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PubMed2004

Molecular characterisation of the SAND protein family: a study based on comparative genomics, structural bioinformatics and phylogeny.

The activities of vertebrate lysosomes are critical to many essential cellular processes. The yeast vacuole is analogous to the mammalian lysosome and is used as a tool to gain insights into vesicle mediated vacuolar/lysosome transport. The protein SAND, which does not contain a SAND domain (PFAM accession number PF01342), has recently been shown to function at the tethering/docking stage of vacuole fusion as a critical component of the vacuole SNARE complex. In this publication we have identified SAND in diverse eukaryotes, from single celled organisms such as the yeasts to complex multi-cellular chordates such as mammals. We have demonstrated subfamily divisions in the SAND proteins and show that in vertebrates, a duplication event gave rise to two SAND sequences. This duplication appears to have occurred during early vertebrate evolution and conceivably with the evolution of lysosomes. Using bioinformatics we predict a secondary structure, solvent accessibility profile and protein fold for the SAND proteins and determine conserved sequence motifs, present in all SAND proteins and those that are specific to subsets. A comprehensive evaluation of yeast and human functional studies in conjunction with our in silico analysis has identified potential roles for some of these motifs.

Amino Acid Sequence↗

PubMed2006

ISfinder: the reference centre for bacterial insertion sequences.

ISfinder (www-is.biotoul.fr) is a dedicated database for bacterial insertion sequences (ISs). It has superseded the Stanford reference center. One of its functions is to assign IS names and to provide a focal point for a coherent nomenclature. It is also the repository for ISs. Each new IS is indexed together with information such as its DNA sequence and open reading frames or potential coding sequences, the sequence of the ends of the element and target sites, its origin and distribution together with a bibliography where available. Another objective is to continuously monitor ISs to provide updated comprehensive groupings or families and to provide some insight into their phylogenies. The site also contains extensive background information on ISs and transposons in general. Online tools are gradually being added. At present an online Blast facility against the entire bank is available. But additional features will include alignment capability, PsiBLAST and HMM profiles. ISfinder also includes a section on bacterial genomes and is involved in annotating the IS content of these genomes. Finally, this database is currently recommended by several microbiology journals for registration of new IS elements before their publication.

DNA Transposable Elements↗

PubMed2026

Multidrug resistance and genomic characteristics of nontypeable Haemophilus influenzae isolates from the respiratory tract of pediatric patients.

UNLABELLED: Nontypeable Haemophilus influenzae (NTHi) is a common colonizer of the human upper respiratory tract and one of the major pathogens responsible for pediatric respiratory tract infections. Given the increasing severity of its multidrug resistance (MDR), this study comprehensively investigated the genomic characteristics of circulating NTHi isolated from sputum and bronchoalveolar lavage fluid (BALF). A total of 104 H. influenzae isolates (69 from sputum; 35 from BALF) were collected from pediatric patients between January 2024 and January 2025. All isolates underwent whole-genome sequencing and antimicrobial susceptibility testing, followed by core/pan-genome phylogenetic analysis, multilocus sequence typing (MLST), and resistome profiling. Among them, 103 were identified as NTHi. We identified 29 known sequence types (STs) and 10 novel STs, with ST-107 (14.4%), ST-57 (10.6%), and ST-11 (8.7%) being the major circulating lineages. However, core-genome phylogenetic analysis provided a more granular view of the genetic variation within these identical STs. All the isolates showed high resistance to ampicillin (98.1%) and cefuroxime (84.6%). Genomically, the multidrug efflux pump gene hmrM was ubiquitous (100%). Ampicillin resistance was predominantly driven by blaTEM-1 carriage (77.9%), with minor contributions from chromosomal ftsI mutations. Fifteen plasmid replicons were predicted from 25 isolates, which highly coincided with the carriage of blaTEM-1 and other acquired resistance genes. This study demonstrates that MDR in pediatric NTHi is primarily driven by acquired resistance genes and chromosomal mutations, with specific resistant clones persisting and enriching under clinical antibiotic pressures. These findings underscore the importance of continuous high-resolution genomic surveillance in guiding rational antibiotic stewardship. IMPORTANCE: This study highlights the critical importance of high-resolution genomic surveillance in managing pediatric nontypeable Haemophilus influenzae (NTHi) infections. By utilizing whole-genome sequencing, we uncovered the pathogen's highly dynamic population structure and complex multidrug resistance (MDR) mechanisms. Crucially, our findings reveal a strong, non-random coupling between core genomic architectures, virulence factors, and MDR elements, driven by dual environmental and pharmacological pressures. This "virulence-MDR" co-evolutionary trend underscores the persistent clinical threat of locally adapted high-risk clones. These findings provide important insights for guiding rational clinical antibiotic stewardship, optimizing treatment strategies, and improving regional infection control.

