Kaiwei Chen, Ling Wei, Shengnan Yu, Ningning He and Fengjuan Zhang
Non-alcoholic fatty liver disease (NAFLD) is a chronic hepatic disease. The incidence and prevalence of NAFLD have increased greatly in recent years, and there is still a lack of effective drugs. Autophagy plays an important role in promoting liver metabolism and maintaining liver homeostasis, and defects in autophagy levels are considered to be related to the development of NAFLD. However, the molecular mechanisms of autophagy in NAFLD still remain unknown. In this study, we identified 6 autophagy-associated hub genes using gene expression profiles obtained from the GSE48452 and GSE89632 datasets. Biomarkers were screened according to gene significance (GS) and module membership (MM) using weighted gene co-expression network analysis (WGCNA), and the immune infiltration landscape of the liver in NAFLD patients was explored using the CIBERSORT algorithm. Subsequently, we analyzed the relationship between liver non-parenchymal cells and autophagy-related hub genes using scRNA-seq data (GSE129516). Finally, we separated the NAFLD patients into two groups based on 6 hub genes by consensus clustering and screened 10 potential autophagy-related small molecules based on the cMAP database.
{"title":"Identification of autophagy-related signatures in nonalcoholic fatty liver disease and correlation with non-parenchymal cells of the liver†","authors":"Kaiwei Chen, Ling Wei, Shengnan Yu, Ningning He and Fengjuan Zhang","doi":"10.1039/D4MO00060A","DOIUrl":"10.1039/D4MO00060A","url":null,"abstract":"<p >Non-alcoholic fatty liver disease (NAFLD) is a chronic hepatic disease. The incidence and prevalence of NAFLD have increased greatly in recent years, and there is still a lack of effective drugs. Autophagy plays an important role in promoting liver metabolism and maintaining liver homeostasis, and defects in autophagy levels are considered to be related to the development of NAFLD. However, the molecular mechanisms of autophagy in NAFLD still remain unknown. In this study, we identified 6 autophagy-associated hub genes using gene expression profiles obtained from the GSE48452 and GSE89632 datasets. Biomarkers were screened according to gene significance (GS) and module membership (MM) using weighted gene co-expression network analysis (WGCNA), and the immune infiltration landscape of the liver in NAFLD patients was explored using the CIBERSORT algorithm. Subsequently, we analyzed the relationship between liver non-parenchymal cells and autophagy-related hub genes using scRNA-seq data (GSE129516). Finally, we separated the NAFLD patients into two groups based on 6 hub genes by consensus clustering and screened 10 potential autophagy-related small molecules based on the cMAP database.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 7","pages":" 469-482"},"PeriodicalIF":3.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141563889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carl M. Kobel, Jenny Merkesvik, Idun Maria Tokvam Burgos, Wanxin Lai, Ove Øyås, Phillip B. Pope, Torgeir R. Hvidsten and Velma T. E. Aho
Holo-omics is the use of omics data to study a host and its inherent microbiomes – a biological system known as a “holobiont”. A microbiome that exists in such a space often encounters habitat stability and in return provides metabolic capacities that can benefit their host. Here we present an overview of beneficial host–microbiome systems and propose and discuss several methodological frameworks that can be used to investigate the intricacies of the many as yet undefined host–microbiome interactions that influence holobiont homeostasis. While this is an emerging field, we anticipate that ongoing methodological advancements will enhance the biological resolution that is necessary to improve our understanding of host–microbiome interplay to make meaningful interpretations and biotechnological applications.
