MétaCan
Menu
← Back to cohort
Record W4402540730 · doi:10.1093/jas/skae234.530

PSIV-23 Transcriptomic analysis revealed the mechanisms of resilience and susceptibility to nonalcoholic steatohepatitis in dairy goats under high-concentrate diet

2024· article· en· W4402540730 on OpenAlexaff
Yue Wang, Sharon Huws, Shengru Wu, Junhu Yao, Leluo Guan

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNonalcoholic steatohepatitisAnimal scienceFood scienceBiologyResilience (materials science)TranscriptomeInternal medicineBiochemistryNonalcoholic fatty liver diseaseFatty liverMedicineGene

Abstract

fetched live from OpenAlex

Abstract Liver health is vital for growth and health of ruminants, which can directly affect their performance. High-concentrate diet (HCD) feeding, a common practice to meet the energy requirements for animal production and growth, has been known to induce liver damage, including nonalcoholic fatty liver (NAFL) and nonalcoholic steatohepatitis (NASH) in ruminants. To date, the regulatory mechanisms of liver metabolic dysfunctions in ruminants induced by HCD are not well defined. To elucidate molecular regulatory mechanisms underlying liver dysfunction in ruminants, we compared liver transcriptome and single-cell transcriptome of dairy goats that had varied responses to the HCD, aiming to underpin the key mechanisms behind resilience and susceptibility to NASH in ruminants. After feeding 30 dairy goats with low-concentrate diet (LCD, n = 15, defined as Con) or high-concentrate diet (HCD, n = 15) for 6 wk, 10 of them developed NASH (defined as NASH), while the other 5 did not showed any NASH symptom (defined as NASH-tolerance, NASH-T) under HCD. Liver tissues were collected to extract RNA. The paired-end RNA-seq library was sequenced with a NovaSeq6000 sequencer (Illumina). Real-time quantitative PCR was conducted to verify the significantly different genes among groups. Liver single-cell transcriptome was performed using 10×Genomics Chromium system. Liver transcriptome analysis revealed that the expression level of genes involved in the interleukin (IL)-17 signaling pathway, including IL-17A (P = 0.035; 0.001), Act1 (P = 0.036; 0.002), FOSB (P = 0.041; 0.048) and IL-1β (P = 0.022; 0.002) in the NASH group were significantly greater than those in the Con and NASH-T groups. Hepatic single-cell RNA sequencing identified seven cell types in the liver, including hepatocytes, macrophages, T cells, endothelial cell, natural killer (NK) cell, hepatic stellate cells (HSCs), B cells. In T cell subset, significantly decreased T helper 17 (TH17) cells and increased regulatory T (Treg) cells were detected in the NASH group compared with Con and NASH-T groups. In TH17 cell, the expression levels of IL-17A (P = 0.004; 0.009) and IL-23R (P = 0.007; 0.01; the surface antibodies of TH17 cells) in NASH group were significantly greater than those in the Con and NASH-T groups. The expression levels of Foxp3 (P < 0.001; 0.013), IL-2R (P < 0.001; 0.001), CTLA-4 (P = 0.005; 0.017) and CD127 (P = 0.004; 0.009; the surface antibodies of Treg cells) in NASH group were significantly less than those in the Con and NASH-T groups. Our results revealed the role of liver immune cells in maintaining tolerance to NASH in dairy goats under HCD, which provides new understanding in pathogenesis of NASH under HCD. Further studies are needed to identify major factors that drive the changes of ratio of TH17/Treg cell.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.300
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Animal Science→Same topicLiver Disease Diagnosis and Treatment→French-language works237,207→