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PubMed2025

Hierarchical metabolic engineering for rewiring cellular metabolism.

Metabolic engineering is a key enabling technology for rewiring cellular metabolism to enhance production of chemicals, biofuels, and materials from renewable resources. However, how to make cells into efficient factories is still challenging due to its robust metabolic networks. To open this door, metabolic engineering has realized great breakthroughs through three waves of technological research and innovations, especially the third wave. To understand the third wave of metabolic engineering better, we discuss its mainstream strategies and examples of its application at five hierarchies, including part, pathway, network, genome, and cell level, and provide insights as to how to rewire cellular metabolism in the context of maximizing product titer, yield, and productivity. Finally, we highlight future perspectives on metabolic engineering for the successful development of cell factories.

Metabolic Engineering↗

PubMed2026

Multi-Omics Analysis Reveals Molecular Networks and Key Pathways Associated with Cysteine- and Methionine-Mediated Biosynthesis of Sulfur-Containing Flavor Metabolites in Lentinula edodes.

Lentinula edodes is renowned for its unique aroma, which is characterized by various volatile sulfur-containing flavor metabolites (SCFMs). Cysteine and methionine could enhance the SCFMs biosynthesis in L. edodes; however, the underlying metabolic pathways remain unclear. To bridge this gap, integrated proteomic and metabolomic analysis were performed to decipher pathways through which cysteine and methionine regulate SCFM biosynthesis. Results showed that exogenous cysteine and methionine supplementation significantly increased the content of lenthionine, the key aroma compound of shiitake mushrooms. Both treatments induced substantial changes in the proteomic and metabolomic profiles. Proteomic analysis revealed that differentially expressed proteins were predominantly enriched in cysteine and methionine metabolism and sulfur metabolism following cysteine treatment, whereas methionine treatment mainly affected proteins associated with tryptophan metabolism and sulfur metabolism. Metabolomic analysis showed that differentially accumulated metabolites were significantly enriched in D-amino acid metabolism and cysteine and methionine metabolism, with glutathione metabolism specifically enriched under cysteine treatment. Integrated omics analysis further uncovered distinct sulfur metabolite-protein regulatory networks under different sulfur nutrition and identified treatment-specific hub proteins. These findings establish a molecular regulatory framework linking SCFM biosynthesis with broader primary metabolic pathways involved in sulfur intermediate generation and regulation, providing new insights into the potential regulatory networks underlying SCFM formation in L. edodes.

Methionine↗

PubMed2025

Phenotypic profiling of carbon utilization of Pectobacterium brasiliense (Pbr1692).

Pectobacterium brasiliense 1692 (Pbr1692) is a necrotrophic pathogen that infects many crops such as potatoes and ornamental plants and derives nutrients from degraded plant tissue. Previous studies have identified Pbr1692 genes required for ecological fitness and virulence, however there is a lack of information on nutrient utilization in Pbr1692. Carbon source utilization profiling in Pbr1692 could provide a platform to decipher its metabolic flexibility and adaptation. This study assessed the nutrient utilization of Pbr1692 in different carbon sources, using Biolog Phenotypic Microarray (PM). An array of carbon sources utilized by Pbr1692 were identified, 32 carbohydrates and 8 carboxylic acids were among the preferred carbon nutrients utilized by Pbr1692. The PM results also revealed that the citric acid cycle, amino acid metabolism, and pentose phosphate metabolic pathways might be used to produce energy for Pbr1692. In addition, growth of Pbr1692 cells in minimal medium supplemented with citric acid, glucose, and aspartic acid retained the typical rod shape, suggesting that nutrient variation did not influence Pbr1692 cell morphology adaptation. This study provides an understanding on the adaptation of Pbr1692 and lays a foundation for understanding carbon metabolism of Pbr1692.

Carbon↗

PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗

PubMed2025

MEANtools integrates multi-omics data to identify metabolites and predict biosynthetic pathways.

During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.

Metabolomics↗

PubMed2026

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.

BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.

Metabolic Networks and Pathways↗

PubMed2025

In silico encounters: harnessing metabolic modelling to understand plant-microbe interactions.

