PubMed

PeerJ. 2022 Nov 23;10:e14312. doi: 10.7717/peerj.14312. eCollection 2022.ABSTRACTThe response of okra to drought stress is very complicated, and the molecular mechanisms underlying this process remains ambiguous up to now. In this study, different degrees of water-stress responses of okra leaf were explained by using transcriptomics and metabolomic approaches. The photosynthesis and glycometabolism in okra leaf were both adversely affected by drought stress, leading to inhibition of the carbohydrate metabolic process, and then influencing the secondary plant metabolism. Further, drought stress disturbed amino acid metabolism, especially for the tyrosine-derived pathway as well as arginine and proline metabolism, which have been shown to be significantly enriched under water withholding conditions based on multi-omics conjoint analysis (transcriptome, proteome and metabolome). In-depth analysis of the internal linkages between differentially expressed transcripts, proteins, and metabolites decidedly indicate that tyrosine metabolism could confer tolerance to drought stress by influencing carbon and nitrogen metabolism. These findings provide a whole framework of the regulation and relationships of major transcripts and peptides related to secondary metabolism, particularly, the role of critical proteins and metabolite involved in the change of amino acid metabolism in response to drought stress.PMID:36444379 | PMC:PMC9700456 | DOI:10.7717/peerj.14312

Food Chem. 2023 Mar 15;404(Pt B):134765. doi: 10.1016/j.foodchem.2022.134765. Epub 2022 Oct 29.ABSTRACTCashew nut is a popular food around the world. The high-resolution profiles and dynamics of metabolomes in cashew fruits are poorly understood till now. In this study, we analyzed the temporal metabolome of cashew nut via a non-targeted method based on UHPLC-Q-Exactive-MS, and analyzed that of cashew apple via a widely targeted method based on UHPLC-QTRAP-MS/MS (MRM). Furthermore, we performed integrative analyses of temporal metabolome and transcriptome data, characterized the accumulation of specific metabolites, and identified the transcriptional changes during cashew fruit development. Specifically, we found that phosphatidylinositol species were the predominant fractions in the unsaturated glycerophospholipids, and we identified a transcription factor that was the potential regulator of phosphatidylinositol biosynthesis. Analysis of cashew apple revealed metabolic genes and transcription factors involved in sugar biosynthesis. Taken together, our results provide insights into metabolic networks during cashew fruit development and generate a valuable resource for further cashew breeding studies.PMID:36444096 | DOI:10.1016/j.foodchem.2022.134765

Food Chem. 2023 Mar 15;404(Pt B):134735. doi: 10.1016/j.foodchem.2022.134735. Epub 2022 Oct 28.ABSTRACTFoxtail millet and its components have hypoglycemic effects on mice, but the role of starch and protein in millet in these effects is unclear. The present study investigated the impact of heat-treated foxtail millet starch and protein on type 2 diabetic mice and the underlying mechanisms, including the influence of gut microbiota and serum metabolic profile. In diabetic mice, the consumption of heat-treated foxtail millet starch and protein reduced, respectively, fasting blood glucose 18.52% and 26.33% and insulin levels 12.22% and 15.96%. In addition, heat-treated foxtail millet starch and protein altered the gut microbiota composition, enriched the abundance of probiotics and short-chain fatty acids producing bacteria, reduced harmful bacteria, and increased fecal short-chain fatty acids concentration. Heat-treated foxtail millet protein had greater effects on gut microbiota composition, whereas heat-treated foxtail millet starch had greater effects on metabolic function. The hypoglycemic potential of heat-treated foxtail millet starch and protein was associated with the modulation of both gut microbiota and serum metabolic profile.PMID:36444094 | DOI:10.1016/j.foodchem.2022.134735

