Production of Minitubers from Potato Seedlings and Advanced Selections

This cooperation between the University of Maine and USDA-ARS was aimed at providing the infrastructure and manpower support for the propagation and advancement of tetraploid potato breeding materials for the USDFA-ARS-Beltsville Potato Breeding Program (K. Haynes, PI)

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

<p>Abstract</p> <p>Background</p> <p>Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information.</p> <p>Results</p> <p>The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations.</p> <p>Conclusion</p> <p>The major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (<it>e.g</it>., LOOCV) and biologically (<it>e.g</it>., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.</p

Reconstruction of Gene Regulatory Modules in Cancer Cell Cycle by Multi-Source Data Integration

Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation

Transcriptional regulatory mechanism of alcohol dehydrogenase 1-deficient mutant of rice for cell survival under complete submergence

10.1186/s12284-016-0124-3Rice915

An early response regulatory cluster induced by low temperature and hydrogen peroxide in seedlings of chilling-tolerant japonica rice

10.1186/1471-2164-8-175BMC Genomics817

Simultaneous Colorimetric Detection of a Variety of Salmonella spp. in Food and Environmental Samples by Optical Biosensing Using Oligonucleotide-Gold Nanoparticles

Optical biosensors for rapid detection of significant foodborne pathogens are steadily gaining popularity due to its simplicity and sensitivity. While nanomaterials such as gold nanoparticles (AuNPs) are commonly used as signal amplifiers for optical biosensors, AuNPs can also be utilized as a robust biosensing platform. Many reported optical biosensors were designed for individual pathogen detection in a single assay and have high detection limit (DL). Salmonella spp. is one of the major causative agents of foodborne sickness, hospitalization and deaths. Unfortunately, there are around 2,000 serotypes of Salmonella worldwide, and rapid and simultaneous detection of multiple strains in a single assay is lacking. In this study, a comprehensive and highly sensitive simultaneous colorimetric detection of nineteen (19) environmental and outbreak Salmonella spp. strains was achieved by a novel optical biosensing platform using oligonucleotide-functionalized AuNPs. A pair of newly designed single stranded oligonucleotides (30-mer) was displayed onto the surface of AuNPs (13 nm) as detection probes to hybridize with a conserved genomic region (192-bases) of ttrRSBCA found on a broad range of Salmonella spp. strains. The sandwich hybridization (30 min, 55°C) resulted in a structural formation of highly stable oligonucleotide/AuNPs-DNA complexes which remained undisturbed even after subjecting to an increased salt concentration (2 M, final), thus allowing a direct discrimination via color change of target (red color) from non-target (purplish-blue color) reaction mixtures by direct observation using the naked eye. In food matrices (blueberries and chicken meat), nineteen different Salmonella spp. strains were concentrated using immunomagnetic separation and then simultaneously detected in a 96-well microplate by oligonucleotide-functionalized AuNPs after DNA preparation. Successful oligonucleotide/AuNPs-DNA hybridization was confirmed by gel electrophoresis while AuNPs aggregation in non-target and control reaction mixtures was verified by both spectrophotometric analysis and TEM images. Results showed that the optical AuNP biosensing platform can simultaneously screen nineteen (19) viable Salmonella spp. strains tested with 100% specificity and a superior detection limit of &lt;10 CFU/mL or g for both pure culture and complex matrices setups. The highly sensitive colorimetric detection system can significantly improve the screening and detection of viable Salmonella spp. strains present in complex food and environmental matrices, therefore reducing the risks of contamination and incidence of foodborne diseases

Upstream regulatory architecture of rice genes: summarizing the baseline towards genus-wide comparative analysis of regulatory networks and allele mining

10.1186/s12284-015-0041-xRice8