{"title":"Integrating host and microbiome biology using holo-omics","authors":"Carl M. Kobel, Jenny Merkesvik, Idun Maria Tokvam Burgos, Wanxin Lai, Ove Øyås, Phillip B. Pope, Torgeir R. Hvidsten and Velma T. E. Aho","doi":"10.1039/D4MO00017J","DOIUrl":"10.1039/D4MO00017J","url":null,"abstract":"<p >Holo-omics is the use of omics data to study a host and its inherent microbiomes – a biological system known as a “holobiont”. A microbiome that exists in such a space often encounters habitat stability and in return provides metabolic capacities that can benefit their host. Here we present an overview of beneficial host–microbiome systems and propose and discuss several methodological frameworks that can be used to investigate the intricacies of the many as yet undefined host–microbiome interactions that influence holobiont homeostasis. While this is an emerging field, we anticipate that ongoing methodological advancements will enhance the biological resolution that is necessary to improve our understanding of host–microbiome interplay to make meaningful interpretations and biotechnological applications.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 7","pages":" 438-452"},"PeriodicalIF":3.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/mo/d4mo00017j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141498537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liuyan Li, Shuqin Ding, Weibiao Wang, Lingling Yang, Gidion Wilson, Yuping Sa, Yue Zhang, Jianyu Chen and Xueqin Ma
Ankylosing spondylitis (AS) is a chronic systemic inflammatory disease that significantly impairs physical function in young individuals. However, the identification of radiographic changes in AS is frequently delayed, and the diagnostic efficacy of biomarkers like HLA-B27 remains moderately effective, with unsatisfactory sensitivity and specificity. In contrast to existing literature, our current experiment utilized a larger sample size and employed both untargeted and targeted UHPLC-QTOF-MS/MS based metabolomics to identify the metabolite profile and potential biomarkers of AS. The results indicated a notable divergence between the two groups, and a total of 170 different metabolites were identified, which were associated with the 6 primary metabolic pathways exhibiting a correlation with AS. Among these, 26 metabolites exhibited high sensitivity and specificity with area under curve (AUC) values greater than 0.8. Subsequent targeted quantitative analysis discovered 3 metabolites, namely 3-amino-2-piperidone, hypoxanthine and octadecylamine, exhibiting excellent distinguishing ability based on the results of the ROC curve and the Random Forest model, thus qualifying as potential biomarkers for AS. Summarily, our untargeted and targeted metabolomics investigation offers novel and precise insights into potential biomarkers for AS, potentially enhancing diagnostic capabilities and furthering the comprehension of the condition's pathophysiology.
{"title":"Serum metabolomics reveals the metabolic profile and potential biomarkers of ankylosing spondylitis†","authors":"Liuyan Li, Shuqin Ding, Weibiao Wang, Lingling Yang, Gidion Wilson, Yuping Sa, Yue Zhang, Jianyu Chen and Xueqin Ma","doi":"10.1039/D4MO00076E","DOIUrl":"10.1039/D4MO00076E","url":null,"abstract":"<p >Ankylosing spondylitis (AS) is a chronic systemic inflammatory disease that significantly impairs physical function in young individuals. However, the identification of radiographic changes in AS is frequently delayed, and the diagnostic efficacy of biomarkers like HLA-B27 remains moderately effective, with unsatisfactory sensitivity and specificity. In contrast to existing literature, our current experiment utilized a larger sample size and employed both untargeted and targeted UHPLC-QTOF-MS/MS based metabolomics to identify the metabolite profile and potential biomarkers of AS. The results indicated a notable divergence between the two groups, and a total of 170 different metabolites were identified, which were associated with the 6 primary metabolic pathways exhibiting a correlation with AS. Among these, 26 metabolites exhibited high sensitivity and specificity with area under curve (AUC) values greater than 0.8. Subsequent targeted quantitative analysis discovered 3 metabolites, namely 3-amino-2-piperidone, hypoxanthine and octadecylamine, exhibiting excellent distinguishing ability based on the results of the ROC curve and the Random Forest model, thus qualifying as potential biomarkers for AS. Summarily, our untargeted and targeted metabolomics investigation offers novel and precise insights into potential biomarkers for AS, potentially enhancing diagnostic capabilities and furthering the comprehension of the condition's pathophysiology.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 8","pages":" 505-516"},"PeriodicalIF":3.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhi Xu, Rui Miao, Tao Han, Yafeng Liu, Jiawei Zhou, Jianqiang Guo, Yingru Xing, Ying Bai, Jing Wu and Dong Hu
Objective: this study evaluates the prognostic relevance of gene subtypes and the role of kinesin family member 2C (KIF2C) in lung cancer progression. Methods: high-expression genes linked to overall survival (OS) and progression-free interval (PFI) were selected from the TCGA-LUAD dataset. Consensus clustering analysis categorized lung adenocarcinoma (LUAD) patients into two subtypes, C1 and C2, which were compared using clinical, drug sensitivity, and immunotherapy analyses. A random forest algorithm pinpointed KIF2C as a prognostic hub gene, and its functional impact was assessed through various assays and in vivo experiments. Results: The study identified 163 key genes and distinguished two LUAD subtypes with differing OS, PFI, pathological stages, drug sensitivity, and immunotherapy response. KIF2C, highly expressed in the C2 subtype, was associated with poor prognosis, promoting cancer cell proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT), with knockdown reducing tumor growth in mice. Conclusion: The research delineates distinct LUAD subtypes with significant clinical implications and highlights KIF2C as a potential therapeutic target for personalized treatment in LUAD.
