clusterMaker2: a major update to clusterMaker, a multi-algorithm clustering app for Cytoscape

Temporal fruit microbiome and immunity dynamics in postharvest apple (Malus x domestica)

The plant immune response plays a central role in maintaining a well-balanced and healthy microbiome for plant health. However, insights into how the fruit immune response and the fruit microbiome influence fruit health after harvest are limited. We investigated the temporal dynamics of the fruit microbiota and host defense gene expression patterns during postharvest storage of apple fruits at room temperature. Our results demonstrate a temporal dynamic shift in both bacterial and fungal community composition during postharvest storage that coincides with a steep-decline in host defense response gene expression associated with pattern-triggered immunity. We observed the gradual appearance of putative pathogenic/spoilage microbes belonging to genera Alternaria (fungi) and Gluconobacter and Acetobacter (bacteria) at the expense of Sporobolomyces and other genera, which have been suggested to be beneficial for plant hosts. Moreover, artificial induction of pattern-triggered immunity in apple fruit with the flg22 peptide delayed the onset of fruit rot caused by the fungal pathogen Penicillium expansum. Our results suggest that the fruit immune response helps to orchestrate a microbiome and that the collapse of the immunity results in the proliferation of spoilage microbes and fruit rot. These findings hold implications for the development of strategies to increase fruit quality and prolong shelf life in fruits and vegetables.

Spatial profiling of ANO7 in prostate tissue: links to AR-signalling-associated lipid metabolism and inflammation

Prostate cancer (PrCa) is highly prevalent in the Western world. Currently, however, there are many unmet needs in PrCa care, for example in distinguishing between clinically significant and indolent cases in early phases of the disease. ANO7 is a prostate-specific gene associated with PrCa risk and prognosis, but its exact function in the prostate remains unclear. This study investigates the role of ANO7 in benign prostatic epithelium using spatial transcriptomics by examining differences between ANO7-expressing and non-expressing epithelial regions and their corresponding stromal compartments. A total of 18,676 protein-coding genes were assessed from prostatectomy samples collected from patients with localised prostate cancer. In the collected sample cohort, ANO7 exhibited a distinct, heterogeneous, on-off epithelial expression pattern, enabling an in-depth analysis of ANO7-dependent processes. ANO7-positive epithelium was predominantly enriched with luminal epithelial cells and a specific NK cell subtype, CD56bright. In contrast, ANO7-negative regions were characterised by enrichment of club cells, inflammation, and features of proliferative inflammatory atrophy. Gene-set enrichment analysis revealed that ANO7 expression is associated with androgen receptor (AR) signalling and lipid metabolism. A detailed analysis of differentially expressed genes identified an ANO7- signature, which consisted of genes co-expressed with ANO7 in luminal cells, that demonstrated high consistency in bulk RNA-sequencing (RNA-seq) data. The ANO7-signature was enriched for AR-regulated genes, which highlighted lipid metabolism processes, particularly arachidonic acid metabolism, as a key metabolic feature of the ANO7-positive epithelium. Furthermore, the ANO7-signature demonstrated clinical significance in low-grade PrCa, correlating with a better response to therapy. In summary, these results highlight the potential role of ANO7 in regulating lipid metabolism associated with androgen signalling in benign luminal cells and low-grade cancer, reinforcing the hypothesis that ANO7 functions as a tumour suppressor. © 2025 The Pathological Society of Great Britain and Ireland.

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing

MAIN CONCLUSION

Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

Deciphering age-related transcriptomic changes in the mouse retinal pigment epithelium

Aging of the retinal pigment epithelium (RPE) leads to a gradual decline in RPE homeostasis over time, significantly impacting retinal health. Understanding the mechanisms underlying RPE aging is crucial for elucidating the background in which many age-related retinal pathologies develop. In this study, we compared the transcriptomes of young and aged mouse RPE and observed a marked upregulation of immunogenic, proinflammatory, and oxidative stress genes in aging RPE. Additionally, aging RPE exhibited dysregulation of pathways associated with visual perception and extracellular matrix production. Research on aging in post-natal quiescent RPE is hindered by the absence of relevant in vitro models. Here, we evaluated an in vitro model of chronologically aged primary human RPE to address this gap and observed gene expression patterns comparable to native-aged RPE. Gene expression profiling in this model highlighted its potential utility in investigating cellular and molecular mechanisms of RPE aging and in screening of therapeutic compounds. In conclusion, our findings underscore the pivotal role of inflammation, immune activation, and oxidative stress in the aging RPE landscape and provide insights into why age increases the risk of retinal pathologies.

