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PubMed2026

Deconstructing the Alternative Lengthening of Telomeres: Integromics Prioritizes Five Master Hubs Dictating Clinical Survival and Therapeutic Vulnerabilities.

The Alternative Lengthening of Telomeres (ALT) pathway drives replicative immortality in aggressive malignancies, particularly sarcomas and gliomas. Clinical ALT stratification has relied on screening for structural ATRX and DAXX mutations. However, this genotypic approach fails to capture the dynamic macro-reprogramming required to sustain ALT. Here, we established and validated a 28-gene transcriptomic signature that captures the ALT-associated transcriptomic phenotype of the ALT phenotype. Using multivariate Cox proportional hazards models and time-dependent ROC analyses, we demonstrate that this signature is a robust, independent predictor of poor overall survival in Sarcoma (SARC) and Lower Grade Glioma (LGG) cohorts, outperforming the prognostic value of traditional ATRX/DAXX mutational status. Genomic mapping revealed this transcriptional synchrony is structurally facilitated by non-random focal clustering on Chromosome 8. To deconstruct the machinery driving this lethal phenotype, we employed an integromic approach, synthesizing protein-protein and metabolic flux networks. Topological algorithms prioritized five indispensable hubs: TP53, ATM, ATR, PCNA, and UBE2I. Gene-metabolite profiling identified PCNA as a bottleneck funneling extreme deoxyribonucleotide (dNTP) demand to sustain break-induced telomeric recombination. To translate these vulnerabilities into actionable treatments, we mapped these hubs to a precision pharmacological network. We propose a multi-targeted strategy combining FDA-approved PARP inhibitors to exploit ATR-mediated synthetic lethality, alongside antimetabolites to induce nucleotide starvation. This study redefines ALT risk stratification and provides a data-driven framework to target and treat resistant ALT-positive tumors.

Alternative Lengthening of Telomeres↗

PubMed2025

Efficacy of Repeated Administration of Cultured Human CD34+ Cells Against Streptozotocin-Induced Diabetic Nephropathy in Rats.

To date, no clinical trial has investigated the potential of CD34+ cells to treat diabetic nephropathy. This study examined the efficacy of human CD34+ cells against diabetic nephropathy in rats. Rats were administered streptozotocin (STZ) intraperitoneally and divided into three groups: normal control, STZ control, and STZ plus cell therapy. The STZ-plus-cell-therapy group was administered human umbilical cord blood-derived CD34+ cells weekly for three weeks. At eight weeks, the rats' renal function, pathology, and transcriptome profiles were assessed. Although blood glucose levels did not differ between the STZ-administered groups, urinary albumin excretion was significantly lower at 6 weeks in the STZ-plus-cell-therapy group than in the STZ control group (p < 0.001). Serum creatinine levels tended to be higher in the STZ control group and lower in the STZ-plus-cell-therapy group. Cell therapy significantly improved mesangial expansion, interstitial fibrosis, peritubular capillary rarefaction, and glomerular macrophage infiltration compared with the STZ control (p < 0.0001). Kidney transcriptomics revealed significant upregulation of genes related to M2 macrophage markers, cell homing, and angiogenesis in the STZ-plus-cell-therapy group. In rats with STZ-induced diabetic nephropathy, human CD34+ cells ameliorated renal injury through their anti-inflammatory and pro-angiogenic effects.

Animals↗

PubMed2026

Amaryllidaceae Alkaloids and Isoquinoline Alkaloids: A Perspective on Historical Approaches to Pathway Elucidation.

