Biology subjects

van Son, M.

Publications and source records attributed to van Son, M..

3 recordsLinked to original sources

bioRxiv2025

Carbon-phosphorous exchange rate constrains growth of arbuscular mycorrhizal fungal networks

Symbiotic nutrient exchange between arbuscular mycorrhizal (AM) fungi and their host plants varies widely depending on their physical, chemical, and biological environment. Yet dissecting this context dependency remains challenging because we lack methods for tracking nutrients such as carbon (C) and phosphorus (P). Here, we developed a new approach to quantitatively estimate C and P fluxes in the AM symbiosis from comprehensive network morphology quantification, achieved by robotic imaging and machine learning based on roughly 100 million hyphal shape measurements. We found that rates of C transfer from the plant and P transfer from the fungus were, on average, related proportionally to one another. This ratio was nearly invariant across AM fungal strains despite contrasting growth phenotypes, but was strongly affected by plant host genotype. Fungal phenotype distributions were bounded by a Pareto front with a shape favoring specialization in an exploration-exploitation trade-off. This means AM fungi can be fast range expanders or fast resource extractors, but not both. Manipulating the C/P exchange rate by swapping the plant host genotype shifted this Pareto front, indicating that the exchange rate constrains possible AM fungal growth strategies. We show by mathematical modeling how AM fungal growth at fixed exchange rate leads to qualitatively different symbiotic outcomes depending on fungal traits and nutrient availability.

biophysics↗

bioRxiv2025

Genome-wide CRISPR knockout screen reveals the landscape of essential genes across the porcine genome

1Characterization of essential genes across the genome is fundamental to understanding cellular functions at a molecular level. While significant progress has been made in characterizing essential genes in human and mouse models, relatively little is known about essential genes in the porcine genome. Pigs are an important production species and are now emerging as valuable models for studying human diseases due to their physiological similarities to humans. To map essential genes across the porcine genome, we have developed a novel porcine genome-wide CRISPR knockout screening library (pGeCKO) and applied it to two porcine cell lines, PK15 and IPEC-J2. We identified 2,245 essential genes in PK15 cells and 919 essential genes in IPEC-J2 cells, with 683 of these shared between both cell lines. Functional analyses revealed that most essential genes are involved in core cellular processes such as cell cycle regulation, DNA replication, transcription, and translation. Comparative analysis with human essential genes from the DepMap project revealed that over half of the genes are shared with humans and the rest are porcine-specific. These porcine-specific essential genes included genes in core functional pathways related to protein and RNA processing as well as many related to N-glycan biosynthesis, signal transduction, and several long-noncoding RNAs. This work provides a new resource for leveraging porcine models in disease research, enhancing our understanding of porcine genetics and its implications for human health.

genomics↗

bioRxiv2023

Large scale sequence-based screen for recessive variants allows for identification and monitoring of rare deleterious variants in pigs

Most deleterious variants are recessive and segregate at relatively low frequency. Therefore, high sample sizes are required to identify these variants. In this study we report a large-scale sequence based genome-wide association study (GWAS) in pigs, with a total of 120,000 Large White and 80,000 Synthetic breed animals imputed to sequence using a reference population of approximately 1,100 whole genome sequenced pigs. We imputed over 20 million variants with high accuracies (R2>0.9) even for low frequency variants (1-5% minor allele frequency). This sequence-based analysis revealed a total of 13 additive and 8 non-additive significant quantitative trait loci (QTLs) for growth rate and backfat thickness. With the non-additive (recessive) model, we identified a deleterious missense SNP in the CDHR2 gene reducing growth rate and backfat in homozygous Large White animals. For the Synthetic breed, we revealed a QTL on chromosome 15 with a frameshift variant in the OBSL1 gene. This QTL has a major impact on both growth rate and backfat, resembling human 3M-syndrome 2 which is related to the same gene. With the additive model, we confirmed known QTLs on chromosomes 1 and 5 for both breeds, including variants in the MC4R and CCND2 genes. On chromosome 1, we disentangled a complex QTL region with multiple variants affecting both traits, harboring 4 independent QTLs in the span of 5 Mb. Together we present a large scale sequence-based association study that provides a key resource to scan for novel variants at high resolution for breeding and to further reduce the frequency of deleterious alleles at an early stage in the breeding program. Author SummaryIn this study we investigated the effect of over 20 million genetic variants on the growth rate and backfat thickness of approximately 140,000 pigs across two commercial breeds, with specific focus on recessive harmful variation. We identified 14 regions with a significant additive effect and 8 regions with a significant recessive effect on these traits. By looking at recessive effects we identified several rare deleterious variants with high impacts on animal fitness. These include a deletion on chromosome 15 in the OBSL1 gene, which leads to a growth reduction of 100 grams a day on average. Interestingly, loss-of-function mutations in OBSL1 are associated with short stature in humans. Looking at additive effects with this high-resolution dataset allowed us to gain more insight into the locus around the MC4R gene on chromosome 1. Here we found a small complex region containing several independent variants affecting both growth rate and backfat. With this study we have shown that by using several gene models and a large dataset, we can identify novel genetic variants at high resolution (<0.01 frequency) with significant impact on animal fitness and production. These results can help us in further eradicating deleterious genetic variants from pig populations.

genomics↗