Humans↗

PubMed2026

RCoxNet: A Deep Learning Framework Integrating Random Walk with Restart, Mutation, and Clinical Data for Cancer Survival Prediction.

Accurate survival prediction in cancer remains challenging due to the sparsity of somatic mutation profiles and the failure of existing models to capture higher-order gene-gene dependencies. Network diffusion methods such as Random Walk with Restart (RWR) can propagate mutation signals across protein-protein interaction (PPI) networks to address sparsity, yet their integration within a deep learning Cox survival framework has not been comprehensively benchmarked across multiple cancer cohorts. We present RCoxNet, a deep learning framework that maps somatic mutation profiles onto a ConsensusPathDB-derived PPI network via RWR, selects prognostic genes by log-rank filtering, and processes network-informed mutation scores through three fully connected hidden layers feeding into a Cox proportional hazards output. RCoxNet was evaluated on The Cancer Genome Atlas (TCGA) cohorts for four cancer types (breast invasive carcinoma [BRCA], lung adenocarcinoma [LUNG], glioblastoma multiforme [GBM], and ovarian serous cystadenocarcinoma [OV]) using 20 independent random splits. The model achieved mean C-index values of 0.807 ± 0.044 (BRCA), 0.750 ± 0.039 (LUNG), 0.704 ± 0.041 (GBM), and 0.668 ± 0.036 (OV), consistently outperforming DeepSurv, Cox-nnet, SurvivalNet, Cox Elastic-Net (Cox-EN), and DeepHit, with statistically significant gains over Cox-EN, Cox-nnet, SurvivalNet, and DeepHit across the majority of cohorts. RCoxNet demonstrates that embedding sparse mutation profiles into a PPI network context substantially improves cancer survival prediction and yields biologically interpretable prognostic features relevant to precision oncology.

cancer survival prediction↗

PubMed2004

Genomics in the immune system.

The analysis of gene expression in tissues, cells, and biologic systems has evolved in the last decade from the analysis of a selected set of genes to an efficient high throughput whole-genome screening approach of potentially all genes expressed in a tissue or cell sample. Development of sophisticated methodologies such as microarray technology allows an open-ended survey to identify comprehensively the fraction of genes that are differentially expressed between samples and define the samples' unique biology. This discovery-based research provides the opportunity to characterize either new genes with unknown function or genes not previously known to be involved in a biologic process. The latter category may hold surprises that sometimes urge us to redirect our thinking. Here, we review the impact of large-scale gene expression profiling by DNA-microarray technology on basic and clinical aspects of immunology.

Autoimmune Diseases↗

PubMed2026

CoSAG-nf: A Scalable Nextflow Pipeline for Co-assembly, Optimization, and Interactive Visualization of High-Throughput Single-Cell Genomes.

