Biology subjects

Yabe, S.

Publications and source records attributed to Yabe, S..

5 recordsLinked to original sources

bioRxiv2026

Independent evolution of flavour and parthenocarpy in fig, one of the earliest domesticated fruit trees

Domestication often modifies multiple traits in concert, but whether fruit quality and reproductive mode share a genetic basis in perennial crops has not been tested at multi-omics resolution. In fig (Ficus carica), one of the earliest domesticated fruit trees and a crop with archaeobotanical evidence of cultivation over 11,000 years ago, domestication yielded both distinctive flavour and parthenocarpy, fruit development without pollination. We integrated whole-genome resequencing, transcriptomics, volatile and metabolite profiling, and taste sensor data across a diverse accession panel, using Bayesian GWAS/TWAS and multi-omics factor analysis. Flavour emerges as an integrated sensory system led by aroma, which modulates taste through cross-modal interactions, with sugar-acid balance as a secondary axis; these quality traits map to numerous independent loci. Parthenocarpy instead traces to a single perfectly concordant chromosome 04 haplotype (Eden) centred on FcMYB101-like, carrying a selective-sweep signature and a derived allele fixed in cultivated figs but rare across the genus. Flavour and reproductive mode thus evolved along separate genomic trajectories, allowing each to be improved independently.

plant biology↗

bioRxiv2026

Chromid-like secondary replicons as key sites of biosynthetic gene clusters in Ktedonobacteria

Soils harbour immense biosynthetic gene cluster (BGC) diversity that can mediate microbial interactions, yet this potential is still mapped unevenly across the tree of life. Ktedonobacteria--a class of actinomycete-like bacteria within phylum Chloroflexota--are widespread in terrestrial environments and repeatedly dominate pioneer communities in extremely oligotrophic volcanic bare-ground soils; however, their secondary metabolism and genome architecture remain poorly characterised. Here, we integrate targeted cultivation using volcanic soils from Mount Zao with genome-resolved metagenomics and public genomes to analyse 183 ktedonobacterial genomes. Using antiSMASH and BiG-SLiCE, we identified 1,546 BGCs comprising 1,162 non-redundant gene-cluster families (GCFs). In our dataset, nearly one quarter of genomes encode [≥]10 distinct GCFs, and several family-level clades show mean GCF counts comparable to those in genus Streptomyces. Most ktedonobacterial BGCs are highly divergent from reference collections and exhibit unusually low intra-genomic redundancy, suggesting broad, underexplored chemotypes. Long-read assemblies from ten strains reveal recurrent 1.6-3.5 Mb chromid-like secondary replicons with chromosome-like composition but distinct maintenance signatures. These replicons are consistently enriched in BGCs and mobility-associated genes, with mobility loci concentrated near BGC boundaries. Collectively, our results expand the current knowledge of the phylogenetic landscape of soil biosynthetic diversity and highlight chromid-like secondary replicons as major genomic reservoirs for specialised metabolism in Ktedonobacteria.

microbiology↗

bioRxiv2025

Horizontal acquisition of nicotine catabolism gene cluster drives the assembly of tobacco root microbiota community

BackgroundPlant roots are hotspots for interactions with soil microbes, where a characteristic bacterial community structure is formed. Plant specialized metabolites often play pivotal roles in this assembly process. However, the molecular basis underlying root microbiota responses to these bioactive compounds, and how such metabolic interactions shape the assembly of host-specific root microbiota, remain largely unknown. Nicotine is a toxic alkaloid predominantly produced by the genus Nicotiana, and the genus Arthrobacter is known as one of the nicotine-degrading bacteria in the tobacco root microbiota. In this study, we used the tobacco-Arthrobacter interaction system as a model and integrated comparative genomics and experimental genetic manipulation assays to uncover the role of bacterial catabolism capacity for host specialized metabolites in shaping host-specific root microbiota. ResultsNicotine catabolism genes are uniquely found in the Arthrobacter strains derived from nicotine-containing environments, and this restricted gene distribution is driven by a plasmid-mediated horizontal gene transfer. To assess the ecological consequences of this genomic adaptation in Arthrobacter fitness in tobacco roots, we conducted adaptation assays under both in vitro and in planta conditions using genetically manipulated Arthrobacter and tobacco mutants, which are impaired in nicotine catabolism and biosynthesis, respectively. Nicotine improves Arthrobacter colonization to the tobacco roots through both catabolism-dependent and catabolism-independent mechanisms. Bacterial community analysis using a synthetic community approach further demonstrated that these metabolic interactions, mediated by tobacco nicotine biosynthesis and its catabolism by Arthrobacter, jointly affect root microbiota composition. ConclusionsOur findings illustrated that bacterial catabolic capacity toward host-derived plant specialized metabolites is key for successful root colonization. This metabolic adaptation is driven by plasmid-mediated horizontal gene transfer and ultimately shapes the structure of the overall root microbiota community.

