Light cues induce protective anticipation of environmental water loss in terrestrial bacteria.

The ecological significance of light perception in nonphotosynthetic bacteria remains largely elusive. In terrestrial environments, diurnal oscillations in light are often temporally coupled to other environmental changes, including increased temperature and evaporation. Here, we report that light functions as an anticipatory cue that triggers protective adaptations to tolerate a future rapid loss of environmental water. We demonstrate this photo-anticipatory stress tolerance in leaf-associated Pseudomonas syringae pv. syringae (Pss) and other plant- and soil-associated pseudomonads. We found that light influences the expression of 30% of the Pss genome, indicating that light is a global regulatory signal, and this signaling occurs almost entirely via a bacteriophytochrome photoreceptor that senses red, far-red, and blue wavelengths. Bacteriophytochrome-mediated light control disproportionally up-regulates water-stress adaptation functions and confers enhanced fitness when cells encounter light prior to water limitation. Given the rapid speed at which water can evaporate from leaf surfaces, such anticipatory activation of a protective response enhances fitness beyond that of a reactive stress response alone, with recurring diurnal wet-dry cycles likely further amplifying the fitness advantage over time. These findings demonstrate that nonphotosynthetic bacteria can use light as a cue to mount an adaptive anticipatory response against a physiologically unrelated but ecologically coupled stress.

Adjusting for gene-specific covariates to improve RNA-seq analysis.

SUMMARY: This article suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by using two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning parameter through cross-validation that maximizes the expected number of hypotheses declared significant. A simulation study demonstrates that our method is comparable to or better than existing methods across realistic scenarios. In data analysis, we find support for our method's premise that the null probability varies with a gene-specific covariate variable. AVAILABILITY AND IMPLEMENTATION: The source code repository is publicly available at https://github.com/hsjeon1217/conditional_method.

Single-parent expression complementation contributes to phenotypic heterosis in maize hybrids.

The dominance model of heterosis explains the superior performance of F1-hybrids via the complementation of deleterious alleles by beneficial alleles in many genes. Genes active in one parent but inactive in the second lead to single-parent expression (SPE) complementation in maize (Zea mays L.) hybrids. In this study, SPE complementation resulted in approximately 700 additionally active genes in different tissues of genetically diverse maize hybrids on average. We established that the number of SPE genes is significantly associated with mid-parent heterosis (MPH) for all surveyed phenotypic traits. In addition, we highlighted that maternally (SPE_B) and paternally (SPE_X) active SPE genes enriched in gene co-expression modules are highly correlated within each SPE type but separated between these two SPE types. While SPE_B-enriched co-expression modules are positively correlated with phenotypic traits, SPE_X-enriched modules displayed a negative correlation. Gene ontology term enrichment analyses indicated that SPE_B patterns are associated with growth and development, whereas SPE_X patterns are enriched in defense and stress response. In summary, these results link the degree of phenotypic MPH to the prevalence of gene expression complementation observed by SPE, supporting the notion that hybrids benefit from SPE complementation via its role in coordinating maize development in fluctuating environments.

"Just right" combinations of adjuvants with nanoscale carriers activate aged dendritic cells without overt inflammation.

BACKGROUND: The loss in age-related immunological markers, known as immunosenescence, is caused by a combination of factors, one of which is inflammaging. Inflammaging is associated with the continuous basal generation of proinflammatory cytokines. Studies have demonstrated that inflammaging reduces the effectiveness of vaccines. Strategies aimed at modifying baseline inflammation are being developed to improve vaccination responses in older adults. Dendritic cells have attracted attention as an age-specific target because of their significance in immunization as antigen presenting cells that stimulate T lymphocytes. RESULTS: In this study, bone marrow derived dendritic cells (BMDCs) were generated from aged mice and used to investigate the effects of combinations of adjuvants, including Toll-like receptor, NOD2, and STING agonists with polyanhydride nanoparticles and pentablock copolymer micelles under in vitro conditions. Cellular stimulation was characterized via expression of costimulatory molecules, T cell-activating cytokines, proinflammatory cytokines, and chemokines. Our results indicate that multiple TLR agonists substantially increase costimulatory molecule expression and cytokines associated with T cell activation and inflammation in culture. In contrast, NOD2 and STING agonists had only a moderate effect on BMDC activation, while nanoparticles and micelles had no effect by themselves. However, when nanoparticles and micelles were combined with a TLR9 agonist, a reduction in the production of proinflammatory cytokines was observed while maintaining increased production of T cell activating cytokines and enhancing cell surface marker expression. Additionally, combining nanoparticles and micelles with a STING agonist resulted in a synergistic impact on the upregulation of costimulatory molecules and an increase in cytokine secretion from BMDCs linked with T cell activation without excessive secretion of proinflammatory cytokines. CONCLUSIONS: These studies provide new insights into rational adjuvant selection for vaccines for older adults. Combining appropriate adjuvants with nanoparticles and micelles may lead to balanced immune activation characterized by low inflammation, setting the stage for designing next generation vaccines that can induce mucosal immunity in older adults.

rmRNAseq: differential expression analysis for repeated-measures RNA-seq data.

