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Journal of Animal Science Oct 2022The objective was to test the hypothesis that supplementation of diets for gestating sows with 25-hydroxycholecalciferol (25-OH-D3) or 1-hydroxycholecalciferol (1-OH-D3)...
Effects of 25-hydroxycholecalciferol (25-OH-D3) and 1-hydroxycholecalciferol (1-OH-D3) on serum bone biomarkers and calcium and phosphorus balance and concentrations of energy in diets without or with microbial phytase fed to sows in late gestation.
The objective was to test the hypothesis that supplementation of diets for gestating sows with 25-hydroxycholecalciferol (25-OH-D3) or 1-hydroxycholecalciferol (1-OH-D3) affects serum biomarkers for bone and increases Ca and P balance and the apparent total tract digestibility (ATTD) of gross energy (GE), and the concentrations of digestible energy (DE) and metabolizable energy (ME) in diets without or with microbial phytase. Sixty multiparous sows were allotted to 1 of 6 diets. Diets were formulated using a 3 × 2 factorial with 3 inclusions of supplemental vitamin D metabolite (no metabolite, 25-OH-D3, or 1-OH-D3) and 2 inclusion levels of microbial phytase (0 or 1,000 units). Sows were housed individually in metabolism crates and feces and urine were collected quantitatively. Results indicated that there was no difference in the ATTD of dry matter (DM) and GE and concentration of DE among the 3 diets containing microbial phytase, but the ATTD of DM and GE and concentration of DE was greater (P < 0.05) in diets containing 1-OH-D3 compared with the diet without a vitamin D metabolite if phytase was not used (interaction; P < 0.05). In diets without microbial phytase, ME was greater in diets containing either one of the 2 vitamin D metabolites than in the diet without a vitamin D metabolite, but among diets with microbial phytase, the ME of the 1-OH-D3 diet was less than of the 25-OH-D3 diet (interaction; P < 0.05). No effect of microbial phytase on concentrations of DE and ME was observed. There was no interaction between supplementation of microbial phytase and vitamin D metabolites for Ca and P balances, and regardless of metabolite supplementation, use of microbial phytase increased (P < 0.05) the ATTD and retention of Ca and P. Regardless of dietary phytase, the ATTD and retention of Ca and P increased (P < 0.05) for sows fed a diet containing one of the vitamin D metabolites compared with sows fed the diet without a vitamin D metabolite. Serum biomarkers for bone resorption or bone tissue synthesis were not affected by experimental diets. In conclusion, the ATTD of DM and GE, concentrations of DE and ME, and Ca and P balance in phytase-free diets fed to sows in late gestation were increased by supplementation with 1-OH-D3 or 25-OH-D3, but no differences between the 2 vitamin D metabolites were observed. Supplementation of diets with microbial phytase increased Ca and P balance, but did not affect DE and ME of diets.
Topics: Pregnancy; Animals; Female; 6-Phytase; Calcium; Calcifediol; Phosphorus; Phosphorus, Dietary; Digestion; Animal Feed; Gastrointestinal Tract; Calcium, Dietary; Diet; Biomarkers; Bone and Bones
PubMed: 36074541
DOI: 10.1093/jas/skac299 -
Hepatology (Baltimore, Md.) Nov 2021Acute kidney injury (AKI) has a poor prognosis in cirrhosis. Given the variability of creatinine, the prediction of AKI and dialysis by other markers is needed. The aim... (Observational Study)
Observational Study
BACKGROUND AND AIMS
Acute kidney injury (AKI) has a poor prognosis in cirrhosis. Given the variability of creatinine, the prediction of AKI and dialysis by other markers is needed. The aim of this study is to determine the role of serum and urine metabolomics in the prediction of AKI and dialysis in an inpatient cirrhosis cohort.
