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PubMed2026

Phenotypic and transcriptomic characterization of biallelic RNU2-2 developmental and epileptic encephalopathy.

OBJECTIVE: A significant proportion of individuals with suspected genetic developmental and epileptic encephalopathies (DEEs) remain unsolved following whole genome sequencing (WGS). Here we describe biallelic RNU2-2 variants causing a recently reported, severe, recessive DEE. METHODS: We screened individuals who have received WGS analyses at the Genomic Medicine Centre Karolinska for Rare Diseases for biallelic RNU2-2 variants. Deep phenotyping was performed through reviewing entire medical histories and phenotypic traits were transcribed to their corresponding Human Phenotype Ontology (HPO) term. HPO terms were used to generate pairwise phenotypic similarity scores and assess for significantly shared phenotype enrichment in the RNU2-2 sub-cohort. RNA sequencing analyses were performed in fibroblast and blood tissues to compare splicing events between RNU2-2 individuals and two independent control groups. RESULTS: We identified 14 individuals from nine families with 12 ultra-rare biallelic RNU2-2 variants clustering in the conserved 5' domains. Genotype data from 13 of 14 individuals has been reported previously as part of a larger cohort. All individuals presented with a highly concordant, severe DEE, characterized by severe to profound intellectual disability, inability to walk or communicate, hyperkinesia, and refractory seizures. Infantile spasms and tonic seizures were the predominant seizure types and a Lennox-Gastaut syndrome-like phenotype was common. These individuals had a significantly similar phenotypic signature when compared with 703 individuals with complex pediatric epilepsies (two-sided Monte Carlo permutation test, p = .005). RNA sequencing analyses showed aberrant splicing, with the most pronounced effects in fibroblast tissues in mutually exclusive exon and alternate 3' splice-site events, which were not detectable in blood. SIGNIFICANCE: We present deep phenotyping data and transcriptomic analyses that provide support for rare, 5' clustering biallelic RNU2-2 variants causing this novel, severe DEE. We propose an RNA sequencing methodology on fibroblast tissue for future validation of RNU2-2 variants.

autosomal recessive disease↗

PubMed2025

CLINICAL AND COGNITIVE PHENOTYPING OF COPY NUMBER VARIANTS ASSOCIATED WITH NEURODEVELOPMENTAL DISORDERS FROM A MULTI-ANCESTRY BIOBANK.

Clinical biobanks with electronic health records (EHRs) linked to genotype data continue to expand yielding an opportunity to further characterize disease-relevant genomic risk factors, yet few recall-by-genotype studies from biobanks have been published to date. For example, copy number variants (CNVs) that significantly increase risk for multiple neurodevelopmental disorders (NDDs) and negatively affect neurocognition, may present in up to 2% of population cohorts, with public health implications for ascertaining NDD CNV carriers. From BioMe, a multi-ancestry biobank derived from the Mount Sinai healthcare system (New York, NY), 892 adult participants were recontacted for deep phenotyping, including 335 NDD CNV carriers as well as comparators, 217 individuals with schizophrenia and 340 controls. Clinical and cognitive assessments were administered to each participant. There was no disclosure of genetic information. Eight percent of recontacted biobank participants completed the study (30 NDD CNV carriers across 15 unique loci, 20 schizophrenia and 23 controls). The study sample had a mean age of 48.8 (10.2) years, was 66% female and of diverse ancestry, 36% African, 34% Hispanic, and 26% European. Overall, 70% of 30 NDD-CNV carriers harbored at least one neuropsychiatric or developmental phenotype, including 40% with mood or anxiety disorders. Further, 22 NDD CNV carriers were significantly impaired compared to controls on digit span backwards (Beta=-1.76, FDR=0.04) and digit span sequencing (Beta=-2.01, FDR=0.04), but higher performing than schizophrenia on verbal learning (Beta=4.5, FDR=0.05). Thirty NDD CNV carriers were successfully recruited from a multi-ancestry biobank, as well as healthy controls and low-functioning individuals with schizophrenia. Deep phenotyping corroborated past reports, while also identifying discordance with EHRs. Future recall-by-genotype studies may further benchmark the study design and elucidate feasibility.

