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PubMed2026

The Rise of Plant Pan-Genomes: From Genome Variation to Predictive Breeding.

Plant pan-genomics is entering a new phase beyond genome variation discovery, requiring a shift from cataloguing genomic diversity toward understanding how variation generates biological function and breeding value. Here, we propose that the future of plant pan-genomics will be shaped by three conceptual transitions. First, structural variation (SV), presence-absence variation (PAV), and haplotype diversity should be interpreted not merely as genomic differences, but as regulatory components that influence gene networks, chromatin organization, and complex traits. Second, the expansion from species-level pan-genomes to genus-level super pan-genomes provides an evolutionary framework for uncovering adaptive genetic modules preserved in wild relatives and overlooked during domestication. Third, integrating pan-genomes with pan-omics, three-dimensional genome analyses, and artificial intelligence will enable the transformation of genomic variation into predictive models for crop improvement. We further propose that the ultimate value of pan-genomes lies not in generating increasingly complete genome collections, but in establishing a mechanistic bridge between genome diversity, biological function, and breeding decisions. This transition will move crop improvement from empirical selection toward rational genome design, where evolutionary diversity can be systematically interpreted, predicted, and engineered.

Journal Article↗
PubMed2006

Solid-phase and bead-based cytokine immunoassay: a comparison.

Cytokines and chemoattractive cytokines (chemokines) are present in a wide variety of body fluids such as plasma, cerebrospinal fluid, bronchoaveolar fluid, amniotic fluid, synovial fluid, middle ear effusion fluid, and urine. Cytokines can be detected using classical solid-phase sandwich immunoassays such as enzyme-linked immunosorbent assay (ELISA) or with a bead based multiplex immunoassay (MIA). The physical chemical properties of the different body fluids (such as pH and total protein content) differ, which may have an impact on the outcome of the cytokine assay. Both ELISA as well as MIA cytokine detection systems are constructed by sandwiching the protein of interest between a capture and reporter antibody. When the biological sample contains heterophilic antibodies (such as in patients with auto-immune diseases), these non-specific antibodies can cause false positive results. During pathological conditions, cytokines may be found over a wide concentration range; likewise have to cover this dynamic range in a similar fashion. The correct (statistical) analysis of standard curves and (multiplexed) data are critical for proper interpretation. Classical ELISA based cytokine assays are robust, easy to use and very well suited for measurement of single cytokines. Due to an increased interest in the integral approach to understand biological processes (the omics era), multiplex immunoassays for detection of cytokines and the interpretation of these assays are gaining popularity.

Animals↗
PubMed2026

JNK acts as a molecular brake of the CDC73 positive feedback loop to modulate osteosarcoma malignant progression via UBR5.

CDC73 is a well-characterized tumor suppressor regulated by stress stimuli, governing progression of diverse human malignancies. Although previous studies have shown that E3 ubiquitin ligase UBR5 drives CDC73 ubiquitination and degradation to modulate tumorigenesis, the mechanisms by which stress-responsive pathways regulate UBR5-mediated CDC73 inactivation and transcriptional reprogramming remain elusive. Here, via integrated analyses of public datasets, multi-omics profiling (assay for transposase-accessible chromatin with sequencing [ATAC-seq], cleavage under targets and tagmentation [CUT&Tag], mRNA sequencing [mRNA-seq]), in vitro/in vivo assays, and molecular approaches including co-immunoprecipitation (Co-IP) and molecular docking, we demonstrate that UBR5 depletion profoundly alters chromatin accessibility and genome-wide transcriptional profiles in a CDC73-dependent manner. UBR5 ablation markedly suppresses osteosarcoma malignant phenotypes in cultured cells and xenograft models, with these effects fully rescued by concurrent CDC73 silencing. Mechanistically, we identify the JNK cascade as the critical upstream regulator: JNK activation sustains CDC73 stability by antagonizing UBR5-mediated CDC73 polyubiquitination, and map Lys257 as the key residue for UBR5-dependent CDC73 ubiquitination and degradation. Collectively, our findings define a novel JNK-dependent UBR5-CDC73 axis that acts as a molecular brake of the CDC73 positive feedback loop to orchestrate transcriptional programs, providing new mechanistic insights into CDC73 post-translational regulation in tumorigenesis and promising therapeutic targets for CDC73-dysregulated diseases.

