GENETICS

Brettanomyces bruxellensis is a yeast species associated with diverse fermentation environments and characterized by extensive genetic diversity, including diploid, autotriploid, and allotriploid lineages resulting from independent hybridization events. These lineages are associated with distinct ecological niches and provide a framework for studying metabolic trait evolution in complex genomes. Nitrate assimilation is a relatively uncommon trait among yeasts and has been reported in B. bruxellensis, but its distribution and evolutionary history within the species remain poorly understood. Here, we combined phenotypic characterization of 151 strains with genomic analyses of 946 whole-genome sequences to investigate nitrate assimilation. Growth assays revealed that nitrate assimilation is widespread but unevenly distributed across genetic lineages, with some populations largely retaining the trait whereas others have frequently lost it. Genomic analyses identified extensive variation affecting the nitrate assimilation gene cluster composed of YNR1, YNI1, and YNT1. Nitrate assimilation was strongly associated with both gene copy number and predicted gene functionality, with nitrate-assimilating strains generally carrying more functional copies of the cluster. Leveraging the complex genomic architecture of the species, we independently analyzed primary and acquired genomes in allotriploid lineages and uncovered contrasting evolutionary trajectories following hybridization. While nitrate assimilation genes were generally maintained in primary genomes, acquired genomes showed a higher prevalence of gene loss and predicted loss-of-function variants, revealing asymmetric dynamics between subgenomes. Altogether, our results suggest that nitrate assimilation represents an ancestral trait that has been differentially maintained across B. bruxellensis lineages through a combination of copy number variation, gene degeneration, and genome-specific evolutionary dynamics. These findings provide new insights into how genome architecture and polyploid evolution shape the maintenance and loss of metabolic traits in an industrially relevant yeast species.

Nuclear pore complexes (NPCs) are nuclear envelope (NE)-embedded protein assemblies that mediate nucleocytoplasmic exchange and interact with the genome, including binding of an NPC component Nup93 to Polycomb chromatin domains. Here, we investigated the in vivo relevance of this relationship in Drosophila, which unusually contains two distinct paralogs of Nup93. Interestingly, we identified a Nup93-2-specific tumorigenic phenotype in larval wings, where depletion of Nup93-2, but not Nup93-1, led to tumor-like overgrowth, reminiscent of Polycomb mutations. Consistently, our transcriptomic analysis revealed a wide-spread loss of gene silencing in Nup93-2-depleted wings, particularly in a Nup93-bound Polycomb domain spanning genes for activators of JAK/STAT signaling. Nup93 paralogs were not found to differ in their effect on NPC biogenesis but strikingly, showed differences in subnuclear localization patterns. While Nup93-1 co-localized exclusively with fully assembled NPCs, Nup93-2 exhibited only partial co-localization and was found at additional NE locations in a tissue-specific manner. Together, our results identify an in vivo silencing role of a Nup93 paralog and suggest that Nup93-2 may form a unique NE-associated complex that targets a subset of Polycomb domains containing growth-promoting genes.

Blumeria graminis f. sp. avenae (Bga), the causal agent of oat powdery mildew, is one of the most host-specialized members of the B. graminis species complex. Despite its agricultural importance, the lack of a high-quality reference genome has limited studies of host specialization, virulence evolution and comparative genomics in this pathogen. Here, we generated the first chromosome-scale genome assembly of Bga using an integrative approach combining long- and short-read sequencing, Hi-C scaffolding and transcriptome data. The Bga genome exhibits hallmark features of powdery mildew fungi, including extensive repeat content and low gene density. Comparative analyses revealed that genome expansion is primarily associated with historical transposable element proliferation rather than recent transpositional activity. Genome organization is consistent with a functionally stratified "one-speed" model, in which genes associated with pathogenicity, including predicted effectors and infection-responsive genes, are preferentially located in transposable element-rich regions characterized by reduced synteny conservation and extended intergenic spaces. In contrast, conserved genes are concentrated in compact genomic regions and maintain strong syntenic conservation across cereal-infecting formae speciales. Hi-C analyses demonstrated a highly structured chromatin architecture and revealed genome organization patterns associated with infection-related gene expression. Comparative genomic analyses indicated that host specialization in Bga is driven by localized diversification of a relatively small subset of genes rather than large-scale genome restructuring. These results provide the first high-quality genomic resource for Bga and offer new insights into the evolutionary mechanisms underlying host specialization in powdery mildew fungi.

