Genomic Analysis of the Hydrocarbon-Producing, Cellulolytic, Endophytic Fungus Ascocoryne sarcoides

The microbial conversion of solid cellulosic biomass to liquid biofuels may provide a renewable energy source for transportation fuels. Endophytes represent a promising group of organisms, as they are a mostly untapped reservoir of metabolic diversity. They are often able to degrade cellulose, and they can produce an extraordinary diversity of metabolites. The filamentous fungal endophyte Ascocoryne sarcoides was shown to produce potential-biofuel metabolites when grown on a cellulose-based medium; however, the genetic pathways needed for this production are unknown and the lack of genetic tools makes traditional reverse genetics difficult. We present the genomic characterization of A. sarcoides and use transcriptomic and metabolomic data to describe the genes involved in cellulose degradation and to provide hypotheses for the biofuel production pathways. In total, almost 80 biosynthetic clusters were identified, including several previously found only in plants. Additionally, many transcriptionally active regions outside of genes showed condition-specific expression, offering more evidence for the role of long non-coding RNA in gene regulation. This is one of the highest quality fungal genomes and, to our knowledge, the only thoroughly annotated and transcriptionally profiled fungal endophyte genome currently available. The analyses and datasets contribute to the study of cellulose degradation and biofuel production and provide the genomic foundation for the study of a model endophyte system

Transition State Charge Stabilization and Acid–Base Catalysis of mRNA Cleavage by the Endoribonuclease RelE

The bacterial toxin RelE is a ribosome-dependent endoribonuclease. It is part of a type II toxin–antitoxin system that contributes to antibiotic resistance and biofilm formation. During amino acid starvation, RelE cleaves mRNA in the ribosomal A-site, globally inhibiting protein translation. RelE is structurally similar to microbial RNases that employ general acid–base catalysis to facilitate RNA cleavage. The RelE active site is atypical for acid–base catalysis, in that it is enriched with positively charged residues and lacks the prototypical histidine-glutamate catalytic pair, making the mechanism of mRNA cleavage unclear. In this study, we use a single-turnover kinetic analysis to measure the effect of pH and phosphorothioate substitution on the rate constant for cleavage of mRNA by wild-type RelE and seven active-site mutants. Mutation and thio effects indicate a major role for stabilization of increased negative change in the transition state by arginine 61. The wild-type RelE cleavage rate constant is pH-independent, but the reaction catalyzed by many of the mutants is strongly dependent on pH, suggestive of general acid–base catalysis. pH–rate curves indicate that wild-type RelE operates with the p<i>K</i>_a of at least one catalytic residue significantly downshifted by the local environment. Mutation of any single active-site residue is sufficient to disrupt this microenvironment and revert the shifted p<i>K</i>_a back above neutrality. pH–rate curves are consistent with K54 functioning as a general base and R81 as a general acid. The capacity of RelE to effect a large p<i>K</i>_a shift and facilitate a common catalytic mechanism by uncommon means furthers our understanding of other atypical enzymatic active sites

Compound gene co-expression profiles.

<p>Each plot shows the quantile-normalized log₂ (RPKM) for each set of genes of co-expressed with a particular compound profile (green 001, red 010, blue 100, cyan 101, purple 110, and black 111) across all 6 conditions (CB, PD4, PD14, AMM, CELL, and OAC). The first three conditions (CB, PD4, and PD14) represent the conditions where the compounds analyzed in this study were detected. The remaining conditions serve as the nulls (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002558#pgen.1002558.s030" target="_blank">Text S1f</a>or details). Within the plots, each line corresponds to a single gene.</p

Coupled transcriptomics and metabolomics to generate pathway predictions.

<p>The top panels (A–D) represent the algorithm schema and the bottom panels (E–H) represent the corresponding steps with data for an example pathway, C8 production. Cyan, green, and purple are used to denote different experimental conditions (1, 2, and 3 and CB, PD4, and PD14 for the schematic and the C8 pathway data, respectively). GC/MS total ion chromatograms (orange box, A & E) are used to generate compound co-occurrence profiles (red box, B & F). These compound co-occurrence profiles are used to group and order the compounds based on patterns of correlation and anti-correlation to build a possible biosynthetic pathway (brown box C & G). Genes for which the expression profile matches the compound profile are considered correlated and therefore likely candidates for the biosynthetic pathway of interest (gray box D & H). Retrosynthesis is then used to match correlated genes with a reaction in the pathway, represented by roman numerals denoted on pathway arrows (brown box, C&G).</p

Analysis of cellulose-related expression.

<p><i>A. sarcoides</i> transcription was profiled when grown on potato-dextrose media for 4 days (PD4), cellulose (CELL) and cellobiose (CB). (A) The total number of genes with quantile normalized log₂(RPKM) greater than 2 was computed for each condition. The venn diagram shows the overlap of these genes across the three conditions. (B) Genes were partitioned according to their homology to the four main CAZY families: Glycoside Hydrolase (GH), Glucosyl Transferase (GT), Carbohydrate Esterase (CE), Carbohydrate Binding Modules (CBM). The homologs were then filtered to include only those genes which showed a standard deviation across the three conditions greater than 0.5. Each family was separately clustered (hierarchical, Euclidean distance, single linkage). The colorbar represents the quantile normalized log₂ (RPKM) value from white (low expression) to dark blue (high expression). Note: CBM can co-occur with all families. Only those genes that had exclusively a CBM domain were clustered in the CBM matrix to avoid duplication. (C) A table of the most highly expressed genes includes genes not directly involved in degradation, such as swollenin and chitin synthase (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002558#s2" target="_blank">Results</a> for more details).</p

Laboratory and home comparison of wrist-activity monitors and polysomnography in middle-aged adults

© 2017, Japanese Society of Sleep Research. Accurate measurement of time at lights out is essential for calculation of several measures of sleep in wrist-activity monitors. While some devices use subjective reporting of time of lights out from a sleep diary, others utilise an automated proprietary scoring algorithm to calculate time at lights out, thereby negating the need for a sleep diary. This study aimed to compare sleep measures from two such devices to polysomnography (PSG) measures (In laboratory) and against each other when worn at home (At home). Fifty middle-aged adults from the Raine Study underwent overnight PSG during which they wore an ActiGraph™ and a Readiband™. They also wore both devices at home for 7 nights. The Readiband uses an automated proprietary algorithm to determine time at lights out whereas the ActiGraph requires completion of a sleep diary noting this time. In laboratory, compared to PSG: Readiband underestimated time at lights out, sleep onset, and wake after sleep onset, overestimated sleep latency and duration (p &lt; 0.001 for all); while ActiGraph underestimated sleep latency and wake after sleep onset and overestimated sleep efficiency and duration (p &lt; 0.001 for all). Similar differences between devices were observed on the laboratory night and when at home. In conclusion, an automated algorithm such as the Readiband may be used in the same capacity as the ActiGraph for the collection of sleep measures including time at sleep onset, sleep duration and time at wake. However, Readiband and ActiGraph measures of sleep latency, efficiency and wake after sleep onset should be interpreted with caution