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At least 991 records · Page 55Linked to original sources
PubMed2026

Japanese encephalitis virus hijacks the host purine biosynthetic network to promote viral replication in neurons.

Japanese encephalitis virus (JEV) is an important neurotropic orthoflavivirus that poses a threat to both human and animal health. However, the mechanism underlying its rapid replication in the central nervous system (CNS) remains poorly understood. In this study, we conducted metabolomic profiling of JEV-infected mouse brains and neurons, revealing a profound reprogramming of central carbon metabolism, particularly an enhancement in nucleotide synthesis. Integrated multi-omics analyses confirmed that JEV infection transcriptionally upregulates key enzymes involved in de novo purine biosynthesis (DNPB), one-carbon (1C) metabolism, and the pentose phosphate pathway (PPP) in neurons. Pharmacological inhibition of the core DNPB enzymes potently suppressed JEV replication in neurons and reduced both viral loads and neuroinflammation in JEV-infected mice, suggesting the essential role of DNPB in JEV replication within CNS. Mechanistically, we delineated the critical functions of both the non-oxidative PPP and MTHFD2-mediated 1C metabolism, which jointly supply essential precursors, such as ribose-5-phosphate and formyl groups, for the de novo biosynthesis of purines required for viral RNA replication. These findings unveil a strategy by which JEV co-opts the host's purine biosynthetic machinery to fulfill the nucleotide demands for its genomic replication, establishing DNPB and its supporting pathways as promising therapeutic targets for infections caused by JEV and other neurotropic viruses.

Animals↗
PubMed2026

Epigenetic and metabolic reprogramming of innate immune cells establishes immunological memory in the Schistosomiasis vector snail Biomphalaria glabrata.

Innate immune memory enables non-vertebrates to mount faster and more effective immune responses upon re-exposure to a previously encountered pathogen, yet its cellular and molecular bases remain poorly understood. The freshwater snail Biomphalaria glabrata, intermediate host of the human parasite Schistosoma mansoni, provides a powerful model to investigate this phenomenon. Here, we show that innate immune memory in B. glabrata is carried by hemocytes and relies on profound metabolic and epigenetic reprogramming initiated during primary infection. Using an integrative multi-omics approach combining transcriptomics, chromatin accessibility profiling, whole-genome bisulfite sequencing and targeted metabolomics, we reveal that the first parasite encounter induces a stable rewiring of hemocyte metabolism and chromatin landscape. This reprogramming primes hemocytes for a massive and rapid transcriptional response upon secondary challenge, characterized by an immune shift toward highly specific humoral effector pathways. Metabolic analyses demonstrate an early switch toward aerobic glycolysis, altered tricarboxylic acid cycle activity and amino acid metabolism, consistent with a Warburg-like metabolic state previously described in vertebrate trained immunity. Notably, metabolic and epigenetic remodeling occurs primarily during the primary infection and remains stable upon secondary exposure, suggesting that immune memory is encoded prior to pathogen re-encounter. Together, our results identify conserved metabolic and epigenetic mechanisms underlying innate immune memory in a non-vertebrate host and provide direct evidence that hemocyte-mediated innate immune memory in B. glabrata shares core features with trained immunity described in vertebrates.

Animals↗
PubMed2007

Molecular classification of breast tumors: toward improved diagnostics and treatments.

Recent advances in gene expression profiling and other "omics" technologies have revolutionized cancer research and hold the potential of also revolutionizing clinical practice. These high-throughout approaches have radically changed our ability to study cells and tissues in a more comprehensive way. Combined with advanced bioinformatics and the possibility to simulate biological processes in computers, this field of "systems biology" allows us to study the organism as a whole entity. This chapter describes the molecular classification and characterization of breast tumors into distinct subtypes by using DNA microarrays and discusses the statistical relationships of the subgroups with clinical features of the disease.

BRCA1 Protein↗
PubMed2007

Expression profiling in granulomatous lung disease.

