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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

Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA.

COVID-19, severe acute respiratory syndrome coronavirus 2, rapidly spread worldwide. Severe and critical patients are expected to rapidly deteriorate. Although several studies have attempted to uncover the mechanisms underlying COVID-19 severity, most have focused on the perturbations of single genes. However, the complex mechanism of COVID-19 involves numerous perturbed genes in a molecular network rather than a single abnormal gene. Thus, we aimed to identify COVID-19 severity-specific markers in the Japanese population using gene network analysis. In order to reveal the severity-specific molecular interplays, we developed a novel computational network biology strategy that measures dissimilarity between networks based on the comprehensive information of gene network (i.e., expression levels of genes and network structure) by using Kullback-Leibler divergence. Monte Carlo simulations demonstrated the effectiveness of our strategy for differential gene network analysis. We applied this method to publicly available whole blood RNA-seq data from the Japan coronavirus disease 2019 Task Force and identified differentially regulated molecular interplays between 368 severe and 105 non-severe samples. Our analysis suggests the gene network between HLA class II, CIITA, and CD74 as a COVID-19 severity specific molecular marker. Although the association between HLA class II and COVID-19 has been demonstrated, our data analysis revealed that the molecular interplay of HLA class II with its target and/or regulator is a crucial marker for COVID-19 severity. Our findings from computational network biology analysis suggest that suppression and activation of the molecular interplay between HLA class II, CIITA, and CD74 provide crucial clues to uncover the mechanisms of COVID-19 severity.

Humans↗
PubMed2026

MetaCCI: meta cell-cell interaction inference and its application to CCIs characteristics of MDS.

MOTIVATION: Cell-cell interactions (CCIs) are fundamental to multicellular organisms and play crucial roles in diverse biological processes and disease mechanisms. Understanding CCIs is vital for deciphering disease pathogenesis and developing therapeutic strategies. Although numerous computational methods have been developed to infer CCIs from complex biological data, most existing approaches rely primarily on single-gene expression levels and ligand-receptor databases, often failing to capture the nuanced network-wide changes characteristic of disease states. RESULT: We propose MetaCCI, a novel computational strategy that integrates meta-information into CCI inference by extending the traditional gene expression-based analysis to a gene regulatory network framework. MetaCCI meticulously combines established ligand-receptor pairs with quantitative insights into gene behavior within complex gene networks, enabling the precise extraction of relevant targets for CCI inference. Subsequently, CCI inference was performed using an eigen cell co-expression network, providing a more holistic view of cell-cell communication. Monte Carlo simulations demonstrated that MetaCCI consistently outperforms existing methods in CCI inference. We applied MetaCCI to characterize cell-cell communication in Myelodysplastic Syndromes (MDS). Our results identified distinct interaction patterns in MDS compared with normal cell populations, specifically highlighting the loss of CCIs between "Dendritic cells and Hematopoietic precursor cells" and between "Dendritic cells and Hematopoietic multipotent progenitor cells" as characteristic features of MDS. Furthermore, FABP5, CD63, and HMGB1 were identified as MDS-specific markers. These findings suggest that diminished CCIs involving dendritic cells, hematopoietic precursor cells, and multipotent progenitor cells are pivotal to MDS pathogenesis. AVAILABILITY AND IMPLEMENTATION: The MetaCCI software is freely available at https://github.com/HeewonGitHub/MetaCCI. An archived version of the software and example datasets used in this study is available at Zenodo: https://doi.org/10.5281/zenodo.20101527.

Myelodysplastic Syndromes↗
PubMed2026

Kv11.1 (hERG) Protein Interaction Networks Connect Endocytic Trafficking to Polygenic Influences on Cardiac Repolarization.

