Methods and Biomarkers for Diagnosing and Monitoring Psychotic
Disorders
Technical Field
The present invention relates to methods of diagnosing or of monitoring
psychotic disorders, in particular schizophrenic disorders (and bipolar
disorders), e.g. using biomarkers. The biomarkers and methods in which they
are employed can be used to assist diagnosis and to assess onset and
development of psychotic disorders. The invention also relates to use of
biomarkers in clinical screening, assessment of prognosis, evaluation of
therapy, for drug screening and drug development.
Background of the invention
Psychosis is a symptom of severe mental illness. Although it is not
exclusively linked to any particular psychological or physical state, it is
particularly associated with schizophrenia, bipolar disorder (manic depression)
and severe clinical depression. Psychosis is characterized by disorders in
basic perceptual, cognitive, affective and judgmental processes. Individuals
experiencing a psychotic episode may experience hallucinations (often auditory
or visual hallucinations), hold paranoid or delusional beliefs, experience
personality changes and exhibit disorganised thinking (thought disorder). This
is sometimes accompanied by features such as a lack of insight into the
unusual or bizarre nature of their behaviour, difficulties with social interaction
and impairments in carrying out the activities of daily living.
Psychosis is not uncommon in cases of brain injury and may occur after
drug use, particularly after drug overdose or chronic use; certain compounds
may be more likely to induce psychosis and some individuals may show
greater sensitivity than others. The direct effects of hallucinogenic drugs are
not usually classified as psychosis, as long as they abate when the drug is
metabolised from the body. Chronic psychological stress is also known to
precipitate psychotic states, however the exact mechanism is uncertain.
Psychosis triggered by stress in the absence of any other mental illness is
known as brief reactive psychosis. Psychosis is thus a descriptive term for a
complex group of behaviours and experiences. Individuals with schizophrenia

can have long periods without psychosis and those with bipolar disorder, or
depression, can have mood symptoms without psychosis.
Hallucinations are defined as sensory perception in the absence of
external stimuli. Psychotic hallucinations may occur in any of the five senses
and can take on almost any form, which may include simple sensations (such
as lights, colours, tastes, smells) to more meaningful experiences such as
seeing and interacting with fully formed animals and people, hearing voices
and complex tactile sensations. Auditory hallucination, particularly the
experience of hearing voices, is a common and often prominent feature of
psychosis. Hallucinated voices may talk about, or to the person, and may
involve several speakers with distinct personas. Auditory hallucinations tend to
be particularly distressing when they are derogatory, commanding or
preoccupying.
Psychosis may involve delusional or paranoid beliefs, classified into
primary and secondary types. Primary delusions are defined as arising out-of-
the-blue and not being comprehensible in terms of normal mental processes,
whereas secondary delusions may be understood as being influenced by the
person's background or current situation, i.e. represent a delusional
interpretation of a "real" situation.
Thought disorder describes an underlying disturbance to conscious
thought and is classified largely by its effects on the content and form of
speech and writing. Affected persons may also show pressure of speech
(speaking incessantly and quickly), derailment or flight of ideas (switching topic
mid-sentence or inappropriately), thought blocking, rhyming or punning.
Psychotic episodes may vary in duration between individuals. In brief
reactive psychosis, the psychotic episode is commonly related directly to a
specific stressful life event, so patients spontaneously recover normal
functioning, usually within two weeks. In some rare cases, individuals may
remain in a state of full blown psychosis for many years, or perhaps have
attenuated psychotic symptoms (such as low intensity hallucinations) present
at most times.
Patients who suffer a brief psychotic episode may have many of the
same symptoms as a person who is psychotic as a result of, for example,

schizophrenia, and this fact has been used to support the notion that psychosis
is primarily a breakdown in some specific biological system in the brain.
Schizophrenia is a major psychotic disorder affecting up to 1% of the
population. It is found at similar prevalence in both sexes and is found
throughout diverse cultures and geographic areas. The World Health
Organization found schizophrenia to be the world's fourth leading cause of
disability that accounts for 1.1% of the total DALYs (Disability Adjusted Life
Years) and 2.8% of YLDs (years of life lived with disability). It was estimated
that the economic cost of schizophrenia exceeded US$ 19 billion in 1991, more
than the total cost of ail cancers in the United States. Early diagnosis and
effective treatment of schizophrenia can improve prognosis and help reduce
the costs associated with this illness.
The clinical syndrome of schizophrenia comprises discrete clinical
features including positive symptoms (hallucination, delusions, disorganization
of thought and bizarre behaviour); negative symptoms (loss of motivation,
restricted range of emotional experience and expression and reduced hedonic
capacity); and cognitive impairments with extensive variation between
individuals. No single symptom is unique to schizophrenia and/or is present in
every case. Despite the lack of homogeneity of clinical symptoms, the current
diagnosis and classification of schizophrenia is still based on the clinical
symptoms presented by a patient. This is primarily because the aetiology of
schizophrenia remains unknown (in fact, the aetiology of most psychiatric
diseases is still unclear) and classification based on aetiology is as yet not
feasible. The clinical symptoms of schizophrenia are often similar to symptoms
observed in other neuropsychiatric and neurodevelopmental disorders.
Due to the complex spectrum of symptoms presented by subjects with
schizophrenic disorders and their similarity to other mental disorders, current
diagnosis of schizophrenia is made on the basis of a complicated clinical
examination/interview of the patient's family history, personal history, current
symptoms (mental state examination) and the presence/absence of other
disorders (differential diagnosis). This assessment allows a "most likely"
diagnosis to be established, leading to the initial treatment plan. To be
diagnosed with schizophrenia, a patient (with few exceptions) should have

psychotic, "loss-of-reality" symptoms for at least six months (DSM IV) and
show increasing difficulty in functioning normally.
The ICD-10 Classification of Mental and Behavioural Disorders,
published by the World Health Organization in 1992, is the manual most
commonly used by European psychiatrists to diagnose mental health
conditions. The manual provides detailed diagnostic guidelines and defines
the various forms of schizophrenia: schizophrenia, paranoid schizophrenia,
hebrephrenic schizophrenia, catatonic schizophrenia, undifferentiated
schizophrenia, post-schizophrenic schizophrenia, residual schizophrenia and
simple schizophrenia.
The Diagnostic and Statistical Manual of Mental Disorders fourth edition
(DSM IV) published by the American Psychiatric Association, Washington D.C.,
1994, has proven to be an authoritative reference handbook for health
professionals both in the United Kingdom and in the United States in
categorising and diagnosing mental health problems. This describes the
diagnostic criteria, subtypes, associated features and criteria for differential
diagnosis of mental health disorders, including schizophrenia, bipolar disorder
and related psychotic disorders.
DSM IV Diagnostic criteria for Schizophrenia
A. Characteristic symptoms: Two (or more) of the following, each present for
a significant portion of time during a 1-month period (or less if successfully
treated): delusions, hallucinations, disorganized speech (e.g., frequent
derailment or incoherence), grossly disorganized or catatonic behaviour,
negative symptoms, i.e., affective flattening, alogia, or avolition. Only one
Criterion A symptom is required if delusions are bizarre or hallucinations
consist of a voice keeping up a running commentary on the person's behaviour
or thoughts, or two or more voices conversing with each other.
B. Social/occupational dysfunction: For a significant portion of the time
since the onset of the disturbance, one or more major areas of functioning such
as work, interpersonal relations, or self-care are markedly below the level
achieved prior to the onset (or when the onset is in childhood or adolescence,
failure to achieve expected level of interpersonal, academic, or occupational
achievement).
C. Duration: Continuous signs of the disturbance persist for at least 6 months.
This 6-month period must include at least 1 month of symptoms (or less if
successfully treated) that meet Criterion A (i.e., active-phase symptoms) and
may include periods of prodromal or residual symptoms. During these
prodromal or residual periods, the signs of the disturbance may be manifested
by only negative symptoms or two or more symptoms listed in Criterion A
present in an attenuated form (e.g., odd beliefs, unusual perceptual
experiences).
D. Schizoaffective and Mood Disorder exclusion: Schizoaffective Disorder
and Mood Disorder With Psychotic Features have been ruled out because
either (1) no Major Depressive Episode, Manic Episode, or Mixed Episode
have occurred concurrently with the active-phase symptoms; or (2) if mood
episodes have occurred during active-phase symptoms, their total duration has
been brief relative to the duration of the active and residual periods.
E. Substance/general medical condition exclusion: The disturbance is not
due to the direct physiological effects of a substance (e.g., a drug of abuse, a
medication) or a general medical condition, so-called "organic" brain
disorders/syndromes.
F. Relationship to a Pervasive Developmental Disorder: If there is a history
of Autistic Disorder or another Pervasive Developmental Disorder, the
additional diagnosis of Schizophrenia is made only if prominent delusions or
hallucinations are also present for at least a month (or less if successfully
treated).
Schizophrenia Subtypes
1. Paranoid Type: A type of Schizophrenia in which the following criteria are
met: preoccupation with one or more delusions (especially with persecutory
content) or frequent auditory hallucinations. None of the following is prominent:
disorganized speech, disorganized or catatonic behaviour, or flat or
inappropriate affect.
2. Catatonic Type: A type of Schizophrenia in which the clinical picture is
dominated by at least two of the following: motoric immobility as evidenced by
catalepsy (including waxy flexibility) or stupor excessive motor activity (that is
apparently purposeless and not influenced by external stimuli), extreme

negativism (an apparently motiveless resistance to all instructions or
maintenance of a rigid posture against attempts to be moved) or mutism,
peculiarities of voluntary movement as evidenced by posturing (voluntary
assumption of inappropriate or bizarre postures), stereotyped movements,
prominent mannerisms, or prominent grimacing echolalia or echopraxia.
3. Disorganized Type: A type of Schizophrenia in which the following criteria
are met: all of the following are prominent: disorganized speech, disorganized
behaviour, flat or inappropriate affect. The criteria are not met for the Catatonic
Type.
4. Undifferentiated Type: A type of Schizophrenia in which symptoms that
meet Criterion A are present, but the criteria are not met for the Paranoid,
Disorganized, or Catatonic Type.
5. Residual Type:. A type of Schizophrenia in which the following criteria are
met: absence of prominent delusions, hallucinations, disorganized speech, and
grossly disorganized or catatonic behaviour. There is continuing evidence of
the disturbance, as indicated by the presence of negative symptoms or two or
more symptoms listed in Criterion A for Schizophrenia, present in an
attenuated form (e.g., odd beliefs, unusual perceptual experiences).
Schizophrenia associated features
Features associated with schizophrenia include: learning problems,
hypoactivity, psychosis, euphoric mood, depressed mood, somatic or sexual
dysfunction, hyperactivity, guilt or obsession, sexually deviant behaviour,
odd/eccentric or suspicious personality, anxious or fearful or dependent
personality, dramatic or erratic or antisocial personality.
Many disorders have similar or even the same symptoms as
schizophrenia: psychotic disorder due to a general medical condition, delirium,
or dementia; substance-induced psychotic disorder; substance-induced
delirium; substance-induced persisting dementia; substance-related disorders;
mood disorder with psychotic features; schizoaffective disorder; depressive
disorder not otherwise specified; bipolar disorder not otherwise specified; mood
disorder with catatonic features; schizophreniform disorder; brief psychotic
disorder; delusional disorder; psychotic disorder not otherwise specified;
pervasive developmental disorders (e.g., autistic disorder); childhood

presentations combining disorganized speech (from a communication disorder)
and disorganized behaviour (from attention-deficit/hyperactivity disorder);
schizotypal disorder; schizoid personality disorder and paranoid personality
disorder.
DSM IV Diagnostic categories for manic depression/bi-polar affective
disorder (BD)
Only two sub-types of bipolar illness have been defined clearly enough
to be given their own DSM categories, Bipolar 1 and Bipolar II.
Bipolar I: This disorder is characterized by manic episodes; the 'high' of the
manic-depressive cycle. Generally this manic period is followed by a period of
depression, although some bipolar I individuals may not experience a major
depressive episode. Mixed states, where both manic or hypomanic symptoms
and depressive symptoms occur at the same time, also occur frequently with
bipolar I patients (for example, depression with the racing thoughts of mania).
Also, dysphoric mania is common, this is mania characterized by anger and
irritability.
Bipolar II: This disorder is characterized by major depressive episodes
alternating with episodes of hypomania, a milder form of mania. Hypomanic
episodes can be a less disruptive form of mania and may be characterized by
low-level, non-psychotic symptoms of mania, such as increased energy or a
more elated mood than usual. It may not affect an individual's ability to function
on a day to day basis. The criteria for hypomania differ from those for mania
only by their shorter duration (at least 4 days instead of 1 week) and milder
severity (no marked impairment of functioning, hospitalization or psychotic
features).
If alternating episodes of depressive and manic symptoms last for two
years and do not meet the criteria for a major depressive or a manic episode
then the diagnosis is classified as a Cyclothymic disorder, which is a less
severe form of bipolar affective disorder. Cyclothymic disorder is diagnosed
over the course of two years and is characterized by frequent short periods of
hypomania and depressive symptoms separated by periods of stability.
Rapid cycling occurs when an individual's mood fluctuates from
depression to hypomania or mania in rapid succession with little or no periods

of stability in between. One is said to experience rapid cycling when one has
had four or more episodes, in a given year, that meet criteria for major
depressive, manic, mixed or hypomanic episodes. Some people who rapid
cycle can experience monthly, weekly or even daily shifts in polarity
(sometimes called ultra rapid cycling).
When symptoms of mania, depression, mixed mood, or hypomania are
caused directly by a medical disorder, such as thyroid disease or a stroke, the
current diagnosis is Mood Disorder Due to a General Medical Condition.
If a manic mood is brought about through an antidepressant, ECT or
through an individual using street drugs, the diagnosis is Substance-Induced
Mood Disorder, with Manic Features.
Diagnosis of Bipolar III has been used to categorise manic episodes
which occur as a result of taking an antidepressant medication, rather than
occurring spontaneously. Confusingly, it has also been used in instances
where an individual experiences hypomania or cyclothymia (i.e. less severe
mania) without major depression.
Mania
Manic Depression is comprised of two distinct and opposite states of
mood, whereby depression alternates with mania. The DSM IV gives a number
of criteria that must be met before a disorder is classified as mania. The first
one is that an individual's mood must be elevated, expansive or irritable. The
mood must be a different one to the individual's usual affective state during a
period of stability. There must be a marked change over a significant period of
time. The person must become very elevated and have grandiose ideas. They
may also become very irritated and may well appear to be 'arrogant' in manner.
The second main criterion for mania emphasizes that at least three of the
following symptoms must have been present to a significant degree: inflated
sense of self importance, decreased need for sleep, increased talkativeness,
flight of ideas or racing thoughts, easily distracted, increased goal-directed
activity. Excessive involvement in activities that can bring pleasure but may
have disastrous consequences (e.g. sexual affairs and spending excessively).
The third criterion for mania in the DSM IV emphasizes that the change in
mood must be marked enough to affect an individual's job performance or

ability to take part in regular social activities or relationships with others. This
third criterion is used to emphasize the difference between mania and
hypomania.
Depression
The DSM IV states that there are a number of criteria by which major
depression is clinically defined. The condition must have been evident for at
least two weeks and must have five of the following symptoms: a depressed
mood for most of the day, almost every day, a loss of interest or pleasure in
almost all activities, almost every day, changes in weight and appetite, sleep
disturbance, a decrease in physical activity, fatigue and loss of energy, feelings
of worthlessness or excessive feelings of guilt, poor concentration levels,
suicidal thoughts.
Both the depressed mood and a loss of interest in everyday activities
must be evident as two of the five symptoms which characterize a major
depression. It is difficult to distinguish the symptoms of an individual suffering
from the depressed mood of manic depression from someone suffering from a
major depression. Dysthymia is a less severe depression than unipolar
depression, but it can be more persistent.
The prolonged process currently needed to achieve accurate diagnosis
of psychotic disorders may delay appropriate treatment, which is likely to have
serious implications for medium to long-term disease outcome. The
development of objective diagnostic methods, tests and tools is urgently
required to help distinguish between psychiatric diseases with similar clinical
symptoms. Objective diagnostic methods and tests for psychotic disorders,
such as schizophrenia and/or bipolar disorder, will assist in monitoring
individuals over the course of illness (treatment response, compliance etc.) and
may also be useful in determining prognosis, as well as providing tools for drug
screening and drug development.
Unfortunately, at present there are no standard, sensitive, specific tests
for psychotic disorders, such as schizophrenia or bipolar disorders.
One biochemical test currently under development for schizophrenia
diagnosis is the niacin skin flush test, based on the observation that there is
failure to respond to the niacin skin test in some schizophrenia patients, due to

abnormal arachidonic acid metabolism. However, the specificity and sensitivity
of this test shows an extreme inconsistency between studies, ranging from
23% to 87%, suggesting that the reliability and validity of this test still need to
be verified.
WO 01/63295 describes methods and compositions for screening,
diagnosis, and determining prognosis of neuropsychiatric or neurological
conditions, including BAD (bipolar affective disorder), schizophrenia and
vascular dementia, for monitoring the effectiveness of treatment in these
conditions and for use in drug development.
Other techniques such as magnetic resonance imaging or positron
emission tomography, based on subtle changes of the frontal and temporal
lobes and the basal ganglia are of little value for the diagnosis, treatment, or
prognosis of schizophrenic disorders in individual patients, since the absolute
size of these reported differences between individuals with schizophrenia and
normal comparison subjects has been generally small, with notable overlap
between the two groups. The role of these neuroimaging techniques is
restricted largely to the exclusion of other conditions which may be
accompanied by schizophrenic symptoms, such as brain tumours or
haemorrhages.
Metabonomic studies can be used to generate a characteristic pattern or
"fingerprint" of the metabolic status of an individual. Metabonomic studies on
biofluids provide information on the biochemical status of the whole organism,
since the composition of a given biofluid is a consequence of the function of the
cells that are intimately concerned with the fluid's composition and secretion.
"Metabonomics" is conventionally defined as "the quantitative
measurement of the multi-parametric metabolic response of living systems to
pathophysiological stimuli or genetic modification". Metabonomics has
developed from the use of 1H NMR spectroscopy to study the metabolic
composition of biofluids, cells, and tissues and from studies utilising pattern
recognition (PR), expert systems and other chemoinformatic tools to interpret
and classify complex NMR-generated metabolic data sets and to extract useful
biological information.

