Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings.

Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.

Pandemic city: Village-in-a-dish unlocks dynamic genetic effects in the brain.

In this issue, Wells et al. combine genetic multiplexing ("village-in-a-dish") and Stem-cell-derived NGN2-accelerated Progenitors (SNaPs) to evaluate genotype-phenotype relationships across 100 donors in the context of Zika virus infection in the developing brain. This resource will be broadly applicable in uncovering how genetic variation underlies risk for neurodevelopmental disorders.

Non-additive effects of schizophrenia risk genes reflect convergent downstream function.

Genetic studies of schizophrenia (SCZ) reveal a complex polygenic risk architecture comprised of hundreds of risk variants, the majority of which are common in the population at-large and confer only modest increases in disorder risk. Precisely how genetic variants with individually small predicted effects on gene expression combine to yield substantial clinical impacts in aggregate is unclear. Towards this, we previously reported that the combinatorial perturbation of four SCZ risk genes ("eGenes", whose expression is regulated by common variants) resulted in gene expression changes that were not predicted by individual perturbations, being most non-additive among genes associated with synaptic function and SCZ risk. Now, across fifteen SCZ eGenes, we demonstrate that non-additive effects are greatest within groups of functionally similar eGenes. Individual eGene perturbations reveal common downstream transcriptomic effects ("convergence"), while combinatorial eGene perturbations result in changes that are smaller than predicted by summing individual eGene effects ("sub-additive effects"). Unexpectedly, these convergent and sub-additive downstream transcriptomic effects overlap and constitute a large proportion of the genome-wide polygenic risk score, suggesting that functional redundancy of eGenes may be a major mechanism underlying non-additivity. Single eGene perturbations likewise fail to predict the magnitude or directionality of cellular phenotypes resulting from combinatorial perturbations. Overall, our results indicate that polygenic risk cannot be extrapolated from experiments testing one risk gene at a time and must instead be empirically measured. By unravelling the interactions between complex risk variants, it may be possible to improve the clinical utility of polygenic risk scores through more powerful prediction of symptom onset, clinical trajectory, and treatment response, or to identify novel targets for therapeutic intervention.

Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders.

Genome-wide association studies reveal the complex polygenic architecture underlying psychiatric disorder risk, but there is an unmet need to validate causal variants, resolve their target genes(s), and explore their functional impacts on disorder-related mechanisms. Disorder-associated loci regulate transcription of target genes in a cell type- and context-specific manner, which can be measured through expression quantitative trait loci. In this review, we discuss methods and insights from context-specific modeling of genetically and environmentally regulated expression. Human induced pluripotent stem cell-derived cell type and organoid models have uncovered context-specific psychiatric disorder associations by investigating tissue-, cell type-, sex-, age-, and stressor-specific genetic regulation of expression. Techniques such as massively parallel reporter assays and pooled CRISPR (clustered regularly interspaced short palindromic repeats) screens make it possible to functionally fine-map genome-wide association study loci and validate their target genes at scale. Integration of disorder-associated contexts with these patient-specific human induced pluripotent stem cell models makes it possible to uncover gene by environment interactions that mediate disorder risk, which will ultimately improve our ability to diagnose and treat psychiatric disorders.

Common genetic variation impacts stress response in the brain.

To explain why individuals exposed to identical stressors experience divergent clinical outcomes, we determine how molecular encoding of stress modifies genetic risk for brain disorders. Analysis of post-mortem brain (n=304) revealed 8557 stress-interactive expression quantitative trait loci (eQTLs) that dysregulate expression of 915 eGenes in response to stress, and lie in stress-related transcription factor binding sites. Response to stress is robust across experimental paradigms: up to 50% of stress-interactive eGenes validate in glucocorticoid treated hiPSC-derived neurons (n=39 donors). Stress-interactive eGenes show brain region- and cell type-specificity, and, in post-mortem brain, implicate glial and endothelial mechanisms. Stress dysregulates long-term expression of disorder risk genes in a genotype-dependent manner; stress-interactive transcriptomic imputation uncovered 139 novel genes conferring brain disorder risk only in the context of traumatic stress. Molecular stress-encoding explains individualized responses to traumatic stress; incorporating trauma into genomic studies of brain disorders is likely to improve diagnosis, prognosis, and drug discovery.

A Framework for Antiracist Curriculum Changes in Nephrology Education.

