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Background: Identifying differentially expressed genes between experimental conditions is still the gold-standard approach to interpret transcriptomic profiles. Alternative approaches based on diversity measures have been proposed to complement the interpretation of such datasets but are only used marginally., Methods: Here, we reinvestigated diversity measures, which are commonly used in ecology, to characterize mice pregnancy microenvironments based on a public transcriptome dataset. Mainly, we evaluated the Tsallis entropy function to explore the potential of a collection of diversity measures for capturing relevant molecular event information., Results: We demonstrate that the Tsallis entropy function provides additional information compared to the traditional diversity indices, such as the Shannon and Simpson indices. Depending on the relative importance given to the most abundant transcripts based on the Tsallis entropy function parameter, our approach allows appreciating the impact of biological stimulus on the inter-individual variability of groups of samples. Moreover, we propose a strategy for reducing the complexity of transcriptome datasets using a maximation of the beta diversity., Conclusions: We highlight that a diversity-based analysis is suitable for capturing complex molecular events occurring during physiological events. Therefore, we recommend their use through the Tsallis entropy function to analyze transcriptomics data in addition to differential expression analyses., Competing Interests: The authors do not declare any conflict of interest.

[This corrects the article DOI: 10.1371/journal.pone.0190334.].

Interferons (IFNs) play a major role in controlling viral infections including HIV/SIV infections. Persistent up-regulation of interferon stimulated genes (ISGs) is associated with chronic immune activation and progression in SIV/HIV infections, but the respective contribution of different IFNs is unclear. We analyzed the expression of IFN genes and ISGs in tissues of SIV infected macaques to understand the respective roles of type I and type II IFNs. Both IFN types were induced in lymph nodes during early stage of primary infection and to some extent in rectal biopsies but not in PBMCs. Induction of Type II IFN expression persisted during the chronic phase, in contrast to undetectable induction of type I IFN expression. Global gene expression analysis with a major focus on ISGs revealed that at both acute and chronic infection phases most differentially expressed ISGs were inducible by both type I and type II IFNs and displayed the highest increases, indicating strong convergence and synergy between type I and type II IFNs. The analysis of functional signatures of ISG expression revealed temporal changes in IFN expression patterns identifying phase-specific ISGs. These results suggest that IFN-γ strongly contribute to shape ISG upregulation in addition to type I IFN.

Aims: Serial invasive endomyocardial biopsies (EMB) remain the gold standard for acute cellular rejection (ACR) diagnosis. However histological grading has several limitations. We aimed to explore the value of myocardial Gene Expression Profiling (GEP) for diagnosing and identifying predictive biomarkers of ACR., Methods: A case-control study nested within a retrospective heart transplant patients cohort included 126 patients with median (IQR) age 50 (41-57) years and 111 (88%) males. Among 1157 EMB performed, 467 were eligible (i.e, corresponding to either ISHLT grade 0 or ≥3A), among which 36 were selected for GEP according to the grading: 0 (CISHLT, n = 13); rejection ≥3A (RISHLT, n = 13); 0 one month before ACR (BRISHLT, n = 10)., Results: We found 294 genes differentially expressed between CISHLT and RISHLT, mainly involved in immune activation, and inflammation. Hierarchical clustering showed a clear segregation of CISHLT and RISHLT groups and heterogeneity of GEP within RISHLT. All EMB presented immune activation, but some RISHLT EMB were strongly subject to inflammation, whereas others, closer to CISHLT, were characterized by structural modifications with lower inflammation level. We identified 15 probes significantly different between BRISHLT and CISHLT, including the gene of the muscular protein TTN. This result suggests that structural alterations precede inflammation in ACR. Linear Discriminant Analysis based on these 15 probes was able to identify the histological status of every 36 samples., Conclusion: Myocardial GEP is a helpful method to accurately diagnose ACR, and predicts rejection one month before its histological occurrence. These results should be considered in cardiac allograft recipients' care., Competing Interests: The authors have declared that no competing interests exist.

Cerebral malaria (CM) caused by Plasmodium falciparum parasites often leads to the death of infected patients or to persisting neurological sequelae despite anti-parasitic treatments. Erythropoietin (EPO) was recently suggested as a potential adjunctive treatment for CM. However diverging results were obtained in patients from Sub-Saharan countries infected with P. falciparum. In this study, we measured EPO levels in the plasma of well-defined groups of P. falciparum-infected patients, from the state of Odisha in India, with mild malaria (MM), CM, or severe non-CM (NCM). EPO levels were then correlated with biological parameters, including parasite biomass, heme, tumor necrosis factor (TNF)-α, interleukin (IL)-10, interferon gamma-induced protein (IP)-10, and monocyte chemoattractant protein (MCP)-1 plasma concentrations by Spearman's rank and multiple correlation analyses. We found a significant increase in EPO levels with malaria severity degree, and more specifically during fatal CM. In addition, EPO levels were also found correlated positively with heme, TNF-α, IL-10, IP-10 and MCP-1 during CM. We also found a significant multivariate correlation between EPO, TNF-α, IL-10, IP-10 MCP-1 and heme, suggesting an association of EPO with a network of immune factors in CM patients. The contradictory levels of circulating EPO reported in CM patients in India when compared to Africa highlights the need for the optimization of adjunctive treatments according to the targeted population.

Background: The Syrian hamster (golden hamster, Mesocricetus auratus) is gaining importance as a new experimental animal model for multiple pathogens, including emerging zoonotic diseases such as Ebola. Nevertheless there are currently no publicly available transcriptome reference sequences or genome for this species., Results: A cDNA library derived from mRNA and snRNA isolated and pooled from the brains, lungs, spleens, kidneys, livers, and hearts of three adult female Syrian hamsters was sequenced. Sequence reads were assembled into 62,482 contigs and 111,796 reads remained unassembled (singletons). This combined contig/singleton dataset, designated as the Syrian hamster transcriptome, represents a total of 60,117,204 nucleotides. Our Mesocricetus auratus Syrian hamster transcriptome mapped to 11,648 mouse transcripts representing 9,562 distinct genes, and mapped to a similar number of transcripts and genes in the rat. We identified 214 quasi-complete transcripts based on mouse annotations. Canonical pathways involved in a broad spectrum of fundamental biological processes were significantly represented in the library. The Syrian hamster transcriptome was aligned to the current release of the Chinese hamster ovary (CHO) cell transcriptome and genome to improve the genomic annotation of this species. Finally, our Syrian hamster transcriptome was aligned against 14 other rodents, primate and laurasiatheria species to gain insights about the genetic relatedness and placement of this species., Conclusions: This Syrian hamster transcriptome dataset significantly improves our knowledge of the Syrian hamster's transcriptome, especially towards its future use in infectious disease research. Moreover, this library is an important resource for the wider scientific community to help improve genome annotation of the Syrian hamster and other closely related species. Furthermore, these data provide the basis for development of expression microarrays that can be used in functional genomics studies.

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel "crowd-based" approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse 'omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.