Biology subjects

Lal, A.

Publications and source records attributed to Lal, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Deep learning design and in vivo validation of Müller glia-specific cis-regulatory elements

Effective recombinant adeno-associated virus gene therapies require promoters that are compact and cell-type-specific. Ideally, promoters should also exhibit functional conservation when tested in model organisms to ensure that preclinical findings translate reliably to human patients. Here, we introduce a deep learning framework for designing cis-regulatory elements (CREs) meeting these criteria, applied to retinal Muller glia (MG). Using single-cell chromatin accessibility data from human and mouse retinas, we trained species-specific models to predict cell-type accessibility, and designed compact CREs using two complementary strategies. In silico validation predicted that the designed CREs exhibit high MG-specific accessibility (on-target) in both species with minimal off-target accessibility across hundreds of human cell types and tissues. Mechanistic analysis revealed that the predicted MG accessibility is driven by the creation of LHX2 motifs. In vivo validation confirmed that the designed CREs successfully restrict reporter expression to MG in the murine retina. Our deep learning framework is highly generalizable and enables the rapid design of compact, on-target, and species-conserved CREs for precision gene therapy.

genomics↗

bioRxiv2026

Agentic systems are adept at solving well-scoped, verifiable problems in computational biology

We introduce CompBioBench, a benchmark of 100 diverse tasks for evaluating agentic systems in computational biology. Unlike mathematics and programming, which more readily admit systematic verification, biological data are inherently noisy and open to interpretation. To enable objective evaluation without reducing tasks to prescriptive checklists, we propose a new benchmark construction strategy based on synthetic/augmented data and metadata scrambling/scrubbing of real datasets to create challenging problems that have a single ground-truth answer and require multi-step reasoning, tool use, bespoke code, and interaction with real-world external resources. The benchmark spans genomics, transcriptomics, epigenomics, single-cell analysis, human genetics, and machine learning workflows. Questions are curated by domain experts to cover a broad range of skills with varying difficulty. We evaluate leading general-purpose agentic systems starting from a bare-minimum environment, requiring them to fetch data and tools as needed to solve each problem. We find strong end-to-end performance, with Codex CLI (GPT 5.4) reaching 83% accuracy, Gemini CLI (3.1 Pro) reaching 82%, Claude Code (Opus 4.6) reaching 81%, and Claude Code (Opus 4.7) reaching 78%. On the hardest questions, Claude Code (Opus 4.6) reaches 69%, Codex CLI (GPT 5.4) reaches 59%, and Gemini CLI (3.1 Pro) reaches 49%. CompBioBench provides a practical testbed for measuring the progress of agentic systems in computational biology and for guiding future benchmark design. Data and a public leaderboard are available at https://huggingface.co/collections/Genentech/compbiobench-v1.

bioinformatics↗

bioRxiv2026

Non-canonical function of the splicing activator U2AF2 in promoting intron retention in the lncRNAs PURPL and MALAT1

Intron retention (IR) is increasingly recognized as a feature of long noncoding RNAs (lncRNAs), yet the mechanisms that shape IR in lncRNAs and the functional consequences of this process remain largely unexplored. To investigate how IR contributes to lncRNA regulation, we performed a genome-wide screen to identify factors controlling IR in the lncRNA PURPL. This approach uncovered a prominent role for U2AF2, which promotes retention of a specific intron in PURPL through a weak polypyrimidine tract. IR of this intron drives nuclear enrichment of PURPL and enhances cell proliferation, revealing biological relevance. Transcriptome-wide analyses showed that although U2AF2 broadly supports canonical splicing consistent with its well-established function in promoting splicing, it also facilitates IR within a distinct subset of RNAs, including the nuclear speckle-associated lncRNA MALAT1. Loss of U2AF2 disrupts MALAT1 speckle localization and using MALAT1 knockout cells reconstituted with wild-type or intron deleted variants, we identified a single intron critical for MALAT1s speckle localization. Deletion of this intron from endogenous MALAT1 impaired speckle localization and reduced cell migration, phenocopying the loss of MALAT1. Together, these findings reveal IR as a key regulatory mechanism governing lncRNA localization and function and uncover an unexpected role for U2AF2 in promoting IR within specific lncRNA contexts.

