Jonathan L. Hess

How might ZNF804A variants influence risk for schizophrenia and bipolar disorder? A literature review, synthesis, and bioinformatic analysis

The gene that encodes zinc finger protein 804A (ZNF804A) became a candidate risk gene for schizophrenia (SZ) after surpassing genome‐wide significance thresholds in replicated genome‐wide association scans and meta‐analyses. Much remains unknown about this reported gene expression regulator; however, preliminary work has yielded insights into functional and biological effects of ZNF804A by targeting its regulatory activities in vitro and by characterizing allele‐specific interactions with its risk‐conferring single nucleotide polymorphisms (SNPs). There is now strong epidemiologic evidence for a role of ZNF804A polymorphisms in both SZ and bipolar disorder (BD); however, functional links between implicated variants and susceptible biological states have not been solidified. Here we briefly review the genetic evidence implicating ZNF804A polymorphisms as genetic risk factors for both SZ and BD, and discuss the potential functional consequences of these variants on the regulation of ZNF804A and its downstream targets. Empirical work and predictive bioinformatic analyses of the alternate alleles of the two most strongly implicated ZNF804A polymorphisms suggest they might alter the affinity of the gene sequence for DNA‐ and/or RNA‐binding proteins, which might in turn alter expression levels of the gene or particular ZNF804A isoforms. Future work should focus on clarifying the critical periods and cofactors regulating these genetic influences on ZNF804A expression, as well as the downstream biological consequences of an imbalance in the expression of ZNF804A and its various mRNA isoforms.

Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia.

The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n=315) and from ex-vivo blood tissues (n=578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia.

Bioinformatic analyses and conceptual synthesis of evidence linking ZNF804A to risk for schizophrenia and bipolar disorder

Advances in molecular genetics, fueled by the results of large‐scale genome‐wide association studies, meta‐analyses, and mega‐analyses, have provided the means of identifying genetic risk factors for human disease, thereby enriching our understanding of the functionality of the genome in the post‐genomic era. In the past half‐decade, research on neuropsychiatric disorders has reached an important milestone: the identification of susceptibility genes reliably associated with complex psychiatric disorders at genome‐wide levels of significance. This age of discovery provides the groundwork for follow‐up studies designed to elucidate the mechanism(s) by which genetic variants confer susceptibility to these disorders. The gene encoding zinc‐finger protein 804 A (ZNF804A) is among these candidate genes, recently being found to be strongly associated with schizophrenia and bipolar disorder via one of its non‐coding mutations, rs1344706. Neurobiological, molecular, and bioinformatic analyses have improved our understanding of ZNF804A in general and this variant in particular; however, more work is needed to establish the mechanism(s) by which ZNF804A variants impinge on the biological substrates of the two disorders. Here, we review literature recently published on ZNF804A, and analyze critical concepts related to the biology of ZNF804A and the role of rs1344706 in schizophrenia and bipolar disorder. We synthesize the results of new bioinformatic analyses of ZNF804A with key elements of the existing literature and knowledge base. Furthermore, we suggest some potentially fruitful short‐ and long‐term research goals in the assessment of ZNF804A.

PTSD blood transcriptome mega-analysis: Shared inflammatory pathways across biological sex and modes of trauma

Transcriptome-wide screens of peripheral blood during the onset and development of posttraumatic stress disorder (PTSD) indicate widespread immune dysregulation. However, little is known as to whether biological sex and the type of traumatic event influence shared or distinct biological pathways in PTSD. We performed a combined analysis of five independent PTSD blood transcriptome studies covering seven types of trauma in 229 PTSD and 311 comparison individuals to synthesize the extant data. Analyses by trauma type revealed a clear pattern of PTSD gene expression signatures distinguishing interpersonal (IP)-related traumas from combat-related traumas. Co-expression network analyses integrated all data and identified distinct gene expression perturbations across sex and modes of trauma in PTSD, including one wound-healing module downregulated in men exposed to combat traumas, one IL-12-mediated signaling module upregulated in men exposed to IP-related traumas, and two modules associated with lipid metabolism and mitogen-activated protein kinase activity upregulated in women exposed to IP-related traumas. Remarkably, a high degree of sharing of transcriptional dysregulation across sex and modes of trauma in PTSD was also observed converging on common signaling cascades, including cytokine, innate immune, and type I interferon pathways. Collectively, these findings provide a broad view of immune dysregulation in PTSD and demonstrate inflammatory pathways of molecular convergence and specificity, which may inform mechanisms and diagnostic biomarkers for the disorder.

The influence of genes on "positive valence systems" constructs: A systematic review.

In 2009, the U.S. National Institute of Mental Health (NIMH) proposed an approach toward the deconstruction of psychiatric nosology under the research domain criteria (RDoC) framework. The overarching goal of RDoC is to identify robust, objective measures of behavior, emotion, cognition, and other domains that are more closely related to neurobiology than are diagnoses. A preliminary framework has been constructed, which has connected molecules, genes, brain circuits, behaviors, and other elements to dimensional psychiatric constructs. Although the RDoC framework has salience in emerging studies, foundational literature that pre‐dated this framework requires synthesis and translation to the evolving objectives and nomenclature of RDoC. Toward this end, we review the candidate‐gene association, linkage, and genome‐wide studies that have implicated a variety of loci and genetic polymorphisms in selected Positive Valence Systems (PVS) constructs. Our goal is to review supporting evidence to currently listed genes implicated in this domain and novel candidates. We systematically searched and reviewed literature based on keywords listed under the June, 2011, edition of the PVS matrix on the RDoC website (http://www.nimh.nih.gov/research-priorities/rdoc/positive-valence-systems-workshop-proceedings.shtml), which were supplemented with de novo keywords pertinent to the scope of our review. Several candidate genes linked to the PVS framework were identified from candidate‐gene association studies. We also identified novel candidates with loose association to PVS traits from genome‐wide studies. There is strong evidence suggesting that PVS constructs, as currently conceptualized under the RDoC initiative, index genetically influenced traits; however, future research, including genetic epidemiological, and psychometric analyses, must be performed.

