1
|
Salihoglu R, Balkenhol J, Dandekar G, Liang C, Dandekar T, Bencurova E. Cat-E: A comprehensive web tool for exploring cancer targeting strategies. Comput Struct Biotechnol J 2024; 23:1376-1386. [PMID: 38596315 PMCID: PMC11001601 DOI: 10.1016/j.csbj.2024.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/26/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024] Open
Abstract
Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.
Collapse
Affiliation(s)
- Rana Salihoglu
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
| | - Johannes Balkenhol
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University Hospital of Wurzburg, 97080 Wurzburg, Germany
| | - Gudrun Dandekar
- Chair of Tissue Engineering and Regenerative Medicine, University Hospital of Wurzburg, 97080 Wurzburg, Germany
| | - Chunguang Liang
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
- Institute of Immunology, Jena University Hospital, Friedrich-Schiller-University, 07743 Jena, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Elena Bencurova
- Department of Bioinformatics, University of Wurzburg, 97074 Wurzburg, Germany
| |
Collapse
|
2
|
Cohen A, Turjeman S, Levin R, Tal S, Koren O. Comparison of canine colostrum and milk using a multi-omics approach. Anim Microbiome 2024; 6:19. [PMID: 38650014 PMCID: PMC11034113 DOI: 10.1186/s42523-024-00309-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND A mother's milk is considered the gold standard of nutrition in neonates and is a source of cytokines, immunoglobulins, growth factors, and other important components, yet little is known about the components of canine milk, specifically colostrum, and the knowledge related to its microbial and metabolic profiles is particularly underwhelming. In this study, we characterized canine colostrum and milk microbiota and metabolome for several breeds of dogs and examined profile shifts as milk matures in the first 8 days post-whelping. RESULTS Through untargeted metabolomics, we identified 63 named metabolites that were significantly differentially abundant between days 1 and 8 of lactation. Surprisingly, the microbial compositions of the colostrum and milk, characterized using 16S rRNA gene sequencing, were largely similar, with only two differentiating genera. The shifts observed, mainly increases in several sugars and amino sugars over time and shifts in amino acid metabolites, align with shifts observed in human milk samples and track with puppy development. CONCLUSION Like human milk, canine milk composition is dynamic, and shifts are well correlated with developing puppies' needs. Such a study of the metabolic profile of canine milk, and its relation to the microbial community, provides insights into the changing needs of the neonate, as well as the ideal nutrition profile for optimal functionality. This information will add to the existing knowledge base of canine milk composition with the prospect of creating a quality, tailored milk substitute or supplement for puppies.
Collapse
Affiliation(s)
- Alisa Cohen
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Sondra Turjeman
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Rachel Levin
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Smadar Tal
- Koret School of Veterinary Medicine, The Hebrew University Veterinary Teaching Hospital, Hebrew University of Jerusalem, Rehovot, Israel
- Tel-Hai Academic College, Upper Galilee, Israel
| | - Omry Koren
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
- Kyung Hee University, Seoul, the Republic of Korea.
| |
Collapse
|
3
|
Liu X, Liu Y, Liu J, Zhang H, Shan C, Guo Y, Gong X, Cui M, Li X, Tang M. Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence. Neural Regen Res 2024; 19:833-845. [PMID: 37843219 PMCID: PMC10664138 DOI: 10.4103/1673-5374.382223] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/19/2023] [Accepted: 06/17/2023] [Indexed: 10/17/2023] Open
Abstract
A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.
