Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networks

Multiple genes in a single GWAS risk locus synergistically mediate aberrant synaptic development and function in human neurons

The mechanistic tie between genome-wide association study (GWAS)-implicated risk variants and disease-relevant cellular phenotypes remains largely unknown. Here, using human induced pluripotent stem cell (hiPSC)-derived neurons as a neurodevelopmental model, we identify multiple schizophrenia (SZ) risk variants that display allele-specific open chromatin (ASoC) and are likely to be functional. Editing the strongest ASoC SNP, rs2027349, near vacuolar protein sorting 45 homolog (VPS45) alters the expression of VPS45, lncRNA AC244033.2, and a distal gene, C1orf54. Notably, the transcriptomic changes in neurons are associated with SZ and other neuropsychiatric disorders. Neurons carrying the risk allele exhibit increased dendritic complexity and hyperactivity. Interestingly, individual/combinatorial gene knockdown shows that these genes alter cellular phenotypes in a non-additive synergistic manner. Our study reveals that multiple genes at a single GWAS risk locus mediate a compound effect on neural function, providing a mechanistic link between a non-coding risk variant and disease-related cellular phenotypes.

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.

Comparative analysis of hippocampal transcriptional features between major depressive disorder patients and animal models

BACKGROUND

Major depressive disorder (MDD) is a psychiatric disorder caused by various etiologies. Chronic stress models are used to simulate the heterogeneous pathogenic processes of depression. However, few studies have compared transcriptional features between stress models and MDD patients.

METHODS

We generated hippocampal transcriptional profiles of the chronic social defeat model by RNA sequencing and downloaded raw data of the same brain region from public databases of the chronic unpredictable mild stress model, the learned helplessness model, and MDD patients. Differential expression and gene co-expression analyses were integrated to compare transcriptional features between stress models and MDD patients.

RESULTS

Each stress model shared 11.4% to 16.3% of differentially expressed genes with MDD patients. Functional analysis at the gene expression level identified altered ensheathment of neurons in both stress models and MDD patients. At the gene network level, each stress model shared 20.9% to 41.6% of co-expressed genes with MDD patients. Functional analysis based on these genes found that axon guidance signaling is the most significantly enriched pathway that was shared by all stress models and MDD patients.

LIMITATIONS

This study was limited by considering only a single brain region and a single sex of stress model animals.

CONCLUSIONS

Our results show that hippocampal transcriptional features of stress models partially overlap with those of MDD patients. The canonical pathways of MDD patients, including ensheathment of neurons, PTEN signaling, and axonal guidance signaling, were shared with all stress models. Our findings provide further clues to understand the molecular mechanisms of depression.

Differential coexpression in human tissues and the confounding effect of mean expression levels

Motivation

Differential coexpression-the alteration of gene coexpression patterns observed in different biological conditions-has been proposed to be a mechanism for revealing rewiring of transcription regulatory networks. Despite wide use of methods for differential coexpression analysis, the phenomenon has not been well-studied. In particular, in many applications, differential coexpression is confounded with differential expression, that is, changes in average levels of expression across conditions. This confounding, despite affecting the interpretation of the differential coexpression, has rarely been studied.

Results

We constructed high-quality coexpression networks for five human tissues and identified coexpression links (gene pairs) that were specific to each tissue. Between 3 and 32% of coexpression links were tissue-specific (differentially coexpressed) and this specificity is reproducible in an external dataset. However, we show that up to 75% of the observed differential coexpression is substantially explained by average expression levels of the genes. 'Pure' differential coexpression independent from differential expression is a minority and is less reproducible in external datasets. We also investigated the functional relevance of pure differential coexpression. Our conclusion is that to a large extent, differential coexpression is more parsimoniously explained by changes in average expression levels and pure links have little impact on network-based functional analysis.

Supplementary information

Supplementary data are available at Bioinformatics online.