J Hazard Mater. 2023 Feb 5;443(Pt B):130384. doi: 10.1016/j.jhazmat.2022.130384. Epub 2022 Nov 11.ABSTRACTMicroplastic (MP) pollution severely impairs the sustainable development of modern agriculture. However, the mechanisms underlying the effects of MP contaminants on nutrient cycles in agroecosystems are poorly understood. In this study, we examined the impacts of two types of MPs, polypropylene (PP) and rubber crumb (RC), on nitrogen (N) transformation and N cycling in soil-peanut system. High concentrations of PP (1% w/w) and RC (1% w/w) inhibited vegetative growth and N uptake in peanut plants by damaging root cells and disturbing soil N cycling. These MPs damaged the plasma membranes of root cells and caused oxidative stress, as evidenced by the decreased number of xylem vessels, which in turn inhibited N uptake by roots. Integrated metagenomic and metabolomic analyses revealed that the differential soil metabolite levels in response to MP treatment affected the microbial community structure in the rhizosphere and the expression of key N cycling-related genes, resulting in altered N transformation and the decreased availability of N in rhizosphere soil. These findings provide the first evidence of the effects of MPs on N uptake in peanut plants and shed light on the importance of rational management of MPs for crop growth and yield in agroecosystems.PMID:36444071 | DOI:10.1016/j.jhazmat.2022.130384

Food Chem. 2023 Mar 15;404(Pt A):134598. doi: 10.1016/j.foodchem.2022.134598. Epub 2022 Oct 14.ABSTRACTCrude polysaccharides extracted from red kidney bean (RK) display significant antidiabetic activity in type 2 diabetic mice, but the underlying mechanism and the core functional component has not been elucidated. In this study, the antidiabetic effect and mechanism of RK are investigated by serum metabolomics and high-throughput sequencing. In addition, the key component was identified by evaluating the improvement on glucose and lipid homeostasis in type 2 diabetic rats. Our data indicated that RK relieved the symptoms of hyperglycemia, hyperlipidemia in STZ-induced diabetic rats. RK not only improved the metabolic disturbance by regulating the biosynthesis of unsaturated fatty acids, but also modified gut microbiota composition by selectively enriching in key genera of Bacteroides, Phascolarctobacterium, Succinivibrio, Blautia. We further found the purified polysaccharides (RKP) were identified as the core biofunctional component in RK. Our present studies provide evidence that RKP are potential effective dietary supplement for type 2 diabetic individuals.PMID:36444040 | DOI:10.1016/j.foodchem.2022.134598

Food Chem. 2023 Mar 15;404(Pt A):134564. doi: 10.1016/j.foodchem.2022.134564. Epub 2022 Oct 12.ABSTRACTChaya (Cnidoscolus chayamansa and C. aconitifolius) is a fast-growing medicinal plant, and its leaves exhibit a strong umami taste. Here metabolite variation and umami-related compounds in the leaves of two chaya species were determined using a multiplatform untargeted-metabolomics approach, electronic tongue, and in silico screening. Metabolite profiles varied between the leaves of the two species and among leaf maturation stages. Young leaves exhibited the highest umami taste intensity, followed by mature and old leaves. Partial least square regression and computational molecular docking analyses revealed five potent umami substances (quinic acid, trigonelline, alanyl-tyrosine, leucyl-glycyl-proline, and leucyl-aspartyl-glutamine) and three known umami compounds (l-glutamic acid, pyroglutamic acid, and 5'-adenosine monophosphate). The five substances were validated as novel umami compounds using electronic tongue assay; leucyl-glycyl-proline exhibited synergism with monosodium glutamate, thereby enhancing the umami taste. Thus, substances contributing to the taste of chaya leaves were successfully identified.PMID:36444036 | DOI:10.1016/j.foodchem.2022.134564

Food Chem. 2023 Mar 15;404(Pt A):134699. doi: 10.1016/j.foodchem.2022.134699. Epub 2022 Oct 19.ABSTRACTChinese indigenous pig breeds have higher intramuscular fat content (IMF) and better meat quality than Western commercial pigs. The differential metabolites and lipids in the skeletal muscle associated with IMF contents and meat flavor in Laiwu and Yorkshire pigs were investigated in this study. As a result, 113 differential metabolites and 54 differential lipids were discovered. Lipidomics revealed that the Laiwu pig had a fast lipid droplet formation and contained more triglyceride than the Yorkshire pig, which was corresponded to its high IMF contents. Both the lipidomics and metabolomics results indicated that the Laiwu pig had a higher mitochondrial content and aerobic respiration, due to its larger percentage of oxidative fibers. In addition, differential metabolites, such as oxoglutaric acid, fumarate, and l-aspartate, were thought to be important flavor precursors contributing to the Laiwu pig's improved pork taste.PMID:36444028 | DOI:10.1016/j.foodchem.2022.134699

