Objectives: Shen-Ling-Bai-Zhu-San (SLBZS) is a classical traditional Chinese herbal formula with spleen-invigorating and dampness-resolving properties. Recent pharmacological studies suggest its potential to regulate immune and metabolic disorders. Type 2 diabetes mellitus (T2D) and ulcerative colitis (UC) often coexist as comorbidities characterized by chronic inflammation, microbial imbalance, and insulin dysregulation, yet effective therapies remain limited. This study aimed to investigate the molecular mechanisms through which SLBZS may benefit T2D–UC comorbidity. Methods: An integrative multi-omics strategy was applied, combining network pharmacology, structural bioinformatics, and ensemble molecular docking–dynamics simulations. These complementary approaches were used to identify SLBZS bioactive compounds, predict their putative targets, and examine their interactions with disease-related biological networks. Results: The analyses revealed that flavonoids in SLBZS act on the SLC6A14/PI3K–AKT signaling axis, thereby modulating immune responses and improving insulin sensitivity. In addition, SLBZS was predicted to regulate the NF-B/MAPK signaling pathways, key hubs linking inflammation and metabolic dysfunction in T2D–UC. These dual actions suggest that SLBZS can intervene in both inflammatory and metabolic processes. Conclusions: SLBZS demonstrates promising therapeutic potential for T2D–UC by targeting interconnected immune–metabolic networks. These findings not only provide mechanistic insights bridging traditional therapeutic concepts with modern pharmacology but also establish a theoretical basis for future experimental validation and clinical application.

The complex interplay of metabolic signaling networks is critical to the pathophysiology of lung cancer. The anabolic mTORC1 kinase and catabolic process of autophagy are key among these regulatory pathways. While their relationship has long been viewed as a matter of simple inhibition, with mTORC1 as a negative regulator of autophagy, new evidence suggests that this relationship may be more nuanced than previously described. Here, we demonstrate that an autophagy-related, ATG4B, is required for mTORC1 activity and is associated with negative clinical outcomes in non-small cell lung cancer (NSCLC). Targeting ATG4B in vitro suppresses cell proliferation, protein synthesis rates, and mTORC1 signaling in NSCLC cell lines. In contrast, overexpressing the ATG4B protease in healthy models of lung tissue increased mTORC1 kinase activity in healthy lung cell models, indicating that an increase in ATG4B is sufficient to drive cellular anabolic signaling. Finally, we found that ATG4B expression is high in NSCLC patient tumors, is elevated in early-stage cancer, and predicts survival in lung adenocarcinoma patients. Taken together, our results demonstrate that ATG4B is required for anabolic behavior in NSCLC, indicating that the autophagic cascade may be a required input for mTORC1 activity and cellular anabolism in lung cancer. These results have implications for the field of cancer biology more broadly, as they indicate that the far from being a simple target of mTORC1, the autophagic cascade may serve as a requisite input for anabolic signaling, casting new light on the relationship between these processes in cancer pathophysiology.

IntroductionSeed deterioration involves oxidative damage and disrupted energy metabolism, yet the genetic mechanisms underlying aging resistance in Allium mongolicum remain unclear.MethodsIn this study, seeds primed with 0.8 mM spermidine (Spd) and stored for varying durations were subjected to transcriptome sequencing, targeted energy metabolite profiling, and assessments of antioxidant systems and energy metabolism enzymes.ResultsWe identified citrate synthase (AmCS) as a pivotal candidate gene involved in delaying aging processes. Under standard growth conditions, AmCS-overexpressing Arabidopsis lines exhibited a 15.55% higher germination rate compared to wild-type (WT), with enhanced activities of superoxide dismutase (SOD) and peroxidase (POD), and a 46.37% increase in ATP content compared to WT. Furthermore, these transgenic lines displayed significant reductions in hydrogen peroxide (H2O2; 35.20%) and malondialdehyde (MDA; 40.40%) accumulation. Mechanistically, AmCS-overexpressing Arabidopsis lines demonstrated heightened mitochondrial functionality, manifested as a 50.26% increase in cellular respiration rate and a 1.41-fold higher NADPH/NADP+ ratio than WT. Yeast two-hybrid assays validated the physical interaction between AmCS and pyruvate dehydrogenase kinase (AmPDK).DiscussionWe demonstrate that the AmCS-AmPDK complex retards seed aging through two key mechanisms: (i) promoting acetyl-CoA flux in the tricarboxylic acid (TCA) cycle and (ii) enhancing NADPH-dependent antioxidant capacity through pentose phosphate pathway activation. Exogenous Spd activates this network by inducing AmCS expression. Our findings establish AmCS as a key genetic regulator for enhancing anti-aging traits in crop breeding, offering prospects for precision breeding and advancements in seed storage practices.