Biobank↗

PubMed2026

The Genetic Architecture of Chronic Cough: From Sensory Hypersensitivity to Treatable Trait.

Chronic cough is a prevalent global clinical disorder with substantial quality-of-life impairment, and refractory cases remain a major unmet medical need. Cough hypersensitivity syndrome is the core pathological mechanism of chronic cough, and growing genetic evidence has confirmed that inherited susceptibility shapes cough hypersensitivity, clinical heterogeneity and therapeutic responsiveness, redefining chronic cough as a biologically mediated sensory-neural disorder rather than a non-specific secondary symptom of airway diseases. This review summarises genetic evidence for chronic cough from family-based studies, pharmacogenomics and genome-wide association studies (GWAS), revealing distinct genetic architectures of chronic dry cough and sputum production, with enrichment of sensory-neural pathway variants and key genetic loci such as replication factor C subunit 1 (RFC1) functional genomic analyses link genetic variation to vagal afferent excitability, and rare genetic neurological disorders further illuminate the neurogenic basis of cough hypersensitivity. Moreover, genetic insights identify tractable treatable traits and rationalise antitussive drug development, supporting genotype-guided patient stratification. We conclude that integrating genetic architecture into clinical phenotyping and translational research provides a critical framework for precision management of chronic cough, and future progress relies on harmonised deep phenotyping and multi-ancestry genetic studies.

RFC1 gene↗

PubMed2026

Genotype-structure-phenotype correlations define divergent natural history in early-onset spastic paraplegia type 4.

Hereditary spastic paraplegia type 4 (SPG4), caused by variants in SPAST, is the most common form of HSP and exhibits a remarkable phenotypic heterogeneity ranging from late-onset pure presentations to severe, early-onset complex disease. Robust genotype-phenotype correlations and detailed natural history data are lacking, limiting clinical trial readiness. We analyzed 206 patients with genetically confirmed SPG4 enrolled across seven international centers, complemented by high-quality literature-derived cases. Deep phenotyping included standardized motor scales, spasticity ratings, developmental milestones, and patient-reported outcomes. We developed an extended essentiality-mapping framework to classify SPAST missense variants by integrating in silico pathogenicity predictions, evolutionary constraint, physicochemical residue connectivity, and variant enrichment within the human spastin hexamer structure. Plasma neurofilament light chain (pNfL) using was quantified using Simoa in 26 patients and 101 controls. We identified 136 distinct SPAST variants, including 10 novel variants. Variant class segregated strongly by inheritance, with de novo cases enriched for missense variants and inherited cases showing a variety of variant classes with enrichment for truncating variants. Longitudinal analysis revealed two latent trajectories: a rapidly progressive severe subgroup enriched for de novo missense variants, and a biphasic moderate subgroup enriched for inherited truncating variants. Patient stratification integrating spastin essentiality mapping (missense variants affecting essential, neutral, or context-dependent residues) with established genetic modifiers (biallelic pathogenic variants or modifier variants in trans) classified patients into predicted severe and moderate subgroups with divergent age at onset and clinical disease progression. The severe subgroup showed early developmental delays, rapid loss of ambulation, and declining quality of life, while the moderate subgroup displayed delayed but accelerating disease progression. pNfL levels were elevated in both subgroups, most pronounced in severe early disease. This study provides the most detailed natural history of SPG4 to date and introduces a biologically informed stratification framework that links variant class and location to divergent clinical trajectories. These data establish clinically meaningful benchmarks and offer a genotype-based framework to improve anticipatory care and optimize trial design for SPG4.

SPAST↗

PubMed2026

Omics in hereditary optic neuropathies: A systematic review of clinical studies with an integrated point of view.

