Search Results (568)

Despite decades of therapeutic advances, cardiovascular diseases remain the leading cause of global mortality, underscoring the need for strategies that move beyond untargeted systemic pharmacotherapy. Synthetic biology introduces a programmable therapeutic paradigm in which engineered gene circuits can sense, compute, and respond to pathological signals with spatiotemporal precision. This review examines the current progress of synthetic gene circuits for cardiovascular therapy, organized across three domains of clinical relevance. The first domain comprises circuits engineered for direct cardiac applications, from inducible switches to classifier systems. This discussion is further expanded by exploring circuits that indirectly target cardiovascular disease; these circuits address upstream risk factors such as cholesterol dysregulation and chronic inflammation. Looking ahead, the focus shifts to orthogonal architectures pioneered in other therapeutic contexts that hold promise for future cardiac applications. This review further discusses the emerging role of computational tools, including gene regulatory network inference and foundation models, in accelerating target discovery. Finally, a modified Design-Build-Test-Learn framework is proposed to overcome translational bottlenecks, thus paving the way for next-generation cardiac therapeutics. Full article

Plants are constantly exposed to a wide range of abiotic stresses that have significant negative impacts on growth and yield. Plant acclimation to these stresses is governed by integrated classical phytohormone and plant peptide hormone signalling networks that control the ability of a plant to survive and adapt to extreme environments. Classical phytohormones, including abscisic acid, auxins, gibberellins, cytokinins, jasmonates, salicylic acid, brassinosteroids, and the recently recognised phytomelatonin, act in concert with peptide-based plant hormones, among which C-terminally encoded peptides (CEPs) play prominent roles in coordinating stress perception, signal transduction, and adaptive responses throughout the plant. These integrated networks control stomatal behaviour, photosynthesis, osmolyte and antioxidant levels, root architecture, and energy metabolism, thereby helping plants maintain homeostasis and optimise survival while sustaining minimal growth under unfavourable conditions. Under stressful conditions, these networks do not operate in isolation but form highly dynamic, context-dependent regulatory circuits in which each physiological process is simultaneously regulated by multiple hormones acting through convergent and overlapping signalling pathways. Phytomelatonin has emerged as a particularly important integrative node within these networks, functioning both as a potent direct antioxidant through sequential ROS-scavenging catabolite cascades and as a bidirectional regulator of classical phytohormone signalling under diverse abiotic stresses. New technologies in the fields of transcriptomics, proteomics, phosphoproteomics, metabolomics, and systems biology have provided new information on the dynamic relationships between classical phytohormones and plant peptide hormones, revealing candidate regulatory nodes and transcription factor networks that mediate stress adaptation at molecular, biochemical, and physiological levels. However, it is important to distinguish between correlative associations identified through omics profiling and causal regulatory relationships validated through rigorous genetic and biochemical experimentation, as most omics-derived candidates remain to be functionally established. Empirical studies demonstrate how these networks can be used to improve crops by increasing stress tolerance through modulating classical phytohormone and plant peptide hormone signalling, including through exogenous phytomelatonin application, CRISPR-mediated hormone pathway editing, and CEP pathway manipulation, to produce resilient cultivars without reducing yields. Although these advances represent significant progress, challenges remain, including the inherent complexity and redundancy of the networks, context-dependence and severity-dependence of hormonal responses, the persistence of a significant translational gap between laboratory findings and field application, and incomplete mechanistic understanding of peptide hormone roles under combined stress conditions. Addressing these challenges will require integrative multi-omics approaches, higher-order computational modelling, and rigorous field-based functional validation alongside emerging tools such as synthetic biology and precision breeding. Full article