RNA sequencing of transformed lymphoblastoid cells from siblings discordant for autism spectrum disorders reveals transcriptomic and functional alterations: Evidence for sex‐specific effects

Genome‐wide expression studies of samples derived from individuals with autism spectrum disorder (ASD) and their unaffected siblings have been widely used to shed light on transcriptomic differences associated with this condition. Females have historically been under‐represented in ASD genomic studies. Emerging evidence from studies of structural genetic variants and peripheral biomarkers suggest that sex‐differences may exist in the biological correlates of ASD. Relatively few studies have explicitly examined whether sex‐differences exist in the transcriptomic signature of ASD. The present study quantified genome‐wide expression values by performing RNA sequencing on transformed lymphoblastoid cell lines and identified transcripts differentially expressed between same‐sex, proximal‐aged sibling pairs. We found that performing separate analyses for each sex improved our ability to detect ASD‐related transcriptomic differences; we observed a larger number of dysregulated genes within our smaller set of female samples (n = 12 sibling pairs), as compared with the set of male samples (n = 24 sibling pairs), with small, but statistically significant overlap between the sexes. Permutation‐based gene‐set analyses and weighted gene co‐expression network analyses also supported the idea that the transcriptomic signature of ASD may differ between males and females. We discuss our findings in the context of the relevant literature, underscoring the need for future ASD studies to explicitly account for differences between the sexes. Autism Res 2017, 10: 439–455.

Why is there selective subcortical vulnerability in ADHD? Clues from postmortem brain gene expression data.

Sub-cortical volumetric differences were associated with attention-deficit/hyperactivity disorder (ADHD) in a recent multi-site, mega-analysis of 1713 ADHD persons and 1529 controls. As there was a wide range of effect sizes among the sub-cortical volumes, it is possible that selective neuronal vulnerability has a role in these volumetric losses. To address this possibility, we used data from Allen Brain Atlas to investigate variability in gene expression profiles between subcortical regions of typically developing brains. We tested the hypothesis that the expression of genes in a set of curated ADHD candidate genes and five a priori selected, biological pathways would be associated with the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) findings. Across the subcortical regions studied by ENIGMA, gene expression profiles for three pathways were significantly correlated with ADHD-associated volumetric reductions: apoptosis, oxidative stress and autophagy. These correlations were strong and significant for children with ADHD, but not for adults. Although preliminary, these data suggest that variability of structural brain anomalies in ADHD can be explained, in part, by the differential vulnerability of these regions to mechanisms mediating apoptosis, oxidative stress and autophagy.

Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary‐level data from previous genome‐wide association studies to determine if commonly varying single nucleotide polymorphisms are shared between psychiatric and immune‐related phenotypes. We estimated heritability and examined pair‐wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics methods. Using LDSC, we observed significant genetic correlations between immune‐related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit‐hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohns disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome‐wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations among the immune‐related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.

SLC9A9 Co-expression modules in autism-associated brain regions.

SLC9A9 is a sodium hydrogen exchanger present in the recycling endosome and highly expressed in the brain. It is implicated in neuropsychiatric disorders, including autism spectrum disorders (ASDs). Little research concerning its gene expression patterns and biological pathways has been conducted. We sought to investigate its possible biological roles in autism‐associated brain regions throughout development. We conducted a weighted gene co‐expression network analysis on RNA‐seq data downloaded from Brainspan. We compared prenatal and postnatal gene expression networks for three ASD‐associated brain regions known to have high SLC9A9 gene expression. We also performed an ASD‐associated single nucleotide polymorphism enrichment analysis and a cell signature enrichment analysis. The modules showed differences in gene constituents (membership), gene number, and connectivity throughout time. SLC9A9 was highly associated with immune system functions, metabolism, apoptosis, endocytosis, and signaling cascades. Gene list comparison with co‐immunoprecipitation data was significant for multiple modules. We found a disproportionately high autism risk signal among genes constituting the prenatal hippocampal module. The modules were enriched with astrocyte and oligodendrocyte markers. SLC9A9 is potentially involved in the pathophysiology of ASDs. Our investigation confirmed proposed functions for SLC9A9, such as endocytosis and immune regulation, while also revealing potential roles in mTOR signaling and cell survival.. By providing a concise molecular map and interactions, evidence of cell type and implicated brain regions we hope this will guide future research on SLC9A9. Autism Res 2017, 10: 414–429.

Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis

Blood‐based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject‐level data from previously published studies by mega‐analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD‐affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta‐data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate‐controlled mixed‐effect linear models were used to identify gene transcripts and co‐expression network modules that were significantly associated with diagnostic status. Permutation‐based gene‐set analysis was used to identify functionally related sets of genes that were over‐ and under‐expressed among ASD samples. Our results were consistent with diminished interferon‐, EGF‐, PDGF‐, PI3K‐AKT‐mTOR‐, and RAS‐MAPK‐signaling cascades, and increased ribosomal translation and NK‐cell related activity in ASD. We explored evidence for sex‐differences in the ASD‐related transcriptomic signature. We also demonstrated that machine‐learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood‐based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD‐related transcriptional differences in circulating immune cells.