Biology subjects

Pinheiro, J. B.

Publications and source records attributed to Pinheiro, J. B..

7 recordsLinked to original sources

bioRxiv2025

GENETIC, MORPHOLOGICAL, AND PHYTOCHEMICAL DIFFERENTIATION OF CARNAVAL - A NEW HOP CULTIVAR ADAPTED TO TROPICAL ENVIRONMENT

AO_SCPLOWBSTRACTC_SCPLOWThe global hops market generates approximately US$8 billion annually, primarily driven by beer production, the worlds most consumed beverage. Despite this global demand, hop cultivation remains concentrated in temperate regions. Brazil, the third-largest beer consumer, imports over 99% of its hops. This study aimed to technically validate a new tropicalized hop cultivar, "Carnaval," developed over six years of breeding and genetic improvement to adapt the crop to Brazilian conditions. A comparative analysis was conducted between Carnaval and two commercial varieties, Comet and Cascade, evaluating morphological traits, production potential, phytochemical composition via GC-MS, and genotyping using 10,541 SNP markers obtained through dd-GBS on the Illumina(R) NextSeq 1000/2000 platform. Carnaval exhibited clear genotypic and phenotypic distinctiveness, with notable differences in cone, leaf, and branch morphology, as well as in alpha (14.0%) and beta (6.9%) acid content and essential oil concentration (0.61%), comprising over 35 components--mainly myrcene, -humulene, selinene, and caryophyllene. Productivity reached 478 kg/ha, comparable to commercial cultivars. Genetic analyses revealed high diversity (HS = 0.35), and population structure showed Carnaval grouping with its female parent Zeus but with distinct genomic characteristics. These findings confirm Carnavals unique identity and its adaptation to tropical conditions, making it a promising alternative for domestic hop production in Brazil.

plant biology↗

bioRxiv2025

Optimizing Soybean Breeding: High-Throughput Phenotyping Technologies for Stink Bug Resistance and High Yields

The stink bug complex is a major agricultural pest for soybean crops, significantly reducing productivity. Genetic resistance is the most effective control strategy, but its quantitative nature and labor-intensive phenotyping make its implementation in breeding programs challenging. This study explored high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with RGB cameras to evaluate a soybean population and identify stink bug resistance by correlating image-derived features and machine learning (ML) models. Using an alpha-lattice design with three replications, we phenotyped 304 soybean lines over two seasons under natural stink bug infestations. We manually evaluated five traits associated with stink bug resistance and correlated them with color, texture, and histogram features from aerial images. Three ML models--AdaBoost, SVM, and MLP-- were tested to predict these traits. VIs, especially the Visible Atmospherically Resistant Index at the first percentile (VARI_P25) and texture-based indices at 45{degrees} and 135{degrees}, effectively predicted traits in stressed environments, particularly during flights near maturation. While ML models showed good predictive ability for yield, healthy seed weight, and maturity, they were less effective for stink bug resistance. Increasing the number of UAV flights modestly improved predictive accuracy, though predicting traits across different seasons remained challenging. Despite this, indices like VARI_25P were valuable for screening and excluding less promising genotypes, optimizing breeding program resources. This pioneering work offers valuable insights and highlights the need for further research to optimize resistance selection, promising significant advances in soybean breeding for stink bug resistance.

genetics↗

bioRxiv2025

First chromosome scale genome of Acrocomia aculeata

The Arecaceae family comprises economically, and ecologically significant palm species widely distributed across tropical and subtropical regions. Among them, Acrocomia aculeata, commonly known as Macauba, has gained attention due to its high oil yield, environmental adaptability, and potential applications in sustainable agriculture and bioenergy production. This study presents the first chromosome scale genome assembly of A. aculeata and the first reference genome of the genus, using Oxford Nanopore Technologies sequencing, PacBio HiFi and Hi-C proximity ligation data. The genome covers 1.94 Gbp in the 15 pseudochromosomes, with N50 of 143.43 Mbp, achieving a base quality (QV) of 76.4 and a k-mer completeness of 90.58% of PacBio HiFi data, with k=21. Interspersed repetitive regions represent 77.36% of the total genome, with long terminal repeat (LTRs) accounting for 54.98%. A total of 28.215 protein-coding genes were predicted. The genome was validated with BUSCO, achieving a completeness of 98.9% for predicted proteins. The first chromosome scale genome of Macauba provide new insights and breakthroughs for studies of conservation genomics and plant breeding programs.

genomics↗

bioRxiv2024

Genetic variations associated with adaptation processes in Acrocomia palms: A comparative study across the Neotropic for future crop improvement

Population genetic research has evolved, focusing on selection processes using single nucleotide polymorphisms (SNP) genotyping techniques to study crop traits and domestication. This study explores the adaptation process of three neotropical palms of Acrocomia, a genus that has high potential for oil extraction. Our research focuses on their genetic structure, evolutionary significance, and implications of the selection signatures for breeding efforts. We employed Genotyping-by-Sequencing (GBS) with a focus on outlier SNP markers for identifying adaptive genetic processes in A. aculeata, A. totai, and A. intumescens across the Neotropic. Our results reveal two major gene pools in A. aculeata, a North American and a South American group mainly influenced by dispersal and biogeographic barriers, with putative selective signatures linked to fatty acid and carotenoid biosynthesis, pathogen resistance, and environmental stress adaptation. A. totai presented a pronounced genetic structure, with SNPs under selection indicating a recent diversification driven by climatic and geological factors, especially in the Pantanal biome. A. intumescens displays genetic structuring shaped by the endemic process of biogeographic barriers within the Caatinga biome, with potential shared ancestry with A. aculeata. Correlations between allele frequencies and climatic variables highlight adaptation to diverse environments, principally semi-arid, with the annual mean temperature being one of the most influential. Candidate genes associated with fatty acid and carotenoid biosynthesis, as well as pathogen resistance and drought tolerance, indicate targets for domestication. Despite challenges in reduced representation sequencing, this study underlines the potential of Acrocomia as a novel crop, offering prospects in oil production, biofuels, and sustainable agriculture. Future efforts should prioritize whole-genome sequencing and genotype-environment interaction studies to realize the full potential of Acrocomia in sustainable development and renewable energy production.

