Periodontal ligament stem cell-derived exosome-loaded Emodin mediated antimicrobial photodynamic therapy against cariogenic bacteria

BACKGROUND

This study was conducted to investigate the efficiency of periodontal ligament (PDL) stem cell-derived exosome-loaded Emodin (Emo@PDL-Exo) in antimicrobial photodynamic therapy (aPDT) on Streptococcus mutans and Lactobacillus acidophilus as the cariogenic bacteria.

MATERIALS AND METHODS

After isolating and characterizing PDL-Exo, the study proceeded to prepare and verify the presence of Emo@PDL-Exo. The antimicrobial effect, anti-biofilm activity, and anti-metabolic potency of Emo, PDL-Exo, and Emo@PDL-Exo were then evaluated with and without irradiation of blue laser at a wavelength of 405 ± 10 nm with an output intensity of 150 mW/cm2 for a duration of 60 s. In addition, the study assessed the binding affinity of Emodin with GtfB and SlpA proteins using in silico molecular docking. Eventually, the study examined the generation of endogenous reactive oxygen species (ROS) and changes in the gene expression levels of gelE and sprE.

RESULTS

The study found that using Emo@PDL-Exo-mediated aPDT resulted in a significant decrease in L. acidophilus and S. mutans by 4.90 ± 0.36 and 5.07 log10 CFU/mL, respectively (P < 0.05). The study found that using Emo@PDL-Exo for aPDT significantly reduced L. acidophilus and S. mutans biofilms by 44.7% and 50.4%, respectively, compared to untreated biofilms in the control group (P < 0.05). Additionally, the metabolic activity of L. acidophilus and S. mutans decreased by 58.3% and 71.2%, respectively (P < 0.05). The molecular docking analysis showed strong binding affinities of Emodin with SlpA and GtfB proteins, with docking scores of -7.4 and -8.2 kcal/mol, respectively. The study also found that the aPDT using Emo@PDL-Exo group resulted in the most significant reduction in gene expression of slpA and gtfB, with a decrease of 4.2- and 5.6-folds, respectively, compared to the control group (P < 0.05), likely due to the increased generation of endogenous ROS.

DISCUSSION

The study showed that aPDT using Emo@PDL-Exo can effectively reduce the cell viability, biofilm activity, and metabolic potency of S. mutans and L. acidophilus. aPDT also significantly reduced the expression levels of gtfB and slpA mRNA due to the increased endogenous ROS generation. The findings suggest that Emo@PDL-Exo-mediated aPDT could be a promising antimicrobial approach against cariogenic microorganisms.

Analysis of PROS1 mutations and clinical characteristics in three Chinese families with hereditary protein S deficiency

We report three heterozygous PROS1 mutations that caused type I protein S deficiency in three unrelated Chinese families. We measured protein S activity and antigen levels for all participants, screened them for mutations in the PROS1 gene. And we employed the calibrated automated thrombin generation (CAT) method to investigate thrombin generation. Numerous bioinformatics tools were utilized to analyze the conservation, pathogenicity of mutation, and spatial structure of the protein S. Phenotyping analysis indicated that all three probands exhibited simultaneous reduced levels of PS:A, TPS:Ag, and FPS:Ag. Genetic testing revealed that proband A harbored a heterozygous c.458_458delA (p.Lys153Serfs*6) mutation in exon 5, proband B carried a heterozygous c.1687C>T (p.Gln563stop) mutation in exon 14, and proband C exhibited a heterozygous c.200A>C (p.Glu67Ala) mutation in exon 2. Bioinformatic analysis predicted that the p.Lys153Serfs*6 frameshift mutation and the p.Gln563stop nonsense mutation in the protein S were classified as "disease-causing." The identification of the novel mutation p.Lys153Serfs*6 in PROS1 enriches the Human Genome Database. Our research suggests that these three mutations (p.Lys153Serfs*6, p.Gln563stop, and p.Glu67Ala) are possibly responsible for the decreased level of protein S in the three families. Furthermore, the evidence also supports the notion that individuals who are asymptomatic but have a family history of PSD can benefit from genetic analysis of the PROS1 gene.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Reconnoitering the sequence and structural analysis of Staphylococcus aureus "A" protein

Background

The Staphylococcus aureus "A" protein plays an essential role in the pathogenicity and virulence of this bacterial species. To gain deeper insights into the protein's characteristics, we conducted an in-depth analysis of its sequence and structure.

Objective

This study aimed to unravel the underlying genetic and structural components that contribute to the protein's functional properties.

Results

Utilizing various bioinformatics tools and techniques, we first examined the protein's primary sequence, identifying key amino acid residues and potential functional domains. Additionally, we employed computational modeling and simulation approaches to determine the tertiary structure of the "A" protein. Through this comprehensive analysis, we discovered novel features and interactions within the protein's structure, shedding light on its potential mechanisms of action. Furthermore, we investigated the protein's evolutionary conservation and compared it with related proteins from other bacterial species.

Conclusions

Overall, our findings provide valuable insights into the sequence and structure of the Staphylococcus aureus "A" protein, which may have implications for understanding its role in pathogenicity and guiding the development of novel therapeutic strategies.

Integrative comparative analysis of avian chromosome evolution by in-silico mapping of the gene ontology of homologous synteny blocks and evolutionary breakpoint regions

Avian chromosomes undergo more intra- than interchromosomal rearrangements, which either induce or are associated with genome variations among birds. Evolving from a common ancestor with a karyotype not dissimilar from modern chicken, two evolutionary elements characterize evolutionary change: homologous synteny blocks (HSBs) constitute common conserved parts at the sequence level, while evolutionary breakpoint regions (EBRs) occur between HSBs, defining the points where rearrangement occurred. Understanding the link between the structural organization and functionality of HSBs and EBRs provides insight into the mechanistic basis of chromosomal change. Previously, we identified gene ontology (GO) terms associated with both; however, here we revisit our analyses in light of newly developed bioinformatic algorithms and the chicken genome assembly galGal6. We aligned genomes available for six birds and one lizard species, identifying 630 HSBs and 19 EBRs. We demonstrate that HSBs hold vast functionality expressed by GO terms that have been largely conserved through evolution. Particularly, we found that genes within microchromosomal HSBs had specific functionalities relevant to neurons, RNA, cellular transport and embryonic development, and other associations. Our findings suggest that microchromosomes may have conserved throughout evolution due to the specificity of GO terms within their HSBs. The detected EBRs included those found in the genome of the anole lizard, meaning they were shared by all saurian descendants, with others being unique to avian lineages. Our estimate of gene richness in HSBs supported the fact that microchromosomes contain twice as many genes as macrochromosomes.

Clinical Prognostic Implications of Wnt Hub Genes Expression in Medulloblastoma

Medulloblastoma is the most common type of pediatric malignant primary brain tumor, and about one-third of patients die due to disease recurrence and most survivors suffer from long-term side effects. MB is clinically, genetically, and epigenetically heterogeneous and subdivided into at least four molecular subgroups: WNT, SHH, Group 3, and Group 4. We evaluated common differentially expressed genes between a Brazilian RNA-seq GSE181293 dataset and microarray GSE85217 dataset cohort of pediatric MB samples using bioinformatics methodology in order to identify hub genes of the molecular subgroups based on PPI network construction, survival and functional analysis. The main finding was the identification of five hub genes from the WNT subgroup that are tumor suppressors, and whose lower expression is related to a worse prognosis for MB patients. Furthermore, the common genes correlated with the five tumor suppressors participate in important pathways and processes for tumor initiation and progression, as well as development and differentiation, and some of them control cell stemness and pluripotency. These genes have not yet been studied within the context of MB, representing new important elements for investigation in the search for therapeutic targets, prognostic markers or for understanding of MB biology.

