Connectivity Map Analysis of a Single-Cell RNA-Sequencing -Derived Transcriptional Signature of mTOR Signaling.

In the connectivity map (CMap) approach to drug repositioning and development, transcriptional signature of disease is constructed by differential gene expression analysis between the diseased tissue or cells and the control. The negative correlation between the transcriptional disease signature and the transcriptional signature of the drug, or a bioactive compound, is assumed to indicate its ability to "reverse" the disease process. A major limitation of traditional CMaP analysis is the use of signatures derived from bulk disease tissues. Since the key driver pathways are most likely dysregulated in only a subset of cells, the "averaged" transcriptional signatures resulting from bulk analysis lack the resolution to effectively identify effective therapeutic agents. The use of single-cell RNA-seq (scRNA-seq) transcriptomic assay facilitates construction of disease signatures that are specific to individual cell types, but methods for using scRNA-seq data in the context of CMaP analysis are lacking. Lymphangioleiomyomatosis (LAM) mutations in TSC1 or TSC2 genes result in the activation of the mTOR complex 1 (mTORC1). The mTORC1 inhibitor Sirolimus is the only FDA-approved drug to treat LAM. Novel therapies for LAM are urgently needed as the disease recurs with discontinuation of the treatment and some patients are insensitive to the drug. We developed methods for constructing disease transcriptional signatures and CMaP analysis using scRNA-seq profiling and applied them in the analysis of scRNA-seq data of lung tissue from naïve and sirolimus-treated LAM patients. New methods successfully implicated mTORC1 inhibitors, including Sirolimus, as capable of reverting the LAM transcriptional signatures. The CMaP analysis mimicking standard bulk-tissue approach failed to detect any connection between the LAM signature and mTORC1 signaling. This indicates that the precise signature derived from scRNA-seq data using our methods is the crucial difference between the success and the failure to identify effective therapeutic treatments in CMaP analysis.

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data.

The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content.

Baseline TREM-1 Whole Blood Gene Expression Does Not Predict Response to Adalimumab Treatment in Patients with Ulcerative Colitis or Crohn's Disease in the SERENE Studies.

BACKGROUND AND AIMS: This study assessed whether baseline triggering receptor expressed on myeloid cells [TREM-1] whole blood gene expression predicts response to anti-TNF therapy in patients with UC or CD. METHODS: TREM-1 whole blood gene expression was analysed by RNA sequencing [RNA-seq] in patients with moderately to severely active UC or CD treated with adalimumab in the Phase 3 SERENE-UC and SERENE-CD clinical trials. The predictive value of baseline TREM-1 expression was evaluated and compared according to endoscopic and clinical response vs non-response, and remission vs non-remission, at Weeks 8 and 52 [SERENE-UC], and Weeks 12 and 56 [SERENE-CD]. RESULTS: TREM-1 expression was analysed in 95 and 106 patients with UC and CD, respectively, receiving standard-dose adalimumab induction treatment. In SERENE-UC, baseline TREM-1 expression was not predictive of endoscopic response [p=0.48], endoscopic remission [p=0.53], clinical response [p=0.58] or clinical remission [p=0.79] at Week 8, or clinical response [p=0.60] at Week 52. However, an association was observed with endoscopic response [p=0.01], endoscopic remission [p=0.048], and clinical remission [p=0.04997] at Week 52. For SERENE-CD, baseline TREM-1 expression was not predictive of endoscopic response [p=0.56], endoscopic remission [p=0.33], clinical response [p=0.07], clinical remission [p=0.65] at Week 12, or endoscopic response [p=0.61], endoscopic remission [p=0.51], clinical response [p=0.62] or clinical remission [p=0.97] at Week 56. CONCLUSIONS: Baseline TREM-1 gene expression did not uniformly predict adalimumab response in SERENE clinical trials. Further research is needed to identify potential blood-based biomarkers predictive of response to anti-TNF therapy in patients with IBD.

Connecting omics signatures and revealing biological mechanisms with iLINCS.

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

Clinical Impact of the Early Use of Monoclonal Antibody LY-CoV555 (Bamlanivimab) on Mortality and Hospitalization Among Elderly Nursing Home Patients: A Multicenter Retrospective Study.

