1. Association of Circadian Rhythm Proteins with Schizophrenia: Insights into
Mechanisms
Mathias Hibbard
NTP 604
Abstract
Numerous groups are reporting associations between abnormalities in the circadian rhythm in
many neuropsychiatric disorders. Abnormalities have also been shown to be especially prevalent
in schizophrenia. Understanding the genetic etiology underlying this association is therefore
important to developing efficacious medication in the future. The overwhelming diversity of
genetic association there are for schizophrenia, though, poses daunting challenges to researchers
in the laboratory, and for text mining to find potential candidate genes. However, a growing
number of online bioinformatics tools and results databases provide a means to surf the genome
and find appropriate candidate genes, and even investigate explanations for the association. Here,
I use a number of such tools to locate genes involved in circadian process that have been tied to
schizophrenia. I then investigate one of these genes, ARNTL, and provide a plausible framework
as to its association with the disorder. It would appear as if ARNTL is subject to drastic changes
in expression, and that reduced expression promotes the development of schizophrenia—however
slightly—as displayed associations with blood triglyceride concentrations and pre-pulse inhibition
performance directions similar to those in schizophrenia. Overall, it appears that circadian
disruption contributes to the development of schizophrenia.
Introduction
The origins of schizophrenia have remained elusive since its initial classification.
Nonetheless, researchers have been able to characterize abnormal brain morphologies (Flaum et
al 1995) and physiologies (Cornblatt & Keiln 1994) associated with the disorder. Such
abnormalities, though, cannot be considered the primary cause of the disorder itself. Rather, the
origins of schizophrenia presumably lie within the genetic code—in rare single nucleotide
polymorphisms (SNP) that impair regular functioning of necessary biochemical circuits.
However, genome-wide association studies (GWAS) have associated innumerably many
SNPs with schizophrenia. These SNPs can impair a number of processes necessary for regular
development and sustained integrity of the nervous system: through altering the transcription of
the gene, conferring a gain of function, or even a loss thereof. Determining how certain SNPs
affect circadian processes is key to refining our understanding of the disorder, as many reports are
beginning to highlight its role in the development of the disorder (Karatsoreos 2014; Boivin 2000).
Such studies were primarily correlational, as abnormal circadian rhythms were shown to be

2. associated with schizophrenia (Hufford et al 2014; Wulff et al 2012). In causal studies, deletion of
key circadian genes were shown to promote the development of psychotic symptoms comparable
to humans later in life (Buchard-Cannon et al 2013; Kimiwada et al 2009).
Specifically, abnormalities in the circadian rhythm have been tied to impaired glyco- and
lipogenesis (Bass & Takahashi 2010, for review), as well as deficient neurogenesis (Malik et al
2015; Bouchard-Cannon et al 2013; Kimiwada et al 2009), and impaired cognitive abilities
(Hufford et al 2014)—all hallmark psychotic symptoms. Of course, the development of
phenotypes such as these are sub-served by a genetic basis; but it is presumable that allelic variants
in non-quintessential circadian genes must accompany deleterious variants in circadian proteins
for the disorder of schizophrenia to fully manifest itself in an individual. Therefore, dysfunction,
under- or over-expression of key circadian proteins may promote the development of
schizophrenia, and may augment characteristics of the disorder with other variants in downstream
biochemical pathways.
Of course, understanding ways in which these SNPs affect not only the function of the
protein, but there impact on biochemical systems as a whole are essential for developing more
efficacious therapeutics, and advancing individualized medicine. However, the sheer genetic
variability present within the schizophrenic population makes classic laboratory assays almost
futile.
Fortunately, online resources such as GWASdb2, GeneNetwork, and the NCBI’s GEO2R
provide substantial datasets from numerous studies that can be manipulated potential associations
and identify genes that may be involved in the disorder’s etiology. In other words, they provide a
more appropriate means to sleuth for candidate genes, understand their role in biochemical
processes, and how they can contribute to the development of a disorder. From these tools,
researchers may develop more robust hypotheses prior to rigorous experimentation in the
laboratory.
All in all, disruptions in the circadian rhythm appear to promote the development of
psychotic symptoms. However, there are numerous processes with which it has been associated,
and attempting to address causality through experimentation in vivo would prove immensely
difficult. However, the diversity of online resources and data-sets online allow for quick
assessments of a particular hypothesis prior to direct work in the lab. Here, a number of these