Hereditary optic neuropathies are characterized by bilateral visual loss due to the degeneration of retinal ganglion cells, resulting in optic nerve degeneration and atrophy. Although the genetic origin of the main isolated and syndromic hereditary optic neuropathies has been characterized, the clinical phenotypes exhibit significant and poorly understood variability in both penetrance and expressivity. Additionally, the genetic and environmental factors that influence the onset of these optic neuropathies remain poorly understood, with limited biomarkers to predict disease progression or as readouts for therapeutic trials. Data-driven omics strategies allow deep phenotyping to improve our understanding of pathophysiological mechanisms and to search for new biomarkers and therapeutic targets. We explore whether the omics strategies applied to patients with hereditary optic neuropathies have provided such new insights. MEDLINE, Web of Science and EMBASE databases were screened for studies with terms relating to hereditary optic neuropathies, transcriptomics, epigenomics, proteomics, metabolomics and lipidomics in clinical studies exploring patients' samples. Out of 1244 references identified, 22 articles were included after double-masked data curation. These articles focused only on the 3 main forms of hereditary optic neuropathies, namely, OPA1-related dominant optic atrophy (n = 4), Leber hereditary optic neuropathy (n = 13), and Wolfram syndrome (n = 5). While the methodological designs and results of these studies were highly heterogeneous, they revealed molecular alterations that we have attempted to discuss at the integrated multi-omics level. This data integration highlighted several common pathophysiological mechanisms such as energetic impairment, endoplasmic reticulum stress, proteotoxic and oxidative stresses, lipid remodeling and altered amino acid and purine metabolisms, while suggesting potential new biomarkers and therapeutic targets. These findings underscore the potential of integrated multi-omics approaches to deepen our understanding of the phenotypic complexity of hereditary optic neuropathies and to support the development of innovative diagnostic and therapeutic strategies.

Humans↗

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

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

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

Journal Article↗

PubMed2025

A genome-wide approach for the discovery of novel repeat expansion disorders in the Undiagnosed Diseases Network cohort.

PURPOSE: The Undiagnosed Diseases Network is a National Institutes of Health funded research study that aims to solve a broad clinical spectrum of challenging rare disease cases. Participants receive care from multiple clinical specialists, who collaborate to perform deep phenotyping and state-of-the-art multiomics analyses. As bioinformatics of short-read sequencing has matured, the discovery of repeat expansion disorders (REDs) is accelerating. REDs comprise approximately 60 characterized disorders, which exhibit a broad spectrum of phenotypes. Thus, a largely unbiased genome-wide approach in a phenotypically diverse sample will add to the diagnostic depth, explore the limits of short-read genome analysis, and establish novel candidate RED loci. METHODS: Here, we present a genome-wide analysis of repeat expansions conducted on 1018 genomes from the Undiagnosed Diseases Network. By leveraging 2 distinct bioinformatics tools, ExpansionHunter Denovo and STRling, we showed that repeat expansions can be accurately detected in short-read genomes. RESULTS: We demonstrated that a genotype-first approach can diagnose atypical cases of known REDs and provide valuable clinical insights. We present clinical details on participants with expansions in ATXN7, DMPK, FMR1, GLS, HTT, RFC1, AFF3, and MARCH6. Importantly, we highlight 2 cases of juvenile Huntington disease that were discovered through our analysis. Finally, we present a list of novel candidate short tandem repeats (TR) that could potentially be pathogenic if expanded. CONCLUSION: Importantly, our approach showcases the bioinformatic advancements in genome analysis for RED detection and highlights its practical applications.

Humans↗

PubMed2025

High throughput screening of eukaryotic release factor 1 variants to enhance noncanonical amino acid incorporation.

Noncanonical amino acids (ncAAs) enable diversification of protein functions, but the efficiency of genetic code expansion (GCE) in eukaryotes is hindered by competition between suppressor tRNAs and release factors. Prior work has identified eukaryotic release factor 1 (eRF1) mutants that improve ncAA incorporation, suggesting that screens for improved variants may lead to further enhancements. Here, we developed a high-throughput system to screen eRF1 mutants in Saccharomyces cerevisiae where eRF1 mutants are coexpressed on a plasmid alongside genomically encoded, wild-type eRF1. This strategy enabled recovery of live cells expressing eRF1 variants that enhance ncAA incorporation, even with mutants known to severely affect cell viability in the absence of WT eRF1 expression. We prepared and screened a million-member library of randomly mutated eRF1 variants for clones exhibiting improved ncAA integration phenotypes. Deep sequencing revealed a diverse set of enriched mutations across all three major domains of eRF1. Interestingly, several enriched mutations identified here are also found in naturally occurring eRF1 homologs from species that recode canonical stop codons. When eRF1 variants were combined with yeast knockout strains also known to enhance ncAA incorporation, this resulted in further improvements to efficiency, highlighting the complementarity of release factor engineering to other GCE enhancement strategies. This work demonstrates that high-throughput engineering of the eukaryotic translational apparatus is a powerful approach to identify previously unknown solutions for enhancing ncAA incorporation, with implications for elucidating and precisely manipulating the molecular functions of essential translational machinery.

