Characterization of QTL and eQTL controlling early Fusarium graminearum infection and deoxynivalenol levels in a Wuhan 1 x Nyubai doubled haploid wheat population.

BACKGROUND: Fusarium head blight (FHB) is a major disease of cereal crops, caused by the fungal pathogen Fusarium graminearum and related species. Breeding wheat for FHB resistance contributes to increase yields and grain quality and to reduce the use of fungicides. The identification of genes and markers for FHB resistance in different wheat genotypes has nevertheless proven challenging. RESULTS: In this study, early infection by F. graminearum was analyzed in a doubled haploid population derived from the cross of the moderately resistant wheat genotypes Wuhan 1 and Nyubai. Three quantitative trait loci (QTL) were identified: 1AL was associated with lower deoxynivalenol content, and 4BS and 5A were associated with reduced F. graminearum infection at 2 days post inoculation. Early resistance alleles were inherited from Wuhan 1 for QTL 1AL and 4BS and inherited from Nyubai for the 5A QTL. Cis and trans expression QTL (eQTL) were identified using RNA-seq data from infected head samples. Hotspots for trans eQTL were identified in the vicinity of the 1AL and 4BS QTL peaks. Among differentially expressed genes with cis eQTL within the QTL support intervals, nine genes had higher expression associated with FHB early resistance, and four genes had higher expression associated with FHB early susceptibility. CONCLUSIONS: Our analysis of genotype and gene expression data of wheat infected by F. graminearum identified three QTL associated with FHB early resistance, and linked genes with eQTL and differential expression patterns to those QTL. These findings may have applications in breeding wheat for early resistance to FHB.

Transcriptome dynamics associated with resistance and susceptibility against fusarium head blight in four wheat genotypes.

BACKGROUND: Fusarium head blight (FHB) of wheat in North America is caused mostly by the fungal pathogen Fusarium graminearum (Fg). Upon exposure to Fg, wheat initiates a series of cellular responses involving massive transcriptional reprogramming. In this study, we analyzed transcriptomics data of four wheat genotypes (Nyubai, Wuhan 1, HC374, and Shaw), at 2 and 4 days post inoculation (dpi) with Fg, using RNA-seq technology. RESULTS: A total of 37,772 differentially expressed genes (DEGs) were identified, 28,961 from wheat and 8811 from the pathogen. The susceptible genotype Shaw exhibited the highest number of host and pathogen DEGs, including 2270 DEGs associating with FHB susceptibility. Protein serine/threonine kinases and LRR-RK were associated with susceptibility at 2 dpi, while several ethylene-responsive, WRKY, Myb, bZIP and NAC-domain containing transcription factors were associated with susceptibility at 4 dpi. In the three resistant genotypes, 220 DEGs were associated with resistance. Glutathione S-transferase (GST), membrane proteins and distinct LRR-RKs were associated with FHB resistance across the three genotypes. Genes with unique, high up-regulation by Fg in Wuhan 1 were mostly transiently expressed at 2 dpi, while many defense-associated genes were up-regulated at both 2 and 4 dpi in Nyubai; the majority of unique genes up-regulated in HC374 were detected at 4 dpi only. In the pathogen, most genes showed increased expression between 2 and 4 dpi in all genotypes, with stronger levels in the susceptible host; however two pectate lyases and a hydrolase were expressed higher at 2 dpi, and acetyltransferase activity was highly enriched at 4 dpi. CONCLUSIONS: There was an early up-regulation of LRR-RKs, different between susceptible and resistant genotypes; subsequently, distinct sets of genes associated with defense response were up-regulated. Differences in expression profiles among the resistant genotypes indicate genotype-specific defense mechanisms. This study also shows a greater resemblance in transcriptomics of HC374 to Nyubai, consistent with their sharing of two FHB resistance QTLs on 3BS and 5AS, compared to Wuhan 1 which carries one QTL on 2DL in common with HC374.

