Moving forward through the in silico modeling of multiple sclerosis: Treatment layer implementation and validation.

Multiple sclerosis is an autoimmune inflammatory disease that affects the central nervous system through chronic demyelination and loss of oligodendrocytes. Since the relapsing-remitting form is the most prevalent, relapse-reducing therapies are a primary choice for specialists. Universal Immune System Simulator is an agent-based model that simulates the human immune system dynamics under physiological conditions and during several diseases, including multiple sclerosis. In this work, we extended the UISS-MS disease layer by adding two new treatments, i.e., cladribine and ocrelizumab, to show that UISS-MS can be potentially used to predict the effects of any existing or newly designed treatment against multiple sclerosis. To retrospectively validate UISS-MS with ocrelizumab and cladribine, we extracted the clinical and MRI data from patients included in two clinical trials, thus creating specific cohorts of digital patients for predicting and validating the effects of the considered drugs. The obtained results mirror those of the clinical trials, demonstrating that UISS-MS can correctly simulate the mechanisms of action and outcomes of the treatments. The successful retrospective validation concurred to confirm that UISS-MS can be considered a digital twin solution to be used as a support system to inform clinical decisions and predict disease course and therapeutic response at a single patient level.

Beyond the state of the art of reverse vaccinology: predicting vaccine efficacy with the universal immune system simulator for influenza.

When it was first introduced in 2000, reverse vaccinology was defined as an in silico approach that begins with the pathogen's genomic sequence. It concludes with a list of potential proteins with a possible, but not necessarily, list of peptide candidates that need to be experimentally confirmed for vaccine production. During the subsequent years, reverse vaccinology has dramatically changed: now it consists of a large number of bioinformatics tools and processes, namely subtractive proteomics, computational vaccinology, immunoinformatics, and in silico related procedures. However, the state of the art of reverse vaccinology still misses the ability to predict the efficacy of the proposed vaccine formulation. Here, we describe how to fill the gap by introducing an advanced immune system simulator that tests the efficacy of a vaccine formulation against the disease for which it has been designed. As a working example, we entirely apply this advanced reverse vaccinology approach to design and predict the efficacy of a potential vaccine formulation against influenza H5N1. Climate change and melting glaciers are critical due to reactivating frozen viruses and emerging new pandemics. H5N1 is one of the potential strains present in icy lakes that can raise a pandemic. Investigating structural antigen protein is the most profitable therapeutic pipeline to generate an effective vaccine against H5N1. In particular, we designed a multi-epitope vaccine based on predicted epitopes of hemagglutinin and neuraminidase proteins that potentially trigger B-cells, CD4, and CD8 T-cell immune responses. Antigenicity and toxicity of all predicted CTL, Helper T-lymphocytes, and B-cells epitopes were evaluated, and both antigenic and non-allergenic epitopes were selected. From the perspective of advanced reverse vaccinology, the Universal Immune System Simulator, an in silico trial computational framework, was applied to estimate vaccine efficacy using a cohort of 100 digital patients.

Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles.

COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.

Reverse Vaccinology for Influenza A Virus: From Genome Sequencing to Vaccine Design.

Reverse vaccinology (RV) consists in the identification of potentially protective antigens expressed by any organism starting from genomic information and derived from in silico analysis, with the aim of promoting the discovery of new candidate vaccines against different types of pathogens. This approach makes use of bioinformatics techniques to screen the whole genomic sequence of a specific pathogen for the identification of the epitopes that could elicit the best immune response. The use of in silico techniques allows to reduce dramatically both the time and cost required for the identification of a potential vaccine, also facilitating the laborious process of selection of those antigens that, with a traditional approach, would be completely impossible to detect or culture. RV methodologies have been successfully applied for the identification of new vaccines against serogroup B meningococcus (MenB), Bacillus anthracis, Streptococcus pneumonia, Staphylococcus aureus, Chlamydia pneumoniae, Porphyromonas gingivalis, Edwardsiella tarda, and Mycobacterium tuberculosis. As a case of study, we will go in depth into the application of RV techniques on Influenza A virus.

A Credibility Assessment Plan for an In Silico Model that Predicts the Dose-Response Relationship of New Tuberculosis Treatments.

Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a particularly desirable goal for diseases such as tuberculosis. The agent-based Universal Immune System Simulator was used to develop an In Silico Trials environment that can predict the dose-response of new therapeutic vaccines against pulmonary tuberculosis, supporting the optimal design of clinical trials. But before such in silico methodology can be used in the evaluation of new treatments, it is mandatory to assess the credibility of this predictive model. This study presents a risk-informed credibility assessment plan inspired by the ASME V&V 40-2018 technical standard. Based on the selected context of use and regulatory impact of the technology, a detailed risk analysis is described together with the definition of all the verification and validation activities and related acceptability criteria. The work provides an example of the first steps required for the regulatory evaluation of an agent-based model used in the context of drug development.

