Molecular omics最新文献

{"title":"Biomarkers of heart failure: advances in omics studies","authors":"Kuo Chi, Jing Liu, Xinghua Li, He Wang, Yanliang Li, Qingnan Liu, Yabin Zhou and Yuan Ge","doi":"10.1039/D3MO00173C","DOIUrl":"10.1039/D3MO00173C","url":null,"abstract":"<p >Heart failure is a complex syndrome characterized by progressive circulatory dysfunction, manifesting clinically as pulmonary and systemic venous congestion, alongside inadequate tissue perfusion. The early identification of HF, particularly at the mild and moderate stages (stages B and C), presents a clinical challenge due to the overlap of signs, symptoms, and natriuretic peptide levels with other cardiorespiratory pathologies. Nonetheless, early detection coupled with timely pharmacological intervention is imperative for enhancing patient outcomes. Advances in high-throughput omics technologies have enabled researchers to analyze patient-derived biofluids and tissues, discovering biomarkers that are sensitive and specific for HF diagnosis. Due to the diversity of HF etiology, it is insufficient to study the diagnostic data of early HF using a single omics technology. This study reviewed the latest progress in genomics, transcriptomics, proteomics, and metabolomics for the identification of HF biomarkers, offering novel insights into the early clinical diagnosis of HF. However, the validity of biomarkers depends on the disease status, intervention time, genetic diversity and comorbidities of the subjects. Moreover, biomarkers lack generalizability in different clinical settings. Hence, it is imperative to conduct multi-center, large-scale and standardized clinical trials to enhance the diagnostic accuracy and utility of HF biomarkers.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 3","pages":" 169-183"},"PeriodicalIF":2.9,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029742","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}

{"title":"Development and application of mass spectrometric molecular networking for analyzing the ingredients of areca nut†","authors":"Jialiang Zhao, Jiachen Shi, Xiaoying Chen, Yuanluo Lei, Tian Tian, Shuang Zhu, Chin-Ping Tan, Yuanfa Liu and Yong-Jiang Xu","doi":"10.1039/D3MO00232B","DOIUrl":"10.1039/D3MO00232B","url":null,"abstract":"<p >Areca nut (<em>Areca catechu</em> L.) is commonly consumed as a chewing food in the Asian region. However, the investigations into the components of areca nut are limited. In this study, we have developed an approach that combines mass spectrometry with feature-based molecular network to explore the chemical characteristics of the areca nut. In comparison to the conventional method, this technique demonstrates a superior capability in annotating unknown compounds present in areca nut. We annotated a total of 52 compounds, including one potential previously unreported alkaloid, one carbohydrate, and one phenol and confirmed the presence of 7 of them by comparing with commercial standards. The validated method was used to evaluate chemical features of areca nut at different growth stages, annotating 25 compounds as potential biomarkers for distinguishing areca nut growth stages. Therefore, this approach offers a rapid and accurate method for the component analysis of areca nut.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 3","pages":" 192-202"},"PeriodicalIF":2.9,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825442","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}

{"title":"The RNA cargo of Myxococcus outer membrane vesicles†","authors":"Martin T. Swain, Emily J. Radford, Allison S. Akanyeti, James H. Hallwood and David E. Whitworth","doi":"10.1039/D3MO00222E","DOIUrl":"10.1039/D3MO00222E","url":null,"abstract":"<p >The outer membrane vesicles (OMVs) secreted by some Gram-negative bacteria contain RNA cargo, which can be introduced into target cells, affecting their cellular processes. To test whether the antimicrobial OMVs secreted by predatory myxobacteria might contain cargo RNA with a role in prey killing, we purified OMVs and cells from four different strains of <em>Myxococcus</em> spp. for RNA-seq transcriptome sequencing. Myxobacterial OMVs contained distinct sets of RNA molecules. The abundance of major cellular transcripts correlated strongly with their abundance in OMVs, suggesting non-specific packaging into OMVs. However, many major cellular transcripts were absent entirely from OMVs and some transcripts were found exclusively in OMVs, suggesting OMV RNA cargo loading is not simply a consequence of sampling the cellular transcriptome. Despite considerable variation in OMV RNA cargo between biological replicates, a small number of transcripts were found consistently in replicate OMV preparations. These ‘core’ OMV transcripts were often found in the OMVs from multiple strains, and sometimes enriched relative to their abundance in cellular transcriptomes. In addition to providing the first transcriptomes for myxobacterial OMVs, and the first cellular transcriptomes for three strains of <em>Myxococcus</em> spp., we highlight five transcripts for further study. These transcripts are ‘core’ for at least two of the three strains of <em>M. xanthus</em> studied, and encode two alkyl hydroperoxidase proteins (AhpC and AhpD), two ribosome-associated inhibitors (RaiA-like) and a DO-family protease. It will be interesting to test whether the transcripts serve a biological function within OMVs, potentially being transported into prey cells for translation into toxic proteins.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 2","pages":" 138-145"},"PeriodicalIF":2.9,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/mo/d3mo00222e?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506937","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}