APPROACH AND RESULTS
Inpatients with cirrhosis from 11 North American Consortium of End-stage Liver Disease centers who provided admission serum/urine when they were AKI and dialysis-free were included. Analysis of covariance adjusted for demographics, infection, and cirrhosis severity was performed to identify metabolites that differed among patients (1) who developed AKI or not; (2) required dialysis or not; and/pr (3) within AKI subgroups who needed dialysis or not. We performed random forest and AUC analyses to identify specific metabolite(s) associated with outcomes. Logistic regression with clinical variables with/without metabolites was performed. A total of 602 patients gave serum (218 developed AKI, 80 needed dialysis) and 435 gave urine (164 developed AKI, 61 needed dialysis). For AKI prediction, clinical factor-adjusted AUC was 0.91 for serum and 0.88 for urine. Major metabolites such as uremic toxins (2,3-dihydroxy-5-methylthio-4-pentenoic acid [DMTPA], N2N2dimethylguanosine, uridine/pseudouridine) and tryptophan/tyrosine metabolites (kynunerate, 8-methoxykyunerate, quinolinate) were higher in patients who developed AKI. For dialysis prediction, clinical factor-adjusted AUC was 0.93 for serum and 0.91 for urine. Similar metabolites as AKI were altered here. For dialysis prediction in those with AKI, the AUC was 0.81 and 0.79 for serum/urine. Lower branched-chain amino-acid (BCAA) metabolites but higher cysteine, tryptophan, glutamate, and DMTPA were seen in patients with AKI needing dialysis. Serum/urine metabolites were additive to clinical variables for all outcomes.
CONCLUSIONS
Specific admission urinary and serum metabolites were significantly additive to clinical variables to predict AKI development and dialysis initiation in inpatients with cirrhosis. These observations can potentially facilitate earlier initiation of renoprotective measures.
Topics: Acute Kidney Injury; Aged; Biomarkers; End Stage Liver Disease; Female; Humans; Liver Cirrhosis; Male; Metabolomics; Middle Aged; Patient Admission; Prognosis; Prospective Studies; Renal Dialysis; Risk Assessment
PubMed: 34002868
DOI: 10.1002/hep.31907 -
Frontiers in Bioengineering and... 2022Microalgae are highly diverse photosynthetic organisms with higher growth rate and simple nutritional requirements. They are evolved with an efficiency to adapt to a... (Review)
Review
Microalgae are highly diverse photosynthetic organisms with higher growth rate and simple nutritional requirements. They are evolved with an efficiency to adapt to a wide range of environmental conditions, resulting in a variety of genetic diversity. Algae accounts for nearly half of global photosynthesis, which makes them a crucial player for CO sequestration. In addition, they have metabolic capacities to produce novel secondary metabolites of pharmaceutical, nutraceutical and industrial applications. Studies have explored the inherent metabolic capacities of microalgae with altered growth conditions for the production of primary and secondary metabolites. However, the production of the targeted metabolites at higher rates is not guaranteed just with the inherent genetic potentials. The strain improvement using genetic engineering is possible hope to overcome the conventional methods of culture condition improvements for metabolite synthesis. Although the advanced gene editing tools are available, the gene manipulation of microalgae remains relatively unexplored. Among the performed gene manipulations studies, most of them focus on primary metabolites with limited focus on secondary metabolite production. The targeted genes can be overexpressed to enhance the production of the desired metabolite or redesigning them using the synthetic biology. A mutant (KOR1) rich in carotenoid and lipid content was developed in a recent study employing mutational breeding in microalgae (Kato, Commun. Biol, 2021, 4, 450). There are lot of challenges in genetic engineering associated with large algal diversity but the numerous applications of secondary metabolites make this field of research very vital for the biotech industries. This review, summarise all the genetic engineering studies and their significance with respect to secondary metabolite production from microalgae. Further, current genetic engineering strategies, their limitations and future strategies are also discussed.
