Biology subjects

Tenson, T.

Publications and source records attributed to Tenson, T..

4 recordsLinked to original sources

bioRxiv2019

Dividing subpopulation of Escherichia coli in stationary phase

The bacterial growth cycle contains different phases: after the growth substrate is exhausted of the toxic waste products accumulate the growth stops. In this non-growing culture the number of colony forming bacteria remains constant or starts to decrease. It has been shown that during prolonged incubation there is constant growth and death of bacteria and certain mutant populations take over the culture. Here we show that the dynamic cell division and death balance can be obtained even before mutants take over the culture.

microbiology↗

bioRxiv2019

Muropeptides Stimulate Growth Resumption from Stationary Phase in Escherichia coli

When nutrients run out, bacteria enter a dormant metabolic state. This low or undetectable metabolic activity helps bacteria to preserve their scant reserves for future, but also diminishes their ability to trace the environment for new growth-promoting substrates. However, neighboring microbial growth is a sure indicator of favorable environment and thus, can serve as a cue for exiting the dormancy. Here we report that for Escherichia coli this cue is the basic peptidoglycan unit (i.e. muropeptide). We show that several forms of muropeptides can stimulate growth resumption of dormant E. coli cells, but the sugar - peptide bond is crucial for activity. We also demonstrate that muropeptides from several different species can induce growth resumption of E. coli and also Pseudomonas aeruginosa. These results, together with the previous identification of muropeptides as germination signal for bacterial spores, makes muropeptides rather universal cue for bacterial growth.

microbiology↗

bioRxiv2019

Critical Nodes of Virus-Host Interaction Revealed Through an Integrated Network Analysis

Viruses are one of the major causes of various acute and chronic infectious diseases and thus a major contributor to the global burden of disease. Several studies have shown how viruses have evolved to hijack basic cellular pathways and evade innate immune response by modulating key host factors and signalling pathways. A collective view of these multiple studies could advance our understanding of viral evasion mechanisms and provide new therapeutic perspectives for the treatment of viral diseases. Here, we performed an integrative meta-analysis to elucidate the 17 different host-virus interactomes. Network and bioinformatics analyses showed how viruses with small genomes efficiently achieve the maximal effect by targeting multifunctional and highly connected host proteins with a high occurrence of disordered regions. We also identified the core cellular process subnetworks that are targeted by all the viruses. Integration with functional RNA interference (RNAi) datasets showed that a large proportion of the targets are required for viral replication. Furthermore, we performed an interactome-informed drug re-purposing screen and identified novel activities for broad-spectrum antiviral agents against hepatitis C virus and human metapneumovirus. Altogether, these orthogonal datasets could serve as a platform for hypothesis generation and follow-up studies to broaden our understanding of the viral evasion landscape.

immunology↗

bioRxiv2018

A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria.

We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) generates a k-mer-based statistical model for predicting a given phenotype and (b) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 460 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets performed with 88% accuracy on the K. pneumoniae test set, 88% on the P. aeruginosa test set and 96.5% on the C. difficile test set. Prediction accuracy was the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets.\n\nPhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).\n\nSummaryPredicting phenotypic properties of bacterial isolates from their genomic sequences has numerous potential applications. A good example would be prediction of antimicrobial resistance and virulence phenotypes for use in medical diagnostics. We have developed a method that is able to predict phenotypes of interest from the genomic sequence of the isolate within seconds. The method uses statistical model that can be trained automatically on isolates with known phenotype. The method is implemented in Python programming language and can be run on low-end Linux server and/or on laptop computers.

bioinformatics↗