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Results for “Partially Identified Models”

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At least 19 recordsLinked to original sources

PubMed2025

Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.

Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, often untestable assumptions that can bias inference. Experimental measurements of load (e.g., qPCR, flow cytometry) offer a solution, but remain costly and uncommon. A recent high-profile study proposed that machine learning could bypass this limitation by predicting microbial load from sequencing data alone. To evaluate this claim, we assembled mutt, the largest public database of paired sequencing and load measurements, spanning 35 studies and over 15,000 samples. Using mutt, we show that published machine learning models fail to generalize: on average they perform worse than a naive baseline that always predicted the training set mean. These failures stem from covariate shift-limited shared taxa between studies, differences in community composition, and differences in preprocessing pipelines-that silently derail model inputs. In contrast, Bayesian partially identified models do not attempt to impute microbial load, but instead propagate scale uncertainty through downstream analyses. Across 30 benchmark datasets, Bayesian partially identified models consistently outperformed normalization and machine learning approaches, providing a principled and reproducible foundation for microbiome inference.

16S rRNA-seq↗

PubMed2026

Murine metabolic HFpEF is associated with altered mitochondrial substrate handling and S-nitrosylation remodeling.

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous condition with incompletely defined myocardial mechanisms. Here, using a two-hit murine model of cardiometabolic HFpEF induced by high-fat diet and endothelial nitric oxide synthase inhibition, we define a mitochondrial metabolic phenotype characterized by altered substrate handling, redox stress, and S-nitrosylation remodeling. While global proteomic changes were modest, metabolomic profiling revealed selective remodeling of tricarboxylic acid cycle intermediates, increased dicarboxylic acids, and altered redox-associated metabolites, consistent with mitochondrial metabolic and redox imbalance in this experimental setting. S-nitrosylation proteomics demonstrated a highly organized and bidirectional remodeling pattern affecting proteins involved in fatty acid/lipid metabolism, carbohydrate metabolism, mitochondrial energy metabolism, amino acid and organic acid metabolism, nucleotide/co-factor metabolism, and redox defense. Stable isotope tracing showed reduced glucose-derived and increased palmitate-derived acetyl-CoA in HFpEF, whereas Na-βHB reduced palmitate contribution and increased βHB-derived acetyl-CoA without restoring glucose contribution, indicating substrate redistribution and preserved ketone oxidation. Na-βHB supplementation increased oligomycin-sensitive respiration in freshly prepared left ventricular tissue, partially normalized selected TCA-cycle intermediates, reduced mitochondrial ROS and the NADH/NAD+ ratio, restored the GSH/GSSG ratio, and improved diastolic function without altering ejection fraction. Together, these findings define a redox-sensitive mitochondrial metabolic state in the HFD/l-NAME model and identify ketone supplementation as a partial metabolic rescue strategy in this context. At the same time, these findings highlight an important limitation of the murine HFD/l-NAME model, which should be interpreted as an experimental system for studying high-fat-induced cardiometabolic stress rather than as a metabolic equivalent of human HFpEF.

Animals↗

PubMed2026

Mechanisms by which carbamoylated high-density lipoprotein (C-HDL) promotes calcific aortic valve disease and exploration of potential targeted therapies.

Calcific aortic valve disease (CAVD) is a progressive fibrocalcific illness for which no effective pharmaceutical treatment exists. This study investigated whether carbamoylated high-density lipoprotein (C-HDL), a defective type of HDL that can develop during inflammation, contributes to CAVD progression and the involved molecular pathways. Male ApoE-/- mice were divided into three groups: CAVD model, cyanate-treated, and inhibitor, and analyzed after 12 weeks. C57BL/6 mice on a regular diet served as blank controls. Serum paraoxonase-1 (PON1), aortic valve calcification, cluster of differentiation 31 (CD31), phosphorylated nuclear factor kappa B p65 (p-p65), NOTCH receptor 1 (NOTCH1), and runt-related transcription factor 2 (RUNX2) were evaluated. In parallel, using RNA sequencing (RNA-seq), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, protein-protein interaction (PPI) network analysis, and quantitative real-time polymerase chain reaction. Cyanate treatment reduced serum PON1 levels, increased von Kossa-positive calcium deposition, and raised CD31, p-p65, NOTCH1, and RUNX2 levels compared with the model group, but Gly partially corrected these effects. Transcriptomic research identified 270 C-HDL-associated differentially expressed genes (DEGs) enriched in pathways associated with inflammatory signaling and NF-κB activity. Five potential hub genes (BIRC6, PIK3R1, ATM, IFIH1, and DDX58) were discovered and verified using qRT-PCR. These data show that C-HDL may accelerate CAVD by disrupting valve endothelial homeostasis and stimulating inflammatory signaling, and they identify potential molecular targets for future functional validation.

