There has been no prior application of matched metagenomics and metatranscriptomics in Clostridioides difficile infection (CDI) evaluating the role of fungi in CDI or identifying community functions that contribute to the development of this disease. We collected diarrheal stools from 49 inpatients (18 of whom tested positive for CDI) under stringent inclusion criteria. We utilized a tiered sequencing approach to identify enriched bacterial and fungal taxa, using 16S and internal transcribed spacer (ITS) rRNA gene amplicon sequencing, with matched metagenomics and metatranscriptomics performed on a subset of the population. Distinct bacterial and fungal compositions distinguished CDI-positive and -negative patients, with the greatest differentiation between the cohorts observed based on bacterial metatranscriptomics. Bipartite network analyses demonstrated that Aspergillus and Penicillium taxa shared a strong positive relationship in CDI patients and together formed negative cooccurring relationships with several bacterial taxa, including the Oscillospira, Comamonadaceae, Microbacteriaceae, and Cytophagaceae. Metatranscriptomics revealed enriched pathways in CDI patients associated with biofilm production primarily driven by Escherichia coli and Pseudomonas, quorum-sensing proteins, and two-component systems related to functions such as osmotic regulation, linoleic acid metabolism, and flagellar assembly. Differential expression of functional pathways unveiled a mechanism by which the causal dysbiosis of CDI may self-perpetuate, potentially contributing to treatment failures. We propose that CDI has a distinct fungus-associated bacteriome, and this first description of metatranscriptomics in human subjects with CDI demonstrates that inflammation, osmotic changes, and biofilm production are key elements of CDI pathophysiology. IMPORTANCE Our data suggest a potential role for fungi in the most common nosocomial bacterial infection in the United States, introducing the concept of a transkingdom interaction between bacteria and fungi in this disease. We also provide the first direct measure of microbial community function in Clostridioides difficile infection using patient-derived tissue samples, revealing antibiotic-independent mechanisms by which C. difficile infection may resist a return to a healthy gut microbiome.

Arsenic is ubiquitous in nature, highly toxic, and is particularly abundant in Southern Asia. While many studies have focused on areas like Bangladesh and West Bengal, India, disadvantaged regions within Nepal have also suffered from arsenic contamination levels, with wells and other water sources possessing arsenic contamination over the recommended WHO and EPA limit of 10 μg/L, some wells reporting levels as high as 500 μg/L. Despite the region's pronounced arsenic concentrations within community water sources, few investigations have been conducted to understand the impact of arsenic contamination on host gut microbiota health. This study aims to examine differential arsenic exposure on the gut microbiome structure within two disadvantaged communities in southern Nepal. Fecal samples (n ¼ 42) were collected from members of the Mahuawa (n ¼ 20) and Ghanashyampur (n ¼ 22) communities in southern Nepal. The 16S rRNA gene was amplified from fecal samples using Illumina-tag PCR and subject to high-throughput sequencing to generate the bacterial community structure of each sample. Bioinformatics analysis and multivariate statistics were conducted to identify if specific fecal bacterial assemblages and predicted functions were correlated with urine arsenic concentration. Our results revealed unique assemblages of arsenic volatilizing and pathogenic bacteria positively correlated with increased arsenic concentration in individuals within the two respective communities. Additionally, we observed that commensal gut bacteria negatively correlated with increased arsenic concentration in the two respective communities. Our study has revealed that arsenic poses a broader human health risk than was previously known. It is influential in shaping the gut microbiome through its enrichment of arsenic volatilizing and pathogenic bacteria and subsequent depletion of gut commensals. This aspect of arsenic has the potential to debilitate healthy humans by contributing to disorders like heart and liver cancers and diabetes, and it has already been shown to contribute to serious diseases and disorders, including skin lesions, gangrene and several types of skin, renal, lung, and liver cancers in disadvantaged areas of the world like Nepal.

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder. Since the advent of the genome-wide association study (GWAS) we have come to understand much about the genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies have increased our ability to prioritize weaker effect alleles, while the recent development of network-based functional prediction has provided a mechanism by which we can use machine learning to reprioritize GWAS hits in the functional context of relevant brain tissues like the hippocampus and amygdala. In parallel with these developments, groups like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) have compiled rich compendia of AD patient data including genotype and biomarker information, including derived volume measures for relevant structures like the hippocampus and the amygdala. In this study we wanted to identify genes involved in AD-related atrophy of these two structures, which are often critically impaired over the course of the disease. To do this we developed a combined score prioritization method which uses the cumulative distribution function of a gene’s functional and positional score, to prioritize top genes that not only segregate with disease status, but also with hippocampal and amygdalar atrophy. Our method identified a mix of genes that had previously been identified in AD GWAS including APOE, TOMM40, and NECTIN2(PVRL2) and several others that have not been identified in AD genetic studies, but play integral roles in AD-effected functional pathways including IQSEC1, PFN1, and PAK2. Our findings support the viability of our novel combined score as a method for prioritizing region- and even cell-specific AD risk genes.

