Biology subjects

Melkonian, C.

Publications and source records attributed to Melkonian, C..

6 recordsLinked to original sources

bioRxiv2026

SynCom101: A web-based platform for the standardized design of functionally tailored synthetic microbial communities

BackgroundSynthetic microbial communities (SynComs) are essential tools for dissecting the causal mechanisms in host-microbiota interactions. To date, however, SynCom design suffers from a lack of standardization, typically oscillating between arbitrary strain selection and computational pipelines that misalign with experimental design. As microbiome research transitions toward functionally defined community systems with reproducible experimental outcomes, there is a strong need for a user-friendly platform that integrates multi-dimensional genomic and/or biological data into a standardized and tailored SynComs design. ResultsHere, we present SynCom101, a web-based platform that democratizes the design of reproducible, hypothesis-driven SynComs. SynCom101 accommodates diverse input formats including genomic annotations and laboratory-obtained phenotypic traits, allowing users to customize their design criteria with high flexibility. The platform utilizes a parsimony algorithm to ensure computational scalability for large datasets, complemented by an optional correlation-aware mode to account for microbial compatibility and co-occurrence patterns when ecological interactions among strains are available. A core innovation of SynCom101 is its suite of trait-weighting modules, which empowers researchers to strategically guide the selection algorithm toward maximal functional trait coverage, the emulation of natural community architectures, or the enrichment of positively correlated microbial assemblages to enhance community stability. We showcase the functionalities of the platform by in silico design of communities from different datasets, demonstrating its capacity to generate concise, functionally prioritized SynComs aligned with targeted design objectives. ConclusionBy providing a transparent, parameter-documented workflow, SynCom101 ensures that community design is no longer a "black box" but a reproducible scientific record. This platform establishes a necessary standard for in silico community assembly, facilitating the transition from descriptive microbiome studies toward high-throughput, predictive functional screening and cross-study comparability. AvailabilitySynCom101 can be accessed via the web interface (https://syncom101.bioinformatics.nl/). The datasets used for case studies are available on Zenodo (https://doi.org/10.5281/zenodo.18310451). The source code is available at Git (https://git.wur.nl/jiayi.jing/syncom101).

bioinformatics↗

bioRxiv2025

Global distribution of honeybee gut microbiome and pesticide-driven adaptations in opportunistic microbial species

Honeybees (Apis mellifera) rely on a specialized gut microbiome shaped by climate, flora, agrochemicals, and dietary supplements. Yet, how these factors alter microbiome composition and function remains unclear. We integrated 16S rRNA and shotgun metagenomics data from eight published studies across six regions, alongside newly generated 16S data from Armenia, to assess how environmental and agrochemical factors influence the honeybee gut microbiome. We also introduced a novel co-abundance and functional analysis pipeline to identify treatment-affected bacterial networks and associated pathways. We observed a stable core of six to twelve phylotypes in amplicon and metagenomic datasets respectively. However, we note significant geographic variation in relative abundances, likely reflecting differences in diet, climate, and local flora. Armenian data revealed distinct seasonal shifts, particularly elevated Commensalibacter in autumn and minor urban-rural differences. Pesticide treatments elicited varying responses: oxalic acid drove pronounced beta-diversity shifts; neonicotinoids had subtler effects, both primarily impacting opportunistic pathogens; and glyphosate disrupted core taxa with stronger effects in newly emerged bees under prolonged exposure. Co-abundance network analysis highlighted that the pesticide-associated community was enriched in adaptive pathways, including potential glyphosate degradation by Pseudomonas, biofilm formation, and aromatic amino acid synthesis. These findings reaffirm the stability of the honeybee core microbiome yet underscore that environmental and anthropogenic stressors induce distinct compositional and functional shifts. We emphasize the need for longitudinal metagenomic approaches that enable high-resolution functional profiling and co-abundance network analysis to clarify how these microbiome shifts impact bee health and colony sustainability.

