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PubMed2005

Cancer drug discovery through collaboration.

It has been two decades since cancer was first described as a genetic disease and researchers offered the promise of early diagnosis and targeted therapies. Today, most cancer patients still await life-saving treatments. Genomics and other '-omics' technologies have revealed a complexity among cancers that makes almost any tumour genetically unique; as a consequence, effective targeted therapies might be suitable only for small subgroups of patients. We suggest that by merging and organizing their core competencies, academia, biotechnology companies and the pharmaceutical industry can address existing bottlenecks in anticancer drug discovery and development.

Antineoplastic Agents↗
PubMed2026

CoxFormer enables spatial omics inference with multimodal generative modeling.

Gene co-expression maps transcriptome-wide gene-gene relationships, yet high-quality estimates cover less than half the genome. Meanwhile, spatial omics either profiles restricted in situ panels or lacks cellular resolution. Extending co-expression transcriptome-wide could overcome these limitations by inferring unassayed gene expression at subcellular resolution. Here we show that CoxFormer integrates literature-derived gene knowledge with co-expression networks from bulk tissues and large-scale single-cell atlases to learn 512-dimensional representations for 32,016 human genes. These embeddings capture functional gene relationships and serve as a generative prior for spatial inference across platforms and modalities. Without requiring a matched single-cell RNA-sequencing reference, CoxFormer supports four applications beyond measured genes: histology-based expression imputation, gene activity prediction from chromatin accessibility, subcellular super-resolution inference, and pathological region detection. Together, CoxFormer extends gene embedding from gene- and cell-level tasks to whole-transcriptome spatial inference, providing a unified framework for biological analysis beyond the limited gene coverage of current spatial omics technologies.

Humans↗
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

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects function. However, the reliability of current benchmarks of spatially aware clustering (SAC) methods is undermined by their narrow focus on Visium and brain tissue datasets and the incorrect interpretation of manual annotation as ground truth. Here we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration and metric evaluation, enabling rapid inclusion of new methods and datasets. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods and shows that anatomical labels commonly used as ground truths are often biased, error prone and unsuitable for benchmarking. Rather than ranking methods, we propose a consensus-guided workflow where descriptive spatial metrics highlight high-entropy regions of method disagreement, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual SAC methods and manual annotations, highlighting the need for iterative, expert-in-the-loop evaluation.

Benchmarking↗
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

Next-generation sequencing in breast cancer: current clinical applications and future directions.

INTRODUCTION: Breast cancer is a heterogeneous disease that claims 670,000 lives by 2022. Omic technologies, particularly next generation sequencing (NGS) offers promising avenues for precision medicine. American Society of Clinical Oncology (ASCO) outlines genomic testing's utility, emphasizing prognostic and diagnostic potential. OBJECTIVES: This review succinctly explores NGS's evolution and clinical applications of NGS in breast cancer, thereby guiding future research to enhance patient care. METHODS: Comprehensive literature searches were conducted using databases such as PubMed, Google Scholar, and ResearchGate, focusing on keywords including breast cancer, HER-2 low breast cancer, circulating tumour DNA, single-cell RNA sequencing, and next-generation sequencing. Peer-reviewed, high-quality articles published in English were selected for inclusion. RESULTS: Previous studies have explored the evolution of NGS technology and its clinical applications in breast cancer, including genomic and transcriptomic characterization, treatment guidance, and resistance prediction. Molecular profiling of challenging entities such as early-onset breast cancer and HER-2 low tumours was summarized, with key findings highlighted. This review also discusses emerging technologies, including circulating DNA and single-cell sequencing, as promising avenues for discovery. CONCLUSION: NGS has revealed the genomic and transcriptomic diversity of breast cancer, identifying actionable alterations associated with chemotherapy response and resistance to therapies such as trastuzumab, TKIs, and CDK4/6 inhibitors. Circulating tumour DNA (ctDNA) shows potential for diagnosis, prediction, prognosis, and monitoring, despite tumour heterogeneity. Single-cell analysis enables exploration of individual cell transcriptomes, though high costs and low throughput remain barriers to widespread adoption. HER2-low tumours continue to pose significant research challenges.

Humans↗
PubMedDate not supplied

Biomarkers in cancer screening, research and detection: present and future: a review.

