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PubMed2025

Knowledge-enhanced protein subcellular localization prediction from 3D fluorescence microscope images.

MOTIVATION: Pinpointing the subcellular location of proteins is essential for studying protein function and related diseases. Advances in spatial proteomics have shown that automatic recognition of protein subcellular localization from images could highly facilitate protein translocation analysis and biomarker discovery, but existing machine-learning works have been mostly limited to processing 2D images. By contrast, 3D images have higher spatial resolution and allow researchers to observe cellular structures in their natural context, but currently, there are only a few studies of 3D image processing for protein distribution analysis due to the lack of data and complexity of modeling. RESULTS: We developed a knowledge-enhanced protein subcellular localization model, KE3DLoc, which could recognize distribution patterns in 3D fluorescence microscope images using deep learning methods. The model designs an image feature extraction module that incorporates information from 3D and 2D projected cells and implements asymmetric loss and confidence weights to address data imbalance and weak cell annotation issues. Besides, considering that the biological knowledge in the Gene Ontology (GO) database can provide valuable support for protein location understanding, the KE3DLoc model incorporates a novel knowledge enhancement module that optimizes the protein representation by related knowledge graphs derived from the GO. Since the image module and the knowledge module calculate features from different levels, KE3DLoc designs protein ID aggregation to enhance the consistency of protein features across different cells. Experimental results on three public datasets have demonstrated that the KE3DLoc significantly outperforms existing methods and provides valuable insights for spatial proteomics research. AVAILABILITY AND IMPLEMENTATION: All datasets and codes used in this study are available at GitHub: https://github.com/PRBioimages/KE3DLoc.

Microscopy, Fluorescence↗

PubMed2026

Decoding protein signatures and protein interactions in oral potentially malignant disorders: a systematic review and network analysis.

BACKGROUND: Proteomic profiling offers thorough insights into protein structure and function, as well as it acts as an essential approach for analyzing molecular changes at the tissue level. However, because of the proteome's diversity and dynamic nature, biomarker discovery remains challenging. By combining proteomics with bioinformatics, the level of understanding in relation to molecular interactions and disease processes can be improved. Through an integrative approach, few limitations can be addressed, thereby promoting proteomic profiling for the discovery of new therapeutic targets and novel biomarkers for a variety of disorders. AIM: To identify differentially expressed protein markers and their key molecular pathways associated with Oral Potentially Malignant Disorders. METHODS: Systematic Review was conducted following the PRISMA guidelines and the protocol registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration ID number CRD42024557545. A comprehensive literature review was performed using electronic databases, yielding 12,797, studies from which 15 eligible articles were selected. The Newcastle-Ottawa Scale was used to assess the risk of bias. Vote counting was performed to identify proteins reported in more than one study. A bipartite network was constructed using Cytoscape to identify shared and disease-specific protein markers. Lesion-wise protein-protein interaction networks were generated using STRING and analysed in Cytoscape to identify highly interconnected hub proteins, and pathway enrichment analysis for these hubs was performed using Reactome. RESULTS: A total of fifteen studies (Leukoplakia (LK) - n = 1, Proliferative Verrucous Leukoplakia (PVL) - n = 2, Oral Submucous Fibrosis (OSMF) - n = 7, and Oral Lichen Planus (OLP) - n = 5) were included. The Newcastle-Ottawa Scale was used to evaluate methodological quality and the quality of studies included in this systematic review was high for 4 articles and moderate in the remaining 11. The most commonly employed technique was mass spectrometry. A total of 318 candidate proteins (LK - 14, PVL - 82, OSMF - 172, and OLP - 50) were identified across the oral potentially malignant disorders. Key markers identified through vote counting included ERO1A, NUCB1, RHOA, and IL36A for PVL; LUM, KRT1, KRT9, ALB, and VIM for OSMF; and ALB, LYZ, HP, HBB, and AMY1A for OLP. The bipartite network showed that OSMF and OLP shared the highest number of proteins, indicating the strongest overlap among lesions. Network analysis further highlighted distinct hub proteins for each lesion: for LK- AMY1A, AMY1B and APOA1; for PVL- CFL1, RHOA and CDC42; for OSMF- HSP90AA1, ENO1 and SERPINA1; and for OLP- HP, B2M, and ORM1. Lesion-specific pathway enrichment revealed that LK was associated with epithelial differentiation, PVL with oncogenic signaling, OSMF with stress-driven fibrosis, and OLP with immune-mediated inflammation. CONCLUSIONS: Proteomic expression offers insights into disease pathogenesis by identifying important molecular changes across OPMDs. However, the majority of biomarkers are still in the exploratory stage due to the considerable variation in lesion types, sample sources, proteomic techniques, and reporting systems. In order to create reliable and clinically applicable biomarkers, future studies should concentrate on combining multi-omics techniques with large-scale, standardized cohorts.