Journal Article↗
PubMed2026

Activation of secondary metabolism in Aspergillus and related filamentous fungi through regulatory engineering.

Filamentous fungi are major contributors to diverse secondary metabolites with broad applications to medicine, agriculture, and biotechnology. Advances in genome sequencing and bioinformatic tools have revealed that fungal genomes encode far more biosynthetic gene clusters (BGCs) than are expressed under normal laboratory conditions, leaving much biosynthetic potential transcriptionally silent. Overcoming this gap between predicted and observed secondary metabolism has become a major challenge in fungal natural product discovery. In this review, we summarize current strategies for activating silent or weakly expressed fungal BGCs through regulatory engineering, with an emphasis on approaches validated in Aspergillus, Penicillium, Monascus, and related filamentous fungi. We focus on genetic and chemical manipulations that enable coordinated activation of multiple biosynthetic pathways through chromatin-level modifiers, global transcriptional regulators, and developmental regulators. By framing these regulators as practical tools rather than solely biological components, we demonstrate their strengths, limitations, and applications in Aspergillus and related filamentous fungi. We further discuss emerging combinatorial and integrative approaches that use regulatory engineering alongside omics technologies and predictive tools, outlining alternatives and future directions for improving the interpretability of silent pathway activation.

Journal Article↗
PubMed2025

Penalized likelihood optimization for censored missing value imputation in proteomics.

Label-free bottom-up proteomics using mass spectrometry and liquid chromatography has long been established as one of the most popular high-throughput analysis workflows for proteome characterization. However, it produces data hindered by complex and heterogeneous missing values, which imputation has long remained problematic. To cope with this, we introduce Pirat, an algorithm that harnesses this challenge using an original likelihood maximization strategy. Notably, it models the instrument limit by learning a global censoring mechanism from the data available. Moreover, it estimates the covariance matrix between enzymatic cleavage products (ie peptides or precursor ions), while offering a natural way to integrate complementary transcriptomic information when multi-omic assays are available. Our benchmarking on several datasets covering a variety of experimental designs (number of samples, acquisition mode, missingness patterns, etc.) and using a variety of metrics (differential analysis ground truth or imputation errors) shows that Pirat outperforms all pre-existing imputation methods. Beyond the interest of Pirat as an imputation tool, these results pinpoint the need for a paradigm change in proteomics imputation, as most pre-existing strategies could be boosted by incorporating similar models to account for the instrument censorship or for the correlation structures, either grounded to the analytical pipeline or arising from a multi-omic approach.

Proteomics↗
PubMed2026

National genomic projects in Asia and Africa: a review.

National genome projects (NGPs) are increasingly shaping precision medicine by improving representation of population-specific genetic diversity. This review compiles findings from NGPs across Asia and Africa, regions that remain underrepresented in global genomic databases despite their extensive demographic and genetic diversity. A total of 53 studies from 24 countries were identified to understand (1) the genomic approach utilized, (2) novel findings that have emerged, and (3) strategies for improving research in these regions. The NGPs implement population-based variome databases (20 NGPs), linear reference genome assemblies (8 NGPs), and graph-based pangenome assemblies (1 NGP). Novel variants ranged between 0.28% (China) and 19.6% (Iran), whereas rare variants accounted for up to 88.9% of the detected variants in the Chinese population. Each NGP documents its country's evolutionary and migration history, which impacts disease frequency and pharmacogenomic variants. Clinically, NGPs revealed strong population stratification in disease-associated and pharmacogenomic variants. For example, the GJB2 rs72474224 hearing-loss variant ranged from 13% in Vietnam and 12% in Hong Kong to 0.0894% in Turkey, while the VKORC1 rs9923231 pharmacogenomic variant reached 89.2% in Taiwan but was 20%-25% in European-related Russian subpopulations. These findings demonstrate that clinically relevant allele frequencies, pathogenicity assessments, and drug-response markers differ substantially across ancestries. This review highlights ongoing efforts and strategies to enhance the representativeness of genomic data through NGPs in Asia and Africa. We also suggest future directions for national projects, including integrating family-based studies, multi-omic data, and standardized pipelines to accelerate discovery and support the equitable implementation of precision medicine.