Performances of bioinformatics tools for the analysis of sequencing data of Mycobacterium tuberculosis complex strains

Whole-genome sequencing of Mycobacterium tuberculosis complex (MTBC) strains is a rapidly growing tool to obtain results regarding the resistance and phylogeny of the strains. We evaluated the performances of two bioinformatics tools for the analysis of whole-genome sequences of MTBC strains. Two hundred and twenty-seven MTBC strains were isolated and whole-genome sequenced at the laboratory of Avicenne Hospital between 2015 and 2021. We investigated the resistance and susceptibility status of strains using two online tools, Mykrobe and PhyResSE. We compared the genotypic and phenotypic resistance results obtained by drug susceptibility testing. Unlike with the Mykrobe tool, sequencing quality data were obtained using PhyResSE: average coverage of 98% and average depth of 119X. We found a similar concordance between phenotypic and genotypic results when determining susceptibility to first-line anti-tuberculosis drugs (95%) with both tools. The sensitivity and specificity of each tool compared to the phenotypic method were respectively 72% [52-87] and 98% [96-99] for Mykrobe and 76% [57-90] and 97% [94-99] for PhyResSE. Mykrobe and PhyResSE were easy to use and efficient. These platforms are accessible to people not trained in bioinformatics and constitute a complementary approach to phenotypic methods for the study of MTBC strains.

IL-6/Stat3 suppresses osteogenic differentiation in ossification of the posterior longitudinal ligament via miR-135b-mediated BMPER reduction

Interleukin-6 (IL-6) has been reported to induce osteogenic differentiation of mesenchymal stem cells for increasing bone regeneration, while the role of IL-6 in osteogenic differentiation during ossification of the posterior longitudinal ligament (OPLL) remains to be determined. The current study aims to explore the downstream mechanism of IL-6 in cyclic tensile strain (CTS)-stimulated OPLL, which involves bioinformatically identified microRNA-135b (miR-135b). Initially, we clinically collected posterior longitudinal ligament (PLL) and ossified PLL tissues, from which ossified PLL cells were isolated, respectively. The obtained data revealed a greater osteogenic property of ossified PLL than non-ossified PLL cells. The effect of regulatory axis comprising IL-6, Stat3, miR-135b, and BMPER on osteogenic differentiation of CTS-stimulated ossified PLL cells was examined with gain- and loss-of-function experiments. BMPER was confirmed as a target gene to miR-135b. Knockdown of BMPER or overexpression of miR-135b inhibited the osteogenic differentiation of CTS-induced ossification in PLL cells. Besides, IL-6 promoted the post-transcriptional process to mature miR-135b via Stat3 phosphorylation. In conclusion, IL-6 inhibited CTS-induced osteogenic differentiation by inducing miR-135b-mediated inhibition of BMPER through Stat3 activation.

Editorial: Advances in genomic and genetic tools, and their applications for understanding embryonic development and human diseases

Significant advances have been recently made in the development of the genetic and genomic platforms. This has greatly contributed to a better understanding of gene expression and regulation machinery. Consequently, this led to considerable progress in unraveling evidence of the genotype-phenotype correlation between normal/abnormal embryonic development and human disease complexity. For example, advanced genomic tools such as next-generation sequencing, and microarray-based CGH have substantially helped in the identification of gene and copy number variants associated with diseases as well as in the discovery of causal gene mutations. In addition, bioinformatic analysis tools of genome annotation and comparison have greatly aided in data analysis for the interpretation of the genetic variants at the individual level. This has unlocked potential possibilities for real advances toward new therapies in personalized medicine for the targeted treatment of human diseases. However, each of these genomic and bioinformatics tools has its limitations and hence further efforts are required to implement novel approaches to overcome these limitations. It could be possible that the use of more than one platform for genotype-phenotype deep analysis is an effective approach to disentangling the cause and treatment of the disease complexities. Our research topic aimed at deciphering these complexities by shedding some light on the recent applications of the basic and advanced genetic/genomic and bioinformatics approaches. These include studying gene-gene, protein-protein, and gene-environment interactions. We, in addition, aimed at a better understanding of the link between normal/abnormal embryonic development and the cause of human disease induction.

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

A missense mutation in TTC8/BBS8 affecting mRNA splicing in patients with non-syndromic retinitis pigmentosa

Splicing disruption is one type of mutation mechanism for disease-predisposing alleles. To date, less than 30 mutations in TTC8/BBS8 have been reported; however, mutations affecting the splice site are rare. Generally missense mutations are assumed to alter protein function; however, reports have shown that mutations in protein coding exons can disrupt splicing by altering exonic splicing silencer or enhancer motifs. Hence, a missense mutation c.1347G > C (p.Q449H) involving final base of the exon 13 in the TTC8, previously identified by us to be linked with non-syndromic autosomal recessive retinitis pigmentosa (arRP), in an Indian family, that might deleteriously affect splicing has been functionally characterized. RNA was isolated, cDNA prepared and amplified using region-specific primers. PCR products were purified and sequenced bi-directionally by Sanger sequencing. Effect of mutation (c.1347G > C) on mRNA splicing has been predicted using bioinformatics tools. We reported that missense mutation (c.1347G > C) at the last base of exon 13 of TTC8 disrupted the canonical donor splice-site resulting in aberrant RNA splicing. A cryptic donor splice-site got activated 77 bases downstream of the authentic splice donor site in intron 13, resulting in the retention of 77 bases of intron 13, and a frameshift leading to pre-mature termination codon in exon 14 at codon 486. Further, duplication of exon 15 and fusion of its duplicated copy occurred with exon 13. The binding site for SC35 protein, normally involved in splicing, also got disrupted (as predicted by SpliceAid2 software), hence, leading to alternative splicing. Our findings strongly suggest that a missense mutation c.1347G > C in TTC8 disrupted the splice donor site causing retention of 77 bases of intron 13, resulting in a frameshift and subsequently introduced a pre-mature termination codon into exon 14, hence creating an altered mRNA transcript. These findings emphasize the significance of examining missense mutations especially in TTC8, to determine their pathogenic role through alternative splicing. Present findings also reiterate the notion that mutations in the TTC8/BBS8 cause phenotypic heterogeneity and does not always follow Mendelian genetics in this ciliopathy.

Molecular docking analysis reveals the functional inhibitory effect of Genistein and Quercetin on TMPRSS2: SARS-COV-2 cell entry facilitator spike protein

BACKGROUND

The Transmembrane Serine Protease 2 (TMPRSS2) of human cell plays a significant role in proteolytic cleavage of SARS-Cov-2 coronavirus spike protein and subsequent priming to the receptor ACE2. Approaching TMPRSS2 as a therapeutic target for the inhibition of SARS-Cov-2 infection is highly promising. Hence, in the present study, we docked the binding efficacy of ten naturally available phyto compounds with known anti-viral potential with TMPRSS2. The aim is to identify the best phyto compound with a high functional affinity towards the active site of the TMPRSS2 with the aid of two different docking software. Molecular Dynamic Simulations were performed to analyse the conformational space of the binding pocket of the target protein with selected molecules.

RESULTS

Docking analysis using PyRx version 0.8 along with AutoDockVina reveals that among the screened phyto compounds, Genistein shows the maximum binding affinity towards the hydrophobic substrate-binding site of TMPRSS2 with three hydrogen bonds interaction ( - 7.5 kcal/mol). On the other hand, molecular docking analysis using Schrodinger identified Quercetin as the most potent phyto compound with a maximum binding affinity towards the hydrophilic catalytic site of TMPRSS2 ( - 7.847 kcal/mol) with three hydrogen bonds interaction. The molecular dynamics simulation reveals that the Quercetin-TMPRSS complex is stable until 50 ns and forms stable interaction with the protein ( - 22.37 kcal/mol of MM-PBSA binding free energy). Genistein creates a weak interaction with the loop residues and hence has an unstable binding and exits from the binding pocket.

