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The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.

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Extensive microdiversity within Prochlorococcus , the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.

Understanding genotype–phenotype relationship is fundamental in biology. With the benefit from next-generation sequencing and high-throughput phenotyping methodologies, there have been generated much genome and phenome data for Saccharomyces cerevisiae. This makes it an excellent model system to understand the genotype–phenotype relationship. In this paper, we presented the reconstruction and application of the yeast pan-genome in resolving genotype–phenotype relationship by a machine learning-assisted approach.

Crude violacein, consisting of violacein and deoxyviolacein, displays many attractive bio-activities in the field of drug therapy. To produce crude violacein from an industrially economic carbon source, we firstly introduced the violacein pathway into Escherichia coli B8/pTRPH1, which was previously engineered to accumulate tryptophan from glucose. A crude violacein production capacity of 0.25 g L OD was obtained using glucose-containing medium. By further overexpressing each of the five genes involved in violacein synthesis pathway, VioE was found as the rate-limiting step for the violacein production. The optimal strain of B8/pTRPH1-pVio-VioE was then used for fed-batch fermentation in a 5-L bioreactor and a crude violacein titer of 4.45 g L, as well as a productivity of 98.7 mg L h, was obtained. This engineered strain showed the highest violacein titer and productivity reported so far. Our optimal strain of E. coli B8/pTRPH1-pVio-VioE by overexpression of the rate-limiting VioE in violacein synthesis pathway was a potential violacein producer by directly using glucose for industrial application.

The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovered enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) was predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtained a thermotolerant strain that outgrew the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.Temperature is the most common environmental and evolutionary factor that shapes the physiology of living cells. Organisms have successfully adapted to survive in diverse temperature ranges 1-3 , where minor deviations from the optimal temperature by merely a few degrees can dramatically impair cell growth. For instance, the model eukaryotic organism Saccharomyces cerevisiae has an optimal growth temperature of ~30°C, whereas a temperature of 42°C is already lethal to the organism 4,5 . Since cell growth fundamentally requires all cellular components to be functional in the temperature window of cell growth, proteins, the most abundant group of biomolecules that carry out the majority of catalytic functions and are also the most sensitive to changes in temperature 5-7 , are considered to have the largest effect on cell physiology in relation to temperature. However, despite all our knowledge of temperature effects at both the cellular and molecular levels, including recent breakthroughs in temperature-dependent protein folding 7-10 and enzyme kinetics 11,12 , the temperature association between proteins and cell physiology is still poorly understood.Multiple studies have attempted to model the temperature effects on cell growth with very few proteome wide parameters. For instance, the dominant activation barrier and the number of essential proteins to cell growth 13 , activation energy of the growth process and the free energy change of protein denaturation 14 and others (reviewed in 15 ). These models showed excellent performance when describing the general cell growth rate at various temperatures, however, they could not pinpoint the specific rate-limiting enzymes, nor predict the amount of improvement in growth rate by replacing these ...

Droplet-based single-cell omics, including single-cell RNA sequencing (scRNAseq), single cell CRISPR perturbations (e.g., CROP-seq) and single-cell protein and transcriptomic profiling (e.g., CITE-seq) hold great promise for comprehensive cell profiling and genetic screening at the single cell resolution, yet these technologies suffer from substantial noise, among which ambient signals present in the cell suspension may be the predominant source. Current efforts to address this issue are highly specific to a certain technology, while a universal model to describe the noise across these technologies may reveal this common source thereby improving the denoising accuracy. To this end, we explicitly examined these unexpected signals and observed a predictable pattern in multiple datasets across different technologies. Based on the finding, we developed single cell Ambient Remover (scAR) which uses probabilistic deep learning to deconvolute the observed signals into native and ambient composition. scAR provides an efficient and universal solution to count denoising for multiple types of single-cell omics data, including single cell CRISPR screens, CITE-seq and scRNAseq. It will facilitate the application of single-cell omics technologies.