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At least 19 recordsLinked to original sources

PubMed2026

Complementary vertebrate Wac models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome.

Monogenic syndromes are associated with neurodevelopmental changes that result in cognitive impairments and neurobehavioral phenotypes, including autism and seizures. Limited studies and resources are available to make meaningful headway into the underlying molecular mechanisms that result in these symptoms. One such example is DeSanto-Shinawi Syndrome (DESSH), a rare disorder caused by pathogenic variants in the WAC gene. Individuals with DESSH syndrome exhibit a recognizable craniofacial gestalt, developmental delay/intellectual disability, neurobehavioral symptoms that include autism, ADHD, behavioral difficulties, and seizures. However, no thorough studies from a vertebrate model exist to understand how these changes occur. To overcome this, we developed both murine and zebrafish Wac/wac deletion mutants and studied whether their phenotypes recapitulate those described in individuals with DESSH syndrome. We first show that the two Wac models exhibit craniofacial and behavioral changes, reminiscent of abnormalities found in DESSH syndrome. In addition, each model revealed impacts on GABAergic neurons and further studies showed that the mouse mutants are susceptible to seizures, changes in brain volumes that are different between sexes and relevant behaviors. Finally, we uncovered transcriptional impacts of Wac loss-of-function in mice that will pave the way for future molecular studies into DESSH. These studies present two new vertebrate models that begin to uncover biological underpinnings of DESSH syndrome and elucidate the biology of Wac.

Animals↗

PubMed2025

Complimentary vertebrate Wac models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome.

Monogenic syndromes are associated with neurodevelopmental changes that result in cognitive impairments, neurobehavioral phenotypes including autism and seizures. Limited studies and resources are available to make meaningful headway into the underlying molecular mechanisms that result in these symptoms. One such example is DeSanto-Shinawi Syndrome (DESSH), a rare disorder caused by pathogenic variants in the WAC gene. Individuals with DESSH syndrome exhibit a recognizable craniofacial gestalt, developmental delay/intellectual disability, neurobehavioral symptoms that include autism, ADHD, behavioral difficulties and seizures. However, no thorough studies from a vertebrate model exist to understand how these changes occur. To overcome this, we developed both murine and zebrafish Wac/wac deletion mutants and studied whether their phenotypes recapitulate those described in individuals with DESSH syndrome. We first show that the two Wac models exhibit craniofacial and behavioral changes, reminiscent of abnormalities found in DESSH syndrome. In addition, each model revealed impacts to GABAergic neurons and further studies showed that the mouse mutants are susceptible to seizures, changes in brain volumes that are different between sexes and relevant behaviors. Finally, we uncovered transcriptional impacts of Wac loss of function in mice that will pave the way for future molecular studies into DESSH. These studies present two new animals that begin to uncover some biological underpinnings of DESSH syndrome and elucidate the biology of Wac.

Journal Article↗

PubMed2026

Bridging the gap between genomic, phenotypic, and modeling data in picophytoplankton-virus interactions.

We present a comprehensive synthesis of recent developments in methodological approaches to combine experimental and genomic studies of picophytoplankton-virus interactions. This synthesis will not only enhance our understanding of these relationships but also stimulate new hypotheses for the interpretation of metagenomic data and enrich current modeling efforts. Marine picophytoplankton are significant primary producers despite their low contribution to biomass. Pan-oceanic metagenomic studies have revealed an astounding genetic diversity within these communities and the astronomical abundance of viruses that infect them. Despite recent advances in understanding the genetic and genomic diversity of picophytoplankton and viruses, the interpretation of these data relies heavily on ecological and physiological insights. Specifically, our understanding of the mechanisms and dynamics governing host-virus interactions is limited because the current wealth of sequence data has not yet been matched with corresponding life-history traits. Linking phenotypes to genotypes in picophytoplankton-virus systems is also essential for accurately modeling their interactions and their impact on the global carbon cycle. Ultimately, the synergy of the three domains allows for a mechanistic interpretation of environmental data.

Chlorophyta↗

PubMed2026

Genomic insights into stroke recovery: cross-phenotype associations.

