UNASSIGNED: Invasive pneumococcal disease (IPD), caused by Streptococcus pneumoniae, is a leading cause of pneumonia, meningitis and septicaemia worldwide, with increased morbidity and mortality in HIV-infected children.
UNASSIGNED: We aimed to compare peripheral blood expression profiles between HIV-infected and uninfected children with pneumococcal meningitis and controls, and between survivors and non-survivors, in order to provide insight into the host inflammatory response leading to poorer outcomes.
UNASSIGNED: Prospective case-control observational study in a tertiary hospital in Malawi.
UNASSIGNED: Children aged 2 months to 16 years with pneumococcal meningitis or pneumonia.
UNASSIGNED: We used the human genome HGU133A Affymetrix array to explore differences in gene expression between cases with pneumococcal meningitis (n=12) and controls, and between HIV-infected and uninfected cases, and validated gene expression profiles for 34 genes using real-time quantitative PCR (RT-qPCR) in an independent set of cases with IPD (n=229) and controls (n=13). Pathway analysis was used to explore genes differentially expressed.
UNASSIGNED: Irrespective of underlying HIV infection, cases showed significant upregulation compared with controls of the following: S100 calcium-binding protein A12 (S100A12); vanin-1 (VNN1); arginase, liver (ARG1); matrix metallopeptidase 9 (MMP9); annexin A3 (ANXA3); interleukin 1 receptor, type II (IL1R2); CD177 molecule (CD177); endocytic adaptor protein (NUMB) and S100 calcium-binding protein A9 (S100A9), cytoskeleton-associated protein 4 (CKAP4); and glycogenin 1 (GYG1). RT-qPCR confirmed differential expression in keeping with microarray results. There was no differential gene expression in HIV-infected compared with HIV-uninfected cases, but there was significant upregulation of folate receptor 3 (FOLR3), S100A12 in survivors compared with non-survivors.
UNASSIGNED: Children with IPD demonstrated increased expression in genes regulating immune activation, oxidative stress, leucocyte adhesion and migration, arginine metabolism, and glucocorticoid receptor signalling.

A number of transcriptome datasets for differential expression (DE) genes have been widely used for understanding organismal biology, but these datasets also contain untapped information that can be used to develop more precise analytical tools. With the use of transcriptome data generated from poplar/canker disease interaction system, we describe a methodology to identify candidate reference genes from high-throughput sequencing data. This methodology will improve the accuracy of RT-qPCR and will lead to better standards for the normalization of expression data. Expression stability analysis from xylem and phloem of Populus bejingensis inoculated with the fungal canker pathogen Botryosphaeria dothidea revealed that 729 poplar transcripts (1.11%) were stably expressed, at a threshold level of coefficient of variance (CV) of FPKM < 20% and maximum fold change (MFC) of FPKM < 2.0. Expression stability and bioinformatics analysis suggested that commonly used house-keeping (HK) genes were not the most appropriate internal controls: 70 of the 72 commonly used HK genes were not stably expressed, 45 of the 72 produced multiple isoform transcripts, and some of their reported primers produced unspecific amplicons in PCR amplification. RT-qPCR analysis to compare and evaluate the expression stability of 10 commonly used poplar HK genes and 20 of the 729 newly-identified stably expressed transcripts showed that some of the newly-identified genes (such as SSU_S8e, LSU_L5e, and 20S_PSU) had higher stability ranking than most of commonly used HK genes. Based on these results, we recommend a pipeline for deriving reference genes from transcriptome data. An appropriate candidate gene should have a unique transcript, constitutive expression, CV value of expression < 20% (or possibly 30%) and MFC value of expression <2, and an expression level of 50-1,000 units. Lastly, when four of the newly identified HK genes were used in the normalization of expression data for 20 differential expressed genes, expression analysis gave similar values to Cufflinks output. The methods described here provide an alternative pathway for the normalization of transcriptome data, a process that is essential for integrating analyses of transcriptome data across environments, laboratories, sequencing platforms, and species.

The Amazon basin includes 1000s of bodies of water, that are sorted according to their color in three types: blackwater, clearwater, and whitewater, which significantly differ in terms of their physicochemical parameters. More than 3,000 species of fish live in the rivers of the Amazon, among them, the sardine, Triportheus albus, which is one of the few species that inhabit all three types of water. The purpose of our study was to analyze if the gene expression of T. albus is determined by the different types of water, that is, if the species presents phenotypic plasticity to live in blackwater, clearwater, and whitewater. Gills of T. albus were collected at well-characterized sites for each type of water. Nine cDNA libraries were constructed, three biological replicates of each condition and the RNA was sequenced (RNA-Seq) on the MiSeq® Platform (Illumina®). A total of 51.6 million of paired-end reads, and 285,456 transcripts were assembled. Considering the FDR ≤ 0.05 and fold change ≥ 2, 13,754 differentially expressed genes were detected in the three water types. Two mechanisms related to homeostasis were detected in T. albus that live in blackwater, when compared to the ones in clearwater and whitewater. The acidic blackwater is a challenging environment for many types of aquatic organisms. The first mechanism is related to the decrease in cellular permeability, highlighting the genes coding for claudin proteins, actn4, itgb3b, DSP, Gap junction protein, and Ca2+-ATPase. The second with ionic and acid-base regulation [rhcg1, slc9a6a (NHE), ATP6V0A2, Na+/K+-ATPase, slc26a4 (pedrin) and slc4a4b]. We suggest T. albus is a good species of fish for future studies involving the ionic and acid-base regulation of Amazonian species. We also concluded that, T. albus, shows well defined phenotypic plasticity for each water type in the Amazon basin.

In recent years, RNA-seq has become an important method in the process of measuring gene expression in various cells and organisms. This chapter will detail all the bioinformatic steps that should be undertaken to determine differentially expressed genes from a typical RNA-seq experiment. Each step will be clearly explained in \"non-bioinformatic\" terminology so that readers embarking on RNA-seq analysis will be able to understand the rationale and reasoning behind each step. Moreover, the exact command lines used to process the data will be presented along with a description of the various flags and commands.

To perform a quantitative analysis with gene-arrays, one must take into account inaccuracies (experimental variations, biological variations and other measurement errors) which are seldom known. In this paper we investigated amplification and noise propagation related errors by measuring intensity dependent variations. Based on a set of control samples, we create confidence intervals for up and down regulations. We validated our method through a qPCR experiment and compared it to standard analysis methods (including loess normalization and filtering methods based on genetic variability). The results reveal that amplification related errors are a major concern.