The FunCoup Cytoscape App: multi-species network analysis and visualization

MOTIVATION

Functional association networks, such as FunCoup, are crucial for analyzing complex gene interactions. To facilitate the analysis and visualization of such genome-wide networks, there is a need for seamless integration with powerful network analysis tools like Cytoscape.

RESULTS

The FunCoup Cytoscape App integrates the FunCoup web service API with Cytoscape, allowing users to visualize and analyze gene interaction networks for 640 species. Users can input gene identifiers and customize search parameters, using various network expansion algorithms like group or independent gene search, MaxLink, and TOPAS. The app maintains consistent visualizations with the FunCoup website, providing detailed node and link information, including tissue and pathway gene annotations. The integration with Cytoscape plugins, such as ClusterMaker2, enhances the analytical capabilities of FunCoup, as exemplified by the identification of the Myasthenia gravis disease module along with potential new therapeutic targets.

AVAILABILITY AND IMPLEMENTATION

The FunCoup Cytoscape App is developed using the Java OSGi framework, with UI components implemented in Java Swing and build support from Maven. The App is available as a JAR file at https://bitbucket.org/sonnhammergroup/funcoup_cytoscape/ repository, and can be downloaded from the Cytoscape App store https://apps.cytoscape.org/apps/funcoup.

Functional Analysis of MS-Based Proteomics Data: From Protein Groups to Networks

Mass spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualization and network analysis of bottom-up MS-based proteomics data sets.

miR-1, miR-133a, miR-29b and skeletal muscle fibrosis in chronic limb-threatening ischaemia

Chronic limb-threatening ischaemia (CLTI), the most severe manifestation of peripheral arterial disease (PAD), is associated with a poor prognosis and high amputation rates. Despite novel therapeutic approaches being investigated, no significant clinical benefits have been observed yet. Understanding the molecular pathways of skeletal muscle dysfunction in CLTI is crucial for designing successful treatments. This study aimed to identify miRNAs dysregulated in muscle biopsies from PAD cohorts. Using MIcroRNA ENrichment TURned NETwork (MIENTURNET) on a publicly accessible RNA-sequencing dataset of PAD cohorts, we identified a list of miRNAs that were over-represented among the upregulated differentially expressed genes (DEGs) in CLTI. Next, we validated the altered expression of these miRNAs and their targets in mice with hindlimb ischaemia (HLI). Our results showed a significant downregulation of miR-1, miR-133a, and miR-29b levels in the ischaemic limbs versus the contralateral non-ischaemic limb. A miRNA target protein-protein interaction network identified extracellular matrix components, including collagen-1a1, -3a1, and -4a1, fibronectin-1, fibrin-1, matrix metalloproteinase-2 and -14, and Sparc, which were upregulated in the ischaemic muscle of mice. This is the first study to identify miR-1, miR-133a, and miR-29b as potential contributors to fibrosis and vascular pathology in CLTI muscle, which supports their potential as novel therapeutic agents for this condition.

Combatting cellular immortality in cancers by targeting the shelterin protein complex

Shelterin proteins (TERF1, TERF2, TPP1, TINF2, POT1) protect telomeres, prevent unwarranted repair activation, and regulate telomerase activity. Alterations in these proteins can lead to cancer progression. This study uses an in-silico approach to examine shelterin in tumour samples across various cancers, employing mutation plots, phylogenetic trees, and sequence alignments. Network pharmacology identified TERF1 as an essential shelterin protein and transcription factors RUNX1, CTCF, and KDM2B as potential biomarkers due to their interactions with miRNAs and shelterin proteins. We performed MCODE analysis to identify subnetworks of ncRNAs interacting with the shelterin proteins. Shelterin expression predicted patient survival in 24 cancer types, with TERF1, TERF2, TINF2, and POT1 significantly expressed in testicular, AML, prostate, breast and renal cancers, respectively, and TPP1 in AML and skin cancer. Spearman and Pearson's analyses showed significant correlations of TERF1 across cancers, with near-significant correlations for all five proteins in different cancer datasets like breast cancer, kidney renal papillary and lung squamous cell carcinoma, skin cutaneous melanoma, etc.,. Shelterin expression correlated with patient survival in breast, renal, lung, skin, uterine, and gastric cancers. Insights into TPP1-associated glycans highlighted glycosylated sites contributing to tumorigenesis. This study provides molecular signatures for further functional and therapeutic research on shelterin, highlighting its potential as a target for anti-cancer therapies and promising prospects for cancer prognosis and prediction.

