Prevalence of Cryptosporidium Infection among Inhabitants of 2 Rural Areas in White Nile State,Sudan

Cryptosporidium , a protozoan parasite that causes watery diarrhea, is found worldwide and is common in areas with low water hygiene. In February 2014, 866 stool samples were collected from the inhabitants of 2 rural areas in White Nile State, Sudan. These stool samples were assessed by performing modified acid-fast staining, followed by examination under a light microscope. The overall positive rate of Cryptosporidium oocysts was 13.3%. Cryptosporidium oocysts were detected in 8.6% stool samples obtained from inhabitants living in the area having water purification systems and in 14.6% stool samples obtained from inhabitants living in the area not having water purification systems. No significant difference was observed in the prevalence of Cryptosporidium infection between men and women (14.7% and 14.1%, respectively). The positive rate of oocysts by age was the highest among inhabitants in their 60s (40.0%). These findings suggest that the use of water purification systems is important for preventing Cryptosporidium infection among inhabitants of these rural areas in Sudan.     

Toxoplasma gondii B1 Gene Detection in Feces of Stray Cats around Seoul,Korea and Genotype Analysis of Two Laboratory-Passaged Isolates

The increasing prevalence of Toxoplasma gondii infection in the human population in the Republic of Korea (= Korea) is due to various reasons such as an increase in meat consumption. However, the importance of cats in transmitting T. gondii infection through oocysts to humans has seldom been assessed. A total of 300 fecal samples of stray cats captured around Seoul from June to August 2013 were examined for T. gondii B1 gene (indicating the presence of oocysts) using nested-PCR. Fourteen (4.7%) of 300 cats examined were positive for B1 gene. Female cats (7.5%) showed a higher prevalence than male cats (1.4%). Cats younger than 3 months (5.5%) showed a higher prevalence than cats (1.5%) older than 3 months. For laboratory passage of the positive samples, the fecal suspension (0.2 ml) of B1 gene positive cats was orally inoculated into experimental mice. Brain tissues of the mice were obtained after 40 days and examined for the presence of tissue cysts. Two isolates were successfully passaged (designated KNIH-1 and KNIH-2) and were molecularly analyzed using the SAG5D and SAG5E gene sequences. The SAG5D and SAG5E gene sequences showed high homologies with the ME49 strain (less virulent strain). The results indicated the importance of stray cats in transmitting T. gondii to humans in Korea, as revealed by detection of B1 gene in fecal samples. T. gondii isolates from cats were successfully passaged in the laboratory for the first time in Korea.     

Intestinal Nematodes from Small Mammals Captured near the Demilitarized Zone,Gyeonggi Province,Republic of Korea

A total of 1,708 small mammals (1,617 rodents and 91 soricomorphs), including Apodemus agrarius (n = 1,400), Microtus fortis (167), Crocidura lasiura (91), Mus musculus (32), Myodes (= Eothenomys) regulus (9), Micromys minutus (6), and Tscherskia (= Cricetulus) triton (3), were live-trapped at US/Republic of Korea (ROK) military training sites near the demilitarized zone (DMZ) of Paju, Pocheon, and Yeoncheon, Gyeonggi Province from December 2004 to December 2009. Small mammals were examined for their intestinal nematodes by necropsy. A total of 1,617 rodents (100%) and 91 (100%) soricomorphs were infected with at least 1 nematode species, including Nippostrongylus brasiliensis, Heligmosomoides polygyrus, Syphacia obvelata, Heterakis spumosa, Protospirura muris, Capillaria spp., Trichuris muris, Rictularia affinis, and an unidentified species. N. brasiliensis was the most common species infecting small mammals (1,060; 62.1%) followed by H. polygyrus (617; 36.1%), S. obvelata (370; 21.7%), H. spumosa (314; 18.4%), P. muris (123; 7.2%), and Capillaria spp. (59; 3.5%). Low infection rates (0.1-0.8%) were observed for T. muris, R. affinis, and an unidentified species. The number of recovered worms was highest for N. brasiliensis (21,623 worms; mean 20.4 worms/infected specimen) followed by S. obvelata (9,235; 25.0 worms), H. polygyrus (4,122; 6.7 worms), and H. spumosa (1,160; 3.7 worms). A. agrarius demonstrated the highest prevalence for N. brasiliensis (70.9%), followed by M. minutus (50.0%), T. triton (33.3%), M. fortis (28.1%), M. musculus (15.6%), C. lasiura (13.2%), and M. regulus (0%). This is the first report of nematode infections in small mammals captured near the DMZ in ROK.     

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.