From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

Molecular Biotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: April 2, 2024

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for discovery, is underpinned by big data, transformative force in current era. Omics characterized its heterogeneity enormity, ushered biological biomedical research into data domain. Acknowledging significance integrating diverse omics strata, known as multi-omics studies, researchers delve intricate interrelationships among various layers. review navigates expansive landscape, showcasing tailored assays each layer through genomes to metabolomes. The sheer volume generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging robust tools. These datasets not only refine classification but also enhance diagnostics foster development therapeutic strategies. Through integration high-throughput focuses targeting modeling multiple disease-regulated networks, validating interactions targets, enhancing potential using network pharmacology approaches. Ultimately, this exploration aims illuminate impact era, shaping future research.

Language: Английский

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Untargeted metabolomics of bladder tissue using liquid chromatography and quadrupole time-of-flight mass spectrometry for cancer biomarker detection

Journal of Pharmaceutical and Biomedical Analysis, Journal Year: 2024, Volume and Issue: 240, P. 115966 - 115966

Published: Jan. 8, 2024

Language: Английский

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Using metabolomics to investigate the relationship between the metabolomic profile of the intestinal microbiota derivatives and mental disorders in inflammatory bowel diseases: a narrative review

Research in Pharmaceutical Sciences, Journal Year: 2025, Volume and Issue: 20(1), P. 1 - 24

Published: Jan. 1, 2025

Individuals with inflammatory bowel disease (IBD) are at a higher risk of developing mental disorders, such as anxiety and depression. The imbalance between the intestinal microbiota its host, known dysbiosis, is one factors, disrupting balance metabolite production their signaling pathways, leading to progression. A metabolomics approach can help identify role gut in disorders associated IBD by evaluating metabolites comprehensively. This narrative review focuses on studies that have comprehensively elucidated altered microbial pathways underlying patients. information was compiled searching PubMed, Web Science, Scopus, Google Scholar from 2005 2023. findings indicated dysbiosis patients leads depression through disturbances metabolism carbohydrates, sphingolipids, bile acids, neurotransmitters, neuroprotective, amino acids. Furthermore, reduction neuroprotective factors increase inflammation observed these also contribute worsening psychological symptoms. Analyzing profile comparing it healthy individuals using advanced technologies like metabolomics, aids early diagnosis prevention disorders. allows for more precise identification microbes responsible production, enabling development tailored dietary pharmaceutical interventions or targeted manipulation microbiota.

Language: Английский

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Association of metabolic dysregulation with treatment response in rectal cancer patients undergoing chemoradiotherapy

BMC Medical Genomics, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 12, 2025

This study aimed to explore the metabolic changes during neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC) by serum metabolomics analysis, and provide new biomarkers for individualized treatment efficacy prediction. Serum samples from 20 LARC before, after NCRT were collected metabolomic analysis. The metabolites analyzed qualitatively quantitatively using gas chromatography-mass spectrometry (GC-MS). Meanwhile, differences profiles at different time points compared significantly changed screened. of altered NCRT. Through we identified that revealed alterations associated pathways. predictive power pre-radiotherapy isocitric acid pro-radiotherapy 3-hydroxy-3-(4'-hydroxy-3'-methoxyphenyl) propionic distinguishing sensitive non-sensitive was markedly high, AUC values 0.875 0.75, respectively. Additional analysis indicated a combined panel yielded even higher values, thereby enhancing accuracy predicting corresponding pathways disorders may be poor outcomes treated cancer, providing prognostic assessment. Further studies validation will help gain insight into mechanism these more basis clinical application.

Language: Английский

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DMoVGPE: predicting gut microbial associated metabolites profiles with deep mixture of variational Gaussian Process experts

BMC Bioinformatics, Journal Year: 2025, Volume and Issue: 26(1)

Published: March 27, 2025

Abstract Background Understanding the metabolic activities of gut microbiome is vital for deciphering its impact on human health. While direct measurement these metabolites through metabolomics effective, it often expensive and time-consuming. In contrast, microbial composition data obtained sequencing more accessible, making a promising resource predicting metabolite profiles. However, current computational models frequently face challenges related to limited prediction accuracy, generalizability, interpretability. Method Here, we present Deep Mixture Variational Gaussian Process Experts (DMoVGPE) model, designed overcome issues. DMoVGPE utilizes dynamic gating mechanism, implemented neural network with fully connected layers dropout regularization, select most relevant experts. During training, refines expert selection, dynamically adjusting their contribution based input features. The model also incorporates an Automatic Relevance Determination (ARD) which assigns relevance scores features by evaluating predictive power. Features linked profiles are given smaller length scales increase influence, while irrelevant down-weighted larger scales, improving both accuracy Conclusions Through extensive evaluations various datasets, consistently achieves higher performance than existing models. Furthermore, our reveals significant associations between specific taxa metabolites, aligning well findings from studies. These results highlight DMoVGPE’s potential provide accurate predictions uncover biologically meaningful relationships, paving way application in disease research personalized healthcare strategies.