MOTIVATION: With the reduction in price of next-generation sequencing technologies, gene expression profiling using RNA-seq has increased the scope of sequencing experiments to include more complex designs, such as designs involving repeated measures. In such designs, RNA samples are extracted from each experimental unit at multiple time points. The read counts that result from RNA sequencing of the samples extracted from the same experimental unit tend to be temporally correlated. Although there are many methods for RNA-seq differential expression analysis, existing methods do not properly account for within-unit correlations that arise in repeated-measures designs. RESULTS: We address this shortcoming by using normalized log-transformed counts and associated precision weights in a general linear model pipeline with continuous autoregressive structure to account for the correlation among observations within each experimental unit. We then utilize parametric bootstrap to conduct differential expression inference. Simulation studies show the advantages of our method over alternatives that do not account for the correlation among observations within experimental units. AVAILABILITY AND IMPLEMENTATION: We provide an R package rmRNAseq implementing our proposed method (function TC_CAR1) at https://cran.r-project.org/web/packages/rmRNAseq/index.html. Reproducible R codes for data analysis and simulation are available at https://github.com/ntyet/rmRNAseq/tree/master/simulation.

Small RNA discovery in the interaction between barley and the powdery mildew pathogen.

BACKGROUND: Plants encounter pathogenic and non-pathogenic microorganisms on a nearly constant basis. Small RNAs such as siRNAs and miRNAs/milRNAs influence pathogen virulence and host defense responses. We exploited the biotrophic interaction between the powdery mildew fungus, Blumeria graminis f. sp. hordei (Bgh), and its diploid host plant, barley (Hordeum vulgare) to explore fungal and plant sRNAs expressed during Bgh infection of barley leaf epidermal cells. RESULTS: RNA was isolated from four fast-neutron immune-signaling mutants and their progenitor over a time course representing key stages of Bgh infection, including appressorium formation, penetration of epidermal cells, and development of haustorial feeding structures. The Cereal Introduction (CI) 16151 progenitor carries the resistance allele Mla6, while Bgh isolate 5874 harbors the AVRa6 avirulence effector, resulting in an incompatible interaction. Parallel Analysis of RNA Ends (PARE) was used to verify sRNAs with likely transcript targets in both barley and Bgh. Bgh sRNAs are predicted to regulate effectors, metabolic genes, and translation-related genes. Barley sRNAs are predicted to influence the accumulation of transcripts that encode auxin response factors, NAC transcription factors, homeodomain transcription factors, and several splicing factors. We also identified phasing small interfering RNAs (phasiRNAs) in barley that overlap transcripts that encode receptor-like kinases (RLKs) and nucleotide-binding, leucine-rich domain proteins (NLRs). CONCLUSIONS: These data suggest that Bgh sRNAs regulate gene expression in metabolism, translation-related, and pathogen effectors. PARE-validated targets of predicted Bgh milRNAs include both EKA (effectors homologous to AVRk1 and AVRa10) and CSEP (candidate secreted effector protein) families. We also identified barley phasiRNAs and miRNAs in response to Bgh infection. These include phasiRNA loci that overlap with a significant proportion of receptor-like kinases, suggesting an additional sRNA control mechanism may be active in barley leaves as opposed to predominant R-gene phasiRNA overlap in many eudicots. In addition, we identified conserved miRNAs, novel miRNA candidates, and barley genome mapped sRNAs that have PARE validated transcript targets in barley. The miRNA target transcripts are enriched in transcription factors, signaling-related proteins, and photosynthesis-related proteins. Together these results suggest both barley and Bgh control metabolism and infection-related responses via the specific accumulation and targeting of genes via sRNAs.

Correction to: small RNA discovery in the interaction between barley and the powdery mildew pathogen.

Following the publication of the original article [1], the authors noted several typesetting errors which are noted in this Correction article.