Cognition and intelligence are central concepts in cognitive science, biology, philosophy of mind, and artificial intelligence, yet these disciplines offer conflicting accounts of what each of them means and how the two notions are related. In many accounts the two notions are used interchangeably, while in others intelligence is defined independently of cognitive processes. Dominant human-centered traditions identify cognition with mental processes associated with brains, whereas life-centered perspectives attribute cognitive capacities to all living systems. This article proposes a relational, life-centered, info-computational framework in which cognition is the ongoing autopoietic and sense-making organization of living systems, while intelligence is the degree of competence with which such organization achieves goal-directed problem solving under novelty, perturbation, and uncertainty. Cognition exists in degrees across living systems, from basal cellular sensing and regulation to increasingly complex cognitive organizations, while intelligence correspondingly appears in degrees in the ability to solve cognitive problems. Current artificial systems can exhibit engineered or derivative intelligence and may implement cognition-like functions, but they are not cognitive in the biological sense. The resulting framework clarifies how human-centered, life-centered, computational, and artificial intelligence can be related. Full article

The CRISPR-Cas systems, identified initially as adaptive immune mechanisms in bacteria and archaea against viral threats, have rapidly evolved into transformative tools in genetic engineering and biotechnology. These RNA-guided systems are broadly classified into Class 1, comprising multi-subunit complexes, and Class 2, characterized by compact single-effector protein, such as Cas9, Cas12, and Cas13. Their remarkable structural and functional diversity enables microorganisms to adapt to diverse ecological niches, offering a vast repertoire of genome-editing strategies. Beyond their natural role in maintaining genome integrity and defense, CRISPR-Cas systems have been extensively repurposed for precise genome modification, transcriptional regulation, epigenetic editing, and nucleic acid detection. Recent advances in computational mining of microbial genomes and metagenomes have uncovered a broad range of novel CRISPR effectors with unique properties, distinct protospacer adjacent motif (PAM) requirements, RNA-targeting capabilities, miniature architectures, and promiscuous cleavage activities that significantly expand the molecular biology toolkit. The development of CRISPR-based technologies such as base editing, prime editing, gene knock-in/out, and live-cell DNA/RNA imaging exemplifies the versatility of these systems. Despite the challenges associated with delivering complex Class 1 systems, both classes are now being actively harnessed across diverse microbial platforms. Concurrently, the CRISPR-Cas research, particularly for guide RNA (gRNA) design and activity prediction, has revolutionized target specificity and editing efficiency. This review presents a comprehensive overview of CRISPR-Cas system diversity, their genomic landscape in microorganisms, and their cutting-edge biotechnological applications. It also emphasizes the transformative potential of CRISPR in synthetic biology, therapeutics, diagnostics, environmental remediation, and agriculture, while also addressing the ethical and biosafety considerations surrounding its deployment. As CRISPR-Cas systems continue to evolve, they stand at the forefront of innovations that bridge natural microbial immunity with engineered precision tools for next-generation biotechnology. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a global health threat, with raw milk serving as a potential reservoir for antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs). This study characterized the resistome and mobilome of raw milk from cows (Bos taurus) and water buffalo (Bubalus bubalis) in the Brazilian Amazon, a region where unpasteurized dairy consumption is culturally ingrained. Methods: Using shotgun metagenomic sequencing, we analyzed 32 pooled milk samples from extensive and semi-intensive farms in the Manaus Metropolitan Region. Results: Sequencing yielded over 3.1 million contigs. While cow milk showed a higher prevalence of positive samples (80%), buffalo milk exhibited a significantly higher abundance and diversity of ARG-associated contigs (301 contigs vs. 85 in cows). Clinically relevant genes were identified, including AbaQ, ArnT, and KpnF, alongside complex multi-AMR cassettes co-occurring with plasmids and widespread viral sequences (dominated by Caudoviricetes). Integrons were ubiquitous in cattle and highly prevalent in buffalo samples. Conclusions: These findings indicate that raw milk in the Amazon harbors a rich reservoir of resistance determinants and MGEs, likely driven by farm-level antibiotic usage. This underscores a critical food safety risk and highlights the need for One Health-based surveillance in the region. Full article