Addressing persistent racial health disparities in cases of kidney disease will first require significant investment in examining how structural racism has influenced our clinical practice and medical education. Improving how we understand and articulate race is critical for achieving this goal. This work begins with ensuring that race's mention within nephrology literature and curricular materials for medical trainees is thoroughly rooted in evidence-based rationale-not to serve as a proxy for polygenic contributions, social determinants of health, or systemic health care barriers. While many institutions are increasingly recognizing the importance of instituting such changes on behalf of the systematically marginalized patient populations who are most affected by these disparities, there is a paucity of guidance on how to critically appraise and revise decades of pathophysiological and epidemiological findings through an antiracist lens. In this article, we propose an inquiry-based framework with case-study examples to help readers recognize improper use of race within nephrology, assess personal and institutional readiness to introduce changes to said content, and generate materials that center evidence-based findings and reject harmful misinterpretations of race.

Modeling gene × environment interactions in PTSD using human neurons reveals diagnosis-specific glucocorticoid-induced gene expression.

Post-traumatic stress disorder (PTSD) can develop following severe trauma, but the extent to which genetic and environmental risk factors contribute to individual clinical outcomes is unknown. Here, we compared transcriptional responses to hydrocortisone exposure in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons and peripheral blood mononuclear cells (PBMCs) from combat veterans with PTSD (n = 19 hiPSC and n = 20 PBMC donors) and controls (n = 20 hiPSC and n = 20 PBMC donors). In neurons only, we observed diagnosis-specific glucocorticoid-induced changes in gene expression corresponding with PTSD-specific transcriptomic patterns found in human postmortem brains. We observed glucocorticoid hypersensitivity in PTSD neurons, and identified genes that contribute to this PTSD-dependent glucocorticoid response. We find evidence of a coregulated network of transcription factors that mediates glucocorticoid hyper-responsivity in PTSD. These findings suggest that induced neurons represent a platform for examining the molecular mechanisms underlying PTSD, identifying biomarkers of stress response, and conducting drug screening to identify new therapeutics.

Common Genetic Variation in Humans Impacts In Vitro Susceptibility to SARS-CoV-2 Infection.

The host response to SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, demonstrates significant interindividual variability. In addition to showing more disease in males, the elderly, and individuals with underlying comorbidities, SARS-CoV-2 can seemingly afflict healthy individuals with profound clinical complications. We hypothesize that, in addition to viral load and host antibody repertoire, host genetic variants influence vulnerability to infection. Here we apply human induced pluripotent stem cell (hiPSC)-based models and CRISPR engineering to explore the host genetics of SARS-CoV-2. We demonstrate that a single-nucleotide polymorphism (rs4702), common in the population and located in the 3' UTR of the protease FURIN, influences alveolar and neuron infection by SARS-CoV-2 in vitro. Thus, we provide a proof-of-principle finding that common genetic variation can have an impact on viral infection and thus contribute to clinical heterogeneity in COVID-19. Ongoing genetic studies will help to identify high-risk individuals, predict clinical complications, and facilitate the discovery of drugs.

Analysis framework and experimental design for evaluating synergy-driving gene expression.

The mechanisms by which genetic risk variants interact with each other, as well as environmental factors, to contribute to complex genetic disorders remain unclear. We describe in detail our recently published approach to resolve distinct additive and synergistic transcriptomic effects after combinatorial manipulation of genetic variants and/or chemical perturbagens. Although first developed for CRISPR-based perturbation studies of isogenic human induced pluripotent stem cell-derived neurons, our methodology can be broadly applied to any RNA sequencing dataset, provided that raw read counts are available. Whereas other differential expression analyses reveal the effect of individual perturbations, here we specifically query interactions between two or more perturbagens, resolving the extent of non-additive (synergistic) interactions between perturbations. We discuss the careful experimental design required to resolve synergistic effects and considerations of statistical power and how to quantify observed synergy between experiments. Additionally, we speculate on potential future applications and explore the obvious limitations of this approach. Overall, by interrogating the effect of independent factors, alone and in combination, our analytic framework and experimental design facilitate the discovery of convergence and synergy downstream of gene and/or treatment perturbations hypothesized to contribute to complex diseases. We think that this protocol can be successfully applied by any scientist with bioinformatic skills and basic proficiency in the R programming language. Our computational pipeline ( https://github.com/nadschro/synergy-analysis ) is straightforward, does not require supercomputing support and can be conducted in a single day upon completion of RNA sequencing experiments.

Common genetic variation in humans impacts in vitro susceptibility to SARS-CoV-2 infection.

The host response to SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, demonstrates significant inter-individual variability. In addition to showing more disease in males, the elderly, and individuals with underlying comorbidities, SARS-CoV-2 can seemingly render healthy individuals with profound clinical complications. We hypothesize that, in addition to viral load and host antibody repertoire, host genetic variants also impact vulnerability to infection. Here we apply human induced pluripotent stem cell (hiPSC)-based models and CRISPR-engineering to explore the host genetics of SARS-CoV-2. We demonstrate that a single nucleotide polymorphism (rs4702), common in the population at large, and located in the 3'UTR of the protease FURIN, impacts alveolar and neuron infection by SARS-CoV-2 in vitro. Thus, we provide a proof-of-principle finding that common genetic variation can impact viral infection, and thus contribute to clinical heterogeneity in SARS-CoV-2. Ongoing genetic studies will help to better identify high-risk individuals, predict clinical complications, and facilitate the discovery of drugs that might treat disease.