MOTIVATION: Single-cell amplified genomes (SAGs) are crucial for resolving intra-population microbial heterogeneity and accurately understanding the metabolic potential of microbial dark matter populations. However, SAGs generated through multiple displacement amplification (MDA) of genomic DNA from single cells with single-copy chromosomes are highly fragmented and prone to contamination, severely hindering high-quality genome reconstruction and functional analysis, which greatly limits their scientific utility. Co-assembly of related SAGs can substantially improve genome quality, but to our knowledge no automated pipeline exists for high-throughput processing, forcing manual implementation of complex workflows that scale poorly to modern dataset sizes. RESULTS: We present CoSAG-nf, an automated high-throughput co-assembly and optimization pipeline for SAGs, implemented following the nf-core framework standards. The pipeline performs alignment-free clustering using sourmash MinHash signatures, then employs iterative tetranucleotide frequency profiling to identify and exclude outlier SAGs from co-assembly groups. CheckM2 quality assessment guides dynamic selection of optimal SAG combinations to optimize genome completeness and minimize contamination. Fully containerized, CoSAG-nf ensures reproducibility and scalability for the high-throughput processing of large-scale SAG datasets across diverse computing environments, including HPC and cloud platforms. The pipeline generates comprehensive HTML reports with quality metrics and taxonomic annotations, providing an end-to-end solution for automated high-throughput single-cell genome reconstruction. AVAILABILITY: CoSAG-nf is freely available under the MIT License at: https://github.com/linfengxu/CoSAG-nf. Archival code repository snapshots are published at zenodo with doi: https://doi.org/10.5281/zenodo.21525244. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗

PubMed2026

The 21-gene recurrence score assay as a tool for predicting recurrence risk and guiding adjuvant treatment selection in early breast cancer.

INTRODUCTION: Estrogen receptor-positive (ER+), HER2-negative breast cancer is the most common breast cancer subtype. While adjuvant endocrine therapy reduces recurrence risk, identifying which patients benefit from the addition of chemotherapy remains a key clinical challenge. The Oncotype DX® 21-gene Recurrence Score assay (Exact Sciences, via Genomic Health, Inc.) was developed to address this by quantifying distant recurrence risk and informing chemotherapy decisions in early-stage ER+/HER2- disease. AREAS COVERED: This diagnostic profile reviews the development, validation, and clinical evidence for Oncotype DX, including findings from the TAILORx and RxPONDER prospective trials and the subsequent development of hybrid tools integrating genomic and clinicopathological data. Alternative multiparameter molecular tests (MammaPrint, Prosigna, EndoPredict, Breast Cancer Index) are summarized and compared. We review international guideline recommendations, decision impact studies, cost-effectiveness evidence, and ongoing trials. EXPERT OPINION: Oncotype DX has strong prognostic evidence and has meaningfully reduced chemotherapy use, though its case as a biomarker predictive of therapeutic effect from chemotherapy rests on trial designs with important limitations. Its independent prognostic contribution beyond comprehensive clinicopathological assessment requires further clarification, and cost-effectiveness varies substantially by indication and healthcare setting.

Humans↗

PubMed2025

Genome-wide identification and expression profiling of the MADS-box gene family in Lavandula angustifolia.

BACKGROUND: MADS-box genes encode transcription factors critical for plant development, particularly floral organogenesis, flowering time regulation, and adaptation to environmental stresses. Among these, the MIKCC-type genes are pivotal regulators in floral developmental processes. Although the evolutionary diversification and functional dynamics of MADS-box genes have been extensively characterized in model plants such as Arabidopsis thaliana and Oryza sativa, their evolutionary relationships and functional profiles in Lavandula angustifolia, an economically significant aromatic plant, remain poorly understood. RESULTS: Genome-wide analysis identified 173 MADS-box genes in L. angustifolia, categorized into type I (Mα: 26; Mβ: 0; Mγ: 10) and type II (MIKCC: 125; MIKC*: 12) based on phylogenetic comparisons with A. thaliana. The MIKCC subgroup was further subdivided into 12 subclasses, including genes central to the ABCDE model of floral organ specification. Structural analyses revealed distinct conserved motifs and exon-intron configurations specific to each subgroup, indicative of functional divergence. Synteny analysis demonstrated Whole Genome Duplication (WGD) and segmental duplications as major contributors to MIKCC gene family expansion, notably among genes linked to floral organ development. Expression profiling via RNA-seq and quantitative real-time PCR (qPCR) showed type II MADS-box genes exhibited higher expression levels with pronounced tissue-specific and developmental stage-specific expression patterns compared to type I genes. Many type II genes displayed significant associations with floral organogenesis, floral transition, and abiotic stress responses, underscoring their essential roles in reproductive development and environmental adaptability in L. angustifolia. CONCLUSIONS: The identification and comprehensive characterization of 173 MADS-box genes in L. angustifolia highlight the significant expansion of the MIKCC subgroup driven primarily by WGD and segmental duplications. The distinct structural features and specific expression patterns observed provide insights into the functional divergence and complexity of these genes, particularly regarding floral organogenesis and adaptation to environmental stress. This study establishes a robust molecular basis for further functional analysis and genetic improvement of aromatic plants.