Opioid-Induced Hyperalgesia Is Associated with Dysregulation of Circadian Rhythm and Adaptive Immune Pathways in the Mouse Trigeminal Ganglia and Nucleus Accumbens

The benefits of opioid-based treatments to mitigate chronic pain can be hindered by the side effects of opioid-induced hyperalgesia (OIH) that can lead to higher consumption and risk of addiction. The present study advances the understanding of the molecular mechanisms associated with OIH by comparing mice presenting OIH symptoms in response to chronic morphine exposure (OIH treatment) relative to control mice (CON treatment). Using RNA-Seq profiles, gene networks were inferred in the trigeminal ganglia (TG), a central nervous system region associated with pain signaling, and in the nucleus accumbens (NAc), a region associated with reward dependency. The biological process of nucleic acid processing was over-represented among the 122 genes that exhibited a region-dependent treatment effect. Within the 187 genes that exhibited a region-independent treatment effect, circadian rhythm processes were enriched among the genes over-expressed in OIH relative to CON mice. This enrichment was supported by the differential expression of the period circadian clock 2 and 3 genes (Per2 and Per3). Transcriptional regulators in the PAR bZip family that are influenced by the circadian clock and that modulate neurotransmission associated with pain and drug addiction were also over-expressed in OIH relative to CON mice. Also notable was the under-expression in OIH relative to CON mice of the Toll-like receptor, nuclear factor-kappa beta, and interferon gamma genes and enrichment of the adaptive immune processes. The results from the present study offer insights to advance the effective use of opioids for pain management while minimizing hyperalgesia.

Axonal myelin decrease in the splenium in major depressive disorder

The corpus callosum has become a key area of interest for researchers in severe mental illness. Disruptions in fractional anisotropy in the callosum have been reported in schizophrenia and major depressive disorder. No change has been reported in oligodendrocyte density and overall size of the callosum in either illness, suggesting that gross morphology is unchanged, but subtler organisational disruption may exist within this structure. Using high-resolution oil immersion microscopy, we examined the cross-sectional area of the nerve fibre and the axonal myelin sheath; and using standard high-resolution light microscopy, we measured the density of myelinated axons. These measurements were made in the splenium of the corpus callosum. Measures were taken in the sagittal plane in the callosal splenium to contrast with the previous similar examination of the callosal genu. Cases of major depressive disorder had significantly decreased mean myelin cross-sectional area (p = 0.014) per axon in the splenium than in controls or schizophrenia groups. There was no significant change in the density of myelinated axons. The results suggest a clear decrease of myelin in the axons of the callosal splenium in MDD, although this type of neuropathological study is unable to clarify whether this is caused by changes during life or has a developmental origin. In contrast with increased myelin in the callosal genu, this result suggests a longitudinal change in callosal myelination in major depressive disorder not present in normal or schizophrenic brains.

Gene expression in cord blood links genetic risk for neurodevelopmental disorders with maternal psychological distress and adverse childhood outcomes

Prenatal exposure to maternal stress and depression has been identified as a risk factor for adverse behavioral and neurodevelopmental outcomes in early childhood. However, the molecular mechanisms through which maternal psychopathology shapes offspring development remain poorly understood. We applied transcriptome-wide screens to 149 umbilical cord blood samples from neonates born to mothers with posttraumatic stress disorder (PTSD; n = 20), depression (n = 31) and PTSD with comorbid depression (n = 13), compared to carefully matched trauma exposed controls (n = 23) and healthy mothers (n = 62). Analyses by maternal diagnoses revealed a clear pattern of gene expression signatures distinguishing neonates born to mothers with a history of psychopathology from those without. Co-expression network analysis identified distinct gene expression perturbations across maternal diagnoses, including two depression-related modules implicated in axon-guidance and mRNA stability, as well as two PTSD-related modules implicated in TNF signaling and cellular response to stress. Notably, these disease-related modules were enriched with brain-expressed genes and genetic risk loci for autism spectrum disorder and schizophrenia, which may imply a causal role for impaired developmental outcomes. These molecular alterations preceded changes in clinical measures at twenty-four months, including reductions in cognitive and socio-emotional outcomes in affected infants. Collectively, these findings indicate that prenatal exposure to maternal psychological distress induces neuronal, immunological and behavioral abnormalities in affected offspring and support the search for early biomarkers of exposures to adverse in utero environments and the classification of children at risk for impaired development.

