Cultivated cotton fiber has undergone transformation from short, coarse fibers found in progenitor wild species to economically important, long, fine fibers grown globally. Morphological transformation requires understanding of development of wild fiber and developmental differences between wild and cultivated fiber.We examined early development of fibers, including abundance and placement on seed surface, nucleus position, presence of vacuoles, and fiber size and shape. Four species were studied using microscopic, morphometric, and statistical methods: Gossypium raimondii (wild D genome), Gossypium herbaceum (cultivated A genome), Gossypium hirsutum (wild tetraploid), and Gossypium hirsutum (cultivated tetraploid). Early fiber development is highly asynchronous in G. raimondii but more synchronous in other taxa. Significant changes associated with domestication include pronounced synchronization of fiber development in G. hirsutum relative to other taxa studied, implicating unconscious selection that shaped early molecular and cellular events, and a delay in some developmental features in fibers of G. herbaceum, including delayed vacuole formation and nuclear migration. Increased fiber cover and synchronized development selection in cultivated cotton may have facilitated both yield and uniformity of the crop. However, for the taxa and developmental timeframe studied, phylogeny is found to play a more important role than domestication in determining early fiber size and shape.

Programmed cell death (PCD) is a biological process that shapes human development. Yet, cancer cells are insusceptible to this process leading to the proliferation of tumors. Research on PCD can produce cancer therapies which increase tumor susceptibility to PCD for tumor eradication.

The exact mechanisms of PCD are currently unknown. My research aims to uncover the role of the gene outsiders in the scheme of PCD in Drosophila melanogaster (fruit fly) embryos. During embryogenesis, Drosophila germ cells travel across the embryo to the gonads for proper development. Mutants with the outsiders gene respond less to PCD resulting in the correct number of germ cells in the gonads, but an excess outlying the peripherals.

To decipher the mechanisms involved in PCD, outsiders will be excised from the genome using the CRISPR-Cas9 genetic engineering technique. This knock-out phenotype will provide insight on the network of PCD for human health applications.

Methods to automatically integrate sequence information with physical and genetic maps are scarce. The Locus Lookup tool enables researchers to define windows of genomic sequence likely to contain loci of interest where only genetic or physical mapping associations are reported. Using the Locus Lookup tool, researchers will be able to locate specific genes more efficiently that will ultimately help them develop a better maize plant. With the availability of the well-documented source code, the tool can be easily adapted to other biological systems.

Background

Rab GTPases are important regulators of endomembrane trafficking, regulating exocytosis, endocytosis and membrane recycling. Many Rab-like proteins exist in plants, but only a subset have been functionally characterized.

Results

Here we report that AtRabD2b and AtRabD2c play important roles in pollen development, germination and tube elongation. AtrabD2b and AtrabD2c single mutants have no obvious morphological changes compared with wild-type plants across a variety of growth conditions. An AtrabD2b/2c double mutant is also indistinguishable from wild-type plants during vegetative growth; however its siliques are shorter than those in wild-type plants. Compared with wild-type plants, AtrabD2b/2c mutants produce deformed pollen with swollen and branched pollen tube tips. The shorter siliques in the AtrabD2b/2c double mutant were found to be primarily due to the pollen defects. AtRabD2b and AtRabD2c have different but overlapping expression patterns, and they are both highly expressed in pollen. Both AtRabD2b and AtRabD2c protein localize to Golgi bodies.

Conclusions

These findings support a partially redundant role for AtRabD2b and AtRabD2c in vesicle trafficking during pollen tube growth that cannot be fulfilled by the remaining AtRabD family members.

Background

The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach.

Results

We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N > 2 groups.

Conclusions

The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies.

Background

Incorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need for computational methods for checking consistency of such annotations against independent sources of evidence and detecting potential annotation errors. We show how a machine learning approach designed to automatically predict a protein's Gene Ontology (GO) functional class can be employed to identify potential gene annotation errors.