Background/Objectives: Circumscribed oval, hypoechoic masses are common on screening breast ultrasound (US), and the vast majority are benign. Triple-receptor negative invasive breast cancer (TNBC) can appear similar, resulting in both human and artificial intelligence (AI) interpretive errors. Purpose: We sought to improve AI performance in distinguishing common benign masses from TNBC through a retrospective model refinement and validation study. Materials and Methods: In an Institutional Review Board-approved HIPAA-compliant protocol, from five academic medical centers, orthogonal ultrasound images of 1771 breast masses 2 cm or smaller were acquired, consisting of cysts, complicated cysts, other benign, and malignancies. Cases were randomized, controlling for lesion class, site, and patient age, with 1446 (including 402, 27.8%, malignancies) used for training and 325 (including 95, 29.2% malignancies) for validation using Koios DS^® (decision support, KDS) software version 2.0. A breast imaging radiologist from each center reviewed images and recorded BI-RADS features and assessment. Demographics, symptoms, and pathology or at least one-year follow-up was recorded. The KDS score was evaluated standalone and in combination with BI-RADS using logistic regression and ROC analysis with focus on specificity at sensitivity of 98%. Results: In training, KDS standalone performed comparably to BI-RADS, and significantly improved BI-RADS malignancy risk prediction (p < 0.001). The 98%–sensitivity threshold for combined KDS + BI-RADS was estimated and kept fixed during validation. In validation, KDS standalone performed similar to BI-RADS with AUC = 0.97 (CI: 0.95–0.98) versus 0.95 (p = 0.22), with sensitivity of 98% (93/95, CI: 95–100%) for both and specificity of 70.9% (163/230, CI: 65.0–76.7%) for KDS versus 63.9% for BI-RADS (147/230, p = 0.10). Combining KDS + BIRADS significantly improved overall performance (AUC 0.98, p < 0.001) and specificity (74.4%, 171/230, p < 0.001) while maintaining sensitivity at 98% (93/95). Conclusions: While KDS alone should not replace BI-RADS, when used in combination with BI-RADS, it can significantly improve specificity for highly accurate (98% sensitivity) triaging management of masses representative of those seen on screening US. Full article

Cardiometabolic diseases, encompassing obesity, insulin resistance, type 2 diabetes (T2D), metabolic dysfunction-associated steatotic liver disease (MASLD), hypertension, and atherosclerotic cardiovascular disease (ASCVD), represent a vast continuum driven by multi-organ network dysregulation. Clinical risk assessment remains dominated by late-stage measures (e.g., fasting glucose, HbA1c, standard lipids). While these assessments predominate the literature and clinical trial endpoints, each incompletely capture early mechanistic risk, inter-individual heterogeneity, and differential response to interventions. Multiomics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiomics, and extracellular vesicle/exosome cargo profiling) expands the biomarker landscape but introduces translational barriers: high dimensionality, cohort heterogeneity, limited causal inference, and insufficient validation pipelines. AI-driven systems biology platforms can support cardiometabolic biomarker discovery and therapeutic translation by enabling systems-level biological inference across heterogeneous datasets, prioritizing mechanism and traceability over purely correlation-based models. GATC Health’s Operon™ platform is described as a proprietary, AI-driven internal scientific computing platform designed to support therapeutic discovery and development decision-making across the pharmaceutical lifecycle, including evaluation of drug efficacy, safety, off-target effects, pharmacokinetics (PK), pharmacodynamics (PD), and overall development risk. Operon evolved from earlier generations of GATC Health’s internal multiomic modeling systems (formerly referred to as the Multiomics Advanced Technology, MAT) and incorporates expanded data types, orchestration layers, validation workflows, and productization frameworks. Operon is operated by GATC scientists and generates structured, productized outputs (e.g., formal assessments, analyses, and decision frameworks) that are reviewed by experts. Operon methodologies have undergone internal validation and independent academic evaluation under blinded conditions, with reported classification performance (true positive rate 86% and true negative rate 91%) in controlled evaluation settings; these performance metrics should not be interpreted as guarantees of clinical success. This review provides a T2D-centered cardiometabolic biomarker landscape with cardiovascular extension and outlines how Operon-enabled multiomic integration and scenario-based simulation can support early screening, endotype stratification, mechanistic interpretation, and precision intervention design, including AI-guided polypharmacology strategies. Full article