Importance Coronavirus disease 2019 (COVID-19) outbreaks are frequent occurrences in nursing homes and long-term care facilities (LTCFs), resulting in subsequent hospitalization and death. Rationale Virus-neutralizing monoclonal antibodies demonstrate a significant decrease in both viral load and hospital transfer rate among patients with mild-to-moderate COVID-19 infection. Objective To assess the clinical outcomes of COVID-19 patients with mild-to-moderate symptoms in LTCFs who received LY-CoV555 as compared to those who did not receive this treatment. Design Retrospective case-control study and logistic regression analysis. Setting LTCFs in New York. Participants Two-hundred forty-six (246) LTCF patients diagnosed with mild-to-moderate COVID-19 infection with positive COVID-19 polymerase chain reaction (PCR) from November 15, 2020, to January 31, 2021. Methods Two-hundred forty-six (246) COVID-19 patients were identified from electronic medical records, out of which 160 cases were exposed to LY-CoV555 treatment (700 mg single dose, intravenous infusion). Eighty-six (86) patients were unexposed controls who did not receive monoclonal antibodies, LY-CoV555. Outcome We assessed the odds of death and hospitalization of exposed cases as compared to unexposed controls. Using logistic regression analysis, we also assessed the risk factors associated with these outcomes in the entire sample population. Results The mean age of the entire sample was 82.4 years. Fifty-two percent (52%) of patients (n = 129) were female and 48% (n = 117) were male. The mean ages of the exposed group and the unexposed group were 81 years and 84 years, respectively. At the end of the study, 92% (148/160) of the exposed group were alive or not transferred to the hospital as compared to 79% (68/86) patients of the unexposed group (OR 3.23, 95% CI: (1.48, 7.31), p-value = 0.0032). Three percent (3%; 5/160) of patients died in the exposed group compared to 10% (9/86) of patients who died in the unexposed group (OR = 0.25, 95% CI: (0.1, 0.85), p-value = 0.0257). Four point thirty-seven percent (4.37%; 7/160) of patients in the exposed group and 10.46% (9/86) of patients in the unexposed group were transferred to the hospital (OR = 0.35, 95% CI: (0.15, 1.08), p-value = 0.0793). Conclusion Early treatment with monoclonal antibody LY-CoV555 is associated with decreased mortality among high-risk patients with mild-to-moderate COVID-19 infection in LTCFs. Although not statistically significant, there was a trend towards a lower risk of hospitalization in patients treated with LY-CoV555.

GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data.

The vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at: https://shiny.ilincs.org/grein , the source code at: https://github.com/uc-bd2k/grein , and the Docker container at: https://hub.docker.com/r/ucbd2k/grein .

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

Rhinovirus infection results in stronger and more persistent genomic dysregulation: Evidence for altered innate immune response in asthmatics at baseline, early in infection, and during convalescence.

BACKGROUND: Rhinovirus (HRV) is associated with the large majority of virus-induced asthma exacerbations in children and young adults, but the mechanisms remain poorly defined. METHODS: Asthmatics and non-asthmatic controls were inoculated with HRV-A16, and nasal epithelial samples were obtained 7 days before, 36 hours after, and 7 days after viral inoculation. RNA was extracted and subjected to RNA-seq analysis. RESULTS: At baseline, 57 genes were differentially expressed between asthmatics and controls, and the asthmatics had decreased expression of viral replication inhibitors and increased expression of genes involved in inflammation. At 36 hours (before the emergence of peak symptoms), 1329 genes were significantly altered from baseline in the asthmatics compared to 62 genes in the controls. At this time point, asthmatics lacked an increase in IL-10 signaling observed in the controls. At 7 days following HRV inoculation, 222 genes were significantly dysregulated in the asthmatics, whereas only 4 genes were dysregulated among controls. At this time point, the controls but not asthmatics demonstrated upregulation of SPINK5. CONCLUSIONS: As judged by the magnitude and persistence of dysregulated genes, asthmatics have a substantially different host response to HRV-A16 infection compared with non-asthmatic controls. Gene expression differences illuminate biologically plausible mechanisms that contribute to a better understanding of the pathogenesis of HRV-induced asthma exacerbations.

A two-step integrated approach to detect differentially expressed genes in RNA-Seq data.

One of the primary objectives of ribonucleic acid (RNA) sequencing or RNA-Seq experiment is to identify differentially expressed (DE) genes in two or more treatment conditions. It is a common practice to assume that all read counts from RNA-Seq data follow overdispersed (OD) Poisson or negative binomial (NB) distribution, which is sometimes misleading because within each condition, some genes may have unvarying transcription levels with no overdispersion. In such a case, it is more appropriate and logical to consider two sets of genes: OD and non-overdispersed (NOD). We propose a new two-step integrated approach to distinguish DE genes in RNA-Seq data using standard Poisson and NB models for NOD and OD genes, respectively. This is an integrated approach because this method can be merged with any other NB-based methods for detecting DE genes. We design a simulation study and analyze two real RNA-Seq data to evaluate the proposed strategy. We compare the performance of this new method combined with the three [Formula: see text]-software packages namely edgeR, DESeq2, and DSS with their default settings. For both the simulated and real data sets, integrated approaches perform better or at least equally well compared to the regular methods embedded in these [Formula: see text]-packages.