{"title":"KIF2C as a potential therapeutic target: insights from lung adenocarcinoma subtype classification and functional experiments†","authors":"Zhi Xu, Rui Miao, Tao Han, Yafeng Liu, Jiawei Zhou, Jianqiang Guo, Yingru Xing, Ying Bai, Jing Wu and Dong Hu","doi":"10.1039/D4MO00044G","DOIUrl":"10.1039/D4MO00044G","url":null,"abstract":"<p > <em>Objective</em>: this study evaluates the prognostic relevance of gene subtypes and the role of kinesin family member 2C (KIF2C) in lung cancer progression. <em>Methods</em>: high-expression genes linked to overall survival (OS) and progression-free interval (PFI) were selected from the TCGA-LUAD dataset. Consensus clustering analysis categorized lung adenocarcinoma (LUAD) patients into two subtypes, C1 and C2, which were compared using clinical, drug sensitivity, and immunotherapy analyses. A random forest algorithm pinpointed KIF2C as a prognostic hub gene, and its functional impact was assessed through various assays and <em>in vivo</em> experiments. <em>Results</em>: The study identified 163 key genes and distinguished two LUAD subtypes with differing OS, PFI, pathological stages, drug sensitivity, and immunotherapy response. KIF2C, highly expressed in the C2 subtype, was associated with poor prognosis, promoting cancer cell proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT), with knockdown reducing tumor growth in mice. <em>Conclusion</em>: The research delineates distinct LUAD subtypes with significant clinical implications and highlights KIF2C as a potential therapeutic target for personalized treatment in LUAD.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 6","pages":" 417-429"},"PeriodicalIF":3.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141469678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Megan Uttley, Grace Horne, Areti Tsigkinopoulou, Francesco Del Carratore, Aliah Hawari, Magdalena Kiezel-Tsugunova, Alexandra C. Kendall, Janette Jones, David Messenger, Ranjit Kaur Bhogal, Rainer Breitling and Anna Nicolaou
Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive in silico models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of in silico and in vitro methods can facilitate a greater understanding of complex biological networks such as the AA cascade.
二十酸是一系列生物活性脂质,包括无处不在的脂肪酸花生四烯酸(AA)的衍生物。二十酸类在炎症中的密切参与促使人们开发出预测性的硅学模型,用于系统级的疾病机制探索、药物开发和动物模型替代。利用集合建模策略,我们开发了 AA 级联的计算模型。这种方法克服了固定参数建模的局限性,使可信的、热力学上可行的预测可视化。我们开发了一种质量评分方法,用于量化相对于实验数据的集合预测的准确性,测量过程的整体不确定性。蒙特卡洛集合建模用于量化预测置信度。使用质谱介质脂质组学测量 HaCaT 表皮角质细胞和 46BR.1N 真皮成纤维细胞在刺激(钙离子诱导剂(A23187)、紫外线辐射、三磷酸腺苷)和环氧化酶抑制剂(吲哚美辛)作用下产生的二十烷酸,证明了模型的适用性。实验结果和预测结果在质量上非常吻合,这表明该模型能够适应在 AA 释放和酶浓度分布方面存在差异的细胞类型。通过扩大网络拓扑结构以包括更多的反应,实验结果和预测结果之间的定量一致性可以得到改善。总之,我们的方法生成了一个适应性强、可调整的 AA 级联集合模型,该模型可量身定制以代表不同的细胞类型,并证明了硅学和体外方法的整合有助于加深对 AA 级联等复杂生物网络的理解。
{"title":"An adaptable in silico ensemble model of the arachidonic acid cascade†","authors":"Megan Uttley, Grace Horne, Areti Tsigkinopoulou, Francesco Del Carratore, Aliah Hawari, Magdalena Kiezel-Tsugunova, Alexandra C. Kendall, Janette Jones, David Messenger, Ranjit Kaur Bhogal, Rainer Breitling and Anna Nicolaou","doi":"10.1039/D3MO00187C","DOIUrl":"10.1039/D3MO00187C","url":null,"abstract":"<p >Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive <em>in silico</em> models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of <em>in silico</em> and <em>in vitro</em> methods can facilitate a greater understanding of complex biological networks such as the AA cascade.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 7","pages":" 453-468"},"PeriodicalIF":3.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/mo/d3mo00187c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.
