Colibactin-induced genotoxicity and colorectal cancer exacerbation critically depends on adhesin-mediated epithelial binding.

Various bacteria are suggested to contribute to colorectal cancer (CRC) development, including pks+ E. coli which produce the genotoxin colibactin that induces characteristic mutational signatures in host epithelial cells. It remains unclear how the highly unstable colibactin molecule is able to access host epithelial cells and its DNA to cause harm. Using the microbiota-dependent ZEB2-transgenic mouse model of invasive CRC, we found that pks+ E. coli drives CRC exacerbation and tissue invasion in a colibactin-dependent manner. Using isogenic mutant strains, we further demonstrate that CRC exacerbation critically depends on expression of the E. coli type-1 pilus adhesin FimH and the F9-pilus adhesin FmlH. Blocking bacterial adhesion using a pharmacological FimH inhibitor attenuates colibactin-mediated genotoxicity and CRC exacerbation. Together, we show that the oncogenic potential of pks+ E. coli critically depends on bacterial adhesion to host epithelial cells and is critically mediated by specific bacterial adhesins. Adhesin-mediated epithelial binding subsequently allows production of the genotoxin colibactin in close proximity to host epithelial cells, which promotes DNA damage and drives CRC development. These findings present promising therapeutic avenues for the development of anti-adhesive therapies aiming at mitigating colibactin-induced DNA damage and inhibiting the initiation and progression of CRC, particularly in individuals at risk for developing CRC.

A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors.

Fungal microorganisms (mycobiota) comprise a small but immunoreactive component of the human microbiome, yet little is known about their role in human cancers. Pan-cancer analysis of multiple body sites revealed tumor-associated mycobiomes at up to 1 fungal cell per 104 tumor cells. In lung cancer, Blastomyces was associated with tumor tissues. In stomach cancers, high rates of Candida were linked to the expression of pro-inflammatory immune pathways, while in colon cancers Candida was predictive of metastatic disease and attenuated cellular adhesions. Across multiple GI sites, several Candida species were enriched in tumor samples and tumor-associated Candida DNA was predictive of decreased survival. The presence of Candida in human GI tumors was confirmed by external ITS sequencing of tumor samples and by culture-dependent analysis in an independent cohort. These data implicate the mycobiota in the pathogenesis of GI cancers and suggest that tumor-associated fungal DNA may serve as diagnostic or prognostic biomarkers.

Mycobacterial infection aggravates Helicobacter pylori-induced gastric preneoplastic pathology by redirection of de novo induced Treg cells.

The two human pathogens Helicobacter pylori and Mycobacterium tuberculosis (Mtb) co-exist in many geographical areas of the world. Here, using a co-infection model of H. pylori and the Mtb relative M. bovis bacillus Calmette-Guérin (BCG), we show that both bacteria affect the colonization and immune control of the respective other pathogen. Co-occurring M. bovis boosts gastric Th1 responses and H. pylori control and aggravates gastric immunopathology. H. pylori in the stomach compromises immune control of M. bovis in the liver and spleen. Prior antibiotic H. pylori eradication or M. bovis-specific immunization reverses the effects of H. pylori. Mechanistically, the mutual effects can be attributed to the redirection of regulatory T cells (Treg cells) to sites of M. bovis infection. Reversal of Treg cell redirection by CXCR3 blockade restores M. bovis control. In conclusion, the simultaneous presence of both pathogens exacerbates the problems associated with each individual infection alone and should possibly be factored into treatment decisions.

The cancer microbiome atlas: a pan-cancer comparative analysis to distinguish tissue-resident microbiota from contaminants.

Studying the microbial composition of internal organs and their associations with disease remains challenging due to the difficulty of acquiring clinical biopsies. We designed a statistical model to analyze the prevalence of species across sample types from The Cancer Genome Atlas (TCGA), revealing that species equiprevalent across sample types are predominantly contaminants, bearing unique signatures from each TCGA-designated sequencing center. Removing such species mitigated batch effects and isolated the tissue-resident microbiome, which was validated by original matched TCGA samples. Gene copies and nucleotide variants can further distinguish mixed-evidence species. We, thus, present The Cancer Microbiome Atlas (TCMA), a collection of curated, decontaminated microbial compositions of oropharyngeal, esophageal, gastrointestinal, and colorectal tissues. This led to the discovery of prognostic species and blood signatures of mucosal barrier injuries and enabled systematic matched microbe-host multi-omic analyses, which will help guide future studies of the microbiome's role in human health and disease.

Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference.

IMPACT STATEMENT: This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome's role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.

