Cartilage-selective genes identified in genome-scale analysis of non-cartilage and cartilage gene expression

<p>Abstract</p> <p>Background</p> <p>Cartilage plays a fundamental role in the development of the human skeleton. Early in embryogenesis, mesenchymal cells condense and differentiate into chondrocytes to shape the early skeleton. Subsequently, the cartilage anlagen differentiate to form the growth plates, which are responsible for linear bone growth, and the articular chondrocytes, which facilitate joint function. However, despite the multiplicity of roles of cartilage during human fetal life, surprisingly little is known about its transcriptome. To address this, a whole genome microarray expression profile was generated using RNA isolated from 18–22 week human distal femur fetal cartilage and compared with a database of control normal human tissues aggregated at UCLA, termed Celsius.</p> <p>Results</p> <p>161 cartilage-selective genes were identified, defined as genes significantly expressed in cartilage with low expression and little variation across a panel of 34 non-cartilage tissues. Among these 161 genes were cartilage-specific genes such as cartilage collagen genes and 25 genes which have been associated with skeletal phenotypes in humans and/or mice. Many of the other cartilage-selective genes do not have established roles in cartilage or are novel, unannotated genes. Quantitative RT-PCR confirmed the unique pattern of gene expression observed by microarray analysis.</p> <p>Conclusion</p> <p>Defining the gene expression pattern for cartilage has identified new genes that may contribute to human skeletogenesis as well as provided further candidate genes for skeletal dysplasias. The data suggest that fetal cartilage is a complex and transcriptionally active tissue and demonstrate that the set of genes selectively expressed in the tissue has been greatly underestimated.</p

Improving the efficiency of genomic loci capture using oligonucleotide arrays for high throughput resequencing

<p>Abstract</p> <p>Background</p> <p>The emergence of next-generation sequencing technology presents tremendous opportunities to accelerate the discovery of rare variants or mutations that underlie human genetic disorders. Although the complete sequencing of the affected individuals' genomes would be the most powerful approach to finding such variants, the cost of such efforts make it impractical for routine use in disease gene research. In cases where candidate genes or loci can be defined by linkage, association, or phenotypic studies, the practical sequencing target can be made much smaller than the whole genome, and it becomes critical to have capture methods that can be used to purify the desired portion of the genome for shotgun short-read sequencing without biasing allelic representation or coverage. One major approach is array-based capture which relies on the ability to create a custom in-situ synthesized oligonucleotide microarray for use as a collection of hybridization capture probes. This approach is being used by our group and others routinely and we are continuing to improve its performance.</p> <p>Results</p> <p>Here, we provide a complete protocol optimized for large aggregate sequence intervals and demonstrate its utility with the capture of all predicted amino acid coding sequence from 3,038 human genes using 241,700 60-mer oligonucleotides. Further, we demonstrate two techniques by which the efficiency of the capture can be increased: by introducing a step to block cross hybridization mediated by common adapter sequences used in sequencing library construction, and by repeating the hybridization capture step. These improvements can boost the targeting efficiency to the point where over 85% of the mapped sequence reads fall within 100 bases of the targeted regions.</p> <p>Conclusions</p> <p>The complete protocol introduced in this paper enables researchers to perform practical capture experiments, and includes two novel methods for increasing the targeting efficiency. Coupled with the new massively parallel sequencing technologies, this provides a powerful approach to identifying disease-causing genetic variants that can be localized within the genome by traditional methods.</p

Ciliary Abnormalities Due to Defects in the Retrograde Transport Protein DYNC2H1 in Short-Rib Polydactyly Syndrome

The short-rib polydactyly (SRP) syndromes are a heterogenous group of perinatal lethal skeletal disorders with polydactyly and multisystem organ abnormalities. Homozygosity by descent mapping in a consanguineous SRP family identified a genomic region that contained DYNC2H1, a cytoplasmic dynein involved in retrograde transport in the cilium. Affected individuals in the family were homozygous for an exon 12 missense mutation that predicted the amino acid substitution R587C. Compound heterozygosity for one missense and one null mutation was identified in two additional nonconsanguineous SRP families. Cultured chondrocytes from affected individuals showed morphologically abnormal, shortened cilia. In addition, the chondrocytes showed abnormal cytoskeletal microtubule architecture, implicating an altered microtubule network as part of the disease process. These findings establish SRP as a cilia disorder and demonstrate that DYNC2H1 is essential for skeletogenesis and growth

Disease Gene Characterization through Large-Scale Co-Expression Analysis

In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis

Comprehensive assessment of expression of insulin signaling pathway components in subcutaneous adipose tissue of women with and without polycystic ovary syndrome

Objective: Insulin resistance is a common feature of polycystic ovary syndrome (PCOS). The insulin signaling pathway consists of two major pathways, the metabolic and the mitogenic cascades. The many components of these pathways have not been comprehensively analyzed for differential expression in insulin-responsive tissues in PCOS. The goal of this study was to determine whether the core elements of the insulin signal transduction cascade were differentially expressed in subcutaneous adipose tissue (SAT) between PCOS and controls. Materials/methods: Quantitative real-time PCR for 36 insulin signaling pathway genes was performed in subcutaneous adipose tissue from 22 white PCOS and 13 healthy controls. Results: Genes in the insulin signaling pathway were not differentially expressed in subcutaneous adipose tissue between PCOS and controls (P > 0.05 for all). Components mainly of the mitogenic pathway were correlated with both androgens and metabolic phenotypes. Expression levels of five genes (MKNK1, HRAS, NRAS, KRAS, and GSK3A) were positively correlated with total testosterone level (ρ > 0, P < 0.05). Inverse correlation was found between expression of six genes (HRAS, MAP2K2, NRAS, MAPK3, GRB2, and SHC1) and metabolic traits (body mass index, fasting glucose, fasting insulin, and HOMA-IR) (ρ < 0, P < 0.05). Conclusions: Differential expression of core insulin signaling pathway components in subcutaneous adipose tissue is not a major contributor to the pathogenesis of PCOS. Correlation between clinical phenotypes and expression of several genes in the mitogenic limb of the insulin signaling pathway suggests mitogenic signaling by insulin may regulate steroidogenesis and glucose homeostasis

Mapping the STK4/Hippo signaling network in prostate cancer cell.

Dysregulation of MST1/STK4, a key kinase component of the Hippo-YAP pathway, is linked to the etiology of many cancers with poor prognosis. However, how STK4 restricts the emergence of aggressive cancer remains elusive. Here, we investigated the effects of STK4, primarily localized in the cytoplasm, lipid raft, and nucleus, on cell growth and gene expression in aggressive prostate cancer. We demonstrated that lipid raft and nuclear STK4 had superior suppressive effects on cell growth in vitro and in vivo compared with cytoplasmic STK4. Using RNA sequencing and bioinformatics analysis, we identified several differentially expressed (DE) genes that responded to ectopic STK4 in all three subcellular compartments. We noted that the number of DE genes observed in lipid raft and nuclear STK4 cells were much greater than cytoplasmic STK4. Our functional annotation clustering showed that these DE genes were commonly associated with oncogenic pathways such as AR, PI3K/AKT, BMP/SMAD, GPCR, WNT, and RAS as well as unique pathways such as JAK/STAT, which emerged only in nuclear STK4 cells. These findings indicate that MST1/STK4/Hippo signaling restricts aggressive tumor cell growth by intersecting with multiple molecular pathways, suggesting that targeting of the STK4/Hippo pathway may have important therapeutic implications for cancer