Inflammatory bowel disease (IBD) is a complex disorder that imposes a growing health burden. Multiple genetic associations have been identified in IBD, but the mechanisms underlying many of these associations are poorly understood. Animal models are needed to bridge this gap, but conventional laboratory mouse strains lack the genetic diversity of human populations. To more accurately model human genetic diversity, we utilized a panel of chromosome (Chr) substitution strains, carrying chromosomes from the wild-derived and genetically divergent PWD/PhJ (PWD) strain on the commonly used C57BL/6J (B6) background, as well as their parental B6 and PWD strains. Two models of IBD were used, TNBS- and DSS-induced colitis. Compared with B6 mice, PWD mice were highly susceptible to TNBS-induced colitis, but resistant to DSS-induced colitis. Using consomic mice, we identified several PWD-derived loci that exhibited profound effects on IBD susceptibility. The most pronounced of these were loci on Chr1 and Chr2, which yielded high susceptibility in both IBD models, each acting at distinct phases of the disease. Leveraging transcriptomic data from B6 and PWD immune cells, together with a machine learning approach incorporating human IBD genetic associations, we identified lead candidate genes, including Itga4, Pip4k2a, Lcn10, Lgmn, and Gpr65 .

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.

No abstract

Infection with SARS-CoV-2 causes COVID-19 and has a well-established set of clinical symptoms. Olfactory and gustatory dysfunction are among the non-life threatening sequalae observed with both acute and chronic SARS-CoV-2 infection. This can lead to the loss of taste and smell and has been observed in large subsets of COVID-19 patients. Although non-life threatening, loss of taste and smell can contribute to decreased quality of life and prevent sufficient nutrient intake, which may negatively affect prognosis and recovery. Despite progress in the treatment of other symptoms caused by COVID-19, there are currently no standardized treatment protocols to mitigate loss of taste and smell caused by SARS-CoV-2 infection and most approaches thus far have evaluated sensory training and regimen-based treatment strategies independently. In this retrospective case series, we demonstrate the effectiveness of a comprehensive, combined treatment protocol for COVID-19-induced taste and smell dysfunction using olfactory and gustatory training in combination with vitamins and supplements, nasal irrigations, nerve stimulation exercises, and anti-inflammatory prophylaxis. Acutely infected patients with COVID-19-related loss of taste and smell were given a daily regimen of zinc, vitamin A, B-complex, vitamin D, and alpha lipoic acid in addition to saline nasal irrigation, fluticasone spray, nerve stimulation exercises, and repeated olfactory-gustatory training. Triamcinalone paste, theophylline, and prednisone were included daily with the observation of partial recovery. At two timepoints over approximately 20–37 days of treatment, taste and smell scores were quantified based on detection of agents included on each sensory training panel. Following this novel and comprehensive “Training ‘N’ Treatment” (TNT) protocol, every patient exhibited a complete recovery of taste and smell. Given the potential to provide relief to the many people with olfactory and gustatory dysfunction following SARS-CoV-2 infection, the effectiveness of this protocol warrants validation in a larger study.

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.