Noncanonical amino acids↗

PubMed2024

Dual diagnosis of achondroplasia and mandibulofacial dysostosis with microcephaly.

BACKGROUND: Achondroplasia and mandibulofacial dysostosis with microcephaly (MFDM) are rare monogenic, dominant disorders, caused by gain-of-function fibroblast growth factor receptor 3 (FGFR3) gene variants and loss-of-function elongation factor Tu GTP binding domain-containing 2 (EFTUD2) gene variants, respectively. The coexistence of two distinct Mendelian disorders in a single individual is uncommon and challenges the traditional paradigm of a single genetic disorder explaining a patient's symptoms, opening new avenues for diagnosis and management. CASE PRESENTATION: We present a case of a female patient initially diagnosed with achondroplasia due to a maternally inherited pathogenic FGFR3 variant. She was referred to our genetic department due to her unusually small head circumference and short stature, which were both significantly below the expected range for achondroplasia. Additional features included distinctive facial characteristics, significant speech delay, conductive hearing loss, and epilepsy. Given the complexity of her phenotype, she was recruited to the DDD (Deciphering Developmental Disorders) study and the 100,000 Genomes project for further investigation. Subsequent identification of a complex EFTUD2 intragenic rearrangement confirmed an additional diagnosis of mandibulofacial dysostosis with microcephaly (MFDM). CONCLUSION: This report presents the first case of a dual molecular diagnosis of achondroplasia and mandibulofacial dysostosis with microcephaly in the same patient. This case underscores the complexity of genetic diagnoses and the potential for coexistence of multiple genetic syndromes in a single patient. This case expands our understanding of the molecular basis of dual Mendelian disorders and highlights the importance of considering the possibility of dual molecular diagnoses in patients with phenotypic features that are not fully accounted for by their primary diagnosis.

Humans↗

PubMed2025

Ophthalmic imaging as a measure of cardiovascular and neurological health: a multi-omic analysis of deep-learning derived phenotypes.

The eye is a recognised source of biomarkers for cardiovascular and neurodegenerative disease risk. Here, we characterise the breadth of these associations and identify biological axes that may mediate them. Using UK Biobank data, we developed a multi-omic analysis pipeline integrating physiological, radiomic, metabolomic, and genomic information. We trained adversarial autoencoders (Ret-AAE) to represent optical coherence tomography (OCT) images and colour fundus photographs as 256-dimensional embeddings. Ret-AAE derived embeddings were associated with a range of cardiovascular and neurodegenerative diseases, including ischaemic heart disease, cerebrovascular disease, Parkinson's disease, and dementia. Examining associations across diverse omics datasets, we provide evidence linking ophthalmic imaging features to neurological and cardiovascular anatomy and function, lipid metabolism, and gene sets associated with neurodegenerative pathology. Collectively, our findings demonstrate that ophthalmic features reflect complex, multisystem biological processes, and reinforce the role of the eye as a composite indicator of systemic health.

Journal Article↗

PubMed2026

A Novel Splice Variant in the COL1A1 Gene Leads to Exon 46 Skipping and Osteogenesis Imperfecta.

