EC2KEGG: a command line tool for comparison of metabolic pathways

The involvement of the intratumoral microbiome in gastric cancer (GC) pathogenesis remains insufficiently characterized, particularly in early gastric cancer (EGC). Here, we explored the relationship between the microbial community and EGC, with a specific focus on Helicobacter pylori (Hp)-negative microbiota. Gastric mucosal microbiota from patients with EGC (n = 76) and healthy controls (HC, n = 39) were analyzed using 16 S rRNA V3-V4 amplicon sequencing. Linear discriminant analysis effect size (LEfSe) and random forest modeling were employed to identify microbial taxa that could serve as potential biomarkers distinguishing EGC from HC. In addition, the functional potential of the microbial communities was predicted using PICRUSt2, with annotation based on KEGG pathways. A total of 115 participants contributed 306 gastric mucosal samples. Significant microbial differences were observed across groups stratified by EGC and Hp status. In EGC-HP-Negative (EHN) group, Firmicutes and Actinobacteria decreased and Proteobacteria increased, accompanied by a reduced Firmicutes/Proteobacteria ratio. LEfSe analysis indicated that Prevotella and Enterococcus were markedly enriched, each presenting LDA scores above 4. In the random forest model, Prevotella was identified as the most influential genus for separating EHN from HC-HP-Negative (HHN), with the classifier achieving an AUC of 0.90. Microbial network complexity was highest in general EGC and lowest in EHN, whereas Hp infection enhanced network connectivity. Functional prediction indicated distinct microbial functional shifts among groups, characterized by enrichment of bile secretion and metabolic pathways in EHN and increased RNA-related pathways in EHP compared with HC. Gastric microbiota in EGC shows distinct alterations both with and without Hp. Prevotella may serve as a promising non-Hp microbial biomarker for early detection, while Hp further shapes microbial composition, network interactions, and pro-inflammatory pathways. These findings highlight the potential of targeting gastric microbiota for early diagnosis and therapeutic strategies in EGC.

Different software processing affects the peak picking and metabolic pathway recognition of metabolomics data

BackgroundMetabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source.ResultsWhile using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme (maltooligosyl-trehalose synthase, EC 5.4.99.15). The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway.ConclusionsFindings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations.

Purpose: Optimization of pancreatic ductal adenocarcinoma (PDAC) patient survival will depend on advancing our understanding of the mechanisms governing tumor cell plasticity and effectively mitigating their invasive and metastatic capabilities. This study aims to investigate the correlation of cancer biomarkers crucial to these processes by integrating molecular signatures with histopathological characteristics using single-cell RNA sequencing (scRNA-seq) and multiplexed IHC/IF. Methods: Single-cell RNA sequencing (scRNA-seq) data from a comprehensive dataset of over 53,000 PDAC cells and validation of lead targets through 14-plex immunofluorescence, using a customized MICS platform (Miltenyi Biotec) and dual color IHC were employed to assess protein expression of defining biomarkers of basal and classical subtypes of PDAC including Keratin 17 (K17) and GATA6. Results: scRNAseq analysis revealed distinct patterns of K17 and GATA6 expression, highlighting intratumoral heterogeneity among PDAC tumors. Specifically, populations of cells expressing K17+/GATA6+ and K17+/GATA6- exhibited significant enrichment in pathways associated with invasion and cell migration, including PI3K/Akt/mTOR, TGF-β, and epithelial-mesenchymal transition (EMT) signaling. Conversely, GATA6-/K17- cells demonstrated enrichment in biosynthetic and metabolic pathways that govern cell cycle regulation. Additionally, mIF data assessment revealed correlations between histomorphological patterns and dynamic shifts in K17 and GATA6 expression within PDAC tumors. Diffusely infiltrative tumor cells (DITCs) uniformly expressed K17, contrasting with the gland-forming components (GFCs) of PDAC, which showed enrichment in GATA6 and sporadic expression of K17. E-cadherin, a key adhesion marker, exhibited the highest expression levels in the glandular and solid components of PDAC, underscoring their epithelial characteristics. Conversely, vimentin, a marker typically associated with epithelial-to-mesenchymal transition (EMT), was significantly expressed in DITCs, suggesting a mesenchymal-like phenotype in these infiltrative cells. Dual color IHC further validated these findings, reinforcing the correlation between morphologic plasticity and the expression patterns of K17 and GATA6, emphasizing their potential to distinguish distinct tumor cell populations within PDAC. Conclusions: These insights suggest that the interaction between these markers may indicate a distinct biological state characterized by altered cellular metabolism and potentially different therapeutic vulnerabilities. The robust association between K17 expression and diffusely infiltrative tumor cells underscores its potential as a marker for the most aggressive forms of this disease. Our results highlight the heterogeneous nature of PDAC and emphasize the critical importance of understanding these molecular distinctions in histopathological contexts to develop targeted therapies aimed at improving patient survival. Citation Format: Lyanne A Delgado- Coka, Brian Nelson, Michael Horowitz, Shayan Sarkar, Natalia Marchenko, Scott Powers, Luisa F Escobar-Hoyos, Kenneth Shroyer. Keratin 17 and GATA6 correlate with diffusely infiltrative versus gland-forming components of PDAC: Uncovering the transitional state in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C070.