Stroke is a major cause of long-term disability with variable recovery. While clinical factors such as initial severity play a role, genetic factors are increasingly recognized as important contributors to stroke recovery. Genotype studies are generally focused on a single post-stroke behavioural domain, but some genes might relate to broad mechanisms of plasticity. This study therefore aimed to identify cross-phenotypic genetic variants associated across two or more stroke recovery domains. DNA from Stroke, Stress, Rehabilitation, and Genetics study participants was genotyped, resulting in 9 814 610 variants. In order to examine cross-phenotypic results, we first conducted genome-wide association studies on the six recovery domains: motor (grip force), cognition (Telephone Montreal Cognitive Assessment), depression (Patient Health Questionnaire-8), stress (Primary Care Post-Traumatic Stress Disorder Screen), functional status (Stroke Impact Scale-Activities of Daily Living), and disability (modified Rankin Scale 0-2 versus 3-6), some of which were tested longitudinally, yielding nine phenotypes. Models were adjusted for age, sex, initial severity (NIH Stroke Scale score), and ancestry. Cross-phenotype associations were identified by evaluating single nucleotide polymorphisms (SNPs) associated (P < 5e-5) with multiple phenotypes. To determine how these genetic variants may relate to biological mechanisms of recovery, we conducted gene enrichment analyses. Participants (n = 565, 59% male) had mild-moderate initial stroke severity (median acute NIH Stroke Scale score = 4). After accounting for the correlation structure among the nine phenotypes, we observed 319 cross-phenotypic SNPs, 3.45 times the expected number. Five of the cross-phenotypic SNPs were linked to genes relevant to neural development, function and plasticity, e.g. ERICH1 (rs11778883-C), FOX3 (rs55726768-G), LIFR-AS1 (rs76401391-T), RPS6KA2 (rs113518460-C) and TUBGCP2 (rs147150392-C), as were enrichments in RAB5-EEA1, CTNNA1-CTNNB1, CIN85-SH3GL2 and ELMO1-DOCK2 complexes. Multiple gene enrichments were found, e.g. Stroke Impact Scale-Activities of Daily Living and Patient Health Questionnaire 8 at 3 months were enriched for CREB phosphorylation, which is important for long-term potentiation. We identified cross-phenotypic SNPs associated with multiple behavioural domains of stroke recovery. Some of these genes encode, or regulate, druggable proteins. These genetic factors are not well captured by clinical or neuroimaging assessments and so provide a unique window into stroke recovery. These findings, if validated, suggest that some genes may be broadly important to stroke recovery.

GWAS↗

PubMed2023

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.

Preprint↗

PubMed2026

Unraveling the Clinical Spectrum of DNASE1L3 Deficiency: Insights from Case Series and Systematic Literature Review.

BACKGROUND: DNASE1L3 deficiency is a rare monogenic cause of lupus and lupus-like autoimmunity resulting from impaired extracellular DNA clearance and sustained immune activation. Although most reported patients present with early-onset systemic lupus erythematosus (SLE), emerging evidence suggests broader phenotypic variability, including vasculitic and overlap manifestations. Whether these presentations represent distinct clinical entities or a continuum of DNASE1L3-associated immune dysregulation remains unclear. We aimed to define the clinical spectrum of DNASE1L3 deficiency and examine the relationship between recurrent pathogenic variants and disease severity. METHODS: We conducted a combined pediatric case series and systematic literature review. Four children with genetically confirmed biallelic DNASE1L3 variants followed at a tertiary pediatric rheumatology centre were retrospectively analysed for clinical, immunological, genetic, treatment, and outcome data. In parallel, a systematic search of PubMed/MEDLINE, Scopus, and Web of Science identified previously reported patients with confirmed biallelic pathogenic or likely pathogenic DNASE1L3 variants and extractable patient-level clinical data. To facilitate cross-case comparison, we applied an exploratory three-tier descriptive framework reflecting increasing disease severity: vasculitic or organ-limited disease (G1), systemic lupus or overlap phenotypes without irreversible organ damage (G2), and severe systemic organ-damaging disease (G3). The assigned grades were descriptive rather than permanent categories, as some patients may meet the criteria for a higher grade if broader systemic manifestations or irreversible organ damage develop during follow-up. FINDINGS: Fifteen reports provided extractable patient-level data, corresponding to 45 unique previously reported patients after accounting for known or probable overlapping reports. Combined with four patients from our centre, the analysis included 49 genetically confirmed individuals. SLE-dominant disease was the most frequent phenotype (27 [60%] of 45), followed by hypocomplementaemic urticarial vasculitis/HUVS-dominant disease (10 [22.2%]) and overlap phenotypes (8 [17.8%]). Renal involvement was reported in 30 (66.7%) of 45 patients, and disease onset occurred by age 3 years in 20 (44.4%). Persistent hypocomplementemia affecting C3 and C4 was frequently reported across the spectrum. Recurrent DNASE1L3 variants were observed across multiple phenotypic and severity grades. Variants such as p.Asn191Ser and p.Thr97Ilefs*2 occurred in patients spanning organ-limited vasculitic disease, lupus overlap phenotypes, and severe multisystem lupus with major organ involvement. CONCLUSION: DNASE1L3 deficiency was associated with a broad clinical spectrum of immune-mediated disease rather than a single clinicopathological entity. The occurrence of identical pathogenic variants across distinct phenotypic and severity states argues against a simple genotype-phenotype model and suggests that additional modifiers influence disease expression.