Results

In a set of 211 previously annotated mouse protein kinases, we found that 201 of the GO annotations returned by AmiGO appear to be inconsistent with the UniProt functions assigned to their human counterparts. In contrast, 97% of the predicted annotations generated using a machine learning approach were consistent with the UniProt annotations of the human counterparts, as well as with available annotations for these mouse protein kinases in the Mouse Kinome database.

Conclusion

We conjecture that most of our predicted annotations are, therefore, correct and suggest that the machine learning approach developed here could be routinely used to detect potential errors in GO annotations generated by high-throughput gene annotation projects.

Recent national reports have indicated a need for significant changes in science higher education, with the inclusion of more studentcentered learning. However, substantial barriers to change exist. These include a lack of faculty awareness and understanding of appropriate pedagogical approaches, large class sizes, the time commitment needed to make these changes, and lack of resources and support. At Iowa State University, the implementation of student-centered learning in introductory biology classes is being facilitated by the use of faculty learning communities (FLCs). Progress toward this goal was assessed via surveys of faculty, including both FLC participants and nonparticipants, to determine their teaching practices and attitudes toward biology education. Two years after the formation of the FLCs, a majority of FLC participants indicated that they had experimented with teaching methods and had worked to clarify learning goals for their classes. To continue these changes and promote a true cultural shift within the program, our next steps are to independently assess faculty progress toward studentcentered learning and changes in student learning gains, as well as to develop a more transparent incentive and reward system for faculty teaching.

Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web.

The G-quadruplex (G4) elements comprise a class of nucleic acid structures formed by stacking of guanine base quartets in a quadruple helix. This G4 DNA can form within or across single-stranded DNA molecules and is mutually exclusive with duplex B-form DNA. The reversibility and structural diversity of G4s make them highly versatile genetic structures, as demonstrated by their roles in various functions including telomere metabolism, genome maintenance, immunoglobulin gene diversification, transcription, and translation. Sequence motifs capable of forming G4 DNA are typically located in telomere repeat DNA and other non-telomeric genomic loci. To investigate their potential roles in a large-genome model plant species, we computationally identified 149,988 non-telomeric G4 motifs in maize (Zea mays L., B73 AGPv2), 29% of which were in non-repetitive genomic regions. G4 motif hotspots exhibited non-random enrichment in genes at two locations on the antisense strand, one in the 5′ UTR and the other at the 5′ end of the first intron. Several genic G4 motifs were shown to adopt sequence-specific and potassium-dependent G4 DNA structures in vitro. The G4 motifs were prevalent in key regulatory genes associated with hypoxia (group VII ERFs), oxidative stress (DJ-1/GATase1), and energy status (AMPK/SnRK) pathways. They also showed statistical enrichment for genes in metabolic pathways that function in glycolysis, sugar degradation, inositol metabolism, and base excision repair. Collectively, the maize G4 motifs may represent conditional regulatory elements that can aid in energy status gene responses. Such a network of elements could provide a mechanistic basis for linking energy status signals to gene regulation in maize, a model genetic system and major world crop species for feed, food, and fuel.

MetaBlast! Virtual Cell (from now on referred to as VC) is a game design solution to a specific scientific and educational problem; expressly, how to make advanced, university level plant biology instruction on molecular and anatomical levels an exciting, efficient learning experience. The advanced technologies of 3D modeling and animation, computer programming and game design are united and tempered with strong, scientific guidance for accuracy and art direction for a powerful visual and audio simulation. The additional strength of intense gaming as a powerful tool aiding memory, logic and problem solving has recently become well recognized. Virtual Cell will provide a unique gaming experience, while transparently teaching scientifically accurate facts and concepts about, in this case, a soybean plant’s inner workings and dependant mechanisms on multiple scales and levels of complexity. Virtual Cell (from now on referred to as VC) in the future may prove to be a reference for other scientific/education endeavors as scientists battle for a more prominent mind share among average citizens. This paper will discuss the difficulties of developing VC, its structure, intended game and educational goals along with additional benefits to both the sciences and gaming industry.