Alkaloid biosynthesis is a central topic in plant specialized metabolism because many alkaloids have ecological, pharmacological, and biotechnological relevance. Isoquinoline alkaloids (IAs) and Amaryllidaceae alkaloids (AAs) are both connected to aromatic amino acid metabolism, but they differ in taxonomic distribution, scaffold-forming chemistry, pathway resolution, and biotechnological development. This review compares the historical and methodological trajectories that have shaped IA and AA pathway elucidation, from compound isolation, radiotracer experiments, and biochemical inference to transcriptomics, metabolomics, functional enzymology, isotope-guided active-tissue identification, regulatory studies, and heterologous pathway reconstruction. In IAs, especially benzylisoquinoline alkaloids, broad genomic and transcriptomic resources have supported candidate gene discovery and functional characterization of several branches, including morphinan, protoberberine, benzophenanthridine, and aporphine-related pathways. In contrast, AA biosynthesis has advanced more recently through function-driven approaches that clarified key steps such as N4OMT-mediated 4'-O-methylation, NBS/NR-mediated norbelladine formation, CYP96T-dependent regioselective oxidative coupling, and transient reconstruction of major scaffold-forming branches. Remaining gaps include the unresolved enzymatic formation of 3,4-dihydroxybenzaldehyde in AAs and incomplete functional validation across less-studied IA scaffold classes. By integrating biochemical logic, omics-guided discovery, enzyme evolution, tissue specificity, regulation, and synthetic biology, this review identifies priorities for future alkaloid pathway discovery and sustainable production.

3,4-dihydroxybenzaldehyde↗

PubMed2025

Multi-omics technologies: Novel tools and methods for assessing nerve injury and regeneration.

Recently, with the rapid advancement of multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, new tools and approaches have been introduced for studying nerve injury and regeneration. This review highlights the application and progress of multi-omics in uncovering the mechanisms of nerve injury, guiding the development of regenerative strategies, and promoting clinical translation. By integrating multi-omics datasets, researchers can comprehensively track dynamic molecular changes following nerve injury, including abnormal gene expression, disrupted protein signaling, altered metabolic programs, and shifts in the immune microenvironment. Single-cell multi-omics technologies resolve cellular heterogeneity, revealing the distinct functions of neurons, glial cells, and immune cell subpopulations during the injury response. Spatially resolved transcriptomics maintain the spatial context of lesion and regeneration sites, enabling precise localization for targeted interventions. Multi-omics technologies not only identify key molecular players involved in nerve regeneration but also create opportunities for personalized medicine. Nonetheless, integrating multi-omics data poses technical challenges, including high dimensionality, batch effects, and algorithmic constraints, while ethical concerns related to stem cell therapy and gene editing require stringent oversight. To transition from structural reconstruction to functional remodeling, future research should emphasize artificial intelligence-driven data integration, organ-on-a-chip modeling, and cross-disciplinary collaboration to overcome existing technical barriers and accelerate the clinical application of neuroregenerative therapies.

artificial intelligence↗

PubMed2025

Distinct Genetic Risk Profile in Aortic Stenosis Compared With Coronary Artery Disease.

IMPORTANCE: Aortic stenosis (AS) and coronary artery disease (CAD) frequently coexist. However, it is unknown which genetic and cardiovascular risk factors might be AS-specific and which could be shared between AS and CAD. OBJECTIVE: To identify genetic risk loci and cardiovascular risk factors with AS-specific associations. DESIGN, SETTING, AND PARTICIPANTS: This was a genomewide association study (GWAS) of AS adjusted for CAD with participants from the European Consortium for the Genetics of Aortic Stenosis (EGAS) (recruited 2000-2020), UK Biobank (recruited 2006-2010), Estonian Biobank (recruited 1997-2019), and FinnGen (recruited 1964-2019). EGAS participants were collected from 7 sites across Europe. All participants were of European ancestry, and information on comorbid CAD was available for all participants. Follow-up analyses with GWAS data on cardiovascular traits and tissue transcriptome data were also performed. Data were analyzed from October 2022 to July 2023. EXPOSURES: Genetic variants. MAIN OUTCOMES AND MEASURES: Cardiovascular traits associated with AS adjusted for CAD. Replication was performed in 2 independent AS GWAS cohorts. RESULTS: A total of 18 792 participants with AS and 434 249 control participants were included in this GWAS adjusted for CAD. The analysis found 17 AS risk loci, including 5 loci with novel and independently replicated associations (RNF114A, AFAP1, PDGFRA, ADAMTS7, HAO1). Of all 17 associated loci, 11 were associated with risk specifically for AS and were not associated with CAD (ALPL, PALMD, PRRX1, RNF144A, MECOM, AFAP1, PDGFRA, IL6, TPCN2, NLRP6, HAO1). Concordantly, this study revealed only a moderate genetic correlation of 0.15 (SE, 0.05) between AS and CAD (P = 1.60 × 10-3). Mendelian randomization revealed that serum phosphate was an AS-specific risk factor that was absent in CAD (AS: odds ratio [OR], 1.20; 95% CI, 1.11-1.31; P = 1.27 × 10-5; CAD: OR, 0.97; 95% CI 0.94-1.00; P = .04). Mendelian randomization also found that blood pressure, body mass index, and cholesterol metabolism had substantially lesser associations with AS compared with CAD. Pathway and transcriptome enrichment analyses revealed biological processes and tissues relevant for AS development. CONCLUSIONS AND RELEVANCE: This GWAS adjusted for CAD found a distinct genetic risk profile for AS at the single-marker and polygenic level. These findings provide new targets for future AS research.