Humans↗

PubMed2024

Massively parallel approaches for characterizing noncoding functional variation in human evolution.

The genetic differences underlying unique phenotypes in humans compared to our closest primate relatives have long remained a mystery. Similarly, the genetic basis of adaptations between human groups during our expansion across the globe is poorly characterized. Uncovering the downstream phenotypic consequences of these genetic variants has been difficult, as a substantial portion lies in noncoding regions, such as cis-regulatory elements (CREs). Here, we review recent high-throughput approaches to measure the functions of CREs and the impact of variation within them. CRISPR screens can directly perturb CREs in the genome to understand downstream impacts on gene expression and phenotypes, while massively parallel reporter assays can decipher the regulatory impact of sequence variants. Machine learning has begun to be able to predict regulatory function from sequence alone, further scaling our ability to characterize genome function. Applying these tools across diverse phenotypes, model systems, and ancestries is beginning to revolutionize our understanding of noncoding variation underlying human evolution.

Humans↗

PubMed2026

Functional role and regulatory network of miR-22-3p in chicken hepatic lipid metabolism.

Although microRNA-22-3p (miR-22-3p) is abundantly expressed in the avian liver, its epigenetic role in lipid homeostasis remains largely uncharacterized. To elucidate its in vivo function, 14-day-old female Qingyuan Partridge chickens were intravenously injected with lentiviral vectors to establish miR-22-3p overexpression and knockdown models. Phenotypic analysis demonstrated that miR-22-3p knockdown significantly elevated hepatic triglyceride (TG) levels (p < 0.05) and drove marked steatosis, whereas its overexpression reduced TG content. Transcriptome sequencing (RNA-Seq) revealed profound metabolic remodeling, identifying 23 core lipid-associated genes (e.g., ELOVL6, FADS2, ACSBG2, and PTGIS) heavily enriched in steroid biosynthesis, fatty acid metabolism, and elongation pathways. In conclusion, miR-22-3p functions as a bidirectional epigenetic rheostat that negatively regulates hepatic lipid deposition by orchestrating a multilayered polygenic network, providing novel molecular targets for mitigating avian metabolic disorders and optimizing production traits in indigenous poultry breeds.

Animals↗

PubMed2025

Simple scaling laws control the genetic architectures of human complex traits.

Genome-wide association studies have revealed that the genetic architectures of complex traits vary widely, including in terms of the numbers, effect sizes, and allele frequencies of significant hits. However, at present we lack a principled way of understanding the similarities and differences among traits. Here, we describe a probabilistic model that combines the effects of mutation, drift, and stabilizing selection at individual sites with a genome-scale model of phenotypic variation. In this model, the architecture of a trait arises from the distribution of selection coefficients of mutations and from two scaling parameters. We fit this model for 95 highly polygenic quantitative traits of different kinds from the UK Biobank. Notably, we infer that all these traits have fairly similar, though not identical, distributions of selection coefficients. This similarity suggests that differences in architectures of highly polygenic traits arise mainly from the two scaling parameters: the mutational target size and heritability per site, which vary by orders of magnitude among traits. When these two scale factors are accounted for, we find that the architectures of all 95 traits are very similar.