Collapse
Affiliation(s)
- Xiaoyan Liu
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Yi Liu
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
- Institute of Animal Husbandry, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu Province, China
| | - Junlin Liu
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Hantao Zhang
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Chaofan Shan
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Yinglu Guo
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Xun Gong
- Department of Rheumatology & Immunology, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Mengmeng Cui
- Department of Neurology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong Province, China
| | - Xiubin Li
- Department of Neurology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong Province, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| |
Collapse
|
4
|
Wen Y, Luo Y, Qiu H, Chen B, Huang J, Lv S, Wang Y, Li J, Tao L, Yang B, Li K, He L, He M, Yang Q, Yu Z, Xiao W, Zhao M, Zou X, Lu R, Gu C. Gut microbiota affects obesity susceptibility in mice through gut metabolites. Front Microbiol 2024; 15:1343511. [PMID: 38450171 PMCID: PMC10916699 DOI: 10.3389/fmicb.2024.1343511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Introduction It is well-known that different populations and animals, even experimental animals with the same rearing conditions, differ in their susceptibility to obesity. The disparity in gut microbiota could potentially account for the variation in susceptibility to obesity. However, the precise impact of gut microbiota on gut metabolites and its subsequent influence on susceptibility to obesity remains uncertain. Methods In this study, we established obesity-prone (OP) and obesity-resistant (OR) mouse models by High Fat Diet (HFD). Fecal contents of cecum were examined using 16S rDNA sequencing and untargeted metabolomics. Correlation analysis and MIMOSA2 analysis were used to explore the association between gut microbiota and intestinal metabolites. Results After a HFD, gut microbiota and gut metabolic profiles were significantly different between OP and OR mice. Gut microbiota after a HFD may lead to changes in eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), a variety of branched fatty acid esters of hydroxy fatty acids (FAHFAs) and a variety of phospholipids to promote obesity. The bacteria g_Akkermansia (Greengene ID: 175696) may contribute to the difference in obesity susceptibility through the synthesis of glycerophosphoryl diester phosphodiesterase (glpQ) to promote choline production and the synthesis of valyl-tRNA synthetase (VARS) which promotes L-Valine degradation. In addition, gut microbiota may affect obesity and obesity susceptibility through histidine metabolism, linoleic acid metabolism and protein digestion and absorption pathways.
Collapse
Affiliation(s)
- Yuhang Wen
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Yadan Luo
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Hao Qiu
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Baoting Chen
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Jingrong Huang
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Shuya Lv
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Yan Wang
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Jiabi Li
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Lingling Tao
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Bailin Yang
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Ke Li
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Lvqin He
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Manli He
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Qian Yang
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Zehui Yu
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Wudian Xiao
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Mingde Zhao
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| | - Xiaoxia Zou
- Suining First People's Hospital, Suining, China
| | - Ruilin Lu
- Suining First People's Hospital, Suining, China
| | - Congwei Gu
- Laboratory Animal Centre, Southwest Medical University, Luzhou, China
- Model Animal and Human Disease Research of Luzhou Key Laboratory, Luzhou, China
| |
Collapse
|
5
|
Shtossel O, Koren O, Shai I, Rinott E, Louzoun Y. Gut microbiome-metabolome interactions predict host condition. Microbiome 2024; 12:24. [PMID: 38336867 PMCID: PMC10858481 DOI: 10.1186/s40168-023-01737-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 12/10/2023] [Indexed: 02/12/2024]
Abstract
BACKGROUND The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment. RESULTS We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]). CONCLUSION These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.
Collapse
Affiliation(s)
- Oshrit Shtossel
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel
| | - Omry Koren
- The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Iris Shai
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ehud Rinott
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel.
| |
Collapse
|
6
|
Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
Collapse
Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
| |
Collapse
|
7
|
Wang Z, Yuan X, Zhu Z, Pang L, Ding S, Li X, Kang Y, Hei G, Zhang L, Zhang X, Wang S, Jian X, Li Z, Zheng C, Fan X, Hu S, Shi Y, Song X. Multiomics Analyses Reveal Microbiome-Gut-Brain Crosstalk Centered on Aberrant Gamma-Aminobutyric Acid and Tryptophan Metabolism in Drug-Naïve Patients with First-Episode Schizophrenia. Schizophr Bull 2024; 50:187-198. [PMID: 37119525 PMCID: PMC10754168 DOI: 10.1093/schbul/sbad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SCZ) is associated with complex crosstalk between the gut microbiota and host metabolism, but the underlying mechanism remains elusive. Investigating the aberrant neurotransmitter processes reflected by alterations identified using multiomics analysis is valuable to fully explain the pathogenesis of SCZ. STUDY DESIGN We conducted an integrative analysis of multiomics data, including the serum metabolome, fecal metagenome, single nucleotide polymorphism data, and neuroimaging data obtained from a cohort of 127 drug-naïve, first-episode SCZ patients and 92 healthy controls to characterize the microbiome-gut-brain axis in SCZ patients. We used pathway-based polygenic risk score (PRS) analyses to determine the biological pathways contributing to genetic risk and mediation effect analyses to determine the important neuroimaging features. Additionally, a random forest model was generated for effective SCZ diagnosis. STUDY RESULTS We found that the altered metabolome and dysregulated microbiome were associated with neuroactive metabolites, including gamma-aminobutyric acid (GABA), tryptophan, and short-chain fatty acids. Further structural and functional magnetic resonance imaging analyses highlighted that gray matter volume and functional connectivity disturbances mediate the relationships between Ruminococcus_torgues and Collinsella_aerofaciens and symptom severity and the relationships between species Lactobacillus_ruminis and differential metabolites l-2,4-diaminobutyric acid and N-acetylserotonin and cognitive function. Moreover, analyses of the Polygenic Risk Score (PRS) support that alterations in GABA and tryptophan neurotransmitter pathways are associated with SCZ risk, and GABA might be a more dominant contributor. CONCLUSIONS This study provides new insights into systematic relationships among genes, metabolism, and the gut microbiota that affect brain functional connectivity, thereby affecting SCZ pathogenesis.
