Identification and Validation of Reference Genes in Clostridium beijerinckii NRRL B-598 for RT-qPCR Using RNA-Seq Data

Obtaining Reliable RT-qPCR Results in Molecular Diagnostics—MIQE Goals and Pitfalls for Transcriptional Biomarker Discovery

In this review, we discuss the development pipeline for transcriptional biomarkers in molecular diagnostics and stress the importance of a reliable gene transcript quantification strategy. Hence, a further focus is put on the MIQE guidelines and how to adapt them for biomarker discovery, from signature validation up to routine diagnostic applications. First, the advantages and pitfalls of the holistic RNA sequencing for biomarker development will be described to establish a candidate biomarker signature. Sequentially, the RT-qPCR confirmation process will be discussed to validate the discovered biomarker signature. Examples for the successful application of RT-qPCR as a fast and reproducible quantification method in routinemolecular diagnostics are provided. Based on the MIQE guidelines, the importance of “key steps” in RT-qPCR is accurately described, e.g., reverse transcription, proper reference gene selection and, finally, the application of automated RT-qPCR data analysis software. In conclusion, RT-qPCR proves to be a valuable tool in the establishment of a disease-specific transcriptional biomarker signature and will have a great future in molecular diagnostics or personalized medicine.

Transcriptomic studies of solventogenic clostridia, Clostridium acetobutylicum and Clostridium beijerinckii

Solventogenic clostridia are not a strictly defined group within the genus Clostridium but its representatives share some common features, i.e. they are anaerobic, non-pathogenic, non-toxinogenic and endospore forming bacteria. Their main metabolite is typically 1-butanol but depending on species and culture conditions, they can form other metabolites such as acetone, isopropanol, ethanol, butyric, lactic and acetic acids, and hydrogen. Although these organisms were previously used for the industrial production of solvents, they later fell into disuse, being replaced by more efficient chemical production. A return to a more biological production of solvents therefore requires a thorough understanding of clostridial metabolism. Transcriptome analysis, which reflects the involvement of individual genes in all cellular processes within a population, at any given (sampling) moment, is a valuable tool for gaining a deeper insight into clostridial life. In this review, we describe techniques to study transcription, summarize the evolution of these techniques and compare methods for data processing and visualization of solventogenic clostridia, particularly the species Clostridium acetobutylicum and Clostridium beijerinckii. Individual approaches for evaluating transcriptomic data are compared and their contributions to advancements in the field are assessed. Moreover, utilization of transcriptomic data for reconstruction of computational clostridial metabolic models is considered and particular models are described. Transcriptional changes in glucose transport, central carbon metabolism, the sporulation cycle, butanol and butyrate stress responses, the influence of lignocellulose-derived inhibitors on growth and solvent production, and other respective topics, are addressed and common trends are highlighted.

Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress

Alicyclobacillus acidoterrestris is a major concern in fruit juice industry due to its spoilage potential of acidic fruit juice. Quantifying the expression levels of functional genes by real-time quantitative polymerase chain reaction (RT-qPCR) is necessary to elucidate the response mechanisms of A. acidoterrestris to acid stress. However, appropriate reference genes (RGs) for data normalization are required to obtain reliable RT-qPCR results. In this study, eight novel candidate RGs were screened based on transcriptome datasets of A. acidoterrestris under acid stress. The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. This study revealed for the first time that A. acidoterrestris had different suitable RGs under different acid conditions, with implications for further deciphering the acid response mechanisms of this spoilage-causing bacterium.

Diversity and Evolution of Clostridium beijerinckii and Complete Genome of the Type Strain DSM 791T

Clostridium beijerinckii is a relatively widely studied, yet non-model, bacterium. While 246 genome assemblies of its various strains are available currently, the diversity of the whole species has not been studied, and it has only been analyzed in part for a missing genome of the type strain. Here, we sequenced and assembled the complete genome of the type strain Clostridium beijerinckii DSM 791T, composed of a circular chromosome and a circular megaplasmid, and used it for a comparison with other genomes to evaluate diversity and capture the evolution of the whole species. We found that strains WB53 and HUN142 were misidentified and did not belong to the Clostridium beijerinckii species. Additionally, we filtered possibly misassembled genomes, and we used the remaining 237 high-quality genomes to define the pangenome of the whole species. By its functional annotation, we showed that the core genome contains genes responsible for basic metabolism, while the accessory genome has genes affecting final phenotype that may vary among different strains. We used the core genome to reconstruct the phylogeny of the species and showed its great diversity, which complicates the identification of particular strains, yet hides possibilities to reveal hitherto unreported phenotypic features and processes utilizable in biotechnology.