Biomarkers provide a powerful and dynamic approach to understanding the spectrum of malignancies with applications in observational and analytic epidemiology, randomized clinical trials, screening, diagnosis and prognosis. Defined as alterations in the constituents of tissues or body fluids, these markers offer a means for homogeneous classification of a disease and risk factor, and they can extend one's basic information about the underlying pathogenesis of disease. The goals in cancer research include finding biomarkers that can be used for the early detection of cancers, design individual therapies, and to identify underlying processes involved in the disease. Because so many myriad processes are involved in the diseased states, the goal is similar to 'finding a needle in a haystack'. However, the development of many -omic technologies, such as genomics and proteomics, has allowed us to monitor a large number of key cellular pathways simultaneously. This has enabled the identification of biomarkers and signalling molecules associated with cell growth, cell death and cellular metabolism. These are also facilitating in monitoring the functional disturbance, molecular and cellular damage, and damage response. This brief review describes the development of biomarkers in cancer research and detection with emphasis on different proteomic tools for the identification and discovery of new biomarkers, different clinical assays to detect various biomarkers in different specimens, role of biomarkers in cancer screening and last but not the least, the challenges in this direction of cancer research.

Biomarkers, Tumor↗
PubMed2006

A strategy capitalizing on synergies: the Reporting Structure for Biological Investigation (RSBI) working group.

In this article we present the Reporting Structure for Biological Investigation (RSBI), a working group under the Microarray Gene Expression Data (MGED) Society umbrella. RSBI brings together several communities to tackle the challenges associated with integrating data and representing complex biological investigations, employing multiple OMICS technologies. Currently, RSBI includes environmental genomics, nutrigenomics and toxicogenomics communities, where independent activities are underway to develop databases and establish data communication standards within their respective domains. The RSBI working group has been conceived as a "single point of focus" for these communities, conforming to general accepted view that duplication and incompatibility should be avoided where possible. This endeavour has aimed to synergize insular solutions into one common terminology between biologically driven standardisation efforts and has also resulted in strong collaborations and shared understanding between those in the technological domain. Through extensive liaisons with many standards efforts, several threads have been woven with the hope that ultimately technology-centered standards and their specific extensions into biological domains of interest will not only stand alone, but will also be able to function together, as interchangeable modules.

Databases, Genetic↗
PubMed2006

Ascorbate as a biosynthetic precursor in plants.

BACKGROUND AND AIMS: l-Ascorbate (vitamin C) has well-documented roles in many aspects of redox control and anti-oxidant activity in plant cells. This Botanical Briefing highlights recent developments in another aspect of l-ascorbate metabolism: its function as a precursor for specific processes in the biosynthesis of organic acids. SCOPE: The Briefing provides a summary of recent advances in our understanding of l-ascorbate metabolism, covering biosynthesis, translocation and functional aspects. The role of l-ascorbate as a biosynthetic precursor in the formation of oxalic acid, l-threonic acid and l-tartaric acid is described, and progress in elaborating the mechanisms of the formation of these acids is reviewed. The potential conflict between the two roles of l-ascorbate in plant cells, functional and biosynthetic, is highlighted. CONCLUSIONS: Recent advances in the understanding of l-ascorbate catabolism and the formation of oxalic and l-tartaric acids provide compelling evidence for a major role of l-ascorbate in plant metabolism. Combined experimental approaches, using classic biochemical and emerging 'omics' technologies, have provided recent insight to previously under-investigated areas.

Ascorbic Acid↗
PubMed2026

New Evidence in Heart Failure: 2026 Update.

Heart failure (HF) remains a major cause of morbidity, mortality, impaired quality of life and healthcare expenditure worldwide. The global burden of HF continues to increase due to population aging, improved survival, and the growing prevalence of cardiovascular, renal, and metabolic comorbidities. Simultaneously, the pace of scientific progress in HF has accelerated considerably. Recent advances have refined our understanding of HF epidemiology, prognosis, and disease trajectories, including emerging concepts of HF improvement, remission, and recovery. The Second Universal Definition of HF has also updated the classification framework, moving beyond the traditional ejection fraction-based categories. HF is now broadly classified into two major phenotypes: heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF). Novel mechanistic insights highlight the role of inflammation, immune activation, metabolic dysfunction, mitochondrial biology, and multisystem interactions in HF progression. There has also been significant progress in the characterization and management of major comorbidities, including chronic kidney disease (CKD), diabetes, obesity, atrial fibrillation (AF), pulmonary hypertension, frailty, malnutrition, and cancer. Diagnostic innovations include novel biomarkers, multi-omics technologies, artificial intelligence-based approaches, advanced imaging techniques, congestion assessment tools, and emerging digital health solutions. Important advances have occurred in specific HF aetiologies, including cardiomyopathies, cardiac amyloidosis (CA), myocarditis, arrhythmia-induced cardiomyopathy (AiCM), and Chagas cardiomyopathy. Therapeutic developments continue to reshape HF management across the spectrum of left ventricular ejection fraction. Recent evidence has focused on optimization of guideline-directed medical therapy in HFrEF, expansion of evidence-based therapies in HFpEF, and growing roles for sodium-glucose cotransporter-2 inhibitors, finerenone, incretin-based therapies, and transcatheter valve interventions. Collectively, these advances support the transition from a predominantly phenotype-based approach towards a more personalized and biologically informed model of HF care, with the potential to further improve outcomes across the entire HF spectrum.

