Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

Unique trajectory of gene family evolution from genomic analysis of nearly all known species in an ancient yeast lineage

Gene gains and losses are a major driver of genome evolution; their precise characterization can provide insights into the origin and diversification of major lineages. Here, we examined gene family evolution of 1,154 genomes from nearly all known species in the medically and technologically important yeast subphylum Saccharomycotina. We found that yeast gene family and genome evolution are distinct from plants, animals, and filamentous ascomycetes and are characterized by small genome sizes and smaller gene numbers but larger gene family sizes. Faster-evolving lineages (FELs) in yeasts experienced significantly higher rates of gene losses-commensurate with a narrowing of metabolic niche breadth-but higher speciation rates than their slower-evolving sister lineages (SELs). Gene families most often lost are those involved in mRNA splicing, carbohydrate metabolism, and cell division and are likely associated with intron loss, metabolic breadth, and non-canonical cell cycle processes. Our results highlight the significant role of gene family contractions in the evolution of yeast metabolism, genome function, and speciation, and suggest that gene family evolutionary trajectories have differed markedly across major eukaryotic lineages.

Systematic profiling of ale yeast protein dynamics across fermentation and repitching

Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself. Saccharomyces cerevisiae (brewing yeast) is among the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of a Saccharomyces cerevisiae ale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout 2 fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics of Saccharomyces cerevisiae ale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.

Recombination, admixture and genome instability shape the genomic landscape of Saccharomyces cerevisiae derived from spontaneous grape ferments

Cultural exchange of fermentation techniques has driven the spread of Saccharomyces cerevisiae across the globe, establishing natural populations in many countries. Despite this, Oceania is thought to lack native populations of S. cerevisiae, only being introduced after colonisation. Here we investigate the genomic landscape of 411 S. cerevisiae isolated from spontaneous grape fermentations in Australia across multiple locations, years, and grape cultivars. Spontaneous fermentations contained highly recombined mosaic strains that exhibited high levels of genome instability. Assigning genomic windows to putative ancestral origin revealed that few closely related starter lineages have come to dominate the genetic landscape, contributing most of the genetic variation. Fine-scale phylogenetic analysis of loci not observed in strains of commercial wine origin identified widespread admixture with European derived beer yeast along with three independent admixture events from potentially endemic Oceanic lineages that was associated with genome instability. Finally, we investigated Australian ecological niches for basal isolates, identifying phylogenetically distinct S. cerevisiae of non-European, non-domesticated origin associated with admixture loci. Our results illustrate the effect commercial use of microbes may have on local microorganism genetic diversity and demonstrates the presence of non-domesticated, potentially endemic lineages of S. cerevisiae in Australian niches that are actively admixing.

Adaptive laboratory evolution for acetic acid-tolerance matches sourdough challenges with yeast phenotypes

Acetic acid tolerance of Saccharomyces cerevisiae is an important trait in sourdough fermentation processes, where the accumulation of acid by the growth of lactic acid bacteria reduces the yeast metabolic activity. In this work, we have carried out adaptive laboratory evolution (ALE) experiments in two sourdough isolates of S. cerevisiae exposed to acetic acid, or alternatively to acetic acid and myriocin, an inhibitor of sphingolipid biosynthesis that sped-up the evolutionary adaptation. Evolution approaches resulted in acetic tolerance, and surprisingly, increased lactic susceptibility. Four evolved clones, one from each parental strain and evolutionary scheme, were selected on the basis of their potential for CO2 production in sourdough conditions. Among them, two showed phenotypic instability characterized by strong lactic sensitivity after several rounds of growth under unstressed conditions, while two others, displayed increased constitutive acetic tolerance with no loss of growth in lactic medium. Genome sequencing and ploidy level analysis of all strains revealed aneuploidies, which could account for phenotypic heterogeneity. In addition, copy number variations (CNVs), affecting specially to genes involved in ion transport or flocculation, and single nucleotide polymorphisms (SNPs) were identified. Mutations in several genes, ARG82, KEX1, CTK1, SPT20, IRA2, ASG1 or GIS4, were confirmed as involved in acetic and/or lactic tolerance, and new determinants of these phenotypes, MSN5 and PSP2, identified.