Humans↗

PubMed2023

Retinal Phenotyping of a Murine Model of Lafora Disease.

Lafora disease (LD) is a progressive neurologic disorder caused by biallelic pathogenic variants in EPM2A or EPM2B, leading to tissue accumulation of polyglucosan aggregates termed Lafora bodies (LBs). This study aimed to characterize the retinal phenotype in Epm2a-/- mice by examining knockout (KO; Epm2a-/-) and control (WT) littermates at two time points (10 and 14 months, respectively). In vivo exams included electroretinogram (ERG) testing, optical coherence tomography (OCT) and retinal photography. Ex vivo retinal testing included Periodic acid Schiff Diastase (PASD) staining, followed by imaging to assess and quantify LB deposition. There was no significant difference in any dark-adapted or light-adapted ERG parameters between KO and WT mice. The total retinal thickness was comparable between the groups and the retinal appearance was normal in both groups. On PASD staining, LBs were observed in KO mice within the inner and outer plexiform layers and in the inner nuclear layer. The average number of LBs within the inner plexiform layer in KO mice were 1743 ± 533 and 2615 ± 915 per mm2, at 10 and 14 months, respectively. This is the first study to characterize the retinal phenotype in an Epm2a-/- mouse model, demonstrating significant LB deposition in the bipolar cell nuclear layer and its synapses. This finding may be used to monitor the efficacy of experimental treatments in mouse models.

Mice↗

PubMed1977

Polymorphism at two loci through selection for linear metric deviation.

A model of phenotypic stabilising selection in which the fitness of an individual depends solely on its phenotype, and not directly on its genetic constitution, is explored algebraically for a system of two linked loci of unequal effect. It is found that selection for metric deviation gives rise to polymorphic gametefrequency equilibria for a variety of fitness regimes. Stability of non-trivial equilibria occurs for a wide range of parameter sets. Stability is facilitated by close linkage and inequality between gene effects. It is suggested that, in general genetic variation may be maintained under stabilising selection when the fitness of double heterozygotes exceeds that of the phenotypically intermediate homozygotes.

Animals↗

PubMed2025

Tulp3 quantitative alleles titrate requirements for viability, brain development, and kidney homeostasis but do not suppress Zfp423 mutations in mice.

Tubby-like protein 3 (TULP3) regulates receptor trafficking in primary cilia and antagonizes SHH signaling. Tulp3 knockout mice are embryonic lethal with developmental abnormalities in multiple organs, while tissue-specific knockouts and viable missense alleles cause polycystic kidney disease. Human patients with TULP3 mutations present with variable, but often multi-organ fibrotic disease. We previously showed that mouse and human Tulp3 expression is negatively regulated by ZNF423, which is required for SHH sensitivity in some progenitor cell models. The level of TULP3 function required to prevent mutant phenotypes has not been known. Here we report a Tulp3 quantitative allelic series, designed by targeting the polypyrimidine tract 5' to the splice acceptor of a critical exon, that shows distinct dose-response effects on viability, brain overgrowth, weight gain, and cystic kidney disease. We find limited evidence for genetic interaction with Zfp423 null or hypomorphic mutations. Together, these results establish an approach to developing quantitative allelic series by exon exclusion, rank-order dose-sensitivity of Tulp3 phenotypes, and model thresholds for TULP3 function to prevent severe outcomes.

Journal Article↗

PubMed2026

A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants.

