Recent Trends in System-Scale Integrative Approaches for Discovering Protective Antigens Against Mycobacterial Pathogens

An Immunoinformatics-Based Study of Mycobacterium tuberculosis Region of Difference-2 Uncharacterized Protein (Rv1987) as a Potential Subunit Vaccine Candidate for Preliminary Ex Vivo Analysis

Mycobacterium tuberculosis (Mtb) is the pathogen that causes tuberculosis and develops resistance to many of the existing drugs. The sole licensed TB vaccine, BCG, is unable to provide a comprehensive defense. So, it is crucial to maintain the immunological response to eliminate tuberculosis. Our previous in silico study reported five uncharacterized proteins as potential vaccine antigens. In this article, we considered the uncharacterized Mtb H37Rv regions of difference (RD-2) Rv1987 protein as a promising vaccine candidate. The vaccine quality of the protein was analyzed using reverse vaccinology and immunoinformatics-based quality-checking parameters followed by an ex vivo preliminary investigation. In silico analysis of Rv1987 protein predicted it as surface localized, secretory, single helix, antigenic, non-allergenic, and non-homologous to the host protein. Immunoinformatics analysis of Rv1987 by CD4 + and CD8 + T-cells via MHC-I and MHC-II binding affinity and presence of B-cell epitope predicted its immunogenicity. The docked complex analysis of the 3D model structure of the protein with immune cell receptor TLR-4 revealed the protein's capability for potential interaction. Furthermore, the target protein-encoded gene Rv1987 was cloned, over-expressed, purified, and analyzed by mass spectrometry (MS) to report the target peptides. The qRT-PCR gene expression analysis shows that it is capable of activating macrophages and significantly increasing the production of a number of key cytokines (TNF-α, IL-1β, and IL-10). Our in-silico analysis and ex vivo preliminary investigations revealed the immunogenic potential of the target protein. These findings suggest that the Rv1987 be undertaken as a potent subunit vaccine antigen and that further animal model immuno-modulation studies would boost the novel TB vaccine discovery and/or BCG vaccine supplement pipeline.

Development of a Trivalent Construct Omp18/AhpC/FlgH Multi Epitope Peptide Vaccine Against Campylobacter jejuni

Campylobacter jejuni (C. jejuni) is one of the major pathogens contributing to the enteritis in humans. Infection can lead to numerous complications, including but not limited to Guillain-Barre syndrome, reactive arthritis, and Reiter’s syndrome. Over the past two decades, joint efforts have been made toward developing a proper strategy of limiting the transmission of C. jejuni to humans. Nevertheless, except for biosecurity measures, no available vaccine has been developed so far. Judging from the research findings, Omp18, AhpC outer membrane protein, and FlgH flagellin subunits of C. jejuni could be adopted as surface protein antigens of C. jejuni for screening dominant epitope thanks to their strong antigenicity, expression of varying strains, and conservative sequence. In this study, bioinformatics technology was adopted to analyze the T-B antigenic epitopes of Omp18, AhpC, and FlgH in C. jejuni strain NCTC11168. Both ELISA and Western Blot methods were adopted to screen the dominant T-B combined epitope. GGS (GGCGGTAGC) sequence was adopted to connect the dominant T-B combined epitope peptides and to construct the prokaryotic expression system of tandem repeats of antigenic epitope peptides. The mouse infection model was adopted to assess the immunoprotective effect imposed by the trivalent T-B combined with antigen epitope peptide based on Omp18/AhpC/FlgH. In this study, a tandem epitope AhpC-2/Omp18-1/FlgH-1 was developed, which was composed of three epitopes and could effectively enhance the stability and antigenicity of the epitope while preserving its structure. The immunization of BALB/c mice with a tandem epitope could induce protective immunity accompanied by the generation of IgG2a antibody response through the in vitro synthesis of IFN-γ cytokines. Judging from the results of immune protection experiments, the colonization of C. jejuni declined to a significant extent, and it was expected that AhpC-2/Omp18-1/FlgH-1 could be adopted as a candidate antigen for genetic engineering vaccine of C. jejuni MAP.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread

In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with predictive monitoring approaches, and epidemiological modeling are illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and the best-fitted distribution is chosen for the prediction of the spread of the disease. The infectious diseases develop over time and we have data on the single variable that is the number of infections that happened, therefore, time series models are fitted and the prediction is done based on the best-fitted model. Monitoring approaches may also be applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. We came up with, how to improve the existing modeling methods, the use of fuzzy variables, and detection of fraud in the available data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.

Immunopeptidomics for next-generation bacterial vaccine development

Antimicrobial resistance is an increasing global threat and alternative treatments substituting failing antibiotics are urgently needed. Vaccines are recognized as highly effective tools to mitigate antimicrobial resistance; however, the selection of bacterial antigens as vaccine candidates remains challenging. In recent years, advances in mass spectrometry-based proteomics have led to the development of so-called immunopeptidomics approaches that allow the untargeted discovery of bacterial epitopes that are presented on the surface of infected cells. Especially for intracellular bacterial pathogens, immunopeptidomics holds great promise to uncover antigens that can be encoded in viral vector- or nucleic acid-based vaccines. This review provides an overview of immunopeptidomics studies on intracellular bacterial pathogens and considers future directions and challenges in advancing towards next-generation vaccines.