Integrated genomics and phenotype microarray analysis of Saccharomyces cerevisiae industrial strains for rice wine fermentation and recombinant protein production

The industrial potential of Saccharomyces cerevisiae has extended beyond its traditional use in fermentation to various applications, including recombinant protein production. Herein, comparative genomics was performed with three industrial S. cerevisiae strains and revealed a heterozygous diploid genome for the 98-5 and KSD-YC strains (exploited for rice wine fermentation) and a haploid genome for strain Y2805 (used for recombinant protein production). Phylogenomic analysis indicated that Y2805 was closely associated with the reference strain S288C, whereas KSD-YC and 98-5 were grouped with Asian and European wine strains, respectively. Particularly, a single nucleotide polymorphism (SNP) in FDC1, involved in the biosynthesis of 4-vinylguaiacol (4-VG, a phenolic compound with a clove-like aroma), was found in KSD-YC, consistent with its lack of 4-VG production. Phenotype microarray (PM) analysis showed that KSD-YC and 98-5 displayed broader substrate utilization than S288C and Y2805. The SNPs detected by genome comparison were mapped to the genes responsible for the observed phenotypic differences. In addition, detailed information on the structural organization of Y2805 selection markers was validated by Sanger sequencing. Integrated genomics and PM analysis elucidated the evolutionary history and genetic diversity of industrial S. cerevisiae strains, providing a platform to improve fermentation processes and genetic manipulation.

Systematic Profiling of Ale Yeast Protein Dynamics across Fermentation and Repitching

Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself. Saccharomyces cerevisiae (brewing yeast) is amongst the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of a Saccharomyces cerevisiae ale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout two fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics of Saccharomyces cerevisiae ale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.

Analyzing and understanding the robustness of bioprocesses

The robustness of bioprocesses is becoming increasingly important. The main driving forces of this development are, in particular, increasing demands on product purities as well as economic aspects. In general, bioprocesses exhibit extremely high complexity and variability. Biological systems often have a much higher intrinsic variability compared with chemical processes, which makes the development and characterization of robust processes tedious task. To predict and control robustness, a clear understanding of interactions between input and output variables is necessary. Robust bioprocesses can be realized, for example, by using advanced control strategies for the different unit operations. In this review, we discuss the different biological, technical, and mathematical tools for the analysis and control of bioprocess robustness.

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (k_app) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.

Integrative Analysis of the Ethanol Tolerance of Saccharomyces cerevisiae

Ethanol (EtOH) alters many cellular processes in yeast. An integrated view of different EtOH-tolerant phenotypes and their long noncoding RNAs (lncRNAs) is not yet available. Here, large-scale data integration showed the core EtOH-responsive pathways, lncRNAs, and triggers of higher (HT) and lower (LT) EtOH-tolerant phenotypes. LncRNAs act in a strain-specific manner in the EtOH stress response. Network and omics analyses revealed that cells prepare for stress relief by favoring activation of life-essential systems. Therefore, longevity, peroxisomal, energy, lipid, and RNA/protein metabolisms are the core processes that drive EtOH tolerance. By integrating omics, network analysis, and several other experiments, we showed how the HT and LT phenotypes may arise: (1) the divergence occurs after cell signaling reaches the longevity and peroxisomal pathways, with CTA1 and ROS playing key roles; (2) signals reaching essential ribosomal and RNA pathways via SUI2 enhance the divergence; (3) specific lipid metabolism pathways also act on phenotype-specific profiles; (4) HTs take greater advantage of degradation and membraneless structures to cope with EtOH stress; and (5) our EtOH stress-buffering model suggests that diauxic shift drives EtOH buffering through an energy burst, mainly in HTs. Finally, critical genes, pathways, and the first models including lncRNAs to describe nuances of EtOH tolerance are reported here.

Current advancements in systems and synthetic biology studies of Saccharomyces cerevisiae

Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.