1H NMR spectra of biofluids and tissues provide a characteristic
metabolic "fingerprint" or profile of the organism from which the biofluid was
obtained for a range of biologically-important endogenous metabolites. This
metabolic profile is characteristically changed by a disease, disorder, toxic
process, or xenobiotic (e.g. drug substance). Quantifiable differences in
metabolite patterns in biofluids and tissues can give information and insight into
the underlying molecular mechanisms of disease or disorder. In the evaluation
of the effects of drugs, each compound or class of compound produces
characteristic changes in the concentrations and patterns of endogenous
metabolites in biofluids.
The metabolic changes can be characterised using automated computer
programs which represent each metabolite measured in the biofluid spectrum
as a co-ordinate in multi-dimensional space.
Metabonomic technology has been used to identify biomarkers of inborn
errors of metabolism, liver and kidney disease, cardiovascular disease, insulin
resistance and neurodegenerative disorders.
The current diagnosis of psychotic disorders, such as schizophrenia,
remains subjective, not only because of the complex spectrum of symptoms
and their similarity to other mental disorders, but also due to the lack of
empirical disease markers. There is a great clinical need for diagnostic tests
and more effective drugs to treat severe mental illnesses.
Recent functional genomics studies suggest that there may be a
metabolic component to the schizophrenia syndrome, but the contribution of
metabolic aspects to psychotic disease is poorly understood. There is some
evidence that abnormal glucoregulation, lipid metabolism and mitochondrial
dysfunction are associated with schizophrenia and affective disorders8'11. But
it is not understood if these metabolic alterations are a cause or effect of the
disorder itself, or whether they occur as a result of medication. Antipsychotic
drug action has been prominently linked to dyslipidemia, but reports of altered
glucose metabolism predate the antipsychotic era (reviewed by Haupt and
Newcomer12) and a recent report aimed at determining the rate of metabolic
syndrome in long-term schizophrenia patients found the prevalence of

metabolic syndrome to be inversely correlated to the daily dose of
antipsychotic drugs13.
It is now widely accepted that both genetic and non-genetic
environmental factors contribute to the aetiopathology of schizophrenia and/or
precipitate the onset of the syndrome. Numerous biological (viral exposure3,
illicit drug use4, perinatal insults5 etc.) and social stressors are considered to be
environmental disease components, likely to interact with a predisposing
genotype. Twin studies are particularly powerful tools for unravelling genetic
and environmental factors responsible for complex disorders. Previous studies
have demonstrated that the likelihood to develop schizophrenia correlates
highly with the level of consanguinity and reaches a concordance rate of about
30-50% for monozygotic twins1,2. Investigations of discordant twins, i.e. twins
in which one twin presents with a disorder and the other twin does not, may
help to disentangle the impact of some of these components. Due to the
difficulties in obtaining brain samples from discordant twins in sufficient
numbers, studies of monozygotic twins discordant for schizophrenia have so
far focused on brain imaging. Twin studies imply that one of the most
consistently reported brain alterations in schizophrenia, i.e. lateral ventricular
enlargement, can been attributed to environmental factors 67
Biomarkers present in readily accessible body fluids, such as blood,
plasma, serum, urine, saliva or cerebrospinal fluid (CSF), may prove useful in
diagnosis of psychotic disorders, aid in predicting and monitoring treatment
response and compliance, and assist in identification of novel drug targets.
Appropriate biomarkers are also important tools in development of new early or
pre-symptomatic treatments designed to improve outcomes or to prevent
pathology.
The validation of biomarkers that can detect early changes specifically
correlated to reversal or progression of mental disorders is essential for
monitoring and optimising interventions. Used as predictors, these biomarkers
can help to identify high-risk individuals and disease sub-groups that may
serve as target populations for chemo-intervention trials; whilst as surrogate
endpoints, biomarkers have the potential for assessing the efficacy and cost
effectiveness of preventative interventions at a speed which is not possible at

present when the incidence of manifest mental disorder is used as the
endpoint.
The transthyretin gene encodes a plasma protein transthyretin (TTR)
that belongs to a group of proteins, including thyroxine-binding globulin and
albumin, which bind and transport thyroid hormones in the blood. TTR
transports thyroxine from the bloodstream to the brain15. It is a single
polypeptide chain of 127 amino acids (14 kDa) and is present in the plasma as
a tetramer of non-covalently bound monomers. TTR is expressed at a high rate
in the brain choroid plexus, from which it is released into the cerebrospinal fluid
(CSF). In peripheral tissues, it is expressed primarily in liver. Only an
estimated 3% of transthyretin in the ventricular CSF and only 10% of the
transthyretin in lumbar CSF are derived from blood16. Under physiological
conditions, the macromolecular complex plays an important physiological role
in vitamin A homeostasis because it binds the specific transport protein for
retinol, the lipocalin retinol-binding protein (RBP). This reduces the glomerular
filtration of the low molecular weight transport protein (21 kDa) in the kidneys.
Any TTR or RBP molecules that are filtered are rapidly bound to megalin, the
multiligand receptor expressed on the luminal surface of the renal proximal
tubules and therefore internalized. Thus, under physiological conditions, TTR
and RBP are present in urine if at all, only in trace amounts. The gene TTR
that encodes transthyretin is in chromosome region 18q11.2-q12.1.
Transthyretin has been associated with Alzheimer's disease and
depression17. It has also been shown that schizophrenia patients treated with
clozapine show differences in transthyretin levels18.
Apolipoproteins function in lipid transport as structural components of
lipoprotein particles, co-factors for enzymes and ligands for cell-surface
receptors. There are five major types of apolipoproteins: ApoA (ApoA1, ApoA
2), ApoB, ApoC (ApoC1, ApoC2, ApoC3, ApoC4), ApoD, and ApoE. In
particular, ApoA1 is the major protein component of high-density lipoproteins;
ApoA4 is thought to act primarily in intestinal lipid absorption; and ApoE is a
blood plasma protein that mediates the transport and uptake of cholesterol and
lipid by way of its high affinity interaction with different cellular receptors,

including the low-density lipoprotein (LDL) receptor. ApoA1 is known to have
cardio-protective properties and play a role in atherosclerosis and diabetes28,29.
Wen et al30 discloses that the lever of ApoA1 in patients which have
undergone therapy with phenothiazine is lower compared to normal controls
from healthy individuals.
Middleton et al31 analysed the expression levels of the Apolipoprotein
gene family cluster.
Summary of the Invention
The present invention is based in part on the results of 1H NMR-based
metabonomics approach to profile plasma from identical twins discordant for
the psychotic disorder schizophrenia (i.e., with one affected twin and one non-
affected twin) and from healthy control sets of twins, to identify a disease-
related metabolic signature.
In one aspect, the invention provides a method of diagnosing or
monitoring a psychotic disorder in a subject, comprising:
(a) providing a test biological sample from said subject,
(b) performing spectral analysis on said test biological sample to provide one or
more spectra, and
(c) comparing said one or more spectra with one or more control spectra.
The invention further provides a method of diagnosing or monitoring a
psychotic disorder in a subject, comprising:
(a) providing a test biological sample from said subject,
(b) performing spectral analysis on said test biological sample to provide one or
more spectra,
(c) analysing said one or more spectra to detect the level of one or more
biomarkers in said spectra, and
(d) comparing the level of said one or more biomarkers in said one or more
spectra with the level of said one or more biomarkers detected in control
spectra.
In a further aspect, the invention provides a method of diagnosing or
monitoring a psychotic disorder, or predisposition thereto, comprising
measuring the level of one or more biomarkers present in a biological sample
taken from a test subject, said biomarkers being selected from: VLDL, LDL and

aromatic species such as plasma proteins. Such methods can be used in
methods of monitoring efficacy of a therapy (e.g. a therapeutic substance) in a
subject having, suspected of having, or of being predisposed to, a psychotic
disorder.
In a further aspect, the invention provides a multi-analyte panel or array
capable of detecting one, two or three biomarkers selected from the group:
VLDL, LDL and aromatic species such as plasma proteins.
A multi-analyte panel is capable of detecting a number of different
analytes. An array can be capable of detecting a single analyte in a number of
samples or, as a multi-analyte array, can be capable of detecting a number of
different analytes in a sample. A multi-analyte panel or multi-analyte array
according to the invention is capable of detecting one or more metabolic
biomarker as described herein, and can be capable of detecting a biomarker or
biomarkers additional to those specifically described herein.
Also provided is a diagnostic or monitoring test kit suitable for
performing a method according to the invention, optionally together with
instructions for use of the kit. The diagnostic or monitoring kit may comprise
one or more biosensors according to the invention, a single sensor, or
biosensor or combination of sensors and/or biosensors may be included in the
kit. A diagnostic or monitoring kit may comprise a panel or an array according
to the invention. A diagnostic or monitoring kit may comprise an assay or
combination of assays according to the invention.
Further provided is the use of one or more biomarkers selected from
VLDL, LDL and aromatic species such as plasma proteins, to diagnose and/or
monitor a psychotic disorder.
Yet further provided is the use of a method, sensor, biosensor, multi-
analyte panel, array or kit according to the invention to identify a substance
capable of modulating a psychotic disorder. A substance capable of
modulating a psychotic disorder may be an anti-psychotic substance useful for
treatment of psychoses, or a pro-psychotic substance which may induce
psychoses.
Additionally provided is a method of identifying a substance capable of
modulating a psychotic disorder in a subject, comprising a method of

monitoring as described herein; particularly preferred identification methods
comprise administering a test substance to a test subject and detecting the
level of one or more biomarkers selected from VLDL, LDL and aromatic
species such as plasma proteins in a biological sample, preferably a whole
blood, serum or plasma sample taken from said subject.
The invention also" relates to the use of a transthyretin peptide
comprising the amino acid sequence shown in SEQ ID NO: 1 or a fragment
thereof as a biomarker for a schizophrenic disorder or predisposition thereto.
The invention further provides a transthyretin peptide biomarker for a
schizophrenic disorder comprising the amino acid sequence shown in SEQ ID
NO: 1 or a fragment thereof.
In a further aspect, the invention provides a method of diagnosing or
monitoring a schizophrenic disorder or predisposition thereto, comprising
detecting and/or quantifying a transthyretin peptide biomarker comprising the
amino acid sequence of SEQ ID NO: 1 or a fragment thereof, present in a
biological sample from a test subject.
A further aspect of the invention provides ligands, such as naturally
occurring or chemically synthesised compounds, capable of specific binding to
the transthyretin peptide biomarker. A ligand according to the invention may
comprise a peptide, an antibody or a fragment thereof, or an aptamer or
oligonucleotide, capable of specific binding to the transthyretin peptide
biomarker. The antibody can be a monoclonal antibody or a fragment thereof
capable of specific binding to the transthyretin peptide biomarker. A ligand
according to the invention may be labelled with a detectable marker, such as a
luminescent, fluorescent, enzyme or radioactive marker; alternatively or
additionally a ligand according to the invention may be labelled with an affinity
tag. Preferably, a ligand according to the invention comprises a peptide, an
antibody or a fragment thereof, or an aptamer or oligonucleotide, capable of
specific binding to a transthyretin peptide biomarker as described herein
wherein the ligand is not an antibody selected from the group as listed in Table
1 or a ligand selected from the group comprising T3, T4 (thyroid hormones),
vitamin A (indirectly by interacting with serum retinol-binding protein),
apolipoprotein Al (ApoAl), noradrenaline oxidation products, and pterins, non-

steroidal anti-inflammatory drugs (NSAIDs), environmental pollutants, such as
polyhalogenated biphenyls and thyromimetic compounds, xanthone derivatives
or natural and synthetic flavonoids.
The present invention provides a method of diagnosing a schizophrenic
disorder or predisposition thereto, comprising detecting and/or quantifying in a
biological sample from a test subject an ApoA1 peptide biomarker comprising
the amino acid sequence of SEQ ID NO: 2 or a fragment thereof.
Biomarkers for schizophrenic disorders are targets for discovery of novel
targets and drug molecules that retard or halt disease progression. As the
level of an ApoA1 peptide biomarker is indicative of disorder and of drug
response, the ApoA1 biomarker is useful for identification of novel therapeutic
compounds in in vitro and/or in vivo assays. The ApoA1 biomarker of the
invention can therefore be employed in methods for screening for compounds
that promote the activity of, or activate the generation of, an ApoA1 peptide
biomarker according to the invention.
Thus, in a further aspect of the invention, there is provided the use of a
substance or ligand capable of stimulating or promoting the generation of an
ApoA1 biomarker peptide said biomarker comprising the amino acid sequence
of SEQ ID NO: 2 or a fragment thereof in the manufacture of a medicament for
the treatment of a schizophrenic disorder or predisposition thereto. Also
provided is the use of a substance or ligand capable of stimulating the activity
of an ApoA1 biomarker peptide, said biomarker comprising the amino acid
sequence of SEQ ID NO: 2 or a fragment thereof in the manufacture of a
medicament for the treatment of a schizophrenic disorder or predisposition
thereto.
The invention also relates to a method for treating a schizophrenic
disorder comprising administering to a patient in need thereof a substance or
ligand capable of stimulating, promoting or activating the activity or the
generation of a peptide comprising the amino acid sequence of SEQ ID NO: 2
or a fragment thereof.
A lower level of plasma protein biomarkers in the test biological sample
relative to the level in a normal control is indicative of the presence of a

psychotic disorder, in particular a schizophrenic disorder, bipolar disorder, or
predisposition thereto.
Methods of monitoring and of diagnosis according to the invention are
useful to confirm the existence of a disorder, or predisposition thereto; to
monitor development of the disorder by assessing onset and progression, or to
assess amelioration or regression of the disorder. Methods of monitoring and
of diagnosis are also useful in methods for assessment of clinical screening,
prognosis, choice of therapy, evaluation of therapeutic benefit, i.e. for drug
screening and drug development.
Efficient diagnosis and monitoring methods provide very powerful
"patient solutions" with the potential for improved prognosis, by establishing the
correct diagnosis, allowing rapid identification of the most appropriate
treatment (thus lessening unnecessary exposure to harmful drug side effects),
reducing "down-time" and relapse rates.
Methods for monitoring efficacy of a therapy can be used to monitor the
therapeutic effectiveness of existing therapies and new therapies in human
subjects and in non-human animals (e.g. in animal models). These monitoring
methods can be incorporated into screens for new drug substances and
combinations of substances
Modulation of a peptide biomarker level is useful as an indicator of the
state of the schizophrenic disorder or predisposition thereto. A decrease in the
level of peptide biomarker over time is indicative of onset or progression, i.e.
worsening of the disorder, whereas an increase in the level of peptide
biomarker indicates amelioration or remission of the disorder.
The identification of biomarkers for schizophrenic disorders permits
integration of diagnostic procedures and therapeutic regimes. Currently there
are significant delays in determining effective treatment and it has not hitherto
been possible to perform rapid assessment of drug response. Traditionally,
many anti-schizophrenic therapies have required treatment trials lasting weeks
to months for a given therapeutic approach. Detection of a peptide biomarker
of the invention can be used to screen subjects prior to their participation in
clinical trials. The biomarker provides a means to indicate therapeutic
response, failure to respond, unfavourable side-effect profile, degree of

medication compliance and achievement of adequate serum drug levels. The
biomarker may be used to provide warning of adverse drug response, a major
problem encountered with all psychotropic medications. Biomarkers are useful
in development of personalized brain therapies, as assessment of response
can be used to fine-tune dosage, minimise the number of prescribed
medications, reduce the delay in attaining effective therapy and avoid adverse
drug reactions. Thus by monitoring a biomarker of the invention, patient care
can be tailored precisely to match the needs determined by the disorder and
the pharmacogenomic profile of the patient, the biomarker can thus be used to
titrate the optimal dose, predict a positive therapeutic response and identify
those patients at high risk of severe side effects.
Biomarker based tests provide a first line assessment of 'new' patients,
and provide objective measures for accurate and rapid diagnosis, in a time
frame and with precision, not achievable using the current subjective
measures.
Furthermore, diagnostic biomarker tests are useful to identify family
members or patients in the "prodromal phase", i.e. those at high risk of
developing overt schizophrenia. This permits initiation of appropriate therapy,
for example low dose antipsychotics, or preventive measures, e.g. managing
risk factors such as stress, illicit drug use or viral infections. These approaches
are recognised to improve outcome and may prevent overt onset of the
disorder.
Biomarker monitoring methods, biosensors and kits are also vital as
patient monitoring tools, to enable the physician to determine whether relapse
is due to a genuine breakthrough or worsening of the disease, poor patient
compliance or substance abuse. If pharmacological treatment is assessed to
be inadequate, then therapy can be reinstated or increased. For genuine
breakthrough disease, a change in therapy can be given if appropriate. As the
biomarker is sensitive to the state of the disorder, it provides an indication of
the impact of drug therapy or of substance abuse.
High-throughput screening technologies based on the biomarkers of the
invention, uses and methods of the invention, e.g. configured in an array
format, are suitable to monitor biomarkers for the identification of potentially

useful therapeutic compounds, e.g. ligands such as natural compounds,
synthetic chemical compounds (e.g. from combinatorial libraries), peptides,
monoclonal or polyclonal antibodies or fragments thereof, capable of
modulating the expression of the biomarkers.
Sequence listing information
SEQ ID NO:1 Amino acid sequence of human transthyretin
PLMVKVLDAV RGSPAINVAV HVFDKAADDT WEPFASGKTS
ESGELHGLTT EEEFVEGIYK VEIDTKSYWK ALGISPFHEH AEVVFTANDS
GPRRYTIAAL LSPYSYSTTA VVTNPKE
SEQ ID No: 2 ApoA1
(Sequences Removed)
Two or more biomarkers described herein may be used in combination.
Each aspect of the invention, as described herein, with respect to a particular
biomarker, may be equally applicable to any other biomarker described herein.
Further, any reference to schizophrenia may equally apply to another
psychotic.
Description of the Invention
The term "biomarker" means a distinctive biological or biologically
derived indicator of a process, event, or condition. Peptide biomarkers can be
used in methods of diagnosis, e.g. clinical screening, and prognosis
assessment; and in monitoring the results of therapy, for identifying patients
most likely to respond to a particular therapeutic treatment, as well as in drug
screening and development. Biomarkers and uses thereof are valuable for
identification of new drug treatments and for discovery of new targets for drug
treatment.

