Folate fortification of rice by metabolic engineering

Rice, the world's major staple crop, is a poor source of essential micronutrients, including folates (vitamin B9). We report folate biofortification of rice seeds achieved by overexpressing two Arabidopsis thaliana genes of the pterin and para-aminobenzoate branches of the folate biosynthetic pathway from a single locus. We obtained a maximal enhancement as high as 100 times above wild type, with 100 g of polished raw grains containing up to four times the adult daily folate requirement.     

Cell-based tissue engineering for lung regeneration

Emphysema is a chronic lung disease characterized by alveolar enlargement and tissue loss. Tissue engineering represents an attractive potential for regeneration of several organ systems. The complex three-dimensional architectural structure of lung parenchyma requiring connections of alveolar units to airways and the pulmonary circulation makes this strategy less optimistic. In the present study, we used Gelfoam sponge as a scaffold material, supplemented with fetal rat lung cells as progenitors, to explore the potential application of cell-based tissue engineering for lung regeneration in adult rats. After injection into lung parenchyma, the sponge showed porous structures similar to alveolar units. It did not induce severe local inflammatory response. Fetal lung cells in the sponge were able to survive in the adult lung for at least 35 days, determined by CMTMR [5-(and-6)-{[(4-chloromethyl)benzoyl]amino}tetramethylrhodamine] labeling. Proliferation of cells within the sponge was demonstrated in vivo by bromodeoxyuridine (BrdU) labeling. Cells formed "alveolar-like structures" at the border between the sponge and the surrounding lung tissue with positive immunohistochemical staining for epithelial and endothelial cells. Neovascularization of the sponge was demonstrated with India ink perfusion. The sponge degraded after several months. This study suggests that cell-based tissue engineering possesses the potential to regenerate alveolar-like structures, an important step towards our ultimate goal of lung regeneration.     

Genetic polymorphisms and plasma levels of transforming growth factor-beta(1) in Chinese patients with tuberculosis in Hong Kong

Susceptibility to tuberculosis (TB) may be affected by host genetic factors. Elevated levels of transforming growth factor-beta 1 (TGF-β₁) were found in plasma of patients with active TB compared with those of healthy contacts. To investigate the association of TGF-β₁ gene polymorphisms (C-509T and T869C) and plasma levels with the risk of TB in Hong Kong Chinese adults, a case-control study was carried out on 174 active TB patients and 174 healthy controls matched for age, gender and smoking. Blood samples from 180 blood donors served as another control group. Genotyping was carried out on genomic DNA using polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP). Plasma TGF-β₁ was measured by commercially available ELISA kit. We found no differences in the distribution of genotypes or alleles of TGF-β₁ gene polymorphisms at C-509T and T869C between patients and either group of healthy controls. Patients with TB had elevated plasma TGF-β₁ levels compared with healthy controls irrespective of their genotypes (p < 0.001). In conclusion, TGF-β₁ gene polymorphism at C-509T and T869C is not associated with TB susceptibility in Hong Kong Chinese adults, but elevated plasma TGF-β₁ levels suggests that this cytokine may play a role in the pathogenesis of tuberculosis.     

Phylogenetic incongruence in the Drosophila melanogaster species group

Drosophila melanogaster and its close relatives are used extensively in comparative biology. Despite the importance of phylogenetic information for such studies, relationships between some melanogaster species group members are unclear due to conflicting phylogenetic signals at different loci. In this study, we use twelve nuclear loci (eleven coding and one non-coding) to assess the degree of phylogenetic incongruence in this model system. We focus on two nodes: (1) the node joining the Drosophila erecta-Drosophila orena, Drosophila melanogaster-Drosophila simulans, and Drosophila yakuba-Drosophila teissieri lineages, and (2) the node joining the lineages leading to the melanogaster, takahashii, and eugracilis subgroups. We find limited evidence for incongruence at the first node; our data, as well as those of several previous studies, strongly support monophyly of a clade consisting of D. erecta-D. orena and D. yakuba-D. teissieri. By contrast, using likelihood based tests of congruence, we find robust evidence for topological incongruence at the second node. Different loci support different relationships among the melanogaster, takahashii, and eugracilis subgroups, and the observed incongruence is not easily attributable to homoplasy, non-equilibrium base composition, or positive selection on a subset of loci. We argue that lineage sorting in the common ancestor of these three subgroups is the most plausible explanation for our observations. Such lineage sorting may lead to biased estimation of tree topology and evolutionary rates, and may confound inferences of positive selection.     

Toxic effects of zinc on anaerobic microbiota from Zimapán Reservoir (Mexico)

The toxic effects of heavy metals have been extensively documented in different organisms. Nevertheless, a lack of information exists with regard to this topic in the case of autochthonous microorganism communities. The aim of this study was to evaluate the toxic effects of zinc on the anaerobic microorganisms present in the sediment and anoxic water of Zimapán Reservoir (Mexico), with particular focus on dissimilatory sulphate reducing bacteria. In the laboratory, a system of enrichment microcosms was set up with sediment and water from the reservoir. ATP, protein, carbohydrates and lactate and alcohol dehydrogenase activity were determined. The physicochemical parameters of the reservoir were evaluated over the course of one year. Sulphate reduction occurred in the reservoir throughout the year, but was most pronounced at the end of the wet season and during winter. In the field, increases in the rate of sulphate reduction coincided with the lowest levels of total phosphorus and hydrosoluble organic carbon. Zinc enrichment was observed to modify protein and carbohydrate content as well as to affect lactate and alcohol dehydrogenase activity. All responses followed a zinc concentration-response relationship and were dependent on reservoir physicochemical parameters. ATP content was used as a biomarker to evaluate the sublethal toxic effects of zinc. The acceptable threshold concentration of zinc in the aquatic and sediment enrichment microcosms was determined to be 0.06mgZn/L and 711.1mgZn/kg, respectively.     

