Literature-Assisted Validation of a Novel Causal Inference Graph in a Sparsely Sampled Multi-Regimen Exercise Data

Background. Causal mechanisms supporting the cardio-metabolic benefits of exercise can be identified for individuals who cannot exercise. With the use of appropriate causal discovery algorithms, the causal pathways can be found for even sparsely sampled data which will help direct drug discovery and pharmaceutical industries to create the appropriate drug to maintain muscles. Objective. The purpose of this study was to infer novel causal source-target interactions active in sparsely sampled data and embed these in a broader causal network extracted from the literature to test their alignment with community-wide prior knowledge and their mechanistic validity in the context of regulatory feedback dynamics. Methods. To this goal, emphasis was placed on the female STRRIDE1/PD dataset to see how the observed data predicts a Causal Directed Acyclic Graph (C-DAG). The analytes in the dataset with greater than 5 missing values were dropped from further analysis to retain a higher confidence among the graphs. The PC, named after its authors Peter and Clark, algorithm was executed for ten thousand iterations on randomly sampled columns of the modified dataset keeping intensity and amount constant as the first two columns to see their effect on the resultant DAG. Out of the 10,000 iterations, interactions that appeared more than 45%, 50%, 65%, 75% and 100% were observed. The interactions that appeared more than 50% of the times were then compared to the literature mined dataset using MedScan Natural Language Processing (NLP) techniques as a part of Pathway Studio. Results. Full consensus across all sub-sampled networks produced 136 interactions that were fully conserved. Of these 136 interactions, 64 were resolved as direct causal interactions, 5 were not direct causal interactions and 67 could only be described as associative. It was found that about 17% of the interactions were recovered from the text mining of the 285 peer-reviewed journals from a total of 64 that were predicted at a 50% consensus. Out of these 11, 4 were completely recovered whereas 7 were only partially recovered. A completely recovered interaction was LDL → ApoB and a partially recovered interaction was HDL → insulin sensitivity. Conclusion. Only 17% of the predicted interactions were found through literature mining, remaining 83% were a mix of novel interactions and self-interactions that need to be worked on further. Of the remaining interactions, 53 remain novel and give insight into how different clinical parameters interact with the cholesterol molecules, biological markers and how they interact with each other

Wright State University\u27s Symposium of Student Research, Scholarship & Creative Activities from Thursday, October 26, 2023

The student abstract booklet is a compilation of abstracts from students\u27 oral and poster presentations at Wright State University\u27s Symposium of Student Research, Scholarship & Creative Activities on October 26, 2023.https://corescholar.libraries.wright.edu/celebration_abstract_books/1001/thumbnail.jp

Terrestrial Very-Long-Baseline Atom Interferometry:Workshop Summary

This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more km-scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions

Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes

Reverse genetics methods, particularly the production of gene knockouts and knockins, have revolutionized the understanding of gene function. High throughput sequencing now makes it practical to exploit reverse genetics to simultaneously study functions of thousands of normal sequence variants and spontaneous mutations that segregate in intercross and backcross progeny generated by mating completely sequenced parental lines. To evaluate this new reverse genetic method we resequenced the genome of one of the oldest inbred strains of mice—DBA/2J—the father of the large family of BXD recombinant inbred strains. We analyzed ~100X wholegenome sequence data for the DBA/2J strain, relative to C57BL/6J, the reference strain for all mouse genomics and the mother of the BXD family. We generated the most detailed picture of molecular variation between the two mouse strains to date and identified 5.4 million sequence polymorphisms, including, 4.46 million single nucleotide polymorphisms (SNPs), 0.94 million intersections/deletions (indels), and 20,000 structural variants. We systematically scanned massive databases of molecular phenotypes and ~4,000 classical phenotypes to detect linked functional consequences of sequence variants. In majority of cases we successfully recovered known genotype-to-phenotype associations and in several cases we linked sequence variants to novel phenotypes (Ahr, Fh1, Entpd2, and Col6a5). However, our most striking and consistent finding is that apparently deleterious homozygous SNPs, indels, and structural variants have undetectable or very modest additive effects on phenotypes