bioinformatics↗

PubMed2026

Spindle Assembly Checkpoint Competency Determines Sensitivity to KIF18A Inhibition in Small-Cell Lung Cancer.

BACKGROUND: Small-cell lung cancer (SCLC) is characterized by pervasive chromosomal instability (CIN) and remains largely refractory to targeted therapies. KIF18A, a motor protein that regulates chromosome alignment during mitosis, has emerged as a selective dependency in CIN-high tumors. Whether this dependency extends to SCLC, a prototypical CIN-high cancer, has not been established, and biomarkers predicting response to KIF18A inhibition, currently in clinical trials, are lacking. METHODS: We integrated analyses of patient tumor datasets, neuroendocrine (NE) and non- NE SCLC cell lines, and functional perturbation models to define the determinants of response to KIF18A inhibition. Chromosomal instability metrics, transcriptional programs, mitotic dynamics, and spindle assembly checkpoint (SAC) function were assessed using genomic profiling, live-cell imaging, genetic perturbation, and pharmacologic inhibition. RESULTS: KIF18A expression was elevated in SCLC tumors and correlated with CIN-associated transcriptional programs, proliferative markers, and NE status; however, these features did not predict sensitivity to KIF18A inhibition. Instead, response was determined by the functional integrity of the SAC. SAC-proficient SCLC cells underwent sustained mitotic arrest followed by apoptotic cell death upon KIF18A inhibition, whereas SAC-defective cells failed to maintain checkpoint activation and survived. Mechanistically, resistant cells exhibited impaired kinetochore recruitment of core SAC components, including MAD1 and BUBR1. Importantly, transient induction of acute CIN through MPS1 inhibition partially restored sensitivity to KIF18A inhibition in resistant models. CONCLUSIONS: This study provides the first mechanistic characterization of KIF18A dependency in SCLC, identifying SAC competency as the primary determinant of response. These findings establish a biologically informed framework for patient stratification and rational combination strategies. TRANSLATIONAL RELEVANCE: Small-cell lung cancer (SCLC) is an aggressive malignancy with few effective targeted therapies and marked chromosomal instability. KIF18A has emerged as a potential therapeutic target in genomically unstable cancers, but biomarkers predicting response to KIF18A inhibition are lacking. We demonstrate that sensitivity to KIF18A inhibition in SCLC is determined not by KIF18A expression, neuroendocrine subtype, or baseline chromosomal instability, but by the functional integrity of the spindle assembly checkpoint (SAC). SCLC cells with intact SAC signaling undergo sustained mitotic arrest and apoptosis upon KIF18A inhibition, whereas SAC-defective cells bypass checkpoint activation and survive aberrant mitosis. Notably, transient induction of acute chromosomal instability through MPS1 inhibition partially restores sensitivity in resistant models. Together, these findings identify mitotic checkpoint competency as a mechanistic determinant and candidate predictive biomarker for KIF18A-targeted therapies, providing a biologically informed framework for patient stratification and rational combination strategies relevant to ongoing KIF18A inhibitor clinical trials.

Journal Article↗

PubMed2026

Shared genetic architecture and therapeutic targets across paediatric immune-mediated diseases.

