Time-course cross-species transcriptomics reveals conserved hepatotoxicity pathways induced by repeated administration of cyclosporine A.

Cyclosporine A (CsA) has shown efficacy against immunity-related diseases despite its toxicity in various organs, including the liver, emphasizing the need to elucidate its underlying hepatotoxicity mechanism. This study aimed to capture the alterations in genome-wide expression over time and the subsequent perturbations of corresponding pathways across species. Six data from humans, mice, and rats, including animal liver tissue, human liver microtissues, and two liver cell lines exposed to CsA toxic dose, were used. The microtissue exposed to CsA for 10 d was analyzed to obtain dynamically differentially expressed genes (DEGs). Single-time points data at 1, 3, 5, 7, and 28 d of different species were used to provide additional evidence. Using liver microtissue-based longitudinal design, DEGs that were consistently up- or down-regulated over time were captured, and the well-known mechanism involved in CsA toxicity was elucidated. Thirty DEGs that consistently changed in longitudinal data were also altered in 28-d rat in-house data with concordant expression. Some genes (e.g. TUBB2A, PLIN2, APOB) showed good concordance with identified DEGs in 1-d and 7-d mouse data. Pathway analysis revealed up-regulations of protein processing, asparagine N-linked glycosylation, and cargo concentration in the endoplasmic reticulum. Furthermore, the down-regulations of pathways related to biological oxidations and metabolite and lipid metabolism were elucidated. These pathways were also enriched in single-time-point data and conserved across species, implying their biological significance and generalizability. Overall, the human organoids-based longitudinal design coupled with cross-species validation provides temporal molecular change tracking, aiding mechanistic elucidation and biologically relevant biomarker discovery.

Pathway-level multi-omics analysis of the molecular mechanisms underlying the toxicity of long-term tacrolimus exposure.

Tacrolimus (TAC)-based treatment is associated with nephrotoxicity and hepatotoxicity; however, the underlying molecular mechanisms responsible for this toxicity have not been fully explored. This study elucidated the molecular processes underlying the toxic effects of TAC using an integrative omics approach. Rats were sacrificed after 4 weeks of daily oral TAC administration at a dose of 5 mg/kg. The liver and kidney underwent genome-wide gene expression profiling and untargeted metabolomics assays. Molecular alterations were identified using individual data profiling modalities and further characterized by pathway-level transcriptomics-metabolomics integration analysis. Metabolic disturbances were mainly related to an imbalance in oxidant-antioxidant status, as well as in lipid and amino acid metabolism in the liver and kidney. Gene expression profiles also indicated profound molecular alterations, including in genes associated with a dysregulated immune response, proinflammatory signals, and programmed cell death in the liver and kidney. Joint-pathway analysis indicated that the toxicity of TAC was associated with DNA synthesis disruption, oxidative stress, and cell membrane permeabilization, as well as lipid and glucose metabolism. In conclusion, our pathway-level integration of transcriptome and metabolome and conventional analyses of individual omics profiles, provided a more comprehensive picture of the molecular changes resulting from TAC toxicity. This study also serves as a valuable resource for subsequent investigations aiming to understand the mechanism underlying the molecular toxicology of TAC.

Characterization of Clofazimine as a Potential Substrate of Drug Transporter.

In this study, we explored clofazimine (CFZ) as a potential substrate of uptake and efflux transporters that might be involved in CFZ disposition, using transporter gene overexpressing cell lines in vitro. The intracellular concentrations of CFZ were significantly increased in the presence of selective inhibitors of P-gp and BCRP, which include verapamil, cyclosporine-A, PSC-833, quinidine, Ko143, and daunorubicin. In a bidirectional transport assay using transwell cultures of cell lines overexpressing P-gp and BCRP, the mean efflux ratios of CFZ were found to be 4.17 ± 0.63 and 3.37 ± 1.2, respectively. The Km and maximum rate of uptake (Vmax) were estimated to be 223.3 ± 14.73 µM and 548.8 ± 87.15 pmol/min/mg protein for P-gp and 381.9 ± 25.07 µM and 5.8 ± 1.22 pmol/min/mg protein for BCRP, respectively. Among the uptake transporters screened, the CFZ uptake rate was increased 1.93 and 3.09-fold in HEK293 cell lines overexpressing OAT1 and OAT3, respectively, compared to the control cell lines, but no significant uptake was observed in cell lines overexpressing OCT1, OCT2, OATP1B1, OATP1B3, OATP2B1, or NTCP. Both OAT1- and OAT3-mediated uptake was inhibited by the selective inhibitors diclofenac, probenecid, and butanesulfonic acid. The Km and Vmax values of CFZ were estimated to be 0.63 ± 0.15 µM and 8.23 ± 1.03 pmol/min/mg protein, respectively, for OAT1 and 0.47 ± 0.1 µM and 17.81 ± 2.19 pmol/min/mg protein, respectively, for OAT3. These findings suggest that CFZ is a novel substrate of BCRP, OAT1, and OAT3 and a known substrate of P-gp in vitro.