Exploring the regulatory role of long non-coding RNA (lncRNA) in plateau yak is crucial to understanding its metabolic network for adapting to extreme environments. By integrating transcriptomic sequencing and co-expression network analysis, the messenger RNA (mRNA) and lncRNA expression characteristics of yak liver at three growth and development stages were systematically analyzed. A total of 35,216 mRNAs and 10,073 lncRNAs were detected. Among the 288 differentially expressed lncRNAs, 88 lncRNAs related to metabolism were screened, and their potential functions in lipid metabolism, collagen remodeling, and protein transport were predicted. The age-dependent expression patterns of some lncRNAs were verified through qRT-PCR (quantitative real-time reverse transcription polymerase chain reaction) experiments, which initially revealed the status and role of lncRNAs in metabolic regulation in yak liver. This study provides new insights into the molecular mechanisms underlying metabolic adaptation in high-altitude species such as yak, and establishes a methodological framework for the screening and identification of functional lncRNAs in non-model organisms.

Objective.To determine whether pre-treatment brain metabolic network patterns measured with18F-FDG PET are associated with treatment response and survival in cancer patients.Approach.Exploratory retrospective study of two independent cohorts: stage III breast cancer patients treated with neoadjuvant chemotherapy and stage IV melanoma patients treated with anti-PD-1 immunotherapy. Metabolic brain network scores were derived from pre-treatment18F-FDG PET scans and evaluated for their ability to stratify good versus poor responders using ROC analysis (AUC). Longitudinal changes in network scores were assessed across follow-up, and progression-free survival (PFS) and overall survival (OS) analyses were performed in the melanoma cohort.Main results.Specific brain networks were associated with treatment outcome; the cognition/language network was the strongest predictor (AUC > 0.84 for distinguishing good vs. poor responders in both cohorts). Good responders showed lower cognition/language scores than poor responders and healthy controls. Longitudinally, cognition/language scores remained stable in good responders, while poor responders exhibited a gradual convergence toward the scores observed in good responders. In the melanoma cohort, lower cognition/language scores were significantly associated with longer PFS and OS.Significance.These findings indicate that metabolic brain network patterns, particularly the cognition/language network, may serve as noninvasive biomarkers linked to treatment efficacy and survival in oncology. The results support a possible complex interaction between brain metabolism, immune response, and clinical outcomes. Key limitations include the retrospective design and lack of direct immune-function and psychometric measures; prospective, multimodal studies are needed to validate these observations and elucidate underlying mechanisms.

Metabolic reprogramming enables tumor cells to survive and proliferate in a nutrient-deficient environment. However, the immunosuppressive tumor microenvironment caused by metabolic reprogramming is often overlooked in current metabolism interventions. Herein, we developed a mito-specific "Trojan Horse" nanoplatform (2-pN@LNPs) coloaded with Niclosamide (Nic) and 2-deoxy-d-glucose (2-DG) to attack key metabolism pathways and synergistically ignite pyroptosis for restoring antitumor immunity. 2-pN@LNPs promoted proton influx across the inner mitochondrial membrane and caused oxidative phosphorylation (OXPHOS) into a futile cycle. Furthermore, 2-pN@LNPs exploited the increased glucose demand to deliver the glycolysis inhibitor 2-DG, causing metabolic network collapse. Both cell and three-dimensional multicellular tumor spheroid results demonstrated superior synergistic metabolic intervention efficacy. The multipath metabolism deprivation leads to irreversible mitochondrial dysfunction, followed by excessive reactive oxygen species accumulation, severe adenosine triphosphate loss, and ultimately exerted a pyroptosis-like micromorphology. Moreover, the synergistic treatment regimen can promote cytotoxic and helper T cells (CD8+/CD4+ T cells) recruitment and M1-type macrophage polarization, facilitating the establishment of a boost in immunological memory to prevent recurrence and metastasis. Overall, this work provides a robust strategy targeting metabolism through mitochondrial uncoupling and glycolysis inhibition, which can effectively improve the antitumor effect, inhibit lung metastasis, and help modulate antitumor immunity.

