Mikula, L. C. | Search bioRxiv

bioRxiv2025

A single transcriptional regulator is crucial for the adaptation of Staphylococcus aureus to diverse niches

The adaptation of versatile multi-host pathogens to various hosts and various niches within hosts is often still poorly understood. The alternative Sigma factor B (SigB) is the master regulator of the general stress response of most gram-positive bacteria, which is a classic case of adaptive plasticity. In Staphylococcus aureus, SigB appears co-opted to function as a switch between intracellular and extracellular niches. During bovine mastitis, low SigB-activity confers an advantage in the milk-rich extracellular niche of the bovine udder. We show that narrowly adapted SigB-deficient strains evolved repeatedly from phenotypically plastic SigB-wildtype strains during persistent mastitis. This genetic assimilation appears driven by the cost of phenotypic plasticity: long time lags in adapting to milk and slow growth. Surprisingly, we observe that mutations causing SigB-deficiency allow even human isolates to grow in milk. While host adaptation often proceeds by mobile genetic elements exchanged between strains, we show how a master regulator in the core genome can drive niche adaptation.

microbiology↗

bioRxiv2023

Inference of Genomic Landscapes using Ordered Hidden Markov Models with Emission Densities (oHMMed)

BackgroundGenomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred. ResultsWe apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations. ConclusionsOur algorithms provide a biologically assumption-free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

bioinformatics↗