Multi-omics network model reveals key genes associated with p-coumaric acid stress response in an industrial yeast strain

The production of ethanol from lignocellulosic sources presents increasingly difficult issues for the global biofuel scenario, leading to increased production costs of current second-generation (2G) ethanol when compared to first-generation (1G) plants. Among the setbacks encountered in industrial processes, the presence of chemical inhibitors from pre-treatment processes severely hinders the potential of yeasts in producing ethanol at peak efficiency. However, some industrial yeast strains have, either naturally or artificially, higher tolerance levels to these compounds. Such is the case of S. cerevisiae SA-1, a Brazilian fuel ethanol industrial strain that has shown high resistance to inhibitors produced by the pre-treatment of cellulosic complexes. Our study focuses on the characterization of the transcriptomic and physiological impact of an inhibitor of this type, p-coumaric acid (pCA), on this strain under chemostat cultivation via RNAseq and quantitative physiological data. It was found that strain SA-1 tend to increase ethanol yield and production rate while decreasing biomass yield when exposed to pCA, in contrast to pCA-susceptible strains, which tend to decrease their ethanol yield and fermentation efficiency when exposed to this substance. This suggests increased metabolic activity linked to mitochondrial and peroxisomal processes. The transcriptomic analysis also revealed a plethora of differentially expressed genes located in co-expressed clusters that are associated with changes in biological pathways linked to biosynthetic and energetical processes. Furthermore, it was also identified 20 genes that act as interaction hubs for these clusters, while also having association with altered pathways and changes in metabolic outputs, potentially leading to the discovery of novel targets for metabolic engineering toward a more robust industrial yeast strain.

Rational and evolutionary engineering of Saccharomyces cerevisiae for production of dicarboxylic acids from lignocellulosic biomass and exploring genetic mechanisms of the yeast tolerance to the biomass hydrolysate

BACKGROUND

Lignosulfonates are significant wood chemicals with a $700 million market, produced by sulfite pulping of wood. During the pulping process, spent sulfite liquor (SSL) is generated, which in addition to lignosulfonates contains hemicellulose-derived sugars-in case of hardwoods primarily the pentose sugar xylose. The pentoses are currently underutilized. If they could be converted into value-added chemicals, overall economic profitability of the process would increase. SSLs are typically very inhibitory to microorganisms, which presents a challenge for a biotechnological process. The aim of the present work was to develop a robust yeast strain able to convert xylose in SSL to carboxylic acids.

RESULTS

The industrial strain Ethanol Red of the yeast Saccharomyces cerevisiae was engineered for efficient utilization of xylose in a Eucalyptus globulus lignosulfonate stream at low pH using CRISPR/Cas genome editing and adaptive laboratory evolution. The engineered strain grew in synthetic medium with xylose as sole carbon source with maximum specific growth rate (µ_max) of 0.28 1/h. Selected evolved strains utilized all carbon sources in the SSL at pH 3.5 and grew with µ_max between 0.05 and 0.1 1/h depending on a nitrogen source supplement. Putative genetic determinants of the increased tolerance to the SSL were revealed by whole genome sequencing of the evolved strains. In particular, four top-candidate genes (SNG1, FIT3, FZF1 and CBP3) were identified along with other gene candidates with predicted important roles, based on the type and distribution of the mutations across different strains and especially the best performing ones. The developed strains were further engineered for production of dicarboxylic acids (succinic and malic acid) via overexpression of the reductive branch of the tricarboxylic acid cycle (TCA). The production strain produced 0.2 mol and 0.12 mol of malic acid and succinic acid, respectively, per mol of xylose present in the SSL.

CONCLUSIONS

The combined metabolic engineering and adaptive evolution approach provided a robust SSL-tolerant industrial strain that converts fermentable carbon content of the SSL feedstock into malic and succinic acids at low pH.in production yields reaching 0.1 mol and 0.065 mol per mol of total consumed carbon sources.. Moreover, our work suggests potential genetic background of the tolerance to the SSL stream pointing out potential gene targets for improving the tolerance to inhibitory industrial feedstocks.

