The metabolism of Chinese Hamster Ovary (CHO) cells in a production environment has been extensively investigated. However, a key metabolic transition, the switch from lactate production to lactate consumption, remains enigmatic. Though commonly observed in CHO cultures, the mechanism(s) by which this metabolic shift is triggered is unknown. Despite this, efforts to control the switch have emerged due to the association of lactate consumption with improved cell growth and productivity. This review aims to consolidate current theories surrounding the lactate switch. The influence of pH, NAD /NADH, pyruvate availability and mitochondrial function on lactate consumption are explored. A hypothesis based on the cellular redox state is put forward to explain the onset of lactate consumption. Various techniques implemented to control the lactate switch, including manipulation of the culture environment, genetic engineering, and cell line selection are also discussed.

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The gene encoding tumor protein p53 is the most frequently mutated gene in human cancer. Mutations in both coding and non-coding regions of TP53 can disrupt the regulatory function of the transcription factor, but the functional impact of different somatic mutations on the global TP53 regulon is complex and poorly understood. To address this, we first proceed with a machine learning (ML) approach, and then propose an integrated computational network modelling approach that reconstructs signalling networks using a comprehensive collection of experimental and predicted regulons, and compares their topology. We evaluate both these approaches in a scrutinized pan-cancer analysis of matched genomics and transcriptomics data from 1,457 cell lines (22 cancer types) and 12,531 clinical samples (54 cancer sub-types). Using a ML approach based on penalized generalized linear regression we were able to predict TP53 mutation, but failed to resolve different mutation types. Thus, to infer the impact of different TP53 mutations we compared the topological characteristics of the optimized and reconstructed (upwards of twenty thousand) gene networks. We demonstrate that by accounting for TP53 mutation characteristics such as i) mutation type (e.g. missense, nonsense), ii) deleterious consequences of the mutation, or iii) mapping to previously identified hotspots, we can infer a much richer understanding of gene expression regulation, than when simply grouping samples based on their mutation/wild type or gene expression status. Our study highlights a powerful strategy exploiting signalling networks to systematically characterize the functional impact of the full spectrum of somatic mutations. This approach can be applied in general to genetic variation, with clear implications for, but not limited to, the biomedical domain and precision medicine.

Mitochondrial quality is implicated as a contributor to declining fertility with aging. We investigated mitochondrial transcripts in oocytes and their associated cumulus cells from mice of different ages using RNA-seq. Mice aged 3 weeks, 9 weeks, and 1 year were superovulated and 48 hr later, oocyte cumulus complexes collected by follicle puncture. We did not detect any major differences that could be attributed to aging. However, mitochondrial RNA transcripts which deviated from the consensus sequence were found at a higher frequency in cumulus cells than in their corresponding oocyte. Previous investigations have shown that variation in the sequence of mtRNA transcripts is substantial, and at least some of this can be accounted for by post-transcriptional modifications which impact base calling during sequencing. Our data would be consistent with either less post-transcriptional modification in mitochondrial RNA from oocytes than cumulus cells or with lower mtDNA mutational load.