The term transthyretin peptide biomarker includes the mature full length
human transthyretin peptide. A preferred transthyretin peptide biomarker (SEQ
ID NO: 1) is, or is derived from, the human transthyretin protein. The peptide of
SEQ ID NO: 1 is the secreted form which does not include the signal (leader)
sequence as found in the precursor. Also included are transthyretin isoforms
and derivatives, for example S-cysteinylated and S-gluthanionylated
transthyretin, which are both common modifications of TTR found in CSF
samples. The peptide biomarker as shown in SEQ ID NO: 1 is found to be
present at lower levels in individuals with first onset psychosis characteristic of
schizophrenia, it is thus useful as a marker for diagnosing and monitoring
schizophrenic disorders or predisposition thereto. According to the invention,
the biomarker may comprise the amino acid sequence shown in SEQ ID NO: 1
or a fragment thereof. For example, the biomarker may comprise one or more
fragments (multiple fragments) of the amino acid sequence shown in SEQ ID
NO:1.
The term ApoA1 peptide biomarker includes the mature full length
human ApoA1 peptide. A preferred ApoAl peptide biomarker is shown in SEQ
ID NO: 1. The peptide biomarker as shown in SEQ ID NO: 2 (Figure 13) is
found to be present at decreased levels in drug-naVve individuals with first-
onset psychosis characteristic of schizophrenic disorders compared to normal
controls. It is thus useful as a marker for diagnosing schizophrenic disorders,
or predisposition thereto.
The term drug-naive patient as used herein means an individual who
has not been treated with any schizophrenia therapeutic substance. Thus in a
preferred embodiment, the invention relates to a method wherein the test
sample is from a test subject wherein the test subject is a first onset drug-naive
individual, and the sample is taken prior to administration of any anti-
schizophrenic therapy to the subject. The control sample is preferably a sample
from a normal individual.
A lower level of the ApoAl peptide biomarker in a test sample relative to
the level in a normal control is indicative of the presence of a schizophrenic
disorder or predisposition thereto. An equivalent or higher level of said peptide
in the test sample relative to the normal control is indicative of the absence of a
schizophrenic disorder or a predisposition thereto.
The term "diagnosis" as used herein encompasses identification,
confirmation, and/or characterisation of a schizophrenic disorder or
predisposition thereto. The term "predisposition" as used herein means that a
subject does not currently present with the disorder, but is liable to be affected
by the disorder in time. Methods of diagnosis according to the invention are
useful to confirm the existence of a disorder, or predisposition thereto. Methods
of diagnosis are also useful in methods for assessment of clinical screening,
prognosis, choice of therapy, evaluation of therapeutic benefit, i.e. for drug
screening and drug development.
Monitoring methods of the invention can be used to monitor onset,
progression, stabilisation, amelioration and/or remission of a psychotic
disorder.
The term "psychotic disorder" as used herein refers to a disorder in
which psychosis is a recognised symptom, this includes neuropsychiatric
(psychotic depression and other psychotic episodes) and neurodevelopmental
disorders (especially Autistic spectrum disorders), neurodegenerative
disorders, depression, mania, and in particular, schizophrenic disorders
(paranoid, catatonic, disorganized, undifferentiated and residual schizophrenia)
and bipolar disorders.
Biological samples that may be tested in a method of the invention
include whole blood, blood serum or plasma, urine, saliva, cerebrospinal fluid
(CSF) or other bodily fluid (stool, tear fluid, synovial fluid, sputum), breath, e.g.
as condensed breath, or an extract or purification therefrom, or dilution thereof.
Biological samples also include tissue homogenates, tissue sections and
biopsy specimens from a live subject, or taken post-mortem. The samples can
be prepared, for example where appropriate diluted or concentrated, and
stored in the usual manner.
A number of spectroscopic techniques can be used to generate spectra,
according to the invention, including NMR spectroscopy and mass
spectrometry. In preferred methods, spectral analysis is performed by NMR
spectroscopy, preferably 1H NMR spectroscopy. One or more spectra may be

generated, a suite of spectra may be measured, including one for small
molecules and another for macromolecule profiles. The spectra obtained may
be subjected to spectral editing techniques. One or two-dimensional NMR
spectroscopy may be performed.
An advantage of using NMR spectroscopy to study complex biomixtures
is that measurements can often be made with minimal sample preparation
(usually with only the addition of 5-10% D20) and a detailed analytical profile of
the whole biological sample can be obtained.
Sample volumes are small, typically 0.3 to 0.5 ml for standard probes,
and as low as 3 ul for microprobes. Acquisition of simple NMR spectra is rapid
and efficient using flow-injection technology. It is usually necessary to
suppress the water NMR resonance.
High resolution NMR spectroscopy (in particular 1H NMR) is particularly
appropriate. The main advantages of using 1H NMR spectroscopy are the
speed of the method (with spectra being obtained in 5 to 10 minutes), the
requirement for minimal sample preparation, and the fact that it provides a non-
selective detector for all metabolites in the biofluid regardless of their structural
type, provided only that they are present above the detection limit of the NMR
experiment and that they contain non-exchangeable hydrogen atoms.
NMR studies of body fluids should ideally be performed at the highest
magnetic field available to obtain maximal dispersion and sensitivity and most
1H NMR studies are performed at 400 MHz or greater, e.g. 600 MHz.
Usually, to assign 1H NMR spectra, comparison is made with control
spectra of authentic materials and/or by standard addition of an authentic
reference standard to the sample. The control spectra employed may be
normal control spectra, generated by spectral analysis of a biological sample
from a normal subject, and/or psychotic disorder control spectra, generated by
spectral analysis of a biological sample from a subject with a psychotic
disorder.
Additional confirmation of assignments is usually sought from the
application of other NMR methods, including, for example, 2-dimensional (2D)
NMR methods, particularly COSY (correlation spectroscopy), TOCSY (total
correlation spectroscopy), inverse-detected heteronuclear correlation methods

such as HMBC (heteronuclear multiple bond correlation), HSQC (heteronuclear
single quantum coherence), and HMQC (heteronuclear multiple quantum
coherence), 2D J-resolved (JRES) methods, spin-echo methods, relaxation
editing, diffusion editing (e.g., both 1D NMR and 2D NMR such as diffusion-
edited TOCSY), and multiple quantum filtering.
By comparison of spectra with normal and/or psychotic disorder control
spectra, the test spectra can be classified as having a normal profile, a
psychotic disorder profile, or a psychotic disorder predisposition profile.
Comparison of spectra may be performed on entire spectra or on
selected regions of spectra. Comparison of spectra may involve an
assessment of the variation in spectral regions responsible for deviation from
the normal spectral profile and in particular, assessment of variation in one or
more biomarkers within those regions.
A limiting factor in understanding the biochemical information from both
1D and 2D-NMR spectra of biofluids, such as plasma, is their complexity. The
most efficient way to compare and investigate these complex multiparametric
data is employ the 1D or 2D NMR metabonomic approach in combination with
computer-based "pattern recognition" (PR) methods and expert systems.
Although the utility of the metabonomic approach is well established, its
full potential has not yet been exploited. The metabolic variation is often subtle,
and powerful analysis methods are required for detection of particular analytes,
especially when the data (e. g., NMR spectra) are so complex.
Metabonomics methods (which employ multivariate statistical analysis
and pattern recognition (PR) techniques, and optionally data filtering
techniques) of analysing data (e.g. NMR spectra) from a test population yield
accurate mathematical models which may subsequently be used to classify a
test sample or subject, and/or in diagnosis.
Comparison of spectra may include one or more chemometric analyses
of the spectra. The term "chemometrics" is applied to describe the use of
pattern recognition (PR) methods and related multivariate statistical
approaches to chemical numerical data. Comparison may therefore comprise
one or more pattern recognition analysis methods, which can be performed by
one or more supervised and/or unsupervised methods.

Pattern recognition (PR) methods can be used to reduce the complexity
of data sets, to generate scientific hypotheses and to test hypotheses, in
general, the use of pattern recognition algorithms allows the identification, and,
with some methods, the interpretation of some non-random behaviour in a
complex system which can be obscured by noise or random variations in the
parameters defining the system. Also, the number of parameters used can be
very large such that visualisation of the regularities or irregularities, which for
the human brain is best in no more than three dimensions, can be difficult.
Usually the number of measured descriptors is much greater than three
and so simple scatter plots cannot be used to visualise any similarity or
disparity between samples. Pattern recognition methods have been used
widely to characterise many different types of problem ranging for example
over linguistics, fingerprinting, chemistry and psychology.
In the context of the methods described herein, pattern recognition is the
use of multivariate statistics, both parametric and non-parametric, to analyse
spectroscopic data, and hence to classify samples and to predict the value of
some dependent variable based on a range of observed measurements. There
are two main approaches. One set of methods is termed "unsupervised" and
these simply reduce data complexity in a rational way and also produce display
plots which can be interpreted by the human eye. The other approach is
termed "supervised" whereby a training set of samples with known class or
outcome is used to produce a mathematical model and this is then evaluated
with independent validation data sets.
Unsupervised techniques are used to establish whether any intrinsic
clustering exists within a data set and consist of methods that map samples,
often by dimension reduction, according to their properties, without reference to
any other independent knowledge, e.g. without prior knowledge of sample
class. Examples of unsupervised methods include principal component
analysis (PCA), non-linear mapping (NLM) and clustering methods such as
hierarchical cluster analysis.
One of the most useful and easily applied unsupervised PR techniques
is principal components analysis (PCA) (see, for example, Kowalski et ai,
1986). Principal components (PCs) are new variables created from linear

combinations of the starting variables with appropriate weighting coefficients.
The properties of these PCs are such that: (i) each PC is orthogonal to
(uncorrelated with) all other PCs, and (ii) the first PC contains the largest part
of the variance of the data set (information content) with subsequent PCs
containing correspondingly smaller amounts of variance.
PCA, a dimension reduction technique, takes m objects or samples,
each described by values in K dimensions (descriptor vectors), and extracts a
set of eigenvectors, which are linear combinations of the descriptor vectors.
The eigenvectors and eigenvalues are obtained by diagonalisation of the
covariance matrix of the data. The eigenvectors can be thought of as a new
set of orthogonal plotting axes, called principal components (PCs). The
extraction of the systematic variations in the data is accomplished by projection
and modelling of variance and covariance structure of the data matrix. The
primary axis is a single eigenvector describing the largest variation in the data,
and is termed principal component one (PC1). Subsequent PCs, ranked by
decreasing eigenvalue, describe successively less variability. The variation in
the data that has not been described by the PCs is called residual variance and
signifies how well the model fits the data. The projections of the descriptor
vectors onto the PCs are defined as scores, which reveal the relationships
between the samples or objects. In a graphical representation (a "scores plot"
or eigenvector projection), objects or samples having similar descriptor vectors
will group together in clusters. Another graphical representation is called a
loadings plot, and this connects the PCs to the individual descriptor vectors,
and displays both the importance of each descriptor vector to the interpretation
of a PC and the relationship among descriptor vectors in that PC. In fact, a
loading value is simply the cosine of the angle which the original descriptor
vector makes with the PC.
Descriptor vectors which fall close to the origin in this plot carry little
information in the PC, while descriptor vectors distant from the origin (high
loading) are important for interpretation.
Thus a plot of the first two or three PC scores gives the "best"
representation, in terms of information content, of the data set in two or three
dimensions, respectively. A plot of the first two principal component scores,

PC1 and PC2 provides the maximum information content of the data in two
dimensions. Such PC maps can be used to visualise inherent clustering
behaviour, for example, for drugs and toxins based on similarity of their
metabonomic responses and hence mechanism of action. Of course, the
clustering information may be in lower PCs and these can also be examined.
Hierarchical Cluster Analysis, another unsupervised pattern recognition
method, permits the grouping of data points which are similar by virtue of being
"near" to one another in some multidimensional space. Individual data points
may be, for example, the signal intensities for particular assigned peaks in an
NMR spectrum. A "similarity matrix" S, is constructed with element ssij = 1-
rij/rijmax' where rij is the interpoint distance between points i and j (e. g.,
Euclidean interpoint distance), and rijmax is the largest interpoint distance for
all points.
The most distant pair of points will have sij equal to 0, since rij then
equals rijmaX. Conversely, the closest pair of points will have the largest sij,
approaching 1. The similarity matrix is scanned for the closest pair of points.
The pair of points is reported with their separation distance, and then the two
points are deleted and replaced with a single combined point. The process is
then repeated iteratively until only one point remains. A number of different
methods may be used to determine how two clusters will be joined, including
the nearest neighbour method (also known as the single link method), the
furthest neighbour method, the centroid method (including centroid link,
incremental link, median link, group average link, and flexible link variations).
The reported connectivities are then plotted as a dendrogram (a tree-like
chart which allows visualisation of clustering), showing sample-sample
connectivities versus increasing separation distance (or equivalent^, versus
decreasing similarity). In the dendrogram the branch lengths are proportional
to the distances between the various clusters and hence the length of the
branches linking one sample to the next is a measure of their similarity. In this
way, similar data points may be identified algorithmically.
Supervised methods of analysis use the class information given for a
training set of sample data to optimise the separation between two or more
sample classes. These techniques include soft independent modelling of class

analogy, partial least squares (PLS) methods, such as projection to latent
discriminant analysis (PLS DA), k-nearest neighbour analysis and neural
networks. Neural networks are a non-linear method of modelling data. A
training set of data is used to develop algorithms that 'learn' the structure of the
data and can cope with complex functions. Several types of neural network
have been applied successfully to predicting toxicity or disease from spectral
information.
Statistical techniques such as one-way analysis of variance (ANOVA)
may also be employed to analyse data.
Methods of the invention involving spectral analysis this may be
performed to provide spectra from biological samples taken on two or more
occasions from a test subject. Spectra from biological samples taken on two or
more occasions from a test subject can be compared to identify differences
between the spectra of samples taken on different occasions. Methods may
include analysis of spectra from biological samples taken on two or more
occasions from a test subject to quantify the level of one or more biomarkers
present in the biological samples, and comparing the level of the one or more
biomarkers present in biological samples taken on two or more occasions.
Diagnostic and monitoring methods of the invention are useful in
methods of assessing prognosis of a psychotic disorder, in methods of
monitoring efficacy of an administered therapeutic substance in a subject
having, suspected of having, or of being predisposed to, a psychotic disorder
and in methods of identifying an anti-psychotic or pro-psychotic substance.
Such methods may comprise comparing the level of the one or more
biomarkers in a test biological sample taken from a test subject with the level
present in one or more samples taken from the test subject prior to
administration of the substance, and/or one or more samples taken from the
test subject at an earlier stage during treatment with the substance.
Additionally, these methods may comprise detecting a change in the level of
the one or more biomarkers in biological samples taken from a test subject on
two or more occasions.
In methods of the invention, in particular those in which spectral analysis
is employed, and in particular when the biological sample is blood or is derived

from blood, e.g. plasma or serum, suitably one or more biomarkers is selected
from: VLDL, LDL and aromatic species such as plasma proteins.
In a 1H NMR-based metabonomics study, alterations in the lipid profile
of both affected and unaffected schizophrenia twins have been identified. Lipid
levels were found to correlate strongly with global function scores for affected
female twins.
These biomarkers of psychotic disorder, in particular schizophrenic
disorders, were identified by extensive metabolic profiling analysis using 1H
NMR spectroscopy in conjunction with computerised pattern recognition
analysis to investigate plasma samples from 21 pairs of monozygotic twins
discordant for schizophrenia and 8 pairs of age-matched healthy control twins.
All samples were obtained under standardized conditions and were annotated
with regards to demographic and clinical details.
The results of these studies show that signals from VLDL, LDL and
aromatic regions relating to plasma proteins, are the most important factors
differentiating ill and healthy co-twins discordant for schizophrenia from healthy
control twins. VLDL and LDL levels were found to be elevated in twins
discordant for schizophrenia compared to normal control twins without
schizophrenia. In the discordant twins, the affected twins had VLDL and LDL
levels that were more elevated than the levels found in the unaffected
discordant twins. This differentiation was very pronounced in female twins. A
close association of VLDLVLDL signals and Global Functioning Scores (DSMIV,
Axis V) was found in female twins suffering from schizophrenia. Discordant
twins had lower plasma protein levels than normal control co-twins, the
greatest reductions in plasma protein being found in the affected twins.
The observed changes in the lipid and aromatic region in twins
discordant for schizophrenia suggests a link between metabolic disturbances
and the aetiopathology of schizophrenia. Although effects of antipsychotic
medication can not be ruled out entirely, the fact that healthy co-twins show a
putative "predisposition" signature implies that these changes are disease-
related, rather than an artifact of medication. The increase in VLDL, LDL and
decrease in aromatic regions (plasma proteins) constitute metabolic

biomarkers that enable differentiation between normal individuals and those
with a psychotic disorder.
Lipid profiles of affected female twins were also found to correlate highly
with Global Functioning Scores (GAF). GAF scores are based on subjective
assessment by a psychiatrist. The correlation between elevation of VLDL and
LDL lipid biomarker levels and GAF score provides an objective means to
confirm and validate the subjective GAF score for diagnosis and monitoring of
psychotic disease such as schizophrenia (and bipolar disorder).
Methods of diagnosing or monitoring according to the invention, may
comprise measuring the level of one or more of the biomarkers present in
biological samples taken on two or more occasions from a test subject.
Comparisons may be made between the level of the biomarkers in samples
taken on two or more occasions. Assessment of any change in the level of the
biomarkers in samples taken on two or more occasions may be performed.
Modulation of the biomarker level is useful as an indicator of the state of the
psychotic disorder or predisposition thereto.
An increase in the level of VLDL or LDL in a biological sample,
preferably in plasma, over time is indicative of onset or progression, i.e.
worsening of the disorder, whereas a decrease in the level of VLDL or LDL
indicates amelioration or remission of the disorder.
A decrease in the level of plasma protein in a biological sample,
preferably in a sample of whole blood, plasma, or serum over time is indicative
of onset or progression, i.e. worsening of the disorder, whereas an increase in
the level of plasma protein indicates amelioration or remission of the disorder.
A method according to the invention may comprise comparing the level
of one or more biomarkers in a biological sample taken from a test subject with
the level present in one or more samples taken from the test subject prior to
commencement of a therapy, and/or one or more samples taken from the test
subject at an earlier stage of a therapy. Such methods may comprise detecting
a change in the amount of the one or more biomarkers in samples taken on
two or more occasions. Methods of the invention are particularly useful in
assessment of anti-psychotic therapies.