Understanding plant-microbe interactions is vital for developing sustainable agricultural practices and mitigating the consequences of climate change on food security. Plant-microbe interactions can improve nutrient acquisition, reduce dependency on chemical fertilizers, affect plant health, growth, and yield, and impact plants' resistance to biotic and abiotic stresses. These interactions are largely driven by metabolic exchanges and can thus be understood through metabolic network modelling. Recent developments in genomics, metagenomics, phenotyping, and synthetic biology now enable researchers to harness the potential of metabolic modelling at the genome scale. Here, we review studies that utilize genome-scale metabolic modelling to study plant-microbe interactions in symbiotic, pathogenic, and microbial community systems. This review catalogues how metabolic modelling has advanced our understanding of the plant host and its associated microorganisms as a holobiont. We showcase how these models can contextualize heterogeneous datasets and serve as valuable tools to dissect and quantify underlying mechanisms. Finally, we consider studies that employ metabolic models as a testbed for in silico design of synthetic microbial communities with predefined traits. We conclude by discussing broader implications of the presented studies, future perspectives, and outstanding challenges.

Plants↗

PubMed2025

Flux-sum coupling analysis of metabolic network models.

Metabolites acting as substrates and regulators of all biochemical reactions play an important role in maintaining the functionality of cellular metabolism. Despite advances in the constraint-based framework for genome-scale metabolic modeling, we lack reliable proxies for metabolite concentrations that can be efficiently determined and that allow us to investigate the relationship between metabolite concentrations in specific metabolic states in the absence of measurements. Here, we introduce a constraint-based approach, the flux-sum coupling analysis (FSCA), which facilitates the study of the interdependencies between metabolite concentrations by determining coupling relationships based on the flux-sum of metabolites. Application of FSCA on metabolic models of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana showed that the three coupling relationships are present in all models and pinpointed similarities in coupled metabolite pairs. Using the available concentration measurements of E. coli metabolites, we demonstrated that the coupling relationships identified by FSCA can capture the qualitative associations between metabolite concentrations and that flux-sum is a reliable proxy for metabolite concentration. Therefore, FSCA provides a novel tool for exploring and understanding the intricate interdependencies between the metabolite concentrations, advancing the understanding of metabolic regulation, and improving flux-centered systems biology approaches.

Escherichia coli↗

PubMed2026

Knowledge-driven interpretable neural networks provide mechanistic insight.

Analyzing omics data in the context of pathway knowledge is critical for understanding the molecular mechanisms underlying pathological changes. However, current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds features into pathway graphs and models reactions analytically, producing interpretable feature hierarchies and subnetworks in which functional associations are estimated to model biological interactions. The approach is agnostic to feature selection, enabling the use of full omics data sets without discarding weak signals. Applications to breast cancer microRNA-gene regulation data and COVID-19 metabolomic data highlight immune and metabolic pathways relevant to disease progression. This framework bridges predictive modeling with mechanistic interpretation and offers a foundation for integrative pathway analysis.

Humans↗

PubMed2026

Dual proximity-based interactome mapping of FKBP51 and FKBP52 uncovers shared metabolic networks.

The 51 kDa FK506-binding protein (FKBP51) has been studied for its involvement in regulating multiple biological systems, particularly as a regulator of steroid hormone receptors, but roles in metabolism, pain response, cell survival, protein turnover, autophagy, immune response, and insulin signaling have also been described. Genetic variants of FKBP51 are associated with various stress-related mental disorders. While recent research has clarified aspects of these processes, the complete range of FKBP51 interactions remains undetermined. FKBP52, a closely related homolog, also affects similar pathways. Recent studies have identified new protein partners for FKBP51 and FKBP52, suggesting an even broader interactome with transient associations. To further characterize interactions, TurboID-based proximity labeling was performed in HeLa cells. Proteomic analysis confirmed known FKBP51 and FKBP52 interactions, while also identifying additional shared and unique binding partners with strong enrichment in metabolic pathways, amino acid biosynthesis, and carbon metabolism. Although FKBP51 and FKBP52 proximal proteins were primarily cytosolic, FKBP51 showed additional associations with exosomal proteins while FKBP52 engaged with additional nuclear proteins. These findings highlight the overlapping roles in metabolic signaling and differentiate pathway-specific partners.

Tacrolimus Binding Proteins↗

PubMed2025

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites.