BACKGROUND: Osteogenesis imperfecta (OI) is a clinical and genetic disorder characterised by bone fragility, growth deficiency and skeletal deformity. Ninety per cent of OI cases are attributable to autosomal dominant variants in the COL1A1 and COL1A2 genes. METHODS: Candidate variants were identified and verified through trio whole-exome sequencing (trio-WES), copy number variation sequencing (CNV-seq) and Sanger sequencing. Minigene splicing assays were performed in HeLa and HEK293T cells with pcDNA3.1 and pcMINI-C vectors to investigate the function of the candidate variants. A systematic review of COL1A1 splicing variants and the corresponding genotype-phenotype spectrum was performed. RESULTS: Trio-WES revealed a novel heterozygous variant in the C-terminal region of the COL1A1 gene: NM_000088.4:c.3423+5G>A. Sanger sequencing confirmed the variant in both the proband (II-2) and her foetus (III-1) who were clinically suspected of having OI. The c.3423+5G>A variant causes complete skipping of Exon 46, as demonstrated by a minigene splicing assay. We retrieved 419 COL1A1 splicing variants from PubMed, excluded 15 without phenotypic data and 2 linked to Ehlers-Danlos syndrome and stratified the remaining 402 variants into three types on the basis of splice site location: (1) Variants at canonical splicing sites (77.8%, 313/402) mostly cause mild phenotypes, whereas a minority may be severe. (2) Intron variants in other locations, such as splice region variants (17.9%, 72/402), usually cause mild clinical phenotypes, and deep intronic splice variants (0.4%, 2/402) that may result in severe phenotypes. (3) Other variants (3.7%, 15/402), such as exon variants or fragment loss, are extremely rare. We also preliminarily discuss the mechanisms underlying phenotypic variability and the characteristics of C-terminal variants. CONCLUSIONS: This intron variant in COL1A1 was classified as likely pathogenic and was confirmed to disrupt COL1A1 expression. The summary analysis results also revealed a correlation among splicing variants, C-terminal region variants and disease, suggesting that variant location provides a useful framework for prognosis prediction.

Female↗

PubMed2026

Single-section multiplex spatial proteomics of immune microenvironments in kidney transplantation.

Characterizing kidney disease is challenged by marked cellular heterogeneity and limited tissue availability from renal biopsies. Conventional diagnostic workflows rely on multiple serial sections for parallel staining, increasing tissue consumption, sampling bias, and loss of spatial information, thereby constraining molecular characterization within intact tissue architecture. High-plex spatial proteomics may overcome these limitations by enabling comprehensive molecular profiling on a single section. Here, we present and evaluate a high-plex cyclic immunofluorescence imaging workflow (MACSima™, Miltenyi Biotec) applied to kidney transplant biopsies, including BK virus nephropathy (BKVN) and focal segmental glomerulosclerosis (FSGS), to characterize spatial immune organization with a focus on complement system components. Feasibility and subcellular resolution were first assessed in a lupus nephritis section, demonstrating compatibility with diagnostic immune panels and preservation of tissue morphology. A 48-marker multiplex panel interrogating immunity, oxidative stress, senescence, and fibrosis was then applied to BKVN samples, including paired pre- and post-treatment biopsies, revealing distinct proteomic patterns and dynamic changes following therapy. In FSGS, a glomerulus-focused panel identified spatially resolved innate and adaptive immune signatures, including complement-related patterns supporting exploratory analysis of glomerular immune architecture. Structural, nuclear, membrane, and phosphorylated signaling markers enabled precise delineation of renal compartments and assessment of cellular states such as proliferation, DNA damage, and pathway activation. The workflow also supported detection of extracellular vesicles in cultured renal cells, highlighting its versatility. Overall, this approach provides a robust, tissue-sparing platform for integrated spatial and molecular profiling of renal biopsies, reducing sampling bias while enabling discovery-level phenotyping from a single section. This unified strategy is particularly suited to kidney transplantation, where diagnosis, therapeutic decision-making, and longitudinal monitoring are closely interconnected.

Kidney Transplantation↗

PubMed2025

Scalable Deep Learning of Histology Images Reveals Genetic and Phenotypic Determinants of Adipocyte Hypertrophy.

