Metatranscriptomics reveals unique microbial small RNAs in the ocean's water column

Microbial gene expression in the environment has recently been assessed via pyrosequencing of total RNA extracted directly from natural microbial assemblages. Several such 'metatranscriptomic' studies1, 2 have reported that many complementary DNA sequences shared no significant homology with known peptide sequences, and so might represent transcripts from uncharacterized proteins. Here we report that a large fraction of cDNA sequences detected in microbial metatranscriptomic data sets are comprised of well-known small RNAs (sRNAs)3, as well as new groups of previously unrecognized putative sRNAs (psRNAs). These psRNAs mapped specifically to intergenic regions of microbial genomes recovered from similar habitats, displayed characteristic conserved secondary structures and were frequently flanked by genes that indicated potential regulatory functions. Depth-dependent variation of psRNAs generally reflected known depth distributions of broad taxonomic groups4, but fine-scale differences in the psRNAs within closely related populations indicated potential roles in niche adaptation. Genome-specific mapping of a subset of psRNAs derived from predominant planktonic species such as Pelagibacter revealed recently discovered as well as potentially new regulatory elements. Our analyses show that metatranscriptomic data sets can reveal new information about the diversity, taxonomic distribution and abundance of sRNAs in naturally occurring microbial communities, and indicate their involvement in environmentally relevant processes including carbon metabolism and nutrient acquisition

Spatiotemporal Dynamics of Virus Infection Spreading in Tissues

Virus spreading in tissues is determined by virus transport, virus multiplication in host cells and the virus-induced immune response. Cytotoxic T cells remove infected cells with a rate determined by the infection level. The intensity of the immune response has a bell-shaped dependence on the concentration of virus, i.e., it increases at low and decays at high infection levels. A combination of these effects and a time delay in the immune response determine the development of virus infection in tissues like spleen or lymph nodes. The mathematical model described in this work consists of reaction-diffusion equations with a delay. It shows that the different regimes of infection spreading like the establishment of a low level infection, a high level infection or a transition between both are determined by the initial virus load and by the intensity of the immune response. The dynamics of the model solutions include simple and composed waves, and periodic and aperiodic oscillations. The results of analytical and numerical studies of the model provide a systematic basis for a quantitative understanding and interpretation of the determinants of the infection process in target organs and tissues from the image-derived data as well as of the spatiotemporal mechanisms of viral disease pathogenesis, and have direct implications for a biopsy-based medical testing of the chronic infection processes caused by viruses, e.g. HIV, HCV and HBV.The research was funded by the Russian Science Foundation (Grant no. 15-11-00029) to G.B., A.M., V.V. A.M. was also partially supported by a grant from the Spanish Ministry of Economy and Competitiveness and FEDER (Grant no. SAF2013-46077-R). S.T. and V.V. were also partially supported by FONDECYT (Chile) project 1150480. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript