Machine learning modeling of patient health signals informs long-term survival on immune checkpoint inhibitor therapy.

System-level patient health signals, as captured by treatment-emergent adverse events (TEAEs), might contain correlates of immune checkpoint inhibitor (ICI) therapy response. Using all TEAEs and a novel machine learning modeling approach, we derived a composite signature predictive of, and potentially specific to, the response to the anti-PD-L1 ICI durvalumab in patients with non-small-cell lung cancer (NSCLC). We trained on data from the durvalumab arm and chemotherapy arm in the MYSTIC clinical trial and tested on data from four independent durvalumab-containing NSCLC trials using only the first 60 days' TEAEs.

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non-small-cell lung cancer.

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical aspects of clinical study design and biomarker discovery but also ensures that the 'right patient' receives the 'right treatment'. However, survival prediction is a highly complex task, given the large number of 'omics; and clinical features, as well as the high degree of freedom that drive patient survival.

Demonstrating a Comprehensive Wastewater-Based Surveillance Approach That Differentiates Globally Sourced Resistomes.

Wastewater-based surveillance (WBS) for disease monitoring is highly promising but requires consistent methodologies that incorporate predetermined objectives, targets, and metrics. Herein, we describe a comprehensive metagenomics-based approach for global surveillance of antibiotic resistance in sewage that enables assessment of 1) which antibiotic resistance genes (ARGs) are shared across regions/communities; 2) which ARGs are discriminatory; and 3) factors associated with overall trends in ARGs, such as antibiotic concentrations. Across an internationally sourced transect of sewage samples collected using a centralized, standardized protocol, ARG relative abundances (16S rRNA gene-normalized) were highest in Hong Kong and India and lowest in Sweden and Switzerland, reflecting national policy, measured antibiotic concentrations, and metal resistance genes.

MetaMLP: A Fast Word Embedding Based Classifier to Profile Target Gene Databases in Metagenomic Samples.

The functional profile of metagenomic samples enables improved understanding of microbial populations in the environment. Such analysis consists of assigning short sequencing reads to a particular functional category. Normally, manually curated databases are used for functional assignment, and genes are arranged into different classes.

Triclosan has a robust, yet reversible impact on human gut microbial composition in vitro.

The recent ban of the antimicrobial compound triclosan from use in consumer soaps followed research that showcased the risk it poses to the environment and to human health. Triclosan has been found in human plasma, urine and milk, demonstrating that it is present in human tissues. Previous work has also demonstrated that consumption of triclosan disrupts the gut microbial community of mice and zebrafish.

A Pilot Study of Chicago Waterways as Reservoirs of Multidrug-Resistant (MDR-Ent) in a High-Risk Region for Community-Acquired MDR-Ent Infection in Children.

Community-acquired multidrug resistant (MDR-Ent) infections continue to increase in the United States. In prior studies, we identified neighboring regions in Chicago, Illinois, where children have 5 to 6 times greater odds of MDR-Ent infections. To prevent community spread of MDR-Ent, we need to identify the MDR-Ent reservoirs.

Comparison of Whole-Genome Sequences of Legionella pneumophila in Tap Water and in Clinical Strains, Flint, Michigan, USA, 2016.

During the water crisis in Flint, Michigan, USA (2014-2015), 2 outbreaks of Legionnaires' disease occurred in Genesee County, Michigan. We compared whole-genome sequences of 10 clinical Legionella pneumophila isolates submitted to a laboratory in Genesee County during the second outbreak with 103 water isolates collected the following year. We documented a genetically diverse range of L.

Enhanced GII.4 human norovirus infection in gnotobiotic pigs transplanted with a human gut microbiota.

The role of commensal microbiota in enteric viral infections has been explored extensively, but the interaction between human gut microbiota (HGM) and human norovirus (HuNoV) is poorly understood. In this study, we established an HGM-Transplanted gnotobiotic (Gn) pig model of HuNoV infection and disease, using an infant stool as HGM transplant and a HuNoV GII.4/2006b strain for virus inoculation.

Identification of discriminatory antibiotic resistance genes among environmental resistomes using extremely randomized tree algorithm.

Background: The interconnectivities of built and natural environments can serve as conduits for the proliferation and dissemination of antibiotic resistance genes (ARGs). Several studies have compared the broad spectrum of ARGs (i.e.

Effects of Dairy Manure-Based Amendments and Soil Texture on Lettuce- and Radish-Associated Microbiota and Resistomes.

Dairy cattle are routinely treated with antibiotics, and the resulting manure or composted manure is commonly used as a soil amendment for crop production, raising questions regarding the potential for antibiotic resistance to propagate from "farm to fork." The objective of this study was to compare the microbiota and "resistomes" (i.e.

Analysis of Temporal Changes in Growth and Gene Expression for Commensal Gut Microbes in Response to the Polyphenol Naringenin.

In this study, the effect of the flavanone naringenin on the growth and genetic expression of the commensal gut microbes, , , and , was analyzed. Analysis of growth curves revealed that was unaffected by naringenin, was slightly enhanced by naringenin, and was severely inhibited by naringenin. Changes in genetic expression due to naringenin were determined using single-molecule RNA sequencing.

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

Background: Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g.

Apigenin Impacts the Growth of the Gut Microbiota and Alters the Gene Expression of Enterococcus.

Apigenin is a major dietary flavonoid with many bioactivities, widely distributed in plants. Apigenin reaches the colon region intact and interacts there with the human gut microbiota, however there is little research on how apigenin affects the gut bacteria. This study investigated the effect of pure apigenin on human gut bacteria, at both the single strain and community levels.

Metagenomic profiling of historic Colorado Front Range flood impact on distribution of riverine antibiotic resistance genes.

Record-breaking floods in September 2013 caused massive damage to homes and infrastructure across the Colorado Front Range and heavily impacted the Cache La Poudre River watershed. Given the unique nature of this watershed as a test-bed for tracking environmental pathways of antibiotic resistance gene (ARG) dissemination, we sought to determine the impact of extreme flooding on ARG reservoirs in river water and sediment. We utilized high-throughput DNA sequencing to obtain metagenomic profiles of ARGs before and after flooding, and investigated 23 antibiotics and 14 metals as putative selective agents during post-flood recovery.

The effect of quercetin on genetic expression of the commensal gut microbes Bifidobacterium catenulatum, Enterococcus caccae and Ruminococcus gauvreauii.

Quercetin is one of the most abundant polyphenols found in fruits and vegetables. The ability of the gut microbiota to metabolize quercetin has been previously documented; however, the effect that quercetin may have on commensal gut microbes remains unclear. In the present study, the effects of quercetin on the commensal gut microbes Ruminococcus gauvreauii, Bifidobacterium catenulatum and Enterococcus caccae were determined through evaluation of growth patterns and cell morphology, and analysis of genetic expression profiles between quercetin treated and non-treated groups using Single Molecule RNA sequencing via Helicos technology.

MetaStorm: A Public Resource for Customizable Metagenomics Annotation.

Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function.

Noncoding RNAs that associate with YB-1 alter proliferation in prostate cancer cells.

The highly conserved, multifunctional YB-1 is a powerful breast cancer prognostic indicator. We report on a pervasive role for YB-1 in which it associates with thousands of nonpolyadenylated short RNAs (shyRNAs) that are further processed into small RNAs (smyRNAs). Many of these RNAs have previously been identified as functional noncoding RNAs (http://www.

Genome-wide transcript profiling reveals novel breast cancer-associated intronic sense RNAs.

Non-coding RNAs (ncRNAs) play major roles in development and cancer progression. To identify novel ncRNAs that may identify key pathways in breast cancer development, we performed high-throughput transcript profiling of tumor and normal matched-pair tissue samples. Initial transcriptome profiling using high-density genome-wide tiling arrays revealed changes in over 200 novel candidate genomic regions that map to intronic regions.

An in-depth map of polyadenylation sites in cancer.

We present a comprehensive map of over 1 million polyadenylation sites and quantify their usage in major cancers and tumor cell lines using direct RNA sequencing. We built the Expression and Polyadenylation Database to enable the visualization of the polyadenylation maps in various cancers and to facilitate the discovery of novel genes and gene isoforms that are potentially important to tumorigenesis. Analyses of polyadenylation sites indicate that a large fraction (∼30%) of mRNAs contain alternative polyadenylation sites in their 3' untranslated regions, independent of the cell type.