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Psychological loss is a common experience that erodes well-being and negatively impacts quality of life. The molecular underpinnings of loss are poorly understood. Here, we investigate the mechanisms of loss using an enrichment removal (ER) paradigm in rats. A comprehensive multi-omics investigation of the basolateral amygdala (BLA) revealed alterations in microglia and extracellular matrix (ECM). Follow-up studies indicated that ER decreased microglia size, complexity, and phagocytosis, suggesting reduced immune surveillance. Loss also substantially increased ECM coverage, specifically targeting perineuronal nets surrounding parvalbumin interneurons, suggesting decreased plasticity and increased inhibition in the BLA following loss. Behavioral analyses suggest that these molecular effects are linked to impaired BLA salience evaluation, reflecting emotional blunting observed in human loss. These loss-like behaviors could be rescued by depleting BLA ECM during removal, helping us understand the mechanisms underlying loss and revealing novel molecular targets to ameliorate its impact.

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

While the pathophysiology of schizophrenia has been extensively investigated using homogenized postmortem brain samples, few studies have examined changes in brain samples with techniques that may attribute perturbations to specific cell types. To fill this gap, we performed microarray assays on mRNA isolated from anterior cingulate cortex (ACC) superficial and deep pyramidal neurons from 12 schizophrenia and 12 control subjects using laser-capture microdissection. Among all the annotated genes, we identified 134 significantly increased and 130 decreased genes in superficial pyramidal neurons, while 93 significantly increased and 101 decreased genes were found in deep pyramidal neurons, in schizophrenia compared to control subjects. In these differentially expressed genes, we detected lamina-specific changes of 55 and 31 genes in superficial and deep neurons in schizophrenia, respectively. Gene set enrichment analysis (GSEA) was applied to the entire pre-ranked differential expression gene lists to gain a complete pathway analysis throughout all annotated genes. Our analysis revealed overrepresented groups of gene sets in schizophrenia, particularly in immunity and synapse-related pathways, suggesting the disruption of these pathways plays an important role in schizophrenia. We also detected other pathways previously demonstrated in schizophrenia pathophysiology, including cytokine and chemotaxis, postsynaptic signaling, and glutamatergic synapses. In addition, we observed several novel pathways, including ubiquitin-independent protein catabolic process. Considering the effects of antipsychotic treatment on gene expression, we applied a novel bioinformatics approach to compare our differential expression gene profiles with 51 antipsychotic treatment datasets, demonstrating that our results were not influenced by antipsychotic treatment. Taken together, we found pyramidal neuron-specific changes in neuronal immunity, synaptic dysfunction, and olfactory dysregulation in schizophrenia, providing new insights for the cell-subtype specific pathophysiology of chronic schizophrenia.

Hyperinflammatory response caused by infections such as Severe Acute Respiratory SyndromeCoronavirus 2 (SARS-CoV-2) increase organ failure, intensive care unit admission, and mortality. Cytokine storm inpatients with Coronavirus Disease 2019(COVID-19) drives this pattern of poor clinical outcomes and is dependent upon the activity of the transcription factor complex nuclear factor kappa-light-chain-enhancer of activated B cells(NF-κB) and its downstream target gene interleukin 6 (IL6). In this study, we compare transcriptomic signatures from a variety of drug-treated or genetically suppressed (i.e.knockdown) cell lines in order to identify a mechanism by which antidepressants such as fluoxetine demonstrate non-serotonergic anti-inflammatory effects. Our results demonstrate a critical role for the IL6 signal transduction protein(IL6ST or gp130) in fluoxetine’s ability to act as a potential therapy for hyperinflammatory states such as asthma and sepsis.

We probed a transcriptomic dataset of pilocarpine-induced TLE using various ontological, machine-learning, and systems-biology approaches. We showed that, underneath the complex and penetrant changes, moderate-to-subtle upregulated homeostatic and downregulated synaptic changes associated with the dentate gyrus and hippocampal subfields could not only predict TLE but various other forms of epilepsy. At the cellular level, pyramidal neurons and interneurons showed disparate changes, whereas the proportion of non-neuronal cells increased steadily. A probabilistic Bayesian network demonstrated an aberrant and oscillating physiological interaction between oligodendrocytes and interneurons in driving seizures. Validating the Bayesian inference, we showed that the cell types driving the seizures were associated with known antiepileptic and epileptic drugs. These findings provide predictive biomarkers of epilepsy, insights into the cellular connections and causal changes associated with TLE and a drug discovery method focusing on these events.