Humans↗
PubMed2026

Epigenetics and childhood obesity: DNA methylation coordinates environment and gene regulation.

Childhood obesity is a complex disorder which results from the combined contribution of genetics, the environment, and development, which is programmed and coordinated by epigenetic mechanisms. Of them, DNA methylation has emerged as an important molecular interface between environmental inputs and changes in gene expression. In this review, we provide an overview of the role of DNA methylation in childhood obesity during the key developmental stages, from prenatal life and childhood to adolescence. We also highlight the available evidence from candidate genes and genome-wide association studies implicating critical loci involved in energy homeostasis and adipogenesis, where DNA methylation is altered. Further, we also provide an overview of how maternal obesity, nutritional status, and bariatric surgery shape offspring's methylation profiles and contribute to the increased risk of programming obesity across generations. Although aberrant methylation patterns are consistently associated with altered metabolic phenotypes, disentangling causality remains a significant challenge. Herein, we highlight emerging approaches, such as rigorous longitudinal cohorts, epigenetic Mendelian randomization, and CRISPR-based epigenome editing, that are beginning to provide the analytical clarity needed to move beyond association. Finally, we examine the potential of DNA methylation signatures to inform early risk stratification and prevention possibilities. Although yet to be clinically validated, whole-genome methylation profiling is increasingly integrated with systems biology and multi-omics frameworks, making the identification of robust, clinically actionable markers more promising. A more precise understanding of how epigenetic processes shape susceptibility to childhood obesity could ultimately support strategies capable of altering lifelong metabolic trajectories.

Humans↗
PubMed2006

Scaling and normalization effects in NMR spectroscopic metabonomic data sets.

Considerable confusion appears to exist in the metabonomics literature as to the real need for, and the role of, preprocessing the acquired spectroscopic data. A number of studies have presented various data manipulation approaches, some suggesting an optimum method. In metabonomics, data are usually presented as a table where each row relates to a given sample or analytical experiment and each column corresponds to a single measurement in that experiment, typically individual spectral peak intensities or metabolite concentrations. Here we suggest definitions for and discuss the operations usually termed normalization (a table row operation) and scaling (a table column operation) and demonstrate their need in 1H NMR spectroscopic data sets derived from urine. The problems associated with "binned" data (i.e., values integrated over discrete spectral regions) are also discussed, and the particular biological context problems of analytical data on urine are highlighted. It is shown that care must be exercised in calculation of correlation coefficients for data sets where normalization to a constant sum is used. Analogous considerations will be needed for other biofluids, other analytical approaches (e.g., HPLC-MS), and indeed for other "omics" techniques (i.e., transcriptomics or proteomics) and for integrated studies with "fused" data sets. It is concluded that data preprocessing is context dependent and there can be no single method for general use.

Algorithms↗
PubMed2026

From molecular responses to environmental monitoring: advances and translational gaps in omics approaches in fish environmental toxicology.