Developmental regression is a severe but poorly understood complication of several neurodevelopmental disorders. In Kleefstra syndrome (KLEFS1), caused by EHMT1 haploinsufficiency, regression often emerges during adolescence or early adulthood and is frequently preceded by marked sleep disturbance. Experimental work implicating EHMT1/G9a in metabolic regulation and stress responses raises the possibility that impaired metabolic resilience contributes to this vulnerability. Here, we aimed to investigate whether altered glucose metabolism is a feature of KLEFS1 and whether it relates to clinical variability, including regression. Through [18F]FDG-PET/CT, individuals with KLEFS1 who had experienced regression (n=4) exhibited a hypometabolic brain profile, whereas one individual who had not experienced regression showed globally elevated metabolic activity. In parallel, G9a mutant flies exhibited increased baseline metabolic rate and neuronal ATP levels together with sleep fragmentation resembling the clinical phenotype. Providing flies with oxidative stress to model KLEFS1 regression further exacerbated sleep disruption and was associated with a reduction in metabolic output. Importantly, adult high sugar feeding in flies prevented oxidative stress-induced worsening of sleep and maintained metabolic stability under challenge. Together, these findings suggest that regression in KLEFS1 and associated sleep disturbances are linked to underlying metabolic vulnerability and impaired maintenance of energy homeostasis under stress.

Standard methods for ancestral sequence reconstruction (ASR) rely on substitution models for the residues in a biological sequence and assume independent evolution across these sites, ignoring the epistatic interactions that shape molecular evolution. In contrast, deep learning models like variational autoencoders (VAEs) can learn low-dimensional representations ("embeddings") of sequences in a protein family that may implicitly handle these dependencies, raising the possibility of performing more accurate ASR by interpolating between extant sequence embeddings within the VAE's latent space. In this study, we test this hypothesis by developing and evaluating a VAE-based ASR pipeline. Benchmarking this approach against established likelihood-based and parsimony methods using various simulations of protein evolution, including scenarios with and without epistasis, we find that the VAE-based approach is consistently and significantly outperformed by standard methods, even in epistatic regimes where it was hypothesized to have an advantage. We further show that this failure is not due to a lack of phylogenetic structure in the latent space, which does contain evolutionary signal. Rather, the primary limitation is the information loss inherent to the autoencoding process: the VAE's decoder cannot generate sequences with sufficient fidelity for the precise demands of ASR.

Background VEXAS syndrome is a late onset autoinflammatory disease caused by somatic UBA1 mutations and characterized by heterogeneous systemic and hematologic manifestations. We aimed to describe all identified Belgian cases through a national multicenter cohort. Methods We conducted a retrospective study across four Belgian tertiary centers. Clinical, biological, genetic, therapeutic, and outcome data were collected using standardized anonymized case report forms. Analyses were descriptive. Results Twenty-one male patients were identified between January 2018 and May 2025. General symptoms such as Fatigue, weight loss and sweating occurred in 95% of cases. The most frequent manifestations were cutaneous (85.7%), hematologic (76.2%), articular (66.7%), thromboembolic (57.1%), chondritis (42.9%), ophthalmologic (38.1%), pulmonary (38.1%). Other manifestations also included vasculitis (61.9%). At diagnosis, 95% had anemia, macrocytic in 57%, and 28.6% had thrombocytopenia. Corticosteroids were the main first line therapy. Second line treatments included anti IL 6 agents (46.7%), JAK inhibitors (20%), and azacitidine (14.3%). Complete remission occurred in 50% of patients receiving anti IL 6 therapy and in 33% treated with either JAK inhibitors or azacitidine. Two patients underwent allogeneic stem cell transplantation, one died from infectious complications. Twenty six infectious episodes were recorded, including opportunistic infections. Six patients (28.6%) died during follow-up, four from infectious complications. Conclusion This first Belgian national cohort confirms the clinical heterogeneity of VEXAS syndrome and highlights substantial infectious morbidity and mortality. Access to targeted second-line therapies, particularly anti IL-6 agents and JAK inhibitors, remains challenging despite apparent clinical benefit.

