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PubMed2023

Phenotypic presentation of Mendelian disease across the diagnostic trajectory in electronic health records.

PURPOSE: To investigate the phenotypic presentation of Mendelian disease across the diagnostic trajectory in the electronic health record (EHR). METHODS: We applied a conceptual model to delineate the diagnostic trajectory of Mendelian disease to the EHRs of patients affected by 1 of 9 Mendelian diseases. We assessed data availability and phenotype ascertainment across the diagnostic trajectory using phenotype risk scores and validated our findings via chart review of patients with hereditary connective tissue disorders. RESULTS: We identified 896 individuals with genetically confirmed diagnoses, 216 (24%) of whom had fully ascertained diagnostic trajectories. Phenotype risk scores increased following clinical suspicion and diagnosis (P < 1 × 10-4, Wilcoxon rank sum test). We found that of all International Classification of Disease-based phenotypes in the EHR, 66% were recorded after clinical suspicion, and manual chart review yielded consistent results. CONCLUSION: Using a novel conceptual model to study the diagnostic trajectory of genetic disease in the EHR, we demonstrated that phenotype ascertainment is, in large part, driven by the clinical examinations and studies prompted by clinical suspicion of a genetic disease, a process we term diagnostic convergence. Algorithms designed to detect undiagnosed genetic disease should consider censoring EHR data at the first date of clinical suspicion to avoid data leakage.

Humans↗

PubMed2025

Multiplex PCR assay for the rapid detection of Klebsiella pneumoniae pathotypes.

Introduction. Klebsiella pneumoniae (Kp) is a major cause of nosocomial infections, with its evolving pathotypes including multidrug-resistant, hypervirulent (hvKp) and convergent strains posing significant diagnostic and treatment challenges due to combined antimicrobial resistance and virulence.Gap Statement. While there is a pressing requirement for thorough detection of Kp pathotypes, current assays in resource-limited environments are unable to effectively focus on essential carbapenemase and hypervirulence genes with the necessary reliability and precision.Aim. To develop and validate a multiplex PCR (m-PCR) assay capable of simultaneously detecting Kp isolates including those carrying partial or full virulence markers, alongside antimicrobial resistance.Methodology. In this study, an m-PCR assay was designed and optimized for the simultaneous detection of key biomarkers associated with hypervirulent (rmpA, rmpA2, iucA, peg344 and iroB), carbapenem-resistant (bla NDM, bla OXA-48-like and bla KPC) and convergent Kp pathotypes in clinical isolates. The assay was evaluated on clinical isolates and validated against whole-genome sequencing (WGS) data for accuracy, specificity and sensitivity.Results. The developed m-PCR assay exhibited 100% specificity when compared to WGS data, successfully detecting all target genes without cross-amplification in ATCC control strains. The assay demonstrated high sensitivity, efficiently amplifying bacterial genomes from minimal DNA input as low as 1 ng µl-1. Additionally, validation through sequencing confirmed the accuracy of detected amplicons.Conclusion. This m-PCR assay offers a rapid, sensitive and specific diagnostic tool for differentiating Kp pathotypes in clinical settings, aiding in timely intervention and improved infection control measures.

Klebsiella pneumoniae↗

PubMed2026

A rapid molecular assay for the detection of hypervirulent Klebsiella pneumoniae in the context of antimicrobial resistance surveillance.