Journal Article↗
PubMed2005

Lessons from controversy: ovarian cancer screening and serum proteomics.

In 2002 a study reported that a blood test, based on pattern-recognition proteomics mass spectroscopy analysis of serum, was nearly 100% sensitive and specific to detect ovarian cancer. Plans to introduce a commercial screening test by early 2004 were delayed amid concerns about whether the approach was reproducible and reliable. In this issue of JNCI, two commentaries discuss whether the initial results are reproducible and whether bias may account for results. This essay describes how threats to validity from chance and bias may cause erroneous results and inflated expectations in the kind of observational research being conducted in several "-omics" fields to assess molecular markers for diagnosis and prognosis of cancer. To address such threats and to realize the potential of new -omics technology will require application of appropriate rules of evidence in the design, conduct, and interpretation of clinical research about molecular markers.

Biomarkers, Tumor↗
PubMed2004

Metabolic profiles to define the genome: can we hear the phenotypes?

There is an increased reliance on genetically modified organisms as a functional genomic tool to elucidate the role of genes and their protein products. Despite this, many models do not express the expected phenotype thought to be associated with the gene or protein. There is thus an increased need to further define the phenotype resultant from a genetic modification to understand how the transcriptional or proteomic network may conspire to alter the expected phenotype. This is best typified by the description of the silent phenotype in genetic manipulations of yeast. High-resolution proton nuclear magnetic resonance ((1)H NMR) spectroscopy provides an ideal mechanism for the profiling of metabolites within biofluids, tissue extracts or, with recent advances, intact tissues. These metabolic datasets can be readily mined using a range of pattern recognition techniques, including hierarchical cluster analysis, principal components analysis, partial least squares and neural networks, with the combined approach being termed metabolomics. This review describes the application of NMR-based metabolomics or metabonomics to genetic and chemical interventions in a number of different species, demonstrating the versatility of such an approach, as well as suggesting how it may be integrated with other "omic" technologies.

Animals↗
PubMed2025

Spatial Metabolomics Reveals the Role of Penicillic Acid in Cheese Rind Microbiome Disruption by a Spoilage Fungus.

Microbial interactions in cheese rinds influence community structure, food safety, and product quality. But the chemical mechanisms that mediate microbial interactions in cheeses and other fermented foods are generally not known. Here, we investigate how the spoilage mold Aspergillus westerdijkiae chemically inhibits beneficial cheese-rind bacteria using a combination of omics technologies. In cheese rind community and co-culture experiments, A. westerdijkiae strongly inhibited most cheese rind community members. In co-culture with Staphylococcus equorum, A. westerdijkiae strongly affected bacterial gene expression, including upregulation of a putative bceAB gene cluster that is associated with resistance to antimicrobial compounds in other bacteria. Mass spectrometry imaging (MSI) revealed spatially localized production of secondary metabolites, including penicillic acid and ochratoxin B at the fungal-bacterial interface. Integration of LC-MS/MS and genome annotations confirmed the presence of additional bioactive metabolites, such as notoamides and circumdatins. Fungal metabolic responses varied by bacterial partner, suggesting species-specific chemical strategies. Notably, penicillic acid levels increased 2.5-fold during interaction with Brachybacterium, and experiments with purified penicillic acid showed inhibition of a range of cheese rind bacteria. These findings show that A. westerdijkiae deploys a context-dependent arsenal of mycotoxins and other metabolites, disrupting microbial community assembly in cheese rinds.

Aspergillus westerdijkiae↗
PubMed2006

Application of genomics in preclinical drug safety evaluation.

Understanding the response of biological systems to xenobiotics is fundamental to the evaluation of drug safety. Toxicologists have traditionally gathered pathological, morphological, chemical and biochemical information from in vivo studies of preclinical species in order to assess drug safety and to determine how new drugs can be safely administered to the human patient population. In recent years the emerging "-omics" technologies have been developed and integrated into preclinical studies in order to better assess drug safety by gaining information on the cellular and molecular events underlying adverse drug reactions. Genomics approaches in particular have become readily available and are being applied in several stages of drug development. The burgeoning literature on what has become known as "toxicogenomics" has for the most part highlighted successful applications of gene expression profiling in predictive toxicology, enabling decisions to be made on the developability of a compound early in the drug development process. It is also becoming apparent that toxicogenomic approaches are good starting points to develop experiments designed to gain a mechanistic insight into drug toxicities within and across species. Gene expression arrays permit the measurement of responses of essentially all the genes in the entire genome to be monitored, and knowledge of the function of the genes affected can identify the potential mechanisms to then be confirmed using conventional biochemical, toxicological and pathological approaches. As toxicologists put these technologies into practice they build up a knowledge base to better characterize toxicities at the molecular level and to make the search for much needed, novel biomarkers of toxicity more achievable.

Drug Evaluation, Preclinical↗
PubMed2026

Phytohormones in fungi: inter-kingdom modulators or fungal self-controlling elements?

SUMMARYLeveraging data from innovative experimental approaches, omics technologies, and bioinformatics, we offer new insights into how fungi communicate with and perceive their environment to achieve ecological success. By integrating comparative data from both the fungal and plant kingdoms, we critically reassessed the evolutionary, biochemical, and functional landscape of phytohormones in fungi, challenging the conventional notion that these molecules serve exclusively as plant regulators or as means of communication with them. Our analysis demonstrates that fungi not only synthesize a diverse array of phytohormones-including auxins, cytokinins, gibberellins, abscisic acid, ethylene, brassinosteroids, salicylic acid, and oxylipins-but also possess hormone-sensing and signal transduction mechanisms architecturally distinct from those of plants. Employing genomics, phylogenetics, and structural analyses, the review uncovers that many hormone biosynthetic and sensing pathways in fungi are evolutionarily ancient, sometimes predating their roles in plant-fungus interactions, and that some pathways show convergence rather than direct gene homology. Fungal phytohormones regulate development, growth, and metabolism, thereby playing key functions in their ecological context. The review emphasizes that, while biosynthetic pathways tend to be highly conserved, perception and signaling mechanisms in fungi are more varied and often remain poorly understood. We conclude that fungi have an intrinsic and autonomous hormonal physiology that underpins their ecological adaptability and success. Collectively, this analysis reframes fungal biology, highlighting the need for deeper investigation into the signaling and regulatory roles of phytohormones in fungi beyond their interactions with plants.

cell signaling↗
PubMed2026

Probiogenomic analysis of functional potential and safety of L. plantarum 8p-a3 and DMC-S1 strains: in silico vs in vitro and in vivo data.

The molecular basis of the beneficial effects and the causes of the negative effects of probiotics are not entirely clear. Clarifying these issues is important for understanding the biology and assessing the safety of the microbes. Omics technologies have opened up new resources for obtaining relevant knowledge. Here, for the first time, we present the results of a comparative analysis of the functional potential and safety of two L. plantarum strains: the approved probiotic 8p-a3 and the Drosophila intestinal resident, which exhibit opposite effects on D. melanogaster as the model host organism. Through genomic analysis, extracellular vesicle studies, and in vitro and in vivo assays, we have identified the common and specific characteristics of the strains. The strains proved to be similar in a set of genes that determine benefits to the host organism, as well as in the presence of some risk factors. Significant differences between the strains are related to genes responsible for adhesion, sialic acid metabolism, mucin degradation, antimicrobial peptides, tannin resistance, and immunomodulation. In silico data correlated with in vitro and in vivo data, with the exception of antimicrobial sensitivity. Pronounced differences between the strains were found in terms of the composition and biological effects of their vesicles. In vivo data on the effects of the strains correlate with the corresponding data of their vesicles in the fruit fly model. The results obtained open up new facets in L. plantarum strains relevant for evaluating the functionality and safety of probiotics.IMPORTANCEUsing a probiogenomic approach, common and specific features regarding functionality and safety were identified in the strains (the approved probiotic strain L. plantarum 8p-a3 and the Drosophila intestinal bacterium L. plantarum DMC-S1), which exhibit opposite effects on the model host organism (D. melanogaster). The genomic analysis was supplemented by the analysis of extracellular vesicles of the strains. Comparative analysis of in silico data in combination with in vitro and in vivo studies was performed, and unexpected capabilities of the strains were discovered. Novel factors, essential for evaluating the safety of probiotics, were identified. New facets in the interplay of probiotic bacterium with host organism have been revealed.