Humans↗

PubMed2000

Comprehensive analyses of prostate gene expression: convergence of expressed sequence tag databases, transcript profiling and proteomics.

Several methods have been developed for the comprehensive analysis of gene expression in complex biological systems. Generally these procedures assess either a portion of the cellular transcriptome or a portion of the cellular proteome. Each approach has distinct conceptual and methodological advantages and disadvantages. We have investigated the application of both methods to characterize the gene expression pathway mediated by androgens and the androgen receptor in prostate cancer cells. This pathway is of critical importance for the development and progression of prostate cancer. Of clinical importance, modulation of androgens remains the mainstay of treatment for patients with advanced disease. To facilitate global gene expression studies we have first sought to define the prostate transcriptome by assembling and annotating prostate-derived expressed sequence tags (ESTs). A total of 55000 prostate ESTs were assembled into a set of 15953 clusters putatively representing 15953 distinct transcripts. These clusters were used to construct cDNA microarrays suitable for examining the androgen-response pathway at the level of transcription. The expression of 20 genes was found to be induced by androgens. This cohort included known androgen-regulated genes such as prostate-specific antigen (PSA) and several novel complementary DNAs (cDNAs). Protein expression profiles of androgen-stimulated prostate cancer cells were generated by two-dimensional electrophoresis (2-DE). Mass spectrometric analysis of androgen-regulated proteins in these cells identified the metastasis-suppressor gene NDKA/nm23, a finding that may explain a marked reduction in metastatic potential when these cells express a functional androgen receptor pathway.

DNA, Complementary↗

PubMed2001

Protein functions and biological contexts.

The availability of a rough draft of the predicted human proteome allows an evaluation of the extent to which the predicted and biochemical functions of proteins are in alignment, and the roles of different technologies and approaches to understanding human diseases and instantiating therapeutics. Microarray technologies at the transcriptomic and proteomic levels can be high throughput and excellent for diagnostic purposes, but their informational outputs are inferior in quality to those emerging from the co- and post-translational levels and from antibody-based molecular anatomy. It is now abundantly clear that data transfer between the transcriptome and proteome is not straightforward, and that increasing emphasis needs to be placed on pure proteomic approaches at the structural, quantitative, cell biological and phenomic levels, with special focus on embryogenic and foetal processes. Finally, the precision genetic engineering that is required to evaluate the functional significance of context-dependent protein interactions underpinned by post-translational modifications and proteolytic cleavage events, is still too time consuming and rudimentary to be implemented on a large scale in the mouse, and basic principles and first order networks will need to be sorted out in even simpler model systems such as Drosophila.

Databases, Protein↗

PubMed2001

Regulation of growth factor induced gene expression by calcium signalling: integrated mRNA and protein expression analysis.