Characterization of Clofazimine Metabolism in Human Liver Microsomal Incubation In Vitro.

Clofazimine [N,5-bis(4-chlorophenyl)-3-[(propane-2-yl)rimino]-3,5-dihydrophenazin-2-amine] is an antimycobacterial agent used as a second-line antituberculosis (anti-TB) drug. Nonetheless, little information is known about the metabolic routes of clofazimine, and the enzymes involved in metabolism. This study aimed to characterize the metabolic pathways and enzymes responsible for the metabolism of clofazimine in human liver microsomes. Eight metabolites, including four oxidative metabolites, three glucuronide conjugates, and one sulfate conjugate were identified, and their structures were deduced based on tandem mass spectrometry (MS/MS) spectra. Hydroxylated clofazimine and hydrated clofazimine was generated even in the absence of the NADPH generating system presumably via a nonenzymatic pathway. Hydrolytic-dehalogenated clofazimine was catalyzed mainly by CYP1A2 whereas hydrolytic-deaminated clofazimine was formed by CYP3A4/A5. In case of glucuronide conjugates, UGT1A1, UGT1A3, and UGT1A9 showed catalytic activity toward hydroxylated and hydrated clofazimine glucuronide whereas hydrolytic-deaminated clofazimine glucuronide was catalyzed by UGT1A4, UGT1A9, UGT1A3, and UGT2B4. Our results suggested that CYP1A2 and CYP3A are involved in the formation of oxidative metabolites while UGT1A1, 1A3, 1A4, 1A9, and 2B4 are involved in the formation of glucuronide conjugates of oxidative metabolites of clofazimine.

Delineation of the molecular mechanisms underlying Colistin-mediated toxicity using metabolomic and transcriptomic analyses.

The mechanisms underlying colistin-induced toxicity are not fully understood. This study used untargeted metabolomics and transcriptomics to elucidate the molecular processes occurring in the liver and kidney of rats after treatment with colistin methanesulfonate (CMS). Rats were treated with 50 mg/kg CMS (high-dose), 25 mg/kg CMS (low-dose), or vehicle control, either as a single dose or once daily for 1 or 4 weeks. We found that metabolic alterations were dose- and treatment duration-dependent in the kidney, whereas mild changes were noted in the liver. Metabolic profiles in the high-dose, low-dose, and control groups of both tissues could be classified using partial least-squares discriminant analysis. Metabolic alterations were associated with the citric acid cycle and related processes, disrupted balance between pro-oxidants and antioxidants, inflammatory responses, and amino acid and nucleic acid metabolism. Gene expression profiles further showed that high-dose treatment was associated with disrupted metabolism, oxidative stress, and proinflammatory signals in the kidney. The expression levels of genes related to the cell cycle, DNA replication, and programmed cell death were also predominantly upregulated. These findings suggested that high-dose treatment was associated with a dramatic increase in cellular kidney injury, while only minor effects were observed in the low-dose group. Almost no significant gene expression was changed in the liver, even with high-dose CMS. In conclusion, untargeted metabolomics and transcriptomics provided better insights into the biological mechanisms underlying colistin-induced nephrotoxicity.

Biological Activities of Lichen-Derived Monoaromatic Compounds.