Only a small fraction of annotated plant genes possess experimentally validated associations with specific phenotypes. Phenotype-associated genes have distinct structural, molecular, and evolutionary characteristics compared with nonvalidated gene models. Here, we develop a simple classifier that uses sequence and evolutionary features, which can be generated for any species with an annotated reference genome assembly, to accurately distinguish phenotype-associated genes from both the overall population of annotated gene models and a specific set of genes identified as being tolerant of premature stop mutations. A model trained solely on genes from maize (Zea mays) identifies and prioritizes rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) genes that are highly enriched in genes with experimentally validated links to phenotypes in both of these evolutionarily distant species. Gene models predicted to have a higher probability of being linked to phenotypes display patterns consistent with known biological properties of phenotype-associated genes. Notably, the sets of genes predicted to have a high probability of being linked to phenotype variation do not consist exclusively of well-characterized gene families but included many uncharacterized gene families carrying domains of unknown function. The quantitative scores generated by this model offer a valuable resource for prioritizing and exploring the vast number of uncharacterized gene models in plants, reducing the risk of failure in future reverse genetic efforts and potentially accelerating gene discovery and functional annotation in crops.

Phenotype↗

PubMed2026

Disruption of GAD1 protein architecture by a novel missense variant in a consanguineous family with autosomal recessive intellectual disability.

BACKGROUND: Intellectual disability represents a heterogeneous group of neurodevelopmental disorders marked by significant impairments in intellectual functioning and adaptive behavior. Among the various causes, genetic factors play a major role, with autosomal recessive intellectual disability (ARID) constituting a genetically diverse subgroup. ARID is prevalent in consanguineous families and arises from homozygous mutations that disrupt critical genes involved in brain development and function. OBJECTIVE: This study aimed to identify disease-causing genetic variants responsible for ARID in a consanguineous Pakistani family and to evaluate the structural and functional impact of a novel variant identified in GAD1 through protein modeling. METHODS: A consanguineous family affected with intellectual disability was enrolled. Whole-exome sequencing was performed on an affected individual, followed by bioinformatics analysis including alignment to the GRCh38 reference genome, variant calling, and annotation. Variants were filtered based on rarity, predicted functional impact, and autosomal recessive inheritance pattern. Candidate variants were validated and assessed by Sanger sequencing and segregation analysis. Protein modeling was performed to evaluate the structural impact of the identified variant. RESULTS: A novel homozygous missense variant NM_000817:c.1700G>A;p.Arg567Gln in GAD1 was identified. Segregation analysis confirmed co-segregation of the variant with the affected phenotype. Protein modeling suggested that the variant may disrupt GAD1 enzymatic function involved in gamma-aminobutyric acid synthesis. CONCLUSION: This study emphasizes the significance of genetic investigation in familial cases and the crucial role that GAD1 mutations play in neurodevelopmental disorders with intellectual disability. The results advance the knowledge of molecular causes of ARID and broaden the mutational range.

Pakistani↗

PubMed2017

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies↗

PubMed2026

MicroRNA-mRNA Networks in Skeletal Muscle of Tailored Pig Models for Dystrophinopathies.