Language: Английский

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Trends in volatile organic compound-based metabolomics for biomarker discovery

Microchemical Journal, Journal Year: 2025, Volume and Issue: unknown, P. 113700 - 113700

Published: April 1, 2025

Language: Английский

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Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation

BMC Cancer, Journal Year: 2024, Volume and Issue: 24(1)

Published: Feb. 16, 2024

Abstract Background Glioma is a primary brain tumor and the assessment of its molecular profile in minimally invasive manner important determining treatment strategies. Among abnormalities gliomas, mutations isocitrate dehydrogenase (IDH) gene are strong predictors sensitivity prognosis. In this study, we attempted to non-invasively diagnose glioma development presence IDH using multivariate analysis plasma mid-infrared absorption spectra for comprehensive sensitive view changes blood components associated with disease genetic mutations. These component discussed terms wavenumbers that contribute differentiation. Methods Plasma samples were collected at our institutes from 84 patients (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 wild-type diffuse glioma, 47 glioblastomas) before initiation 72 healthy participants. FTIR-ATR obtained each sample, PLS discriminant was performed absorbance wavenumber fingerprint region biomolecules as explanatory variable. This data used distinguish participants Results The derived classification algorithm distinguished 83% accuracy (area under curve (AUC) receiver operating characteristic (ROC) = 0.908) diagnosed mutation 75% (AUC 0.752 ROC) cross-validation 30% total test data. suggest an increase ratio β-sheet structures conformational composition proteins glioma. Furthermore, these more pronounced gliomas. Conclusions infrared could be gliomas high degree accuracy. spectral shape protein band showed significantly higher than participants, aggregation distinct feature

Language: Английский

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Exploring machine learning for untargeted metabolomics using molecular fingerprints

Computer Methods and Programs in Biomedicine, Journal Year: 2024, Volume and Issue: 250, P. 108163 - 108163

Published: April 8, 2024

Metabolomics, the study of substrates and products cellular metabolism, offers valuable insights into an organism's state under specific conditions has potential to revolutionise preventive healthcare pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited incompletely annotated metabolic pathways. This study, inspired by well-established in drug discovery, employs machine learning metabolite fingerprints explore relationship their structure responses experimental beyond known pathways, shedding light processes. It evaluates fingerprinting effectiveness representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, interpretable features. Feature importance analysis is then applied reveal key chemical configurations affecting classification, identifying related groups. The approach tested two datasets: one Ataxia Telangiectasia another endothelial cells low oxygen. Machine molecular predicts effectively, feature aligns unveiling new affected groups for further study. In conclusion, presented leverages strengths discovery address critical issues research aims bridge gap between these disciplines. work lays foundation future this direction, possibly exploring alternative encodings models.

Language: Английский

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Postmortem metabolomics: influence of time since death on the level of endogenous compounds in human femoral blood. Necessary to be considered in metabolome study planning?

Metabolomics, Journal Year: 2024, Volume and Issue: 20(3)

Published: May 9, 2024

Abstract Introduction The (un)targeted analysis of endogenous compounds has gained interest in the field forensic postmortem investigations. blood metabolome is influenced by many factors, and specimens are considered particularly challenging due to unpredictable decomposition processes. Objectives This study aimed systematically investigate influence time since death on its relevance designing studies. Methods Femoral samples 427 authentic cases, were collected at two points after (854 total; t1: admission institute, 1.3–290 h; t2: autopsy, 11–478 median ∆ t = 71 h). All analyzed using an untargeted approach, peak areas determined for 38 (acylcarnitines, amino acids, phospholipids, others). Differences between t2 t1 assessed Wilcoxon signed-ranked test ( p < 0.05). Moreover, all n 854) binned into groups (6 h, 12 or 24 h intervals) compared Kruskal–Wallis/Dunn’s multiple comparison tests 0.05 each) effect estimated death. Results Except serine, threonine, PC 34:1, tested analytes revealed statistically significant changes (highest increase 166%). Unpaired 854 in-between indicated similar results. Significant differences typically observed within first later than 48 death, respectively. Conclusions To improve consistency comprehensive data evaluation studies, it seems advisable only include 2 days

Language: Английский

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Multifunctional-separation-mode ion chromatography method for determining major metabolites during multiple parallel fermentation of rice wine

Analytical Methods, Journal Year: 2024, Volume and Issue: 16(25), P. 4045 - 4053

Published: Jan. 1, 2024

Facile and effective analysis methods are desirable for elucidating the behaviours of metabolites during fermentation reactions.

Language: Английский

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