Hypervirulent Klebsiella pneumoniae (hvKP) represents an emerging clinical and public-health concern, particularly as hypervirulence increasingly converges with multidrug resistance. Current diagnostic approaches rely on phenotypic assays, such as the string test, or on whole-genome sequencing (WGS), both of which have limitations in specificity, turnaround time, standardization, and feasibility for routine surveillance. To address this gap, we developed a multiplex real-time PCR assay targeting key hvKP-associated virulence loci, including siderophore systems, hypermucoviscosity regulators, and additional markers linked to invasive potential. The assay was evaluated on 110 K. pneumoniae clinical isolates and 9 positive blood cultures, using WGS and the string test as comparators. The molecular panel demonstrated high concordance with WGS for principal virulence determinants, correctly identifying all high-virulence (score 4) profiles, and most intermediate profiles. Against WGS, the assay yielded a sensitivity of 82% and a specificity of 73%; performance against the string test was 96% and 87%, respectively. Direct testing from blood culture pellets yielded results consistent with both WGS and DNA-based PCR for the limited number of targets detected, supporting the technical feasibility of this approach. However, broader validation is needed to confirm performance in this specimen type. Overall, this multiplex PCR assay provides a targeted molecular screening approach for the rapid identification of hvKP-associated virulence profiles. Its agreement with genomic data supports its potential utility as an accessible complement to WGS for hvKP surveillance, although further workflow optimization will be required before broader routine implementation.IMPORTANCEThe global emergence of hypervirulent and multidrug-resistant K. pneumoniae represents a major public-health threat, as the convergence of virulence and antimicrobial resistance dramatically limits therapeutic options and increases the likelihood of severe, invasive, and potentially untreatable infections. Rapid identification of essential virulence determinants is therefore critical for timely clinical management and for preventing onward transmission. However, current diagnostic approaches are either insufficiently sensitive or require substantial resources, limiting their routine use. By providing a rapid and targeted molecular assay capable of detecting the principal loci associated with hypervirulent K. pneumoniae and by demonstrating the preliminary feasibility of its use directly on blood culture pellets previously identified as Klebsiella spp. by MALDI-TOF MS, this work provides a pragmatic approach for early virulence profiling. Implementation of such assays can significantly enhance epidemiological surveillance, support tailored patient management, and reduce the spread of high-risk K. pneumoniae lineages in both community and healthcare environments.

Klebsiella pneumoniae↗

PubMed2025

Uncovering the genetic architecture of ME/CFS: a precision approach reveals impact of rare monogenic variation.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disabling and heterogeneous disorder lacking validated biomarkers or targeted therapies. Clinical variability and elusive pathophysiology hinder progress toward effective diagnostics and treatment. Core symptoms include persistent fatigue, post-exertional malaise, unrefreshing sleep, cognitive dysfunction, and pain. We tested whether an individualized, “n-of-1” genomic and transcriptomic framework combined with comprehensive, participant-informed phenotyping could reveal molecular signatures unique to each patient. METHODS: Clinical-grade whole-genome sequencing was conducted in 31 affected individuals from 25 families, with RNA-seq performed on a subset (16 affected, 7 unaffected) using blood samples. Machine-learning assisted variant triage, transcript-aware damage prediction, and expert review identified pathogenic or likely pathogenic variants in 8 of 25 probands (32%) and 12 of 31 affected individuals (39%). RESULTS: Findings revealed marked genetic heterogeneity, including large-effect rare and more common variants. Implicated pathways included ATP generation, oxidative phosphorylation, fatty acid oxidation; regulation of glycolysis, amino acid and lipid turnover; ion and solute homeostasis; synaptic signaling, excitability, oxygen transport, and muscle integrity, resilience, and post-exertional recovery; previously implicated processes. Plausible modifiers influencing disease onset, severity, and relapsing–remitting patterns and possibly explaining intrafamilial variability and inconsistent findings across studies, were also identified. Despite gene-level diversity, downstream effects converged on impaired energy production, reduced stress resilience, and vulnerability to post-exertional metabolic failure; disruptions consistent with core ME/CFS symptoms of exertional intolerance, cognitive fog, and fatigue. CONCLUSIONS: Our findings support the hypothesis that at least a subset of ME/CFS cases represent distinct molecular disorders that converge on shared physiological pathways. Validation in larger, more diverse cohorts will be essential to test this hypothesis and establish generalizability, but increase size alone is unlikely to resolve causation in a disorder defined by rarity, heterogeneity, and molecular complexity. We suggest that progress will require experimental designs that integrate individual-level genomic data with deep, participant-informed deep phenotyping, capturing the combined effects of rare and common variants and environmental modifiers on disease expression and progression. We believe that an individualized precision medicine framework will uncover molecular drivers and modifiers of ME/CFS previously obscured by heterogeneity, enabling biologically informed stratification, improved trial design, biomarker discovery, and targeted interventions in this historically neglected condition.