CONCLUSION

The compounds, Quercetin and Genistein, can inhibit the TMPRSS2 guided priming of the spike protein. The compounds could reduce the interaction of the host cell with the type I transmembrane glycoprotein to prevent the entry of the virus. The critical finding is that compared to Genistein, Quercetin exhibits higher binding affinity with the catalytic unit of TMPRSS2 and forms a stable complex with the target. Thus, enhancing our innate immunity by consuming foods rich in Quercetin and Genistein or developing a novel drug in the combination of Quercetin and Genistein could be the brilliant choices to prevent SARS-Cov-2 infection when we consider the present chaos associated with vaccines and anti-viral medicines.

Expression profiling of exosomal miRNAs derived from different stages of infection in mice infected with Echinococcus granulosus protoscoleces using high-throughput sequencing

Echinococcosis is a worldwide zoonosis. The mechanism of the establishment, growth, and persistence of parasites in the host has not been fully understood. Exosomes are found to be a way of information exchange between parasites and hosts. They exist in various body fluids widely. There are few studies on host-derived exosomes and their miRNA expression profiles at different infection time points. In this study, BALB/c mice were intraperitoneally infected with protricercariae. Exosomes were extracted from plasma (0, 3, 9, and 20 weeks post infection), and the expression profiles of exosome miRNA in the peripheral blood of mice were determined using RNA-sequencing. Compared to the 0 week groups, 24, 35, and 22 differentially expressed miRNAs were detected in infected mouse at the three infection stages, respectively. The results showed that there were significant differences in the miRNAs of exosomes at different infection time points. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to annotate the different miRNAs. The results showed that the biological pathways of parasites changed significantly at different stages of infection, with many significant and abundant pathways involved in cell differentiation, inflammation, and immune response, such as MAPK signaling pathway, Th17 cell differentiation, Wnt signaling pathway, FoxO signaling pathway, Notch signaling pathway, etc. These results suggest that miRNA may be an important regulator of interactions between Echinococcus granulosus and host. The data provided here provide valuable information to increase understanding of the regulatory function of microRNAs in the host microenvironment and the mechanism of host-parasite interaction. This may help us to find targets for Echinococcus granulosus to escape host immune attack and control Echinococcus granulosus infection in the future.

Identification of potential microRNA groups for the diagnosis of hepatocellular carcinoma (HCC) using microarray datasets and bioinformatics tools

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and the third cause of cancer-related death worldwide. Potential microRNAs have been reported as biomarkers for early detection of HCC as well as novel molecular targets for HCC treatment. Various tissue expression profiles of miRNAs using three microarray datasets from groups in Asia (2), Europe, America (GSE147892, GSE21362, GSE74618, GSE40744) and multiple bioinformatics tools were integrated to determine the most significant miRNA groups to assist in the diagnosis of HCC. Statistical analyses identified at least 30 miRNAs with 17 up-regulated and 13 down-regulated in HCC-related tumor tissues. All the miRNAs also showed relevance to the hallmarks of cancer such as cell proliferation, invasion, metastasis, angiogenesis, metabolism, epithelial-mesenchymal transition and apoptosis. Expression levels of miRNAs observed in the European group showed up-regulation at 5–37% compared to both Asian and American groups. Interestingly, four miRNAs divided into two groups as miR-182-5p/miR-1269a and miR-199a/miR-422a were the most promising for diagnosis of HCC patients from healthy controls, with AUC values of 0.902 and 0.892, respectively. Results provided evidence of the correlation between potential miRNAs and HCC that could be useful for disease diagnosis based on in-depth analyses of large case numbers and cohort studies.

Bioinformatics Tools to Understand Notch

As a result of the culmination of data, and the fast-paced advancement of new research, all the biological information collected can make it difficult to sort data. This is oftentimes experienced when learning about the human genome. Fortunately, with the advancement of technology, the field of bioinformatics has emerged which has allowed for the creation of a variety of biological databases. These biological databases provide a condensed reservoir of organized information that is easy to use and topic-specific. Here, we provide a list of 39 biological databases that help break down the fundamental details of a gene. This chapter uses the NOTCH1 gene as an example to demonstrate how biological databases can be used to extract gene information. Five sections were created to highlight the major areas needed to build a comprehensive foundation of NOTCH1. The first section lists databases containing basic gene and protein product information. The next section consists of protein interactions and signaling pathway databases which are essential in understanding the biological processes a gene product is involved in. Gene expression and disease databases are the next two sections which are connected since disease results from the aberrant expression of a gene product. The last database section examines model organisms which serve a key role in the study of human genetic diseases. Using these databases, we can elucidate NOTCH1's gene/protein structure, expression, and vital physiological function through the Notch signaling pathway.

Accurate Ensemble Prediction of Somatic Mutations with SMuRF2

Accurate identification of somatic mutations is crucial for discovery and identification of driver mutations in cancer tumors. Here, we describe the updated Somatic Mutation calling method using a Random Forest (SMuRF2), an ensemble method that combines the predictions and auxiliary features from individual mutation callers using supervised machine learning. SMuRF2 provides an efficient workflow to predict both somatic point mutations (SNVs) and small insertions/deletions (indels) in cancer genomes and exomes. We describe the latest method and provide a detailed tutorial for running SMuRF2.

MicroRNA Targeting

MicroRNAs (miRNAs) are small noncoding elements that play essential roles in the posttranscriptional regulation of biochemical processes. miRNAs recognize and target multiple mRNAs; therefore, investigating miRNA dysregulation is an indispensable strategy to understand pathological conditions and to design innovative drugs. Targeting miRNAs in diseases improve outcomes of several therapeutic strategies thus, this present study highlights miRNA targeting methods through experimental assays and bioinformatics tools. The first part of this review focuses on experimental miRNA targeting approaches for elucidating key biochemical pathways. A growing body of evidence about the miRNA world reveals the fact that it is not possible to uncover these molecules' structural and functional characteristics related to the biological processes with a deterministic approach. Instead, a systemic point of view is needed to truly understand the facts behind the natural complexity of interactions and regulations that miRNA regulations present. This task heavily depends both on computational and experimental capabilities. Fortunately, several miRNA bioinformatics tools catering to nonexperts are available as complementary wet-lab approaches. For this purpose, this work provides recent research and information about computational tools for miRNA targeting research.

Rational Design of Anti-Angiogenic Peptides to Inhibit VEGF/VEGFR2 Interactions for Cancer Therapeutics

BACKGROUND

Angiogenesis is a critical physiological process that plays a key role in tumor progression, metastatic dissemination, and invasion. In the last two decades, the vascular endothelial growth factor (VEGF) signaling pathway has been the area of extensive researches. VEGF executes its special effects by binding to vascular endothelial growth factor receptors (VEGFRs), particularly VEGFR-2.

OBJECTIVE

The inhibition of VEGF/VEGFR2 interaction is known as an effective cancer therapy strategy. The current study pointed to design and model an anti-VEGF peptide based on VEGFR2 binding regions.