Validation of a Standard Protocol to Assess the Fermentative and Chemical Properties of Saccharomyces cerevisiae Wine Strains

This paper reports on a common experiment performed by 17 Research Units of the Italian Group of Microbiology of Vine and Wine (GMVV), which belongs to the Scientific Society SIMTREA, with the aim to validate a protocol for the characterization of wine strains of Saccharomyces cerevisiae. For this purpose, two commercial S. cerevisiae strains (EC 1118 and AWRI796) were used to carry out inter-laboratory-scale comparative fermentations using both synthetic medium and grape musts and applying the same protocol to obtain reproducible, replicable, and statistically valid results. Ethanol yield, production of acetic acid, glycerol, higher alcohols, and other volatile compounds were assessed. Moreover, the Fourier transform infrared spectroscopy was also applied to define the metabolomic fingerprint of yeast cells from each experimental trial. Data were standardized as unit of compounds or yield per gram of sugar (glucose and fructose) consumed throughout fermentation, and analyzed through parametric and non-parametric tests, and multivariate approaches (cluster analysis, two-way joining, and principal component analysis). The results of experiments carried out by using synthetic must showed that it was possible to gain comparable results from three different laboratories by using the same strains. Then, the use of the standardized protocol on different grape musts allowed pointing out the goodness and the reproducibility of the method; it showed the main traits of the two yeast strains and allowed reducing variability amongst independent batches (biological replicates) to acceptable levels. In conclusion, the findings of this collaborative study contributed to the validation of a protocol in a specific synthetic medium and in grape must and showed how data should be treated to gain reproducible and robust results, which could allow direct comparison of the experimental data obtained during the characterization of wine yeasts carried out by different research laboratories.

The Role of Ancestral Duplicated Genes in Adaptation to Growth on Lactate, a Non-Fermentable Carbon Source for the Yeast Saccharomyces cerevisiae

The cell central metabolism has been shaped throughout evolutionary times when facing challenges from the availability of resources. In the budding yeast, Saccharomyces cerevisiae, a set of duplicated genes originating from an ancestral whole-genome and several coetaneous small-scale duplication events drive energy transfer through glucose metabolism as the main carbon source either by fermentation or respiration. These duplicates (~a third of the genome) have been dated back to approximately 100 MY, allowing for enough evolutionary time to diverge in both sequence and function. Gene duplication has been proposed as a molecular mechanism of biological innovation, maintaining balance between mutational robustness and evolvability of the system. However, some questions concerning the molecular mechanisms behind duplicated genes transcriptional plasticity and functional divergence remain unresolved. In this work we challenged S. cerevisiae to the use of lactic acid/lactate as the sole carbon source and performed a small adaptive laboratory evolution to this non-fermentative carbon source, determining phenotypic and transcriptomic changes. We observed growth adaptation to acidic stress, by reduction of growth rate and increase in biomass production, while the transcriptomic response was mainly driven by repression of the whole-genome duplicates, those implied in glycolysis and overexpression of ROS response. The contribution of several duplicated pairs to this carbon source switch and acidic stress is also discussed.

Characterization of Cold-Tolerant Saccharomyces cerevisiae Cheongdo Using Phenotype Microarray

The cold-tolerant yeast Saccharomyces cerevisiae is industrially useful for lager fermentation, high-quality wine, and frozen dough production. S. cerevisiae Cheongdo is a recent isolate from frozen peach samples which has a good fermentation performance at low temperatures and desirable flavor profiles. Here, phenotype microarray was used to investigate industrial potentials of S. cerevisiae Cheongdo using 192 carbon sources. Compared to commercial wine yeast S. cerevisiae EC1118, Cheongdo showed significantly different growth rates on 34 substrates. The principal component analysis of the results highlighted that the better growth of Cheongdo on galactose than on EC1118 was the most significant difference between the two strains. The intact GAL4 gene and the galactose fermentation performance at a low temperatures suggested that S. cerevisiae Cheongdo is a promising host for industrial fermentation rich in galactose, such as lactose and agarose.