Humans↗

PubMed2026

Convergent IGF2 overexpression in pheochromocytoma/paraganglioma: insights from Beckwith-Wiedemann syndrome.

Beckwith-Wiedemann syndrome (BWS) is an imprinting disorder characterized by overgrowth and tumor predisposition, caused by dysregulated expression of genes on chromosome 11p15.5. An association between BWS and pheochromocytoma/paraganglioma (PPGL) has been suggested in isolated case reports over the past fifty years, but the molecular basis for this link remains unclear. We identified four patients with BWS who developed metastatic PPGL and investigated IGF2 pathway activation in these tumors and in PPGL across various genotypes. Pan-cancer transcriptomic analysis of The Cancer Genome Atlas (TCGA) demonstrated that PPGL overexpresses IGF2, with pseudohypoxic tumors exhibiting higher expression compared to other molecular clusters. Loss of heterozygosity and loss of imprinting at 11p15.5 partially explain this overexpression, with PPGL additionally demonstrating globally elevated expression of imprinted genes compared to most other tumor types, suggesting a broader relaxation of genomic imprinting. Cognate receptor profiling revealed that PPGLs are equipped to respond to IGF2 signaling, with high expression of IGF1R and insulin receptor isoform A (IR-A). Immunohistochemistry confirmed IGF2 protein overexpression in both BWS-associated and genotypically diverse sporadic PPGLs. Our results indicate that IGF2 overexpression is a convergent molecular feature of PPGL across genotypes and suggest the IGF2 pathway as a potential diagnostic and therapeutic target.

Humans↗

PubMed2026

Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.

Prosthetic joint infections (PJIs) remain among the most devastating complications of arthroplasty, imposing substantial clinical, economic and patient burdens. Although culture-based diagnostics underpin current clinical practice, PJIs are biofilm-driven infections shaped by taxonomic diversity, spatial organization, host responses and surface interactions, meaning conventional approaches provide only a partial and often decontextualized view of the infection process. We examine how convergent methodologies can transform PJI research by integrating approaches that have traditionally been studied in isolation, including sequencing, transcriptomics, metabolomics, advanced imaging and culture-based characterization. We discuss how whole-genome sequencing, shotgun metagenomics, transcriptomic and metabolomic approaches resolve pathogen identity, functional activity and adaptive persistence and how cross-scale imaging and spatial biology techniques reveal where microbes colonize, interact and survive across implant surfaces. We highlight emerging opportunities to unify these datasets into coherent frameworks that capture both the molecular and physical dimensions of PJIs. Integrating these complementary approaches will enable a multi-layered understanding of PJIs that link composition, function and spatial organization. Ultimately, this provides a foundation for predictive diagnostics, precision antimicrobial strategies and improved implant design and supports a shift towards more effective, mechanism-informed management of implant-associated infection.

Prosthesis-Related Infections↗

PubMed2026

SeqUIaSCOPE: multi-omics data integration platform for single-patient clinical oncology pathway exploration.

SUMMARY: SeqUIaSCOPE is an open-source platform designed for routine clinical oncology diagnostics through case-centric integration and visualization of genomic variants, fusion events, and expression profiles. The platform combines molecular-level validation via embedded genome browsing with systems-level interpretation through dynamic pathway visualization, enabling geneticists to assess how alterations converge across biological networks. Flexible reporting with customizable templates accommodates diverse institutional requirements, while secure cluster-based or local deployment ensures compliance with data protection policies, making advanced multi-omics diagnostics accessible to academic and clinical institutions. AVAILABILITY AND IMPLEMENTATION: SeqUIaSCOPE is freely available on GitHub at https://github.com/BioIT-CEITEC/sequiascope under the MIT license and archived at Zenodo (https://zenodo.org/records/21338445). Due to the sensitive nature of patient data, the repository provides simulated datasets that mimic the structure of real clinical data for testing and exploration. Documentation and a live demo accompany these datasets, allowing users to explore the application without any prior setup. The repository also includes a Helm chart for Kubernetes deployment and Docker containers for local deployment, ensuring compatibility across Linux, macOS, and Windows. No user registration is required, and all data remains on local or institutional infrastructure.