Fish occupy a central position in aquatic ecosystems and serve as important bioindicators for environmental monitoring, as well as powerful translational models for understanding toxic mechanisms conserved across higher vertebrates. In recent years, omics techniques have proven to be powerful tools to address complex environmental questions that conventional toxicology methods cannot answer. Despite this potential, a critical translational gap remains between molecular findings and their use in ecological risk assessment frameworks. This review critically synthesizes advances across omics techniques including epigenomics, transcriptomics, metabolomics and proteomics and their integration. Special emphasis is placed on methodological considerations and practical aspects of these techniques in fish environmental toxicology and environmental monitoring. Evidence from single-omics studies suggests conserved biomarker signatures across species while characterizing complex phenomena like non-monotonic dose-response relationships, mixture toxicity and transgenerational and stereoselective effects with implications for population level monitoring. Multi-omics studies, especially those involving triple omics, further enhance mechanistic resolution by reconstructing adverse outcome pathways. We further evaluate using case studies when additional molecular layers provide critical insight and when they offer limited advantage, a strategic distinction with direct implications in environmental monitoring programmes. Finally, current limitations and future directions that will ultimately bridge the translational gap and hold promise for advancing mechanistic ecotoxicology and predictive environmental monitoring are discussed.

Animals↗
PubMed2006

Concept of sample in OMICS technology.

Fundamental biological processes can now be studied by applying the full range of OMICS technologies (genomics, transcriptomics, proteomics, metabolomics, and beyond) to the same biological sample. Clearly, it would be desirable if the concept of sample were shared among these technologies, especially as up until the time a biological sample is prepared for use in a specific OMICS assay, its description is inherently technology independent. Sharing a common informatic representation would encourage data sharing (rather than data replication), thereby reducing redundant data capture and the potential for error. This would result in a significant degree of harmonization across different OMICS data standardization activities, a task that is critical if we are to integrate data from these different data sources. Here, we review the current concept of sample in OMICS technologies as it is being dealt with by different OMICS standardization initiatives and discuss the special role that the newly formed Genomic Standards Consortium (GSC) might have to play in this domain.

Animals↗
PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗
PubMed2026

Do Multi-Omics Approaches Improve the Diagnosis of Microbial Overgrowth Syndromes?

PURPOSE OF REVIEW: This review investigates how advances in breath testing (BT), small bowel (SB) culture, metagenomics, metatranscriptomics, transcriptomics and proteomics are reshaping the definition and diagnosis of small intestinal bacterial overgrowth (SIBO). It also discusses whether SIBO should be redefined as part of a larger group of microbial overgrowth syndromes. RECENT FINDINGS: Recent studies identify distinct hydrogen-, methane-, and hydrogen sulfide-associated overgrowth phenotypes, termed SIBO, intestinal methanogen overgrowth (IMO), and intestinal sulfide overproduction (ISO). SB sampling shows that these conditions involve different microbial patterns and functional activity, symptoms, and host responses. Quantitative shotgun metagenomics provides greater taxonomic and functional resolution than culture, while metatranscriptomics reveals active microbial pathways. On top of that, host transcriptomics and proteomics contribute to the better understanding of the predominant microbial effects in host cellular mechanisms in each of the distinct small bowel overgrowth types. SIBO has been increasingly identified as a disorder of microbial ecology and function rather than bacterial quantity alone. Integrating BT with SB sampling and multi-omics approaches may improve classification, clarify symptom mechanisms, and support a more individualized treatment, although standardized methods and further clinical validation remain necessary.

Humans↗
PubMed2026

Machine learning and multi-omics clustering to map cellular rewiring and immune evasion in ccRCC.

Immune checkpoint blockade (ICB) efficacy in clear cell renal cell carcinoma (ccRCC) is limited by tumor microenvironment (TME) heterogeneity. Because traditional bulk-derived models lack spatial resolution, we developed an integrated framework connecting macroscopic survival risks to microscopic TME structures. We applied ten algorithms to establish multi-omics subtypes and evaluated 101 machine-learning combinations across three independent cohorts to generate a Consensus Machine Learning-driven Signature (CMLS). The signature's spatial and cellular origins were decoded using spatial transcriptomics (ST) and a 140,000-cell scRNA-seq atlas. Expression of key genes was experimentally validated via RT-qPCR in 17 paired ccRCC clinical tissues. We identified two molecular subtypes with distinct clinical and epigenetic profiles. SuperPC optimization yielded a 24-gene CMLS serving as an independent prognostic factor. scRNA-seq and ST deconvolution revealed these signals predominantly originate from cancer-associated fibroblasts (CAFs) and malignant epithelial cells, which collaborate to drive spatial immune exclusion. RT-qPCR confirmed significant overexpression of five core CMLS genes in ccRCC versus adjacent normal tissues. Low CMLS scores correlated with enhanced ICB responsiveness, whereas high-CMLS tumors demonstrated specific vulnerability to dasatinib and dabrafenib. The CMLS translates spatial immune-exclusion dynamics into a quantifiable metric, outperforming tumor mutational burden in predicting ICB benefits, providing a robust tool for patient stratification in ccRCC.

