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Oncogenic fusions both drive hematologic cancers and enable precise measurable residual disease tracking, yet whether residual fusion products can be therapeutically controlled remains incompletely defined. Emerging genomic, RNA, and proteostasis strategies now frame fusion control as a layer-matched therapeutic strategy.
Cryptosporidium parvum is a ubiquitous enteric parasite that infects a diverse range of vertebrate species. The detection of C. parvum can be confounded when oocysts are intermittently shed below an assay's limit of detection, yielding a false-negative result. We therefore investigated the utility of Cryptosporidium parvum virus 1 (CSpV1), a putative symbiont of Cryptosporidium parvum, as a surrogate target for detecting the parasite in bovine calves. Using real-time polymerase chain reaction (qPCR), we tested 422 samples for Cryptosporidium spp., C. parvum-associated targets, and CSpV1. Among the 189 samples positive for at least one target, CSpV1 was detected in 24 (12.70%) samples without concurrent detection of Cryptosporidium. Additionally, we analyzed CSpV1 genomic sequences to ascertain its value as an epidemiological biomarker. Evaluation of dsRNA1 amino acid sequences identified country-associated patterns, suggesting potential utility for geographic distribution analyses. These findings suggest that CSpV1 may serve as a biological signature of C. parvum and support further investigation into its usefulness as an adjunct molecular target.
Immune checkpoint inhibitor (ICI) therapy commonly leads to adverse events such as ICI-mediated diarrhea and colitis (IMDC). Fecal microbiota transplantation (FMT) remains an option for patients with refractory colitis. We report a multi-omics profiling of patients receiving first-line FMT for IMDC. In our preliminary analysis, 10 (76.9%) patients achieve clinical response, with a median time to clinical improvement of 1.5 (1-10.5) days. Among responder patients with baseline and follow-up samples, 6 (75%) show an increase in alpha-diversity post-FMT. Pre-FMT samples show an increase in the abundance scores of plasma cells, neutrophils, macrophages (M1 and M2), memory activated and resting memory CD4+ T cells, CD8+ T cells, T follicular helper (Tfh) cells, and regulatory T cells (Tregs), all of which decrease post-FMT. In a small cohort of patients, we identify potential mechanisms for FMT response and demonstrate that first-line FMT in patients with IMDC (NCT04038619) can be effective.
The DNBSEQ-T20×2 (T20) sequencer, developed by MGI Tech, enables cost-effective human whole-genome sequencing (WGS) at 30× coverage for less than $100 per genome. Here, we evaluate the sequencing performance and data quality of the T20 platform by benchmarking it against the established DNBSEQ-T7 (T7) sequencer using 507 samples derived from blood (N = 75), stool (N = 242), and saliva (N = 190). The T20 exhibited lower sequencing quality metrics compared with the T7, with Q20 scores of 95.76%-95.83% and Q30 scores of 87.25%-87.40%, compared with 97.81%-97.93% and 93.26%-93.60%, respectively, for T7 data. Quality differences were more evident toward the end of reads, and PCR-free libraries sequenced on the T20 showed similar reductions in quality scores. The median empirical base error rate estimated from 102 ZymoBIOMICS samples was 0.33%. The T20 demonstrated comparable coverage uniformity to the T7 and showed high concordance in microbiome composition analysis, with a median Bray-Curtis dissimilarity of 0.02. Variant calling performance was highly consistent between the two platforms. Among variants with non-missing genotype calls on both platforms, 94.92% of SNPs and 87.20% of InDels showed concordant genotypes between T20 and T7. Overall, the T20 delivers reliable sequencing accuracy and reproducibility for large-scale genomic and microbiome studies, providing a cost-effective alternative for high-throughput sequencing applications.