A method of diagnosis of or monitoring according to the invention may
comprise quantifying the one or more biomarkers in a test biological sample
taken from a test subject and comparing the level of the one or more
biomarkers present in said test sample with one or more controls. The control
can be selected from a normal control and/or a psychotic disorder control. The
control used in a method of the invention can be one or more controls selected
from the group consisting of: the level of biomarker found in a normal control
sample from a normal subject, a normal biomarker level; a normal biomarker
range, the level in a sample from a subject with a schizophrenic disorder,
bipolar disorder, related psychotic disorder, or a diagnosed predisposition
thereto; a schizophrenic disorder marker level, a bipolar disorder marker level,
a related psychotic disorder marker level, a schizophrenic disorder marker
range, a bipolar disorder marker range and a related psychotic disorder marker
range.
Biological samples can be taken at intervals over the remaining life, or a
part thereof, of a subject. Suitably, the time elapsed between taking samples
from a subject undergoing diagnosis or monitoring will be 3 days, 5 days, a
week, two weeks, a month, 2 months, 3 months, 6 or 12 months. Samples
may be taken prior to and/or during and/or following an anti-psychotic therapy,
such as an anti-schizophrenic or anti-bipolar disorder therapy.
Measurement of the level of a biomarker can be performed by any
method suitable to identify the amount of the biomarker in a biological sample
taken from a patient or a purification of or extract from the sample or a dilution
thereof. Measuring the level of a biomarker present in a sample may include
determining the concentration of the biomarker present in the sample. Such
quantification may be performed directly on the sample, or indirectly on an
extract therefrom, or on a dilution thereof. In methods of the invention, in
addition to measuring the concentration of the biomarker in a biological
sample, which is preferably whole blood, plasma or serum, the concentration of
the biomarker may be tested in a different biological sample taken from the test
subject, e.g. CSF, urine, saliva, or other bodily fluid (stool, tear fluid, synovial
fluid, sputum), breath, e.g. as condensed breath, or an extract or purification
therefrom, or dilution thereof. Biological samples also include tissue

homogenates, tissue sections and biopsy specimens from a live subject, or
taken post-mortem. The samples can be prepared, for example where
appropriate diluted or concentrated, and stored in the usual manner.
Biomarker levels can be measured by one or more methods selected
from the group consisting of: spectroscopy methods such as NMR (nuclear
magnetic resonance), or mass spectroscopy (MS); SELDI (-TOF), MALDI (-
TOF), a 1-D gel-based analysis, a 2-D gel-based analysis, liquid
chromatography (e.g. high pressure liquid chromatography (HPLC) or low
pressure liquid chromatography (LPLC)), thin-layer chromatography, and LC-
MS-based techniques. Appropriate LC MS techniques include ICAT® (Applied
Biosystems, CA, USA), or iTRAQ® (Applied Biosystems, CA, USA).
Measurement of a biomarker may be performed by a direct or indirect
detection method. A biomarker may be detected directly, or indirectly, via
interaction with a ligand or ligands, such as an enzyme, binding receptor or
transporter protein, antibody, peptide, aptamer, or oligonucleotide, or any
synthetic chemical receptor or compound capable of specifically binding the
biomarker. The ligand may possess a detectable label, such as a luminescent,
fluorescent or radioactive label and/or an affinity tag.
The term "antibody" as used herein includes, but is not limited to:
polyclonal, monoclonal, bispecific, humanised or chimeric antibodies, single
chain antibodies, Fab fragments and F (ab')2 fragments, fragments produced by
a Fab expression library, anti-idiotypic (anti-Id) antibodies, and epitope-binding
fragments of any of the above. The term "antibody" as used herein also refers
to immunoglobulin molecules and immunologically-active portions of
immunoglobulin molecules, i. e., molecules that contain an antigen binding site
that specifically binds an antigen. The immunoglobulin molecules of the
invention can be of any class (e. g., IgG, IgE, IgM, IgD and IgA) or subclass of
immunoglobulin molecule.
Metabolite biomarkers as described herein are suitably measured by
conventional chemical or enzymatic methods (which may be direct or indirect
and or may not be coupled), electrochemical, fluorimetric, luminometric,
spectrophotometric, fluorimetric, luminometric, spectrometric, polarimetric,
chromatographic (e.g. HPLC) or similar techniques.

For enzymatic methods, consumption of a substrate in the reaction, or
generation of a product of the reaction, may be detected, directly or indirectly,
as a means of measurement.
VLDL and LDL biomarkers can be detected and levels measured using
various detection systems including liquid-phase chemical methods
(immunoseparation and separation with polyanion surfactant/detergent
combinations), physical methods for separation of lipoproteins (e.g.,
electrophoresis, capillary isotachophoresis, and chromatography), which may
be performed in conjunction with enzymatic assays e.g. the cholesterol
esterase-cholesterol oxidase (peroxidase) enzymatic assay, as well as indirect
methods such as NMR.
In normal individuals VLDL and LDL levels in serum/plasma are
generally 85mg/dl +/- 15% and 30mg/dl +/- 10% for VLDL and LDL
respectively, thus levels above these are diagnostic of psychotic disorder,
especially schizophrenia, a bipolar disorder, or a predisposition thereto.
Aromatic species biomarkers such as plasma proteins can be detected
and levels measured using methods including, but not limited to, ultraviolet
absorbance and colorimetric methods such as Bradford assay, Lowry assay,
and BCA assay.
The biomarkers of the invention are preferably detected and measured
using mass spectrometry-based techniques; chromatography-based
techniques; enzymatic detection systems (by direct or indirect measurements);
or using sensors, e.g. with sensor systems with amperometric, potentiometric,
conductimetric, impedance, magnetic, optical, acoustic or thermal transducers.
A sensor may incorporate a physical, chemical or biological detection
system. An example of a sensor is a biosensor, i.e. a sensor with a biological
recognition system, e.g. based on a nucleic acid, such as an oligonucleotide
probe or aptamer, or a protein such as an enzyme, binding protein, receptor
protein, transporter protein or antibody.
The biosensor may incorporate an immunological method for detection
of the biomarker, an electrical, thermal, magnetic, optical (e.g. hologram) or
acoustic technologies. Using such biosensors, it is possible to detect the target
biomarker at the anticipated concentrations found in biological samples.

Methods of the invention are suitable for clinical screening, assessment
of prognosis, monitoring the results of therapy, identifying patients most likely
to respond to a particular therapeutic treatment, for drug screening and
development, and to assist in identification of new targets for drug treatment.
The identification of key biomarkers specific to a disease is central to
integration of diagnostic procedures and therapeutic regimes.
Methods of the invention may further comprise one or more
assessments to diagnose and/or monitor a psychotic disorder in a subject.
Assessment may be a clinical assessment, carried out by a clinician in
accordance with accepted assessment protocols, e.g. global functioning score
(GAF) or SCID, and/or may involve a self-assessment by the subject. Rating
scales may be used to assist diagnosis and/or monitoring. GAF and SCID are
assessed on the basis of a clinical interview. It is preferred that assessments,
such as global functioning score, are made at (i.e. the same day as) or around
(i.e. within a few days of) the time of collection of the test biological sample
from the subject. This is particularly useful as a tool for diagnosing and
monitoring female subjects, in which VLDL and LDL levels were found to have
a very close inverse correlation with the clinical assessment as determined by
global functioning score.
Using predictive biomarkers such as those described herein, appropriate
diagnostic tools such as sensors and biosensors can be developed,
accordingly, in methods and uses of the invention, detecting and quantifying
one or more biomarkers can be performed using a sensor or biosensor.
A sensor or biosensor according to the invention is a psychotic disorder
sensor or biosensor capable of quantifying one, two, or three biomarkers
selected from the group: VLDL, LDL and aromatic species such as plasma
proteins.
The sensor or biosensor may incorporate detection methods and
systems as described herein for detection of the biomarker. Sensors or
biosensors may employ electrical (e.g. amperometric, potentiometric,
conductimetric, or impedance detection systems), thermal (e.g. transducers),
magnetic, optical (e.g. hologram) or acoustic technologies. In a sensor or
biosensor according to the invention the level of one, two, or three biomarkers
can be detected by one or more methods selected from: direct, indirect or
coupled enzymatic, spectrophotometric, fluorimetric, luminometric,
spectrometric, polarimetric and chromatographic techniques. Particularly
preferred sensors or biosensors comprise one or more enzymes used directly
or indirectly via a mediator, or using a binding, receptor or transporter protein,
coupled to an electrical, optical, acoustic, magnetic or thermal transducer.
Using such biosensors, it is possible to detect the level of target biomarkers at
the anticipated concentrations found in biological samples.
A biomarker or biomarkers of the invention can be detected using a
sensor or biosensor incorporating technologies based on "smart" holograms, or
high frequency acoustic systems, such systems are particularly amenable to
"bar code" or array configurations.
In smart hologram sensors (Smart Holograms Ltd, Cambridge, UK), a
holographic image is stored in a thin polymer film that is sensitised to react
specifically with the biomarker. On exposure, the biomarker reacts with the
polymer leading to an alteration in the image displayed by the hologram. The
test result read-out can be a change in the optical brightness, image, colour
and/or position of the image. For qualitative and semi-quantitative applications,
a sensor hologram can be read by eye, thus removing the need for detection
equipment. A simple colour sensor can be used to read the signal when
quantitative measurements are required. Opacity or colour of the sample does
not interfere with operation of the sensor. The format of the sensor allows
multiplexing for simultaneous detection of several substances. Reversible and
irreversible sensors can be designed to meet different requirements, and
continuous monitoring of a particular biomarker of interest is feasible.
Suitably, biosensors for detection of the biomarker of the invention are
coupled, i.e. they combine biomolecular recognition with appropriate means to
convert detection of the presence, or quantitation, of the biomarker in the
sample into a signal. Biosensors can be adapted for "alternate site" diagnostic
testing, e.g. in the ward, outpatients' department, surgery, home, field and
workplace.
Biosensors to detect the biomarker of the invention include acoustic,
plasmon resonance, holographic and microengineered sensors. Imprinted

recognition elements, thin film transistor technology, magnetic acoustic
resonator devices and other novel acousto-electrical systems may be
employed in biosensors for detection of the biomarkers of the invention.
Methods involving detection and/or quantification of the biomarker of the
invention can be performed using bench-top instruments, or can be
incorporated onto disposable, diagnostic or monitoring platforms that can be
used in a non-laboratory environment, e.g. in the physician's office or at the
patient's bedside. Suitable sensors or biosensors for performing methods of
the invention include "credit" cards with optical or acoustic readers. Sensors or
biosensors can be configured to allow the data collected to be electronically
transmitted to the physician for interpretation and thus can form the basis for e-
neuromedicine.
A higher level of the VLDL and/or LDL biomarkers in the test biological
sample relative to the level in a normal control is indicative of the presence of a
psychotic disorder, in particular a schizophrenic disorder, bipolar disorder, or
predisposition thereto.
The invention also comprises detecting and/or quantifying a
transthyretin peptide biomarker, preferably comprising the amino acid
sequence of SEQ ID NO: 1, or a fragment thereof, in a test biological sample
from a test subject and comparing the level of peptide present in said test
sample with one or more controls.
The invention further comprises detecting and/or quantifying an ApoA1
peptide biomarker comprising the amino acid sequence of SEQ ID NO: 2, or a
fragment thereof, in a test biological sample from a test subject and comparing
the level of peptide present in said test sample with one or more controls.
The control used in a method of the invention can be one or more
controls selected from the group consisting of: the level of biomarker found in a
normal control sample from a normal subject, a normal biomarker level or a
normal biomarker concentration range.
Suitably, the test and the normal control sample will be the same type of
sample, e.g. the level in a test serum sample will be compared with the level in
a control serum sample.

A preferred method of diagnosing a schizophrenic disorder or
predisposition thereto, comprises:
(a) quantifying the amount of a peptide biomarker comprising SEQ ID NO: 1 or
2, or a fragment thereof present in a test biological sample, and
(b) comparing the amount of said peptide in said test sample with the amount
present in a normal control biological sample from a normal subject.
A lower level of the transthyretin peptide biomarker in the test sample
relative to the level in the normal control is indicative of the presence of a
schizophrenic disorder or predisposition thereto. An equivalent or higher level
of said peptide in the test sample relative to the normal control is indicative of
absence of a schizophrenic disorder and/or absence of a predisposition
thereto.
Efficient diagnosis and monitoring methods provide very powerful
"patient solutions" with the potential for improved prognosis, by establishing the
correct diagnosis, allowing rapid identification of the most appropriate
treatment (thus lessening unnecessary exposure to harmful drug side effects),
reducing "down-time" and relapse rates.
Also provided is a method of monitoring efficacy of a therapy for a
schizophrenic disorder in a subject having such a disorder, suspected of
having such a disorder or of being predisposed thereto, comprising detecting
and/or quantifying a transthyretin peptide, preferably comprising the amino acid
sequence of SEQ ID NO: 1, or a fragment thereof, present in a biological
sample from said subject. In monitoring methods, test samples may be taken
on two or more occasions. The method may further comprise comparing the
level of the biomarker present in the test sample with one or more controls
and/or with one or more previous test samples taken earlier from the same test
subject, e.g. prior to commencement of therapy, and/or from the same test
subject at an earlier stage of therapy. The method may comprise detecting a
change in the level of the biomarker in test samples taken on different
occasions.
The invention provides a method for monitoring efficacy of therapy for a
schizophrenic disorder in a subject, comprising:

(a) quantifying the amount of a transthyretin peptide biomarker, preferably
comprising the amino acid sequence of SEQ ID NO: 1 or a fragment thereof, in
a test biological sample taken from said subject, and
(b) comparing the amount of said peptide in said test sample with the amount
present in one or more controls and/or one or more previous test samples
taken at an earlier time from said same test subject.
An increase in the level of the peptide biomarker in the test sample
relative to the level in a previous test sample taken earlier from the same test
subject is indicative of a beneficial effect, e.g. stabilisation or improvement, of
said therapy on the disorder, suspected disorder or predisposition thereto.
Methods for monitoring efficacy of a therapy can be used to monitor the
therapeutic effectiveness of existing therapies and new therapies in human
subjects and in non-human animals (e.g. in animal models). These monitoring
methods can be incorporated into screens for new drug substances and
combinations of substances.
Suitably, the time elapsed between taking samples from a subject
undergoing diagnosis or monitoring will be 3 days, 5 days, a week, two weeks,
a month, 2 months, 3 months, 6 or 12 months. Samples may be taken prior to
and/or during and/or following an anti-schizophrenic disorder therapy.
Samples can be taken at intervals over the remaining life, or a part thereof, of a
subject.
Quantifying the amount of the biomarker present in a sample may
include determining the concentration of the peptide biomarker present in the
sample. Detecting and/or quantifying may be performed directly on the
sample, or indirectly on an extract therefrom, or on a dilution thereof.
Detecting and/or quantifying can be performed by any method suitable
to identify the presence and/or amount of a specific protein in a biological
sample from a patient or a purification of extract of a biological sample or a
dilution thereof. In methods of the invention, quantifying may be performed by
measuring the concentration of the peptide biomarker in the sample or
samples. Biological samples that may be tested in a method of the invention
include cerebrospinal fluid (CSF), whole blood, blood serum, urine, saliva, or
other bodily fluid (stool, tear fluid, synovial fluid, sputum), breath, e.g. as

condensed breath, or an extract or purification therefrom, or dilution thereof.
Biological samples also include tissue homogenates, tissue sections and
biopsy specimens from a live subject, or taken post-mortem. Preferably, the
sample is CSF or blood serum. The samples can be prepared, for example
where appropriate diluted or concentrated, and stored in the usual manner.
Detection and/or quantification of transthyretin peptide biomarkers may
be performed by detection of the peptide biomarker or of a fragment thereof,
e.g. a fragment with C-terminal truncation, and/or with N-terminal truncation.
Fragments are suitably greater than 4 amino acids in length. Preferably,
fragments are in the range of from about 6 to about 50 amino acids in length.
The biomarker may be directly detected, e.g. by SELDI or MALDI-TOF.
Alternatively, the biomarker may be detected, directly or indirectly, via
interaction with a ligand or ligands such as an antibody or a biomarker-binding
fragment thereof, or other peptide, or ligand, e.g. aptamer, or oligonucleotide,
capable of specifically binding the biomarker. The ligand may possess a
detectable label, such as a luminescent, fluorescent or radioactive label, and/or
an affinity tag. Ligands include, for example:
(1) in vivo: T3, T4 (thyroid hormones), vitamin A (indirectly by interacting with
serum retinol-binding protein), apolipoprotein Al (ApoAl), noradrenaline
oxidation products, and pterins.
(2) in vitro (most of them pharmacological agents): some non-steroidal anti-
inflammatory drugs (NSAIDs), environmental pollutants, such as
polyhalogenated biphenyls and thyromimetic compounds, xanthone derivatives
as well as natural and synthetic flavonoids. Other ligands may be antibodies as
listed in Table 1.
For example, methods relating to detecting, monitoring, diagnosing
and/or quantifying can be performed by one or more methods selected from
the group consisting of: SELDI (-TOF), MALDI (-TOF), a 1-D gel-based
analysis, a 2-D gel-based analysis, Mass spec (MS), LC and LC-MS-based
techniques. Appropriate LC MS techniques include ICAT® (Applied
Biosystems, CA, USA), or iTRAQ® (Applied Biosystems, CA, USA). Liquid
chromatography (e.g. high pressure liquid chromatography (HPLC) or low

