Biology subjects

Zapata, G.

Publications and source records attributed to Zapata, G..

5 recordsLinked to original sources

bioRxiv2026

Scan Cluster: A versatile database-independent prediction tool for multi-genome identification of homologous gene clusters.

The exploration of colocalized gene sets, such as Biosynthetic Gene Clusters (BGCs) and symbiotic islands, is fundamental in modern genome mining. However, many existing prediction tools rely heavily on curated databases or predefined rules, inherently biasing detection toward known clusters. To address these limitations, we introduce Scan Cluster, a robust and flexible Python-based bioinformatic tool designed to identify user-defined, conserved gene clusters across diverse genomes without database-driven constraints. Scan Cluster leverages BLAST or HMMER to detect homologous proteins and evaluates their colocalization, accommodating complex evolutionary events such as orientation inversions, the insertion of alien genes, gene deletions, and the integration of insertion sequences. Beyond identification, the software performs progressive multiple cluster alignments and generates distance trees to assess cluster similarity, producing outputs ready for visualization in iTOL and Clinker. We validated Scan Cluster against standard tools like antiSMASH and DeepBGC, demonstrating high accuracy in delineating complete cluster boundaries. Its utility was further confirmed through the analysis of the nos and complex nod-nif-fix symbiotic gene clusters in rhizobia strains, successfully tracking genetic decay and grouping diverse architectures. Scan Cluster provides an accessible, low-resource framework to explore the evolutionary and functional dynamics of novel genetic clusters.

bioinformatics↗

bioRxiv2026

Compromised striatal structure and function in mouse models of RARB-related disorder

Dominant variants in the retinoic acid receptor beta (RARB) gene cause a complex disorder known as RARB-related disorder (RARB-RD), characterized by multiple congenital anomalies, global developmental delay, and dystonia. RARB-RD variants have been classified as either gain-of-function (GOF) or dominant-negative (DN) based on their cell-based transcriptional responses to retinoids. To investigate the mechanisms underlying this disorder, we generated mouse models carrying either the p.R387C or p.L402P RARB-RD variant, previously categorized as GOF and DN, respectively. Homozygous mice for either RARB-RD variant died perinatally with colonic aganglionosis, while heterozygous mice survived and recapitulated several features of RARB-RD. In addition to microphthalmia, both RarbR387C/+and RarbL402P/+ mice exhibited progressive coordination deficits, increased active-phase locomotor activity, and cognitive impairment in the novel object recognition test. In contrast, mice heterozygous for a null allele of Rarb (Rarb+/-) did not display these abnormalities. In the brain, Rarb is predominantly expressed in the two major populations of projection neurons of the striatum recognizable by the expression of dopamine receptors D1R/Drd1 and D2R/Drd2. Marker analysis revealed a reduction in Drd2-expressing neurons without changes in Drd1-expressing neurons in both RARB-RD models. Furthermore, RARB-RD mice showed partial resistance to the cataleptic effects of haloperidol, a D2R-specific antagonist. These behavioral, cellular, and dopaminergic deficits--though not the cognitive impairments--have previously been observed in Rarb-/- mice. To determine whether the in vitro effects of RARB-RD variants correlate with distinct transcriptional signatures in vivo, we compared the striatal transcriptome of RarbR387C/+, RarbL402P/+, Rarb-/- and Rarb+/-mice with their littermate controls. We found that the heterozygous RARB-RD variants and the homozygous null allele affected a large subset of common genes, with putative direct RARB targets predominantly downregulated. Notably, the transcriptional impact of the RARB-RD variants was more profound than that of the null allele, regardless of zygosity. Additionally, transcriptional changes in RARB-RD mice extensively overlapped with those observed in mouse models of Huntingtons disease, suggesting shared mechanisms affecting neuronal survival in the striatum. We conclude that the p.R387C and p.L402P variants similarly compromise striatal integrity and function, likely through a DN mechanism. Progressive emergence of most neurologic deficits highlights a potential therapeutic window. Our results support the development of strategies aimed at silencing RARB-RD alleles.

