Biology subjects

Hassani-Pak, K.

Publications and source records attributed to Hassani-Pak, K..

5 recordsLinked to original sources

bioRxiv2026

Comparative analysis of 15 chromosome-scale near T2T assemblies of Brazilian Fusarium graminearum isolates

Fusarium graminearum is the causative agent of Fusarium head blight of wheat, yet genomic resources for Brazilian populations, where it is the dominant species, remain limited to a single reference genome. We report fifteen near telomere-to-telomere (T2T) assemblies of Brazilian F. graminearum isolates generated with Oxford Nanopore sequencing. Each genome was assembled into four nuclear chromosomes, with 118 of 120 telomeres resolved, BUSCO completeness of 99.199.3%, and 14,80214,894 predicted genes per isolate. The genomes are structurally conserved, showing no chromosome-scale rearrangements and uniform transposable element content. Orthology assignment placed 90.4% of genes in the core genome. This suggests that the large phenotypic variance in aggressiveness observed between isolates likely resides in regulatory differences and/or within the small and largely uncharacterised accessory fraction. Ultimately, these assemblies provide a high-resolution resource for the global FHB community and a foundation for comparative analysis of a previously underrepresented population.

bioinformatics↗

bioRxiv2025

Revisiting the Central Dogma: the distinct roles of genome, methylation, transcription, and translation on protein expression in Arabidopsis thaliana

BackgroundWe investigated the flow of information from genome sequence to protein expression implied by the Central Dogma, to determine the impact of intermediate genomic levels in plants. ResultsWe performed genomic profiling of rosettes in two Arabidopsis accessions, Col-0 and Can-0, and assembled their genomes using long reads and chromatin interaction data. We measured gene and protein expression in biological replicates grown in a controlled environment, also measuring CpG methylation, ribosome-associated transcript levels and tRNA abundance. Each omic level is highly reproducible between biological replicates and between accessions despite their 0.5% sequence divergence; the single best predictor of any level in one accession is the corresponding level in the other. Within each accession, gene codon frequencies accurately model both mRNA and protein expression. The effects of a codon on mRNA and protein expression are highly correlated but are unrelated to genome-wide codon frequencies or to tRNA levels which instead match genome-wide amino acid frequencies. Ribosome-associated transcripts closely track mRNA levels. ConclusionsIn the absence of environmental perturbation, neither methylation, tRNA nor ribosome-associated transcript levels add appreciable information about constitutive protein abundance beyond that in DNA codon frequencies and mRNA expression levels. The impact of constitutive gbM is mostly explained by gene codon composition. tRNA abundance tracks overall amino acid demand. However, genetic differences between accessions associate with differential gbM by inflating differential expression variation. Our data show that the Central Dogma holds only if both sequence and abundance information in mRNA are considered.

plant biology↗

bioRxiv2020

KnetMiner - Intelligent search and visualisation of connected data to explain complex traits and diseases

Generating new ideas and scientific hypotheses is often the result of extensive literature and database reviews, overlaid with scientists own novel data and a creative process of making connections that were not made before. We have developed a comprehensive approach to guide this technically challenging data integration task and to make knowledge discovery and hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes of scientific literature and biological research to find and visualise links between the genetic and biological properties of complex traits and diseases. Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. We have developed KnetMiner knowledge graphs and applications for a range of species including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. KnetMiner is available at http://knetminer.org.

bioinformatics↗

bioRxiv2019

A co-expression network in hexaploid wheat reveals mostly balanced expression and lack of significant gene loss of homeologous meiotic genes upon polyploidization

Polyploidization has played an important role in plant evolution. However, upon polyploidization the process of meiosis must adapt to ensure the proper segregation of increased numbers of chromosomes to produce balanced gametes. It has been suggested that meiotic gene (MG) duplicates return to a single copy following whole genome duplication to stabilise the polyploid genome. Therefore, upon the polyploidization of wheat, a hexaploid species with three related (homeologous) genomes, the stabilization process may have involved rapid changes in content and expression of MGs on homeologous chromosomes (homeologs). To examine this hypothesis, sets of candidate MGs were identified in wheat using co-expression network analysis and orthology informed approaches. In total, 130 RNA-Seq samples from a range of tissues including wheat meiotic anthers were used to define co-expressed modules of genes. Three modules were significantly correlated with meiotic tissue samples but not with other tissue types. These modules were enriched for GO terms related to cell cycle, DNA replication and chromatin modification, and contained orthologs of known MGs. Overall 74.4 % of genes within these meiosis-related modules had three homeologous copies which was similar to other tissue-related modules. Amongst wheat MGs identified by orthology, rather than co-expression, the majority (93.7 %) were either retained in hexaploid wheat at the same number of copies (78.4 %) or increased in copy number (15.3%) compared to ancestral wheat species. Furthermore, genes within meiosis-related modules showed more balanced expression levels between homeologs than genes in non-meiosis-related modules. Taken together our results do not support extensive gene loss nor changes in homeolog expression of MGs upon wheat polyploidization. The construction of the MG co-expression network allowed identification of hub genes and provided key targets for future studies.\n\nAuthor summaryAll flowering plants have undergone a polyploidization event(s) during their evolutionary history. One of the biggest challenges faced by a newly-formed polyploid is meiosis, an essential event for sexual reproduction and fertility. This process must adapt to discriminate between multiple related chromosomes and to ensure their proper segregation to produce fertile gametes. The meiotic mechanisms responsible for the stabilisation of the extant polyploids remain poorly understood except in wheat, where there is now a better understanding of these processes. It has been proposed that meiotic adaptation in established polyploids could involve meiotic gene loss following the event of polyploidization. To test this hypothesis in hexaploid wheat, we have computationally predicted sets of hexaploid wheat meiotic genes based on expression data from different tissue types, including meiotic anther tissue, and orthology informed approaches. We have calculated homeolog expression patterns and number of gene copies for the predicted meiotic genes and compared them with proper control gene sets. Our findings did not support any significant meiotic gene loss upon wheat polyploidization. Furthermore, wheat meiotic genes showed more balanced expression levels between homeologs than non-meiotic genes.

bioinformatics↗

bioRxiv2019

Validation and characterisation of a wheat GENIE3 network using an independent RNA-Seq dataset

Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently, as part of an analysis of the RefSeqv1.0 wheat transcriptome, a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we have used an independent and publicly-available RNA-Seq dataset to validate the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-analysed the RNA-Seq data against the RefSeqv1.0 genome and identified a de novo set of differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of NAM-A1 overlap significantly with the de novo DEGs, more than would be expected for a random transcription factor. Based on high levels of overlap between GENIE3-predicted target genes and the de novo DEGs, we also identified a set of candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and homoeolog expression levels and found that only homoeologous transcription factors are likely to share predicted targets in common. However, homoeologs in dynamic triads, i.e. with higher variation in homoeolog expression levels across tissues, are less likely to share predicted targets than stable triads. This suggests that homoeologs in dynamic triads are more likely to act on distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies.

plant biology↗