Granulomatous lung diseases, such as sarcoidosis, hypersensitivity pneumonitis, Wegener's granulomatosis, and chronic beryllium disease, along with granulomatous diseases of known infectious etiologies, such as tuberculosis, are major causes of morbidity and mortality throughout the world. Clinical manifestations of these diseases are highly heterogeneous, and the determinants of disease susceptibility and clinical course (e.g., resolution vs. chronic, progressive fibrosis) are largely unknown. The underlying pathogenic mechanisms of these diseases also remain poorly understood. Within this context, these diseases have been approached using genomic and proteomic technologies to allow us to identify patterns of gene/protein expression that track with clinical disease or to identify new pathways involved in disease pathogenesis. The results from these initial studies highlight the potential for these "-omics" approaches to reveal novel insights into the pathogenesis of granulomatous lung disease and provide new tools to improve diagnosis, clinical classification, course prediction, and response to therapy. Realizing this potential will require collaboration among multidisciplinary groups with expertise in the respective technologies, bioinformatics, and clinical medicine for these complex diseases.

Gene Expression Profiling↗
PubMedDate not supplied

Transcriptomics and proteomics: tools for optimising phytoremediation activities.

Negligent industrial development has greatly contributed to environmental pollution through the contamination of water and soil by xenobiotic organic chemicals Remedial strategies to deal with chemical pollution require reliable methods to identify and monitor contamination, as well as effective procedures to attenuate or to eliminate the pollutant. In the food chain, plants are ideally placed as early bio-indicators of environmental pollution as they experience and respond to environmental toxicants sooner than organisms at higher trophic levels. Furthermore, some plants are capable of detoxifying anthropogenic chemicals by metabolic transformation and could prove useful for the remediation of contaminated water and soil: so-called phytoremediation. So far research technologies aimed at developing plants for bio-indication/bio-monitoring and for remediation have largely relied on standardised chemical and biochemical procedures to evaluate phytotoxicity, metabolic fate and persistence of organic pollutants in plants. The next stage in the evolution of these plant-based technologies is the improvement and optimisation of any innate phytoremediation activities identified in selected plants. In general, uptake followed by metabolism and compartmentation is responsible for the detoxification of organic xenobiotics in plants. These are complex cellular systems that may be organised in well-defined pathways and are often controlled by large families of genes. In order to elucidate complex traits such as detoxification, an emerging idea is to make use of global approaches such as the new "omic" technologies to identify molecular changes in plant tissues exposed to specific organic xenobiotics. From expression profiles at the messenger RNA level, transcriptomics permit the identification of function-related gene clusters and at the protein level proteomics provide information on where, when and at what level specific proteins accumulate. We conclude that these global approaches may be a useful way of widening screening capability to identify appropriate molecular markers that can be used to improve detoxification activity.

Arabidopsis↗
PubMed2005

Glial fibrillary acidic protein and related glial proteins as biomarkers of neurotoxicity.

A variety of '-omic' technologies are being increasingly applied in preclinical safety assessments. Such approaches, however, have not been implemented in neurotoxicity safety evaluations. Current regulatory guidelines for assessing neurotoxicity emphasise reliance on traditional histopathological stains and behavioural testing batteries. Although these methods may be sufficient to detect some neurotoxic effects, they lack both the sensitivity and specificity required for broad-scale neurotoxicity screening. The glial reaction to nervous system damage, often termed gliosis, represents a hallmark of all types of nervous system injury. As such, the development and implementation of gliosis biomarkers represents a broadly applicable approach for neurotoxicity safety assessment. Using a panel of known neurotoxic agents, the authors have shown that the astroglial protein, glial fibrillary acidic protein (GFAP), can serve as one such biomarker of neurotoxicity. Qualitative and quantitative analysis of GFAP has shown this biomarker to be a sensitive and specific indicator of the neurotoxic condition. The implementation of GFAP and related glial biomarkers in neurotoxicity screens may serve as the basis for further development of molecular signatures predictive of adverse effects on the nervous system.

Biomarkers↗
PubMed2025

Systems biology successes and areas for opportunity in prostate cancer.