OBJECTIVES: Paediatric-onset immune-mediated inflammatory diseases (IMIDs), including juvenile idiopathic arthritis and related rheumatic diseases, remain genetically undercharacterised. We aimed to define shared and category-specific genetic architecture across paediatric IMIDs, compare signals with adult IMIDs, and identify therapeutic opportunities. METHODS: We analysed 24 paediatric IMIDs classified as autoimmune, polygenic-autoinflammatory, mixed-pattern, or allergic. Genome-wide association analyses included 18,086 cases and 131,019 controls of European ancestry. We estimated single nucleotide polymorphism (SNP)-based heritability, genetic correlations, and polygenic overlap; performed subset-based meta-analysis; and conducted functional annotation, gene prioritisation, pathway and protein network analyses, adult-IMID comparison, and drug-target prioritisation. RESULTS: SNP-based heritability ranged from 28.9% for allergic IMIDs to 61.9% for autoimmune IMIDs. Genetic correlation and polygenic modelling supported partial sharing across categories with category-specific components. Meta-analysis identified 39 genome-wide significant loci outside the Major Histocompatibility Complex (MHC) region, including 15 previously unreported loci; 19 loci were shared between categories. Gene-prioritisation and protein interaction analyses identified a core MHC-centred antigen-presentation network, with category-enriched modules involving complement, innate/barrier pathways, epithelial biology, and type 2 immunity. Enriched pathways included nuclear factor κB signalling, T helper 17 related pathways, Janus kinase-signal transducer and activator of transcription signalling, programmed cell death protein 1/programmed death‑ligand 1, cytotoxic T‑lymphocyte associated protein 4 regulation, and osteoclast differentiation, several of which are relevant to rheumatic diseases. Paediatric IMIDs shared broad polygenic architecture with adult IMIDs, whereas top-ranked genes converged strongly with adult rheumatic diseases. Priority Index analysis identified 178 high-scoring genes, including 43 approved or investigational IMID drug targets. CONCLUSIONS: Paediatric-onset IMIDs share core pathways with adult forms but exhibit distinct genetic architecture shaped by age-specific immune and neurodevelopmental biology. These findings provide a genomic framework for paediatric precision medicine, guiding classification, risk prediction, and therapeutic development.

Humans↗

PubMed2024

A computational model for bacteriophage ϕX174 gene expression.

Bacteriophage ϕX174 has been widely used as a model organism to study fundamental processes in molecular biology. However, several aspects of ϕX174 gene regulation are not fully resolved. Here we construct a computational model for ϕX174 and use the model to study gene regulation during the phage infection cycle. We estimate the relative strengths of transcription regulatory elements (promoters and terminators) by fitting the model to transcriptomics data. We show that the specific arrangement of a promoter followed immediately by a terminator, which occurs naturally in the ϕX174 genome, poses a parameter identifiability problem for the model, since the activity of one element can be partially compensated for by the other. We also simulate ϕX174 gene expression with two additional, putative transcription regulatory elements that have been proposed in prior studies. We find that the activities of these putative elements are estimated to be weak, and that variation in ϕX174 transcript abundances can be adequately explained without them. Overall, our work demonstrates that ϕX174 gene regulation is well described by the canonical set of promoters and terminators widely used in the literature.

Gene Expression Regulation, Viral↗

PubMed2024

Elucidating the clinical and genetic spectrum of inositol polyphosphate phosphatase INPP4A-related neurodevelopmental disorder.

PURPOSE: Biallelic INPP4A variants have recently been associated with severe neurodevelopmental disease in single-case reports. Here, we expand and elucidate the clinical-genetic spectrum and provide a pathomechanistic explanation for genotype-phenotype correlations. METHODS: Clinical and genomic investigations of 30 individuals were undertaken alongside molecular and in silico modelling and translation reinitiation studies. RESULTS: We characterize a clinically variable disorder with cardinal features, including global developmental delay, severe-profound intellectual disability, microcephaly, limb weakness, cerebellar signs, and short stature. A more severe presentation associated with biallelic INPP4A variants downstream of exon 4 has additional features of (ponto)cerebellar hypoplasia, reduced cerebral volume, peripheral spasticity, contractures, intractable seizures, and cortical visual impairment. Our studies identify the likely pathomechanism of this genotype-phenotype correlation entailing translational reinitiation in exon 4 resulting in an N-terminal truncated INPP4A protein retaining partial functionality, associated with less severe disease. We also identified identical reinitiation site conservation in Inpp4a-/- mouse models displaying similar genotype-phenotype correlation. Additionally, we show fibroblasts from a single affected individual exhibit disrupted endocytic trafficking pathways, indicating the potential biological basis of the condition. CONCLUSION: Our studies comprehensively characterize INPP4A-related neurodevelopmental disorder and suggest genotype-specific clinical assessment guidelines. We propose that the potential mechanistic basis of observed genotype-phenotype correlations entails exon 4 translation reinitiation.