QQS orphan gene and its interactor NF-YC4 reduce susceptibility to pathogens and pests.

Enhancing the nutritional quality and disease resistance of crops without sacrificing productivity is a key issue for developing varieties that are valuable to farmers and for simultaneously improving food security and sustainability. Expression of the Arabidopsis thaliana species-specific AtQQS (Qua-Quine Starch) orphan gene or its interactor, NF-YC4 (Nuclear Factor Y, subunit C4), has been shown to increase levels of leaf/seed protein without affecting the growth and yield of agronomic species. Here, we demonstrate that overexpression of AtQQS and NF-YC4 in Arabidopsis and soybean enhances resistance/reduces susceptibility to viruses, bacteria, fungi, aphids and soybean cyst nematodes. A series of Arabidopsis mutants in starch metabolism were used to explore the relationships between QQS expression, carbon and nitrogen partitioning, and defense. The enhanced basal defenses mediated by QQS were independent of changes in protein/carbohydrate composition of the plants. We demonstrate that either AtQQS or NF-YC4 overexpression in Arabidopsis and in soybean reduces susceptibility of these plants to pathogens/pests. Transgenic soybean lines overexpressing NF-YC4 produce seeds with increased protein while maintaining healthy growth. Pull-down studies reveal that QQS interacts with human NF-YC, as well as with Arabidopsis NF-YC4, and indicate two QQS binding sites near the NF-YC-histone-binding domain. A new model for QQS interaction with NF-YC is speculated. Our findings illustrate the potential of QQS and NF-YC4 to increase protein and improve defensive traits in crops, overcoming the normal growth-defense trade-offs.

Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning.

The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.

A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets.

BACKGROUND: Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding. RESULTS: We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions. CONCLUSIONS: The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO .

Discovery and Characterization of the 3-Hydroxyacyl-ACP Dehydratase Component of the Plant Mitochondrial Fatty Acid Synthase System.

We report the characterization of the Arabidopsis (Arabidopsis thaliana) 3-hydroxyacyl-acyl carrier protein dehydratase (mtHD) component of the mitochondrial fatty acid synthase (mtFAS) system, encoded by AT5G60335. The mitochondrial localization and catalytic capability of mtHD were demonstrated with a green fluorescent protein transgenesis experiment and by in vivo complementation and in vitro enzymatic assays. RNA interference (RNAi) knockdown lines with reduced mtHD expression exhibit traits typically associated with mtFAS mutants, namely a miniaturized morphological appearance, reduced lipoylation of lipoylated proteins, and altered metabolomes consistent with the reduced catalytic activity of lipoylated enzymes. These alterations are reversed when mthd-rnai mutant plants are grown in a 1% CO2 atmosphere, indicating the link between mtFAS and photorespiratory deficiency due to the reduced lipoylation of glycine decarboxylase. In vivo biochemical feeding experiments illustrate that sucrose and glycolate are the metabolic modulators that mediate the alterations in morphology and lipid accumulation. In addition, both mthd-rnai and mtkas mutants exhibit reduced accumulation of 3-hydroxytetradecanoic acid (i.e. a hallmark of lipid A-like molecules) and abnormal chloroplastic starch granules; these changes are not reversible by the 1% CO2 atmosphere, demonstrating two novel mtFAS functions that are independent of photorespiration. Finally, RNA sequencing analysis revealed that mthd-rnai and mtkas mutants are nearly equivalent to each other in altering the transcriptome, and these analyses further identified genes whose expression is affected by a functional mtFAS system but independent of photorespiratory deficiency. These data demonstrate the nonredundant nature of the mtFAS system, which contributes unique lipid components needed to support plant cell structure and metabolism.

Stability of Single-Parent Gene Expression Complementation in Maize Hybrids upon Water Deficit Stress.

Heterosis is the superior performance of F1 hybrids compared with their homozygous, genetically distinct parents. In this study, we monitored the transcriptomic divergence of the maize (Zea mays) inbred lines B73 and Mo17 and their reciprocal F1 hybrid progeny in primary roots under control and water deficit conditions simulated by polyethylene glycol treatment. Single-parent expression (SPE) of genes is an extreme instance of gene expression complementation, in which genes are active in only one of two parents but are expressed in both reciprocal hybrids. In this study, 1,997 genes only expressed in B73 and 2,024 genes only expressed in Mo17 displayed SPE complementation under control and water deficit conditions. As a consequence, the number of active genes in hybrids exceeded the number of active genes in the parental inbred lines significantly independent of treatment. SPE patterns were substantially more stable to expression changes by water deficit treatment than other genotype-specific expression profiles. While, on average, 75% of all SPE patterns were not altered in response to polyethylene glycol treatment, only 17% of the remaining genotype-specific expression patterns were not changed by water deficit. Nonsyntenic genes that lack syntenic orthologs in other grass species, and thus evolved late in the grass lineage, were significantly overrepresented among SPE genes. Hence, the significant overrepresentation of nonsyntenic genes among SPE patterns and their stability under water limitation might suggest a function of these genes during the early developmental manifestation of heterosis under fluctuating environmental conditions in hybrid progeny of the inbred lines B73 and Mo17.

