Biology subjects

Molla, G.

Publications and source records attributed to Molla, G..

7 recordsLinked to original sources

bioRxiv2026

Variation in enhancer activity across brain regions defines neurological disease risk and shapes cellular pathology

Genetic variants associated with neurological traits are enriched in enhancers active in neurons. However, there are many types of neurons with varying functions across the brain, making it difficult to pinpoint insights into disease pathology. We set out to investigate whether cell type and brain region specificity of transcriptional enhancers could reveal new insights into the cellular pathology of neurological traits. We performed H3K27ac ChIP-seq on neurons and glia sorted from post-mortem tissue for six brain regions in triplicate and integrated these datasets with existing single-cell ATAC-seq data. While 87% of neuronal enhancers had similar activity across brain regions, we identified 40,049 neuronal regulatory elements that vary in activity across brain regions, which we termed Brain Region Variable Elements (BRVEs). Some BRVEs reflect differences in cell composition, such as a high proportion of medium spiny neurons (MSNs) in the nucleus accumbens, whereas others capture developmental trajectories, including enhancers with robust activity across telencephalic regions that are inactive in the diencephalic hypothalamus. Genetic variants within BRVEs are disproportionately enriched for the heritability of neurological traits, capturing far more heritability than neuronal enhancers with uniform activity across the brain. Additionally, genes linked to BRVEs are also more likely to be implicated in neuropsychiatric disorders by differential expression studies. Finally, we present a new method, GWAS-LOCATE, that leverages variation in enhancer activity across the brain to assign 23,494 GWAS loci to specific cell types and brain regions, including 9.8% linked to MSNs. This includes a BMI risk locus linked to the transcription factor ISL1. ISL1 knockdown in iPSC-derived MSNs revealed differentially expressed genes enriched for BMI heritability, supporting a role for ISL1-mediated MSN pathways with BMI. Collectively, these findings demonstrate that the enhancer specificity across the brain provides a powerful framework for dissecting complex trait biology and revealing cellular pathology.

genetics↗

bioRxiv2026

Organism-scale annotation with Pan-human Azimuth

Single-cell atlases now span many human tissues, but inconsistent annotations across studies limit their utility as a unified reference. We introduce Pan-human Azimuth, a supervised neural network that maps human cells from diverse tissues and datasets onto a single hierarchical organism-scale typology. Developed through NIH HuBMAP, the model is trained on a uniquely curated corpus designed to maximize diversity across tissues and technologies while enforcing uniform, interpretable annotations and stringent quality control. The use of a single organism-wide reference enables us to map tens of millions of cells in the Tabula Sapiens and scBaseCamp repositories, perform cross-tissue comparisons across thousands of samples, and identify striking tissue specialization among fibroblast states. Pan-human Azimuth naturally extends to annotating spatial transcriptomic data, recovering canonical kidney cortical structures and distinguishing glomerular states consistent with expert pathology. We release Pan-human Azimuth alongside cloud, R, and Python interfaces to facilitate standardized organism-wide single-cell analysis.

genomics↗

bioRxiv2026

Bioinf-Farma: supervised integration of epitope prediction and recombinant protein developability for automated vaccine candidate prioritization

Vaccine antigen discovery requires prioritizing protein candidates according to both immunogenic potential and recombinant expression feasibility. These properties are typically evaluated using separate computational tools, requiring researchers to integrate heterogeneous outputs through ad hoc workflows. Here, we present BIOINF-farma, a modular platform integrating epitope prediction and developability assessment for rational antigen selection within a unified environment. Candidates can be submitted as amino acid sequences or three-dimensional structures. When experimental structures are unavailable, BIOINF-farma automatically searches for models in AlphaFold DB or performs structure prediction using Boltz-2, ensuring a standardized structural representation for downstream analyses. Antigenicity is quantified by combining structure-based conformational epitope signals (MLCE/REBELOT-BEPPE) and sequence-based linear epitope propensity scores (BepiPred 3.0) into a protein-level Antigenicity Score, with a classification threshold optimized on a manually curated validation dataset. Developability is evaluated through two supervised Random Forest meta-learners that integrate three solubility predictors (DeepSoluE, SoluProt, Protein-Sol) and three thermal stability predictors (TemStaPro, ProLaTherm, BertThermo), whose outputs are combined into an Expression Efficiency Score (EES). By integrating complementary predictive signals, the meta-learning framework achieves greater accuracy and robustness than individual predictors while maintaining performance across a broad range of sequence identities. The Antigenicity Score effectively discriminates antigenic from non-antigenic proteins with a large effect size, whereas EES successfully distinguishes soluble from insoluble outcomes on an independent panel of recombinant proteins expressed in Escherichia coli. BIOINF-farma jointly assesses antigenicity and expression feasibility within a single framework. Its modular architecture facilitates the incorporation of future predictive methods, while its web-based interface makes the full pipeline accessible to users without programming expertise, supporting rapid candidate triage in vaccine research and emerging pathogen responses. Author SummaryVaccine development begins with a critical step: identifying, among the many proteins encoded in a pathogen genome, those most suitable as candidate antigens. A promising candidate must satisfy two requirements that are rarely evaluated together. It must be recognized by the immune system, so that vaccination elicits a protective response; and it must be amenable to recombinant production, since antigens that cannot be obtained in sufficient quantity and quality are of limited practical use. Current computational tools typically address only one of these aspects, and researchers must integrate their outputs manually, through procedures that are time-consuming and prone to inconsistency. We developed BIOINF-farma, an automated platform that brings these two assessments into a single analytical framework. Starting from a protein sequence or an experimental structure, the platform retrieves or predicts a three-dimensional model, evaluates the proteins antigenic potential by combining complementary epitope predictors, and estimates its expression feasibility by integrating multiple solubility and stability predictors through supervised machine learning. A web-based interface makes the full workflow available to experimental immunologists and vaccine developers without requiring computational expertise, supporting rational candidate prioritization in routine vaccine research and during emerging pathogen responses.

