Welcome to the Maloof Lab
Whole genome sequence of synthetically derived Brassica napus inbred cultivar Da-Ae.
Davis JT et al., G3 (Bethesda) (2023)
Recruiting grad students (and soon a postdoc) to a new NSF funded project. If interested click on links below.
Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development.
Baker RL et al., PLoS Genet (2019)
Maloof and Harmer Labs 2016 Summer Party
A New Advanced Backcross Tomato Population Enables High Resolution Leaf QTL Mapping and Gene Identification.
Fulop D et al., G3 (Bethesda) (2016)
Structured Light-Based 3D Reconstruction System for Plants.
Nguyen TT et al., Sensors (Basel) (2015)
Shade avoidance components and pathways in adult plants revealed by phenotypic profiling.
Nozue K et al., PLoS Genet (2015)
2015 Undergraduate Research Conference
Polymorphism identification and improved genome annotation of Brassica rapa through Deep RNA sequencing.
Devisetty UK et al., G3 (Bethesda) (2014)
Comparative transcriptomics reveals patterns of selection in domesticated and wild tomato.
Koenig D et al., Proc Natl Acad Sci U S A (2013)
Brassica napus, a globally important oilseed crop, is an allotetraploid hybrid species with two subgenomes originating from B. rapa and B. oleracea. The presence of two highly similar subgenomes has made the assembly of a complete draft genome challenging and has also resulted in natural homoeologous exchanges between the genomes, resulting in variations in gene copy number, which further complicates assigning sequences to correct chromosomes. Despite these challenges, high quality draft genomes of this species have been released. Using third generation sequencing and assembly technologies, we generated a new genome assembly for the synthetic Brassica napus cultivar Da-Ae. Through the use of long reads, linked-reads, and Hi-C proximity data, we assembled a new draft genome that provides a high quality reference genome of a synthetic Brassica napus. In addition, we identified potential hotspots of homoeologous exchange between subgenomes within Da-Ae, based on their presence in other independently-derived lines. The occurrence of these hotspots may provide insight into the genetic rearrangements required for B. napus to be viable following the hybridization of B. rapa and B. oleracea.
Biodiversity is critical for the health of ecosystems, our biosphere, and humankind. However, biodiversity is threatened by habitat loss and climate change, which has resulted in the acceleration of species extinctions across the world. Within species, the extinction or persistence of populations determines whether a species will survive at all. Thus, understanding the factors that contribute to population persistence or extinction is critical for predicting future population dynamics and managing biodiversity in a changing world. Population persistence is determined by genetic composition, ecological habitat, environmental stresses, and interactions among these factors. This project will integrate advances in genomics, remote sensing, and statistical modeling to develop new predictive models of population persistence and extinction. The models created in this project will be critical for understanding population dynamics, predicting responses to future change, and providing tools to direct the implementation of genetically-informed conservation strategies.
Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use hierarchical Bayesian Function-Valued Trait (FVT) models to estimate Brassica rapa growth curves throughout ontogeny, across two treatments, and in two growing seasons. We find genetic variation for plasticity of growth rates and final sizes, but not the inflection point (transition from accelerating to decelerating growth) of growth curves. There are trade-offs between growth rate and duration, indicating that selection for maximum yields at early harvest dates may come at the expense of late harvest yields and vice versa. We generate eigengene modules and determine which are co-expressed with FVT traits using a Weighted Gene Co-expression Analysis. Independently, we seed a Mutual Rank co-expression network model with FVT traits to identify specific genes and gene networks related to FVT. GO-analyses of eigengene modules indicate roles for actin/cytoskeletal genes, herbivore resistance/wounding responses, and cell division, while MR networks demonstrate a close association between metabolic regulation and plant growth. We determine that combining FVT Quantitative Trait Loci (QTL) and MR genes/WGCNA eigengene expression profiles better characterizes phenotypic variation than any single data type (i.e. QTL, gene, or eigengene alone). Our network analysis allows us to employ a targeted eQTL analysis, which we use to identify regulatory hotspots for FVT. We examine cis vs. trans eQTL that mechanistically link FVT QTL with structural trait variation. Colocalization of FVT, gene, and eigengene eQTL provide strong evidence for candidate genes influencing plant height. The study is the first to explore eQTL for FVT, and specifically do so in agroecologically relevant field settings.
Also a goodbye party for Dan Fulop, Amanda Schrager-Lavelle and Mike Covington
Quantitative Trait Locus (QTL) mapping is a powerful technique for dissecting the genetic basis of traits and species differences. Established tomato mapping populations between domesticated tomato (Solanum lycopersicum) and its more distant interfertile relatives typically follow a near isogenic line (NIL) design, such as the Solanum pennellii Introgression Line (IL) population, with a single wild introgression per line in an otherwise domesticated genetic background. Here we report on a new advanced backcross QTL mapping resource for tomato, derived from a cross between the M82 tomato cultivar and S. pennelli This so-called Backcrossed Inbred Line (BIL) population is comprised of a mix of BC2 and BC3 lines, with domesticated tomato as the recurrent parent. The BIL population is complementary to the existing S. pennellii IL population, with which it shares parents. Using the BILs we mapped traits for leaf complexity, leaflet shape, and flowering time. We demonstrate the utility of the BILs for fine-mapping QTL, particularly QTL initially mapped in the ILs, by fine-mapping several QTL to single or few candidate genes. Moreover, we confirm the value of a backcrossed population with multiple introgressions per line, such as the BILs, for epistatic QTL mapping. Our work was further enabled by the development of our own statistical inference and visualization tools, namely a heterogeneous Hidden Markov Model for genotyping the lines, and by using state of the art sparse regression techniques for QTL mapping.
Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance.
Shade from neighboring plants limits light for photosynthesis; as a consequence, plants have a variety of strategies to avoid canopy shade and compete with their neighbors for light. Collectively the response to foliar shade is called the shade avoidance syndrome (SAS). The SAS includes elongation of a variety of organs, acceleration of flowering time, and additional physiological responses, which are seen throughout the plant life cycle. However, current mechanistic knowledge is mainly limited to shade-induced elongation of seedlings. Here we use phenotypic profiling of seedling, leaf, and flowering time traits to untangle complex SAS networks. We used over-representation analysis (ORA) of shade-responsive genes, combined with previous annotation, to logically select 59 known and candidate novel mutants for phenotyping. Our analysis reveals shared and separate pathways for each shade avoidance response. In particular, auxin pathway components were required for shade avoidance responses in hypocotyl, petiole, and flowering time, whereas jasmonic acid pathway components were only required for petiole and flowering time responses. Our phenotypic profiling allowed discovery of seventeen novel shade avoidance mutants. Our results demonstrate that logical selection of mutants increased success of phenotypic profiling to dissect complex traits and discover novel components.
Laksmhidevi, Christina, and Amanjot present their work. Nice Job!
The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes-R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)-using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/.
Although applied over extremely short timescales, artificial selection has dramatically altered the form, physiology, and life history of cultivated plants. We have used RNAseq to define both gene sequence and expression divergence between cultivated tomato and five related wild species. Based on sequence differences, we detect footprints of positive selection in over 50 genes. We also document thousands of shifts in gene-expression level, many of which resulted from changes in selection pressure. These rapidly evolving genes are commonly associated with environmental response and stress tolerance. The importance of environmental inputs during evolution of gene expression is further highlighted by large-scale alteration of the light response coexpression network between wild and cultivated accessions. Human manipulation of the genome has heavily impacted the tomato transcriptome through directed admixture and by indirectly favoring nonsynonymous over synonymous substitutions. Taken together, our results shed light on the pervasive effects artificial and natural selection have had on the transcriptomes of tomato and its wild relatives.
The Maloof Lab is in the Department of Plant Biology at the University of California, Davis.
Because plants are rooted in place they have a remarkable ability to withstand a wide range of environmental conditions. They do so by altering their growth, development and physiology to suit their current environment. We are interested in determining the genes and mechanisms underlying this fascinating phenotypic plasticity and how the genetic pathways have changed over time to allow adaptation to different environments.
To achieve these goals we use molecular and quantitative genetics and genomics techniques coupled with bioinformatics and statistical analyses. We work with the Streptanthus species complex, Brassica rapa, Tomato, and Arabidopsis.
Adaptation to climate niches
How do plants adapt to different climates? Plants undergo critical developmental transitions that must be matched to their local environment to enable success. For example, germination must occur when environmental conditions are favorable for seedling establishment and growth. Flowering must occur when pollinators are present and when conditions can support resource investment into seed. The proper timing for these events varies widely in different climates. Perennials growing in the foothills of California’s Sierra Nevada must cope with significant summer heat and drought and therefore often germinate in the fall, grow in the winter, and flower in the spring. This strategy does work not for plants at higher elevation where many feet of snow cover the ground from November to May or June. In collaboration with Jenny Gremer, Sharon Strauss, and Johanna Schmitt we are studying the environmental cues that plants in the Streptanthus clade use to determine germination and reproductive timing, how the use of these cues varies in populations and species adapted to different climates, and the evolution of genetic networks associated with these changes.
Predicting population persistence or extinction
Biodiversity is critical for the health of ecosystems, our biosphere, and humankind. However, biodiversity is threatened by habitat loss and climate change, which has resulted in the acceleration of species extinctions across the world. The ability to predict how the genetic composition of populations impacts their long-term persistence or extinction in different and changing environments requires integrating analysis techniques and data across diverse fields. In collaboration with Jenny Gremer, Troy Magney, and Denneal Jamison, we are developing integrative demographic models that incorporate genetic, physiological, and life history traits to improve multi-generational predictions of population persistence and extinction for Streptanthus tortuosus (Mountain Jewelflower). The resulting integrative demography models will provide a road map for conservation biologists and managers to use genomic information to predict effects of different conservation strategies, such as assisted migration or introducing genetic variation, which can be applied across wild, managed, and agricultural species and populations.
How plants sense and respond to their neighbors
Light is essential for plant growth. Perhaps as a consequence, plants have an intricate set of photoreceptors and responses that they use to optimize their development and physiology to suit their light environment. We study the downstream mechanisms underlying these responses and how plants have evolved differences in their light perception and responses that allow them to thrive in different environments. We are interested in both the genetic and molecular basis of variation in light response as well as the adaptive consequences. A combination of molecular and quantitative genetics and genomics is used in Arabidopsis, Tomato, and Brassica