There is considerable indirect evidence that growth factor induced changes in the intracellular concentration of calcium play an important role in the regulation of the mammalian cell cycle. However, the precise mechanism by which this may be achieved remains unclear. Here we show that SKF-96365, an inhibitor of growth factor induced capacitative calcium entry (CCE), inhibits cell cycle progression by preventing entry into S phase. SKF-96365 changes the temporal profile of growth factor induced calcium signalling and recent studies have shown that alterations in the temporal and spatial patterns of calcium signalling can differentially regulate gene expression. We have therefore sought to examine the effect of inhibition of CCE on growth factor induced gene expression during G1. To achieve this we have initiated a combined transcriptomic and proteomic approach to measure CCE regulated gene expression using cDNA arrays and two-dimensional polyacrylamide gel electrophoresis, respectively. The initial results of this on-going analysis are reported here. They reveal that inhibition of CCE influences the expression of 29 genes at the mRNA level and 22 genes at the protein level. We report the identification of the mRNAs whose expression is altered by inhibition of CCE and describe the potential functional significance of some of these changes. The value of integrating a transcriptomic and two-dimensional gel electrophoresis based proteomic approach to studies of gene expression is discussed.

3T3 Cells↗

PubMed2026

Livestock Multi-Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation.

Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.

Animals↗

PubMed2026

A Multifaceted Interplay Among Hemophagocytosis, Interleukin-18, and Type I Interferon Distinguishes Still Disease From Other Autoinflammatory Diseases.

OBJECTIVE: The unknown pathophysiology and the lack of specific features for systemic juvenile idiopathic arthritis and adult-onset Still disease (collectively known as Still disease; SD) delay diagnosis and appropriate treatment. The goal of this study was to identify features and mechanisms that distinguish SD from other systemic autoinflammatory diseases (SAID). METHODS: Using the SomaScan assay and RNA sequencing (RNA-Seq), we determined the plasma proteomes and immune cell microRNA (miRNA) and RNA transcriptomes of 372 patients with SAID, respectively. Proteomic findings were validated by enzyme-linked immunosorbent assays. SD (n = 72) and non-SD SAIDs (n = 300) were compared to identify distinguishing features of SD. We performed integrated and unbiased analyses of all data sets using weighted gene correlation network analysis to identify feature modules that characterize SD and stratify patients. RESULTS: Elevated plasma heme oxygenase 1 (HO-1) and interleukin-18 (IL-18) strongly correlate and characterize SD but do not associate with general inflammation. SD was characterized by ferroptosis in plasma, type I interferon (IFN) signaling in monocyte transcriptomes, and elevated natural killer cell miRNA-146a-5p, which is an IL-18 induced miRNA. Finally, we identified feature modules that distinguish SD from other SAIDs and stratified patients with SD into two distinct subgroups not attributable to disease activity or inflammation but hemophagocytosis. CONCLUSION: This unprecedented large omics data set of SAIDs revealed that complex interactions among hemophagocytosis, IL-18, and type I IFN signaling characterize SD. Furthermore, two distinct subgroups in patients with SD were distinguished by the degree of hemophagocytic activity. Finally, the large proteomics and RNA-Seq data sets generated in this study can serve as an invaluable resource for the further investigation of SD and other SAIDs.

Humans↗

PubMed2026

Complement Activation Linked to Type II Interferon Signaling in Still Disease.