Humans↗

PubMed2026

Spectral-Proteomic Integration Analysis (SPIA) Deciphers Molecular Trajectories of Breast Cancer and Enables Multitarget Therapeutic Assessment.

Raman spectroscopy and mass spectrometry-based proteomics offer deeply complementary yet largely disconnected views of cancer biology: the former provides a label-free, real-time biochemical phenotype, while the latter delivers a quantitative inventory of specific protein effectors. Bridging this gap remains a fundamental challenge in analytical biomedicine. Here, we introduce Spectral-Proteomic Integration Analysis (SPIA)─a novel, data-driven integrative framework that systematically links Raman spectroscopic phenotypes with quantitative proteomic profiles through machine learning and statistical correlation. Using a DMBA-induced rat breast cancer model with and without Toremifene (TOR) intervention, SPIA dynamically maps tumor microenvironment remodeling, capturing progressive collagen deposition and lipid metabolic reprogramming. An SVM classifier trained on Raman spectra achieves exceptional diagnostic accuracy (AUC ≥ 99.0%) and successfully predicts TOR therapeutic response. Proteomic analysis identifies 1,350 differentially expressed proteins, with convergent machine learning feature selection (LASSO, Random Forest, XGBoost) pinpointing core regulators including Luc7l2, Nucb1, Cbx3, and Csnk2a1. Crucially, Spearman correlation analysis between key Raman bands and core DEPs reveals strong, statistically robust associations (median ρ ∼ 0.75 in the 1533-1669 cm-1 region), empirically validating SPIA's core integrative logic. Leveraging this multimodal map, we elucidate a multitarget mechanism for TOR involving concurrent suppression of collagen deposition and correction of aberrant lipid metabolism. SPIA establishes a powerful, generalizable paradigm for integrating phenotypic and molecular data, with broad implications for biomarker discovery, drug mechanism elucidation, and precision oncology.

Animals↗

PubMed2025

Whole genome sequencing of Yersinia pestis isolates from Central Asian natural plague foci revealed the role of adaptation to different hosts and environmental conditions in shaping specific genotypes.

The genetic diversity and biovar classification of Yersinia isolates from Central Asia were investigated using whole-genome sequencing. In total, 98 isolates from natural plague foci were sequenced using the MiSeq platform. Computational pipelines were developed for accurate assembly of Y. pestis replicons, including small cryptic plasmids, and for identifying genetic polymorphisms. A panel of 99 diagnostic polymorphisms was established, enabling the distinction of dominant Medievalis isolates derived from desert and upland regions. Evidence of convergent evolution was observed in polymorphic allele distributions across genetically distinct Y. pestis biovars, Y. pseudotuberculosis, and other Y. pestis strains, likely driven by adaptation to similar environmental conditions. Genetic polymorphisms in the napA, araC, ssuA, and rhaS genes, along with transposon and CRISPR-Cas insertion patterns, were confirmed as suitable tools for identifying Y. pestis biovars, although their homoplasy suggests limited utility for phylogenetic inference. Notably, a novel cryptic plasmid, pCKF, previously associated with the strain of the population 2.MED0 from the Central-Caucasus high-altitude autonomous plague focus, was detected in a genetically distinct isolate of 2.MED1 population from the Ural-Embi region, indicating potential plasmid transfer across the 2.MED lineage. These findings emphasize the need for ongoing genomic surveillance to monitor the spread of virulence-associated genetic elements and to improve our understanding of Y. pestis evolution and ecology.

Yersinia pestis↗

PubMed1979

Fractured proximal femur in Newcastle upon Tyne.