QQS orphan gene regulates carbon and nitrogen partitioning across species via NF-YC interactions.

The allocation of carbon and nitrogen resources to the synthesis of plant proteins, carbohydrates, and lipids is complex and under the control of many genes; much remains to be understood about this process. QQS (Qua-Quine Starch; At3g30720), an orphan gene unique to Arabidopsis thaliana, regulates metabolic processes affecting carbon and nitrogen partitioning among proteins and carbohydrates, modulating leaf and seed composition in Arabidopsis and soybean. Here the universality of QQS function in modulating carbon and nitrogen allocation is exemplified by a series of transgenic experiments. We show that ectopic expression of QQS increases soybean protein independent of the genetic background and original protein content of the cultivar. Furthermore, transgenic QQS expression increases the protein content of maize, a C4 species (a species that uses 4-carbon photosynthesis), and rice, a protein-poor agronomic crop, both highly divergent from Arabidopsis. We determine that QQS protein binds to the transcriptional regulator AtNF-YC4 (Arabidopsis nuclear factor Y, subunit C4). Overexpression of AtNF-YC4 in Arabidopsis mimics the QQS-overexpression phenotype, increasing protein and decreasing starch levels. NF-YC, a component of the NF-Y complex, is conserved across eukaryotes. The NF-YC4 homologs of soybean, rice, and maize also bind to QQS, which provides an explanation of how QQS can act in species where it does not occur endogenously. These findings are, to our knowledge, the first insight into the mechanism of action of QQS in modulating carbon and nitrogen allocation across species. They have major implications for the emergence and function of orphan genes, and identify a nontransgenic strategy for modulating protein levels in crop species, a trait of great agronomic significance.

Root Type-Specific Reprogramming of Maize Pericycle Transcriptomes by Local High Nitrate Results in Disparate Lateral Root Branching Patterns.

The adaptability of root system architecture to unevenly distributed mineral nutrients in soil is a key determinant of plant performance. The molecular mechanisms underlying nitrate dependent plasticity of lateral root branching across the different root types of maize are only poorly understood. In this study, detailed morphological and anatomical analyses together with cell type-specific transcriptome profiling experiments combining laser capture microdissection with RNA-seq were performed to unravel the molecular signatures of lateral root formation in primary, seminal, crown, and brace roots of maize (Zea mays) upon local high nitrate stimulation. The four maize root types displayed divergent branching patterns of lateral roots upon local high nitrate stimulation. In particular, brace roots displayed an exceptional architectural plasticity compared to other root types. Transcriptome profiling revealed root type-specific transcriptomic reprogramming of pericycle cells upon local high nitrate stimulation. The alteration of the transcriptomic landscape of brace root pericycle cells in response to local high nitrate stimulation was most significant. Root type-specific transcriptome diversity in response to local high nitrate highlighted differences in the functional adaptability and systemic shoot nitrogen starvation response during development. Integration of morphological, anatomical, and transcriptomic data resulted in a framework underscoring similarity and diversity among root types grown in heterogeneous nitrate environments.

Non-syntenic genes drive RTCS-dependent regulation of the embryo transcriptome during formation of seminal root primordia in maize (Zea mays L.).

