Genomics, Proteomics & Bioinformatics

Kruppel associated box zinc finger proteins (KZFPs) form the largest family of transcriptional regulators in mammals, yet most remain uncharacterized. Here we established a scalable framework to probe KZFP function. An arrayed inducible overexpression screen of 366 human KZFPs in K562 cells identified factors that alter cellular proliferation, enabling functional prioritization. Integrative transcriptomic, chromatin and proteomic analyses revealed diverse mechanisms, including transposable element-linked repression (ZNF43), promoter proximal regulation (ZNF257), and SCAN domain dependent transcriptional activation (ZNF498/ZSCAN25 and ZNF18). These results highlight the functional diversity of KZFPs and provide a strategy for their annotation.

Esophageal squamous cell carcinoma (ESCC) is still lack of clinically molecular subtyping and effective therapeutic strategies. Herein, a total of 46 paired tissue samples of esophageal squamous cell carcinoma (ESCC) were collected and subjected to a systematic proteogenomic evaluation. Consensus assessment of the ESCC-related transcriptomes and TCGA dataset revealed several consensual modes of gene expression related to ESCC specificity, with 8 plasma-detectable hub proteins that could discriminate ESCC from others. Three ESCC molecular subtypes were defined and validated based on proteome data, including pCC1 with activated immune response and best survival outcome, pCC2 as cell cycle subtype with relative worse outcome, and pCC3 with worst outcome that expressed more cell adhesion related proteins. Furthermore, we proposed potential therapeutic strategies for improving survival outcomes in patients with different ESCC molecular subtypes. This integrative proteogenomic analysis provided a novel view of ESCC-dependent molecular information.

Aim: To comprehensively characterize the salivary proteome in periodontitis using Orbitrap Astral data-independent acquisition mass spectrometry (DIA-MS), identify an atlas of differentially expressed proteins (DEPs), and develop a machine learning-derived multi-protein biomarker panel for non-invasive diagnosis of stage III/IV periodontitis. Materials and Methods: Unstimulated saliva samples from 199 participants (periodontal health/gingivitis, n=120; stage III/IV periodontitis, n=79) were analyzed by Orbitrap Astral DIA-MS. DEPs were identified, and pathway enrichment analysis was performed. A two-tier machine learning pipeline, integrating pathway-based feature selection with cross-validated evaluation, was applied to identify the optimal diagnostic panel. Results: Orbitrap Astral DIA-MS quantified 5,597 salivary proteins and 1,966 DEPs (|log2FC|>0.5, FDR<0.05). Pathway analysis identified 14 periodontitis-relevant KEGG pathways, including Th17 cell differentiation, IL-17 signaling, neutrophil extracellular trap formation, and complement and coagulation cascades. A four-protein panel (TEC, RAC1, MAPK14, KRT17) achieved an area under the curve (AUC) of 0.985 plus-or-minus sign 0.010, with 83% sensitivity and 100% specificity. The panel was corroborated using public datasets. Conclusions: To our knowledge, this study represents the first application of Orbitrap Astral DIA mass spectrometry in periodontitis research, establishing a disease-specific DEPs atlas and a salivary biomarker panel with high diagnostic accuracy for stage III/IV periodontitis, providing a foundation for future external validation studies.

O_LIPlants produce diverse bouquets of specialized metabolites (SMs), yet only a fraction of the vast phytochemical space has been explored to date. Comparative analysis of SM profiles can reveal hotspots of biochemical novelty, while systematic profiling across taxonomic levels does presently not cover large plant families. C_LIO_LITo study core and accessory SM profiles in the Brassicaceae plant family, we fingerprinted 14 species by Liquid-Chromatography Mass-Spectrometry (LCMS/MS). We develop standardized experimental and computational workflows integrating in silico annotation tools to study consensus compound class and substructure distributions of SMs. Furthermore, we investigate the congruence of chemotaxonomy and species phylogeny across an extended panel of 17 species. C_LIO_LIUnique metabolite profiles were outstanding in Camelina sativa, Capsella rubella, and B. vulgaris, with the largest unique terpenoid profile annotated in C. sativa, accounting for 33.5% and 55.6% in positive and negative ionization mode, respectively. Substructure motifs were found to overlap with compound class predictions, highlighted for triterpenoids in Camelinodae. Furthermore, dual-tissue chemotaxonomic clustering resembled relationships of Brassica subgenomes across tissues. C_LIO_LIWe anticipate that our systematic approach can serve as a blueprint for investigating biochemical diversity in other plant lineages and can boost the characterization of plant natural product pathways. C_LI

Elongases are essential enzymes in the biosynthesis of sex pheromones in many lepidopteran species. Together with desaturases, they determine the carbon skeletons of many pheromone precursors, thereby contributing to the production of species-specific chemical signals. However, to date, such fatty acyl elongase gene has not been functionally characterized. The rice leaffolder, Cnaphalocrocis medinalis, utilizes a blend of C18 monounsaturated aldehydes and alcohols as its sex pheromone, implying a critical elongation step from C16 precursors. In this study, we performed pheromone gland transcriptome analysis and identified 45 candidate biosynthetic genes. Functional assays in Nicotiana benthamiana showed that the {Delta}11 desaturase Cmed070400 produces (Z)-11-hexadecenoic acid, which serves as the substrate for elongation. Multiple elongases catalyzed its conversion to (Z)-13-octadecenoic acid, with Cmed092440 showing the highest activity. These findings provide the first experimental evidence for elongase-mediated formation of C18 pheromone precursors in C. medinalis. The identification of a minimal set of functionally active enzymes further enables reconstruction of this pathway in plant systems, offering a basis for sustainable production of pheromone precursors for pest management applications.

Single-cell foundation models enable reusable representations and streamlined analysis workflows, yet rigorous evaluation of their performance and robustness in real-world pharmaceutical settings remain underexplored. Here, we benchmarked leading single-cell foundation models (scGPT; scGPT_CP, a continually pretrained checkpoint of scGPT; scFoundation; scMulan; CellFM) against established baseline methods (scVI; Harmony) for data integration using over 1.5 million cells from clinical and preclinical samples. Performance was assessed using well-established and complementary metrics for technical correction and biological structure preservation. We further introduced robustness-oriented rankings to summarize metric trade-offs and quantify performance consistency across datasets and evaluation settings. Our findings show that fine-tuning improved technical correction performance; among the foundation models, fine-tuned scGPT_CP performed best. However, the baseline scVI was the top overall performer, ranking first by our multi-metric Leximax ranking and achieving the highest Pareto Front-1 hit. Collectively, our study provides practical insights for adapting foundation models to real-world drug design and development.

Plants produce the most diverse blends of specialized metabolites on earth. Natural products derived from plants are valuable resources for drug development, food chemistry, and crop resistance breeding. Phenotypes of specialized metabolite profiles can be captured by untargeted mass-spectrometry across species phylogeny, tissues, and genotypes. Here, we collected metabolic fingerprints of 17 Brassicaceae species across three tissues (paired leaf and root; flower) using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in positive and negative ionization mode. Corresponding metadata has been refined for reuse according to ReDU guidelines, and for integration with public genomic and transcriptomic data. Standardization of in vitro growth conditions, and data processing workflows enables integration of acquired raw and processed data across platforms for single- and multi-omics analysis. Further, the inclusion of tissue-specific metabolic profiles across ploidy levels, as well as across crop species and wild relatives, makes this dataset a valuable resource for natural product discovery.

The advances of sequencing technologies and the availability of high-quality genome assemblies for many genotypes per species, give the opportunity to improve sequence alignment rate and quality, and the variant calling accuracy by including all genomic variations in a graph reference, called a pangenome graph. Because the process of building and analysing a pangenome graph is still complex, with related software packages under development, there is an important need for releasing user-friendly pipelines for this emerging research area. Pan1C is a pipeline based on a chromosome-by-chromosome graph construction strategy. It integrates two complementary strategies for building pangenomes and produces informative metric plots and graphics using a large set of tools. By benchmarking Pan1C on human, fungal, and wheat assemblies, which span a wide range of genome sizes and complexities, we showed the interest of Pan1C for assembly and graph validation as well as for performing primary analyses.

In our society, ageing, longevity, and neurodegenerative diseases are major concerns of public health. Recently, in Drosophila, we have identified a new cluster of three snoRNAs, including jouvence, and showed that each of them affect longevity and neurodegeneration. As these snoRNAs are required in the epithelium of the gut, these results point-out a causal relationship between the epithelium of the gut and the neurodegenerative lesions through the metabolic parameters, indicating a gut-brain axis. Here, we demonstrate that each snoRNA pseudouridylates a specific site on ribosomal-RNA, which consequently affects the amount of ribosomes as well as the translational efficacy. Moreover, using TRAP experiment assay, we also show that these lacks of pseudouridylations modify the translation of specific genes involved in lipid metabolism. Consequently, these lead to a chronic deregulation of trigycerides and sterols levels, whose correlate to an increase of neurogenerative lesions in old flies, as well as to a modification of longevity.

