Biology subjects

Pallaseni, A.

Publications and source records attributed to Pallaseni, A..

4 recordsLinked to original sources

bioRxiv2023

The interplay of DNA repair context with target sequence predictably biasses Cas9-generated mutations

Mutagenic outcomes of CRISPR/Cas9-generated double-stranded breaks depend on both the sequence flanking the cut and cellular DNA damage repair. The interaction of these features has been largely unexplored, limiting our ability to understand and manipulate the outcomes. Here, we measured how the absence of 18 repair genes changed frequencies of 83,680 unique mutational outcomes generated by Cas9 double-stranded breaks at 2,838 synthetic target sequences in mouse embryonic stem cells. This large scale survey allowed us to classify the outcomes in an unbiased way, generating hypotheses about new modes of double-stranded break repair. Our data indicate a specialised role for Prkdc (DNA-PKcs protein) and Polm (Pol) in creating 1bp insertions that match the nucleotide on the proximal side of the Cas9 cut with respect to the protospacer-adjacent motif (PAM), a variable involvement of Nbn (NBN) and Polq (Pol{theta}) in the creation of different deletion outcomes, and a unique class of uni-directional deletion outcomes that are dependent on both end-protection gene Xrcc5 (Ku80) and the resection gene Nbn (NBN). We used the knowledge of the reproducible variation across repair milieus to build predictive models of the mutagenic outcomes of Cas9 scission that outperform the current standards. This work improves our understanding of DNA repair gene function, and provides avenues for more precise modulation of CRISPR/Cas9-generated mutations.

genetics↗

bioRxiv2021

Predicting efficiency of writing short sequences into the genome using prime editing

Short sequences can be precisely written into a selected genomic target using prime editing. This ability facilitates protein tagging, correction of pathogenic deletions, and many other exciting applications. However, it remains unclear what types of sequences prime editors can easily insert, and how to choose optimal reagents for a desired outcome. To characterize features that influence insertion efficiency, we designed a library of 2,666 sequences up to 69 nt in length and measured the frequency of their insertion into four genomic sites in three human cell lines, using different prime editor systems. We discover that insertion sequence length, nucleotide composition and secondary structure all affect insertion rates, and that mismatch repair proficiency is a strong determinant for the shortest insertions. Combining the sequence and repair features into a machine learning model, we can predict insertion frequency for new sequences with R = 0.69. The tools we provide allow users to choose optimal constructs for DNA insertion using prime editing.

molecular biology↗

bioRxiv2021

Predicting base editing outcomes using position-specific sequence determinants

Nucleotide-level control over DNA sequences is poised to power functional genomics studies and lead to new therapeutics. CRISPR/Cas base editors promise to achieve this ability, but the determinants of their activity remain incompletely understood. We measured base editing frequencies in two human cell lines for two cytosine and two adenine base editors at [~]14,000 target sequences. Base editing activity is sequence-biased, with largest effects from nucleotides flanking the target base, and is correlated with measures of Cas9 guide RNA efficiency. Whether a base is edited depends strongly on the combination of its position in the target and the preceding base, with a preceding thymine in both editor types leading to a wider editing window, while a preceding guanine in cytosine editors and preceding adenine in adenine editors to a narrower one. The impact of features on editing rate depends on the position, with guide RNA efficacy mainly influencing bases around the centre of the window, and sequence biases away from it. We use these observations to train a machine learning model to predict editing activity per position for both adenine and cytosine editors, with accuracy ranging from 0.49 to 0.72 between editors, and with better generalization performance across datasets than existing tools. We demonstrate the usefulness of our model by predicting the efficacy of potential disease mutation correcting guides, and find that most of them suffer from more unwanted editing than corrected outcomes. This work unravels the position-specificity of base editing biases, and provides a solution to account for them, thus allowing more efficient planning of base edits in experimental and therapeutic contexts.

genetics↗

bioRxiv2018

Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data

Gene expression is controlled by networks of transcription factors and regulators, but the structure of these networks is as yet poorly understood and is thus inferred from data. Recent work has shown the efficacy of information theoretical approaches for network reconstruction from single cell transcriptomic data. Such methods use information to estimate dependence between every pair of genes in the dataset, then edges are inferred between top-scoring pairs. Dependence, however, does not indicate significance, and the definition of \"top-scoring\" is often arbitrary and a priori related to expected network size. This makes comparing networks across datasets difficult, because networks of a similar size are not necessarily similarly accurate. We present a method for performing formal hypothesis tests on putative network edges derived from information theory, bringing together empirical Bayes and work on theoretical null distributions for information measures. Thresholding based on empirical Bayes allows us to control network accuracy according to how we intend to use the network. Using single cell data from mouse pluripotent stem cells, we recover known interactions and suggest several new interactions for experimental validation (using a stringent threshold) and discover high-level interactions between sub-networks (using a more relaxed threshold). Furthermore, our method allows for the inclusion of prior information. We use in-silico data to show that even relatively poor quality prior information can increase the accuracy of a network, and demonstrate that the accuracy of networks inferred from single cell data can sometimes be improved by priors from population-level ChIP-Seq and qPCR data.

systems biology↗