Using Hospital Activity Analysis (HAA) data as a diagnostic index followed by record linkage procedures, a retrospective survey was carried out of patients aged 65 and over from the Newcastle area admitted to hospital with fractures of the proximal femur. Annual incidence rates for the Newcastle area of 5.6 per 1000 in females and 2.3 per 1000 in males were observed. These rates are probably 7% lower than true rates owing to patients with multiple fractures being allocated to diagnostic codes other than fractured proximal femur in HAA files. Incidence rates increased steeply with age, and rates in the sexes tended to converge at higher ages. Proportionately large numbers of patients were admitted in the winter months. The mean length of hospital stay was 75.2 days of which an average of 47.8 were spent in acute orthopaedic units: this was equivalent to the continuous occupation of 51.1% of acute orthopaedic beds in Newcastle. At one hospital, mean length of stay was 64% greater than at the other and the weekly discharge rates were suggestive of partly prescriptive discharge. Of the patients studied 35.6% died in hospital, and actuarial analysis shows that risk of death fell from initially high values to a nadir at four to six weeks and then showed a secondary rise.

Aged↗

PubMed2026

Towards precision medicine for brain arteriovenous malformations.

Recent advances in cerebrovascular genomics, single-cell biology, pharmacology, and gene editing technology are transforming our understanding of brain arteriovenous malformations (bAVMs) - a leading cause of pediatric hemorrhagic stroke. Once considered static anatomical defects, bAVMs are now recognized as dynamic, genetically driven lesions resulting from somatic mutations in KRAS, BRAF, and pathways involved in arteriovenous specification, angiogenesis, and vascular remodeling. By integrating human genetics, animal models, and endovascular innovations, researchers have uncovered convergent mechanisms that link endothelial Ras/MAPK hyperactivation to abnormal vessel growth and higher rupture risk. These insights provide a foundation for precision medicine approaches that combine molecular diagnostics - such as liquid or endoluminal biopsies - with mutation-specific pharmacotherapies and emerging CRISPR-based gene editing strategies. We suggest that genotype-guided interventions, tailored by spatial and developmental cerebrovascular context, could ultimately reclassify bAVMs from surgically incurable malformations to treatable molecular conditions.

Humans↗

PubMed2026

Integrated landscape of salivary metagenome and multi-biofluid metabolome characterizes a microbial-metabolic axis in upper gastrointestinal cancer progression.

BACKGROUND: Upper gastrointestinal cancer (UGIC) imposes a major global health burden, yet the stage-specific molecular changes along the microbial-metabolic axis remain limited understood. We aimed to delineate this molecular landscape across UGIC progression and evaluate its potential as non-invasive methods for precision screening. RESULTS: Derived from a multi-center population-based UGIC screening program, we enrolled 420 individuals, stratified into normal, low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and UGIC (n = 105 per group). Integrated salivary metagenomics and paired salivary/plasma metabolomics were performed to capture local and systemic dysregulation. We uncovered distinct stage-specific divergence during UGIC progression: profound remodeling of the salivary microbiota (104 differential species) and salivary metabolomics (80 differential metabolites) initiated early at the LGIN stage, whereas plasma metabolic dysregulation (40 differential metabolites) peaked significantly later at the HGIN stage. Integrative analysis revealed salivary microbiota related more closely with salivary metabolome than plasma metabolome. Moreover, statistical evidence suggested that dysbiotic salivary microbiota was associated with altered lysine- and tryptophan-related catabolic pathways converging on Acetyl-CoA-related metabolic nodes, supporting a potential metabolic mechanism in precancerous lesions. Finally, the discriminative model integrating metagenomic and metabolomic markers demonstrated promising diagnostic performance in distinguishing these precancerous lesions (LGIN: area under the curve [AUC] = 0.83; HGIN: AUC = 0.77) and UGIC (AUC = 0.76) from normal. CONCLUSION: This study characterizes a stage-specific microbial-metabolic axis that facilitates the comprehensive understanding of UGIC pathogenesis. These multi-biofluid signatures offer a promising non-invasive triage strategy for detecting precancerous lesions and optimizing endoscopic resource allocation. Video Abstract.