Flow cytometry is widely used for high-throughput single-cell analysis. However, its data analysis relies on either costly commercial software or programming-intensive open-source tools. To bridge this gap, we developed FlowWeb, a freely accessible, web-based platform that combines the flexibility of the R/Bioconductor ecosystem with an intuitive graphical user interface. FlowWeb enables integrated workflows for data handling, quality control, gating, visualization and statistical analysis within a unified environment. FlowWeb integrates raw data, metadata, and analytical state within synchronized Bioconductor structures, enabling coherent analysis and visualization workflows. FlowWeb supports both manual and automated data-driven gating workflows. To evaluate its performance, we applied FlowWeb to an in-house flow cytometry dataset and compared its automated cell cycle and gating workflows to established commercial tools. FlowWebs automated cell cycle workflow produced consistent and reproducible results across replicates and demonstrated high concordance with reference analyses, highlighting the platforms robustness. FlowWebs advanced visualization tools include a wide range of fully customizable individual, overlay, and statistical plots. To enhance usability and reproducibility, the FlowWeb platform provides optional user-accounts that allow storage of reusable configurations, including quality control presets, gating definitions, and plot templates. By lowering technical barriers without compromising analytical rigor, FlowWeb facilitates accessible, reproducible, and scalable flow cytometry data analysis for a broad range of users in research and clinical settings.

Motivation: The rapid expansion of proteomic data has created new opportunities for large-scale integrative analyses. However, substantial variability across platforms, experimental designs, and processing pipelines limits direct quantitative comparisons among studies. Differential proteomic changes between conditions are often considered to be more reproducible than absolute abundances and may therefore provide a robust basis for cross-dataset integration. However, the systematic ability of differential change-based approaches to capture biologically meaningful relationships across heterogeneous datasets remains unclear. Results: We developed a differential-change framework and applied it to public proteomic datasets. Pairwise contrasts were defined as differential proteomic profiles, and the concordance of up- and down-regulated proteins was quantified using odds ratios. Significant profile pairs were visualized as an integrative network. The treatment of anti-cancer drug doxorubicin vs control (MCF-7) comparison emerged as a central hub, with breast cancer proteome profiles clustering around it and associating with tumor stage (p = 0.03). Enrichment analysis revealed overrepresentation of lipid- and cholesterol-related pathways. Availability and implementation: The source code for proteome network integration is available at https://github.com/manakanishizaki/proteome-network-integration.git.

Drug-target interaction prediction is significant in the hit identification phase of drug discovery, enabling the identification of potential drug candidates for downstream optimization. Traditional computational methods have some drawbacks in their ability to represent 3D structural data for both molecules and target proteins, which is required for the intricate protein-ligand interactions that regulate binding affinity. In this approach, we propose a graph transformer-based model (GTStrDTI) that combines an intragraph attention mechanism with cross-modal attention to enrich the representation of both the drug molecule and target protein. This approach comprehensively models both intramolecular structural features and intermolecular interactions, thereby enhancing binding affinity prediction performance. A thorough evaluation on benchmark datasets such as KIBA, DAVIS, and BindingDB_Kd shows that our approach surpasses the state-of-the-art methods under challenging target cold-start settings. Our analysis found that augmenting graph-based 3D structural protein target (C-alpha contact graphs from PDB with threshold distance of 5[A]) and incorporating molecule adjacency information, boosts predictive performance, thus contributing towards narrowing the gap between computational and experimental research.

The maintenance of protein homeostasis is vital for all cells. Alteration in protein handling underlies several diseases. The small molecule sephin1 is a promising clinical candidate against proteostasis disruption, but its mechanism of action is still uncertain. Our experimental evidence shows that sephin1 binds G-actin and drives actin cytoskeleton misfolding, and eventually, Golgi disintegration. At first, sephin1 impairs the autophagic flux and elicits the phosphorylation of the subunit of eIF2 and the ER-stress independent expression of CHOP via GCN2 kinase. Sephin1 also inhibits the mammalian target of rapamycin (mTORC1), activates the transcription Factor EB (TFEB), drives the expression of TFEB-direct target genes, and eventually stimulates the autophagy lysosomal pathway. Our results reveal that the actin cytoskeleton may regulate autophagy via mTORC1-TFEB complemented with the GCN2-eIF2-CHOP signaling pathway.

