Bioactive and flavor compounds in cocoa liquor and their traceability over the major steps of cocoa post-harvesting processes

The production of fine-flavor cocoa represents a promising avenue to enhance socioeconomic development in Colombia and Latin America. Premium chocolate is obtained through a post-harvesting process, which relies on semi-standardized techniques. The change in the metabolic profile during cocoa processing considerably impacts flavor and nutraceutical properties of the final product. Understanding this impact considering both volatiles and non-volatile compounds is crucial for process and product re-engineering of cocoa post-harvesting. Consequently, this work studied the metabolic composition of cocoa liquor by untargeted metabolomics and lipidomics. This approach offered a comprehensive view of cocoa biochemistry, considering compounds associated with bioactivity and flavor in cocoa liquor. Their variations were traced back over the cocoa processing (i.e., drying, and roasting), highlighting their impact on flavor development and the nutraceutical properties. These results represent the basis for future studies aimed to re-engineer cocoa post-harvesting considering the variation of key flavor and bioactive compounds over processing.

Saccharomyces cerevisiae biofactory to produce naringenin using a systems biology approach and a bicistronic vector expression strategy in flavonoid production

IMPORTANCE

Flavonoids are a group of compounds generally produced by plants with proven biological activity, which have recently beeen recommended for the treatment and prevention of diseases and ailments with diverse causes. In this study, naringenin was produced in adequate amounts in yeast after in silico design. The four genes of the involved enzymes from several organisms (bacteria and plants) were multi-expressed in two vectors carrying each two genes linked by a short viral peptide sequence. The batch kinetic behavior of the product, substrate, and biomass was described at lab scale. The engineered strain might be used in a more affordable and viable bioprocess for industrial naringenin procurement.

Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity of Cannabis sativa Leaf Extracts

Over the past decades, Colombia has suffered complex social problems related to illicit crops, including forced displacement, violence, and environmental damage, among other consequences for vulnerable populations. Considerable effort has been made in the regulation of illicit crops, predominantly Cannabis sativa, leading to advances such as the legalization of medical cannabis and its derivatives, the improvement of crops, and leaving an open window to the development of scientific knowledge to explore alternative uses. It is estimated that C. sativa can produce approximately 750 specialized secondary metabolites. Some of the most relevant due to their anticancer properties, besides cannabinoids, are monoterpenes, sesquiterpenoids, triterpenoids, essential oils, flavonoids, and phenolic compounds. However, despite the increase in scientific research on the subject, it is necessary to study the primary and secondary metabolism of the plant and to identify key pathways that explore its great metabolic potential. For this purpose, a genome-scale metabolic reconstruction of C. sativa is described and contextualized using LC-QTOF-MS metabolic data obtained from the leaf extract from plants grown in the region of Pesca-Boyaca, Colombia under greenhouse conditions at the Clever Leaves facility. A compartmentalized model with 2101 reactions and 1314 metabolites highlights pathways associated with fatty acid biosynthesis, steroids, and amino acids, along with the metabolism of purine, pyrimidine, glucose, starch, and sucrose. Key metabolites were identified through metabolomic data, such as neurine, cannabisativine, cannflavin A, palmitoleic acid, cannabinoids, geranylhydroquinone, and steroids. They were analyzed and integrated into the reconstruction, and their potential applications are discussed. Cytotoxicity assays revealed high anticancer activity against gastric adenocarcinoma (AGS), melanoma cells (A375), and lung carcinoma cells (A549), combined with negligible impact against healthy human skin cells.