Chemical Derivatization Strategy for Extending the Identification of MHC Class I Immunopeptides.

Neoantigen-based therapeutic vaccines have a high potential impact on tumor eradication and patient survival. Mass spectrometry (MS)-based immunopeptidomics has the capacity to identify tumor-associated epitopes and pinpoint mutation-bearing major histocompatibility complex (MHC)-binding peptides. This approach presents several challenges, including the identification of low-abundance peptides. In addition, MHC peptides have much lower MS/MS identification rates than tryptic peptides due to their shorter sequence and lack of basic amino acid at C-termini. In this study, we report the development and application of a novel chemical derivatization strategy that combines the analysis of native, dimethylated, and alkylamidated peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to expand the coverage of the MHC peptidome. The results revealed that dimethylation increases hydrophobicity and ionization efficiency of MHC class I peptides, while alkylamidation significantly improves the fragmentation by producing more y-ions during MS/MS fragmentation. Thus, the combination of dimethylation and alkylamidation enabled the identification of peptides that could not be identified from the analysis of their native form. Using this strategy, we identified 3148 unique MHC I peptides from HCT 116 cell lines, compared to only 1388 peptides identified in their native form. Among these, 10 mutation-bearing peptides were identified with high confidence, indicating that this chemical derivatization strategy is a promising approach for neoantigen discovery in clinical applications.

Bioinformatics identification of new targets for improving low temperature stress tolerance in spring and winter wheat.

BACKGROUND: Phenotypic studies in Triticeae have shown that low temperature-induced protective mechanisms are developmentally regulated and involve dynamic acclimation processes. Understanding these mechanisms is important for breeding cold-resistant wheat cultivars. In this study, we combined three computational techniques for the analysis of gene expression data from spring and winter wheat cultivars subjected to low temperature treatments. Our main objective was to construct a comprehensive network of cold response transcriptional events in wheat, and to identify novel cold tolerance candidate genes in wheat. RESULTS: We assigned novel cold stress-related roles to 35 wheat genes, uncovered novel transcription (TF)-gene interactions, and identified 127 genes representing known and novel candidate targets associated with cold tolerance in wheat. Our results also show that delays in terms of activation or repression of the same genes across wheat cultivars play key roles in phenotypic differences among winter and spring wheat cultivars, and adaptation to low temperature stress, cold shock and cold acclimation. CONCLUSIONS: Using three computational approaches, we identified novel putative cold-response genes and TF-gene interactions. These results provide new insights into the complex mechanisms regulating the expression of cold-responsive genes in wheat.

Glycoproteomic comparison of clinical triple-negative and luminal breast tumors.

Triple-negative (TN) breast cancer accounts for ∼ 15% of breast cancers and is characterized by a high likelihood of relapse and a lack of targeted therapies. In contrast, luminal-type tumors that express the estrogen and progesterone receptors (ER+/PR+) and lack expression of human epidermal growth factor receptor 2 (Her2-) are treated with targeted hormonal therapy and carry a better prognosis. To identify potential targets for the development of future therapeutics aimed specifically at TN breast cancers, we have used a hydrazide-based glycoproteomic workflow to compare protein expression in clinical tumors from nine TN (Her2-/ER-/PR-) and nine luminal (Her2-/ER+/PR+) patients. Using a label-free LC-MS based approach, we identified and quantified 2264 proteins. Of these, 90 proteins were more highly expressed and 86 proteins were underexpressed in the TN tumors relative to the luminal tumors. The expression level of four of these potential targets was verified in the original set of tumors by Western blot and correlated well with our mass-spectrometry-based quantification. Furthermore, 30% of the proteins differentially expressed between luminal and TN tumors were validated in a larger cohort of 406 TN and 469 luminal tumors through corresponding differences in their mRNA expression in publically available microarray data. A group of 29 of these differentially expressed proteins was shown to correctly classify 88% of TN and luminal tumors using microarray data of their associated mRNA levels. Interestingly, even within a group of TN breast cancer patients, the expression levels of these same mRNAs were able to significantly predict patient survival, suggesting that these proteins play a role in the aggressiveness seen in TN tumors. This study provides a comprehensive list of potential targets for the development of diagnostic and therapeutic agents specifically aimed at treating TN breast cancer and demonstrates the utility of using publicly available microarray data to further prioritize potential targets.