Polygenic scores (PGS) capture the combined effect of many common genetic variants on quantitative traits and disease risk, yet their functional consequences at the protein level remain poorly defined. Here, we integrated quantitative and interaction proteomics to resolve how polygenic liability for cardiac repolarization manifests in human cells. We studied human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from donors with extreme PGS for QT interval duration, a clinically relevant electrophysiologic trait associated with arrhythmia risk. Global quantitative proteomics revealed increased abundance of mitochondrial proteins in high-PGS cardiomyocytes. To define protein network-level effects on a key repolarizing ion channel, we performed multiplexed affinity purification-mass spectrometry (AP-MS) of Kv11.1. While mitochondrial changes did not directly explain Kv11.1-associated complexes, interactome analysis revealed increased association of Kv11.1 with myosin motor proteins and endosomal recycling machinery in high-PGS cells. These findings suggest altered channel trafficking dynamics of Kv11.1, distinct from the trafficking defects observed in monogenic Kv11.1 variants. Together, these data show that integrating global and interaction proteomics can resolve how polygenic variation reshapes protein networks. Future work using these methods could connect genomic risk to subcellular remodeling and our work provides a generalizable framework to probe the proteomic basis of complex traits. SIGNIFICANCE STATEMENT: Polygenic scores (PGS) predict disease risk, but how biological pathways are influenced by these common variants remains difficult to define. We generated human induced pluripotent stem cells from individuals with extreme high- and low- PGS for QT interval, a key electrocardiographic measure linked to arrhythmia risk. By combining global proteomics and interactomics for a common ion channel involved in regulating the QT interval (Kv11.1) we found potential mechanisms that are influenced by common genetic traits in patients. Our work provides an approach to connect polygenic scores to pathway-level molecular mechanisms in human cells and a general framework for uncovering how complex genetic architecture drives disease-relevant biology.

AP-MS↗
PubMed2026

NAViFluX: a visualization‑centric platform for interactive analysis, refinement and design of genome‑scale metabolic networks.

MOTIVATION: Genome-scale metabolic network (GSMN) models enable flux-based metabolite fate discovery, metabolic engineering, drug target identification, and multi-omics integration. However, programming requirements, architectural complexity, and limited visualization support impede its adoption by the broader scientific community. Existing tools exclusively specialize in GSMN analyses or visualization while lacking important features such as pathway-specific views, database-integrated refinement, and comprehensive enrichment and perturbation analyses. RESULTS: Here, we present NAViFluX (metabolic Network Analysis and Visualization of Flux), a visualization-centric, web browser-based tool that unifies native pathway/subsystem map generation, interactive model refinement via KEGG/BiGG, pathway merging and modules for flux computations, topology, and functional enrichment all within network views. Using three independent case studies on Escherichia coli, the utility of NAViFluX for characterization of nutrient-specific metabolic adaptations, enhancing gene essentiality predictions and interpretability, and rational design of an optimized carbon-fixing metabolic state is demonstrated. AVAILABILITY AND IMPLEMENTATION: All source code and supplementary files associated with the case studies are publicly available via Zenodo at https://zenodo.org/records/19107831. NAViFluX can be easily installed as a standalone software through https://github.com/bnsb-lab-iith/NAViFluX.

Metabolic Networks and Pathways↗
PubMed2025

Historical introgression as a driver of diversification of diploid Picris (Compositae) in the Mediterranean Basin.

The Mediterranean Basin is recognized as one of the world's most prominent biodiversity hotspots, where past climatic changes have driven range shifts, secondary contact between populations, and gene exchange. This study investigates the impact of historical introgression on the diversification of diploid members of the genus Picris (Compositae). Using nuclear and plastid genome data obtained through the Hyb-Seq approach, we assess whether introgression contributed to the evolution of the Mediterranean Picris, potentially giving rise to multiple regional endemics. We also test whether introgression was associated with the transfer of traits such as life strategy and fruit morphology, which are involved in habitat-specific adaptation. Phylogenetic network analysis revealed two major introgression events that shaped evolutionary trajectories within the genus. The earliest and most complex events involved the Turkish endemic P. campylocarpa, which hybridized with the most recent common ancestor (MRCA) of the P. cyprica-P. pauciflora lineage and with the MRCA of the B1 subclade, comprising the P. hieracioides group and the P. scaberrima-P. strigosa lineage. The latter introgression preceded shifts from iteroparity to semelparity and from heterocarpy to homocarpy, ruling out an adaptive introgression origin for these traits. Nevertheless, all detected historical introgression events contributed to the diversification of diploid Picris taxa.