Female↗

PubMed2026

Beyond genes: EpiSwitch® and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Long COVID (LC19), post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) are clinically distinct disorders that share substantial symptom overlap, including persistent fatigue, cognitive impairment, autonomic dysfunction, and immune dysregulation. Although these conditions differ in diagnosis and clinical presentation, their underlying biological mechanisms remain poorly understood and may involve convergent regulatory pathways. METHODS: The EpiSwitch® 3D genomics platform and Orion knowledgebase were used to integrate chromosome conformation signatures with genome-wide association study (GWAS)-derived datasets across ME/CFS, LC19, PTSD, RA, and MS. Three-dimensional genomic anchors were mapped to coding genes and analysed using STRING protein-protein interaction networks and Cytoscape-based systems biology approaches. Disease-specific anchor datasets were generated and compared at both gene and network levels to identify shared biological processes and regulatory mechanisms. RESULTS: Analysis of the ME/CFS dataset identified 552 unique 3D genomic anchors mapped to 567 genes, with analogous disease-specific anchor sets generated for LC19, PTSD, RA, and MS. Direct overlap between disease-associated genes was limited; however, higher-order network analyses revealed substantial interconnectivity and convergence across conditions. Shared biological pathways included immune and cytokine signalling, interferon responses, mitochondrial function, metabolic regulation, and neuroendocrine processes. Highly connected hub genes included immune regulatory nodes such as LAG3 and components of the mTOR signalling pathway, implicating T-cell exhaustion, chronic immune activation, and immunometabolic dysregulation as common mechanisms underlying these disorders. CONCLUSIONS: These findings support a systems-level model in which clinically overlapping fatigue-associated syndromes arise from perturbations of interconnected regulatory networks rather than discrete disease-specific pathways. Despite limited genetic overlap, substantial convergence at the network level suggests shared biological architecture across ME/CFS, LC19, PTSD, RA, and MS. The identification of common regulatory pathways provides a mechanistic framework for the development of cross-disease diagnostic and therapeutic strategies. By capturing dynamic regulatory states, 3D genomic biomarkers offer significant potential for objective blood-based diagnostics, patient stratification, and the identification of shared therapeutic targets across complex chronic disorders. These findings support the application of precision medicine approaches and may accelerate the development of novel interventions for fatigue-associated multisystem diseases.

Humans↗

PubMed2026

MicroRNAs in Veterinary Viral Diseases: A Comprehensive Review from Molecular Mechanisms to Clinical Translation.

MicroRNAs (miRNAs) are small non-coding RNA molecules, approximately 22 nucleotides in length, that regulate post-transcriptional gene expression and have emerged as pivotal modulators of host-virus interactions. Veterinary viral diseases continue to pose substantial challenges to animal health, livestock productivity, food security, and public health, particularly due to their zoonotic potential. While miRNA research has advanced considerably, a comprehensive and critically integrated understanding of their biological functions and clinical applications across veterinary viral diseases remains incomplete. This comprehensive critical narrative synthesis addresses four overarching research questions: (1) What conserved and species-specific miRNA-mediated mechanisms govern major veterinary viral diseases? (2) What contextual factors determine antiviral vs. proviral duality? (3) To what extent do circulating miRNA signatures offer diagnostic and prognostic utility? (4) What translational barriers currently prevent clinical implementation, and how can the One Health framework help overcome them? Integrating three interconnected dimensions-molecular mechanisms, pathogen-specific responses, and translational applications-the review synthesizes evidence across PRRSV, avian oncogenic viruses (MDV, ALV), the immunosuppressive IBDV, FMD, BVDV, Ebola, Hendra, Rabies, and aquatic viral diseases. A key contribution of this review is the proposal of a four-axis contextual framework that explains the antiviral/proviral duality of miRNAs, and a 'One miRNA, One Health' convergence model with a concrete implementation roadmap. Key findings include: (a) a four-axis contextual framework (cell type, infection stage, viral strain, host-viral miRNA competition) that explains the antiviral/proviral duality; (b) virus-encoded miRNAs (v-miRNAs) as lower-risk therapeutic targets due to their absence from uninfected host genomes; (c) circulating miRNA biomarkers validated only at proof-of-concept stage (TRL 1-3), with no veterinary product yet at TRL ≥4; and (d) zoonotic conservation of miR-155, miR-146a, miR-21, and miR-122 across human and veterinary pathogens, supporting a 'One miRNA, One Health' convergence strategy. Critical short-term priorities are standardized pre-analytical protocols, open-access veterinary miRNA databases, and multicenter validation in natural infection cohorts.