Systems biology approaches have been applied to prostate cancer to model how individual cellular and molecular components interact to influence cancer development, progression, and treatment responses. The integration of multi-omic experimental data with computational models has provided insights into the molecular characteristics of prostate cancer and emerging treatment strategies that have the potential to improve patient outcomes. Here, we highlight recent advancements that have emerged from systems modeling in prostate cancer. These include descriptions of the molecular landscape of prostate cancer and how genomic alterations inform computational models of disease progression, how evolutionary processes give rise to mechanisms of therapeutic resistance, and the development of innovative treatment strategies such as adaptive therapy. We also highlight current challenges in prostate cancer that can be addressed through systems biology approaches. These include tumor heterogeneity, poor immunotherapy response, a paucity of experimental model systems, and the ongoing translation of computational models for clinical decision making. Leveraging systems biology approaches has the potential to lead to a better understanding of the disease and better patient outcomes in the treatment of prostate cancer.

Humans↗
PubMed2026

Somatic genetic alterations in pituitary neuroendocrine tumors.

The molecular characterization of pituitary neuroendocrine tumors (PitNETs) has progressed pronouncedly in recent years, unraveling the molecular pathways driving initiation and progression of different PitNET types and allowing a better understanding of their biology. The most frequent recurring somatic driver alterations were recognized in corticotroph PitNETs (USP8, USP48, BRAF) and somatotroph PitNETs (GNAS) and, much less frequently, in lactotroph PitNETs (SF3B1). Additional well-characterized somatic driver alterations, including TP53, ATRX, and DAXX, are enriched in aggressive corticotroph tumors. Identification of new molecular markers and delineation of their clinical phenotypes are enabling further subclassification of PitNETs based on tumor molecular profiles, with earlier recognition of more aggressive variants. These molecular markers also provide an opportunity for new targeted therapies. Beyond single-gene alterations, epigenetic modifications, such as DNA methylation, histone modifications, and noncoding RNA dysregulation, are emerging as important contributors to PitNET pathogenesis and potential therapeutic targets. Multi-omics approaches encompassing genomics, transcriptomics, epigenomics, and proteomics are transforming PitNET classification. In this review, we provide a comprehensive, data-driven update on somatic driver alterations, epigenetic alterations, converging signaling pathways, and the related emerging therapeutic targets in PitNETs, integrating pooled analyses from published cohorts.

Humans↗
PubMed2004

Metabonomics and its role in drug development and disease diagnosis.

This article describes the new approach known as metabonomics, a combination of data-rich analytical chemistry and chemometrics for profiling metabolism in complex systems. Two main analytical approaches have been used, namely nuclear magnetic resonance spectroscopy and mass spectrometry, however, most literature on mammalian systems pertains to nuclear magnetic resonance spectroscopy. The technologies are reviewed in relation to other -omics, and how these methods can be applied to drug safety assessment, characterization of genetically modified animal models of disease, diagnosis of human disease and drug therapy monitoring is demonstrated.

Animals↗
PubMed2005

Cancer proteomics: many technologies, one goal.

A major goal of the National Cancer Institute is to alleviate patient pain, suffering and death associated with cancer by the year 2015. This goal does not insinuate a cure for cancer, but rather the development of diagnostics and therapeutics that will eventually decrease cancer morbidity and mortality. A part of meeting this goal is to leverage the enormous data-gathering capabilities of proteomic technologies to discover disease-specific biomarkers in serum, plasma, urine, tissues and other biologic samples. The rapid advance in available technologies that have been spurred by the -omics era, has enabled biologic samples to be surveyed for biomarkers in ways never before possible. However, it is not yet clear which specific technologies will be the most successful. Therefore, proteomic laboratories within the National Cancer Institute are taking a multipronged approach to identify disease-specific biomarkers. This review discusses some of these approaches in their context of meeting the National Cancer Institute's 2015 goal.

Animals↗
PubMed2005

Improved understanding of gene expression regulation using systems biology.

This article reviews the current state of systems biology approaches, including the experimental tools used to generate 'omic' data and computational frameworks to interpret this data. Through illustrative examples, systems biology approaches to understand gene expression and gene expression regulation are discussed. Some of the challenges facing this field and the future opportunities in the systems biology era are highlighted.

Animals↗
PubMed2006

Endocrinology: the next 60 years.

The next 60 years promise to arouse the interest of scientists and clinicians while challenging the central dogmas of endocrine physiology. In this review we consider the fundamental changes in the understanding of endocrine physiology that have taken place in recent years and the new hormones discovered. We discuss how the brain is emerging as an important regulator of endocrine and neuroendocrine circuits. Advances in molecular biology techniques and the use of genomics and other -omics in furthering our understanding of endocrine physiology are also discussed. Finally, we propose that in 2066 we may prescribe designer hormones to healthy subjects.