3. resources were used to identify genes involved in both the circadian rhythm and schizophrenia,
and assess how they might promote the development of psychotic symptoms.
Methods
Overview of methods
A number of online tools were used to better understand the role of the circadian rhythm
in manifestation of schizophrenia. Beginning with GEO2R, protein expression level changes were
recorded from a metabolic insult study (Masumoto et al 2009). The results were then compared to
gene-disease association databases via the MSET package in R. Significant genes lists were then
run through ToppCluster to identify genes involved in the circadian rhythm. To better understand
the role of these genes, circadian genes identified in the ToppCluster analysis were fed into
STRING to observe interactions between them. From here, a single gene, ARNTL—shown to
regulate the expression of the others (GeneCards)—was chosen to conduct the remainder of the
analysis upon.
To gain an understanding of how ARNTL—and the circadian rhythm in general—can
contribute toward the development schizophrenia, relevant SNPs were first identified via
GWAS2db, and were then located on the genome via the UCSC genome browser to determine its
location in the gene region. A quantitative trait loci analysis (QTL) was conducted on hippocampal
ARNTL expression across ten BXD populations to better understand how the gene is regulated.
Finally, correlations to phenotypes characteristic of schizophrenia were conducted via
GeneNetwork to support the role of ARNTL and the circadian rhythm in the development of
schizophrenia.
List of sites used within the analysis
GeneNetwork < http://www.genenetwork.org/webqtl/main.py >
BXD Published Phenotypes Database < http://www.genenetwork.org/dbdoc/BXDPublish.html >
QTLMiner < http://genenetwork.org/webqtl/main.pyFormID=qtlminer >
NCBI GEO2R < http://www.ncbi.nlm.nih.gov/geo/geo2r/ >
ToppCluster Database < https://toppcluster.cchmc.org/ >
String < http://string-db.org/ >
Genome Wide Association Study Database < http://jjwanglab.org/gwasdb/ >
University of California Santa Cruz (UCSC) Genome Browser < http://genome.ucsc.edu/ >
Schizophrenia DISEASES Database
Schizophrenia GAD Database
Schizophrenia_huge_over1pub Database

4. Schizophrenia Malacards Database
MSET Package in R
Results
Associated Proteins
The NCBI’s GEO2R analysis allows for the retrieval of gene expression patterns between
experimental groups within a study. The resulting list allows for comparison between gene-lists
comprising of genes that have been associated with diseases. As for this analysis, a study focusing
on metabolic insult (Masumoto et al 2009) was chosen for GEO2R analysis, and was compared to
schizophrenia gene-lists via the MSET package in R. The analysis revealed only one list that was
significantly associated with the metabolic insult study, Schizophrenia_Huge_over1pub (figure 1
A); however, numerous other sets approached significance (data not shown). Nonetheless, the
A
B
C
Figure 1 : Determination of genes in the metabolic insult study that were common to both schizophrenia and
the circadian rhythm. Images generated from comparison between schizophrenia-gene databases via MSET (A),
interaction analysis via string (B), and an expanded view of the interaction network generated in string (C).
[Legend :