Antiviral therapy↗

PubMed2026

Expanding the Genomic Spectrum of NHLRC2-Associated FINCA Disease: Integrated Bioinformatic Characterization of a Novel Deep Intronic Variant Predicted to Activate a Pseudoexon.

NHLRC2-associated FINCA disease is an ultra-rare autosomal recessive multisystem disorder caused by biallelic pathogenic variants in NHLRC2. Its mutational spectrum and genotype-phenotype correlations remain incompletely defined, and the contribution of non-coding variants is poorly understood. Here, we report a male infant with a severe FINCA-like phenotype, including early-onset hemolytic anemia, pulmonary involvement, neurodevelopmental impairment, growth failure, recurrent infections, and fatal progression at 8.5 months. Whole-genome sequencing identified a compound heterozygous NHLRC2 genotype comprising the previously reported pathogenic missense variant c.442G>T (p.Asp148Tyr) and a novel deep intronic variant, c.331+6863A>G. Segregation analysis confirmed inheritance from different parents. Integrated genomic and splicing analysis predicted that c.331+6863A>G creates a strong cryptic donor splice site and supports pseudoexon inclusion. Reconstruction of the predicted aberrant transcript indicated premature termination and potential susceptibility to nonsense-mediated mRNA decay. To our knowledge, this is the first reported deep intronic NHLRC2 variant predicted to activate pseudoexon inclusion. Although experimental validation was unavailable, convergent clinical, segregation, population, and computational evidence supports c.331+6863A>G as the most plausible second disease-associated allele. This case expands the genomic spectrum of NHLRC2-associated FINCA disease and highlights the diagnostic value of phenotype-driven whole-genome sequencing.

Humans↗

PubMed2026

Machine learning-enabled multi-omics discovery of prognostic biomarkers and signaling targets in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains difficult to subtype using single omics layers. We conducted an exploratory investigation integrating reverse-phase protein array (RPPA) and DNA methylation data from the cancer genome atlas (TCGA)- pancreatic adenocarcinoma (PAAD) to assess the feasibility of multi-omics subtyping, alongside a supervised machine learning analysis of a small gene expression omnibus (GEO) transcriptomic cohort (n = 26) to identify candidate diagnostic genes. RPPA-based K-means clustering suggested a weak, possible two-subtype structure (silhouette ≈ 0.16) that remained unassociated with overall survival (log-rank p = 0.113) and lacked independent prognostic value. An independently performed similarity network fusion (SNF) analysis integrating RPPA and methylation data showed low concordance with RPPA-derived subtypes (Adjusted Rand Index (ARI) = 0.014), indicating limited convergence between molecular modalities. Supervised machine learning analysis of the GEO cohort using a fully nested leave-one-out cross-validation pipeline achieved a mean (area under the curve) AUC of 0.896 across four classifiers and identified four-fold-stable candidate genes (ESCO2, COL17A1, BCL2L14, and SOWAHB). However, this gene panel demonstrated limited external validity across two independent PDAC cohorts (log-rank p = 0.438 for both GSE62452 and GSE28735), indicating limited generalizability despite robust internal performance. Collectively, these findings provide limited evidence for a robust, prognostically significant multi-omics subtype or a validated diagnostic gene signature; instead, this study serves as a hypothesis-generating resource and highlights the importance of rigorous cross-validation and independent external validation in small-sample transcriptomic biomarker discovery.

Humans↗

PubMed2026

Proteomics identify disease-associated variants in patients with rare diseases undiagnosed after genome sequencing.

