Glucose-lactose mixture feeds in industry-like conditions: a gene regulatory network analysis on the hyperproducing Trichoderma reesei strain Rut-C30.

BACKGROUND: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network of T. reesei Rut-C30. RESULTS: Experimental results on the Rut-C30 hyperproducing strain confirmed the impact of sugar mixtures on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network built using BRANE Cut software reveals three sub-networks related to i) a positive correlation between lactose concentration and cellulase production, ii) a particular dependence of the lactose onto the ß-glucosidase regulation and iii) a negative regulation of the development process and growth. CONCLUSIONS: This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1, clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the ß-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains in industry-like conditions.

Translational study of the whole transcriptome in rats and genetic polymorphisms in humans identifies LRP1B and VPS13A as key genes involved in tolerance to cocaine-induced motor disturbances.

Motor disturbances strongly increase the burden of cocaine use disorder (CUDs). The objective of our translational study was to identify the genes and biological pathways underlying the tolerance to cocaine-induced motor effects. In a 5-day protocol measuring motor tolerance to cocaine in rats (N = 40), modeling the motor response to cocaine in patients, whole-genome RNA sequencing was conducted on the ventral and dorsal striatum to prioritize a genetic association study in 225 patients with severe CUD who underwent thorough phenotypic (cocaine-induced hyperlocomotion, CIH; and cocaine-induced stereotypies, CIS) and genotypic [571,000 polymorphisms (SNPs)] characterization. We provide a comprehensive description of the rat striatal transcriptomic response to cocaine in our paradigm. Repeated vs. acute cocaine binge administration elicited 27 differentially expressed genes in the ventral striatum and two in the dorsal striatum. One gene, Lrp1b, was differentially expressed in both regions. In patients, LRP1B was significantly associated with both CIS and CIH. CIH was also associated with VPS13A, a gene involved in a severe neurological disorder characterized by hyperkinetic movements. The LRP1B minor allele rs7568970 had a significant protective effect against CIS (558 SNPs, Bonferroni-corrected p = 0.02) that resisted adjustment for confounding factors, including the amount of cocaine use (adjusted beta = -0.965 and -2.35 for heterozygotes and homozygotes, respectively, p < 0.01). Using hypothesis-free prioritization of candidate genes along with thorough methodology in both the preclinical and human analysis pipelines, we provide reliable evidence that LRP1B and VPS13A are involved in the motor tolerance to cocaine in CUD patients, in line with their known pathophysiology.

Isolation and differential transcriptome of vascular smooth muscle cells and mid-capillary pericytes from the rat brain.

Brain mural cells form a heterogeneous family which significantly contributes to the maintenance of the blood-brain barrier and regulation of the cerebral blood flow. Current procedures to isolate them cannot specifically separate their distinct subtypes, in particular vascular smooth muscle cells (VSMCs) and mid-capillary pericytes (mcPCs), which differ among others by their expression of smooth muscle actin (SMA). We herein describe an innovative method allowing SMA+ VSMCs and SMA- mcPCs to be freshly isolated from the rat cerebral cortex. Using differential RNA-Seq analysis, we then reveal the specific gene expression profile of each subtype. Our results refine the current description of the role of VSMCs in parenchymal cortical arterioles at the molecular level and provide a unique platform to identify the molecular mechanisms underlying the specific functions of mcPCs in the brain vasculature.

Aozan: an automated post-sequencing data-processing pipeline.

MOTIVATION: Data management and quality control of output from Illumina sequencers is a disk space- and time-consuming task. Thus, we developed Aozan to automatically handle data transfer, demultiplexing, conversion and quality control once a run has finished. This software greatly improves run data management and the monitoring of run statistics via automatic emails and HTML web reports. AVAILABILITY AND IMPLEMENTATION: Aozan is implemented in Java and Python, supported on Linux systems, and distributed under the GPLv3 License at: http://www.outils.genomique.biologie.ens.fr/aozan/ . Aozan source code is available on GitHub: https://github.com/GenomicParisCentre/aozan . CONTACT: aozan@biologie.ens.fr.

Comparison of library preparation methods reveals their impact on interpretation of metatranscriptomic data.

BACKGROUND: Metatranscriptomics is rapidly expanding our knowledge of gene expression patterns and pathway dynamics in natural microbial communities. However, to cope with the challenges of environmental sampling, various rRNA removal and cDNA synthesis methods have been applied in published microbial metatranscriptomic studies, making comparisons arduous. Whereas efficiency and biases introduced by rRNA removal methods have been relatively well explored, the impact of cDNA synthesis and library preparation on transcript abundance remains poorly characterized. The evaluation of potential biases introduced at this step is challenging for metatranscriptomic samples, where data analyses are complex, for example because of the lack of reference genomes. RESULTS: Herein, we tested four cDNA synthesis and Illumina library preparation protocols on a simplified mixture of total RNA extracted from four bacterial species. In parallel, RNA from each microbe was tested individually. cDNA synthesis was performed on rRNA depleted samples using the TruSeq Stranded Total RNA Library Preparation, the SMARTer Stranded RNA-Seq, or the Ovation RNA-Seq V2 System. A fourth experiment was made directly from total RNA using the Encore Complete Prokaryotic RNA-Seq. The obtained sequencing data were analyzed for: library complexity and reproducibility; rRNA removal efficiency and bias; the number of genes detected; coverage uniformity; and the impact of protocols on expression biases. Significant variations, especially in organism representation and gene expression patterns, were observed among the four methods. TruSeq generally performed best, but is limited by its requirement of hundreds of nanograms of total RNA. The SMARTer method appears the best solution for smaller amounts of input RNA. For very low amounts of RNA, the Ovation System provides the only option; however, the observed biases emphasized its limitations for quantitative analyses. CONCLUSIONS: cDNA and library preparation methods may affect the outcome and interpretation of metatranscriptomic data. The most appropriate method should be chosen based on the available quantity of input RNA and the quantitative or non-quantitative objectives of the study. When low amounts of RNA are available, as in most metatranscriptomic studies, the SMARTer method seems to be the best compromise to obtain reliable results. This study emphasized the difficulty in comparing metatranscriptomic studies performed using different methods.