A machine learning and directed network optimization approach to uncover TP53 regulatory patterns.

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.

No evidence for age-related differences in mitochondrial RNA quality in the female germline.

Abstract: 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 h later, oocyte cumulus complexes were 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. Lay summary: Women become less fertile as they age. Shortage of energy contributes to this, caused by a decline in the quality of mitochondria (the powerhouses of the cell) in the egg. Genes are the blueprint for the cell. They are made of DNA which is copied into an RNA message, or instructions, for making proteins. We counted differences in the RNA message of developing eggs and the cells that support them during development (cumulus cells). We compared the number of these differences in mice of different ages. These age groups represent mice had not reached puberty, those of prime reproductive age, and old mothers. We did not find any differences linked to the age of the mice. However, we did find differences between the egg and the cumulus cells. In most cases, there were lower levels of mutations in eggs than there were in cumulus cells.

Mechanisms driving the lactate switch in Chinese hamster ovary cells.

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.