cell biology↗

bioRxiv2025

Nona: A unifying multimodal masking framework for functional genomics

The non-coding genome encodes complex regulatory logic that orchestrates gene expression and cell identity. While machine learning models for functional genomics have advanced our understanding of the cis-regulatory code, sequence-to-function models, DNA language models, and generative models have evolved as separate paradigms despite probing the same underlying regulatory biology. We introduce Nona, a multimodal masked modeling framework that unifies these paradigms by learning jointly from DNA sequence and base-resolution functional genomics data. Beyond unifying existing modeling paradigms, Nona enables entirely new modeling objectives. We demonstrate its versatility through three applications: (1) a context-aware sequence-to-function model that improves local predictions by up to 13% by correcting systematic errors in sequence-to-function predictions; (2) a functional language model that integrates functional data into language modeling, learns relevant regulatory sequence motifs, and enables regulatory element design through masked discrete diffusion; (3) functional genotyping, which reveals an unrecognized privacy vulnerability in processed ATAC-seq data and re-identifies individuals from genetic databases with perfect accuracy. Together, these results establish masking as a universal interface for integrated modeling of functional genomics data, unifying disparate approaches while opening new directions for understanding and engineering the regulatory genome.

genomics↗

bioRxiv2025

Human wastewater contamination drives the emergence of novel multidrug resistant bacteria in the Galapagos marine ecosystem.

Since the publication of Charles Darwins On the Origin of Species in 1859, the Galapagos archipelago has become emblematic of natural selection and evolution. While the lens of evolution in the Galapagos has traditionally focused on iconic megafauna, including finches, marine iguanas, and giant tortoises, the marine environment is also home to diverse microbial ecosystems that are constantly evolving under selective pressure from environmental factors such as human activity. We focused on the second most populated island within the archipelago -- San Cristobal -- an island that has experienced rapid urbanization and intense international tourism pressure. Using a lab-free approach, we spatiotemporally mapped wastewater contamination around San Cristobal. On-site metagenomic sequencing revealed a stark shift in genera and a higher count of antimicrobial resistance genes at wastewater-associated sites. Over 40% of lactose-fermenting Enterobacteriaceae isolates collected from sewage and wastewater outfall exhibited multidrug resistance (MDR). Long-read sequencing and de novo assembly of bacterial genomes and plasmids from MDR Escherichia coli revealed frequent and rapid reassortment of antimicrobial resistance genes on plasmids, generating unique antimicrobial resistance profiles. This study not only provides a framework for conducting antimicrobial resistance research in low-resource settings but also underscores the impact of wastewater contamination on the environmental AMR landscape and highlights potential threats to human and animal health.

microbiology↗

bioRxiv2025

Sigma-1 Receptor Promotes Glycolysis in Neuronal Systems by Suppressing GRIM19

Sigma-1 receptor (S1R) is a Ca2+ sensitive, ligand-operated receptor chaperone protein present on the endoplasmic reticulum (ER) membrane and more specifically at the mitochondria-associated ER membrane (MAM). Upon activation by ER calcium depletion or ligand binding, S1R can increase calcium efflux from the ER into the mitochondria by chaperoning IP3 receptor type3 (Ip3R3). Mitochondrial metabolism has an intricate relationship with glycolysis. Despite S1R affecting mitochondria, the relevance of S1R to glycolysis and its impact on the overall cellular energy metabolism is not known. This study utilizes wild-type (Wt) and S1R knockout (S1R KO) Neuro2a (N2a) cells and Wt and S1R KO mice for primary culture of cortical neurons studies and longitudinal in-vivo imaging. In this manuscript we describe the fundamental functions of S1R on glycolysis, mitochondrial activity and NAD+/NADH metabolism, keystone coenzymes essential for glycolysis and for mitochondrial activity. Both N2a cells and cortical neurons lacking S1R had reduced glycolytic activity, and increased mitochondria complex I protein GRIM19 but no change in mitochondrial oxygen consumption. Furthermore, we observed an increased NAD+/NADH ratio in S1R KO condition. Positron emission tomography revealed decreased [18F]fluorodeoxyglucose brain uptake in S1R KO mice. We observed that knocking down GRIM19 in S1R KO condition rescued the glycolysis deficit. Altogether, these data show for the first time that S1R modulates glycolysis and NAD metabolism in various neuronal systems. This new insight on the S1R function may lead to new therapeutic applications of S1R ligands where compromised glycolysis and cellular NAD+/NADH ratios occur such as aging and neurodegeneration.

cell biology↗

bioRxiv2025

HNRNPH1-mediated splicing events regulate EIF4G1 transcript variant composition and the organization of the AURKA 5' UTR

