Biology subjects

Yousefi, N.

Publications and source records attributed to Yousefi, N..

6 recordsLinked to original sources

bioRxiv2026

Phenotypic and genomic changes associated with the evolutionary loss of heterostyly in primroses

Trait loss has been documented across broad phylogenetic scales and dissected genetically within species, but how the genetic basis of loss varies among lineages and over evolutionary time remains poorly understood. Heterostyly, a floral polymorphism, represents an ideal case study, as it has been lost repeatedly across angiosperms. In Primula, heterostyly is controlled by the S-locus supergene, and loss-of-function mutations in CYP cause transitions to homostyly. We investigated the critically endangered Primula boveana, whose floral morphology is debated, in the diploid, self-compatible P. sect. Sphondylia. We quantified herkogamy dynamics during anthesis, performed controlled pollination experiments, assembled a chromosome-scale genome, compared S-locus structure and gene expression with heterostylous relatives, and reconstructed phylogenetic relationships within the section. Our results revealed that P. boveana is homostylous, with a disrupted CYP. Herkogamy declines during anthesis, resulting in early anther-stigma contact, autonomous selfing, and reproductive assurance. Thus, unlike other homostylous Primula species, these developmental changes do not represent a delayed selfing strategy. These findings link developmental changes in herkogamy with autonomous selfing and support CYP inactivation as the genetic basis of long homostyly in P. boveana. The extensive degeneration of CYP suggests that prolonged relaxed selection promotes progressive degeneration of non-functional S-genes following heterostyly loss.

evolutionary biology↗

bioRxiv2025

Distinct genomic architectures but the same gene underlie the convergent evolution of a plant supergene

AO_SCPLOWBSTRACTC_SCPLOWEvolution reflects a balance between innovation and constraint, often repurposing existing components in new contexts. Convergent evolution exemplifies this interplay, with similar traits evolving independently in different species, yet the genomic mechanisms enabling such repeatability remain poorly understood. Here, by analyzing ten chromosome-scale genome assemblies, including seven newly generated, we discovered that the S-locus supergene (a cluster of tightly linked genes controlling a floral dimorphism called distyly) arose independently multiple times within the primrose family, Darwins iconic system for studying distyly. In each case, the same gene was independently duplicated and co-opted, yet the resulting genomic architectures differed, ranging from hemizygous (present on one chromosome copy) to heterozygous (on both copies). These diverse architectures shaped supergene evolution differently, with genetic degeneration occurring only in the heterozygous case. By uncovering multiple mechanisms for supergene origins, our work shows how convergent evolution can produce similar phenotypes by reusing the same genetic building blocks while exploring distinct genomic configurations.

evolutionary biology↗

bioRxiv2025

Fate of a supergene in the shift from diploidy to polyploidy

Despite the evolutionary importance of supergenes, their properties in polyploids remain unexplored. Polyploid genomes are expected to undergo chromosomal rearrangements and gene losses over time, potentially affecting supergene architecture. The iconic distyly supergene (S-locus), controlling a floral heteromorphism with two self-incompatible morphs, has been well-documented in diploids, but remains unknown in polyploids. Primula, the classic model for distyly since Darwin, is ancestrally diploid and distylous, yet polyploid, homostylous species with a single, self-compatible floral morph evolved repeatedly. The intraspecific loss of distyly is associated with small loss-of-function mutations in the S-locus CYPT gene controlling style length and female self-incompatibility. Over longer timescales, relaxed selection on CYPT should generate greater accumulation of larger mutations, including exon and gene loss. By analyzing the first assembled genome of an allotetraploid, homostylous species (Primula grandis) in a comparative framework, we discovered two, nearly identical S-locus alleles in the same subgenome, suggesting it originated via inter-specific hybridization between a homostylous and a distylous progenitor. Conformant to predictions from theory, the macroevolutionary loss of distyly coincided with considerable degeneration of CYPT, while other S-locus genes remained largely unaffected, suggesting the shift to homostyly preceded and facilitated polyploid establishment. At the whole-genome level, we found minimal subgenome dominance -- as expected, given the inferred recent origin of P. grandis -- and highly reduced genetic diversity, congruently with its narrow distribution and self-compatibility. This study provides the first comparison of a supergene across ploidy levels and reproductive systems, contributing new knowledge on the previously unknown fate of supergenes in polyploids. SIGNIFICANCEThis study advances knowledge on genome evolution by elucidating how supergenes (clusters of tightly linked genes) evolve across species with different sets of chromosomes and reproductive systems. By analyzing the newly assembled genome of the polyploid, self-compatible Primula grandis in a broad framework, we provide the first comparison of the distyly supergene between diploid outcrossers and polyploid self-fertilizers. We discovered one pair of identical supergene alleles in the same subgenome, rather than one pair per subgenome, revealing the species originated via a cross between a self-compatible and a self-incompatible progenitor. Conformant to theory, the gene controlling female self-incompatibility and style length (CYPT) was considerably degenerated, because of relaxed selection over time, with the rest of the supergene largely unaffected.

