The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

Background  

Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data.     

Genome-Wide Association of Carbon and Nitrogen Metabolism in the Maize Nested Association Mapping Population

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C₄ metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.Carbon (C) and nitrogen (N) metabolism are the basis for life on Earth. The production, balance, and tradeoffs of C and N metabolism are critical to all plant growth, yield, and local adaptation (Coruzzi and Bush, 2001; Coruzzi et al., 2007). In plants, there is a critical balance between the tissues that are producing energy (sources) and those using it (sinks), as the identities and locations of these vary through time and developmental stage (Smith et al., 2004). While a great deal of research has focused on the key genes and proteins involved in these processes (Wang et al., 1993; Kim et al., 2000; Takahashi et al., 2009), relatively little is known about the natural variation within a species that fine-tunes these processes in individual plants.In addition, a key aspect of core C metabolism involves the nature of plant photosynthesis. While the majority of plants use standard C₃ photosynthetic pathways, some, including maize (Zea mays) and many other grasses, use C₄ photosynthesis to concentrate CO₂ in bundle sheath cells to avoid wasteful photorespiration (Sage, 2004). Under some conditions (such as drought or high temperatures), C₄ photosynthesis is much more efficient than C₃ photosynthesis. Since these conditions are expected to become more prevalent in the near future due to climate change, various research groups are working to convert C₃ crop species to C₄ metabolism in order to boost crop production and food security (Sage and Zhu, 2011). Beyond this, better understanding of both C₃ and C₄ metabolic pathways will aid efforts to breed crops for superior yield, N-use efficiency, and other traits important for global food production.In the last two decades, quantitative trait locus (QTL) mapping, first with linkage analysis and later with association mapping, has been used to dissect C and N metabolism in several species, including Arabidopsis (Arabidopsis thaliana; Mitchell-Olds and Pedersen, 1998; Keurentjes et al., 2008; Lisec et al., 2008; Sulpice et al., 2009), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Hirel et al., 2001; Limami et al., 2002; Zhang et al., 2006, 2010a, 2010b). These studies identified key genetic regions underlying variation in core C and N metabolism, many of which include candidate genes known to be involved in these processes.Previous studies of genetic variation for C and N metabolism are limited by the fact that they identified trait loci only through linkage mapping in artificial families or through association mapping across populations of unrelated individuals. Linkage mapping benefits from high statistical power due to many individuals sharing the same genotype at any given location, but it suffers from low resolution due to the limited number of generations (and hence recombination events) since the initial founders. Association mapping, in turn, enjoys high resolution due to the long recombination histories of natural populations but suffers from low power, since most genotypes occur in only a few individuals. In addition, many of these studies focused on C and N in artificial settings (e.g. greenhouses or growth chambers) instead of field conditions, running the risk that important genetic loci could be missed if the conditions do not include important (and potentially unknown) natural environmental variables.To address these issues and improve our understanding of C and N metabolism in maize, we used a massive and diverse germplasm resource, the maize nested association mapping (NAM) population (Buckler et al., 2009; McMullen et al., 2009), to evaluate genetic variation underlying the accumulation of 12 targeted metabolites in maize leaf tissue under field conditions. This population was formed by mating 25 diverse maize lines to the reference line, B73, and creating a 200-member biparental family from each of these crosses. The entire 5,000-member NAM population thus combines the strengths of both linkage and association mapping (McMullen et al., 2009), and it has been used to identify QTLs for important traits such as flowering time (Buckler et al., 2009), disease resistance (Kump et al., 2011; Poland et al., 2011), and plant architecture (Tian et al., 2011; Peiffer et al., 2013). Most importantly, this combination of power and resolution frequently resolves associations down to the single-gene level, even when using field-based data.The metabolites we profiled are key indicators of photosynthesis, respiration, glycolysis, and protein and sugar metabolism in the plant (Sulpice et al., 2009). By taking advantage of a robotized metabolic phenotyping platform (Gibon et al., 2004), we performed more than 100,000 assays across 12,000 samples, with two independent samples per experimental plot. Raw data and the best linear unbiased predictors (BLUPs) of these data were included as part of a study of general functional variation in maize (Wallace et al., 2014), but, to our knowledge, this is the first in-depth analysis of these metabolic data. We find strong correlations among several of the metabolites, and we also find extensive pleiotropy among the different traits. Many of the top QTLs are also near or within candidate genes relating to C and N metabolism, thus identifying targets for future breeding and selection. These results provide a powerful resource for those working with core C and N metabolism in plants and for improving maize performance in particular.     

