SEARCH · Search PubMed

Results for “genomic structural equation modeling”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

849 recordsLinked to original sources

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↗

PubMed2025

Coupling of spectroscopy and nitrogen-oxygen isotopes unveils the mechanisms of dissolved organic matter and nitrate pollution in lakes within the agro-pastoral transition zone.

Lakes in arid and semi-arid regions are subjected to severe ecological stress, such as organic pollution, eutrophication, and salinization, due to climate change and human activities. This study investigates Chagannur Lake, a typical arid-region lake that is representative and ecologically sensitive in Northern China's agro-pastoral ecotone, to uncover its pollution characteristics and mechanisms. We employed fluorescence spectroscopy and stable isotope analysis to trace dissolved organic matter (DOM) and nitrate sources. The DOM composition was dominated by microbial metabolic byproducts and protein-like substances, suggesting that microbial processes are key to organic matter transformation. Source apportionment revealed that pollutants primarily originated from livestock and poultry manure (37.6 %), agricultural fertilizers (35.6 %), and soil erosion (24.7 %), with agricultural fertilizers contributing most significantly in the Gogstai River (63.3 %). A structural equation model (SEM) coupling spectral and mass spectrometric data revealed that microbial transformation significantly impairs the lake's self-purification capacity, thereby promoting pollutant accumulation (path coefficient = 0.91,*p < 0.05). Moreover, microbial processes link endogenous and exogenous pollution, a mechanism effectively traced by isotopic and fluorescence indices (path coefficient = 0.55, ⁎⁎p < 0.01). These findings enhance the understanding of pollution sources and transformation mechanisms in arid-region lakes and offer foundational theoretical support for policymakers engaged in pollution control strategies.

Lakes↗

PubMed2026

Glymphatic dysfunction mediates inflammation-driven vascular burden and cognitive decline in cerebral small vessel disease.

BACKGROUND: Cerebral small vessel disease (CSVD) is increasingly recognized as a disorder involving microvascular dysfunction, impaired perivascular clearance, and inflammatory processes. However, how systemic inflammatory burden, neurovascular coupling (NVC), glymphatic MRI markers, vascular lesion burden, and cognition are interrelated remains unclear. MATERIALS AND METHODS: In this prospective study, 155 patients with CSVD and 70 healthy controls (HCs) underwent multimodal MRI. NVC was quantified using the cerebral blood flow/fractional amplitude of low-frequency fluctuations ratio. Glymphatic function was assessed via the diffusion tensor image analysis along the perivascular space (ALPS) index, choroid plexus volume (CPV), and perivascular space (PVS) fractions. Structural equation modeling (SEM) was employed to evaluate the direct and indirect effects of inflammatory markers on vascular burden and cognitive performance. RESULTS: Patients with CSVD exhibited significantly diminished NVC (specifically in the right median cingulate and left frontal gyri) and impaired glymphatic function (lower ALPS-index; higher CPV and PVS fractions) compared to HCs. SEM revealed that inflammatory biomarkers exerted both a direct effect on vascular burden and a substantial indirect effect (accounting for 66.3% of the total effect) mediated through two pathways: a single-mediation path via glymphatic function (42.8%) and a serial-mediation path via NVC and glymphatic function (23.5%). Increased vascular burden was significantly associated with poorer cognitive performance. CONCLUSION: Inflammation drives CSVD progression and cognitive decline primarily through the disruption of NVC and glymphatic clearance mechanisms. These findings highlight glymphatic dysfunction as a critical mediator of inflammation-related structural brain damage.

Humans↗

PubMed2026

Ramu stunt virus genome reveals previously unreported segments and nucleocapsid domain duplication in Mechlorovirus.

Ramu stunt virus (RmSV), a member of the genus Mechlorovirus within the family Phenuiviridae, was previously described as a six-segmented RNA virus infecting sugarcane. In this study, we re-examined type material and additional isolates using high-throughput sequencing and RT-PCR validation, revealing that RmSV possesses a nine-segmented genome, making it the largest reported in the Phenuiviridae. This expanded architecture includes duplicated RNA segments (RNA 2a and RNA 2b) encoding nucleocapsid-like proteins and two novel segments (RNA 7 and RNA 8). Comparative analysis showed that RNA 2a and 2b share about 84% amino acid identity, while RNA 5 encodes a third nucleocapsid homolog, indicating unprecedented domain redundancy. Structural modeling confirmed that all three nucleocapsid proteins maintain a conserved fold despite low sequence identity, with electrostatic mapping suggesting differential RNA-binding potential. Additionally, RNA 6 encodes a hypothetical protein structurally similar to the rice stripe virus disease-specific S-protein, implicating a role in symptom development. Transcript abundance analysis revealed RNA 6 as the most highly expressed segment across isolates. These findings revise the genomic composition of RmSV, highlight mechanisms of genome plasticity and adaptive evolution in plant-infecting bunyaviruses, and underscore practical implications for diagnostic assay design, resistance breeding, and biosecurity surveillance.

