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.

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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.

The Severe Acute Respiratory Syndrome Corona Virus 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 FDA approved drug that treats 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 pro-immune 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 pro-immune 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. Keywords: COVID19, Oxytocin, Immune, LINCS, Transcriptomic Signature

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.