HNRNPH1 is a regulator of alternative splicing, but few studies have defined the splicing events it mediates. Here, we used short- and long-read RNA sequencing to interrogate the transcriptome-wide effects of HNRNPH1 depletion and its regulation of specific splicing events. Differential alternative splicing analysis revealed effects on the transcriptome that involved all splice event categories. We confirmed HNRNPH1s regulation of a splicing event involving TCF3-exons 18a and 18b that encode distinct TCF3 transcription factor isoforms. Extending this finding, we present evidence that in neuroblastoma, HNRNPH1 is a MYCN target, potentially explaining the higher levels of HNRNPH1 and TCF3-exon 18a transcript variants in this tumor type. Analysis of two skipped exon events determined that HNRNPH1 regulates the splicing of exons encoding part of the EIF4G1 translation initiation factors N-terminus and an exon included in the 5UTR of specific transcript variants encoding the mitotic kinase AURKA. Using reporter constructs, we show this AURKA 5UTR exon enhances expression, suggesting HNRNPH1 could contribute to regulating AURKA protein levels. Our findings highlight HNRNPH1s roles in regulating the expression of proteins with diverse cellular functions. Key pointsO_LIHNRNPH1 regulates the expression of proteins with diverse cellular functions, including proteins involved in the regulation of gene expression and essential cellular mechanisms. C_LIO_LIDepletion of HNRNPH1 alters the expression of specific protein-coding EIF4G1 transcript variants. C_LIO_LIHNRNPH1 mediates the inclusion of an AURKA 5UTR exon that enhances protein expression. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/667222v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@13780f1org.highwire.dtl.DTLVardef@f276d8org.highwire.dtl.DTLVardef@588245org.highwire.dtl.DTLVardef@d08551_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

GAME: Genomic API for Model Evaluation

The rapid expansion of genomics datasets and the application of machine learning has produced sequence-to-activity genomics models with ever-expanding capabilities. However, benchmarking these models on practical applications has been challenging because individual projects evaluate their models in ad hoc ways, and there is substantial heterogeneity of both model architectures and benchmarking tasks. To address this challenge, we have created GAME, a system for large-scale, community-led standardized model benchmarking on user-defined evaluation tasks. We borrow concepts from the Application Programming Interface (API) paradigm to allow for seamless communication between pre-trained models and benchmarking tasks, ensuring consistent evaluation protocols. Because all models and benchmarks are inherently compatible in this framework, the continual addition of new models and new benchmarks is easy. We also developed a Matcher module powered by a large language model (LLM) to automate ambiguous task alignment between benchmarks and models. Containerization of these modules enhances reproducibility and facilitates the deployment of models and benchmarks across computing platforms. By focusing on predicting underlying biochemical phenomena (e.g. gene expression, open chromatin, DNA binding), we ensure that tasks remain technology-independent. We provide examples of benchmarks and models implementing this framework, and anticipate that the community will contribute their own, leading to an ever-expanding and evolving set of models and evaluation tasks. This resource will accelerate genomics research by illuminating the best models for a given task, motivating novel functional genomic benchmarks, and providing a more nuanced understanding of model abilities.

bioinformatics↗

bioRxiv2025

p53-induced RNA-binding protein ZMAT3 inhibits transcription of a hexokinase to suppress mitochondrial respiration

The tumor suppressor p53 is a transcription factor that controls the expression of hundreds of genes. Emerging evidence indicates that the p53-induced RNA-binding protein ZMAT3 acts as a key splicing regulator that contributes to p53-dependent tumor suppression in vitro and in vivo. However, the mechanism by which ZMAT3 functions within the p53 pathway remains largely unclear. Here, we discovered a function of ZMAT3 in inhibiting transcription of HKDC1, a hexokinase that regulates glucose metabolism and mitochondrial respiration. Quantitative proteomics revealed HKDC1 as the most significantly upregulated protein in ZMAT3-depleted colorectal cancer cells. ZMAT3 depletion resulted in increased mitochondrial respiration, which was rescued by simultaneous depletion of HKDC1, suggesting that HKDC1 is a critical downstream effector of ZMAT3. Unexpectedly, ZMAT3 did not bind to HKDC1 RNA or DNA; however, proteomic analysis of the ZMAT3 interactome identified its interaction with the oncogenic transcription factor JUN. ZMAT3 depletion enhanced JUN binding to the HKDC1 locus, leading to increased HKDC1 transcription that was rescued upon JUN depletion, suggesting that JUN activates HKDC1 transcription in ZMAT3-depleted cells. Collectively, these findings uncover a mechanism by which ZMAT3 regulates transcription through JUN and demonstrate that HKDC1 is a key component of the ZMAT3-regulated transcriptome in the context of mitochondrial respiration regulation.

cancer biology↗

bioRxiv2024

Decoding sequence determinants of gene expression in diverse cellular and disease states