OBJECTIVE: Still disease (SD) is an autoinflammatory syndrome characterized by innate immune dysregulation. Although complement can drive inflammation, its involvement in SD remains to be defined. Thus, we aimed to assess complement activation in SD. METHODS: Complement was assessed using transcriptomic, proteomic, and in vitro approaches. RNA sequencing of monocytes was performed in healthy donors (n = 15), those with nonsystemic juvenile idiopathic arthritis (JIA; n = 8), patients with SD at onset (n = 19) and remission (n = 18), and those with macrophage activation syndrome (n = 2). Whole-blood NanoString analysis of complement and interferon (IFN)-related gene expression was conducted in patients with SD (active n = 41, inactive n = 33) and JIA (n > 600). Complement products and inflammatory mediators were measured by Luminex and enzyme-linked immunosorbent assay. Functional complement activity was evaluated in SD (active n = 30, inactive n = 67) and JIA sera (n = 12). In vitro assays examined monocytic C1q induction and complement-mediated CD8+ T cell activation. RESULTS: Transcriptomic analysis of monocytes from patients with SD at onset revealed enrichment of the complement cascade compared with patients in remission (adjusted P = 3.7 × 10-36), ranking among the top 10 up-regulated pathways. Classical complement genes (C1QB/C1QC) were markedly up-regulated in onset SD compared with patients with remission SD and JIA. Patients with active SD showed increased C1q, C3a, C5a, and terminal complement complex protein levels, with enhanced functional classical complement activity. Whole-blood C1QB/C1QC expression correlated with IFN-related markers, including interleukin-18, CXCL9, and CXCL10. Recombinant IFN-γ induced monocytic C1q, whereas C1q enhanced IFN-γ production by CD8+ T cells, supporting a feed-forward loop. CONCLUSION: SD is characterized by complement activation with marked up-regulation of C1q, which is closely linked to IFN-γ/type II signaling.

Journal Article↗

PubMed2026

CASTOR1 Regulates Humoral Immune Responses and Contributes to the Pathogenesis of Systemic Lupus Erythematosus.

OBJECTIVE: CASTOR1 senses arginine and regulates mammalian target of rapamycin complex 1 (mTORC1), a central metabolic signaling molecule. This study aimed to elucidate the roles of CASTOR1 in humoral immune responses. METHODS: We analyzed human B cell transcriptomes from healthy controls and patients with systemic lupus erythematosus (SLE) via correlation analysis and gene set variation analysis using our database, Immune Cell Gene Expression Atlas from the University of Tokyo. Castor1-deficient and B cell-specific Castor1-deficient mice were used for analyses of serum immunoglobulins and autoantibodies, urinary proteins, renal pathology, gene expression, and flow cytometry in spleen and bone marrow cells. The culture supernatant of splenic B cells was used for immunoglobulin (Ig) analysis. RESULTS: Transcriptomic analysis of bulk RNA sequencing data from various B cell subsets in patients with SLE (n = 136; n = 129 included in the primary analysis) revealed a correlation between CASTOR1 expression and disease activity, with CASTOR1 expression in plasmablasts inversely correlated with Systemic Lupus Erythematosus Disease Activity Index 2000 (r = -0.32, P = 0.00031). Castor1-deficient mice exhibited increased plasma cell populations in the spleen and bone marrow, elevated serum IgG levels, production of anti-double-stranded DNA antibodies, and glomerulonephritis with IgG deposits, reflecting SLE-like autoimmunity. Moreover, B cell-specific Castor1-deficient mice showed increased plasma cell counts, elevated serum IgG levels, and glomerulonephritis, indicating that Castor1 might regulate systemic humoral immunity via a B cell-intrinsic mechanism. CONCLUSION: CASTOR1 plays a regulatory role in humoral immunity and may contribute to the pathogenesis of autoimmune diseases such as SLE, representing a potential therapeutic target.

Journal Article↗

PubMed2026

Human Monocytic Models Reveal Genotype-Dependent Inflammatory Programs in VEXAS Syndrome.