Promzea: a pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the Maize Development Atlas.

BACKGROUND: The discovery of genetic networks and cis-acting DNA motifs underlying their regulation is a major objective of transcriptome studies. The recent release of the maize genome (Zea mays L.) has facilitated in silico searches for regulatory motifs. Several algorithms exist to predict cis-acting elements, but none have been adapted for maize. RESULTS: A benchmark data set was used to evaluate the accuracy of three motif discovery programs: BioProspector, Weeder and MEME. Analysis showed that each motif discovery tool had limited accuracy and appeared to retrieve a distinct set of motifs. Therefore, using the benchmark, statistical filters were optimized to reduce the false discovery ratio, and then remaining motifs from all programs were combined to improve motif prediction. These principles were integrated into a user-friendly pipeline for motif discovery in maize called Promzea, available at http://www.promzea.org and on the Discovery Environment of the iPlant Collaborative website. Promzea was subsequently expanded to include rice and Arabidopsis. Within Promzea, a user enters cDNA sequences or gene IDs; corresponding upstream sequences are retrieved from the maize genome. Predicted motifs are filtered, combined and ranked. Promzea searches the chosen plant genome for genes containing each candidate motif, providing the user with the gene list and corresponding gene annotations. Promzea was validated in silico using a benchmark data set: the Promzea pipeline showed a 22% increase in nucleotide sensitivity compared to the best standalone program tool, Weeder, with equivalent nucleotide specificity. Promzea was also validated by its ability to retrieve the experimentally defined binding sites of transcription factors that regulate the maize anthocyanin and phlobaphene biosynthetic pathways. Promzea predicted additional promoter motifs, and genome-wide motif searches by Promzea identified 127 non-anthocyanin/phlobaphene genes that each contained all five predicted promoter motifs in their promoters, perhaps uncovering a broader co-regulated gene network. Promzea was also tested against tissue-specific microarray data from maize. CONCLUSIONS: An online tool customized for promoter motif discovery in plants has been generated called Promzea. Promzea was validated in silico by its ability to retrieve benchmark motifs and experimentally defined motifs and was tested using tissue-specific microarray data. Promzea predicted broader networks of gene regulation associated with the historic anthocyanin and phlobaphene biosynthetic pathways. Promzea is a new bioinformatics tool for understanding transcriptional gene regulation in maize and has been expanded to include rice and Arabidopsis.

Cell surface profiling of cultured cells by direct hydrazide capture of oxidized glycoproteins.

Glycoproteins are a particularly interesting subset of the cellular proteome as a high proportion of proteins present on the extracellular cell surface are glycosylated. These cell surface proteins are ideal targets for biologic drug therapies or for diagnostics tests. Here, we describe a modification of the well-described Cell Surface Capture (CSC) method for the selective isolation and identification of cell surface glycoproteins that contain N-linked carbohydrates. This modification, which we refer to as Direct Cell Surface Capture (D-CSC), is based on oxidation of cell surface glycans on intact cells, followed by direct conjugation of the oxidized oligosaccharides to a solid support using hydrazide chemistry, with no biotinylation step. As a proof-of-principle, we applied D-CSC to the analysis of cell surface membrane proteins of three adherent cancer cell lines (A549, OVCAR3, and U87MG) and compared our results to those published using the well-established Cell Surface Capture (CSC) method, demonstrating comparable selectivity for cell surface proteins. •A method enabling the identification of cell surface proteins from cells in culture is described.•Application of this method to profile the cell surface on three different cancer cell lines is included.

Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data.

Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

Differential expression of receptors mediating receptor-mediated transcytosis (RMT) in brain microvessels, brain parenchyma and peripheral tissues of the mouse and the human.

Receptor-mediated transcytosis (RMT) is a principal pathway for transport of macromolecules essential for brain function across the blood-brain barrier (BBB). Antibodies or peptide ligands which bind RMT receptors are often co-opted for brain delivery of biotherapeutics. Constitutively recycling transferrin receptor (TfR) is a prototype receptor utilized to shuttle therapeutic cargos across the BBB. Several other BBB-expressed receptors have been shown to mediate transcytosis of antibodies or protein ligands including insulin receptor (INSR) and insulin-like growth factor-1 receptor (IGF1R), lipid transporters LRP1, LDLR, LRP8 and TMEM30A, solute carrier family transporter SLC3A2/CD98hc and leptin receptor (LEPR). In this study, we analyzed expression patterns of genes encoding RMT receptors in isolated brain microvessels, brain parenchyma and peripheral organs of the mouse and the human using RNA-seq approach. IGF1R, INSR and LRP8 were highly enriched in mouse brain microvessels compared to peripheral tissues. In human brain microvessels only INSR was enriched compared to either the brain or the lung. The expression levels of SLC2A1, LRP1, IGF1R, LRP8 and TFRC were significantly higher in the mouse compared to human brain microvessels. The protein expression of these receptors analyzed by Western blot and immunofluorescent staining of the brain microvessels correlated with their transcript abundance. This study provides a molecular transcriptomics map of key RMT receptors in mouse and human brain microvessels and peripheral tissues, important to translational studies of biodistribution, efficacy and safety of antibodies developed against these receptors.

Seed storage protein gene promoters contain conserved DNA motifs in Brassicaceae, Fabaceae and Poaceae.

BACKGROUND: Accurate computational identification of cis-regulatory motifs is difficult, particularly in eukaryotic promoters, which typically contain multiple short and degenerate DNA sequences bound by several interacting factors. Enrichment in combinations of rare motifs in the promoter sequence of functionally or evolutionarily related genes among several species is an indicator of conserved transcriptional regulatory mechanisms. This provides a basis for the computational identification of cis-regulatory motifs. RESULTS: We have used a discriminative seeding DNA motif discovery algorithm for an in-depth analysis of 54 seed storage protein (SSP) gene promoters from three plant families, namely Brassicaceae (mustards), Fabaceae (legumes) and Poaceae (grasses) using backgrounds based on complete sets of promoters from a representative species in each family, namely Arabidopsis (Arabidopsis thaliana (L.) Heynh.), soybean (Glycine max (L.) Merr.) and rice (Oryza sativa L.) respectively. We have identified three conserved motifs (two RY-like and one ACGT-like) in Brassicaceae and Fabaceae SSP gene promoters that are similar to experimentally characterized seed-specific cis-regulatory elements. Fabaceae SSP gene promoter sequences are also enriched in a novel, seed-specific E2Fb-like motif. Conserved motifs identified in Poaceae SSP gene promoters include a GCN4-like motif, two prolamin-box-like motifs and an Skn-1-like motif. Evidence of the presence of a variant of the TATA-box is found in the SSP gene promoters from the three plant families. Motifs discovered in SSP gene promoters were used to score whole-genome sets of promoters from Arabidopsis, soybean and rice. The highest-scoring promoters are associated with genes coding for different subunits or precursors of seed storage proteins. CONCLUSION: Seed storage protein gene promoter motifs are conserved in diverse species, and different plant families are characterized by a distinct combination of conserved motifs. The majority of discovered motifs match experimentally characterized cis-regulatory elements. These results provide a good starting point for further experimental analysis of plant seed-specific promoters and our methodology can be used to unravel more transcriptional regulatory mechanisms in plants and other eukaryotes.