Hyperinflammatory response caused by infections such as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) increases organ failure, intensive care unit admission, and mortality. Cytokine storm in patients with Coronavirus Disease 2019 (COVID-19) drives this pattern of poor clinical outcomes and is dependent upon the activity of the transcription factor complex nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kappaB) and its downstream target gene interleukin 6 (IL6) which interacts with IL6 receptor (IL6R) and the IL6 signal transduction protein (IL6ST or gp130) to regulate intracellular inflammatory pathways. In this study, we compare transcriptomic signatures from a variety of drug-treated or genetically suppressed (i.e. knockdown) cell lines in order to identify a mechanism by which antidepressants such as fluoxetine demonstrate non-serotonergic, anti-inflammatory effects. Our results demonstrate a critical role for IL6ST and NF-kappaB Subunit 1 (NFKB1) in fluoxetine’s ability to act as a potential therapy for hyperinflammatory states such as asthma, sepsis, and COVID-19., Graphical abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide pandemic, infecting over 16 million people worldwide with a significant mortality rate. However, there is no current Food and Drug Administration-approved drug that treats coronavirus disease 2019 (COVID-19). Damage to T lymphocytes along with the cytokine storm are important factors that lead to exacerbation of clinical cases. Here, we are proposing intravenous oxytocin (OXT) as a candidate for adjunctive therapy for COVID-19. OXT has anti-inflammatory and proimmune adaptive functions. Using the Library of Integrated Network-Based Cellular Signatures (LINCS), we used the transcriptomic signature for carbetocin, an OXT agonist, and compared it to gene knockdown signatures of inflammatory (such as interleukin IL-1β and IL-6) and proimmune markers (including T cell and macrophage cell markers like CD40 and ARG1). We found that carbetocin’s transcriptomic signature has a pattern of concordance with inflammation and immune marker knockdown signatures that are consistent with reduction of inflammation and promotion and sustaining of immune response. This suggests that carbetocin may have potent effects in modulating inflammation, attenuating T cell inhibition, and enhancing T cell activation. Our results also suggest that carbetocin is more effective at inducing immune cell responses than either lopinavir or hydroxychloroquine, both of which have been explored for the treatment of COVID-19.

The common molecular mechanisms underlying psychiatric disorders are not well understood. Prior attempts to assess the pathological mechanisms responsible for psychiatric disorders have been limited by biased selection of comparable disorders, datasets/cohort availability, and challenges with data normalization. Here, using DisGeNET, a gene-disease associations database, we sought to expand such investigations in terms of number and types of diseases. In a top-down manner, we analyzed an unbiased cluster of 36 psychiatric disorders and comorbid conditions at biological pathway, cell-type, drug-target, and chromosome levels and deployed density index, a novel metric to quantify similarities (close to 1) and dissimilarities (close to 0) between these disorders at each level. At pathway level, we show that cognition and neurotransmission drive the similarity and are involved across all disorders, whereas immune-system and signal-response coupling (cell surface receptors, signal transduction, gene expression, and metabolic process) drives the dissimilarity and are involved with specific disorders. The analysis at the drug-target level supports the involvement of neurotransmission-related changes across these disorders. At cell-type level, dendrite-targeting interneurons, across all layers, are most involved. Finally, by matching the clustering pattern at each level of analysis, we showed that the similarity between the disorders is influenced most at the chromosomal level and to some extent at the cellular level. Together, these findings provide first insights into distinct cellular and molecular pathologies, druggable mechanisms associated with several psychiatric disorders and comorbid conditions and demonstrate that similarities between these disorders originate at the chromosome level and disperse in a bottom-up manner at cellular and pathway levels.

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions.

BackgroundIn silico data exploration is a key first step of exploring a research question. There are many publicly available databases and tools that offer appealing features to help with such a task. However, many applications lack exposure or are constrained with unfriendly or outdated user interfaces. Thus, it follows that there are many resources that are relevant to investigation of medical disorders that are underutilized.ResultsWe developed an R Shiny web application, called Kaleidoscope, to address this challenge. The application offers access to several omics databases and tools to let users explore research questions in silico. The application is designed to be user- friendly with a unified user interface, while also scalable by offering the option of uploading user-defined datasets. We demonstrate the application features with a starting query of a single gene (Disrupted in schizophrenia 1, DISC1) to assess its protein-protein interactions network. We then explore expression levels of the gene network across tissues and cell types in the brain, as well as across 34 schizophrenia versus control differential gene expression datasets.ConclusionKaleidoscope provides easy access to several databases and tools under a unified user interface to explore research questions in silico. The web application is open-source and freely available at https://kalganem.shinyapps.io/Kaleidoscope/. This application streamlines the process of in silico data exploration for users and expands the efficient use of these tools to stakeholders without specific bioinformatics expertise.

The treatment of CNS disorders, and in particular psychiatric illnesses, lacks disease-altering therapeutics for many conditions. This is likely due to regulatory challenges involving the high cost and slow-pace of drug development for CNS disorders as well as due to limited understanding of disease causality. Repurposing drugs for new indications have lower cost and shorter development timeline compared to that of de novo drug development. Historically, empirical drug-repurposing is a standard practice in psychiatry; however, recent advances in characterizing molecules with their structural and transcriptomic signatures along with ensemble of data analysis approaches, provides informed and cost-effective repurposing strategies that ameliorate the regulatory challenges. In addition, the potential to incorporate ontological approaches along with signature-based repurposing techniques addresses the various knowledge-based challenges associated with CNS drug development. In this review we primarily discuss signature-basedin silicoapproaches to drug repurposing, and its integration with data science platforms for evidence-based drug repurposing. We contrast variousin silicoand empirical approaches and discuss possible avenues to improve the clinical relevance. These concepts provide a promising new translational avenue for developing new therapies for difficult to treat disorders, and offer the possibility of connecting drug discovery platforms and big data analytics with personalized disease signatures.