Non-small cell lung cancer (NSCLC) persists as a major contributor to global cancer morbidity and mortality. This study explores the complex interplay between bile acid metabolism (BAM), tumor invasion, and the immune microenvironment in NSCLC pathogenesis. While traditionally known for digestive functions, bile acids are crucial signaling molecules, and their metabolic pathways, coupled with invasion-related genes (IRGs), are increasingly implicated in tumorigenesis. Using the NSCLC dataset GSE225620, we identified 109 differentially expressed genes at the intersection of BAM and invasion (BAM&IRDEGs). Functional analysis revealed their significant enrichment in metabolic pathways, including fatty acid metabolism, which are critical for shaping the tumor microenvironment and fostering aggressive growth. Immune infiltration analysis indicated significant remodeling, particularly highlighting a strong correlation between activated CD8⁺ T cells and central memory CD4⁺ T cells. Key genes, including the glutamine transporter SLC1A5 and the fatty acid translocase CD36, emerged as critical nodes. Our analysis suggests these genes contribute to an immunosuppressive microenvironment by fueling tumor metabolic reprogramming and inducing CD8⁺ T cell dysfunction through altered lipid and glutamine metabolism. Leveraging these key genes, we established a therapeutic monitoring model using LASSO regression and support vector machine (SVM) algorithms. The model demonstrated robust performance in predicting treatment efficacy, underscoring its potential for monitoring therapeutic response and guiding individualized treatment decisions. Our findings highlight the clinical relevance of the metabolic-immune axis in NSCLC and propose that key BAM&IRDEGs, particularly SLC1A5 and CD36, are not only potential therapeutic targets but also promising candidates for inclusion in liquid biopsy panels. Such non-invasive tools could enable dynamic monitoring of disease progression and early detection of relapse, paving the way for more precise NSCLC management.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12672-025-03925-x.

Sex-specific proteomic analysis of epileptic brain tissues from Pten knockout mice and human refractory epilepsy