Muscle fatty infiltration (MFI), the pathological replacement of muscle by adipose tissue in chronic diseases, lacks comprehensive genetic characterization despite known cellular contributors. Elucidating its genetic architecture and clinical correlations could reveal therapeutic targets for this debilitating condition. We performed GWAS on 33,300 participants' genomic/MRI data, identifying MFI-associated loci. Fine-mapping (GCTA-COJO/FUMA), Mendelian randomization (tissue-specific genes, plasma proteins, metabolites), and genetic correlation (LDSC) analyses were conducted. KLF5's functional role was validated through inhibition experiments in fibro-adipogenic progenitors (FAPs) and murine immobilization-induced MFI models. GWAS revealed 91 significant SNPs across 26 loci, with risk genes enriched in olfactory transduction and JAK-STAT pathways. Multi-omics integration identified, KLF5 as a key transcriptional regulator, CHRDL2/HLA-E as circulating risk protein and phosphatidylcholines/triglycerides as causal metabolites, and (4) genetic correlations between MFI and metabolic/musculoskeletal disorders. Experimentally, KLF5 suppression reduced adipo-fibrogenic FAP differentiation and improved muscle histology in mice. Our study delineates MFI's polygenic basis, establishes clinical-metabolic relationships, and mechanistically validates KLF5 as a target. These findings provide a framework for treating MFI through metabolic modulation or KLF5 inhibition, with broader implications for muscle-degenerative comorbidities.

Diabetic neuropathy (DN) is a major and debilitating complication of diabetes mellitus, marked by progressive nerve dysfunction, chronic pain, and degeneration of both peripheral and autonomic neurons. Its complex pathophysiology involves persistent hyperglycemia, metabolic imbalance, vascular dysfunction, oxidative stress, and inflammation. Recent advances in mechanobiology have implicated that PIEZO1, a mechanosensitive ion channel, has emerged as a central player in mechanotransduction and is increasingly implicated in the pathophysiology of diabetic neuropathy. This review provides insights into the role of PIEZO1 in diabetic complications, particularly under conditions of chronic hyperglycemia, where its aberrant activation contributes to neuronal injury, oxidative stress, and inflammatory signalling. PIEZO1 modulates calcium influx in neurons, glia, endothelial cells, and immune cells, triggering downstream cascades that are intimately linked with neurodegeneration, chronic pain, and microvascular dysfunction. In diabetic neuropathy, PIEZO1 overexpression exacerbates nerve damage by disrupting Schwann cell function, impairing blood-nerve barrier integrity, and promoting neuroinflammation. Its expression in dorsal root ganglia further implicates it in the sensitization of nociceptive pathways and neuropathic pain. Beyond neural tissues, PIEZO1 modulate survival of pancreatic β-cell, endothelial responses to shear stress, and immune cell polarization, positioning it at the intersection of metabolic, vascular, and inflammatory networks. Emerging evidence from animal models and cellular studies underscores the therapeutic potential of PIEZO1-targeted interventions, including channel inhibitors like GsMTx4 and novel approaches such as electromagnetic field modulation. Given its broad mechanobiological significance and pathophysiological relevance, PIEZO1 represents a promising, multidimensional target for disease-modifying therapies in diabetic neuropathy and related chronic complications.

