Daniel S. Tylee

On the outside, looking in: A review and evaluation of the comparability of blood and brain “-omes”

In this article, we review studies detailing the correspondence between peripheral blood and brain tissue across various domains of high‐throughput ‐omic analysis in order to provide a context for evaluating blood‐based biomarker studies. Specifically, we reviewed seven studies comparing patterns of DNA methylation (i.e., an aspect of the epigenome), eight articles comparing patterns of gene expression (i.e., the transcriptome), and three articles comparing patterns of protein expression (i.e., the proteome). Our review of the epigenomic literature suggests that CpG‐island methylation levels are generally highly correlated (r = 0.90) between blood and brain. Our review of transcriptomic studies suggests that between 35% and 80% of known transcripts are present in both brain and blood tissue samples; estimates of cross‐tissue correlation in expression levels were found to range from 0.25 to 0.64, with stronger correlations observed among particular subsets of genes. Relative to the epigenome and transcriptome, the proteome has not been as fully compared between brain and blood samples, highlighting an important area for future work as whole‐proteome profiling methods mature. Beyond reviewing the relevant studies, we discuss some of the assumptions, methodological issues, and gaps in knowledge that should be addressed in order to better understand how the multiple “‐omes” of the brain are reflected in the peripheral blood. A better understanding of these relationships is a critical precursor to the validation of biomarkers for brain disorders.

Gene networks specific for innate immunity define post-traumatic stress disorder.

The molecular factors involved in the development of Post-Traumatic Stress Disorder (PTSD) remain poorly understood. Previous transcriptomic studies investigating the mechanisms of PTSD apply targeted approaches to identify individual genes under a cross-sectional framework lack a holistic view of the behaviours and properties of these genes at the system-level. Here we sought to apply an unsupervised gene-network based approach to a prospective experimental design using whole-transcriptome RNA-Seq gene expression from peripheral blood leukocytes of U.S. Marines (N=188), obtained both pre- and post-deployment to conflict zones. We identified discrete groups of co-regulated genes (i.e., co-expression modules) and tested them for association to PTSD. We identified one module at both pre- and post-deployment containing putative causal signatures for PTSD development displaying an over-expression of genes enriched for functions of innate-immune response and interferon signalling (Type-I and Type-II). Importantly, these results were replicated in a second non-overlapping independent dataset of U.S. Marines (N=96), further outlining the role of innate immune and interferon signalling genes within co-expression modules to explain at least part of the causal pathophysiology for PTSD development. A second module, consequential of trauma exposure, contained PTSD resiliency signatures and an over-expression of genes involved in hemostasis and wound responsiveness suggesting that chronic levels of stress impair proper wound healing during/after exposure to the battlefield while highlighting the role of the hemostatic system as a clinical indicator of chronic-based stress. These findings provide novel insights for early preventative measures and advanced PTSD detection, which may lead to interventions that delay or perhaps abrogate the development of PTSD.

Blood-based gene-expression predictors of PTSD risk and resilience among deployed marines: a pilot study.

Susceptibility to PTSD is determined by both genes and environment. Similarly, gene‐expression levels in peripheral blood are influenced by both genes and environment, and expression levels of many genes show good correspondence between peripheral blood and brain. Therefore, our objectives were to test the following hypotheses: (1) pre‐trauma expression levels of a gene subset (particularly immune‐system genes) in peripheral blood would differ between trauma‐exposed Marines who later developed PTSD and those who did not; (2) a predictive biomarker panel of the eventual emergence of PTSD among high‐risk individuals could be developed based on gene expression in readily assessable peripheral blood cells; and (3) a predictive panel based on expression of individual exons would surpass the accuracy of a model based on expression of full‐length gene transcripts. Gene‐expression levels were assayed in peripheral blood samples from 50 U.S. Marines (25 eventual PTSD cases and 25 non‐PTSD comparison subjects) prior to their deployment overseas to war‐zones in Iraq or Afghanistan. The panel of biomarkers dysregulated in peripheral blood cells of eventual PTSD cases prior to deployment was significantly enriched for immune genes, achieved 70% prediction accuracy in an independent sample based on the expression of 23 full‐length transcripts, and attained 80% accuracy in an independent sample based on the expression of one exon from each of five genes. If the observed profiles of pre‐deployment mRNA‐expression in eventual PTSD cases can be further refined and replicated, they could suggest avenues for early intervention and prevention among individuals at high risk for trauma exposure.

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.

Blood-based gene-expression biomarkers of post-traumatic stress disorder among deployed marines: A pilot study.

The etiology of post-traumatic stress disorder (PTSD) likely involves the interaction of numerous genes and environmental factors. Similarly, gene-expression levels in peripheral blood are influenced by both genes and environment, and expression levels of many genes show good correspondence between peripheral blood and brain tissues. In that context, this pilot study sought to test the following hypotheses: (1) post-trauma expression levels of a gene subset in peripheral blood would differ between Marines with and without PTSD; (2) a diagnostic biomarker panel of PTSD among high-risk individuals could be developed based on gene-expression in readily assessable peripheral blood cells; and (3) a diagnostic panel based on expression of individual exons would surpass the accuracy of a model based on expression of full-length gene transcripts. Gene-expression levels in peripheral blood samples from 50 U.S. Marines (25 PTSD cases and 25 non-PTSD comparison subjects) were determined by microarray following their return from deployment to war-zones in Iraq or Afghanistan. The original sample was carved into training and test subsets for construction of support vector machine classifiers. The panel of peripheral blood biomarkers achieved 80% prediction accuracy in the test subset based on the expression of just two full-length transcripts (GSTM1 and GSTM2). A biomarker panel based on 20 exons attained an improved 90% accuracy in the test subset. Though further refinement and replication of these biomarker profiles are required, these preliminary results provide proof-of-principle for the diagnostic utility of blood-based mRNA-expression in PTSD among trauma-exposed individuals.

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.

Machine-learning classification of 22q11.2 deletion syndrome: a diffusion tensor imaging study

Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a genetic neurodevelopmental syndrome that has been studied intensively in order to understand relationships between the genetic microdeletion, brain development, cognitive function, and the emergence of psychiatric symptoms. White matter microstructural abnormalities identified using diffusion tensor imaging methods have been reported to affect a variety of neuroanatomical tracts in 22q11.2DS. In the present study, we sought to combine two discovery-based approaches: (1) white matter query language was used to parcellate the brains white matter into tracts connecting pairs of 34, bilateral cortical regions and (2) the diffusion imaging characteristics of the resulting tracts were analyzed using a machine-learning method called support vector machine in order to optimize the selection of a set of imaging features that maximally discriminated 22q11.2DS and comparison subjects. With this unique approach, we both confirmed previously-recognized 22q11.2DS-related abnormalities in the inferior longitudinal fasciculus (ILF), and identified, for the first time, 22q11.2DS-related anomalies in the middle longitudinal fascicle and the extreme capsule, which may have been overlooked in previous, hypothesis-guided studies. We further observed that, in participants with 22q11.2DS, ILF metrics were significantly associated with positive prodromal symptoms of psychosis.

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.

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.

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.