The placenta remains poorly studied despite being implicated in many pregnancy and chronic disorders. Trophoblast invasion and nutrient transport are critical placental processes where defects can lead to preeclampsia and other diseases. We set out to characterize protein pathways underlying those processes, using mouse as a model. We profiled the placenta proteome and phosphoproteome at embryonic day (e)7.5, when trophoblast invasion peaks, and at e9.5, when nutrient transport is occurring, and integrated this data with RNA-seq.

Comparing the unmodified proteome and the transcriptome revealed that most upregulated proteins are not the result of transcript changes. However, genes upregulated at both levels reflected expected functions, such as enrichment of migration processes at e7.5 and of metabolic processes at e9.5. Proteins that were only upregulated at the protein level contained potentially novel genes involved in migration and patterning, and indicated that the placenta at e9.5 is under stress.

The phosphoproteome revealed novel phosphosites on placental transcription factors (TFs) that were conserved in human and differentially phosphorylated in our dataset, indicating an important role for the sites in modulating TF function. When we combined the phosphoproteome with the other datasets, we found further clues that e9.5 placenta is a stressful environment, and we identified a posttranscriptionally and posttranslationally regulated network at this timepoint.

This analysis provides a systems-level view of gene expression patterns at two critical timepoints of placental development, and opens the door for experimental validation of potentially novel proteins, phosphosites, and pathways that may be critical for normal placental function.

Telomeres, the ends of linear chromosomes, are composed of simple tandem repeats which are usually G·C rich. Telomeres are essential for chromosome stability, organizing the nuclear architecture and ensuring complete replication of the chromosomal terminus. To understand how telomeres carry out these fundamental cellular roles, one must understand the structural and dynamic properties of telomeric repeat sequences. Structural and genetic approaches were taken to learn more about telomeric nucleic acids;The structural portion of my research concerned an unusual DNA structure formed by the C-rich strand of telomeric DNA. Telomeric C-strand sequences form non-Watson-Crick structures in supercoiled plasmids at low pH. Absorbance thermal denaturation, chemical modification and non-denaturing gel electrophoresis showed that telomeric C-strand oligonucleotides form stable structures at low pH. H1[superscript]'-H1[superscript]' nuclear Overhauser effects indicated that these structures were four-stranded. In addition, these four-stranded C-structures were shown to mediate recognition and binding of identical nucleic acid sequences. Thus, a novel nucleic acid dimerization motif was discovered;The genetic portion of my research concerned analysis of a Tetrahymena mutant with short telomeres. This mutant was heterozygous for a telomerase RNA mutation. Telomerase is a ribonucleoprotein that uses its RNA component as a template for addition of telomeric repeats to chromosome termini. Therefore, telomerase is involved in telomere length regulation, a process that has been implicated in both aging and cancer. The mutant telomerase RNA gene caused telomere shortening when introduced into wildtype cells, and thus identifies a functionally important domain of the telomerase RNA. Although mutant telomerase activity was indistinguishable from wildtype activity in vitro, cells expressing high levels of the mutant telomerase RNA exhibited lethal phenotypes that were due to the presence of very short telomeres.

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.

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.

The eukaryotic alga, Chlamydomonas reinhardtii, acclimates to limiting CO2 conditions by the induction of the CO2-concentrating mechanism (CCM) – a complex system of changes in its metabolism, gene expression patterns, and physiology – to compensate for the reduction in the amount of available CO2 and counter the hindrance to its ability to photosynthesize and grow. LCIB, a gene upregulated in such conditions, encodes a protein potentially involved in uptake of CO2 into the cell and in preventing the leakage of CO2 out of the cell. This protein is indispensable for growth in air-level CO2 (~350-400 ppm), since mutants in this gene are unable to grow (hence they are called air dier mutants). Several mutants that have second-site alterations that restore growth in air-level CO2 (i.e., suppress the air dier phenotype) have been isolated. Identifying the genes that are mutated in these suppressors and the functions of the encoded proteins will help us better discern the role of LCIB and comprehend the workings of the entire mechanism.

To identify the locus of the mutated gene in one suppressor mutant, crosses were performed between the mutant strain and a non-mutant, polymorphic wild-type strain, and a large population of recombinant progeny that segregated against the mutation of interest was amassed. Using a strain that has unique single nucleotide polymorphisms (SNPs) as the non-mutant parent allowed us to seek a particular characteristic (the polymorphisms) in the region of interest in the genome of the progeny. With a sequenced genome, a library of SNPs in the polymorphic strain, and a pool of the genomic DNA from the entire population, we mapped the mutation to a specific region of the genome and narrowed potential candidates down to a small number of genes. By cosegregation analysis, we were able to confirm one of the candidates, LCI15, as the implicated gene. Preliminary functional analyses with semi-quantitative RT-PCR and Western immunoblots reveal the LCI15 protein as possibly playing an overarching role in regulation in the CCM and offer terms for discussing potential methods by which the lack of LCI15 might potentially mask the deleterious effects of the absence of LCIB.

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 Association of American Colleges and Universities ranks multiple process of science (POS) skills among the top-10 skills employers seek in college graduates. As part of an effort to explore and align the emphasis on POS skills in our science departments, we sought three things: (a) to determine if faculty and students felt enough time was devoted to POS skills, (b) to identify the skills that science students and faculty felt were important to acquire from an undergraduate education, and (c) to evaluate whether there were differences in these views among disciplines. We found that faculty and students agreed on the amount of time spent in class on POS skills, but students thought that amount of time was sufficient, whereas faculty did not. Further, students and faculty placed a high importance on the POS skills of problem solving/critical thinking, communicating results, and interpreting data. We did not find differences among faculty of different science disciplines on the most highly ranked POS skills, but we did in students. The findings of this study have informed curricular discussions and decisions.

Plant epidermal cells express unique molecular machinery that juxtapose the assembly of intracellular lipid components and the unique extracellular cuticular lipids that are unidirectionally secreted to plant surfaces. In maize (Zea mays), mutations at the glossy2 (gl2) locus affect the deposition of extracellular cuticular lipids. Sequence-based genome scanning identified a new Gl2 homolog in the maize genome, namely Gl2-like. Both the Gl2-like and Gl2 genes are members of the BAHD superfamily of acyltransferases, with close sequence similarity to the Arabidopsis (Arabidopsis thaliana) CER2 gene. Transgenic experiments demonstrated that Gl2-like and Gl2 functionally complement the Arabidopsis cer2 mutation, with differential influences on the cuticular lipids and the lipidome of the plant, particularly affecting the longer alkyl chain acyl lipids, especially at the 32-carbon chain length. Site-directed mutagenesis of the putative BAHD catalytic HXXXDX-motif indicated that Gl2-like requires this catalytic capability to fully complement the cer2 function, but Gl2 can accomplish complementation without the need for this catalytic motif. These findings demonstrate that Gl2 and Gl2-like overlap in their cuticular lipid function, but have evolutionarily diverged to acquire nonoverlapping functions.

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.