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In Print: Last Week's Microarray Papers of Note: Aug 28, 2012

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A gene expression profile of stem cell pluripotentiality and differentiation is conserved across diverse solid and hematopoietic cancers.

Genome Biol. 2012 Aug 21;13(8):R71.

Palmer N, Schmid P, Berger B, Kohane I.

The authors identify mRNA transcription patterns associated with pluripotent stem cells and then use this profile to derive a quantitative measure of "stem cell-like" gene expression activity. They demonstrate how their 189-gene signature "stratifies a variety of stem cell, malignant, and normal tissue samples by their relative plasticity and state of differentiation within Concordia, a diverse gene expression database consisting of 3,209 Affymetrix HGU133+ 2.0 microarray assays." The authors also demonstrate how the stem-like signature serves as a "proxy for tumor grade" in a variety of solid tumors, including brain, breast, lung, and colon.


Genetic linkage of soil carbon pools and microbial functions in subtropical freshwater wetlands in response to experimental warming.

Appl Environ Microbiol. 2012 Aug 24. [Epub ahead of print]

Wang H, He Z, Lu Z, Zhou J, Van Nostrand JD, Xu X, Zhang Z.

The authors conducted a field microcosm experiment to examine the impact of soil warming in freshwater wetlands on different organic carbon pools and associated microbial functional responses. They used a DNA microarray called GeoChip 4.0 to determine microbial gene diversity and functional potential for carbon degradation. The team found that experimental warming "significantly increased soil porewater-dissolved organic [carbon] and phosphorus concentrations, leading to a higher potential for C emission and P export." The findings suggest that soil warming enhances fungal decomposition of lignin-like compounds, "leading to greater microbially mediated C losses than previously estimated in freshwater wetlands."


Evaluation of a fosmid-clone-based microarray for comparative analysis of swine fecal metagenomes.

J Microbiol. 2012 Aug;50(4):684-8.

Park SJ, Kim DH, Jung MY, Kim SJ, Kim H, Kim YH, Chae JC, Rhee SK.

The researchers fabricated a metagenome array comprised of a glass slide arrayed with fosmid clone DNA generated from swine feces. A comparison of swine fecal metagenomes with the array "suggested that the microbial community composition of swine intestine could be dependent on the health state of swine."


A cancer/testis antigen microarray to screen autoantibody biomarkers of non-small cell lung cancer.

Cancer Lett. 2012 Aug 22. [Epub ahead of print]

Shan Q, Lou X, Xiao T, Zhang J, Sun H, Gao Y, Cheng S, Wu L, Xu N, Liu S.

The researchers used a low-density protein microarray, which consisted of 72 cancer/testis antigens and 6 non-cancer/testis antigens, to screen for lung cancer-related autoantibodies. They found that a cancer/testis antigen panel consisting of NY-ESO-1, XAGE-1, ADAM29, and MAGEC1 had sensitivity and specificity values of 33 percent and 96 percent, respectively. The markers preferentially detected non-small cell lung cancer, but did not detect breast cancer or non-cancer lung disease, the authors reported.


O-antigen and virulence profiling of Shiga toxin-producing Escherichia coli by a rapid and cost-effective DNA microarray colorimetric method.

Front Cell Infect Microbiol. 2012;2:61.

Quiñones B, Swimley MS, Narm KE, Patel RN, Cooley MB, Mandrell RE.

The authors combined the use of DNA microarrays with the ampliPHOX colorimetric method to detect and genotype Shiga-toxin-producing Escherichia coli (STEC). They designed a low-density 30-mer oligonucleotide DNA array to target O-antigen gene clusters of 11 E. coli serogroups (O26, O45, O91, O103, O104, O111, O113, O121, O128, O145, and O157) that have been associated with the majority of STEC infections. The DNA microarray also targeted 11 virulence genes, encoding adhesins, cytotoxins, proteases, and receptor proteins that have also been associated with STEC infections. "Results from the validation experiments demonstrated that this microarray-based colorimetric method allowed for a rapid and accurate genotyping of STEC reference strains from environmental and clinical sources and from distinct geographical locations," the authors concluded.


Identification of microRNAs changed in the neonatal lungs in response to hyperoxia exposure.

Physiol Genomics. 2012 Aug 21. [Epub ahead of print]

Bhaskaran M, Xi D, Wang Y, Huang C, Narasaraju T, Shu W, Zhao C, Xiao X, More S, Breshears M,
Liu L.

The authors set out to identify miRNAs that are differentiated in bronchopulmonary dysplasia, a chronic lung disease of premature infants. The team used an miRNA microarray and real-time PCR to identify changed miRNAs and their target genes in neonatal rat lungs in response to hyperoxia exposure. They found down-regulation of five miRNAs — miR-342, miR-335, miR-150, miR-126* and miR-151* — and up-regulation of two miRNAs — miR-21 and miR-34a. Additional DNA microarray analysis identified several genes with conserved binding sites for these altered miRNAs.


Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis.

BMC Med Genomics. 2012 Aug 21;5(1):35.

Turnbull AK, Kitchen RR, Larionov AA, Renshaw L, Dixon JM, Sims AH.

The authors set out to determine whether "platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis" of publicly available data from Affymetrix GeneChips and Illumina BeadArrays. Using Affymetrix and Illumina data from the Microarray Quality Control Project and elsewhere, the researchers evaluated several approaches to directly integrate intensity-level expression data from the two platforms. They found that two approaches — ComBat and cross platform normalization — "significantly outperform mean-centering and distance-weighted discrimination in terms of minimizing inter-platform variance." In particular, they found that distance-weighted discrimination "removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets."

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