Biology subjects

Petralia, F.

Publications and source records attributed to Petralia, F..

11 recordsLinked to original sources

bioRxiv2024

HIV-1 infection is associated with depletion of germinal center B cells and a decrease in IgA+ plasma cells in the GI tract

Gastrointestinal (GI) B cells and plasma cells (PCs) are critical to mucosal homeostasis and the host response to HIV-1 infection. Here, high resolution mapping of human B cells and PCs sampled from the colon and ileum during both viremic and suppressed HIV-1 infection identified a reduction in germinal center (GC) B cells and follicular dendritic cells (FDCs) during HIV-1 viremia. IgA+ PCs are the major cellular output of intestinal GCs and were significantly reduced during viremic HIV-1 infection. PC-associated transcriptional perturbations, including type I interferon signaling, persisted in antiretroviral therapy (ART)-treated individuals, suggesting ongoing disruption of the intestinal immune milieu during ART. GI humoral immune perturbations were associated with changes in the intestinal microbiome composition and systemic inflammation. These findings highlight a key immune defect in the GI mucosa due to HIV-1 viremia. One Sentence SummaryIntestinal germinal center B cell reduction in HIV-1 infection linked to reduced IgA+ plasma cells and systemic inflammation.

immunology↗

bioRxiv2023

Myeloid cell influx into the colonic epithelium is associated with disease severity and non-response to anti-Tumor Necrosis Factor Therapy in patients with Ulcerative Colitis.

Ulcerative colitis (UC) is an idiopathic chronic inflammatory disease of the colon with sharply rising global prevalence. Dysfunctional epithelial compartment (EC) dynamics are implicated in UC pathogenesis although EC-specific studies are sparse. Applying orthogonal high-dimensional EC profiling to a Primary Cohort (PC; n=222), we detail major epithelial and immune cell perturbations in active UC. Prominently, reduced frequencies of mature BEST4+OTOP2+ absorptive and BEST2+WFDC2+ secretory epithelial enterocytes were associated with the replacement of homeostatic, resident TRDC+KLRD1+HOPX+ {gamma}{delta}+ T cells with RORA+CCL20+S100A4+ TH17 cells and the influx of inflammatory myeloid cells. The EC transcriptome (exemplified by S100A8, HIF1A, TREM1, CXCR1) correlated with clinical, endoscopic, and histological severity of UC in an independent validation cohort (n=649). Furthermore, therapeutic relevance of the observed cellular and transcriptomic changes was investigated in 3 additional published UC cohorts (n=23, 48 and 204 respectively) to reveal that non-response to anti-Tumor Necrosis Factor (anti-TNF) therapy was associated with EC related myeloid cell perturbations. Altogether, these data provide high resolution mapping of the EC to facilitate therapeutic decision-making and personalization of therapy in patients with UC.

immunology↗

bioRxiv2023

Gut-associated lymphoid tissue attrition associates with response to anti-α4β7 therapy in ulcerative colitis

Targeting the 4{beta}7-MAdCAM-1 axis with vedolizumab (VDZ) is a front-line therapeutic paradigm in ulcerative colitis (UC). However, mechanism(s) of action (MOA) of VDZ remain relatively undefined. Here, we examined three distinct cohorts of patients with UC (n=83, n=60, and n=21), to determine the effect of VDZ on the mucosal and peripheral immune system. Transcriptomic studies with protein level validation were used to study drug MOA using conventional and transgenic murine models. We found a significant decrease in colonic and ileal naive B and T cells and circulating gut-homing plasmablasts ({beta}7+) in VDZ-treated patients, pointing to gut-associated lymphoid tissue (GALT) targeting by VDZ. Murine Peyers patches (PP) demonstrated a significant loss cellularity associated with reduction in follicular B cells, including a unique population of epithelium-associated B cells, following anti-4{beta}7 antibody (mAb) administration. Photoconvertible (KikGR) mice unequivocally demonstrated impaired cellular entry into PPs in anti-4{beta}7 mAb treated mice. In VDZ-treated, but not anti-tumor necrosis factor-treated UC patients, lymphoid aggregate size was significantly reduced in treatment responders compared to non-responders, with an independent validation cohort further confirming these data. GALT targeting represents a novel MOA of 4{beta}7-targeted therapies, with major implications for this therapeutic paradigm in UC, and for the development of new therapeutic strategies.

