Molecular omics最新文献

The concentration of many transcription factors exhibits high cell-to-cell variability due to differences in synthesis, degradation, and cell size. Whether the functions of these factors are robust to fluctuations in concentration, and how this may be achieved, is poorly understood. Across two independent panels of breast cancer cells, we show that the average whole cell concentration of YAP decreases as a function of cell area. However, the nuclear concentration distribution remains constant across cells grouped by size, across a 4–8 fold size range, implying unperturbed nuclear translocation despite the falling cell wide concentration. Both the whole cell and nuclear concentration was higher in cells with more DNA and CycA/PCNA expression suggesting periodic synthesis of YAP across the cell cycle offsets dilution due to cell growth and/or cell spreading. The cell area – YAP scaling relationship extended to melanoma and RPE cells. Integrative analysis of imaging and phospho-proteomic data showed the average nuclear YAP concentration across cell lines was predicted by differences in RAS/MAPK signalling, focal adhesion maturation, and nuclear transport processes. Validating the idea that RAS/MAPK and cell cycle regulate YAP translocation, chemical inhibition of MEK or CDK4/6 increased the average nuclear YAP concentration. Together, this study provides an example case, where cytoplasmic dilution of a protein, for example through cell growth, does not limit a cognate cellular function. Here, that same proteins translocation into the nucleus.

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) untargeted metabolomics has become the gold standard for the profiling of low-molecular-weight compounds. Recently, this discipline has raised great interest in forensic sciences, especially in the field of toxicology and for post-mortem interval estimation. The current study aims at evaluating three extraction protocols and two LC-MS/MS assays run in both positive and negative modes, to identify the most suitable method to conduct post-mortem metabolomic profiling of bone tissue. A fragment of the anterior tibia of a 82 years-old male sampled from a human taphonomy facility was powdered via freeze-milling. The powdered sub-samples were extracted in five replicates per protocol. Methods tested were (I) a biphasic chloroform–methanol–water protocol, (II) a single phase methanol–water protocol, and (III) a single phase methanol–acetonitrile–water protocol. LC-MS/MS analyses were carried out via high performance liquid chromatography, either on hydrophilic interaction (HILIC) or on reversed-phase (C18) columns in both positive and negative ionisation modes, coupled with a Q-TOF mass spectrometer. Results suggest that the highest consistency between replicates and quality control samples was obtained with the single phase extractions (i.e., methanol–acetonitrile–water), whilst the ideal combination of instrumental set up HILIC chromatography in positive ionisation mode and of C18 chromatography in negative ionisation mode. For the purpose of forensic investigations, a combination of a single phase extraction and the two aforementioned chromatographic and mass spectrometry modes could represent an ideal set up for obtaining bone metabolomic profiles from taphonomically altered bones.

Extracellular vesicles (EVs) represent an attractive source of biomarkers due to their biomolecular cargo. The aim of this study was to identify candidate protein biomarkers from plasma-derived EVs of patients with liver cirrhosis (LC) and hepatocellular carcinoma (HCC). Plasma-derived EVs from healthy participants (HP), LC, and HCC patients (eight samples each) were subjected to label-free quantitative proteomic analysis using LC-MS/MS. A total of 248 proteins were identified, and differentially expressed proteins (DEPs) were obtained after pairwise comparison. We found that DEPs mainly involve complement cascade activation, coagulation pathways, cholesterol metabolism, and extracellular matrix components. By choosing a panel of up- and down-regulated proteins involved in cirrhotic and carcinogenesis processes, TGFBI, LGALS3BP, C7, SERPIND1, and APOC3 were found to be relevant for LC patients, while LRG1, TUBA1C, TUBB2B, ACTG1, C9, HP, FGA, FGG, FN1, PLG, APOB and ITIH2 were associated with HCC patients, which could discriminate both diseases. In addition, we identified the top shared proteins in both diseases, which included LCAT, SERPINF2, A2M, CRP, and VWF. Thus, our exploratory proteomic study revealed that these proteins might play an important role in the disease progression and represent a panel of candidate biomarkers for the prognosis and diagnosis of LC and HCC.

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.

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.

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.

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.

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.

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.

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.