Potential Molecular Mechanisms of Rare Anti-Tumor Immune Response by SARS-CoV-2 in Isolated Cases of Lymphomas

Recently, two cases of complete remission of classical Hodgkin lymphoma (cHL) and follicular lymphoma (FL) after SARS-CoV-2 infection were reported. However, the precise molecular mechanism of this rare event is yet to be understood. Here, we hypothesize a potential anti-tumor immune response of SARS-CoV-2 and based on a computational approach show that: (i) SARS-CoV-2 Spike-RBD may bind to the extracellular domains of CD15, CD27, CD45, and CD152 receptors of cHL or FL and may directly inhibit cell proliferation. (ii) Alternately, upon internalization after binding to these CD molecules, the SARS-CoV-2 membrane (M) protein and ORF3a may bind to gamma-tubulin complex component 3 (GCP3) at its tubulin gamma-1 chain (TUBG1) binding site. (iii) The M protein may also interact with TUBG1, blocking its binding to GCP3. (iv) Both the M and ORF3a proteins may render the GCP2-GCP3 lateral binding where the M protein possibly interacts with GCP2 at its GCP3 binding site and the ORF3a protein to GCP3 at its GCP2 interacting residues. (v) Interactions of the M and ORF3a proteins with these gamma-tubulin ring complex components potentially block the initial process of microtubule nucleation, leading to cell-cycle arrest and apoptosis. (vi) The Spike-RBD may also interact with and block PD-1 signaling similar to pembrolizumab and nivolumab- like monoclonal antibodies and may induce B-cell apoptosis and remission. (vii) Finally, the TRADD interacting “PVQLSY” motif of Epstein-Barr virus LMP-1, that is responsible for NF-kB mediated oncogenesis, potentially interacts with SARS-CoV-2 M(pro), NSP7, NSP10, and spike (S) proteins, and may inhibit the LMP-1 mediated cell proliferation. Taken together, our results suggest a possible therapeutic potential of SARS-CoV-2 in lymphoproliferative disorders

The complete genome sequence of Corynebacterium pseudotuberculosis FRC41 isolated from a 12-year-old girl with necrotizing lymphadenitis reveals insights into gene-regulatory networks contributing to virulence

Trost E, Ott L, Schneider J, et al. The complete genome sequence of Corynebacterium pseudotuberculosis FRC41 isolated from a 12-year-old girl with necrotizing lymphadenitis reveals insights into gene-regulatory networks contributing to virulence. BMC Genomics. 2010;11(1): 728

Genomic Islands: an overview of current software tools and future improvements

Microbes are highly diverse and widely distributed organisms. They account for ~60% of Earth’s biomass and new predictions point for the existence of 1011 to 1012 species, which are constantly sharing genes through several different mechanisms. Genomic Islands (GI) are critical in this context, as they are large regions acquired through horizontal gene transfer. Also, they present common features like genomic signature deviation, transposase genes, flanking tRNAs and insertion sequences. GIs carry large numbers of genes related to specific lifestyle and are commonly classified in Pathogenicity, Resistance, Metabolic or Symbiotic Islands. With the advent of the next-generation sequencing technologies and the deluge of genomic data, many software tools have been developed that aim to tackle the problem of GI prediction and they are all based on the prediction of GI common features. However, there is still room for the development of new software tools that implements new approaches, such as, machine learning and pangenomics based analyses. Finally, GIs will always hold a potential application in every newly invented genomic approach as they are directly responsible for much of the genomic plasticity of bacteria

GIPSy2 - Genomic Island Prediction Software 2

<p>Dealing with genomic mobility is a complex task for today's predictors. In fact, with the increasing number of genomes available, there is a constant demand for software that can handle multiple inputs and provide results quickly and reliably. With this in mind, we present GIPSy2, a new version of a well-established software for predicting bacterial genomic islands. The new version allows the addition of multiple inputs and can process 300 <i>Escherichia coli</i> genomes in only two hours of running time. In addition, statistical methods based on principal component analysis (PCA), singular value decomposition (SVD), logistic regression, support vector machine (SVM), and Fisher's exact test are used to provide statistical values associated with each prediction. In the comparative analyses presented in this study, we have shown that the predictions of the new version are, on the whole, equivalent to or better than the results of the previous version. Incongruent results between the two versions were analyzed individually in terms of predictive element content, but they still point to the efficiency of the new version.</p&gt

Density parameter estimation for finding clusters of homologous proteins-tracing actinobacterial pathogenicity lifestyles

Abstract Motivation: Homology detection is a long-standing challenge in computational biology. To tackle this problem, typically all-versus-all BLAST results are coupled with data partitioning approaches resulting in clusters of putative homologous proteins. One of the main problems, however, has been widely neglected: all clustering tools need a density parameter that adjusts the number and size of the clusters. This parameter is crucial but hard to estimate without gold standard data at hand. Developing a gold standard, however, is a difficult and time consuming task. Having a reliable method for detecting clusters of homologous proteins between a huge set of species would open opportunities for better understanding the genetic repertoire of bacteria with different lifestyles. Results: Our main contribution is a method for identifying a suitable and robust density parameter for protein homology detection without a given gold standard. Therefore, we study the core genome of 89 actinobacteria. This allows us to incorporate background knowledge, i.e. the assumption that a set of evolutionarily closely related species should share a comparably high number of evolutionarily conserved proteins (emerging from phylum-specific housekeeping genes). We apply our strategy to find genes/proteins that are specific for certain actinobacterial lifestyles, i.e. different types of pathogenicity. The whole study was performed with transitivity clustering, as it only requires a single intuitive density parameter and has been shown to be well applicable for the task of protein sequence clustering. Note, however, that the presented strategy generally does not depend on our clustering method but can easily be adapted to other clustering approaches. Availability: All results are publicly available at http://transclust.mmci.uni-saarland.de/actino_core/ or as Supplementary Material of this article. Contact:  [email protected] Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p