Comparative cellular immune responses in calves after infection with Mycobacterium avium subsp. paratuberculosis, M. avium subsp. avium, M. kansasii and M. bovis

In the present study, calves were infected with Mycobacterium avium subsp. paratuberculosis (MAP), Mycobacterium avium subsp. avium (M. avium), Mycobacterium kansasii (M. kansasii), or Mycobacterium bovis (M. bovis) to determine differences in cellular immunity. Comparative cellular responses were assessed upon stimulation of cells with mycobacterial whole cell sonicates respective of each infection group. Antigen-specific whole blood interferon gamma (IFN-γ) responses were observed in all infection groups compared to noninfected control calves, however, responses were more robust for M. bovis calves. Upon antigen stimulation of PBMCs, secretion of IFN-γ and IL-10 was higher for M. bovis calves compared to other infection groups. In contrast, IL-12 secretion was lower for M. bovis calves compared to MAP infected calves. Within the total PBMC population, higher numbers of CD4+, CD8+, and γδ TCR + T cells were observed for MAP and M. avium calves compared to M. bovis calves. This aligned with higher expression of CD26 on these subpopulations for MAP and M. avium calves, as well. In contrast, greater expression of CD25 was observed on CD4+ and γδ TCR + T cells and natural killer cells for M. bovis calves. Overall, similarities in cellular immune responses were observed between the closely related MAP and M. avium during infection of calves. In contrast, significant differences were noted between calves infected with MAP and M. bovis. This suggests that host immune responses to different mycobacteria may impact interpretation of diagnostic tools based upon their cellular immunity.

Serum proteomics of active tuberculosis patients and contacts reveals unique processes activated during Mycobacterium tuberculosis infection

Tuberculosis (TB) is the most lethal infection among infectious diseases. The specific aim of this study was to establish panels of serum protein biomarkers representative of active TB patients and their household contacts who were either infected (LTBI) or uninfected (EMI-TB Discovery Cohort, Pontevedra Region, Spain). A TMT (Tamdem mass tags) 10plex-based quantitative proteomics study was performed in quintuplicate containing a total of 15 individual serum samples per group. Peptides were analyzed in an LC-Orbitrap Elite platform, and raw data were processed using Proteome Discoverer 2.1. A total of 418 proteins were quantified. The specific protein signature of active TB patients was characterized by an accumulation of proteins related to complement activation, inflammation and modulation of immune response and also by a decrease of a small subset of proteins, including apolipoprotein A and serotransferrin, indicating the importance of lipid transport and iron assimilation in the progression of the disease. This signature was verified by the targeted measurement of selected candidates in a second cohort (EMI-TB Verification Cohort, Maputo Region, Mozambique) by ELISA and nephelometry techniques. These findings will aid our understanding of the complex metabolic processes associated with TB progression from LTBI to active disease.

Multi-epitope based vaccine against yellow fever virus applying immunoinformatics approaches

Yellow fever disease is considered a re-emerging major health issue which has caused recent outbreaks with a high number of deaths. Tropical countries, mainly African and South American, are the most affected by Yellow fever outbreaks. Despite the availability of an attenuated vaccine, its use is limited for some groups such as pregnant and nursing women, immunocompromised and immunosuppressed patients, elderly people >65 years, infants <6 months and patients with biological disorders like thymus disorders. In order to achieve new preventive measures, we applied immunoinformatics approaches to develop a multi-epitope-based subunit vaccine for Yellow fever virus. Different epitopes, related to humoral and cell-mediated immunity, were predicted for complete polyproteins of two Yellow fever strains (Asibi and 17 D vaccine). Those epitopes common for both strains were mapped into a set of 137 sequences of Yellow fever virus, including 77 sequences from a recent outbreak at the state of Minas Gerais, southeast Brazil. Therefore, the present work uses robust bioinformatics approaches for the identification of a multi-epitope vaccine against the Yellow fever virus. Our results indicate that the identified multi-epitope vaccine might stimulate humoral and cellular immune responses and could be a potential vaccine candidate against Yellow fever virus infection. Hence, it should be subjected to further experimental validations. Communicated by Ramaswamy H. Sarma.

Chloroplast-based inducible expression of ESAT-6 antigen for development of a plant-based vaccine against tuberculosis

Mycobacterium tuberculosis causes tuberculosis in humans. The major disease burden of tuberculosis lies in developing countries. Lack of an effective vaccine for adults is one of the major hurdles for controlling this deadly disease. In the present study, 6 kDa early secretory antigenic target (ESAT-6) of M. tuberculosis was inducibly expressed in chloroplasts of Nicotiana tabacum. The expression of ESAT-6 in chloroplasts was controlled by T7 promoter that was activated by nuclear-generated signal peptide. Tobacco plants, containing nuclear component, were transformed via biolistic bombardment with pEXP-T7-ESAT-6 obtained by Gateway® cloning. Transformation and homoplasmic status of transplastomic plants was confirmed by polymerase chain reaction and Southern blotting. Plants were induced for protein expression by spraying with 5% ethanol for 1 day, 3 days, 7 days and 10 days. ESAT-6 protein was detected by immunoblot analysis and maximum protein was obtained for 10 days induced plants that was estimated to accumulate up to 1.2% of total soluble fraction of protein. Transplastomic plants showed completely normal morphology. Transplastomic and untransformed plants became slightly chlorotic upon prolonged exposure to ethanol until 10 days. Taken together, this data could help in the development of an antigen-based subunit vaccine against tuberculosis.