Diploidy↗
PubMed2025

Comprehensive circRNA expression profile and hub genes screening during human liver development.

BACKGROUND: Understanding the expression of non-coding RNA in the liver during embryonic development provides important insights into liver diseases. Therefore, we investigated circular RNA (circRNA) roles in human liver development, an unexplored research domain. METHODS: Using high-throughput sequencing and bioinformatics, we analysed foetal liver samples across developmental stages (7-20 weeks post-conception). Differentially expressed (DE) genes were identified and subjected to enrichment analysis using Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG), and Disease Ontology (DO). Modular analysis was performed using the Search Tool for Retrieval of Interacting Genes (STRING), followed by construction of a protein-protein interaction (PPI) network using Cytoscape software. The key genes were screened using Molecular Complex Detection (MCODE). The mRNA levels of hub genes were validated using quantitative reverse transcription polymerase chain reaction (qRT-PCR). RESULTS: There were 645 DE circRNAs and 5,145 DE mRNAs between human livers at the three growth stages (HB, EH, and LH). It was found that the activity of circRNAs was boosted remarkably in the hepatoblastic stage. Enrichment analysis found they mainly involved in nervous system regulation of liver function, embryonic organ development and digestive system development. In addition, DE circRNAs were primarily involved in the PI3K-AKT, MAPK and calcium pathways, potentially contributing to adult liver diseases. Notably, only hsa_circ_001471 and novel_circ_017382 were simultaneously identified at all stages and were persistently downregulated. A co-expression regulatory network involving these circRNAs was established. Three hub genes (LGR5, FOXL1 and RSPO3) were identified from the PPI network of 167 genes and may play key roles in human liver development. The RT-qPCR validation results were in agreement with the sequencing data. CONCLUSIONS: Our findings provide the first insights into the roles and regulatory networks of circRNAs in human liver development, laying the groundwork for further investigations of molecular and signalling networks.

Humans↗
PubMed2025

Interrogating functional connectivity of in vitro neural glia tissue model modulated through integrative control of matrix stiffness and a neurotrophic factor.

Brain function emerges from intricate cellular communication within neural networks. Both In silico neuronal models and primary neuron cells have revealed that the branching architecture of individual neurons determines the bioelectrical signal propagation pattern and dynamics. However, whether stem cell-differentiated neurons can build functional connectivity regulated by neuronal morphology has yet to be determined. Here, we hypothesized that neurite length, branching, or both factors would regulate the functional connectivity of the stem cell-differentiated neural network. We examined this hypothesis by differentiating mouse cortical neural stem cells (NSCs) on Matrigel substrates with varying storage moduli, both with and without basic fibroblast growth factor (bFGF). Interestingly, with bFGF, Matrigel with a storage modulus (G') of 100 Pa drives NSCs to differentiate into neurons with more dendritic branches, while the gel with G' of 50 Pa led to the development of longer neurites with fewer branches. Notably, branch-rich neural networks exhibited an increased frequency of calcium transients. Using a MATLAB-based analysis pipeline incorporating graph theory, we constructed spatial and temporal calcium activity maps, revealing that branching complexity, more than neurite length, correlates with the density and strength of functional neural circuits. Overall, this study demonstrates that the dendritic branching of neurons, modulated with matrix stiffness and neurotrophic factors, is a key element in enhancing the electrophysiological functionality of the stem cell-differentiated neural network. This finding will have a significant impact on efforts to reconstruct functional neural tissue models, advancing both regenerative therapies and unexplored applications, including biological computing.