The reconstruction of ecosystem responses to past climate change has historically focused on large vertebrates. In contrast, small vertebrates with potentially stricter habitat preferences have been neglected in ancient DNA studies despite their potential utility as proxies for inferring geographic and temporal changes in habitat. Aotearoa New Zealand's acanthisittid wrens are a speciose group of tiny perching birds, including the mātuhituhi | bush wren (Xenicus longipes ssp.). Despite its relatively recent extinction in the 1970s, very little is known about this enigmatic bird. Here we sequence mitochondrial genomes and nuclear ultra conserved genomic elements from 32 historical bush wren specimens to reconstruct their evolutionary history. We also genetically sex specimens and reanalyse their plumage to reconstruct aspects of bush wren plumage variation. Our analyses suggest North and South Island bush wren populations diverged 2.6 million years ago when narrowing and closure of Plio-Pleistocene seaways allowed colonisation of new habitats, followed by rapid glaciation-driven diversification of South Island populations 470,000-94,000 years ago. Genetic sexing allowed an accurate reconstruction of ontogenetic, sexual, and geographic variation in plumage. Our multidisciplinary data supports recognition of North and South Island populations as separate species, and the description of a new subspecies X. longipes perditus subsp. nov. This research shows how ecosystems can buffer against the impacts of climate change up to an ecological tipping point, which has important lessons for conservation management in a fast-changing world.
Specific recognition between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to adaptive immunity, yet accurate prediction of TCR-pMHC specificity remains challenging. Existing models mainly rely on sequence features or isolated molecular structures, limiting their ability to capture interface-level determinants within the ternary recognition complex. Here, we constructed the multimodal TCR-pMHC ternary complex (MM-TCR) data set, integrating paired TCR-pMHC sequences, V/J gene annotations, and modeled TCR-pMHC complex structures refined by short molecular dynamics-based relaxation. Based on MM-TCR, we developed TCRspec, an interpretable multimodal framework combining sequence embeddings, gene-usage features, and complex-level structural representations. Under a stringent CD-HIT TCR-cluster-disjoint split, TCRspec achieved an average AUROC of 0.896 and AUPRC of 0.882 across seven antigen-specific test data sets, outperforming representative baseline models. Cross-validation and ablation analyses confirmed the contribution of ternary complex structural information and MD-refined structures. In independent OOD peptide-TCR systems, TCRspec retained discriminative performance and identified model-inferred peptide positions associated with TCR recognition, providing a structure-informed framework for TCR specificity prediction.
Copper and copper-based materials have gained increasing attention for the functional modification of implantable medical devices intended for prolonged soft-tissue contact, including vascular stents, catheters, and intrauterine devices. Owing to their broad-spectrum antimicrobial activity, redox reactivity, and involvement in angiogenesis and cellular signaling, copper-based systems offer significant potential for multifunctional surface engineering. However, achieving a balance between antibacterial efficacy, corrosion behavior, controlled ion release, and cytocompatibility remains a critical challenge. This PRISMA-compliant systematic review analyzes copper-containing materials and surface modification strategies for soft-tissue biomedical applications. A structured search of Scopus, Web of Science, and PubMed (2015-2025) identified 65 eligible studies. The review encompasses bulk copper-containing alloys, electrochemical and chemical surface modification techniques, physical vapor deposition approaches, and advanced hybrid systems integrating copper with polymers, hydrogels, or metal-phenolic networks. Across the reviewed literature, antibacterial performance was strongly dependent on copper concentration, microstructural distribution, and spatiotemporal ion release profiles. Moderate, well-controlled copper incorporation frequently improved antibacterial efficacy while maintaining acceptable hemocompatibility and cytocompatibility, particularly in vascular and blood-contacting devices. In contrast, excessive copper loading often accelerated corrosion and induced adverse cellular responses. Emerging multifunctional architectures demonstrated improved regulation of biological interactions, enabling simultaneous antibacterial, antithrombotic, and proendothelial effects. Overall, copper-based surface technologies represent a versatile platform for soft-tissue implant modification. Future translational progress will require precise control of copper release kinetics and comprehensive long-term in vivo validation to ensure safety and sustained therapeutic performance. From the authors' perspective, the most promising future direction involves multifunctional copper-based hybrid coatings capable of dynamically regulating ion release, host tissue integration, and antibacterial performance simultaneously. Strategies integrating hierarchical architectures, stimulus-responsive release systems, and clinically scalable fabrication methods are expected to play a key role in translating copper-containing surfaces from experimental concepts toward commercially viable soft-tissue biomedical devices.
