Chrats Melkonian

Utrecht University | UU · Division of Theoretical Biology and Bioinformatics

Deciphering the metabolism of microbial species is crucial for understanding their function within complex ecosystems. Genome-scale metabolic models (GSMMs), which predict metabolic traits based on the enzymes encoded in a genome, are promising tools to study microbial ecosystems when genome sequences can be obtained. However, constructing GSMMs fo...

Cheese fermentation and flavour formation are governed by complex biochemical reactions driven by polymicrobial activity. While the compositional dynamics of cheese microbiomes is relatively well mapped, the mechanistic role of microbial interactions in flavour formation is yet unknown. We microbially and metabolically characterised a year-long Che...

So far, only members of the bacterial phyla Proteobacteria and Verrucomicrobia are known to grow methanotrophically under aerobic conditions. Here we report that this metabolic trait is also observed within the Actinobacteria. We enriched and cultivated a methanotrophic Mycobacterium from an extremely acidic biofilm growing on a cave wall at a gase...

Climate change increases sugar content in grapes, resulting in unwanted increase in ethanol content of wine. Lachancea thermotolerans ferments glucose and fructose into both ethanol and lactate, decreasing final ethanol content and positively affecting wine acidity. Reported Lachancea thermotolerans strains show big variation in lactate production...

Metabolic interactions between auxotrophs and prototrophs in microbial communities are understudied. Yu et al. showed how intracellular as well as intercellular metabolism affects community fitness in the absence and presence of abiotic stress, that is, drugs.

The behaviour of microbial communities depends on environmental factors and on the interactions of the community members. This is also the case for urinary tract infection (UTI) microbial communities. Here, we devise a computational approach that uses indices of complementarity and competition based on metabolic gene annotation to rapidly predict p...

A key question in microbial ecology is what the driving forces behind the persistence of large biodiversity in natural environments are. We studied a microbial community with more than 100 different types of species which evolved in a 15-years old bioreactor with benzene as the main carbon and energy source and nitrate as the electron acceptor. Usi...

The yeast Lachancea thermotolerans converts consumed sugar partly to lactic acid instead of ethanol and is therefore used together with Saccharomyces cerevisiae to produce wines with a lower alcohol content. Being able to distinguish these yeasts is important for quality control and quantitative assessment of the contributions of both yeasts to win...

A key question in microbial ecology is what the driving forces behind the persistence of large biodiversity in natural environments are. We studied a microbial community with more than 100 different types of species which evolved in a 15-years old bioreactor with benzene as the main carbon and free energy source and nitrate as the electron acceptor...

Some strains of Oenococcus oeni possess cinnamoyl esterase activity that can be relevant in the malolactic stage of wine production liberating hydroxycinnamic acids that are precursors of volatile phenols responsible for sensory faults. The objective of this study was to better understand the basis of the differential activity between strains. Afte...

Microbial life usually takes place in a community where individuals interact, by competition for nutrients, cross-feeding, inhibition by end-products, but also by their spatial distribution. Lactic acid bacteria are prominent members of microbial communities responsible for food fermentations. Their niche in a community depends on their own propert...

Although there is an extensive tradition of research into the microbes that underlie the winemaking process, much remains to be learnt. We combined the high-throughput sequencing (HTS) tools of metabarcoding and metagenomics, to characterize how microbial communities of Riesling musts sampled at four different vineyards, and their subsequent sponta...

Heatmap visualization of the drafted genomic bins in vineyard 4 (v4) and vineyard 5 (v5) assigned with unique 6394 KEGG Orthology annotated functions using affinity propagation with Pearson correlation on the presence and absence table. Cluster colors are based on unsupervised clustering from affinity propagation.

Absolute counts of functions with taxonomy separation from eggNOG results with the top 6 groups included: bacteria, eukaryotes (without fungi), opisthokonts, other fungus, family Saccharomycetaceae, and virus. Functions compared between vineyard 4 (W3 and W4) and vineyard 5 (W9 and W10) show abundances and differences caused mainly by two groups: b...

Affinity propagation clustering of mapped taxa using Pearson correlation in metagenomic data of vineyard 4. (A) The species which had an increase of their abundance during the fermentation were grouped to cluster 1. (B) Second cluster included Vitis vinifera, Erysiphe necator, and Botrytis cinerea. (C) Third cluster was wine related yeasts driven c...

Metabarcoding performance. Sample read numbers from metabarcoding mapped against ITS2 gene copy numbers with samples colored and shaped according to fermentation behavior. The data show that samples with stuck fermentation behavior had the lowest amounts of both reads and ITS2 gene copy numbers. Blue triangle: stuck fermentation behavior, orange ci...

Summary of selected 10 samples used in metagenomic sequencing.

Shotgun library construction method comparison.

Details of samples and measured wine parameters.

An overview of OTUs assignment per sample using ITS2 metabarcoding.

BGI 2.0 adapters sequences.

(A) Summary of metagenomic sequencing data generated. The classified reads and unclassified reads were mapped to a curated database using Kraken and Bracken. (B) The relative abundance of number of mapped reads to each taxon in curated database per sample found in at least once.

Mean Ct values (±standard deviation) from qPCR of three extraction methods in different sample types. n = 5 for ferment, while n = 2 for other. Colors correspond to the three methods: dark gray: BEST, light gray: BEMT, gray: NEBNext. Refer to Supplementary File S1 for more details.

(A) Summary of binning details and (B) percentage of recruitment for the metagenomic data of vineyards 4 and 5. Percentage of recruitment summarizes the mean coverage of each split in each bin, and normalize every bin with respect to each other. It is critical to remember that these values do not take the unassembled data into account.