Molecular omics最新文献

Cancer remains the second leading cause of death worldwide, surpassed only by cardiovascular disease. From the different types of cancer, pancreatic cancer (PaC) has one of the lowest survival rates, with a survival rate of about 20% after the first year of diagnosis and about 8% after 5 years. The lack of highly sensitive and specific biomarkers, together with the absence of symptoms in the early stages, determines a late diagnosis, which is associated with a decrease in the effectiveness of medical intervention, regardless of its nature – surgery and/or chemotherapy. This review provides an updated overview of recent studies combining multi-OMICs approaches (e.g., proteomics, metabolomics) with analytical tools, highlighting the synergy between high-throughput molecular data generation and precise analytical tools such as LC–MS, GC–MS and MALDI-TOF MS. This combination significantly improves the detection, quantification and identification of biomolecules in complex biological systems and represents the latest advances in understanding PaC management and the search for effective diagnostic tools. Large-scale data analysis coupled with bioinformatics tools enables the identification of specific genetic mutations, gene expression patterns, pathways, networks, protein modifications and metabolic signatures associated with PaC pathogenesis, progression and treatment response through the integration of multi-OMICs data.

Brevetoxins are a type of neurotoxin produced in red tide blooms. Northern quahogs (M. mercenaria) are extensively used in commercial aquaculture farming, and early-stage metabolomics studies can provide early warnings of brevetoxins for farmers. In this study, NMR-based metabolomics was performed to investigate the response of clam gills and digestive glands under a series of sublethal doses of brevetoxins. Our study showed that the brevetoxin PbTx-2 had minimal influence on the physical activities of M. mercenaria for a short exposure time (24 hours). However, major metabolic level perturbations were observed in the clam gill extracts from the 1 ppb treatment. In addition, in the low concentration (0.1 ppb) study, clam gills showed combinational metabolite perturbations, as observed by an OPLS-DA study. The highly disturbed metabolites in the gill samples were the upregulated serine, glucose, hypotaurine, and glycine and the downregulated lactate, leucine, isoleucine, threonine, biotin, taurine, and valine. The results indicated that the brevetoxin PbTx-2 potentially affects glycolysis, glycine, serine, and threonine metabolism, taurine and hypotaurine metabolism, and biotin metabolism. While the digestive gland had less significantly changed metabolites, the potential combinational metabolite changes from PCA were observed from the 5-ppb treatment. Glucose and glycine are the primary metabolites that showed high contributions to the OPLS-DA model, which indicates the potential influence of digestive activities. The study indicated that metabolomic analysis of the gills and digestive glands of M. mercenaria is a feasible method to monitor the toxicity of brevetoxins, especially under sublethal doses in marine water.

The present work aimed to examine the primary mechanisms of liver damage, namely the impact of gut-derived endotoxins along the gut–liver axis and adipose-derived free fatty acids along the adipose–liver axis. These processes are known to play a significant role in the development of hepatic inflammation and steatosis. Although possible overlapping in the pathogenesis was expected, these processes have unique pathophysiological consequences. Therefore, we used HepG2 cells as a model system to investigate the impact of lipopolysaccharides (LPS) and free fatty acid (FFA; albumin conjugated palmitic acid) on the intracellular metabolome. Although both LPS and FFA triggered the expression of nuclear factor κB (NFκB)-dependent inflammation, only LPS treatment was able to trigger a Toll-like receptor 4 (TLR4) dependent response. The intracellular cytoprotective enzymatic levels (catalase, peroxidase, glutathione) were increased due to FFA but lowered due to LPS. The free-radical neutralizing efficacies of cell-free metabolites of FFA-treated cells were better than those of the LPS-treated ones. The use of untargeted metabolomics allowed for the identification of distinct metabolic pathway enrichments, providing further insights into the differential effects of LPS and FFA on the metabolism of hepatocytes. Collectively, the current study highlights the distinct impacts of endotoxemia and lipotoxicity on the metabolome of hepatocytes, hence offering valuable insights into hepatocellular function.