5. Schizophrenia_Huge_over1pub database yielded a list of fifteen genes that were common to both
the disorder and metabolic insult.
To deduce genes within the list that were involved in the circadian rhythm, the gene-list
was fed into the ToppCluster database. The ToppCluster database provides numerous enrichment
studies that correlate genes to biological functions, functional interactions, drug responses, etc. As
for the MSET, ToppCluster analysis revealed that three of the genes were involved in the circadian
rhythm: ARNT, PER3, and CSNK1E.
To better understand the biochemical network to which these genes contribute, ARNTL,
PER3 and CSNK1E were fed into the STRING software. STRING allows for the visualization of
associations between genes, such as co-expression and functional interactions. The STRING
analysis revealed that these three genes were associated across a number of lines (figure 1 B).
Expanding the network within STRING revealed that the genes were part of a dense network of
other circadian genes (Figure 1 C). Presumably, then, deleterious variants within ARNTL, PER3,
Figure 2 : Location of SNP within ARNTL intron correlated significantly with schizophrenia in the study
sample. A. Image generated via the UCSD genome browser. The red box indicates the location of the ARNTL SNP
associated with schizophrenia. B. Mutation identified in the study sample (A to G)
A
B

6. and CSNK1E disturb regular functions of the network. For the remainder of the analysis, a focus
will be given to ARNTL and its possible role in the etiology of schizophrenia. ARNTL was chosen
as it precedes the other genes in the circadian rhythm, and acts as a positive regulator of the cycle
at large.
There are three possibilities surrounding the problem in ARNTL’s function. One is that
mutations in the exome lead to dysfunctionality, such that it can no longer directly influence the
cell cycle. Similarly, mutations in non-coding regions may inhibit the protein’s transcription,
whether it be through delayed transcription or even complete silencing. Alternatively to both, the
protein may become overexpressed such that it exerts a disproportionately large effect on the
circadian rhythm.
Figure 3 : Quantitative trait loci analysis in BXD populations reveals that ARNTL is subject to cis-regulation.
Image generated from mapping the QTL data in Genenetwork.

7. Relevant SNPs
GWAS2db may be used to locate SNPs that have been correlated to disease phenotypes.
For this study, the program was used to locate SNPs within the ARNTL gene that have been
associated with schizophrenia. Only one SNP within the ARNTL gene region was significantly
associated with schizophrenia (rs4757144, p = 5.00e-6). Although only one SNP displayed an
outright association with schizophrenia, numerous other SNPs were associated with fatty acid
metabolism and diabetes mellitus (data not shown), both of which are common characteristics of
schizophrenia (Schoepf et al 2012; Carney et al 2006).
Figure 4 : Positive correlation between BXD hippocampal ARNTL expression levels and performance on a
measure presumed to be reflective of schizophrenic behavior, the pre-pulse inhibition paradigm. Image generated
from comparing BXD QTL data to BXD phenotypes in GeneNetwork.

8. The University of California Santa Cruz (UCSC) Genome Browser was then used to
pinpoint the location of this SNP within ARNTL, which was found to be intergenic.
Quantitative Trait Locus Analyses
As the SNP is intergenic, it unlikely affects the function of the protein product. However,
there is the possibility that the intron affects transcription of the protein. Unfortunately, though,
the brevity of transcriptional analysis for ARNTL in humans is apparent in the literature.
Therefore, to determine the degree to which ARNTL is subject to changes in expression will be
examined via the BXD mouse data-sets on the informatics tool, GeneNetwork.
Figure 5 : Negative correlation between BXD hippocampal ARNTL expression and blood triglyceride
concentration. Image generated from comparing BXD QTL data to BXD phenotypes in GeneNetwork.

