Encapsulation Thermogenic Preadipocytes for Transplantation into Adipose Tissue Depots

Cell encapsulation was developed to entrap viable cells within semi-permeable membranes. The engrafted encapsulated cells can exchange low molecular weight metabolites in tissues of the treated host to achieve long-term survival. The semipermeable membrane allows engrafted encapsulated cells to avoid rejection by the immune system. The encapsulation procedure was designed to enable a controlled release of bioactive compounds, such as insulin, other hormones, and cytokines. Here we describe a method for encapsulation of catabolic cells, which consume lipids for heat production and energy dissipation (thermogenesis) in the intra-abdominal adipose tissue of obese mice. Encapsulation of thermogenic catabolic cells may be potentially applicable to the prevention and treatment of obesity and type 2 diabetes. Another potential application of catabolic cells may include detoxification from alcohols or other toxic metabolites and environmental pollutants.     

Cross-Platform Prediction of Gene Expression Signatures

Gene expression signatures can predict the activation of oncogenic pathways and other phenotypes of interest via quantitative models that combine the expression levels of multiple genes. However, as the number of platforms to measure genome-wide gene expression proliferates, there is an increasing need to develop models that can be ported across diverse platforms. Because of the range of technologies that measure gene expression, the resulting signal values can vary greatly. To understand how this variation can affect the prediction of gene expression signatures, we have investigated the ability of gene expression signatures to predict pathway activation across Affymetrix and Illumina microarrays. We hybridized the same RNA samples to both platforms and compared the resultant gene expression readings, as well as the signature predictions. Using a new approach to map probes across platforms, we found that the genes in the signatures from the two platforms were highly similar, and that the predictions they generated were also strongly correlated. This demonstrates that our method can map probes from Affymetrix and Illumina microarrays, and that this mapping can be used to predict gene expression signatures across platforms.     

Inflammation and Race and Gender Differences in Computerized Tomography‐measured Adipose Depots

A growing body of evidence has consistently shown a correlation between obesity and chronic subclinical inflammation. It is unclear whether the size of specific adipose depots is more closely associated with concentrations of inflammatory markers than overall adiposity. This study investigated the relationship between inflammatory markers and computerized tomography‐derived abdominal visceral and subcutaneous fat and thigh intermuscular and subcutaneous fat in older white and black adults. Data were from 2,651 black and white men and women aged 70–79 years participating in the Health, Aging, and Body Composition (Health ABC) study. Inflammatory markers, interleukin‐6 (IL‐6), C‐reactive protein (CRP), and tumor necrosis factor‐α (TNF‐α) were obtained from serum samples. Abdominal visceral and subcutaneous fat and thigh intermuscular and subcutaneous fat were quantified on computerized tomography images. Linear regression analysis was used to evaluate the cross‐sectional relationship between specific adipose depots and inflammatory markers in four race/gender groups. As expected, blacks have less visceral fat than whites and women less visceral fat than men. However, abdominal visceral adiposity was most consistently associated with significantly higher IL‐6 and CRP concentrations in all race/gender groups (P < 0.05), even after controlling for general adiposity. Thigh intermuscular fat had an inconsistent but significant association with inflammation, and there was a trend toward lower inflammatory marker concentration with increasing thigh subcutaneous fat in white and black women. Despite the previously established differences in abdominal fat distribution across gender and race, visceral fat remained a significant predictor of inflammatory marker concentration across all four subgroups examined.     

High Throughput Screening of Gene Expression Signatures

This paper focuses on microarray image analysis and discusses a completely automated approach to image processing, which eliminates human intervention. A system for automated image processing is described, which is capable of processing image files in a batch-mode thus allowing high-throughput of microarray image analysis. Grid-placement and spot finding are achieved without operator's help. The software eliminates noise signals from the data analysis process and minimizes operator's involvement in the procedure.     

Unique Microbial Signatures of the Alien Hawaiian Marine Sponge Suberites zeteki

Invasive species poses a threat to the world’s oceans. Alien sponges account for the majority of introduced marine species in the isolated Hawaiian reef ecosystems. In this study, cultivation-dependent and cultivation-independent techniques were applied to investigate microbial consortia associated with the alien Hawaiian marine sponge Suberites zeteki. Its microbial communities were diverse with representatives of Actinobacteria, Firmicutes, α- and γ-Proteobacteria, Bacteroidetes, Chlamydiae, Planctomycetes, and Cyanobacteria. Specifically, the genus Chlamydia was identified for the first time from marine sponges, and two genera (Streptomyces and Rhodococcus) were added to the short list of culturable actinobacteria from sponges. Culturable microbial communities were dominated by Bacillus species (63%) and contained actinobacterial species closely affiliated with those from habitats other than marine sponges. Cyanobacterial clones were clustered with free-living cyanobacteria from water column and other environmental samples; they show no affiliation with other sponge-derived cyanobacteria. The low sequence similarity of Planctomycetes, Chlamydiae, and α-Proteobacteria clones to other previously described sequences suggested that S. zeteki may contain new lineages of these bacterial groups. The microbial diversity of S. zeteki was different from that of other studied marine sponges. This is the first report on microbial communities of alien marine invertebrate species. For the first time, it provides an insight into microbial structure within alien marine sponges in the Hawaiian marine ecosystems.     

