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1.
Bioinformatic and experimental characterization of SEN1998: a conserved gene carried by the Enterobacteriaceae-associated ROD21-like family of genomic islands.
Piña-Iturbe, A, Hoppe-Elsholz, G, Fernández, PA, Santiviago, CA, González, PA, Bueno, SM
Scientific reports. 2022;(1):2435
Abstract
Genomic islands (GIs) are horizontally transferred elements that shape bacterial genomes and contributes to the adaptation to different environments. Some GIs encode an integrase and a recombination directionality factor (RDF), which are the molecular GI-encoded machinery that promotes the island excision from the chromosome, the first step for the spread of GIs by horizontal transfer. Although less studied, this process can also play a role in the virulence of bacterial pathogens. While the excision of GIs is thought to be similar to that observed in bacteriophages, this mechanism has been only studied in a few families of islands. Here, we aimed to gain a better understanding of the factors involved in the excision of ROD21 a pathogenicity island of the food-borne pathogen Salmonella enterica serovar Enteritidis and the most studied member of the recently described Enterobacteriaceae-associated ROD21-like family of GIs. Using bioinformatic and experimental approaches, we characterized the conserved gene SEN1998, showing that it encodes a protein with the features of an RDF that binds to the regulatory regions involved in the excision of ROD21. While deletion or overexpression of SEN1998 did not alter the expression of the integrase-encoding gene SEN1970, a slight but significant trend was observed in the excision of the island. Surprisingly, we found that the expression of both genes, SEN1998 and SEN1970, were negatively correlated to the excision of ROD21 which showed a growth phase-dependent pattern. Our findings contribute to the growing body of knowledge regarding the excision of GIs, providing insights about ROD21 and the recently described EARL family of genomic islands.
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2.
Antibody VH domain sequence analysis by a bioinformatics approach based on electronic amino acid properties may help to predict paratop location.
Srdic-Rajic, T, Metlas, R
Immunology letters. 2022;:55-57
Abstract
Gene as the basic functional unit of DNA encodes information about the product such as protein. The majority of proteins realize function through protein-protein interactions involving short protein motifs. However, some proteins such as antibodies are established by the rearrangement of several (V-D-J) gene segments with the potential addition of nontemplated nucleotides that may change information encoded by the respective gene segment used. Antibody VH domain sequence analysis by ISM bioinformatics approach that is based on amino acids physicochemical features, enable to distinguish the contribution of the information encoded by VH gene or generated during VDJ gene recombination for antibody-antigen interaction. The data presented in this report revealed the significance of CDRH3 for the interaction of antibody specific for immunogenic molecules while CDRH3 contribution is minor for antibody interaction with nonimmunogenic molecules such as haptens and native mammalian dsDNA. Thus, paratopes might be located in the CDRH3 or VH regions.
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3.
Large-scale characterization of the macrolide resistome reveals high diversity and several new pathogen-associated genes.
Lund, D, Kieffer, N, Parras-Moltó, M, Ebmeyer, S, Berglund, F, Johnning, A, Larsson, DGJ, Kristiansson, E
Microbial genomics. 2022;(1)
Abstract
Macrolides are broad-spectrum antibiotics used to treat a range of infections. Resistance to macrolides is often conferred by mobile resistance genes encoding Erm methyltransferases or Mph phosphotransferases. New erm and mph genes keep being discovered in clinical settings but their origins remain unknown, as is the type of macrolide resistance genes that will appear in the future. In this study, we used optimized hidden Markov models to characterize the macrolide resistome. Over 16 terabases of genomic and metagenomic data, representing a large taxonomic diversity (11 030 species) and diverse environments (1944 metagenomic samples), were searched for the presence of erm and mph genes. From this data, we predicted 28 340 macrolide resistance genes encoding 2892 unique protein sequences, which were clustered into 663 gene families (<70 % amino acid identity), of which 619 (94 %) were previously uncharacterized. This included six new resistance gene families, which were located on mobile genetic elements in pathogens. The function of ten predicted new resistance genes were experimentally validated in Escherichia coli using a growth assay. Among the ten tested genes, seven conferred increased resistance to erythromycin, with five genes additionally conferring increased resistance to azithromycin, showing that our models can be used to predict new functional resistance genes. Our analysis also showed that macrolide resistance genes have diverse origins and have transferred horizontally over large phylogenetic distances into human pathogens. This study expands the known macrolide resistome more than ten-fold, provides insights into its evolution, and demonstrates how computational screening can identify new resistance genes before they become a significant clinical problem.
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4.
Abiotic Stress and Belowground Microbiome: The Potential of Omics Approaches.
Sandrini, M, Nerva, L, Sillo, F, Balestrini, R, Chitarra, W, Zampieri, E
International journal of molecular sciences. 2022;(3)
Abstract
Nowadays, the worldwide agriculture is experiencing a transition process toward more sustainable production, which requires the reduction of chemical inputs and the preservation of microbiomes' richness and biodiversity. Plants are no longer considered as standalone entities, and the future of agriculture should be grounded on the study of plant-associated microorganisms and all their potentiality. Moreover, due to the climate change scenario and the resulting rising incidence of abiotic stresses, an innovative and environmentally friendly technique in agroecosystem management is required to support plants in facing hostile environments. Plant-associated microorganisms have shown a great attitude as a promising tool to improve agriculture sustainability and to deal with harsh environments. Several studies were carried out in recent years looking for some beneficial plant-associated microbes and, on the basis of them, it is evident that Actinomycetes and arbuscular mycorrhizal fungi (AMF) have shown a considerable number of positive effects on plants' fitness and health. Given the potential of these microorganisms and the effects of climate change, this review will be focused on their ability to support the plant during the interaction with abiotic stresses and on multi-omics techniques which can support researchers in unearthing the hidden world of plant-microbiome interactions. These associated microorganisms can increase plants' endurance of abiotic stresses through several mechanisms, such as growth-promoting traits or priming-mediated stress tolerance. Using a multi-omics approach, it will be possible to deepen these mechanisms and the dynamic of belowground microbiomes, gaining fundamental information to exploit them as staunch allies and innovative weapons against crop abiotic enemies threatening crops in the ongoing global climate change context.
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5.
Challenges and limitations in the studies of glycoproteins: A computational chemist's perspective.
Balli, OI, Uversky, VN, Durdagi, S, Coskuner-Weber, O
Proteins. 2022;(2):322-339
Abstract
Experimenters face challenges and limitations while analyzing glycoproteins due to their high flexibility, stereochemistry, anisotropic effects, and hydration phenomena. Computational studies complement experiments and have been used in characterization of the structural properties of glycoproteins. However, recent investigations revealed that computational studies face significant challenges as well. Here, we introduce and discuss some of these challenges and weaknesses in the investigations of glycoproteins. We also present requirements of future developments in computational biochemistry and computational biology areas that could be necessary for providing more accurate structural property analyses of glycoproteins using computational tools. Further theoretical strategies that need to be and can be developed are discussed herein.
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6.
Computational Analysis of Mutations in the Receptor-Binding Domain of SARS-CoV-2 Spike and Their Effects on Antibody Binding.
Bozdaganyan, ME, Shaitan, KV, Kirpichnikov, MP, Sokolova, OS, Orekhov, PS
Viruses. 2022;(2)
Abstract
Currently, SARS-CoV-2 causing coronavirus disease 2019 (COVID-19) is responsible for one of the most deleterious pandemics of our time. The interaction between the ACE2 receptors at the surface of human cells and the viral Spike (S) protein triggers the infection, making the receptor-binding domain (RBD) of the SARS-CoV-2 S-protein a focal target for the neutralizing antibodies (Abs). Despite the recent progress in the development and deployment of vaccines, the emergence of novel variants of SARS-CoV-2 insensitive to Abs produced in response to the vaccine administration and/or monoclonal ones represent a potential danger. Here, we analyzed the diversity of neutralizing Ab epitopes and assessed the possible effects of single and multiple mutations in the RBD of SARS-CoV-2 S-protein on its binding affinity to various antibodies and the human ACE2 receptor using bioinformatics approaches. The RBD-Ab complexes with experimentally resolved structures were grouped into four clusters with distinct features at sequence and structure level. The performed computational analysis indicates that while single amino acid replacements in RBD may only cause partial impairment of the Abs binding, moreover, limited to specific epitopes, the variants of SARS-CoV-2 with multiple mutations, including some which were already detected in the population, may potentially result in a much broader antigenic escape. Further analysis of the existing RBD variants pointed to the trade-off between ACE2 binding and antigenic escape as a key limiting factor for the emergence of novel SAR-CoV-2 strains, as the naturally occurring mutations in RBD tend to reduce its binding affinity to Abs but not to ACE2. The results provide guidelines for further experimental studies aiming to identify high-risk RBD mutations that allow for an antigenic escape.