bioinformatics↗

bioRxiv2026

Drosophila melanogaster as a platform for the functional expression of engineered PET-degrading enzymes

Insects offer promising opportunities for organic waste bioconversion; however, they cannot efficiently degrade synthetic polymers such as polyethylene terephthalate (PET). Here, we generated transgenic Drosophila melanogaster lines to express in vitro-evolved variants of two PET-degrading enzymes with distinct biochemical properties: an engineered Ideonella sakaiensis PETase variant (TS-{Delta}IsPET) and a leaf-branch compost cutinase variant (TA-{Delta}LCC). Both enzymes, fused to a Drosophila gut-derived secretory signal, were produced and secreted by both Drosophila cultured S2R+ cells and transgenic larvae. Both enzymes were glycosylated upon secretion, a post-translational modification that did not abolish their catalytic activity. Notably, TA-{Delta}LCC displayed [~]6-fold higher esterase activity than TS-{Delta}IsPET in larval extracts and TA-{Delta}LCC-containing extracts depolymerised PET nanoparticles in vitro under enzyme-favourable conditions. Transgenic flies showed normal development, fertility and survival. Morphological and biochemical analysis confirmed that TA-{Delta}LCC expression did not alter midgut structure and function. Together, these results establish Drosophila melanogaster as a model for functional expression and comparative evaluation of engineered PET-degrading enzymes and identify TA-{Delta}LCC as a promising candidate for exploitation in insect species relevant to plastic contaminated waste bioconversion. HighlightsO_LITransgenic D. melanogaster enables in vivo study of engineered PET enzymes C_LIO_LIEngineered TS-{Delta}IsPET and TA-{Delta}LCC are functional in larval extracts C_LIO_LITA-{Delta}LCC was selected for PET nanoparticle assays due to higher pNPA activity C_LIO_LID. melanogaster model enables comparative evaluation of PET-degrading enzymes C_LI

biochemistry↗

bioRxiv2024

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRAs Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

bioinformatics↗

bioRxiv2024

A Panoramic View of Cell Population Dynamics in Mammalian Aging

To elucidate the aging-associated cellular population dynamics throughout the body, here we present PanSci, a single-cell transcriptome atlas profiling over 20 million cells from 623 mouse tissue samples, encompassing a range of organs across different life stages, sexes, and genotypes. This comprehensive dataset allowed us to identify more than 3,000 unique cellular states and catalog over 200 distinct aging-associated cell populations experiencing significant depletion or expansion. Our panoramic analysis uncovered temporally structured, organ- and lineage-specific shifts of cellular dynamics during lifespan progression. Moreover, we investigated aging-associated alterations in immune cell populations, revealing both widespread shifts and organ-specific changes. We further explored the regulatory roles of the immune system on aging and pinpointed specific age-related cell population expansions that are lymphocyte-dependent. The breadth and depth of our cell-omics methodology not only enhance our comprehension of cellular aging but also lay the groundwork for exploring the complex regulatory networks among varied cell types in the context of aging and aging-associated diseases. One Sentence SummaryPanSci, a single-cell transcriptome atlas of over 20 million cells throughout the mouse lifespan, unveils the temporal architecture of aging-associated cellular population dynamics, organ-specific immune cell shifts, and the lymphocytes role in organismal aging.

genomics↗

bioRxiv2022

Dictionary learning for integrative, multimodal, and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets represents a powerful alternative to unsupervised analysis. Reference datasets, however, are predominantly constructed from single-cell RNA-seq data, and cannot be used to annotate datasets that do not measure gene expression. Here we introduce bridge integration, a method to harmonize singlecell datasets across modalities by leveraging a multi-omic dataset as a molecular bridge. Each cell in the multi-omic dataset comprises an element in a dictionary, which can be used to reconstruct unimodal datasets and transform them into a shared space. We demonstrate that our procedure can accurately harmonize transcriptomic data with independent single cell measurements of chromatin accessibility, histone modifications, DNA methylation, and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to substantially improve computational scalability, and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach aims to broaden the utility of single-cell reference datasets and facilitate comparisons across diverse molecular modalities. AvailabilityInstallation instructions, documentations, and vignettes are available at http://www.satijalab.org/seurat

genomics↗