Humans↗

PubMed2026

Integrated Metabolomic and Transcriptomic Analysis Reveals Tissue-Specific Secondary Metabolic Differentiation and Indole Alkaloid Accumulation in Evodia rutaecarpa.

Evodia rutaecarpa is a valuable medicinal plant, yet its non-medicinal tissues remain largely underexplored. Here, we integrated ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics and RNA sequencing (RNA-seq) transcriptomics to systematically profile the metabolic and transcriptional landscapes of roots, stems, leaves, and flowers of Evodia rutaecarpa (Juss.) Benth. Our aim was to characterize tissue-specific metabolic differentiation and its underlying transcriptional regulatory mechanisms. Metabolomic analysis, employing principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) with robust model parameters (R2Y > 0.9, Q2 > 0.5), identified 3090 differential metabolite features (variable importance in projection, VIP > 1.0; p < 0.05) across the four tissues, which exhibited distinct tissue-specific clustering patterns. Integrated Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and weighted gene co-expression network analysis (WGCNA) revealed that roots specifically accumulated quinolone alkaloids and flavonoid glycosides, accompanied by the coordinated upregulation of genes involved in flavonoid and phenylpropanoid biosynthetic pathways. In contrast, stems, leaves, and flowers were enriched in indole alkaloids (evodiamine and rutaecarpine) and volatile oil precursors, with concurrent upregulation of genes involved in tryptophan metabolism and indole alkaloid biosynthesis (e.g., tryptophan decarboxylase, TDC; s N-methyltransferase, NMT). Notably, leaves and flowers displayed particularly high accumulation levels of these bioactive alkaloids, suggesting their potential as alternative sources for industrial and pharmaceutical applications. WGCNA further identified multiple transcription factors and structural gene modules tightly correlated with evodiamine accumulation, offering promising candidate regulators for future biosynthetic pathway engineering. Collectively, this multi-omics integration study systematically elucidates the tissue-partitioned secondary metabolism of Evodia rutaecarpa (Juss.) Benth. and provides a solid scientific foundation for full-plant resource utilization, targeted development of non-medicinal tissues, and future metabolic engineering of indole alkaloid production.

Evodia rutaecarpa↗

PubMed2024

Synergistic removal of total petroleum hydrocarbons and antibiotic resistance genes in Yellow River Delta wetlands contaminated soil composting regulated by biogas slurry addition.

The interactive effects between the emerging contaminant antibiotic resistance genes (ARGs) and the traditional pollutant total petroleum hydrocarbons (TPHs) in contaminated soils remain unclear. The synergistic removal of TPHs and ARGs from composted contaminated soil, along with the microbial mechanisms driven by the addition of biogas slurry, have not yet been investigated. This study explored the impact of biogas slurry on the synergistic degradation mechanisms and bacterial community dynamics of ARGs and TPHs in compost derived from contaminated soil. The addition of biogas slurry resulted in a reduction of targeted ARGs and mobile genetic elements (MGEs) by 9.96%-95.70% and 13.32%-97.66%, respectively. Biogas slurry changed the succession of bacterial communities during composting, thereby reducing the transmission risk of ARGs. Pseudomonas, Cellvibrio, and Devosia were identified as core microorganisms in the synergistic degradation of ARGs and TPHs. According to the partial least squares path model, temperature and NO3- indirectly influenced the removal of ARGs and TPHs by directly regulating the abundance and composition of host microbes and MGEs. In summary, the results of this study contribute to the high-value utilization of biogas slurry and provide methodological support for the low-cost remediation of contaminated soils.

Composting↗

PubMed2026

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning.

Non-identically distributed data is a major challenge in Federated Learning (FL). Personalized FL tackles this by balancing local model adaptation with global model consistency. One variant, partial FL, leverages the observation that early layers learn more transferable features by federating only early layers. However, current partial FL approaches use predetermined, architecture-specific rules to select layers, limiting their applicability. We introduce Principled Layer-wise-FL (PLayer-FL), which uses a novel federation sensitivity metric to identify layers that benefit from federation. This metric, inspired by model pruning, quantifies each layer's contribution to cross-client generalization after the first training epoch, identifying a transition point in the network where the benefits of federation diminish. We first demonstrate that our federation sensitivity metric shows strong correlation with established generalization measures across diverse architectures. Next, we show that PLayer-FL outperforms existing FL algorithms on a range of tasks, also achieving more uniform performance improvements across clients.