BACKGROUND: Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) are X-linked dystrophinopathies caused by mutations in the dystrophin (DMD) gene. A common DMD-causing mutation in humans is exon 52 deletion (DMDΔ52), which disrupts the reading frame and abolishes dystrophin expression. Therapeutic skipping of exon 51 or 53 can restore the reading frame, producing a truncated but functional protein and generating a BMD-like phenotype. Porcine models recapitulating DMDΔ52 (DMD) and DMDΔ51-52 (BMD-like) were used to identify molecular differences and condition-specific miRNA-mRNA networks. METHODS: Skeletal muscle (triceps brachii) from four DMD, four BMD, and five wild-type (WT) pigs at 3.5 months of age underwent stranded total RNA-seq and small RNA-seq. Differentially expressed mRNAs (|log2FC| ≥ 1, adj. p ≤ 0.05) and miRNAs (adj. p ≤ 0.05) were identified with DESeq2. miRNA-mRNA networks were constructed using RNAhybrid predictions (MFE < -25 kcal/mol, seed pairing) filtered by inverse Pearson correlation. RESULTS: Compared with WT, DMD muscle exhibited 1440 upregulated and 487 downregulated genes, characterized by strong repression of structural, contractile, calcium-handling and metabolic genes (e.g., MYBPC2, MYL3, MYLK2, CACNA2D3, CACNA2D4) and marked upregulation of inflammatory mediators and innate immune receptors (e.g., IL6, IL18, IL1R1, CCR1/2/5, TLR1/2/4/7/9). In contrast, BMD muscle showed partial restoration of these pathways and clustered closer to WT in global expression profiles. Distinct miRNA signatures were observed between DMD and BMD. Differential expression analysis identified 22 upregulated and 12 downregulated miRNAs in DMD versus WT and 36 upregulated and 21 downregulated miRNAs in BMD versus WT. Integration of miRNA and mRNA data yielded extensive regulatory networks (1013 unique pairs for upregulated miRNAs in DMD; 2679 pairs for downregulated miRNAs in BMD). Two condition-specific miRNAs emerged as strong biomarker candidates: ssc-miR-296-3p (upregulated exclusively in DMD, targeting 228 genes enriched in muscle structure and fatty acid metabolism) and ssc-miR-423-5p (elevated specifically in BMD, targeting 67 genes involved in calcium signalling and tissue development). Several dysregulated miRNAs, including miR-199a-5p and miR-199b, overlapped with those reported in human DMD and other muscular dystrophies. CONCLUSIONS: Exon 51 skipping in the DMDΔ52 background partially restores key transcriptional programmes in skeletal muscle but does not fully normalize them to WT patterns. The identification of condition-specific miRNAs highlights post-transcriptional regulatory differences between DMD and BMD, positioning them as promising biomarkers and therapeutic targets. These findings underscore the translational value of porcine dystrophinopathy models for mechanistic studies and preclinical evaluation of RNA-targeted interventions.

Animals↗

PubMed2026

Detection of antibiotic heteroresistance in clinical microbiology: current and emerging methodologies.

BACKGROUND: Antibiotic heteroresistance (HR) is characterised by the coexistence of susceptible and resistant subpopulations within an apparently isogenic bacterial isolate. Because routine antimicrobial susceptibility testing (AST) primarily assesses the dominant population, HR may escape detection, potentially leading to discrepancies between laboratory susceptibility categorisation and the underlying bacterial population structure. OBJECTIVES: To provide a critical and practice-oriented evaluation of current and emerging methodologies for HR detection and to discuss their strengths, limitations, and potential for clinical implementation. SOURCES: Narrative review based on PubMed searches, complemented by screening of key reference lists and relevant EUCAST and CLSI documents. Peer-reviewed literature was prioritised. CONTENT: Phenotypic approaches, particularly population analysis profiling, remain the reference method for HR definition, but their labour-intensive workflows, long turnaround times, and limited standardisation restrict routine implementation. Alternative strategies, including modified AST assays, metabolic assays, and single-cell platforms, offer gains in speed or throughput but require broader validation. Molecular approaches such as quantitative PCR, droplet digital PCR, targeted deep sequencing, and whole-genome sequencing improve detection of minority resistance determinants. Emerging computational frameworks, including machine learning models integrating phenotypic and genomic data, represent a promising frontier for scalable HR prediction. IMPLICATIONS: Available evidence supports the clinical relevance of HR, although its association with adverse outcomes varies across bacterial species and antibiotic classes. Harmonised methodologies and clinically validated interpretive criteria are needed to support integration of HR assessment into routine diagnostics. Prospective multicentre studies and further standardisation, including engagement with EUCAST and CLSI, will be important to advance clinical implementation.

Antimicrobial resistance↗

PubMed2026

Hologenomic interactions promote the higher-order evolvability of phenotypic complexity.

Current models for evolvability and complexity generally focus on mutational and regulatory processes in the host genome alone, limiting their ability to explain the origin, inheritance, and dynamics of many phenotypes. We describe a framework treating multigenome interactions in the holobiont as a central process that impacts the genotype-phenotype map, expanding the dimensionality of mechanisms producing heritable variation, generating novel traits, and exploring adaptive trajectories. These mechanisms can promote both complex phenotypic innovation and evolutionary systems drift. Many evolutionary pathways and novelties cannot be fully understood from host data alone but require consideration of hologenomic targets of selection. We outline hypotheses and methods to quantify and evaluate their impacts as a fundamental macroevolutionary process.

cellular innovation↗