{"title":"Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach","authors":"Priyanka Choudhury, Sanjukta Dasgupta, Parthasarathi Bhattacharyya, Sushmita Roychowdhury and Koel Chaudhury","doi":"10.1039/D3MO00266G","DOIUrl":"10.1039/D3MO00266G","url":null,"abstract":"<p >Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 6","pages":" 366-389"},"PeriodicalIF":3.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141148744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ünzile Güven Gülhan, Emrah Nikerel, Tunahan Çakır, Fatih Erdoğan Sevilgen and Saliha Durmuş
Enterotypes have been shown to be an important factor for population stratification based on gut microbiota composition, leading to a better understanding of human health and disease states. Classifications based on compositional patterns will have implications for personalized microbiota-based solutions. There have been limited enterotype based studies on colorectal adenoma and cancer. Here, an enterotype-based meta-analysis of fecal shotgun metagenomic studies was performed, including 1579 samples of healthy controls (CTR), colorectal adenoma (ADN) and colorectal cancer (CRC) in total. Gut microbiota of healthy people were clustered into three enterotypes (Ruminococcus-, Bacteroides- and Prevotella-dominated enterotypes). Reference-based enterotype assignments were performed for CRC and ADN samples, using the supervised machine learning algorithm, K-nearest neighbors. Differential abundance analyses and random forest classification were conducted on each enterotype between healthy controls and CRC–ADN groups, revealing novel enterotype-specific microbial markers for non-invasive CRC screening strategies. Furthermore, we identified microbial species unique to each enterotype that play a role in the production of secondary bile acids and short-chain fatty acids, unveiling the correlation between cancer-associated gut microbes and dietary patterns. The enterotype-based approach in this study is promising in elucidating the mechanisms of differential gut microbiome profiles, thereby improving the efficacy of personalized microbiota-based solutions.
{"title":"Species-level identification of enterotype-specific microbial markers for colorectal cancer and adenoma†","authors":"Ünzile Güven Gülhan, Emrah Nikerel, Tunahan Çakır, Fatih Erdoğan Sevilgen and Saliha Durmuş","doi":"10.1039/D4MO00016A","DOIUrl":"10.1039/D4MO00016A","url":null,"abstract":"<p >Enterotypes have been shown to be an important factor for population stratification based on gut microbiota composition, leading to a better understanding of human health and disease states. Classifications based on compositional patterns will have implications for personalized microbiota-based solutions. There have been limited enterotype based studies on colorectal adenoma and cancer. Here, an enterotype-based meta-analysis of fecal shotgun metagenomic studies was performed, including 1579 samples of healthy controls (CTR), colorectal adenoma (ADN) and colorectal cancer (CRC) in total. Gut microbiota of healthy people were clustered into three enterotypes (<em>Ruminococcus</em>-, <em>Bacteroides</em>- and <em>Prevotella</em>-dominated enterotypes). Reference-based enterotype assignments were performed for CRC and ADN samples, using the supervised machine learning algorithm, K-nearest neighbors. Differential abundance analyses and random forest classification were conducted on each enterotype between healthy controls and CRC–ADN groups, revealing novel enterotype-specific microbial markers for non-invasive CRC screening strategies. Furthermore, we identified microbial species unique to each enterotype that play a role in the production of secondary bile acids and short-chain fatty acids, unveiling the correlation between cancer-associated gut microbes and dietary patterns. The enterotype-based approach in this study is promising in elucidating the mechanisms of differential gut microbiome profiles, thereby improving the efficacy of personalized microbiota-based solutions.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 6","pages":" 397-416"},"PeriodicalIF":3.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenrui Ji, Xiaomin Xie, Guirong Bai, Yanting He, Ling Li, Li Zhang and Dan Qiang
Many individuals with pre-diabetes eventually develop diabetes. Therefore, profiling of prediabetic metabolic disorders may be an effective targeted preventive measure. We aimed to elucidate the metabolic mechanism of progression of pre-diabetes to type 2 diabetes mellitus (T2DM) from a metabolic perspective. Four sets of plasma samples (20 subjects per group) collected according to fasting blood glucose (FBG) concentration were subjected to metabolomic analysis. An integrative approach of metabolome and WGCNA was employed to explore candidate metabolites. Compared with the healthy group (FBG < 5.6 mmol L−1), 113 metabolites were differentially expressed in the early stage of pre-diabetes (5.6 mmol L−1 ⩽ FBG < 6.1 mmol L−1), 237 in the late stage of pre-diabetes (6.1 mmol L−1 ⩽ FBG < 7.0 mmol L−1), and 245 in the T2DM group (FBG 7.0 mmol L−1). A total of 27 differentially expressed metabolites (DEMs) were shared in all comparisons. Among them, L-norleucine was downregulated, whereas ethionamide, oxidized glutathione, 5-methylcytosine, and alpha-D-glucopyranoside beta-D-fructofuranosyl were increased with the rising levels of FBG. Surprisingly, 15 (11 lyso-phosphatidylcholines, L-norleucine, oxidized glutathione, arachidonic acid, and 5-oxoproline) of the 27 DEMs were ferroptosis-associated metabolites. WGCNA clustered all metabolites into 8 modules and the pathway enrichment analysis of DEMs showed a significant annotation to the insulin resistance-related pathway. Integrated analysis of DEMs, ROC and WGCNA modules determined 12 potential biomarkers for pre-diabetes and T2DM, including L-norleucine, 8 of which were L-arginine or its metabolites. L-Norleucine and L-arginine could serve as biomarkers for pre-diabetes. The inventory of metabolites provided by our plasma metabolome offers insights into T2DM physiology metabolism.