immunology↗

bioRxiv2023

Decomprolute: A benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution is a reliable way to disentangle the diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA-seq) rather than protein levels in tumor cells. While gene expression is less expensive to measure, protein levels provide a more accurate view of immune markers. To facilitate the development as well as improve the reproducibility and reusability of multi-omic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic data sets. Decomprolute incorporates the large-scale multiomic data sets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. The platform consists of modular architecture and it comes with well-defined input and output formats at each module. As a result, it is robust and extendable easily with additional algorithms or analyses. The platform is available for access and use at http://pnnl-compbio.github.io/decomprolute. MotivationTo provide a comprehensive platform for algorithm developers and researchers to benchmark and run tumor deconvolution algorithms on multiomic data.

cancer biology↗

bioRxiv2022

Anti-integrin αvβ6 autoantibodies are a novel predictive biomarker in ulcerative colitis

Background and AimsBetter biomarkers for prediction of ulcerative colitis (UC) development and prognostication are needed. Anti-integrin v{beta}6 autoantibodies (anti-v{beta}6) have been described in UC patients. Here, we tested for the presence of anti-v{beta}6 antibodies in the pre-clinical phase of UC and studied their association with disease-related outcomes after diagnosis. MethodsAnti-v{beta}6 were measured in 4 longitudinal serum samples collected from 82 subjects who later developed UC and 82 matched controls from a Department of Defense pre-clinical cohort (PREDICTS). In a distinct, external validation cohort (GEM), we tested 12 pre-UC subjects and 49 matched controls. Further, anti-v{beta}6 were measured in 2 incident UC cohorts (COMPASS n=55 and OSCCAR n=104) and associations between anti-v{beta}6 and UC-related outcomes were defined using Cox proportional-hazards model. ResultsAnti-v{beta}6 were significantly higher among individuals who developed UC compared to controls up to 10 years before diagnosis in PREDICTS. The anti-v{beta}6 seropositivity was 12.2% 10 years before diagnosis and increased to 52.4% at the time of diagnosis in subjects who developed UC compared with 2.7% in controls across the 4 timepoints. Anti-v{beta}6 predicted UC development with an AUC of at least 0.8 up to 10 years before diagnosis. The presence of anti-v{beta}6 in pre-clinical UC samples was validated in the GEM cohort. Finally, high anti-v{beta}6 was associated with a composite of adverse UC-outcomes including hospitalization, disease extension, colectomy, systemic steroid use and/or escalation to biologic therapy in recently diagnosed UC. ConclusionAnti-integrin v{beta}6 auto-antibodies precede the clinical diagnosis of UC by up to 10 years and are associated with adverse UC-related outcomes.

immunology↗

bioRxiv2021

CPTAC Pancancer Phosphoproteomics Kinase Enrichment Analysis with ProKAP Provides Insights into Immunogenic Signaling Pathways

The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive phosphoproteomics and proteomics data for tumor and tumor-adjacent normal tissue across multiple cancer types. This dataset provides an unprecedented opportunity to systematically characterize pan-cancer kinase activities, which is essential for coupling tumor subtypes with kinase inhibitors as potential treatment. In this work, we performed Kinase Enrichment Analysis (KEA) using a CPTAC phosphoproteomics dataset to identify putative differences in kinase state between tumor and normal tissues within and across five types of cancer. We then implemented an interactive web-portal, the ProTrack Kinase Activity Portal (ProKAP), for querying, visualizing, and downloading the derived pan-cancer kinase activity scores together with the corresponding sample metadata, and protein and phosphoprotein expression profiles. To illustrate the usage of this digital resource, we analyzed the association between kinase activity scores and immune subtypes of clear cell renal cell carcinoma (ccRCC) derived from the CPTAC ccRCC study. We found multiple kinases, whose inhibition has been suggested to have therapeutic effect in other tumor types, are highly active in CD8+-enriched ccRCC tumors. The ProTrack Kinase Activity Portal (ProKAP) is available at: https://pancan-kea3.cptac-data-view.org.