Effect of short-term liver X receptor activation on epidermal barrier features in mild to moderate atopic dermatitis: A randomized controlled trial.

BACKGROUND: Liver X receptors (LXRs) are involved in maintaining epidermal barrier and suppressing inflammatory responses in model systems. The LXR agonist VTP-38543 showed promising results in improving barrier function and inflammatory responses in model systems. OBJECTIVE: To assess the safety, tolerability, cellular and molecular changes, and clinical efficacy of the topical VTP-38543 in adults with mild to moderate atopic dermatitis (AD). METHODS: A total of 104 ambulatory patients with mild to moderate AD were enrolled in this randomized, double-blind, vehicle-controlled trial between December 2015 and September 2016. VTP-38543 cream in 3 concentrations (0.05%, 0.15%, and 1.0%) or placebo was applied twice daily for 28 days. Pretreatment and posttreatment skin biopsy specimens were obtained from a subset of 33 patients. Changes in SCORing of Atopic Dermatitis, Eczema Area and Severity Index, Investigator's Global Assessment, and tissue biomarkers (by real-time polymerase chain reaction and immunostaining) were evaluated. RESULTS: Topical VTP-38543 was safe and well tolerated. VTP-38543 significantly increased messenger RNA (mRNA) expression of epidermal barrier differentiation (loricrin and filaggrin, P = .02) and lipid (adenosine triphosphate-binding cassette subfamily G member 1 and sterol regulatory element binding protein 1c, P < .01) measures and reduced epidermal hyperplasia markers (thickness, keratin 16 mRNA). VTP-38543 nonsignificantly suppressed cellular infiltrates and down-regulated mRNA expression of several TH17/TH22-related (phosphatidylinositol 3, S100 calcium-binding protein A12) and innate immunity (interleukin 6) markers. CONCLUSION: Topical VTP-38543 is safe and well tolerated. Its application led to improvement in barrier differentiation and lipids. Longer-term studies are needed to clarify whether a barrier-based approach can induce meaningful suppression of immune abnormalities. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT02655679.

Integration of pan-cancer transcriptomics with RPPA proteomics reveals mechanisms of epithelial-mesenchymal transition.

Integrating data from multiple regulatory layers across cancer types could elucidate additional mechanisms of oncogenesis. Using antibody-based protein profiling of 736 cancer cell lines, along with matching transcriptomic data, we show that pan-cancer bimodality in the amounts of mRNA, protein, and protein phosphorylation reveals mechanisms related to the epithelial-mesenchymal transition (EMT). Based on the bimodal expression of E-cadherin, we define an EMT signature consisting of 239 genes, many of which were not previously associated with EMT. By querying gene expression signatures collected from cancer cell lines after small-molecule perturbations, we identify enrichment for histone deacetylase (HDAC) inhibitors as inducers of EMT, and kinase inhibitors as mesenchymal-to-epithelial transition (MET) promoters. Causal modeling of protein-based signaling identifies putative drivers of EMT. In conclusion, integrative analysis of pan-cancer proteomic and transcriptomic data reveals key regulatory mechanisms of oncogenic transformation.

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.

GEN3VA: aggregation and analysis of gene expression signatures from related studies.

BACKGROUND: Genome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expression Omnibus (GEO) for potential reuse. However, these repositories currently do not provide simple interfaces to systematically analyze collections of related studies. RESULTS: Here we present GENE Expression and Enrichment Vector Analyzer (GEN3VA), a web-based system that enables the integrative analysis of aggregated collections of tagged gene expression signatures identified and extracted from GEO. Each tagged collection of signatures is presented in a report that consists of heatmaps of the differentially expressed genes; principal component analysis of all signatures; enrichment analysis with several gene set libraries across all signatures, which we term enrichment vector analysis; and global mapping of small molecules that are predicted to reverse or mimic each signature in the aggregate. We demonstrate how GEN3VA can be used to identify common molecular mechanisms of aging by analyzing tagged signatures from 244 studies that compared young vs. old tissues in mammalian systems. In a second case study, we collected 86 signatures from treatment of human cells with dexamethasone, a glucocorticoid receptor (GR) agonist. Our analysis confirms consensus GR target genes and predicts potential drug mimickers. CONCLUSIONS: GEN3VA can be used to identify, aggregate, and analyze themed collections of gene expression signatures from diverse but related studies. Such integrative analyses can be used to address concerns about data reproducibility, confirm results across labs, and discover new collective knowledge by data reuse. GEN3VA is an open-source web-based system that is freely available at: http://amp.pharm.mssm.edu/gen3va .