High production of fatty alcohols in Escherichia coli with fatty acid starvation

Background: Microbial biofuel synthesis attracting increasing attention. Great advances have been made in producing fatty alcohols from fatty acyl-CoAs and fatty acids in Escherichia coli. However, the low titers and limited knowledge regarding the basic characteristics of fatty alcohols, such as location and toxicity, have hampered large-scale industrialization. Further research is still needed.</p

TALENs-Assisted Multiplex Editing for Accelerated Genome Evolution To Improve Yeast Phenotypes

Genome editing is an important tool for building novel genotypes with a desired phenotype. However, the fundamental challenge is to rapidly generate desired alterations on a genome-wide scale. Here, we report TALENs (transcription activator-like effector nucleases)-assisted multiplex editing (TAME), based on the interaction of designed TALENs with the DNA sequences between the critical TATA and GC boxes, for generating multiple targeted genomic modifications. Through iterative cycles of TAME to induce abundant semirational <i>indels</i> coupled with efficient screening using a reporter, the targeted fluorescent trait can be continuously and rapidly improved by accumulating multiplex beneficial genetic modifications in the evolving yeast genome. To further evaluate its efficiency, we also demonstrate the application of TAME for significantly improving ethanol tolerance of yeast in a short amount of time. Therefore, TAME is a broadly generalizable platform for accelerated genome evolution to rapidly improve yeast phenotypes

Additional file 1: of Genome shuffling of the nonconventional yeast Pichia anomala for improved sugar alcohol production

Fig. S1. The colorimetric assay of sugar alcohols. a The flow chart of the colorimetric method for sugar alcohol screening. b The correlation of the two sugar alcohol-detection methods by linear regression. H and C represent the HPLC and colorimetric methods, respectively. Fig. S2. Comparison of the DNA content among the parent and shuffled strains, as determined by flow cytometry. The DNA content is shown for a haploid control strain S. cerevisiae BY4741, haploid parent strain P. anomala HP, diploid strain P. anomala TIB-x229 and shuffled strains GS2-1, GS2-2 and GS2-3

An NGS-Independent Strategy for Proteome-Wide Identification of Single Amino Acid Polymorphisms by Mass Spectrometry

Detection of proteins containing single amino acid polymorphisms (SAPs) encoded by nonsynonymous SNPs (nsSNPs) can aid researchers in studying the functional significance of protein variants. Most proteogenomic approaches for large-scale SAPs mapping require construction of a sample-specific database containing protein variants predicted from the next-generation sequencing (NGS) data. Searching shotgun proteomic data sets against these NGS-derived databases allowed for identification of SAP peptides, thus validating the proteome-level sequence variation. Contrary to the conventional approaches, our study presents a novel strategy for proteome-wide SAP detection without relying on sample-specific NGS data. By searching a deep-coverage proteomic data set from an industrial thermotolerant yeast strain using our strategy, we identified 337 putative SAPs compared to the reference genome. Among the SAP peptides identified with stringent criteria, 85.2% of SAP sites were validated using whole-genome sequencing data obtained for this organism, which indicates high accuracy of SAP identification with our strategy. More interestingly, for certain SAP peptides that cannot be predicted by genomic sequencing, we used synthetic peptide standards to verify expression of peptide variants in the proteome. Our study has provided a unique tool for proteogenomics to enable proteome-wide direct SAP identification and capture nongenetic protein variants not linked to nsSNPs

Distinct Proteome Remodeling of Industrial <i>Saccharomyces cerevisiae</i> in Response to Prolonged Thermal Stress or Transient Heat Shock

To gain a deep understanding of yeast-cell response to heat stress, multiple laboratory strains have been intensively studied via genome-wide expression analysis for the mechanistic dissection of classical heat-shock response (HSR). However, robust industrial strains of <i>Saccharomyces cerevisiae</i> have hardly been explored in global analysis for elucidation of the mechanism of thermotolerant response (TR) during fermentation. Herein, we employed data-independent acquisition and sequential window acquisition of all theoretical mass spectra based proteomic workflows to characterize proteome remodeling of an industrial strain, ScY01, responding to prolonged thermal stress or transient heat shock. By comparing the proteomic signatures of ScY01 in TR versus HSR as well as the HSR of the industrial strain versus a laboratory strain, our study revealed disparate response mechanisms of ScY01 during thermotolerant growth or under heat shock. In addition, through proteomics data-mining for decoding transcription factor interaction networks followed by validation experiments, we uncovered the functions of two novel transcription factors, Mig1 and Srb2, in enhancing the thermotolerance of the industrial strain. This study has demonstrated that accurate and high-throughput quantitative proteomics not only provides new insights into the molecular basis for complex microbial phenotypes but also pinpoints upstream regulators that can be targeted for improving the desired traits of industrial microorganisms

Distinct Proteome Remodeling of Industrial <i>Saccharomyces cerevisiae</i> in Response to Prolonged Thermal Stress or Transient Heat Shock

To gain a deep understanding of yeast-cell response to heat stress, multiple laboratory strains have been intensively studied via genome-wide expression analysis for the mechanistic dissection of classical heat-shock response (HSR). However, robust industrial strains of <i>Saccharomyces cerevisiae</i> have hardly been explored in global analysis for elucidation of the mechanism of thermotolerant response (TR) during fermentation. Herein, we employed data-independent acquisition and sequential window acquisition of all theoretical mass spectra based proteomic workflows to characterize proteome remodeling of an industrial strain, ScY01, responding to prolonged thermal stress or transient heat shock. By comparing the proteomic signatures of ScY01 in TR versus HSR as well as the HSR of the industrial strain versus a laboratory strain, our study revealed disparate response mechanisms of ScY01 during thermotolerant growth or under heat shock. In addition, through proteomics data-mining for decoding transcription factor interaction networks followed by validation experiments, we uncovered the functions of two novel transcription factors, Mig1 and Srb2, in enhancing the thermotolerance of the industrial strain. This study has demonstrated that accurate and high-throughput quantitative proteomics not only provides new insights into the molecular basis for complex microbial phenotypes but also pinpoints upstream regulators that can be targeted for improving the desired traits of industrial microorganisms