Abstract (Objective) Dietary fiber is pivotal in enhancing intestinal development, modulating microbial diversity, and maintaining gut homeostasis in pigs. However, the dynamic processes by which dietary fiber reshapes gut microbiota remain poorly characterized. This study employed an in vitro gut microbial fermentation model to investigate the succession patterns of microbial communities and functional dynamics during arabinoxylan utilization in Jinhua (JH, Chinese native pigs) and Duroc × Landrace × Yorkshire (DLY) pigs, aiming to elucidate the microbial mechanisms underlying divergent arabinoxylan metabolic capacities and provide theoretical insights for optimizing dietary fiber utilization. (Methods) Fecal microbiota from six healthy post-weaning JH and DLY pigs was subjected to in vitro arabinoxylan fermentation. Samples were collected at 1, 3, 6, 9, 12, 15, 18, 21, 24, 48, and 72 h to analyze pH, β-xylanase and α-L-arabinofuranosidase activity, and short-chain fatty acid (SCFA) profiles via gas chromatography. 16S rRNA sequencing tracked microbial community dynamics, while time-series data integration and linear regression modeling established interactions between co-abundance response groups (CARGs) and SCFA metabolism. (Results) Key findings: 1) Gut microbiome variation exhibited similar short-term microbial responses to arabinoxylan, with microbial diversity and enzyme activity involved in arabinoxylan degradation stabilizing after rapid initial shifts. Arabinoxylan fermentation progressed through three time-dependent phases, corroborated by microbial distance and KEGG functional shifts. The microbial network closeness centrality decreased from the first fermentation stage to the second stage and then increased in the final stage, indicating that the metabolic changes primarily occurred during the middle phase. Enterobacter and Roseobacter were the primary degrading bacteria, with the enrichment of fiber-degrading taxa in JH. Clostridium and Lactobacillus were secondary degraders. 2) Niche differentiation and metabolic outputs depended on baseline microbiota. JH maintained higher α-diversity than DLY (P< 0.05) at most time points except 3, 9, 18, and 72 h, and higher total SCFAs at 1, 9, 48, and 72 h (P< 0.05). Notably, pentose and glucuronate interconversions were representative of the predicted metabolic functions that distinguish JH and DLY. 3) Time-resolved microbial networks revealed that arabinoxylan-induced metabolic divergence relied on complex ecological interactions. SCFA levels were associated with CARG abundance and microbial interactions during fermentation. Regression models identified Limosilactobacillus mucosae, Lactobacillus delbrueckii subsp. jakobsenii, Limosilactobacillus balticus, and Limosilactobacillus agrestimuris as acetate-associated taxa. (Conclusion) While both breeds shared core arabinoxylan utilization mechanisms, the microbiome in JH demonstrated superior SCFA metabolism. Higher baseline diversity facilitated enhanced fiber degradation, with niche dynamics dictating metabolic outcomes. CARGs served as robust predictors of SCFA production. This study underscores the pivotal role of intricate microecological networks in dietary fiber metabolism, advancing mechanistic understanding of fermentation kinetics for precision nutritional strategies.

Integrated physiological, biochemical and transcriptomic analyses revealed key genes regulating ascorbic acid biosynthesis during sweet potato development

High temperature (HT, ≥ 38°C) impairs maize (Zea mays L.) yield by disrupting pollination, yet mechanisms in female reproductive organs remain elusive. Maize silks, the essential tissues for pollen capture and pollen tube growth, are particularly sensitive to HT, are highly vulnerable to HT. Here, we combined phenotypic, physiological, metabolic and transcriptomic analyses under controlled HT (40/30°C) and control (32/22°C) conditions to dissect mechanisms underlying HT-induced silk growth inhibition (SGI) and silk pollination dysfunction (SPD). HT reduced silk emergence by ~20% but decreased seed set by ~50%, indicating SPD dominated kernel loss over SGI. HT significantly downregulated key genes of the silks that encode sucrose transporters, sugars will eventually be exported through transporters and glycolytic enzymes (hexokinase; 6-phosphofructokinase; pyruvate kinase), restricting energy metabolism required for silk elongation and pollen tube growth. Concurrently, HT elevated abscisic acid and indole-3-acetic acid while suppressing zeatin riboside, brassinolide and jasmonic acid levels, collectively driving SGI. SPD was primarily linked to oxidative damage via suppressed flavonoid biosynthesis (chalcone synthase, flavonol synthase and peroxidase) and impaired reactive oxygen species (ROS) scavenging. Specifically, HT induced a negative correlation between ZmARF1 and ZmSOD3 expression, suggesting compromised ROS clearance that exacerbated silk structural damage. These findings provide new insights into the metabolic, hormonal and transcriptional regulatory networks that govern silk thermotolerance, providing potential molecular targets for breeding heat-resilient maize varieties.

