Comparative pan-genome analysis of Piscirickettsia salmonis reveals genomic divergences within genogroups

Indexación: Scopus.Piscirickettsia salmonis is the etiological agent of salmonid rickettsial septicemia, a disease that seriously affects the salmonid industry. Despite efforts to genomically characterize P. salmonis, functional information on the life cycle, pathogenesis mechanisms, diagnosis, treatment, and control of this fish pathogen remain lacking. To address this knowledge gap, the present study conducted an in silico pan-genome analysis of 19 P. salmonis strains from distinct geographic locations and genogroups. Results revealed an expected open pan-genome of 3,463 genes and a core-genome of 1,732 genes. Two marked genogroups were identified, as confirmed by phylogenetic and phylogenomic relationships to the LF-89 and EM-90 reference strains, as well as by assessments of genomic structures. Different structural configurations were found for the six identified copies of the ribosomal operon in the P. salmonis genome, indicating translocation throughout the genetic material. Chromosomal divergences in genomic localization and quantity of genetic cassettes were also found for the Dot/Icm type IVB secretion system. To determine divergences between core-genomes, additional pan-genome descriptions were compiled for the so-termed LF and EM genogroups. Open pan-genomes composed of 2,924 and 2,778 genes and core-genomes composed of 2,170 and 2,228 genes were respectively found for the LF and EM genogroups. The core-genomes were functionally annotated using the Gene Ontology, KEGG, and Virulence Factor databases, revealing the presence of several shared groups of genes related to basic function of intracellular survival and bacterial pathogenesis. Additionally, the specific pan-genomes for the LF and EM genogroups were defined, resulting in the identification of 148 and 273 exclusive proteins, respectively. Notably, specific virulence factors linked to adherence, colonization, invasion factors, and endotoxins were established. The obtained data suggest that these genes could be directly associated with inter-genogroup differences in pathogenesis and host-pathogen interactions, information that could be useful in designing novel strategies for diagnosing and controlling P. salmonis infection. © 2017 Nourdin-Galindo, Sánchez, Molina, Espinoza-Rojas, Oliver, Ruiz, Vargas-Chacoff, Cárcamo, Figueroa, Mancilla, Maracaja-Coutinho and Yañez.https://www.frontiersin.org/articles/10.3389/fcimb.2017.00459/ful

Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool

Transcriptome analyses have increased our understanding of the molecular mechanisms underlying human diseases. Most approaches aim to identify significant genes by comparing their expression values between healthy subjects and a group of patients with a certain disease. Given that studies normally contain few samples, the heterogeneity among individuals caused by environmental factors or undetected illnesses can impact gene expression analyses. We present a systematic analysis of sample heterogeneity in a variety of gene expression studies relating to inflammatory and infectious diseases and show that novel immunological insights may arise once heterogeneity is addressed. The perturbation score of samples is quantified using nonperturbed subjects (i.e., healthy subjects) as a reference group. Such a score allows us to detect outlying samples and subgroups of diseased patients and even assess the molecular perturbation of single cells infected with viruses. We also show how removal of outlying samples can improve the “signal” of the disease and impact detection of differentially expressed genes. The method is made available via the mdp Bioconductor R package and as a user-friendly webtool, webMDP, available at http://mdp.sysbio.tools

Long non-coding RNAs are involved in multiple immunological pathways in response to vaccination

Understanding the mechanisms of vaccine-elicited protection contributes to the development of new vaccines. The emerging field of Systems Vaccinology provides detailed information on host responses to vaccination and has been successfully applied to study the molecular mechanisms of several vaccines. Long noncoding RNAs (lncRNAs) are crucially involved in multiple biological processes but their role in vaccine-induced immunity has not been explored. We performed an analysis of over 2,000 blood transcriptome samples from 17 vaccine cohorts to identify lncRNAs potentially involved with antibody responses to Influenza and Yellow Fever vaccines. We have created an online database where all results from this analysis can be accessed easily. We found that lncRNAs participate in distinct immunological pathways related to vaccine-elicited responses. Among them, we showed that the expression of lncRNA FAM30A was high in B-cells and correlates with the expression of Immunoglobulin genes located in its genomic vicinity. We also identified altered expression of these lncRNAs in RNA-seq data from a new cohort of children following immunization with intranasal live attenuated influenza vaccine, suggesting a common role across several diverse vaccines. Taken together, these findings provide the first evidence that lncRNAs play a significant impact on the immune responses induced by vaccination

Genomic positional conservation identifies topological anchor point (tap)RNAs linked to developmental loci

The mammalian genome is transcribed into large numbers of long noncoding RNAs (lncRNAs), but the definition of functional lncRNA groups has proven difficult, partly due to their low sequence conservation and lack of identified shared properties. Here we consider positional conservation across mammalian genomes as an indicator of functional commonality. We identify 665 conserved lncRNA promoters in mouse and human genomes that are preserved in genomic position relative to orthologous coding genes. The identified positionally conserved lncRNA genes are primarily associated with developmental transcription factor loci with which they are co-expressed in a tissue-specific manner. Strikingly, over half of all positionally conserved RNAs in this set are linked to distinct chromatin organization structures, overlapping the binding sites for the CTCF chromatin organizer and located at chromatin loop anchor points and borders of topologically associating domains (TADs). These topological anchor point (tap)RNAs possess conserved sequence domains that are enriched in potential recognition motifs for Zinc Finger proteins. Characterization of these non-coding RNAs and their associated coding genes shows that they are functionally connected: they regulate each other ′s expression and influence the metastatic phenotype of cancer cells in vitro in a similar fashion. Thus, interrogation of positionally conserved lncRNAs identifies a new subset of tapRNAs with shared functional properties. These results provide a large dataset of lncRNAs that conform to the ″extended gene″ model, in which conserved developmental genes are genomically and functionally linked to regulatory lncRNA loci across mammalian evolution

Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci

BACKGROUND: The mammalian genome is transcribed into large numbers of long noncoding RNAs (lncRNAs), but the definition of functional lncRNA groups has proven difficult, partly due to their low sequence conservation and lack of identified shared properties. Here we consider promoter conservation and positional conservation as indicators of functional commonality. RESULTS: We identify 665 conserved lncRNA promoters in mouse and human that are preserved in genomic position relative to orthologous coding genes. These positionally conserved lncRNA genes are primarily associated with developmental transcription factor loci with which they are coexpressed in a tissue-specific manner. Over half of positionally conserved RNAs in this set are linked to chromatin organization structures, overlapping binding sites for the CTCF chromatin organiser and located at chromatin loop anchor points and borders of topologically associating domains (TADs). We define these RNAs as topological anchor point RNAs (tapRNAs). Characterization of these noncoding RNAs and their associated coding genes shows that they are functionally connected: they regulate each other’s expression and influence the metastatic phenotype of cancer cells in vitro in a similar fashion. Furthermore, we find that tapRNAs contain conserved sequence domains that are enriched in motifs for zinc finger domain-containing RNA-binding proteins and transcription factors, whose binding sites are found mutated in cancers. CONCLUSIONS: This work leverages positional conservation to identify lncRNAs with potential importance in genome organization, development and disease. The evidence that many developmental transcription factors are physically and functionally connected to lncRNAs represents an exciting stepping-stone to further our understanding of genome regulation

The Role of Neuregulin 1 in Schizophrenia: A Bioinformatics Approach

*Context:* Notwithstanding the great number of studies on the etiology and pathophysiology of schizophrenia, both issues remain far from being fully understood. Schizophrenia seems to be related to several biochemical abnormalities, which point to a multi-factor etiology and pathophysiology, as well as to the perspective that several etiologically diverse disorders might coexist within this nosographic entity. On the other hand, identical twins reveal a high concordance for schizophrenia. From that standpoint, the perspective that structurally-related proteins may play an important and yet non-deterministic role seems attractive. Among these proteins, it is suggestive that Neuregulin 1 exerts a pivotal role. 
*Objective:* This paper aims to uncover the most prominent relations that Neuregulin 1 establishes with schizophrenia. 
*Method:* Several bioinformatical methods are used in order to present: 
1. A visual representation of Neuregulin 1’s main molecular pathways, associated with a discussion about their importance to schizophrenia research; 
2. A new heatmap of Neuregulin 1 and its receptor’s expression in brain tissues most relevant to the understanding of schizophrenia, created after the development of new R programming scripts (described elsewhere), which facilitate the analysis of gene expression profiles in public datasets; 
3. A conceptual map of the literature retrieved using the keywords ‘Neuregulin 1 and human’ in PubMed, followed by a discussion of the most relevant sub-topics. 
*Results:* Neuregulin 1 polymorphisms affect several brain tissues and contribute to the etiology and pathophysiology of schizophrenia. Suggestively, Neuregulin 1 partially bridges the 'molecular gap' that schizophrenia establishes in relation to bipolar disorder and Alzheimer disease, which involves genes that affect several brain networks, at the same time that they are highly dependent on noxious environmental variables to be triggered

Draft Genome of Chilean Honeybee (Apis mellifera) Gut Strain Lactobacillus kunkeei MP2.

Here, we report the first draft genome sequence of Lactobacillus kunkeei strain MP2, isolated from a Chilean honeybee gut. The sequenced genome has a total size of 1.58 Mb distributed into 44 contigs and 1,356 protein-coding sequences

A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.

Genomic Islands (GIs) are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP--Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me

Medium Voltage Network Planning Considering Distributed Generation and Demand Side Management

Hepatitis C virus (HCV) infection frequently persists despite substantial virus-specific immune responses and the combination of pegylated interferon (INF)-alpha and ribavirin therapy. Major histocompatibility complex class I restricted CD8+ T cells are responsible for the control of viraemia in HCV infection, and several studies suggest protection against viral infection associated with specific HLAs. The reason for low rates of sustained viral response (SVR) in HCV patients remains unknown. Escape mutations in response to cytotoxic T lymphocyte are widely described; however, its influence in the treatment outcome is ill understood. Here, we investigate the differences in CD8 epitopes frequencies from the Los Alamos database between groups of patients that showed distinct response to pegylated alpha-INF with ribavirin therapy and test evidence of natural selection on the virus in those who failed treatment, using five maximum likelihood evolutionary models from PAML package. The group of sustained virological responders showed three epitopes with frequencies higher than Non-responders group, all had statistical support, and we observed evidence of selection pressure in the last group. No escape mutation was observed. Interestingly, the epitope VLSDFKTWL was 100% conserved in SVR group. These results suggest that the response to treatment can be explained by the increase in immune pressure, induced by interferon therapy, and the presence of those epitopes may represent an important factor in determining the outcome of therapy

Mean shift algorithm procedure for a data point x_<i>i</i>.

<p>The bold filled circles with arrows represent the iteration, while the pointed circles represent the window used in density estimation until the convergence is achieved at the <i>n</i>th iteration.</p