LifeStyle-Specific-Islands (LiSSI): Integrated Bioinformatics Platform for Genomic Island Analysis

Distinct bacteria are able to cope with highly diverse lifestyles; for instance, they can be free living or host-associated. Thus, these organisms must possess a large and varied genomic arsenal to withstand different environmental conditions. To facilitate the identification of genomic features that might influence bacterial adaptation to a specific niche, we introduce LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning (Random Forest with feature selection, model tuning and robustness analysis). In summary, our strategy aims to identify conserved consecutive homology sequences (islands) in genomes and to identify the most discriminant islands for each lifestyle

Genetics and Molecular Research

p.290-1294.Despite the existence of a vaccine against diphtheria, this disease remains endemic and is reemerging in several regions due to many factors, including variations in genes coding for virulence factors. One common feature of virulence factors is their high concentration in pathogenicity islands (PAIs), very unstable regions acquired via horizontal gene transfer, which has lead to the emergence of various bacterial pathogens. The 13 putative PAIs in Corynebacterium diphtheriae NCTC 13129 and the reemergence of this disease point to the great variability in the PAIs of this species, which may reflect on bacterial life style and physiological versatility. We investigated the relationships between the large number of PAIs in C. diphtheriae and the possible implications of their plasticity in virulence. The GenoFrag software was used to design primers to analyze the genome plasticity of two pathogenicity islands of the reference strain (PiCds 3 and 8) in 11 different strains. We found that PiCd 3 was absent in only two strains, showing genes playing putative important roles in virulence and that only one strain harbored PiCd 8, due to its location in a putative “hotspot” for horizontal gene transfer events

Proksee sessions with GIPSy2 comparisons

&lt;p&gt;&lt;a href="https://proksee.ca"&gt;Proksee&lt;/a&gt; sessions related to comparative analyses performed with GIPSy2.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Supporting material 1&lt;/i&gt;. Comparative analyses carried out with the chromosome of &lt;i&gt;Acinetobacter baumannii&lt;/i&gt; strain AYE against the chromosome of &lt;i&gt;A. baumannii&lt;/i&gt; strain SDF.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Supporting material 2&lt;/i&gt;. Comparative analysis carried out with the chromosome I of &lt;i&gt;Burkholderia pseudomallei&lt;/i&gt; strain K96243 against the chromosome I of &lt;i&gt;B. pseudomallei&lt;/i&gt; strain 668.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Supporting material 3&lt;/i&gt;. Comparative analysis carried out with the chromosome II of &lt;i&gt;Burkholderia pseudomallei&lt;/i&gt; strain K96243 against the chromosome chromosome II of &lt;i&gt;B. pseudomallei&lt;/i&gt; strain 668.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Supporting material 4&lt;/i&gt;. Comparative analyses carried out with the chromosome of &lt;i&gt;Escherichia coli&lt;/i&gt; CFT073 against the chromosome of &lt;i&gt;E. coli&lt;/i&gt; strain K-12 substrain MG1655.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Supporting material 5&lt;/i&gt;. Comparative analyses carried out with the chromosome of &lt;i&gt;Mesorhizobium japonicum&lt;/i&gt; MAFF 303099 against the chromosome of &lt;i&gt;Mesorhizobium sp&lt;/i&gt;. strain BNC1.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Supporting material 6&lt;/i&gt;. Comparative analyses carried out on the chromosome of &lt;i&gt;Mesorhizobium japonicum&lt;/i&gt; MAFF 303099 against the chromosomes of &lt;i&gt;Mesorhizobium sp&lt;/i&gt;. strain BNC1, &lt;i&gt;M. loti&lt;/i&gt; strain TONO, &lt;i&gt;M. loti&lt;/i&gt; strain R88b and &lt;i&gt;M. japonicum&lt;/i&gt; strain R7Astar.&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt

Bacteriocin Producing Streptococcus agalactiae Strains Isolated from Bovine Mastitis in Brazil

Antibiotic resistance is one of the biggest health challenges of our time. We are now facing a post-antibiotic era in which microbial infections, currently treatable, could become fatal. In this scenario, antimicrobial peptides such as bacteriocins represent an alternative solution to traditional antibiotics because they are produced by many organisms and can inhibit bacteria, fungi, and/or viruses. Herein, we assessed the antimicrobial activity and biotechnological potential of 54 Streptococcus agalactiae strains isolated from bovine mastitis. Deferred plate antagonism assays revealed an inhibition spectrum focused on species of the genus Streptococcus&mdash;namely, S. pyogenes, S. agalactiae, S. porcinus, and S. uberis. Three genomes were successfully sequenced, allowing for their taxonomic confirmation via a multilocus sequence analysis (MLSA). Virulence potential and antibiotic resistance assessments showed that strain LGMAI_St_08 is slightly more pathogenic than the others. Moreover, the mreA gene was identified in the three strains. This gene is associated with resistance against erythromycin, azithromycin, and spiramycin. Assessments for secondary metabolites and antimicrobial peptides detected the bacteriocin zoocin A. Finally, comparative genomics evidenced high similarity among the genomes, with more significant similarity between the LGMAI_St_11 and LGMAI_St_14 strains. Thus, the current study shows promising antimicrobial and biotechnological potential for the Streptococcus agalactiae strains