neuroscience↗

bioRxiv2025

Integrated Metabolic Complex Genetic Interaction 1 Network of Chromosome 4p Loss in Basal Breast Cancer

Basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and exhibits a recurrent large chromosomal deletion in chromosome 4p (chr4p). Chr4p loss is associated with poor survival, evolves early in tumorigenesis and confers on cells a proliferative state. Here, we map the integrated metabolic complex genetic interaction network of chr4p in basal breast cancer to identify targetable vulnerabilities. Differential gene expression analysis of patient derived xenografts and cancer cell models revealed that chr4p loss is associated with changes in cellular energetics and reduction/oxidation balance. Analysis of DepMap pooled genome-wide CRISPR-Cas9 screens identified complex genetic interactions specific to chr4p deletion in basal breast cancer cell models. Functional assays revealed that chr4p loss is associated with disrupted mitochondrial respiratory function and reduced glycolytic capacity, suggesting metabolic rewiring. Increased reactive oxygen species and lipid peroxidation compromised antioxidant defense mechanisms. Ultimately, this study sheds light on targeted therapies for basal breast cancer harboring large chromosomal deletions.

genomics↗

bioRxiv2025

PARPAL: PARalog Protein Redistribution using Abundance and Localization in Yeast Database

Whole-genome duplication (WGD) events are common across various organisms however the retention and evolution of WGD paralogs is not fully understood. Quantitative measure of protein redistribution in response to the deletion of their WGD paralog provides insight into sources of gene retention. Here, we describe PARPAL (PARalog Protein Retribution using Abundance and Localization in Yeast), a web database that houses results of high-content screening and deep learning neural network analysis of the redistribution of 164 proteins reflecting how their subcellular localization and protein abundance change in response to their paralog deletion. We interrogated a total of 82 paralog pairs in two genetic backgrounds for a total of [~]3,500 micrographs of [~]460,000 cells. PARPAL also links to other studies on trigenic interactions, protein-protein interactions and protein abundance. PARPAL is available at https://parpal.c3g-app.sd4h.ca and is a valuable resource for the yeast community interested in understanding the retention and evolution of paralogs and can help researchers to investigate protein dynamics of paralogs in other organisms.

systems biology↗

bioRxiv2017

Capture-based DNA methylation sequencing facilitates diagnosis and reveals potential pathogenic mechanisms in teratogenic diabetes exposure

Diabetic embryopathy (DE) describes a spectrum of birth defects associated with a teratogenic exposure to maternal diabetes in utero. These defects strongly overlap the phenotypes of known genetic syndromes; however, the pathogenic mechanisms underlying DE remain uncertain and there are no definitive tests that distinguish the diagnosis. Here, we explore the potential of DNA methylation as both a diagnostic biomarker and a means of informing disease pathogenesis in DE. Capture-based bisulfite sequencing was used to compare patterns of DNA methylation at 2,800,516 sites genome-wide in seven DE neonates and 11 healthy neonates, including five with in utero diabetes exposure. DE infants had significantly lower global DNA methylation (ANOVA, Tukey HSD p=0.045) than diabetes-unexposed, healthy controls (UH), with multiple sites showing large (mean methylation difference = 16.6%) and significant (p<0.001) differential methylation between the two groups. We found that a subset of 237 highly differentially methylated loci could accurately distinguish DE infants from both UH and diabetes-exposed healthy infants (sensitivity 80% -100%). Differentially methylated sites were enriched in intergenic (p<3.52x10-15) and intronic (p<0.001) regions found proximal to genes either associated with Mendelian syndromes that overlap the DE phenotype (e.g. TRIO, ANKRD11), or known to influence early organ development (e.g. BRAX1, RASA3). Further, by integrating information on cis-sequence variation, we found that 39.3% of loci with evidence for allele-specific methylation also showed differential methylation between DE and controls. Our study suggests a role for aberrant DNA methylation and cis-sequence variation in the pathogenesis of DE, and highlights the diagnostic potential of DNA methylation for teratogenic birth defects.

genetics↗