SHERLOCK-GBA: CRISPR-Cas13a Diagnostics and Neuro-Immune Mapping

Project Overview

This repository establishes an independent, end-to-end computational framework for the precision molecular surveillance of Plasmodium falciparum and its systemic impact on host physiology. The project integrates a CRISPR-Cas13a (SHERLOCK) guide RNA (gRNA) design pipeline with high-dimensional modeling of the Gut-Brain Axis (GBA).

By targeting the Kelch13 (PfK13) propeller domain—the primary genetic determinant of artemisinin resistance—this workflow enables the "ultrasensitive" detection of pathogen genotypes. These diagnostic outputs are then correlated with microglial activation signatures (CX3CR1/Iba1) to parameterize the neuro-inflammatory sequelae of systemic infection.

Pipeline Architecture

• Script 01: Cas13a gRNA Engineering for AT-Rich Genomes

  • Implements a custom sliding window algorithm to generate 28nt spacers, adhering to the biophysical constraints of the Cas13a (SHERLOCK) effector protein.
  • Optimizes for a 25-45% GC-content threshold to address the unique AT-rich genomic architecture of the Plasmodium genome, ensuring diagnostic specificity.
  • Filters and isolates candidate guides (n=25) targeting critical drug-resistance SNPs within the PfK13 domain.

• Script 02: Neuro-Immune Correlation and GBA Modeling

  • Constructs a statistical framework linking CRISPR-detected parasite loads to host hypothalamic microglial set-points.
  • Models the negative correlation ($r = -0.946$) between microglial activation (Iba1 intensity) and the expression of homeostatic regulatory checkpoints (CX3CR1).
  • Quantifies the collapse of neuro-immune resilience as a function of pathogen genotype severity.

Visual Results

All technical outputs proving diagnostic viability and systemic correlation are securely archived in the results/ and figures/ directories. The GBA_Microglia_Correlation.png plot specifically demonstrates a robust negative correlation between Iba1-mediated activation and CX3CR1-mediated regulation, providing a computational proof-of-concept for the predictive value of molecular genotyping in neuro-immunology.

Future Directions: In Vitro and In Vivo Validation

To empirically validate the computational gRNA designs and the GBA-crosstalk model, an integrated experimental approach is proposed:

1. Diagnostic Validation: The engineered Cas13a guides will be synthesized and screened using a fluorescence-based SHERLOCK assay. Synthetic PfK13 RNA fragments containing artemisinin-resistance SNPs will be used to quantify the "collateral cleavage" efficiency and limit of detection (LoD) for each candidate gRNA.
2. Gut-Brain Axis Modeling: Utilizing a microfluidic Gut-on-a-Chip system, the impact of Plasmodium-derived metabolites or extracellular vesicles on human-derived microglial cell lines will be assessed. High-resolution confocal microscopy and qRT-PCR will quantify the predicted shift from a homeostatic (CX3CR1-high) to an activated (Iba1-high) microglial phenotype.
3. In Vivo Assessment: Balb/c mice will be infected with Plasmodium berghei (as a proxy for severe malaria) to monitor real-time gut-barrier permeability and hypothalamic microglial remodeling, bridging the gap between in-silico genotyping and functional systemic pathology.

Dependencies & Reproducibility

This project utilizes the renv package manager to guarantee 100% computational reproducibility across research environments. The primary R packages required include:

• Biostrings (Bioconductor): The core engine for high-performance sequence analysis and gRNA design.
• Tidyverse & ggplot2: Utilized for data wrangling and generating publication-ready GBA correlation visualizations.
• BiocManager: Essential for managing specialized bioinformatics dependencies.

How to Run the Pipeline

1. Clone this repository to your local machine.
2. Open the SHERLOCK-GBA.Rproj file in RStudio.
3. Run renv::restore() in the R console to install the exact dependency versions.
4. Execute scripts/01_gRNA_Design.R and scripts/02_GBA_Correlation.R in sequential order.

License

This project is licensed under the MIT License - see the LICENSE file for details.