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

Humans↗

PubMed2026

Multimodal Analysis Reveals Aberrant Expression of SUMO2 and Its Significant Association With Key Mechanisms of Metabolic Pathways in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. However, the role of small ubiquitin-like modifier 2 (SUMO2), a core member of the small ubiquitin-like modifier (SUMO) family, regarding its expression patterns and metabolism-related functions in HCC remains inadequately understood. METHODS: A multidimensional analytical framework was applied, integrating immunohistochemistry (153 HCC vs. 21 non-HCC samples), proteomics (159 paired samples), bulk transcriptomics (3240 HCC vs. 2267 non-HCC samples), single-cell RNA sequencing (RNA-seq) (10 HCC vs. 8 non-HCC samples), spatial transcriptomics, and external CRISPR/Cas9 functional genomics data. Systematic analyses included standardized mean difference (SMD), pathway enrichment, pseudotime trajectory inference, in silico knockout, cell-cell communication, metabolic flux scoring, immune infiltration, clinical correlation, drug sensitivity prediction, and molecular docking. RESULTS: At the protein level, immunohistochemistry (nuclear positivity) and external proteomic data collectively demonstrated consistent SUMO2 overexpression in HCC. Consistent upregulation was also observed at the mRNA level across large-scale cohorts. Single-cell RNA-seq and spatial transcriptomics localized SUMO2 enrichment to malignant hepatocytes and tumor-dominant regions. CRISPR-mediated SUMO2 knockout suppressed proliferation in multiple HCC cell lines. Mechanistically, high SUMO2 expression was significantly associated with metabolic reprogramming involving glycolysis/gluconeogenesis, pyruvate metabolism, and the tricarboxylic acid cycle. SUMO2-high malignant hepatocyte subpopulations exhibited enhanced activity of the macrophage migration inhibitory factor signaling axis and enhanced iron-sensor interactions. Further, the immune infiltration analysis revealed a negative correlation between SUMO2 expression and M1 macrophages and a positive correlation between follicular helper T cells and regulatory T cells. Clinically, elevated SUMO2 levels were found to be associated with adverse prognostic features. Furthermore, high SUMO2 expression was associated with increased sensitivity to dasatinib, and molecular docking simulations predicted potential binding between SUMO2 and dasatinib, with a Vina score of -8.5 kcal/mol. CONCLUSIONS: SUMO2 is aberrantly expressed at the protein, mRNA, single-cell, and spatial transcriptomic levels in HCC and is significantly associated with metabolic reprogramming and altered migration inhibitory factor (MIF)-mediated intercellular communication, suggesting its potential as a novel biomarker for diagnosis and treatment.

Humans↗

PubMed2025

Engineering of xylose metabolic pathways in Rhodotorula toruloides for sustainable biomanufacturing.

The oleaginous yeast Rhodotorula toruloides is a promising microbial cell factory for the sustainable production of biofuels and value-added chemicals from renewable carbon sources. Unlike the conventional yeast Saccharomyces cerevisiae, R. toruloides can naturally metabolize xylose, the second most abundant sugar in lignocellulosic hydrolysates. However, its native xylose metabolism is inefficient, characterized by slow xylose uptake and accumulation of D-arabitol. Moreover, despite its phenotype, research on the enzymes involved in xylose metabolism has yet to reach a consensus. Therefore, this review provides a comprehensive analysis of the non-canonical xylose metabolism in R. toruloides, focusing on the properties of key enzymes involved in xylose metabolism. Native xylose reductase and xylitol dehydrogenase exhibit broad substrate promiscuity compared to their counterparts in the xylose-fermenting Scheffersomyces stipitis. Additionally, the absence of xylulokinase expression under xylose-utilizing conditions redirects metabolism toward D-arabitol accumulation. Consequently, D-arabitol dehydrogenases and ribulokinase play essential roles in the xylose metabolism of R. toruloides. These findings highlight the fundamental differences between R. toruloides xylose metabolism and the oxidoreductase pathways observed in other xylose-fermenting yeast, providing insights for metabolic engineering strategies to improve xylose utilization and enhance bioconversion of cellulosic hydrolysates to different bioproducts by R. toruloides.

Xylose↗

PubMed2025

Versatile sugar and valerate metabolic pathways in Paraburkholderia xenovorans LB400 enable tailored poly(3-hydroxybutyrate-co-3-hydroxyvalerate) production.