Sequence-to-function models that predict gene expression from genomic DNA sequence have proven valuable for many biological tasks, including understanding cis-regulatory syntax and interpreting non-coding genetic variants. However, current state-of-the-art models have been trained largely on bulk expression profiles from healthy tissues or cell lines, and have not learned the properties of precise cell types and states that are captured in large-scale single-cell transcriptomic datasets. Thus, they lack the ability to perform these tasks at the resolution of specific cell types or states across diverse tissue and disease contexts. To address this gap, we present Decima, a model that predicts the cell type- and condition- specific expression of a gene from its surrounding DNA sequence. Decima is trained on single-cell or single-nucleus RNA sequencing data from over 22 million cells, and successfully predicts the cell type-specific expression of unseen genes based on their sequence alone. Here, we demonstrate Decimas ability to reveal the cis-regulatory mechanisms driving cell type-specific gene expression and its changes in disease, to predict non-coding variant effects at cell type resolution, and to design regulatory DNA elements with precisely tuned, context-specific functions.

genomics↗

bioRxiv2024

gReLU: A comprehensive framework for DNA sequence modeling and design

Deep learning models are increasingly being used to perform a variety of tasks on DNA sequences, such as predicting tissue- and cell type-specific sequence activity, deriving cis-regulatory rules, predicting non-coding variant effects, and designing synthetic regulatory sequences. However, these models require specialized knowledge to build, train and interpret correctly. In addition, the field is hampered by the lack of interoperability between models and software built by different groups. Here we present gReLU, a comprehensive software framework that enables users to easily execute advanced sequence modeling pipelines, including data preprocessing, model training, hyperparameter tuning, evaluation, interpretation, variant effect prediction, and design of novel regulatory elements. The software is accompanied by a model zoo containing state-of-the-art pre-trained models that can easily be downloaded, applied, and fine-tuned. This framework and resources will accelerate research across the field of DNA sequence modeling and enable the effective design of synthetic regulatory elements.

genomics↗

bioRxiv2024

regLM: Designing realistic regulatory DNA with autoregressive language models

Cis-regulatory elements (CREs), such as promoters and en-hancers, are DNA sequences that regulate the expression of genes. The activity of a CRE is influenced by the order, composition and spacing of sequence motifs that bind to proteins called transcription factors (TFs). Synthetic CREs with specific properties are needed for biomanufacturing as well as for many therapeutic applications including cell and gene therapy. Here, we present regLM, a framework to design synthetic CREs with desired properties, such as high, low or cell type-specific activity, using autoregressive language models in conjunction with supervised sequence-to-function models. We used our framework to design synthetic yeast promoters and cell type-specific human enhancers. We demonstrate that the synthetic CREs generated by our approach are not only predicted to have the desired functionality but also contain biological features similar to experimentally validated CREs. regLM thus facilitates the design of realistic regulatory DNA elements while providing insights into the cis-regulatory code.

genomics↗

bioRxiv2023

Polygraph: A Software Framework for the Systematic Assessment of Synthetic Regulatory DNA Elements

The design of regulatory elements is pivotal in gene and cell therapy, where DNA sequences are engineered to drive elevated and cell-type specific expression. However, the systematic assessment of synthetic DNA sequences without robust metrics and easy-to-use software remains challenging. Here, we introduce Polygraph, a Python framework that evaluates synthetic DNA elements, based on features like diversity, motif and k-mer composition, similarity to endogenous sequences, and screening with predictive and foundational models. Polygraph is the first instrument for assessing synthetic regulatory sequences, enabling faster progress in therapeutic interventions and improving our understanding of gene regulatory mechanisms.

bioinformatics↗

bioRxiv2023

Bench-ML: A Benchmarking Web Interface for Machine Learning Methods and Models in Genomics

Machine learning is a complex but essential technology in genomics data analysis and its popularity has increased the rate of new methodological approaches published but this raises the question of how models should be benchmarked and validated. Bench-ML is a generalizable and easy to use web interface for benchmarking and validation that can preprocess data, train, test, evaluate and compare machine learning algorithms for genomics. It makes benchmarking machine learning methods more accessible by enabling genomics scientists to perform end-to-end analyses, visualize results and evaluate performance or metrics to compare methods and models by providing a point of reference using only a web browser. To improve something it needs to be measured; To benchmark and evaluate models Bench-ML provides several strategies, methodologies, and tools to generate measurements and visualizations to track experiments to help identify areas of opportunity using metrics such as loss and accuracy, model visualization, learning and saturation curves, principal component analysis, feature scoring, confusion matrix, regression for training and test data, mean absolute error, etc. Bench-ML explains the different options to test and validate machine and deep learning models to identify problematic areas and potentially improve performance. Bench-ML provides several strategies to improve performance like showing when a model is not performing or when different hyperparameters values could be needed, it also helps fine tune hyperparameter values and to identify accuracy across multiple classes and from these classes which class could affect performance. The selection, development, and comparison of machine learning methods and models in genomics datasets can be a daunting task based on the goals of a particular study or the target problem. Machine learning is very good at pattern recognition but modeling the world is much more than that so how to know if a machine learning method or model is performing at a good sensitivity and specificity in large genomics datasets is still a big problem and this is where Bench-ML can help.