Design and Assembly of a Biofactory for (2S)-Naringenin Production in Escherichia coli: Effects of Oxygen Transfer on Yield and Gene Expression

The molecule (2S)-naringenin is a scaffold molecule with several nutraceutical properties. Currently, (2S)-naringenin is obtained through chemical synthesis and plant isolation. However, these methods have several drawbacks. Thus, heterologous biosynthesis has emerged as a viable alternative to its production. Recently, (2S)-naringenin production studies in Escherichia coli have used different tools to increase its yield up to 588 mg/L. In this study, we designed and assembled a bio-factory for (2S)-naringenin production. Firstly, we used several parametrized algorithms to identify the shortest pathway for producing (2S)-naringenin in E. coli, selecting the genes phenylalanine ammonia lipase (pal), 4-coumarate: CoA ligase (4cl), chalcone synthase (chs), and chalcone isomerase (chi) for the biosynthetic pathway. Then, we evaluated the effect of oxygen transfer on the production of (2S)-naringenin at flask (50 mL) and bench (4 L culture) scales. At the flask scale, the agitation rate varied between 50 rpm and 250 rpm. At the bench scale, the dissolved oxygen was kept constant at 5% DO (dissolved oxygen) and 40% DO, obtaining the highest (2S)-naringenin titer (3.11 ± 0.14 g/L). Using genome-scale modeling, gene expression analysis (RT-qPCR) of oxygen-sensitive genes was obtained.

Omics approaches to understand cocoa processing and chocolate flavor development: A review

The global market of chocolate has increased worldwide during the last decade and is expected to reach a value of USD 200 billion by 2028. Chocolate is obtained from different varieties of Theobroma cacao L, a plant domesticated more than 4000 years ago in the Amazon rainforest. However, chocolate production is a complex process requiring extensive post-harvesting, mainly involving cocoa bean fermentation, drying, and roasting. These steps have a critical impact on chocolate quality. Standardizing and better understanding cocoa processing is, therefore, a current challenge to boost the global production of high-quality cocoa worldwide. This knowledge can also help cocoa producers improve cocoa processing management and obtain a better chocolate. Several recent studies have been conducted to dissect cocoa processing via omics analysis. A vast amount of data has been produced regarding omics studies of cocoa processing performed worldwide. This review systematically analyzes the current data on cocoa omics using data mining techniques and discusses opportunities and gaps for cocoa processing standardization from this data. First, we observed a recurrent report in metagenomics studies of species of the fungi genus Candida and Pichia as well as bacteria from the genus Lactobacillus, Acetobacter, and Bacillus. Second, our analyzes of the available metabolomics data showed clear differences in the identified metabolites in cocoa and chocolate from different geographical origin, cocoa type, and processing stage. Finally, our analysis of peptidomics data revealed characteristic patterns in the gathered data including higher diversity and lower size distribution of peptides in fine-flavor cocoa. In addition, we discuss the current challenges in cocoa omics research. More research is still required to fill gaps in central matter in chocolate production as starter cultures for cocoa fermentation, flavor evolution of cocoa, and the role of peptides in the development of specific flavor notes. We also offer the most comprehensive collection of multi-omics data in cocoa processing gathered from different research articles.

Lipidomic profiling of bioactive lipids during spontaneous fermentations of fine-flavor cocoa

The impact of cocoa lipid content on chocolate quality has been extensively described. Nevertheless, few studies have elucidated the cocoa lipid composition and their bioactive properties, focusing only on specific lipids. In the present study the lipidome of fine-flavor cocoa fermentation was analyzed using LC-MS-QTOF and a Machine Learning model to assess potential bioactivity was developed. Our results revealed that the cocoa lipidome, comprised mainly of fatty acyls and glycerophospholipids, remains stable during fine-flavor cocoa fermentations. Also, several Machine Learning algorithms were trained to explore potential biological activity among the identified lipids. We found that K-Nearest Neighbors had the best performance. This model was used to classify the identified lipids as bioactive or non-bioactive, nominating 28 molecules as potential bioactive lipids. None of these compounds have been previously reported as bioactive. Our work is the first untargeted lipidomic study and systematic effort to investigate potential bioactivity in fine-flavor cocoa lipids.

Multiscale Approach to Dairy Products Design

Dairy products are among the most popular nutritious foods in the world. Understanding the relationship between the composition, process, and structural properties at different scales (molecular, microscopic, and macroscopic) is fundamental to designing dairy products. This review highlights the need to analyze this relationship from different scales as an essential step during product design through a multiscale approach.