Journal Article↗

PubMed2026

Discovery of antimicrobial peptides from incomplete biosynthetic gene clusters to combat multidrug-resistant bacteria.

The escalating crisis of multidrug-resistant bacteria necessitates innovative antibiotic discovery platforms. Conventional antimicrobial peptide (AMP) mining often relies on complete biosynthetic gene clusters (BGCs), leaving fragmented genomic resources underexplored. Here, we present an evolution-inspired approach to reconstruct and predict AMPs from partial BGCs. Applying this strategy to 954 Paenibacillus genomes identifies five polymyxin-like peptides, NP001-NP005, with broad in vitro activity. Crucially, in murine models of polymyxin-resistant infection, NP001 reduced bacterial burdens by up to 1,000-fold in a thigh infection model and improved survival (50% vs. 0%) in a lethal peritonitis model. Structural simulations and biophysical assays revealed that NP001 maintains high affinity for bacterial membranes and effectively binds to MCR-1-modified lipid A, a key colistin-resistance mechanism. Moreover, Leu at position 10 of NP001 plays a key role in antibacterial activity against MCR-1-resistant bacteria. Our work establishes a generalizable framework for AMP discovery and introduces a promising therapeutic candidate, NP001, which effectively counteracts polymyxin-resistant pathogens.

Multigene Family↗

PubMed2026

Understanding tumor adaptations and resistance to MET inhibitors in MET-altered non-small cell lung cancer.

AIM: Type Ib MET inhibitors are clinically active in selected MET-altered non-small cell lung cancer, particularly tumors with MET exon 14 skipping or MET amplification, but acquired resistance remains incompletely understood. Here, we investigated resistance across biologically distinct MET-altered contexts, including MET exon 14 skipping, MET amplification, and MET overexpression. METHODS: Paired baseline and progression samples from seven patients treated with tepotinib or capmatinib were analyzed using spatial transcriptomics, whole-exome sequencing, RNA sequencing, CRISPR screening, and drug-combination assays. Patient-derived cultures and resistant cell-line models were used to explore resistance-associated changes. RESULTS: MET inhibitor resistance was heterogeneous, with persistence of the initial MET alteration in most evaluable cases and emergence of patient-specific genomic events. Three main resistance-associated, often overlapping, routes were identified: on-target MET evolution through kinase-domain alterations; extracellular matrix and tumor-microenvironment remodeling, including collagen and fibronectin upregulation, complement-related signaling, and partial EMT-associated programs; and bypass signaling involving EGFR/HER, MAPK, and PI3K/Akt pathways. In vitro models reproduced several tumor-cell-intrinsic features but only partially captured microenvironment-associated changes. CONCLUSIONS: MET inhibitor resistance in this cohort involved overlapping, context-dependent genomic, phenotypic, and signaling adaptations, supporting combination strategies for MET-altered lung cancer.

CRISPR screen↗

PubMed2025

Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders.

Biomarker research in psychopathology increasingly employs high-dimensional Omics approaches. Yet, proteomics based on human hair remain largely unexplored, despite its potential to efficiently capture stable biological signals accumulated over weeks to months. This study leveraged machine learning to investigate the potential of the hair proteome-all detectable peptides and proteins-as a biomarker source for stress-associated psychopathology. We analyzed protein profiles from hair segments of women with non-suicidal self-injury disorder and healthy controls (N = 68). Of 1114 identified proteins, 611 were sufficiently abundant for analyses. Partial Least Squares Discriminant Analysis achieved stable 84.4 % cross-validated accuracy for classification of clinical groups (p < .001), outperforming models based on data-derived clusters (60 %), stress-related proteins (73 %), and simulated hair cortisol from meta-analytic effect sizes (53-59 %). Predicted class probabilities strongly correlated with clinical symptoms and well-being (r > .60). Key predictive proteins were linked to pain perception, oxidative stress, and cholesterol homeostasis. Approximately 15 % of proteins differed significantly between groups, with the strongest candidates related to ribosomal function-an emerging target in depression. These findings establish hair proteomics as a promising, non-invasive biomarker source for psychiatric research with potential clinical applications in risk assessment and personalized interventions.