BackgroundRecent studies suggest that intratumoral microbiome and altered metabolic networks play crucial roles in pancreatic cancer (PC) progression. However, the precise interplay between microbial communities and tumor metabolism in PC remains poorly understood. This study aims to investigate the impact of the intratumoral microbiome, the metabolic landscape, and their interactions on PC development.Methods16S rDNA sequencing and Untargeted metabolomic profiling were performed on 47 paired pancreatic cancer and adjacent normal tissues to analyze their intratumoral microbiome and metabolic landscapes. Bioinformatics tools were used to conduct differential microbiome abundance analysis and pathway enrichment. A correlation analysis was performed to identify key microbiota-metabolite interactions.Results16S rDNA sequencing revealed significant differences in the abundance and diversity (α-diversity and β-diversity) of the intratumoral microbiome in PC. The predominant species in pancreatic cancer were Pseudomonas. Enrichment analysis showed that amino acid metabolic pathways, including Arginine and Proline Metabolism, Arginine Biosynthesis, were significantly enriched in PC. Untargeted metabolomics identified 298 metabolites that were significantly altered in PC (fold change > 1.5, P-value < 0.05). These included amino acid metabolites such as Lys-Leu, Pro-Leu, Arg-Leu, Lys-Val, His-Lys, and others. Functional enrichment analysis highlighted several metabolic pathways that play important roles in pancreatic cancer, including Glycine, Serine, and Threonine Metabolism, Amino Acid Biosynthesis, Metabolic Pathways and Cysteine and Methionine Metabolism. Correlation analysis between microbiome and metabolic data revealed significant associations between Pseudomonas and several metabolites, including Alpha-ketoisovaleric acid, 16-hydroxyhexadecanoic acid, Myristic acid, Nonanoic acid (the Spearman correlation coefficient r, 0.5 ≤|r|≤ 1 and P-value < 0.05).ConclusionThis study suggests a relationship between the microbiome and metabolism in pancreatic cancer. We observed that Pseudomonas contributes to altered amino acid metabolism, but whether this interaction is causal and the mechanisms underlying it remain unclear. Further experimental validation is required before considering microbiome-targeted metabolic interventions as viable therapeutic strategies.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12943-025-02458-9.

Antibiotic-induced perturbations in C-N metabolic networks, and associated gene pathways in soybean (Glycine max) seedlings.

Efficiency of removing estradiol from soil by twice-ball milled magnetic biochar: Mechanism and microbial interaction.

Metabolomics reveals that Da Chuanxiong Formula ameliorates cerebral ischemia/reperfusion injury via regulating membrane phospholipid homeostasis.

Carboxylic acid metabolism in cancer: Mechanisms, microenvironment interactions, and therapeutic opportunities.

Temperature adaptability drives functional diversity and horizontal gene transfer within microbial communities in Daqu solid-state fermentation.

Chebulinic acid from Chebulae fructus alleviates influenza virus-induced acute lung injury by inhibiting IDO1-Kyn axis activation.

Sequence alignment, pattern matching, and mining are important cornerstones in bioinformatics, and they include identifying genome structure, protein function, and biological metabolic regulatory network. However, because it helps speed up the dealing process, the parallel sequential pattern recognition method has gained attention as data volume has increased. This review summarizes the GPU-based sequence alignment, pattern matching, and mining with the tools and their applications in bioinformatics. After giving an overview of the background, this review first introduces the concept and database of sequence alignment, pattern matching, and mining. Then, the basic architecture and parallel computing principle of GPU are briefly described. Next, the design of GPU-based algorithms and optimization strategies in sequence alignment, pattern matching, and mining are listed in detail. By comparing and analyzing the existing research, the summarization of the advantages and challenges of GPU application in bioinformatics are given. Finally, the future research direction is prospected, including the further development of the algorithm combined with machine learning and deep learning.

Transcriptomic shifts in Microtus ochrogaster neurogenic niches reveal psychiatric-risk pathways engaged by pair-bond formation.