METHOD

The large-scale peptide mutation screening was used to achieve a potent peptide with high binding affinity to VEGF for possible application in inhibition of VEGF/VEGFR2 interaction. The AntiCP and Peptide Ranker servers were used to generate the possible peptides library with anticancer activities and prediction of peptides bioactivity. Then, the interaction of VEGF and all library peptides were analyzed using Hex 8.0.0 and ClusPro tools. A number of six peptides with favorable docking scores were achieved. All of the best docking scores of peptides in complexes with VEGF were evaluated to confirm their stability, using molecular dynamics simulation (MD) with the help of the GROMACS software package.

RESULTS

As a result, two antiangiogenic peptides with 13 residues of PepA (NGIDFNRDFFLGL) and PepC (NGIDFNRDKFLFL) were achieved and introduced to inhibit VEGF/VEGFR2 interactions.

CONCLUSIONS

In summary, this study provided new insights into peptide-based therapeutics development for targeting VEGF signaling pathway in tumor cells. PepA and PepC are recommended as potentially promising anticancer agents for further experimental evaluations.

Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Because of their wide network of interactions, miRNAs have become the focus of many studies over the past decade, particularly in animal species. To streamline the number of potential wet lab experiments, the use of miRNA target prediction tools is currently the first step undertaken. However, the predictions made may vary considerably depending on the tool used, which is mostly due to the complex and still not fully understood mechanism of action of miRNAs. The discrepancies complicate the choice of the tool for miRNA target prediction. To provide a comprehensive view of this issue, we highlight in this review the main characteristics of miRNA-target interactions in bilaterian animals, describe the prediction models currently used, and provide some insights for the evaluation of predictor performance.

Capabilities of bioinformatics tools for optimizing physicochemical features of proteins used in Nano biosensors: A short overview of the tools related to bioinformatics

Protein-protein ligand is one of the most detection methods used in Nano biosensors. Based on the advantage of specific docking between two special 3D structures, they have become a potent candidate in bioanalysis and Nanodiagnostic tools. These tools lease users to do a simple, fast, cost-effective, sensitive, and specific detection of molecular biomarkers in real samples. Recent advantages of using protein-protein ligand Nano-biosensors application is remarkable due to its special docking that refers to each protein unique 3D conformation. However, it challenges different problems such as low rate of docking and hard process for fixation on the basic layer. These challenges make developers to optimize the structure and functions of proteins. The process has different Nano scale calculation that could be done with algorithms and solutions are available as bioinformatics tools. This article aimed to have a short overview of the abilities of bioinformatics tools for modeling and optimization of physiochemical features of proteins in Nano scale.

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Nano biosensors use different strategies which based on docking between two molecules to detect and identify different proteins.

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Molecular docking between transducer in Nano biosensors and proteins rely on physicochemical features of transducer, protein and docking strategy.

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Nano bioinformatics use bioinformatics tools and algorithms as a collective solution for developing functional structure in Nano scale.

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Nano bioinformatics use different bioinformatics tools to optimize physicochemical features of proteins as a new approach in Nano biosensors and drug discovery.

Predicting HLA-DPB1 permissive probabilities through a DPB1 prediction service towards the optimization of HCT donor selection

Despite its demonstrated importance in hematopoietic cell transplantation, the HLA-DPB1 locus is only typed in one in five unrelated donors in the United States. Addressing this issue, we developed a DPB1 Prediction Service that leverages seven-locus haplotype frequencies (HLA-A ∼ C ∼ B ∼ DRB3/4/5 ∼ DRB1 ∼ DQB1 ∼ DPB1) to extend the imputation of six-locus HLA typing (HLA-A ∼ C ∼ B ∼ DRB3/4/5 ∼ DRB1 ∼ DQB1) to the HLA-DPB1 locus, including the novel prediction of HLA-DPB1 TCE groups to calculate donor-recipient TCE permissive match probabilities. Simulations of current-day patient searches reveal the service can fill in missing gaps for another four in five donors that appears on lists. To validate its performance, samples of 206,328 registered donors and 5,218 donor-recipient pairs with known high-resolution HLA-DPB1 typing were used for predicted-versus-observed comparisons. These comparisons demonstrated that the predictions were correct for 11.9-19.7% of HLA-DPB1 genotypes, 64.9-70.0% of TCE groups, and 61.0% of permissive match categories. Although HLA-DPB1 match predictions must be confirmed by additional typing, knowledge of TCE match probabilities facilitates rapid and improved identification of best donor options, especially for populations of color. Thus, we developed the TCE Prediction Tool user interface for a pilot program with several transplant centers to preview the accuracy and utility of this prediction framework, which provides valuable upfront optimization of donor selection.

An updated overview and classification of bioinformatics tools for MicroRNA analysis, which one to choose?

The term 'MicroRNA' (miRNA) refers to a class of small endogenous non-coding RNAs (ncRNAs) regenerated from hairpin transcripts. Recent studies reveal miRNAs' regulatory involvement in essential biological processes through translational repression or mRNA degradation. Recently, there is a growing body of literature focusing on the importance of miRNAs and their functions. In this respect, several databases have been developed to manage the dispersed data produced. Therefore, it is necessary to know the parameters and characteristics of each database to benefit their data. Besides, selecting the correct database is of great importance to scientists who do not have enough experience in this field. A comprehensive classification along with an explanation of the information contained in each database leads to facilitating access to these resources. In this regard, we have classified relevant databases into several categories, including miRNA sequencing and annotation, validated/predicted miRNA targets, disease-related miRNA, SNP in miRNA sequence or target site, miRNA-related pathways, or gene ontology, and mRNA-miRNA interactions. Hence, this review introduces available miRNA databases and presents a convenient overview to inform researchers of different backgrounds to find suitable miRNA-related bioinformatics web tools and relevant information rapidly.

A brief review of RNA modification related database resources

To date, over 150 different RNA modifications have been identified, playing crucial roles in biological processes and disease pathogenesis. Thanks to the advancement of high-throughput sequencing technologies employed for transcriptome-wide mapping, a bunch of RNA modification databases have emerged as an exciting area, which promotes further investigation of the mechanisms and functions of these modified ribonucleotides. This article introduces the high-throughput sequencing technique developed for transcriptome-wide mapping of RNA modifications, as well as the procedures and main techniques of building these databases from the developers' perspective. It also reviews existing RNA modification databases in terms of their main functions, species, the number of sites they collected, the annotations, and the tools they provided. From the view of users, we further analyze and compare these databases in terms of their functions. For instance, these databases can be applied to record chemical structures and biosynthetic pathways, or unravel the epi-transcriptome comprehensively, or only investigate specific features of RNA modifications. Additionally, the limitations of the existing approaches are discussed, and some future suggestions are offered.

Computational Biology Analysis of COVID-19 Receptor-Binding Domains: A Target Site for Indocyanine Green Through Antimicrobial Photodynamic Therapy

Introduction: The receptor-binding domain (RBD) in SARS-CoV-2 binds strongly to angiotensin-converting enzyme 2 (ACE2) receptors and causes coronavirus disease 2019 (COVID-19). Antimicrobial photodynamic therapy (aPDT) is a well-established treatment option for the treatment of several viral infections. This in silico study was conducted to target the RBD of SARS-CoV-2 as a target site for aPDT. Methods: SARS-CoV-2-RBD was selected as a novel target for indocyanine green (ICG) as a photosensitizer during aPDT to exploit its molecular modeling, hierarchical nature of protein structure, and physico-chemical properties using several bioinformatic tools. The binding mode of the RBD to ICG was assessed via protein-ligand docking. Results: The results of a computational biology analysis revealed that SARS-CoV-2-RBD has 223 amino acids with a molecular weight of 25098.40 Da. RBD is most similar to 6W41 with an E-value of 4e-167, identity of 100%, and query cover of 100%. The aliphatic index of the RBD protein sequences was 71.61, suggesting that the protein is stable in a broad spectrum of temperatures. The predicted structure of RBD showed that it is a protein with a positive charge and a random coil structure (69.51%). Four ligands were modeled in this entry, including one N-acetyl-D-glucosamine (NAG), one glycerol (GOL), and two sulfate ions (SO4 ), to which ICG desires to bind in the molecular docking analysis. Conclusion: Molecular modeling and simulation analysis showed that SARS-CoV-2-RBD could be a substrate for binding to ICG during aPDT to control the spread of COVID-19.

