Molecular omics最新文献

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.

Pancreatic β-cells are composed of different subtypes that play a key role in the control of insulin secretion and thereby control glucose homeostasis. In vitro differentiation of human induced pluripotent stem cells (hiPSCs) into 3D spheroids leads to the generation of β-cell subtypes and thus to the development of islet-like structures. Using this cutting-edge cell model, the aim of the study was to decipher the signaling signature that underlines β-cell subtypes, with a focus on the search for the activity of motifs of important transcription regulators (TRs). The investigation was performed using data from previous single-cell sequencing analysis introduced into the integrated system for motif activity response analysis (ISMARA) of transcription regulators. We extracted the matrix of important TRs activated in the β-cell subpopulation and bi-hormonal-like β-cells. Based on these TRs and their targets, we built specific regulatory networks for main cell subpopulations. Our data confirmed the transcriptomic heterogeneity of the β-cell subtype lineage and suggested a mechanism that could account for the differentiation of β-cell subtypes during pancreas development. We do believe that our findings could be instrumental for understanding the mechanisms that affect the balance of β-cell subtypes, leading to impaired insulin secretion in type 2 diabetes.

Obstructive sleep apnea (OSA) is characterized by intermittent hypoxic environments at the cellular level and is an independent risk factor for the development of cardiovascular disease. Endothelial cell (EC) dysfunction precedes the development of cardiovascular disease; however, the mechanisms by which ECs respond to these intermittent hypoxic events are poorly understood. To better understand EC responses to hypoxia, we examined the effects of sustained hypoxia (SH) and intermittent hypoxia (IH) on the activation of HIF-1α in ECs. While SH stabilized HIF-1α and led to its nuclear localization, IH did not activate HIF-1α and the expression of its target genes. Using RNA-sequencing, we evaluated transcriptional responses of ECs to hypoxia. SH induced the expression of HIF-1α and hypoxia response genes, while IH affected cell-cycle regulation genes. A cytoscape protein–protein interaction network for EC response to hypoxia was created with differentially expressed genes. The network comprises cell-cycle regulation, inflammatory signaling via NF-κB and response to VEGF stimulus subnetworks on which SH and IH had distinct activities. As OSA is associated with elevated catecholamines, we investigated the effect of epinephrine on the EC response to SH and IH. Transcriptomic responses under IH and epinephrine revealed protein–protein interaction networks emphasizing distinct subnetworks, including cytokine-mediated TNFα signaling via NF-κB, Wnt/LRP/DKK signaling and cell cycle regulation. This study reveals differential transcriptomic responses under SH and IH characterised by HIF-1α transcriptional response induced only by SH, but not by IH. The study also features the potential molecular events that may occur at the vascular level in OSA.

To illustrate epigenetic heterogeneity, versatile tools of single-cell ChIP-seq (scChIP-seq) are essential for both convenience and accuracy. We developed MobiChIP, a compatible ChIP-seq library construction method based on current sequencing platforms for single-cell applications. MobiChIP efficiently captures fragments from tagmented nuclei across various species and allows sample mixing from different tissues or species. This strategy offers robust nucleosome amplification and flexible sequencing without customized primers. MobiChIP reveals regulatory landscapes of chromatin with active (H3K27ac) and repressive (H3K27me3) histone modification in peripheral blood mononuclear cells (PBMCs) and accurately identifies epigenetic repression of the Hox gene cluster, outperforming ATAC-seq. Meanwhile, we also integrated scChIP-seq with scRNA-seq to further illustrate cellular genetic and epigenetic heterogeneity.

Forensic science, an interdisciplinary field encompassing the collection, examination, and presentation of evidence in legal proceedings, has recently embraced lipidomics as a valuable tool. Lipidomics, a subfield of metabolomics, specializes in the analysis of lipid structures and functions, offering insights into biological processes that can aid forensic investigations. While not a substitute for DNA analysis in personal identification, lipidomics complements this technique by focusing on small biological molecules, with distinct sample requirements. This review comprehensively explores the current applications of lipidomics in forensic science. The review commences with an introduction to the concept and historical background of lipidomics, subsequently delving into its utilization in diverse areas such as drug analysis, ethyl alcohol and substitute assessment, latent fingermark detection, fire debris analysis, and seafood authentication. By showcasing the various biological materials and methods employed, this review underscores the potential of lipidomics as a powerful adjunct in forensic investigations.

Coupling size exclusion chromatography (SEC) with mass spectrometry-based proteomics enables investigating protein complexes, with degradomic profiling providing deeper insights into complex-associated proteolytic processing and retaining of cleavage products. This study aims to map protein complex formation upon inflammasome activation in bone marrow-derived dendritic cells (BMDCs) from gasdermin D-deficient mice, focusing on proteolytic enzymes and truncated proteins in higher molecular weight complexes. Cultured BMDCs were primed with LPS and subsequently treated with nigericin or Val-boroPro (VbP). SEC-fractionated proteins were TMT-labelled and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS). We identified 6862 proteins and 70 802 peptides, including 14 714 semi-tryptic peptides indicating elevated endogenous proteolytic processing. The sequence motif of numerous cleavage sites maps to caspase-like activity. Inflammasome activation was corroborated by elevated levels of apoptosis-associated speck-like protein containing a CARD (ASC) in higher molecular weight (MW) fractions and increased IL-1β levels in low MW fractions upon nigericin or VbP treatment. The majority of truncated cleavage products remained within their corresponding, higher MW protein complexes while caspase-specific cleavage products of Rho-associated protein kinase 1, gelsolin, and AP-2 complex subunit alpha-2 dissociated to lower MW fractions. SEC profiles identified 174 proteases, with cell surface proteases forming high MW complexes, including ADAMs and DPP4 but not MMP14. VbP treatment led to the accumulation of ISG15 in low MW fractions while RNA polymerase II coactivator p15 shifted to higher MW fractions. This study demonstrates that SEC-coupled proteomics and degradomic profiling offer unique insights into protein complex dynamics and proteolytic processes upon inflammasome activation.