Seeder: discriminative seeding DNA motif discovery.

MOTIVATION: The computational identification of transcription factor binding sites is a major challenge in bioinformatics and an important complement to experimental approaches. RESULTS: We describe a novel, exact discriminative seeding DNA motif discovery algorithm designed for fast and reliable prediction of cis-regulatory elements in eukaryotic promoters. The algorithm is tested on biological benchmark data and shown to perform equally or better than other motif discovery tools. The algorithm is applied to the analysis of plant tissue-specific promoter sequences and successfully identifies key regulatory elements.

Computational selection of antibody-drug conjugate targets for breast cancer.

The selection of therapeutic targets is a critical aspect of antibody-drug conjugate research and development. In this study, we applied computational methods to select candidate targets overexpressed in three major breast cancer subtypes as compared with a range of vital organs and tissues. Microarray data corresponding to over 8,000 tissue samples were collected from the public domain. Breast cancer samples were classified into molecular subtypes using an iterative ensemble approach combining six classification algorithms and three feature selection techniques, including a novel kernel density-based method. This feature selection method was used in conjunction with differential expression and subcellular localization information to assemble a primary list of targets. A total of 50 cell membrane targets were identified, including one target for which an antibody-drug conjugate is in clinical use, and six targets for which antibody-drug conjugates are in clinical trials for the treatment of breast cancer and other solid tumors. In addition, 50 extracellular proteins were identified as potential targets for non-internalizing strategies and alternative modalities. Candidate targets linked with the epithelial-to-mesenchymal transition were identified by analyzing differential gene expression in epithelial and mesenchymal tumor-derived cell lines. Overall, these results show that mining human gene expression data has the power to select and prioritize breast cancer antibody-drug conjugate targets, and the potential to lead to new and more effective cancer therapeutics.

Silicon and plant disease resistance against pathogenic fungi.

Silicon (Si) is a bioactive element associated with beneficial effects on mechanical and physiological properties of plants. Silicon alleviates abiotic and biotic stresses, and increases the resistance of plants to pathogenic fungi. Several studies have suggested that Si activates plant defense mechanisms, yet the exact nature of the interaction between the element and biochemical pathways leading to resistance remains unclear. Silicon possesses unique biochemical properties that may explain its bioactivity as a regulator of plant defense mechanisms. It can act as a modulator influencing the timing and extent of plant defense responses in a manner reminiscent of the role of secondary messengers in induced systemic resistance; it can also bind to hydroxyl groups of proteins strategically involved in signal transduction; or it can interfere with cationic co-factors of enzymes influencing pathogenesis-related events. Silicon may therefore interact with several key components of plant stress signaling systems leading to induced resistance.

The protective role of silicon in the Arabidopsis-powdery mildew pathosystem.

The role and essentiality of silicon (Si) in plant biology have been debated for >150 years despite numerous reports describing its beneficial properties. To obtain unique insights regarding the effect of Si on plants, we performed a complete transcriptome analysis of both control and powdery mildew-stressed Arabidopsis plants, with or without Si application, using a 44K microarray. Surprisingly, the expression of all but two genes was unaffected by Si in control plants, a result contradicting reports of a possible direct effect of Si as a fertilizer. In contrast, inoculation of plants, treated or not with Si, altered the expression of a set of nearly 4,000 genes. After functional categorization, many of the up-regulated genes were defense-related, whereas a large proportion of down-regulated genes were involved in primary metabolism. Regulated defense genes included R genes, stress-related transcription factors, genes involved in signal transduction, the biosynthesis of stress hormones (SA, JA, ethylene), and the metabolism of reactive oxygen species. In inoculated plants treated with Si, the magnitude of down-regulation was attenuated by >25%, an indication of stress alleviation. Our results demonstrate that Si treatment had no effect on the metabolism of unstressed plants, suggesting a nonessential role for the element but that it has beneficial properties attributable to modulation of a more efficient response to pathogen stress.