Biology subjects

Chater, J. M.

Publications and source records attributed to Chater, J. M..

5 recordsLinked to original sources

bioRxiv2026

Genome Assembly of the Green Revolution Wheat Cultivar Pavon 76 Establishes a Reference for CIMMYT-Derived Wheat

Common wheat (Triticum aestivum L.) is a cornerstone of global food security. However, the standard reference genome, IWGSC RefSeq v2.1, is derived from wheat cultivar Chinese Spring, a historically important lineage that is not representative of modern cultivated wheat. In contrast, Pavon 76 is a CIMMYT Green Revolution wheat cultivar grown worldwide and is present in the pedigrees of numerous elite wheats, as well as in the genetic background of over 600 cytogenetic stocks used in studies ranging from recombination to male sterility. As no Pavon 76 reference has been reported, interpretation of these Pavon 76-derived stocks has relied on the genetically distant IWGSC RefSeq v2.1. To address this gap, we generated PacBio HiFi reads and Hi-C read pairs to assemble Pavon 76s genome. The final assembly comprises the expected 21 chromosomes, spans 15 Gb, and has an N50 of 710 Mb. Annotation identified 167,201 genes, of which 89.5% were functionally annotated. Comparative sequence and gene synteny analysis between Pavon 76 and IWGSC RefSeq v2.1 revealed significant structural divergence, establishing that IWGSC RefSeq v2.1 is not fully representative of modern wheats. This robust Pavon 76 assembly establishes the first chromosome-scale de novo assembly of a CIMMYT-derived wheat and provides a foundation for future genetic and genomic studies.

genomics↗

bioRxiv2026

Combating citrus greening disease by simultaneously targeting the pathogen and callose-mediated phloem occlusion

Huanglongbing (HLB), caused by Candidatus Liberibacter asiaticus (CLas), disrupts sugar transport in citrus due to bacterial proliferation and excessive callose deposition that blocks sieve elements. Unblocking phloem may reduce symptoms, but it is unclear whether targeting CLas or suppressing callose deposition is more critical, as both contribute to phloem dysfunction. This study evaluated whether an integrated strategy targeting both CLas and callose-mediated phloem blockage could more effectively restore phloem transport, carbohydrate allocation, and productivity in HLB-affected citrus. Two complementary field experiments assessed the effects of an antibiotic, a callose inhibitor, and their combination on phloem recovery and source-sink dynamics. The first experiment focused on treatment-induced changes in carbon transport between source and sink shortly after delivery via stem infiltration, capturing immediate physiological responses. The second experiment assessed cumulative carbon allocation to determine sustained effects on assimilate transport and fruit development. Callose inhibitor treatment increased sugar transport but this immediate response did not yield lasting benefits. Antibiotic treatment enhanced carbon import into developing fruits, but these effects were not consistently supported by other physiological indicators. In contrast, the combined treatment reduced callose deposition, improved phloem conductivity and carbon allocation during high sink demand, decreased fruit abscission, and increased the total carbon accumulated in the fruits after physiological fruit drop. Collectively, these results demonstrate that impaired source-sink balance in HLB-affected citrus results from the combination of both pathogen load and host callose accumulation, supporting an integrated strategy that combines pathogen suppression with modulation of host phloem function to improve orchard productivity.

plant biology↗

bioRxiv2020

CancerMIRNome: a web server for interactive analysis and visualization of cancer miRNome data

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10,998 samples from 33 TCGA projects and 21,993 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

bioinformatics↗

bioRxiv2020

Extended Application of Genomic Selection to Screen Multi-Omics Data for Prognostic Signatures of Prostate Cancer

Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e., miRNAs) significantly increased outcome predictability when compared with panels consisting of small numbers of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multi-omics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multi-omics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer.

bioinformatics↗

bioRxiv2018

Inference of Chromosome-length Haplotypes Using Genomic Data of Three to Five Single Gametes

Knowledge of chromosome-length haplotypes will not only advance our understanding of the relationship between DNA and phenotypes, but also promote a variety of genetic applications. Here we present Hapi, an innovative method for chromosomal haplotype inference using only 3 to 5 gametes. Hapi outperformed all existing haploid-based phasing methods in terms of accuracy, reliability, and cost efficiency in both simulated and real gamete datasets. This highly cost-effective phasing method will make large-scale haplotype studies feasible to facilitate human disease studies and plant/animal breeding. In addition, Hapi can detect meiotic crossovers in gametes, which has promise in the diagnosis of abnormal recombination activity in human reproductive cells.

bioinformatics↗