Biology subjects

Hicks, M.

Publications and source records attributed to Hicks, M..

3 recordsLinked to original sources

bioRxiv2018

Unsupervised integration of multimodal dataset identifies novel signatures of health and disease

Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. We collected 1,385 data features from diverse modalities, including metabolome, microbiome, genetics and advanced imaging, from 1,253 individuals and from a longitudinal validation cohort of 1,083 individuals. We utilized an ensemble of unsupervised machine learning techniques to identify multimodal biomarker signatures of health and disease risk. In particular, our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers, which were used to cluster individuals into distinct health profiles. Cluster membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and BMI. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We also identified an early disease signature for hypertension, and individuals at-risk for a poor metabolic health outcome. We found novel associations between an uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages -- an essential step towards personalized, preventative health risk assessment.

bioinformatics↗

bioRxiv2017

Functional characterization of 3D-protein structures informed by human genetic diversity

Sequence variation data of the human proteome can be used to analyze 3-dimensional (3D) protein structures to derive functional insights. We used genetic variant data from nearly 150,000 individuals to analyze 3D positional conservation in 4,390 protein structures using 481,708 missense and 264,257 synonymous variants. Sixty percent of protein structures harbor at least one intolerant 3D site as defined by significant depletion of observed over expected missense variation. We established an Angstrom-scale distribution of annotated pathogenic missense variants and showed that they accumulate in proximity to the most intolerant 3D sites. Structural intolerance data correlated with experimental functional read-outs in vitro. The 3D structural intolerance analysis revealed characteristic features of ligand binding pockets, orthosteric and allosteric sites. The identification of novel functional 3D sites based on human genetic data helps to validate, rank or predict drug target binding sites in vivo.

genomics↗

bioRxiv2016

The human functional genome defined by genetic diversity

Large scale efforts to sequence whole human genomes provide extensive data on the non-coding portion of the genome. We used variation information from 11,257 human genomes to describe the spectrum of sequence conservation in the population. We established the genome-wide variability for each nucleotide in the context of the surrounding sequence in order to identify departure from expectation at the population level (context-dependent conservation). We characterized the population diversity for functional elements in the genome and identified the coordination of conserved sequences of distal and cis enhancers, chromatin marks, promoters, coding and intronic regions. The most context-dependent conserved regions of the genome are associated with unique functional annotations and a genomic organization that spreads up to one megabase. Importantly, these regions are enriched by over 100-fold of non-coding pathogenic variants. This analysis of human genetic diversity thus provides a detailed view of sequence conservation, functional constraint and genomic organization of the human genome. Specifically, it identifies highly conserved non-coding sequences that are not captured by analysis of interspecies conservation and are greatly enriched in disease variants.

genomics↗