Humans↗
PubMed2025

LimROTS: a hybrid method integrating empirical Bayes and reproducibility-optimized statistics for robust differential expression analysis.

MOTIVATION: Differential expression analysis plays a vital role in omics research enabling precise identification of features that associate with different phenotypes. This process is critical for uncovering biological differences between conditions, such as disease versus healthy states. In proteomics, several statistical methods have been used, ranging from simple t-tests to more advanced methods like DEqMS, limma and ROTS. However, a flexible method for reproducibility-optimized statistics tailored for clinical omics data has been lacking. RESULTS: In this study, we developed LimROTS, a hybrid method that integrates a linear regression model and the empirical Bayes approach with reproducibility optimized statistics, to create a novel moderated ranking statistic, for robust and flexible analysis of proteomics data. We validated its performance using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques for benchmarking. This hybrid approach improves accuracy and reproducibility of complex proteomics data, making LimROTS a powerful tool for high-dimensional omics data analysis. AVAILABILITY AND IMPLEMENTATION: LimROTS has been implemented as an R/Bioconductor package, available at https://doi.org/doi:10.18129/B9.bioc.LimROTS. Additionally, the code used in this study is available in GitHub repository https://github.com/AliYoussef96/LimROTSmanuscript.

Bayes Theorem↗
PubMed2005

Integrating data from biological experiments into metabolic networks with the DBE information system.

Modern 'omics'-technologies result in huge amounts of data about life processes. For analysis and data mining purposes this data has to be considered in the context of the underlying biological networks. This work presents an approach for integrating data from biological experiments into metabolic networks by mapping the data onto network elements and visualising the data enriched networks automatically. This methodology is implemented in DBE, an information system that supports the analysis and visualisation of experimental data in the context of metabolic networks. It consists of five parts: (1) the DBE-Database for consistent data storage, (2) the Excel-Importer application for the data import, (3) the DBE-Website as the interface for the system, (4) the DBE-Pictures application for the up- and download of binary (e. g. image) files, and (5) DBE-Gravisto, a network analysis and graph visualisation system. The usability of this approach is demonstrated in two examples.

Computational Biology↗
PubMed2006

Advances in plant proteomics.

With the avalanche of genomic information and improvements in analytical technology, proteomics is becoming increasingly important for the study of many different aspects of plant functions. Since proteins serve as important components of major signaling and biochemical pathways, studies at protein levels are essential to reveal molecular mechanisms underlying plant growth, development, and interactions with the environment. The plant proteome is highly complex and dynamic. Although great strides need to be taken towards the ultimate goal of characterizing all the proteins in a proteome, current technologies have provided immense opportunities for high-throughput proteomic studies that have gone beyond simple protein identification to analyzing various functional aspects, such as quantification, PTM, subcellular localization, and protein-protein interactions. In this review of plant proteomics, advances in protein fractionation, separation, and MS will be outlined. Focus will be on recent development in functional analysis of plant proteins, which paves the way towards the comprehensive integration with transcriptomics, metabolomics, and other large scale "-omics" into systems biology.

Algorithms↗
PubMed2026

Pan-cancer analysis identifies KANSL2 as a cell-cycle-associated regulator of tumor progression and immunity in liver hepatocellular carcinoma.

