Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism

GlSIRT1 deacetylates and activates pyruvate kinase to improve pyruvate content and enhance heat stress resistance in Ganoderma lucidum

Heat stress is a prevalent environmental stressor. Previous studies have shown that heat stress drives many cellular changes in Ganoderma lucidum. Interestingly, glycolysis is activated during heat stress, which could contribute to increased heat resistance. However, the molecular mechanisms underlying the enhanced heat resistance of G. lucidum following heat exposure are not yet fully understood. In this study, we explored the possibility that acetylation modification plays a significant role in responses to abiotic stress. After heat treatment, an enhanced interaction between the deacetylase GlSIRT1 and pyruvate kinase (PK) was observed, and the acetylation level of PK was decreased. Further studies revealed that GlSIRT1 increases PK activity through deacetylation, thereby increasing pyruvate content. Consistent with these findings, both PK activity and pyruvate content were reduced in GlSIRT1 knockdown strains, which exhibited greater sensitivity to heat stress compared to the wild-type (WT) strain. Collectively, our results reveal a novel molecular mechanism by which heat treatment increases pyruvate content.

Footprint-based functional analysis of multiomic data

Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms.

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Functional information on signalling pathways, metabolism and gene regulation can be found across multiple types of omic data.

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One way to extract such information is to consider these data as the footprint of the activity of enzymes and pathways.

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Information on enzyme/pathway activities and omic data can be integrated together to contextualise multi-scale networks.

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Such an approach can lead to the discovery of regulatory events spanning across multiple biological processes.