An application of the Shapley value to the analysis of co-expression networks

We study the problem of identifying relevant genes in a co-expression network using a (cooperative) game theoretic approach. The Shapley value of a cooperative game is used to asses the relevance of each gene in interaction with the others, and to stress the role of nodes in the periphery of a co-expression network for the regulation of complex biological pathways of interest. An application of the method to the analysis of gene expression data from microarrays is presented, as well as a comparison with classical centrality indices. Finally, making further assumptions about the a priori importance of genes, we combine the game theoretic model with other techniques from cluster analysis.

Treatment resistant depression: A multi-scale, systems biology approach

An estimated 50% of depressed patients are inadequately treated by available interventions. Even with an eventual recovery, many patients require a trial and error approach, as there are no reliable guidelines to match patients to optimal treatments and many patients develop treatment resistance over time. This situation derives from the heterogeneity of depression and the lack of biomarkers for stratification by distinct depression subtypes. There is thus a dire need for novel therapies. To address these known challenges, we propose a multi-scale framework for fundamental research on depression, aimed at identifying the brain circuits that are dysfunctional in several animal models of depression as well the changes in gene expression that are associated with these models. When combined with human genetic and imaging studies, our preclinical studies are starting to identify candidate circuits and molecules that are altered both in models of disease and in patient populations. Targeting these circuits and mechanisms can lead to novel generations of antidepressants tailored to specific patient populations with distinctive types of molecular and circuit dysfunction.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Cerebellum Transcriptome of Mice Bred for High Voluntary Activity Offers Insights into Locomotor Control and Reward-Dependent Behaviors

The role of the cerebellum in motivation and addictive behaviors is less understood than that in control and coordination of movements. High running can be a self-rewarding behavior exhibiting addictive properties. Changes in the cerebellum transcriptional networks of mice from a line selectively bred for High voluntary running (H) were profiled relative to an unselected Control (C) line. The environmental modulation of these changes was assessed both in activity environments corresponding to 7 days of Free (F) access to running wheel and to Blocked (B) access on day 7. Overall, 457 genes exhibited a significant (FDR-adjusted P-value < 0.05) genotype-by-environment interaction effect, indicating that activity genotype differences in gene expression depend on environmental access to running. Among these genes, network analysis highlighted 6 genes (Nrgn, Drd2, Rxrg, Gda, Adora2a, and Rab40b) connected by their products that displayed opposite expression patterns in the activity genotype contrast within the B and F environments. The comparison of network expression topologies suggests that selection for high voluntary running is linked to a predominant dysregulation of hub genes in the F environment that enables running whereas a dysregulation of ancillary genes is favored in the B environment that blocks running. Genes associated with locomotor regulation, signaling pathways, reward-processing, goal-focused, and reward-dependent behaviors exhibited significant genotype-by-environment interaction (e.g. Pak6, Adora2a, Drd2, and Arhgap8). Neuropeptide genes including Adcyap1, Cck, Sst, Vgf, Npy, Nts, Penk, and Tac2 and related receptor genes also exhibited significant genotype-by-environment interaction. The majority of the 183 differentially expressed genes between activity genotypes (e.g. Drd1) were under-expressed in C relative to H genotypes and were also under-expressed in B relative to F environments. Our findings indicate that the high voluntary running mouse line studied is a helpful model for understanding the molecular mechanisms in the cerebellum that influence locomotor control and reward-dependent behaviors.

Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis

Antidepressant efficacy is insufficient, unpredictable and poorly understood in major depressive episode (MDE). Gene expression studies allow for the identification of significantly dysregulated genes but can limit the exploration of biological pathways. In the present study, we proposed a gene coexpression analysis to investigate biological pathways associated with treatment response predisposition and their regulation by microRNAs (miRNAs) in peripheral blood samples of MDE and healthy control subjects. We used a discovery cohort that included 34 MDE patients that were given 12-week treatment with citalopram and 33 healthy controls. Two replication cohorts with similar design were also analyzed. Expression-based gene network was built to define clusters of highly correlated sets of genes, called modules. Association between each module's first principal component of the expression data and clinical improvement was tested in the three cohorts. We conducted gene ontology analysis and miRNA prediction based on the module gene list. Nine of the 59 modules from the gene coexpression network were associated with clinical improvement. The association was partially replicated in other cohorts. Gene ontology analysis demonstrated that 4 modules were associated with cytokine production, acute inflammatory response or IL-8 functions. Finally, we found 414 miRNAs that may regulate one or several modules associated with clinical improvement. By contrast, only 12 miRNAs were predicted to specifically regulate modules unrelated to clinical improvement. Our gene coexpression analysis underlines the importance of inflammation-related pathways and the involvement of a large miRNA program as biological processes predisposing associated with antidepressant response.

Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility

Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery.

Apolipoprotein E*4 (APOE*4) Genotype Is Associated with Altered Levels of Glutamate Signaling Proteins and Synaptic Coexpression Networks in the Prefrontal Cortex in Mild to Moderate Alzheimer Disease

It has been hypothesized that Alzheimer disease (AD) is primarily a disorder of the synapse. However, assessment of the synaptic proteome in AD subjects has been limited to a small number of proteins and often included subjects with end-stage pathology. Protein from prefrontal cortex gray matter of 59 AD subjects with mild to moderate dementia and 12 normal elderly subjects was assayed using targeted mass spectrometry to quantify 191 synaptically expressed proteins. The profile of synaptic protein expression clustered AD subjects into two groups. One of these was characterized by reduced expression of glutamate receptor proteins, significantly increased synaptic protein network coexpression, and associated withApolipoprotein E*4 (APOE*4) carrier status. The second group, by contrast, showed few differences from control subjects. A subset of AD subjects had altered prefrontal cortex synaptic proteostasis for glutamate receptors and their signaling partners. Efforts to therapeutically target glutamate receptors in AD may have outcomes dependent on APOE*4 genotype.

Differential activation of immune/inflammatory response-related co-expression modules in the hippocampus across the major psychiatric disorders

The Stanley Neuropathology Consortium Integrative Database (SNCID, http://sncid.stanleyresearch.org) is a data-mining tool that includes 379 neuropathology data sets from hippocampus, as well as RNA-Seq data measured in 15 well-matched cases in each of four groups: schizophrenia, bipolar disorder (BPD), major depression (MD) and unaffected controls. We analyzed the neuropathology data from the hippocampus to identify those abnormalities that are shared between psychiatric disorders and those that are specific to each disorder. Of the 379 data sets, 20 of them showed a significant abnormality in at least one disorder as compared with unaffected controls. GABAergic markers and synaptic proteins were mainly abnormal in schizophrenia and the two mood disorders, respectively. Two immune/inflammation-related co-expression modules built from RNA-seq data from both schizophrenia and controls combined were associated with disease status, as well as negatively correlated with the GABAergic markers. The correlation between immune-related modules and schizophrenia was replicated using microarray data from an independent tissue collection. Immune/inflammation-related co-expression modules were also built from RNA-seq data from BPD cases or from MD cases but were not preserved when using data from control cases. Moreover, there was no overlap in the genes that comprise the immune/inflammation response-related modules across the different disorders. Thus, there appears to be differential activation of the immune/inflammatory response, as determined by co-expression of genes, which is associated with the major psychiatric disorders and which is also associated with the abnormal neuropathology in the disorders.

Blood-based gene expression signatures of medication-free outpatients with major depressive disorder: integrative genome-wide and candidate gene analyses

Several microarray-based studies have investigated gene expression profiles in major depressive disorder (MDD), yet with highly variable findings. We examined blood-based genome-wide expression signatures of MDD, focusing on molecular pathways and networks underlying differentially expressed genes (DEGs) and behaviours of hypothesis-driven, evidence-based candidate genes for depression. Agilent human whole-genome arrays were used to measure gene expression in 14 medication-free outpatients with MDD who were at least moderately ill and 14 healthy controls matched pairwise for age and sex. After filtering, we compared expression of entire probes between patients and controls and identified DEGs. The DEGs were evaluated by pathway and network analyses. For the candidate gene analysis, we utilized 169 previously prioritized genes and examined their case-control separation efficiency and correlational co-expression network in patients relative to controls. The 317 screened DEGs mapped to a significantly over-represented pathway, the "synaptic transmission" pathway. The protein-protein interaction network was also significantly enriched, in which a number of key molecules for depression were included. The co-expression network of candidate genes was markedly disrupted in patients. This study provided evidence for an altered molecular network along with several key molecules in MDD and confirmed that the candidate genes are worthwhile targets for depression research.

