bioRxiv Science⌕ Search

Biology subjects

Mamaghani, T.

Publications and source records attributed to Mamaghani, T..

3 recordsLinked to original sources

Upper respiratory tract bacterial microbiome features associated with Streptococcus pneumoniae colonisation, environmental and demographic factors among older children in a rural setting in The Gambia

Abstract The upper respiratory tract (URT) microbiome plays a vital role in respiratory health, with older children (5-14 years old) serving as key reservoirs for the transmission of bacteria to younger individuals (< 5 years old). However, the URT microbiome in this age group remains poorly characterised, particularly in relation to colonisation by the pathobiont Streptococcus pneumoniae. To address this gap, we analysed URT samples collected over eight visits from 134 healthy children aged 5-14 years in The Gambia, using culture-dependent methods to detect S. pneumoniae and culture-independent 16S rRNA sequencing to characterise the microbiome in parallel. 16S rRNA sequencing identified Streptococcus and Prevotella as the dominant microbiome constituents. When integrating longitudinal sampling data, we observed temporal instability with frequent switches in microbiome profiles over time. URT samples colonised with S. pneumoniae were associated with increased abundance of Moraxella, Streptococcus and Haemophilus, whereas non-colonised URT samples were enriched with Granulicatella and Filifactor. S. pneumoniae colonisation was also associated with lower Shannon diversity compared with non-colonised samples. Age and season were the strongest determinants of microbiome composition; increasing age was associated with decreasing prevalence and abundance of taxa including Haemophilus and Moraxella, while gram-positive taxa were predominantly enriched in the dry season. These findings provide new insight into the URT microbiome of older children in The Gambia and highlight Granulicatella and Filifactor as potential contributors to colonisation resistance against S. pneumoniae.

microbiology↗

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 and complementary proteomics to map transcription, RNA processing, and protein abundance in both cells. In Syn1.0, full-length sequencing resolved 459 operons encompassing 911 genes, revealing pervasive RNA processing with a strong 3' bias. Analysis of the division and cell-wall cluster showed how transcriptional context explained the restoration of genes required for normal cell division in Syn3A. Most antisense and intergenic transcription in Syn1.0 reflected low-level transcriptional noise arising from inherited mis-annotation, read-through, and synthetic sequences. Much of that transcription was lost in Syn3A after genome minimization. Reducing the genome unexpectedly altered the expression of several retained genes by deleting promoters, most prominently reducing expression of the nucleoid protein HupA and central-carbon enzymes. Meanwhile, one third of the coding mRNA pool was allocated to a single 21-gene ribosomal-protein operon, while several other ribosomal proteins had reduced transcript abundance, which possibly led to imbalanced ribosome assembly. Expression of RNA polymerase and central-carbon metabolism declined at both mRNA and protein levels whereas RNase Y degradosome abundance increased. These shifts in the synthetic evolution of Syn3A suggest plausible mechanisms for its reduced chromosome contacts and slower growth. Together, these results show that genome minimization alters not only gene content but also the transcriptional context and resource allocation of retained genes. The analysis and visualization that are shared via Jupyter Notebook provide the RNA-level foundation for whole-cell modeling of the minimal cell.

synthetic biology↗

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↗