Retinitis Pigmentosa (RP) is a group of inherited retinal degenerations for which most subtypes lack effective drug treatments. This challenge is particularly critical for autosomal dominant (ad) RP, which is often unsuitable for gene replacement therapy. To address this challenge, we screened an FDA-approved compound library using a zebrafish adRP model expressing a human RHODOPSIN transgene with the Q344X mutation. The screen evaluated drug effects on larval visual behavior by assessing the visual-motor response (VMR). Four compounds significantly improved VMR in Q344X zebrafish: amitriptyline, difluprednate, maprotiline, and prednisolone. Further characterization revealed that these hits act through distinct mechanisms, including reducing rod death, promoting rod neogenesis, and enhancing the function of extraocular photoreceptors. Together, these findings demonstrate the potential to repurpose these drugs for adRP caused by the RHO Q344X mutation, providing preclinical candidates and revealing potential targets for future drug development.

Grapevines (Vitis) belonging to grape family (Vitaceae) are symbolic fruit crops pivotal to human civilization. The evolutionary history of grapevines divergent from other Vitaceae plants remains mysterious, requiring a family-wide whole-genome phylogenomic analysis. Here, we conduct chromosome-level phylogenomics to investigate the origin and evolution of grapevines using 29 genome assemblies of five genera Vitis, Parthenocissus, Ampelopsis, Tetrastigma, and Cissus, 27 of which are newly released in this study. Phylogenomic and macrosynteny analysis unanimously support Ampelopsis as a sister lineage to Parthenocissus, placing both closer to Vitis, with introgression and incomplete lineage sorting contributing to these relationships. Ancestral genome reconstruction delineates the major chromosome rearrangement events in Vitaceae karyotype evolution, highlighting the conserved karyotype in Vitis and the extensive karyotypic reorganization in Tetrastigma and Cissus. Pan-3D genome analysis highlights the contributions of structural variants (SVs) to the variation of A/B compartments and topologically associated domains (TADs), revealing a strong purifying selection of SVs at TAD boundaries. We further demonstrate that Helitron transposons drive the expansion and expression regulation of NLR immune-receptor genes in Vitis. Importantly, we discovered an NLR gene VbRpv35 from wild grapevine V. bellula resistant to downy mildew (DM), whose heterologous expression in V. vinifera confers enhanced DM resistance. Taken together, we provide phylogenomic insight into the origin and evolution of grapevines and valuable resources for grapevine improvement and understanding angiosperm evolution.

Understanding crop population history requires genetic material that predates modern breeding. In rice (Oryza sativa), extant landraces have revealed a broad regional structure within indica, but the historical depth of these patterns remains uncertain. Here, we analyzed a historical collection of rice landraces assembled in Southeast Asia from 1957 to 1958 (henceforth the Hamada collection). Short-read resequencing yielded 66 high-quality historical samples (seven from North Vietnam, 30 from South Vietnam, 13 from Cambodia, two from Laos, and 14 from Thailand). When integrated with published rice panels, all historical accessions were assigned to indica, and the major regional structure previously described for extant landraces was recovered. Within the Hamada collection, South Vietnamese, Cambodian, and Thai accessions formed a largely continuous mainland Southeast Asian group, whereas North Vietnamese accessions were clearly distinct and comprised two differentiated groups corresponding to two traditional growth seasons (fifth- and tenth-month rice). The fifth-month rice accessions were assigned to the previously reported distinct Vietnam-I5 cluster, showing that this lineage already existed before modern breeding. Admixture graph, f-statistics, and qpWave analyses further indicated that Vietnam-I5 is closely related to a China-associated lineage with a distinct admixture event that cannot be adequately attributed to sampled indica, japonica, or aus groups. Together, these results show that the present-day regional structure of indica was established before modern cultivar replacement, and highlight northern Vietnam as a historical zone of lineage differentiation between Chinese and mainland Southeast Asian rice.