Despite the introduction of genome sequencing (GS) for rare disease diagnostics, a genetic cause is not identified in most patients. Here, we explored the potential of proteomics to improve the diagnostic yield in 424 patients with rare diseases from the 100,000 Genomes Project (100kGP) without a genetic diagnosis. Serum proteomic profiling was performed using the Olink Explore 1536 assay (N = 1463 proteins). For 13 patients without genetic diagnoses, detection of lower serum protein "outliers" (z-score < -2) led to confirmed genetic diagnoses by resolving variants of uncertain significance or prioritizing genes for targeted GS reanalysis. For 23 additional patients without genetic diagnoses (64% of findings), we identified candidate gene-disease links and variants through convergent evidence from lower protein outliers and variants ranked through the variant prioritization tool Exomiser. For example, we identified a candidate heterozygous missense variant [Genome Aggregation Database (gnomAD) minor allele frequency = 0.006%] in tyrosine kinase with immunoglobulin-like and epidermal growth factor homology domains 1 (TIE1) that was only present in a patient with lower TIE1 serum abundance (z-score = -5.12) and their father, both of whom were affected by the same monogenic cardiac disorder, but in no other individuals from the 100kGP. Missense (52.5%) and splice region (27.5%) variants accounted for most diagnostic or candidate variants prioritized. This proof-of-principle study demonstrated that serum proteomics can support rare disease diagnosis and identify disease-causing genes in patients undiagnosed after GS, although successful implementation will likely depend on tissue specificity of protein expression, detectability in blood, proteomic platform coverage, and sensitivity.

Humans↗

PubMed2026

Comparative chloroplast genomics of six Bupleurum (Apiaceae) accessions: candidate barcodes, phylogeny based on available plastomes, and candidate RNA-editing sites.

INTRODUCTION: Bupleurum L. (Apiaceae), a taxonomically intricate genus of about 190 species and a source of Radix Bupleuri (Chai Hu), is difficult to discriminate because of convergent morphology, infraspecific variation, and limited genomic sampling. This study aimed to characterize plastome variation, identify and validate candidate molecular markers, reconstruct plastid phylogenetic relationships, and assess candidate plastid RNA-editing sites in Bupleurum. METHODS: We assembled six plastomes from subgenus Bupleurum, screened 51 Bupleurum plastomes for diagnostic loci, reconstructed whole-plastome and partitioned protein-coding-sequence phylogenies, and predicted plastid C-to-U RNA-editing candidates across the six newly assembled plastomes using a PREP-Cp-compatible workflow. Candidate barcode performance was evaluated against the reference plastome phylogenies, and codon-based models were used to test for positive selection. RESULTS: The plastomes were 154,496-155,778 bp with the canonical quadripartite structure and GC contents of 37.67-37.73%. Gene content was stable (131-132 genes; 86-87 protein-coding genes); B. falcatum subsp. cernuum lacked ycf15 but contained an additional inverted-repeat-associated ycf1 annotation. A/U-ending synonymous codons were favoured. Finite pairwise Ka/Ks estimates were below 1 for most genes, and site-specific codon models detected no positive selection. Each plastome contained 55-61 pure microsatellites, dominated by A/T mononucleotide motifs. MarkerSeek ranked 265 features and identified atpF-atpH, petA-psbJ, rpl32-trnL-UAG, and ycf1 as leading candidate barcodes. ycf1 recovered 38 of 41 nodes strongly supported by both reference trees, whereas a partitioned four-locus analysis recovered 40 of 41 and distinguished all 51 accession sequences. However, only one of seven multi-accession operational binomial groups was monophyletic, and only one showed a positive local barcode gap. The whole-plastome phylogeny recovered Bupleurum as monophyletic relative to Chamaesium. The two sampled Penninervia accessions occupied early-diverging positions without forming an exclusive clade. B. falcatum subsp. cernuum was sister to B. ranunculoides, with B. ranunculoides subsp. telonense sister to that pair. A partitioned 74-CDS analysis recovered the same key relationships and 45 of 50 internal bipartitions. Across the six newly assembled plastomes, 57-63 nonsynonymous C-to-U candidates were predicted per accession (367 total) in 21-22 genes; 269 affected the second codon position and 98 the first. DISCUSSION: Bupleurum plastomes are structurally conservative but retain localised divergence useful for marker development. Concordant whole-plastome and CDS genealogies support genus monophyly, whereas sparse Penninervia sampling and maternal plastid inheritance preclude rejecting traditional subgeneric classification. The predicted RNA-editing sites represent candidates for future experimental validation rather than an established Bupleurum editome. These genomic resources support authentication, conservation, and evolutionary research in Bupleurum.

Apiaceae↗