Humans↗

PubMed2025

Functional metaproteomics for enzyme discovery.

Discovery of microbial biocatalysts traditionally relied on activity screening of isolated bacterial strains. However, since most microorganisms cannot be cultivated in the lab, such an approach leaves the majority of the microbial enzyme diversity untapped. Metagenomic approaches, in which the DNA from a microbial community is directly isolated and then used either for the creation of an expression library or for sequencing and metagenome annotation have alleviated this shortcoming to an extent, but have their own limitations: the generation of large expression libraries is time-consuming and their screening is costly, while metagenome annotation can infer biocatalytic function only from prior knowledge. We have thus developed a functional metaproteomic approach, which combines the immediacy of traditional activity screening with the comprehensiveness of a meta-omics approach. Briefly, the whole metaproteome of an environmental sample is separated on a 2-D gel, biocatalytically active proteins are visualized in-gel through zymography, and those candidate biocatalysts are then identified through mass spectrometry, searching against a metagenome-derived database obtained from the very same environmental sample. Here we explain the process in detail, with a focus on esterases, and give guidelines on how to develop a functional metaproteomic workflow for enzyme discovery.

Proteomics↗

PubMed2025

Exploring the causal relationship between plasma proteins and postherpetic neuralgia: a Mendelian randomization study.

BACKGROUND: The proteome represents a valuable resource for identifying therapeutic targets and clarifying disease mechanisms in neurological disorders. This study investigated potential causal relationships between plasma proteins and postherpetic neuralgia (PHN). METHODS: We conducted a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary statistics from the Decode Genetics dataset (4,907 plasma proteins) and the FinnGen database (490 PHN cases and 435,371 controls). Instrumental variables (IVs) were selected based on relevance, independence, and exclusivity. Causal associations were assessed using inverse-variance weighted (IVW), MR-Egger regression, simple mode, weighted mode, and weighted median methods. Sensitivity analyses, including leave-one-out tests, evaluated result robustness, while colocalization analysis examined shared causal variants between traits. RESULTS: Eight plasma proteins showed significant associations with PHN (PFDR < 0.05). Higher levels of ATRN, PIANP, and CD48 correlated with increased PHN risk, whereas elevated KIR2DL5A, GPI, SEMG2, EIF4B, and HFE2 levels were associated with reduced risk. Sensitivity analyses supported these findings and excluded genetic pleiotropy as a major confounding factor. Colocalization analysis did not detect shared causal variants (PPH4 < 0.8). CONCLUSION: These results suggest a potential causal role for eight plasma proteins in PHN pathogenesis. While these proteins may serve as biomarkers or therapeutic candidates, further validation is required. This study advances understanding of PHN pathophysiology and supports future investigations into diagnostic and therapeutic strategies.

Mendelian randomization↗

PubMed2026

Identification of Immune Response-Related Proteomic Biomarkers in Moyamoya Disease Using Serum Olink Proteomics.

Moyamoya disease, a rare chronic cerebrovascular disorder, requires invasive digital subtraction angiography (DSA) for diagnosis. This study employed high-throughput proteomics to identify plasma biomarkers for Moyamoya disease diagnosis. We conducted immunopanel analysis using the Olink platform to evaluate 92 immune-related proteins in plasma samples from 88 Moyamoya disease patients and 88 healthy controls. Key proteins were identified through differential expression analysis, GO, and KEGG enrichment analysis. A diagnostic model was constructed using LASSO regression, Boruta algorithm, and machine learning models including random forest and XGBoost. Validation of these proteins was performed using GEO external data sets, followed by prediction of potential therapeutic drugs and molecular docking validation through pharmacogenomic databases. A total of 44 differentially expressed proteins were identified through the Olink immunopanel, with 12 downregulated and 32 upregulated. GO and KEGG analyses revealed significant enrichment of these proteins in innate immune responses and signaling pathways such as NF-kB and MAPK. Through LASSO, random forest, and protein under-area analysis, four potential biomarkers for Moyamoya disease (MGMT, SIT1, PRDX1, TRAF2) were identified. A diagnostic model using these proteins showed the highest AUC value with the XGBoost model. Additionally, TRAF2 and PRDX1 exhibited significant expression differences in Moyamoya disease patients within the GEO data set. Our study revealed the immune landscape of Moyamoya disease, identified four biomarkers, and established a variety of diagnostic models.

