CloudATAC: a cloud-based framework for ATAC-Seq data analysis.

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore, we present Cloud ATAC, an open-source, cloud-based interactive framework with a scalable, flexible, and streamlined analysis framework based on the best practices approach for pooled-cell and single-cell ATAC-seq data. These frameworks use on-demand computational power and memory, scalability, and a secure and compliant environment provided by the Google Cloud. Additionally, we leverage Jupyter Notebook's interactive computing platform that combines live code, tutorials, narrative text, flashcards, quizzes, and custom visualizations to enhance learning and analysis. Further, leveraging GPU instances has significantly improved the run-time of the single-cell framework. The source codes and data are publicly available through NIH Cloud lab https://github.com/NIGMS/ATAC-Seq-and-Single-Cell-ATAC-Seq-Analysis. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

The single-cell opioid responses in the context of HIV (SCORCH) consortium.

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

Coordination among frequent genetic variants imparts substance use susceptibility and pathogenesis.

Determining the key genetic variants is a crucial step to comprehensively understand substance use disorders (SUDs). In this study, utilizing whole exome sequences of five multi-generational pedigrees with SUDs, we used an integrative omics-based approach to uncover candidate genetic variants that impart susceptibility to SUDs and influence addition traits. We identified several SNPs and rare, protein-function altering variants in genes, GRIA3, NCOR1, and SHANK1; compound heterozygous variants in LNPEP, LRP1, and TBX2, that play a significant role in the neurotransmitter-neuropeptide axis, specifically in the dopaminergic circuits. We also noted a greater frequency of heterozygous and recessive variants in genes involved in the structural and functional integrity of synapse receptors, CHRNA4, CNR2, GABBR1, DRD4, NPAS4, ADH1B, ADH1C, OPRM1, and GABBR2. Variant analysis in upstream promoter regions revealed regulatory variants in NEK9, PRRX1, PRPF4B, CELA2A, RABGEF1, and CRBN, crucial for dopamine regulation. Using family-and pedigree-based data, we identified heterozygous recessive alleles in LNPEP, LRP1 (4 frameshift deletions), and TBX2 (2 frameshift deletions) linked to SUDs. GWAS overlap identified several SNPs associated with SUD susceptibility, including rs324420 and rs1229984. Furthermore, miRNA variant analysis revealed notable variants in mir-548 U and mir-532. Pathway studies identified the presence of extensive coordination among these genetic variants to impart substance use susceptibility and pathogenesis. This study identified variants that were found to be overrepresented among genes of dopaminergic circuits participating in the neurotransmitter-neuropeptide axis, suggesting pleiotropic influences in the development and sustenance of chronic substance use. The presence of a diverse set of haploinsufficient variants in varying frequencies demonstrates the existence of extraordinary coordination among them in attributing risk and modulating severity to SUDs.

Identification of potential inhibitors for drug-resistant EGFR mutations in non-small cell lung cancer using whole exome sequencing data.

Epidermal growth factor receptor (EGFR) gene mutations are prevalent in about 50% of lung adenocarcinoma patients. Highly effective tyrosine kinase inhibitors (TKIs) targeting the EGFR protein have revolutionized treatment for the prevalent and aggressive lung malignancy. However, the emergence of new EGFR mutations and the rapid development of additional drug resistance mechanisms pose substantial challenge to the effective treatment of NSCLC. To investigate the underlying causes of drug resistance, we utilized next-generation sequencing data to analyse the genetic alterations in different tumor genomic states under the pressure of drug selection. This study involved a comprehensive analysis of whole exome sequencing data (WES) from NSCLC patients before and after treatment with afatinib and osimertinib with a goal to identify drug resistance mutations from the post-treatment WES data. We identified five EGFR single-point mutations (L718A, G724E, G724K, K745L, V851D) and one double mutation (T790M/L858R) associated with drug resistance. Through molecular docking, we observed that mutations, G724E, K745L, V851D, and T790M/L858R, have negatively affected the binding affinity with the FDA-approved drugs. Further, molecular dynamic simulations revealed the detrimental impact of these mutations on the binding efficacy. Finally, we conducted virtual screening against structurally similar compounds to afatinib and osimertinib and identified three compounds (CID 71496460, 73292362, and 73292545) that showed the potential to selectively inhibit EGFR despite the drug-resistance mutations. The WES-based study provides additional insight to understand the drug resistance mechanisms driven by tumor mutations and helps develop potential lead compounds to inhibit EGFR in the presence of drug resistance mutations.

iCluF: an unsupervised iterative cluster-fusion method for patient stratification using multiomics data.