Designing reagents for protein labeling is crucial for investigating cellular events and developing new therapeutics. Historically, much effort has been focused on labeling lysine and arginine residues due to their abundance on the protein periphery. The chemo-selectivity of these reagents is a challenging yet crucial parameter for deciphering properties specifically associated with the targeted amino acid. Consequently, there is a growing demand for new conjugation reagents and workflows that facilitate selective binding to amino acids other than lysine, cysteine, and arginine. Tyrosine, an aromatic amino acid, occurs moderately on the protein periphery, with its phenolic ring often buried within the tertiary protein structure. This presents a challenging environment for tyrosine-specific protein bioconjugation efforts. The hydrophobic aromatic side chain of tyrosine is known to engage in π-stacking interactions, while the hydroxyl group of the phenyl ring can participate in hydrogen bonding and form tyrosyl radicals involved in electron transfer. 4-Phenyl-3H-1,2,4-triazole-3,5(4H)-dione (PTAD) has been previously investigated for its ability to bind to tyrosine. This work presents an extensive structural proteomics investigation of tyrosine labeling across samples of varying complexity, ranging from peptides and proteins to entire cell lysates. Mass spectrometry is utilized to study the behavior of tyrosine-labeled samples through tandem mass spectrometry experiments. We believe these studies will offer valuable insights into tyrosine bioconjugation with PTAD and demonstrate its potential as a covalent labeling reagent for chemical proteomics research.

Cisplatin-based concurrent chemoradiotherapy (CCRT) is the standard treatment for cervical patients with locally advanced disease. Despite the improved survival rates and prognosis observed in patients undergoing CCRT, over 30–40% do not achieve complete response and are at risk of locoregional recurrence. Targeting crucial molecules that confer resistance may improve the clinical outcomes of the treatment resistant patient cohort. Herein, we employed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based phosphoproteomic approach to identify the altered phosphophorylation events, activated kinases and dysregulated pathways involved in treatment resistance. We quantified 2531 unique phosphopeptides mapping to 1099 proteins of which 74 proteins were differentially phosphorylated between the cohorts. Pathway analysis revealed dysregulation of the DNA repair pathway and the proteins involved in DNA repair in the non-responder cohort. Additionally, we identified kinase signature associated with CCRT resistance. Kinases such as CSNK2A1, PRKDC, PLK-1, NEK2, ATM and CDK1 are predicted to be activated in non-responders. In particular, we showed that CSNK2A1 is involved in oncogenesis of cervical cancer and pharmacological inhibition led to reduced cell proliferation, migration and colony formation. Moreover, the combination of the CSNK2A1 inhibitor, silmitasertib with cisplatin demonstrated synergism (combination index < 1) and yielded a beneficial reduction in dosage. The dose reduced combination potentially reduced the proliferative, migratory and colony formation ability in vitro. Our findings highlight the potential of phosphoproteomics to identify clinically significant targets and pathways implicated in CCRT resistance. Our study also indicates that combination therapy could serve as an effective treatment strategy to improve the efficacy of patients undergoing CCRT.

The application and development of fast and simple screening methods for the authentication of foods has increased continuously in recent years. A widely used analytical technique is Fourier transform near-infrared spectroscopy (FT-NIR). Despite the simple application of FT-NIR analysis, the analyses are usually carried out on benchtop devices in the laboratory. However small, inexpensive and mobile NIR devices could be used on-site. Despite the simple use of FT-NIR analysis, the examinations are usually carried out on a stationary benchtop device in a laboratory. However, in order to be able to perform the application directly on site, the application of small, cost-effective and mobile NIR devices for food analysis is crucial. In this study, both, a benchtop NIR instrument and a handheld NIR device with a lower resolution and analyzed wavenumber range were applied for the differentiation of strawberries from different geographical origins. Distinguishing German and non-German strawberries using linear discriminant analysis (LDA) yielded an accuracy of 91.9% and 84.0% using the benchtop and the handheld devices, respectively. Relevant variables could be assigned to lipids, carbohydrates and proteins. Overall, our study demonstrated for the first time that analyzing the geographical origin of strawberries using NIR spectroscopy is also possible by means of a handheld device.

Memory disorder (MD) is a neurodegenerative disease with an increasing incidence rate that adversely affects the quality of life of patients. Qifu Yin (QFY), a classic traditional Chinese medicine formula used for treating dementia, is known for its neuroprotective properties, although its mechanism of action requires further exploration. In this study, D-galactose combined with aluminum chloride was used to establish an MD rat model, and behavior, histopathology, and related indicators were used to evaluate the pharmacodynamics of the formula in the rats. Furthermore, brain tissues were examined using pseudo-targeted lipidomics analysis, and candidate ion pairs were screened through mass spectrometry using UPLC-Q/Orbitrap HRMS. An sMRM detection method for candidate ion pairs was developed using UHPLC-Q-TRAP-MS/MS and validated. This approach was applied to the lipidomics study of QFY in improving MD. Differential metabolites screened through pseudo-targeted lipidomics were analyzed by employing network pharmacology, and the pathway was verified to explore their mechanism of action. Results demonstrated that QFY could improve memory impairment. A total of 1052 ion pairs were constructed in the pseudo-targeted lipidomics analysis, identifying 33 differential metabolites and 5 metabolic pathways. Furthermore, 31 differential metabolites in MD rats treated with QFY were significantly reversed. Immunohistochemical analysis showed that QFY could inhibit the expression of inflammatory factors. Network pharmacological analysis showed that the calcium signaling pathway was the main signaling pathway, and QFY could significantly reverse the expression levels of mRNA and protein. Thus, QFY can improve memory impairment in rats, which may be related to the regulation of oxidative stress, lipid metabolism disorder and the calcium signaling pathway.

