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Simple Summary: Breast cancer is diagnosed in 2.3 million women each year and kills 685,000 (~30% of patients) worldwide. The most dangerous breast cancer is triple-negative breast cancer (TNBC). TNBC is very diverse, with 12 underlying subtypes. This great deal of patient diversity, along with the lack of broad-application targetable mechanistic markers (such as ER, PR, and HER2, which are present in other breast cancer subtypes but missing in TNBC), gives TNBC patients the worst outcomes of any breast cancer type. We aim to remedy this by exploring the molecular mechanisms across TNBC samples and subtypes. Molecular mechanisms are potentially targetable for treatment. We explore these options as part of our RNA-sequencing analysis. Our novel findings include highly accurate mechanistic markers identified using machine learning methods, including CIDEC (97.1% accuracy alone). Additionally, we found TNBC subtype-differentiating mechanistic markers, including PDE3B, CFD, IFNG, and ADM, which are targets with known therapeutics and potential for drug repurposing. Background/Objectives: Breast cancer is diagnosed in 2.3 million women each year and kills 685,000 (~30% of patients) worldwide. The prognosis for many breast cancer subtypes has improved due to treatments targeting the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). In contrast, patients with triple-negative breast cancer (TNBC) tumors, which lack all three commonly targeted membrane markers, more frequently relapse and have lower survival rates due to a lack of tumor-selective TNBC treatments. We aim to investigate TNBC mechanistic markers that could be targeted for treatment. Methods: We performed a secondary TNBC analysis of 196 samples across 10 publicly available bulk RNA-sequencing studies to better understand the molecular mechanism(s) of disease and predict robust mechanistic markers that could be used to improve the mechanistic understanding of and diagnostic capabilities for TNBC. Results: Our analysis identified ~12,500 significant differentially expressed genes (FDR-adjusted p-value < 0.05), including KIF14 and ELMOD3, and two significantly modulated pathways. Additionally, our novel findings include highly accurate mechanistic markers identified using machine learning methods, including CIDEC (97.1% accuracy alone), CD300LG, ASPM, and RGS1 (98.9% combined accuracy), as well as TNBC subtype-differentiating mechanistic markers, including the targets PDE3B, CFD, IFNG, and ADM, which have associated therapeutics that can potentially be repurposed to improve treatment options. We then experimentally and computationally validated a subset of these findings. Conclusions: The results of our analyses can be used to better understand the mechanism(s) of disease and contribute to the development of improved diagnostics and/or treatments for TNBC. [ABSTRACT FROM AUTHOR]

Introduction: Bovine mastitis caused by Escherichia coli compromises animal health and inflicts substantial product losses in dairy farming. It may manifest as subclinical through severe acute disease and can be transient or persistent in nature. Little is known about bacterial factors that impact clinical outcomes or allow some strains to outcompete others in the mammary gland (MG) environment. Mastitis-associated E. coli (MAEC) may have distinctive characteristics which may contribute to the varied nature of the disease. Given their high levels of intraspecies genetic variability, virulence factors of commonly used MAEC model strains may not be relevant to all members of this group. Methods: In this study, we sequenced the genomes of 96 MAEC strains isolated from cattle with clinical mastitis (CM). We utilized clinical severity data to perform genome-wide association studies to identify accessory genes associated with strains isolated from mild or severe CM, or with high or low competitive fitness during in vivo competition assays. Genes associated with mastitis pathogens or commensal strains isolated from bovine sources were also identified. Results: A type-2 secretion system (T2SS) and a chitinase (ChiA) exported by this system were strongly associated with pathogenic isolates compared with commensal strains. Deletion of chiA from MAEC isolates decreased their adherence to cultured bovine mammary epithelial cells. Discussion: The increased fitness associated with strains possessing this gene may be due to better attachment in the MG. Overall, these results provide a much richer understanding of MAEC and suggest bacterial processes that may underlie the clinical diversity associated with mastitis and their adaptation to this unique environment. [ABSTRACT FROM AUTHOR]

Background/Objectives: Systemic lupus erythematosus (lupus) and B-cell lymphoma (lymphoma) co-occur at higher-than-expected rates and primarily depend on B cells for their pathology. These observations implicate shared inflammation-related B cell molecular mechanisms as a potential cause of co-occurrence. Methods: We consequently implemented a novel Immune Imbalance Transcriptomics (IIT) algorithm and applied IIT to lupus, lymphoma, and healthy B cell RNA-sequencing (RNA-seq) data to find shared and contrasting mechanisms that are potential therapeutic targets. Results: We observed 7143 significantly dysregulated genes in both lupus and lymphoma. Of those genes, we found 5137 to have a significant immune imbalance, defined as a significant dysregulation by both diseases, as analyzed by IIT. Gene Ontology (GO) term and pathway enrichment of the IIT genes yielded immune-related "Neutrophil Degranulation" and "Adaptive Immune System", which validates that the IIT algorithm isolates biologically relevant genes in immunity and inflammation. We found that 344 IIT gene products are known targets for established and/or repurposed drugs. Among our results, we found 48 known and 296 novel lupus targets, along with 151 known and 193 novel lymphoma targets. Known disease drug targets in our IIT results further validate that IIT isolates genes with disease-relevant mechanisms. Conclusions: We anticipate the IIT algorithm, together with the shared and contrasting gene mechanisms uncovered here, will contribute to the development of immune-related therapeutic options for lupus and lymphoma patients. [ABSTRACT FROM AUTHOR]

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org.

