Detection and validation of single feature polymorphisms in cowpea (L. Walp) using a soybean genome array-0

detected in the SFP probe region detected by RPP (underlined). I, IT93K-503-1; C, CB46; G, cowpea methyl filtered sequence; S, target sequence from soybean SIF.<p><b>Copyright information:</b></p><p>Taken from "Detection and validation of single feature polymorphisms in cowpea (L. Walp) using a soybean genome array"</p><p>http://www.biomedcentral.com/1471-2164/9/107</p><p>BMC Genomics 2008;9():107-107.</p><p>Published online 28 Feb 2008</p><p>PMCID:PMC2270837.</p><p></p

Detection and validation of single feature polymorphisms in cowpea (L. Walp) using a soybean genome array-4

Ltered cowpea sequence; S, target sequence from soybean SIF. The target SFP probe number is given in parenthesis.<p><b>Copyright information:</b></p><p>Taken from "Detection and validation of single feature polymorphisms in cowpea (L. Walp) using a soybean genome array"</p><p>http://www.biomedcentral.com/1471-2164/9/107</p><p>BMC Genomics 2008;9():107-107.</p><p>Published online 28 Feb 2008</p><p>PMCID:PMC2270837.</p><p></p

Genome distribution of rice orthologs of wheat genes represented on the wheat genome array

<p><b>Copyright information:</b></p><p>Taken from "Mapping translocation breakpoints using a wheat microarray"</p><p></p><p>Nucleic Acids Research 2007;35(9):2936-2943.</p><p>Published online 16 Apr 2007</p><p>PMCID:PMC1888831.</p><p>© 2007 The Author(s)</p> Percent of wheat probe sets (-axis) with rice gene models on each of 12 rice chromosomes (-axis)

Detection and validation of single feature polymorphisms in cowpea (L. Walp) using a soybean genome array-3

Wpea methyl filtered sequence and soybean SIF are in black. The position of SFP probe number 2 detected by the RPP method is underlined. Arrows indicate SNPs. I, IT93K-503-1; C, CB46; G, cowpea methyl filtered sequence; S, target sequence from soybean SIF.<p><b>Copyright information:</b></p><p>Taken from "Detection and validation of single feature polymorphisms in cowpea (L. Walp) using a soybean genome array"</p><p>http://www.biomedcentral.com/1471-2164/9/107</p><p>BMC Genomics 2008;9():107-107.</p><p>Published online 28 Feb 2008</p><p>PMCID:PMC2270837.</p><p></p

Distribution Profiling of Circulating MicroRNAs in Serum

Circulating microRNAs (miRNAs) are potential biomarkers useful in cancer diagnosis. They have been found to be bound to various carriers like proteins, lipoprotein particles, and exosomes. It is likely that only miRNAs in particular carriers, but not the overall quantity, are directly related to cancer development. Herein, we developed a method for rapid separation of different miRNA carriers in serum using asymmetrical flow field flow fractionation (AF4). Sera from two healthy individuals (control) or from two cancer patients (case) were fractionated. Six fractions enriching different types of miRNA carriers, such as the lipoprotein particles and exosomes, were collected. The quantities of eight selected miRNAs in each fraction were obtained by RT-qPCR to yield their distribution profiles among the carriers. Larger changes in miRNA quantity between the control and the case were detected in the fractionated results compared to the sum values. Statistical analysis on the distribution profiles also proved that, the quantities of 4 miRNAs within particular fractions showed significant difference between the controls and the cases. On the contrary, if the overall quantity of the miRNA was subject to the same statistical analysis, only 2 miRNAs exhibited significant difference. Moreover, principle component analysis revealed good separation between the controls and the cases with the fractionated miRNA amounts. All in all, we have demonstrated that, our method enables comprehensive screening of the distribution of circulating miRNAs in the carriers. The obtained distribution profile enlarges the miRNA expression difference between healthy individuals and cancer patients, facilitating the discovery of specific miRNA biomarkers for cancer diagnosis

Saliva Microbiota Carry Caries-Specific Functional Gene Signatures

<div><p>Human saliva microbiota is phylogenetically divergent among host individuals yet their roles in health and disease are poorly appreciated. We employed a microbial functional gene microarray, HuMiChip 1.0, to reconstruct the global functional profiles of human saliva microbiota from ten healthy and ten caries-active adults. Saliva microbiota in the pilot population featured a vast diversity of functional genes. No significant distinction in gene number or diversity indices was observed between healthy and caries-active microbiota. However, co-presence network analysis of functional genes revealed that caries-active microbiota was more divergent in non-core genes than healthy microbiota, despite both groups exhibited a similar degree of conservation at their respective core genes. Furthermore, functional gene structure of saliva microbiota could potentially distinguish caries-active patients from healthy hosts. Microbial functions such as <i>Diaminopimelate epimerase</i>, <i>Prephenate dehydrogenase</i>, <i>Pyruvate-formate lyase</i> and <i>N-acetylmuramoyl-L-alanine amidase</i> were significantly linked to caries. Therefore, saliva microbiota carried disease-associated functional signatures, which could be potentially exploited for caries diagnosis.</p></div

Conservation of function genes encoded in saliva microbiota among human hosts.

<p>The <i>x</i>-axis stands for the number of saliva microbiota (i.e. hosts) included. The <i>y</i>-axis is the number of shared functional genes among the hosts, representing the means of 100 iterations. Error bars represent standard deviations.</p

Functional patterns of the ten healthy and ten caries-active human saliva microbiota.

<p>(<b>A</b>) Relative abundance of the functional genes among the 19 gene categories on HuMiChip 1.0. (<b>B</b>) Relative diversity of the functional genes among the 19 gene categories on HuMiChip 1.0.</p

Amino acid (AA) associated gene co-presence sub-networks in the healthy and caries-active host groups.

<p>Genes in the AA-associated pathway include AA transport and metabolism and AA synthesis. The sub-networks in the H (<b>A</b>) and C groups (<b>B</b>) were shown and compared, where the largest module in the H Group consists of 337 genes yet only 74 genes were found in the largest module in the C Group. The top five most abundant genes in each network were labeled with different colors.</p

Background information and microbial diversity of the healthy and caries-active saliva samples.

<p>Background information and microbial diversity of the healthy and caries-active saliva samples.</p