evolutionary biology↗

bioRxiv2024

The Primula edelbergii S-locus is an example of a jumping supergene

Research on supergenes, non-recombining genomic regions housing tightly linked genes that control complex phenotypes, has gained prominence in genomics, with supergenes having been described in most eukaryotic lineages. Heterostyly, a floral heteromorphism promoting outcrossing in several angiosperm families, is controlled by the S-locus supergene. Historically, the S-locus has been studied primarily in closely related Primula species and, more recently, in other groups that independently evolved heterostyly. However, it remains unknown whether genetic architecture and composition of the S-locus are maintained among species that share a common origin of heterostyly and subsequently diverged across larger time scales. To address this research gap, we present a chromosome-scale genome assembly of Primula edelbergii, a species that shares the same origin of heterostyly with Primula veris (whose S-locus has been characterized) but diverged from it ca. 18 million years ago. Comparative genomic analyses between P. edelbergii and P. veris allowed us to show, for the first time, that the S-locus can jump (i.e. translocate) between chromosomes. Additionally, we found that four S-locus genes were maintained across time but were reshuffled within the supergene, seemingly without affecting their expression. Furthermore, we confirmed that S-locus hemizygosity counteracts genetic degeneration, otherwise expected in supergenes. Finally, we investigated P. edelbergii evolutionary history within Ericales in terms of whole genome duplications and transposable element accumulation. In summary, our work provides a valuable resource for comparative analyses aimed at investigating the genetics of heterostyly and the pivotal role of supergenes in shaping the evolution of complex phenotypes.

genomics↗

bioRxiv2022

BindingSiteAugmentedDTA: Enabling A Next-Generation Pipeline for Interpretable Prediction Models in Drug-Repurposing

While research into Drug-Target Interaction (DTI) prediction is fairly mature, generalizability and interpretability are not always addressed in the existing works in this field. In this paper, we propose a deep learning-based framework, called BindingSite-AugmentedDTA, which improves Drug-Target Affinity (DTA) predictions by reducing the search space of potential binding sites of the protein, thus making the binding affinity prediction more efficient and accurate. Our BindingSite-AugmentedDTA is highly generalizable as it can be integrated with any DL-based regression model, while it significantly improves their prediction performance. Also, unlike many existing models, our model is highly interpretable due to its architecture and self-attention mechanism, which can provide a deeper understanding of its underlying prediction mechanism by mapping attention weights back to protein binding sites. The computational results confirm that our framework can enhance the prediction performance of seven state-of-the-art DTA prediction algorithms in terms of 4 widely used evaluation metrics, including Concordance Index (CI), Mean Squared Error (MSE), modified squared correlation coefficient [Formula], and the Area Under the Precision Curve (AUPC). We also contribute to the two most commonly used DTA benchmark datasets, namely Kiba and Davis, by including additional information on 3D structure of all proteins contained in these two datasets. We manually extracted this information from Protein Data Bank (PDB) files of proteins available at https://www.uniprot.org/. Furthermore, we experimentally validate the practical potential of our proposed framework through in-lab experiments. We measure the binding interaction between several drug candidate compounds for the inhibition of binding between (SARS-CoV-2 S-protein RBD) Spike and ACE-2 (host cell binding target) proteins. We then compare the computationally-predicted results against the ones experimentally-observed in the laboratory. The relatively high agreement between computationally-predicted and experimentally-observed binding interactions supports the potential of our framework as the next-generation pipeline for prediction models in drug repurposing.

bioinformatics↗

bioRxiv2021

AttentionSiteDTI: Attention Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites

In this study, we introduce and implement an interpretable graph-based deep learning prediction model, which utilizes protein binding sites along with self-attention to learn which protein binding sites interact with a given ligand. Our proposed model enables interpretability by identifying the protein binding sites that contribute the most towards the Drug-Target Interaction. Results on three benchmark datasets show improved performance compared to previous graph-based models. More significantly, unlike previous studies our model performance remains close to the optimal performance when tested with new proteins (ie., high generalizablity). Through multidisciplinary collaboration, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict binding interaction of some candidate compounds with a target protein, then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally-predicted and experimentally-observed (measured) DTIs illustrates the potential of our method as an effective pre-screening tool in drug re-purposing applications.

bioinformatics↗