MASALAH-MASALAH PEMBELAJARAN YANG DIHADAPI WIDYAISWARA : Studi Kasus Pada Lembaga Diktat Pemda Tk.I Propinsi Bengkulu

<div><p>Rat strains differ dramatically in their susceptibility to mammary carcinogenesis. On the assumption that susceptibility genes are conserved across mammalian species and hence inform human carcinogenesis, numerous investigators have used genetic linkage studies in rats to identify genes responsible for differential susceptibility to carcinogenesis. Using a genetic backcross between the resistant Copenhagen (Cop) and susceptible Fischer 344 (F344) strains, we mapped a novel mammary carcinoma susceptibility (<i>Mcs30</i>) locus to the centromeric region on chromosome 12 (LOD score of ∼8.6 at the D12Rat59 marker). The <i>Mcs30</i> locus comprises approximately 12 Mbp on the long arm of rat RNO12 whose synteny is conserved on human chromosome 13q12 to 13q13. After analyzing numerous genes comprising this locus, we identified <i>Fry</i>, the rat ortholog of the furry gene of <i>Drosophila melanogaster,</i> as a candidate <i>Mcs</i> gene. We cloned and determined the complete nucleotide sequence of the 13 kbp <i>Fry</i> mRNA. Sequence analysis indicated that the <i>Fry</i> gene was highly conserved across evolution, with 90% similarity of the predicted amino acid sequence among eutherian mammals. Comparison of the <i>Fry</i> sequence in the Cop and F344 strains identified two non-synonymous single nucleotide polymorphisms (SNPs), one of which creates a putative, de novo phosphorylation site. Further analysis showed that the expression of the <i>Fry</i> gene is reduced in a majority of rat mammary tumors. Our results also suggested that FRY activity was reduced in human breast carcinoma cell lines as a result of reduced levels or mutation. This study is the first to identify the <i>Fry</i> gene as a candidate <i>Mcs</i> gene. Our data suggest that the SNPs within the <i>Fry</i> gene contribute to the genetic susceptibility of the F344 rat strain to mammary carcinogenesis. These results provide the foundation for analyzing the role of the human <i>FRY</i> gene in cancer susceptibility and progression.</p></div

Chapter 15: Disease Gene Prioritization

<div><p>Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.</p></div

MC4R-centered protein-protein interaction network.

<p>The figure illustrates protein-protein interaction neighborhood of the human melanocortin 4 receptor (MC4R) as illustrated by the confidence view of the STRING 8.3 server. The nodes of the graph represent human proteins and the connections illustrate their known or predicted, direct and indirect interactions. The connection between any two protein-nodes is based on the available information mined from relevant databases and literature. The network includes all protein interactions that have >0.9 estimated probability.</p

The available data sources and gene prioritization tools.

<p>There is a wide range of data sources that can be used to infer the above-described pieces of evidence. The existing tools try to take advantage of many (if not all) of them. This table summarizes the collections and methodologies that make current state of the art in gene prioritization possible. Note, not all resources mentioned here are utilized by all gene prioritization tools nor are all data sources available listed. Moreover, some resources may be classified as more than one data-type. Many of the resources reported here are available electronically through the gene prioritization portal <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002902#pcbi.1002902-Tranchevent3" target="_blank">[124]</a>.</p

PolySearch gene-disease associations.

<p>PolySearch uses PubMed lookup results to prioritize diseases associated with a given gene. Here, screen shots of the top two results (where available; sorted by relevancy score metric) from PolySearch are shown. According to these, BRCA1 and PIK3CA are associated with breast cancer, while MC4R and CLC1 are not. These results quantitatively confirm intuitive inferences made from simple PubMed searches.</p

Correlating cross-species phenotypes.

<p>Phenotypes of wild-type (top) and PAX6 ortholog mutations (bottom) in human, mouse, zebrafish, and fly can be described with the EQ method suggested by Washington et al <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002902#pcbi.1002902-Washington1" target="_blank">[59]</a>. Once phenotypic descriptions are standardized across species, genotypic variations can be assessed as well.</p

Overview of gene prioritization data flow.

<p>In order to prioritize disease-gene candidates various pieces of information about the disease and the candidate genetic interval are collected (green layer). These describe the biological relationships and concepts (blue layer) relating the disease to the possible causal genes. Note, the blue layer (representing the biological meaning) should ideally be blind to the content green layer (information collection); <i>i.e.</i> any resource that describes the needed concepts may be used by a gene prioritization method.</p

Adding faces and names increases engagement.

<p>Adding faces and names increases engagement.</p

Comics have a way of going viral.

<p>Comics have a way of going viral.</p

Adding information can create a “Vennster” (the intersection of a Venn diagram and monster).

<p>Adding information can create a “Vennster” (the intersection of a Venn diagram and monster).</p