microbiology↗

bioRxiv2023

Improving genome-scale metabolic models of incomplete genomes with deep learning

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 for uncultured bacteria is challenging, as metagenome-assembled genomes are typically incomplete, leading to metabolic reconstructions with numerous gaps. Existing methodologies often fill these gaps with the minimum set of reactions necessary to simulate an objective function such as growth. Here we introduce an artificial intelligence-based alternative: the Deep Neural Network Guided Imputation Of Reactomes (DNNGIOR). The DNNGIOR neural network learns weights for missing reactions in incomplete GSMMs from patterns in the presence and absence of metabolic reactions in genomes spanning the bacterial domain. We identified two important factors contributing to prediction accuracy: (1) the frequency of reaction across all bacteria, and (2) the phylogenetic distance between the query and the genomes in the training dataset. Reactions that occur in > 30% of the training genomes can be accurately predicted (Mean F1 score = 0.85). The weights generated by the DNNGIOR network improved the gap-filling of incomplete GSMMs, when assessed on a large and phylogenetically diverse testing dataset and a small set of high-quality manually curated models. The accuracy of DNNGIOR was on average 14 times greater than the standard unweighted gap-filling for draft reconstructions, and 2-9 times greater for manually curated models. DNNGIOR models could also simulate experimentally measured carbon usage profiles with similar accuracy as CarveMe. DNNGIOR is available at https://github.com/MGXlab/DNNGIOR or as a pip package (https://pypi.org/project/dnngior/).

bioinformatics↗

bioRxiv2023

A fast method to distinguish between fermentative and respiratory metabolisms in single yeast cells

Saccharomyces cerevisiae adapts its metabolism according to nutrient availability. Typically, it rapidly ferments glucose to ethanol, and then shifts to respiration when glucose becomes limited. However, our understanding of the regulation of metabolism is largely based on population averages, whereas nutrient transitions may cause heterogeneous responses at the individual cell level. Although protein expression can be followed at the single-cell level as a proxy for metabolic modes, direct assessment of the contribution of respiration or (respiro)fermentation to energy metabolism is lacking. Here we describe a method to quickly differentiate between fermentative and respiratory metabolisms in individual cells of budding yeast. The method explores the use of the fluorescent FRET-based biosensor yAT1.03 to measure cytosolic ATP, coupled with the respiratory inhibitor Antimycin A. For the method validation, we used cells under fermentative and respiratory regimes from batch and chemostat cultures. Upon Antimycin A addition, we observed a sharp decrease of the normalized FRET ratio for respiratory cells; respirofermentative cells showed no response. Next, we tracked the changes in metabolism during the diauxic shift of a glucose pre-grown batch culture. Following glucose exhaustion, the entire cell population experienced a progressive rise in intracellular ATP produced via respiration, suggesting a uniform and gradual increase in respiratory capacity as cells pick up growth in a medium with ethanol as the sole carbon source. Overall, the combination of yAT1.03 with Antimycin A is a robust tool to distinguish fermentative from respiratory yeast cells, offering a new single-cell opportunity to study yeast metabolism. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/546324v1_figs1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1db7087org.highwire.dtl.DTLVardef@1b8b839org.highwire.dtl.DTLVardef@46d641org.highwire.dtl.DTLVardef@9c4aac_HPS_FORMAT_FIGEXP M_FIG Identification of fermentative and respiratory metabolisms in yeast cells using an ATP sensor coupled with a respiration inhibitor. (a) yAT1.03 consists of a donor (tdTomato) and an acceptor (ymTq2{Delta}11) domain linked by a binding domain with affinity to ATP. When ATP binds to the binding domain, donor and acceptor come together and the Forster energy is transferred from the first to the second domain. When expressed in in vivo cells the sensor allows real time measurements of ATP changes. (b) Depending on the growth conditions, yeast cells expressing yAT1.03 show a distinct response after being pulsed with the respiratory inhibitor Antimycin A (AA). The drop in ATP levels in respiratory cells caused by AA results from the inhibition of the mitochondrial electron transport chain. (c) Distinct metabolic responses to an AA pulse pre-, during and post-diauxic shift reveal distinct metabolic phenotypes. C_FIG

systems biology↗

bioRxiv2021

A simple coloring method to distinguish colonies of the yeasts Lachancea thermotolerans and Saccharomyces cerevisiae on agar media

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 wine fermentations. Commonly used methods to routinely distinguish these organisms are indirect or rely on commercial products of undisclosed composition. Here we describe that adding bromocresol purple to agar media induces Lachancea colonies to develop a brown color, whereas Saccharomyces colonies remain white.

microbiology↗

bioRxiv2020

Biodiversity and niche partitioning in an anaerobic benzene degrading culture

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. We demonstrate that only a few community members are able to degrade benzene, and that most of the others feed on the metabolic left-overs or on the contents of dead cells making up a food web with different trophic levels. As a result of niche partitioning, a high species richness is maintained and the complexity of a natural community is stabilized in a relatively simple environment. This view highlights the importance of species interactions and interdependencies, which drive microbial community structure and function. These mechanisms may well be conserved across ecosystems.

ecology↗