{"title":"Complementarity of two proteomic data analysis tools in the identification of drug-metabolising enzymes and transporters in human liver†","authors":"Areti-Maria Vasilogianni, Sarah Alrubia, Eman El-Khateeb, Zubida M. Al-Majdoub, Narciso Couto, Brahim Achour, Amin Rostami-Hodjegan and Jill Barber","doi":"10.1039/D3MO00144J","DOIUrl":"10.1039/D3MO00144J","url":null,"abstract":"<p >Several software packages are available for the analysis of proteomic LC-MS/MS data, including commercial (<em>e.g.</em> Mascot/Progenesis LC-MS) and open access software (<em>e.g.</em> MaxQuant). In this study, Progenesis and MaxQuant were used to analyse the same data set from human liver microsomes (<em>n</em> = 23). Comparison focussed on the total number of peptides and proteins identified by the two packages. For the peptides exclusively identified by each software package, distribution of peptide length, hydrophobicity, molecular weight, isoelectric point and score were compared. Using standard cut-off peptide scores, we found an average of only 65% overlap in detected peptides, with surprisingly little consistency in the characteristics of peptides exclusively detected by each package. Generally, MaxQuant detected more peptides than Progenesis, and the additional peptides were longer and had relatively lower scores. Progenesis-specific peptides tended to be more hydrophilic and basic relative to peptides detected only by MaxQuant. At the protein level, we focussed on drug-metabolising enzymes (DMEs) and transporters, by comparing the number of unique peptides detected by the two packages for these specific proteins of interest, and their abundance. The abundance of DMEs and SLC transporters showed good correlation between the two software tools, but ABC showed less consistency. In conclusion, in order to maximise the use of MS datasets, we recommend processing with more than one software package. Together, Progenesis and MaxQuant provided excellent coverage, with a core of common peptides identified in a very robust way.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 2","pages":" 115-127"},"PeriodicalIF":2.9,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/mo/d3mo00144j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135659648","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}

{"title":"Systematic characterization of m6A proteomics across 12 cancer types: a multi-omics integration study†","authors":"Hongru Li, Yunke Jiang, Jiajin Chen, Zaiming Li, Ruyang Zhang, Yongyue Wei, Yang Zhao, Sipeng Shen and Feng Chen","doi":"10.1039/D3MO00171G","DOIUrl":"10.1039/D3MO00171G","url":null,"abstract":"<p >The modification patterns of <em>N</em>6-methyladenosine (m6A) regulators and interacting genes are deeply involved in tumors. However, the effect of m6A modification patterns on human proteomics remains largely unknown. We evaluated the molecular characteristics and clinical relevance of m6A modification proteomics patterns among 1013 pan-cancer samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). More than half of the m6A proteins were expressed at higher levels in tumor tissues and presented oncogenic characteristics. Furthermore, we performed multi-omics analyses integrating with transcriptomics data of m6A regulators and interactive coding and non-coding RNAs and developed a m6A multi-omics signature to identify potential m6A modification target proteins across global proteomics. It was significantly associated with overall survival in nine cancer types, tumor mutation burden (<em>P</em> = 0.01), and immune checkpoints including <em>PD-L1</em> (<em>P</em> = 4.9 × 10<small>⁻⁸</small>) and <em>PD-1</em> (<em>P</em> &lt; 0.01). We identified 51 novel proteins associated with the multi-omics signature (<em>P</em><small>_FDR</small> &lt; 0.05). These proteins were functional through pathway enrichment analyses. The protein with the highest hit frequency was CHORDC1, which was significantly up-regulated in tumor tissues in nine cancer types. Its higher abundance was significantly associated with a poorer prognosis in seven cancer types. The identified m6A target proteins might provide infomation for the study of molecular mechanism of cancer.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 2","pages":" 103-114"},"PeriodicalIF":2.9,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71522064","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}

