Analysis of the molecular basis of Saccharomyces cerevisiae mutant with high nucleic acid content by comparative transcriptomics.

Ribonucleic acid (RNA) and its degradation products are important functional components widely used in the food industry. Transcription analysis was used to explore the genetic mechanism underlying nucleic acid synthesis in the chemical mutant Saccharomyces cerevisiae strain BY23-195 with high nucleic acid content. Results showed that ribosome biogenesis, meiosis, RNA transport, mitogen-activated protein kinase (MAPK) signaling pathway, tryptophan metabolism, carbon metabolism, and longevity regulating pathway are closely related to the high nucleic acid metabolism of S. cerevisiae. Fourteen most promising genes were selected to evaluate the effect of single-gene deletion or overexpression on the RNA synthesis of S. cerevisiae. Compared with the RNA content of the parent strain BY23, that of mutant strains BY23-HXT1, BY23-ΔGSP2 and BY23-ΔCTT1 increased by 8.19%, 11.60% and 14.00%, respectively. The possible reason why HXT1, GSP2, and CTT1 affect RNA content is by regulating cell fitness. This work was the first to report that regulating the transcription of HXT1, GSP2, and CTT1 could increase the RNA content of S. cerevisiae. This work also provides valuable knowledge on the genetic mechanism of high nucleic acid synthesis in S. cerevisiae and new strategies for increasing its RNA content.

Increased RNA production in Saccharomyces cerevisiae by simultaneously overexpressing FHL1, IFH1, and SSF2 and deleting HRP1.

Ribonucleic acid (RNA) and its degradation products are widely used in the food industry. In this study, we constructed Saccharomyces cerevisiae mutants with FHL1, IFH1, SSF1, and SSF2 overexpression and HRP1 deletion, individually to evaluate the effect on RNA production. The RNA content of recombinant strains W303-1a-FHL1, W303-1a-SSF2, and W303-1a-ΔHRP1 was increased by 14.94%, 24.4%, and 19.36%, respectively, compared with the RNA content of the parent strain. However, W303-1a-IFH1 and W303-1a-SSF1 showed no significant change in RNA production compared with the parent strain. IFH1 and FHL1 encode Ifh1p and Fhl1p, respectively, which combine to form a complex that plays a key role in the transcription of the ribosomal protein (RP) gene. Ssf2p, encoded by SSF2, plays an important role in ribosome biosynthesis and Hrp1p is a negative regulator of cell growth in S. cerevisiae. Subsequently, a high RNA production strain, W112, was constructed by simultaneously overexpressing FHL1, IFH1, and SSF2 and deleting HRP1. The RNA content of W112 was 38.8% higher than the parent strain. The growth performance, RP transcription levels, and rRNA content were also investigated in the recombinant strains. This study provides a new strategy for the construction of S. cerevisiae strains containing large amounts of RNA, and it will make a significant contribution to progress in the nucleic acid industry. KEY POINTS: • Simultaneously overexpressing FHL1, IFH1, and SSF2 and deleting HRP1 can significantly increases RNA production. • The production of RNA increased by 38.8% in Saccharomyces cerevisiae. • The cell size and growth rate of the strains with higher RNA content also increased.

Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.

OBJECTIVE: Assessing risk of adverse outcomes among patients with chronic liver disease has been challenging due to non-linear disease progression. We previously developed accurate prediction models for fibrosis progression and clinical outcomes among patients with advanced chronic hepatitis C (CHC). The primary aim of this study was to validate fibrosis progression and clinical outcomes models among a heterogeneous patient cohort. DESIGN: Adults with CHC with ≥3 years follow-up and without hepatic decompensation, hepatocellular carcinoma (HCC), liver transplant (LT), HBV or HIV co-infection at presentation were analyzed (N = 1007). Outcomes included: 1) fibrosis progression 2) hepatic decompensation 3) HCC and 4) LT-free survival. Predictors included longitudinal clinical and laboratory data. Machine learning methods were used to predict outcomes in 1 and 3 years. RESULTS: The external cohort had a median age of 49.4 years (IQR 44.3-54.3); 61% were male, 80% white, and 79% had genotype 1. At presentation, 73% were treatment naïve and 31% had cirrhosis. Fibrosis progression occurred in 34% over a median of 4.9 years (IQR 3.2-7.6). Clinical outcomes occurred in 22% over a median of 4.4 years (IQR 3.2-7.6). Model performance for fibrosis progression was limited due to small sample size. The area under the receiver operating characteristic curve (AUROC) for 1 and 3-year risk of clinical outcomes was 0.78 (95% CI 0.73-0.83) and 0.76 (95% CI 0.69-0.81). CONCLUSION: Accurate assessments for risk of clinical outcomes can be obtained using routinely collected data across a heterogeneous cohort of patients with CHC. These methods can be applied to predict risk of progression in other chronic liver diseases.