Collapse
Affiliation(s)
- Zhuo Wang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University; Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Xiuxia Yuan
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
| | - Zijia Zhu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University; Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Lijuan Pang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
| | - Shizhi Ding
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University; Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Xue Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
| | - Yulin Kang
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Gangrui Hei
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
| | - Liyuan Zhang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
| | - Xiaoyun Zhang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
| | - Shuying Wang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
| | - Xuemin Jian
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University; Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhiqiang Li
- The Affiliated Hospital of Qingdao University and the Biomedical Sciences Institute of Qingdao University, Qingdao Branch of SJTU Bio-X Institutes, Qingdao University, Qingdao, China
| | - Chenxiang Zheng
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University; Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoduo Fan
- Psychotic Disorders Program, UMass Memorial Medical Center, University of Massachusetts Medical School, Worcester, MA, USA
| | - Shaohua Hu
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yongyong Shi
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University; Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai, China
- The Affiliated Hospital of Qingdao University and the Biomedical Sciences Institute of Qingdao University, Qingdao Branch of SJTU Bio-X Institutes, Qingdao University, Qingdao, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University; Henan International Joint Laboratory of Biological Psychiatry; Henan Psychiatric Transformation Research Key Laboratory/Zhengzhou University, Zhengzhou, China
| |
Collapse
|
8
|
Malwe AS, Sharma VK. Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome. Front Microbiol 2023; 14:1254073. [PMID: 38116528 PMCID: PMC10728657 DOI: 10.3389/fmicb.2023.1254073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/12/2023] [Indexed: 12/21/2023] Open
Abstract
A highly complex, diverse, and dense community of more than 1,000 different gut bacterial species constitutes the human gut microbiome that harbours vast metabolic capabilities encoded by more than 300,000 bacterial enzymes to metabolise complex polysaccharides, orally administered drugs/xenobiotics, nutraceuticals, or prebiotics. One of the implications of gut microbiome mediated biotransformation is the metabolism of xenobiotics such as medicinal drugs, which lead to alteration in their pharmacological properties, loss of drug efficacy, bioavailability, may generate toxic byproducts and sometimes also help in conversion of a prodrug into its active metabolite. Given the diversity of gut microbiome and the complex interplay of the metabolic enzymes and their diverse substrates, the traditional experimental methods have limited ability to identify the gut bacterial species involved in such biotransformation, and to study the bacterial species-metabolite interactions in gut. In this scenario, computational approaches such as machine learning-based tools presents unprecedented opportunities and ability to predict the gut bacteria and enzymes that can potentially metabolise a candidate drug. Here, we have reviewed the need to identify the gut microbiome-based metabolism of xenobiotics and have provided comprehensive information on the available methods, tools, and databases to address it along with their scope and limitations.
Collapse
Affiliation(s)
| | - Vineet K. Sharma
- MetaBioSys Lab, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, India
| |
Collapse
|
9
|
Fernandez ME, Martinez-Romero J, Aon MA, Bernier M, Price NL, de Cabo R. How is Big Data reshaping preclinical aging research? Lab Anim (NY) 2023; 52:289-314. [PMID: 38017182 DOI: 10.1038/s41684-023-01286-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/10/2023] [Indexed: 11/30/2023]
Abstract
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.