Humans↗

PubMed2026

Proteomic Profiling Captures Residual Cardiovascular Risk Beyond the PREVENT Model in Individuals With Cardiovascular-Kidney-Metabolic Syndrome Stages 2-3.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome reflects complex pathobiological interactions among metabolic disorders, kidney injury, and cardiovascular disease (CVD). Stages 2 and 3 represent critical phases of disease progression characterised by high pathological heterogeneity. This study aimed to develop a CVD protein risk score (PRS) for this population and evaluate its incremental predictive value over the PREVENT model. METHODS: This study included 24 017 participants with CKM Stages 2-3 from the UK Biobank. Using 2923 plasma proteins measured via the Olink platform, a PRS was developed in a training set (n = 19 218) using the LASSO method. In the validation set (n = 4799), the incremental predictive performance of this score over the PREVENT model was assessed using Harrell's C-statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS: A risk score comprising 63 proteins was constructed, primarily reflecting inflammation, kidney injury and matrix remodelling. Key proteins included growth differentiation factor 15 (GDF15), hepatitis A virus cellular receptor 1 (HAVCR1), matrix metallopeptidase 12 (MMP12) and NT-proBNP. In the validation set, after adjusting for PREVENT risk factors, individuals in the high PRS group had a 2.56-fold higher risk of CVD compared to those in the low score group (HR: 2.56, 95% CI: 1.96-3.37). Integrating the score into the PREVENT model improved the C-statistic by 0.034 (0.672-0.706) and achieved a 10-year NRI of 15.8% (95% CI: 9.5%-20.9%) and an IDI of 2.2% (95% CI: 1.3%-3.3%). CONCLUSION: Combining the PREVENT model with the PRS developed in this study enhances the prediction of future CVD events in the CKM Stages 2-3 population. This approach facilitates the capture of residual risk and supports precision risk stratification and management for this high-risk group.

Humans↗

PubMed2026

Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma.

BACKGROUND: The combination of immune checkpoint inhibitors (ICIs) with anti-angiogenic agents is the preferred first-line therapy option for patients with advanced hepatocellular carcinoma (HCC), yet only a subset of patients responds, urging the quest for prediction biomarkers. We aimed to integrate genomics with radiology to propose an immune-derived radiogenomics biomarker of response to such combination immunotherapy and evaluate its added value in clinical context. METHODS: We integrated bulk RNA sequencing (RNA-seq) and proteomics data of 994 HCC patients with single-cell RNA-seq data of 11 samples across multiple datasets to identify an immune-related signature (IRS) that may influence sensitivity or resistance to such combined immunotherapy strategy, followed by verification of selected marker genes using immunohistochemistry and cytological experiments. We then trained/validated a cross-modality radiogenomics biomarker using machine learning based on TCIA database that was further tested in multi-scale independent cohorts covering 754 HCC patients. RESULTS: Integrative multi-omics analysis identifed a parsimonious 2-gene prognostic signature including KPNA2 and SMG5 that was significantly associated with immune heterogeneity and response to combination immunotherapy. Machine-learning pipeline exported the optimal 4-feature radiogenomics biomarker using support vector machine that significantly discriminated prognosis (hazard ratio 1.415–1.890; p < 0.05 for all) and modestly predicted response to ICI plus anti-angiogenic therapy (area under the curve 0.720–0.829) in independent retrospective series across major imaging modalities (computed tomography/magnetic resonance imaging). In a prospective neoadjuvant cohort, this biomarker also showed favorable performance for predicting pathological response and tumor recurrence, accompanied by biological validation through single-cell RNA-seq analysis of pre-treatment biopsies. CONCLUSIONS: Our study provides a cross-device-cross-modal radiogenomics biomarker that can improve patient selection for emerging ICI plus anti-angiogenic therapy with novel potential therapeutic targets in HCC.

Humans↗

PubMed2026

engGNN: a dual-graph neural network for omics-based disease classification and feature selection.

Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, small sample sizes, and intricate biological networks pose major challenges for reliable prediction and meaningful interpretation. Graph neural networks offer a promising way to integrate prior knowledge by encoding feature relationships as graphs. Yet, existing methods typically rely solely on either an externally curated feature graph or a data-driven generated graph, which limits their ability to capture complementary information. To address this, we propose the external and generated Graph Neural Network (engGNN), a dual-graph framework that jointly leverages both external biological networks and data-driven generated graphs. Specifically, engGNN constructs a biologically informed undirected feature graph from established network databases and complements it with a directed feature graph derived from tree-ensemble models. This dual-graph design produces more comprehensive representations, thereby improving predictive performance and interpretability. Through extensive simulation studies and real-world applications to three independent gene expression datasets, engGNN consistently demonstrates strong classification performance compared with competitive baselines. Beyond classification, engGNN provides feature- and source-level interpretability, enabling biologically meaningful analyses such as pathway enrichment analysis. Taken together, these results highlight engGNN as a robust, flexible, and interpretable framework for disease classification and biomarker discovery in high-dimensional omics contexts.

Graph Neural Networks↗

PubMed2024

Myoferlin: A Potential Marker of Response to Radiation Therapy and Survival in Locally Advanced Rectal Cancer.

PURPOSE: Patients with locally advanced rectal cancer often require neoadjuvant chemoradiation therapy to downstage the disease, but the response is variable with no predictive biomarkers. We have previously revealed through proteomic profiling that myoferlin is associated with response to radiation therapy. The aims of this study were to further validate this finding and explore the potential for myoferlin to act as a prognostic and/or therapeutic target. METHODS AND MATERIALS: Immunohistochemical analysis of a tissue microarray (TMA) for 111 patients was used to validate the initial proteomic findings. Manipulation of myoferlin was achieved using small interfering RNA, a small molecular inhibitor (wj460), and a CRISPR-Cas9 knockout cell line. Radiosensitization after treatment was assessed using 2-dimensional clonogenic assays, 3-dimensional spheroid models, and patient-derived organoids. Underlying mechanisms were investigated using electrophoresis, immunofluorescence, and immunoblotting. RESULTS: Analysis of both the diagnostic biopsy and tumor resection samples confirmed that low myoferlin expression correlated with a good response to neoadjuvant long-course chemoradiation therapy. High myoferlin expression was associated with spread to local lymph nodes and worse 5-year survival (P = .01; hazard ratio, 3.5; 95% CI, 1.27-10.04). This was externally validated using the Stratification in Colorectal Cancer database. Quantification of myoferlin using immunoblotting in immortalized colorectal cancer cell lines and organoids demonstrated that high myoferlin expression was associated with increased radioresistance. Biological and pharmacologic manipulation of myoferlin resulted in significantly increased radiosensitivity across all cell lines in 2-dimensional and 3-dimensional models. After irradiation, myoferlin knockdown cells had a significantly impaired ability to repair DNA double-strand breaks. This appeared to be mediated via nonhomologous end-joining. CONCLUSIONS: We have confirmed that high expression of myoferlin in rectal cancer is associated with poor response to neoadjuvant therapy and worse long-term survival. Furthermore, the manipulation of myoferlin led to increased radiosensitivity in vitro. This suggests that myoferlin could be targeted to enhance the sensitivity of patients with rectal cancer to radiation therapy, and further work is required.

Humans↗

PubMed2024

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Proteomics↗

PubMed2026

The Role of Small Segmental Duplications in Generating Identical Isoforms Through Alternative Splicing Sites.

Alternative splicing plays a crucial role in expanding proteomic diversity but can also generate identical isoforms under certain conditions. While mutually exclusive splicing of tandem exons has occasionally been reported to produce identical isoforms, the extent to which other splicing events contribute to this phenomenon remains unclear. In this study, we demonstrate that alternative 5' and 3' splice site selection can also lead to the formation of identical isoforms, providing an additional type of splicing event for functional redundancy in transcriptomes. To address this, we analyzed reference genome annotations from 15 plant species, including Arabidopsis thaliana and wheat (Triticum aestivum), obtained from the RefSeq database. Identical isoforms were computationally defined as transcripts with distinct exon-intron structures but identical coding sequences. Our analysis reveals that the majority of alternative 5' and 3' fragments originate from small segmental duplications, suggesting that sequence repetition within gene regions facilitates the emergence of such splicing patterns. We also observed differences in the annotated 5' UTRs of some identical isoforms. However, since the alternative splicing sites themselves were not located within UTRs, these differences may reflect annotation uncertainty rather than genuine AS-derived variation. Given that UTR predictions in reference databases are not always precise, such observations should be interpreted cautiously. Expression analysis using an isoform-specific k-mer approach confirmed that identical isoforms can be differentially regulated. These findings suggest that, beyond expanding protein diversity, alternative splicing can also generate redundant isoforms that are differentially expressed at the RNA level, indicating potential regulatory roles. By elucidating the structural and regulatory factors contributing to the formation and retention of identical isoforms, our study provides new insights into the evolutionary and functional significance of alternative splicing in plants.