Axonal myelin increase in the callosal genu in depression but not schizophrenia

BACKGROUND

Abnormalities in the anterior inter-hemispheric connectivity have previously been implicated in major depressive disorder. Disruptions in fractional anisotropy in the callosum and fornix have been reported in schizophrenia and major depressive disorder. Oligodendrocyte density and overall size of the callosum and fornix show no alteration in either illness, suggesting that gross morphology is unchanged but more subtle organizational disruption may exist within these brain regions in mood and affective disorders.

METHOD

Using high-resolution oil-immersion microscopy we examined the cross-sectional area of the nerve fibre and the axonal myelin sheath, and using standard high-resolution light microscopy we measured the density of myelinated axons. These measurements were made in the genu of the corpus callosum and the medial body of the fornix at its most dorsal point. Measures were taken in the sagittal plane in the callosal genu and in the coronal plane at the most dorsal part of the fornix body.

RESULTS

Cases of major depressive disorder had significantly greater mean myelin cross-sectional area (p = 0.017) and myelin thickness (p = 0.004) per axon in the genu than in control or schizophrenia groups. There was no significant change in the density of myelinated axons, and no changes observed in the fornix.

CONCLUSION

The results suggest a clear increase of myelin in the axons of the callosal genu in MDD, although this type of neuropathological study is unable to clarify whether this is caused by changes during life or has a developmental origin.

A null model for Pearson coexpression networks

Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the number of samples is small, yielding a non-negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The threshold is theoretically derived by means of an analytic approach and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data. We show, on synthetic and array datasets, that the proposed threshold is effective in eliminating all false positive links, with an offsetting cost in terms of false negative detected edges.

Altered glutamate protein co-expression network topology linked to spine loss in the auditory cortex of schizophrenia

BACKGROUND

Impaired glutamatergic signaling is believed to underlie auditory cortex pyramidal neuron dendritic spine loss and auditory symptoms in schizophrenia. Many schizophrenia risk loci converge on the synaptic glutamate signaling network. We therefore hypothesized that alterations in glutamate signaling protein expression and co-expression network features are present in schizophrenia.

METHODS

Gray matter homogenates were prepared from auditory cortex gray matter of 22 schizophrenia and 23 matched control subjects, a subset of whom had been previously assessed for dendritic spine density. One hundred fifty-five selected synaptic proteins were quantified by targeted mass spectrometry. Protein co-expression networks were constructed using weighted gene co-expression network analysis.

RESULTS

Proteins with evidence for altered expression in schizophrenia were significantly enriched for glutamate signaling pathway proteins (GRIA4, GRIA3, ATP1A3, and GNAQ). Synaptic protein co-expression was significantly decreased in schizophrenia with the exception of a small group of postsynaptic density proteins, whose co-expression increased and inversely correlated with spine density in schizophrenia subjects.

CONCLUSIONS

We observed alterations in the expression of glutamate signaling pathway proteins. Among these, the novel observation of reduced ATP1A3 expression is supported by strong genetic evidence indicating it may contribute to psychosis and cognitive impairment phenotypes. The observations of altered protein network topology further highlight the complexity of glutamate signaling network pathology in schizophrenia and provide a framework for evaluating future experiments to model the contribution of genetic risk to disease pathology.

