RNA-binding protein CPEB1 remodels host and viral RNA landscapes.

Host and virus interactions occurring at the post-transcriptional level are critical for infection but remain poorly understood. Here, we performed comprehensive transcriptome-wide analyses revealing that human cytomegalovirus (HCMV) infection results in widespread alternative splicing (AS), shortening of 3' untranslated regions (3' UTRs) and lengthening of poly(A)-tails in host gene transcripts. We found that the host RNA-binding protein CPEB1 was highly induced after infection, and ectopic expression of CPEB1 in noninfected cells recapitulated infection-related post-transcriptional changes. CPEB1 was also required for poly(A)-tail lengthening of viral RNAs important for productive infection. Strikingly, depletion of CPEB1 reversed infection-related cytopathology and post-transcriptional changes, and decreased productive HCMV titers. Host RNA processing was also altered in herpes simplex virus-2 (HSV-2)-infected cells, thereby indicating that this phenomenon might be a common occurrence during herpesvirus infections. We anticipate that our work may serve as a starting point for therapeutic targeting of host RNA-binding proteins in herpesvirus infections

Metagenomic insights into anaerobic metabolism along an Arctic peat soil profile.

A metagenomic analysis was performed on a soil profile from a wet tundra site in northern Alaska. The goal was to link existing biogeochemical knowledge of the system with the organisms and genes responsible for the relevant metabolic pathways. We specifically investigated how the importance of iron (Fe) oxides and humic substances (HS) as terminal electron acceptors in this ecosystem is expressed genetically, and how respiratory and fermentative processes varied with soil depth into the active layer and into the upper permafrost. Overall, the metagenomes reflected a microbial community enriched in a diverse range of anaerobic pathways, with a preponderance of known Fe reducing species at all depths in the profile. The abundance of sequences associated with anaerobic metabolic processes generally increased with depth, while aerobic cytochrome c oxidases decreased. Methanogenesis genes and methanogen genomes followed the pattern of CH4 fluxes: they increased steeply with depth into the active layer, but declined somewhat over the transition zone between the lower active layer and the upper permafrost. The latter was relatively enriched in fermentative and anaerobic respiratory pathways. A survey of decaheme cytochromes (MtrA, MtrC and their homologs) revealed that this is a promising approach to identifying potential reducers of Fe(III) or HS, and indicated a possible role for Acidobacteria as Fe reducers in these soils. Methanogens appear to coexist in the same layers, though in lower abundance, with Fe reducing bacteria and other potential competitors, including acetogens. These observations provide a rich set of hypotheses for further targeted study

Transcriptome-wide analysis of PGC-1α–binding RNAs identifies genes linked to glucagon metabolic action

The peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) is a transcriptional coactivator that controls expression of metabolic/energetic genes, programming cellular responses to nutrient and environmental adaptations such as fasting, cold, or exercise. Unlike other coactivators, PGC-1α contains protein domains involved in RNA regulation such as serine/arginine (SR) and RNA recognition motifs (RRMs). However, the RNA targets of PGC-1α and how they pertain to metabolism are unknown. To address this, we performed enhanced ultraviolet (UV) cross-linking and immunoprecipitation followed by sequencing (eCLIP-seq) in primary hepatocytes induced with glucagon. A large fraction of RNAs bound to PGC-1α were intronic sequences of genes involved in transcriptional, signaling, or metabolic function linked to glucagon and fasting responses, but were not the canonical direct transcriptional PGC-1α targets such as OXPHOS or gluconeogenic genes. Among the top-scoring RNA sequences bound to PGC-1α were Foxo1, Camk1δ, Per1, Klf15, Pln4, Cluh, Trpc5, Gfra1, and Slc25a25 PGC-1α depletion decreased a fraction of these glucagon-induced messenger RNA (mRNA) transcript levels. Importantly, knockdown of several of these genes affected glucagon-dependent glucose production, a PGC-1α-regulated metabolic pathway. These studies show that PGC-1α binds to intronic RNA sequences, some of them controlling transcript levels associated with glucagon action

Percent abundance of genomic sequences from selected taxa with known respiratory pathways.

<p>FeRB, Fe-reducing bacteria consist of Geobacteraceae (incuding <i>Pelobacter</i> and <i>Desulfomonas</i>), <i>Rhodoferax (Albidiferax) ferrireducens, Shewanella, Carboxydothermus</i> and <i>Anaeromyxobacter</i>. SRB, sulfate reducing bacteria include the Desulfobacterales, Desulfovibrionales and Desulfurococcales. Dehalo, Dehalorespirers include <i>Anaeromyxobacter</i>, <i>Carboxydothermus, Dechloromonas</i>, and <i>Dehalococcoides</i>. Strict fermenters (Strict Ferm) include Clostridiales and <i>Bacteroides</i>. Syntrophic bacteria include Syntrophaceae, Syntrophobacteraceae and Syntrophomonadaceae. All the taxa shown varied significantly with depth (P<0.001) by the Pearson chi-square test.</p

Taxonomic representation of key genes in fermentative pathways found in all four metagenomes.

a<p>includes butyryl and 3-hydroxybutyryl-CoA hydrogenases, enoyl-CoA hydratase, and acetyl-CoA acetyltransferase.</p>b<p>includes (in decreasing order) Chloroflexi, Euryarchaeota, Verrucomicrobia, Cyanobacteria, Deinococcus, Planctomycetes and Spirochaetes.</p>c<p>tests the null hypothesis that the percentages are the same across the three columns.</p><p>Legend: Similarities to these genes (as annotated in SEED) were combined by the genus in which they were found and the 50 most abundant genera for each gene were aggregated into phyla (or classes for the Proteobacteria).</p

Maximum likelihood phylogenetic tree of decaheme cytochrome protein sequences with high similarity to metagenomic sequences.

<p>(Mean log E value = −13.3, mean % identity = 70.5%, mean alignment length = 47.7). The code in square brackets represents the number of similar sequences (superscript) from the four layers, with A-D corresponding to shallow-deep. Different sequences from the same genome are differentiated by the last 4 digits of the MD5 number in the M5nr database (<a href="http://tools.metagenomics.anl.gov/m5nr/m5nr.cgi" target="_blank">http://tools.metagenomics.anl.gov/m5nr/m5nr.cgi</a>). Sequences were aligned based on the ten CxxCH heme-binding domains. MtrA Opitutus has only 7–8 canonical heme-binding domains (eighth domain = TxxCH), and so this sequence was used as an outgroup.</p

Comparison of Barrow soil metagenomes presented in this study (all layers combined) with published Waseca farm soil metagenome [6].

a<p>Pearson chi-squared statistic (χ², and corresponding P value) compares proportions of sequences between the two soils.</p