{"title":"Metabolomic approaches to dissect dysregulated metabolism in the progression of pre-diabetes to T2DM†","authors":"Wenrui Ji, Xiaomin Xie, Guirong Bai, Yanting He, Ling Li, Li Zhang and Dan Qiang","doi":"10.1039/D3MO00130J","DOIUrl":"10.1039/D3MO00130J","url":null,"abstract":"<p >Many individuals with pre-diabetes eventually develop diabetes. Therefore, profiling of prediabetic metabolic disorders may be an effective targeted preventive measure. We aimed to elucidate the metabolic mechanism of progression of pre-diabetes to type 2 diabetes mellitus (T2DM) from a metabolic perspective. Four sets of plasma samples (20 subjects per group) collected according to fasting blood glucose (FBG) concentration were subjected to metabolomic analysis. An integrative approach of metabolome and WGCNA was employed to explore candidate metabolites. Compared with the healthy group (FBG < 5.6 mmol L<small><sup>−1</sup></small>), 113 metabolites were differentially expressed in the early stage of pre-diabetes (5.6 mmol L<small><sup>−1</sup></small> ⩽ FBG < 6.1 mmol L<small><sup>−1</sup></small>), 237 in the late stage of pre-diabetes (6.1 mmol L<small><sup>−1</sup></small> ⩽ FBG < 7.0 mmol L<small><sup>−1</sup></small>), and 245 in the T2DM group (FBG <img> 7.0 mmol L<small><sup>−1</sup></small>). A total of 27 differentially expressed metabolites (DEMs) were shared in all comparisons. Among them, <small>L</small>-norleucine was downregulated, whereas ethionamide, oxidized glutathione, 5-methylcytosine, and alpha-<small>D</small>-glucopyranoside beta-<small>D</small>-fructofuranosyl were increased with the rising levels of FBG. Surprisingly, 15 (11 lyso-phosphatidylcholines, <small>L</small>-norleucine, oxidized glutathione, arachidonic acid, and 5-oxoproline) of the 27 DEMs were ferroptosis-associated metabolites. WGCNA clustered all metabolites into 8 modules and the pathway enrichment analysis of DEMs showed a significant annotation to the insulin resistance-related pathway. Integrated analysis of DEMs, ROC and WGCNA modules determined 12 potential biomarkers for pre-diabetes and T2DM, including <small>L</small>-norleucine, 8 of which were <small>L</small>-arginine or its metabolites. <small>L</small>-Norleucine and <small>L</small>-arginine could serve as biomarkers for pre-diabetes. The inventory of metabolites provided by our plasma metabolome offers insights into T2DM physiology metabolism.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 5","pages":" 333-347"},"PeriodicalIF":2.9,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/mo/d3mo00130j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140829963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weibing Pan‡, Tianwei Yun, Xin Ouyang, Zhijun Ruan, Tuanjie Zhang, Yuhao An, Rui Wang and Peng Zhu
Kidney stone disease (KSD, also named renal calculi, nephrolithiasis, or urolithiasis) is a common urological disease entailing the formation of minerals and salts that form inside the urinary tract, frequently caused by diabetes, high blood pressure, hypertension, and monogenetic components in most patients. 10% of adults worldwide are affected by KSD, which continues to be highly prevalent and with increasing incidence. For the identification of novel therapeutic targets in KSD, we adopted high-throughput sequencing and mass spectrometry (MS) techniques in this study and carried out an integrative analysis of exosome proteomic data and DNA methylation data from blood samples of normal and KSD individuals. Our research delineated the profiling of exosomal proteins and DNA methylation in both healthy individuals and those afflicted with KSD, finding that the overexpressed proteins and the demethylated genes in KSD samples are associated with immune responses. The consistency of the results in proteomics and epigenetics supports the feasibility of the comprehensive strategy. Our insights into the molecular landscape of KSD pave the way for a deeper understanding of its pathogenic mechanism, providing an opportunity for more precise diagnosis and targeted treatment strategies for KSD.