Background/Objectives: Although clinically distinct, bipolar disorder (BP), schizophrenia (SZ), major depressive disorder (MDD), and social anxiety disorder (SAD) share fundamental biology. We mapped these transdiagnostic systemic mechanisms. Methods: Weighted Gene Co-Expression Network Analysis (WGCNA) of peripheral blood RNA-Seq datasets evaluated module preservation, hub gene disruption, and microRNA (miRNA) networks. Results: Seven modules showed robust cross-disease preservation. Overall, 56 of 105 candidate hub genes exhibited altered expression, with 22 passing the false discovery rate (FDR) correction. Hubs like IL1B, TLR2, and MMP9 dominated networks linked to altered inflammatory signaling and structural remodeling. Downregulated ribosomal hubs characterized systemic metabolic stress. Discussion: These signatures capture extensive systemic dysregulation. Inflammation and metabolic shifts correlate strongly with pathways regulating chronic neuroinflammation, epigenetic control, and dendritic pruning. Computational models suggest these cascades evade miRNA controls, potentially compromising structural neural plasticity. Conclusions: This shared transcriptomic architecture challenges rigid diagnostic boundaries. Identifying systemic immune dysregulation and translational alterations as core pathogenic denominators provides a rationale for transdiagnostic therapies targeting upstream systemic networks to mitigate neural vulnerabilities. Full article

This study investigates shared molecular mechanisms between pyoderma gangrenosum (PG) and inflammatory bowel disease (IBD) and systematically evaluates the therapeutic potential of JAK inhibitors targeting this pathway. Despite the clear clinical comorbidity, the core inflammatory pathways driving cross-tissue associations between the two diseases remain unclear. Furthermore, systematic mechanistic evidence is lacking regarding whether JAK inhibitors act by regulating shared pathological pathways in patients with comorbidities. To address this, this study integrated PG skin and IBD intestinal transcriptome data, single-cell transcriptomic data, and genome-wide association study (GWAS) meta-data from public databases. It employed a multi-level computational biology approach combining Mendelian randomization, weighted gene co-expression network analysis, protein interaction network construction, molecular docking simulations, and system dynamics modeling. The results revealed that genetic analysis confirmed IBD as a causal risk factor for PG, precisely identifying six shared genetic loci. Transcriptomic analysis identified a cross-tissue conserved inflammatory module centered on the JAK-STAT pathway, with JAK2 and STAT3 identified as network hubs. Molecular docking predicted high affinity of baricitinib for both JAK1 and JAK2, while system dynamics modeling demonstrated that its intervention effectively suppresses signaling in the shared inflammatory network. This study reveals the molecular basis of the “gut–skin axis” comorbidity between PG and IBD from a multi-omics integration perspective. It provides predictive computational evidence for the use of JAK inhibitors in targeted comorbidity therapy. Baricitinib is identified as a particularly promising candidate. These findings advance the transition from empirical drug use to mechanism-guided precision treatment strategies. Although this study provides multiscale computational simulation evidence, the lack of direct experimental validation of these predicted results necessitates further confirmation through in vitro and in vivo experiments. Full article

X-ray free-electron lasers (XFELs) have revolutionized structural biology by enabling “diffraction-before-destruction” and capturing the ultrafast dynamics of life. However, the intrinsic sparsity and noise of XFEL diffraction snapshots, often complicated by multi-lattice overlaps, create a formidable computational bottleneck that limits data utilization and structural fidelity. Here, we present MCDPS-SFX, a robust indexing framework based on a reference-based, whole-pattern matching principle integrated with parallelized iterative refinement. By exhaustively sampling orientation space and progressively rejecting outliers, MCDPS-SFX significantly outperforms legacy algorithms—more than doubling crystal yields in heterogeneous datasets (e.g., 21,807 vs. 8792 for MOSFLM)—and achieves highly competitive yields comparable to state-of-the-art indexers, such as extracting over 90,000 lattices in the lysozyme benchmark. We demonstrate its efficacy on standard benchmarks and technically demanding G-protein-coupled receptor (GPCR) systems, including the rhodopsin–arrestin complex and the glucagon receptor. MCDPS-SFX consistently produces high-quality data statistics, enabling the high-resolution visualization of functionally critical, flexible regions such as phosphorylated receptor tails. Our results provide a powerful tool for enhancing the scientific output of XFEL experiments, offering a robust alternative for maximizing information recovery from weakly diffracting or overlapping crystalline samples. Full article