Molecular diversity and hydrolytic enzymes production abilities of soil bacteria

Soil is an integral part of ecosystem which is niche for varieties of microflora. The present study was investigated to isolate varied strains of bacteria from soil samples of three different geographical regions of Tamil Nadu (India) and evaluate their hydrolytic enzymes (amylase, cellulase, and inulinase) producing potentialities. Among 72 bacterial cultures isolated from Ambattur Industrial Estate, Neyveli Lignite Corporation, and Arignar Anna Zoological Park regions, 41.66, 38.88, and 36.11% of isolates were observed amylase, cellulase, and inulinase producers, respectively. On the other hand, 20.83% of total bacteria isolated from all three regions exhibited concurrent production of amylase, cellulase, and inulinase. Potent isolates depicting maximum enzyme activities were identified as Bacillus anthracis strain ALA1, Bacillus cereus strain ALA3, Glutamicibacter arilaitensis strain ALA4, and Bacillus thuringiensis strain ALA5 based on molecular characterization tools. Further, the thermodynamics parameters, open reading frames (ORFs) regions, and guanine-cytosine (GC) content were determined by distinct bioinformatics tools using 16S rRNA sequences of strains. Minimum free energy values for strain ALA1, strain ALA3, strain ALA4, and strain ALA5 were calculated as −480.73, −478.76, −496.63, and −479.03 kcal/mol, respectively. Mountain plot and entropy predicted the hierarchical representation of RNA secondary structure. The GC content of sequence for strain ALA1, strain ALA3, strain ALA4, and strain ALA5 was calculated as 53.06, 52.94, 56.78, and 53.06%, respectively. Nine ORFs were obtained for strain ALA1, strain ALA3, and strain ALA5 while 10 ORFs were observed for strain ALA4. Additionally, bootstrap tree demonstrated close resemblance of strains with existing bacteria of similar genus. Findings showed higher variability of bacterial diversity as hydrolytic enzymes producers in the investigated geographical regions.

LncRNA regulation: New frontiers in epigenetic solutions to drug chemoresistance

Long-noncoding RNAs (lncRNAs) have been shown to participate in sensitizing or de-sensitizing cancer cells to chemical drugs during cancer therapeutics. Notably, a plethora of lncRNAs have been confirmed to be associated with epigenetic controllers and regulate histone protein modification or DNA methylation states in the process of gene transcription. This correlation between lncRNAs and epigenetic regulators can induce the expression of core genes to trigger drug resistance. In addition, epigenetic signatures are considered to be effective and attractive biomarkers for monitoring drug therapeutic effects because they are inheritable, dynamic, and reversible. Therefore, the regulatory mechanism between lncRNAs and epigenetic machinery can serve as a novel indicator and target to overcome or reverse drug resistance in cancer therapy. In this review, we also presented a curated selection of computational tools (including online databases and network analysis) in the area of epigenetics. A classic workflow for lncRNA expression network analysis is presented, providing guidance for non-bioinformaticians to identify significant correlation between lncRNAs and other biomolecules.

BMT: Bioinformatics mini toolbox for comprehensive DNA and protein analysis

Background Bioinformatics tools are of great significance and are used in different spheres of life sciences. There are wide variety of tools available to perform primary analysis of DNA and protein but most of them are available on different platforms and many remain undetected. Accessing these tools separately to perform individual task is uneconomical and inefficient. Objective Our aim is to bring different bioinformatics models on a single platform to ameliorate scientific research. Hence, our objective is to make a tool for comprehensive DNA and protein analysis. Methods To develop a reliable, straight-forward and standalone desktop application we used state of the art python packages and libraries. Bioinformatics Mini Toolbox (BMT) is combination of seven tools including FastqTrimmer, Gene Prediction, DNA Analysis, Translation, Protein analysis and Pairwise and Multiple alignment. Results FastqTrimmer assists in quality assurance of NGS data. Gene prediction predicts the genes by homology from novel genome on the basis of reference sequence. Protein analysis and DNA analysis calculates physiochemical properties of nucleotide and protein sequences, respectively. Translation translates the DNA sequence into six open reading frames. Pairwise alignment performs pairwise global and local alignment of DNA and protein sequences on the basis or multiple matrices. Multiple alignment aligns multiple sequences and generates a phylogenetic tree. Conclusion We developed a tool for comprehensive DNA and protein analysis. The link to download BMT is https://github.com/nasiriqbal012/BMT_SETUP.git.

Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources

Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals.

The HLA-DP peptide repertoire from human respiratory syncytial virus is focused on major structural proteins with the exception of the viral polymerase

The recognition by specific T helper cells of viral antigenic peptides complexed with HLA class II molecules exposed on the surface of antigen presenting cells is the first step of the complex cascade of immunological events that generates the protective cellular and humoral immune responses. The HLA class II-restricted helper immune response is critical in the control and the clearance of human respiratory syncytial virus (HRSV) infection, a pathogen with severe health risk in pediatric, immunocompromised and elderly populations. In this study, a mass spectrometry analysis was used to identify HRSV ligands bound to HLA-DP class II molecules present on the surface of HRSV-infected cells. Among the thousands of cellular peptides bound to HLA class II proteins in the virus-infected cells, sixty-four naturally processed viral ligands, most of them included in complex nested set of peptides, were identified bound to HLA-DP molecules. These viral ligands arose from five of six major structural HRSV proteins: attachment, fusion, matrix, nucleoprotein, and phosphoprotein. In contrast, no HLA-DP ligands were identified from polymerase protein, the largest HRSV protein that includes half of the viral proteome. These findings have important implications for analysis of the helper immune response as for antiviral vaccine design. SIGNIFICANCE: The existence of a supertype including five alleles that bind a peptide repertoire very similar make HLA-DP class II molecules an interesting target for the design of vaccines. Here, we analyze the HLA-DP-restricted peptide repertoire against the human respiratory syncytial virus, a pathogen that represents a high health risk in infected pediatric, immunocompromised and elderly populations. This repertoire is focused on major structural proteins with the exception of the viral polymerase.

Delving into the Characteristic Features of "Menace" Mycobacterium tuberculosis Homologs: A Structural Dynamics and Proteomics Perspectives

The global increase in the morbidity/mortality rate of Mycobacterial infections, predominantly renascent tuberculosis, leprosy, and Buruli ulcers have become worrisome over the years. More challenging is the incidence of resistance mediated by mutant Mycobacterium strains against front-line antitubercular drugs. Homologous to all Mycobacteria species is the GlcNAc-6-phosphate deacetylase (NagA) which catalyzes essential amino sugars synthesis required for cell wall architecture, hence, metamorphosing into an important pharmacological target for curtailing virulence and drug-resistance. This study used integrated bioinformatics methods, MD simulations, and DynaMut and PolyPhen2 to; explore unique features, monitor dynamics, and analyze the functional impact of non-synonymous single-nucleotide polymorphisms of the six NagA of most ruinous Mycobacterium species; tuberculosis (Mtb), smegmatis (MS), marinum (MM), ulcerans, africanum, and microti respectively. This approach is essential for multi-targeting and could result in the identification of potential polypharmacological antitubercular compounds. Comparative sequential analyses revealed ≤ 50% of the overall structure, including the catalytic Asp267 and reactive Cys131, remained conserved. Interestingly, MS-NagA and MM-NagA possess unique hydrophobic isoleucine (Ile) residues at their active sites in contrast to leucine (Leu) found in other variants. More so, unique to the active sites of the NagA is a 'subunit loop' that covers the active site; probably crucial in binding (entry and exit) mechanisms of targeted NagA inhibitors. Relatively, nsSNP mutations exerted a destabilizing effect on the native NagA conformation. Structural and dynamical insights provided, basically pin-pointed the "Achilles' heel" explorable for the rational drug design of target-specific 'NagA' inhibitors potent against a wide range of mycobacterial diseases.

