Biology subjects

Jonsson, V.

Publications and source records attributed to Jonsson, V..

3 recordsLinked to original sources

bioRxiv2025

E2F1 induces a G0-G1 reentry transcriptional program without changing chromatin accessibility

Quiescent cells actively repress cell-cycle genes via chromatin-based mechanisms to maintain a non-dividing state, yet remain poised to reenter upon stimulation. E2F1, a canonical activator of cell-cycle genes, is sufficient to induce reentry from quiescence, but how it overcomes chromatin-mediated repression remains unclear. Here, we show that inducible E2F1 expression triggers exit from quiescence and progression through the cycle without changes in chromatin accessibility, by harnessing regulatory elements with limited, pre-existing accessibility. Using time-resolved transcriptomics, we demonstrate that E2F1 induces an accelerated transcriptional program compared to serum. Unlike serum, which triggers broad chromatin remodeling, E2F1-induced activation occurs in a context of limited accessibility. ChIP-seq reveals that E2F1 directly binds target sites in quiescent cells to upregulate canonical genes. Biochemical reconstitution shows that E2F1 binds nucleosomes and accesses internal E2F sites within histone-wrapped DNA. These findings suggest that E2F1 can engage nucleosome-associated DNA and initiate transcription without major chromatin reorganization, redefining transcription factor-chromatin dynamics during cell fate transitions and establishing E2F1 as a potent regulator of cell-cycle reentry.

cell biology↗

bioRxiv2021

What is the role of the environment in the emergence of novel antibiotic resistance genes? - A modelling approach

It is generally accepted that intervention strategies to curb antibiotic resistance cannot solely focus on human and veterinary medicine but must also consider environmental settings. While the environment clearly has a role in the transmission of resistant bacteria, it is less clear what role it plays in the emergence of novel types of resistance. It has been suggested that the environment constitutes an enormous recruitment ground for resistance genes to pathogens, but the extent to which this actually happens is unknown. In this study, we built a model framework for resistance emergence and used the available quantitative data on the relevant processes to identify the steps which are limiting the appearance of antibiotic resistance determinants in human or animal pathogens. We also assessed the effect of uncertainty in the available data on the model results. We found that in a majority of scenarios, the environment would only play a minor role in the emergence of novel resistance genes. However, the uncertainty around this role is enormous, highlighting an urgent need of more quantitative data to understand the role of the environment in antibiotic resistance development. Specifically, more data is most needed on the fitness costs of antibiotic resistance gene (ARG) carriage, the degree of dispersal of resistant bacteria from the environment to humans, but also the rates of mobilization and horizontal transfer of ARGs. Quantitative data on these processes is instrumental to determine which processes that should be targeted for interventions to curb development and transmission of resistance.

microbiology↗

bioRxiv2018

Quantifying point-mutations in shotgun metagenomic data

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available from http://microbiology.se/software/mumame

bioinformatics↗