Animals↗
PubMed2026

Shared genetic architecture and therapeutic targets across paediatric immune-mediated diseases.

OBJECTIVES: Paediatric-onset immune-mediated inflammatory diseases (IMIDs), including juvenile idiopathic arthritis and related rheumatic diseases, remain genetically undercharacterised. We aimed to define shared and category-specific genetic architecture across paediatric IMIDs, compare signals with adult IMIDs, and identify therapeutic opportunities. METHODS: We analysed 24 paediatric IMIDs classified as autoimmune, polygenic-autoinflammatory, mixed-pattern, or allergic. Genome-wide association analyses included 18,086 cases and 131,019 controls of European ancestry. We estimated single nucleotide polymorphism (SNP)-based heritability, genetic correlations, and polygenic overlap; performed subset-based meta-analysis; and conducted functional annotation, gene prioritisation, pathway and protein network analyses, adult-IMID comparison, and drug-target prioritisation. RESULTS: SNP-based heritability ranged from 28.9% for allergic IMIDs to 61.9% for autoimmune IMIDs. Genetic correlation and polygenic modelling supported partial sharing across categories with category-specific components. Meta-analysis identified 39 genome-wide significant loci outside the Major Histocompatibility Complex (MHC) region, including 15 previously unreported loci; 19 loci were shared between categories. Gene-prioritisation and protein interaction analyses identified a core MHC-centred antigen-presentation network, with category-enriched modules involving complement, innate/barrier pathways, epithelial biology, and type 2 immunity. Enriched pathways included nuclear factor κB signalling, T helper 17 related pathways, Janus kinase-signal transducer and activator of transcription signalling, programmed cell death protein 1/programmed death‑ligand 1, cytotoxic T‑lymphocyte associated protein 4 regulation, and osteoclast differentiation, several of which are relevant to rheumatic diseases. Paediatric IMIDs shared broad polygenic architecture with adult IMIDs, whereas top-ranked genes converged strongly with adult rheumatic diseases. Priority Index analysis identified 178 high-scoring genes, including 43 approved or investigational IMID drug targets. CONCLUSIONS: Paediatric-onset IMIDs share core pathways with adult forms but exhibit distinct genetic architecture shaped by age-specific immune and neurodevelopmental biology. These findings provide a genomic framework for paediatric precision medicine, guiding classification, risk prediction, and therapeutic development.

Humans↗
PubMed1975

Neurons from fetal rat brain in a new cell culture system: a multidisciplinary analysis.

A new culture system for cells from the mammalian brain was developed by a modification of a previously established technique. This modification involved the use of fluorodeoxyuridine and adult horse serum. The cultures contained large, easily visualized neurons both isolated from other neurons and in networks of varying complexity. These cells were large enough to permit reliable intracellular electrophysiologic recording and were often sufficiently dispersed to allow examination of membrane responses to iontophoretically applied neurotransmitter candidates. Many responses characteristic of central neurons in situ were seen, including evoked and spontaneous action potentials, complex patterns of inhibitory and excitatory post-synaptic potentials, and neurotransmitter-induced membrane responses. These preparations were examined by phase contrast microscopy, by light microscopy after silver impregnation and by Nomarski interference optics. Total choline acetyltransferase (CAT) activity was little changed and specific activity was increased in the new culture system as compared with the earilier system. Conditions which gave the highest specific activity of CAT also provided the best cultures from the standpoint of electrophysiologic and morphologic analysis. This new approach will allow, in culture, detailed multidisciplinary analyses of individual neurons and small networks of neurons from the mammalian brain.

Acetylcholine↗
PubMed2025

Transmission of extended spectrum β-lactamase-producing Escherichia coli and antimicrobial resistance gene flow across One Health compartments in eastern Africa: a whole-genome sequence analysis from a prospective cohort study.

