A critical reanalysis of the relationship between genomics and well-being.

Fredrickson et al. [Fredrickson BL, et al. (2013) Proc Natl Acad Sci USA 110(33):13684-13689] claimed to have observed significant differences in gene expression related to hedonic and eudaimonic dimensions of well-being. Having closely examined both their claims and their data, we draw substantially different conclusions. After identifying some important conceptual and methodological flaws in their argument, we report the results of a series of reanalyses of their dataset. We first applied a variety of exploratory and confirmatory factor analysis techniques to their self-reported well-being data. A number of plausible factor solutions emerged, but none of these corresponded to Fredrickson et al.'s claimed hedonic and eudaimonic dimensions. We next examined the regression analyses that purportedly yielded distinct differential profiles of gene expression associated with the two well-being dimensions. Using the best-fitting two-factor solution that we identified, we obtained effects almost twice as large as those found by Fredrickson et al. using their questionable hedonic and eudaimonic factors. Next, we conducted regression analyses for all possible two-factor solutions of the psychometric data; we found that 69.2% of these gave statistically significant results for both factors, whereas only 0.25% would be expected to do so if the regression process was really able to identify independent differential gene expression effects. Finally, we replaced Fredrickson et al.'s psychometric data with random numbers and continued to find very large numbers of apparently statistically significant effects. We conclude that Fredrickson et al.'s widely publicized claims about the effects of different dimensions of well-being on health-related gene expression are merely artifacts of dubious analyses and erroneous methodology.

Whole-genome expression profiling of the marine diatom Thalassiosira pseudonana identifies genes involved in silicon bioprocesses.

Formation of complex inorganic structures is widespread in nature. Diatoms create intricately patterned cell walls of inorganic silicon that are a biomimetic model for design and generation of three-dimensional silica nanostructures. To date, only relatively simple silica structures can be generated in vitro through manipulation of known diatom phosphoproteins (silaffins) and long-chain polyamines. Here, we report the use of genome-wide transcriptome analyses of the marine diatom Thalassiosira pseudonana to identify additional candidate gene products involved in the biological manipulation of silicon. Whole-genome oligonucleotide tiling arrays and tandem mass spectrometry identified transcripts for >8,000 genes, approximately 3,000 of which were not previously described and included noncoding and antisense RNAs. Gene-specific expression profiles detected a set of 75 genes induced only under low concentrations of silicon but not under low concentrations of nitrogen or iron, alkaline pH, or low temperatures. Most of these induced gene products were predicted to contain secretory signals and/or transmembrane domains but displayed no homology to known proteins. Over half of these genes were newly discovered, identified only through the use of tiling arrays. Unexpectedly, a common set of 84 genes were induced by both silicon and iron limitations, suggesting that biological manipulation of silicon may share pathways in common with iron or, alternatively, that iron may serve as a required cofactor for silicon processes. These results provide insights into the transcriptional and translational basis for the biological generation of elaborate silicon nanostructures by these ecologically important microbes.

In-depth query of large genomes using tiling arrays.

Identification of the transcribed regions in the newly sequenced genomes is one of the major challenges of postgenomic biology. Among different alternatives for empirical transcriptome mapping, whole-genome tiling array experiment emerged as the most comprehensive and unbiased approach. This relatively new method uses high-density oligonucleotide arrays with probes chosen uniformly from both strands of the entire genomes including all genic and intergenic regions. By hybridizing the arrays with tissue specific or pooled RNA samples, a genome-wide picture of transcription can be derived. This chapter discusses computational tools and techniques necessary to successfully conduct genome tiling array experiments.

Global identification of noncoding RNAs in Saccharomyces cerevisiae by modulating an essential RNA processing pathway.