Seminal roots of maize are pivotal for early seedling establishment. The maize mutant rootless concerning crown and seminal roots (rtcs) is defective in seminal root initiation during embryogenesis. In this study, the transcriptomes of wild-type and rtcs embryos were analyzed by RNA-Seq based on histological results at three stages of seminal root primordia formation. Hierarchical clustering highlighted that samples of each genotype grouped together along development. Determination of their gene activity status revealed hundreds of genes specifically transcribed in wild-type or rtcs embryos, while K-mean clustering revealed changes in gene expression dynamics between wild-type and rtcs during embryo development. Pairwise comparisons of rtcs and wild-type embryo transcriptomes identified 131 transcription factors among 3526 differentially expressed genes [false discovery rate (FDR) <5% and |log2Fc|≥1]. Among those, functional annotation highlighted genes involved in cell cycle control and phytohormone action, particularly auxin signaling. Moreover, in silico promoter analyses identified putative RTCS target genes associated with transcription factor action and hormone metabolism and signaling. Significantly, non-syntenic genes that emerged after the separation of maize and sorghum were over-represented among genes displaying RTCS-dependent expression during seminal root primordia formation. This might suggest that these non-syntenic genes came under the transcriptional control of the syntenic gene rtcs during seminal root evolution. Taken together, this study provides first insights into the molecular framework underlying seminal root initiation in maize and provides a starting point for further investigations of the molecular networks underlying RTCS-dependent seminal root initiation.

Complexity and specificity of the maize (Zea mays L.) root hair transcriptome.

Root hairs are tubular extensions of epidermis cells. Transcriptome profiling demonstrated that the single cell-type root hair transcriptome was less complex than the transcriptome of multiple cell-type primary roots without root hairs. In total, 831 genes were exclusively and 5585 genes were preferentially expressed in root hairs [false discovery rate (FDR) ≤1%]. Among those, the most significantly enriched Gene Ontology (GO) functional terms were related to energy metabolism, highlighting the high energy demand for the development and function of root hairs. Subsequently, the maize homologs for 138 Arabidopsis genes known to be involved in root hair development were identified and their phylogenetic relationship and expression in root hairs were determined. This study indicated that the genetic regulation of root hair development in Arabidopsis and maize is controlled by common genes, but also shows differences which need to be dissected in future genetic experiments. Finally, a maize root view of the eFP browser was implemented including the root hair transcriptome of the present study and several previously published maize root transcriptome data sets. The eFP browser provides color-coded expression levels for these root types and tissues for any gene of interest, thus providing a novel resource to study gene expression and function in maize roots.

Nonsyntenic genes drive highly dynamic complementation of gene expression in maize hybrids.

Maize (Zea mays) displays an exceptional level of structural genomic diversity, which is likely unique among higher eukaryotes. In this study, we surveyed how the genetic divergence of two maize inbred lines affects the transcriptomic landscape in four different primary root tissues of their F1-hybrid progeny. An extreme instance of complementation was frequently observed: genes that were expressed in only one parent but in both reciprocal hybrids. This single-parent expression (SPE) pattern was detected for 2341 genes with up to 1287 SPE patterns per tissue. As a consequence, the number of active genes in hybrids exceeded that of their parents in each tissue by >400. SPE patterns are highly dynamic, as illustrated by their excessive degree of tissue specificity (80%). The biological significance of this type of complementation is underpinned by the observation that a disproportionally high number of SPE genes (75 to 82%) is nonsyntenic, as opposed to all expressed genes (36%). These genes likely evolved after the last whole-genome duplication and are therefore younger than the syntenic genes. In summary, SPE genes shape the remarkable gene expression plasticity between root tissues and complementation in maize hybrids, resulting in a tissue-specific increase of active genes in F1-hybrids compared with their inbred parents.

Extreme-phenotype genome-wide association study (XP-GWAS): a method for identifying trait-associated variants by sequencing pools of individuals selected from a diversity panel.

Although approaches for performing genome-wide association studies (GWAS) are well developed, conventional GWAS requires high-density genotyping of large numbers of individuals from a diversity panel. Here we report a method for performing GWAS that does not require genotyping of large numbers of individuals. Instead XP-GWAS (extreme-phenotype GWAS) relies on genotyping pools of individuals from a diversity panel that have extreme phenotypes. This analysis measures allele frequencies in the extreme pools, enabling discovery of associations between genetic variants and traits of interest. This method was evaluated in maize (Zea mays) using the well-characterized kernel row number trait, which was selected to enable comparisons between the results of XP-GWAS and conventional GWAS. An exome-sequencing strategy was used to focus sequencing resources on genes and their flanking regions. A total of 0.94 million variants were identified and served as evaluation markers; comparisons among pools showed that 145 of these variants were statistically associated with the kernel row number phenotype. These trait-associated variants were significantly enriched in regions identified by conventional GWAS. XP-GWAS was able to resolve several linked QTL and detect trait-associated variants within a single gene under a QTL peak. XP-GWAS is expected to be particularly valuable for detecting genes or alleles responsible for quantitative variation in species for which extensive genotyping resources are not available, such as wild progenitors of crops, orphan crops, and other poorly characterized species such as those of ecological interest.