Never Change a Brewing Yeast? Why Not, There Are Plenty to Choose From

Fermented foods and particularly beer have accompanied the development of human civilization for thousands of years. Saccharomyces cerevisiae, the dominant yeast in the production of alcoholic beverages, probably co-evolved with human activity. Considering that alcoholic fermentations emerged worldwide, the number of strains used in beer production nowadays is surprisingly low. Thus, the genetic diversity is often limited. This is among others related to the switch from a household brewing style to a more artisan brewing regime during the sixteenth century and latterly the development of single yeast isolation techniques at the Carlsberg Research Laboratory in 1883, resulting in process optimizations in the brewing industry. However, due to fierce competition within the beer market and the increasing demand for novel beer styles, diversification is becoming increasingly important. Moreover, the emergence of craft brewing has influenced big breweries to rediscover yeast as a significant contributor to a beer's aroma profile and realize that there is still room for innovation in the fermentation process. Here, we aim at giving a brief overview on how currently used S. cerevisiae brewing yeasts emerged and comment on the rationale behind replacing them with novel strains. We will present potential sources of yeasts that have not only been used in beer brewing before, including natural sources and sources linked to human activity but also an overlooked source, such as yeast culture collections. We will briefly comment on common yeast isolation techniques and finally touch on additional challenges for the brewing industry in replacing their current brewer's yeasts.

Wild Yeast for the Future: Exploring the Use of Wild Strains for Wine and Beer Fermentation

The continuous usage of single Saccharomyces cerevisiae strains as starter cultures in fermentation led to the domestication and propagation of highly specialized strains in fermentation, resulting in the standardization of wines and beers. In this way, hundreds of commercial strains have been developed to satisfy producers’ and consumers’ demands, including beverages with high/low ethanol content, nutrient deprivation tolerance, diverse aromatic profiles, and fast fermentations. However, studies in the last 20 years have demonstrated that the genetic and phenotypic diversity in commercial S. cerevisiae strains is low. This lack of diversity limits alternative wines and beers, stressing the need to explore new genetic resources to differentiate each fermentation product. In this sense, wild strains harbor a higher than thought genetic and phenotypic diversity, representing a feasible option to generate new fermentative beverages. Numerous recent studies have identified alleles in wild strains that could favor phenotypes of interest, such as nitrogen consumption, tolerance to cold or high temperatures, and the production of metabolites, such as glycerol and aroma compounds. Here, we review the recent literature on the use of commercial and wild S. cerevisiae strains in wine and beer fermentation, providing molecular evidence of the advantages of using wild strains for the generation of improved genetic stocks for the industry according to the product style.

Innovative Tools and Strategies for Optimizing Yeast Cell Factories

Metabolic engineering (ME) aims to develop efficient microbial cell factories that can produce a wide variety of valuable compounds, ideally at the highest yield and from various feedstocks. We summarize recent developments in ME methods for tailoring different yeast cell factories (YCFs). In particular, we highlight the most timely and cutting-edge molecular tools and strategies for biosynthetic pathway optimization (including genome-editing tools), combinatorial transcriptional and post-transcriptional engineering (cis/trans regulators), dynamic control of metabolic fluxes (e.g., rewiring of primary metabolism), and spatial reconfiguration of metabolic pathways. Finally, we discuss challenges and perspectives for adaptive laboratory evolution (ALE) of yeast to advance ME of microbial cell factories.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

Evolution of Ty1 copy number control in yeast by horizontal transfer and recombination