RationaleEpilepsy presents significant sex-based disparities in prevalence and manifestation. Epidemiological studies reveal that epilepsy is more prevalent in males, with lesional types being more common, whereas idiopathic generalized epilepsies are more frequently observed in females. These differences stress the importance of considering sex-specific factors in epilepsy diagnosis, treatment, and mechanistic research using preclinical models. To elucidate potential molecular differences that could explain these disparities and inform personalized treatment strategies, we conducted a proteomic analysis of epileptic brain tissues from both an experimental mouse model of genetic epilepsy and humans with drug-resistant epilepsy (DRE).MethodsWe employed mass spectrometry-based proteomic analysis on brain tissues from DRE patients and thePtenknockout (KO) mouse model of genetic epilepsy with focal cortical dysplasia. Mouse samples included hippocampi from adult wild-type (WT) andPtenKO mice (4-5 per group and sex). Human samples included temporal cortex from 12 DRE adult patients (7 males, 5 females) and 5 non-epileptic (NE) controls (2 males, 3 females). Brain biopsies were collected with patients’ informed consent under approved IRB protocols (Indiana University Health Biorepository). Proteomic profiles were analyzed using principal component analysis (PCA) along with volcano plots to identify significant changes in protein expression. The enrichment analysis of differentially expressed proteins was conducted by Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway.ResultsPCA revealed distinct clustering of brain proteomes between epilepsy and control cases in both human and mice, with 390 proteins showing significant differences in human and 437 proteins in mouse samples. These proteins are primarily associated with ion channels, synaptic processes, and neuronal energy regulation. In the mouse model, males have more pronounced proteomic changes than females, with enrichment in metabolic pathways and VEGF signaling pathway, indicating a more severe vascular permeability impairment in males. In human DRE cases, 118 proteins were significantly changed by comparing epileptic females to males. Pathway analysis revealed changes in metabolic pathways and the HIF-1 signaling pathway, indicating that altered neuronal activity and inflammation may lead to increased oxygen consumption.ConclusionThese findings highlight significant differences between epilepsy and control brain samples in both humans and mice. Sex-specific analysis revealed distinct pathway enrichments between females and males, with males exhibiting a broader range of alterations, suggesting more extensive proteomic alterations. This study offers valuable insights into potential underlying mechanisms of epilepsy and underscores the importance of considering sex as a key factor in epilepsy research and therapeutic development.

Tomato (Solanum lycopersicum) is among the most important commercial horticultural crops worldwide. The crop quality and production is largely hampered due to the fungal pathogen Alternaria solani causing necrotrophic foliage early blight disease. Crop plants usually respond to the biotic challenges with altered metabolic composition and physiological perturbations. We have deciphered altered metabolite composition, modulated metabolic pathways and identified metabolite biomarkers in A. solani-challenged susceptible tomato variety Kashi Aman using Liquid Chromatography-Mass Spectrometry (LC–MS) based metabolomics. Alteration in the metabolite feature composition of pathogen-challenged (m/z 9405) and non-challenged (m/z 9667) plant leaves including 8487 infection-exclusive and 8742 non-infection exclusive features was observed. Functional annotation revealed putatively annotated metabolites and pathway mapping indicated their enrichment in metabolic pathways, biosynthesis of secondary metabolites, ubiquinone and terpenoid-quinones, brassinosteroids, steroids, terpenoids, phenylpropanoids, carotenoids, oxy/sphingolipids and metabolism of biotin and porphyrin. PCA, multivariate PLS-DA and OPLS-DA analysis showed sample discrimination. Significantly up regulated 481 and down regulated 548 metabolite features were identified based on the fold change (threshold ≥ 2.0). OPLS-DA model based on variable importance in projection (VIP scores) and FC threshold (> 2.0) revealed 41 up regulated discriminant metabolite features annotated as sphingosine, fecosterol, melatonin, serotonin, glucose 6-phosphate, zeatin, dihydrozeatin and zeatin-β-d-glucoside. Similarly, 23 down regulated discriminant metabolites included histidinol, 4-aminobutyraldehyde, propanoate, tyramine and linalool. Melatonin and serotonin in the leaves were the two indoleamines being reported for the first time in tomato in response to the early blight pathogen. Receiver operating characteristic (ROC)-based biomarker analysis identified apigenin-7-glucoside, uridine, adenosyl-homocysteine, cGMP, tyrosine, pantothenic acid, riboflavin (as up regulated) and adenosine, homocyctine and azmaline (as down regulated) biomarkers. These results could aid in the development of metabolite-quantitative trait loci (mQTL). Furthermore, stress-induced biosynthetic pathways may be the potential targets for modifications through breeding programs or genetic engineering for improving crop performance in the fields.