Pre-mRNA splicing is essential for gene expression, yet how disruption of core spliceosomal factors produces tissue- and developmental stage-specific phenotypes remains poorly understood. Here, we investigated the in vivo function of the conserved spliceosomal kinase PRPF-4 in C. elegans using endogenous reporter analysis, conditional protein depletion, and transcriptome-wide analysis of alternative splicing and gene expression. We found that PRPF-4 is broadly expressed throughout development and is continuously required for postembryonic development, with distinct requirements in the pharynx, nervous system, and germline. Acute PRPF-4 depletion rapidly disrupts alternative splicing across thousands of transcripts, with exon skipping representing the predominant class of affected events. In addition, PRPF-4 depletion results in a robust transcriptome shift with induction of components of the spliceosome and repression of ciliary and ion transport-related transcripts. These findings establish PRPF-4 as a central regulator of RNA metabolism and demonstrate the far-reaching effects on gene expression caused by loss of core spliceosomal components.

The interpretation of missense variants remains a major challenge in clinical genetics. "Meta-domains" aggregate population and pathogenic variation across homologous Pfam domain instances in the human proteome, providing per-residue context for interpreting variants of uncertain significance (VUS). Our 2019 implementation, MetaDome, is widely used and named in clinical variant-classification guidelines. Here we present the MetaDome 2027 update, featuring a comprehensively updated dataset and GRCh38 support. The redesigned pipeline enables incremental updates of GENCODE, UniProtKB/Swiss-Prot, Pfam, gnomAD, and ClinVar while maintaining 100% sequence-identity gene-to-protein mapping. Annotated Pfam domain instances grew 14.9% from 71,419 to 82,069 and meta-domain-eligible Pfam families ([≥]2 human occurrences) by 73.3% from 3,334 to 5,778; Pfam domains are annotated to 92% of human proteins. Approximately 43% of mapped protein-coding nucleotides (14.3 million in GRCh38, 13.8 million in GRCh37) are in a meta-domain; in GRCh38 67.9% (37,692 of 55,548) of pathogenic or likely pathogenic ClinVar missense variants fall at such a position. We show how MetaDome helped reclassify a de novo missense VUS in RALA and identify 52,463 ClinVar missense VUS for which meta-domains supply otherwise unavailable pathogenic evidence. MetaDome is freely available at www.metadome.app.

DNMT3A is a de novo DNA methyltransferase whose recruitment to chromatin regulates its function. Missense mutations within the chromatin-binding PWWP domain are associated with diverse human disorders, yet how mutations in the same domain produce distinct phenotypes remains unclear. Here we systematically characterise 19 clinically reported mutations in the PWWP domain of DNMT3A that are associated with Heyn-Sproul-Jackson syndrome (HESJAS), paraganglioma (PG) and clonal haematopoiesis (CH). We show that all PWWP-domain mutations associated with HESJAS abolished interaction with H3K36me2 modified nucleosomes, defining this as a consistent biochemical feature of HESJAS. In contrast, mutations from all disease classes differentially altered DNA binding of the PWWP domain, driven by alterations in the net charge of the domain. However, these effects are largely overcome by inclusion of the DNNMT3A1 N-terminal region, which is absent from its embryonic isoform, suggesting that PWWP mutations may differentially affect DNMT3A function through development. Changes in the thermal stability of the isolated PWWP domain mutants did not directly translate into altered stability of full-length DNMT3A1 in cells. We show that HESJAS mutations can affect the intramolecular interaction between the PWWP and adjacent ADD domain, an interaction proposed to contribute to the autoinhibitory function of the ADD domain. However, not all mutations behaved in the same way, suggesting that multiple factors govern the intramolecular autoinhibition of DNMT3A. Together, this study advances our understanding of the molecular mechanisms by which DNMT3A PWWP-domain mutations are mechanistically heterogeneous, providing a biochemical framework that contributes to distinct disease phenotypes.

Drug resistance emerges repeatedly in outbreaks of Candida fungal pathogens, but little is known about its origins or persistence. Here, we investigated the evolutionary processes shaping echinocandin resistance in Candida auris, a globally emerging and predominantly clonal fungal pathogen. Genome-wide association across over 600 isolates identified mutations in the {beta}-1,3-glucan synthase gene FKS1 as the most significant driver of resistance to an echinocandin drug. Ancestral reconstruction of this population traced shared resistance mutations among small groups typically consisting of 2-3 closely related isolates, but clusters could include up to 16 isolates. Nearly all resistant clusters consisted of isolates collected in the same year and region, consistent with local transmission. To further examine population-level selection, we measured adaptive signatures in FKS1 and the highly diverged paralog FKS2 across 22,000 genomes. This revealed excess nonsynonymous polymorphisms in FKS1, primarily due to independent, recurrent mutations at resistance hotspots, consistent with parallel evolution and incomplete fixation of adaptive alleles. In FKS2, there is no evidence of hotspots and little support for diversifying selection. Together, these results indicate that resistance mutations emerge under strong genetic constraint, with adaptation restricted to only one FKS homolog and predominantly at mutational hotspots.

