DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases.

Biofilm production plays a clinically significant role in the pathogenicity of many bacteria, limiting our ability to apply antimicrobial agents and contributing in particular to the pathogenesis of chronic infections. Bacteriophage depolymerases, leveraged by these viruses to circumvent biofilm mediated resistance, represent a potentially powerful weapon in the fight against antibiotic resistant bacteria. Such enzymes are able to degrade the extracellular matrix that is integral to the formation of all biofilms and as such would allow complementary therapies or disinfection procedures to be successfully applied. In this manuscript, we describe the development and application of a machine learning based approach towards the identification of phage depolymerases. We demonstrate that on the basis of a relatively limited number of experimentally proven enzymes and using an amino acid derived feature vector that the development of a powerful model with an accuracy on the order of 90% is possible, showing the value of such approaches in protein functional annotation and the discovery of novel therapeutic agents.

Dormant non-culturable Mycobacterium tuberculosis retains stable low-abundant mRNA.

BACKGROUND: Dormant Mycobacterium tuberculosis bacilli are believed to play an important role in latent tuberculosis infection. Previously, we have demonstrated that cultivation of M. tuberculosis in K(+)-deficient medium resulted in generation of dormant cells. These bacilli were non-culturable on solid media (a key feature of dormant M. tuberculosis in vivo) and characterized by low metabolism and tolerance to anti-tuberculosis drugs. The dormant bacteria demonstrated a high potential to reactivation after K(+) reintroduction even after prolonged persistence under rifampicin. In this work, we studied the transcriptome and stability of transcripts in persisting dormant bacilli under arrest of mRNA de novo synthesis. RESULTS: RNA-seq-based analysis of the dormant non-culturable population obtained under rifampicin exposure revealed a 30-50-fold decrease of the total mRNA level, indicating global transcriptional repression. However, the analysis of persisting transcripts displayed a cohort of mRNA molecules coding for biosynthetic enzymes, proteins involved in adaptation and repair processes, detoxification, and control of transcription initiation. This 'dormant transcriptome' demonstrated considerable stability during M. tuberculosis persistence and mRNA de novo synthesis arrest. On the contrary, several small non-coding RNAs showed increased abundance on dormancy. Interestingly, M. tuberculosis entry into dormancy was accompanied by the cleavage of 23S ribosomal RNA at a specific point located outside the ribosome catalytic center. CONCLUSIONS: Dormant non-culturable M. tuberculosis bacilli are characterized by a global transcriptional repression. At the same time, the dormant bacilli retain low-abundant mRNAs, which are considerably stable during in vitro persistence, reflecting their readiness for translation upon early resuscitation steps. Increased abundance of non-coding RNAs on dormancy may indicate their role in the entry into and maintenance of M. tuberculosis dormant non-culturable state.

Differences in brain transcriptomes of closely related Baikal coregonid species.

The aim of this work was to get deeper insight into genetic factors involved in the adaptive divergence of closely related species, specifically two representatives of Baikal coregonids--Baikal whitefish (Coregonus baicalensis Dybowski) and Baikal omul (Coregonus migratorius Georgi)--that diverged from a common ancestor as recently as 10-20 thousand years ago. Using the Serial Analysis of Gene Expression method, we obtained libraries of short representative cDNA sequences (tags) from the brains of Baikal whitefish and omul. A comparative analysis of the libraries revealed quantitative differences among ~4% tags of the fishes under study. Based on the similarity of these tags with cDNA of known organisms, we identified candidate genes taking part in adaptive divergence. The most important candidate genes related to the adaptation of Baikal whitefish and Baikal omul, identified in this work, belong to the genes of cell metabolism, nervous and immune systems, protein synthesis, and regulatory genes as well as to DTSsa4 Tc1-like transposons which are widespread among fishes.