Plant metabolism is essential for coordinating growth, development, and defense under changing environmental conditions. Plants continuously adjust their metabolic pathways to balance resource allocation between growth and immune responses. Under stress, metabolic reprogramming redirects energy and resources toward the production of defense compounds and activation of immune signaling pathways. These changes involve complex interactions among primary metabolism, specialised metabolites, and regulatory networks, including calcium signaling, reactive oxygen species, and phytohormones. Advances in metabolomics and multi-omics technologies have improved understanding of the metabolic control of plant immunity; however, knowledge remains fragmented, and an integrated framework linking metabolism, development, and defense is still emerging. This review examines plant immunometabolism by highlighting the dynamic relationships between metabolic networks and immune functions during development and stress. It discusses pathways that influence growth, stress-induced metabolic shifts linked to defense, and how signaling interacts with metabolism. Progress in metabolomics, transcriptomics, proteomics, and computational modeling that supports systems-level analysis of plant metabolism is also summarized. In addition, potential applications in agriculture and biotechnology, including metabolic engineering, genome editing, and metabolomics-based breeding, are considered in relation to crop resilience. By integrating metabolism, signaling, and systems biology, this review provides a broad perspective on how metabolic reprogramming shapes the growth–defense trade-off in plants and outlines future directions for developing climate-resilient crops. Full article

Pregnancy represents a distinct immunological and physiological state that modifies maternal susceptibility to SARS-CoV-2 and influences the clinical and biological course of COVID-19. Accumulating evidence indicates that the interaction between viral entry determinants, gestation-specific immune modulation, placental endocrine–angiogenic pathways, and systemic inflammatory responses underlies the characteristic manifestations of SARS-CoV-2 infection during pregnancy. This review consolidates current understanding of SARS-CoV-2 viral structure, receptor biology, and the gestational regulation of key entry cofactors, including ACE2, TMPRSS2, NRP1, CTSL and FURIN, within reproductive and placental tissues. The review further integrates documented mechanisms of cytokine-mediated immune dysregulation, endothelial injury, thrombo-inflammation, and steroidogenic alteration observed in affected pregnancies, and examines their contribution to placental malperfusion, preeclampsia-like presentations, fetal growth abnormalities and preterm birth. Published molecular and computational studies characterising trophoblast antiviral defenses, receptor expression patterns, and structural determinants of Spike–ACE2 affinity are synthesised to contextualise the biological basis of placental susceptibility and the rarity of confirmed transplacental transmission. Current evidence on maternal clinical outcomes, fetal and neonatal consequences, vaccination efficacy, therapeutic considerations and contemporary management guidelines is also critically reviewed. By integrating molecular, immunological, pathological and clinical insights, this article provides a comprehensive framework for understanding the interaction between SARS-CoV-2 infection and pregnancy-specific physiology, with implications for risk assessment, preventive strategies and maternal–fetal care. Full article