Alternative Splicing↗

PubMed2025

Annotation matters: the effect of structural gene annotation on orthology inference.

MOTIVATION: In silico gene annotation, the process of identifying the genes present in a genome, remains a challenging task. As genome assemblies rapidly increase, the corresponding gene models and repertoires often fall short in quality. Despite advances in annotation methods, a lack of community standards means that most published gene annotations result from ad hoc pipelines. As a result, only a few species have nearly complete and accurate gene models. This annotation quality is thought to affect downstream analyses, including orthology inference, often the first step of comparative genomics studies. RESULTS: We show that different annotation methods yield markedly distinct orthology inferences. We compared orthology assignments of gene models obtained by four prominent protein-coding gene model sources: the NCBI Eukaryotic Genome Annotation Pipeline, the Ensembl Gene Annotation System, the UniProt Reference Proteomes, and Augustus 3.4 (an ab initio pipeline). We observe significant discrepancies between sources, namely in the proportion of orthologous genes per genome, the completeness of Hierarchical Orthologous Groups, and the accuracy and recall of the predicted orthologs on a standard orthology benchmark.

Molecular Sequence Annotation↗

PubMed2026

Multi-omics identification of therapeutic targets of compound sappan decoction in hepatocellular carcinoma.

BACKGROUND: Compound sappan decoction (CSD) is a multi-herbal traditional Chinese medicine formulation with clinical relevance in hepatocellular carcinoma (HCC). However, its therapeutic mechanisms remain unclear. METHODS: Bioactive compounds of CSD were identified and standardized using pharmacological and chemical databases. Potential targets were predicted via multiple target inference platforms. HCC-related genes were curated from comprehensive disease databases. Summary-data-based Mendelian randomization (SMR) was conducted to infer causal relationships between compound targets and HCC risk using large-scale quantitative trait loci (QTL) datasets and HCC genome-wide association study data. Colocalization analysis, protein-protein interaction (PPI) network construction, and GO/KEGG enrichment were performed on SMR-identified targets. Molecular docking evaluated binding affinities of representative compounds to prioritized targets. RESULTS: A total of 784 overlapping genes between predicted CSD targets and HCC-related genes were subjected to SMR analysis. Among these, 22 targets were significantly associated with HCC risk based on transcriptomic or proteomic QTLs and showed colocalization evidence. Notably, four targets (ADRB2, APOE, SYK, and PGF) were supported by both replication in an independent cohort and strong colocalization. These 22 targets were enriched in apoptosis, PI3K-Akt signaling, redox metabolism, and detoxification pathways. PPI analysis revealed central hubs including MMP9, BCL2, CASP1, and MCL1. Molecular docking demonstrated strong binding of APOE to quercetin, PGF to luteolin-7-olate, and SYK to kaempferol. CONCLUSIONS: CSD may exert therapeutic effects on HCC through modulation of genetically validated targets involved in tumor progression, inflammation, and metabolic reprogramming, supporting its potential clinical utility as an adjunctive treatment strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s12672-026-04740-8.

Caesalpinia↗

PubMed2025

Rehabilomics Strategies Enabled by Cloud-Based Rehabilitation: Scoping Review.

BACKGROUND: Rehabilomics, or the integration of rehabilitation with genomics, proteomics, metabolomics, and other "-omics" fields, aims to promote personalized approaches to rehabilitation care. Cloud-based rehabilitation offers streamlined patient data management and sharing and could potentially play a significant role in advancing rehabilomics research. This study explored the current status and potential benefits of implementing rehabilomics strategies through cloud-based rehabilitation. OBJECTIVE: This scoping review aimed to investigate the implementation of rehabilomics strategies through cloud-based rehabilitation and summarize the current state of knowledge within the research domain. This analysis aims to understand the impact of cloud platforms on the field of rehabilomics and provide insights into future research directions. METHODS: In this scoping review, we systematically searched major academic databases, including CINAHL, Embase, Google Scholar, PubMed, MEDLINE, ScienceDirect, Scopus, and Web of Science to identify relevant studies and apply predefined inclusion criteria to select appropriate studies. Subsequently, we analyzed 28 selected papers to identify trends and insights regarding cloud-based rehabilitation and rehabilomics within this study's landscape. RESULTS: This study reports the various applications and outcomes of implementing rehabilomics strategies through cloud-based rehabilitation. In particular, a comprehensive analysis was conducted on 28 studies, including 16 (57%) focused on personalized rehabilitation and 12 (43%) on data security and privacy. The distribution of articles among the 28 studies based on specific keywords included 3 (11%) on the cloud, 4 (14%) on platforms, 4 (14%) on hospitals and rehabilitation centers, 5 (18%) on telehealth, 5 (18%) on home and community, and 7 (25%) on disease and disability. Cloud platforms offer new possibilities for data sharing and collaboration in rehabilomics research, underpinning a patient-centered approach and enhancing the development of personalized therapeutic strategies. CONCLUSIONS: This scoping review highlights the potential significance of cloud-based rehabilomics strategies in the field of rehabilitation. The use of cloud platforms is expected to strengthen patient-centered data management and collaboration, contributing to the advancement of innovative strategies and therapeutic developments in rehabilomics.