OBJECTIVES: VEXAS syndrome is a severe X-linked autoinflammatory disorder caused by somatic mutations in ubiquitin-like modifier activating enzyme 1 (UBA1), with clinical outcomes that vary by UBA1 genotype. We aimed to elucidate genotype-specific inflammatory programs and identify potential therapeutic targets. METHODS: We conducted longitudinal deep phenotyping, including whole-blood RNA sequencing (RNA-seq) and clinical activity assessment. Peripheral blood samples were analyzed by single-cell RNA-seq. Human monocytic cell lines harboring each major UBA1 mutation (p.Met41Val, p.Met41Thr, or p.Met41Leu) were generated and subjected to transcriptomic and functional analyses. RESULTS: Thirteen patients with VEXAS syndrome contributed a total of 79 RNA-seq samples. Among genes upregulated in VEXAS syndrome, RNASE1 showed the strongest correlation with longitudinal disease activity (r = 0.70, FDR < 0.05) and was upregulated in patients' monocytes. In UBA1-mutant monocytic cell lines, genotype-dependent ubiquitination defects were observed in a graded manner (p.Met41Val > p.Met41Thr > p.Met41Leu), even in the absence of exogenous stimuli. These defects were accompanied by unfolded protein response activation, increased pro-inflammatory cytokine production, progressive cell death, and RNASE1 upregulation, all following the same graded pattern, recapitulating patient genotype-phenotype associations. Transcriptomic analyses demonstrated enrichment of pro-inflammatory, interferon, and necroptosis signatures in more severe genotypes. Notably, inhibition of receptor-interacting protein kinase 3 (RIPK3) markedly attenuated all pathological features, including RNASE1 upregulation. CONCLUSIONS: Our UBA1-mutant monocytic cell-line models, representing three distinct genotypes, recapitulate genotype-dependent inflammatory phenotypes that can be modulated by RIPK3 inhibition, providing a translational platform for mechanistic investigation and precision therapy development in VEXAS syndrome.

Journal Article↗

PubMed2026

POFUT1 Serves as an Independent Prognostic Factor and Therapeutic Target by Activating the PI3K/AKT Pathway in Glioma.

OBJECTIVE: Protein O-fucosyltransferase 1 (POFUT1) has been implicated in several malignancies, but its functional and prognostic significance in glioma remains insufficiently defined. This study evaluated whether POFUT1 expression is associated with glioma progression, patient outcome, and PI3K/AKT pathway activity. METHODS: Public glioma transcriptome datasets from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were analyzed and compared with clinical samples collected from 123 glioma patients. POFUT1 protein levels in clinical specimens were determined by immunohistochemical staining, and its association with patient outcome was analyzed using survival curves. In vitro, glioma cell growth, motility, and invasiveness were examined using MTT and Transwell assays. The effect of POFUT1 on tumor formation was further tested in a subcutaneous xenograft model. RNA sequencing, KEGG pathway enrichment, and pharmacological inhibition were then used to explore the mechanism linking POFUT1 to PI3K/AKT signaling. RESULTS: POFUT1 expression was higher in glioma than in normal brain tissue and increased with tumor grade. Patients with high POFUT1 levels had shorter overall survival, and multivariate Cox analyses supported POFUT1 as an independent prognostic indicator. Incorporating POFUT1 into a nomogram improved prediction of 1-, 3-, and 5-year survival. Functionally, POFUT1 knockdown reduced glioma cell growth, motility, invasion, and xenograft expansion, whereas POFUT1 overexpression produced the opposite phenotype. Transcriptomic and protein analyses indicated that POFUT1 enhanced PI3K/AKT signaling. The PI3K inhibitor LY294002 weakened the tumor-promoting effects caused by POFUT1 overexpression. CONCLUSION: POFUT1 as a key driver of glioma malignancy, predominantly through activating the PI3K-AKT signaling pathway. These findings highlight POFUT1 as a promising novel therapeutic target for aggressive glioma.

Glioma↗

PubMed2004

IL-8 induces a specific transcriptional profile in human neutrophils: synergism with LPS for IL-1 production.