Theobroma cacao L. cultivar CCN 51: a comprehensive review on origin, genetics, sensory properties, production dynamics, and physiological aspects

Many decades of improvement in cacao have aided to obtain cultivars with characteristics of tolerance to diseases, adaptability to different edaphoclimatic conditions, and higher yields. In Ecuador, as a result of several breeding programs, the clone CCN 51 was obtained, which gradually expanded through the cacao-production regions of Ecuador, Colombia, Brazil and Peru. Recognized for its high yield and adaptability to different regions and environments, it has become one of the most popular clones for breeding programs and cultivation around the world. This review aims to summarize the current evidence on the origin, genetics, morphological, volatile compounds, and organoleptic characteristics of this clone. Physiological evidence, production dynamics, and floral biology are also included to explain the high yield of CCN 51. Thus, characteristics such as osmotic adjustment, long pollen longevity, and fruit formation are further discussed and associated with high production at the end of the dry period. Finally, the impact of this popular clone on the current and future cacao industry will be discussed highlighting the major challenges for flavor enhancement and its relevance as a platform for the identification of novel genetic markers for cultivar improvement in breeding programs.

Dissecting fine-flavor cocoa bean fermentation through metabolomics analysis to break down the current metabolic paradigm

Cocoa fermentation plays a crucial role in producing flavor and bioactive compounds of high demand for food and nutraceutical industries. Such fermentations are frequently described as a succession of three main groups of microorganisms (i.e., yeast, lactic acid, and acetic acid bacteria), each producing a relevant metabolite (i.e., ethanol, lactic acid, and acetic acid). Nevertheless, this view of fermentation overlooks two critical observations: the role of minor groups of microorganisms to produce valuable compounds and the influence of environmental factors (other than oxygen availability) on their biosynthesis. Dissecting the metabolome during spontaneous cocoa fermentation is a current challenge for the rational design of controlled fermentations. This study evaluates variations in the metabolic fingerprint during spontaneous fermentation of fine flavor cocoa through a multiplatform metabolomics approach. Our data suggested the presence of two phases of differential metabolic activity that correlate with the observed variations on temperature over fermentations: an exothermic and an isothermic phase. We observed a continuous increase in temperature from day 0 to day 4 of fermentation and a significant variation in flavonoids and peptides between phases. While the second phase, from day four on, was characterized for lower metabolic activity, concomitant with small upward and downward fluctuations in temperature. Our work is the first to reveal two phases of metabolic activity concomitant with two temperature phases during spontaneous cocoa fermentation. Here, we proposed a new paradigm of cocoa fermentation that considers the changes in the global metabolic activity over fermentation, thus changing the current paradigm based only on three main groups of microorganism and their primary metabolic products.

Rational Design of Photo-Electrochemical Hybrid Devices Based on Graphene and Chlamydomonas reinhardtii Light-Harvesting Proteins

Dye-sensitized solar cells (DSSCs) have been highlighted as the promising alternative to generate clean energy based on low pay-back time materials. These devices have been designed to mimic solar energy conversion processes from photosynthetic organisms (the most efficient energy transduction phenomenon observed in nature) with the aid of low-cost materials. Recently, light-harvesting complexes (LHC) have been proposed as potential dyes in DSSCs based on their higher light-absorption efficiencies as compared to synthetic dyes. In this work, photo-electrochemical hybrid devices were rationally designed by adding for the first time Leu and Lys tags to heterologously expressed light-harvesting proteins from Chlamydomonas reinhardtii, thus allowing their proper orientation and immobilization on graphene electrodes. The light-harvesting complex 4 from C. reinhardtii (LHC4) was initially expressed in Escherichia coli, purified via affinity chromatography and subsequently immobilized on plasma-treated thin-film graphene electrodes. A photocurrent density of 40.30 ± 9.26 μA/cm2 was measured on devices using liquid electrolytes supplemented with a phosphonated viologen to facilitate charge transfer. Our results suggest that a new family of graphene-based thin-film photovoltaic devices can be manufactured from rationally tagged LHC proteins and opens the possibility to further explore fundamental processes of energy transfer for biological components interfaced with synthetic materials.

Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO2 levels

The increase in atmospheric CO2 due to anthropogenic activities is generating climate change, which has resulted in a subsequent rise in global temperatures with severe environmental impacts. Biological mitigation has been considered as an alternative for environmental remediation and reduction of greenhouse gases in the atmosphere. In fact, the use of easily adapted photosynthetic organisms able to fix CO2 with low-cost operation is revealing its high potential for industry. Among those organism, the algae Chlamydomonas reinhardtii have gain special attention as a model organism for studying CO2 fixation, biomass accumulation and bioenergy production upon exposure to several environmental conditions. In the present study, we studied the Chlamydomonas response to different CO2 levels by comparing metabolomics and transcriptomics data with the predicted results from our new-improved genomic-scale metabolic model. For this, we used in silico methods at steady dynamic state varying the levels of CO2. Our main goal was to improve our capacity for predicting metabolic routes involved in biomass accumulation. The improved genomic-scale metabolic model presented in this study was shown to be phenotypically accurate, predictive, and a significant improvement over previously reported models. Our model consists of 3726 reactions and 2436 metabolites, and lacks any thermodynamically infeasible cycles. It was shown to be highly sensitive to environmental changes under both steady-state and dynamic conditions. As additional constraints, our dynamic model involved kinetic parameters associated with substrate consumption at different growth conditions (i.e., low CO2-heterotrophic and high CO2-mixotrophic). Our results suggest that cells growing at high CO2 (i.e., photoautotrophic and mixotrophic conditions) have an increased capability for biomass production. In addition, we have observed that ATP production also seems to be an important limiting factor for growth under the conditions tested. Our experimental data (metabolomics and transcriptomics) and the results predicted by our model clearly suggest a differential behavior between low CO2-heterotrophic and high CO2-mixotrophic growth conditions. The data presented in the current study contributes to better dissect the biological response of C. reinhardtii, as a dynamic entity, to environmental and genetic changes. These findings are of great interest given the biotechnological potential of this microalga for CO2 fixation, biomass accumulation, and bioenergy production.

A synthetic multi-cellular network of coupled self-sustained oscillators

Engineering artificial networks from modular components is a major challenge in synthetic biology. In the past years, single units, such as switches and oscillators, were successfully constructed and implemented. The effective integration of these parts into functional artificial self-regulated networks is currently on the verge of breakthrough. Here, we describe the design of a modular higher-order synthetic genetic network assembled from two independent self-sustained synthetic units: repressilators coupled via a modified quorum-sensing circuit. The isolated communication circuit and the network of coupled oscillators were analysed in mathematical modelling and experimental approaches. We monitored clustering of cells in groups of various sizes. Within each cluster of cells, cells oscillate synchronously, whereas the theoretical modelling predicts complete synchronization of the whole cellular population to be obtained approximately after 30 days. Our data suggest that self-regulated synchronization in biological systems can occur through an intermediate, long term clustering phase. The proposed artificial multicellular network provides a system framework for exploring how a given network generates a specific behaviour.

The fraction of cells that resume growth after acetic acid addition is a strain-dependent parameter of acetic acid tolerance in Saccharomyces cerevisiae

High acetic acid tolerance of Saccharomyces cerevisiae is a relevant phenotype in industrial biotechnology when using lignocellulosic hydrolysates as feedstock. A screening of 38 S. cerevisiae strains for tolerance to acetic acid revealed considerable differences, particularly with regard to the duration of the latency phase. To understand how this phenotype is quantitatively manifested, four strains exhibiting significant differences were studied in more detail. Our data show that the duration of the latency phase is primarily determined by the fraction of cells within the population that resume growth. Only this fraction contributed to the exponential growth observed after the latency phase, while all other cells persisted in a viable but non-proliferating state. A remarkable variation in the size of the fraction was observed among the tested strains differing by several orders of magnitude. In fact, only 11 out of 10(7)  cells of the industrial bioethanol production strain Ethanol Red resumed growth after exposure to 157 mM acetic acid at pH 4.5, while this fraction was 3.6 × 10(6) (out of 10(7)  cells) in the highly acetic acid tolerant isolate ATCC 96581. These strain-specific differences are genetically determined and represent a valuable starting point to identify genetic targets for future strain improvement.