BACKGROUND: White adipose tissue dysfunction has emerged as a critical factor in cardiometabolic disease development, yet the cellular microstructure and genetic architecture of adipocyte morphology remain poorly explored. METHODS: We introduce Adipocyte U-Net 2.0, an advanced deep learning method for the semantic segmentation of adipose tissue histology, enabling analysis of over 27 million adipocytes from 2,667 individuals. FINDINGS: Our approach revealed that adipocyte hypertrophy associates with metabolic dysfunction, including increased fasting glucose, glycated hemoglobin, leptin, and triglycerides, with decreased adiponectin and HDL cholesterol levels. Through the largest genome-wide association study of adipocyte size to date (NSubcutaneous = 2,066, NVisceral = 1,878), we identified four genome-wide significant loci: two in sex-combined analysis (rs73184721 in NAALADL2 and rs200047724 in NRXN3) and two female-specific variants (rs140503338 and rs11656704 in ULK2). Notably, these genetic associations showed congruent relationships with cardiometabolic traits, suggesting shared biological mechanisms. INTERPRETATION: Our findings demonstrate the utility of deep learning for adipocyte phenotyping at scale and provide novel insights into the genetic basis of adipocyte morphology and its relationship to metabolic disease.

Journal Article↗

PubMed2022

TTC5 syndrome: Clinical and molecular spectrum of a severe and recognizable condition.

Biallelic mutations in the TTC5 gene have been associated with autosomal recessive intellectual disability (ARID) and subsequently with an ID syndrome including severe speech impairment, cerebral atrophy, and hypotonia as clinical cornerstones. A TTC5 role in IDs has been proposed based on the physical interaction of TTC5 with p300, and possibly reducing p300 co-activator complex activity, similarly to what was observed in Menke-Hennekam 1 and 2 patients (MKHK1 and 2) carrying, respectively, mutations in exon 30 and 31 of CREBBP and EP300, which code for the TTC5-binding region. Recently, TTC5-related brain malformation has been linked to tubulinopathies due to the function of TTC5 in tubulins' dynamics. We reported seven new patients with novel or recurrent TTC5 variants. The deep characterization of the molecular and phenotypic spectrum confirmed TTC5-related disorder as a recognizable, very severe neurodevelopmental syndrome. In addition, other relevant clinical aspects, including a severe pre- and postnatal growth retardation, cryptorchidism, and epilepsy, have emerged from the reversal phenotype approach and the review of already published TTC5 cases. Microcephaly and facial dysmorphism resulted in being less variable than that documented before. The TTC5 clinical features have been compared with MKHK1 published cases in the hypothesis that clinical overlap in some characteristics of the two conditions was related to the common p300 molecular pathway.

Exons↗

PubMed2026

Spectral Transforms as a Tool to Optimize Digital Phenotyping in Biological Images.

Modern livestock breeding has mastered genotyping. Genome-wide association studies, genomic selection, and SNP arrays enable genetic merit prediction at lower cost. However, phenotyping remains the bottleneck, as manual measurement is slow, expensive, subjective, and unable to capture spatial or temporal trait organization. Digital phenotyping via artificial intelligence could resolve this, but deep learning requires thousands of labelled examples, impractical when phenotyping cost itself limits datasets to hundreds of individuals. This creates a paradox: AI could accelerate phenotyping but requires large numbers of samples to train the models. Here, we demonstrate that integrating computer vision with machine learning offers sample-efficient digital phenotyping using eggshell colour as a model system. Rather than learning features from scratch (deep learning), we engineer physically motivated features via Wavelet transforms that decompose images into multi-scale spatial components. Wavelet features captured 14.2 percentage points more variance (R2 = 0.976 vs. 0.834, p < 0.001) than standard colorimetry, with 50% better sample efficiency (achieving at n = 60 what colorimetry required n = 120). Variance decomposition revealed 77% of discriminative capacity derives from spatial patterns (bands, spots, gradients) invisible to scalar averages. Additionally, we identified "cryptic phenotypes" (3.3%) where spatial patterns contradicted average colour, cases where colorimeters failed but Wavelets succeeded. The underlying principle-that spatial decomposition can recover organizational information lost by scalar averaging-may be applicable to other traits with spatial or temporal structure, such as marbling, dermatitis, or pigmentation rhythms, although whether comparable performance gains would be observed remains to be tested empirically. Hence, for breeding programs implementing genomic selection, computer vision-based digital phenotyping captures complex trait variation without massive training datasets, addressing the bottleneck that increasingly limits genetic progress as genotyping becomes trivial.

