Yeasts in sustainable bioethanol production: a review

Bioethanol has been identified as the mostly used biofuel worldwide since it significantly contributes to the reduction of crude oil consumption and environmental pollution. It can be produced from various types of feedstocks such as sucrose, starch, lignocellulosic and algal biomass through fermentation process by microorganisms. Compared to other types of microoganisms, yeasts especially Saccharomyces cerevisiae is the common microbes employed in ethanol production due to its high ethanol productivity, high ethanol tolerance and ability of fermenting wide range of sugars. However, there are some challenges in yeast fermentation which inhibit ethanol production such as high temperature, high ethanol concentration and the ability to ferment pentose sugars. Various types of yeast strains have been used in fermentation for ethanol production including hybrid, recombinant and wild-type yeasts. Yeasts can directly ferment simple sugars into ethanol while other type of feedstocks must be converted to fermentable sugars before it can be fermented to ethanol. The common processes involves in ethanol production are pretreatment, hydrolysis and fermentation. Production of bioethanol during fermentation depends on several factors such as temperature, sugar concentration, pH, fermentation time, agitation rate, and inoculum size. The efficiency and productivity of ethanol can be enhanced by immobilizing the yeast cells. This review highlights the different types of yeast strains, fermentation process, factors affecting bioethanol production and immobilization of yeasts for better bioethanol production

Stress modulation as a means to improve yeasts for lignocellulose bioconversion

The second-generation (2G) fermentation environment for lignocellulose conversion presents unique challenges to the fermentative organism that do not necessarily exist in other industrial fermentations. While extreme osmotic, heat, and nutrient starvation stresses are observed in sugar- and starch-based fermentation environments, additional pre-treatment-derived inhibitor stress, potentially exacerbated by stresses such as pH and product tolerance, exist in the 2G environment. Furthermore, in a consolidated bioprocessing (CBP) context, the organism is also challenged to secrete enzymes that may themselves lead to unfolded protein response and other stresses. This review will discuss responses of the yeast Saccharomyces cerevisiae to 2G-specific stresses and stress modulation strategies that can be followed to improve yeasts for this application. We also explore published –omics data and discuss relevant rational engineering, reverse engineering, and adaptation strategies, with the view of identifying genes or alleles that will make positive contributions to the overall robustness of 2G industrial strains

Process development for the continuous production of heterologous proteins by the industrial yeast, Komagataella phaffii

The current trend in industrial biotechnology is to move from batch or fed-batch fermentations to continuous operations. The success of this transition will require the development of genetically stable production strains, the use of strong constitutive promoters, and the development of new medium formulations that allow an appropriate balance between cell growth and product formation. We identified genes that showed high expression in Komagataella phaffii during different steady-state conditions and explored the utility of promoters of these genes (Chr1-4_0586 and FragB_0052) in optimizing the expression of two different r-proteins, human lysozyme (HuLy), and the anti-idiotypic antibody fragment, Fab-3H6, in comparison with the widely used glyceraldehyde-3-phosphate dehydrogenase promoter. Our results showed that the promoter strength was highly dependent on the cultivation conditions and thus constructs should be tested under a range of conditions to determine both the best performing clone and the ideal promoter for the expression of the protein of interest. An important benefit of continuous production is that it facilitates the use of the genome-scale metabolic models in the design of strains and cultivation media. In silico flux distributions showed that production of either protein increased the flux through aromatic amino acid biosynthesis. Tyrosine supplementation increased the productivity for both proteins, whereas tryptophan addition did not cause any significant change and, phenylalanine addition increased the expression of HuLy but decreased that of Fab-3H6. These results showed that a genome-scale metabolic model can be used to assess the metabolic burden imposed by the synthesis of a specific r-protein and then this information can be used to tailor a cultivation medium to increase production

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer

Purification, biochemical characterization and gene sequencing of a thermostable raw starch digesting alpha-amylase from Geobacillus thermoleovorans subsp stromboliensis subsp nov.

This study reports the purification and biochemical characterization of a raw starch-digesting alpha-amylase from Geobacillus thermoleovorans subsp. stromboliensis subsp. nov. (strain Pizzo(T)). The molecular weight was estimated to be 58 kDa by SDS-PAGE. The enzyme was highly active over a wide range of pH from 4.0-10.0. The optimum temperature of the enzyme was 70A degrees C. It showed extreme thermostability in the presence of Ca2+, retaining 50% of its initial activity after 90 h at 70A degrees C. The enzyme efficiently hydrolyzed 20% (w/v) of raw starches, concentration normally used in starch industries. The alpha-amylase showed an high stability in presence of many organic solvents. In particular the residual activity was of 73% in presence of 15% (v/v) ethyl alcohol, which corresponds to ethanol yield in yeast fermentation process. By analyzing its complete amyA gene sequence (1,542 bp), the enzyme was proposed to be a new alpha-amylase

An integrative analysis of transcriptomic response of ethanol tolerant strains to ethanol in Saccharomyces cerevisiae

The accumulation of ethanol is one of the main environmental stresses that Saccharomyces cerevisiae cells are exposed to in industrial alcoholic beverage and bioethanol production processes. Despite the known impacts of ethanol, the molecular mechanisms underlying ethanol tolerance are still not fully understood. Novel gene targets leading to ethanol tolerance were previously identified via a network approach and the investigations of the deletions of these genes resulted in the improved ethanol tolerance of pmt7 Delta/pmt7 Delta and yhl042w Delta/yhl042w Delta strains. In the present study, an integrative system based approach was used to investigate the global transcriptional changes in these two ethanol tolerant strains in response to ethanol and hence to elucidate the mechanisms leading to the observed tolerant phenotypes. In addition to strain specific biological processes, a number of common and already reported biological processes were found to be affected in the reference and both ethanol tolerant strains. However, the integrative analysis of the transcriptome with the transcriptional regulatory network and the ethanol tolerance network revealed that each ethanol tolerant strain had a specific organization of the transcriptomic response. Transcription factors around which most important changes occur were determined and active subnetworks in response to ethanol and functional clusters were identified in all strains