Typing methods based on whole genome sequencing data

Whole genome sequencing (WGS) of foodborne pathogens has become an effective method for investigating the information contained in the genome sequence of bacterial pathogens. In addition, its highly discriminative power enables the comparison of genetic relatedness between bacteria even on a sub-species level. For this reason, WGS is being implemented worldwide and across sectors (human, veterinary, food, and environment) for the investigation of disease outbreaks, source attribution, and improved risk characterization models. In order to extract relevant information from the large quantity and complex data produced by WGS, a host of bioinformatics tools has been developed, allowing users to analyze and interpret sequencing data, starting from simple gene-searches to complex phylogenetic studies. Depending on the research question, the complexity of the dataset and their bioinformatics skill set, users can choose between a great variety of tools for the analysis of WGS data. In this review, we describe the relevant approaches for phylogenomic studies for outbreak studies and give an overview of selected tools for the characterization of foodborne pathogens based on WGS data. Despite the efforts of the last years, harmonization and standardization of typing tools are still urgently needed to allow for an easy comparison of data between laboratories, moving towards a one health worldwide surveillance system for foodborne pathogens.

Bioinformatics Tools and Resources for Cancer Immunotherapy Study

In recent years, cancer immunotherapy has emerged as a highly promising approach to treat patients with cancer, as the patient's own immune system is harnessed to attack cancer cells. However, the application of these approaches is still limited to a minority of patients with cancer and it is difficult to predict which patients will derive the greatest clinical benefit.One of the challenges faced by the biomedical community in the search of more effective biomarkers is the fact that translational research efforts involve collecting and accessing data at many different levels: from the type of material examined (e.g., cell line, animal models, clinical samples) to multiple data type (e.g., pharmacodynamic markers, genetic sequencing data) to the scale of a study (e.g., small preclinical study, moderate retrospective study on stored specimen sets, clinical trials with large cohorts).This chapter reviews several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing (WES), RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization.

Designing and Analyzing the Structure of DT-STXB Fusion Protein as an Anti-tumor Agent: An in Silico Approach

BACKGROUND & OBJECTIVE

A main contest in chemotherapy is to obtain regulator above the biodistribution of cytotoxic drugs. The utmost promising strategy comprises of drugs coupled with a tumor-targeting bearer that results in wide cytotoxic activity and particular delivery. The B-subunit of Shiga toxin (STxB) is nontoxic and possesses low immunogenicity that exactly binds to the globotriaosylceramide (Gb3/CD77). Gb3/CD77 extremely expresses on a number of human tumors such as pancreatic, colon, and breast cancer and acts as a functional receptor for Shiga toxin (STx). Then, this toxin can be applied to target Gb3-positive human tumors. In this study, we evaluated DT390-STXB chimeric protein as a new anti-tumor candidate via genetically fusing the DT390 fragment of DT538 (Native diphtheria toxin) to STxB.

METHODS

This study intended to investigate the DT390- STxB fusion protein structure in silico. Considering the Escherichia coli codon usage, the genomic construct was designed. The properties and the structure of the protein were determined by an in silico technique. The mRNA structure and the physicochemical characteristics, construction, and the stability of the designed chimeric protein were analyzed using computational and bioinformatics tools and servers. Hence, the GOR4 and I-TASSER online web servers were used to predict the secondary and tertiary structures of the designed protein.

RESULTS

The results demonstrated that codon adaptation index (CAI) of dt390-stxB chimeric gene raised from 0.6 in the wild type to 0.9 in the chimeric optimized gene. The mfold data revealed that the dt390-stxB mRNA was completely stable to be translated effectively in the novel host. The normal activity of the fusion protein determined by considering the secondary and tertiary structure of each construct. Energy calculation data indicated that the thermodynamic ensemble for mRNA structure was -427.40 kJ/mol. The stability index (SI) of DT390-STxB was 36.95, which is quite appropriate to preserve the stability of the construct. Ultimately, the DT390-STxB was classified as a steady fusion protein according to the Ramachandran plot.

CONCLUSION

Our results showed that DT390-STXB was a stable chimeric protein and it can be recruited as a candidate of novel anti-tumor agents for the development of breast cancer treatment.

Nonribosomal peptides in fungal cell factories: from genome mining to optimized heterologous production

Fungi are notoriously prolific producers of secondary metabolites including nonribosomal peptides (NRPs). The structural complexity of NRPs grants them interesting activities such as antibiotic, anti-cancer, and anti-inflammatory properties. The discovery of these compounds with attractive activities can be achieved by using two approaches: either by screening samples originating from various environments for their biological activities, or by identifying the related clusters in genomic sequences thanks to bioinformatics tools. This genome mining approach has grown tremendously due to recent advances in genome sequencing, which have provided an incredible amount of genomic data from hundreds of microbial species. Regarding fungal organisms, the genomic data have revealed the presence of an unexpected number of putative NRP-related gene clusters. This highlights fungi as a goldmine for the discovery of putative novel bioactive compounds. Recent development of NRP dedicated bioinformatics tools have increased the capacity to identify these gene clusters and to deduce NRPs structures, speeding-up the screening process for novel metabolites discovery. Unfortunately, the newly identified compound is frequently not or poorly produced by native producers due to a lack of expression of the related genes cluster. A frequently employed strategy to increase production rates consists in transferring the related biosynthetic pathway in heterologous hosts. This review aims to provide a comprehensive overview about the topic of NRPs discovery, from gene cluster identification by genome mining to the heterologous production in fungal hosts. The main computational tools and methods for genome mining are herein presented with an emphasis on the particularities of the fungal systems. The different steps of the reconstitution of NRP biosynthetic pathway in heterologous fungal cell factories will be discussed, as well as the key factors to consider for maximizing productivity. Several examples will be developed to illustrate the potential of heterologous production to both discover uncharacterized novel compounds predicted in silico by genome mining, and to enhance the productivity of interesting bio-active natural products.

QTL-BSA: A Bulked Segregant Analysis and Visualization Pipeline for QTL-seq

In recent years, the application of Whole Genome Sequencing (WGS) on plants has generated sufficient data for the identification of trait-associated genomic loci or genes. A high-throughput genome-assisted QTL-seq strategy, combined with bulked-segregant analysis and WGS of two bulked populations from a segregating progeny with opposite phenotypic trait values, has gained increasing popularities in research community. However, there is no publicly available user friendly software for the identification and visualization. Hence, we developed a tool named QTL-BSA (QTL-bulked segregant analysis and visualization pipeline), which could facilitate the rapid identification and visualization of candidate QTLs from QTL-seq. As a proof-of-concept study, we have applied the tool for the rapid discovery and the identification of genes related with the partial blast resistance in rice. Genomic region of the major QTL identified on chromosome 6, is located between 1.52 and 4.32 Mb, which is consistent with previous studies (2.39-4.39 Mb). We also derived the gene and QTLs functional annotation of this region. QTL-BSA offers a comprehensive solution to facilitate a wide range of programming and visualization tasks in QTL-seq analysis, is expected to be used widely by the research community.