Elevated levels of butyric acid in the jejunum of an animal model of broiler chickens: from early onset of Clostridium perfringens infection to clinical disease of necrotic enteritis

BACKGROUND

Necrotic enteritis (NE) is an economically important disease of broiler chickens caused by Clostridium perfringens (CP). The pathogenesis, or disease process, of NE is still not clear. This study aimed to identify the alterations of metabolites and metabolic pathways associated with subclinical or clinical NE in CP infected birds and to investigate the possible variations in the metabolic profile of birds infected with different isolates of CP.

METHODOLOGY

Using a well-established NE model, the protein content of feed was changed abruptly before exposing birds to CP isolates with different toxin genes combinations (cpa, cpb2, netB, tpeL; cpa, cpb2, netB; or cpa, cpb2). Metabolomics analysis of jejunal contents was performed by a targeted, fully quantitative LC-MS/MS based assay.

RESULTS

This study detected statistically significant differential expression of 34 metabolites including organic acids, amino acids, fatty acids, and biogenic amines, including elevation of butyric acid at onset of NE in broiler chickens. Subsequent analysis of broilers infected with CP isolates with different toxin gene combinations confirmed an elevation of butyric acid consistently among 21 differentially expressed metabolites including organic acids, amino acids, and biogenic amines, underscoring its potential role during the development of NE. Furthermore, protein-metabolite network analysis revealed significant alterations in butyric acid and arginine-proline metabolisms.

CONCLUSION

This study indicates a significant metabolic difference between CP-infected and non-infected broiler chickens. Among all the metabolites, butyric acid increased significantly in CP-infected birds compared to non-infected healthy broilers. Logistic regression analysis revealed a positive association between butyric acid (coefficient: 1.23, P < 0.01) and CP infection, while showing a negative association with amino acid metabolism. These findings suggest that butyric acid could be a crucial metabolite linked to the occurrence of NE in broiler chickens and may serve as an early indicator of the disease at the farm level. Further metabolomic experiments using different NE animal models and field studies are needed to determine the specificity and to validate metabolites associated with NE, regardless of predisposing factors.

Multi-omics reveals new links between Fructosamine-3-Kinase (FN3K) and core metabolic pathways

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the Tree of Life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) HepG2 cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), and carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3K and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. Our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance and NAD-dependent metabolic processes are altered.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Illuminating the functions of the understudied Fructosamine-3-kinase (FN3K) using a multi-omics approach reveals new links to lipid, carbon, and co-factor metabolic pathways

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the tree of life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3Ks and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. By identifying a potential link between FN3Ks, redox regulation, and NAD-dependent metabolic processes, our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance is altered.

Multiplatform Metabolomics Characterization Reveals Novel Metabolites and Phospholipid Compositional Rules of Haemophilus influenzae Rd KW20

Haemophilus influenzae is a gram-negative bacterium of relevant clinical interest. H. influenzae Rd KW20 was the first organism to be sequenced and for which a genome-scale metabolic model (GEM) was developed. However, current H. influenzae GEMs are unable to capture several aspects of metabolome nature related to metabolite pools. To directly and comprehensively characterize the endometabolome of H. influenzae Rd KW20, we performed a multiplatform MS-based metabolomics approach combining LC-MS, GC-MS and CE-MS. We obtained direct evidence of 15-20% of the endometabolome present in current H. influenzae GEMs and showed that polar metabolite pools are interconnected through correlating metabolite islands. Notably, we obtained high-quality evidence of 18 metabolites not previously included in H. influenzae GEMs, including the antimicrobial metabolite cyclo(Leu-Pro). Additionally, we comprehensively characterized and evaluated the quantitative composition of the phospholipidome of H. influenzae, revealing that the fatty acyl chain composition is largely independent of the lipid class, as well as that the probability distribution of phospholipids is mostly related to the conditional probability distribution of individual acyl chains. This finding enabled us to provide a rationale for the observed phospholipid profiles and estimate the abundance of low-level species, permitting the expansion of the phospholipidome characterization through predictive probabilistic modelling.