KANSL2, a core component of the NSL histone acetyltransferase complex, has been implicated in tumorigenesis. However, its pan-cancer relevance and functional role in liver hepatocellular carcinoma (LIHC) remain unclear. Multi-omics data from TCGA, GEO, and HPA were integrated to systematically evaluate KANSL2 expression, clinical significance, genomic alterations, and immune associations across cancers. Functional enrichment, immune infiltration analyses, and single-cell transcriptomics were performed. In vitro assays were conducted to validate the biological effects of KANSL2 in LIHC cells. KANSL2 is broadly upregulated across cancers and exhibits strong diagnostic performance. Elevated KANSL2 expression correlates with unfavorable prognosis, particularly in LIHC. Mechanistically, KANSL2 and its co-expressed genes are enriched in cell-cycle progression. KANSL2 expression is also closely associated with immune infiltration and immunoregulatory signaling within the tumor microenvironment, with single-cell data indicating preferential expression in proliferative T-cell subsets. Functional experiments demonstrate that KANSL2 silencing suppresses proliferation, migration, and invasion, and induces G2/M phase arrest in LIHC cells. Notably, its effects on apoptosis are limited, suggesting that KANSL2 primarily drives tumor progression through cell-cycle-dependent mechanisms. This study identifies KANSL2 as a key regulator of tumor progression and immune remodeling in LIHC. By promoting malignancy predominantly via cell-cycle control, KANSL2 represents a promising biomarker for diagnosis and prognosis, and a potential therapeutic target.

Carcinoma, Hepatocellular↗
PubMed2026

Pan-cancer analysis identifies APOC1 as a TAM-derived modulator of adaptive immune resistance and predictor of therapeutic response.

BACKGROUND: Apolipoprotein C1 (APOC1) has been implicated in several malignancies, yet its expression patterns, clinical significance, and immunomodulatory roles across cancer types remain poorly characterized. METHODS: We performed a comprehensive multi-omic analysis of APOC1 across 33 cancer types integrating transcriptomic, proteomic, genomic, epigenomic, and pharmacogenomic data from TCGA, GTEx, CPTAC, and multiple independent external cohorts. Immune infiltration was assessed using seven complementary algorithms. Spatial transcriptomics and single-cell RNA sequencing were employed to determine the cellular source of APOC1 expression. RESULTS: APOC1 upregulation in most cancers was associated with cancer type-specific prognosis. After adjustment for clinical covariates and macrophage infiltration, high APOC1 remained an independent adverse factor in KIRC, LGG, and STAD. APOC1 expression positively correlated with genomic instability hallmarks, including homologous recombination deficiency and aneuploidy, with these associations largely independent of immune infiltration; in contrast, associations with tumor mutational burden were substantially confounded by macrophage abundance. Immune infiltration analysis revealed a pattern consistent with adaptive immune resistance: APOC1 correlated positively with immune-activating signatures (STAT1, MHC-II, TCR signaling) and immunosuppressive M2 macrophages and Tregs, yet negatively with anti-tumor effectors (activated NK cells, dendritic cells). Spatial transcriptomics and single-cell RNA sequencing identified tumor-associated macrophages (TAMs) as the primary cellular source of APOC1, with transcripts co-localizing with CD68 in tissue sections. APOC1 expression correlated with multiple immune checkpoint molecules and was elevated in responders to immune checkpoint blockade, consistent with an inflamed yet regulated tumor microenvironment. Pharmacogenomic analyses revealed that APOC1-high tumors display distinct drug response profiles, characterized by resistance to MAPK pathway inhibitors and potential sensitivity to the HDAC inhibitor Entinostat. CONCLUSION: This pan-cancer analysis establishes APOC1 as a context-dependent biomarker and a TAM-derived modulator of adaptive immune resistance, with prognostic and therapeutic implications across malignancies. APOC1-expressing TAMs represent a potential target for combination immunotherapy strategies.

APOC1↗