Malachy T. Campbell

Introgression of Novel Traits from a Wild Wheat Relative Improves Drought Adaptation in Wheat

Agropyron elongatum introgression into bread wheat (Triticum aestivum) improves root traits for drought adaptation. Root architecture traits are an important component for improving water stress adaptation. However, selection for aboveground traits under favorable environments in modern cultivars may have led to an inadvertent loss of genes and novel alleles beneficial for adapting to environments with limited water. In this study, we elucidate the physiological and molecular consequences of introgressing an alien chromosome segment (7DL) from a wild wheat relative species (Agropyron elongatum) into cultivated wheat (Triticum aestivum). The wheat translocation line had improved water stress adaptation and higher root and shoot biomass compared with the control genotypes, which showed significant drops in root and shoot biomass during stress. Enhanced access to water due to higher root biomass enabled the translocation line to maintain more favorable gas-exchange and carbon assimilation levels relative to the wild-type wheat genotypes during water stress. Transcriptome analysis identified candidate genes associated with root development. Two of these candidate genes mapped to the site of translocation on chromosome 7DL based on single-feature polymorphism analysis. A brassinosteroid signaling pathway was predicted to be involved in the novel root responses observed in the A. elongatum translocation line, based on the coexpression-based gene network generated by seeding the network with the candidate genes. We present an effective and highly integrated approach that combines root phenotyping, whole-plant physiology, and functional genomics to discover novel root traits and the underlying genes from a wild related species to improve drought adaptation in cultivated wheat.

Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice

The genetic basis of dynamic salinity stress responses is elucidated using image-based phenomics and functional association analysis. Salinity affects a significant portion of arable land and is particularly detrimental for irrigated agriculture, which provides one-third of the global food supply. Rice (Oryza sativa), the most important food crop, is salt sensitive. The genetic resources for salt tolerance in rice germplasm exist but are underutilized due to the difficulty in capturing the dynamic nature of physiological responses to salt stress. The genetic basis of these physiological responses is predicted to be polygenic. In an effort to address this challenge, we generated temporal imaging data from 378 diverse rice genotypes across 14 d of 90 mm NaCl stress and developed a statistical model to assess the genetic architecture of dynamic salinity-induced growth responses in rice germplasm. A genomic region on chromosome 3 was strongly associated with the early growth response and was captured using visible range imaging. Fluorescence imaging identified four genomic regions linked to salinity-induced fluorescence responses. A region on chromosome 1 regulates both the fluorescence shift indicative of the longer term ionic stress and the early growth rate decline during salinity stress. We present, to our knowledge, a new approach to capture the dynamic plant responses to its environment and elucidate the genetic basis of these responses using a longitudinal genome-wide association model.

Image Harvest: an open-source platform for high-throughput plant image processing and analysis

Highlight Image Harvest is an open-source software for high-throughput image processing and analysis that is integrated with the Open Science Grid and provides computational resources to process large image datasets.

Genetic and Molecular Characterization of Submergence Response Identifies Subtol6 as a Major Submergence Tolerance Locus in Maize

Maize is highly sensitive to short term flooding and submergence. Early season flooding reduces germination, survival and growth rate of maize seedlings. We aimed to discover genetic variation for submergence tolerance in maize and elucidate the genetic basis of submergence tolerance through transcriptional profiling and linkage analysis of contrasting genotypes. A diverse set of maize nested association mapping (NAM) founder lines were screened, and two highly tolerant (Mo18W and M162W) and sensitive (B97 and B73) genotypes were identified. Tolerant lines exhibited delayed senescence and lower oxidative stress levels compared to sensitive lines. Transcriptome analysis was performed on these inbreds to provide genome level insights into the molecular responses to submergence. Tolerant lines had higher transcript abundance of several fermentation-related genes and an unannotated Pyrophosphate-Dependent Fructose-6-Phosphate 1-Phosphotransferase gene during submergence. A coexpression network enriched for CBF (C-REPEAT/DRE BINDING FACTOR: C-REPEAT/DRE BINDING FACTOR) genes, was induced by submergence in all four inbreds, but was more activated in the tolerant Mo18W. A recombinant inbred line (RIL) population derived from Mo18W and B73 was screened for submergence tolerance. A major QTL named Subtol6 was mapped to chromosome 6 that explains 22% of the phenotypic variation within the RIL population. We identified two candidate genes (HEMOGLOBIN2 and RAV1) underlying Subtol6 based on contrasting expression patterns observed in B73 and Mo18W. Sources of tolerance identified in this study (Subtol6) can be useful to increase survival rate during flooding events that are predicted to increase in frequency with climate change.