Collapse
Affiliation(s)
- Maria Emilia Fernandez
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jorge Martinez-Romero
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Miguel A Aon
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Michel Bernier
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nathan L Price
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rafael de Cabo
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
| |
Collapse
|
10
|
Shen Y, An Z, Huyan Z, Shu X, Wu D, Zhang N, Pellegrini N, Rubert J. Lipid complexation reduces rice starch digestibility and boosts short-chain fatty acid production via gut microbiota. NPJ Sci Food 2023; 7:56. [PMID: 37853069 PMCID: PMC10584848 DOI: 10.1038/s41538-023-00230-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
In this study, two rice varieties (RS4 and GZ93) with different amylose and lipid contents were studied, and their starch was used to prepare starch-palmitic acid complexes. The RS4 samples showed a significantly higher lipid content in their flour, starch, and complex samples compared to GZ93. The static in vitro digestion highlighted that RS4 samples had significantly lower digestibility than the GZ93 samples. The C∞ of the starch-lipid complex samples was found to be 17.7% and 18.5% lower than that of the starch samples in GZ93 and RS4, respectively. The INFOGEST undigested fractions were subsequently used for in vitro colonic fermentation. Short-chain fatty acids (SCFAs) concentrations, mainly acetate, and propionate were significantly higher in starch-lipid complexes compared to native flour or starch samples. Starch-lipid complexes produced a distinctive microbial composition, which resulted in different gene functions, mainly related to pyruvate, fructose, and mannose metabolism. Using Model-based Integration of Metabolite Observations and Species Abundances 2 (MIMOSA2), SCFA production was predicted and associated with the gut microbiota. These results indicated that incorporating lipids into rice starch promotes SCFA production by modulating the gut microbiota selectively.
Collapse
Affiliation(s)
- Yi Shen
- State Key Laboratory of Rice Biology, Key Laboratory of the Ministry of Agriculture for Nuclear-Agricultural Sciences, Zhejiang University, Hangzhou, 310058, PR China
- Food Quality and Design Group, Wageningen University & Research, P. O. Box 17, 6700 AA, Wageningen, The Netherlands
| | - Zengxu An
- State Key Laboratory of Rice Biology, Key Laboratory of the Ministry of Agriculture for Nuclear-Agricultural Sciences, Zhejiang University, Hangzhou, 310058, PR China
| | - Zongyao Huyan
- Food Quality and Design Group, Wageningen University & Research, P. O. Box 17, 6700 AA, Wageningen, The Netherlands
| | - Xiaoli Shu
- State Key Laboratory of Rice Biology, Key Laboratory of the Ministry of Agriculture for Nuclear-Agricultural Sciences, Zhejiang University, Hangzhou, 310058, PR China
- Hainan Institute of Zhejiang University, Yazhou Bay Science and Technology City, Yazhou District, Sanya, 572025, PR China
| | - Dianxing Wu
- State Key Laboratory of Rice Biology, Key Laboratory of the Ministry of Agriculture for Nuclear-Agricultural Sciences, Zhejiang University, Hangzhou, 310058, PR China
- Hainan Institute of Zhejiang University, Yazhou Bay Science and Technology City, Yazhou District, Sanya, 572025, PR China
| | - Ning Zhang
- State Key Laboratory of Rice Biology, Key Laboratory of the Ministry of Agriculture for Nuclear-Agricultural Sciences, Zhejiang University, Hangzhou, 310058, PR China
| | - Nicoletta Pellegrini
- Food Quality and Design Group, Wageningen University & Research, P. O. Box 17, 6700 AA, Wageningen, The Netherlands
- Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, via Sondrio 2/A, Udine, 33100, Italy
| | - Josep Rubert
- Food Quality and Design Group, Wageningen University & Research, P. O. Box 17, 6700 AA, Wageningen, The Netherlands.
| |
Collapse
|
11
|
Gautam A, Bhowmik D, Basu S, Zeng W, Lahiri A, Huson DH, Paul S. Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification. Brief Bioinform 2023; 24:bbad325. [PMID: 37771003 DOI: 10.1093/bib/bbad325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/12/2023] [Indexed: 09/30/2023] Open
Abstract
A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.