Molecular and Genetic Characterization of Depression: Overlap with other Psychiatric Disorders and Aging

Genome-wide expression and genotyping technologies have uncovered the genetic bases of complex diseases at unprecedented rates; However despite its heavy burden and high prevalence, the molecular characterization of major depressive disorder (MDD) has lagged behind. Transcriptome studies report multiple brain disturbances but are limited by small sample sizes. Genome-wide association studies (GWAS) report weak results but suggest overlapping genetic risk with other neuropsychiatric disorders. We performed systematic molecular characterization of altered brain function in MDD, using meta-analysis of differential expression in eight gene array studies in three corticolimbic brain regions in 101 subjects. The identified "metaA-MDD" genes suggest altered neurotrophic support, brain plasticity and neuronal signaling in MDD. Notably, metaA-MDD genes display low connectivity and hubness in coexpression networks, and uniform genomic distribution, consistent with diffuse polygenic mechanisms. We next integrated these findings with results from over 1800 published GWAS and show that genetic variations nearby metaA-MDD genes predict greater risk for neuropsychiatric disorders and notably for age-related phenotypes, but not for other medical illnesses, including those frequently co-morbid with depression, or body characteristics. Collectively, the intersection of unbiased investigations of gene function (transcriptome) and structure (GWAS) provides novel leads to investigate molecular mechanisms of MDD and suggest common biological pathways between depression, other neuropsychiatric diseases, and brain aging.

Dendritic spine alterations in schizophrenia

Schizophrenia is a chronic illness affecting approximately 0.5-1% of the world's population. The etiology of schizophrenia is complex, including multiple genes, and contributing environmental effects that adversely impact neurodevelopment. Nevertheless, a final common result, present in many subjects with schizophrenia, is impairment of pyramidal neuron dendritic morphology in multiple regions of the cerebral cortex. In this review, we summarize the evidence of reduced dendritic spine density and other dendritic abnormalities in schizophrenia, evaluate current data that informs the neurodevelopment timing of these impairments, and discuss what is known about possible upstream sources of dendritic spine loss in this illness.

Gene expression in teratogenic exposures: a new approach to understanding individual risk

The phenomenon of partial or incomplete penetrance is common to many paradigms of exposure to teratogens, where only some of the exposed individuals exhibit developmental defects. We here argue that the most widely used experimental approaches in reproductive toxicology do not take partial penetrance into account, and are thus likely to miss differences between affected and unaffected individuals that contribute to susceptibility for teratogenesis. We propose that focus on the variation between exposed individuals could help to discover factors that may play a causative role for abnormal developmental processes that occur with incomplete penetrance.

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene-gene expression correlations are a common source of molecular networks because they can be extracted from high-dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming 'hub genes' direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi-omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease-related prioritization of genes and molecular systems and network meta-analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality-lethality relationship, integration with machine learning approaches and network pharmacology.

Complex network analysis of CA3 transcriptome reveals pathogenic and compensatory pathways in refractory temporal lobe epilepsy

We previously described - studying transcriptional signatures of hippocampal CA3 explants - that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). Here we developed a methodology for complex network visualization (3D) and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree) and of interconnection between node neighbors (concentric node degree). Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i) obtained complete transcriptional networks (CO) for FS and NFS phenotypic groups; ii) examined how CO and DE networks are related; iii) characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i) DE hubs and VIPs are evenly distributed inside the CO networks; ii) most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network. Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes are highly interconnected in transcriptional networks.

Meta-analysis of gene coexpression networks in the post-mortem prefrontal cortex of patients with schizophrenia and unaffected controls

Background

Gene expression profiling of the postmortem human brain is part of the effort to understand the neuropathological underpinnings of schizophrenia. Existing microarray studies have identified a large number of genes as candidates, but efforts to generate an integrated view of molecular and cellular changes underlying the illness are few. Here, we have applied a novel approach to combining coexpression data across seven postmortem human brain studies of schizophrenia.

Results

We generated separate coexpression networks for the control and schizophrenia prefrontal cortex and found that differences in global network properties were small. We analyzed gene coexpression relationships of previously identified differentially expressed ‘schizophrenia genes’. Evaluation of network properties revealed differences for the up- and down-regulated ‘schizophrenia genes’, with clustering coefficient displaying particularly interesting trends. We identified modules of coexpressed genes in each network and characterized them according to disease association and cell type specificity. Functional enrichment analysis of modules in each network revealed that genes with altered expression in schizophrenia associate with modules representing biological processes such as oxidative phosphorylation, myelination, synaptic transmission and immune function. Although a immune-function enriched module was found in both networks, many of the genes in the modules were different. Specifically, a decrease in clustering of immune activation genes in the schizophrenia network was coupled with the loss of various astrocyte marker genes and the schizophrenia candidate genes.