Selective sweep suggests transcriptional regulation may underlie Plasmodium vivax resilience to malaria control measures in Cambodia

In Cambodia, where Plasmodium vivax and Plasmodium falciparum are coendemic and intense multimodal malaria-control interventions have reduced malaria incidence, P. vivax malaria has proven relatively resistant to such measures. We performed comparative genomic analyses of 150 P. vivax and P. falciparum isolates to determine whether different evolutionary strategies might underlie this species-specific resilience. Demographic modeling and tests of selection show that, in contrast to P. falciparum, P. vivax has experienced uninterrupted growth and positive selection at multiple loci encoding transcriptional regulators. In particular, a strong selective sweep involving an AP2 transcription factor suggests that P. vivax may use nuanced transcriptional approaches to population maintenance. Better understanding of P. vivax transcriptional regulation may lead to improved tools to achieve elimination

The Pharmacoepigenomics Informatics Pipeline and H-GREEN Hi-C Compiler: Discovering Pharmacogenomic Variants and Pathways with the Epigenome and Spatial Genome

Over the last decade, biomedical science has been transformed by the epigenome and spatial genome, but the discipline of pharmacogenomics, the study of the genetic underpinnings of pharmacological phenotypes like drug response and adverse events, has not. Scientists have begun to use omics atlases of increasing depth, and inferences relating to the bidirectional causal relationship between the spatial epigenome and gene expression, as a foundational underpinning for genetics research. The epigenome and spatial genome are increasingly used to discover causative regulatory variants in the significance regions of genome-wide association studies, for the discovery of the biological mechanisms underlying these phenotypes and the design of genetic tests to predict them. Such variants often have more predictive power than coding variants, but in the area of pharmacogenomics, such advances have been radically underapplied. The majority of pharmacogenomics tests are designed manually on the basis of mechanistic work with coding variants in candidate genes, and where genome wide approaches are used, they are typically not interpreted with the epigenome. This work describes a series of analyses of pharmacogenomics association studies with the tools and datasets of the epigenome and spatial genome, undertaken with the intent of discovering causative regulatory variants to enable new genetic tests. It describes the potent regulatory variants discovered thereby to have a putative causative and predictive role in a number of medically important phenotypes, including analgesia and the treatment of depression, bipolar disorder, and traumatic brain injury with opiates, anxiolytics, antidepressants, lithium, and valproate, and in particular the tendency for such variants to cluster into spatially interacting, conceptually unified pathways which offer mechanistic insight into these phenotypes. It describes the Pharmacoepigenomics Informatics Pipeline (PIP), an integrative multiple omics variant discovery pipeline designed to make this kind of analysis easier and cheaper to perform, more reproducible, and amenable to the addition of advanced features. It described the successes of the PIP in rediscovering manually discovered gene networks for lithium response, as well as discovering a previously unknown genetic basis for warfarin response in anticoagulation therapy. It describes the H-GREEN Hi-C compiler, which was designed to analyze spatial genome data and discover the distant target genes of such regulatory variants, and its success in discovering spatial contacts not detectable by preceding methods and using them to build spatial contact networks that unite disparate TADs with phenotypic relationships. It describes a potential featureset of a future pipeline, using the latest epigenome research and the lessons of the previous pipeline. It describes my thinking about how to use the output of a multiple omics variant pipeline to design genetic tests that also incorporate clinical data. And it concludes by describing a long term vision for a comprehensive pharmacophenomic atlas, to be constructed by applying a variant pipeline and machine learning test design system, such as is described, to thousands of phenotypes in parallel. Scientists struggled to assay genotypes for the better part of a century, and in the last twenty years, succeeded. The struggle to predict phenotypes on the basis of the genotypes we assay remains ongoing. The use of multiple omics variant pipelines and machine learning models with omics atlases, genetic association, and medical records data will be an increasingly significant part of that struggle for the foreseeable future.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145835/1/ariallyn_1.pd