Food Chem. 2023 Mar 15;404(Pt A):134591. doi: 10.1016/j.foodchem.2022.134591. Epub 2022 Oct 14.ABSTRACTHyperlipidemia can directly cause metabolic diseases that seriously endanger disorder and metabolism and gut health. Tea polyphenol (TP) and epigallocatechin gallate (EGCG) was found to improve blood lipid levels and gut microbiota. This study aimed to investigate the effects of TP and EGCG on alleviating hyperlipidemia and liver fat accumulation with physiology, genomics, and metabolomics. Results showed that both TP and EGCG reduced body weight, and TP showed advantages in the decrease of serum cholesterol and triglycerides in hyperlipidemic rats induced by the high-fat diet. Moreover, EGCG may protect liver function via reducing the glycerophospholipids increased by high-fat diet intervention. TP remodeled the gut microbiota composition and enriched the abundance of beneficial bacteria (Bacteroides, Faecalibacterium, Parabacteroides, Akkermansia), and EGCG may improve gut health via promoting the acid-producing bacteria (such as Butyricimonas, Desulfovibrio). The above results provided new insights into the hypolipidemic mechanism of TP and EGCG.PMID:36444016 | DOI:10.1016/j.foodchem.2022.134591

J Allergy Clin Immunol Pract. 2022 Oct;10(10):2774-2775. doi: 10.1016/j.jaip.2022.08.004. Epub 2022 Oct 7.NO ABSTRACTPMID:36444000 | DOI:10.1016/j.jaip.2022.08.004

Proteomics Clin Appl. 2022 Nov 28:e2200042. doi: 10.1002/prca.202200042. Online ahead of print.ABSTRACTBACKEGROUND: Lipidomics and metabolomics are closely related to tumor phenotypes, and serum lipoprotein subclasses and small-molecule metabolites are considered as promising biomarkers for breast cancer (BC) diagnosis. This study aimed to explore potential biomarker models based on lipidomic and metabolomic analysis that could distinguish BC from healthy controls (HCs) and triple-negative BC (TNBC) from non-TNBC.METHODS: Blood samples were collected from 114 patients with BC and 75 healthy controls (HCs). A total of 112 types of lipoprotein subclasses and 30 types of small-molecule metabolites in the serum were detected by 1 H-NMR. All lipoprotein subclasses and small-molecule metabolites were subjected to a three-step screening process in the order of significance (P < 0.05), univariate regression (P < 0.1), and lasso regression (non-zero coefficient). Discriminant models of BC versus HCs and TNBC versus non-TNBC were established using binary logistic regression.RESULTS: We developed a valid discriminant model based on three-biomarker panel (formic acid, TPA2, and L6TG) that could distinguish patients with BC from HCs. The area under the receiver operating characteristic curve (AUC) was 0.999 (95% confidence interval [CI]: 0.995-1.000) and 0.990 (95% CI: 0.959-1.000) in the training and validation sets, respectively. Based on the panel (D-dimer, CA15-3, CEA, L5CH, glutamine, and ornithine), a discriminant model was established to differentiate between TNBC and non-TNBC, with AUC of 0.892 (95% CI: 0.778-0.967) and 0.905 (95% CI: 0.754-0.987) in the training and validation sets, respectively.CONCLUSION: This study revealed lipidomic and metabolomic differences between BC versus HCs and TNBC versus non-TNBC. Two validated discriminatory models established against lipidomic and metabolomic differences can accurately distinguish BC from HCs and TNBC from non-TNBC.IMPACT: Two validated discriminatory models can be used for early BC screening and help BC patients avoid time-consuming, expensive, and dangerous BC screening. This article is protected by copyright. All rights reserved.PMID:36443927 | DOI:10.1002/prca.202200042