Collapse
Affiliation(s)
- Anupam Gautam
- Algorithms in Bioinformatics, Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- International Max Planck Research School "From Molecules to Organisms", Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Cluster of Excellence: EXC 2124: Controlling Microbes to Fight Infection, Tübingen, Germany
| | - Debaleena Bhowmik
- Cell Biology and Physiology Division, CSIR-Indian Institute of Chemical Biology, Kolkata, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Sayantani Basu
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Wenhuan Zeng
- Algorithms in Bioinformatics, Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Cluster of Excellence: EXC 2064: Machine Learning: New Perspectives for Science, University of Tübingen, Tübingen, Germany
| | - Abhishake Lahiri
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
- Infectious Diseases and Immunology Division, CSIR-Indian Institute of Chemical Biology, Kolkata, India
- Centre for Health Science and Technology, JIS Institute of Advanced Studies and Research Kolkata, JIS University, West Bengal, India
| | - Daniel H Huson
- Algorithms in Bioinformatics, Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- International Max Planck Research School "From Molecules to Organisms", Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Cluster of Excellence: EXC 2124: Controlling Microbes to Fight Infection, Tübingen, Germany
| | - Sandip Paul
- Centre for Health Science and Technology, JIS Institute of Advanced Studies and Research Kolkata, JIS University, West Bengal, India
| |
Collapse
|
12
|
Meydan Y, Baldini F, Korem T. pymgpipe: microbiome metabolic modeling in Python. J Open Source Softw 2023; 8:5545. [PMID: 37885608 PMCID: PMC10600976 DOI: 10.21105/joss.05545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Affiliation(s)
- Yoli Meydan
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Federico Baldini
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, United States of America
| | - Tal Korem
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, United States of America
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, United States of America
| |
Collapse
|
13
|
Bartmanski BJ, Rocha M, Zimmermann-Kogadeeva M. Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism. Curr Opin Chem Biol 2023; 75:102324. [PMID: 37207402 PMCID: PMC10410306 DOI: 10.1016/j.cbpa.2023.102324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.
Collapse
Affiliation(s)
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Campus of Gualtar, Braga, Portugal
| | | |
Collapse
|
14
|
Jacobs JP, Lagishetty V, Hauer MC, Labus JS, Dong TS, Toma R, Vuyisich M, Naliboff BD, Lackner JM, Gupta A, Tillisch K, Mayer EA. Multi-omics profiles of the intestinal microbiome in irritable bowel syndrome and its bowel habit subtypes. Microbiome 2023; 11:5. [PMID: 36624530 PMCID: PMC9830758 DOI: 10.1186/s40168-022-01450-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that is thought to involve alterations in the gut microbiome, but robust microbial signatures have been challenging to identify. As prior studies have primarily focused on composition, we hypothesized that multi-omics assessment of microbial function incorporating both metatranscriptomics and metabolomics would further delineate microbial profiles of IBS and its subtypes. METHODS Fecal samples were collected from a racially/ethnically diverse cohort of 495 subjects, including 318 IBS patients and 177 healthy controls, for analysis by 16S rRNA gene sequencing (n = 486), metatranscriptomics (n = 327), and untargeted metabolomics (n = 368). Differentially abundant microbes, predicted genes, transcripts, and metabolites in IBS were identified by multivariate models incorporating age, sex, race/ethnicity, BMI, diet, and HAD-Anxiety. Inter-omic functional relationships were assessed by transcript/gene ratios and microbial metabolic modeling. Differential features were used to construct random forests classifiers. RESULTS IBS was associated with global alterations in microbiome composition by 16S rRNA sequencing and metatranscriptomics, and in microbiome function by predicted metagenomics, metatranscriptomics, and metabolomics. After adjusting for age, sex, race/ethnicity, BMI, diet, and anxiety, IBS was associated with differential abundance of bacterial taxa such as Bacteroides dorei; metabolites including increased tyramine and decreased gentisate and hydrocinnamate; and transcripts related to fructooligosaccharide and polyol utilization. IBS further showed transcriptional upregulation of enzymes involved in fructose and glucan metabolism as well as the succinate pathway of carbohydrate fermentation. A multi-omics classifier for IBS had significantly higher accuracy (AUC 0.82) than classifiers using individual datasets. Diarrhea-predominant IBS (IBS-D) demonstrated shifts in the metatranscriptome and metabolome including increased bile acids, polyamines, succinate pathway intermediates (malate, fumarate), and transcripts involved in fructose, mannose, and polyol metabolism compared to constipation-predominant IBS (IBS-C). A classifier incorporating metabolites and gene-normalized transcripts differentiated IBS-D from IBS-C with high accuracy (AUC 0.86). CONCLUSIONS IBS is characterized by a multi-omics microbial signature indicating increased capacity to utilize fermentable carbohydrates-consistent with the clinical benefit of diets restricting this energy source-that also includes multiple previously unrecognized metabolites and metabolic pathways. These findings support the need for integrative assessment of microbial function to investigate the microbiome in IBS and identify novel microbiome-related therapeutic targets. Video Abstract.