Motivation: Patient stratification is crucial for the effective treatment or management of heterogeneous diseases, including cancers. Multiomic technologies facilitate molecular characterization of human diseases; however, the complexity of data warrants the need for the development of robust data integration tools for patient stratification using machine-learning approaches. Results: iCluF iteratively integrates three types of multiomic data (mRNA, miRNA, and DNA methylation) using pairwise patient similarity matrices built from each omic data. The intermediate omic-specific neighborhood matrices implement iterative matrix fusion and message passing among the similarity matrices to derive a final integrated matrix representing all the omics profiles of a patient, which is used to further cluster patients into subtypes. iCluF outperforms other methods with significant differences in the survival profiles of 8581 patients belonging to 30 different cancers in TCGA. iCluF also predicted the four intrinsic subtypes of Breast Invasive Carcinomas with adjusted rand index and Fowlkes-Mallows scores of 0.72 and 0.83, respectively. The Gini importance score showed that methylation features were the primary decisive players, followed by mRNA and miRNA to identify disease subtypes. iCluF can be applied to stratify patients with any disease containing multiomic datasets. Availability and implementation: Source code and datasets are available at https://github.com/GudaLab/iCluF_core.

A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data.

The premise for this study emanated from the need to understand SARS-CoV-2 infections at the molecular level and to develop predictive tools for managing COVID-19 severity. With the varied clinical outcomes observed among infected individuals, creating a reliable machine learning (ML) model for predicting the severity of COVID-19 became paramount. Despite the availability of large-scale genomic and clinical data, previous studies have not effectively utilized multi-modality data for disease severity prediction using data-driven approaches. Our primary goal is to predict COVID-19 severity using a machine-learning model trained on a combination of patients' gene expression, clinical features, and co-morbidity data. Employing various ML algorithms, including Logistic Regression (LR), XGBoost (XG), Naïve Bayes (NB), and Support Vector Machine (SVM), alongside feature selection methods, we sought to identify the best-performing model for disease severity prediction. The results highlighted XG as the superior classifier, with 95% accuracy and a 0.99 AUC (Area Under the Curve), for distinguishing severity groups. Additionally, the SHAP analysis revealed vital features contributing to prediction, including several genes such as COX14, LAMB2, DOLK, SDCBP2, RHBDL1, and IER3-AS1. Notably, two clinical features, the absolute neutrophil count and Viremia Categories, emerged as top contributors. Integrating multiple data modalities has significantly improved the accuracy of disease severity prediction compared to using any single modality. The identified features could serve as biomarkers for COVID-19 prognosis and patient care, allowing clinicians to optimize treatment strategies and refine clinical decision-making processes for enhanced patient outcomes.

Neurobehavioral Characterization of Perinatal Oxycodone-Exposed Offspring in Early Adolescence.

The opioid epidemic has received considerable attention, but the impact on perinatal opioid-exposed (POE) offspring remains underexplored. This study addresses the emerging public health challenge of understanding and treating POE children. We examined two scenarios using preclinical models: offspring exposed to oxycodone (OXY) in utero (IUO) and acute postnatal OXY (PNO). We hypothesized exposure to OXY during pregnancy primes offspring for neurodevelopmental deficits and severity of deficits is dependent on timing of exposure. Notable findings include reduced head size and brain weight in offspring. Molecular analyses revealed significantly lower levels of inflammasome-specific genes in the prefrontal cortex (PFC). Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) highlighted the enrichment of genes associated with mitochondrial and synapse dysfunction in POE offspring. Western blot analysis validated IPA predictions of mitochondrial dysfunction in PFC-derived synaptosomes. Behavioral studies identified significant social deficits in POE offspring. This study presents the first comparative analysis of acute PNO- and IUO-offspring during early adolescence finding acute PNO-offspring have considerably greater deficits. The striking difference in deficit severity in acute PNO-offspring suggests that exposure to opioids in late pregnancy pose the greatest risk for offspring well-being.