Clostridium difficile is a bacterial pathogen of the gut that causes nearly 500,000 infections per year in the United States, with 20 to 30 percent of cases re‐occurring. Little is known about how C. difficile modulates the gut's fungal community and how this dysbiosis may perpetuate its reocurrence. This study aimed to contribute to the understanding of the disease's mechanism by identifying bacterial and fungal community structures and the bacterial‐fungal interactions in C. difficile infected (CDI) patients. Forty‐nine diarrheal stool samples, 18 CDI and 31 non‐CDI, were collected from hospitalized patients. The taxonomic marker, or “thumbprint,” regions of DNA, 16S rRNA for bacteria and ITS for fungi, were isolated and sequenced to determine the microbial communities. Metagenomic and metatranscriptomic analyses were also preformed to further characterize gut microbial structure and function. Bioinformatic analysis of the 16S rRNA and ITS data revealed a greater number of fungal taxa enriched in CDI samples than non‐CDI samples. Further, co‐occurrence network analysis using the program CoNet displayed negative correlations between the fungal taxon Candida and several bacterial taxa in CDI samples. These findings indicate that CDI infections may create an environment that allows fungal communities to bloom and possibly suppress commensal bacteria. Additionally, bioinformatic analysis of the metatrancriptomic and metagenomic data revealed that pathways involving biofilm formation, inflammation, flagellar assembly, and two‐component systems involving osmotic regulation were enriched in CDI samples. These functionalities may allow C. difficile to persist in the gut, and therefore may lead to failed treatments and reoccurrences. Our data suggest that the differential bacterial and fungal communities in CDI and non‐CDI patients may lead to high recurrence rates. The enriched fungal community in CDI patients, may be perpetuating the dysbiosis, thus leading to the development of new therapeutic approaches.Support or Funding InformationThis research was supported by a grant from the American Society of Colon & Rectal Surgeons in 2017 (ASCRS Research Foundation Benign Colorectal Disease Grant RFP‐002). This research was also supported by a grant to Juniata College from the Howard Hughes Medical Institute (http://www.hhmi.org) through the Precollege and Undergraduate Science Education Program, as well as by the National Science Foundation (http://www.nsf.gov) through NSF award DBI‐1248096.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Early-life seizures (ELS) are associated with persistent cognitive deficits such as ADHD and memory impairment. These co-morbidities have a dramatic negative impact on the quality of life of patients. Therapies that improve cognitive outcomes have enormous potential to improve patients’ quality of life. Our previous work in a rat flurothyl-induction model showed that administration of adrenocorticotropic hormone (ACTH) at time of seizure induction led to improved learning and memory in the animals despite no effect on seizure latency or duration. Administration of dexamethasone (Dex), a corticosteroid, did not have the same positive effect on learning and memory and has even been shown to exacerbate injury in a rat model of temporal lobe epilepsy. We hypothesized that ACTH exerted positive effects on cognitive outcomes through beneficial changes to gene expression and proposed that administration of ACTH at seizure induction would return gene-expression in the brain towards the normal pattern of expression in the Control animals whereas Dex would not. Twenty-six Sprague-Dawley rats were randomized into vehicle- Control, and ACTH-, Dex-, and vehicle-ELS. Rat pups were subjected to 60 flurothyl seizures from P5 to P15. After seizure induction, brains were removed and the hippocampus and PFC were dissected, RNA was extracted and sequenced, and differential expression analysis was performed using generalized estimating equations. Differential expression analysis showed that ACTH pushes gene expression in the brain back to a more normal state of expression through enrichment of pathways involved in supporting homeostatic balance and down-regulating pathways that might contribute to excitotoxic cell-damage post-ELS.

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Gene prioritization within mapped disease-risk loci from genome-wide association studies (GWAS) remains one of the central bioinformatic challenges of human genetics. This problem is abundantly clear in Alzheimer’s Disease (AD) which has several dozen risk loci, but no therapeutically effective drug target. Dominant strategies emphasize alignment between molecular quantitative trait loci (mQTLs) and disease risk loci, under the assumption that cis-regulatory drivers of gene expression or protein abundance mediate disease risk. However, mQTL data do not capture clinically relevant time points or they derive from bulk tissue. These limitations are particularly significant in complex diseases like AD where access to diseased tissue occurs only in end-stage disease, while genetically encoded risk events accumulate over a lifetime. Network-based functional predictions, where bioinformatic databases of gene interaction networks are used to learn disease-associated gene networks to prioritize genes, complement mQTL-based prioritization. The choice of input network, however, can have a profound impact on the output gene rankings, and the optimal tissue network may not be known a priori. Here, we develop a natural extension of the popular NetWAS approach to gene prioritization that allows us to combine information from multiple networks at once. We applied our multi-network (MNFP) approach to AD GWAS data to prioritize candidate genes and compared the results to baseline, single-network models. Finally, we applied the models to prioritize genes in recently mapped AD risk loci and compared our prioritizations to the state-of-the-art mQTL approach used to functionally prioritize genes within those loci. We observed a significant concordance between the top candidates prioritized by our MNFP method and those prioritized by the mQTL approach. Our results show that network-based functional predictions are a strong complement to mQTL-based approaches and are significant to the AD genetics community as they provide a strong functional rationale to mechanistically follow-up novel AD-risk candidates.

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