Background: During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions.

Phylogeographical analyses have become commonplace for a myriad of organisms with the advent of cheap DNA sequencing technologies. Bayesian model-based clustering is a powerful tool for detecting important patterns in such data and can be used to decipher even quite subtle signals of systematic differences in molecular variation. Here, we introduce two upgrades to the Bayesian Analysis of Population Structure (BAPS) software, which enable 1) spatially explicit modeling of variation in DNA sequences and 2) hierarchical clustering of DNA sequence data to reveal nested genetic population structures. We provide a direct interface to map the results from spatial clustering with Google Maps using the portal http://www.spatialepidemiology.net/ and illustrate this approach using sequence data from Borrelia burgdorferi. The usefulness of hierarchical clustering is demonstrated through an analysis of the metapopulation structure within a bacterial population experiencing a high level of local horizontal gene transfer. The tools that are introduced are freely available at http://www.helsinki.fi/bsg/software/BAPS/.

Bayesian inference, Genetic structure, Spatial modeling, Statistical learning theory, Unsupervised classification,

NK and T cell-derived IFN-γ is a key cytokine that stimulates innate immune responses and directs adaptive T cell response toward Th1 type. IL-15, IL-18, and IL-21 have significant roles as activators of NK and T cell functions. We have previously shown that IL-15 and IL-21 induce the expression of IFN-γ, T-bet, IL-12Rβ2, and IL-18R genes both in NK and T cells. Now we have studied the effect of IL-15, IL-18, and IL-21 on IFN-γ gene expression in more detail in human NK and T cells. IL-15 clearly activated IFN-γ mRNA expression and protein production in both cell types. IL-18 and IL-21 enhanced IL-15-induced IFN-γ gene expression. IL-18 or IL-21 alone induced a modest expression of the IFN-γ gene but a combination of IL-21 and IL-18 efficiently up-regulated IFN-γ production. We also show that IL-15 activated the binding of STAT1, STAT3, STAT4, and STAT5 to the regulatory sites of the IFN-γ gene. Similarly, IL-21 induced the binding of STAT1, STAT3, and STAT4 to these elements. IL-15- and IL-21-induced STAT1 and STAT4 activation was verified by immunoprecipitation with anti-phosphotyrosine Abs followed by Western blotting with anti-STAT1 and anti-STAT4 Abs. IL-18 was not able to induce the binding of STATs to IFN-γ gene regulatory sites. IL-18, however, activated the binding of NF-κB to the IFN-γ promoter NF-κB site. Our results suggest that both IL-15 and IL-21 have an important role in activating the NK cell-associated innate immune response.

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OBJECTIVE-The objective of this study is to quantitate expression of genes possibly contributing to insulin resistance and fat deposition in the human liver.RESEARCH DESIGN AND METHODS-A total of 24 subjects who had varying amounts of histologically determined fat in the liver ranging from normal (n ϭ 8) to steatosis due to a nonalcoholic fatty liver (NAFL) (n ϭ 16) were studied. The mRNA concentrations of 21 candidate genes associated with fatty acid metabolism, inflammation, and insulin sensitivity were quantitated in liver biopsies using real-time PCR. In addition, the subjects were characterized with respect to body composition and circulating markers of insulin sensitivity. . PPAR␥ coactivator 1 (PGC1) was significantly lower in subjects with NAFL than in those without. Genes significantly associated with obesity included nine genes: plasminogen activator inhibitor 1, PPAR␥, PPAR␦, MCP-1, CCL3 (macrophage inflammatory protein [MIP]-1␣), PPAR␥2, carnitine palmitoyltransferase (CPT1A), FABP4, and FABP5. The following parameters were associated with liver fat independent of obesity: serum adiponectin, insulin, C-peptide, and HDL cholesterol concentrations and the mRNA concentrations of MCP-1, MIP-1␣, ACSL4, FABP4, FABP5, and LPL. RESULTS-TheCONCLUSIONS-Genes involved in fatty acid partitioning and binding, lipolysis, and monocyte/macrophage recruitment and inflammation are overexpressed in the human fatty liver.

Toll-like receptors (TLRs) mediate host cell activation by various microbial components. TLR2, TLR3, TLR4, TLR7, TLR8, and TLR9 are the receptors that have been associated with virus-induced immune response. We have previously reported that all these TLRs, except TLR9, are expressed at mRNA levels in human monocyte-derived macrophages. Here we have studied TLR2, TLR3, TLR4, and TLR7/8 ligand-induced IFN-α, IFN-β, IL-28, and IL-29 expression in human macrophages. IFN-α pretreatment of macrophages was required for efficient TLR3 and TLR4 agonist-induced activation of IFN-α, IFN-β, IL-28, and IL-29 genes. TLR7/8 agonist weakly activated IFN-α, IFN-β, IL-28, and IL-29 genes, whereas TLR2 agonist was not able to activate these genes. IFN-α enhanced TLR responsiveness in macrophages by up-regulating the expression of TLR3, TLR4, and TLR7. IFN-α also enhanced the expression of TLR signaling molecules MyD88, TIR domain-containing adaptor inducing IFN-β, IκB kinase-ε, receptor interacting protein 1, and IFN regulatory factor 7. Furthermore, the activation of transcription factor IFN regulatory factor 3 by TLR3 and TLR4 agonists was dependent on IFN-α pretreatment. In conclusion, our results suggest that IFN-α sensitizes cells to microbial recognition by up-regulating the expression of several TLRs as well as adapter molecules and kinases involved in TLR signaling.

Enterococcus faecium has recently emerged as an important multiresistant nosocomial pathogen. Defining population structure in this species is required to provide insight into the existence, distribution, and dynamics of specific multiresistant or pathogenic lineages in particular environments, like the hospital. Here, we probe the population structure of E. faecium using Bayesian-based population genetic modeling implemented in Bayesian Analysis of Population Structure (BAPS) software. The analysis involved 1,720 isolates belonging to 519 sequence types (STs) (491 for E. faecium and 28 for Enterococcus faecalis). E. faecium isolates grouped into 13 BAPS (sub)groups, but the large majority (80%) of nosocomial isolates clustered in two subgroups (2-1 and 3-3). Phylogenetic and eBURST analysis of BAPS groups 2 and 3 confirmed the existence of three separate hospital lineages (17, 18, and 78), highlighting different evolutionary trajectories for BAPS 2-1 (lineage 78) and 3-3 (lineage 17 and lineage 18) isolates. Phylogenomic analysis of 29 E. faecium isolates showed agreement between BAPS assignment of STs and their relative positions in the phylogenetic tree. Odds ratio calculation confirmed the significant association between hospital isolates with BAPS 3-3 and lineages 17, 18, and 78. Admixture analysis showed a scarce number of recombination events between the different BAPS groups. For the E. faecium hospital population, we propose an evolutionary model in which strains with a high propensity to colonize and infect hospitalized patients arise through horizontal gene transfer. Once adapted to the distinct hospital niche, this subpopulation becomes isolated, and recombination with other populations declines.