In bacterial genomes, there are two mechanisms to terminate the DNA transcription: the “intrinsic” or Rho-independent termination and the Rho-dependent termination. Intrinsic terminators are characterized by a RNA hairpin followed by a run of 6–8 U residues relatively easy to identify using one of the numerous available prediction programs. In contrast, Rho-dependent termination is mediated by the Rho protein factor that, firstly, binds to ribosome-free mRNA in a site characterized by a C > G content and then reaches the RNA polymerase to induce its release. Conversely on intrinsic terminators, the computational prediction of Rho-dependent terminators in prokaryotes is a very difficult problem because the sequence features required for the function of Rho are complex and poorly defined. This is the reason why it still does not exist an exhaustive Rho-dependent terminators prediction program. In this study we introduce RhoTermPredict, the first published algorithm for an exhaustive Rho-dependent terminators prediction in bacterial genomes. RhoTermPredict identifies these elements based on a previously proposed consensus motif common to all Rho-dependent transcription terminators. It essentially searches for a 78 nt long RUT site characterized by a C > G content and with regularly spaced C residues, followed by a putative pause site for the RNA polymerase. We tested RhoTermPredict performances by using available genomic and transcriptomic data of the microorganism Escherichia coli K-12, both in limited-length sequences and in the whole-genome, and available genomic sequences from Bacillus subtilis 168 and Salmonella enterica LT2 genomes. We also estimated the overlap between the predictions of RhoTermPredict and those obtained by the predictor of intrinsic terminators ARNold webtool. Our results demonstrated that RhoTermPredict is a very performing algorithm both for limited-length sequences (F1-score obtained about 0.7) and for a genome-wide analysis. Furthermore the degree of overlap with ARNold predictions was very low. Our analysis shows that RhoTermPredict is a powerful tool for Rho-dependent terminators search in the three analyzed genomes and could fill this gap in computational genomics. We conclude that RhoTermPredict could be used in combination with an intrinsic terminators predictor in order to predict all the transcription terminators in bacterial genomes.

Erythroid commitment and differentiation are regulated by the coordinated action of a host of transcription factors, including GATA2 and GATA1. Here, we explored GATA-mediated transcriptional regulation through the integrative analysis of gene expression, chromatin modifications, and GATA factors' binding in human multipotent hematopoietic stem/progenitor cells, early erythroid progenitors, and late precursors. A progressive loss of H3K27 acetylation and a diminished usage of active enhancers and super-enhancers were observed during erythroid commitment and differentiation. GATA factors mediate transcriptional changes through a stage-specific interplay with regulatory elements: GATA1 binds different sets of regulatory elements in erythroid progenitors and precursors and controls the transcription of distinct genes during commitment and differentiation. Importantly, our results highlight a pivotal role of promoters in determining the transcriptional program activated upon erythroid differentiation. Finally, we demonstrated that GATA1 binding to a stage-specific super-enhancer sustains the expression of the KIT receptor in human erythroid progenitors.

Gut Microbiota (GM) dysbiosis associates with Atherosclerotic Cardiovascular Diseases (ACVD), but whether this also holds true in subjects without clinically manifest ACVD represents a challenge of personalized prevention. We connected exposure to diet (self-reported by food diaries) and markers of Subclinical Carotid Atherosclerosis (SCA) with individual taxonomic and functional GM profiles (from fecal metagenomic DNA) of 345 subjects without previous clinically manifest ACVD. Subjects without SCA reported consuming higher amounts of cereals, starchy vegetables, milky products, yoghurts and bakery products versus those with SCA (who reported to consume more mechanically separated meats). The variety of dietary sources significantly overlapped with the separations in GM composition between subjects without SCA and those with SCA (RV coefficient between nutrients quantities and microbial relative abundances at genus level = 0.65, p-value = 0.047). Additionally, specific bacterial species (Faecalibacterium prausnitzii in the absence of SCA and Escherichia coli in the presence of SCA) are directly related to over-representation of metagenomic pathways linked to different dietary sources (sulfur oxidation and starch degradation in absence of SCA, and metabolism of amino acids, syntheses of palmitate, choline, carnitines and Trimethylamine n-oxide in presence of SCA). These findings might contribute to hypothesize future strategies of personalized dietary intervention for primary CVD prevention setting.

