Effects of galectin-1 on immunomodulatory properties of human monocyte-derived dendritic cells

Our study aimed to evaluate the effects of Gal-1 in dose depending manner on maturation and immunomodulatory properties of monocyte-derived (Mo) DCs in-vitro. The effects were analyzed by monitoring their phenotypic characteristics, cytokine profile, and the ability to direct the immune response in the co-culture with allogeneic CD4⁺T cells. Gal-1 reduced the expression of CD80 and CD86 molecules on MoDCs compared to untreated MoDCs. Gal-1 at concentrations of 1 and 6 μg/mL significantly reduced IL-12 production, while the concentration of 3 μg/mL led to its significant increase. Gal-1 in all concentrations induced a significant increase in the production of IL-10. Treatment of MoDCs with 3 and 6 μg/mL of Gal-1 stimulated the production of IL-2 and IFN-γ in the co-culture with CD4⁺T lymphocytes. This study demonstrated a dual immunomodulatory effect of Gal-1 on MoDCs in terms of immune stimulation and immune suppression, depending on the applied concentration.

In silico Analysis Suggests Common Appearance of scaRNAs in Type II Systems and Their Association With Bacterial Virulence

In addition to its well-established defense function, CRISPR/Cas can also exhibit crucial non-canonical activity through endogenous gene expression regulation, which was found to mainly affect bacterial virulence. These non-canonical functions depend on scaRNA, which is a small RNA encoded outside of CRISPR array, that is typically flanked by a transcription start site (TSS) and a terminator, and is in part complementary to another small CRISPR/Cas-associated RNA (tracrRNAs). Identification of scaRNAs is however largely complicated by the scarcity of RNA-Seq data across different bacteria, so that they were identified only in a relatively rare CRISPR/Cas subtype (IIB), and the possibility of finding them in other Type II systems is currently unclear. This study presents the first effort toward systematic detection of small CRISPR/Cas-associated regulatory RNAs, where obtained predictions can guide future experiments. The core of our approach is ab initio detection of small RNAs from bacterial genome, which is based on jointly predicting transcription signals - TSS and terminators - and homology to CRISPR array repeat. Particularly, we employ our improved approach for detecting bacterial TSS, since accurate TSS detection is the main limiting factor for accurate small RNA prediction. We also explore how our predictions match to available RNA-Seq data and analyze their conservation across related bacterial species. In Type IIB systems, our predictions are consistent with experimental data, and we systematically identify scaRNAs throughout this subtype. Furthermore, we identify scaRNA:tracrRNA pairs in a number of IIA/IIC systems, where the appearance of scaRNAs co-occurs with the strains being pathogenic. RNA-Seq and conservation analysis show that our method is well suited for predicting CRISPR/Cas-associated small RNAs. We also find possible existence of a modified mechanism of CRISPR-associated small RNA action, which, interestingly, closely resembles the setup employed in biotechnological applications. Overall, our findings indicate that scaRNA:tracrRNA pairs are present in all subtypes of Type II systems, and point to an underlying connection with bacterial virulence. In addition to formulating these hypotheses, careful manual curation that we performed, makes an important first step toward fully automated predictor of CRISPR/Cas-associated small RNAs, which will allow their large scale analysis across diverse bacterial genomes.

Mix-and-matching as a promoter recognition mechanism by ECF σ factors

Background

Transcription initiation is in bacteria exhibited by different σ factors, most of which fall within σ⁷⁰ family. This family is diverse, ranging from the housekeeping Group I (RpoDs), to Group IV (ECF) σ factors, that transcribe smaller regulons under more stringent conditions. RpoDs employ a kinetic mix-and-match mechanism, where promoter elements complement each other binding strengths in achieving sufficient transcription activity. On the other hand, it is assumed that ECF σs, which are the most distant from the housekeeping σ factors, cannot exhibit mix-and-matching. However, mix-and-matching for ECF σ factors was not quantitatively checked before, and recent results show a much larger flexibility in the promoter recognition by the members of this group.

