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BACKGROUND: Bovine tuberculosis (bTB) is an enduring contagious disease of cattle that has caused substantial losses to the global livestock industry. Despite large-scale eradication efforts, bTB continues to persist. Current bTB tests rely on the measurement of immune responses in vivo (skin tests), and in vitro (bovine interferon-γ release assay). Recent developments are characterized by interrogating the expression of an increasing number of genes that participate in the immune response. Currently used assays have the disadvantages of limited sensitivity and specificity, which may lead to incomplete eradication of bTB. Moreover, bTB that reemerges from wild disease reservoirs requires early and reliable diagnostics to prevent further spread. In this work, we use high-throughput sequencing of the peripheral blood mononuclear cells (PBMCs) transcriptome to identify an extensive panel of genes that participate in the immune response. We also investigate the possibility of developing a reliable bTB classification framework based on RNA-Seq reads. METHODOLOGY/PRINCIPAL FINDINGS: Pooled PBMC mRNA samples from unaffected calves as well as from those with disease progression of 1 and 2 months were sequenced using the Illumina Genome Analyzer II. More than 90 million reads were splice-aligned against the reference genome, and deposited to the database for further expression analysis and visualization. Using this database, we identified 2,312 genes that were differentially expressed in response to bTB infection (p

The fat body is the main organ of intermediary metabolism in insects and the principal source of hemolymph proteins. As part of our ongoing efforts to understand mosquito fat body physiology and to identify novel targets for insect control, we have conducted a transcriptome analysis of the fat body of Aedes aegypti before and in response to blood feeding.We created two fat body non-normalized EST libraries, one from mosquito fat bodies non-blood fed (NBF) and another from mosquitoes 24 hrs post-blood meal (PBM). 454 pyrosequencing of the non-normalized libraries resulted in 204,578 useable reads from the NBF sample and 323,474 useable reads from the PBM sample. Alignment of reads to the existing reference Ae. aegypti transcript libraries for analysis of differential expression between NBF and PBM samples revealed 116,912 and 115,051 matches, respectively. De novo assembly of the reads from the NBF sample resulted in 15,456 contigs, and assembly of the reads from the PBM sample resulted in 15,010 contigs. Collectively, 123 novel transcripts were identified within these contigs. Prominently expressed transcripts in the NBF fat body library were represented by transcripts encoding ribosomal proteins. Thirty-five point four percent of all reads in the PBM library were represented by transcripts that encode yolk proteins. The most highly expressed were transcripts encoding members of the cathepsin b, vitellogenin, vitellogenic carboxypeptidase, and vitelline membrane protein families.The two fat body transcriptomes were considerably different from each other in terms of transcript expression in terms of abundances of transcripts and genes expressed. They reflect the physiological shift of the pre-feeding fat body from a resting state to vitellogenic gene expression after feeding.

A complete assessment of population growth and viability from field census data often requires complex data manipulations, statistical routines, mathematical tools, programming environments, and graphical capabilities. We therefore designed an R package called popbio to facilitate both the construction and analysis of projection matrix models. The package consists primarily of the R translation of MATLAB code found in Caswell (2001) and Morris and Doak (2002) for the analysis of projection matrix models. The package also includes methods to estimate vital rates and construct projection matrix models from census data typically collected in plant demography studies. In these studies, vital rates can often be estimated directly from annual censuses of tagged individuals using transition frequency tables. Because the construction of projection matrix models requires careful management of census data, we describe the steps to construct a projection matrix in detail.