Intracellular colonization and persistent infection by the kinetoplastid protozoan parasite, Trypanosoma cruzi, underlie the pathogenesis of human Chagas disease. To obtain global insights into the T. cruzi infective process, transcriptome dynamics were simultaneously captured in the parasite and host cells in an infection time course of human fibroblasts. Extensive remodeling of the T. cruzi transcriptome was observed during the early establishment of intracellular infection, coincident with a major developmental transition in the parasite. Contrasting this early response, few additional changes in steady state mRNA levels were detected once mature T. cruzi amastigotes were formed. Our findings suggest that transcriptome remodeling is required to establish a modified template to guide developmental transitions in the parasite, whereas homeostatic functions are regulated independently of transcriptomic changes, similar to that reported in related trypanosomatids. Despite complex mechanisms for regulation of phenotypic expression in T. cruzi, transcriptomic signatures derived from distinct developmental stages mirror known or projected characteristics of T. cruzi biology. Focusing on energy metabolism, we were able to validate predictions forecast in the mRNA expression profiles. We demonstrate measurable differences in the bioenergetic properties of the different mammalian-infective stages of T. cruzi and present additional findings that underscore the importance of mitochondrial electron transport in T. cruzi amastigote growth and survival. Consequences of T. cruzi colonization for the host include dynamic expression of immune response genes and cell cycle regulators with upregulation of host cholesterol and lipid synthesis pathways, which may serve to fuel intracellular T. cruzi growth. Thus, in addition to the biological inferences gained from gene ontology and functional enrichment analysis of differentially expressed genes in parasite and host, our comprehensive, high resolution transcriptomic dataset provides a substantially more detailed interpretation of T. cruzi infection biology and offers a basis for future drug and vaccine discovery efforts.

BackgroundParasites of the genus Leishmania are the causative agents of leishmaniasis, a group of diseases that range in manifestations from skin lesions to fatal visceral disease. The life cycle of Leishmania parasites is split between its insect vector and its mammalian host, where it resides primarily inside of macrophages. Once intracellular, Leishmania parasites must evade or deactivate the host's innate and adaptive immune responses in order to survive and replicate.ResultsWe performed transcriptome profiling using RNA-seq to simultaneously identify global changes in murine macrophage and L. major gene expression as the parasite entered and persisted within murine macrophages during the first 72 h of an infection. Differential gene expression, pathway, and gene ontology analyses enabled us to identify modulations in host and parasite responses during an infection. The most substantial and dynamic gene expression responses by both macrophage and parasite were observed during early infection. Murine genes related to both pro- and anti-inflammatory immune responses and glycolysis were substantially upregulated and genes related to lipid metabolism, biogenesis, and Fc gamma receptor-mediated phagocytosis were downregulated. Upregulated parasite genes included those aimed at mitigating the effects of an oxidative response by the host immune system while downregulated genes were related to translation, cell signaling, fatty acid biosynthesis, and flagellum structure.ConclusionsThe gene expression patterns identified in this work yield signatures that characterize multiple developmental stages of L. major parasites and the coordinated response of Leishmania-infected macrophages in the real-time setting of a dual biological system. This comprehensive dataset offers a clearer and more sensitive picture of the interplay between host and parasite during intracellular infection, providing additional insights into how pathogens are able to evade host defenses and modulate the biological functions of the cell in order to survive in the mammalian environment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2237-2) contains supplementary material, which is available to authorized users.

Protozoan parasites of the genus Leishmania are the etiological agents of leishmaniasis, a group of diseases with a worldwide incidence of 0.9–1.6 million cases per year. We used RNA-seq to conduct a high-resolution transcriptomic analysis of the global changes in gene expression and RNA processing events that occur as L. major transforms from non-infective procyclic promastigotes to infective metacyclic promastigotes. Careful statistical analysis across multiple biological replicates and the removal of batch effects provided a high quality framework for comprehensively analyzing differential gene expression and transcriptome remodeling in this pathogen as it acquires its infectivity. We also identified precise 5′ and 3′ UTR boundaries for a majority of Leishmania genes and detected widespread alternative trans-splicing and polyadenylation. An investigation of possible correlations between stage-specific preferential trans-splicing or polyadenylation sites and differentially expressed genes revealed a lack of systematic association, establishing that differences in expression levels cannot be attributed to stage-regulated alternative RNA processing. Our findings build on and improve existing expression datasets and provide a substantially more detailed view of L. major biology that will inform the field and potentially provide a stronger basis for drug discovery and vaccine development efforts.

Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example, if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here, we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions. We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff and root mean squared error of qsmooth compared to other global normalization methods. A software implementation is available from https://github.com/stephaniehicks/qsmooth.

GDC-0853 is a selective, reversible, and non-covalent inhibitor of Bruton’s tyrosine kinase (BTK) that does not require interaction with the Cys481 residue for activity. In this first-in-human phase 1 study we evaluated safety, tolerability, pharmacokinetics, and activity of GDC-0853 in patients with relapsed or refractory non-Hodgkin lymphoma (NHL) or chronic lymphocytic leukemia (CLL). Twenty-four patients, enrolled into 3 cohorts, including 6 patients who were positive for the C481S mutation, received GDC-0853 at 100, 200, or 400 mg once daily, orally. There were no dose limiting toxicities. GDC-0853 was well tolerated and the maximum tolerated dose (MTD) was not reached due to premature study closure. Common adverse events (AEs) in ≥ 15% of patients regardless of causality included fatigue (37%), nausea (33%), diarrhea (29%), thrombocytopenia (25%), headache (20%), and abdominal pain, cough, and dizziness (16%, each). Nine serious AEs were reported in 5 patients of whom 2 had fatal outcomes (confirmed H1N1 influenza and influenza pneumonia). A third death was due to progressive disease. Eight of 24 patients responded to GDC-0853: 1 complete response, 4 partial responses, and 3 partial responses with lymphocytosis, including 1 patient with the C481S mutation. Two additional C481S mutation patients had a decrease in size of target tumors (–23% and –44%). These data demonstrate GDC-0853 was generally well-tolerated with antitumor activity.

BackgroundCount data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. Commonly used count data normalization approaches like library size scaling/rarefaction/subsampling cannot correct for compositional or any other relevant technical bias that is uncorrelated with library size.ResultsWe demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it.ConclusionsCompositional bias, induced by the sequencing machine, confounds inferences of absolute abundances. We present a normalization technique for compositional bias correction in sparse sequencing count data, and demonstrate its improved performance in metagenomic 16s survey data. Based on the distribution of technical bias estimates arising from several publicly available large scale 16s count datasets, we argue that detailed experiments specifically addressing the influence of compositional bias in metagenomics are needed.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5160-5) contains supplementary material, which is available to authorized users.

Hepatocellular carcinoma (HCC) develops in the context of chronic inflammatory liver disease and has an extremely poor prognosis. An immunosuppressive tumor microenvironment may contribute to therapeutic failure in metastatic HCC. Here, we identified unique molecular signatures pertaining to HCC disease progression and tumor immunity by analyzing genome-wide RNA-Seq data derived from HCC patient tumors and non-tumor cirrhotic tissues. Unsupervised clustering of gene expression data revealed a gradual suppression of local tumor immunity that coincided with disease progression, indicating an increasingly immunosuppressive tumor environment during HCC disease advancement. IHC examination of the spatial distribution of CD8+ T cells in tumors revealed distinct intra- and peri-tumoral subsets. Differential gene expression analysis revealed an 85-gene signature that was significantly upregulated in the peri-tumoral CD8+ T cell-excluded tumors. Notably, this signature was highly enriched with components of underlying extracellular matrix, fibrosis, and epithelial–mesenchymal transition (EMT). Further analysis condensed this signature to a core set of 23 genes that are associated with CD8+ T cell localization, and were prospectively validated in an independent cohort of HCC specimens. These findings suggest a potential association between elevated fibrosis, possibly modulated by TGF-β, PDGFR, SHH or Notch pathway, and the T cell-excluded immune phenotype. Indeed, targeting fibrosis using a TGF-β neutralizing antibody in the STAM™ model of murine HCC, we found that ameliorating the fibrotic environment could facilitate redistribution of CD8+ lymphocytes into tumors. Our results provide a strong rationale for utilizing immunotherapies in HCC earlier during treatment, potentially in combination with anti-fibrotic therapies.

Count data derived from high-throughput DNA sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. We demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it.