Humans↗

PubMed2026

Machine Learning and Metabolomics to Characterize Warburg-Like Metabolic Subtypes in Human Retinal Endothelial Cells Exposed to Risk Factors Associated With Proliferative Diabetic Retinopathy.

PURPOSE: High glucose (HG), hypoxia (Hyp), and their combination are major risk factors for proliferative diabetic retinopathy (PDR). Although these conditions induce features of the Warburg-like metabolic reprogramming in human retinal endothelial cells (HRECs), it remains unclear whether they produce distinct metabolic and angiogenic subtypes. This study aimed to characterize the Warburg-like-associated metabolic heterogeneity induced by these PDR-related risk factors and evaluate the ability of supervised machine-learning models to distinguish these subtypes. METHODS: HRECs were cultured under normoglycemic, HG, Hyp (2% O2), and combined HG-Hyp conditions. Untargeted LC-MS/MS metabolomics quantified metabolites spanning carbohydrates, amino acids, nucleotides, and lipids. Principal component analysis (PCA) assessed overall metabolic variation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified metabolic pathways associated with angiogenesis. In vitro angiogenesis assays measured endothelial tube formation and branching. Nine supervised classifiers (decision tree, logistic regression, naïve Bayes, random forest, K-Nearest Neighbors, neural network, gradient boosting, AdaBoost, and Support Vector Machine) were trained on the highest-ranked metabolites selected by the Information Gain Ratio feature-ranking approach. Model performance was evaluated using 10-fold cross-validation, leave-one-out cross-validation (LOOCV), permutation testing, and a classifier stability analysis under biologically meaningful distributional shift using an independent chemically induced hypoxia model (CoCl2). RESULTS: PCA revealed partial separation of metabolic profiles across conditions, indicating different Warburg-like metabolic subtypes. The combined HG-Hyp condition exhibited enhanced angiogenic potential relative to either HG or Hyp alone. KEGG pathway enrichment analysis identified fatty acid biosynthesis and elongation among the most significantly enriched pathways in HRECs under combined HG-Hyp conditions, alongside amino sugar and nucleotide sugar metabolism, glycerophospholipid metabolism, the pentose phosphate pathway, and glycolysis/gluconeogenesis. Supervised machine-learning classifiers distinguished these metabolic subtypes, with AdaBoost and gradient Boosting showing the most balanced, reproducible performance across 10-fold cross-validation, LOOCV, and permutation testing, and remaining the most reliable classifiers under domain-shift testing (area under the curve = 0.88, P = 0.0061). CONCLUSIONS: In this exploratory analysis, HG, Hyp, and their combination drive metabolically and functionally distinct subtypes of Warburg-like metabolic reprogramming in HRECs, with HG-Hyp in combination producing a highly angiogenic phenotype. Boosting-based ensemble classifiers provide a promising framework for detecting these subtypes even under domain-shift conditions, warranting validation in larger independent datasets. TRANSLATIONAL RELEVANCE: Integrating metabolomics with machine-learning classification offers a strategy to identify Warburg-like metabolic subtypes in retinal endothelial cells, providing insights into angiogenic mechanisms and guiding the development of targeted diagnostics or therapeutics for PDR.

Humans↗

PubMed2024

Loss-of-function in RBBP5 results in a syndromic neurodevelopmental disorder associated with microcephaly.

PURPOSE: Epigenetic dysregulation has been associated with many inherited disorders. RBBP5 (HGNC:9888) encodes a core member of the protein complex that methylates histone 3 lysine-4 and has not been implicated in human disease. METHODS: We identify 5 unrelated individuals with de novo heterozygous variants in RBBP5. Three nonsense/frameshift and 2 missense variants were identified in probands with neurodevelopmental symptoms, including global developmental delay, intellectual disability, microcephaly, and short stature. Here, we investigate the pathogenicity of the variants through protein structural analysis and transgenic Drosophila models. RESULTS: Both missense p.(T232I) and p.(E296D) variants affect evolutionarily conserved amino acids located at the interface between RBBP5 and the nucleosome. In Drosophila, overexpression analysis identifies partial loss-of-function mechanisms when the variants are expressed using the fly Rbbp5 or human RBBP5 cDNA. Loss of Rbbp5 leads to a reduction in brain size. The human reference or variant transgenes fail to rescue this loss and expression of either missense variant in an Rbbp5 null background results in a less severe microcephaly phenotype than the human reference, indicating both missense variants are partial loss-of-function alleles. CONCLUSION: Haploinsufficiency of RBBP5 observed through de novo null and hypomorphic loss-of-function variants is associated with a syndromic neurodevelopmental disorder.

