Biology subjects

Brier, T. A.

Publications and source records attributed to Brier, T. A..

4 recordsLinked to original sources

bioRxiv2025

Unraveling the Transcriptional Landscape within a Minimized Bacterium via Comparative Analysis

Stochastic nature of gene expression leads to the complex formation of the bacterial transcriptome and proteome. In contrast to typical transcriptome studies, we employ a near wild-type, Syn1.0, of the naturally genome-reduced Mycoplasmas, and the dramatically further genome-reduced JCVI-syn3A thus avoiding additional contributions from many non-essential cellular functions. To aid in profiling the transcriptional landscape within these bacteria, we present a bioinformatic analysis of the genetic sequence motifs implicated in modulating the stochastic gene expression events, coupled with genome-wide short-read (Illumina) and long-read (Oxford Nanopore Technologies and Pacfic Biosciences) RNA sequencing. The bioinformatic analysis coupled with information from structural studies assigns strengths of the Shine-Dalgarno signatures and identifies both transcription initiation and termination sites, leading to predictions of RNA isoforms in Syn1.0 (and related organisms). The long-read and short-read RNA sequencing characterized the predicted transcriptional activity, and the long-read methods provide direct insight into the RNA isoform complexity within Syn1.0. Comparison of the RNA sequencing results with that of the bioinformatic analysis highlights the inability of bioinformatics alone to capture the results of bacterial transcription without including effects of RNA degradation. This study emphasizes the need for comparative analysis and potential dangers of genome reduction, exemplified through the discovery of altered gene expression patterns of JCVI-syn1.0 and JCVI-syn3A, achieved via the union of our transcriptome study with their proteomics data. Analysis of the transcriptomics data sets through a Jupyter notebook allows any genomic region to be easily examined. Table of Content Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/681674v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@78a9e0org.highwire.dtl.DTLVardef@1d8c9fcorg.highwire.dtl.DTLVardef@1b4e129org.highwire.dtl.DTLVardef@2a8c3f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Assembly of Macromolecular Complexes in the Whole-Cell Model of a Minimal Cell

Macromolecular complexes in the genetically minimized bacterium, JCVI-syn3A, support gene expression (RNA polymerase, ribosome, degradosome), metabolism (ABC transporters, ATP synthase) and chromosome dynamics. In this work, we further incorporate the assembly of 21 unique macromolecular complexes into the existing whole-cell kinetic model of Syn3A. The synthesis and translocation of protein subunits in membrane complexes occur through distinct pathways. A range of 2D association rates for membrane complexes were considered to guarantee a high yield of assembly given the existing time scales of gene expression. By alleviating the undesired kinetically trapped intermediates in ATP synthase assembly, the efficiency was improved. The assembly of RNA polymerase, ribosome, and degradosome influence the speed and efficiency of protein synthesis. Collectively, this model predicted time-dependent cellular behaviors consistent with experiments. A machine learning analysis of the time-dependent metabolomics and metabolic fluxes highlighted the effect of introducing complex assembly into our whole-cell model.

biophysics↗

bioRxiv2025

Bringing the Genetically Minimal Cell to Life on a Computer in 4D

We present a whole-cell spatial and kinetic model for the 100 minute cell cycle of the genetically minimal bacterium, JCVI-syn3A. This is the first simulation of a complete cell cycle in 4D including all genetic information processes, metabolic networks, growth, and cell division. Integrating hybrid computational methods, dynamics of the morphological transformations were achieved. Growth is driven by synthesis of lipids and membrane proteins and constrained by new fluorescence imaging data. Chromosome replication and segregation is controlled by essential SMC and topoisomerase proteins in Brownian dynamics simulations with replication rates responding to dNTP pools from metabolism. The model captures the origin to terminus ratio measured in our DNA sequencing and recovers other experimental measurements like doubling time, mRNA half-lives, protein distributions, and ribosome counts. Because of stochasticity, each replicate cell is unique. Not only do we predict average behavior for partitioning to daughter cells, we predict the heterogeneity among them.

biophysics↗

bioRxiv2020

Stochastic Analysis Probes the Role of the Chaperone Hfq in E. coli Sugar Shock Response

Small RNAs (sRNAs) play a crucial role in the regulation of bacterial gene expression by silencing the translation of target mRNAs. SgrS is an sRNA that relieves glucose-phosphate stress, or "sugar shock" in E. coli. The power of single cell measurements is their ability to obtain population level statistics that illustrate cell-to-cell variation. Here, we utilize single molecule super-resolution microscopy in single E. coli cells coupled with stochastic modeling to analyze glucose-phosphate stress regulation by SgrS. We present a kinetic model that captures the combined effects of transcriptional regulation, gene replication and chaperone mediated RNA silencing in the SgrS regulatory network. This more complete kinetic description, simulated stochastically, recapitulates experimentally observed cellular heterogeneity and characterizes the binding of SgrS to the chaperone protein Hfq as a slow process that not only stabilizes SgrS but also may be critical in restructuring the sRNA to facilitate association with its target ptsG mRNA.

biophysics↗