Teng, L. | Search bioRxiv

bioRxiv2018

Polar targeting and assembly of the Legionella Dot/Icm type IV secretion system (T4SS) by T6SS-related components

Legionella pneumophila, the causative agent of Legionnaires disease, survives and replicates inside amoebae and macrophages by injecting a large number of protein effectors into the host cells cytoplasm via the Dot/Icm type IVB secretion system (T4BSS). Previously, we showed that the Dot/Icm T4BSS is localized to both poles of the bacterium and that polar secretion is necessary for the proper targeting of the Legionella containing vacuole (LCV). Here we show that polar targeting of the Dot/Icm core-transmembrane subcomplex (DotC, DotD, DotF, DotG and DotH) is mediated by two Dot/Icm proteins, DotU and IcmF, which are able to localize to the poles of L. pneumophila by themselves. Interestingly, DotU and IcmF are homologs of the T6SS components TssL and TssM, which are part of the T6SS membrane complex (MC). We propose that Legionella co-opted these T6SS components to a novel function that mediates subcellular localization and assembly of this T4SS. Finally, in depth examination of the biogenesis pathway revealed that polar targeting and assembly of the Legionella T4BSS apparatus is mediated by an innovative \"outside-inside\" mechanism.

microbiology↗

bioRxiv2018

Bi-clustering interpretation and prediction of correlation between gene expression and protein abundance

Most organisms transcript and protein level only moderately correlate for various reasons, such as regulation of transcription and protein degradation. Better prediction and understanding the correlation between gene expression and protein abundance has been possible by harnessing the matching RNA/protein datasets produced by modern high-throughput RNA-Seq and mass spectrometry methods. In this work, we have utilized some well-studied matching RNA/protein datasets, and explored for the first time a bi-clustering method to cluster genes that have consistent correlation patterns between gene expression and protein abundance. The clustering results have been interpreted from the perspective of both transcriptomic and proteomic features, which show that mRNA half-life, protein half-life and protein structure in concert significantly affect the correlation of gene expression and protein abundance. With these and other carefully selected features, a prediction model based on individual clusters, called Cluster-based Linear prediction Model (CLM), was built and tested on mouse liver mitochondrial, mouse brainstem mitochondrial, Saccharomyces cerevisiae and Danio rerio datasets. CLM could find genes for which protein abundance can be predicted from mRNA data. In summary, based on bi-clustering, feature selection and CLM model, we have established a new and valuable cluster-based protein abundance prediction method.

bioinformatics↗