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7.
Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.
Tasmia, SA, Kibria, MK, Tuly, KF, Islam, MA, Khatun, MS, Hasan, MM, Mollah, MNH
Scientific reports. 2022;(1):2632
Abstract
Serine phosphorylation is one type of protein post-translational modifications (PTMs), which plays an essential role in various cellular processes and disease pathogenesis. Numerous methods are used for the prediction of phosphorylation sites. However, the traditional wet-lab based experimental approaches are time-consuming, laborious, and expensive. In this work, a computational predictor was proposed to predict serine phosphorylation sites mapping on Schizosaccharomyces pombe (SP) by the fusion of three encoding schemes namely k-spaced amino acid pair composition (CKSAAP), binary and amino acid composition (AAC) with the random forest (RF) classifier. So far, the proposed method is firstly developed to predict serine phosphorylation sites for SP. Both the training and independent test performance scores were used to investigate the success of the proposed RF based fusion prediction model compared to others. We also investigated their performances by 5-fold cross-validation (CV). In all cases, it was observed that the recommended predictor achieves the largest scores of true positive rate (TPR), true negative rate (TNR), accuracy (ACC), Mathew coefficient of correlation (MCC), Area under the ROC curve (AUC) and pAUC (partial AUC) at false positive rate (FPR) = 0.20. Thus, the prediction performance as discussed in this paper indicates that the proposed approach may be a beneficial and motivating computational resource for predicting serine phosphorylation sites in the case of Fungi. The online interface of the software for the proposed prediction model is publicly available at http://mollah-bioinformaticslab-stat.ru.ac.bd/PredSPS/ .
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8.
Prediction and Evolution of the Molecular Fitness of SARS-CoV-2 Variants: Introducing SpikePro.
Pucci, F, Rooman, M
Viruses. 2021;(5)
Abstract
The understanding of the molecular mechanisms driving the fitness of the SARS-CoV-2 virus and its mutational evolution is still a critical issue. We built a simplified computational model, called SpikePro, to predict the SARS-CoV-2 fitness from the amino acid sequence and structure of the spike protein. It contains three contributions: the inter-human transmissibility of the virus predicted from the stability of the spike protein, the infectivity computed in terms of the affinity of the spike protein for the ACE2 receptor, and the ability of the virus to escape from the human immune response based on the binding affinity of the spike protein for a set of neutralizing antibodies. Our model reproduces well the available experimental, epidemiological and clinical data on the impact of variants on the biophysical characteristics of the virus. For example, it is able to identify circulating viral strains that, by increasing their fitness, recently became dominant at the population level. SpikePro is a useful, freely available instrument which predicts rapidly and with good accuracy the dangerousness of new viral strains. It can be integrated and play a fundamental role in the genomic surveillance programs of the SARS-CoV-2 virus that, despite all the efforts, remain time-consuming and expensive.
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9.
Causal Inference in Microbiome Medicine: Principles and Applications.
Lv, BM, Quan, Y, Zhang, HY
Trends in microbiology. 2021;(8):736-746
Abstract
Microorganisms that colonize the mammalian skin and cavity play critical roles in various physiological functions of the host. Numerous studies have revealed strong associations between the microbiota and multiple diseases. However, association does not mean causation. To clarify the mechanisms underlying microbiota-mediated diseases, research is moving from associative analyses to causation studies. In this article, we first introduce the principles of the computational methods for causal inference, and then discuss the applications of these methods in microbiome medicine. Furthermore, we examine the reliability of theoretically inferred causality by the interventionist framework. Finally, we show the potential of confirmed causality in microbiota-targeted therapy, especially in personalized dietary intervention. We conclude that a comprehensive understanding of the causal relationships between diets, microbiota, host targets, and diseases is critical to future microbiome medicine.
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10.
An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies.
Gao, B, Chi, L, Zhu, Y, Shi, X, Tu, P, Li, B, Yin, J, Gao, N, Shen, W, Schnabl, B
Biomolecules. 2021;(4)
Abstract
The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been widely applied to study the gut microbiome, including 16S rRNA, 18S rRNA, internal transcribed spacer (ITS) sequencing, shotgun metagenomic sequencing, metatranscriptomic sequencing and viromic sequencing. Various software tools were developed to analyze different sequencing data. In this review, we summarize commonly used computational tools for gut microbiome data analysis, which extended our understanding of the gut microbiome in health and diseases.