Poly(3-hydroxybutyrate) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) polymers are accumulated by diverse prokaryotes. Their distinct monomer compositions enable their use as tailored bioplastics. The aims were to characterize the poly(3-hydroxybutyrate) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) synthesis by Paraburkholderia xenovorans LB400 using different sugars and valerate, and to gain genome-oriented insights into polyhydroxyalkanoate production. d-Glucose, d-mannitol, d-gluconate, and d-xylose were evaluated as sole carbon sources or supplemented with valerate. Polyhydroxyalkanoates synthesized by strain LB400 were characterized through GC-MS, GC-FID, FTIR, and 1H and 13C-NMR. P. xenovorans LB400 reached 1.00-1.39 g L-1 of dry cell weight (DCW) with a P(3HB) content of 21-43% w w-1 when grown on different sugars. The addition of valerate to the sugar-grown LB400 cultures yielded a DCW of 1.79 to 2.29 g L-1 and a P(3HB-co-3HV) content of 50.0‒51.2% w w-1, with varying 3HV compositions (28‒43 mol%). The highest 3HV incorporation was observed with d-xylose and valerate. Genomic analyses of strain LB400 revealed key elements of sugar metabolism influencing growth, polymer accumulation, and monomer composition. LB400 genome encodes the PhaJ-like R-specific hydratase and FadJ epimerase, which are potentially useful for modulating copolymer composition. PHA production under bioreactor conditions was evaluated. In a bioreactor fed with d-glucose, LB400 achieved a P(3HB) concentration of 2.2 g L-1. These findings highlight the metabolic versatility of P. xenovorans LB400 in utilizing diverse sugars to produce either P(3HB) or tailor-made P(3HB-co-3HV), supporting the development of bioplastics for specific applications. KEY POINTS: • Strain LB400 produced P(3HB-co-3HV) from various sugars and valerate. • Sugar type drives LB400 PHA copolymer synthesis and composition. • Strain LB400 PHA production was scaled up to a bioreactor.

Polyesters↗

PubMed2026

Gut microbiota dynamics and metabolic pathways associated with bleomycin-induced pulmonary fibrosis progression.

BACKGROUND: Pulmonary fibrosis (PF) is a progressive respiratory disease characterized by epithelial injury, aberrant repair and excessive extracellular matrix deposition. Although the gut-lung axis is increasingly implicated in respiratory disorders, stage-resolved characterization of gut microbiota taxonomic and functional potential during PF development is limited. METHODS: We established a bleomycin-induced murine PF model and performed cross-sectional shotgun metagenomic sequencing of fecal samples from separate cohorts at three defined stages: baseline (control), day 7 (early fibrosis; M7), and day 14 (established fibrosis; M14). Microbial taxonomy, alpha/beta diversity, and predicted functional capacity were inferred using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Carbohydrate-Active enZymes (CAZy) annotations; associations were assessed using Procrustes and Spearman correlation analyses. RESULTS: Histopathology and immunohistochemistry confirmed progressive fibrogenesis with increased TGF-β1 and α-SMA expression. Compared with baseline, bleomycin-treated groups exhibited stage-specific shifts in gut microbial composition, including depletion of mucin-associated taxa (e.g., Prevotella, Akkermansia muciniphila) and expansion of Muribaculaceae- and Clostridiaceae-affiliated taxa. Alpha and beta diversity metrics differed across groups. KEGG/CAZy-based annotations revealed predicted, stage-dependent changes in microbial metabolic potential, including early reductions in pathways related to amino acid and glycan metabolism (M7) and later increases in predicted starch/sucrose catabolism, phosphotransferase system (PTS) representation, and secondary bile acid biosynthesis (M14). Correlation analyses linked compositional shifts to these predicted functional changes. CONCLUSION: In a stage-resolved, cross-sectional study, bleomycin-associated pulmonary fibrosis was accompanied by compositional and predicted functional alterations in the gut microbiota. These data identify candidate taxa and predicted pathways for follow-up mechanistic testing, but functional (metabolomic) and causality experiments are required to confirm whether and how microbial changes contribute to PF pathogenesis.

Animals↗

PubMed2025

Evolution and applications of genome-scale metabolic models in yeast systems biology studies.