Maiden outbreak of chikungunya in dongguan city, guangdong province, china: epidemiological characteristics

Background

This study was conducted to identify epidemiological characteristics of the first documented CHIK fever outbreak in China and evaluate the effect of the preventive measures taken.

Methodology/Principal Findings

From September 1 to October 29, 2010, China''s first documented outbreak of CHIK fever occurred in the Xincun community of Wanjiang District of Dongguan city, Guangdong province; 253 case-patients were recorded, of which 129 were laboratory confirmed, with an attack rate of 1%. Before September 18^th the number of CHIK fever cases remained relatively low in the Xincun community; from September 19^th onwards, the number of cases increased drastically, with an outbreak peak on October 4^th. Cases were distributed across nine small village groups in the Xincun community, with an attack rate of 0–12% at the village level. The household attack rates ranged between 20% and 100%. No significant difference was found in the attack rate between males and females. There was a significant difference in the attack rate in different age groups (chi-square = 18.35, p = 0.005); highest in patients aged 60 years or older and the lowest in patients aged under 10. The major clinical characteristics of patients are fever (100%), joint pain (79%) and rash (54%). Phylogenetic analysis of the E1 gene on the five earliest confirmed cases showed that the strains of CHIKV isolated from their sera were highly homologous (up to 99%) with isogeneic strains isolated in Thailand in 2009. After control measures were taken, including killing adult mosquitoes and cleaning breeding habitats of Aedes mosquitoes, the Breteau index and Mosq-ovitrap index decreased rapidly, and the outbreak ended on October 29.

Conclusion/Significance

The infection source of the outbreak was imported. Cases showed obvious temporal, spatial, and population aggregation during the outbreak. Comprehensive control measures based on reducing the density of Aedes mosquitoes were effective in controlling the epidemic.     

一类一级饱和反应系统的极限环

本文研究生化反应中一类饱和反应的数学模型：应用微分方程定性理论,完整地解决了该系统极限环的存在性、唯一性和不存在性等问题．     

miR-22 regulates C2C12 myoblast proliferation and differentiation by targeting TGFBR1

Recently, miR-22 was found to be differentially expressed in different skeletal muscle growth period, indicated that it might have function in skeletal muscle myogenesis. In this study, we found that the expression of miR-22 was the most in skeletal muscle and was gradually up-regulated during mouse myoblast cell (C2C12 myoblast cell line) differentiation. Overexpression of miR-22 repressed C2C12 myoblast proliferation and promoted myoblast differentiation into myotubes, whereas inhibition of miR-22 showed the opposite results. During myogenesis, we predicted and verified transforming growth factor beta receptor 1 (TGFBR1), a key receptor of the TGF-β/Smad signaling pathway, was a target gene of miR-22. Then, we found miR-22 could regulate the expression of TGFBR1 and down-regulate the Smad3 signaling pathway. Knockdown of TGFBR1 by siRNA suppressed the proliferation of C2C12 cells but induced its differentiation. Conversely, overexpression of TGFBR1 significantly promoted proliferation but inhibited differentiation of the myoblast. Additionally, when C2C12 cells were treated with different concentrations of transforming growth factor beta 1 (TGF-β1), the level of miR-22 in C2C12 cells was reduced. The TGFBR1 protein level was significantly elevated in C2C12 cells treated with TGF-β1. Moreover, miR-22 was able to inhibit TGF-β1-induced TGFBR1 expression in C2C12 cells. Altogether, we demonstrated that TGF-β1 inhibited miR-22 expression in C2C12 cells and miR-22 regulated C2C12 cell myogenesis by targeting TGFBR1.