Characterizing Different Modes of Interplay Between Rap1 and H3 Using Inducible H3-depletion Yeast.

Histones and transcription factors (TFs) are two important DNA-binding proteins that interact, compete, and together regulate transcriptional processes in response to diverse internal and external stimuli. Condition-specific depletion of histones in Saccharomyces cerevisiae using a galactose-inducible H3 promoter provides a suitable framework for examining transcriptional alteration resulting from reduced nucleosome content. However, the effect on DNA binding activities of TFs is yet to be fully explored. In this work, we combine ChIP-seq of H3 with RNA-seq to elucidate the genome-scale relationships between H3 occupancy patterns and transcriptional dynamics before and after global H3 depletion. ChIP-seq of Rap1 is also conducted in the H3-depletion and control treatments, to investigate the interplay between this master regulator TF and nucleosomal H3, and to explore the impact on diverse transcriptional responses of different groups of target genes and functions. Ultimately, we propose a working model and testable hypotheses regarding the impact of global and local H3 depletion on transcriptional changes. We also demonstrate different potential modes of interaction between Rap1 and H3, which sheds light on the potential multifunctional regulatory capabilities of Rap1 and potentially other pioneer factors.

Optimizing Ethanol Production in Saccharomyces cerevisiae at Ambient and Elevated Temperatures through Machine Learning-Guided Combinatorial Promoter Modifications.

Bioethanol has gained popularity in recent decades as an ecofriendly alternative to fossil fuels due to increasing concerns about global climate change. However, economically viable ethanol fermentation remains a challenge. High-temperature fermentation can reduce production costs, but Saccharomyces cerevisiae yeast strains normally ferment poorly under high temperatures. In this study, we present a machine learning (ML) approach to optimize bioethanol production in S. cerevisiae by fine-tuning the promoter activities of three endogenous genes. We created 216 combinatorial strains of S. cerevisiae by replacing native promoters with five promoters of varying strengths to regulate ethanol production. Promoter replacement resulted in a 63% improvement in ethanol production at 30 °C. We created an ML-guided workflow by utilizing XGBoost to train high-performance models based on promoter strengths and cellular metabolite concentrations obtained from ethanol production of 216 combinatorial strains at 30 °C. This strategy was then applied to optimize ethanol production at 40 °C, where we selected 31 strains for experimental fermentation. This reduced experimental load led to a 7.4% increase in ethanol production in the second round of the ML-guided workflow. Our study offers a comprehensive library of promoter strength modifications for key ethanol production enzymes, showcasing how machine learning can guide yeast strain optimization and make bioethanol production more cost-effective and efficient. Furthermore, we demonstrate that metabolic engineering processes can be accelerated and optimized through this approach.

Overexpression of Pyruvate Carboxylase Is Correlated With Colorectal Cancer Progression and Supports Growth of Invasive Colon Cancer HT-29 Cell Line.

BACKGROUND/AIM: Pyruvate carboxylase (PC) is a major anaplerotic enzyme for generating oxaloacetate for the TCA cycle and also a key enzyme in gluconeogenesis, de novo fatty acid and amino acid synthesis in normal cells. Recent studies have identified PC overexpression in different cancers, such as breast and lung. However, the involvement of PC in colorectal cancer (CRC) is unclear. Our purpose was to investigate the PC expression levels and its correlations with potentially relevant clinical-pathological parameters in CRC. MATERIALS AND METHODS: PC expression levels in tissues from 60 Thai CRC patients were investigated by immunohistochemistry while a clonogenic assay was performed for determining cell growth of HT-29 cells with PC knockdown. RESULTS: Our results showed for the first time that high PC expression levels were significantly correlated with late stage of the cancer, perineural invasion and lymph node metastasis. The overexpression of PC was also significantly associated with poor overall and disease-free survival times of CRC patients. In addition, suppression of cancer cell growth was found in PC-deficient cell lines using CRISPR-Cas9. CONCLUSION: The overexpression levels of PC were correlated with CRC progression and survival times. Therefore, PC might serve as a potential clinical prognostic marker for colorectal cancer.