genomics↗

bioRxiv2024

Genotyping-By-Sequencing and DNA array for genomic prediction in soybean oil composition

Soybean oil is intended for various purposes, such as cooking oil and biodiesel. The oil composition changes the shelf life, palatability, and how healthy this oil is for the human diet. Genomic selection jointly uses these traits, phenotypes, and markers from one of the available genotyping platforms to increase genetic gain over time. This study aims to evaluate the impact of different genotyping platforms, DNA arrays, and Genotyping-by-Sequencing (GBS) on genomic selection in relation to the composition of fatty acids in soybean oil and total oil content. We used different quality control parameters, such as heterozygote rate, minor allele frequency, and missing data rate in ten combinations, and two prediction models, BayesB and BRR. To compare the impact of the genotyping approaches, we calculated the principal components analysis from the kinship matrices, the SNP density, and the traits prediction accuracies for each approach. Principal component analysis showed that the DNA array explained better the population genetic architecture. On the other hand, prediction accuracies varied between the different genotyping platforms and only GBS was affected under different quality control parameters. Although the DNA array has important and well-studied polymorphisms for soybeans and is stable, it also has ascertainment bias. GBS, although not stable and requires more robust quality control, can discover alleles specific to the population under study. As soybean oil is used for different functions and the fatty acid profiles are different for each objective, the work constitutes a critical study and direction for improving the composition of soybean oil.

genetics↗

bioRxiv2022

A novel image-based approach for soybean seed phenotyping using machine learning techniques

Soybean is one of the most important sources of protein and vegetable oil in the world. Given its increasing demand, the increment in yield has only been possible due to investments in research and production technology, especially in breeding programs. One of the main factors influencing soybean yield is the seed morphology; however, its analyses are hampered by the lack of efficient computational approaches with not only accurate results, but also a high flexibility to user preferences and needs. In this context, the present work provides a methodological framework for: (i) seed segmentation in soybean images; (ii) seed morphological evaluation; and (iii) image-based prediction of the hundred-seed weight trait. We used genotypes from a partial diallel cross design, which aimed at obtaining genotypes with high agronomic performance. In addition to the measurement of the hundred-seed weight, we collected RGB images of seeds of each plot. For image segmentation, we created an in-house image processing pipeline, which enabled a full morphological seed evaluation. For predicting the hundred-seed weight, we compared different machine learning algorithms using as input the morphological characteristics obtained, and also features from state-of-the-art convolutional neural network (CNN) architectures. The image segmentation methodology showed to be highly efficient, as more than 98% of the seeds in the images were correctly identified. Even if the seeds were close, the segmentation strategy could separate them into independent image components. In addition to supplying a highly accurate decision support system for soybean breeders, we verified the morphological phenotyping adaptability in other plant species, fully assessing the pipeline generalization. We consider the use of this methodology highly advantageous, as the method is entirely based on widely used morphological operations, which results in an easy implementation and low computational costs. Using these morphological measures, we could estimate machine learning models for predicting the hundred-seed weight, achieving considerable predictive accuracy. The same results were observed for CNN-obtained features, showing the efficiency of the morphological measurements as feature extractors. The possibility of obtaining seed morphological characteristics provides a valuable tool for the continuous and efficient development of new soybean cultivars in breeding programs aimed at long-term genetic gain. Additionally, through a faster seed image acquisition workflow, with less chance of errors and low cost, it is also possible to make predictions of important soybean characteristics. The work conducted has the potential to help future research and the industry to develop automated phenotyping tools, incorporating the proposed analytical workflows.

plant biology↗

bioRxiv2021

Selective signatures and high genome-wide diversity in traditional Brazilian manioc (Manihot esculenta Crantz) varieties

Knowledge about crops genetic diversity is essential to promote effective use and conservation of their genetic resources, because genetic diversity enables farmers to adapt their crops to specific needs and is the raw material for breeding efforts. Currently, manioc (Manihot esculenta ssp. esculenta) is one of the most important food crops in the world and has the potential to help achieve food security in the context of on-going climate changes. In this study we assessed the patterns of genome-wide diversity of traditional Brazilian manioc varieties conserved in the gene bank of the Luiz de Queiroz College of Agriculture, University of Sao Paulo. We used single nucleotide polymorphisms to evaluate the organization of genetic diversity and to identify selective signatures contrasting varieties from different biomes with samples of maniocs wild relative M. esculenta ssp. flabellifolia. We identified signatures of selection putatively associated with resistance genes, plant development and response to abiotic stresses. This presumed adaptive variation might have been important for the initial domestication and for the crops diversification in response to cultivation in different environments. The neutral variation revealed high levels of genetic diversity within groups of varieties from different biomes and low to moderate genetic divergence among biomes. These results reflect the complexity of maniocs biology and its evolutionary dynamics under traditional cultivation. Our results exemplify how the smallholder practices contribute to the conservation of maniocs genetic resources, maintaining variation of potential adaptive significance and high levels of neutral genetic diversity.

genetics↗