microbiology↗

bioRxiv2019

Choosing the optimal population for a genome-wide association study: a simulation using whole-genome sequences from rice

A genome-wide association study (GWAS) needs to have a suitable population. The factors that affect a GWAS, e.g. population structure, sample size, and sequence analysis and field testing costs, need to be considered. Mixture populations containing subpopulations of different genetic backgrounds may be suitable populations. We conducted simulation experiments to see if a population with high genetic diversity, e.g., a diversity panel, should be added to a target population, especially when the target population harbors small genetic diversity. The target population was 112 accessions of Oryza sativa subsp. japonica, mainly developed in Japan. We combined the target population with three populations that had higher genetic diversities. These were 100 indica accessions, 100 japonica accessions, and 100 accessions with various genetic backgrounds. The results showed that the GWAS power with a mixture population was generally higher than with a separate population. Also, the GWAS optimal population varied depending on the fixation index FST of the quantitative trait nucleotide (QTN) and its polymorphism of QTN in each population. When a QTN is polymorphic in a target population, a target population combined with a higher diversity population improves the QTN detection power. Investigating FST and the expected heterozygosity He as factors influencing the detection power, we showed that SNPs with high FST or low He are less likely to be detected by GWAS with mixture populations. Sequenced/genotyped germplasm collections can improve the GWAS detection power by using a subset of them with a target population.\n\nCore ideas (3-5 impact statements, 85 char max for each)\n\n- Genome-wide association studies with mixture populations are expected to improve the detection power of novel genes due to the increase of the sample size although the influence of population structure is a concern.\n- When a quantitative trait nucleotide (QTN) is polymorphic in a target population, a combination of the target population and a population with higher diversity than the target population improves the detection power of the QTN.\n- We found that the fixation index (FST) and the expected heterozygosity (He) were strongly related to the detection power of QTNs.\n- Germplasm collections which have been already sequenced/genotyped are useful for improving the detection power of GWAS without any addition of sequence costs by using a subset of them with a target population.

genomics↗

bioRxiv2019

Coupling Day Length Data and Genomic Prediction tools for Predicting Time-Related Traits under Complex Scenarios

Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; however, when the traits have intermediate optima (phenology stages) this implementation might not be the most convenient. GS might deliver high rank correlations but incurring in serious bias. Days to heading (DTH) is a crucial development stage in rice for regional adaptability with significant impact in yield potential. The objectives of this research consisted in the development of a method that accurately predicts time related traits like DTH in unobserved environments. For this, we developed an implementation that incorporate day length information (DL) in the prediction process for two relevant scenarios: CV0, predicting tested genotypes in unobserved environments (C method); and CV00, predicting untested genotypes in unobserved environments (CB method). The use of DL has advantages over weather data since it can be determined in advance just by knowing the location and planting date. The proposed methods showed that DL information significantly helps to improve the predictive ability of DTH in unobserved environments. Under CV0, the C method returned an root-mean-square-error (RMSE) of 3.9 days, a Pearson Correlation (PC) of 0.98 and the differences between the predicted and observed environmental means (EMD) ranged between -4.95 and 4.67 days. For CV00, the CB method returned an RMSE of 7.3 days, a PC of 0.93 and the EMD ranged between -6.4 and 4.1 days while the conventional GS implementation produced an RMSE of 18.1 days, a PC of 0.41 and the EMD ranged between -31.5 and 28.7 days.

genomics↗