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.

Analysis of Malassezia Lipidome Disclosed Differences Among the Species and Reveals Presence of Unusual Yeast Lipids.

Malassezia yeasts are lipid dependent and part of the human and animal skin microbiome. However, they are also associated with a variety of dermatological conditions and even cause systemic infections. How these yeasts can live as commensals on the skin and switch to a pathogenic stage has long been a matter of debate. Lipids are important cellular molecules, and understanding the lipid metabolism and composition of Malassezia species is crucial to comprehending their biology and host-microbe interaction. Here, we investigated the lipid composition of Malassezia strains grown to the stationary phase in a complex Dixon medium broth. In this study, we perform a lipidomic analysis of a subset of species; in addition, we conducted a gene prediction analysis for the detection of lipid metabolic proteins. We identified 18 lipid classes and 428 lipidic compounds. The most commonly found lipids were triglycerides (TAG), sterol (CH), diglycerides (DG), fatty acids (FAs), phosphatidylcholine (PC), phosphatidylethanolamine (PE), ceramides, cholesteryl ester (CE), sphingomyelin (SM), acylcarnitine, and lysophospholipids. Particularly, we found a low content of CEs in Malassezia furfur, atypical M. furfur, and Malassezia pachydermatis and undetectable traces of these components in Malassezia globosa, Malassezia restricta, and Malassezia sympodialis. Remarkably, uncommon lipids in yeast, like diacylglyceryltrimethylhomoserine and FA esters of hydroxyl FAs, were found in a variable concentration in these Malassezia species. The latter are bioactive lipids recently reported to have antidiabetic and anti-inflammatory properties. The results obtained can be used to discriminate different Malassezia species and offer a new overview of the lipid composition of these yeasts. We could confirm the presence and the absence of certain lipid-biosynthesis genes in specific species. Further analyses are necessary to continue disclosing the complex lipidome of Malassezia species and the impact of the lipid metabolism in connection with the host interaction.

In vitro and in silico characterization of metagenomic soil-derived cellulases capable of hydrolyzing oil palm empty fruit bunch.

Diversification of raw material for biofuel production is of interest to both academia and industry. One attractive substrate is a renewable lignocellulosic material such as oil palm (Elaeis guineensis Jacq.) empty fruit bunch (OPEFB), which is a byproduct of the palm oil industry. This study aimed to characterize cellulases active against this substrate. Cellulases with activity against OPEFB were identified from a metagenomic library obtained from DNA extracted from a high-Andean forest ecosystem. Our findings show that the highest cellulolytic activities were obtained at pH and temperature ranges of 4-10 and 30 °C-60 °C, respectively. Due to the heterogeneous character of the system, degradation profiles were fitted to a fractal-like kinetic model, evidencing transport mass transfer limitations. The sequence analysis of the metagenomic library inserts revealed three glycosyl hydrolase families. Finally, molecular docking simulations of the cellulases were carried out corroborating possible exoglucanase and ß-glucosidase activity.

Phage ΦPan70, a Putative Temperate Phage, Controls Pseudomonas aeruginosa in Planktonic, Biofilm and Burn Mouse Model Assays.

Pseudomonas aeruginosa is one of the Multi-Drug-Resistant organisms most frequently isolated worldwide and, because of a shortage of new antibiotics, bacteriophages are considered an alternative for its treatment. Previously, P. aeruginosa phages were isolated and best candidates were chosen based on their ability to form clear plaques and their host range. This work aimed to characterize one of those phages, ΦPan70, preliminarily identified as a good candidate for phage-therapy. We performed infection curves, biofilm removal assays, transmission-electron-microscopy, pulsed-field-gel-electrophoresis, and studied the in vivo ΦPan70 biological activity in the burned mouse model. ΦPan70 was classified as a member of the Myoviridae family and, in both planktonic cells and biofilms, was responsible for a significant reduction in the bacterial population. The burned mouse model showed an animal survival between 80% and 100%, significantly different from the control animals (0%). However, analysis of the ΦPan70 genome revealed that it was 64% identical to F10, a temperate P. aeruginosa phage. Gene annotation indicated ΦPan70 as a new, but possible temperate phage, therefore not ideal for phage-therapy. Based on this, we recommend genome sequence analysis as an early step to select candidate phages for potential application in phage-therapy, before entering into a more intensive characterization.

Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia.

BACKGROUND: Chronic lymphocytic leukemia (CLL) is an incurable malignancy of mature B-lymphocytes, characterized as being a heterogeneous disease with variable clinical manifestation and survival. Mutational statuses of rearranged immunoglobulin heavy chain variable (IGVH) genes has been consider one of the most important prognostic factors in CLL, but despite of its proven value to predict the course of the disease, the regulatory programs and biological mechanisms responsible for the differences in clinical behavior are poorly understood. METHODS: In this study, (i) we performed differential gene expression analysis between the IGVH statuses using multiple and independent CLL cohorts in microarrays platforms, based on this information, (ii) we constructed a simplified protein-protein interaction (PPI) network and (iii) investigated its structure and critical genes. This provided the basis to (iv) develop a Boolean model, (v) infer biological regulatory mechanism and (vi) performed perturbation simulations in order to analyze the network in dynamic state. RESULTS: The result of topological analysis and the Boolean model showed that the transcriptional relationships of IGVH mutational status were determined by specific regulatory proteins (PTEN, FOS, EGR1, TNF, TGFBR3, IFGR2 and LPL). The dynamics of the network was controlled by attractors whose genes were involved in multiple and diverse signaling pathways, which may suggest a variety of mechanisms related with progression occurring over time in the disease. The overexpression of FOS and TNF fixed the fate of the system as they can activate important genes implicated in the regulation of process of adhesion, apoptosis, immune response, cell proliferation and other signaling pathways related with cancer. CONCLUSION: The differences in prognosis prediction of the IGVH mutational status are related with several regulatory hubs that determine the dynamic of the system.

Computational approaches for evaluating the effect of sequence variations and the intrinsically disordered C-terminal region of the Helicobacter pylori CagA protein on the interaction with tyrosine kinase Src.

The Helicobacter pylori CagA protein was the first bacterial oncoprotein to be identified as important in the development of human malignancies such as gastric cancer. It is not clear how it is able to deregulate a set of cell control mechanisms to induce carcinogenesis following translocation into human gastric epithelial cells. It is likely, however, that structural variations in the CagA sequence alter its affinity with the host proteins inducing differences in the pathogenicity of different H. pylori strains. Using the recently elucidated N-terminal 3D structure of H. pylori CagA, information on the full cagA gene sequence, and intrinsically disordered protein structure predictions methods we evaluated the interaction of different CagA variants with the kinase Src. An automated docking followed by molecular dynamics simulations were performed to explore CagA interaction modes with Src, one of its cellular partners. The computational approach let us establish that even in the presence of the same number and type of EPIYA motifs, CagA protein can reveal different spatial distributions. Based on the lowest affinity energy and higher number of interactions it was established that the principal forces governing the CagA-Src interaction are electrostatic. Results showed that EPIYA-D models presents higher affinity with some host proteins than EPIYA-C. Thus, we highlight the importance and advantage of the use of computational tools in combining chemical and biological data with bioinformatics for modeling and prediction purposes in some cases where experimental techniques present limitations.

Quorum quenching analysis in Pseudomonas aeruginosa and Escherichia coli: network topology and inhibition mechanism effect on the optimized inhibitor dose.

The discovery of quorum sensing as a mechanism for regulating specific phenotypes in bacteria based on population density has conveyed attention to find molecules capable of interfering quorum sensing networks (QSN) in a process coined quorum quenching. Here, we examined the dynamics of Escherichia coli AI-2 and Pseudomonas aeruginosa QSN exposed to signal degraders or competitors for binding transcriptional regulators. Stability analysis was performed for E. coli and P. aeruginosa finding no multistability in E. coli. However, our model allowed to discern that quenchers influence P. aeruginosa QSN multistability by reducing the interval of the amount of molecules of the extracellular signal that originate several steady states. We proposed a simulated annealing algorithm to optimize the quencher dose based on stochastic kinetics. E. coli QSN requires around 640 while P. aeruginosa QSN needs 253 quencher molecules per microorganism. This dose was found to be negatively influenced by the quencher-signal affinity.