Tender coconut water tastes sweet and is enjoyed by consumers, but its commercial development is restricted by an extremely short shelf life, which cannot be explained by existing research. UPLC-MS/MS-based metabolomics methods were used to identify and statistically analyze metabolites in coconut water under refrigerated storage. A multivariate statistical analysis method was used to analyze the UPLC-MS/MS datasets from 35 tender coconut water samples stored for 0–6 weeks. In addition, we identified other differentially expressed metabolites by selecting p-values and fold changes. Hierarchical cluster analysis and association analysis were performed with the differentially expressed metabolites. Metabolic pathways were analyzed using the KEGG database and the MetPA module of MetaboAnalyst. A total of 72 differentially expressed metabolites were identified in all groups. The OPLS-DA score chart showed that all samples were well grouped. Thirty-one metabolic pathways were enriched in the week 0–1 samples. The results showed that after a tender coconut is peeled, the maximum storage time at 4 °C is 1 week. Analysis of metabolic pathways related to coconut water storage using the KEGG and MetPA databases revealed that amino acid metabolism is one of the main causes of coconut water quality deterioration.

Environmental concentrations of antibiotics alter the zebrafish gut microbiome structure and potential functions.

BackgroundThe KEGG PATHWAY database provides a plethora of pathways for a diversity of organisms. All pathway components are directly linked to other KEGG databases, such as KEGG COMPOUND or KEGG REACTION. Therefore, the pathways can be extended with an enormous amount of information and provide a foundation for initial structural modeling approaches. As a drawback, KGML-formatted KEGG pathways are primarily designed for visualization purposes and often omit important details for the sake of a clear arrangement of its entries. Thus, a direct conversion into systems biology models would produce incomplete and erroneous models.ResultsHere, we present a precise method for processing and converting KEGG pathways into initial metabolic and signaling models encoded in the standardized community pathway formats SBML (Levels 2 and 3) and BioPAX (Levels 2 and 3). This method involves correcting invalid or incomplete KGML content, creating complete and valid stoichiometric reactions, translating relations to signaling models and augmenting the pathway content with various information, such as cross-references to Entrez Gene, OMIM, UniProt ChEBI, and many more.Finally, we compare several existing conversion tools for KEGG pathways and show that the conversion from KEGG to BioPAX does not involve a loss of information, whilst lossless translations to SBML can only be performed using SBML Level 3, including its recently proposed qualitative models and groups extension packages.ConclusionsBuilding correct BioPAX and SBML signaling models from the KEGG database is a unique characteristic of the proposed method. Further, there is no other approach that is able to appropriately construct metabolic models from KEGG pathways, including correct reactions with stoichiometry. The resulting initial models, which contain valid and comprehensive SBML or BioPAX code and a multitude of cross-references, lay the foundation to facilitate further modeling steps.

BackgroundThe role of circulating metabolome in cognitive impairment is limited and inconclusive. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors.MethodIn the community‐based Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance (NMR) spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired group according to their performance in two objective cognitive tests. A two‐step strategy of cross‐sectional discovery followed by prospective validation was applied to identify cognitive impairment‐related metabolites. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately‐to‐severely impaired) during the 2‐year follow‐up to 204 cognitively normal controls by age and sex with a 1:3 ratio and conducted conditional logistic regression. Metabolite set enrichment analysis was performed to identify cognitive impairment‐related metabolic pathways. We constructed prediction models for incident cognitive impairment using Lasso regression.ResultWe identified 28 metabolites cross‐sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were each prospectively associated with 28%, 28%, 29%, and 33% lower risk of incident cognitive impairment. Based on incident cognitive impairment‐related metabolites, three enriched metabolic pathways (valine, leucine, and isoleucine biosynthesis; valine, leucine, and isoleucine degradation; and aminoacyl‐tRNA biosynthesis) were found. Incorporating 13 metabolite biomarkers selected by Lasso regression into the traditional risk factors‐based prediction model substantially improved the prediction performance on incident cognitive impairment (AUCs: 0.839 vs. 0.703, P <0.001).ConclusionThe study identified specific plasma metabolites and potential enriched metabolic pathways related to cognitive impairment. Utilizing the selected metabolites substantially improved the prediction performance for cognitive impairment.

