CriSNPr: a single interface for the curated and de novo design of gRNAs for CRISPR diagnostics using diverse Cas systems

  1. Asgar H Ansari
  2. Manoj Kumar
  3. Sajal Sarkar
  4. Souvik Maiti
  5. Debojyoti Chakraborty  Is a corresponding author
  1. CSIR Institute of Genomics and Integrative Biology, India
  2. CSIR-Institute of Genomics and Integrative Biology, India

Abstract

CRISPR-based diagnostics (CRISPRDx) have improved clinical decision-making, especially during the COVID-19 pandemic, by detecting nucleic acids and identifying variants. This has been accelerated by the discovery of new and engineered CRISPR effectors, which have expanded the portfolio of diagnostic applications to include a broad range of pathogenic and non-pathogenic conditions. However, each diagnostic CRISPR pipeline necessitates customized detection schemes based on the fundamental principles of the Cas protein used, its guide RNA (gRNA) design parameters, and the assay readout. This is especially relevant for variant detection, a low-cost alternative to sequencing-based approaches for which no in silico pipeline for the ready-to-use design of CRISPR-based diagnostics currently exists. In this manuscript, we fill this lacuna using a unified webserver, CriSNPr (CRISPR-based SNP recognition), which provides the user with the opportunity to de-novo design gRNAs based on six CRISPRDx proteins of choice (Fn/enFnCas9, LwCas13a, LbCas12a, AaCas12b, and Cas14a) and query for ready-to-use oligonucleotide sequences for validation on relevant samples. Furthermore, we provide a database of curated pre-designed gRNAs as well as target/off-target for all human and SARS-CoV-2 variants reported thus far. CriSNPr has been validated on multiple Cas proteins, demonstrating its broad and immediate applicability across multiple detection platforms. CriSNPr can be found at http://crisnpr.igib.res.in/.

Data availability

The current manuscript is a computational study, so no new data has been generated for this manuscript. Experimental validation results have been presented in figures in the manuscript. The source code and related datasets have been indicated in the manuscript and also uploaded here: http://crisnpr.igib.res.in/download. All other validation data have been presented in the main manuscript itself.

The following previously published data sets were used

Article and author information

Author details

  1. Asgar H Ansari

    Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, Delhi, India
    Competing interests
    The authors declare that no competing interests exist.
  2. Manoj Kumar

    Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, Delhi, India
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0772-1399
  3. Sajal Sarkar

    CSIR Institute of Genomics and Integrative Biology, New Delhi, India
    Competing interests
    The authors declare that no competing interests exist.
  4. Souvik Maiti

    Chemical and Systems Biology, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
    Competing interests
    The authors declare that no competing interests exist.
  5. Debojyoti Chakraborty

    CSIR Institute of Genomics and Integrative Biology, New Delhi, India
    For correspondence
    debojyoti.chakraborty@igib.in
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1460-7594

Funding

CSIR (HCP23)

  • Souvik Maiti
  • Debojyoti Chakraborty

EMBO (GAP252)

  • Debojyoti Chakraborty

Lady Tata Memorial Trust (GAP198)

  • Debojyoti Chakraborty

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2023, Ansari et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,758
    views
  • 254
    downloads
  • 7
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Asgar H Ansari
  2. Manoj Kumar
  3. Sajal Sarkar
  4. Souvik Maiti
  5. Debojyoti Chakraborty
(2023)
CriSNPr: a single interface for the curated and de novo design of gRNAs for CRISPR diagnostics using diverse Cas systems
eLife 12:e77976.
https://doi.org/10.7554/eLife.77976

Share this article

https://doi.org/10.7554/eLife.77976

Further reading

    1. Computational and Systems Biology
    2. Developmental Biology
    Rachael Kuintzle, Leah A Santat, Michael B Elowitz
    Research Article

    The Notch signaling pathway uses families of ligands and receptors to transmit signals to nearby cells. These components are expressed in diverse combinations in different cell types, interact in a many-to-many fashion, both within the same cell (in cis) and between cells (in trans), and their interactions are modulated by Fringe glycosyltransferases. A fundamental question is how the strength of Notch signaling depends on which pathway components are expressed, at what levels, and in which cells. Here, we used a quantitative, bottom-up, cell-based approach to systematically characterize trans-activation, cis-inhibition, and cis-activation signaling efficiencies across a range of ligand and Fringe expression levels in Chinese hamster and mouse cell lines. Each ligand (Dll1, Dll4, Jag1, and Jag2) and receptor variant (Notch1 and Notch2) analyzed here exhibited a unique profile of interactions, Fringe dependence, and signaling outcomes. All four ligands were able to bind receptors in cis and in trans, and all ligands trans-activated both receptors, although Jag1-Notch1 signaling was substantially weaker than other ligand-receptor combinations. Cis-interactions were predominantly inhibitory, with the exception of the Dll1- and Dll4-Notch2 pairs, which exhibited cis-activation stronger than trans-activation. Lfng strengthened Delta-mediated trans-activation and weakened Jagged-mediated trans-activation for both receptors. Finally, cis-ligands showed diverse cis-inhibition strengths, which depended on the identity of the trans-ligand as well as the receptor. The map of receptor-ligand-Fringe interaction outcomes revealed here should help guide rational perturbation and control of the Notch pathway.

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Pierre Barrat-Charlaix, Richard A Neher
    Research Article

    As pathogens spread in a population of hosts, immunity is built up, and the pool of susceptible individuals are depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host’s immune systems adapt, and older immune responses wane, such that escape variants only enjoy a growth advantage for a limited time. If variant growth dynamics and reshaping of host-immunity operate on comparable time scales, viral adaptation is determined by eco-evolutionary interactions that are not captured by models of rapid evolution in a fixed environment. Here, we use a Susceptible/Infected model to describe the interaction between an evolving viral population in a dynamic but immunologically diverse host population. We show that depending on strain cross-immunity, heterogeneity of the host population, and durability of immune responses, escape variants initially grow exponentially, but lose their growth advantage before reaching high frequencies. Their subsequent dynamics follows an anomalous random walk determined by future escape variants and results in variant trajectories that are unpredictable. This model can explain the apparent contradiction between the clearly adaptive nature of antigenic evolution and the quasi-neutral dynamics of high-frequency variants observed for influenza viruses.