Convalescent Plasma for the Treatment of Severe COVID-19 Infection in Cancer Patients.

BACKGROUND: Patients with malignancy are particularly vulnerable to infection with Severe Acute Respiratory Disease-Coronavirus-2 (SARS-CoV-2) given their immunodeficiency secondary to their underlying disease and cancer-directed therapy. We report a case series of patients with cancer who received convalescent plasma, an investigational therapy for severe Coronavirus Disease 2019 (COVID-19). METHODS: Patients with cancer were identified who received convalescent plasma. Enrolled patients had confirmed COVID-19 with severe or life-threatening disease and were transfused with convalescent plasma from donors with a SARS-CoV-2 anti-spike antibody titer of ≥ 1:320 dilution. Oxygen requirements and clinical outcomes of interests were captured as well as laboratory parameters at baseline and 3 days after treatment. RESULTS: We identified 24 patients with cancer, 14 of whom had a hematological malignancy, who were treated with convalescent plasma. Fifteen patients (62.5%) were on cancer-directed treatment at the time of COVID-19 infection. After a median of hospital duration of 9 days, 13 patients (54.2%) had been discharged home, 1 patient (4.2%) was still hospitalized, and 10 patients had died (41.7%). Non-intubated patients, particularly those on nasal cannula alone, had favorable outcomes. Three mild febrile non-hemolytic transfusion reactions were observed. C-reactive protein significantly decreased after 3 days of treatment, while other laboratory parameters including ferritin and D-dimer remained unchanged. CONCLUSIONS: Convalescent plasma may be a promising therapy in cancer patients with COVID-19.

Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data.

The 'discovery' stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur 'outside' the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias.

Identification and therapeutic rescue of autophagosome and glutamate receptor defects in C9ORF72 and sporadic ALS neurons.

Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron disease with diverse etiologies. Therefore, the identification of common disease mechanisms and therapeutics targeting these mechanisms could dramatically improve clinical outcomes. To this end, we developed induced motor neuron (iMN) models from C9ORF72 and sporadic ALS (sALS) patients to identify targets that are effective against these types of cases, which together comprise ~90% of patients. We find that iMNs from C9ORF72 and several sporadic ALS patients share two common defects - impaired autophagosome formation and the aberrant accumulation of glutamate receptors. Moreover, we show that an anticoagulation-deficient form of activated protein C, 3K3A-APC, rescues these defects in both C9ORF72 and sporadic ALS iMNs. As a result, 3K3A-APC treatment lowers C9ORF72 dipeptide repeat protein (DPR) levels, restores nuclear TDP-43 localization, and rescues the survival of both C9ORF72 and sporadic ALS iMNs. Importantly, 3K3A-APC also lowers glutamate receptor levels and rescues proteostasis in vivo in C9ORF72 gain- and loss-of-function mouse models. Thus, motor neurons from C9ORF72 and at least a subset of sporadic ALS patients share common, early defects in autophagosome formation and glutamate receptor homeostasis and a single therapeutic approach may be efficacious against these disease processes.

Haploinsufficiency leads to neurodegeneration in C9ORF72 ALS/FTD human induced motor neurons.

An intronic GGGGCC repeat expansion in C9ORF72 is the most common cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), but the pathogenic mechanism of this repeat remains unclear. Using human induced motor neurons (iMNs), we found that repeat-expanded C9ORF72 was haploinsufficient in ALS. We found that C9ORF72 interacted with endosomes and was required for normal vesicle trafficking and lysosomal biogenesis in motor neurons. Repeat expansion reduced C9ORF72 expression, triggering neurodegeneration through two mechanisms: accumulation of glutamate receptors, leading to excitotoxicity, and impaired clearance of neurotoxic dipeptide repeat proteins derived from the repeat expansion. Thus, cooperativity between gain- and loss-of-function mechanisms led to neurodegeneration. Restoring C9ORF72 levels or augmenting its function with constitutively active RAB5 or chemical modulators of RAB5 effectors rescued patient neuron survival and ameliorated neurodegenerative processes in both gain- and loss-of-function C9ORF72 mouse models. Thus, modulating vesicle trafficking was able to rescue neurodegeneration caused by the C9ORF72 repeat expansion. Coupled with rare mutations in ALS2, FIG4, CHMP2B, OPTN and SQSTM1, our results reveal mechanistic convergence on vesicle trafficking in ALS and FTD.