Diabetic chronic wounds have become a major challenge for clinical treatment due to their complex pathological microenvironment, including persistent inflammatory response, angiogenesis disorder, excessive oxidative stress, and susceptible infection. Traditional dressings as a passive barrier have difficulty meeting the above multiple treatment needs. Electrospinning technology, with its ability to mimic the fibrous network structure of the natural extracellular matrix (ECM), offers a high specific surface area, controllable porosity, and excellent drug-loading capacity, making it an ideal platform for developing a new generation of multifunctional wound dressings. This article provides a systematic review of the research progress on electrospun nanofiber dressings in the treatment of diabetic wounds, focusing on the design evolution from basic single-layer structures to advanced complex structures and elucidating the mechanisms of action and quantifiable effects of each structural type in addressing specific pathological challenges. We also compared the current status of clinical translation for electrospun dressings with that of other advanced wound care platforms and proposed a standardized preclinical evaluation framework. A large number of research data show that these advanced designs can effectively improve the quality of healing. Finally, this paper points out the challenges faced by this field, such as scalable fabrication, in vivo reliability of smart systems, and long-term biosafety, and provides theoretical basis and technical reference for the design of efficient and intelligent electrostatic spinning diabetic wound dressings.
In order to identify new and genome-wide significant loci for eating behavior traits (cognitive restraint, uncontrolled eating and emotional eating), we conducted a meta-GWAS with seven studies of European ancestry (n = 11,250). Eating behavior was assessed using the Three-Factor Eating Questionnaire. Genotype effects of single studies were estimated using additive models adjusting for age, sex, BMI, and principal components and single study results were combined by fixed-effect meta-analysis.For cognitive restraint and uncontrolled eating, no genome-wide significant association could be detected. For emotional eating, one genomic region on chromosome 5 comprising two polymorphisms attained genome-wide significance (P = 4.0×10-8 for rs6877636 and P = 3.2×10-8 for rs6897090). The minor alleles were associated with higher emotional eating scores (β=0.093 ± 0.017), with a similar direction of effect in each study. Both SNPs, in near perfect linkage disequilibrium, mapped to RP11-24P24.1, a processed pseudogene of ornithine decarboxylase 1 (ODC1). Enrichment analysis revealed a significant overlap between genome-wide BMI-associated variants and nominal emotional eating variants, supporting the hypothesis that shared genetic factors may influence both eating behavior traits and obesity risk. Finally, we observed a number of interesting associations reaching suggestive significance (P < 10-6) involving BMI candidate genes, including a suggestive association between FTO variants and cognitive restraint (rs9922708, β = 0.069, P = 5.9×10-7).In conclusion, our meta-GWAS identified for the first time a robust chromosomal region associated with emotional eating in seven studies. Given that emotional eating strongly influences body weight but is often stigmatized, recognizing genetic susceptibility to certain eating behaviors may help reduce stigma and alleviate guilt.
The glucagon receptor (GCGR) and gastric inhibitory polypeptide receptor (GIPR) are class B GPCRs that regulate glucose homeostasis and energy balance, making them key targets for type 2 diabetes and obesity. Achieving preferential Gs signaling at these receptors with small molecules remains an unmet challenge. Here, we report SIM1, developed through optimization of the PCO371 scaffold, which exhibits preferential Gs signaling at GCGR and GIPR with minimal detectable β-arrestin recruitment and substantially improved efficacy at GIPR. Cryo-EM structures of SIM1-GCGR-Gs (2.53 Å) and SIM1-GIPR-Gs (2.74 Å) reveal a shared intracellular allosteric interface at the receptor-G protein coupling region, distinct from extracellular peptide recognition. Structural comparison with GLP1R suggests that intracellular conformational constraints contribute to differential SIM1 responsiveness, which is restored by targeted mutations. Guided by these insights, analogs SIM2 and SIM3 exhibited up to 20-fold enhanced potency while maintaining an apparent preferential Gs signaling profile. These findings reveal a conserved intracellular allosteric activation mechanism across multiple class B GPCRs and identify SIM1 and its analogs as valuable chemical tools for investigating receptor-specific intracellular allosteric regulation and G protein-preferential signaling.