IL-8 is an inflammatory CXC chemokine involved in neutrophil recruitment and activation in various inflammatory conditions. The transcriptional profile induced by IL-8 in human neutrophils was analyzed using high-density oligonucleotide arrays and compared with that of the prototypic phagocyte activator LPS. As expected, LPS induced a major effect on the cell transcriptome, upregulating 116 (0.93%) and downregulating 70 (0.56%) of the transcripts. IL-8 induced a less profound modulation of the cell transcriptome, with upregulation of 30 (0.25%) and downregulation of 6 (0.04%) of the transcripts. Although the two proinflammatory mediators induced partially overlapping transcriptional profiles (50.0% of IL-8-responsive genes were concordantly regulated by LPS), IL-8 also modulated a significant number of genes unresponsive to LPS, including soluble mediators, membrane receptors, signaling molecules, and regulators of transcription and translation. A set of IL-8-inducible genes was related to cell motility, possibly a strategy to prepare for migration into tissues. Analysis of the IL-8-responsive gene IL-1beta at the protein level revealed that transcript induction was not followed by protein production. Neutrophils stimulated with IL-8, however, showed a significant increase in IL-1beta secretion after subsequent exposure to LPS. Thus, the effect of IL-8 at the transcriptional level could provide a synergistic effect with microbial products for neutrophil activation.

Gene Expression↗

PubMed2006

Neuroproteomics - the tasks lying ahead.

The brain is unquestionably the most fascinating organ. Despite tremendous progress, current knowledge falls short of being able to explain its function. An emerging approach toward improved understanding of the molecular mechanisms underlying brain function is neuroproteomics. Today's neuroscientists have access to a battery of versatile technologies both in transcriptomics and proteomics. The challenge is to choose the right strategy in order to generate new hypotheses on how the brain works. The goal of this review is therefore two-fold: first we recall the bewildering cellular, molecular, and functional complexity in the brain, as this knowledge is fundamental to any study design. In fact, an impressive complexity on the molecular level has recently re-emerged as a central theme in large-scale analyses. Then we review transcriptomics and proteomics technologies, as both are complementary. Finally, we comment on the most widely used proteomics techniques and their respective strengths and drawbacks. We conclude that for the time being, neuroproteomics should focus on its strengths, namely the identification of posttranslational modifications and protein-protein interactions, as well as the characterization of highly purified subproteomes. For global expression profiling, emphasis should be put on further development to significantly increase coverage.

Brain↗

PubMed2026

Revealing the Shared Genetic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Traits Through Genomic Structural Equation Modeling.

Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.

Humans↗

PubMed2026

The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups.

T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we aggregated 2314 transcriptomes (15 cohorts, age: 0.8-90.8 years). An extended unsupervised approach defined 17 main clusters and 3 subclusters in samples with high blast fractions. Supervised analyses added an overarching immature T-ALL (early T cell precursor [ETP]-like) definition and resolved the LMO2 γδ-like subtype. All clusters contained samples from at least two cohorts. Characteristic genomic driver enrichments were consistent across cohorts, while gene-expression clusters did not correspond exclusively to single driver events but also reflected developmental origins. A machine-learning classifier based on ALLCatchR, our B-cell acute lymphoblastic leukemia (B-ALL) classifier, identified these 20 transcriptomic subtypes and the immature T-ALL (ETP-like) signature with 0.995-1.0 accuracy in a validation set (n = 203). Testing the classifier on a second hold-out data set (n = 265 samples) showed that 92.7% of predictions matched with corresponding driver alterations. Across all samples, 83.2% of cases received high-confidence predictions, 7.3% candidate predictions, and 9.5% remained unclassified, largely because of low blast fractions. We identified a novel gene-expression cluster markedly enriched (P < 0.001) for clonal hematopoiesis mutations (IDH2 R140Q, DNMT3A) and a stem-/progenitor cell-like gene expression. This novel clonal hematopoiesis-related T-ALL subtype was observed in six cohorts and accounted for 8.9% of adults and 39.5% of patients aged >50 years. We extended ALLCatchR into ALLCatchR2, a free R package that now enables B-/T-lineage separation, gene-expression subtyping, blast estimation, and developmental annotation to harmonize T-ALL classification across studies and clinical contexts.

Journal Article↗