Blood-Brain Barrier↗
PubMed2025

Precision Genomics: A Reality Having Universal Impact in a New Era of Psychiatry - Lessons Learned, Past and Present.

Addiction neuroscience explores the complex interplay between genetic, neurobiological, environmental, and socio-spiritual factors underlying substance and behavioral addictions. Over the past three decades, research in this domain has identified critical molecular and epigenetic mechanisms-particularly those affecting dopaminergic signaling and reward pathways-that contribute to both vulnerability and resilience to addictive behaviors. Central to this understanding is the concept of reward deficiency syndrome (RDS), first introduced by Kenneth Blum, which posits that hypodopaminergic functioning predisposes individuals to seek maladaptive rewards. Advances in neurogenetics, including the identification of key polymorphisms such as the DRD2 A1 allele, have paved the way for precision tools like the genetic addiction risk severity (GARS®) test. This test, alongside pro-dopaminergic nutraceutical interventions like KB220, demonstrates the potential for early detection and individualized treatment of "pre-addiction" risk states. Despite ongoing reliance on opioids for opioid use disorder (OUD), emerging paradigms advocate for dopamine homeostasis through non-addictive, integrative approaches. Furthermore, the integration of whole genome sequencing data can be used for Genome-Wide Association Studies (GWAS), multi-omics, and machine learning into clinical practice holds promise for advancing personalized medicine in addiction treatment. As the field progresses, addressing health equity and improving genomic representation across populations remain critical goals. This evolving framework underscores the importance of leveraging genomic insights to prevent, predict, and personalize interventions for addiction and mental illness at scale.

Disorder↗
PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗
PubMed2026

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗
PubMed2026

The role of KIAA1467 in breast cancer: insights from pan-cancer and single-cell sequencing analysis.

BACKGROUND: Improving the response rate of single-agent immune checkpoint blockade (ICB) urgently requires the discovery of new therapeutic targets for combinatorial regimens. Analyses of tumor microenvironment (TME)-associated biomarkers have verified that KIAA1467 drives the formation of an immune-excluded, non-inflamed TME in breast cancer (BRCA). This study systematically explores the expression pattern, prognostic value, immune regulatory function, biological effects, and drug resistance relevance of FAM234B (also known as KIAA1467) in BRCA. METHODS: We performed pan-cancer survival analysis using The Cancer Genome Atlas (TCGA) datasets. Multi-omics bioinformatics analyses were conducted to evaluate KIAA1467 expression across malignancies. Single-cell RNA sequencing (scRNA-seq) data from GSE176078 was utilized to localize KIAA1467 expression at the cellular level. Immunohistochemistry and western blot assays validated KIAA1467 expression in BRCA clinical specimens. Correlation analyses were implemented to assess relationships between KIAA1467 expression, clinicopathological features, immune modulators, tumor-infiltrating immune cells, and p53 mutation status. Functional enrichment analysis uncovered relevant signaling pathways. Bioinformatic half maximal inhibitory concentration (IC50) prediction and in vitro cellular experiments were applied to evaluate associations between KIAA1467 and chemotherapeutic drug sensitivity. RESULTS: TCGA pan-cancer survival analysis demonstrated that elevated KIAA1467 expression significantly predicted shortened overall survival in BRCA and multiple other tumor types. KIAA1467 displayed distinct expression patterns across cancers, with prominent upregulation in BRCA. scRNA-seq confirmed enriched KIAA1467 expression within BRCA cells, and its upregulation in BRCA tissues was further verified by immunohistochemistry and western blot. High KIAA1467 expression was positively correlated with advanced tumor grade and lymphatic metastasis. KIAA1467 showed negative correlations with most immune modulators and core immune checkpoint molecules, as well as tumor-infiltrating immune cells in the TME, implying its potential function in tumor immune evasion. Low KIAA1467 expression was tightly linked to p53 mutations. Enrichment analysis indicated participation of KIAA1467 in epithelial-mesenchymal transition, apoptosis and cell cycle arrest. Furthermore, high KIAA1467 expression corresponded to higher estimated IC50 values of cisplatin, gefitinib, paclitaxel and gemcitabine, consistent with reduced chemosensitivity observed in vitro. CONCLUSIONS: This study reveals the multifaceted oncogenic role of KIAA1467 in BRCA. KIAA1467 participates in remodeling an immunosuppressive TME, correlates with malignant progression and chemoresistance, and may serve as a promising candidate target to optimize ICB-based combination therapy for BRCA. These findings offer new perspectives for the clinical treatment and comprehensive management of BRCA.