pressure liquid chromatography (LPLC)), thin-layer chromatography, NMR
(nuclear magnetic resonance) spectroscopy could also be used.
Methods for diagnosis or monitoring according to the invention may
comprise analysing a biological sample, e.g. cerebrospinal fluid (CSF) or
serum, by SELDI TOF, MALDI TOF and other methods using mass
spectrometry to detect the presence or level of the peptide biomarker
comprising SEQ ID NO: 1 or 2 or a fragment thereof. Such techniques may be
used for relative and absolute quantification and also to assess the ratio of the
biomarker according to the invention with other biomarkers that may be
present. These methods are also suitable for clinical screening, prognosis,
monitoring the results of therapy, identifying patients most likely to respond to a
particular therapeutic treatment, for drug screening and development, and
identification of new targets for drug treatment.
Surface enhanced laser deionization ionization (SELDI) mass
spectrometry is a powerful tool for identifying a characteristic "fingerprint" of
proteins and peptides in body fluids and tissues for a given condition, e.g. drug
treatments and diseases19. This technology utilizes protein chips to capture
proteins/peptides and a time-of-flight mass spectrometer (tof-MS) to quantitate
and calculate the mass of compounds ranging from small molecules and
peptides of less than 1,000 Da up to proteins of 500 kDa. Quantifiable
differences in protein/peptide patterns can be statistically evaluated using
automated computer programs which represent each protein/peptide measured
in the biofluid spectrum as a coordinate in multi-dimensional space. This
approach has been most successful in the field of clinical biomarker discovery
as it can be used as a diagnostic tool without knowing the biomarkers' identity.
The SELDI system also has a capability of running hundreds of samples in a
single experiment. In addition, all the signals from SELDI mass spectrometry
are derived from native proteins/peptides (unlike some other proteomics
technologies which require protease digestion), thus directly reflecting the
underlying physiology of a given condition.
Detecting and/or quantifying the transthyretin peptide biomarker may be
performed using any method based on immunological, peptide, aptamer or
synthetic recognition. For example, the method may involve an antibody, or a

fragment thereof capable of specific binding to the transthyretin peptide
biomarker, e.g. to a peptide comprising or consisting of the amino acid
sequence shown in SEQ ID NO: 1 or 2 or a fragment thereof. Suitable
antibodies that bind human TTR are commercially available, and are listed in
TaKlo 1
(Table Removed)
Suitable immunological methods include sandwich immunoassays, such
as sandwich ELISA in which the detection of the peptide biomarkers is
performed using two antibodies which recognize different epitopes on the
peptide biomarker (see examples); radioimmunoassays (RIA), direct or
competitive enzyme-linked immunosorbent assays (ELISA), enzyme
immunoassays (EIA), western blotting, immunoprecipitation and any particle-
based immunoassay (e.g. using gold, silver, or latex particles, magnetic
particles, or Q-dots). Immunological methods may be performed, for example,
in microtitre plate or strip format.
In methods and uses of the invention in which the amount of the
transthyretin biomarker peptide of SEQ ID NO: 1 or a fragment thereof present
in a test sample from a test subject is measured, detection of a lower level of
the biomarker peptide in the test sample compared to the level found in a
normal control sample from a normal individual is indicative of a schizophrenic
disorder or a predisposition thereto in the test subject. For example in serum,
the amount of transthyretin peptide of SEQ ID NO: 1, a fragment or derivative
thereof, detected in a sample from a test subject with a schizophrenic disorder
or predisposition thereto will generally be at least 15% lower than the amount
of the transthyretin peptide found in a normal control sample. In the prefrontal

cortex, the decrease of transthyretin expression is about 40%. In CSF samples,
the decrease is about 20% compared to TTR levels in normal control samples.
According to the invention, it is also possible to assess, for example
using mass spectrometry or other suitable techniques, a decrease of the TTR
peptide of SEQ ID No 1 or a fragment thereof by reference to the levels of a
control peptide or protein. Such peptide may be another biomarker for a
schizophrenic disorder.
In methods and uses of the invention in which the amount, level or
concentration of the ApoA1 biomarker peptide of SEQ ID NO: 2 or a fragment
thereof present in a test sample from a test subject is measured, detection of a
lower level of the biomarker peptide in the test sample compared to the level
found in a normal control sample from a normal individual is indicative of a
schizophrenic disorder or a predisposition thereto in the test subject. For
example, the level of peptide of SEQ ID NO: 2 detected in a test sample from a
test drug-naive subject with a schizophrenic disorder or predisposition thereto
will generally be at least about 10% to about 80%, preferably about 18% to
about 60%, lower than the amount of the peptide found in a normal control
sample.
Biological samples that may be tested in a method of the invention
include cerebrospinal fluid (CSF), whole blood, blood serum, plasma, red blood
cells, liver cells, urine, saliva, or other tissue or bodily fluid (stool, tear fluid,
synovial fluid, sputum), breath, e.g. as condensed breath, or an extract or
purification therefrom, or dilution thereof. Biological samples also include
tissue homogenates, tissue sections and biopsy specimens from a live subject,
or taken post-mortem. The samples can be prepared, for example where
appropriate diluted or concentrated, and stored in the usual manner.
In a serum sample from a test drug-naTve subject with a schizophrenic
disorder or predisposition thereto, the level of the ApoA1 biomarker peptide
comprising SEQ ID NO: 2 or a fragment thereof detected and/or quantified
according to the methods of the invention will be about 10% to about 25%
lower, preferably about 18% lower, than the amount of the peptide found in a
normal control serum sample.
In a red blood cells sample from a test drug-naiVe subject with a
schizophrenic disorder or predisposition thereto, the level of the ApoA1
biomarker peptide comprising SEQ ID NO: 2 or a fragment thereof detected
and/or quantified according to the methods of the invention will preferably be
about 50% to about 70% lower, preferably about 60% lower, than the amount
of the peptide found in a normal control red blood cell sample.
In liver cells from a test drug-naive subject with a schizophrenic disorder
or predisposition thereto, the level of ApoA1 peptide biomarker peptide
comprising SEQ ID NO: 2 or a fragment thereof detected and/or quantified
according to the methods of the invention will preferably be about 20% to about
40% lower, preferably about 30% lower, than the amount of the peptide found
in a normal control liver cell sample.
In a CSF sample from a test drug-naive subject with a schizophrenic
disorder or predisposition thereto, the level of ApoA1 peptide biomarker
peptide comprising SEQ ID NO: 2 or a fragment thereof detected and/or
quantified according to the methods of the invention will preferably be about
20% to about 40% lower, preferably about 30% to about 35% lower, than the
amount of the peptide found in a normal control CSF sample.
Detection and/or quantification of ApoA1 peptide biomarkers may be
performed by detection of the peptide biomarker or of a fragment thereof, e.g. a
fragment with C-terminal truncation, or with N-terminal truncation. Fragments
are suitably greater than 4 amino acids in length.
The biomarker may be directly detected, e.g. by SELDI, MALDI-TOF.
Alternatively, the biomarker may be detected directly or indirectly via interaction
with any naturally occurring, biologically derived or synthetic ligand or ligands
such as an antibody or a biomarker-binding fragment thereof, or other peptide,
or ligand, e.g. aptamer, or oligonucleotide, or chemically-synthesised binding
partner, capable of specifically binding the biomarker. Ligands used in the
methods of the invention may possess a detectable label, such as a
luminescent, coloured, metallic, magnetic, fluorescent or radioactive label,
and/or an affinity tag (e.g Arg-tag, calmodulin-binding peptide, cellulose-binding
domain, DsbA, c-myc-tag, glutathione S-transferase, FLAG-tag, HAT-tag, His-
tag, maltose-binding protein, NusA, S-tag, SBP-tag, Strep-tag, or thioredoxin).

The marker may also comprise nanoparticles. Quantum dots (Qdots) may be
used. Qdots are core/shell CdSe/ZnS nanocrystals of a few nanometers in
size, which can be conjugated to biomolecules.
The ligand may also be labelled with up-converting phosphors. Up-
converting phosphors are uniform submicron up-converting phosphors
microspheres that can be synthesised and coated with biologically active
probes, such as antibodies. They are materials that emit visible light upon
excitation with near infra-red light.
Depending on the nature of the ligand, Fluorescence Resonance Energy
Transfer (FRET), channelling assays (e.g. Luminescent oxygen channelling, for
example using LOCI® latex particles conjugated to the biomolecule) or
proximity assays may be used for detection.
Furthermore, surface plasmon resonance (SPR) may be used as a
label-free sensing tool.
Detecting and/or quantifying can be performed by one or more methods
selected from the group consisting of: LC, UPLC, CZE, SELDI (-TOF), MALDI
(-TOF), a 1-D gel-based analysis, a 2-D gel-based analysis, e.g. Differential In
Gel Electrophoresis (2D-DIGE), Mass spec (MS) and LC-MS-based
techniques. Appropriate LC MS techniques include ICAT® (Applied
Biosystems, CA, USA), or iTRAQ® (Applied Biosystems, CA, USA). Liquid
chromatography (e.g. high pressure liquid chromatography (HPLC) or low
pressure liquid chromatography (LPLC)), thin-layer chromatography, NMR
(nuclear magnetic resonance) spectroscopy could also be used.
Methods for diagnosis according to the invention may comprise
analysing a biological sample, e.g. cerebrospinal fluid (CSF), serum or plasma,
by SELDI TOF or MALDI TOF to detect the presence or level of the peptide
biomarker of SEQ ID NO: 2 or a fragment thereof.
Detecting and/or quantifying the ApoA1 peptide biomarker may be
performed using an immunological method, involving an antibody, or a
fragment thereof capable of specific binding to the ApoA1 peptide biomarker,
e.g. an antibody to a peptide consisting of the amino acid sequence shown in
SEQ ID NO: 2 or a fragment thereof. Suitable immunological methods include
sandwich immunoassays, such as sandwich ELISA in which the detection of

the peptide biomarkers is performed using two antibodies which recognize
different epitopes on the peptide biomarker; radioimmunoassays (RIA), direct
or competitive enzyme linked immunosorbent assays (ELISA) or any
modification or embodiment thereof, enzyme-immuno assays (EIA), western
blotting, immunoprecipitation and any particle-based immunoassay (e.g. using
gold, silver, or latex particles, magnetic particles, or Q-dots). Immunological
methods may be performed, for example, in microtitre plate or strip format.
Biosensors according to the invention may comprise a ligand or ligands,
as described herein, capable of specific binding to the peptide biomarker.
Such biosensors are useful in detecting and/or quantifying a peptide of the
invention.
Also provided is an array, pattern or signature comprising a ligand as
described herein capable of specific binding to a peptide biomarker.
Diagnostic or monitoring kits are provided for performing methods of the
invention. Such kits will suitably comprise a ligand as described herein, for
detection and/or quantification of the peptide biomarker, and/or a biosensor,
and/or an array as described herein, optionally together with instructions for
use of the kit.
Also provided by the invention is the use of a ligand as described herein,
which may be naturally occurring or chemically synthesised, and is suitably a
peptide, antibody or fragment thereof, aptamer or oligonucleotide, or the use of
a biosensor of the invention, or an array of the invention, or a kit of the
invention to detect and/or quantify the peptide biomarker or a fragment thereof.
In this use, the detection and/or quantification can be performed on a biological
sample, such as CSF, whole blood, blood serum, tear fluid, urine, saliva, or
other bodily fluid, breath, e.g. as condensed breath, or an extract or purification
therefrom, or dilution thereof.
Thus, in a further aspect of the invention, there is provided the use of a
ligand, as described herein, which can be a peptide, antibody or fragment
thereof or aptamer or oligonucleotide according to the invention; or the use of a
biosensor according to the invention, or an array according to the invention; or
a kit according to the invention, to identify a substance capable of stimulating,
promoting or activating the generation of a peptide biomarker.

Also there is provided a method of identifying a substance capable of
stimulating, promoting or activating the generation of a peptide biomarker, the
peptide biomarker preferably comprising the amino acid sequence of SEQ ID
NO: 1 or 2, or a fragment thereof, in a subject, comprising administering a test
substance to a subject animal and detecting and/or quantifying levels of the
peptide biomarker present in a test sample from the subject.
Any suitable animal may be used as a subject non-human animal, for
example a non-human primate, horse, cow, pig, goat, zebrafish, sheep, dog,
cat, fish, rodent, e.g. guinea pig, rat or mouse; insect (e.g. Drosophila),
amphibian (e.g. Xenopus) or C. elegans.
The test substance can be a known chemical or pharmaceutical
substance, such as, but not limited to, an anti-schizophrenic disorder
therapeutic, or the test substance can be a novel synthetic or natural chemical
entity, or a combination of two or more of the aforesaid substances.
There is provided a method of identifying a substance capable of
stimulating, promoting or activating the generation of a peptide biomarker,
preferably comprising the amino acid sequence of SEQ ID NO: 1 or 2, or a
fragment thereof, in a subject, comprising exposing a test cell to a test
substance and monitoring levels of the peptide biomarker within said test cell,
or secreted by said test cell. The test cell could be prokaryotic, however it is
preferred that a eukaryotic cell be employed in cell-based testing methods.
Suitably, the eukaryotic cell is a yeast cell, insect cell, Drosophila cell,
amphibian cell (e.g. from Xenopus), C. elegans cell or is a cell of human, non-
human primate, equine, bovine, porcine, caprine, ovine, canine, feline, piscine,
rodent or murine origin.
In methods for identifying substances of potential therapeutic use, non-
human animals or cells can be used that are capable of expressing human
transthyretin polypeptides.
Screening methods also encompass a method of identifying a ligand
capable of binding to a peptide biomarker according to the invention,
comprising incubating a test substance in the presence of the peptide
biomarker in conditions appropriate for binding, and detecting and/or
quantifying binding of the peptide to said test substance.
High-throughput screening technologies based on the biomarkers, uses
and methods of the invention, e.g. configured in an array, pattern or signature
format, are suitable to monitor biomarker signatures for the identification of
potentially useful therapeutic compounds, e.g. ligands such as natural
compounds, synthetic chemical compounds (e.g. from combinatorial libraries),
peptides, monoclonal or polyclonal antibodies or fragments thereof, capable of
binding the biomarker.
Methods of the invention can be performed in array, pattern or signature
format, e.g. on a chip, or as a multiwell array. As described above, other
techniques, such as mass spectrometry can also be used. Methods can be
adapted into platforms for single tests, or multiple identical or multiple non-
identical tests, and can be performed in high throughput format. Methods of
the invention may comprise performing one or more additional, different tests
to confirm or exclude diagnosis, and/or to further characterise a condition.
The invention further provides a substance, e.g. a ligand, identified or
identifiable by an identification or screening method or use of the invention.
Such substances may be capable of stimulating, promoting or activating,
directly or indirectly, the activity of a peptide biomarker, or of stimulating,
promoting or activating generation of the peptide biomarker. The term
substances includes substances that do not directly bind the peptide biomarker
and directly induce expression of the peptide biomarker or promote or activate
a function, but instead indirectly induce expression of the peptide biomarker or
promote/activate a function of the peptide biomarker. Ligands are also
included in the term substances; ligands of the invention (e.g. a natural or
synthetic chemical compound, peptide, aptamer, oligonucleotide, antibody or
antibody fragment) are capable of binding, preferably specific binding, to a
peptide biomarker.
The invention further provides the use of a substance or ligand
according to the invention in the treatment of a schizophrenic disorder or
predisposition thereto.
Also provided is the use of a substance according to the invention as a
medicament.

Yet further provided is the use of a substance according to the invention
in the manufacture of a medicament for the treatment of a schizophrenic
disorder or predisposition thereto.
A kit for diagnosing or monitoring a schizophrenic disorder or
predisposition thereto is provided. Suitably a kit according to the invention may
contain one or more components selected from the group: a ligand specific for
a peptide biomarker, a peptide biomarker, a control, a reagent, and a
consumable; optionally together with instructions for use of the kit.
The terms "treating" or "treatment" as used herein with reference to
therapeutic uses of the biomarker of the invention describe the management or
care of a patient for the purposes of combating disease, and include the
administration of the active agents to asymptomatic individuals, for example to
prevent the onset of the symptoms or complications (i.e. prophylaxis).
Also, there is provided a method for identifying a schizophrenic disorder
therapeutic substance, wherein said substance is capable of promoting the
generation of an ApoA1 peptide biomarker, said method comprising
administering said substance to a test subject, and detecting and/or quantifying
the level of ApoA1 peptide biomarker in said test subject. In another
embodiment, there is provided a method for identifying a schizophrenic
disorder therapeutic substance wherein said substance is capable of promoting
the activity of an ApoA1 peptide biomarker, said method comprising
administering said substance to a test subject, and detecting and/or quantifying
the activity of ApoA1 peptide biomarker in said test subject. An increase in the
level or activity of an ApoA1 biomarker peptide indicates that the substance is
schizophrenic disorder therapeutic substance. Preferably, the ApoA1 peptide
biomarker according to these methods comprises SEQ ID NO:2, a fragment
thereof or a non-human ApoA1 homolog.
The term "therapeutic substance" as used herein defines a substance
that has therapeutic, i.e. curative/beneficial properties and treats a
schizophrenic disorder, alleviates the symptoms thereof or prevents the onset
of a schizophrenic disorder. Thus, the substance is for use in the treatment of
schizophrenia.