BACKGROUND: The One Health paradigm considers interdependence of human, animal, and environmental health. However, there is little evidence from high-income countries to support the importance of a One Health approach to addressing spread of antimicrobial resistance (AMR). Given AMR is a global threat, understanding how the close interactions of humans with animals and the environment in low-income settings affect the spread of AMR is important. We aimed to investigate diversity and transmission of extended spectrum β-lactamase (ESBL)-producing Escherichia coli across household-linked One Health compartments using genomic data. METHODS: We sequenced whole genomes of ESBL-producing E coli isolates from humans, animals, and the environment from a prospective, longitudinal cohort study conducted in Malawi (April 29, 2019, to Dec 3, 2020) and Uganda (July 16, 2020, to Aug 6, 2021). In the cohort study, 259 households were enrolled at baseline in Malawi and 92 in Uganda from a mix of urban, peri-urban, and rural areas. Households were followed up at months 1, 3, and 6 in Malawi and at months 1, 2, and 4 in Uganda. Samples collected at each visit included human and animal stool, environmental samples from hand-contact areas, food, and water, and broader environmental samples such as river water. Samples were cultured in buffered peptone water and then ESBL chromogenic agar to isolate ESBL-producing E coli. ESBL-producing E coli isolates underwent whole-genome sequencing. We performed phylogenetic analyses, and in-silico multi-locus sequence typing, characterised AMR determinants and linked genotypes with sample location, ecological source, and other covariates. We performed fine-scale single nucleotide polymorphism (SNP) and network analysis to infer strain and plasmid transmission across ecological compartments. The primary outcome was colonisation with ESBL-producing E coli. Secondary outcomes were genomic clusters and ESBL genomic determinants within and between One Health compartments. FINDINGS: We found high diversity of ESBL-producing E coli, with 170 sequence types and 166 genomic clusters identified from 2344 genomes, including 1814 genomes from Malawi (907 human, 221 animal, and 686 environmental) and 530 genomes from Uganda (380 human, 147 animal, and three environmental). Sequence type (ST)131 dominated in Malawi (209 [11·5%] of 1814 genomes), and ST10 dominated in Uganda (45 [8·5%] of 530 genomes). Common ESBL genes blaCTX-M-15 (1604 [68·4%] of 2344 genomes) and blaCTX-M-27 (336 [14·3%] of 2344 genomes) were carried on a complex network of 55 and 30 different plasmids. This diversity of plasmids presented multiple pathways for dissemination and revealed high force of selection. Phylogenetic analyses revealed common intermixing of isolates between humans, animals, and the environment. SNP transmission analysis revealed ecologically overlapping clusters, suggesting ESBL-producing E coli co-circulation both within and between compartments with frequent spillover events. Applying a five-SNP threshold, we inferred 463 human-environment transmission events, 146 human-animal events, and 142 animal-environment events. INTERPRETATION: Our work suggests that a One Health approach is crucial to addressing AMR in eastern Africa. Improving water, sanitation, and hygiene systems will create a safer environment, reduce spillovers of AMR bacteria between compartments, and eventually reduce AMR reservoirs in the environment and in animals. FUNDING: Medical Research Council, National Institute for Health and Care Research, and Wellcome Trust.

Humans↗
PubMed2026

Functional analysis of Candida albicans protein kinases identifies Crk1 as a modulator of epithelial cell damage.