A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets.

The COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and computer simulation in the development or regulatory evaluation of a medical intervention, are making the difference to better predict the efficacy and safety of new vaccines. An integrated bioinformatics pipeline that merges the prediction power of different software that act at different scales for evaluating the elicited response of human immune system against every pathogen is proposed. As a working example, we applied this problem solving protocol to predict the cross-reactivity of pre-existing vaccination interventions against SARS-CoV-2.

Correction: Genovese et al. In Vitro Antibacterial, Anti-Adhesive and Anti-Biofilm Activities of Krameria lappacea (Dombey) Burdet & B.B. Simpson Root Extract against Methicillin-Resistant Staphylococcus aureus Strains. Antibiotics 2021, 10, 428.

The authors would like to make the following corrections to the published paper [...].

In Vitro Antibacterial, Anti-Adhesive and Anti-Biofilm Activities of Krameria lappacea (Dombey) Burdet & B.B. Simpson Root Extract against Methicillin-Resistant Staphylococcus aureus Strains.

Methicillin-resistant Staphylococcus aureus (MRSA) represents a serious threat to public health, due to its large variety of pathogenetic mechanisms. Accordingly, the present study aimed to investigate the anti-MRSA activities of Krameria lappacea, a medicinal plant native to South America. Through Ultra-High-Performance Liquid Chromatography coupled with High-Resolution Mass spectrometry, we analyzed the chemical composition of Krameria lappacea root extract (KLRE). The antibacterial activity of KLRE was determined by the broth microdilution method, also including the minimum biofilm inhibitory concentration and minimum biofilm eradication concentration. Besides, we evaluated the effect on adhesion and invasion of human lung carcinoma A549 cell line by MRSA strains. The obtained results revealed an interesting antimicrobial action of this extract, which efficiently inhibit the growth, biofilm formation, adhesion and invasion of MRSA strains. Furthermore, the chemical analysis revealed the presence in the extract of several flavonoid compounds and type-A and type-B proanthocyanidins, which are known for their anti-adhesive effects. Taken together, our findings showed an interesting antimicrobial activity of KLRE, giving an important contribution to the current knowledge on the biological activities of this plant.

Comparison of Cell Culture with Three Conventional Polymerase Chain Reactions for Detecting Chlamydophila pneumoniae in Adult's Pharyngotonsillitis.

Chlamydophila pneumoniae is an intracellular pathogen responsible for respiratory tract infections. The isolation of the microorganism from clinical specimens is essential for a diagnosis. However, the identification of C. pneumoniae by cell cultures is very difficult besides strongly depending on the sample conditions. The study aimed to investigate, in adult patients with pharyngotonsillitis, the frequency of Chlamydophila pneumoniae detection by cell cultures and three conventional PCRs (a conventional PCR targeting the 16S rRNA gene and two nested PCRs, targeting the 16S rRNA gene and the ompA gene, respectively). The presence of chlamydial inclusion in cell cultures was observed in 11/94 samples (11.70%) by IFA. C. pneumoniae DNA was detected in 12/94 (12.76%) specimens by the 16S rRNA gene nested PCR, 4/94 (4.26%) by ompA gene nested PCR, and in 2/94 (2.13%) by 16S rRNA single-step PCR. Our data show poor agreement between the three applied DNA-amplification methods; in fact, only 16S rRNA gene nested PCR showed a statistically significant difference. Moreover, this result allowed us to achieve a definitive confirmation of the previous finding and to avoid the risk of an overestimation of the C. pneumoniae as a pathogen in pharyngotonsillitis.

Streptococcus agalactiae in pregnant women: serotype and antimicrobial susceptibility patterns over five years in Eastern Sicily (Italy).

