Full-length transcriptomics and proteomics reveal how genome minimization reshapes gene expression in synthetic bacteria
JCVI-syn1.0 (Syn1.0) and JCVI-syn3A (Syn3A), genetically synthetic and genome-reduced versions of the naturally occurring bacterium Mycoplasma mycoides, are landmark platforms for defining the gene set required for life, yet how their genomes are expressed at the RNA level remains uncharacterized. We combined full-length PacBio and native long-read RNA sequencing with short-read quantification…
Full-length transcriptomics and proteomics have shed light on how genome minimization impacts gene expression in synthetic bacteria. Researchers examined two genetically reduced versions of Mycoplasma mycoides, JCVI-syn1.0 and JCVI-syn3A, to understand the gene set required for life. By combining full-length PacBio and native long-read RNA sequencing with short-read quantification and proteomics, they mapped transcription, RNA processing, and protein abundance in both cells.
In Syn1.0, full-length sequencing identified 459 operons containing 911 genes, indicating extensive RNA processing with a strong 3' bias. The division and cell-wall cluster analysis demonstrated how transcriptional context helped restore genes essential for normal cell division in Syn3A. The majority of antisense and intergenic transcription in Syn1.0 resulted from low-level transcriptional noise due to inherited mis-annotation, read-through, and synthetic sequences, which were largely absent in Syn3A following genome minimization.
Surprisingly, minimizing the genome altered the expression of several retained genes by modifying promoters. This change notably reduced the expression of the nucleoid protein HupA and central-carbon enzymes. Furthermore, one-third of the coding mRNA pool was dedicated to a single 21-gene ribosomal-protein operon, while several other ribosomal proteins had decreased transcript abundance, potentially leading to imbalanced ribosome assembly.
Expression of RNA polymerase and central-carbon metabolism decreased both at the mRNA and protein levels, while the abundance of the RNase Y degradosome increased. These alterations in Syn3A suggest plausible mechanisms for its reduced chromosome contacts and slower growth.
Overall, these findings indicate that genome minimization not only impacts gene content but also affects the transcriptional context and resource allocation of retained genes. The shared RNA-level analysis and visualization through Jupyter Notebook serve as a foundation for whole-cell modeling of the minimal cell.
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