Unique Behavioral Characteristics and microRNA Signatures in a Drug Resistant Epilepsy Model

Background

Pharmacoresistance is a major issue in the treatment of epilepsy. However, the mechanism underlying pharmacoresistance to antiepileptic drugs (AEDs) is still unclear, and few animal models have been established for studying drug resistant epilepsy (DRE). In our study, spontaneous recurrent seizures (SRSs) were investigated by video-EEG monitoring during the entire procedure.

Methods/Principal Findings

In the mouse pilocarpine-induced epilepsy model, we administered levetiracetam (LEV) and valproate (VPA) in sequence. AED-responsive and AED-resistant mice were naturally selected after 7-day treatment of LEV and VPA. Behavioral tests (open field, object exploration, elevated plus maze, and light-dark transition test) and a microRNA microarray test were performed. Among the 37 epileptic mice with SRS, 23 showed significantly fewer SRSs during administration of LEV (n = 16, LEV sensitive (LS) group) or VPA (n = 7, LEV resistant/VPA sensitive (LRVS) group), while 7 epileptic mice did not show any amelioration with either of the AEDs (n = 7, multidrug resistant (MDR) group). On the behavioral assessment, MDR mice displayed distinctive behaviors in the object exploration and elevated plus maze tests, which were not observed in the LS group. Expression of miRNA was altered in LS and MDR groups, and we identified 4 miRNAs (miR-206, miR-374, miR-468, and miR-142-5p), which were differently modulated in the MDR group versus both control and LS groups.

Conclusion

This is the first study to identify a pharmacoresistant subgroup, resistant to 2 AEDs, in the pilocarpine-induced epilepsy model. We hypothesize that modulation of the identified miRNAs may play a key role in developing pharmacoresistance and behavioral alterations in the MDR group.     

An Adipose Tissue Atlas: An Image-Guided Identification of Human-like BAT and Beige Depots in Rodents

1. Download high-res image (247KB)
2. Download full-size image

     

Concurrent Gene Signatures for Han Chinese Breast Cancers

The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers.We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival.Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. Prognostic comparisons with published gene expression signatures showed a better discerning ability of concurrent genes, many of which were rarely identifiable if expression data were pre-selected by phenotype correlations or variability of individual genes.We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer.     

Conserved Non-Coding Regulatory Signatures in Arabidopsis Co-Expressed Gene Modules

Complex traits and other polygenic processes require coordinated gene expression. Co-expression networks model mRNA co-expression: the product of gene regulatory networks. To identify regulatory mechanisms underlying coordinated gene expression in a tissue-enriched context, ten Arabidopsis thaliana co-expression networks were constructed after manually sorting 4,566 RNA profiling datasets into aerial, flower, leaf, root, rosette, seedling, seed, shoot, whole plant, and global (all samples combined) groups. Collectively, the ten networks contained 30% of the measurable genes of Arabidopsis and were circumscribed into 5,491 modules. Modules were scrutinized for cis regulatory mechanisms putatively encoded in conserved non-coding sequences (CNSs) previously identified as remnants of a whole genome duplication event. We determined the non-random association of 1,361 unique CNSs to 1,904 co-expression network gene modules. Furthermore, the CNS elements were placed in the context of known gene regulatory networks (GRNs) by connecting 250 CNS motifs with known GRN cis elements. Our results provide support for a regulatory role of some CNS elements and suggest the functional consequences of CNS activation of co-expression in specific gene sets dispersed throughout the genome.     

Gene Expression Profiles Identify Inflammatory Signatures in Dendritic Cells

Dendritic cells (DCs) constitute a heterogeneous group of antigen-presenting leukocytes important in activation of both innate and adaptive immunity. We studied the gene expression patterns of DCs incubated with reagents inducing their activation or inhibition. Total RNA was isolated from DCs and gene expression profiling was performed with oligonucleotide microarrays. Using a supervised learning algorithm based on Random Forest, we generated a molecular signature of inflammation from a training set of 77 samples. We then validated this molecular signature in a testing set of 38 samples. Supervised analysis identified a set of 44 genes that distinguished very accurately between inflammatory and non inflammatory samples. The diagnostic performance of the signature genes was assessed against an independent set of samples, by qRT-PCR. Our findings suggest that the gene expression signature of DCs can provide a molecular classification for use in the selection of anti-inflammatory or adjuvant molecules with specific effects on DC activity.