Polygenic risk scores (PRS) have emerged as promising tools for stratifying inherited disease risk, yet their translation into clinical practice is constrained by a critical and frequently unmet requirement: demonstration that scores derived in one cohort retain discriminatory value when applied independently in a different population. For suspected familial hypercholesterolemia (FH), a substantial proportion of patients that meet clinical criteria still test negative for a monogenic cause. These patients are presumed to carry polygenic LDL-C burden, yet PRS validation is still scarce. Here, we evaluated a published hypercholesterolemia PRS (PGS000936) spanning 5,386 genome-wide loci. We tested the score in 117 monogenic-negative hypercholesterolemia cases and 496 controls from the German general population genotyped on the Illumina Global Screening Array v3.0, with imputation to approximately 11 million variants. The score was applied without retraining, using externally derived {beta}-coefficients. The PRS clearly distinguished cases from controls (p < 2x10-16), with a mean PRS of 0.97 in cases versus 0.52 in controls. Decile analysis revealed a seven-fold increase in odds of hypercholesterolemia in the top 10% of the distribution (OR 7.55; 95% CI 4.36-13.07; p < 0.0001). In 94 cases for which clinical data was available, higher PRS was associated with significantly higher LDL-C levels before treatment. Importantly, the PRS distribution in the German control cohort was shifted relative to that of the control cohort in the initial study, suggesting that population-matched calibration is required for accurate odds ratio estimation and proper interpretation of PRS values. These findings show that the published hypercholesterolemia PRS (PGS000936) can effectively stratify risk and identify individuals with a high genetic burden in a real-world German clinical setting, supporting its clinical utility when appropriately calibrated for the local population.

Background. Patient stratification from multi-omics and clinical data is essential for uncovering disease heterogeneity and moving toward more personalized treatment strategies. However, integrating heterogeneous data layers while identifying robust patient strata remains challenging. Methods. We introduce Reduced Fusion of Multi-Omics Stratification (RedFuMOS), a novel three-step approach for patient stratification based on mixed-type multi-omics data. RedFuMOS extends Similarity Network Fusion to accommodate mixed-type data layers and layer-specific similarity measures for data integration, includes a dimensionality reduction step to mitigate the curse of dimensionality, and performs patient stratification using density-based hierarchical clustering with HDBSCAN. It also implemented an automated optimization procedure to identify the best set of hyperparameters, minimizing the need for manual tuning. Results. RedFuMOS outperformed six state-of-the-art tools for multi-omics patient stratification in a comprehensive simulated benchmarking study, which also confirmed that, although computationally expensive, the dimensionality reduction step is crucial for achieving good stratification performance. Additionally, RedFuMOS identified two clinically relevant patient strata in a small real-world cohort of patients with Philadelphia chromosome-positive chronic myeloid leukaemia. Conclusion. RedFuMOS provides a flexible framework for integrating heterogeneous multi-omics and clinical data. RedFuMOS is available as an R package at http://github.com/delucasara/RedFuMOS.

Standard polygenic risk scores (PRSs) are constructed based on additive genome-wide association study (GWAS) summary statistics. Nonlinear machine learning methods have been increasingly applied to construct PRSs directly from individual-level data, with the aim of improving predictive performance over standard PRSs through their ability to model non-additive genetic effects. However, their superiority across studies has been inconsistent, and the conditions under which they provide meaningful improvements remain unclear. We combined theoretical analysis, simulations and a real-world application to investigate when two widely used nonlinear machine learning methods, random forest and XGBoost, outperform standard PRSs. Theoretical analysis showed that standard PRSs can implicitly capture part of the genetic variance attributable to nonadditive genetic effects through their contributions to marginal SNP effects, thereby losing less information than commonly assumed. Although nonlinear models have a higher theoretical potential, their greater flexibility incurs a bias-variance trade-off that can limit predictive gains at finite sample sizes. Simulations showed that XGBoost outperformed the standard PRS only when the genetic architecture involves a sufficiently large proportion of interaction genetic variance concentrated across relatively few interaction effects and large training samples were available. Random forest consistently underperformed the standard PRS. In an application to ischemic heart disease prediction using UK Biobank data, XGBoost showed no meaningful improvement in predictive performance over the standard PRS, whereas random forest again performed worse. Together, these findings suggest that nonlinear machine learning do not uniformly outperform standard PRSs; rather, their relative performance depends jointly on genetic architecture and training sample size. Our study helps to reconcile the inconsistent results reported across previous studies and provides a framework for identifying settings in which more complex PRS models are likely to be beneficial.

