Gut microbiome dynamics and associations with mortality in critically ill patients.

BACKGROUND: Critical illness and care within the intensive care unit (ICU) leads to profound changes in the composition of the gut microbiome. The impact of such changes on the patients and their subsequent disease course remains uncertain. We hypothesized that specific changes in the gut microbiome would be more harmful than others, leading to increased mortality in critically ill patients. METHODS: This was a prospective cohort study of critically ill adults in the ICU. We obtained rectal swabs from 52 patients and assessed the composition the gut microbiome using 16 S rRNA gene sequencing. We followed patients throughout their ICU course and evaluated their mortality rate at 28 days following admission to the ICU. We used selbal, a machine learning method, to identify the balance of microbial taxa most closely associated with 28-day mortality. RESULTS: We found that a proportional ratio of four taxa could be used to distinguish patients with a higher risk of mortality from patients with a lower risk of mortality (p = .02). We named this binarized ratio our microbiome mortality index (MMI). Patients with a high MMI had a higher 28-day mortality compared to those with a low MMI (hazard ratio, 2.2, 95% confidence interval 1.1-4.3), and remained significant after adjustment for other ICU mortality predictors, including the presence of the acute respiratory distress syndrome (ARDS) and the Acute Physiology and Chronic Health Evaluation (APACHE II) score (hazard ratio, 2.5, 95% confidence interval 1.4-4.7). High mortality was driven by taxa from the Anaerococcus (genus) and Enterobacteriaceae (family), while lower mortality was driven by Parasutterella and Campylobacter (genera). CONCLUSIONS: Dysbiosis in the gut of critically ill patients is an independent risk factor for increased mortality at 28 days after adjustment for clinically significant confounders. Gut dysbiosis may represent a potential therapeutic target for future ICU interventions.

A map of cis-regulatory elements and 3D genome structures in zebrafish.

The zebrafish (Danio rerio) has been widely used in the study of human disease and development, and about 70% of the protein-coding genes are conserved between the two species1. However, studies in zebrafish remain constrained by the sparse annotation of functional control elements in the zebrafish genome. Here we performed RNA sequencing, assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation with sequencing, whole-genome bisulfite sequencing, and chromosome conformation capture (Hi-C) experiments in up to eleven adult and two embryonic tissues to generate a comprehensive map of transcriptomes, cis-regulatory elements, heterochromatin, methylomes and 3D genome organization in the zebrafish Tübingen reference strain. A comparison of zebrafish, human and mouse regulatory elements enabled the identification of both evolutionarily conserved and species-specific regulatory sequences and networks. We observed enrichment of evolutionary breakpoints at topologically associating domain boundaries, which were correlated with strong histone H3 lysine 4 trimethylation (H3K4me3) and CCCTC-binding factor (CTCF) signals. We performed single-cell ATAC-seq in zebrafish brain, which delineated 25 different clusters of cell types. By combining long-read DNA sequencing and Hi-C, we assembled the sex-determining chromosome 4 de novo. Overall, our work provides an additional epigenomic anchor for the functional annotation of vertebrate genomes and the study of evolutionarily conserved elements of 3D genome organization.

A supervised learning framework for chromatin loop detection in genome-wide contact maps.

Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of orthogonal data types such as ChIA-PET, HiChIP, Capture Hi-C, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here, we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. We compare Peakachu with current enrichment-based approaches, and find that Peakachu identifies a unique set of short-range interactions. We show that our models perform well in different platforms, across different sequencing depths, and across different species. We apply this framework to predict chromatin loops in 56 Hi-C datasets, and release the results at the 3D Genome Browser.

CD4 T cells control development and maintenance of brain-resident CD8 T cells during polyomavirus infection.