Allelic variants of OsHKT1;1 underlie the divergence between indica and japonica subspecies of rice (Oryza sativa) for root sodium content

Salinity is a major factor limiting crop productivity. Rice (Oryza sativa), a staple crop for the majority of the world, is highly sensitive to salinity stress. To discover novel sources of genetic variation for salt tolerance-related traits in rice, we screened 390 diverse accessions under 14 days of moderate (9 dS·m-1) salinity. In this study, shoot growth responses to moderate levels of salinity were independent of tissue Na+ content. A significant difference in root Na+ content was observed between the major subpopulations of rice, with indica accessions displaying higher root Na+ and japonica accessions exhibiting lower root Na+ content. The genetic basis of the observed variation in phenotypes was elucidated through genome-wide association (GWA). The strongest associations were identified for root Na+:K+ ratio and root Na+ content in a region spanning ~575 Kb on chromosome 4, named Root Na+ Content 4 (RNC4). Two Na+ transporters, HKT1;1 and HKT1;4 were identified as candidates for RNC4. Reduced expression of both HKT1;1 and HKT1;4 through RNA interference indicated that HKT1;1 regulates shoot and root Na+ content, and is likely the causal gene underlying RNC4. Three non-synonymous mutations within HKT1;1 were present at higher frequency in the indica subpopulation. When expressed in Xenopus oocytes the indica-predominant isoform exhibited higher inward (negative) currents and a less negative voltage threshold of inward rectifying current activation compared to the japonica-predominant isoform. The introduction of a 4.5kb fragment containing the HKT1;1 promoter and CDS from an indica variety into a japonica background, resulted in a phenotype similar to the indica subpopulation, with higher root Na+ and Na+:K+. This study provides evidence that HKT1;1 regulates root Na+ content, and underlies the divergence in root Na+ content between the two major subspecies in rice.

A comprehensive image-based phenomic analysis reveals the complex genetic architecture of shoot growth dynamics in rice (Oryza sativa)

Functional mapping uncovers the genetic architecture of shoot growth dynamics. Gibberellic acid is an underlying component for natural variation for shoot growth dynamics in rice. Genomic prediction is effective for improving early growth dynamics.

A Comparison of a Campus Cluster and Open Science Grid Platforms for Protein-Guided Assembly Using Pegasus Workflow Management System

Scientific workflows are a useful tool for managing large and complex computational tasks. Due to its intensive resource requirements, the scientific workflows are often executed on distributed platforms, including campus clusters, grids and clouds. In this paper we build a scientific workflow for blast2cap3, the protein-guided assembly, using the Pegasus Workflow Management System (Pegasus WMS). The modularity of blast2cap3 allows us to decompose the existing serial approach on multiple tasks, some of which can be run in parallel. Afterwards, this workflow is deployed on two distributed execution platforms: Sandhills, the University of Nebraska Campus Cluster, and the Open Science Grid (OSG). We compare and evaluate the performance of the built workflow for the both platforms. Furthermore, we also investigate the influence of the number of clusters of transcripts in the blast2cap3 workflow over the total running time. The performed experiments show that the Pegasus WMS implementation of blast2cap3 significantly reduces the running time compared to the current serial implementation of blast2cap3 for more than 95 %. Although OSG provides more computational resources than Sandhills, our workflow experimental runs have better running time on Sandhills. Moreover, the selection of 300 clusters of transcripts gives the optimum performance with the resources allocated from Sandhills.

Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes

With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multitrait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.

Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits

Understanding the genetic basis of dynamic plant phenotypes has largely been limited due to lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image-based phenotyping platforms has provided the plant science researchers with an effective means to non-destructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g. genome-wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits, and provide a robust framework for modeling traits trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice. Here, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over a conventional single time point analyses for discovering loci associated with shoot growth trajectories. This RR approach uncovers persistent, as well as time-specific, transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements.

Using LASSO in gene co-expression network for genome-wide identification of gene interactions responding to salt stress in rice

In many applications, such as gene co-expression network analyses, data arises with a huge number of covariates while the size of sample is comparatively small. To improve the accuracy of prediction, variable selection is often used to get a sparse solution by forcing coefficients of variables contributing less to the observed response variable to zero. Various algorithms were developed for variable selection, but LASSO is well known for its statistical accuracy, computational feasibility and broad applicability to adaptation. In this project, we applied LASSO to the gene co-expression network of rice with salt stress to discover key gene interactions for salt-tolerance related phenotypes. The dataset we have is a high-dimensional one, having 50K genes from 100 samples, with the issue of multicollinearity for fitting linear regression - the expression level of genes in the same pathway tends to be highly correlated. The property of LASSO with sparse parameters is naturally suitable to identify gene interactions of interest in this dataset. After biologically functional modules in the co-expression network was identified, the major changed expression patterns were further selected by LASSO regression to establish a linear relationship between gene expression profiles and physiological responses, such as sodium/potassium condenses, with salt stress. Five modules of intensively co-expressed genes, from 45 to 291 genes, were identified by our method with significant P-values, which indicate these modules are significantly associated with physiological responses to stress. Genes in these modules have functions related to ion transport, osmotic adjustment, and oxidative tolerance. For example, LOC_Os7g47350 and LOC_Os07g37320 are co-expressed gene in the same module 15. Both are ion transporter genes and have higher gene expression levels for rice with low sodium levels with salt stress.