Citrus fruits are valued not only for their nutritional benefits but also for their rich phytochemical content. Metabolomics has emerged as a comprehensive technique for assessing the chemical composition of fruits. The botanical connection between leaves, flowers, and fruits is reflected in both their structure and chemical composition, particularly in the flow of nutrients between plant organs. We introduced a new logarithm ratio-based approach to compare metabolite profiles between fruits and leaves. We hypothesize that this method allows for the analysis of multiple citrus metabolomic profiles to reveal known and novel correlation patterns, reflecting the dynamic connections between metabolic sources. To test this hypothesis, we leveraged comprehensive leaf metabolomic profiles from over 200 accessions in the Givaudan Citrus Variety Collection and reviewed published metabolomics data for fruits and juices of matching citrus types. By employing logarithm-transformed metabolic ratios within each dataset, we accounted for systematic differences across metabolomic platforms, achieving an unbiased analysis.

Photosynthesis-related traits, encompassing aspects such as absorbance, chlorophyll content, fluorescence, quenching ratio of incoming light, and photochemical efficiency, are pivotal characteristics directly related to photosynthesis carbon gain with important implications for overall tree physiology and fruit production. In this study, we conducted an examination of 71 citrus accessions and varieties, utilizing over 56,000 single nucleotide polymorphisms and 37 leaf reflectance parameters. Pairwise correlation analysis revealed clusters of interrelated traits organized into four distinct groups. The observed trait variations were found to have associations with the citrus phylogeny. Through genome-wide association studies, we identified a total of 125 genomic loci and 189 potentially associated genes responsible for the observed trait variations. The potential network and biological pathways influencing the variability of these biophysical variables within the citrus collection are also discussed.

This study utilized the Fourier-based method to analyze the morphology of over 4000 leaves from more than 190 accessions selected from the Givaudan Citrus Variety Collection at the University of California Riverside, one of the world’s most diverse collections of citrus and closely related genera. Our analysis revealed significant variations in leaf morphology among the major citrus species groups, and hybrid varieties produced through breeding exhibited intermediate leaf morphology compared to their parental citrus species. We found a positive correlation between leaf area in native citrus species and temperature in lower tropical latitudes, while negative/positive associations between aspect ratio and temperature/rainfall were also observed, respectively. These results suggest that citrus leaves may have evolved into larger but thinner leaves to increase their photosynthetic capacity per unit area while maintaining water balance by reducing water loss through transpiration. Our analysis also indicates that the existing biodiversity observed in citrus species can be attributed to their migrations across the foothills of the Himalayas, southward to the islands of Indonesia, and northward to the islands of Japan. Our study supports the hypothesis that citrus species have adapted to warm areas lacking extreme daily and nightly temperatures, where a large number of species of origin are found. Overall, this study presents a promising approach to investigate the morphological variation in citrus leaves, which could potentially aid in the selection and breeding of citrus cultivars with superior physiological traits and deepen our understanding of citrus diversity.

Abstract Premise The measurement of leaf morphometric parameters from digital images can be time‐consuming or restrictive when using digital image analysis softwares. The Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high‐throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification. Methods and Results MuLES uses contrasting pixel color values to distinguish between leaf objects and their background area, eliminating the need for color threshold–based methods or color correction cards typically required in other software methods. The leaf morphometric parameters measured by this software, especially leaf aspect ratio, were able to distinguish between large populations of different accessions for the same species in a high‐throughput manner. Conclusions MuLES provides a simple method for the rapid measurement of leaf morphometric parameters in large plant populations from digital images and demonstrates the ability of leaf aspect ratio to distinguish between closely related plant types.

Our recent algorithm, Hapi , infers chromosome-scale haplotypes using genomic data of a small number of single gametes. Its advanced version, IIIandMe , is proposed here to achieve comparable phasing accuracy with as few as three gametes, pushing the analysis to its limit. The new method is validated with simulation and a citrus gamete dataset. The rapid advances in genotyping technologies promise a broad application of IIIandMe in disclosing important genetic information.

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 10 554 samples from 33 TCGA projects and 28 633 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.

ABSTRACT MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising biomarkers for a variety of human diseases, including 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 diagnostic biomarkers for early cancer detection. The vast amount of miRNA expression data from tens of thousands of samples in various types of cancers generated by The Cancer Genome Atlas (TCGA) and circulating miRNA data produced by many large-scale circulating miRNA profiling studies provide extraordinary opportunities for the discovery and validation of miRNA signatures in cancer. Novel and user-friendly tools are desperately needed to facilitate the data mining of such valuable cancer miRNome datasets. To fill this void, we developed CancerMIRNome, a web server for interactive analysis and visualization of cancer miRNome data based on TCGA and public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and functions have been packaged in CancerMIRNome, allowing for a pan-cancer analysis of a miRNA of interest across multiple cancer types and a comprehensive analysis of cancer miRNome at the dataset level. The CancerMIRNome web server is freely available at http://bioinfo.jialab-ucr.org/CancerMIRNome.