Lichen-derived monoaromatic compounds are bioactive compounds, associated with various pharmacological properties: antioxidant, antifungal, antiviral, cytotoxicity, and enzyme inhibition. However, little is known about data regarding alpha-glucosidase inhibition and antimicrobial activity. Very few compounds were reported to have these activities. In this paper, a series of monoaromatic compounds from a lichen source were isolated and structurally elucidated. They are 3,5-dihydroxybenzoic acid (1), 3,5-dihydroxybenzoate methyl (2), 3,5-dihydroxy-4-methylbenzoic acid (3), 3,5-dihydroxy-4-methoxylbenzoic acid (4), 3-hydroxyorcinol (5), atranol (6), and methyl hematommate (7). To obtain more derivatives, available compounds from the previous reports such as methyl ß-orsellinate (8), methyl orsellinate (9), and D-montagnetol (10) were selected for bromination. Electrophilic bromination was applied to 8-10 using NaBr/H2O2 reagents to yield products methyl 5-bromo-ß-orsellinate (8a), methyl 3,5-dibromo-orsellinate (9a), 3-bromo-D-montagnetol (10a), and 3,5-dibromo-D-montagnetol (10b). Compounds were evaluated for alpha-glucosidase inhibition and antimicrobial activity against antibiotic-resistant, pathogenic bacteria Enterococcus faecium, Staphylococcus aureus, and Acinetobacter baumannii. Compound 4 showed stronger alpha-glucosidase inhibition than others with an IC50 value of 24.0 µg/mL. Synthetic compound 9a exhibited remarkable activity against Staphylococcus aureus with a MIC value of 4 µg/mL. Molecular docking studies were performed to confirm the consistency between in vitro and in silico studies.

Characterization and Antioxidant Activity Determination of Neutral and Acidic Polysaccharides from Panax Ginseng C. A. Meyer.

Panax ginseng (P. ginseng) is the most widely consumed herbal plant in Asia and is well-known for its various pharmacological properties. Many studies have been devoted to this natural product. However, polysaccharide's components of ginseng and their biological effects have not been widely studied. In this study, white ginseng neutral polysaccharide (WGNP) and white ginseng acidic polysaccharide (WGAP) fractions were purified from P. ginseng roots. The chemical properties of WGNP and WGAP were investigated using various chromatography and spectroscopy techniques, including high-performance gel permeation chromatography, Fourier-transform infrared spectroscopy, and high-performance liquid chromatography with an ultra-violet detector. The antioxidant, anti-radical, and hydrogen peroxide scavenging activities were evaluated in vitro and in vivo using Caenorhabditis elegans as the model organism. Our in vitro data by ABTS (2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid), reducing power, ferrous ion chelating, and hydroxyl radical scavenging activity suggested that the WGAP with significantly higher uronic acid content and higher molecular weight exhibits a much stronger antioxidant effect as compared to that of WGNP. Similar antioxidant activity of WGAP was also confirmed in vivo by evaluating internal reactive oxygen species (ROS) concentration and lipid peroxidation. In conclusion, WGAP may be used as a natural antioxidant with potent scavenging and metal chelation properties.

Systemic and Local Metabolic Alterations in Sleep-Deprivation-Induced Stress: A Multiplatform Mass-Spectrometry-Based Lipidomics and Metabolomics Approach.

Sleep deprivation (SD) is known to be associated with metabolic disorders and chronic diseases. Complex metabolic alterations induced by SD at omics scale and the associated biomarker candidates have been proposed. However, in vivo systemic and local metabolic shift patterns of the metabolome and lipidome in acute and chronic partial SD models remain to be elucidated. In the present study, the serum, hypothalamus, and hippocampus CA1 of sleep-deprived rats (SD rats) from acute and chronic sleep restriction models were analyzed using three different omics platforms for the discovery and mechanistic assessment of systemic and local SD-induced dysregulated metabolites. We found a similar pattern of systemic metabolome alterations between two models, for which the area under the curve (AUC) of receiver operating characteristic curves was AUC = 0.847 and 0.930 with the pseudotargeted and untargeted metabolomics approach, respectively. However, SD-induced systemic lipidome alterations were significantly different and appeared to be model-dependent (AUC = 0.374). Comprehensive pathway analysis of the altered lipidome and metabolome in the hypothalamus indicated the abnormal behavior of eight metabolic and lipid metabolic pathways. The metabolic alterations of the hippocampus CA1 was subtle in two SD models. Collectively, these results extend our understanding of the quality of sleep and suggest metabolic targets in developing diagnostic biomarkers for better SD control.