Genome-level longitudinal expression of signaling pathways and gene networks in pediatric septic shock

We have conducted longitudinal studies focused on the expression profiles of signaling pathways and gene networks in children with septic shock. Genome-level expression profiles were generated from whole blood-derived RNA of children with septic shock (n=30) corresponding to day one and day three of septic shock, respectively. Based on sequential statistical and expression filters, day one and day three of septic shock were characterized by differential regulation of 2,142 and 2,504 gene probes, respectively, relative to controls (n=15). Venn analysis demonstrated 239 unique genes in the day one dataset, 598 unique genes in the day three dataset, and 1,906 genes common to both datasets. Functional analyses demonstrated time-dependent, differential regulation of genes involved in multiple signaling pathways and gene networks primarily related to immunity and inflammation. Notably, multiple and distinct gene networks involving T cell- and MHC antigen-related biology were persistently downregulated on both day one and day three. Further analyses demonstrated large scale, persistent downregulation of genes corresponding to functional annotations related to zinc homeostasis. These data represent the largest reported cohort of patients with septic shock subjected to longitudinal genome-level expression profiling. The data further advance our genome-level understanding of pediatric septic shock and support novel hypotheses.     

Isolation of Laribacter hongkongensis, a novel bacterium associated with gastroenteritis, from drinking water reservoirs in Hong Kong

AIMS: Freshwater fish has been found to be the reservoir of Laribacter hongkongensis, a recently discovered bacterium associated with community-acquired gastroenteritis. However, little is known about the ecology of this bacterium in the aquatic environment. We carried out a surveillance study to investigate the presence of L. hongkongensis in water and freshwater fish from 10 drinking water reservoirs in Hong Kong. METHODS AND RESULTS: Using membrane filtration, L. hongkongensis was isolated from the waters of six reservoirs, with numbers ranging from 1 to 12 CFU l(-1). Higher recovery rates were observed in summer and during days of higher water and ambient temperatures. Of 27 freshwater fish collected from the reservoirs, L. hongkongensis was recovered from the intestines of two fish, a Goldfish and a Nile tilapia. Overall, 35 different pulsed-field gel electrophoresis patterns are found among the 59 isolates recovered from water and the two isolates from freshwater fish. CONCLUSIONS: The present report represents the first to demonstrate the presence of L. hongkongensis in natural water environments. SIGNIFICANCE AND IMPACT OF THE STUDY: Although it is unlikely that treated, drinking water is an important source of L. hongkongensis-associated gastroenteritis, one should be aware of the possibility of other contaminated water as a source of human infection.     

A multi-stage approach to clustering and imputation of gene expression profiles

MOTIVATION: Microarray experiments have revolutionized the study of gene expression with their ability to generate large amounts of data. This article describes an alternative to existing approaches to clustering of gene expression profiles; the key idea is to cluster in stages using a hierarchy of distance measures. This method is motivated by the way in which the human mind sorts and so groups many items. The distance measures arise from the orthogonal breakup of Euclidean distance, giving us a set of independent measures of different attributes of the gene expression profile. Interpretation of these distances is closely related to the statistical design of the microarray experiment. This clustering method not only accommodates missing data but also leads to an associated imputation method. RESULTS: The performance of the clustering and imputation methods was tested on a simulated dataset, a yeast cell cycle dataset and a central nervous system development dataset. Based on the Rand and adjusted Rand indices, the clustering method is more consistent with the biological classification of the data than commonly used clustering methods. The imputation method, at varying levels of missingness, outperforms most imputation methods, based on root mean squared error (RMSE). AVAILABILITY: Code in R is available on request from the authors.     

Graph-based consensus clustering for class discovery from gene expression data

MOTIVATION: Consensus clustering, also known as cluster ensemble, is one of the important techniques for microarray data analysis, and is particularly useful for class discovery from microarray data. Compared with traditional clustering algorithms, consensus clustering approaches have the ability to integrate multiple partitions from different cluster solutions to improve the robustness, stability, scalability and parallelization of the clustering algorithms. By consensus clustering, one can discover the underlying classes of the samples in gene expression data. RESULTS: In addition to exploring a graph-based consensus clustering (GCC) algorithm to estimate the underlying classes of the samples in microarray data, we also design a new validation index to determine the number of classes in microarray data. To our knowledge, this is the first time in which GCC is applied to class discovery for microarray data. Given a pre specified maximum number of classes (denoted as K(max) in this article), our algorithm can discover the true number of classes for the samples in microarray data according to a new cluster validation index called the Modified Rand Index. Experiments on gene expression data indicate that our new algorithm can (i) outperform most of the existing algorithms, (ii) identify the number of classes correctly in real cancer datasets, and (iii) discover the classes of samples with biological meaning. AVAILABILITY: Matlab source code for the GCC algorithm is available upon request from Zhiwen Yu.