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- Bousquet, J, et al.
(författare)
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Nrf2-interacting nutrients and COVID-19: time for research to develop adaptation strategies
- 2020
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Ingår i: Clinical and translational allergy. - : Wiley. - 2045-7022. ; 10:1, s. 58-
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Tidskriftsartikel (refereegranskat)abstract
- There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPARγ:Peroxisome proliferator-activated receptor, NFκB: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2α:Elongation initiation factor 2α). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT1R axis (AT1R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity.
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- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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- Hevler, JF, et al.
(författare)
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MRPS36 provides a structural link in the eukaryotic 2-oxoglutarate dehydrogenase complex
- 2023
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Ingår i: Open biology. - : The Royal Society. - 2046-2441. ; 13:3, s. 220363-
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Tidskriftsartikel (refereegranskat)abstract
- The tricarboxylic acid cycle is the central pathway of energy production in eukaryotic cells and plays a key part in aerobic respiration throughout all kingdoms of life. One of the pivotal enzymes in this cycle is 2-oxoglutarate dehydrogenase complex (OGDHC), which generates NADH by oxidative decarboxylation of 2-oxoglutarate to succinyl-CoA. OGDHC is a megadalton protein complex originally thought to be assembled from three catalytically active subunits (E1o, E2o, E3). In fungi and animals, however, the protein MRPS36 has more recently been proposed as a putative additional component. Based on extensive cross-linking mass spectrometry data supported by phylogenetic analyses, we provide evidence that MRPS36 is an important member of the eukaryotic OGDHC, with no prokaryotic orthologues. Comparative sequence analysis and computational structure predictions reveal that, in contrast with bacteria and archaea, eukaryotic E2o does not contain the peripheral subunit-binding domain (PSBD), for which we propose that MRPS36 evolved as an E3 adaptor protein, functionally replacing the PSBD. We further provide a refined structural model of the complete eukaryotic OGDHC of approximately 3.45 MDa with novel mechanistic insights.
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- Kerali, HR, et al.
(författare)
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Long term pavement performance trials and data analysis in the UK
- 1991
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Ingår i: Proceedings of Strategic Highway Research Program and Traffic Safety on Two Continents. Conference in Gothenburg, Sweden, September 18-20, 1991. - Linköping : Statens väg- och transportforskningsinstitut. ; , s. 67-84
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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