Background: Genome-scale metabolic models (GEMs) are foundational tools for systems biology, enabling quantitative interrogation of human metabolism across physiological and pathological states. However, many legacy reconstructions exhibit heterogeneous identifier usage, incomplete pathway integration, and limited thermodynamic refinement, constraining reproducibility, interoperability, and translational applicability. Methods: We present EHMN 2026, an update of the Edinburgh Human Metabolic Network. The reconstruction was refined through systematic identifier reconciliation using MetaNetX and ChEBI mappings, duplicate reaction consolidation, thermodynamic directionality assessment, and structured pathway annotation via Reactome. The final model was encoded in Systems Biology Markup Language (SBML) Level 3 Version 2 with the Flux Balance Constraints (FBC2) package, ensuring explicit gene–protein–reaction (GPR) representation and compatibility with modern constraint-based modelling toolchains. Results: EHMN 2026 comprises 11 compartments, 14,321 metabolites (species), and 22,642 reactions, supported by 3996 gene products. Of all reactions, 9638 (42.6%) contain GPR associations, linking metabolic transformations to 2887 unique Ensembl gene identifiers (ENSG). Pathway integration yielded 2194 unique Reactome identifiers, providing structured pathway-level organisation of metabolic functions. Thermodynamic refinement reduced infeasible energy-generating cycles and improved reaction directionality coherence while preserving global network connectivity. The reconstruction is fully SBML-compliant and portable across major modelling platforms. Compared with Recon3D and Human1, EHMN 2026 uniquely combines native Reactome reaction-level annotation, systematic MetaNetX identifier harmonisation, documented thermodynamic cycle elimination (37 cycles, 0 remaining), and an 11-compartment architecture supporting organelle-specific modelling—features designed for QSP and multi-layer integration applications. Conclusions: EHMN 2026 delivers a rigorously harmonised, thermodynamically refined, and pathway-annotated human metabolic reconstruction with enhanced annotation depth and standards-based interoperability. By combining genome-scale coverage with structured gene and pathway integration, the model establishes a robust computational backbone for reproducible metabolic analysis and provides a scalable foundation for future multi-layer systems pharmacology and integrative modelling frameworks. Full article

To better understand the complexity of biological systems, research has shifted from a reductionist to a holistic approach, expanding the focus from single genes to a genome-scale view of gene activity and regulation. This is known as transcriptomics, a continuously growing field generating gene expression signatures from different technologies. A comparable paradigm shift has occurred in computational systems biology with the implementation of Artificial Intelligence (AI) learning models for gene expression analysis and integration. These models enable transcriptome-based profiling to address challenges of data heterogeneity, integration, and updating, assisting human intelligence and enhancing their ability to retrieve, analyze, integrate, and generate data recursively, thanks to their intrinsic predictive, inferential, reinforcement, and generative capabilities. Additionally, while scientists worldwide are still learning how to leverage AI methods that can maintain the human-in-the-loop, a new fundamental change is emerging: agentic AI, which can autonomously act and employ other AI methods to pursue its objectives. As a futuristic perspective, the proposed data analysis pipeline imagines agentic AI systems allowing the automated retrieval and pre-processing of heterogeneous transcriptomics data, analysis and integration with other omics datasets, performed with an incremental updating and recurrent analysis (IURA) model that could allow the detection of guideline updates (e.g., disease reclassification) and the generation of new hypotheses, such as candidate biomarkers or transcriptome–phenotype correlations. Since personalized medicine could derive profound benefits from its use, this scenario also raises important considerations regarding the advantages and concerns associated with the use of scientific AI agents in research and clinical practice. Full article

Ducks, once considered mere reservoirs, now serve as both victims and amplifiers of persistent highly pathogenic avian influenza (HPAI) virus cycles in wild populations. The molecular pathogenesis of HPAI is shaped by complex, dysregulated molecular networks, necessitating a systems biology approach that integrates computational modeling of host–pathogen interactions. Despite recent advances, a comprehensive understanding of the signaling pathways, molecular mechanisms, and hub genes driving HPAI H5N1 pathogenesis in avian hosts remains incomplete. This study addresses this gap by employing an integrated multi-omics strategy—combining transcriptomic, proteomic, and phosphoproteomic analyses—to map the signaling networks and key host factors involved in HPAI H5N1 infection in duck lung tissue. Our network analysis revealed activation of RIG-I-like receptor, toll-like receptor, NOD-like receptor, NF-κB, and JAK/STAT signaling pathways. Phosphoproteomic profiling independently confirmed the activation of these pathways, supporting the integrated network findings. Key regulatory hub genes identified include STAT1, DDX58 (RIG-I), MYD88, NFKBIA, NFKB1, IRF7, SOCS3, ACTB, TLR4, TLR7, IL-6, CASP1, and CASP8, which form a central hub in duck antiviral immunity. Some of these genes may represent promising targets for therapeutic or vaccine development against avian influenza. Collectively, this work delineates the critical signaling pathways and hub genes underlying HPAI H5N1 pathogenesis in ducks through comprehensive multi-omics integration. Full article