Conclusion

Our novel network-based approach for evaluating gene coexpression provides results that converge with existing evidence from genetic and genomic studies to support an immunological link to the pathophysiology of schizophrenia.

Biological substrates underpinning diagnosis of major depression

Major depression is characterized by low mood, a reduced ability to experience pleasure and frequent cognitive, physiological and high anxiety symptoms. It is also the leading cause of years lost due to disability worldwide in women and men, reflecting a lifelong trajectory of recurring episodes, increasing severity and progressive treatment resistance. Yet, antidepressant drugs at best treat only one out of every two patients and have not fundamentally changed since their discovery by chance >50 yr ago. This status quo may reflect an exaggerated emphasis on a categorical disease classification that was not intended for biological research and on oversimplified gene-to-disease models for complex illnesses. Indeed, genetic, molecular and cellular findings in major depression suggest shared risk and continuous pathological changes with other brain-related disorders. So, an alternative is that pathological findings in major depression reflect changes in vulnerable brain-related biological modules, each with their own aetiological factors, pathogenic mechanisms and biological/environment moderators. In this model, pathological entities have low specificity for major depression and instead co-occur, combine and interact within individual subjects across disorders, contributing to the expression of biological endophenotypes and potentially clinical symptom dimensions. Here, we discuss current limitations in depression research, review concepts of gene-to-disease biological scales and summarize human post-mortem brain findings related to pyramidal neurons, γ-amino butyric acid neurons, astrocytes and oligodendrocytes, as prototypical brain circuit biological modules. Finally we discuss nested aetiological factors and implications for dimensional pathology. Evidence suggests that a focus on local cell circuits may provide an appropriate integration point and a critical link between underlying molecular mechanisms and neural network dysfunction in major depression.

[Endophenotypes: the molecular biology point of view]

Endophenotypes are proposed for a better understanding of the molecular substrate underlying psychiatric disorders vulnerability. In this review, we discuss key points of the definition of endophenotypes from the molecular biology point of view. First, we examine the concept of heritability of endophenotype, which does not directly explain the molecular mechanisms responsible for the studied disorder Indeed, we discuss the necessity to better decipher the functional role of polymorphisms associated to endophenotypes, especially if those endophenotypes would be assigned a clinical and biological value. The complexity of endophenotypes definition and use in psychiatric research is also illustrated by the complexity of the human genome organization and gene networks as well as by the gene x environment interactions and also the possible existence of phenocopies.

Causal co-expression method with module analysis to screen drugs with specific target

The considerable increase of investment in research and development by the pharmaceutical industry over the past three decades has not added the number of approved new drugs. An important issue ignored by drug discovery practice is the multi-dimensional interaction network between drugs and their targets. Thus, it is essential to view drug actions through the lens of network biology. In the current study, based on the co-expression network of transcription factors and their downstream genes, we proposed a novel approach, called causal co-expression method with module analysis, to screen drugs with specific target and fewer side effects. We presented a causal co-expression method with module analysis and it could be used in analyzing the microarray data of different drug candidates. At first, the differential wiring value (DW) was calculated to find some causal transcription factors (TFs) by combining with differential expression genes in the regulated networks. After the discovery of the causal TFs, co-expression module analysis method was applied to mine molecular pharmacology pathways around these causal TFs at molecular level. We applied our methods to two drug candidates, Argyrin A and Bortezomib, both with anti-cancer activities. We first obtained some differentially expressed transcription factors of cells treated with Argyrin A or Bortezomib. Nearly all these transcription factors are associated with the tumor suppressor protein p27kip1. Furthermore, module analysis showed that Bortezomib inhibited tumor growth not specifically by cell cycle and cell proliferation pathway, but through many basic metabolic processes which result in cell toxicity. In contrast, Argyrin A had influence on cell cycle, and was involved in DNA damage repair at the same time, showing that Argyrin A was a more suitable drug for anti-cancer treatment. Our study revealed that the causal co-expression method with module analysis was effective and can be used as a tool to evaluate drug candidates.