9. GeneNetwork allows for the aggregation of numerous datasets with a focus on a specific
phenotype. The pooled data-sets may then be used for quantitative trait loci (QTL) mapping to
determine regions of the genome that likely regulate the phenotype of interest. As for this study,
the phenotype of interest was ARNTL expression, with specific reference to the hippocampus. Ten
BXD hippocampal ARNTL expression were pooled in the analysis. The QTL analysis revealed an
impressive LRS value (LRS = 124) centered on the ARNTL gene region (Figure 4).
Though the precise transcriptional-modifying SNPs within the gene-region cannot be
known from this analysis, the analysis nonetheless suggests that ARNTL is subject to drastic
changes in expression. Differing levels of expression may then affect other biochemical pathways,
and perhaps even behavior.
ARNTL Expression – Phenotype Correlations
Extending upon the QTL analysis from the previous section, the aggregated data was then
correlated to a variety of phenotypes, as per the GeneNetwork algorithms. Specifically, phenotypes
purported to be relevant to schizophrenia were sought.
Schizophrenic individuals tend to perform poorly on the pre-pulse inhibition paradigm,
displaying a lack of inhibition relative to controls (Swerdlow et al 1994). A similar paradigm
designed for rodents is considered to be emulative of the human-based paradigm. Of importance
is the correlation between performance on the pre-pulse inhibition paradigm and hippocampal
ARNTL expression. The two variables, represented in figure 4, show a moderately positive, but
significant, correlation (r = .410; p = 1.36e-2).
Another characteristic of schizophrenic individuals is larger concentrations of fatty acids
within the blood relative to controls (Arvindakshan et al 2003). Once again, hippocampal ARNTL
expression was significantly correlated—albeit sleight—to blood-triglyceride concentrations
(Figure 5). However, the variables displayed a negative correlation with one another (r = -.460; p
= 5.58e-3).
Discussion
Comparisons between schizophrenia gene-set databases and genes displaying treatment-
induced changes in expression from the metabolism study (Masumoto et al 2009) revealed a
moderate list of genes common to both. To determine which, if any, genes were also associated
with the circadian rhythm, the list was fed into ToppCluster. Sure enough, three genes—ARNTL,
CSNK1E, and PER3—were associated with the circadian rhythm. Closer examination revealed

10. that ARNTL acted early in the circadian rhythm, acting as a transcriptional regulator for the other
genes. For these reasons, it was chosen as focus for the remainder of the analysis.
Sleuthing GWAS2db revealed that one SNP within ARNTL had been previously correlated
to schizophrenia. This SNP was found to be intergenic via the UCSD genome browser. The dearth
of SNPs associated with schizophrenia, though, should not be taken as evidence against a potential
role for ARNTL in the generation of the disease. This is because many other SNPs were associated
with impaired fatty acid metabolism, and diabetes mellitus—both of which are often co-morbid
with schizophrenia and its progression (Schoepf et al 2012; Carney et al 2006).
As the associated SNP was intergenic, the gene likely promoted schizophrenic
development—at least in the sample studied—through irregular levels of expression, either too
much or too little.
The QTL analysis suggested that ARNTL is primarily transcribed in a cis-regulatory
manner, as the only region displaying significant associations with ARNTL expression were, in
fact, around the ARNTL protein itself. The robust LRS signal further suggests that ARNTL—at
least in the BXD population—is subject to a wide degree of expression level diversity. Therefore,
ARNTL is likely tied to the development of schizophrenia through aberrant levels of expression.
Phenotypic correlations lend further support to aberrant ARNTL expression levels in
schizophrenia. Above average fatty acid concentrations in the circulatory system are characteristic
of schizophrenia (Arvindakshan et al 2003), and, in the comparison, greater ARNTL expression
correlated with reduced triglyceride levels in BXD blood samples. Furthermore, performance on
the pre-pulse inhibition paradigm (A classic test designed to probe disinhibition in schizophrenia
and in models of the disorder (Swerdlow et al 1994)) improved with increasing levels of ARNTL
expression. Therefore, deficient expression of ARNTL promoted phenotypes similar to those in
schizophrenia.
In culmination, these results suggest that impaired circadian processes contribute to the
development of schizophrenia. As ARNTL is a positive regulator of the circadian rhythm
(GeneCards), it is likely that under-expression impairs the gross progression of the circadian
rhythm, such that down-stream transcription of important regulatory genes such as PER1, 2 and 3
will not occur at optimal levels. Reduced expression of regulatory genes such as ARNTL would,
presumably, impair other processes throughout the body that are reliant upon PER signaling,
among other ARNTL target genes.