MADS Domain Proteins↗

PubMed2025

Identifying key palmitoylation-associated genes in endometriosis through genomic data analysis.

BACKGROUND: Palmitoylation, a post-translational lipid modification, has garnered increasing attention for its role in inflammatory processes and tumorigenesis. Emerging evidence suggests a potential association between palmitoylation and inflammatory responses in the pathogenesis of endometriosis. However, the precise mechanistic interplay remains elusive, necessitating further investigation. METHODS: This study integrated transcriptomic analysis and Mendelian randomization (MR) to identify a causal gene set implicated in endometriosis. Differentially expressed genes (DEGs) were first identified in the training dataset using the limma package in R. Weighted gene co-expression network analysis (WGCNA) was subsequently performed, leveraging Single Sample Gene Set Enrichment Analysis (ssGSEA)-derived scores of palmitoylation-related genes (PRGs) as phenotypic traits to identify key modular genes. The intersection of these key modular genes with DEGs yielded a refined gene set. Machine learning algorithms were then applied to further optimize gene selection, followed by external validation, immune infiltration analysis, RNA network construction, and exploration of potential targeted drug candidates. RESULTS: Through a rigorous screening process, VRK1, GALNT12, and RMI1 emerged as key genes associated with palmitoylation, exhibiting significant downregulation in endometriosis samples (P < 0.05), indicative of a potential protective role. Immune infiltration analysis further revealed strong correlations between these genes and M2 macrophages as well as resting Natural Killer (NK) cells. Additionally, investigations into the targeted RNA network and drug association profiling provided novel insights, laying the groundwork for future high-quality validation studies. CONCLUSIONS: This study employed a comprehensive analytical framework to identify palmitoylation-associated key genes in endometriosis. The integration of immunoinfiltration analysis, RNA network construction, and drug association profiling offers valuable insights for advancing clinical diagnostics, disease monitoring, and therapeutic development in endometriosis.

Humans↗

PubMed2026

A comprehensive survey of genetic variants in neuroblastoma.

BACKGROUND: Neuroblastoma (NB) is the most common extracranial solid tumor in children and is characterized by marked clinical and molecular heterogeneity. Genomic alterations play a critical role in NB pathogenesis; however, population-specific mutational features remain insufficiently characterized, particularly among Chinese patients. METHODS: Whole-exome sequencing (WES) was performed on tumor, para-tumor, and matched peripheral blood samples from nine pathologically confirmed Chinese patients with NB. Somatic variant profiles were compared with four publicly available NB datasets from cBioPortal, published in 2012, 2013, 2015, and 2023. Mutational patterns, recurrently altered genes, and Gene Ontology (GO) enrichment were analyzed using R version 4.3.2 and clusterProfiler version 4.10.0. RESULTS: A total of 77 missense variants were identified in our cohort. Single-nucleotide polymorphisms (SNPs) represented the predominant variant type, and C > T substitutions were the most frequent nucleotide change. MAP1A variants, comprising two missense variants in one patient, and RBM33 variants, comprising two distinct variants in two patients, were detected in our cohort and, to the best of our knowledge, have not been previously reported in NB, although their frequencies were low. No MYCN amplification or variants in ALK, ATRX, or DAXX were detected. Comparative analysis with the cBioPortal datasets revealed no somatic variants universally shared across all cohorts. In addition, high-risk patients exhibited distinct mutational patterns, with enrichment of the Gene Ontology term "collagen-containing extracellular matrix." CONCLUSIONS: These findings highlight the molecular diversity of NB and suggest the presence of potential population-specific genetic features in Chinese patients. The low-frequency MAP1A and RBM33 variants identified in this cohort warrant further validation in larger, independent cohorts. Moreover, the enrichment of extracellular matrix-related pathways in high-risk NB supports further investigation of tumor-microenvironment interactions as potential therapeutic targets.