{"title":"A blood-based multi-omic landscape for the molecular characterization of kidney stone disease†","authors":"Weibing Pan‡, Tianwei Yun, Xin Ouyang, Zhijun Ruan, Tuanjie Zhang, Yuhao An, Rui Wang and Peng Zhu","doi":"10.1039/D3MO00261F","DOIUrl":"10.1039/D3MO00261F","url":null,"abstract":"<p >Kidney stone disease (KSD, also named renal calculi, nephrolithiasis, or urolithiasis) is a common urological disease entailing the formation of minerals and salts that form inside the urinary tract, frequently caused by diabetes, high blood pressure, hypertension, and monogenetic components in most patients. 10% of adults worldwide are affected by KSD, which continues to be highly prevalent and with increasing incidence. For the identification of novel therapeutic targets in KSD, we adopted high-throughput sequencing and mass spectrometry (MS) techniques in this study and carried out an integrative analysis of exosome proteomic data and DNA methylation data from blood samples of normal and KSD individuals. Our research delineated the profiling of exosomal proteins and DNA methylation in both healthy individuals and those afflicted with KSD, finding that the overexpressed proteins and the demethylated genes in KSD samples are associated with immune responses. The consistency of the results in proteomics and epigenetics supports the feasibility of the comprehensive strategy. Our insights into the molecular landscape of KSD pave the way for a deeper understanding of its pathogenic mechanism, providing an opportunity for more precise diagnosis and targeted treatment strategies for KSD.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 5","pages":" 322-332"},"PeriodicalIF":2.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sreejata Dutta, Dinesh Pal Mudaranthakam, Yanming Li and Mihaela E. Sardiu
Omics data sets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these data sets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there has been limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach, we introduce PerSEveML, an interactive web-based tool that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.
由于 Omics 数据集具有维度高、规模大和非线性结构等特点,通常会给计算带来挑战。在出现罕见事件时,分析这些数据集变得尤为困难。机器学习(ML)方法在分析罕见事件方面已经获得了广泛的关注,但对整合 ML 技术以理解潜在生物学的生物信息学工具的探索仍然有限。在我们之前开发的综合机器学习方法计算框架的基础上,我们推出了基于网络的交互式工具 PerSEveML,该工具利用众包智能预测罕见事件并确定特征选择结构。PerSEveML 通过评估指标全面概述了综合方法,帮助用户了解单个 ML 方法对预测过程的贡献。此外,PerSEveML 还能计算熵和等级分数,直观地将输入特征组织成一个由选定、未选定和波动类别组成的持久结构,帮助研究人员发现有关潜在生物学的有意义的假设。我们已经在三个不同的复杂生物数据集上对 PerSEveML 进行了评估,这些数据集包含从小到大的极其罕见的事件,并证明了它生成有效假设的能力。PerSEveML 可在 https://biostats-shinyr.kumc.edu/PerSEveML/ 和 https://github.com/sreejatadutta/PerSEveML 上查阅。
{"title":"PerSEveML: a web-based tool to identify persistent biomarker structure for rare events using an integrative machine learning approach†","authors":"Sreejata Dutta, Dinesh Pal Mudaranthakam, Yanming Li and Mihaela E. Sardiu","doi":"10.1039/D4MO00008K","DOIUrl":"10.1039/D4MO00008K","url":null,"abstract":"<p >Omics data sets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these data sets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there has been limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach, we introduce PerSEveML, an interactive web-based tool that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 5","pages":" 348-358"},"PeriodicalIF":2.9,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/mo/d4mo00008k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}