11. Perhaps reduced expression, or even loss of function of other positive regulators of the
circadian rhythm contribute to the development of schizophrenia and other psychiatric disorders.
Inability to complete the cycle efficiently—at any point, including through reduced expression of
ARNTL—could disturb regular circadian control over other processes throughout the body.
Should this be case, impediments to the circadian rhythm will result in dysregulated
metabolism, hindered neurogenesis, and ultimately culminate in the manifestation of cognitive,
affective, and behavioral abnormalities present in cases of schizophrenia. However, a dysregulated
circadian rhythm should not be considered the sole progenitor of the disorder, as many other genes
likely promote the development of schizophrenia on their own, or in conjunction with aberrations
to the circadian rhythm (Horvath & Mirnics 2015; Sullivan et al 2003).
Conclusion
Schizophrenia is complex disorder of many genes and factors. Tough circadian dysfunction
is unlikely the whole story, these results provide insight into a shared mechanism through which
characteristic phenotypes of schizophrenia may develop. ARNTL, the primary gene examined
within this study, shows significant associations to phenotypes related to schizophrenia. The
relationship is likely due to under-expression of ARNTL, as reduced synthesis could effectively
halt the circadian rhythm. Questions remain regarding how under-expression of this gene, among
others, could affect relevant downstream pathways. Answering these questions will allow for novel
drug treatments, and more careful administration of current anti-psychotics.
References
1. ARNTL Gene - GeneCards | BMAL1 Protein | BMAL1 Antibody. (n.d.). Retrieved May 10, 2016, from http://www.genecards.org/cgi-
bin/carddisp.pl?gene=ARNTL&keywords=ARNTL
2. Arvindakshan, M., Sitasawad, S., Debsikdar, V., Ghate, M., Evans, D., Horrobin, D. F., … Mahadik, S. P. (2003). Essential
polyunsaturated fatty acid and lipid peroxide levels in never-medicated and medicated schizophrenia patients. Biological Psychiatry,
53(1), 56–64. http://doi.org/10.1016/S0006-3223(02)01443-9
3. Bass, J., & Takahashi, J. S. (2010). Circadian Integration of Metabolism and Energetics. Science, 330(6009), 1349–1354.
http://doi.org/10.1126/science.1195027
4. Berger, G. E., Wood, S. J., Pantelis, C., Velakoulis, D., Wellard, R. M., & Mcgorry, P. D. (2002). Implications of lipid biology for the
pathogenesis of schizophrenia. Australian and New Zealand Journal of Psychiatry, 36(3), 355–366. http://doi.org/10.1046/j.1440-
1614.2001.01021.x
5. Boivin, D. (2000). Influence of sleep-wake and circadian rhythm disturbances in psychiatric disorders. Journal of Psychiatry and
Neuroscience, 25(5), 446–458.
6. Bouchard-Cannon, P., Mendoza-Viveros, L., Yuen, A., Kærn, M., & Cheng, H.-Y. M. (2013). The Circadian Molecular Clock Regulates
Adult Hippocampal Neurogenesis by Controlling the Timing of Cell-Cycle Entry and Exit. Cell Reports, 5(4), 961–973.
http://doi.org/10.1016/j.celrep.2013.10.037
7. Carney, C. P., Jones, L., & Woolson, R. F. (2006). Medical Comorbidity in Women and Men with Schizophrenia: A Population-Based
Controlled Study. Journal of General Internal Medicine, 21(11), 1133–1137. http://doi.org/10.1111/j.1525-1497.2006.00563.x
8. Cornblatt, B. A., & Keilp, J. G. (1994). Impaired attention, genetics, and the pathophysiology of schizophrenia. Schizophrenia
Bulletin, 20(1), 31-46.
9. Flaum, M., O’Leary, D. S., Swayze II, V. W., Miller, D. D., Arndt, S., & Andreasen, N. C. (1995). Symptom dimensions and brain
morphology in schizophrenia and related psychotic disorders. Journal of Psychiatric Research, 29(4), 261–276.
http://doi.org/10.1016/0022-3956(94)00046-T
10. Horváth, S., & Mirnics, K. (2015). Schizophrenia as a Disorder of Molecular Pathways. Biological Psychiatry, 77(1), 22–28.
http://doi.org/10.1016/j.biopsych.2014.01.001