Rubrivivax benzoatilyticus strain JA2 is an anoxygenic phototrophic bacterium, able to grow under different growth modes. Particularly under chemotrophic conditions, it produces novel Trp-melanin, anthocyanin-like, and pyomelanin pigments. However, the underlying molecular adaptations of strain JA2 that lead to the formation of novel metabolites under chemotrophic conditions remain unexplored. The present study used iTRAQ-based global proteomic and metabolite profiling to unravel the biochemical processes operating under the L-tryptophan-fed chemotrophic state. Exometabolite profiling of L-tryptophan fed chemotrophic cultures revealed production of diverse indolic metabolites, many of which are hydroxyindole derivatives, along with unique pigmented metabolites. Proteomic profiling revealed a global shift in the proteome and detected 2411 proteins, corresponding to 61.8% proteins expressed. Proteins related to signalling, transcription-coupled translation, stress, membrane transport, and metabolism were highly differentially regulated. Extensive rewiring of amino acid, fatty acid, lipid, and energy metabolism was observed under L-tyrptophan fed chemotrophic conditions. Moreover, energy conservation and cell protection strategies such as efflux pumps involved in the efflux of aromatic compounds were activated. The study demonstrated a correlation between some of the detected indole derivatives and the up-regulation of proteins associated with L-tryptophan catabolism, indicating a possible role of aromatic mono/dioxygenases in the formation of hydroxyindole derivatives and pigments under chemotrophic conditions. The overall study revealed metabolic flexibility in utilizing aromatic compounds and molecular adaptations of strain JA2 under the chemotrophic state.

Human cerumen analysis is an innovative and non-invasive trend in diagnosing diseases. Recently, new cerumen volatile-based methods using binary (volatile presence/absence) and semiquantitative (volatile intensity) data approaches have shown great potential in detecting biomarkers for cancer, chronic and rare diseases, and xenobiotic exposures. However, to date, the impacts of demographic factors such as body mass index (BMI), sex, age, and ethnicity/race in cerumen data have not been widely described, which can hamper interpretation in biomarker discovery investigations. This study examined the effects of such factors in cerumen, defining the baseline volatile organic metabolites (VOMs) across different physiological groups. Cerumen samples from seventy volunteers were analyzed using headspace/gas chromatography–mass spectrometry (HS/GC–MS) and multivariate statistical analysis using binary and semiquantitative data approaches. In the binary data approach, several VOMs exhibited patterns of high occurrence in some specific demographic groups. However, no pattern of discrimination that could be attributed to demographic factors was observed. In the semiquantitative approach, the relative abundance of cerumen VOMs was more impacted by sex and BMI than age and ethnicity/race. In summary, we describe how cerumen VOM occurrence and abundance are affected by patient phenotype, which can pave the way for more personalized medicine in future cerumen volatile-based methods.

Extensive research has confirmed the widespread presence of microorganisms in the human body and their crucial impact on human health, with drugs being an effective method of regulation. Hence it is essential to identify potential microbe–drug associations (MDAs). Owing to the limitations of wet experiments, such as high costs and long durations, computational methods for binary classification tasks have become valuable alternatives for traditional experimental approaches. Since validated negative MDAs are absent in existing datasets, most methods randomly sample negatives from unlabeled data, which evidently leads to false negative issues. In this manuscript, we propose a novel model based on graph representation learning and positive-unlabeled learning (GRL–PUL), to infer potential MDAs. Firstly, we screen reliable negative samples by applying weighted matrix factorization and the PU-bagging strategy on the known microbe–drug bipartite network. Then, we combine muti-model attributes and constructed a microbe–drug heterogeneous network. After that, graph attention auto-encoder module, an encoder combining graph convolutional networks and graph attention networks, is introduced to extract informative embeddings based on the microbe–drug heterogeneous network. Lastly, we adopt a modified random forest as the final classifier. Comparison experiments with five baseline models on three benchmark datasets show that our model surpasses other methods in terms of the AUC, AUPR, ACC, F1-score and MCC. Moreover, several case studies show that GRL–PUL could capably predict latent MDAs. Notably, we further verify the effectiveness of a reliable negative sample selection module by migrating it to other state-of-the-art models, and the experimental results demonstrate its ability to substantially improve their prediction performance.