The said test subject according to a method for identifying a
schizophrenia disorder therapeutic substance may be any suitable animal,
preferably a non-human animal, for example a non-human primate, horse, cow,
pig, goat, sheep, dog, cat, fish, rodent, e.g. guinea pig, rabbit, rat or mouse;
insect (e.g. Drosophila), amphibian (e.g. Xenopus) or C. elegans.
~ The test substance can be a known chemical or pharmaceutical
substance, such as, but not limited to, an anti-schizophrenic therapeutic or a
known anti-psychotic; or the test substance can be novel synthetic or natural
chemical entity, or a combination of two or more of the aforesaid substances.
The invention further provides an in vitro method of identifying a
schizophrenic disorder therapeutic substance wherein said substance is
capable of stimulating or promoting the generation of an ApoA1 peptide
biomarker, said method comprising exposing a test cell to a test substance and
detecting an increased level of said biomarker peptide or a fragment thereof
within said test cell or secreted by said test cell. Also provided is an in vitro
method of identifying a schizophrenic disorder therapeutic substance wherein
said substance is capable of stimulating or promoting the activity of an ApoA1
peptide biomarker, said method comprising exposing a test cell to a test
substance and detecting an increased activity of said biomarker peptide or a
fragment thereof within said test cell or secreted by said test cell. Preferably,
the ApoA1 peptide biomarker according to these in vitro methods comprises
SEQ ID NO:2, a fragment thereof or a non-human ApoA1 homolog.
Suitably, the eukaryotic cell can be selected from a yeast cell, insect
cell, Drosophila cell, amphibian cell (e.g. from Xenopus), C. elegans cell or the
cell can be of human, non-human primate, equine, bovine, leporine, porcine,
caprine, ovine, canine, feline, piscine, rodent or murine origin.
In methods for identifying substances of potential therapeutic use, non-
human animals or cells can be used that are capable of expressing human
ApoA1 polypeptides. Alternatively, the non-human cells may express their
endogenous ApoA1.
Screening methods also encompass a method of identifying a ligand
capable of binding to an ApoA1 peptide biomarker according to the invention,
comprising incubating a test substance in the presence of the peptide

biomarker in conditions appropriate for binding, and detecting and/or
quantifying binding of the peptide to said test substance.
Also provided is a substance identified by a method according to the
invention.
Diagnostic or monitoring kits are provided for performing methods of the
invention. Such kits will suitably comprise a ligand as described herein
capable of specific binding to the ApoA1 peptide biomarker, for detection
and/or quantification of the ApoA1 peptide biomarker, and/or a biosensor,
and/or an array as described herein, optionally together with instructions for
use of the kit. In another aspect, the invention provides a kit for diagnosing or
monitoring a schizophrenic disorder or predisposition thereto. Suitably, a kit
according to the invention may contain one or more components selected from
the group: a ligand specific for an ApoA1 peptide biomarker, an ApoA1 peptide
biomarker or a structural/shape mimic of an ApoA1 peptide biomarker, a
control, a reagent, and a consumable; optionally together with instructions for
use of the kit.
Methods of the invention can be performed in multi-analyte panel or
array format, e.g. on a chip, or as a multiwell array. Methods can be adapted
into platforms for single tests, or multiple identical or multiple non-identical
tests, and can be performed in high throughput format. Methods of the
invention may comprise performing one or more additional, different tests to
confirm or exclude diagnosis, and/or to further characterise a psychotic
condition.
List of Figures
Figure 1 Metabonomic analysis of plasma samples from monozygotic
twins discordant for schizophrenia and control twins. (A) Partial 1H NMR
spectrum of plasma samples from a pair of representative twins discordant with
schizophrenia (the affected co-twin in grey and the unaffected in black)
illustrate changes in lipid regions- (CH2)n and CH3 lipids. (B) and (C) PLS-DA
scores plots showing differentiation of control twin from unaffected and affected
twins with schizophrenia as determined by the 1H NMR plasma spectra. The
unaffected co-twin shows an intermediate position between controls and the
schizophrenic co-twin.

Figure 2 Metabonomic analysis of plasma samples from female twins
discordant for schizophrenia and female control twins. PLS-DA scores plots
(Fig 2A) of female monozygotic twins showing a clear differentiation of control
twins, unaffected twins and the schizophrenic twins as determined by the 1H
NMR plasma spectra. The loading plots demonstrate that LDL (0.86 and 1.26),
VLDL (0.9 and 1.3) and aromatic regions (-7.5) are the key chemical shifts that
contribute to the separation. There is a high degree of similarity between Fig
1CandFig2B.
Figure 3 Negative correlations between global functioning score (DSM
IV, Axis V) and two key chemical shifts (1.24-1.28ppm; A and 1.28-1.32ppm; B)
primarily corresponding to LDL and VLDL levels in female twin plasma. The R2
are shown in each plots.
Figure 4 Metabonomic analysis of plasma samples from male
discordant twins with and without schizophrenia and male control twins. PLS-
DA scores plots (A) showing no differentiation between male control twins and
unaffected male twins whilst schizophrenic twins show a moderate
differentiation from male control twins and male discordant twins. Glucose level
(3.2-3.9ppm) and signals from aromatic region (~7.9ppm) and 1.04-1.06-
1.12ppm regions were found to be the major contributing factor for separation
as illustrated in the loading plots (B).
Figure 5 Protein/peptide profiling of CSF samples from first-onset, drug-
naive schizophrenia patients using SELDI mass spectrometry. A: A typical
CSF protein/peptide spectrum using an anion exchanger chip (Q10; 50mM
Tris-HCI, pH9.0) showing the m/z range of about 10,000-15,000 from a healthy
volunteer.
B: The peak intensity of protein/peptide peaks from SELDI spectra were
analyzed using PCA and PLS-DA models. A 3D PLS-DA scores plot indicates
clusters of healthy volunteers (in black) and untreated, drug-naive
schizophrenia patients (in grey).
C: and D PLS-DA scores and loadings plots. The scores plot is similar to (B)
but only the first two components were used to discriminate healthy controls
and patients. The loading plot as shown in (D) indicates the key protein/peptide
peaks contributing the most towards the separation in (C).

Figure 6 Down-regulation of three different forms of transthyretin in CSF
from first onset, drug-nai've schizophrenia patients. Examples of CSF spectra
from healthy volunteers and patients with schizophrenia within 6-17kDa are
shown in (A). The peak cluster indicated (arrow) is enlarged in (B). Statistical
details of each sub-peak are listed in the table below. On-chip reduction of
CSF peptide/protein with p-mecaptoethanol at room temperature showed that
the three peaks 13,741, 13,875, and 13,923 Da were reduced into a single
peak (C), suggesting they are different S-cysteinylated derivatives of the same
protein. To identify the identity, CSF samples from a healthy volunteer and a
schizophrenia patient were applied to an anion exchanger column (HyperD)
and eluted with pH9-pH3 buffers. A major band was eluted at ~14-15kQa in
pH3 fraction (D, left panel). The band was identified as transthyretin using LC-
MS/MS (D, right panel) and the sequence coverage is shown. In addition, the
band was confirmed to be the peak cluster around 13.5-14kDa in the spectra
by eluting the proteins from the band and running on a NP20 chip to match the
mass (E).
Figure 7 Transthyretin levels in sera of first onset, drug-naive
schizophrenia patients and prefrontal cortex post-mortem tissue from
schizophrenia patients. The serum samples from the same patients whose
CSF protein profiles were measured in Figure 5 and 8 were included in this
study. Figure 7A shows serum transthyretin levels in schizophrenia patients
significantly decreased by -15% compared to control subjects. Data are shown
in Mean ± S.D. *p=0.007 (t test). Figure 7B indicates no correlation between
serum transthyretin and CSF SELDI signals from one of the transthyretin
isoforms (m/z=13,741) in the 2nd sample set (for demographics, see table 5).
Similar results were found when comparing with signals from other isoforms in
CSF (data not shown). Figure 7C shows a -40% decrease of transthyretin
expression in prefrontal cortex of 5 schizophrenia patients and 5 control
subjects. For demographic details, see Table 6.
Figure 8 PLS-DA analysis of CSF protein/peptide profiles from an
independent validation sample set containing 18 first-onset, drug-naive
schizophrenia patients and 40 healthy volunteers. The demographic details of
this sample set are listed in Table 5. The PLS-DA scores plot showed a

separation between patients (black) and healthy volunteers (grey). The
loadings plot indicates transthyretin protein signals between 13,600 and 14,000
found in the first experiment (see Figure 5).
Figure 9. Down-regulation of CSF ApoA1 levels in first-onset drug-naTve
schizophrenia patients. A: Strong anion-exchange (Q10) ProteinChip arrays
were used to profile CSF proteins and peptides. A representative CSF SELDI
spectrum showing the profile of proteins/peptides with a mass-to-charge ratio
between m/z= 4,000 to 70,000 m/z. B: a gel view of 14 representative spectra
showing apoA1 protein (-28 kDa) to be reduced (-35%; p=0.00001) in
schizophrenia samples compared to controls. The adjacent histogram depicts
the mean +/- SD of ApoA1 levels (41 CSF samples from first-onset drug-naTve
schizophrenia patients were compared to 40 matched control samples). C: The
~ 28kDa peak was gel purified (arrow) and the excised protein was sequenced
using LC-MS/MS (right panel). The sequence coverage that is highlighted in
bold corresponds to part of the published ApoA1 amino acid sequence. D: the
band was confirmed to be the peak cluster around 28kDa in the spectra by
immuno-capturing the proteins in CSF samples with an anti-ApoA1 antibody
on-chip (RS-100 ProteinChip). CSF samples were applied to RS-100 chips
coupled with (lower panel) or without (upper panel) anti-ApoA1 antibody. The
proteins bound to the chips were analyzed by SELDI-TOF. The captured
ApoA1 protein shows an m/z at 28kDa. E: Western blot analysis showing
ApoA1 expression in the prefrontal cortex of 8 schizophrenia patients and 8
healthy volunteers. A trend towards down-regulation was observed (-32%;
p=0.07, t test). The protein loadings are shown below the blot.
Figure 10. 2-D DIGE analysis of liver from schizophrenia patients
(n=15) and controls (n=15). A: A typical 2D gel image of liver protein extracts.
ApoA1 protein (the corresponding spot is indicated by an arrow) was one of the
significantly altered proteins. B: ApoA1 levels were found to be significantly
reduced in livers from schizophrenia patients (-30%; p=0.017). The histogram
depicts the mean +/- SD of the relative standardised abundance. C: LC-
MS/MS analysis of trypsinized peptides from the gel spot showed that three
peptide fragments were derived from apoA1 protein. The Mascot score and
sequence coverage are shown in the table.

Figure 11 2-D DIGE analysis of red blood cells (RBC) from
schizophrenia patients (n=20) and controls (n=20), illustrating a decrease in
ApoA1 protein expression. A: A typical 2D gel image of the unfractionated RBC
proteome, showing a dominant expression of haemoglobin proteins (pl~7). B:
A typical 2D gel image after removing dominant proteins (i.e. haemoglobin) by
a Ficoll density gradient. The arrow and spot indicates the position the apoA1
spot on the gel. C: ApoA1 levels were found to be significantly reduced in RBC
from schizophrenia patients (-60%.; p=0.0034). The histogram depicts the
mean +/- SD of the relative standardised abundance. D: LC-MS/MS analysis
of trypsinized peptides from the gel spot identified that eight peptide fragments
were derived from ApoA1 protein. The Mascot score and sequence coverage
are shown in the table.
Figure 12 Down-regulation of serum ApoA1 levels in schizophrenia. A:
ELISA analysis of apoA1 levels in sera of first-onset drug-naive schizophrenia
patients (n=35) and healthy volunteers (n=63). The mean value +/- S.D. of
apoA1 concentrations in schizophrenia patients and controls is shown.
p=0.00039 (t test). B: Correlation analysis of CSF and serum ApoA1 levels
from the same individuals. No correlation was found between serum and CSF
levels for either the control or patient group.
Figure 13 illustrates that a high sensitivity of about 89% and a
specificity of about 73% can be achieved when combining 2 biomarkers for
PCA analysis.
Examples
The invention will be further understood by reference to the Examples
provided below.
Example 1
Plasma samples from 21 pairs of monozygotic twins discordant for
schizophrenia and 16 matched control twins were collected under standardised
conditions by Dr Fuller Torrey, Stanley Medical Research Institute, Bethesda,
USA. All study participants gave their written informed consent and the original
study was approved by an Institutional Review Board. The GAF of each
individual was derived by consensus of the two interviewers who did the SCID

interview Structured Clinical Interview for DSM-IV-TR (SC1D). SCID is a clinical
rating scale which involves a semi-structured diagnostic interview designed to
assist clinicians, researchers, and trainees in making reliable DSM-IV
psychiatric diagnoses. The plasma was obtained from both twins
simultaneously as part of a lymphocyte collection aphoresis procedure carried
out at mid-morning, with both twins having been on similar diets and residing in
a hotel together. Blood plasma samples (50ul) were made up to a final volume
of 500ul by the addition of D2O in preparation for 1H NMR analysis. Plasma
samples were diluted to a final volume of 550ul by the addition of isotonic
saline solution containing 10% D20 for the NMR field-frequency lock.
Twin samples were divided into aliquots and stored at -80°C. None of
the samples underwent more than 3 freeze-thaw cycles prior to acquisition of
NMR spectra. All experiments were performed under blind and randomized
conditions. Plasma samples (50ul) were made up to a final volume of 500ul by
the addition of D20 in preparation for 1H NMR analysis. Plasma samples were
diluted to a final volume of 550ul by the addition of isotonic saline solution
containing 10% D20 for the NMR field-frequency lock.
1H NMR Spectroscopy of Plasma Samples:
Standard 1-D 600MHz 1H NMR spectra were acquired for all samples
using a pre-saturation pulse sequence to effect suppression of the water
resonance (pulse sequence: relaxation delay-90o-ti-90°-tm-90°-acquire FID;
Bruker Analytische GmbH, Rheinstetten, Germany). In this pulse sequence, a
secondary radio frequency irradiation is applied specifically at the water
resonance frequency during the relaxation delay of 2s and the mixing period
(tm=100ms), with ti fixed at 3us. Typically 256 transients were acquired at 300K
into 32K data points, with a spectral width of 6000Hz and an acquisition time of
1.36s per scan. Prior to Fourier transformation, the free induction decays
(FID's) were multiplied by an exponential weight function corresponding to a
line-broadening factor of 0.3Hz.
Data Reduction and Pattern Recognition Procedures:
To evaluate efficiently the metabolic variability within and between
biofluids derived from patients and controls, spectra were data reduced using
the software program AMIX (Analysis of Mixtures version 2.5, Bruker

Rheinstetten, Germany) and exported into SIMCA-P (version 10.5, Umetrics
AB, Umea, Sweden) where a range of multivariate statistical analyses were
conducted. Initially principal components analysis (PCA) was applied to the
data in order to discern the presence of inherent similarities in spectral profiles.
Where the classification of 1H NMR spectra was influenced by exogenous
contaminants, the spectral regions containing those signals were removed from
statistical analysis. In order to confirm the biomarkers differentiating between
the schizophrenia patients and matched controls, projection to latent structure
discriminant analysis (PLS-DA) was employed. Where appropriate, data were
subjected to one-way analysis of variance (ANOVA) using the Statistical
Package for Social Scientists (SPSS/PC 13; SPSS, Chicago). Where the F
ratio gave P<0.05, comparisons between individual group means were made
by Dunnett T3 test at significance levels of P=0.05.
Results
Plots of PLS-DA scores based on 1H NMR spectra of plasma from 21
pairs of monozygotic twins discordant for schizophrenia and 16 matched
control twins differentiated affected and unaffected twins from age-matched
control twins (Figure 1A and 1B). The loading coefficients indicated that
resonances from VLDL (0.92-0.88 ppm and 1.28-1.32ppm), LDL (0.84-
0.88ppm and 1.24-28ppm) and aromatic groups (-87.5; most likely
representing plasma proteins) were predominantly responsible for the
separation (Table 3; Figure 1C). Co-twins with schizophrenia showed a 23%
(p=0.015; ANOVA) increase in plasma VLDL signals (1.28-1.32ppm) compared
to control twins. Corresponding unaffected co-twins were also found to have
increased 1.28-1.32ppm signals, however, differences were not quite
significant for the unaffected group (p=0.18; ANOVA). LDL levels in the three
groups showed a trend similar to that of the VLDL signals but, again, did not
reach statistical significance (data not shown). In addition, discordant
schizophrenia twins had lower plasma protein levels represented by aromatic
signals around 7.5ppm (14% and 8% reduction for the affected and unaffected
co-twins respectively; p<0.01). No difference was observed in HDL signals
(0.6-0.7ppm) between the groups. Further analyses showed a much more
pronounced differentiation of female twins (Figure 2). The key chemical shifts

that differentiated the groups are listed in Table 3. Interestingly, PLS-DA
analyses between the female affected and healthy discordant twins alone
showed that the same scores and loading plots that significantly separated the
discordant twins from control twins is responsible for the separation between
the discordant twins themselves. This implies that the identified metabolic
alterations are a genuine disease-related signature. Furthermore, signals
between 1.24-1.28ppm (mainly LDL) correlated strongly with scores obtained
from the DSM IV Axis V Global Assessment of Functioning (GAF) Scale
(R2=0.62, Figure 3), which represents one of the most widely used methods for
assessing impairment among patients with psychiatric disorders. The rating is
made on a scale from 1 to 100 with ratings of 1-10 representing severe
impairment and ratings of 90 or more indicating superior functioning (DSMIV;
Moos et al., 2002). Plasma VLDL signals (1.28-1.32ppm) of female twins also
show a strong correlation with GAF scores (R2=0.54; Figure 3). No correlation
was found when all twins or male twins alone were considered (data not
shown). Age did not appear to have an effect on disease-related chemical
shifts. However, antipsychotics drug exposure (measured as fluphenezine
equivalent) also correlated with GAF scores and metabolic signature
respectively of the female twins.
On the other hand, corresponding plots of PLS-DA scores of plasma 1H
NMR spectra derived from male twins discordant for schizophrenia showed a
less prominent differentiation between affected and unaffected twins (Figure
4A). Unlike the female twins, the loading coefficients indicated that resonances
from the aromatic region, corresponding to plasma proteins, are mainly
responsible for the separation amongst male twins (Figure 4B). No correlation
was found between the glucose signals and antipsychotic treatment, age,
duration of illness, substance abuse and GAF scores (data not shown) for male
twins. No significant difference was found between male control twins and
unaffected co-twins (Figure 4A).
Discussion of Example 1
The present study examined the metabolic plasma profiles of a total of
42 monozygotic twins discordant for schizophrenia and 16 matched control
twins using 1H NMR in order to explore the role of genetic and environmental

factors contributing to schizophrenia. The result show that signals from VLDL,
LDL and aromatic regions are the most important factors differentiating ill and
healthy co-twins discordant for schizophrenia from control twins. Interestingly,
this differentiation was much more pronounced for female twins.
Overall, similar metabolic changes were observed in male and female
schizophrenia twins, in the female group a potential predisposing disease
signature was found in unaffected co-twins. This could imply a greater genetic
loading for female twins. A marked sex difference in schizophrenia is a well
established fact; female schizophrenia patients have, on average, a later age
of onset and better prognosis. This has been attributed to protective effect of
oestrogens. Women suffering from acute psychotic episodes have been
shown to exhibit lower levels of oestrogen (Huber et ai, 2005). Oestrogens are
known to have neuroprotective properties and may reduce cell death
associated with excitotoxicity as well as oxidative stress.
In female twins suffering from schizophrenia, alterations were highly
associated with disease severity as well as exposure to typical antipsychotics,
making it difficult to evaluate the contribution of environmental factors and drug
effects. However, several lines of evidence suggest that the effect is not a drug
effect: in that similar changes were identified in unaffected co-twins; also, anti-
psychotic medication was not found to correlate with Global Functioning
Scores in affected male twins.
One of the most interesting findings in this study is the close association
of VLDL/LDL signals and Global Functioning Scores (DSMIV, Axis V) in female
subjects. This is apparently the first report showing a strong correlation
between a subjectively-derived clinical rating score and an objective biomarker;
Thus these biomarkers are useful as an aid in diagnosis and in establishing
clinical response.
(Table Removed)