Transposable elements constitute a large fraction of most eukaryotic genomes. Insertion of mobile DNA sequences typically has deleterious effects on host fitness, and thus diverse mechanisms have evolved to control mobile element proliferation. Mobility of the Ty1 retrotransposon in Saccharomyces yeasts is regulated by copy number control (CNC) mediated by a self-encoded restriction factor derived from the Ty1 gag capsid gene that inhibits virus-like particle function. Here, we survey a panel of wild and human-associated strains of S. cerevisiae and S. paradoxus to investigate how genomic Ty1 content influences variation in Ty1 mobility. We observe high levels of mobility for a tester element with a gag sequence from the canonical Ty1 subfamily in permissive strains that either lack full-length Ty1 elements or only contain full-length copies of the Ty1' subfamily that have a divergent gag sequence. In contrast, low levels of canonical Ty1 mobility are observed in restrictive strains carrying full-length Ty1 elements containing a canonical gag sequence. Phylogenomic analysis of full-length Ty1 elements revealed that Ty1' is the ancestral subfamily present in wild strains of S. cerevisiae, and that canonical Ty1 in S. cerevisiae is a derived subfamily that acquired gag from S. paradoxus by horizontal transfer and recombination. Our results provide evidence that variation in the ability of S. cerevisiae and S. paradoxus strains to repress canonical Ty1 transposition via CNC is regulated by the genomic content of different Ty1 subfamilies, and that self-encoded forms of transposon control can spread across species boundaries by horizontal transfer.

Chromosomal-level assembly of Juglans sigillata genome using Nanopore, BioNano, and Hi-C analysis

BACKGROUND

Juglans sigillata, or iron walnut, belonging to the order Juglandales, is an economically important tree species in Asia, especially in the Yunnan province of China. However, little research has been conducted on J. sigillata at the molecular level, which hinders understanding of its evolution, speciation, and synthesis of secondary metabolites, as well as its wide adaptability to its plateau environment. To address these issues, a high-quality reference genome of J. sigillata would be useful.

FINDINGS

To construct a high-quality reference genome for J. sigillata, we first generated 38.0 Gb short reads and 66.31 Gb long reads using Illumina and Nanopore sequencing platforms, respectively. The sequencing data were assembled into a 536.50-Mb genome assembly with a contig N50 length of 4.31 Mb. Additionally, we applied BioNano technology to identify contacts among contigs, which were then used to assemble contigs into scaffolds, resulting in a genome assembly with scaffold N50 length of 16.43 Mb and contig N50 length of 4.34 Mb. To obtain a chromosome-level genome assembly, we constructed 1 Hi-C library and sequenced 79.97 Gb raw reads using the Illumina HiSeq platform. We anchored ∼93% of the scaffold sequences into 16 chromosomes and evaluated the quality of our assembly using the high contact frequency heat map. Repetitive elements account for 50.06% of the genome, and 30,387 protein-coding genes were predicted from the genome, of which 99.8% have been functionally annotated. The genome-wide phylogenetic tree indicated an estimated divergence time between J. sigillata and Juglans regia of 49 million years ago on the basis of single-copy orthologous genes.

CONCLUSIONS

We provide the first chromosome-level genome for J. sigillata. It will lay a valuable foundation for future research on the genetic improvement of J. sigillata.

Linking genetic, metabolic, and phenotypic diversity among Saccharomyces cerevisiae strains using multi-omics associations

BACKGROUND

The selection of bioengineering platform strains and engineering strategies to improve the stress resistance of Saccharomyces cerevisiae remains a pressing need in bio-based chemical production. Thus, a systematic effort to exploit genotypic and phenotypic diversity to boost yeast's industrial value is still urgently needed.

RESULTS

We analyzed 5,400 growth curves obtained from 36 S. cerevisiae strains and comprehensively profiled their resistances against 13 industrially relevant stresses. We observed that bioethanol and brewing strains exhibit higher resistance against acidic conditions; however, plant isolates tend to have a wider range of resistance, which may be associated with their metabolome and fluxome signatures in the tricarboxylic acid cycle and fatty acid metabolism. By deep genomic sequencing, we found that industrial strains have more genomic duplications especially affecting transcription factors, showing that they result from disparate evolutionary paths in comparison with the environmental strains, which have more indels, gene deletions, and strain-specific genes. Genome-wide association studies coupled with protein-protein interaction networks uncovered novel genetic determinants of stress resistances.

CONCLUSIONS

These resistance-related engineering targets and strain rankings provide a valuable source for engineering significantly improved industrial platform strains.