Functional genomic analysis of systemic cell division regulation in legumes

Legumes develop root nodules from pluripotent stem cells in the rootpericycle in response to mitogenic activation by a decorated chitin-likenodulation factor synthesized in Rhizobium bacteria. The soybean genes encoding the receptor for such signals were cloned using map-based cloning approaches. Pluripotent cells in the root pericycle and the outer or inner cortex undergo repeated cell divisions to initiate a composite nodule primordium that develops to a functional nitrogen-fixing nodule. The process itself is autoregulated, leading to the characteristic nodulation of the upper root system. Autoregulation of nodulation (AON) in all legumes is controlled in part by a leucine-rich repeat receptor kinase gene (GmNARK). Mutations of GmNARK, and its other legume orthologues, result in abundant nodulation caused by the loss of a yet-undefined negative nodulation repressor system. AON receptor kinases are involved in perception of a long distance, root-derived signal, to negatively control nodule proliferation. GmNARK and LjHAR1 are expressed in phloem parenchyma. GmNARK kinase domain interacts with Kinase Associated Protein Phosphatase (KAPP). NARK gene expression did not mirror biological NARK activity in nodulation control, as q-RT-PCR in soybean revealed high NARK expression in roots, root tips, leaves, petioles, stems and hypocotyls, while shoot and root apical meristems were devoid of NARK RNA. High through-put transcript analysis in soybean leaf and root indicated that major genes involved in JA synthesis or response are preferentially down-regulated in leaf but not root of wild type, but not NARK mutants, suggesting that AON signaling may in part be controlled by events relating to hormone metabolism. Ethylene and abscisic acid insensitive mutants of L. japonicus are described. Nodulation in legumes has significance to global economies and ecologies, as the nitrogen input into the biosphere allows food, feed and biofuel production without the inherent costs associated with nitrogen fertilization [1]. Nodulation involves the production of a new organ capable of nitrogen fixation [2] and as such is an excellent system to study plant – microbe interaction, plant development, long distance signaling and functional genomics of stem cell proliferation [3, 4]. Concerted international effort over the last 20 years, using a combination of induced mutagenesis followed by gene discovery (forward genetics), and molecular/biochemical approaches revealed a complex developmental pathway that ‘loans’ genetic programs from various sources and orchestrates these into a novel contribution. We report our laboratory’s contribution to the present analysis in the field.

Analysis of human breast milk cells: gene expression profiles during pregnancy, lactation, involution, and mastitic infection

The molecular processes underlying human milk production and the effects of mastitic infection are largely unknown because of limitations in obtaining tissue samples. Determination of gene expression in normal lactating women would be a significant step toward understanding why some women display poor lactation outcomes. Here, we demonstrate the utility of RNA obtained directly from human milk cells to detect mammary epithelial cell (MEC)-specific gene expression. Milk cell RNA was collected from five time points (24 h prepartum during the colostrum period, midlactation, two involutions, and during a bout of mastitis) in addition to an involution series comprising three time points. Gene expression profiles were determined by use of human Affymetrix arrays. Milk cells collected during milk production showed that the most highly expressed genes were involved in milk synthesis (e.g., CEL, OLAH, FOLR1, BTN1A1, and ARG2), while milk cells collected during involution showed a significant downregulation of milk synthesis genes and activation of involution associated genes (e.g., STAT3, NF-kB, IRF5, and IRF7). Milk cells collected during mastitic infection revealed regulation of a unique set of genes specific to this disease state, while maintaining regulation of milk synthesis genes. Use of conventional epithelial cell markers was used to determine the population of MECs within each sample. This paper is the first to describe the milk cell transcriptome across the human lactation cycle and during mastitic infection, providing valuable insight into gene expression of the human mammary gland.

A comparison of collection techniques for gene expression analysis of human oral taste tissue

Variability in human taste perception is associated with both genetic and environmental factors. The influence of taste receptor expression on this variability is unknown, in part, due to the difficulty in obtaining human oral tissue that enables quantitative expression measures of taste genes. In a comparison of six current techniques (Oragene RNeasy Kit, Isohelix swab, Livibrush cytobrush, tongue saliva, cheek saliva collection, and fungiform papillae biopsy), we identify the fungiform papillae biopsy is the optimal sampling technique to analyse human taste gene expression. The fungiform papillae biopsy resulted in the highest RNA integrity, enabling amplification of all the assessed taste receptor genes (TAS1R1, TAS1R2, TAS1R3, SCNN1A and CD36) and taste tissue marker genes (NCAM1, GNAT3 and PLCβ2). Furthermore, quantitative expression was observed in a subset of taste genes assessed from the saliva collection techniques (cheek saliva, tongue saliva and Oragene RNA kit). These saliva collection techniques may be useful as a non-invasive alternative sampling technique to the fungiform papillae biopsy. Identification of the fungiform papillae biopsy as the optimal collection method will facilitate further research into understanding the effect of gene expression on variability in human taste perception.

An association rule analysis framework for complex physiological and genetic data

Physiological and genetic information has been critical to the successful diagnosis and prognosis of complex diseases. In this paper, we introduce a support-confidence-correlation framework to accurately discover truly meaningful and interesting association rules between complex physiological and genetic data for disease factor analysis, such as type II diabetes (T2DM). We propose a novel Multivariate and Multidimensional Association Rule mining system based on Change Detection (MMARCD). Given a complex data set u i (e.g. u 1 numerical data streams, u 2 images, u 3 videos, u 4 DNA/RNA sequences) observed at each time tick t, MMARCD incrementally finds correlations and hidden variables that summarise the key relationships across the entire system. Based upon MMARCD, we are able to construct a correlation network for human diseases. © 2012 Springer-Verlag.

Identification and characterization of rhizospheric microbial diversity by 16S ribosomal RNA gene sequencing

In the present study, samples of rhizosphere and root nodules were collected from different areas of Pakistan to isolate plant growth promoting rhizobacteria. Identification of bacterial isolates was made by 16S rRNA gene sequence analysis and taxonomical confirmation on EzTaxon Server. The identified bacterial strains were belonged to 5 genera i.e. Ensifer, Bacillus, Pseudomona, Leclercia and Rhizobium. Phylogenetic analysis inferred from 16S rRNA gene sequences showed the evolutionary relationship of bacterial strains with the respective genera. Based on phylogenetic analysis, some candidate novel species were also identified. The bacterial strains were also characterized for morphological, physiological, biochemical tests and glucose dehydrogenase (gdh) gene that involved in the phosphate solublization using cofactor pyrroloquinolone quinone (PQQ). Seven rhizoshperic and 3 root nodulating stains are positive for gdh gene. Furthermore, this study confirms a novel association between microbes and their hosts like field grown crops, leguminous and non-leguminous plants. It was concluded that a diverse group of bacterial population exist in the rhizosphere and root nodules that might be useful in evaluating the mechanisms behind plant microbial interactions and strains QAU-63 and QAU-68 have sequence similarity of 97 and 95% which might be declared as novel after further taxonomic characterization.