Biology subjects

Mahajan, R.

Publications and source records attributed to Mahajan, R..

3 recordsLinked to original sources

bioRxiv2026

Deep learning-guided design of cell type-specific AAV promoters

Precise cell type targeting is critical for both clinical and experimental applications of adeno-associated viral (AAV) vectors, yet engineering vectors with cell type-specific activity remains a challenge. Here, we compared three strategies leveraging single-cell chromatin accessibility data to design cell type-specific AAV promoters, including a deep learning-based method to generate de novo regulatory sequences. When applied to target retinal ganglion cells or horizontal cells in mouse retina, deep learning-guided design consistently outperformed rational approaches, yielding synthetic promoters with stronger and more specific expression in vivo. Synthetic AAV promoters supported diverse transgenes, enabling the recording and ablation of targeted cells. Promoter activity was also maintained in human retinal organoids, suggesting that deep learning-designed sequences may be suitable for translation. Our findings highlight the potential of deep learning to synthesize cell type-specific AAV promoters and establish a versatile platform for cell type targeting with broad implications for gene therapy and basic research.

synthetic biology↗

bioRxiv2023

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 element-gene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancer-promoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

genomics↗

bioRxiv2020

A rapid, low cost, and highly sensitive SARS-CoV-2 diagnostic based on whole genome sequencing

Early detection of infection with SARS-CoV-2 is key to managing the current global pandemic, as evidence shows the virus is most contagious on or before symptom onset. Here, we introduce a low-cost, high-throughput method for diagnosing and studying SARS-CoV-2 infection. Dubbed Pathogen-Oriented Low-Cost Assembly & Re-Sequencing (POLAR), this method amplifies the entirety of the SARS-CoV-2 genome. This contrasts with typical RT-PCR-based diagnostic tests, which amplify only a few loci. To achieve this goal, we combine a SARS-CoV-2 enrichment method developed by the ARTIC Network (https://artic.network/) with short-read DNA sequencing and de novo genome assembly. Using this method, we can reliably (>95% accuracy) detect SARS-CoV-2 at a concentration of 84 genome equivalents per milliliter (GE/mL). Almost all diagnostic methods currently authorized for use by the United States Food and Drug Administration with the Coronavirus Disease 2019 (COVID-19) Emergency Use Authorization require larger concentrations of the virus to achieve this degree of accuracy. In addition, we can reliably assemble the SARS-CoV-2 genome in the sample, often with no gaps and perfect accuracy. The genotypic data contained in these genome assemblies enable the more effective analysis of disease spread than is possible with an ordinary binary diagnostic. These data can also help identify vaccine and drug targets. Finally, we show that the diagnoses obtained using POLAR of both positive and negative clinical nasopharyngeal swab samples 100% match the diagnoses obtained in a clinical diagnostic lab using the Center for Disease Controls 2019-Novel Coronavirus test. Using POLAR, a single person can manually process 192 samples over an 8- hour experiment at the cost of [~]$36 per patient (as of December 7th, 2022), enabling a 24-hour turnaround with sequencing and data analysis time. We anticipate that further testing and refinement will allow greater sensitivity in this approach.

genomics↗