Prions, once mainly studied for their pathogenic roles, are now gaining recognition as adaptive elements in microbial physiology. Over one-third of wild yeast isolates harbor prion proteins, yet their impact on host-microbe interactions remains poorly characterized. Given the ecological dominance of yeasts in the Drosophila mycobiome, we leveraged the Drosophila melanogaster-Saccharomyces cerevisiae system to investigate how the mycobiome-derived prion, [MRPL10+], modulates host physiology. We show that flies exposed to [MRPL10+] yeast exhibit significantly enhanced cold tolerance and increased locomotor activity. This effect persists with heat-killed yeast and diluted culture, suggesting a stable, potent bioactive factor. Using the genetically diverse Drosophila Global Diversity Lines (GDL), we identified natural variation in responsiveness to [MRPL10+] yeast. Genome-wide association and functional RNAi screening revealed a gut-brain signaling axis involving genes critical for digestion, intercellular communication, transcription regulation, and neural transmission. Notably, serotonin and octopamine pathways were essential for [MRPL10+]-induced changes in cold tolerance and locomotion, implicating neuromodulatory circuits in prion-mediated microbial signaling. Our findings establish a mechanistic link between a fungal prion and host metabolic and neural adaptation. This work provides the first genetic dissection of a prion-mediated host-microbe interaction, laying the groundwork for investigating beneficial prions in complex microbial communities and highlighting a new dimension of the mycobiomes influence on animal physiology.

Genes encoding novel protein sequences are a ubiquitous feature of genomes. They fuel molecular and cellular evolutionary innovations and frequently contribute to species-specific characteristics. We are now unravelling the processes by which they originate, including de novo from noncoding sequences and through extreme divergence, yet how much and what types of novel proteins evolve through each process is still unclear Does the mechanism of origination shape the structural and functional potential of the resulting proteins? Here, we conducted a broad computational investigation of genetic and protein novelty at the scale of the entire subphylum of Saccharomycotina yeasts. We detected more than 5,000 robust de novo genes across 332 species and compared them to more than 10,000 novel genes resulting from extreme sequence divergence, revealing two distinct modes of evolution of novelty. A remarkable 40% of de novo proteins are predicted to localize to mitochondria compared to only 15% of divergent, with the latter also being substantially longer and more disordered. A detailed analysis of conservatively predicted tertiary structures of novel proteins shows that "invention" of novel folds can happen through both processes but is more likely to occur de novo. We also illustrate cases of evolutionary "re-invention" of existing protein folds from non-coding sequences. Our work deepens our understanding of the origins and importance of novel proteins opening new directions for further structural and functional characterization.

Paternally-inherited epigenetic information can influence phenotype in offspring (1). Here, we identify a critical mechanistic contribution of KDM6A (UTX), an X-linked histone modifier and tumor suppressor, in regulating transmissible epigenetic information in mammalian sperm. Paternal loss of KDM6A increases cancer risk in genetically wild type offspring, but how Kdm6a knockout sperm transmit this effect at the molecular level is unknown (2). We find that KDM6A functions in spermatogenesis to promote methylation of histone H3 lysine 4 (H3K4) via selective interaction with the COMPASS complex methyltransferase KMT2C (MLL3). KMT2C and KDM6A are coordinately recruited to promoters of active genes in spermatogenic cells, contrasting with recruitment to intergenic enhancers in other cell types (3, 4). Loss of KDM6A disrupts H3K4 methylation at promoters of tumor suppressor genes in spermatogonia, and some of these defects persist in epididymal sperm and correspond to impaired expression in preimplantation embryos. These genes are also misregulated in normal and malignant hematopoietic tissue of genetically wild type offspring, indicating that impaired H3K4 methylation in KDM6A-deficient male germ cells may preferentially alter regulation of tumor suppressor gene networks in development across generations.