Tissue-resident memory CD8 T (TRM) cells defend against microbial reinfections at mucosal barriers; determinants driving durable TRM cell responses in non-mucosal tissues, which often harbor opportunistic persistent pathogens, are unknown. JC polyomavirus (JCPyV) is a ubiquitous constituent of the human virome. With altered immunological status, JCPyV can cause the oft-fatal brain demyelinating disease progressive multifocal leukoencephalopathy (PML). JCPyV is a human-only pathogen. Using the mouse polyomavirus (MuPyV) encephalitis model, we demonstrate that CD4 T cells regulate development of functional antiviral brain-resident CD8 T cells (bTRM) and renders their maintenance refractory to systemic CD8 T cell depletion. Acquired CD4 T cell deficiency, modeled by delaying systemic CD4 T cell depletion until MuPyV-specific CD8 T cells have infiltrated the brain, impacted the stability of CD8 bTRM, impaired their effector response to reinfection, and rendered their maintenance dependent on circulating CD8 T cells. This dependence of CD8 bTRM differentiation on CD4 T cells was found to extend to encephalitis caused by vesicular stomatitis virus. Together, these findings reveal an intimate association between CD4 T cells and homeostasis of functional bTRM to CNS viral infection.

An unsupervised machine learning method for discovering patient clusters based on genetic signatures.

INTRODUCTION: Many chronic disorders have genomic etiology, disease progression, clinical presentation, and response to treatment that vary on a patient-to-patient basis. Such variability creates a need to identify characteristics within patient populations that have clinically relevant predictive value in order to advance personalized medicine. Unsupervised machine learning methods are suitable to address this type of problem, in which no a priori class label information is available to guide this search. However, it is challenging for existing methods to identify cluster memberships that are not just a result of natural sampling variation. Moreover, most of the current methods require researchers to provide specific input parameters a priori. METHOD: This work presents an unsupervised machine learning method to cluster patients based on their genomic makeup without providing input parameters a priori. The method implements internal validity metrics to algorithmically identify the number of clusters, as well as statistical analyses to test for the significance of the results. Furthermore, the method takes advantage of the high degree of linkage disequilibrium between single nucleotide polymorphisms. Finally, a gene pathway analysis is performed to identify potential relationships between the clusters in the context of known biological knowledge. DATASETS AND RESULTS: The method is tested with a cluster validation and a genomic dataset previously used in the literature. Benchmark results indicate that the proposed method provides the greatest performance out of the methods tested. Furthermore, the method is implemented on a sample genome-wide study dataset of 191 multiple sclerosis patients. The results indicate that the method was able to identify genetically distinct patient clusters without the need to select parameters a priori. Additionally, variants identified as significantly different between clusters are shown to be enriched for protein-protein interactions, especially in immune processes and cell adhesion pathways, via Gene Ontology term analysis. CONCLUSION: Once links are drawn between clusters and clinically relevant outcomes, Immunochip data can be used to classify high-risk and newly diagnosed chronic disease patients into known clusters for predictive value. Further investigation can extend beyond pathway analysis to evaluate these clusters for clinical significance of genetically related characteristics such as age of onset, disease course, heritability, and response to treatment.

Identification of a rare LAMB4 variant associated with familial diverticulitis through exome sequencing.

Diverticulitis is a chronic disease of the colon in which diverticuli, or outpouching through the colonic wall, become inflamed. Although recent observations suggest that genetic factors may play a significant role in diverticulitis, few genes have yet been implicated in disease pathogenesis and familial cases are uncommon. Here, we report results of whole exome sequencing performed on members from a single multi-generational family with early onset diverticulitis in order to identify a genetic component of the disease. We identified a rare single nucleotide variant in the laminin ß 4 gene (LAMB4) that segregated with disease in a dominant pattern and causes a damaging missense substitution (D435N). Targeted sequencing of LAMB4 in 148 non-familial and unrelated sporadic diverticulitis patients identified two additional rare variants in the gene. Immunohistochemistry indicated that LAMB4 localizes to the myenteric plexus of colonic tissue and patients harboring LAMB4 variants exhibited reduced LAMB4 protein levels relative to controls. Laminins are constituents of the extracellular matrix and play a major role in regulating the development and function of the enteric nervous system. Reduced LAMB4 levels may therefore alter innervation and morphology of the enteric nervous system, which may contribute to colonic dysmotility associated with diverticulitis.