bioinformatics↗

bioRxiv2022

The efficiency of Grignard Pure™ to inactivate airborne SARS-CoV-2 surrogate

Grignard Pure (GP) is a unique and proprietary blend of Triethylene Glycol (TEG) and inert ingredients designed for continuous antimicrobial treatment of air. GP received approval from the US EPA under its Section 18 Public Health Emergency Exemption program for use in seven states. This study characterizes the efficacy of GP for inactivating MS2 bacteriophage - a non-enveloped virus widely used as a surrogate for SARs-CoV-2. Experiments measured the decrease in the airborne viable MS2 concentration in the presence of different concentrations of GP from 60 to 90 minutes, accounting for both natural die-off and settling of MS2. Experiments were conducted both by introducing GP aerosol into air containing MS2 and by introducing airborne MS2 into air containing GP aerosol. GP is consistently able to rapidly reduce viable MS2 bacteriophage concentration by 2-3 logs at GP concentrations of 0.02 mg/m3 to 0.5 mg/m3 (corresponding to TEG concentrations of 0.012 mg/m3 to 0.287 mg/m3). Related GP efficacy experiments by the US EPA, as well as GP (TEG) safety and toxicology, are also discussed. SynopsisLimited research on the germicidal properties of triethylene glycol against airborne pathogens was conducted during the 1940s and 50s. This paper investigates the inactivation rate of airborne bacteriophage MS2 by Grignard Pure product, containing a unique and proprietary blend of Triethylene Glycol (TEG) and inert ingredients.

microbiology↗

bioRxiv2022

Matrin3 regulates cell proliferation and spindle dynamics by regulating CDC14B alternative splicing

Matrin3 is an RNA-binding protein that affects diverse RNA-related processes, including mRNA splicing. While Matrin3 has been intensively studied in neurodegenerative diseases, its function in cancer remains unclear. Here, we discovered Matrin3-mediated regulation of mitotic spindle dynamics in colorectal cancer (CRC) cells. We identified bound and regulated Matrin3-target RNAs transcriptome-wide in CRC cells and found that Matrin3 broadly modulates mRNA splicing patterns. Among the top Matrin3 targets, we focused on CDC14B and found that Matrin3 loss increased inclusion of an exon containing a premature termination codon into the CDC14B transcript and simultaneous down-regulation of the standard CDC14B transcript. Selective knockdown of the CDC14B standard transcript phenocopied Matrin3 knockdown and exhibited reduced proliferation and defects in mitotic spindle formation, suggesting that CDC14B is a key downstream effector of Matrin3. Collectively, these data reveal an important role for the Matrin3/CDC14B axis in control of CRC cell proliferation and mitotic spindle formation.

molecular biology↗

bioRxiv2022

Efficient Pangenome Construction through Alignment-Free Residue Pangenome Analysis (ARPA)

Protein sequences can be transformed into vectors composed of counts for each amino acid (vector of Residue Counts; vRC) that are mathematically tractable and retain information about homology. We use vRCs to perform alignment-free, residue-based, pangenome analysis (ARPA; https://github.com/Arnavlal/ARPA). ARPA is 70-90 times faster at identifying homologous gene clusters compared to standard techniques, and offers rapid calculation, visualization, and novel phylogenetic approaches for pangenomes.

bioinformatics↗

bioRxiv2022

Accelerating single-cell genomic analysis with GPUs

Single-cell genomic technologies are rapidly improving our understanding of cellular heterogeneity in biological systems. In recent years, technological and computational improvements have continuously increased the scale of single-cell experiments, and now allow for millions of cells to be analyzed in a single experiment. However, existing software tools for single-cell analysis do not scale well to such large datasets. RAPIDS is an open-source suite of Python libraries that use GPU computing to accelerate data science workflows. Here, we report the use of RAPIDS and GPU computing to accelerate single-cell genomic analysis workflows and present open-source examples that can be reused by the community.

bioinformatics↗