Over the last few decades, computational genomics has tremendously contributed to decipher biology from genome sequences and related data. Considerable effort has been devoted to the prediction of transcription promoter and terminator sites that represent the essential “punctuation marks” for DNA transcription. Computational prediction of promoters in prokaryotes is a problem whose solution is far from being determined in computational genomics. The majority of published bacterial promoter prediction tools are based on a consensus-sequences search and they were designed specifically for vegetative σ70 promoters and, therefore, not suitable for promoter prediction in bacteria encoding a lot of σ factors, like actinomycetes. In this study we investigated the possibility to identify putative promoters in prokaryotes based on evolutionarily conserved motifs, and focused our attention on GC-rich bacteria in which promoter prediction with conventional, consensus-based algorithms is often not-exhaustive. Here, we introduce G4PromFinder, a novel algorithm that predicts putative promoters based on AT-rich elements and G-quadruplex DNA motifs. We tested its performances by using available genomic and transcriptomic data of the model microorganisms Streptomyces coelicolor A3(2) and Pseudomonas aeruginosa PA14. We compared our results with those obtained by three currently available promoter predicting algorithms: the σ70consensus-based PePPER, the σ factors consensus-based bTSSfinder, and PromPredict which is based on double-helix DNA stability. Our results demonstrated that G4PromFinder is more suitable than the three reference tools for both the genomes. In fact our algorithm achieved the higher accuracy (F1-scores 0.61 and 0.53 in the two genomes) as compared to the next best tool that is PromPredict (F1-scores 0.46 and 0.48). Consensus-based algorithms produced lower performances with the analyzed GC-rich genomes. Our analysis shows that G4PromFinder is a powerful tool for promoter search in GC-rich bacteria, especially for bacteria coding for a lot of σ factors, such as the model microorganism S. coelicolor A3(2). Moreover consensus-based tools and, in general, tools that are based on specific features of bacterial σ factors seem to be less performing for promoter prediction in these types of bacterial genomes.

Background: the neoplastic B cells of the Helicobacter pylori-related low-grade gastric mucosa-associated lymphoid tissue (MALT) lymphoma proliferate in response to H. pylori, however, the nature of the H. pylori antigen responsible for proliferation is still unknown. The purpose of the study was to dissect whether CagY might be the H. pylori antigen able to drive B cell proliferation. Methods: the B cells and the clonal progeny of T cells from the gastric mucosa of five patients with MALT lymphoma were compared with those of T cell clones obtained from five H. pylori–infected patients with chronic gastritis. The T cell clones were assessed for their specificity to H. pylori CagY, cytokine profile and helper function for B cell proliferation. Results: 22 of 158 CD4⁺ (13.9%) gastric clones from MALT lymphoma and three of 179 CD4⁺ (1.7%) clones from chronic gastritis recognized CagY. CagY predominantly drives Interferon-gamma (IFN-γ) and Interleukin-17 (IL-17) secretion by gastric CD4⁺ T cells from H. pylori-infected patients with low-grade gastric MALT lymphoma. All MALT lymphoma-derived clones dose dependently increased their B cell help, whereas clones from chronic gastritis lost helper activity at T-to-B-cell ratios greater than 1. Conclusion: the results obtained indicate that CagY drives both B cell proliferation and T cell activation in gastric MALT lymphomas.

Haploidentical hematopoietic stem cell transplantation (h-HSCT) represents an efficient curative approach for patients affected by hematologic malignancies in which the reduced intensity conditioning induces a state of immunologic tolerance between donor and recipient. However, opportunistic viral infections greatly affect h-HSCT clinical outcomes. NK cells are the first lymphocytes that recover after transplant and provide a prompt defense against human cytomegalovirus (HCMV) infection/reactivation. By undertaking a longitudinal single-cell computational profiling of multiparametric flow cytometry, we show that HCMV accelerates NK cell immune reconstitution together with the expansion of CD158b1b2j ^pos /NKG2A ^neg /NKG2C ^pos /NKp30 ^lo NK cells. The frequency of this subset correlates with HCMV viremia, further increases in recipients experiencing multiple episodes of viral reactivations, and persists for months after the infection. The transcriptional profile of FACS-sorted CD158b1b2j ^pos NK cells confirmed the ability of HCMV to deregulate NKG2C, NKG2A, and NKp30 gene expression, thus inducing the expansion of NK cells with adaptive traits. These NK cells are characterized by the downmodulation of several gene pathways associated with cell migration, the cell cycle, and effector-functions, as well as by a state of metabolic/cellular exhaustion. This profile reflects the functional impairments of adaptive NK cells to produce IFN-γ, a phenomenon also due to the viral-induced expression of lymphocyte-activation gene 3 (LAG-3) and programmed cell death protein 1 (PD-1) checkpoint inhibitors.