Wavelet transform↗

PubMed2026

An Integrative Morphological and Genomic Analysis With a Refined Fluorescence In Situ Hybridization (FISH) Threshold and Novel Kinase Fusions in a Large Asian Cohort of Spitzoid Neoplasms.

Differentiating atypical Spitz tumors (ASTs) from true Spitz melanomas (SMs) and conventional melanomas with spitzoid features (MSFs) remains a formidable diagnostic challenge. Because current molecular epidemiological data are overwhelmingly derived from Caucasian cohorts, the genomic landscape of Asian populations remains largely unexplored. To elucidate the molecular progression landscape and refine the diagnostic criteria, we performed a comprehensive multimodal analysis-integrating histomorphology, immunohistochemistry, multiprobe fluorescence in situ hybridization (FISH), and targeted RNA/DNA-based next-generation sequencing (NGS)-on a cohort of 140 spitzoid neoplasms. This cohort, comprising 126 ASTs, 8 SMs, and 6 MSFs, represents the largest Asian cohort to date. Malignant phenotype strongly correlated with lesional asymmetry, deep atypical mitoses, a sheet-like growth pattern, diffuse preferentially expressed antigen of melanoma positivity, and significant loss of p16 expression (64.3% in SM/MSF vs 9.5% in ASTs; P < .0001). Building upon the established melanoma FISH criteria, we optimized a prognostic threshold of ≥2 FISH abnormalities specifically tailored for spitzoid neoplasms. We demonstrated that isolated single chromosomal aberrations (particularly MYB loss) are relatively stable events that are frequent in indolent ASTs, whereas our refined ≥2 threshold yielded 100% sensitivity and 92.5% specificity for predicting regional lymph node metastasis/local recurrence. Molecularly, NGS identified mutually exclusive initiating driver alterations (comprising kinase fusions and HRAS mutations) in 89.9% of true Spitz neoplasms, a remarkably high prevalence suggesting a distinct genetic background in Asian populations. We also characterized 5 entirely novel kinase fusions (ZNF24::ROS1, PCBP1::ROS1, NUMA1::RET, CBWD1::ALK, and TPR::NTRK1). Furthermore, NGS definitively segregated true Spitz neoplasms from morphological mimics (MSF), which lacked fusions and were driven by canonical genomic alterations of the conventional melanoma pathway. Integrating these genomic landscapes validated a stepwise progression model. Although isolated kinase fusions drove indolent ASTs, malignant SM invariably harbored concurrent pathogenic secondary alterations, demonstrating a profound reliance on CDKN2A/B, TP53, and CDK4 aberrations. Ultimately, we propose an integrated diagnostic algorithm combining morphological evaluation, the refined FISH threshold, and comprehensive NGS profiling, providing a precise, evidence-based framework for pathway classification and clinical management of spitzoid neoplasms.

fluorescence in situ hybridization↗

PubMed2026

Deep soil layers show the most pronounced genetic variation in wheat root length.

Wheat is one of the most important cereals worldwide, yet significant gaps remain in our understanding of genetic variability in root traits, especially those associated with deeper rooting that support resource acquisition in challenging environments. Root traits are typically controlled by many genes with small effects and often display low heritability. Our aim was to develop a statistical approach to analyse root variation across soil depth and to determine where genetic differences in root intensity are most detectable. An experiment was conducted at the RadiMax semi-field facility, which is designed to measure deep root systems. Five years of phenotypic data recorded each June produced observations from 1500 rows. Each row captured root intensity across the soil profile from 0.6 m to 2.6 m, enabling detailed analysis of vertical root distribution. Across the five years, 513 winter wheat cultivars were grown in the facility, and among those 409 were genotyped with SNP chips. Depth-resolved regression models with random coefficients were used to quantify genetic and non-genetic variation in root intensity across soil depths, while accounting for spatial variation between rows. Random variation within rows was found to be constant across depths. The models showed that genetic variance for cumulative root intensity increased substantially below 1.1 m, with the deepest layers exhibiting the largest differences between wheat lines. Narrow-sense heritability of point measurements peaked at approximately 1.5 m ([Formula: see text]).

Genetic variability↗