Streptococcus agalactiae (also known Group B Streptococcus or GBS) represents the main pathogen responsible for early- and late-onset infections in newborns. The present study aimed to determine the antimicrobial susceptibility pattern and the capsular serotypes of GBS isolated in Eastern Sicily over 5 years, from January 2015 to December 2019. A total of 3494 GBS were isolated from vaginal swabs of pregnant women (37-39 weeks), as recommended by the Centers for Disease Control and Prevention. Capsular polysaccharide's typing of GBS was determined by a commercial latex agglutination test containing reagents to serotypes I-IX. The antimicrobial resistance pattern of GBS was determined through the disk diffusion method (Kirby-Bauer) and the double-disk diffusion test on Mueller-Hinton agar plates supplemented with 5% defibrinated sheep blood, according to the guidelines of the Clinical and Laboratory Standards Institute. Serotypes III (1218, 34.9%) and V (1069, 30.6%) were the prevalent colonizers, followed by not typable (570, 16.3%) and serotypes Ia (548, 15.7%), Ib (47, 1.3%), II (40, 1.1%), and IV (2, 0.1%). All 3494 clinical isolates were susceptible to cefditoren and vancomycin. Resistance to penicillin, ampicillin, levofloxacin, clindamycin, and erythromycin was observed in 6 (0.2%), 5 (0.1%), 161 (4.6%), 1090 (31.2%), and 1402 (40.1%) of the strains, respectively. Most of erythromycin-resistant GBS (1090/1402) showed the cMLSB phenotype, 276 the M phenotype, and 36 the iMLSB phenotype. Our findings revealed a higher prevalence of serotype III and a relevant resistance rate, among GBS strains, to the most frequently used antibiotics in antenatal screening.

EpiMethEx: a tool for large-scale integrated analysis in methylation hotspots linked to genetic regulation.

BACKGROUND: DNA methylation is an epigenetic mechanism of genomic regulation involved in the maintenance of homeostatic balance. Dysregulation of DNA methylation status is one of the driver alterations occurring in neoplastic transformation and cancer progression. The identification of methylation hotspots associated to gene dysregulation may contribute to discover new prognostic and diagnostic biomarkers, as well as, new therapeutic targets. RESULTS: We present EpiMethEx (Epigenetic Methylation and Expression), a R package to perform a large-scale integrated analysis by cyclic correlation analyses between methylation and gene expression data. For each gene, samples are segmented according to the expression levels to select genes that are differentially expressed. This stratification allows to identify CG methylation probesets modulated among gene-stratified samples. Subsequently, the methylation probesets are grouped by their relative position in gene sequence to identify wide genomic methylation events statically related to genetic modulation. CONCLUSIONS: The beta-test study showed that the global methylation analysis was in agreement with scientific literature. In particular, this analysis revealed a negative association between promoter hypomethylation and overexpression in a wide number of genes. Less frequently, this overexpression was sustained by intragenic hypermethylation events.

Immunological effects of occupational exposure to lead (Review).

It is well-known that occupational and environmental exposure to several factors, including benzene, heavy metals, chemicals and mineral fibers, is associated with the risk of developing a great number of diseases. Numerous studies have been carried out in order to investigate the mechanisms of toxicity of these substances, with particular regard to the possible toxic effects on the immune system. However, little is known about the influence of heavy metals, such as lead, on the immune system in human populations. Lead is a heavy metal still used in many industrial activities. Human exposure to lead can induce various biological effects depending upon the level and duration of exposure, such as toxic effects on haematological, cardiovascular, nervous and reproductive systems. Several studies demonstrated that exposure to lead is associated to toxic effects also on the immune system, thus increasing the incidence of allergy, infectious disease, autoimmunity or cancer. However, the effects of lead exposure on the human immune system are not conclusive, mostly in occupationally exposed subjects; nevertheless some immunotoxic abnormalities induced by lead have been suggested. In particular, in vivo, in vitro and ex vivo lead is able to improve T helper 2 (Th2) cell development affecting Th1 cell proliferation. Further studies are required to better understand the mechanisms of lead immunotoxicity and the ability of lead to affect preferentially one type of immune response.

Epigenetic alterations and occupational exposure to benzene, fibers, and heavy metals associated with tumor development (Review).

The chronic occupational exposure to contaminants and carcinogens leads to the development of cancer. Over the past decades, many carcinogens have been found in the occupational environment and their presence is often associated with an increased incidence of cancer. According to the International Agency for Research on Cancer (IARC), the majority of carcinogens are classified as 'probable' and 'possible' human carcinogens, while, direct evidence of carcinogenicity is provided in epidemiological and experimental studies. Additionally, accumulating evidence suggests that epigenetic alterations may be early indicators of genotoxic and non-genotoxic carcinogen exposure. In the present review, the relationship between exposures to benzene, mineral fibers, metals and epigenetic alterations are discussed as the most important cancer risk factors during work activities.

Computational modeling in melanoma for novel drug discovery.

INTRODUCTION: There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. AREAS COVERED: This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. EXPERT OPINION: Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.