A Pan-Draft Metabolic Model Reflects Evolutionary Diversity across 332 Yeast Species

Yeasts are increasingly employed in synthetic biology as chassis strains, including conventional and non-conventional species. It is still unclear how genomic evolution determines metabolic diversity among various yeast species and strains. In this study, we constructed draft GEMs for 332 yeast species using two alternative procedures from the toolbox RAVEN v 2.0. We found that draft GEMs could reflect the difference in yeast metabolic potentials, and therefore, could be utilized to probe the evolutionary trend of metabolism among 332 yeast species. We created a pan-draft metabolic model to account for the metabolic capacity of every sequenced yeast species by merging all draft GEMs. Further analysis showed that the pan-reactome of yeast has a “closed” property, which confirmed the great conservatism that exists in yeast metabolic evolution. Lastly, the quantitative correlations among trait similarity, evolutionary distances, genotype, and model similarity were thoroughly investigated. The results suggest that the evolutionary distance and genotype, to some extent, determine model similarity, but not trait similarity, indicating that multiple mechanisms shape yeast trait evolution. A large-scale reconstruction and integrative analysis of yeast draft GEMs would be a valuable resource to probe the evolutionary mechanism behind yeast trait variety and to further refine the existing yeast species-specific GEMs for the community

HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer

Horizontal gene transfer (HGT) is an important driver in genome evolution, gain-of-function, and metabolic adaptation to environmental niches. Genome-wide identification of putative HGT events has become increasingly practical, given the rapid growth of genomic data. However, existing HGT analysis toolboxes are not widely used, limited by their inability to perform phylogenetic reconstruction to explore potential donors, and the detection of HGT from both evolutionarily distant and closely related species.In this study, we have developed HGTphyloDetect, which is a versatile computational toolbox that combines high-throughput analysis with phylogenetic inference, to facilitate comprehensive investigation of HGT events. Two case studies with Saccharomyces cerevisiae and Candida versatilis demonstrate the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, HGTphyloDetect enables phylogenetic analysis to illustrate a likely path of gene transmission among the evolutionarily distant or closely related species.The HGTphyloDetect computational toolbox is designed for ease of use and can accurately find HGT events with a very low false discovery rate in a high-throughput manner. The HGTphyloDetect toolbox and its related user tutorial are freely available at https:// github.com/SysBioChalmers/HGTphyloDetect

Deep learning-based k(cat) prediction enables improved enzyme-constrained model reconstruction

Enzyme turnover numbers (k(cat)) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured k(cat) data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput k(cat) prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture k(cat) changes for mutated enzymes and identify amino acid residues with a strong impact on k(cat) values. We applied this approach to predict genome-scale k(cat) values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted k(cat) values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale

NPS: A Framework for Accurate Program Sampling Using Graph Neural Network

With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling. Program sampling is a crucial step in microprocessor design, as it selects representative simulation points for workload simulation. While SimPoint has been the de-facto approach for decades, its limited expressiveness with Basic Block Vector (BBV) requires time-consuming human tuning, often taking months, which impedes fast innovation and agile hardware development. This paper introduces Neural Program Sampling (NPS), a novel framework that learns execution embeddings using dynamic snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding generation, leveraging an application's code structures and runtime states. AssemblyNet serves as NPS's graph model and neural architecture, capturing a program's behavior in aspects such as data computation, code path, and data flow. AssemblyNet is trained with a data prefetch task that predicts consecutive memory addresses. In the experiments, NPS outperforms SimPoint by up to 63%, reducing the average error by 38%. Additionally, NPS demonstrates strong robustness with increased accuracy, reducing the expensive accuracy tuning overhead. Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings

Engineering cofactor metabolism for improved protein and glucoamylase production in Aspergillus niger

Background: Nicotinamide adenine dinucleotide phosphate (NADPH) is an important cofactor ensuring intracellular redox balance, anabolism and cell growth in all living systems. Our recent multi-omics analyses of glucoamylase (GlaA) biosynthesis in the filamentous fungal cell factory Aspergillus niger indicated that low availability of NADPH might be a limiting factor for GlaA overproduction. Results: We thus employed the Design-Build-Test-Learn cycle for metabolic engineering to identify and prioritize effective cofactor engineering strategies for GlaA overproduction. Based on available metabolomics and 13C metabolic flux analysis data, we individually overexpressed seven predicted genes encoding NADPH generation enzymes under the control of the\ua0Tet-on gene switch in two A. niger recipient strains, one carrying a single and one carrying seven glaA gene copies, respectively, to test their individual effects on GlaA and total protein overproduction. Both strains were selected to understand if a strong pull towards glaA biosynthesis (seven gene copies) mandates a higher NADPH supply compared to the native condition (one gene copy). Detailed analysis of all 14 strains cultivated in shake flask cultures uncovered that overexpression of the gsdA gene (glucose 6-phosphate dehydrogenase), gndA gene (6-phosphogluconate dehydrogenase) and maeA gene (NADP-dependent malic enzyme) supported GlaA production on a subtle (10%) but significant level in the background strain carrying seven glaA gene copies. We thus performed maltose-limited chemostat cultures combining metabolome analysis for these three isolates to characterize metabolic-level fluctuations caused by cofactor engineering. In these cultures, overexpression of either the gndA or maeA gene increased the intracellular NADPH pool by 45% and 66%, and the yield of GlaA by 65% and 30%, respectively. In contrast, overexpression of the gsdA gene had a negative effect on both total protein and glucoamylase production. Conclusions: This data suggests for the first time that increased NADPH availability can indeed underpin protein and especially GlaA production in strains where a strong pull towards GlaA biosynthesis exists. This data also indicates that the highest impact on GlaA production can be engineered on a genetic level by increasing the flux through the pentose phosphate pathway (gndA gene) followed by engineering the flux through the reverse TCA cycle (maeA gene). We thus propose that NADPH cofactor engineering is indeed a valid strategy for metabolic engineering of A. niger to improve GlaA production, a strategy which is certainly also applicable to the rational design of other microbial cell factories.[Figure not available: see fulltext.]