Extracellular matrix↗

PubMed2004

Transcriptomic changes in human breast cancer progression as determined by serial analysis of gene expression.

INTRODUCTION: Genomic and transcriptomic alterations affecting key cellular processes such us cell proliferation, differentiation and genomic stability are considered crucial for the development and progression of cancer. Most invasive breast carcinomas are known to derive from precursor in situ lesions. It is proposed that major global expression abnormalities occur in the transition from normal to premalignant stages and further progression to invasive stages. Serial analysis of gene expression (SAGE) was employed to generate a comprehensive global gene expression profile of the major changes occurring during breast cancer malignant evolution. METHODS: In the present study we combined various normal and tumor SAGE libraries available in the public domain with sets of breast cancer SAGE libraries recently generated and sequenced in our laboratory. A recently developed modified t test was used to detect the genes differentially expressed. RESULTS: We accumulated a total of approximately 1.7 million breast tissue-specific SAGE tags and monitored the behavior of more than 25,157 genes during early breast carcinogenesis. We detected 52 transcripts commonly deregulated across the board when comparing normal tissue with ductal carcinoma in situ, and 149 transcripts when comparing ductal carcinoma in situ with invasive ductal carcinoma (P < 0.01). CONCLUSION: A major novelty of our study was the use of a statistical method that correctly accounts for the intra-SAGE and inter-SAGE library sources of variation. The most useful result of applying this modified t statistics beta binomial test is the identification of genes and gene families commonly deregulated across samples within each specific stage in the transition from normal to preinvasive and invasive stages of breast cancer development. Most of the gene expression abnormalities detected at the in situ stage were related to specific genes in charge of regulating the proper homeostasis between cell death and cell proliferation. The comparison of in situ lesions with fully invasive lesions, a much more heterogeneous group, clearly identified as the most importantly deregulated group of transcripts those encoding for various families of proteins in charge of extracellular matrix remodeling, invasion and cell motility functions.

Apoptosis↗

PubMed2002

The IUPS Physiome Project. International Union of Physiological Sciences.

Modern medicine is currently benefiting from the development of new genomic and proteomic techniques, and also from the development of ever more sophisticated clinical imaging devices. This will mean that the clinical assessment of a patient's medical condition could, in the near future, include information from both diagnostic imaging and DNA profile or protein expression data. The Physiome Project of the International Union of Physiological Sciences (IUPS) is attempting to provide a comprehensive framework for modelling the human body using computational methods which can incorporate the biochemistry, biophysics and anatomy of cells, tissues and organs. A major goal of the project is to use computational modelling to analyse integrative biological function in terms of underlying structure and molecular mechanisms. To support that goal the project is establishing web-accessible physiological databases dealing with model-related data, including bibliographic information, at the cell, tissue, organ and organ system levels. This paper discusses the development of comprehensive integrative mathematical models of human physiology based on patient-specific quantitative descriptions of anatomical structures and models of biophysical processes which reach down to the genetic level.

Biophysics↗

PubMed2005

Supervised enzyme network inference from the integration of genomic data and chemical information.

