Integrative multiomics profiling of passion fruit reveals the genetic basis for fruit color and aroma.

Passion fruit (Passiflora edulis) possesses a complex aroma and is widely grown in tropical and subtropical areas. Here, we conducted the de novo assembly, annotation, and comparison of PPF (P. edulis Sims) and YPF (P. edulis f. flavicarpa) reference genomes using PacBio, Illumina, and Hi-C technologies. Notably, we discovered evidence of recent whole-genome duplication events in P. edulis genomes. Comparative analysis revealed 7.6∼8.1 million single nucleotide polymorphisms, 1 million insertions/deletions, and over 142 Mb presence/absence variations among different P. edulis genomes. During the ripening of yellow passion fruit, metabolites related to flavor, aroma, and color were substantially accumulated or changed. Through joint analysis of genomic variations, differentially expressed genes, and accumulated metabolites, we explored candidate genes associated with flavor, aroma, and color distinctions. Flavonoid biosynthesis pathways, anthocyanin biosynthesis pathways, and related metabolites are pivotal factors affecting the coloration of passion fruit, and terpenoid metabolites accumulated more in PPF. Finally, by heterologous expression in yeast (Saccharomyces cerevisiae), we functionally characterized 12 terpene synthases. Our findings revealed that certain TPS homologs in both YPF and PPF varieties produce identical terpene products, while others yield distinct compounds or even lose their functionality. These discoveries revealed the genetic and metabolic basis of unique characteristics in aroma and flavor between the 2 passion fruit varieties. This study provides resources for better understanding the genome architecture and accelerating genetic improvement of passion fruits.

Telomere-to-telomere assembly of cassava genome reveals the evolution of cassava and divergence of allelic expression.

Cassava is a crucial crop that makes a significant contribution to ensuring human food security. However, high-quality telomere-to-telomere cassava genomes have not been available up to now, which has restricted the progress of haploid molecular breeding for cassava. In this study, we constructed two nearly complete haploid resolved genomes and an integrated, telomere-to-telomere gap-free reference genome of an excellent cassava variety, 'Xinxuan 048', thereby providing a new high-quality genomic resource. Furthermore, the evolutionary history of several species within the Euphorbiaceae family was revealed. Through comparative analysis of haploid genomes, it was found that two haploid genomes had extensive differences in linear structure, transcriptome features, and epigenetic characteristics. Genes located within the highly divergent regions and differentially expressed alleles are enriched in the functions of auxin response and the starch synthesis pathway. The high heterozygosity of cassava 'Xinxuan 048' leads to rapid trait segregation in the first selfed generation. This study provides a theoretical basis and genomic resource for molecular breeding of cassava haploids.

Systematic characterization and prediction of coenzyme A-associated proteins using sequence and network information.

Coenzyme A-associated proteins (CAPs) are a category of functionally important proteins involved in multiple biological processes through interactions with coenzyme A (CoA). To date, unfortunately, the specific differences between CAPs and other proteins have yet to be systemically investigated. Moreover, there are no computational methods that can be used specifically to predict these proteins. Herein, we characterized CAPs from multifaceted viewpoints and revealed their specific preferences. Compared with other proteins, CAPs were more likely to possess binding regions for CoA and its derivatives, were evolutionarily highly conserved, exhibited ordered and hydrophobic structural conformations, and tended to be densely located in protein-protein interaction networks. Based on these biological insights, we built seven classifiers using predicted CoA-binding residue distributions, word embedding vectors, remote homolog numbers, evolutionary conservation, amino acid composition, predicted structural features and network properties. These classifiers could effectively identify CAPs in Homo sapiens, Mus musculus and Arabidopsis thaliana. The complementarity among the individual classifiers prompted us to build a two-layer stacking model named CAPE for improving prediction performance. We applied CAPE to identify some high-confidence candidates in the three species, which were tightly associated with the known functions of CAPs. Finally, we extended our algorithm to cross-species prediction, thereby developing a generic CAP prediction model. In summary, this work provides a comprehensive survey and an effective predictor for CAPs, which can help uncover the interplay between CoA and functionally relevant proteins.