{"title":"Untargeted metabolomics analysis reveals spatial metabolic heterogeneity in different intestinal segments of type 1 diabetic mice†","authors":"Kaiyan Gong, Junli Chen, Xiaoli Yin, Mengjun Wu, Hong Zheng and Lingling Jiang","doi":"10.1039/D3MO00163F","DOIUrl":"10.1039/D3MO00163F","url":null,"abstract":"<p >Type 1 diabetes (T1D) has been reported to cause systematic metabolic disorders, but metabolic changes in different intestinal segments of T1D remain unclear. In this study, we analyzed metabolic profiles in the jejunum, ileum, cecum and colon of streptozocin-induced T1D and age-matched control (CON) mice by an LC-MS-based metabolomics method. The results show that segment-specific metabolic disorders occurred in the gut of T1D mice. In the jejunum, we found that T1D mainly led to disordered amino acid metabolism and most amino acids were significantly lower relative to CON mice. Moreover, fatty acid metabolism was disrupted mainly in the ileum, cecum and colon of T1D mice, such as arachidonic acid, alpha-linolenic acid and linoleic acid metabolism. Thus, our study reveals spatial metabolic heterogeneity in the gut of T1D mice and provides a metabolic view on diabetes-associated intestinal diseases.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 2","pages":" 128-137"},"PeriodicalIF":2.9,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135505601","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}

{"title":"Long-term physical inactivity induces significant changes in biochemical pathways related to metabolism of proteins and glycerophospholipids in mice†","authors":"Bijayashree Sahu, Sunil Pani, Gourabamani Swalsingh, Unmod Senapati, Punyadhara Pani, Benudhara Pati, Subhasmita Rout, Rimjhim Trivedi, Ritu Raj, Suchanda Dey, Amar Jeet, Dinesh Kumar and Naresh C. Bal","doi":"10.1039/D3MO00127J","DOIUrl":"10.1039/D3MO00127J","url":null,"abstract":"<p >Physical inactivity affects multiple organ systems, including the musculoskeletal system, which upsets the delicate balance of several secretory factors leading to metabolic derailment. This reduces contractile recruitment of the skeletal muscle with dampening of its oxidative capacity resulting in impaired intramuscular lipid metabolism and substrate utilization. We hypothesized that this altered phenotype would also have an indispensable effect on circulatory cytokines and the level of metabolic intermediates. In this study, comparison between sedentary (SED) and exercised (EXER) animal models showed that organismal metabolic parameters (body mass, oxygen utilization and glucose tolerance) are altered based on physical activity. Our data suggest that cytokines linked to glycemic excursions (insulin, c-peptide, glucagon) and their passive regulators (leptin, BDNF, active ghrelin, and GIP) exhibit changes in the SED group. Furthermore, some of the proinflammatory cytokines and myokines were upregulated in SED. Interestingly, serum metabolite analysis showed that the levels of glucogenic amino acids (alanine, glycine, tryptophan, proline and valine), nitrogenous amino acids (ornithine, asparagine, and glutamine) and myogenic metabolites (taurine, creatine) were altered due to the level of physical activity. A pyrimidine nucleoside (uridine), lipid metabolite (glycerol) and ketone bodies (acetoacetate and acetate) were found to be altered in SED. A Spearman rank correlation study between SED and CTRL showed that cytokines build a deformed network with metabolites in SED, indicating significant modifications in amino acids, phosphatidylinositol phosphate and glycerophospholipid metabolic pathways. Overall, long-term physical inactivity reorganizes the profile of proinflammatory cytokines, glucose sensing hormones, and protein and glycerophospholipid metabolism, which might be the initial factors of metabolic diseases due to SED.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 1","pages":" 64-77"},"PeriodicalIF":2.9,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71425267","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}

{"title":"Correction: Generation of β-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids","authors":"Lisa Morisseau, Fumiya Tokito, Stéphane Poulain, Valérie Plaisance, Valérie Pawlowski, Soo Hyeon Kim, Cécile Legallais, Rachid Jellali, Yasuyuki Sakai, Amar Abderrahmani and Eric Leclerc","doi":"10.1039/D3MO90033A","DOIUrl":"10.1039/D3MO90033A","url":null,"abstract":"<p >Correction for ‘Generation of β-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids’ by Lisa Morisseau <em>et al.</em>, <em>Mol. Omics</em>, 2023, https://doi.org/10.1039/d3mo00050h.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 10","pages":" 823-823"},"PeriodicalIF":2.9,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2023/mo/d3mo90033a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41165889","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}