The cotton aphid, Aphis gossypii, is a globally significant agricultural pest whose microbiota plays vital roles in its physiology and adaptation. However, the dynamics of bacterial communities across its developmental stages remain poorly understood. This study employed full-length 16S rRNA gene sequencing to characterize the microbiota structure, diversity, and functional potential in nine developmental stages of A. gossypii, including egg, nymph (1-, 3-, 5-, 7-day-old), and adult (1-, 3-, 5-, 7-day-old). Results revealed Proteobacteria (72.75–95.51%) as the dominant phylum across all stages, with Buchnera aphidicola (primary obligate symbiont) constituting over 23.83% of bacterial abundance and peaking in eggs (≈80%). Alpha diversity indices (Shannon, Simpson) indicated significantly higher microbial diversity in nymphs compared to adults, suggesting stage-specific ecological interactions. While beta diversity analysis showed no structural clustering by developmental stage, functional predictions highlighted enrichment in metabolic pathways (>73% of genes), though limitations in 16S-based functional inference were noted. Notably, facultative symbionts like Hamiltonella or Serratia were absent, contrasting with other aphid systems. Dynamic shifts in Buchnera titer and the prominence of Delftia tsuruhatensis and Enterobacter hormaechei implied potential roles in host adaptation. These findings highlight the persistent dominance of the obligate symbiont Buchnera aphidicola across all developmental stages, despite quantitative fluctuations in its abundance, alongside stage-specific shifts in facultative bacterial communities, offering insights into novel targets for microbiome-driven pest management strategies. Further multi-omics approaches are warranted to validate functional contributions of these microbial communities.

The β‐galactoside binding lectins, termed galectins, have garnered immense scientific interest for their ability to regulate diverse biologic processes, from adaptive immune responses to epithelial cell biology. Yet, an understanding of the role of galectins in regulating developmental processes has been limited by the functional redundancies across galectins which exist in vertebrates. Thus, we utilized the model organism Drosophila, which contains a single galectin ortholog, to ask what are the developmental requirements for galectins in metazoans. Using CRISPR/Cas9, we generated deletions in the galectin locus which efficiently eliminated galectin protein (Dmgal) across tissues. Intriguing, loss of galectin is not essential in Drosophila as animals ecclose with relatively minimal phenotypes. Among these, dmgal‐deficient animals are significantly smaller than their wild‐type counterparts. Moreover, when challenged to nutrient‐deprivation or Paraquat‐toxicity, dmgal‐deficient animals show marked sensitivity compared to their wild‐type animals. Transcriptomics analysis reveals that dmgal‐deficient animals show significantly different profiles than their wild‐type counterparts, with enrichment in metabolic pathways. Similarly, metabolomics shows distinct profiles from dmgal‐deficient and wild‐type animals with significant alterations in amino acid and nutrient‐sensing pathways. Finally, analysis of the Drosophila fat body reveals alterations in fat content, and tissue specific deletion of galectin in this organ renders animals sensitive to nutrient loss. Taken together, these findings uncover a novel role for dmgal in regulating Drosophila metabolism in the Drosophila fat body, and may provide clues into the role of galectins in regulating insulin sensitivity and NASH/NAFLD in vertebrates.

Transcriptome and lipidome integration unveils mechanisms of fatty liver formation in Shitou geese