BMC Pharmacol Toxicol. 2022 Nov 28;23(1):87. doi: 10.1186/s40360-022-00626-x.ABSTRACTTacrolimus (Tac) is a common immunosuppressant that used in organ transplantation. However, its therapeutic index is narrow, and it is prone to adverse side effects, along with an increased risk of toxicity, namely, cardio-, nephro-, hepato-, and neurotoxicity. Prior metabolomic investigations involving Tac-driven toxicity primarily focused on changes in individual organs. However, extensive research on multiple matrices is uncommon. Hence, in this research, the authors systemically evaluated Tac-mediated toxicity in major organs, namely, serum, brain, heart, liver, lung, kidney, and intestines, using gas chromatography-mass spectrometry (GC-MS). The authors also employed multivariate analyses, including orthogonal projections to the latent structure (OPLS) and t-test, to screen 8 serum metabolites, namely, D-proline, glycerol, D-fructose, D-glucitol, sulfurous acid, 1-monopalmitin (MG (16:0/0:0/0:0)), glycerol monostearate (MG (0:0/18:0/0:0)), and cholesterol. Metabolic changes within the brain involved alterations in the levels of butanamide, tartronic acid, aminomalonic acid, scyllo-inositol, dihydromorphine, myo-inositol, and 11-octadecenoic acid. Within the heart, the acetone and D-fructose metabolites were altered. In the liver, D-glucitol, L-sorbose, palmitic acid, myo-inositol, and uridine were altered. In the lung, L-lactic acid, L-5-oxoproline, L-threonine, phosphoric acid, phosphorylethanolamine, D-allose, and cholesterol were altered. Lastly, in the kidney, L-valine and D-glucose were altered. Our findings will provide a systematic evaluation of the metabolic alterations in target organs within a Tac-driven toxicity mouse model.PMID:36443830 | DOI:10.1186/s40360-022-00626-x

Sci Rep. 2022 Nov 28;12(1):20469. doi: 10.1038/s41598-022-24309-z.ABSTRACTCordyceps sinensis is a second-class nationally-protected medicinal fungus and functional food. Cordyceps sinensis resources are endangered, and finding new medicinal materials is a fast and economical way to meet the current demonstrated demand, which can effectively solve the shortage of C. sinensis resources. In this study, the metabolite characteristics of Cordyceps were comprehensively revealed by LC-QTOF-MS technology. The maxent model can be used to predict the habitat suitability distribution of Cordyceps and screen out the main climatic factors affecting its distribution. The correlation model between climate factors and chemical components was established by Pearson correlation analysis. Finally, based on the analysis of climate factors and metabolites, we will analyze the high correlation species with C. sinensis, and develop them as possible alternative species of C. sinensis in the future. The results showed that the suitable area of Cordyceps cicadae demonstrated a downward trend, while that of C. sinensis, Cordyceps militaris and Cordyceps gunnii demonstrated an upwards trend. The suitable areas all shifted to the northwest. The temperature seasonality and max temperature of the warmest month are the maximum climatic factors affecting nucleosides. Compared with C. sinensis, the metabolic spectrum similarities of C. cicadae, C. militaris, and C. gunnii were 94.42%, 80.82%, and 91.00%, respectively. Cordyceps sinensis, C. cicadae, and C. gunnii were correlated well for compounds and climate factors. This study will explore whether C. cicadae, C. militaris and C. gunnii can be used as substitutes for C. sinensis. Our results may provide a reference for resource conservation and sustainable utilization of endangered C. sinensis.PMID:36443322 | PMC:PMC9705360 | DOI:10.1038/s41598-022-24309-z

BMC Bioinformatics. 2022 Nov 28;22(Suppl 10):632. doi: 10.1186/s12859-022-04964-9.ABSTRACTBACKGROUND: Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient's responses to numerous cancer drugs are needed for personalized treatment for cancer. By using molecular profiles of cancer cell lines available from Cancer Cell Line Encyclopedia (CCLE) and anticancer drug responses available in the Genomics of Drug Sensitivity in Cancer (GDSC), we will build computational models to predict anticancer drug responses from molecular features.RESULTS: We propose a novel deep neural network model that integrates multi-omics data available as gene expressions, copy number variations, gene mutations, reverse phase protein array expressions, and metabolomics expressions, in order to predict cellular responses to known anti-cancer drugs. We employ a novel graph embedding layer that incorporates interactome data as prior information for prediction. Moreover, we propose a novel attention layer that effectively combines different omics features, taking their interactions into account. The network outperformed feedforward neural networks and reported 0.90 for [Formula: see text] values for prediction of drug responses from cancer cell lines data available in CCLE and GDSC.CONCLUSION: The outstanding results of our experiments demonstrate that the proposed method is capable of capturing the interactions of genes and proteins, and integrating multi-omics features effectively. Furthermore, both the results of ablation studies and the investigations of the attention layer imply that gene mutation has a greater influence on the prediction of drug responses than other omics data types. Therefore, we conclude that our approach can not only predict the anti-cancer drug response precisely but also provides insights into reaction mechanisms of cancer cell lines and drugs as well.PMID:36443676 | DOI:10.1186/s12859-022-04964-9