Genome-scale metabolic models (GEMs) can be used to simulate the metabolic network of an organism in a systematic and holistic way. Different yeast species, including Saccharomyces cerevisiae, have emerged as powerful cell factories for bioproduction. Recently, with the dedicated efforts from the scientific community, significant progress has been made in the development of yeast GEMs. Numerous versions of yeast GEMs and the derived multiscale models have been released, facilitating integrative omics analysis and rational strain design for different types of yeast cell factories. These advancements reflected the evolution and maturation of yeast GEMs together with a model ecosystem around them. This review will summarize the development and expansion of yeast GEMs and discuss their applications in yeast systems biology studies. It is anticipated that yeast GEMs will continue to play an increasingly important role in pioneering yeast physiological and metabolic studies in coming years.

Systems Biology↗

PubMed2026

Adolescent depression as a systemic multimorbidity catalyst: integrated genetic and metabolic pathway analysis.

BACKGROUND: Although adolescent depression has been linked to individual chronic conditions, its broader role in shaping multimorbidity risk remains understudied. METHODS: A total of 87,562 UK Biobank participants were included, of whom 18,851 had documented adolescent depression. Cox proportional hazards models were applied to evaluate associations between adolescent depression and 24 chronic diseases, followed by stratified analyses by sex and age. Two-sample Mendelian randomization (MR) was then conducted to infer causality for diseases showing significant associations. Genomic colocalization analyses were performed using relevant GWAS data to identify shared causal variants. Mediation analyses were performed to detect possible mediating factors, including the frailty index, KDM biological age acceleration, allostatic load and 30 circulating biomarkers. RESULTS: Adolescent depression was associated with elevated risk for 12 chronic diseases, with strongest associations for hypothyroidism (HR = 1.29 [1.18-1.42]), diabetes (HR = 1.25 [1.13-1.38]) and chronic obstructive pulmonary disease (COPD) (HR = 1.74 [1.50-2.01]). Risks were notably higher among females and younger adults. MR confirmed likely causal relationships for hypothyroidism (OR = 1.45 [1.03-2.05]), diabetes (OR = 1.01 [1.01-1.02]) and COPD (OR = 1.04 [1.02-1.06]). Genomic colocalization revealed a shared genetic signal at the CDSN/PSORS1C1 locus between adolescent depression and hypothyroidism. Mediation analyses revealed disease-specific pathways: creatinine for hypothyroidism, testosterone for diabetes, KDM biological ageing for COPD and frailty index across all three conditions. CONCLUSIONS: Adolescent depression confers systemic vulnerability through genetic and metabolic mechanisms, with amplified risks in females and individuals aged ≤55 years. These findings support early, integrated interventions to mitigate long-term multimorbidity.

Humans↗

PubMed2025

Uncovering potential biomarkers and metabolic pathways in systemic lupus erythematosus and lupus nephritis through integrated microbiome and metabolome analysis.

OBJECTIVE: This study aims to explore the relationship between gut microbiota and fecal metabolomic profiles in patients with systemic lupus erythematosus (SLE), with and without lupus nephritis (LN), in order to identify potentially relevant biomarkers and better understand their association with disease progression. METHODS: Fecal samples from 15 healthy controls (HC) and 36 SLE patients (18 SLE-nonLN and 18 SLE-LN) were analyzed using 16S rRNA gene sequencing and untargeted metabolomics. Differential microbial taxa and metabolites were identified using Linear Discriminant Analysis Effect Size (LEfSe) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Receiver Operating Characteristic (ROC) curve analyses were used to assess the potential clinical relevance of selected metabolites. RESULTS: Beta diversity analysis demonstrated distinct microbial clustering between groups (p < 0.05). SLE-LN samples showed an increased relative abundance of Proteobacteria and decreased Firmicutes compared to SLE-nonLN. Metabolomic profiling identified multiple differentially abundant metabolites, with notable enrichment in primary bile acid biosynthesis pathways (e.g., Glycocholic acid, AUC = 0.951). In the SLE-nonLN group, increased Glycoursodeoxycholic acid levels (AUC = 0.922) were observed in pathways related to taurine and hypotaurine metabolism. Correlation analysis indicated a negative association between Escherichia-Shigella and bile acid levels (p < 0.01). CONCLUSION: This integrative analysis suggests that patients with SLE and LN harbor distinct gut microbiota and metabolomic profiles. The identified microbial taxa and metabolites may have potential as non-invasive biomarkers and could contribute to a better understanding of SLE pathogenesis and progression.

Humans↗