UNLABELLED: The commensal and pathogenic lifestyles of the opportunistic fungal pathogen Candida albicans require complex signaling networks regulated by protein kinases. To investigate the role of C. albicans protein kinases at the intestinal epithelial interface, we screened a comprehensive protein kinase deletion library for the capacity of the mutants to damage intestinal epithelial cells (IEC). Mutants showing altered IEC cytotoxicity relative to the wild type were further analyzed for their growth and morphology, focusing on hyper-damaging strains to identify kinases that rather prevent host cell damage. Deletion of CRK1 caused increased IEC-specific damage, despite slower growth, reduced hyphal length, and reduced adhesion as compared to wild-type cells. While tissue invasion levels and the formation of transcellular tunnels of the crk1Δ/Δ mutant were increased, the translocation capacity through the IEC barrier was reduced. Transcriptional and metabolic profiling suggested a role for Crk1 in metabolic adaptation to carbon and nitrogen sources, which was validated by showing that high glucose and amino acids are required for crk1Δ/Δ to cause increased IEC damage. Deletion of CRK1 rendered C. albicans more susceptible to cell wall and membrane stressors, but caused higher resistance to a catalase-specific and histidine biosynthesis inhibitor. This phenotypic pattern of medium- and epithelial cell type-specific cytotoxicity displayed by a C. albicans protein kinase mutant suggests that Crk1 regulates processes linked to carbon and amino acid metabolism that are relevant to interactions with intestinal epithelial cells. IMPORTANCE: Microbial signal transduction pathways regulate adaptation to changing environmental conditions and facilitate the success of many microbes during interactions with their hosts. The fungal pathobiont Candida albicans exists as a harmless commensal on mucosal surfaces of most humans but can also cause superficial and invasive infections under certain circumstances. Both lifestyles require complex signaling networks, predominantly regulated by protein kinases. The C. albicans genome was predicted to encode 108 protein kinases, yet nearly 50% remain uncharacterized. We aimed to dissect the role of C. albicans protein kinases during the transition from commensal to pathogen. We showed that multiple protein kinase genes are involved in epithelial cell damage. Particularly, the protein kinase gene Crk1 was of interest because deletion of CRK1 caused increased damage to intestinal epithelial cells under distinct conditions. Our study links Crk1 with regulation of metabolic processes relevant for commensalism and pathogenicity of C. albicans.

Candida albicans↗
PubMed2026

Toward Class Imbalance and Uncertainty in Powder XRD Analysis: A Dual-Channel Fusion Network for Space Group Classification.

Accurate identification of space groups from powder X-ray diffraction (pXRD) is essential for understanding crystal structures and accelerating materials discovery. However, this task remains highly challenging due to inherent peak overlap, experimental noise, and the complexity of the 230-class classification problem. To address the critical issues of class imbalance and data scarcity, we first design a general physics-informed data augmentation pipeline. We then propose a dual-channel fusion uncertainty-aware network (DFUN) for automated space group classification. The DFUN architecture integrates two complementary feature representations: convolutional features extracted directly from raw diffraction profiles and domain-specific peak descriptors. These distinct representations are adaptively fused through a gating mechanism. Furthermore, to mitigate the inherent long-tailed distribution of crystallographic data, we employ a hybrid loss function that combines Focal Loss with Label Smoothing. Finally, we incorporate Monte Carlo Dropout to provide predictive uncertainty estimation, thereby enabling not only accurate classification but also a crucial assessment of the model's reliability. Evaluated on large-scale simulated data and two public data sets (opXRD and RRUFF), DFUN outperforms the evaluated baseline methods across the reported metrics. The framework also provides uncertainty-aware predictions, establishing DFUN as a robust and interpretable solution for high-throughput automated crystallographic analysis from powder diffraction.

Uncertainty↗
PubMed1977

Heterogeneity of the kinetoplast DNA molecules of Trypanosoma cruzi.

Kinetoplast DNA (kDNA) of the culture form of Trypanosoma cruzi is cleaved by restriction endonucleases (HpaII, HindII, EcoRI, and HaeIII). The analysis of the cleavage patterns proves that the minicircles (free circulargenome units) are heterogeneous in base sequences. The same results are obtained with the complex kDNA network which is composed of the association of minicircles and linear molecules. Kinetic studies of the renaturation of kDNA previously cleaved by HpaII into fragments of the genome unit size show at least two populations of molecules. About 75% of these molecules correspond to the fast renaturing population having the molecular complexity of the minicircles. The molecules of the slow renaturing population have a much higher molecular complexity than the minicircles and do not seem to be related to the majority of the long linear molecules.