Animals↗
PubMed2026

Cell-type specific activation of the cGAS-STING pathway in tumor immunotherapy: mechanisms and therapeutic implications.

BACKGROUND: The cyclic GMP–AMP synthase–stimulator of interferon genes (cGAS–STING) pathway acts as a pivotal innate immune sensor that detects cytosolic DNA and links genomic instability to antitumor immune activation. Therapeutic activation of this pathway has garnered substantial interest as a strategy to enhance cancer immunotherapy by promoting dendritic cell maturation, augmenting antigen presentation, and facilitating cytotoxic lymphocyte infiltration. However, the functional outcomes of cGAS–STING signaling are highly context dependent and influenced by both cell type and tumor microenvironmental (TME) conditions. MAIN BODY: Recent advances in single-cell and spatial transcriptomic profiling have revealed profound heterogeneity in cGAS–STING activation across distinct cellular and regional compartments within tumors. Acute and spatially restricted activation of the pathway can elicit potent antitumor immune responses, whereas chronic or dysregulated signaling may promote immune tolerance and tumor progression. Moreover, metabolic stress, epigenetic silencing, and microenvironmental immunosuppressive factors such as TGF-β and IL-10 can further modulate STING activity, leading to resistance to immunotherapy. Current translational efforts focus on next-generation STING agonists, nanoparticle-based delivery systems, and rational combination strategies with immune checkpoint blockade and metabolic modulators to overcome tumor-intrinsic resistance and minimize systemic toxicity. CONCLUSIONS: Understanding the cell-type-specific and spatial dynamics of cGAS–STING signaling is crucial for the rational design of precision immunotherapies. Future research should emphasize context-dependent modulation of STING activity to maximize therapeutic benefit while limiting adverse effects. Integrating multi-omics technologies and spatially guided drug delivery may ultimately enable personalized modulation of the cGAS–STING axis, transforming it into a clinically effective and safe strategy for cancer immunotherapy.

Humans↗
PubMed2026

Characterization of age-related changes in the gut microbiome and metabolome of Kunming dogs and their associations with police performance.

BACKGROUND: Gut microbiota plays a pivotal role in regulating the host's central nervous system (CNS) activity and behavior. However, its influence on the police performance of Kunming dogs and the underlying mechanisms remain largely unexplored. This study was the first to apply multi-omics technologies to investigate the dynamic variations in gut microbiota and their metabolic profiles across different ages of Kunming dogs. Furthermore, we systematically examined the associations between these microbial alterations and police performance metrics, providing a theoretical foundation for enhancing the working capabilities of Kunming dogs through targeted modulation of intestinal microecology. RESULTS: The study showed that puppies, young dogs and adult dogs had significantly better police performance than elderly dogs, with young dogs exhibiting the highest scores. Analysis of 16S rRNA sequencing demonstrated that gut microbial diversity and stability were highest during the young dog stage, gradually declining with age. Metagenomic analysis revealed that the abundance of Lactobacillus acidophilus, Lactobacillus johnsonii, Limosilactobacillus reuteri, Ligilactobacillus animalis and Muribaculum gordoncarteri were strongly correlated with police performance. The results of metagenome-assembled genomes (MAGs) indicated that the above species have functional genes involved in GABAergic and glutamatergic synapse pathways. Furthermore, metabolomic analysis showed that differential metabolites were enriched in the neuroactive ligand-receptor interaction pathway, in which GABA (γ-aminobutyric acid), histamine and tyramine metabolites were positively correlated with the above species and police performance. CONCLUSION: The species L. acidophilus, L. johnsonii, L. reuteri, L. animalis, and M. gordoncarteri, which were enriched in the gut of puppies and young Kunming dogs, may potentially influence the nervous system through the production of neurotransmitters and neuromodulators, suggesting a possible association with police performance. Video Abstract.

Animals↗