PubMed: 35402414
DOI: 10.3389/fbioe.2022.836056 -
Microbial Cell (Graz, Austria) Aug 2017Lysosomal storage diseases (LSDs) arise from monogenic deficiencies in lysosomal proteins and pathways and are characterized by a tissue-wide accumulation of a vast... (Review)
Review
Lysosomal storage diseases (LSDs) arise from monogenic deficiencies in lysosomal proteins and pathways and are characterized by a tissue-wide accumulation of a vast variety of macromolecules, normally specific to each genetic lesion. Strategies for treatment of LSDs commonly depend on reduction of the offending metabolite(s) by substrate depletion or enzyme replacement. However, at least 44 of the ~50 LSDs are currently recalcitrant to intervention. Murine models have provided significant insights into our understanding of many LSD mechanisms; however, these systems do not readily permit phenotypic screening of compound libraries, or the establishment of genetic or gene-environment interaction networks. Many of the genes causing LSDs are evolutionarily conserved, thus facilitating the application of models system to provide additional insight into LSDs. Here, we review the utility of yeast models of 3 LSDs: Batten disease, cystinosis, and Niemann-Pick type C disease. We will focus on the translation of research from yeast models into human patients suffering from these LSDs. We will also discuss the use of yeast models to investigate the penetrance of LSDs, such as Niemann-Pick type C disease, into more prevalent syndromes including viral infection and obesity.
PubMed: 28913343
DOI: 10.15698/mic2017.09.588 -
Redox Biology Aug 2017Changes in plasma concentration of small organic metabolites could be due to their altered production or urinary excretion and changes in their urine concentration may...
Gene and protein expressions and metabolomics exhibit activated redox signaling and wnt/β-catenin pathway are associated with metabolite dysfunction in patients with chronic kidney disease.
Changes in plasma concentration of small organic metabolites could be due to their altered production or urinary excretion and changes in their urine concentration may be due to the changes in their filtered load, tubular reabsorption, and/or altered urine volume. Therefore, these factors should be considered in interpretation of the changes observed in plasma or urine of the target metabolite(s). Fasting plasma and urine samples from 180 CKD patients and 120 age-matched healthy controls were determined by UPLC-HDMS-metabolomics and quantitative real-time RT-PCR techniques. Compared with healthy controls, patients with CKD showed activation of NF-κB and up-regulation of pro-inflammatory and pro-oxidant mRNA and protein expression as well as down-regulation of Nrf2-associated anti-oxidant gene mRNA and protein expression, accompanied by activated canonical Wnt/β-catenin signaling. 124 plasma and 128 urine metabolites were identified and 40 metabolites were significantly altered in both plasma and urine. Plasma concentration and urine excretion of 25 metabolites were distinctly different between CKD and controls. They were related to amino acid, methylamine, purine and lipid metabolisms. Logistic regression identified four plasma and five urine metabolites. Parts of them were good correlated with eGFR or serum creatinine. 5-Methoxytryptophan and homocystine and citrulline were good correlated with both eGFR and creatinine. Clinical factors were incorporated to establish predictive models. The enhanced metabolite model showed 5-methoxytryptophan, homocystine and citrulline have satisfactory accuracy, sensitivity and specificity for predictive CKD. The dysregulation of CKD was related to amino acid, methylamine, purine and lipid metabolisms. 5-methoxytryptophan, homocystine and citrulline could be considered as additional GFR-associated biomarker candidates and for indicating advanced renal injury. CKD caused dysregulation of the plasma and urine metabolome, activation of inflammatory/oxidative pathway and Wnt/β-catenin signaling and suppression of antioxidant pathway.
Topics: Adult; Aged; Antioxidants; Case-Control Studies; Female; Gene Expression Profiling; Gene Expression Regulation; Humans; Male; Metabolomics; Middle Aged; Oxidation-Reduction; Plasma; Proteomics; Renal Insufficiency, Chronic; Urine; Wnt Signaling Pathway
PubMed: 28343144
DOI: 10.1016/j.redox.2017.03.017 -
Pathobiology : Journal of... 2018Metabolite levels can be measured non-invasively using in vivo 1H magnetic resonance spectroscopy (MRS). These tumour metabolite profiles are highly characteristic for...