Telomere Biology Disorders (TBDs) are a genetically heterogeneous and often under-recognized cause of Bone Marrow Failure Syndromes (BMFS), driven by defective telomere maintenance and progressive telomere attrition. We performed an integrated genomic, telomeric and computational analysis in 118 subjects presenting clinical features of BMFS to delineate the contribution of Telomere Regulatory Genes (TRGs) variants to disease pathogenesis. Whole exome sequencing (WES) identified pathogenic (18.18%), likely pathogenic (27.27%) and rare variants of uncertain significance (54.54%) in 27 subjects (22.9%) across five TRGs: RTEL1, TERT, TINF2, NOP10, and WRAP53. Telomere Length (TL) assessment revealed significant telomere shortening in TRG variant-positive subjects compared with age-matched controls, with the most profound attrition observed in individuals harboring de novo TINF2 gene variants. RTEL1 emerged as the most frequently affected gene, with recurrent clustering of variants within its C-terminal regulatory region. A familial NOP10 variant, Asp12His, segregated with cutaneous pigmentation and hematological abnormalities consistent with the established role of NOP10 in dyskeratosis congenita, further broadening the known mutational spectrum of the gene. Structure-guided in-silico analyses predicted that both novel and recurrent variants disrupt protein stability, telomerase assembly or trafficking and shelterin complex integrity. Reduced TERT expression and a significant inverse correlation between telomere length and clinical severity further underscored the functional impact of TRG defects. Collectively, this study provides the first comprehensive characterization of TRG variants in the Indian BMFS cohort and highlights the utility of integrating genomic sequencing, telomere length measurement and computational modeling to improve diagnostic precision, variant interpretation and clinical stratification in TBDs.
Pediatric adrenocortical tumors are rare, clinically heterogeneous neoplasms with unpredictable outcomes and limited treatment options. Through integrated multi-omic analysis of 214 pediatric adrenocortical tumors combining DNA methylation profiling, transcriptomics, chromatin accessibility, and spatial deconvolution, we identify four distinct risk groups. A high-risk subgroup is characterized by CpG island hypermethylation, chromosomal instability, and dismal survival. These tumors exhibit transcriptional co-activation of WNT signalling and activator protein-1 transcriptional programs and display balanced admixture of zona glomerulosa and zona fasciculata/reticularis-like cells. Spatial analysis reveals zona glomerulosa cells as WNT signaling hubs driving intercellular crosstalk. Mechanistically, the histone deacetylase inhibitor entinostat reverses promoter methylation, silences activator protein-1 activity, and induces apoptotic reprogramming in tumor models. These findings establish a molecular framework for risk stratification and identify actionable therapeutic vulnerabilities, providing an essential resource for studying this molecularly uncharted pediatric malignancy.
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Streptococcus pneumoniae is highly diverse, comprising over 100 serotypes and hundreds of genomic lineages amid widespread vaccination. While it can cause invasive pneumococcal disease (IPD) which exhibits pronounced seasonal spikes, the interplay between pneumococcal diversity and environmental drivers remains unexplored. Here we analysed 59,017 IPD cases over 19 years from South Africa, incorporating 4,350 genome-sequenced isolates, using Bayesian spatiotemporal models to link environmental exposure and pneumococcal diversity. Cumulatively, across an 8-week period, moderate relative humidity (33-49%) and cold minimum temperatures (4-10 °C) increased IPD risk by 5% and 4%, respectively. Conversely, warm maximum temperatures (27-38 °C) were associated with up to a 10% increased risk within a week of exposure. There was a positive association between air pollution (PM2.5) and IPD, although it varied by age, disease presentation, and most notably serotype and lineage. Specifically, the lag time between PM2.5 exposure and disease onset varied by serotype, with only serotypes 4, 8 and 23F conferring an immediate IPD risk. High prevalence of GPSC21 lineage (serotype 19F) also modified the pollution response, shifting the lag structure to produce immediate risk of disease following high PM2.5 exposure. Our results demonstrate that pneumococcal population structure shapes air quality risk which in turn can shape the fitness landscape of microbial populations. Integration of these data may inform public health policy.