12. 11. Hufford, M. R., Davis, V. G., Hilt, D., Dgetluck, N., Geffen, Y., Loebel, A., … Keefe, R. S. E. (2014). Circadian rhythms in cognitive
functioning among patients with schizophrenia: Impact on signal detection in clinical trials of potential pro-cognitive therapies.
Schizophrenia Research, 159(1), 205–210. http://doi.org/10.1016/j.schres.2014.07.018
12. Karatsoreos, I. N. (2014). Links between circadian rhythms and psychiatric disease. Frontiers in behavioral neuroscience, 8.
13. Kimiwada, T., Sakurai, M., Ohashi, H., Aoki, S., Tominaga, T., & Wada, K. (2009). Clock genes regulate neurogenic transcription
factors, including NeuroD1, and the neuronal differentiation of adult neural stem/progenitor cells. Neurochemistry International, 54(5–
6), 277–285. http://doi.org/10.1016/j.neuint.2008.12.005
14. Kohsaka, A., Laposky, A. D., Ramsey, K. M., Estrada, C., Joshu, C., Kobayashi, Y., … Bass, J. (2007). High-Fat Diet Disrupts
Behavioral and Molecular Circadian Rhythms in Mice. Cell Metabolism, 6(5), 414–421. http://doi.org/10.1016/j.cmet.2007.09.006
15. Masumoto, S., Akimoto, Y., Oike, H., & Kobori, M. (2009). Dietary phloridzin reduces blood glucose levels and reverses Sglt1
expression in the small intestine in streptozotocin-induced diabetic mice. Journal of agricultural and food chemistry, 57(11), 4651-
4656.
16. Malik, A., Kondratov, R. V., Jamasbi, R. J., & Geusz, M. E. (2015). Circadian Clock Genes Are Essential for Normal Adult
Neurogenesis, Differentiation, and Fate Determination. PLOS ONE, 10(10), e0139655. http://doi.org/10.1371/journal.pone.0139655
17. Overall, R. (2009). Genetics of the hippocampal transcriptome in mouse: a systematic survey and online neurogenomics resource.
Frontiers in Neuroscience. http://doi.org/10.3389/neuro.15.003.2009
18. Schoepf, D., Potluri, R., Uppal, H., Natalwala, A., Narendran, P., & Heun, R. (2012). Type-2 diabetes mellitus in schizophrenia:
Increased prevalence and major risk factor of excess mortality in a naturalistic 7-year follow-up. European Psychiatry, 27(1), 33–42.
http://doi.org/10.1016/j.eurpsy.2011.02.009
19. Sullivan PF, Kendler KS, & Neale MC. (2003). Schizophrenia as a complex trait: Evidence from a meta-analysis of twin studies.
Archives of General Psychiatry, 60(12), 1187–1192. http://doi.org/10.1001/archpsyc.60.12.1187
20. Swerdlow NR, Braff DL, Taaid N, & Geyer MA. (1994). Assessing the validity of an animal model of deficient sensorimotor gating in
schizophrenic patients. Archives of General Psychiatry, 51(2), 139–154. http://doi.org/10.1001/archpsyc.1994.03950020063007
21. Wulff, K., Dijk, D.-J., Middleton, B., Foster, R. G., & Joyce, E. M. (2012). Sleep and circadian rhythm disruption in schizophrenia. The
British Journal of Psychiatry, 200(4), 308–316. http://doi.org/10.1192/bjp.bp.111.096321