Example 2
Extensive protein/peptide profiling analysis of CSF samples from a total
of 139 CSF samples (80 controls and 59 first onset, drug-naive schizophrenia
patients) was performed using SELDI mass spectrometry in combination with
computerized pattern recognition analysis. Highly significant and reproducible
differences were found in samples obtained from first-onset, drug-naTve patients
with a diagnosis of paranoid schizophrenia as compared to age-matched

(Table Removed)(Table Removed)
Sensitivity is defined as the proportion of true positives it detects of all the positives.
2Specificity is defined as the proportion of true negatives it detects of all the negatives.
Clinical samples
The Ethical committee of the Medical Faculty of the University of Cologne
reviewed and approved the protocol of this study and the procedures for sample
collection and analysis. All study participants gave their written informed
consent. All clinical investigations were conducted according to the principles
expressed in the Declaration of Helsinki. CSF and serum samples were
collected from drug-naive patients diagnosed with first episode paranoid
schizophrenia or brief psychotic disorder due to duration of illness (DSM-IV
295.30 or 298.8, n=59) and from demographically matched healthy volunteers
(n=80) (Tables 4 and 5). Fresh-frozen prefrontal cortex tissue (Brodmann area
9) from gray matter of 8 schizophrenia and 8 well-matched control individuals
was obtained from the Neuropathology Consortium of the Stanley brain
collection (Stanley Medical Research Institute, USA).
Preparation of CSF Samples for SELDI Analysis
5 u.1 of each CSF sample was applied to the chips with different chemical
properties at various pH conditions. The best condition was chosen at pH 9.0 on
strong anion exchanger Q10 chip based on number and separation of peaks
resolved. Briefly, the array spots were pre-activated twice with binding buffer
(100mM Tris-HCI, pH9.0) at room temperature for 10 min on a shaker
(frequency= 600 rpm). 50jil binding buffer composition was added into each
protein spot prior to the addition of 5 µl CSF sample. The protein chips were
incubated on a shaker for 60 min at room temperature. The chips were washed
twice with binding buffer and once with H20, and then air-dried. The chips were

then sequentially treated twice with 0.6 pi of a 100% saturated sinapinic acid
(3,5-dimethoxy-4-hydroxycinnamic acid) in 50% acetonitrile and 0.5%
trifluoroacetic acid. The chips were analyzed with the Ciphergen ProteinChip
Reader (Ciphergen ProteinChip System Series 4000). Each sample was
analyzed twice to confirm reproducibility in identifying the differentially
expressed proteins.
SELDI-TOF-MS Analysis
The arrays were analyzed with the Ciphergen ProteinChip System Series
4000 (Ciphergen Biosystems, USA). Mass spectra of proteins were generated
by using an average of 254 laser shots at a laser intensity of 1800 arbitrary
units. For data acquisition, the detection size range was between 3 and 200
kDa. The laser was focused at 10 kDa. The mass-to-charge ratio (mfz) of each
of the proteins captured on the array surface was determined according to
externally calibrated standards (Ciphergen Biosystems; USA): bovine insulin
(5,733.6 Da), human ubiquitin (8,564.8 Da), bovine cytochrome c (12,230.9 Da),
bovine superoxide dismutase (15,591.4 Da), horseradish peroxidase (43,240
Da) and BSA (66,410 Da). The data were analyzed with PROTEINCHIP data
analysis software version 3.0 and Ciphergen Express Software 3.0 (Ciphergen
Biosystems; USA). The Ciphergen Express Software 3.0 was used to compile
all spectra and autodetect quantified mass peaks. Peak labelling was completed
by using second-pass peak selection with 0.2% of the mass window, and
estimated peaks were added. The peak information of all spectra was exported
for further statistic analyses.
Peptide and protein identification
Identification of the schizophrenia specific peptides was performed by a
combination of purification step (either on-chip) followed by C18 Zip-Tip
purification. Typically, 10µl CSF samples from each the control and
schizophrenia groups were subjected to Q10 protein chips at pH 9.0 (50mM
Tris-HCI). Proteins/peptides bound to the chip were eluted with 5jil elution buffer
(30% acetonitrile, 50mM sodium acetate pH3.0) by pipetting and was desalted
using a C18 Ziptip according to manufacturer's manual. The peptides eluted
with 0.1% formic acid/50% aqueous acetonitrile (2µl) were further examined by

MALDI mass spectrometry for confirmation of the peak in CSF samples from
schizophrenia patients. The eluted peptides were also loaded into a C18 nano-
column linked with ESI-MS/MS (Applied Biosystems, USA) for de novo
sequencing.
For protein biomarkers, CSF proteins were purified from pooled CSF by a
combination of anion exchange chromatography (HyperD F; Ciphergen
Biosystems; USA) followed by SDS-PAGE. The bands around the matched
mass were cut out and the proteins from 1/3 of the excised protein band was
eluted passively using previous described method20 to confirm the mass in the
spectrum. The rest of the protein band was in-gel digested with trypsin (1:50;
Promega, UK) overnight at room temperature. The resulting peptide mixtures
were then sequenced using LC-MS/MS (Applied Biosystems, USA).
S-cysteinylated or S-glutathionylated isoforms (which are isoforms are
generated in vivo) of proteins were confirmed by comparing the spectra before
and after on-chip reduction using {3-mecaptoethanol. In brief, CSF protein and
peptide binding was performed as described above and in the final step each
spot was washed with 100ul 1mM HEPES pH 7.5. The proteins and peptides on
the chips were then reduced with 1/40 p-mecaptoethanol (1 µ!) for 30min at
room temperature. 1ml of water was added onto each spot and evaporated.
This procedure was repeated twice. Matrix was then added on and data were
acquired using ProteinChip Reader (Ciphergen ProteinChip System Series
4000).
Quantitative analysis of transthyretin in human serum samples by
enzyme-linked immunosorbent assays (ELISA)
Samples were defrosted from -80°C and vortexed for 10 min before
experimental work. All samples were assayed blind to the clinical conditions.
The identity of all subjects was blind by a code number until all biochemical
analyses were completed.
Transthyretin standard (Sigma, UK), controls and patient-derived human
serum samples were diluted 1000 times with phosphate buffered saline, pH 7.4,
(Sigma, UK), Transthyretin standard and samples were then loaded onto ELISA
Maxisorb plates (Nunc™, Denmark) and incubated for 1 h. All samples were

tested in duplicate. After washing with Washing buffer (0.03% Tween 20 in
PBS), the plates were blocked with 5% skimmed milk powder for 60 min. 100JJ.I
transthyretin antibody (DakoCytomation, Denmark, 1:500 diluted in 2.5%
skimmed milk powder) was incubated in 96-well plates for 60 min. The plates
were washed four times with Washing buffer followed by addition of 100 pi
secondary antibody (anti-rabbit HPP-linked IgG (Cell Signalling, UK; 1:2000) to
each well and incubated for 60 min. After washing with Washing Buffer three
times, 100 pi substrate (TMB One solution, Promega) was added into each well
and the mixture was incubated at room temperature for 10 min. The plate was
read at 450nm (BIO-RAD, Model 680).
Western blot analysis
The preparation of human brain samples for Western blot analysis and
the details of performing Western blotting were as described previously21. In
brief, equivalent amounts of protein (30 ug per sample) were resolved
electrophoretically on 10% polyacrylamide gels and transferred onto
nitrocellulose, which was then incubated with primary antibody (anti-
transthyretin, DakoCytomation) in 3% milk-PBS overnight at 4°C, followed by
incubation of a secondary antibody (HRP conjugated anti-rabbit secondary
antibody (Cell Signaling, 1:2500) at room temperature for 1 hr. Enhanced
chemiluminescence (LumiGluTM, Cell Signaling) was used to detect signals
from the blot. Consistency of protein loading and transfer was determined by
Ponseau S staining.
Statistic analysis
Multivariate statistical analysis including principal component analysis
(PCA), partial least squares discriminate analysis (PLS-DA) and PLS were
employed to summarize the data output from Ciphergen Express. Holdout
cross-validation was performed three times so that the sensitivity and specificity
of the PLS model could be estimated. In each of the three rounds of holdout
cross-validation, one third of the samples were randomly selected to form the
validation data and the remaining samples were used as the training data. All
multivariate analyses were performed using SIMCA-P+ 10 (Umetrics AB,
Sweden). Sensitivity is defined as the proportion of true positives it detects of all
the positives and specificity is defined as the proportion of true negatives it

detects of all of the negatives. Where appropriate, t test was performed using
the Statistical Package for Social Scientists (SPSS/PC+; SPSS, Chicago).
Alterations of CSF protein/peptide profiles in first-onset, drug-naive,
paranoid schizophrenia patients
In a first set of experiments protein/peptide profiles of CSF samples from
41 first-onset, drug-naive, paranoid schizophrenia patients and 40
demographically matched healthy volunteers were examined using SELDI mass
spectrometry. CSF proteins and peptides were profiled using Q10 (strong anion
exchanger) chips at pH 9.0. An example of the CSF protein/peptide profile of a
healthy volunteer is shown in Figure 5A. Approximately 75 peaks can be readily
detected with a signal to noise ratio >5 under this Q10 protein chip binding
condition. Plots of PLS-DA scores based on SELDI spectra of CSF samples
showed a clear differentiation between healthy volunteers and drug-naive
patients with first onset, paranoid schizophrenia (Figure 5B and C). Similar
results were found using principle component analysis (data not shown). The
loading plot showed significant reductions in clusters of peaks between 13,600-
14,000Da. The sensitivity and specificity of this model based on holdout cross
validation was 80% and 95%, respectively (Table 7).
Identification of the 13.6-14kDa protein cluster as transthyretin
The protein cluster between 13.6-14.1 kDa contained four peaks (Figure
6B), three of which were consistently down-regulated in CSF from first onset,
drug-naive schizophrenia patients (p<0.01; Figure 6B, bottom panel). Studies
have suggested that these peaks may be from S-cysteinylated or S-
glutathionylated derivatives of transthyretin protein 22,23, a thyroid hormone-
binding protein that transports thyroxine from the bloodstream to the brain. On-
chip reduction of CSF peptide/protein performed using p-mercaptoethanol at
room temperature showed that the three peaks 13,741, 13,875, and 13,923Da
were reduced to a single peak (Figure 6C), confirming they were derived from
the same protein. To identify the protein, a pair of CSF samples from a healthy
volunteer and a schizophrenia patient were applied to an anion exchanger
column (HyperD) and eluted with pH 9- pH 3 buffers. A major band ~13-15kD
was eluted in the pH3 fraction (Figure 6D, left panel). The band was confirmed
to be the peak cluster around 13.6-14kDa in the SELDI spectrum by eluting the

protein from the band and running on a NP20 chip to match the mass (Figure
66E). This protein was then digested with trypsin and sequenced using LC-
MS/MS. The protein was identified as transthyretin (Figure 6D, right panel).
Down-regulation of transthyretin in serum samples from the same
subjects as well as prefrontal cortex tissue from schizophrenia patients
It has been estimated that 3% of transthyretin in the ventricular CSF and
10% of the transthyretin in lumbar CSF are derived from blood. To evaluate the
contribution of bfood transthyretin to the changes found in CSF in
schizophrenia, serum transthyretin levels taken from the same individuals (at
the same time when CSF was collected) who had been studied in Figures 5 and
8 (for demographics, see Tables 4 and 5) were investigated using ELISA. A
moderate but significant decrease of transthyretin in sera was found from
schizophrenia patients compared to controls (15% decrease, p=0.0007, t test)
(Figure 7A). However, no correlation between CSF and serum transthyretin
levels from the same individuals was found, suggesting that transthyretin levels
are regulated independently in CSF and serum (Figure 7B). Transthyretin levels
were decreased in both CSF and serum samples. However, there is no
correlation of CSF transthyretin levels and serum transthyretin level.
Interestingly, a ~4o% down-regulation of transthyretin in post-mortem
prefrontal cortex from schizophrenia patients as compared to controls using
Western blot was found (Figure 7C).
Validation of protein/peptide biomarkers in an independent sample set
The biomarker model in Figure 5 was validated using an independent
sample set consisting of a further 18 first-onset, drug-naive schizophrenia
patients and 40 demographically matched healthy volunteers. These samples
were run using identical conditions as in the previously described experiment.
PLS-DA scores and loadings plots showed a very similar result as found in
Figure 5 with in the cluster of 13,600-14,000 proteins (Figure 8). This suggests
that these identified alterations in CSF proteins and peptides are a consistent
finding and thus may reflect genuinely the early pathophysiology of
schizophrenia. The sensitivity and specificity of this model was 95% and 98%,
respectively (Table 7).
(Table Removed)
Discussion of Example 2
Initial analysis of SELDI spectra of a total of 81 CSF samples (41
schizophrenia; 40 controls) showed a differential distribution of samples from
drug-naTve patients with first onset paranoid schizophrenia away from healthy
volunteer samples (Figure 5B, 5C and 5D). The protein/peptide profile of CSF
was found to be characteristically altered in paranoid schizophrenia patients
and a key alteration was the down-regulation of transthyretin around 14kDa.
These schizophrenia specific protein/peptide changes were replicated/validated
in an independent sample set (n=58) using identical conditions (Figure 8). Both
experiments achieved an astonishingly high specificity (rate of true negative) of
95/98% and a sensitivity of 80/90%, respectively (Table 7). This means that
virtually no control samples clustered with the schizophrenia group (Figure 5B
and 5C). For a high diagnostic validity and consequent therapeutic
interventions an accurate identification of those individuals who truly have the
disease is most critical.
A moderate but consistent decrease of transthyretin was observed in CSF
from first onset schizophrenia patients. ELISA results on the serum samples
collected from the identical individuals whose CSF samples were investigated in
this study showed that there is a -15% decrease in transthyretin levels in serum
(p=0.0007, t test; Figure 7A), however, there was no correlation between the
levels of serum transthyretin and SELDI signals from CSF transthyretin,
suggesting that liver derived transthyretin may not contribute to the down-
regulation in CSF (Figure 7B). Experiments perfusing isolated sheep brains
showed that all newly synthesized transthyretin was secreted from the choroid
plexus towards the ventricles. The synthesis of this protein is required for the
transport of thyroxine16. Thus, the decreased level of transthyretin in CSF
suggests a lowered thyroxine transport in brains of schizophrenia patients.
Indeed, the results found in this study showing a down-regulation in
transthyretin protein in post-mortem brain tissue from schizophrenia patients
(Figure 7C) further support this notion. It is noteworthy that thyroid dysfunction
is relatively common in patients with schizophrenia24'25 and indeed other
psychiatric disorders26, possibly genetically linked to the disorders. In addition,
in patients with severe forms of both hypo- and hyper-thyroidism, psychotic

symptoms may occur and the clinical picture frequently resembles that of
schizophrenia27, which may imply that an increase in CNS thyroxine function
may be linked. Interestingly, long-term administration of clozapine has been
shown to induce transthyretin expression in rat hippocampus and cerebral
cortex18, implying that clozapine enhances CNS thyroxine function in light of the
results herein, supporting the clinical relevance of transthyretin in the early
pathophysiology of schizophrenia.
The application of SELDI mass spectrometry can provide an efficient
means for early diagnosis of paranoid schizophrenia.
Example 3
Clinical samples
The protocols of this study including procedures for sample collection
and analysis were approved by ethical committees. Informed consent was given
in writing by all participants and clinical investigations were conducted
according to the principles expressed in the Declaration of Helsinki. CSF and
serum samples were collected from drug-naive patients diagnosed with first
episode paranoid schizophrenia or brief psychotic disorder due to duration of
illness (DSM-IV 295.30 or 298.8; n=41 for CSF; n=35 for serum; Table 8) and
from demographically matched healthy volunteers (n=40 for CSF; n=63 for
serum; Table 8).
For post-mortem studies, fresh-frozen prefrontal cortex tissue (Brodmann
area 9; 8 schizophrenia and 8 well matched control individuals) and liver
samples (15 schizophrenia and 15 well matched controls) were obtained. The
demographic details are listed in Table 8.
For red blood cell (RBC) experiments, a total of 40 blood samples (7 first-
onset, drug-naive schizophrenia patients and 13 schizophrenia patients treated
with atypical antipsychotic medication as well as 20 demographically-matched
healthy volunteers, see Table 8 for demographic details) were collected from
two centres using an identical sample collection procedure.