Collapse
Affiliation(s)
- Jonathan P Jacobs
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Division of Gastroenterology, Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
| | - Venu Lagishetty
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Megan C Hauer
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Jennifer S Labus
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tien S Dong
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Division of Gastroenterology, Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Ryan Toma
- Viome Life Sciences, Bellevue, WA, USA
| | | | - Bruce D Naliboff
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Jeffrey M Lackner
- Division of Behavioral Medicine, Department of Medicine, Jacobs School of Medicine, University at Buffalo, SUNY, Buffalo, NY, USA
| | - Arpana Gupta
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kirsten Tillisch
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Integrative Medicine, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Emeran A Mayer
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- G. Oppenheimer Center for Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| |
Collapse
|
15
|
Agamah FE, Bayjanov JR, Niehues A, Njoku KF, Skelton M, Mazandu GK, Ederveen THA, Mulder N, Chimusa ER, 't Hoen PAC. Computational approaches for network-based integrative multi-omics analysis. Front Mol Biosci 2022; 9:967205. [PMID: 36452456 PMCID: PMC9703081 DOI: 10.3389/fmolb.2022.967205] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 10/20/2022] [Indexed: 08/27/2023] Open
Abstract
Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.
Collapse
Affiliation(s)
- Francis E. Agamah
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jumamurat R. Bayjanov
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Anna Niehues
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Kelechi F. Njoku
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Michelle Skelton
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Gaston K. Mazandu
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- African Institute for Mathematical Sciences, Cape Town, South Africa
| | - Thomas H. A. Ederveen
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Emile R. Chimusa
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle, United Kingdom
| | - Peter A. C. 't Hoen
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| |
Collapse
|
16
|
Fortuin S, Soares NC. The Integration of Proteomics and Metabolomics Data Paving the Way for a Better Understanding of the Mechanisms Underlying Microbial Acquired Drug Resistance. Front Med (Lausanne) 2022; 9:849838. [PMID: 35602483 PMCID: PMC9120609 DOI: 10.3389/fmed.2022.849838] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
Due to an increase in the overuse of antimicrobials and accelerated incidence of drug resistant pathogens, antimicrobial resistance has become a global health threat. In particular, bacterial antimicrobial resistance, in both hospital and community acquired transmission, have been found to be the leading cause of death due to infectious diseases. Understanding the mechanisms of bacterial drug resistance is of clinical significance irrespective of hospital or community acquired since it plays an important role in the treatment strategy and controlling infectious diseases. Here we highlight the advances in mass spectrometry-based proteomics impact in bacterial proteomics and metabolomics analysis- focus on bacterial drug resistance. Advances in omics technologies over the last few decades now allows multi-omics studies in order to obtain a comprehensive understanding of the biochemical alterations of pathogenic bacteria in the context of antibiotic exposure, identify novel biomarkers to develop new drug targets, develop time-effectively screen for drug susceptibility or resistance using proteomics and metabolomics.
Collapse
Affiliation(s)
- Suereta Fortuin
- African Microbiome Institute, General Internal Medicine, Stellenbosch University, Cape Town, South Africa
- *Correspondence: Suereta Fortuin
| | - Nelson C. Soares
- College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
- Nelson C. Soares
| |
Collapse
|
17
|
Beale DJ, Jones OAH, Bose U, Broadbent JA, Walsh TK, van de Kamp J, Bissett A. Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience. Emerg Top Life Sci 2022:ETLS20210261. [PMID: 35403668 DOI: 10.1042/ETLS20210261] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/17/2022] [Accepted: 03/22/2022] [Indexed: 12/15/2022]
Abstract
Current environmental monitoring efforts often focus on known, regulated contaminants ignoring the potential effects of unmeasured compounds and/or environmental factors. These specific, targeted approaches lack broader environmental information and understanding, hindering effective environmental management and policy. Switching to comprehensive, untargeted monitoring of contaminants, organism health, and environmental factors, such as nutrients, temperature, and pH, would provide more effective monitoring with a likely concomitant increase in environmental health. However, even this method would not capture subtle biochemical changes in organisms induced by chronic toxicant exposure. Ecosurveillance is the systematic collection, analysis, and interpretation of ecosystem health-related data that can address this knowledge gap and provide much-needed additional lines of evidence to environmental monitoring programs. Its use would therefore be of great benefit to environmental management and assessment. Unfortunately, the science of ‘ecosurveillance’, especially omics-based ecosurveillance is not well known. Here, we give an overview of this emerging area and show how it has been beneficially applied in a range of systems. We anticipate this review to be a starting point for further efforts to improve environmental monitoring via the integration of comprehensive chemical assessments and molecular biology-based approaches. Bringing multiple levels of omics technology-based assessment together into a systems-wide ecosurveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.
Collapse
|