AIMS
Metabolite levels can be measured non-invasively using in vivo 1H magnetic resonance spectroscopy (MRS). These tumour metabolite profiles are highly characteristic for tumour type in childhood brain tumours; however, the relationship between metabolite values and conventional histopathological characteristics has not yet been fully established. This study systematically tests the relationship between metabolite levels detected by MRS and specific histological features in a range of paediatric brain tumours.
METHODS
Single-voxel MRS was performed routinely in children with brain tumours along with the clinical imaging prior to treatment. Metabolites were quantified using LCModel. Histological features were assessed semi-quantitatively for 27 children on H&E and immunostained slides, blind to the metabolite values. Statistical analysis included 2-tailed independent-samples t tests and 2-tailed Spearman rank correlation tests.
RESULTS
Ki67, cellular atypia, and mitosis correlated positively with choline metabolites, and phosphocholine in particular. Apoptosis and necrosis were both associated with lipid levels, with the relationship dependent on the use of long or short echo time MRS acquisitions. Neuronal components correlated negatively and glial components positively with N-acetyl-aspartate. Glial components correlated positively with myoinositol.
CONCLUSION
Metabolite levels in children's brain tumours measured by MRS are closely associated with key histological features routinely assessed by histopathologists in the diagnostic process. This further elucidates our understanding of this important non-invasive diagnostic tool and strengthens our understanding of the relationship between metabolites and histological features.
Topics: Apoptosis; Biomarkers, Tumor; Brain; Brain Neoplasms; Child; Humans; Ki-67 Antigen; Magnetic Resonance Spectroscopy; Necrosis; Staining and Labeling
PubMed: 29428932
DOI: 10.1159/000458423 -
Journal of Inherited Metabolic Disease Jan 2020Inborn errors of metabolism cause disease because of accumulation of a metabolite before the blocked step or deficiency of an essential metabolite downstream of the...
Inborn errors of metabolism cause disease because of accumulation of a metabolite before the blocked step or deficiency of an essential metabolite downstream of the block. Treatments can be directed at reducing the levels of a toxic metabolite or correcting a metabolite deficiency. Many disorders have been treated successfully first in a single patient because we can measure the metabolites and adjust treatment to get them as close as possible to the normal range. Examples are drawn from Komrower's description of treatment of homocystinuria and the author's trials of treatment in bile acid synthesis disorders (3β-hydroxy-Δ -C -steroid dehydrogenase deficiency and Δ -3-oxosteroid 5β-reductase deficiency), neurotransmitter amine disorders (aromatic L-amino acid decarboxylase [AADC] and tyrosine hydroxylase deficiencies), and vitamin B6 disorders (pyridox(am)ine phosphate oxidase deficiency and pyridoxine-dependent epilepsy [ALDH7A1 deficiency]). Sometimes follow-up shows there are milder and more severe forms of the disease and even variable clinical manifestations but by measuring the metabolites we can adjust the treatment to get the metabolites into the normal range. Biochemical measurements are not subject to placebo effects and will also show if the disorder is improving spontaneously. The hypothesis that can then be tested for clinical outcome is whether getting metabolite(s) into a target range leads to an improvement in an outcome parameter such as abnormal liver function tests, hypokinesia, epilepsy control etc. The metabolite-guided approach to treatment is an example of personalized medicine and is a better way of determining efficacy for disorders of variable severity than a randomized controlled clinical trial.
Topics: 3-Hydroxysteroid Dehydrogenases; Administration, Oral; Bile Acids and Salts; Epilepsy; Humans; Metabolic Diseases; Pyridoxal Phosphate; Pyridoxaminephosphate Oxidase; Pyridoxine; Randomized Controlled Trials as Topic; Vitamin B 6; Vitamin B 6 Deficiency
PubMed: 31222759
DOI: 10.1002/jimd.12139 -
Frontiers in Nutrition 2022The human gut microbiota has been proposed to serve as a multifunctional organ in host metabolism, contributing effects to nutrient acquisition, immune response, and...