Genome, Viral↗

PubMed2026

A conserved distal-tail helical extension defines a tailspike attachment architecture in Gram-negative siphophages.

Rapid growth of bacteriophage genome collections has outpaced functional annotation of tail-tip proteins, limiting comparative analysis of host-recognition structures. Starting from a shared distal-tail gene organization in the Salmonella phages 9NA and Jersey, I developed a morphogenetic bioinformatic framework integrating gene synteny, sequence comparison, profile hidden Markov model (HMM) screening, structural evidence, structure-aware searching, and AlphaFold modeling. Comparison with the experimentally characterized lambda and Sf11 tail assemblies identified a predominantly alpha-helical C-terminal extension of the distal-tail (DT) protein associated with tailspike attachment, termed the distal-tail helical extension (DT-helix). Screening 541,986 proteins from 5167 complete NCBI RefSeq tailed-phage genomes, followed by evidence-based evaluation of sequence, genomic context, and structural architecture, identified 165 curated DT-helical-extension-associated phages. Their DT proteins segregated into six sequence groups. In the four principal multi-member groups, cognate tailspikes showed group-specific conservation in proximal N-terminal regions but substantially greater downstream diversity, consistent with sequence constraint at the DT-tailspike attachment boundary. A complementary ProstT5/Foldseek search supported the established groups but revealed no convincing additional highly divergent family. Together with the experimentally characterized Sf11 attachment interface, these findings define a recurrent morphogenetic architecture linking conserved distal-tail scaffolds to more variable receptor-binding proteins across siphophages infecting Gram-negative bacteria. Although universal exchangeability is not established, the identified scaffold-receptor-binding boundaries provide a framework for molecular characterization and rational phage engineering. Accession-level information for the 165 curated phages is available through PhageTailDB.

Viral Tail Proteins↗

PubMed2026

Innovation-related perception as a key driver of alternative protein acceptance: evidence from an early-stage model for cultivated meat and algae-/microalgae-based alternative protein products in Italy.

Alternative proteins are increasingly considered part of the transition toward more sustainable food systems, yet their diffusion depends critically on consumer acceptance. This study investigates the early-stage acceptance of two alternative protein categories in Italy-cultivated meat and algae-/microalgae-based alternative protein products. Focusing on the first three phases of acceptance, the analysis examines how innovation-related perception (IRP) shapes consumer perceived value (CPV), consumer perceived risk (CPR), and subsequent affective (AFF), cognitive (COG), and conative (CON) responses. Data were collected through an online survey administered to 238 Italian respondents and analysed using partial least squares structural equation modelling (PLS-SEM). The results show that IRP is the main upstream driver of early-stage acceptance in both product domains: more favourable perceptions strongly increase perceived value and reduce perceived risk. In turn, CPV exerts a much stronger influence than CPR on both affective and cognitive attitudes. A tentative cross-model comparison suggests only a descriptive variation in the final transition toward conative acceptance: affective and cognitive responses were both significant in the two models, with a relatively larger affective coefficient for cultivated meat and more balanced coefficients for algae-/microalgae-based products. Overall, the findings support a process-based interpretation of alternative protein acceptance and highlight the central role of innovation-related perception in shaping early consumer responses. These results provide relevant implications for communication strategies, product positioning, and policy actions aimed at improving the acceptability of alternative proteins in food cultures characterised by strong culinary traditions.

Italy↗

PubMed2026

Long-term petroleum pollution alters soil microbial communities via electron transfer capacity: Evidence from a 35-year chronosequence.