New SDC function prediction based on protein-protein interaction using bioinformatics tools

The precise roles for SDC have been complex to specify. Assigning and reanalyzing protein and peptide identification to novel protein functions is one of the most important challenges in postgenomic era. Here, we provide SDC molecular description to support, contextualize and reanalyze the corresponding protein-protein interaction (PPI). From SDC-1 data mining, we discuss the potential of bioinformatics tools to predict new biological rules of SDC. Using these methods, we have assembled new possibilities for SDC biology from PPI data, once, the understanding of biology complexity cannot be capture from one simple question.

Molecular aspect of silver nanoparticles regulated embryonic development in Zebrafish (Danio rerio) by Oct-4 expression

With the enhancement of commercial manifestation of silver nanoparticles, concerned has risen on their accumulation in aquatic system and consequent effects on fish development and metabolism. In this study, experiments were conducted to assess the impacts of silver nanoparticles on early life cycles of fish considering Zebrafish (Danio rerio) as experimental model. Silver nanoparticles were synthesized through chemical reduction method and characterized through UV-visible spectroscopy, dynamic light scattering (DLS), and HR-TEM. Different sub lethal doses of nanosilver were applied (13.6, 21.6, 42.4, 64, and 128 μgL^-1) to post-fertilization phases of Zebrafish embryos and their interaction effects were monitored up to six days period. No significant morphological variations were observed at 13.6, 21.6, 42.4 μgL^-1 dose of silver nanoparticles, whereas 64 and 128 μgL^-1 exposure dose exhibited bending in myotome, deformity in tail region, somites, notochord and swelling in anterior and posterior region of embryos and larva. Hatching performances analysis elicited highest hatching success in 13.6 and 21.6 μgL^-1 doses of silver nanoparticles followed by positive and negative control, whereas exposure dose of 64 and 128 μgL^-1 exhibited comparatively lower success. Western blot analysis were conducted on developing hatchlings with Oct4 antibody and at 13.6 and 21.6 μgL^-1dose,it showed over expression elucidating stimulatory role of nanosilver in these mentioned doses. In silico analysis depicted a firm interaction of nanosilver with Oct4 revealing their key role in growth stimulation of developing embryos. The study demonstrates the function of nanosilver as a growth promoter rather only as a toxicant in fish metabolic system.

Statistical and Multivariate Analysis of MS-Based Plant Metabolomics Data

Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification.

FledFold: A Novel Software for RNA Secondary Structure Prediction

Background

RNA secondary structure is essential to understand the mechanism of RNAs.

Method

In this paper, fledFold, a novel software for RNA secondary structure prediction, is introduced. It combines both thermodynamic and kinetic factors of RNA secondary structures and can predict RNA secondary structures from their primary sequences with local personal computers.

Results

FledFold is implemented in C++ under Windows 7 and could run on windows 7 or later version with at least 2 GB of RAM. Fledfold is user friendly and could output results with multiple formats.

Conslusion

FledFold will be a valuable tool for RNA researches and it could be downloaded freely from http://www.jlucomputer.com/fledfold.php.

In-silico design and molecular docking evaluation of peptides derivatives from bacteriocins and porcine beta defensin-2 as inhibitors of Hepatitis E virus capsid protein

Hepatitis E virus (HEV) is considered the main etiological agent that causes acute hepatitis. It is estimated that 20 million cases occur annually worldwide, reaching mortality rates of 28% in pregnant women. To date, available treatments and vaccines have not been entirely effective. In this study, six antiviral peptides derived from the sequences of porcine Beta-Defensin-2 and bacteriocins Nisin and Subtilosin were generate using in silico tools in order to propose new antiviral agents. Through the use of molecular docking, interactions between the HEV capsid protein and the six new antiviral peptide candidates were evaluated. A peptide of 15 residues derived from Subtilosin showed the best docking energy (-7.0 kcal/mol) with the capsid protein. This is the first report to our knowledge involving a non-well study viral protein interacting with peptides susceptibles to being synthesized, and that could be subsequently evaluated in vitro; moreover, this study provide novel information on the nature of the dimerization pocket of the HEV capsid protein, and could help to understand the first steps in the viral replication cycle, needed for the virus entry to the host cell.

Deciphering the role of microRNA - A step by step guide

MicroRNAs (miRNAs), are small non-coding RNAs of approximately 22 nucleotides in length, playing an important role in regulating gene expression post-transcriptionally. Understanding the effect of miRNA regulation in a pathway-specific manner unravels the approaches adopted to apprehend biological mechanisms, the information, which is scanty for researchers, not primed already for miR related research. Here, we describe a quick perspective in 5 steps with probable approaches and assays at every level to unravel the specific role of a microRNA, miR-145a-5p, as an example. This perspective as a guide would help in identifying novel targets for a microRNA, as shown for miR-145a-5p, which down-regulated the mRNA expression of ADD3 and BRCA2, using bioinformatic tools and experimental assays.

Evaluation of GeneMarker® HTS for improved alignment of mtDNA MPS data, haplotype determination, and heteroplasmy assessment

Existing software has not allowed for effective alignment of mitochondrial (mt) DNA sequence data generated using a massively parallel sequencing (MPS) approach, combined with the ability to perform a detailed assessment of the data. The regions of sequence that are typically difficult to align are homopolymeric stretches, isolated patterns of SNPs (single nucleotide polymorphisms), and INDELs (insertions/deletions). A custom software solution, GeneMarker^® HTS, was developed and evaluated to address these limitations, and to provide a user-friendly interface for forensic practitioners and others interested in mtDNA analysis of MPS data. GeneMarker^® HTS generates an exportable consensus mtDNA sequence that produces phylogenetically correct SNP and INDEL calls using a customizable motif-based alignment algorithm. Sequence data from 500 individuals, with various alignment asymmetries and levels of heteroplasmy, were used to assess the software. Accuracy in producing mtDNA haplotypes, the ability to correctly identify low-level heteroplasmic sequence variants, and the user-based features of the software were evaluated. Analyzed sequences yielded correct mtDNA haplotypes, and heteroplasmic variants were properly identified with minimal manual interpretation. The software offers numerous user-defined parameters for filtering the data that address the interests of researchers and practitioners, and provides multiple options for viewing and navigating through the data.

Evaluation of a hypothetical protein for serodiagnosis and as a potential marker for post-treatment serological evaluation of tegumentary leishmaniasis patients

The serodiagnosis for tegumentary leishmaniasis (TL) presents problems related to the sensitivity and/or specificity of the tests. In the present study, an enzyme-linked immunosorbent assay (ELISA) technique was used to evaluate the performance from a Leishmania braziliensis hypothetical protein, LbHyM, in an attempt to compare its serological reactivity with a soluble Leishmania antigenic preparation (SLA) for the serodiagnosis of cutaneous (CL) and mucosal (ML) leishmaniasis. LbHyM was predicted to be a kinesin-like protein by bioinformatics tools. Serum samples were collected from both CL and ML patients, as well as from those with Chagas disease and from healthy subjects living in endemic or non-endemic areas of TL. Also, sera were collected from patients before and after the treatments, seeking to evaluate their serological follow-up in relation to the anti-protein and anti-parasite antibody levels. When an ELISA-rLbHyM assay was performed, it proved to be significantly more sensitive than ELISA-L. braziliensis SLA in detecting both CL and ML patients. Also, when using sera from Chagas disease patients, the ELISA-rLbHyM proved to be more specific than ELISA-SLA. The anti-protein and anti-parasite antibody levels were also evaluated 6 months after the treatments, and treated patients showed significantly lower levels of specific-rLbHyM antibodies, when compared to the anti-parasite antibody levels. In conclusion, the ELISA-rLbHyM assay can be considered a confirmatory serological technique for the serodiagnosis of L. braziliensis infection and can also be used in the serological follow-up of treated patients, aiming to correlate the low anti-protein antibody levels with the improvement of the healthy state of the patients.

