Biology subjects

Remm, M.

Publications and source records attributed to Remm, M..

4 recordsLinked to original sources

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

Gene content of the fish-hunting cone snail Conus consors

BackgroundConus consors is a fish-hunting cone snail that lives in the tropical waters of the Indo-Pacific region. Cone snails have attracted scientific interest for the amazing potency of their venom, which consists of a complex mixture of small proteins known as conopeptides, many of which act as ion channel and receptor modulators with high selectivity.\n\nResultsWe have analysed publicly available transcriptomic sequences from 8 tissues of Conus consors and complemented the transcriptome data with the data from genomic DNA reads. We identified 17,715 full-length protein sequences from the transcriptome. In addition, we predicted 168 full-length or partial conopeptide sequences and characterized gene structures of several conopeptide superfamilies.

genomics↗

bioRxiv2019

AluMine: alignment-free method for the discovery of polymorphic Alu element insertions

BackgroundRecently, alignment-free sequence analysis methods have gained popularity in the field of personal genomics. These methods are based on counting frequencies of short k-mer sequences, thus allowing faster and more robust analysis compared to traditional alignment-based methods.\n\nResultsWe have created a fast alignment-free method, AluMine, to analyze polymorphic insertions of Alu elements in the human genome. We tested the method on 2,241 individuals from the Estonian Genome Project and identified 28,962 potential polymorphic Alu element insertions. Each tested individual had on average 1,574 Alu element insertions that were different from those in the reference genome. In addition, we propose an alignment-free genotyping method that uses the frequency of insertion/deletion-specific 32-mer pairs to call the genotype directly from raw sequencing reads. Using this method, the concordance between the predicted and experimentally observed genotypes was 98.7%. The running time of the discovery pipeline is approximately 2 hours per individual. The genotyping of potential polymorphic insertions takes between 0.4 and 4 hours per individual, depending on the hardware configuration.\n\nConclusionsAluMine provides tools that allow discovery of novel Alu element insertions and/or genotyping of known Alu element insertions from personal genomes within few hours.

genomics↗

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↗