Biology subjects

Boer, M. D.

Publications and source records attributed to Boer, M. D..

3 recordsLinked to original sources

bioRxiv2025

Widespread synchronization of codon usage in functionally related genes

The usage of synonymous codons varies along the genome, with strong biases in conserved and highly expressed genes that are optimized for efficient translation. The extent to which selection shapes codon usage in other genes, as well as the associations between gene function, gene expression, and codon usage, remains an important open question. We developed and optimized a novel approach to detect synchronized changes in codon usage patterns and applied it to 15,005 species-representative bacterial genomes spanning the 15 most represented phyla. We show that codon usage is extensively shaped by selection in both highly and lowly expressed genes, with at least [~]20-46% of gene families showing synchronized codon usage evolution across genomes. We reveal that gene pairs with parallel codon usage adaptation are co-expressed, co-regulated, metabolically connected, and functionally associated. By identifying synchronized codon usage evolution between gene pairs, we have generated a genome-wide set of functional associations reflecting correlated expression across species. This underappreciated layer of coordinated codon usage adaptation has important implications for function discovery and engineering.

genomics↗

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↗

bioRxiv2019

Modeling of root nitrate responses suggests preferential foraging arises from the integration of demand, supply and local presence signals

A plants fitness to a large extent depends on its capacity to adapt to spatio-temporally varying environmental conditions. One such environmental condition to which plants display extensive phenotypic plasticity is soil nitrate levels and patterns. In response to heterogeneous nitrate distribution, plants show a preferential foraging response, enhancing root growth in high nitrate patches and repressing root growth in low nitrate locations beyond a level that can be explained from local nitrate sensing. Although various molecular players involved in this preferential foraging behavior have been identified, how these together shape root system adaptation has remained unresolved. Here we use a simple modeling approach in which we incrementally incorporate the various known molecular pathways to investigate the combination of regulatory mechanisms that underly preferential root nitrate foraging. Our model suggests that instead of a thus far not discovered growth suppressing supply signal, growth reduction on the low nitrate side may simply arise from a reduced root foraging and increased competition for carbon. Additionally, our work suggests that the long distance CK signaling involved in root growth increase in high nitrate patches may represent a supply signal specifically functioning in modulating demand signaling strength. We illustrate how this integration of demand and supply signals prevents excessive preferential foraging under conditions in which demand is not met by sufficient supply and a more generic foraging in search of nitrate should be maintained.

plant biology↗