Sutradhar, A. | bioRxiv Science

bioRxiv2024

Synthetic genome modules designed for programmable silencing of functions and chromosomes

Unlike in bacteria, eukaryotes rarely cluster sets of genes in their genomes according to function, instead having most genes spread randomly across different chromosomes and loci. However, with the advent of genome engineering, synthetic co-location of genes that together encode a cell function has now become possible. Here, using Saccharomyces cerevisiae we demonstrate the feasibility of reorganising a set of yeast genes encoding a cell function, tryptophan biosynthesis, into a synthetic genome module by deleting these genes and their regulatory elements from their native genomic loci while in parallel reconstructing them into gene cluster format by synthetic DNA assembly. As part of synthetic module design, loxPsym sequences recognised by Cre recombinase are placed between all module genes, and we leverage these for a novel master regulation system we call dCreSIR. Using dCreSIR we externally control silencing of synthetic modules by targeted binding of chromatin recruiters to loxPsym sites and this leads to inhibition of local transcription. We further show that dCreSIR can go beyond modules and be used to specifically downregulate expression across an entire synthetic yeast chromosome containing loxPsym sites. Together, our work offers insights into yeast genome organisation and establishes new principles and tools for the future design and construction of modular synthetic yeast genomes.

synthetic biology↗

bioRxiv2023

Transcriptome-wide meta-analysis of codon usage in Escherichia coli

The preference for synonymous codons, termed codon usage bias (CUB), is a fundamental feature of coding sequences, with distinct preferences being observed across species, genomes and genes. Accurately quantifying codon usage frequencies is useful for a range of applications, from guiding mRNA vaccine design, to elucidating protein folding and uncovering co-evolutionary relationships. However, current methods are either based on a single genome assembly, lack functional stratification, or are extremely outdated. To address this, we adopted a data-driven approach and developed Codon Usage Bias estimation from RNA-sequencing data (CUBSEQ), a fully automatic meta-analysis pipeline to estimate CUB at the trascriptome-level and for gene panels. Here, we used CUBSEQ to perform, to our knowledge, the largest and most comprehensive CUB analysis of the transcriptome and highly expressed genes in Escherichia coli, using RNA sequencing data from 6,763 samples across 72 strains. By capturing sequence variants of these genes through variant calls, we constructed a per-sample representation of the E. coli transcriptome revealing a rich mutational landscape. We then identified a set of 81 highly expressed genes with consistent expression patterns across strains, sample library size and experimental conditions, and found significant differences in CUB compared to transcriptome-wide genes and alternative codon usage tables. Finally, we found codons with a high relative frequency were often associated with a larger repertoire of isoaccepting tRNAs and not necessarily high tRNA abundance.

genomics↗