BMC Bioinformatics. 2022 Nov 28;23(1):508. doi: 10.1186/s12859-022-05068-0.ABSTRACTBACKGROUND: Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study.RESULTS: We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered.CONCLUSIONS: We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.PMID:36443658 | DOI:10.1186/s12859-022-05068-0

BMC Plant Biol. 2022 Nov 29;22(1):549. doi: 10.1186/s12870-022-03939-5.ABSTRACTBACKGROUND: Salicylic acid (SA) is a major plant hormone that mediates the defence pathway against pathogens. SA accumulates in highly variable amounts depending on the plant-pathogen system, and several enzyme activities participate in the restoration of its levels. Gentisic acid (GA) is the product of the 5-hydroxylation of SA, which is catalysed by S5H, an enzyme activity regarded as a major player in SA homeostasis. GA accumulates at high levels in tomato plants infected by Citrus Exocortis Viroid (CEVd), and to a lesser extend upon Pseudomonas syringae DC3000 pv. tomato (Pst) infection.RESULTS: We have studied the induction of tomato SlS5H gene by different pathogens, and its expression correlates with the accumulation of GA. Transient over-expression of SlS5H in Nicotiana benthamiana confirmed that SA is processed by SlS5H in vivo. SlS5H-silenced tomato plants were generated, displaying a smaller size and early senescence, together with hypersusceptibility to the necrotrophic fungus Botrytis cinerea. In contrast, these transgenic lines exhibited an increased defence response and resistance to both CEVd and Pst infections. Alternative SA processing appears to occur for each specific pathogenic interaction to cope with SA levels. In SlS5H-silenced plants infected with CEVd, glycosylated SA was the most discriminant metabolite found. Instead, in Pst-infected transgenic plants, SA appeared to be rerouted to other phenolics such as feruloyldopamine, feruloylquinic acid, feruloylgalactarate and 2-hydroxyglutarate.CONCLUSION: Using SlS5H-silenced plants as a tool to unbalance SA levels, we have studied the re-routing of SA upon CEVd and Pst infections and found that, despite the common origin and role for SA in plant pathogenesis, there appear to be different pathogen-specific, alternate homeostasis pathways.PMID:36443652 | DOI:10.1186/s12870-022-03939-5

Nat Metab. 2022 Nov 28. doi: 10.1038/s42255-022-00689-4. Online ahead of print.ABSTRACTThe branched-chain aminotransferase isozymes BCAT1 and BCAT2, segregated into distinct subcellular compartments and tissues, initiate the catabolism of branched-chain amino acids (BCAAs). However, whether and how BCAT isozymes cooperate with downstream enzymes to control BCAA homeostasis in an intact organism remains largely unknown. Here, we analyse system-wide metabolomic changes in BCAT1- and BCAT2-deficient mouse models. Loss of BCAT2 but not BCAT1 leads to accumulation of BCAAs and branched-chain α-keto acids (BCKAs), causing morbidity and mortality that can be ameliorated by dietary BCAA restriction. Through proximity labelling, isotope tracing and enzymatic assays, we provide evidence for the formation of a mitochondrial BCAA metabolon involving BCAT2 and branched-chain α-keto acid dehydrogenase. Disabling the metabolon contributes to BCAT2 deficiency-induced phenotypes, which can be reversed by BCAT1-mediated BCKA reamination. These findings establish a role for metabolon formation in BCAA metabolism in vivo and suggest a new strategy to modulate this pathway in diseases involving dysfunctional BCAA metabolism.PMID:36443523 | DOI:10.1038/s42255-022-00689-4

Nat Microbiol. 2022 Nov 28. doi: 10.1038/s41564-022-01266-x. Online ahead of print.ABSTRACTDespite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.PMID:36443458 | DOI:10.1038/s41564-022-01266-x