Petroleum pollution poses a serious threat to soil ecosystems, especially in areas surrounding oil wells, where contamination should not be overlooked. Through a 35-year longitudinal study of soils surrounding oil wells, we demonstrate that petroleum hydrocarbons accumulate predominantly in the top 10 cm of soil, reducing the electron acceptor capacity (EAC) by 61.59 % (from 12.68 to 4.87 μmole-/gC) and decreasing the electron transfer capacity (ETC) by 43 %. Structural equation modeling identified ETC as the critical mediator of microbial community shifts, with EAC playing a pivotal role in sustaining redox processes. Notably, hydrocarbon accumulation triggered a microbial succession: The abundance of Actinomycetota (including genera Rhodococcus, Arthrobacter, and Rubrobacter) showed the most significant fluctuations within 2 years, while Pseudomonadota (genera Methylobacter, Thiobacillus, and Pseudomonas), which were dominant in uncontaminated soils, decreased markedly during this period. This transition coincided with peak microbial dysbiosis (microbial dysbiosis index in 2022 reached 31.41 times that of controls). Within two to four years following mild petroleum stress, the bacterial community established a new structural configuration, revealing a crucial window for ecological recovery. The coupling between ETC reduction and microbial succession highlights the pivotal role of electron flux in soil recovery. Our findings establish a mechanistic framework for ETC-targeted restoration strategies to enhance bioremediation in petroleum-contaminated soils.

Soil Microbiology↗

PubMed2026

Multi-omics reveal microbial functional traits and antifungal metabolites associated with lower Pseudogymnoascus destructans loads in bat cave soils.

White-nose syndrome, caused by Pseudogymnoascus destructans (Pd), is a major fungal disease threatening hibernating bats. Cave soils can serve as environmental reservoirs for Pd, yet the microbial and biochemical mechanisms underlying naturally low Pd burdens in some cave environments remain poorly understood. Here, we integrated soil microbiome profiling, metagenomics, metabolomics, multi-omics network analysis, and in vitro validation to investigate the ecological and functional basis of differential Pd loads in hibernating bat caves in Northeast China. The three caves shared cold, humid, and weakly acidic microenvironments, but differed significantly in electrical conductivity, soil water content, nutrient availability, and extracellular enzyme activities. Soil microbial communities showed significant inter-cave variation in composition, diversity, and niche breadth, with stochastic processes contributing substantially to community assembly. Environmental variables, particularly pH and Pd load, were important predictors of microbial community structure. Functional analyses revealed that the low-Pd Gezi Cave was enriched in genes associated with organic carbon degradation, nitrogen input and retention, and secondary metabolism. Metabolomic profiling further identified cave-specific metabolite signatures, among which Biochanin A, 4-Hydroxybenzaldehyde, Vanillin, and Arachidonic acid were negatively correlated with Pd loads. Integrated pathway and network analyses showed that differential genes and metabolites jointly mapped to secondary metabolite biosynthesis, aminobenzoate degradation, and flavonoid degradation pathways, forming a microbe-metabolite-functional gene coupling network involving key taxa such as Rhodococcus, Pseudorhodoplanes, and Rhodoplanes. In vitro assays confirmed that 4-Hydroxybenzaldehyde, Coumarin, and Vanillin inhibited Pd growth. Structural equation modelling further indicated that environmental heterogeneity was associated with variation in Pd loads through microbial functional attributes and metabolite profiles. These findings suggest that naturally low-Pd cave soils are associated with coordinated environmental filtering, microbial functional specialization, and antifungal metabolite production, providing mechanistic insight into microbial and biochemical constraints on Pd persistence in cave reservoirs.

Animals↗

PubMed2026

Introgression shapes the genomic conflict landscape of Malus, providing evidence for a reticulate backbone in a woody crop lineage.

Phylogenomic discordance is widespread across plants, but its evolutionary significance is often obscured when conflict is treated primarily as analytical noise rather than as evidence of underlying processes. In woody lineages in particular, incomplete lineage sorting, introgression, and genome duplication can interact over long timescales to produce complex genomic histories that are not adequately summarized by a strictly bifurcating tree. Here, we use Malus as a model woody genus to investigate how these processes structure conflict across a genus-scale, accession-based phylogenomic framework. Using broad taxon sampling, hundreds of nuclear loci, plastid genomes, and genome-wide SNP summaries, we reconstruct a robust nuclear backbone for sampled Malus lineages and evaluate where discordance is concentrated and which processes best explain it. Nuclear analyses resolve eight major clades, whereas conflict is non-random and localized to recurrent hotspots rather than evenly distributed across the tree. Cytonuclear discordance is similarly concentrated, especially around Clade H, represented by sampled accessions of M. tschonoskii, where localized plastid-nuclear disagreement is consistent with candidate plastid capture or organellar introgression. Multiple complementary analyses further indicate that the strongest conflict is not explained by ILS alone, but instead reflects lineage-structured introgression, while polyploid complexes represent additional localized sources of evolutionary complexity. Together, these results provide evidence for a reticulate genomic backbone in Malus and show how integrating nuclear, plastid, and genome-wide conflict analyses can help distinguish background discordance from process-specific signals in woody plant radiations. Several lineage-level reticulation hypotheses identified here should now be tested with broader population-level sampling and curated reference accessions.

