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Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002.

ABSTRACT: Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to Synechococcus sp. PCC 7002; we also outline how it can be adapted to any species or community with available multi-omic data. For complete details on the use and execution of this protocol, please refer to Vijayakumar et al. (2020).

SUBMITTER: Vijayakumar S

PROVIDER: S-EPMC8488602 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium <i>Synechococcus</i> sp. PCC 7002.

Vijayakumar Supreeta S Angione Claudio C

STAR protocols 20210929 4

Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to <i>Synechococcus</i> sp. PCC 7002; we a ...[more]

PMID: 34632416

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Project description:RNA degradation is an important process that influences the ultimate concentration of individual proteins inside cells. While the main enzymes that facilitate this process have been identified, global maps of RNA turnover are available for only a few species. Even in these cases, there are few sequence elements that are known to enhance or destabilize a native transcript; even fewer confer the same effect when added to a heterologous transcript. To address this knowledge gap, we assayed genome-wide RNA degradation in the cyanobacterium Synechococcus sp. strain PCC 7002 by collecting total RNA samples after stopping nascent transcription with rifampin. We quantified the abundance of each position in the transcriptome as a function of time using RNA-sequencing data and later analyzed the global mRNA decay map using machine learning principles. Half-lives, calculated on a per-ORF (open reading frame) basis, were extremely short, with a median half-life of only 0.97 min. Despite extremely rapid turnover of most mRNA, transcripts encoding proteins involved in photosynthesis were both highly expressed and highly stable. Upon inspection of these stable transcripts, we identified an enriched motif in the 3' untranslated region (UTR) that had similarity to Rho-independent terminators. We built statistical models for half-life prediction and used them to systematically identify sequence motifs in both 5' and 3' UTRs that correlate with stabilized transcripts. We found that transcripts linked to a terminator containing a poly(U) tract had a longer half-life than both those without a poly(U) tract and those without a terminator.IMPORTANCE RNA degradation is an important process that affects the final concentration of individual mRNAs, affecting protein expression and cellular physiology. Studies of how RNA is degraded increase our knowledge of this fundamental process as well as enable the creation of genetic tools to manipulate RNA stability. By studying global transcript turnover, we searched for sequence elements that correlated with transcript (in)stability and used these sequences to guide tool design. This study probes global RNA turnover in a cyanobacterium, Synechococcus sp. strain PCC 7002, that both has a unique array of RNases that facilitate RNA degradation and is an industrially relevant strain that could be used to convert CO2 and sunlight into useful products.

Project description:BackgroundFuture sustainable energy production can be achieved using mass cultures of photoautotrophic microorganisms, which are engineered to synthesize valuable products directly from CO2 and sunlight. As cyanobacteria can be cultivated in large scale on non-arable land, these phototrophic bacteria have become attractive organisms for production of biofuels. Synechococcus sp. PCC 7002, one of the cyanobacterial model organisms, provides many attractive properties for biofuel production such as tolerance of seawater and high light intensities.ResultsHere, we performed a systems analysis of an engineered ethanol-producing strain of the cyanobacterium Synechococcus sp. PCC 7002, which was grown in artificial seawater medium over 30 days applying a 12:12 h day-night cycle. Biosynthesis of ethanol resulted in a final accumulation of 0.25% (v/v) ethanol, including ethanol lost due to evaporation. The cultivation experiment revealed three production phases. The highest production rate was observed in the initial phase when cells were actively growing. In phase II growth of the producer strain stopped, but ethanol production rate was still high. Phase III was characterized by a decrease of both ethanol production and optical density of the culture. Metabolomics revealed that the carbon drain due to ethanol diffusion from the cell resulted in the expected reduction of pyruvate-based intermediates. Carbon-saving strategies successfully compensated the decrease of central intermediates of carbon metabolism during the first phase of fermentation. However, during long-term ethanol production the producer strain showed clear indications of intracellular carbon limitation. Despite the decreased levels of glycolytic and tricarboxylic acid cycle intermediates, soluble sugars and even glycogen accumulated in the producer strain. The changes in carbon assimilation patterns are partly supported by proteome analysis, which detected decreased levels of many enzymes and also revealed the stress phenotype of ethanol-producing cells. Strategies towards improved ethanol production are discussed.ConclusionsSystems analysis of ethanol production in Synechococcus sp. PCC 7002 revealed initial compensation followed by increasing metabolic limitation due to excessive carbon drain from primary metabolism.

Dataset Information

Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002.

Publications

Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium <i>Synechococcus</i> sp. PCC 7002.

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