Predicting and improving complex beer flavor through machine learning

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

Orthogonal LoxPsym sites allow multiplexed site-specific recombination in prokaryotic and eukaryotic hosts

Site-specific recombinases such as the Cre-LoxP system are routinely used for genome engineering in both prokaryotes and eukaryotes. Importantly, recombinases complement the CRISPR-Cas toolbox and provide the additional benefit of high-efficiency DNA editing without generating toxic DNA double-strand breaks, allowing multiple recombination events at the same time. However, only a handful of independent, orthogonal recombination systems are available, limiting their use in more complex applications that require multiple specific recombination events, such as metabolic engineering and genetic circuits. To address this shortcoming, we develop 63 symmetrical LoxP variants and test 1192 pairwise combinations to determine their cross-reactivity and specificity upon Cre activation. Ultimately, we establish a set of 16 orthogonal LoxPsym variants and demonstrate their use for multiplexed genome engineering in both prokaryotes (E. coli) and eukaryotes (S. cerevisiae and Z. mays). Together, this work yields a significant expansion of the Cre-LoxP toolbox for genome editing, metabolic engineering and other controlled recombination events, and provides insights into the Cre-LoxP recombination process.

Antibiotic dose and nutrient availability differentially drive the evolution of antibiotic resistance and persistence

Effective treatment of bacterial infections proves increasingly challenging due to the emergence of bacterial variants that endure antibiotic exposure. Antibiotic resistance and persistence have been identified as two major bacterial survival mechanisms, and several studies have shown a rapid and strong selection of resistance or persistence mutants under repeated drug treatment. Yet, little is known about the impact of the environmental conditions on resistance and persistence evolution and the potential interplay between both phenotypes. Based on the distinct growth and survival characteristics of resistance and persistence mutants, we hypothesized that the antibiotic dose and availability of nutrients during treatment might play a key role in the evolutionary adaptation to antibiotic stress. To test this hypothesis, we combined high-throughput experimental evolution with a mathematical model of bacterial evolution under intermittent antibiotic exposure. We show that high nutrient levels during antibiotic treatment promote selection of high-level resistance, but that resistance mainly emerges independently of persistence when the antibiotic concentration is sufficiently low. At higher doses, resistance evolution is facilitated by the preceding or concurrent selection of persistence mutants, which ensures survival of populations in harsh conditions. Collectively, our experimental data and mathematical model elucidate the evolutionary routes toward increased bacterial survival under different antibiotic treatment schedules, which is key to designing effective antibiotic therapies.

Metabolism: How a eukaryote adapted to life without respiration

A new study finds that Schizosaccharomyces japonicus, a eukaryote that lost the ability to respire, modified its central carbon metabolism to maintain efficient ATP production, cofactor regeneration, and amino-acid production. This remarkable metabolic flexibility opens new avenues towards applications.

Do microbes have a memory? History-dependent behavior in the adaptation to variable environments

Microbes are constantly confronted with changes and challenges in their environment. A proper response to these environmental cues is needed for optimal cellular functioning and fitness. Interestingly, past exposure to environmental cues can accelerate or boost the response when this condition returns, even in daughter cells that have not directly encountered the initial cue. Moreover, this behavior is mostly epigenetic and often goes hand in hand with strong heterogeneity in the strength and speed of the response between isogenic cells of the same population, which might function as a bet-hedging strategy. In this review, we discuss examples of history-dependent behavior (HDB) or “memory,” with a specific focus on HDB in fluctuating environments. In most examples discussed, the lag time before the response to an environmental change is used as an experimentally measurable proxy for HDB. We highlight different mechanisms already implicated in HDB, and by using HDB in fluctuating carbon conditions as a case study, we showcase how the metabolic state of a cell can be a key determining factor for HDB. Finally, we consider possible evolutionary causes and consequences of such HDB.

Overlapping functions of the yeast oxysterol-binding protein homologues

The Saccharomyces cerevisiae genome encodes seven homologues of the mammalian oxysterol-binding protein (OSBP), a protein implicated in lipid trafficking and sterol homeostasis. To determine the functions of the yeast OSBP gene family (OSH1-OSH7), we used a combination of genetics, genomics, and sterol lipid analysis to characterize OSH deletion mutants. All 127 combinations and permutations of OSH deletion alleles were constructed. Individual OSH genes were not essential for yeast viability, but the elimination of the entire gene family was lethal. Thus, the family members shared an essential function. In addition, the in vivo depletion of all Osh proteins disrupted sterol homeostasis. Like mutants that affect ergosterol production, the viable combinations of OSH deletion alleles exhibited specific sterol-related defects. Although none of the single OSH deletion mutants was defective for growth, gene expression profiles revealed that each mutant had a characteristic molecular phenotype. Therefore, each gene performed distinct nonessential functions and contributed to a common essential function. Our findings indicated that OSH genes performed a multitude of nonessential roles defined by specific subsets of the genes and that most shared at least one essential role potentially linked to changes in sterol lipid levels.