Malus↗

PubMed2026

Machine learning-ready genomic biomarkers: ATF3 polymorphisms predict postoperative analgesic demand through AI-compatible phenotyping.

PURPOSE: To determine whether ATF3 polymorphisms can serve as genetic biomarkers for machine learning-based precision analgesia by establishing a genotype-phenotype association suitable for predictive modeling of postoperative opioid requirements. METHODS: In a prospective cohort of 167 adults undergoing abdominal surgery, ATF3 SNPs rs3122721 and rs3125293 were genotyped. A structured dataset architecture was developed to represent genetic profiles as input features for supervised learning models, enabling translational analysis of genotype‑dependent opioid consumption over 72 h. RESULTS: Patients with homozygous genotypes of the ATF3 SNPs had significantly higher opioid requirements than non‑carriers, despite reporting similar subjective pain scores. This consistent genotype‑dependent pattern provided a clinically relevant phenotype suitable for integration into predictive algorithms. CONCLUSION: ATF3 genotyping offers a promising biomarker for computationally informed precision analgesia. By linking genomic variability to clinically meaningful outcomes within a structured clinical and genomic framework, this approach supports the future development of risk-stratified clinical decision-support systems to optimize postoperative pain management.Trial registration ChiCTR1900021991, registered 30 April 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00480-9.

ATF3↗

PubMed2026

Nitrogen sources and concentrations shape algal odor compounds: Key drivers of β-cyclocitral and β-ionone in water bodies of the lower Yangtze River.

Taste and odor (T&O) compounds derived from cyanobacterial blooms pose escalating threats to freshwater security worldwide, yet the drivers of specific T&O metabolites remain poorly constrained. Here, we investigated the dual effects of nitrogen (N) sources and concentrations on the production of β-cyclocitral and β-ionone, two algal-derived T&O compounds, through integrated field surveys (54 sites across lakes and rivers) in the eutrophic lower Yangtze River, China, and laboratory cultivation of typical cyanobacteria (Microcystis aeruginosa and Pseudanabaena cinerea). Our field data revealed that the concentrations of β-cyclocitral and β-ionone in lakes and rivers were not significantly different, but increased with the trophic level index. Redundancy analysis and Mantel analysis showed that Microcystis and Pseudanabaena were potentially dominant contributors to β-cyclocitral and β-ionone in the water column. Structural equation modeling and variation partitioning analysis showed that enhanced nitrate (NO3--N) significantly promoted the production of these compounds. Laboratory experiments demonstrated that inorganic N (NaNO₃) maximized total T&O yields by promoting algal biomass, whereas organic N (urea and glutamic acid) elevated the T&O production per unit biomass by 1.5- to 9.5-fold. Notably, Pseudanabaena exhibited a 2.3-fold higher β-ionone yield than Microcystis, with greater sensitivity to N concentrations. Our study highlights the critical role of nitrogen pollution, both source and concentration, in the production of T&O compounds by phytoplankton and provides reference data for managing T&O issues in rivers and shallow lakes.

Norisoprenoids↗

PubMed2026

Integrated exome and mitochondrial genome sequencing reveals the genetic landscape of primary mitochondrial diseases: findings from a large Tunisian cohort.