Oil palm (Elaeis) is the primary source of global vegetable oil. Interspecific hybrids of Elaeis exhibit pronounced heterosis by integrating two distinct subgenomes into a single nucleus, effectively combining the high yield of African oil palm (E. guineensis) with the high unsaturated fatty acid content and disease resistance of American oil palm (E. oleifera). However, the genetic basis underlying heterosis is still unclear. Here, we combine phased genome assembly, comparative genomics, evolutionary genomics and haplotype-aware transcriptomics to unravel the genetic architecture of heterosis of hybrid oil palm. We assemble the highly heterozygous F1 genome ('Reyou 40', 3.75% heterozygosity) into a complete 1.73 Gb T2T haplotype (HapG) and a 1.84 Gb near-T2T haplotype (HapO with17 gaps). Despite 91.56% sequence identity, HapG and HapO diverged in LTR-RT occurrence and PAV affected genes, showing complementary biases in lipid metabolism and stress responses, respectively. Evolutionary genomics revealed that ancient WGDs preserved the palm family. Whereas lineage-specific lipid-related gene expansions in oil palm. Six ancient introgressed regions (~64 Mb) in HapG were reshaped by transposable elements and tandem duplication, showing an enrichment of genes related to resistance and lipid metabolism. Transcriptomically, 82.2% of allelic gene pairs maintained balanced expression, accompanied by parental functional complementarity and dosage buffering, revealing a potential regulatory basis for coordinating parental genetic differences in the hybrid genome. These haplotype-resolved genomic resources offer vital targets for understanding heterosis and accelerating oil palm molecular breeding.

Bacterial genome-wide association studies (GWAS) offer a powerful approach to identify the genetic basis of a trait measured in a set of sequenced isolates. As the number of sequenced isolates has grown, the limiting factor for GWAS has become phenotyping enough isolates to achieve statistical power. To overcome the need for large-scale phenotyping, we developed Bayesian Adaptive Sequential Sampling GWAS (BASS-GWAS), which couples Bayesian adaptive experimental design with a sparse regression model to select maximally informative isolates for phenotypic testing. BASS-GWAS efficiently recovered causal loci for three antimicrobial resistance traits in Neisseria gonorrhoeae, requiring many fewer phenotyped isolates than random sampling. We applied BASS-GWAS to discover variants enabling gyrBD429N-dependent cross-resistance to the novel topoisomerase inhibitors zoliflodacin and gepotidacin. After phenotyping fewer than 30 isolates, we identified and then validated both parCD86N and a gyrA-parE-based pathway as enabling cross-resistance. BASS-GWAS provides a practical and statistically principled solution for efficient bacterial GWAS.

Unmeasured confounding threatens causal inference and replicability in observational multi-omic studies across variable environments. Genetic instrumental variables (Mendelian randomization) and negative-control calibration each address complementary sources of unmeasured confounding, yet no existing framework unifies them for omics-scale mediation analysis. We introduce ICONIC, an R package that embeds genetic instruments and negative controls within a proximal causal inference framework for total-effect and mediation analysis. ICONIC implements eight estimators spanning five confounding-control strategies, supports continuous, binary, and time-to-event outcomes, and provides extensive diagnostics including sensitivity analyses that map estimator performance across plausible assumptions. Ground-truth benchmarks are calibrated to real-omics covariance structures via a hybrid generative model (GAN + feature-level Gaussian copula) rather than parametric simulation, and a companion planning tool predicts performance gains from collecting additional omic data. We demonstrate ICONIC in two case studies: identifying placental transcriptomic mediators of gestational diabetes on birth weight (n = 164), and tumor-expression mediators of smoking intensity on lung cancer survival (n = 494). Notably, ICONIC's diagnostics recommended different estimation strategies across the two scenarios, reflecting differences in the likely influence of unmeasured confounding. ICONIC is freely available at https://github.com/sbresnahan/iconic/.

Spinal presynaptic inhibitory interneurons are thought to regulate proprioceptive sensory feedback to shape motor output, however, their specific contribution to motor behavior has been difficult to assess, partially due to the lack of a specific genetic handle. Here, we identify Sall3 as the transcription factor required for the establishment and maintenance of GABApre axo-axonic synapses on proprioceptive Ia afferent terminals. Loss of Sall3 in mice selectively eliminates GABApre boutons on Ia afferent terminals, resulting in altered sensory-evoked motor responses and impaired skilled locomotor behaviors. Together, these findings establish Sall3 as a key regulator of GABApre circuit development and provide a genetic framework for understanding how presynaptic inhibition shapes sensorimotor integration.