Noncoding RNAs (ncRNAs) perform essential cellular tasks and play key regulatory roles in all organisms. Although several new ncRNAs in yeast were recently discovered by individual studies, to our knowledge no comprehensive empirical search has been conducted. We demonstrate a powerful and versatile method for global identification of previously undescribed ncRNAs by modulating an essential RNA processing pathway through the depletion of a key ribonucleoprotein enzyme component, and monitoring differential transcriptional activities with genome tiling arrays during the time course of the ribonucleoprotein depletion. The entire Saccharomyces cerevisiae genome was scanned during cell growth decay regulated by promoter-mediated depletion of Rpp1, an essential and functionally conserved protein component of the RNase P enzyme. In addition to most verified genes and ncRNAs, expression was detected in 98 antisense and intergenic regions, 74 that were further confirmed to contain previously undescribed RNAs. A class of ncRNAs, located antisense to coding regions of verified protein-coding genes, is discussed in this article. One member, HRA1, is likely involved in 18S rRNA maturation.

Identification of transcribed sequences in Arabidopsis thaliana by using high-resolution genome tiling arrays.

Using a maskless photolithography method, we produced DNA oligonucleotide microarrays with probe sequences tiled throughout the genome of the plant Arabidopsis thaliana. RNA expression was determined for the complete nuclear, mitochondrial, and chloroplast genomes by tiling 5 million 36-mer probes. These probes were hybridized to labeled mRNA isolated from liquid grown T87 cells, an undifferentiated Arabidopsis cell culture line. Transcripts were detected from at least 60% of the nearly 26,330 annotated genes, which included 151 predicted genes that were not identified previously by a similar genome-wide hybridization study on four different cell lines. In comparison with previously published results with 25-mer tiling arrays produced by chromium masking-based photolithography technique, 36-mer oligonucleotide probes were found to be more useful in identifying intron-exon boundaries. Using two-dimensional HPLC tandem mass spectrometry, a small-scale proteomic analysis was performed with the same cells. A large amount of strongly hybridizing RNA was found in regions "antisense" to known genes. Similarity of antisense activities between the 25-mer and 36-mer data sets suggests that it is a reproducible and inherent property of the experiments. Transcription activities were also detected for many of the intergenic regions and the small RNAs, including tRNA, small nuclear RNA, small nucleolar RNA, and microRNA. Expression of tRNAs correlates with genome-wide amino acid usage.

Genome-wide transcriptional analysis of flagellar regeneration in Chlamydomonas reinhardtii identifies orthologs of ciliary disease genes.

The important role that cilia and flagella play in human disease creates an urgent need to identify genes involved in ciliary assembly and function. The strong and specific induction of flagellar-coding genes during flagellar regeneration in Chlamydomonas reinhardtii suggests that transcriptional profiling of such cells would reveal new flagella-related genes. We have conducted a genome-wide analysis of RNA transcript levels during flagellar regeneration in Chlamydomonas by using maskless photolithography method-produced DNA oligonucleotide microarrays with unique probe sequences for all exons of the 19,803 predicted genes. This analysis represents previously uncharacterized whole-genome transcriptional activity profiling study in this important model organism. Analysis of strongly induced genes reveals a large set of known flagellar components and also identifies a number of important disease-related proteins as being involved with cilia and flagella, including the zebrafish polycystic kidney genes Qilin, Reptin, and Pontin, as well as the testis-expressed tubby-like protein TULP2.

Global snapshot of a protein interaction network-a percolation based approach.

MOTIVATION: Biologically significant information can be revealed by modeling large-scale protein interaction data using graph theory based network analysis techniques. However, the methods that are currently being used draw conclusions about the global features of the network from local connectivity data. A more systematic approach would be to define global quantities that measure (1) how strongly a protein ties with the other parts of the network and (2) how significantly an interaction contributes to the integrity of the network, and connect them with phenotype data from other sources. In this paper, we introduce such global connectivity measures and develop a stochastic algorithm based upon percolation in random graphs to compute them. RESULTS: We show that, in terms of global connectivities, the distribution of essential proteins is distinct from the background. This observation highlights a fundamental difference between the essential and the non-essential proteins in the network. We also find that the interaction data obtained from different experimental methods such as immunoprecipitation and two-hybrid techniques contribute differently to network integrities. Such difference between different experimental methods can provide insight into the systematic bias present among these techniques. SUPPLEMENTARY INFORMATION: The full list of our results can be found in the supplemental web site http://www.nas.nasa.gov/Groups/SciTech/nano/msamanta/projects/percolation/index.php

Predicting protein functions from redundancies in large-scale protein interaction networks.

Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.