Primary mitochondrial diseases are a heterogeneous group of neurometabolic disorders recognized as the most common metabolic genetic diseases. They manifest at any age, affecting any tissue or organ, especially those with high energy demands, and are caused by pathogenic variants in both mitochondrial and nuclear genomes. Here, we aimed to describe the genetic spectrum of a Tunisian pediatric cohort with suspected mitochondrial diseases. We recruited 47 unrelated families who underwent exome sequencing as a first-tier test followed by whole mitochondrial genome sequencing for unsolved cases. Dedicated bioinformatic pipelines and prediction tools were used to determine the potential disease-causing variants. Sanger sequencing confirmed the presence and segregation within parents. For the newly identified variants, structural modeling was conducted to study the impact of these variants on protein structure and motions. Dual genome sequencing yielded a molecular diagnosis in 33/47 families (70%) and 18/47 (38%) showed disease-causing variants in genes encoding mitochondrial proteins. Among them, four families disclosed novel variants in FASTKD2, SERAC1 and GATB, which were supported by in-depth in silico and structural analyses demonstrating their deleterious effect. The remaining families (32%, 15/47) disclosed other metabolic and neurological disorders. An exome-first strategy delivers a high diagnostic yield in Tunisia, where consanguinity remains high and simultaneously captures mitochondrial and non-mitochondrial etiologies. Mitochondrial sequencing remains indispensable in the case of an inconclusive exome. Thus, our data expand the clinical and genetic spectrum of primary mitochondrial diseases in Tunisia, an underrepresented and admixed population.

Humans↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗

PubMed2026

Bioprospecting microbial genomes to expand the biocatalytic toolbox of rubber oxygenases.

A set of rubber oxygenases was discovered through phylogenetic analysis and AI-based structural modeling of complexes of the putative enzymes with a substrate mimicking cis-1,4-polyisoprene. Sixteen candidate proteins were selected from thermophilic microorganisms, all sequence-related to the Latex clearing protein from Streptomyces sp. K30 (LcpK30). Sequence truncation and solubility tags were then evaluated to enhance protein expression, with the SUMO tag proving to be the most effective. Including LcpK30, nine heme-containing oxygenases were successfully expressed in E. coli NEB 10-beta cells, purified (35-157 mg L-1 yield) and characterized. Steady-state kinetics revealed significant rubber latex-degrading properties for six of them, with the truncated SUMO-fused LcpK30 (SUMO-LcpK30T) showing activity in agreement with literature. Notably, the catalytic efficiencies of all the expressed homologs lay within one order of magnitude and the oxygenase from Thermomonospora echinospora was found to be particularly promising in terms of activity, especially at high latex concentrations (more than 1% w/v). The analysis of reaction mixtures by both HPLC and HPLC-MS confirmed the oxidation of cis-1,4-polyisoprene to form the expected isoprenoid oligomers (n = 2-12), whose distribution was consistent with the usual endo-type cleavage pattern in all but one case. This bioprospecting effort afforded a platform of new rubber-degrading enzymes with diverse efficiencies and product profiles, capable of adapting to targeted applications.

Oxygenases↗

PubMed2026

Comparative genomic and proteomic analysis reveals orthogroup structured evolution of tick protease inhibitors.

Protease inhibitors (PIs) play central roles in regulating endogenous proteolysis and host-parasite interactions in ticks. However, the evolutionary architecture underlying their diversification across tick lineages remains insufficiently resolved. Here, we performed a genome-wide comparative analysis of predicted proteomes from 14 tick species to systematically characterize PI repertoires. In total, 4931 putative PIs were identified and grouped into 20 families using the MEROPS classification system. Further, PI families such as Antistasin, WAP-type, and Pacifastin, which have not previously been systematically reported in tick genomes, were classified. Orthogroup inference demonstrated that PI expansion is structured at the level of evolutionary lineages rather than uniformly across families. By stratifying orthogroups according to duplication burden and taxonomic conservation, we identified a broadly conserved single-copy core under strong purifying selection. Motif level analysis of serpin reactive center loops further revealed conservation of inhibitory specificity within single copy orthogroups and diversification of key functional residues in duplication-associated lineages. Integration of secretion prediction and tissue-resolved proteomics from Hyalomma anatolicum and Rhipicephalus microplus demonstrated that evolutionary stratification is reflected at the protein level. Together, these findings provide an orthogroup-resolved evolutionary framework linking duplication dynamics, molecular evolution, and tissue-level protein deployment. This integrative approach offers a systematic basis for prioritizing conserved and diversified PI lineages for future functional and anti-tick intervention studies.

Animals↗

PubMed2026

Genome-wide identification, structural characterization, and evolutionary analysis of growth-related gene families in African catfish (Clarias gariepinus).

