{"doi":"10.17615/qacc-g522","title":"Interstrain differences in liver injury and one-carbon metabolism in alcohol-fed mice","abstract":"Alcoholic liver injury is a major public health issue worldwide. Even though the major mechanisms of this disease have been established over the past decades, little is known about genetic susceptibility factors that may predispose individuals who abuse alcoholic beverages to liver damage and subsequent pathological conditions. We hypothesized that a panel of genetically diverse mouse strains may be used to examine the role of ER stress and one-carbon metabolism in the mechanism of inter-individual variability in alcoholic liver injury. We administered alcohol (up to 27 mg/kg/d) in high fat diet using intragastric intubation model for 28 days to male mice from 14 inbred strains (129S1/SvImJ, AKR/J, BALB/cJ, BALB/cByJ, BTBR T+tf/J, C3H/HeJ, C57BL/10J, DBA/2J, FVB/NJ, KK/HIJ, MOLF/EiJ, NZW/LacJ, PWD/PhJ, and WSB/EiJ). Profound inter-strain differences (more than 3-fold) in alcohol-induced steatohepatitis were observed among the strains in spite of consistently high levels of urine alcohol that was monitored throughout the study. We found that endoplasmic reticulum stress genes were induced only in strains with the highest liver injury. Liver glutathione and methyl donor levels were affected in all strains, albeit to a different degree. Most pronounced effects that were closely associated with the degree of liver injury were hyperhomocysteinemia and strain-dependent differences in expression patterns of one-carbon metabolism-related genes.","journal":"UNC Libraries","year":2020,"id":117634,"datarank":0.20164014031326505,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.09766806322927322,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.09766806322927322,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9475,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":303876,"name":"Oksana Kosyk","orcid":"0000-0003-0873-3180","position":1,"is_corresponding":false},{"id":548886,"name":"Svitlana Shymonyak","orcid":null,"position":2,"is_corresponding":false},{"id":548887,"name":"Masato Tsuchiya","orcid":null,"position":3,"is_corresponding":false},{"id":476851,"name":"Volodymyr Tryndyak","orcid":"0000-0002-8319-2954","position":4,"is_corresponding":false},{"id":548285,"name":"Hiroshi Kono","orcid":"0000-0001-6843-0814","position":5,"is_corresponding":false},{"id":548888,"name":"Cheng Ji","orcid":null,"position":6,"is_corresponding":false},{"id":299483,"name":"Ivan Rusyn","orcid":"0000-0001-9340-7384","position":7,"is_corresponding":false},{"id":424441,"name":"Stepan Melnyk","orcid":"0000-0002-0629-9723","position":8,"is_corresponding":false},{"id":335445,"name":"Levan Muskhelishvili","orcid":null,"position":9,"is_corresponding":false},{"id":453064,"name":"Igor P. Pogribny","orcid":"0000-0002-5248-9843","position":10,"is_corresponding":false},{"id":548284,"name":"Neil Kaplowitz","orcid":"0000-0002-9424-393X","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:13:54.951170Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}