Genetic variants that associate with cirrhosis have pleiotropic effects on human traits

Background and AimsCirrhosis is characterized by extensive fibrosis of the liver and is a major cause of liver‐related mortality. Cirrhosis is partially heritable but genetic contributions to cirrhosis have not been systemically explored. Here, we carry out association analyses with cirrhosis in two large biobanks and determine the effects of cirrhosis associated variants on multiple human disease/traits.MethodsWe carried out a genome‐wide association analysis of cirrhosis as a diagnosis in UK BioBank (UKBB; 1088 cases vs. 407 873 controls) and then tested top‐associating loci for replication with cirrhosis in a hospital‐based cohort from the Michigan Genomics Initiative (MGI; 875 cases of cirrhosis vs. 30 346 controls). For replicating variants or variants previously associated with cirrhosis that also affected cirrhosis in UKBB or MGI, we determined single nucleotide polymorphism effects on all other diagnoses in UKBB (PheWAS), common metabolic traits/diseases and serum/plasma metabolites.ResultsUnbiased genome‐wide association study identified variants in/near PNPLA3 and HFE, and candidate variant analysis identified variants in/near TM6SF2, MBOAT7, SERPINA1, HSD17B13, STAT4 and IFNL4 that reproducibly affected cirrhosis. Most affected liver enzyme concentrations and/or aspartate transaminase‐to‐platelet ratio index. PheWAS, metabolic trait and serum/plasma metabolite association analyses revealed effects of these variants on lipid, inflammatory and other processes including new effects on many human diseases and traits.ConclusionsWe identified eight loci that reproducibly associate with population‐based cirrhosis and define their diverse effects on human diseases and traits.See Editorial on Page 281Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153621/1/liv14321_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153621/2/liv14321.pd

Body Composition and Genetic Lipodystrophy Risk Score Associate With Nonalcoholic Fatty Liver Disease and Liver Fibrosis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150618/1/hep41391.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150618/2/hep41391_am.pd

17‐Beta Hydroxysteroid Dehydrogenase 13 Is a Hepatic Retinol Dehydrogenase Associated With Histological Features of Nonalcoholic Fatty Liver Disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148371/1/hep30350.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148371/2/hep30350_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148371/3/hep30350-sup-0001-Supinfo.pd

Large-scale experimental studies show unexpected amino acid effects on protein expression and solubility in vivo in E. coli

The biochemical and physical factors controlling protein expression level and solubility in vivo remain incompletely characterized. To gain insight into the primary sequence features influencing these outcomes, we performed statistical analyses of results from the high-throughput protein-production pipeline of the Northeast Structural Genomics Consortium. Proteins expressed in E. coli and consistently purified were scored independently for expression and solubility levels. These parameters nonetheless show a very strong positive correlation. We used logistic regressions to determine whether they are systematically influenced by fractional amino acid composition or several bulk sequence parameters including hydrophobicity, sidechain entropy, electrostatic charge, and predicted backbone disorder. Decreasing hydrophobicity correlates with higher expression and solubility levels, but this correlation apparently derives solely from the beneficial effect of three charged amino acids, at least for bacterial proteins. In fact, the three most hydrophobic residues showed very different correlations with solubility level. Leu showed the strongest negative correlation among amino acids, while Ile showed a slightly positive correlation in most data segments. Several other amino acids also had unexpected effects. Notably, Arg correlated with decreased expression and, most surprisingly, solubility of bacterial proteins, an effect only partially attributable to rare codons. However, rare codons did significantly reduce expression despite use of a codon-enhanced strain. Additional analyses suggest that positively but not negatively charged amino acids may reduce translation efficiency in E. coli irrespective of codon usage. While some observed effects may reflect indirect evolutionary correlations, others may reflect basic physicochemical phenomena. We used these results to construct and validate predictors of expression and solubility levels and overall protein usability, and we propose new strategies to be explored for engineering improved protein expression and solubility

Insulin Resistance Exacerbates Genetic Predisposition to Nonalcoholic Fatty Liver Disease in Individuals Without Diabetes

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149741/1/hep41353.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149741/2/hep41353_am.pd

Structural genomics target selection for the New York consortium on membrane protein structure

The New York Consortium on Membrane Protein Structure (NYCOMPS), a part of the Protein Structure Initiative (PSI) in the USA, has as its mission to establish a high-throughput pipeline for determination of novel integral membrane protein structures. Here we describe our current target selection protocol, which applies structural genomics approaches informed by the collective experience of our team of investigators. We first extract all annotated proteins from our reagent genomes, i.e. the 96 fully sequenced prokaryotic genomes from which we clone DNA. We filter this initial pool of sequences and obtain a list of valid targets. NYCOMPS defines valid targets as those that, among other features, have at least two predicted transmembrane helices, no predicted long disordered regions and, except for community nominated targets, no significant sequence similarity in the predicted transmembrane region to any known protein structure. Proteins that feed our experimental pipeline are selected by defining a protein seed and searching the set of all valid targets for proteins that are likely to have a transmembrane region structurally similar to that of the seed. We require sequence similarity aligning at least half of the predicted transmembrane region of seed and target. Seeds are selected according to their feasibility and/or biological interest, and they include both centrally selected targets and community nominated targets. As of December 2008, over 6,000 targets have been selected and are currently being processed by the experimental pipeline. We discuss how our target list may impact structural coverage of the membrane protein space

A new framework for host-pathogen interaction research

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed