Project description:Model-guided chassis strain design has the potential to accelerate cellfactory development. In this experiment genetic targets were identified in silico and implemented in vivo to design a yeast chassis strain for enhanced production of succinic, malic and fumaric acid. The phenotype of engineered chassis strains was further optimised through adaptive laboratory evolution. RNA-seq analysis of engineered yeast chassis strains, evolved strains and wild-type (CEN.PK background)was performed to determine the effect of engineered gene deletions and evolution on the transcriptome.
Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being “recalibrated” (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration.
Project description:D-lactic acid is a three-carbon organic acid with a chiral structure and can improve the thermostability of polylactic acid. Microorganisms such as the methylotrophic yeast Pichia pastoris, which lack the natural ability to produce or accumulate high amounts of D-lactic acid, have been engineered to produce it in high titers. However, tolerance to D-lactic acid remains a challenge. In this study, we demonstrate that cell flocculation improves tolerance to D-lactic acid and leads to increased D-lactic acid production in Pichia pastoris. By incorporating a flocculation gene from Saccharomyces cerevisiae (ScFLO1) into P. pastoris KM71, we created a strain (KM71-ScFlo1) that demonstrated up to a 1.6-fold improvement in specific growth rate at high D-lactic acid concentrations. Furthermore, integrating a D-lactate dehydrogenase gene from Leuconostoc pseudomesenteroides (LpDLDH) into KM71-ScFlo1 resulted in an engineered strain (KM71-ScFlo1-LpDLDH) that can produce D-lactic acid at a titer of 5.12 0.35 g/L in 48 hours , a 2.6-fold improvement over the control strain lacking ScFLO1 expression. Transcriptomics analysis of this strain provided insights into the mechanism of increased tolerance to D-lactic acid including the upregulations of genes involved in lactate transport and iron metabolism. Overall, our work represents an advancement in the efficient microbial production of D-lactic acid by manipulating yeast flocculation.
Project description:Agricultural wastes and other non-food sources can be used to produce biofuels. Despite multiple attempts using engineered yeast strains expressing exogenous genes, the native Saccharomyces cerevisiae produces low amount of second generations of biofuels. Here, we focused on Znf1, a non-fermentable carbon transcription factor and the suppressor protein Bud21 to overcome this challenge. Several mutants of engineered S. cerevisiae strains were engineered to enhance production of biofuels and xylose-derived compounds such as xylitol. This study demonstrates Znf1's novel transcriptional regulatory control of xylose and offer an initial step toward a more sustainable production of advanced biofuels from xylose.
Project description:The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to develop synthetic oligoribonucleotides as spike-in standards.
Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being M-bM-^@M-^\recalibratedM-bM-^@M-^] (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units M-BM- at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration. Four human RNA samples with equimolar ERCC spike-in standards were sequenced on Illumina. Two human brain/liver/muscle RNA mixtures with dynamic range of ERCC spike-in standards were sequenced on SOLiD.