Anal Bioanal Chem. 2022 Nov 28. doi: 10.1007/s00216-022-04384-x. Online ahead of print.ABSTRACTLiquid chromatography mass spectrometry (LC-MS) has emerged as a mainstream strategy for metabolomics analyses. One advantage of LC-MS is that it can serve both as a biomarker discovery tool and as a platform for clinical diagnostics. Consequently, it offers an exciting opportunity to potentially transition research studies into real-world clinical tools. One important distinction between research versus diagnostics-based applications of LC-MS is throughput. Clinical LC-MS must enable quantitative analyses of target molecules in hundreds or thousands of samples each day. Currently, the throughput of these clinical applications is limited by the chromatographic gradient lengths, which-when analyzing complex metabolomics samples-are difficult to conduct in under ~ 3 min per sample without introducing serious quantitative analysis problems. To address this shortcoming, we developed sequential quantification using isotope dilution (SQUID), an analytical strategy that combines serial sample injections into a continuous isocratic mobile phase to maximize throughput. SQUID uses internal isotope-labelled standards to correct for changes in LC-MS response factors over time. We show that SQUID can detect microbial polyamines in human urine specimens (lower limit of quantification; LLOQ = 106 nM) with less than 0.019 normalized root mean square error. Moreover, we show that samples can be analyzed in as little as 57 s. We propose SQUID as a new, high-throughput LC-MS tool for quantifying small sets of target biomarkers across large cohorts.PMID:36443449 | DOI:10.1007/s00216-022-04384-x

J Med Virol. 2022 Nov 28. doi: 10.1002/jmv.28355. Online ahead of print.ABSTRACTThe genetic association of COVID-19 with its complications has not been fully understood. This study aimed to identify variants and haplotypes of candidate genes implicated in COVID-19 related traits by combining the literature review and pathway analysis. In order to explore such genes, the protein-protein interactions and relevant pathways of COVID-19-associated genes were assessed. A number of variants on candidate genes were identified from genome-wide association studies (GWASs) which were associated with COVID-19 related traits (p˂10-6 ). Haplotypic blocks were assessed using haplotypic structures among the 1000 Genomes Project (r2 ≥0.8, D'≥0.8). Further functional analyses were performed on the selected variants. The results demonstrated that a group of variants in ACE and AGT genes were significantly correlated with COVID-19 related traits. Three haplotypes were identified to be involved in the blood metabolites levels and the development of blood pressure. Functional analyses revealed that most GWAS index variants were expression quantitative trait loci (eQTL) and had transcription factor binding sites, exonic splicing enhancers, or silencer activities. Furthermore, the proxy haplotype variants, rs4316, rs4353, rs4359, and three variants, namely rs2493133, rs2478543, and rs5051, were associated with blood metabolite and systolic blood pressure, respectively. These variants exerted more regulatory effects compared with other GWAS variants. The present study indicates that the genetic variants and candidate haplotypes of COVID-19 related genes are associated with blood pressure and blood metabolites. However, further observational studies are warranted to confirm these results. This article is protected by copyright. All rights reserved.PMID:36443248 | DOI:10.1002/jmv.28355

Anim Sci J. 2022 Jan;93(1):e13785. doi: 10.1111/asj.13785.ABSTRACTEctopic fat is defined by the deposition of adipose tissue within non-adipose tissue such as skeletal muscle. Japanese Black cattle (Wagyu) are characterized by the ability to accumulate high amounts of intramuscular adipose tissue. Obese conditions enhance the accumulation of ectopic fat. This review shows the effects of subcutaneous and visceral fat distribution on Wagyu intramuscular adipogenesis. Obese conditions also stimulate the macrophage infiltration into adipose tissues. Adipose tissue macrophages have reported to regulate adipose tissue growth and ectopic fat accumulation in humans and rodents. Wagyu is characterized by the higher capacity for intramuscular adipogenesis than Holsteins. This review discusses the depot-specific effects of macrophage infiltration among subcutaneous, visceral, and intramuscular adipose tissue on intramuscular adipogenesis in Wagyu and Holstein cattle. Recently, metabolome analysis has been used to identify obesity-related biomarkers by comparing the biological samples between lean and obese patients. This review introduces the metabolomic profiles of plasma and intramuscular adipose tissue between Wagyu and Holsteins.PMID:36443236 | DOI:10.1111/asj.13785