Anthrax vaccine design: strategies to achieve comprehensive protection against spore, bacillus, and toxin

The successful use of Bacillus anthracis as a lethal biological weapon has prompted renewed research interest in the development of more effective vaccines against anthrax. The disease consists of three critical components: spore, bacillus, and toxin, elimination of any of which confers at least partial protection against anthrax. Current remedies rely on postexposure antibiotics to eliminate bacilli and pre- and postexposure vaccination to target primarily toxins. Vaccines effective against toxin have been licensed for human use, but need improvement. Vaccines against bacilli have recently been developed by us and others. Whether effective vaccines will be developed against spores is still an open question. An ideal vaccine would confer simultaneous protection against spores, bacilli, and toxins. One step towards this goal is our dually active vaccine, designed to destroy both bacilli and toxin. Existing and potential strategies towards potent and effective anthrax vaccines are discussed in this review

The dawn of the liquid biopsy in the fight against cancer

ABSTRACT Cancer is a molecular disease associated with alterations in the genome, which, thanks to the highly improved sensitivity of mutation detection techniques, can be identified in cell-free DNA (cfDNA) circulating in blood, a method also called liquid biopsy. This is a non-invasive alternative to surgical biopsy and has the potential of revealing the molecular signature of tumors to aid in the individualization of treatments. In this review, we focus on cfDNA analysis, its advantages, and clinical applications employing genomic tools (NGS and dPCR) particularly in the field of oncology, and highlight its valuable contributions to early detection, prognosis, and prediction of treatment response

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

A master autoantigen-ome links alternative splicing, female predilection, and COVID-19 to autoimmune diseases

Chronic and debilitating autoimmune sequelae pose a grave concern for the post-COVID-19 pandemic era. Based on our discovery that the glycosaminoglycan dermatan sulfate (DS) displays peculiar affinity to apoptotic cells and autoantigens (autoAgs) and that DS-autoAg complexes cooperatively stimulate autoreactive B1 cell responses, we compiled a database of 751 candidate autoAgs from six human cell types. At least 657 of these have been found to be affected by SARS-CoV-2 infection based on currently available multi-omic COVID data, and at least 400 are confirmed targets of autoantibodies in a wide array of autoimmune diseases and cancer. The autoantigen-ome is significantly associated with various processes in viral infections, such as translation, protein processing, and vesicle transport. Interestingly, the coding genes of autoAgs predominantly contain multiple exons with many possible alternative splicing variants, short transcripts, and short UTR lengths. These observations and the finding that numerous autoAgs involved in RNA-splicing showed altered expression in viral infections suggest that viruses exploit alternative splicing to reprogram host cell machinery to ensure viral replication and survival. While each cell type gives rise to a unique pool of autoAgs, 39 common autoAgs associated with cell stress and apoptosis were identified from all six cell types, with several being known markers of systemic autoimmune diseases. In particular, the common autoAg UBA1 that catalyzes the first step in ubiquitination is encoded by an X-chromosome escape gene. Given its essential function in apoptotic cell clearance and that X-inactivation escape tends to increase with aging, UBA1 dysfunction can therefore predispose aging women to autoimmune disorders. In summary, we propose a model of how viral infections lead to extensive molecular alterations and host cell death, autoimmune responses facilitated by autoAg-DS complexes, and ultimately autoimmune diseases. Overall, this master autoantigen-ome provides a molecular guide for investigating the myriad of autoimmune sequalae to COVID-19 and clues to the rare adverse effects of the currently available mRNA and viral vector-based COVID vaccines

A repertoire of 124 potential autoantigens for autoimmune kidney diseases identified by dermatan sulfate affinity enrichment of kidney tissue proteins.

Autoantigens are the molecular targets in autoimmune diseases. They are a cohort of seemingly unrelated self-molecules present in different parts of the body, yet they can trigger a similar chain of autoimmune responses such as autoantibody production. We previously reported that dermatan sulfate (DS) can bind self-molecules of dying cells to stimulate autoreactive CD5+ B cells to produce autoantibodies. The formation of autoantigen-DS complexes converts the normally non-antigenic self-molecules to none-self antigens, and thus DS-affinity represents a common underlying biochemical property for autoantigens. This study sought to apply this property to identify potential autoantigens in the kidney. Total proteins were extracted from mouse kidney tissues and loaded onto DS-Sepharose resins. Proteins without affinity were washed off the resins, whereas those with increasing DS-affinity were eluted with step gradients of increasing salt strength. Fractions with strong and moderate DS-affinity were sequenced by mass spectrometry and yielded 25 and 99 proteins, respectively. An extensive literature search was conducted to validate whether these had been previously reported as autoantigens. Of the 124 proteins, 79 were reported autoantigens, and 19 out of 25 of the strong-DS-binding ones were well-known autoantigens. Moreover, these proteins largely fell into the two most common autoantibody categories in autoimmune kidney diseases, including 40 ANA (anti-nuclear autoantibodies) and 25 GBM (glomerular basement membrane) autoantigens. In summary, this study compiles a large repertoire of potential autoantigens for autoimmune kidney diseases. This autoantigen-ome sheds light on the molecular etiology of autoimmunity and further supports our hypothesis DS-autoantigen complexes as a unifying principle of autoantigenicity

Genes commonly selected before and after PSM.

BackgroundAnalysis of omics data that contain multidimensional biological and clinical information can be complex and make it difficult to deduce significance of specific biomarker factors.MethodsWe explored the utility of propensity score matching (PSM), a statistical technique for minimizing confounding factors and simplifying the examination of specific factors. We tested two datasets generated from cohorts of colorectal cancer (CRC) patients, one comprised of immunohistochemical analysis of 12 protein markers in 544 CRC tissues and another consisting of RNA-seq profiles of 163 CRC cases. We examined the efficiency of PSM by comparing pre- and post-PSM analytical results.ResultsUnlike conventional analysis which typically compares randomized cohorts of cancer and normal tissues, PSM enabled direct comparison between patient characteristics uncovering new prognostic biomarkers. By creating optimally matched groups to minimize confounding effects, our study demonstrates that PSM enables robust extraction of significant biomarkers while requiring fewer cancer cases and smaller overall patient cohorts.ConclusionPSM may emerge as an efficient and cost-effective strategy for multiomic data analysis and clinical trial design for biomarker discovery.</div

Comparison of protein marker expression between groups before and after PSM.

Comparison of protein marker expression between groups before and after PSM.</p