The maize brown midrib4 (bm4) gene encodes a functional folylpolyglutamate synthase.

Mutations in the brown midrib4 (bm4) gene affect the accumulation and composition of lignin in maize. Fine-mapping analysis of bm4 narrowed the candidate region to an approximately 105 kb interval on chromosome 9 containing six genes. Only one of these six genes, GRMZM2G393334, showed decreased expression in mutants. At least four of 10 Mu-induced bm4 mutant alleles contain a Mu insertion in the GRMZM2G393334 gene. Based on these results, we concluded that GRMZM2G393334 is the bm4 gene. GRMZM2G393334 encodes a putative folylpolyglutamate synthase (FPGS), which functions in one-carbon (C1) metabolism to polyglutamylate substrates of folate-dependent enzymes. Yeast complementation experiments demonstrated that expression of the maize bm4 gene in FPGS-deficient met7 yeast is able to rescue the yeast mutant phenotype, thus demonstrating that bm4 encodes a functional FPGS. Consistent with earlier studies, bm4 mutants exhibit a modest decrease in lignin concentration and an overall increase in the S:G lignin ratio relative to wild-type. Orthologs of bm4 include at least one paralogous gene in maize and various homologs in other grasses and dicots. Discovery of the gene underlying the bm4 maize phenotype illustrates a role for FPGS in lignin biosynthesis.

Post-weaning blood transcriptomic differences between Yorkshire pigs divergently selected for residual feed intake.

BACKGROUND: Improving feed efficiency (FE) of pigs by genetic selection is of economic and environmental significance. An increasingly accepted measure of feed efficiency is residual feed intake (RFI). Currently, the molecular mechanisms underlying RFI are largely unknown. Additionally, to incorporate RFI into animal breeding programs, feed intake must be recorded on individual pigs, which is costly and time-consuming. Thus, convenient and predictive biomarkers for RFI that can be measured at an early age are greatly desired. In this study, we aimed to explore whether differences exist in the global gene expression profiles of peripheral blood of 35 to 42 day-old pigs with extremely low (more efficient) and high RFI (less efficient) values from two lines that were divergently selected for RFI during the grow-finish phase, to use such information to explore the potential molecular basis of RFI differences, and to initiate development of predictive biomarkers for RFI. RESULTS: We identified 1972 differentially expressed genes (DEGs) (q ≤ 0.15) between the low (n = 15) and high (n = 16) RFI groups of animals by using RNA sequencing technology. We validated 24 of 37 selected DEGs by reverse transcription-quantitative PCR (RT-qPCR) in a joint analysis of 24 (12 per line) of the 31 samples already used for RNA-seq plus 24 (12 per line) novel samples from the same contemporary group of pigs. Using an analysis of the 24 novel samples alone, only nine of the 37 selected DEGs were validated. Genes involved in small molecule biosynthetic process, antigen processing and presentation of peptide antigen via major histocompatibility complex (MHC) class I, and steroid biosynthetic process were overrepresented among DEGs that had higher expression in the low versus high RFI animals. Genes known to function in the proteasome complex or mitochondrion were also significantly enriched among genes with higher expression in the low versus high RFI animals. Alternatively, genes involved in signal transduction, bone mineralization and regulation of phosphorylation were overrepresented among DEGs with lower expression in the low versus high RFI animals. The DEGs significantly overlapped with genes associated with disease, including hyperphagia, eating disorders and mitochondrial diseases (q < 1E-05). A weighted gene co-expression network analysis (WGCNA) identified four co-expression modules that were differentially expressed between the low and high RFI groups. Genes involved in lipid metabolism, regulation of bone mineralization, cellular immunity and response to stimulus were overrepresented within the two modules that were most significantly differentially expressed between the low and high RFI groups. We also found five of the DEGs and one of the co-expression modules were significantly associated with the RFI phenotype of individual animals (q < 0.05). CONCLUSIONS: The post-weaning blood transcriptome was clearly different between the low and high RFI groups. The identified DEGs suggested potential differences in mitochondrial and proteasomal activities, small molecule biosynthetic process, and signal transduction between the two RFI groups and provided potential new insights into the molecular basis of RFI in pigs, although the observed relationship between the post-weaning blood gene expression and RFI phenotype measured during the grow-finish phase was not strong. DEGs and representative genes in co-expression modules that were associated with RFI phenotype provide a preliminary list for developing predictive biomarkers for RFI in pigs.