MOTIVATION: The metabolic network is an important biological network which relates enzyme proteins and chemical compounds. A large number of metabolic pathways remain unknown nowadays, and many enzymes are missing even in known metabolic pathways. There is, therefore, an incentive to develop methods to reconstruct the unknown parts of the metabolic network and to identify genes coding for missing enzymes. RESULTS: This paper presents new methods to infer enzyme networks from the integration of multiple genomic data and chemical information, in the framework of supervised graph inference. The originality of the methods is the introduction of chemical compatibility as a constraint for refining the network predicted by the network inference engine. The chemical compatibility between two enzymes is obtained automatically from the information encoded by their Enzyme Commission (EC) numbers. The proposed methods are tested and compared on their ability to infer the enzyme network of the yeast Saccharomyces cerevisiae from four datasets for enzymes with assigned EC numbers: gene expression data, protein localization data, phylogenetic profiles and chemical compatibility information. It is shown that the prediction accuracy of the network reconstruction consistently improves owing to the introduction of chemical constraints, the use of a supervised approach and the weighted integration of multiple datasets. Finally, we conduct a comprehensive prediction of a global enzyme network consisting of all enzyme candidate proteins of the yeast to obtain new biological findings. AVAILABILITY: Softwares are available upon request.

Algorithms↗

PubMed2006

Whole-genome analysis of the SHORT-ROOT developmental pathway in Arabidopsis.

Stem cell function during organogenesis is a key issue in developmental biology. The transcription factor SHORT-ROOT (SHR) is a critical component in a developmental pathway regulating both the specification of the root stem cell niche and the differentiation potential of a subset of stem cells in the Arabidopsis root. To obtain a comprehensive view of the SHR pathway, we used a statistical method called meta-analysis to combine the results of several microarray experiments measuring the changes in global expression profiles after modulating SHR activity. Meta-analysis was first used to identify the direct targets of SHR by combining results from an inducible form of SHR driven by its endogenous promoter, ectopic expression, followed by cell sorting and comparisons of mutant to wild-type roots. Eight putative direct targets of SHR were identified, all with expression patterns encompassing subsets of the native SHR expression domain. Further evidence for direct regulation by SHR came from binding of SHR in vivo to the promoter regions of four of the eight putative targets. A new role for SHR in the vascular cylinder was predicted from the expression pattern of several direct targets and confirmed with independent markers. The meta-analysis approach was then used to perform a global survey of the SHR indirect targets. Our analysis suggests that the SHR pathway regulates root development not only through a large transcription regulatory network but also through hormonal pathways and signaling pathways using receptor-like kinases. Taken together, our results not only identify the first nodes in the SHR pathway and a new function for SHR in the development of the vascular tissue but also reveal the global architecture of this developmental pathway.

Arabidopsis↗

PubMed1993

Cytogenetic profiling using fluorescence in situ hybridization (FISH) and comparative genomic hybridization (CGH).

Fluorescence in situ hybridization (FISH) and comparative genomic hybridization (CGH) allow cytogenetic analyses of primary tumors without culture. CGH allows detection and mapping of allelic imbalance by simultaneous in situ hybridization of differentially labeled tumor (green fluorescing) and normal DNA (red fluorescing) to a normal human metaphase spread. Regions of increased or decreased copy number in the tumor are mapped onto the normal metaphase chromosomes as increases or decreases in the green to red fluorescence ratio. This technique gives a comprehensive assessment of gene dosage imbalance throughout the tumor. However, it is limited, at present, to fairly large tumors containing few normal cells. FISH, on the other hand, allows analysis of DNA sequence copy number at specific loci in single nuclei. A wide variety of DNA probes is available for FISH, including chromosome-specific probes which hybridize to alpha-satellite pericentromeric DNA regions (to detect changes in specific chromosome copy number and overall ploidy) and specific locus probes targeting 20-150 kilobase sequences (to detect specific amplifications, deletions, breakpoints, or rearrangements). FISH using these probes has been applied to interphase nuclei in touch preparations, smears from fine needle aspirates, and thin (< 6 microns) and thick (> 20 microns) sections cut from formalin-fixed, paraffin-embedded tissue. Analysis of thick sections allows accurate actual signal enumeration within the histological context. This approach may allow analysis of subtle premalignant, early malignant, and infiltrating tumors in which malignant cells must be differentiated from nonmalignant cells.(ABSTRACT TRUNCATED AT 250 WORDS)