Transcriptomic data is highly sensitive to cancer state and progression, making transcriptome-based foundation models a great promise for diverse clinical ontological inference. However, analyses of transcriptome are conventionally hindered by technical batch effects and limited generalization across platforms. Here, we introduce RNABag, a foundation model designed to generalize well to external datasets. In particular, the model only focuses on highly variable genes to reduce noise; and extensive data augmentation was utilized to pretrain RNABag to learn robust representations, invariant to batch variations. We demonstrate that RNABag achieves superior performance in pan-cancer tissue-of-origin classification and cancer detection in internal validation sets, as well as in zero-shot generalization to external cohorts and in-house clinical samples. Furthermore, RNABag, after specialized finetuning, exhibits strong capabilities in a wide range of clinical applications. The model effectively stratifies patient survival and predicts relapse risks, highlighting key molecular pathways driving tumor progression. Crucially, we extend RNABags utility to liquid biopsies, achieving high diagnostic accuracy in plasma cfRNA and tumor-educated platelets (TEPs), thereby supporting its application in non-invasive cancer monitoring. Interpretability analysis revealed pivotal role of tumor immune escape in the cancer induced plasma cfRNA signals. In summary, our study indicates that cancer states and progression may be monitored in details and precision via comprehensive modeling of transcriptome across biopsy modalities.

Inflammasomes are cytosolic innate immune sensors that, once activated by a pathogenic threat, lead to activation of the inflammatory Caspase-1. Inflammasome activation and its consequences have been studied extensively in myeloid cells and in overexpression systems. Recent studies have identified cell type specific effects that are not fully explained by the known cleavage targets of Caspase-1. Here, we identified targets of caspase cleavage using mass spectrometry in primary intestinal epithelial cells by specifically activating the NAIP-NLRC4 inflammasome. We have taken an unbiased approach and developed a novel method for analyzing mass spectrometry data for evidence of caspase activity. Our approach can also be applied to existing proteomic datasets to establish the presence of caspase activity under various biological conditions. These results lay the groundwork for future studies on mechanisms of caspase-induced processes such as intestinal epithelial cell extrusion.

A key step in analyzing population-level spatial proteomic data is to delineate consistently defined spatial domains across samples. Domain detection is particularly challenging for cancer tissues, which have complex spatial domains with elongated or branching geometries. To address these challenges, we present Concordia, a Graph Neural Network (GNN)-based framework that uses augmented graphs to capture complex spatial domains, and it is designed to analyze thousands of tissues simultaneously to obtain consistently defined domains. Applied to a lung cancer dataset, Concordia uncovers a spatially defined cancer associated fibroblast subset linked to clinical outcomes that cannot be identified using protein expression alone.

Diet is an important ecological modulator of the oral microbiome, yet population-level evidence on a broader spectrum of food components remains limited. This cross-sectional study investigated associations among dietary intake, oral rinse microbiome, and oral disease conditions in a nationally representative sample of United States adults from the National Health and Nutrition Examination Survey. A total of 3,254 participants with oral rinse microbiome sequencing data were included, with oral conditions classified as oral health, caries-only, periodontitis-only, or co-existing disease. Dietary intake was assessed using 24-hour dietary recalls and summarized as dietary indices and energy-adjusted food components. Associations between diet and the oral microbiome were evaluated using community-level analyses, regression models, mediation analyses, and unsupervised clustering, while accounting for oral conditions. This study found that dietary intake, as a combined variable set, explained 3.6% of the variance in oral rinse microbial community structure; this was comparable to oral disease status or smoking and larger than sociodemographic factors. Healthier dietary profiles, including higher health-associated dietary index scores and greater vegetable and fruit intake, were associated with taxa commonly linked to oral health (e.g., Neisseria, Cardiobacterium and Lautropia). In contrast, added sugars, alcoholic drinks, cured meat, potatoes, dairy products, and higher dietary inflammatory index scores showed opposite association patterns. Mediation analyses suggested that coordinated microbial groups may partly link dietary exposures with oral disease outcomes, particularly for vegetables and added sugars. Additionally, three population-level dietary patterns were identified, among which the plant-rich pattern was associated with more favorable oral health and microbial profiles enriched in nitrate-reducing commensals, including Neisseria and Haemophilus. Overall, dietary intake was associated with oral microbiota composition and oral health conditions, supporting ecological influences of dietary components beyond sugar on oral bacteria and dental diseases. Longitudinal studies are needed to clarify the direction and causality of these relationships.