{"title":"Cronobacter sakazakii infection implicates multifaceted neuro-immune regulatory pathways of Caenorhabditis elegans†","authors":"Lappasi Mohanram VenkataKrishna, Boopathi Balasubramaniam, T. J. Sushmitha, V. Ravichandiran and Krishnaswamy Balamurugan","doi":"10.1039/D3MO00167A","DOIUrl":"10.1039/D3MO00167A","url":null,"abstract":"<p >The neural pathways of <em>Caenorhabditis elegans</em> play a crucial role in regulating host immunity and inflammation during pathogenic infections. To understand the major neuro-immune signaling pathways, this study aimed to identify the key regulatory proteins in the host <em>C. elegans</em> during <em>C. sakazakii</em> infection. We used high-throughput label-free quantitative proteomics and identified 69 differentially expressed proteins. KEGG analysis revealed that <em>C. sakazakii</em> elicited host immune signaling cascades primarily including mTOR signaling, axon regeneration, metabolic pathways (<em>let-363</em> and <em>acox-1.4</em>), calcium signaling <em>(mlck-1)</em>, and longevity regulating pathways (<em>ddl-2</em>), respectively. The abrogation in functional loss of mTOR-associated players deciphered that <em>C. sakazakii</em> infection negatively regulated the lifespan of mutant worms (<em>akt-1</em>, <em>let-363</em> and <em>dlk-1</em>), including physiological aberrations, such as reduced pharyngeal pumping and egg production. Additionally, the candidate pathway proteins were validated by transcriptional profiling of their corresponding genes. Furthermore, immunoblotting showed the downregulation of mTORC2/SGK-1 during the later hours of pathogen exposure. Overall, our findings profoundly provide an understanding of the specificity of proteome imbalance in affecting neuro-immune regulations during <em>C. sakazakii</em> infection.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 1","pages":" 48-63"},"PeriodicalIF":2.9,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41206541","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}

{"title":"Generation of β-like cell subtypes from differentiated human induced pluripotent stem cells in 3D spheroids†","authors":"Lisa Morisseau, Fumiya Tokito, Stéphane Poulain, Valerie Plaisance, Valerie Pawlowski, Soo Hyeon Kim, Cécile Legallais, Rachid Jellali, Yasuyuki Sakai, Amar Abderrahmani and Eric Leclerc","doi":"10.1039/D3MO00050H","DOIUrl":"10.1039/D3MO00050H","url":null,"abstract":"<p >Since the identification of four different pancreatic β-cell subtypes and bi-hormomal cells playing a role in the diabetes pathogenesis, the search for <em>in vitro</em> models that mimics such cells heterogeneity became a key priority in experimental and clinical diabetology. We investigated the potential of human induced pluripotent stem cells to lead to the development of the different β-cells subtypes in honeycomb microwell-based 3D spheroids. The glucose-stimulated insulin secretion confirmed the spheroids functionality. Then, we performed a single cell RNA sequencing of the spheroids. Using a knowledge-based analysis with a stringency on the pancreatic markers, we extracted the β-cells INS+/UCN3+ subtype (11%; β1-like cells), the INS+/ST8SIA1+/CD9− subtype (3%, β3-like cells) and INS+/CD9+/ST8SIA1-subtype (1%; β2-like cells) consistently with literature findings. We did not detect the INS+/ST8SIA1+/CD9+ cells (β4-like cells). Then, we also identified four bi-hormonal cells subpopulations including δ-like cells (INS+/SST+, 6%), γ-like cells (INS+/PPY+, 3%), α-like-cells (INS+/GCG+, 6%) and ε-like-cells (INS+/GHRL+, 2%). Using data-driven clustering, we extracted four progenitors’ subpopulations (with the lower level of INS gene) that included one population highly expressing inhibin genes (INHBA+/INHBB+), one population highly expressing KCNJ3+/TPH1+, one population expressing hepatocyte-like lineage markers (HNF1A+/AFP+), and one population expressing stem-like cell pancreatic progenitor markers (SOX2+/NEUROG3+). Furthermore, among the cycling population we found a large number of REST+ cells and CD9+ cells (CD9+/SPARC+/REST+). Our data confirm that our differentiation leads to large β-cell heterogeneity, which can be used for investigating β-cells plasticity under physiological and pathophysiological conditions.</p>","PeriodicalId":19065,"journal":{"name":"Molecular omics","volume":" 10","pages":" 810-822"},"PeriodicalIF":2.9,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10211184","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}