Chromosome Aberrations↗

PubMed2005

Identification of coexpressed gene clusters in a comparative analysis of transcriptome and proteome in mouse tissues.

A major advantage of the mouse model lies in the increasing information on its genome, transcriptome, and proteome, as well as in the availability of a fast growing number of targeted and induced mutant alleles. However, data from comparative transcriptome and proteome analyses in this model organism are very limited. We use DNA chip-based RNA expression profiling and 2D gel electrophoresis, combined with peptide mass fingerprinting of liver and kidney, to explore the feasibility of such comprehensive gene expression analyses. Although protein analyses mostly identify known metabolic enzymes and structural proteins, transcriptome analyses reveal the differential expression of functionally diverse and not yet described genes. The comparative analysis suggests correlation between transcriptional and translational expression for the majority of genes. Significant exceptions from this correlation confirm the complementarities of both approaches. Based on RNA expression data from the 200 most differentially expressed genes, we identify chromosomal colocalization of known, as well as not yet described, gene clusters. The determination of 29 such clusters may suggest that coexpression of colocalizing genes is probably rather common.

Animals↗

PubMed2004

Protein network inference from multiple genomic data: a supervised approach.

MOTIVATION: An increasing number of observations support the hypothesis that most biological functions involve the interactions between many proteins, and that the complexity of living systems arises as a result of such interactions. In this context, the problem of inferring a global protein network for a given organism, using all available genomic data about the organism, is quickly becoming one of the main challenges in current computational biology. RESULTS: This paper presents a new method to infer protein networks from multiple types of genomic data. Based on a variant of kernel canonical correlation analysis, its originality is in the formalization of the protein network inference problem as a supervised learning problem, and in the integration of heterogeneous genomic data within this framework. We present promising results on the prediction of the protein network for the yeast Saccharomyces cerevisiae from four types of widely available data: gene expressions, protein interactions measured by yeast two-hybrid systems, protein localizations in the cell and protein phylogenetic profiles. The method is shown to outperform other unsupervised protein network inference methods. We finally conduct a comprehensive prediction of the protein network for all proteins of the yeast, which enables us to propose protein candidates for missing enzymes in a biosynthesis pathway. AVAILABILITY: Softwares are available upon request.

Artificial Intelligence↗

PubMed2025

A survey of dietary effects on tRNA abundance and modifications.

Transfer RNAs (tRNAs) play a central role in protein translation and are increasingly recognized as dynamic regulators of gene expression. Both physiological and environmental signals can modulate tRNA abundance and chemical modifications, yet the impact of dietary cues on the tRNA landscape remains poorly understood. Here, we investigated the effects of two distinct dietary interventions-low-protein and high-fat diets-on tRNA abundance and modification profiles across multiple mouse tissues. We conducted a comprehensive analysis of tRNA abundance and modification changes in response to these nutritional challenges using RNA mass spectrometry and Ordered Two-Template Relay sequencing (OTTR-seq), a modified-base-sensitive tRNA sequencing method. Our results reveal both shared and tissue-specific alterations in abundance and modifications of specific nuclear and mitochondrial genome-encoded tRNAs in response to dietary conditions at isotype, isoacceptor, and isodecoder levels. As many of the tissue-specific or diet-responsive tRNA modifications have been previously reported to affect decoding efficiency or translational fidelity, these results have implications for understanding translational adaptation in response to dietary conditions.

Journal Article↗