Integrative lipidomic and transcriptomic analysis of X-linked adrenoleukodystrophy reveals distinct lipidome signatures between adrenomyeloneuropathy and childhood cerebral adrenoleukodystrophy.

Precise pathophysiology with respect to the phenotypic variations and severity of X-ALD, specifically between adrenomyeloneuropathy (AMN) and childhood cerebral adrenoleukodystrophy (CCALD), has not been fully discovered. Herein, a systematic analysis using multi-layered lipidomics and transcriptomics was conducted to elucidate distinctive metabolic biosignatures among healthy control, AMN, and CCALD. Significant alterations regarding the accumulation of very long chain fatty acids were found in various lipid species such as phospholipids, glycerolipids, and sphingolipids. Remarkably, TG and CER that are physiologically essential were markedly down-regulated in CCALD than AMN. Transcriptomic analysis further supported the robustness of our findings by providing valuable information on the gene expressions of the regulatory factors. For instance, regulators of sphingolipid catabolism (SMPD1, CERK, and SPHK1) and TG anabolism (GPAM, GPAT2, and MBOAT2) were more up-regulated in AMN than in CCALD. These observations, among others, were in line with the recognized alterations of the associated lipidomes. In conclusion, the homeostatic imbalance of the complex lipid networks may be pathogenically important in X-ALD and the particular dysregulations of TG and CER may further influence the severity of CCALD among X-ALD patients.

High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer.

The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal cancer (CRC). Different random forests (RF)-based feature selection methods including the area under the curve (AUC)-RF, Boruta, and Vita were used and the diagnostic performance of the proposed biosignatures was benchmarked using RF, logistic regression, naïve Bayes, and k-nearest neighbors models. All models showed satisfactory performance in which RF appeared to be the best. For instance, regarding the RF model, the following were observed: mean accuracy 0.998 (standard deviation (SD) < 0.003), mean specificity 0.999 (SD < 0.003), and mean sensitivity 0.998 (SD < 0.004). Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Some biomarkers were found to be enriched in epithelial cell signaling in Helicobacter pylori infection and inflammatory processes. The overexpression of TGFBI and S100A2 was associated with poor disease-free survival while the down-regulation of NR5A2, SLC4A4, and CD177 was linked to worse overall survival of the patients. In conclusion, novel transcriptome signatures to improve the diagnostic accuracy in CRC are introduced for further validations in various clinical settings.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

INTRODUCTION: Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients. OBJECTIVES: In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

The Dominant Role of Forkhead Box Proteins in Cancer.

Forkhead box (FOX) proteins are multifaceted transcription factors that are significantly implicated in cancer, with various critical roles in biological processes. Herein, we provide an overview of several key members of the FOXA, FOXC, FOXM1, FOXO and FOXP subfamilies. Important pathophysiological processes of FOX transcription factors at multiple levels in a context-dependent manner are discussed. We also specifically summarize some major aspects of FOX transcription factors in association with cancer research such as drug resistance, tumor growth, genomic alterations or drivers of initiation. Finally, we suggest that targeting FOX proteins may be a potential therapeutic strategy to combat cancer.

High-Throughput Screening and Prediction Model Building for Novel Hemozoin Inhibitors Using Physicochemical Properties.

It is essential to continue the search for novel antimalarial drugs due to the current spread of resistance against artemisinin by Plasmodium falciparum parasites. In this study, we developed in silico models to predict hemozoin inhibitors as a potential first-step screening for novel antimalarials. An in vitro colorimetric high-throughput screening assay of hemozoin formation was used to identify hemozoin inhibitors from 9,600 structurally diverse compounds. The physicochemical properties of positive hits and randomly selected compounds were extracted from the ChemSpider database; they were used for developing prediction models to predict hemozoin inhibitors using two different approaches, i.e., traditional multivariate logistic regression and Bayesian model averaging. Our results showed that a total of 224 positive-hit compounds exhibited the ability to inhibit hemozoin formation, with 50% inhibitory concentrations (IC50s) ranging from 3.1 µM to 199.5 µM. The best model according to traditional multivariate logistic regression included the three variables octanol-water partition coefficient, number of hydrogen bond donors, and number of atoms of hydrogen, while the best model according to Bayesian model averaging included the three variables octanol-water partition coefficient, number of hydrogen bond donors, and index of refraction. Both models had a good discriminatory power, with area under the curve values of 0.736 and 0.781 for the traditional multivariate model and Bayesian model averaging, respectively. In conclusion, the prediction models can be a new, useful, and cost-effective approach for the first screen of hemozoin inhibition-based antimalarial drug discovery.