Chemical Phenomena↗
PubMed1979

Health-seeking behavior and social networks of the aged living in single-room occupancy hotels.

The elderly who reside in single-room occupancy (SRO) hotels often have been depicted as "isolates," lacking the interest or ability to engage primary or secondary support systems. This characterization has not enhanced understanding of how the SRO aged are able to survive in the community. With the use of network analysis techniques, this study demonstrates the inaccuracy of the assertion that these old persons lack significant social support. The data pointed to differences in network size, complexity, intensity, connectedness, and directionality in relation to varying degrees of physical and psychiatric health.

Aged↗
PubMed1979

[Quantitation of myelofibrosis in blood diseases by electronic image analysis (author's transl)].

Normal and pathologic reticulin networks colored black by silver nitrate can be automatically quantitated by electronic image analysis. By using this technique, different parameters can be obtained, such as the average density, the surface of network meshes, the thickness of the fibers, the complexity of the reticulum, and the heterogeneity of the myelofibrosis distribution. All of these parameters were obtained in 83 osteomedullar biopsies of blood diseases (primary splenomegaly, chronic myeloid leukemia, polycythermia vera, acute leukemia, and aplastic anemia). We have shown that there is no relation between the different parameters obtained and the medullary richness, hematopoietic center, or patient survival. On the other hand, the histomorphometric parameters can be used to distinguish acute leukemia and chronic myeloid leukemia myelofibrosis, while the parameters in primary splenomegaly are shown to be very heterogeneous.

Acute Disease↗
PubMed2022

A graph-based approach for the visualisation and analysis of bacterial pangenomes.

BACKGROUND: The advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species. Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation. RESULTS: Starting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences. CONCLUSIONS: We anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.

Bacteria↗
PubMed2025

FACS-Proteomics strategy toward extracellular vesicles single-phenotype characterization in biological fluids: exploring the role of leukocyte-derived EVs in multiple sclerosis.

BACKGROUND: The isolation and proteomics characterization of extracellular vesicles (EVs) from body fluids is challenging due to their vast heterogeneity. We have recently demonstrated that Fluorescence-activated Cell Sorting (FACS) efficiently isolates the whole EV circulating compartment directly from untouched body fluids enabling a comprehensive EV proteomics analysis. RESULTS: Here, we characterized, for the first time, a single-phenotype EV subset by sorting leukocyte-derived EVs (Leuko EVs) from peripheral blood and tears of healthy volunteers. Using an optimized and patented staining protocol of the whole EV compartment we identified and excluded non-EV particles, debris and damaged EVs. We further isolated, using an anti-CD45 antibody, Leuko EVs (CD45+ EVs), reaching a high level of purity (> 90%). Purified Leuko EVs were characterized using atomic force microscopy, nanoparticle tracking, and shotgun proteomics analysis revealing a similar coded protein cargo in both biological fluids. Subsequently, the same workflow was applied to tears from Relapsing-Remitting Multiple Sclerosis (RRMS) patients, revealing a Leuko EVs protein cargo enrichment that reflects the neuroinflammatory condition characteristics of RRMS. This enrichment was evidenced by the activation of upstream regulators TGFB1 and NFE2L2, which are associated with inflammatory responses. Additionally, the analysis identified markers indicative of endothelial cell proliferation and the development of enhanced vascular networks, with AGNPT2 and VEGF emerging as activated upstream regulators. These findings indicate the complex interplay between inflammation and angiogenesis in RRMS. CONCLUSIONS: In conclusion, our combined FACS-Proteomics strategy offers a promising approach for biomarker discovery, analysing cell-specific EV phenotypes directly from untouched body fluids, advancing the clinical value of tears EVs and improving the understanding of EV-mediated processes in vivo. Data are available via ProteomeXchange with the identifier PXD049036 and in EV-TRACK knowledgebase with ID: EV240150.

Humans↗