The somatotropic axis encompassing growth hormone (GH), insulin-like growth factor (IGF), myostatin (MSTN), and prolactin (PRL) signalling cascades is the master regulator of somatic growth, metabolism, and development in vertebrates. African catfish (Clarias gariepinus), a commercially pivotal aquaculture species, now possesses a chromosome-level reference genome (CGAR_prim_01v2); however, a systematic, genome-wide characterization spanning all five interconnected growth-related gene families has not previously been undertaken in this species. Here, we identified and characterized 15 growth-related genes spanning gh1, ghra, ghrb, Igf1, Igf2a, Igf2b, igf1ra, Igf1rb, Igf2r, Mstna, Mstnb, prl, prlra, prlrb, and smtlb distributed across 13 chromosomes. Complete one-to-one orthology with zebrafish confirmed strong dosage-balance conservation across >120 million years of teleost divergence. Physicochemical analysis resolved a clear biochemical dichotomy between compact, basic secreted ligands (19.88-45.81 kDa; pI up to 10.02) and large, acidic, heavily glycosylated membrane receptors (56.82-270.80 kDa; pI 4.85-5.97). Phylogenetic analysis confirmed 3R whole-genome duplication origins for all paralog pairs, while synteny analysis revealed a disruption of the ancestral gh1-prl chromosomal block in C. gariepinus, a finding that warrants further comparative and functional investigation. This genomic atlas provides the sequence and structural information including exon-intron boundaries, domain architecture, and chromosomal coordinates needed as a prerequisite for future marker-assisted selection and CRISPR-based myostatin-editing efforts in African catfish aquaculture, though translation into applied breeding outcomes will require subsequent functional and expression studies.

Animals↗

PubMed2026

Genome-wide SNP data support species boundaries in sympatric Polylepis Ruiz & Pav. (Rosaceae) species from Bolivia and Ecuador.

Species delimitation in the South American genus Polylepis is notoriously challenging due to high morphological similarity and phenotypic plasticity, likely driven by hybridization and gene flow. Previous phylogenetic studies suggested that genetic structure aligns more strongly with geography than with taxonomy, questioning existing species concepts and hampering conservation efforts. We used double-digest RAD sequencing (ddRADseq) to generate genome-wide SNP data for 11 Polylepis species sampled across multiple localities in Bolivia and Ecuador. Population genetic analyses, phylogenetic inference, and network approaches were combined to assess whether genetic structure aligns more closely with taxonomy or geography. Morphologically defined species formed largely cohesive genetic lineages across regions, with species identity explaining substantially more genetic variation than locality. While localized admixture and reticulation were detected among closely related taxa, widespread species showed strong genetic cohesion and clear separation from congeners. Our results indicate that the sampled Polylepis species from Bolivia and Ecuador maintain distinct genetic identities despite localized signals consistent with gene flow. This genome-wide support for current taxonomy highlights Polylepis as a valuable model for studying speciation under gene flow and indicates that multiple geographic sampling will be essential in reconstructing a robust phylogeny of the genus, with important implications for conservation planning in Andean montane forests.

Bolivia↗

PubMed2026

A system-level metastable model of cancer evolution: integrating replication stress, cell cycle deregulation and chromosomal instability.

INTRODUCTION: Cancer cell proliferation occurs within the context of persistent genomic instability. In this review, we propose the RS-CCD-CIN axis as a systems-level framework in which replication stress (RS), cell cycle deregulation (CCD) and chromosomal instability (CIN) form an interdependent triad that shapes tumour evolution. This axis represents a constrained metastable state in which genomic instability is tolerated and buffered. The objective of this review is to synthesize the current understanding of how the RS-CCD-CIN axis contributes to tumour heterogeneity, adaptability and therapy response. DISCUSSION: Evidence indicates that RS, CCD and CIN operate as a dynamic, interconnected network rather than as independent processes. Replication stress induces DNA damage and mutagenesis, while partial checkpoint disruption permits cells with unresolved lesions to proliferate. Chromosomal instability generates both structural and numerical alterations, contributing to intratumoural heterogeneity. Together, these processes facilitate adaptation to environmental and therapeutic pressures. Extrachromosomal DNA, micronuclei formation and cytosolic DNA signalling, including the cGAS-STING pathway, connect genomic instability to adaptive responses and immune modulation. Single-cell and spatial profiling reveal temporal and spatial variability in RS, CCD and CIN states, highlighting the limitations of static biomarkers. Therapeutically, targeting individual components often yields limited durability, whereas approaches that simultaneously perturb multiple aspects of the RS-CCD-CIN axis may improve clinical outcomes. CONCLUSIONS: This review highlights the RS-CCD-CIN axis as a fragile and metastable architecture that supports cancer evolution, while also being susceptible to collapse. A deeper understanding of this interconnected framework may inform the development of therapeutic strategies and enhance the management of resistance.

Humans↗