Association between nutritional status and dengue infection: a systematic review and meta-analysis.

BACKGROUND: Dengue infection has various clinical manifestations, often with unpredictable clinical evolutions and outcomes. Several factors including nutritional status have been studied to find the relationship with dengue severity. However, the nutritional status had conflicting effects on the complication of dengue in some previous studies. Therefore, we conducted a systematic review and performed a meta-analysis to analyze the association between nutritional status and the outcome of dengue infection. METHODS: Eleven electronic databases and manual searching of reference lists were used to identify the relevant studies published before August 2013. At least two authors worked independently in every step to select eligible studies and extract data. Dengue severity in the included studies must be classified into three categories: dengue fever (DF), dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). RESULTS: Thirteen articles that met the inclusion criteria came to final analysis. A meta-analysis using fixed- or random-effects models was conducted to calculate pooled odds ratios (OR) with corresponding 95 % confidence intervals. It has shown that there was no statistically significant association between DHF group and DSS group in malnutritional and overweight/obesity patients with OR: 1.17 (95 % CI: 0.99-1.39), 1.31 (0.91-1.88), respectively. A significantly inverse relation between DF and DHF groups of malnutritional patients was revealed (OR = 0.71, 95 % CI: 0.56-0.90). Our meta-analysis also indicated a statistically significant negative correlation between malnourished children with dengue virus infection and healthy children (OR = 0.46, 95 % CI: 0.3-0.70). When analyzing patients with normal nutrition status, we found out that there was a significantly negative relationship between DHF and DSS groups (0.87; 95 % CI: 0.77-0.99). Other comparisons of DSS with DF/DHF groups, DSS/DHF with DF groups, and DHF with DF groups in normal nutritional patients showed no significant correlation. However, the findings should be interpreted cautiously because all significant associations were lost after removing of the largest study. CONCLUSIONS: Results from previous studies failed to show any solid consistency regarding the association between the nutritional status and dengue infection. Consequently, the effects of nutritional status on dengue disease outcome has been controversial. Further studies are recommended to clarify the impact of nutritional status on dengue infection.

Participants' understanding of informed consent in clinical trials over three decades: systematic review and meta-analysis.

OBJECTIVE: To estimate the proportion of participants in clinical trials who understand different components of informed consent. METHODS: Relevant studies were identified by a systematic review of PubMed, Scopus and Google Scholar and by manually reviewing reference lists for publications up to October 2013. A meta-analysis of study results was performed using a random-effects model to take account of heterogeneity. FINDINGS: The analysis included 103 studies evaluating 135 cohorts of participants. The pooled proportion of participants who understood components of informed consent was 75.8% for freedom to withdraw at any time, 74.7% for the nature of study, 74.7% for the voluntary nature of participation, 74.0% for potential benefits, 69.6% for the study's purpose, 67.0% for potential risks and side-effects, 66.2% for confidentiality, 64.1% for the availability of alternative treatment if withdrawn, 62.9% for knowing that treatments were being compared, 53.3% for placebo and 52.1% for randomization. Most participants, 62.4%, had no therapeutic misconceptions and 54.9% could name at least one risk. Subgroup and meta-regression analyses identified covariates, such as age, educational level, critical illness, the study phase and location, that significantly affected understanding and indicated that the proportion of participants who understood informed consent had not increased over 30 years. CONCLUSION: The proportion of participants in clinical trials who understood different components of informed consent varied from 52.1% to 75.8%. Investigators could do more to help participants achieve a complete understanding.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management.

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Omics analysis unveils changes in the metabolome and lipidome of Caenorhabditis elegans upon polydopamine exposure.