KIAA1467↗
PubMed2026

Decoding age-stratified clinical and molecular heterogeneity in male breast cancer through multiomic profiling.

OBJECTIVE: Age-associated molecular heterogeneity is well described in female breast cancer but remains insufficiently characterized in male breast cancer (MBC). We profiled age-stratified clinical and molecular differences between younger (&#x2264;55 years) male breast cancer (YMBC) and older (>55 years) male breast cancer (OMBC). METHODS: We retrospectively analyzed 347 patients with MBC diagnosed at Fudan University Shanghai Cancer Center by integrating clinicopathological data, RNA sequencing, and whole-exome sequencing (WES). Survival, differential expression, and mutational signature analyses were performed. Tumor microenvironment features were inferred using xCell and ESTIMATE, and weighted gene co-expression network analysis (WGCNA) was conducted to identify age-associated co-expression modules. Candidate therapeutics were prioritized using the Genomics of Drug Sensitivity in Cancer (GDSC) resource and evaluated using patient-derived organoids (PDOs). RESULTS: Compared with OMBC, YMBC more frequently had human epidermal growth factor receptor 2 (HER2)-positive status (14.91% vs. 4.02%) and triple-negative tumors (4.92% vs. 1.78%), and had worse 5-year recurrence-free survival (hazard ratio=2.19, P=0.018). Transcriptomic analyses indicated enrichment of neural-related programs and reduced immune-related signaling in YMBC, and xCell/ESTIMATE supported lower immune infiltration. Consistently, WGCNA identified age-associated modules linking neural-related programs with reduced immune infiltration. Immunohistochemistry supported increased perineural invasion and lower CD8+ T cell infiltration in YMBC. GDSC-guided prioritization with PDO testing nominated sepantronium bromide (YM155) as a candidate vulnerability in YMBC. WES showed a higher NBPF10 mutation frequency in YMBC (54.5% vs. 14.3%, P<0.05). CONCLUSIONS: Integrated multi-omics profiling revealed age-stratified clinical and molecular heterogeneity in MBC. YMBC patients demonstrated inferior recurrence-free survival, neural signaling enrichment, an immune-cold microenvironment, and enriched NBPF10 mutations. These findings support age as a meaningful stratification variable in MBC risk assessment and treatment planning, and highlight the need for caution when considering treatment de-escalation in younger patients, while nominating YM155 as a candidate agent for prospective evaluation.

Male breast cancer↗
PubMed2026

Enhanced chromatin compaction is associated with de novo expression of a nuclear microprotein, global loss of H3 acetylation and local transcriptional changes in retinal rod photoreceptors.

We have limited understanding of how aging alters gene expression and remodels cellular architecture in post-mitotic neurons. The inverted nuclear organization of mouse rod photoreceptors provides a unique model to gain mechanistic insights into age-associated decline in neuronal function. We have generated and integrated multi-omic datasets including 3D-genome topology, histone modifications, chromatin accessibility, DNA methylation and transcriptome of rod photoreceptors from young- and aged-mice. We show that aging drives global chromatin compaction, with regional alterations enriched at active chromatin. Epigenomic and transcriptional changes broadly correlate with chromatin dynamics as validated by high resolution microscopy. We uncover a megabase-sized genomic region with multi-level alterations, including de novo transcription of Gm7239, which encodes a functional microprotein carrying histone acetyltransferase-inhibitor domain. Overexpression of Gm7239 is associated with global loss of histone H3 acetylation, highlighting a potential new axis of genomic regulation in aging. Finally, we identify multiple significant local transcriptional alterations in non-annotated regions and genes associated with age-related macular degeneration. Our studies link age-related chromatin landscape changes with gene expression that may influence rod function and vulnerability to diseases.

Journal Article↗