Cloud Computing↗

PubMed2026

13C Stable Isotope Tracing-Based MFA Reveals the Contribution of Glucose to Glycolytic and TCA Fluxes and Its Application in Depression Research.

Metabolomics is widely applied to dissect metabolic pathways and their correlations with biological phenotypes. Unlike genomics and proteomics, metabolites exhibit substantial heterogeneity in chemical structure, physicochemical properties, and biological origin. Accordingly, pathway enrichment and annotation relying merely on alterations in metabolite abundance are prone to incomplete coverage, ionization bias, and ambiguous annotation, which inevitably impair the accuracy of pathway interpretation. Metabolic flux analysis (MFA) coupled with stable isotope-resolved metabolomics (SIRM) offers a powerful quantitative framework for tracing in vivo carbon flow and estimating reaction fluxes across key metabolic nodes. Glucose metabolism lies at the core of systemic energy homeostasis; however, most current investigations are confined to cell lines or in vitro systems, and a simple, easy-to-implement computational pipeline for in vivo glucose flux analysis in animal models is still lacking. Herein, we established an in vivo 13C-labeling-based MFA workflow to trace and resolve the systemic metabolic fate of glucose in rats. The pipeline covers tracer administration, sample preparation, LC-MS detection, isotopologue data acquisition and correction, construction of a glucose-metabolism-related metabolite database, MFA model establishment, and metabolic flux quantification. By infusing rats with [U-13C6]-glucose and [U-13C3]-sodium L-lactate, we precisely characterized the in vivo metabolic fates of circulating glucose and lactate and quantified their respective contributions to glycolytic flux and tricarboxylic acid (TCA) cycle flux. We further applied this workflow to profile energy metabolic reprogramming in depression. The results revealed a systemic shift toward aerobic glycolysis in rats exposed to chronic unpredictable mild stress (CUMS). Overall, the expanded application of this MFA strategy can provide mechanistic and quantitative insights into the regulation of metabolic pathways.

Animals↗

PubMed2026

Pilot metaproteomic profiling reveals bacterial diversity and potential medical and veterinary relevance of tick microbiomes in northern Algeria.

Ticks are major ectoparasites and vectors of pathogens affecting humans, livestock, and wildlife. They harbor diverse microbial communities that may influence tick biology and interactions with microorganisms; however, functional information on tick-associated microbiomes remains limited, particularly in North Africa. In this pilot study, we applied a metaproteomic approach based on high-resolution tandem mass spectrometry to characterize bacterial communities associated with three tick species collected in Algeria: Rhipicephalus sanguineus sensu lato, Hyalomma aegyptium, and Hyalomma dromedarii. Peptide spectra were assigned to taxa using a two-step database search strategy based on NCBInr, and bacterial composition and relative abundance were compared across tick species and sampling locations. A total of 40 bacterial genera belonging to 32 families and four phyla were identified. Microbiome composition differed significantly between tick genera and collection locations, suggesting an influence of species-specific and geographical factors on microbial community structure. Dominant genera included Streptomyces, Bacillus, Clostridium, Escherichia, Flavobacterium, Paenibacillus, and Providencia. Peptides related to Coxiella spp. were frequently detected, consistent with previous reports of Coxiella-like endosymbionts in ticks. This pilot study provides a first metaproteomic characterization of tick-associated communities in Algeria. The results reveal species- and location-associated differences in microbial composition and highlight the potential of metaproteomics for exploring tick-associated microbiomes in North Africa.

Animals↗