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations

Hotspots, trends, and advice: a 10-year visualization-based analysis of painting therapy from a scientometric perspective

PurposeResearch on painting therapy is available worldwide and painting therapy is widely applied as a psychological therapy in different fields with diverse clients. As an evidence-based psychotherapy, previous studies have revealed that painting therapy has favorable therapeutic effects. However, limited studies on painting therapy used universal data to assemble in-depth evidence to propose a better recommendation on it for the future use. Large-scale retrospective studies that used bibliometric methodology are lacking. Therefore, this study presented a broad view of painting therapy and provided an intensively analytical insight into the structure of knowledge regarding painting therapy employing bibliometric analysis of articles. CiteSpace software was used to evaluate scientific research on painting therapy globally published from January 2011 to July 2022.MethodsPublications related to painting therapy from 2011 to 2022 were searched using the Web of Science database. This study employed bibliometric techniques to perform co-citation analysis of authors, visualize collaborations between countries/regions as network maps, and analyze keywords and subjects relevant to painting therapy by using CiteSpace software.ResultsIn total, 871 articles met the inclusion criteria. We found that the number of painting therapy publications generally trended incrementally. The United States and United Kingdom made the most contributions to painting therapy research and had the greatest impact on the practical application in other countries. Arts in Psychotherapy and Frontiers in Psychology occupied key publishing positions in this research field. The application groups were mainly children, adolescents, and females, and Western countries paid high attention to painting therapy. The main areas of application of painting therapy were Alzheimer’s disease and other psychosomatic disease fields. Identified research priorities for painting therapy were emotion regulation and mood disorder treatment, personality disorder treatment, personal self-esteem enhancement, and medical humanistic care. Three keywords, “depression,” “women,” and “recovery,” had the strongest citation bursts, which emphasized the research trends.ConclusionThe general trend for painting therapy research is positive. Our findings provide useful information for researchers on painting therapy to determine new directions in relate to popular issues, collaborators, and research frontiers. Painting therapy holds a promising future, and further studies could explore the clinical implications of this therapy in terms of mechanisms and criteria for assessing efficacy

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology

Fabrication of Patterned Graphene FETs Array

AbstractA new approach of fabrication of back-gated graphene FETs array based on the nano-wall channel between the source and the drain were investigated. Patterned metal film on three-dimentional nano-wall structure prepared by photo lithography was used as the source and the drain to bond the graphite foil. With SU-8 process in MEMS and lift off processes, metal is sputtered exactly onto the SU-8 patterns and forms electrodes.The potential transistor design relying only on a single sheet could be achieved by placing the graphene sheet film on the nano-wall channel between the source and the drain. Chemically derived graphene samples are then transferred onto 300nm SiO2/highly doped Si which serves as the back gate.A gas sensing region is expected to be present because the graphene sheet segment has great adsorption capacity of gas with the help of the nano-wall structure betwee the source and the drain strips. This method has offered potential convenience for future research on graphene properties, such as the anomalously quantized Hall effects, large charge carrier mobility and so on, and demonstrated a great potential application of novel structured FETs based on graphene

Kinetic Models of Metabolism

This chapter introduces the kinetic models of metabolism followed by examples on the construction of kinetic models as well as applications. With the Michaelis-Menten formulation, the influence of enzyme properties, enzyme abundance, and metabolite concentration on the dynamic behavior of a reaction can be explained mechanistically. Kinetic models mechanistically represent the processes that take place within a cell, and these models are made up of a series of ordinary differential equations. A kinetic model requires the definition of rate equations and their respective parameters for each of the reactions, which are currently unknown for many of the reactions contained in genome-scale models. Reaction kinetics can be described with mathematical expressions where the reaction rates are functions of kinetic parameters and the concentration of metabolites. Approximative rate expression is also adopted in the kinetic model reconstruction. Estimation of parameters in rate expressions is essential for having good predictive performance of a kinetic model