Humans↗

PubMed2026

CRISPR screening identifies DTX4 governing alveolar macrophage cholesterol efflux in pulmonary alveolar proteinosis.

Pulmonary alveolar proteinosis (PAP) is a rare pulmonary syndrome characterized by impaired surfactant clearance, driven by dysfunctional cholesterol efflux in alveolar macrophages (AMs). However, the molecular determinants governing AM cholesterol homeostasis remain incompletely defined. Here, through a genome-wide CRISPR screen in foamy macrophages and bulk RNA sequencing of AMs from PAP patients, we identify DTX4 as a pivotal regulator of cholesterol efflux in AMs. In mice, AAV-mediated silencing of DTX4 led to excessive AM lipid accumulation, exacerbated proteinosis, increased lung opacities, and deteriorated pulmonary function. Similarly, DTX4 depletion in primary AMs impaired cholesterol efflux and promoted intracellular lipid deposition. Conversely, AM-specific overexpression of DTX4 in the Csf2ra-/- PAP model markedly alleviated lipid accumulation, mitigated alveolar proteinosis, restored lung densities, and rescued pulmonary function. Mechanistically, DTX4 stabilizes the GM-CSF receptor via an E3-independent interaction to sustain JAK2/STAT5 signaling, which reciprocally maintains DTX4 transcription. This positive-feedback loop drives PPARγ expression, and its disruption in PAP impairs cholesterol efflux, a defect partially reversible by ectopic PPARγ expression. Collectively, our findings identify DTX4 as a central orchestrator of AM cholesterol efflux and surfactant homeostasis, positioning it as a promising therapeutic target for PAP.

Animals↗

PubMed2026

Genetic Associations with Temporal Modeling of Alzheimer's Disease Progression Supports a Novel Paradigm for Disease Risk.

A major challenge in Alzheimer's disease (AD) research is predicting who will develop AD, how it progresses, and how to slow, prevent, or reverse progression. Here, we apply a data-driven timeline inference framework to sparse longitudinal blood metabolomics data to reconstruct AD timelines and derive individual-specific timeline progression rates. Inferred temporal locations for each metabolomics sample along the AD timeline closely track clinical severity, while timeline progression rates capture inter-individual differences in the speed of pathophysiological progression. Genome-wide association studies of timeline progression rate identify novel loci distinct from those in AD case-control studies, notably showing no effect of the major risk locus APOE. These findings support a multidimensional paradigm of AD risk in which disease potential and progression act as partially independent factors. By explicitly modeling disease dynamics, this work reveals genetic contributions not captured by traditional approaches and provides a framework for studying AD and other progressive disorders.

Journal Article↗

PubMed2026

Dynamic transcriptomic landscape from bulk RNA-seq reveals critical mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 modules for deep second-degree burn wound healing.

Burn injuries constitute a significant global health challenge, with deep partial-thickness burns (deep second-degree) posing particular clinical concerns due to prolonged healing and high scarring risks stemming from reticular dermis damage. Current therapeutic strategies remain largely empirical, reflecting limited understanding of stage-specific regulatory mechanisms. This study systematically investigated the molecular basis of deep partial-thickness burn repair by establishing murine models and performing RNA-seq analysis across healing phases (0, 3, 7, 14 days post-burn, dpb). Integrated bioinformatics revealed pivotal ceRNA and PPI networks, identifying hif1a (hypoxia-responsive immunomodulator) and col1a1 (ECM remodeling hub) as nodal regulators. Mechanistically, mmu-miR-101a-3p and mmu-miR-181a-5p were validated as post-transcriptional repressors of col1a1 and hif1a, respectively. Our work pioneers the discovery of the mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 axes as master regulators of burn repair, offering novel therapeutic targets. The multi-omics dataset and molecular networks established herein provide a foundational resource for wound healing research.

MicroRNAs↗