The human gut microbiota has been proposed to serve as a multifunctional organ in host metabolism, contributing effects to nutrient acquisition, immune response, and digestive health. Fasting during Ramadan may alter the composition of gut microbiota through changes in dietary behavior, which ultimately affects the contents of various metabolites in the gut. Here, we used liquid chromatography-mass spectrometry-based metabolomics to investigate the composition of fecal metabolites in Chinese and Pakistani individuals before and after Ramadan fasting. Principal component analysis showed distinct separation of metabolite profiles among ethnic groups as well as between pre- and post-fasting samples. After Ramadan fasting, the Chinese and Pakistani groups showed significant differences in their respective contents of various fecal metabolites. In particular, L-histidine, lycofawcine, and cordycepin concentrations were higher after Ramadan fasting in the Chinese group, while brucine was enriched in the Pakistani group. The KEGG analysis suggested that metabolites related to purine metabolism, 2-oxocarboxylic acid metabolism, and lysine degradation were significantly enriched in the total subject population pre-fasting vs. post-fasting comparisons. Several bacterial taxa were significantly correlated with specific metabolites unique to each ethnic group, suggesting that changes in fecal metabolite profiles related to Ramadan fasting may be influenced by associated shifts in gut microbiota. The fasting-related differences in fecal metabolite profile, together with these group-specific correlations between taxa and metabolites, support our previous findings that ethnic differences in dietary composition also drive variation in gut microbial composition and diversity. This landscape view of interconnected dietary behaviors, microbiota, and metabolites contributes to the future development of personalized, diet-based therapeutic strategies for gut-related disorders.
PubMed: 35600819
DOI: 10.3389/fnut.2022.845086 -
The Science of the Total Environment Oct 2023Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the...
Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the availability of measured data, these in silico tools are trained and validated primarily on drug metabolism data. In this work, we assessed the capacity of five leading metabolite structure predictors to represent the metabolism of agrochemicals observed in rats. More specifically, we tested the ability of SyGMa, GLORY, GLORYx, BioTransformer 3.0, and MetaTrans to correctly predict and rank the experimentally observed metabolites of a set of 85 parent compounds. We found that the models were able to recover about one to two-thirds of the experimentally observed first-generation, second-generation and third-generation metabolites, confirming their value in applications such as metabolite identification. However, precision was low for all investigated tools and did not exceed approximately 18 % for the pool of first-generation metabolites and 2 % for the pool of compounds representing the first three generations of metabolites. The variance in prediction success rates was high across the individual metabolic maps, meaning that outcomes depend strongly on the specific compound under investigation. We also found that the predictions for individual parent compounds differed strongly between the tools, particularly between those built on orthogonal technologies (e.g., rule-based and end-to-end machine learning approaches). This renders ensemble model strategies promising for improving success rates. Overall, the results of this benchmark study show that there is still considerable room for the improvement of metabolite structure predictors left. Our discussion points out several avenues to progress. The bottleneck in method development certainly has been, and will remain, for the foreseeable future, the limited quantity and quality of available measured data on small-molecule metabolism.
Topics: Rats; Animals; Agrochemicals; Machine Learning; Xenobiotics; Inactivation, Metabolic
PubMed: 37355108
DOI: 10.1016/j.scitotenv.2023.165039 -
Bioinformatics (Oxford, England) Jun 2023Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes...
MOTIVATION
Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a "multi-omics" network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes.
RESULTS
Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance.
AVAILABILITY AND IMPLEMENTATION
The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep.
Topics: Humans; Multiomics; Metabolome; Neoplasms; Computer Simulation; Gene Regulatory Networks
PubMed: 37216914
DOI: 10.1093/bioinformatics/btad333