Results

To this end, we quantitatively investigate mix-and-matching in two canonical ECF σ family members (σ^E and σ^W), for which we use a biophysics based model of transcription initiation. For σ^E, we perform a separate analysis for in-vitro active and in-vitro inactive promoters, which allows us investigating how mix-and-matching depends on the external factors that may control transcription activity in the in-vitro inactive set. We show that the promoter elements of canonical ECF σs significantly complement each other strengths, where such mix-and-matching is in the in-vitro active set even stronger compared to the correlations observed for the housekeeping σs. This complementation however significantly decreases for the in-vitro inactive set, which we propose is due to mix-and-matching with regulatory sequences outside of the canonical promoter elements. In line with this proposition, we show that a conserved spacer element, which appears in the in-vitro inactive promoter set, significantly increases the promoter element complementation. While RpoD promoter elements mix-and-match to achieve sufficient total transcription activity, for σ^E they complement each other to achieve sufficiently strong total binding affinity, which we relate to differences in physiological responses between the two groups of σ factors.

Conclusion

Despite a common notion that smaller σ factor specificity leads to a larger mix-and-matching, we here obtain a larger promoter element complementation for σ^E compared to RpoDs. Finally, to explain this finding, we propose a simple model which relates the size of σ factor regulon with the extent of mix-and-matching, based on an assumption of a selection pressure on promoters that are near the non-specific binding boundary to remain functional.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0865-z) contains supplementary material, which is available to authorized users.

Bioinformatics as a first-line approach for understanding bacteriophage transcription

Current approach to understanding bacteriophage transcription strategies during infection includes a combination of experimental and bioinformatics approaches, which is often time and resource consuming. Given the exponentially growing number of sequenced bacteriophage genomes, it becomes sensible asking to what extent one can understand bacteriophage transcription by using bioinformatics methods alone. We here argue that a suitable choice of computational methods may provide a highly efficient first-line approach for underst-anding bacteriophage transcription.

Inferring bacteriophage infection strategies from genome sequence: analysis of bacteriophage 7-11 and related phages

Background

Analyzing regulation of bacteriophage gene expression historically lead to establishing major paradigms of molecular biology, and may provide important medical applications in the future. Temporal regulation of bacteriophage transcription is commonly analyzed through a labor-intensive combination of biochemical and bioinformatic approaches and macroarray measurements. We here investigate to what extent one can understand gene expression strategies of lytic phages, by directly analyzing their genomes through bioinformatic methods. We address this question on a recently sequenced lytic bacteriophage 7 - 11 that infects bacterium Salmonella enterica.

Results

We identify novel promoters for the bacteriophage-encoded σ factor, and test the predictions through homology with another bacteriophage (phiEco32) that has been experimentally characterized in detail. Interestingly, standard approach based on multiple local sequence alignment (MLSA) fails to correctly identify the promoters, but a simpler procedure that is based on pairwise alignment of intergenic regions identifies the desired motifs; we argue that such search strategy is more effective for promoters of bacteriophage-encoded σ factors that are typically well conserved but appear in low copy numbers, which we also verify on two additional bacteriophage genomes. Identifying promoters for bacteriophage encoded σ factors together with a more straightforward identification of promoters for bacterial encoded σ factor, allows clustering the genes in putative early, middle and late class, and consequently predicting the temporal regulation of bacteriophage gene expression, which we demonstrate on phage 7-11.

Conclusions

While MLSA algorithms proved highly useful in computational analysis of transcription regulation, we here established that a simpler procedure is more successful for identifying promoters that are recognized by bacteriophage encoded σ factor/RNA polymerase. We here used this approach for predicting sequence specificity of a novel (bacteriophage encoded) σ factor, and consequently inferring phage 7-11 transcription strategy. Therefore, direct analysis of bacteriophage genome sequences is a plausible first-line approach for efficiently inferring phage transcription strategies, and may provide a wealth of information on transcription initiation by diverse σ factors/RNA polymerases.