Zika virus (ZIKV) is a member of the Flavivirus genus of positive-sense single-stranded RNA viruses, which includes Dengue, West Nile, Yellow Fever, and other mosquito-borne arboviruses. Infection by ZIKV can be difficult to distinguish from infection by other mosquito-borne Flaviviruses due to high sequence similarity, serum antibody cross-reactivity, and virus co-circulation in endemic areas. Indeed, existing serological methods are not able to consistently differentiate ZIKV from other Flaviviruses, which makes it extremely difficult to accurately calculate the incidence rate of Zika-associated Guillain-Barre in adults, microcephaly in newborns, or asymptomatic infections within a geographical area. In order to identify Zika-specific peptide regions that could be used as serology reagents, we have applied comparative genomics and protein structure analyses to identify amino acid residues that distinguish each of 10 Flavivirus species and subtypes from each other by calculating the specificity, sensitivity, and surface exposure of each residue in relevant target proteins. For ZIKV we identified 104 and 116 15-mer peptides in the E glycoprotein and NS1 non-structural protein, respectively, that contain multiple diagnostic sites and are located in surface-exposed regions in the tertiary protein structure. These sensitive, specific, and surface-exposed peptide regions should serve as useful reagents for seroprevalence studies to better distinguish between prior infections with any of these mosquito-borne Flaviviruses. The development of better detection methods and diagnostic tools will enable clinicians and public health workers to more accurately estimate the true incidence rate of asymptomatic infections, neurological syndromes, and birth defects associated with ZIKV infection.

BackgroundThe recent emergence of the 2009 pandemic influenza A/H1N1 virus has highlighted the value of free and open access to influenza virus genome sequence data integrated with information about other important virus characteristics.DesignThe Influenza Research Database (IRD, http://www.fludb.org) is a free, open, publicly-accessible resource funded by the U.S. National Institute of Allergy and Infectious Diseases through the Bioinformatics Resource Centers program. IRD provides a comprehensive, integrated database and analysis resource for influenza sequence, surveillance, and research data, including user-friendly interfaces for data retrieval, visualization and comparative genomics analysis, together with personal log in-protected 'workbench' spaces for saving data sets and analysis results. IRD integrates genomic, proteomic, immune epitope, and surveillance data from a variety of sources, including public databases, computational algorithms, external research groups, and the scientific literature.ResultsTo demonstrate the utility of the data and analysis tools available in IRD, two scientific use cases are presented. A comparison of hemagglutinin sequence conservation and epitope coverage information revealed highly conserved protein regions that can be recognized by the human adaptive immune system as possible targets for inducing cross-protective immunity. Phylogenetic and geospatial analysis of sequences from wild bird surveillance samples revealed a possible evolutionary connection between influenza virus from Delaware Bay shorebirds and Alberta ducks.ConclusionsThe IRD provides a wealth of integrated data and information about influenza virus to support research of the genetic determinants dictating virus pathogenicity, host range restriction and transmission, and to facilitate development of vaccines, diagnostics, and therapeutics.

The rapid evolution of SARS-CoV-2 has fueled its global proliferation since its discovery in 2019, with several notable variants having been responsible for increases in cases of coronavirus disease 2019 (COVID-19). Analyses of codon bias and usage in these variants between phylogenetic clades or lineages may grant insights into the evolution of SARS-CoV-2 and identify target codons indicative of evolutionary or mutative trends that may prove useful in tracking or defending oneself against emerging strains. We processed a cohort of 120 SARS-CoV-2 genome sequences through a statistical and bioinformatic pipeline to identify codons presenting evidence of selective pressure as well as codon coevolution. We report the identification of two codon sites in the orf8 and N genes demonstrating such evidence with real-world impacts on pathogenicity and transmissivity. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer diagnosis worldwide accounting for 1 out of every 8 cancer diagnoses. The elevated expression of Thymidine Kinase 1 (TK1) is associated with more aggressive tumor grades, including breast cancer. Recent studies indicate that TK1 may be involved in cancer pathogenesis; however, its direct involvement in breast cancer has not been identified. Here, we evaluate potential pathogenic effects of elevated TK1 expression by comparing HCC 1806 to HCC 1806 TK1-knockdown cancer cells (L133). Transcriptomic profiles of HCC 1806 and L133 cells showed cell cycle progression, apoptosis, and invasion as potential pathogenic pathways affected by TK1 expression. Subsequent in-vitro studies confirmed differences between HCC 1806 and L133 cells in cell cycle phase progression, cell survival, and cell migration. Expression comparison of several factors involved in these pathogenic pathways between HCC 1806 and L133 cells identified p21 and AKT3 transcripts were significantly affected by TK1 expression. Creation of a protein-protein interaction map of TK1 and the pathogenic factors we evaluated predict that the majority of factors evaluated either directly or indirectly interact with TK1. Our findings argue that TK1 elevation directly increases HCC 1806 cell pathogenicity and is likely occurring by p21- and AKT3-mediated mechanisms to promote cell cycle arrest, cellular migration, and cellular survival. [ABSTRACT FROM AUTHOR]

Background: Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods: We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results: As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients. [ABSTRACT FROM AUTHOR]