Biology subjects

Janjuha, S.

Publications and source records attributed to Janjuha, S..

6 recordsLinked to original sources

bioRxiv2023

Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning

Prime editing is a powerful genome editing technology, but its efficiency varies depending on the pegRNA design and target locus. Existing computational models for predicting prime editing rates are limited by their focus on specific edit types and by omitting the local chromatin environment. In our study, we developed machine learning models that predict prime editing efficiencies across a wide range of edit types up to 15 bp (PRIDICT2.0) and in different chromatin contexts (ePRIDICT). Both models can be accessed at www.pridict.it.

molecular biology↗

bioRxiv2023

Prime editing of the beta-1 adrenoceptor in the brain reprograms mouse behavior

Prime editing is a highly versatile genome editing technology that holds great potential for treating genetic diseases1, 2. While in vivo prime editing has recently been conducted in the brain, liver, heart, and retina3-6, application of this technology to modulate neural circuits in the brain has not been reported yet. Here, we employ adeno-associated viral vectors to deliver optimized intein-split prime editors into the brain of mice. Delivery into newborn pups via intracerebroventricular injection resulted in up to 44.0% editing at the Dnmt1 locus in the cortex (on average 34.8{+/-}9.8% after 6 months). In addition, we obtained up to 28.1% editing at the Adrb1 locus in the cortex (on average 14.7{+/-}11.6% after 6 months). The introduced Adrb1A187V mutation is a naturally occurring variant of the {beta}1-adrenergic receptor, which has previously been linked to increased activity and natural short sleep7. Similarly, we observed an increase in the activity and exploratory behavior of treated animals. This study demonstrates the potential of prime editing for treating genetic diseases in the central nervous system and for reprogramming molecular pathways that modulate animal behavior.

genetics↗

bioRxiv2021

Loss of Rnf31 and Vps4b sensitizes pancreatic cancer to T cell-mediated killing

Pancreatic ductal adenocarcinoma (PDA) is an inherently immune cell deprived tumor, characterized by desmoplastic stroma and suppressive immune cells. Here we systematically dissected PDA intrinsic mechanisms of immune evasion by in vitro and in vivo CRISPR screening, and identified Rnf31 and Vps4b as essential factors required for escaping CD8+ T cell-killing. Using murine PDA cells and human PDA organoids, we demonstrate that Rnf31 protects from TNF-mediated caspase 8 cleavage and subsequent apoptosis induction. For Vps4b we found that inactivation impairs autophagy, resulting in increased accumulation of CD8+ T cell-derived granzyme B and subsequent tumor cell lysis. Orthotopic transplantation of Rnf31- or Vps4b deficient pancreatic tumors, moreover, revealed increased CD8+ T cell infiltration and effector function, and markedly reduced tumor growth in mice. Our work uncovers vulnerabilities in PDA that might be exploited to render these tumors more susceptible to the immune system.

cancer biology↗

bioRxiv2021

A single-cell atlas of de novo β-cell regeneration reveals the contribution of hybrid β/δ cells to diabetes recovery in zebrafish

Regeneration-competent species possess the ability to reverse the progression of severe diseases by restoring the function of the damaged tissue. However, the cellular dynamics underlying this capability remain unexplored. Here, we use single-cell transcriptomics to map de novo {beta}-cell regeneration during induction and recovery from diabetes in zebrafish. We show that the zebrafish has evolved two distinct types of somatostatin-producing {delta}-cells, which we term {delta}1- and {delta}2-cells. Moreover, we characterize a small population of glucose-responsive islet cells, which share the hormones and fate-determinants of both {beta}- and {delta}1-cells. The transcriptomic analysis of {beta}-cell regeneration reveals that {beta}/{delta} hybrid cells constitute a prominent source of insulin-expression during diabetes recovery. Using in vivo calcium imaging and cell tracking, we further show that the hybrid cells form de novo and acquire glucose-responsiveness in the course of regeneration. The overexpression of dkk3, a gene enriched in hybrid cells, increases their formation in the absence of {beta}-cell injury. Finally, interspecies comparison shows that plastic {delta}1-cells are partially related to PP-cells in the human pancreas. Our work provides an atlas of {beta}-cell regeneration and indicates that the rapid formation of glucose-responsive hybrid cells contributes to the resolution of diabetes in zebrafish

developmental biology↗

bioRxiv2020

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable conversion of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have vast potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on thousands of lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2018

Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their trans criptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the predictive power of GERAS to identify genome-wide molecular factors that correlate with aging. We show that one of these factors, junb, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling to detect pro-aging factors and candidate genes associated with aging.

bioinformatics↗