Deciphering the impact of the mitochondrial negative regulator MCJ on host-microbiota interactions in experimental ulcerative colitis
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) encompasses two types of idiopathic intestinal diseases, ulcerative colitis (UC) and Crohn's disease.Both are chronic, heterogeneous, and severe inflammatory disorders that primarily affect the intestine.Although the specific underlying cause of UC is unknown, it is considered the result of a complex interaction between the microbiota, immune system, host genetics and environmental factors.Recent evidence has demonstrated potential links between mitochondrial dysfunction and IBD.Indeed, mitochondrial function are decreased in active UC patients, en estadios tempranos la evolucin de la enfermedad y la respuesta a la terapia con agentes anti-TNF en pacientes con CU.Por otra parte, este estudio nos ha permitido identificar posibles firmas microbianas en las heces asociadas con la progresin de la enfermedad y la respuesta a terapias, que podran servir como biomarcadores predictivos, permitiendo la estratificacin de los pacientes.It is emerging as a global disease with a sharp increase in worldwide incidence and prevalence.Industrialization has largely affected humans health by dramatically changing transportation, agriculture, manufacturing, urbanization and diet.Hence, shifts in manufacturing have led to increased air pollution, and fibers from diets started to be less plant-based (Windsor et al., 2019).Industrialization has accelerated IBD incidence in newly industrialized countries, principally occurring in the Western World, which includes Europe, North America and Australia.Remarkably, UC is more prevalent than CD and the first UC reports were detected in the 1800s in the Western World.Precisely, Samuel Wilks first described UC in 1859 (Gajendran et al., 2019).From the nineteenth century onward, the incidence of IBD has been rising continuously (Kaplan, 2015).Regarding the incidence of UC between 1990 and 2016, the highest incidence was recorded in developed countries such as Canada, USA, North Europe, Australia and Figure 5. Toll-like receptors, NOD-like receptors and their signaling pathways.TLR1 and TLR6 recognize their ligands (Triacylated and diacylated lipopeptides respectively) as heterodimers with TLR2.TLR4 recognizes lipopolysaccharide (LPS) from gram negative bacteria.TLR3, TLR4, TLR5, TLR7, and TLR9 are currently thought to deliver their signal by forming homodimers after interacting with their ligands.TLR3, TLR7/8, and TLR9 are intracellular TLRs, located inside the endosome that recognize nucleic acids.NOD1 and NOD2 function as intracellular receptors for bacterial peptidoglycan fragments.While NOD1 activity is triggered by D-glutamyl-meso-diaminopimelic acid (DAP), NOD2 is activated by muramyl dipeptides (MDPs).Therefore, these receptors activate several transcription factors including the nuclear factor (NF)-kB and AP-1 by both TLRs and NLRs, and IRF3 and IRF7 (Interferon pathway) by TLRs, which results in the control of the inflammation, immune regulation, survival and proliferation.AP-1: activator protein 1; CpG-ODN: CpG oligodeoxynucleotides; dsRNA: double-strand RNA; IkB: NFKB Inhibitor Alpha; IKK: IkappaB kinase; IRF3/7: interferon regulatory factor 3/5/7; NF-k: nuclear factor kappa B; ssRNA: single-stranded RNA virus; TIRAP: TIR domain-containing adaptor protein; TRAF3/6: TNF receptor-associated factor 3/6; TRIF: TIR-domain-containing adapter-inducing interferon-.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".