Single-molecule genomics (SMG) has transformed our ability to study the mechanisms that regulate the genome by enabling profiling of the activity of regulatory factors on individual DNA molecules genome-wide. SMG is able to quantify molecular heterogeneity and the co-occurrence of regulatory events, including epigenetic modifications, transcription factor binding and chromatin organization on single DNA molecules. SMG reveals dynamics of chromatin interactions that cannot be measured by conventional genomics assays. Therefore, SMG offers a unique platform to study how regulatory events combine to control genome activity. In this Expert Recommendation article, we provide a practical guide for adopting SMG and outline best practices.
Somatic mosaicism is increasingly recognized as a pervasive feature of the human brain and a potential contributor to neurological disease across the lifespan. Unlike germline variants, somatic variants arise post-zygotically and are unevenly distributed across regions, cell types and even individual neurons, enabling focal biological effects that can scale to network-level dysfunction. In this Review, we synthesize current evidence that developmental timing, clonal architecture and cell-type-specific selective pressures shape how somatic variants influence brain structure and function. Early embryonic variants can produce broad regional clones and severe phenotypes, whereas later events are usually more restricted; with ageing, ongoing DNA damage and imperfect repair generate private variants that might cumulatively reduce cellular resilience. We also summarize advances in detection approaches, including bulk, error-corrected and single-cell sequencing, and discuss their strengths and current limitations for clinical translation. Emerging data link brain somatic variants to neurodevelopmental and neurodegenerative phenotypes, supporting a unified framework in which mosaic genetics bridges focal lesions and distributed neurological syndromes. Integrating genomic, cellular and physiological analyses in longitudinal human studies will be essential to define causality, identify biomarkers and guide future targeted interventions.
Among the more than 90 identified genetic risk loci for late-onset Alzheimer's disease (AD) and related dementias, the apolipoprotein E (APOE) gene ɛ2/ɛ3/ɛ4 polymorphisms remain the longstanding benchmark for genetic disease risk with a consistently large effect across studies1-10. Despite this massive signal, the exact mechanisms by which ɛ4 increases and ɛ2 decreases dementia risk remain poorly understood. Notably, recent trials of anti-amyloid therapies suggest less efficacy and higher risks of severe side effects in ε4 carriers11-13, hampering the treatment of those with the highest unmet need. To improve our understanding of the genetic architecture of AD in the context of its main genetic driver, we performed genome-wide association studies (GWASs) stratified by ε4 and ε2 carrier status. HP1BP3, SLC50A1, PTPRC, NPAS3, DDHD1, CHST9, SMYD2, PRAMEF1 and GFRA1 emerged as new genomic signals for AD risk, appearing only when stratified by APOE carrier status. DDHD1 appeared especially promising, showing protective effects in ε4 carriers, being identified as an expression quantitative trait locus and being involved in rare neuronal diseases. Such APOE-stratified insights may help understand and overcome side effects, inform clinical trial enrollment strategies, and create the scientific basis for targeted, mechanism-driven therapies in neurodegenerative diseases.
Polygenic risk scores (PRSs) trained on multiancestry data can improve prediction in under-represented groups, but large linked genetic and health datasets capturing broad human diversity remain limited. Using 245,388 whole-genome sequences from the All of Us research program (AoU) together with UK Biobank data, we developed multiancestry PRSs for 32 traits and diseases. We evaluated how ancestry, methodology and genetic architecture influenced PRS performance across ancestrally diverse AoU participants. Increased diversity in the AoU improved PRS accuracy for several traits, especially in under-represented populations. However, maximizing sample size by meta-analyzing AoU and UK Biobank was not universally optimal: for less polygenic traits, AoU-only training performed best in African ancestry participants, consistent with ancestry-enriched effects. Individual PRS accuracy declined linearly with increasing ancestry divergence from the discovery GWAS, but this decay was attenuated using multiancestry training data. These findings underscore the value of more representative biobanks for equitable PRS performance.