Bioinformatics Tools for the Discovery of New Nonribosomal Peptides

This chapter helps in the use of bioinformatics tools relevant to the discovery of new nonribosomal peptides (NRPs) produced by microorganisms. The strategy described can be applied to draft or fully assembled genome sequences. It relies on the identification of the synthetase genes and the deciphering of the domain architecture of the nonribosomal peptide synthetases (NRPSs). In the next step, candidate peptides synthesized by these NRPSs are predicted in silico, considering the specificity of incorporated monomers together with their isomery. To assess their novelty, the two-dimensional structure of the peptides can be compared with the structural patterns of all known NRPs. The presented workflow leads to an efficient and rapid screening of genomic data generated by high throughput technologies. The exploration of such sequenced genomes may lead to the discovery of new drugs (i.e., antibiotics against multi-resistant pathogens or anti-tumors).

RNA Systems Biology for Cancer: From Diagnosis to Therapy

It is due to the advances in high-throughput omics data generation that RNA species have re-entered the focus of biomedical research. International collaborate efforts, like the ENCODE and GENCODE projects, have spawned thousands of previously unknown functional non-coding RNAs (ncRNAs) with various but primarily regulatory roles. Many of these are linked to the emergence and progression of human diseases. In particular, interdisciplinary studies integrating bioinformatics, systems biology, and biotechnological approaches have successfully characterized the role of ncRNAs in different human cancers. These efforts led to the identification of a new tool-kit for cancer diagnosis, monitoring, and treatment, which is now starting to enter and impact on clinical practice. This chapter is to elaborate on the state of the art in RNA systems biology, including a review and perspective on clinical applications toward an integrative RNA systems medicine approach. The focus is on the role of ncRNAs in cancer.

The Topology Prediction of Membrane Proteins: A Web-Based Tutorial

There is a great need for development of educational materials on the transfer of current bioinformatics knowledge to undergraduate students in bioscience departments. In this study, it is aimed to prepare an example in silico laboratory tutorial on the topology prediction of membrane proteins by bioinformatics tools. This laboratory tutorial is prepared for biochemistry lessons at bioscience departments (biology, chemistry, biochemistry, molecular biology and genetics, and faculty of medicine). The tutorial is intended for students who have not taken a bioinformatics course yet or already have taken a course as an introduction to bioinformatics. The tutorial is based on step-by-step explanations with illustrations. It can be applied under supervision of an instructor in the lessons, or it can be used as a self-study guide by students. In the tutorial, membrane-spanning regions and α-helices of membrane proteins were predicted by internet-based bioinformatics tools. According to the results achieved from internet-based bioinformatics tools, the algorithms and parameters used were effective on the accuracy of prediction. The importance of this laboratory tutorial lies on the facts that it provides an introduction to the bioinformatics and that it also demonstrates an in silico laboratory application to the students at natural sciences. The presented example education material is applicable easily at all departments that have internet connection. This study presents an alternative education material to the students in biochemistry laboratories in addition to classical laboratory experiments.

Using in silico techniques: Isolation and characterization of an insect cuticle-degrading-protease gene from Beauveria bassiana

Cuticle-degrading-proteases (CDPs) secreted by Beauveria spp. are pivotal biocontrol substances, possessing commercial potential for developing bio-pesticides. Therefore, a thoughtful and contemplative understanding and assessment of the structural and functional features of these proteases would markedly assist the development of biogenic pesticides. Computational molecular biology is a new facile alternative approach to the tedious experimental molecular biology; therefore, by using bioinformatics tools, we isolated and characterized an insect CDP gene from Beauveria bassiana 70 s.l. genomic DNA. The CDP gene (1240 bp with GeneBank accession no. KT804651.1) consisted of three introns and four CDS exons, and shared 74-100% sequence identity to the reference CDP genes. Its phylogenetic tree results showed a unique evolution pattern, and the predicted amino acid peptide (PAAP) consisted of 344 amino acid residues with pI, molecular weight, instability index, grand average hydropathicity value and aliphatic index of 7.2, 35.4 kDa, 24.45, -0.149, and 76.63, respectively. The gene possessed 74-89% amino acid sequence similarity to the 12 reference strains. Three motifs (Peptidase_S8 subtilase family) were detected in the PAAP, and the computed 3D structure possessed 79.09% structural identity to alkaline serine proteases. The PAAP had four (three serine proteases and one Pyridoxal-dependent decarboxylase) conserved domains, a disulfide bridge, two calcium binding sites, MY domain, and three predicted active sites in the serine family domains. These results will set the groundwork for further exploitation of proteases and understanding the mechanism of disease caused by cuticle-degrading-serine-proteases from entomopathogenic fungi.

HoloVir: A Workflow for Investigating the Diversity and Function of Viruses in Invertebrate Holobionts

Abundant bioinformatics resources are available for the study of complex microbial metagenomes, however their utility in viral metagenomics is limited. HoloVir is a robust and flexible data analysis pipeline that provides an optimized and validated workflow for taxonomic and functional characterization of viral metagenomes derived from invertebrate holobionts. Simulated viral metagenomes comprising varying levels of viral diversity and abundance were used to determine the optimal assembly and gene prediction strategy, and multiple sequence assembly methods and gene prediction tools were tested in order to optimize our analysis workflow. HoloVir performs pairwise comparisons of single read and predicted gene datasets against the viral RefSeq database to assign taxonomy and additional comparison to phage-specific and cellular markers is undertaken to support the taxonomic assignments and identify potential cellular contamination. Broad functional classification of the predicted genes is provided by assignment of COG microbial functional category classifications using EggNOG and higher resolution functional analysis is achieved by searching for enrichment of specific Swiss-Prot keywords within the viral metagenome. Application of HoloVir to viral metagenomes from the coral Pocillopora damicornis and the sponge Rhopaloeides odorabile demonstrated that HoloVir provides a valuable tool to characterize holobiont viral communities across species, environments, or experiments.

Web resources for microbial data

There are multitudes of web resources that are quite useful for the microbial scientific research community. Here, we provide a brief introduction on some of the most notable microbial web resources and an evaluation of them based upon our own user experience.

Tissue-specific mtDNA abundance from exome data and its correlation with mitochondrial transcription, mass and respiratory activity

Eukaryotic cells contain a population of mitochondria, variable in number and shape, which in turn contain multiple copies of a tiny compact genome (mtDNA) whose expression and function is strictly coordinated with the nuclear one. mtDNA copy number varies between different cell or tissues types, both in response to overall metabolic and bioenergetics demands and as a consequence or cause of specific pathological conditions. Here we present a novel and reliable methodology to assess the effective mtDNA copy number per diploid genome by investigating off-target reads obtained by whole-exome sequencing (WES) experiments. We also investigate whether and how mtDNA copy number correlates with mitochondrial mass, respiratory activity and expression levels. Analyzing six different tissues from three age- and sex-matched human individuals, we found a highly significant linear correlation between mtDNA copy number estimated by qPCR and the frequency of mtDNA off target WES reads. Furthermore, mtDNA copy number showed highly significant correlation with mitochondrial gene expression levels as measured by RNA-Seq as well as with mitochondrial mass and respiratory activity. Our methodology makes thus feasible, at a large scale, the investigation of mtDNA copy number in diverse cell-types, tissues and pathological conditions or in response to specific treatments.