Polydopamine (PDA) is an insoluble biopolymer with a dark brown-black color that forms through the autoxidation of dopamine. Because of its outstanding biocompatibility and durability, PDA holds enormous promise for various applications, both in the biomedical and non-medical domains. To ensure human safety, protect health, and minimize environmental impacts, the assessment of PDA toxicity is important. In this study, metabolomics and lipidomics assessed the impact of acute PDA exposure on Caenorhabditis elegans (C. elegans). The findings revealed a pronounced perturbation in the metabolome and lipidome of C. elegans at the L4 stage following 24 hours of exposure to 100 µg/mL PDA. The changes in lipid composition varied based on lipid classes. Increased lipid classes included lysophosphatidylethanolamine, triacylglycerides, and fatty acids, while decreased species involved in several sub-classes of glycerophospholipids and sphingolipids. Besides, we detected 37 significantly affected metabolites in the positive and 8 in the negative ion modes due to exposure to PDA in C. elegans. The metabolites most impacted by PDA exposure were associated with purine metabolism, biosynthesis of valine, leucine, and isoleucine; aminoacyl-tRNA biosynthesis; and cysteine and methionine metabolism, along with pantothenate and CoA biosynthesis; the citrate cycle (TCA cycle); and beta-alanine metabolism. In conclusion, PDA exposure may intricately influence the metabolome and lipidome of C. elegans. The combined application of metabolomics and lipidomics offers additional insights into the metabolic perturbations involved in PDA-induced biological effects and presents potential biomarkers for the assessment of PDA safety.

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

Cyclosporine A-induced systemic metabolic perturbations in rats: A comprehensive metabolome analysis.

A better understanding of cyclosporine A (CsA)-induced nephro- and hepatotoxicity at the molecular level is necessary for safe and effective use. Utilizing a sophisticated study design, this study explored metabolic alterations after long-term CsA treatment in vivo. Rats were exposed to CsA with 4, 10, and 25 mg/kg for 4 weeks and then sacrificed to obtain liver, kidney, urine, and serum for untargeted metabolomics analysis. Differential network analysis was conducted to explore the biological relevance of metabolites significantly altered by toxicity-induced disturbance. Dose-dependent toxicity was observed in all biospecimens. The toxic effects were characterized by alterations of metabolites related to energy metabolism and cellular membrane composition, which could lead to the cholestasis-induced accumulation of bile acids in the tissues. The unfavorable impacts were also demonstrated in the serum and urine. Intriguingly, phenylacetylglycine was increased in the kidney, urine, and serum treated with high doses versus controls. Differential correlation network analysis revealed the strong correlations of deoxycytidine and guanosine with other metabolites in the network, which highlighted the influence of repeated CsA exposure on DNA synthesis. Overall, prolonged CsA administration had system-level dose-dependent effects on the metabolome in treated rats, suggesting the need for careful usage and dose adjustment.

Metabolic phenotyping and global functional analysis facilitate metabolic signature discovery for tuberculosis treatment monitoring.

Tracking alterations in polar metabolite and lipid levels during anti-tuberculosis (TB) interventions is an emerging biomarker discovery and validation approach due to its sensitivity in capturing changes and reflecting on the host status. Here, we employed deep plasma metabolic phenotyping to explore the TB patient metabolome during three phases of treatment: at baseline, during intensive phase treatment, and upon treatment completion. Differential metabolites (DMs) in each period were determined, and the pathway-level biological alterations were explored by untargeted metabolomics-guided functional interpretations that bypassed identification. We identified 41 DMs and 39 pathways that changed during intensive phase completion. Notably, levels of certain amino acids including histidine, bile acids, and metabolites of purine metabolism were dramatically increased. The altered pathways included those involved in the metabolism of amino acids, glycerophospholipids, and purine. At the end of treatment, 44 DMs were discovered. The levels of glutamine, bile acids, and lysophosphatidylinositol significantly increased compared to baseline; the levels of carboxylates and hypotaurine declined. In addition, 37 pathways principally associated with the metabolism of amino acids, carbohydrates, and glycan altered at treatment completion. The potential of each DM for diagnosing TB was examined using a cohort consisting of TB patients, those with latent infections, and controls. Logistic regression revealed four biomarkers (taurine, methionine, glutamine, and acetyl-carnitine) that exhibited excellent performance in differential diagnosis. In conclusion, we identified metabolites that could serve as useful metabolic signatures for TB management and elucidated underlying biological processes affected by the crosstalk between host and TB pathogen during treatment.