bioinformatics↗

bioRxiv2021

BayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data

To understand immune activation and evasion mechanisms in cancer, one crucial step is to characterize the composition of immune and stromal cells in the tumor microenvironment (TME). Deconvolution analysis based on bulk transcriptomic data has been used to estimate cell composition in TME. However, these algorithms are sub-optimal for proteomic data, which has hindered research in the rapidly growing field of proteogenomics. Moreover, with the increasing prevalence of multi-omics studies, there is an opportunity to enhance deconvolution analysis by utilizing paired proteomic and transcriptomic profiles of the same tissue samples. To bridge these gaps, we propose BayesDeBulk, a new method for estimating the immune/stromal cell composition based on bulk proteomic and gene expression data. BayesDeBulk utilizes the information of known cell-type-specific markers without requiring their absolute abundance levels as prior knowledge. We compared BayesDeBulk with existing tools on synthetic and real data examples, demonstrating its superior performance and versatility. AvailabilitySoftware available at http://www.BayesDeBulk.com/ ContactFor any information, please contact francesca.petralia@mssm.edu

bioinformatics↗

bioRxiv2020

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗

bioRxiv2020

CITEseq analysis of non-small-cell lung cancer lesions reveals an axis of immune cell activation associated with tumor antigen load and TP53 mutations

Immunotherapy is becoming a mainstay in the treatment of NSCLC. While tumor mutational burden (TMB) has been shown to correlate with response to immunotherapy, little is known about the relation of the baseline immune response with the tumor genotype. Here, we profiled 35 early stage NSCLC lesions using multiscale single cell sequencing. Unsupervised clustering identified in a subset of patients a key cellular module consisting of PDCD1+ CXCL13+ activated T cells, IgG+ plasma cells, and SPP1+ macrophages, referred to as the lung cancer activation module (LCAMhi). Transcriptional data from two NSCLC cohorts confirmed a subset of patients with LCAMhi enrichment, which was independent of overall immune cell content. The LCAMhi module strongly correlated with TMB, expression of cancer testis antigens, and with TP53 mutations in smokers and non-smokers. These data establish LCAM as a key mode of immune cell activation associated with high tumor antigen load and driver mutations.

immunology↗

bioRxiv2020

ProNetView-ccRCC: A web-based portal to interactively explore clear cell renal cell carcinoma proteogenomics networks

To better understand the molecular basis of cancer, the NCIs Clinical Proteomics Tumor Analysis Consortium (CPTAC) has been performing comprehensive large-scale proteogenomic characterizations of multiple cancer types. Gene/protein regulatory networks has subsequently been derived based on these proteogenomic profiles, which serve as useful tools to gain system-level understanding of the molecular regulatory factories underlying the diseases. On the other hand, it remains a challenge to effectively visualize and navigate the resulting network models, which capture higher order structures in the proteogenomic profiles. There is a pressing need to have a new open community resource tool for intuitive visual exploration, interpretation and communication of these gene/protein regulatory networks by the cancer research community. In this work, we introduce ProNetView-ccRCC (http://ccrcc.cptac-network-view.org/), an interactive web-based network exploration portal for investigating phosphopeptide co-expression network inferred based on the CPTAC clear cell renal cell carcinoma (ccRCC) phosphoproteomics data. ProNetView-ccRCC enables quick, user-intuitive visual interactions with the ccRCC tumor phosphoprotein co-expression network comprised of 3,614 genes, as well as 30 functional pathway-enriched network modules. Users can interact with the network portal and can conveniently query for association between abundance of each phosphopeptide in the network and clinical variables such as tumor grade.

bioinformatics↗

bioRxiv2020

ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive multi-omics data resources of deep proteogenomic profiles for multiple cancer types. To enable the broader community of biological and medical researchers to intuitively query, explore, and download data and analysis results from various CPTAC projects, we built a prototype user-friendly web application called "ProTrack" with the CPTAC clear cell renal cell carcinoma (ccRCC) data set (http://ccrcc.cptac-data-view.org). Here we describe the salient features of this application which provides a dynamic, comprehensive, and granular visualization of the rich proteogenomic data. Statement of SignificanceThe CPTAC initiative (https://proteomics.cancer.gov/) has generated multi-omics data for multiple cancer types to understand the proteogenomic aberrations of these malignancies. Collectively this effort has so far produced a large data resource for the research community, including high-throughput profiles for proteome, phosphoproteome, whole exome, whole genome, transcriptome, and DNA methylome. To make this valuable data-resource useful to the larger research community, there is a pressing need for development of user-friendly, readily accessible, and easily shared analytic and visualization tools for aligning multi-omics data and exploring alterations in key cancer genes, to drive and support new biological hypotheses. To bridge this gap, we have developed CPTAC ProTrack, an interactive web application which uses a multilayered, client-server architecture in order to deliver an interactive web experience to any user of a modern web-browser. This tool is intentionally designed accessible for researchers, biologists, and clinicians who are interested in multi-omic data without any need to code.

bioinformatics↗