Table 8
(Table Removed)
Preparation of RBC samples
Blood samples were collected in anticoagulant EDTA tubes prior to cell
isolation and protein extraction (see below). To purify RBCs, 40 ml of freshly
drawn blood was diluted with 40 ml of PBS. The diluted blood was gently
layered on half volume of a density gradient separation medium
(HISTOPAQUE®-1077, Sigma) and centrifuged at 750 x g for 10 min. Isolated
RBC were then collected from the bottom of the tube and frozen at -80°C. RBC
were lysed with erythrocyte lysis buffer (Qiagen, UK) in 1:5 ratios at 4°C for 15
minutes. Proteins were extracted by precipitation using 100 mM ammonium
acetate in methanol overnight at -20°C and resuspended in ASB14 buffer (8 M
urea, 2% ASB14, 5 mM magnesium acetate, 20 mM Tris-HCI, 1% Triton-X100,
pH 8,) containing complete protease inhibitor cocktail (Roche, Switzerland) and
phosphatase inhibitors (1mM sodium pyrophosphate, 1mM sodium
orthovanadate, 10 mM (3-glycerophosphate, and 50mM sodium fluoride). Protein

concentration was determined using a detergent-compatible protein assay kit
(BioRes). The highly abundant protein, haemoglobin, was first pre-fractionated
from the RBC proteome using a ZOOM® IEF Fractionator (Invitrogen). This is a
simple and convenient method to reproducibly fractionate cell lysate on the
basis of isoelectric point (pi) using solution phase isoelectric focussing (IEF).
Fractionated proteins with a pi between 6.2 and 10, containing Hb were
discarded. The remaining fractions from each individual patient/control (pi 3-6.2)
were pooled and proteins were re-extracted by ammonium acetate precipitation
and subjected to 2D-DIGE analysis.
2D-DIGE analysis
2D-DIGE analyses of liver samples were performed using 24 cm, pH4-7,
IDG DryStrips. The detail procedures are as described previously (18).
CSF protein profiling using SELDI-TOF analysis and protein biomarker
identification
5 ul samples of each CSF was applied to protein chips with different
chemical properties at various pH conditions. The best condition was chosen at
pH 9.0 on strong anion exchanger Q10 chip, based on number and separation
of peaks resolved. Briefly, the array spots were pre-activated twice with binding
buffer (100mM Tris-HCI, pH 9.0) at room temperature for 10 minutes on a
shaker (frequency= 600 rpm). 50 ul binding buffer was added into each spot
prior to the addition of the 5 pi CSF sample. The protein chips were incubated
on a shaker for 60 min at room temperature, then washed twice with binding
buffer, once with H2O, and air-dried. The chips were then sequentially treated
twice with 0.6ul of a 100% saturated sinapinic acid (3, 5-dimethoxy-4-
hydroxycinnamic acid) in 50% acetonitrile and 0.5% trifluoroacetic acid. The
chips were analyzed using the Ciphergen ProteinChip Reader (Ciphergen
ProteinChip System Series 4000). Each sample was analyzed twice to confirm
reproducibility in identifying the differentially expressed proteins. Mass spectra
of proteins/peptides were generated by using an average of 254 laser shots at a
laser intensity of 1800 arbitrary units. For data acquisition, the detection size
range was between 3 and 200 kDa. The laser was focused at 10 kDa. The
mass-to-charge ratio (m/z) of each of the proteins captured on the array surface
was determined relative to external calibration standards (Ciphergen

Biosystems; USA): bovine insulin (5,733.6 Da), human ubiquitin (8,564.8 Da),
bovine cytochrome c (12,230.9 Da), bovine superoxide dismutase (15,591.4
Da), horseradish peroxidase (43,240 Da) and BSA (66,410 Da). The data were
analyzed with PROTEINCHIP data analysis software version 3.0 and using
Ciphergen Express Software 3.0 (Ciphergen Biosystems; USA). The Ciphergen
Express Software 3.0 was used to compile all spectra and autodetect quantified
mass peaks. Peak labelling was completed by using second-pass peak
selection with 0.2% of the mass window, and estimated peaks were added. The
statistic analyses of peak information were performed using Ciphergen Express
Software 3.0.
For identification of protein biomarkers, CSF proteins were purified from
pooled CSF by a combination of anion exchange chromatography (HyperD F;
Ciphergen Biosystems; USA) followed by SDS-PAGE. The band expected
correspond to the SELDI peak was cut from the gel and the gel band was in-gel
digested with trypsin (1:50; Promega, UK) overnight at room temperature. The
resulting peptide mixtures were then analyzed by LC-ESI-MS/MS (QSTAR,
Applied Biosystems, USA) and the protein identified by database searching
using Mascot software (Matrix Science, London). To confirm the gel band is the
protein of interest, an antibody capture experiment was performed. Briefly, 2 pi
of antibody (0.2mg/ml) was coupled to RS100 reactive chip surface, followed by
blocking with 2M Tris-HCI (pH8.0) at room temperature according to the
manufacturer's protocol. 5 pi CSF samples were then applied directly to spots
with or without antibody coupling and incubated for 1 hr. After washing 5 times
with 10 pi HEPES buffer (50mM, pH7.2), the chips were analyzed with
Ciphergen ProteinChip System Series 4000.
Western blot analysis
Western blot analysis of brain tissues has been described previously
(32). Briefly, after determining the protein concentration, samples were diluted in
sample buffer (Invitrogen), to a final concentration of 4mg/ml. 30 u.g of protein
was loaded into each well and separated on 4%-12% SDS pre-cast-gel
(Invitrogen) alongside ApoA1 standard (Sigma) as a positive control. Separated
proteins were transferred onto nitrocellulose membranes at room temperature.

The nitrocellulose membranes were incubated with blocking solution (5% dried
skimmed milk) for 60 minutes at room temperature followed by incubation with
anti-human ApoA1 polyclonal antibody (1:1000) (CalBiochem) overnight at 4SC.
Membranes were washed four times with wash buffer and then incubated with
horseradish peroxidase-conjugated secondary antibody (Cell Signalling,
1:5000) at room temperature for 1 hr. Chemiluminescent visualization (GE
Heathcare) was used to visualize the signals.
ELISA
Serum samples were randomized and the identity of all subjects was
blinded by a code number until all biochemical analyses were completed.
ApoA1 standard (Sigma, UK) alongside human serum samples from patients
and control subjects were diluted 1:1000 with phosphate buffered saline, pH 7.4
(PBS, Sigma, UK). ApoA1 standard and samples were then loaded onto ELISA
Maxisorb plates (Nunc™, Denmark) and incubated for 1 hr. After washing with
washing buffer (0.03% Tween 20 in PBS), the plates were blocked with 5%
dried skimmed milk powder in PBS for 60 minutes. 100 u.l ApoA1 primary
antibody (rabbit) (CalBiochem, UK; 1:1000) was incubated in 96-well plates for
60 minutes. The plates were washed four times with wash buffer followed by the
addition of 100 ul anti-rabbit secondary antibody (Cell Signalling, UK; 1:2000) to
each well, and incubated for 60 minutes. All incubations were carried out on a
shaker (600 rpm) at room temperature. Finally, after washing four times with
wash buffer, 100ul substrate (TMB One solution, Promega) was then added to
each well and incubated at room temperature for 10 minutes. The plates were
read with a plate reader (BIO-RAD, Model 680) at 450 nm. Statistical analysis of
serum samples was performed by t-test using the Statistical Package for Social
Scientists (SPSS/PC+; SPSS, Chicago). All measurements were replicated in
an independent experiment.
(Table Removed)
Example 4
Changes in ApoA1 (apolipoprotein A1) and TTR (transthyretin) proteins
were initially identified in CSF from first-onset drug-na'ive schizophrenia
patients. Both were found to be significantly down-regulated in CSF.
This Example investigated changes in ApoA1 and TTR in serum. Thus
ELISA assays for both proteins were established and 27 schizophrenia (first-
onset drug naive) and 48 healthy volunteer sera were investigated. Both
proteins were found to be significantly reduced in schizophrenia serum (apoA1:
-18%; p= 0.00039 and TTR: -15% ; p=0.0007). See Fig. 13.

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CLAIMS
1. A method of diagnosing or monitoring a psychotic disorder in a subject,
comprising:
(a) providing a sample from said subject,
(b) performing spectral analysis on said sample to provide one or more spectra,
and
(c) comparing said one or more spectra with one or more control spectra.

2. A method according to claim 1, wherein the spectral analysis is
performed by NMR spectroscopy.
3. A method according to claim 1 or claim 2, wherein the spectral analysis is
performed by 1H NMR spectroscopy.
4. A method according to any preceding claim, wherein the one or more
control spectra comprise normal control spectra.
5. A method according to any preceding claim, wherein the one or more
control spectra comprise psychotic disorder control spectra.
6. A method according to any preceding claim, wherein said comparing
comprises classifying spectra of a sample as having a normal profile, psychotic
disorder profile, or psychotic disorder predisposition profile.
7. A method according to any preceding claim, wherein said comparing
comprises one or more chemometric analyses.
8. A method according to any preceding claim, wherein said comparing
comprises a pattern recognition analysis.
9. A method according to claim 8, wherein the pattern recognition analysis
is performed by one or more supervised and/or unsupervised methods.
10. A method according to claim 9, wherein the one or more unsupervised
methods are selected from a principle components analysis (PCA), non-linear
mapping (NLM) and a clustering method.
11. A method according to claim 9 or claim 10, wherein the one or more
supervised methods are selected from a soft independent modelling of class
analogy, a partial least squares (PLS) method, a k-nearest neighbour analysis
and a neural network.

12. A method according to any preceding claim, comprising performing
spectral analyses to provide spectra from samples taken on two or more
occasions from the subject.
13. A method according to claim 12, comprising comparing spectra from
samples taken on two or more occasions from the subject.
14. A method according to any preceding claim, wherein said comparing
comprises assessing variation in one or more biomarkers present in said
spectra.
15. A method of diagnosing or monitoring a psychotic disorder in a subject,
comprising:

(a) providing a sample from said subject,
(b) performing spectral analysis on said sample to provide one or more spectra,
(c) analysing said one or more spectra to detect the level of one or more
biomarkers present in said one or more spectra, and
(d) comparing the level of said one or more biomarkers detected in said one or
more spectra with the level of said one or more biomarkers detected in control
spectra.

16. A method according to claim 15, comprising analysing spectra from
samples taken on two or more occasions from the subject, to quantify one or
more biomarkers present in the samples, and comparing the levels of the one or
more biomarkers present in the samples.
17. A method according to any one of claims 14 to 16, further comprising
detecting a change in the level of the one or more biomarkers in samples taken
from the subject on two or more occasions.
18. A method according to any preceding claim, wherein the one or more
biomarkers are selected from a transthyretin peptide comprising SEQ ID NO: 1
or a fragment thereof, an ApoA1 peptide comprising SEQ ID NO: 2 or a
fragment thereof, VLDL, LDL and aromatic species such as plasma proteins.
19. A method of diagnosing or monitoring a psychotic disorder, or
predisposition thereto, comprising measuring the level of one or more
biomarkers present in a sample taken from a subject, said biomarkers being
selected from a transthyretin peptide comprising SEQ ID NO: 1 or a fragment

thereof, an ApoA1 peptide comprising SEQ ID NO: 2 or a fragment thereof,
VLDL, LDL and aromatic species such as plasma proteins.
20. A method of monitoring efficacy of a therapy in a subject having,
suspected of having, or of being predisposed to, a psychotic disorder,
comprising a method according to claim 19.
21. A method according to claim 19 or claim 20, comprising measuring the
level of the one or more biomarkers present in samples taken on two or more
occasions from the subject.
22. A method according to claim 21, comprising comparing the levels of the
one or more biomarkers present in samples taken on two or more occasions
from the subject.
23. A method according to any one of claims 19 to 21, comprising comparing
the levels of the one or more biomarkers in a sample taken from the subject with
the level present in one or more samples taken from the subject prior to
commencement of a therapy, and/or one or more samples taken from the
subject at an earlier stage of a therapy.
24. A method according to any one of claims 19 to 23, comprising detecting
a change in the amount of the one or more biomarkers in samples taken on two
or more occasions.
25. A method according to any one of claims 19 to 24, wherein the therapy is
an anti-psychotic disorder therapy.
26. A method according to any one of claims 19 to 25, comprising comparing
the amount of the one or more biomarkers present in a sample with the level in
one or more controls.
27. A method according to claim 26, wherein the controls are a normal
control and/or a psychotic disorder control.
28. A method according to any one of claims 14 to 27, wherein the level of
one or more biomarkers is detected by analysis of NMR spectra.
29. A method according to any one of claims 14 to 28, wherein the level of
one or more biomarkers is detected by one or more methods selected from
NMR, SELDI (-TOF), MALDI (-TOF), a 1 -D gel-based analysis, a 2-D gel-based
analysis, mass spectrometry (MS) and LC-MS-based technique.

30. A method according to any one of claims 14 to 28, wherein the level of
one or more biomarkers is detected by one or more methods selected from
direct or indirect, coupled or uncoupled enzymatic methods, electrochemical,
spectrophotometric, fluorimetric, luminometric, spectrometric, polarimetric and
chromatographic techniques, or an immunological method such as ELISA.
31. A method according to any one of claims 14 to 30, wherein the biomarker
is VLDL and/or LDL and the level thereof is detected by one or more methods
selected from a liquid-phase chemical method, a physical method for separation
of lipoproteins and an enzymatic assay.
32. A method according to any one of claims 14 to 31, wherein the level of
plasma proteins is detected by one or more method selected from ultraviolet
absorbance and a colorimetric method.
33. A method according to any one of claims 14 to 32, wherein the level of
one or more biomarkers is detected using a sensor or biosensor comprising one
or more enzymes, binding, receptor or transporter proteins, antibody, synthetic
receptors or other selective binding molecules for direct or indirect detection of
the biomarkers, said detection being coupled to an electrical, optical, acoustic,
magnetic or thermal transducer.
34. A method according to any preceding claim, wherein the sample is
selected from whole blood, blood serum, blood plasma or an extract or
purification therefrom, or dilution thereof.
35. A method according to any preceding claim, comprising quantifying one
or more biomarkers in a further sample taken from the subject.
36. A method according to claim 35, wherein the further biological sample is
selected from CSF, urine, saliva, or other bodily fluid, or breath, condensed
breath, or an extract or purification therefrom, or dilution thereof.
37. A method according to any preceding claim, wherein the subject is drug-
naive.
38. A method according to any preceding claim, wherein the psychotic
disorder is a schizophrenic disorder.
39. A method according to claim 38, wherein the schizophrenic disorder is
selected from paranoid, catatonic, disorganized, undifferentiated and residual
schizophrenia.

40. A method according to any one of claims 1 to 37, wherein the psychotic
disorder is a bipolar disorder.
41. A method according to any preceding claim, further comprising a clinical
or self-assessment of the subject.
42. A method according to claim 41, wherein the clinical assessment is a
SCID or global functioning score assessment.
43. A method according to claim 41 or claim 42, wherein the assessment is
made at or about the time of collection of the sample from the subject.
44. A method according to any preceding claim, wherein the subject is a
female subject.
45. A method of monitoring efficacy of a therapeutic substance in a subject
having, suspected of having, or of being predisposed to, a psychotic disorder,
comprising the steps of a method according to any of claims 1 to 36.
46. A method of identifying an anti-psychotic substance, comprising the
steps of a method according to any of claims 1 to 36.
47. A method of identifying a pro-psychotic substance, comprising the steps
of a method according to claims 1 to 36.
48. A method according to any one of claims 45 to 47, comprising comparing
the level of one or more biomarkers in a sample taken from the subject with the
level present in one or more samples taken from the subject prior to
administration of the substance, and/or one or more samples taken from the
subject at an earlier stage during treatment with the substance.
49. A psychotic disorder sensor capable of quantifying one or more
biomarkers selected from a transthyretin peptide comprising SEQ ID NO: 1 or a
fragment thereof, an ApoA1 peptide comprising SEQ ID NO: 2 or a fragment
thereof, VLDL, LDL and aromatic species such as plasma proteins .
50. A sensor according to claim 49, wherein the one or more biomarkers is
quantifiable by one or more methods selected from direct, indirect or coupled
enzymatic, spectrophotometric, fluorimetric, luminometric, spectrometric,
polarimetric and chromatographic techniques.
51. A sensor according to claim 49 or claim 50, comprising a component
selected from enzymes, binding, receptor or transporter proteins, antibody or
fragment thereof, synthetic receptors or other selective binding molecules for

direct or indirect detection of the one or more biomarkers, said component
being coupled to an electrical, optical, acoustic, magnetic or thermal transducer.
52 An array or multi-analyte panel capable of detecting one or more
biomarkers as defined in claim 49.
53. The use of one or more biomarkers as defined in claim 49, to diagnose
and/or monitor a psychotic disorder.
54. A method of identifying a substance capable of modulating a psychotic
disorder, comprising administering a test substance to a subject and detecting
the level of one or more biomarkers as defined in claim 49, in a sample taken
from said subject.
55. A method according to claim 54, wherein the sample is selected from the
group consisting of: whole blood, blood serum, blood plasma or an extract or
purification therefrom, or dilution thereof.
56. A ligand capable of specific binding to a peptide biomarker as defined in
claim 49.
57. A ligand according to claim 56, which comprises a peptide or optomer.
58. A ligand according to claim 56, which is an antibody.
59. A ligand according to claim 58, wherein the antibody is a monoclonal
antibody.
60. A ligand according to claim 56, which is not a ligand as listed herein.
61. A ligand according to any one of claims 56 to 60, labeled with a directly
or indirectly detectable marker.
62. A ligand according to claim 61, wherein the detectable marker is a
luminescent, fluorescent, enzyme or radioactive marker.
63. A ligand according to any one of claims 56 to 62, labeled with an affinity
tag.
64. A sensor comprising a ligand according to any one of claims 56 to 63.
65. An array comprising a ligand according to any one of claims 56 to 63.
66. A method of identifying a substance capable of stimulating, promoting or
activating the generation of a peptide biomarker as defined in claim 49,
comprising administering a test substance to a subject animal and detecting
and/or quantifying the peptide biomarker present in said subject.

67. A method of identifying a substance capable of stimulating, promoting or
activating the generation of a transthyretin peptide biomarker as defined in
claim 49, comprising exposing a test cell to a test substance and monitoring
levels of the peptide within said test cell or secreted by said test cell.
68. A method according to claim 67, wherein the test cell is a eukaryotic cell.
69. A method according to claim 67 or 68, wherein the eukaryotic cell is a
yeast cell, insect cell, Drosophila cell, amphibian cell (e.g. from Xenopus) or C.
elegans cell, or is a cell of human, non-human primate, equine, bovine, porcine,
caprine, ovine, canine, feline, piscine, rodent, or murine origin.
70. A method according to any one of claims 67 to 69, wherein said animal
or cell is a non-human animal or cell engineered to be capable of expressing
the peptide.
71. A method of identifying a ligand capable of binding to a peptide
biomarker as defined in claim 49, comprising incubating a test substance in the
presence of the peptide under conditions appropriate for binding, and detecting
and/or quantifying binding of said peptide to said test substance.
72. A method of identifying a ligand capable of specific binding to a peptide
biomarker as defined in claim 49, comprising incubating a test substance in the
presence of the peptide, and detecting and/or quantifying specific binding of
said peptide to said test substance.
73. The use of a substance or ligand according to any one of claims 56 to 63
and 66 to 72, in the treatment of a psychotic disorder or a predisposition
thereto.
74. The use of a substance or ligand according to any one of claims 56 to 63
and 66 to 72, in the manufacture of a medicament for the treatment of a
psychotic disorder or predisposition thereto.
75. The invention according to any preceding claim, wherein biomarkers are
used and are a combination of the peptide biomarkers.