Concept and application of a computational vaccinology workflow

Recent developments in systems biology and genetic engineering toward design of vaccines for TB

Tuberculosis (TB) is one of the most prevalent diseases worldwide. The currently available Bacillus Calmette-Guérin vaccine is not sufficient in protecting against pulmonary TB. Although many vaccines have been evaluated in clinical trials, but none of them yet has proven to be more successful. Thus, new strategies are urgently needed to design more effective TB vaccines. The emergence of new technologies will undoubtedly accelerate the process of vaccine development. This review summarizes the potential and validated applications of emerging technologies, including: systems biology (genomics, proteomics, and transcriptomics), genetic engineering, and other computational tools to discover and develop novel vaccines against TB. It also discussed that the significant implementation of these approaches will play crucial roles in the development of novel vaccines to cure and control TB.

COVID-19: A Review on the Novel Coronavirus Disease Evolution, Transmission, Detection, Control and Prevention

Three major outbreaks of the coronavirus, a zoonotic virus known to cause respiratory disease, have been reported since 2002, including SARS-CoV, MERS-CoV and the most recent 2019-nCoV, or more recently known as SARS-CoV-2. Bats are known to be the primary animal reservoir for coronaviruses. However, in the past few decades, the virus has been able to mutate and adapt to infect humans, resulting in an animal-to-human species barrier jump. The emergence of a novel coronavirus poses a serious global public health threat and possibly carries the potential of causing a major pandemic outbreak in the naïve human population. The recent outbreak of COVID-19, the disease caused by SARS-CoV-2, in Wuhan, Hubei Province, China has infected over 36.5 million individuals and claimed over one million lives worldwide, as of 8 October 2020. The novel virus is rapidly spreading across China and has been transmitted to 213 other countries/territories across the globe. Researchers have reported that the virus is constantly evolving and spreading through asymptomatic carriers, further suggesting a high global health threat. To this end, current up-to-date information on the coronavirus evolution and SARS-CoV-2 modes of transmission, detection techniques and current control and prevention strategies are summarized in this review.

COVID-19: A Review on the Novel Coronavirus Disease Evolution, Transmission, Detection, Control and Prevention

Three major outbreaks of the coronavirus, a zoonotic virus known to cause respiratory disease, have been reported since 2002, including SARS-CoV, MERS-CoV and the most recent 2019-nCoV, or more recently known as SARS-CoV-2. Bats are known to be the primary animal reservoir for coronaviruses. However, in the past few decades, the virus has been able to mutate and adapt to infect humans, resulting in an animal-to-human species barrier jump. The emergence of a novel coronavirus poses a serious global public health threat and possibly carries the potential of causing a major pandemic outbreak in the naïve human population. The recent outbreak of COVID-19, the disease caused by SARS-CoV-2, in Wuhan, Hubei Province, China has infected over 36.5 million individuals and claimed over one million lives worldwide, as of 8 October 2020. The novel virus is rapidly spreading across China and has been transmitted to 213 other countries/territories across the globe. Researchers have reported that the virus is constantly evolving and spreading through asymptomatic carriers, further suggesting a high global health threat. To this end, current up-to-date information on the coronavirus evolution and SARS-CoV-2 modes of transmission, detection techniques and current control and prevention strategies are summarized in this review.

Investigating genetic-and-epigenetic networks, and the cellular mechanisms occurring in Epstein-Barr virus-infected human B lymphocytes via big data mining and genome-wide two-sided NGS data identification

Epstein-Barr virus (EBV), also known as human herpesvirus 4, is prevalent in all human populations. EBV mainly infects human B lymphocytes and epithelial cells, and is therefore associated with their various malignancies. To unravel the cellular mechanisms during the infection, we constructed interspecies networks to investigate the molecular cross-talk mechanisms between human B cells and EBV at the first (0-24 hours) and second (8-72 hours) stages of EBV infection. We first constructed a candidate genome-wide interspecies genetic-and-epigenetic network (the candidate GIGEN) by big database mining. We then pruned false positives in the candidate GIGEN to obtain the real GIGENs at the first and second infection stages in the lytic phase by their corresponding next-generation sequencing data through dynamic interaction models, the system identification approach, and the system order detection method. The real GIGENs are very complex and comprise protein-protein interaction networks, gene/microRNA (miRNA)/long non-coding RNA regulation networks, and host-virus cross-talk networks. To understand the molecular cross-talk mechanisms underlying EBV infection, we extracted the core GIGENs including host-virus core networks and host-virus core pathways from the real GIGENs using the principal network projection method. According to the results, we found that the activities of epigenetics-associated human proteins or genes were initially inhibited by viral proteins and miRNAs, and human immune responses were then dysregulated by epigenetic modification. We suggested that EBV exploits viral proteins and miRNAs, such as EBNA1, BPLF1, BALF3, BVRF1 and miR-BART14, to develop its defensive mechanism to defeat multiple immune attacks by the human immune system, promotes virion production, and facilitates the transportation of viral particles by activating the human genes NRP1 and CLIC5. Ultimately, we propose a therapeutic intervention comprising thymoquinone, valpromide, and zebularine to act as inhibitors of EBV-associated malignancies.

Peptide Vaccines: New Trends for Avoiding the Autoimmune Response

Background:

Several marketed antiviral vaccines (such as that against hepatitis virus A and/or B, influenza virus, human papillomavirus, yellow fever virus, measles, rubella and mumps viruses) may elicit various autoimmune reactions.

Results:

The cause of autoimmune response due to vaccination may be: 1. the adjuvant which is regularly added to the vaccine (especially in the case of various oil substrates), 2. the specific viral component itself (a protein or glycoprotein potentially possessing cross-reactive epitopes) and/or 3. contamination of the vaccine with traces of non-viral proteins mostly cellular in origin. Believing that peptide vaccines might represent an optimal solution for avoiding the above-mentioned problems, we discuss the principles of rational design of a typical peptide vaccine which should contain oligopeptides coming either from the selected structural virion components (i.e.capsid proteins and/or envelop glycoproteins or both) or from the virus-coded non-structural polypeptides. The latter should be equally immunogenic as the structural virus proteins. Describing the feasibility of identification and design of immunogenic epitopes, our paper also deals with possible problems of peptide vaccine manufacturing. The presented data are in part based on the experience of our own, in part, they are coming from the results published by others.

Conclusion:

Any peptide vaccine should be able to elicit relevant and specific antibody formation, as well as an efficient cell-mediated immune response. Consequently, the properly designed peptide vaccine is expected to consist of carefully selected viral peptides, which should stimulate the receptors of helper T/CD4 cells as well as of cytotoxic (T/CD8) lymphocytes.

Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is ("Isolate-Inactivate-Inject") Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview

Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. “New vaccines” are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/reemergence of old and new infectious agents at the animal–human interface. Conventional vaccinology (the so-called “vaccinology 1.0”) was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan (“vaccinology 3.0”). This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration.

Detection of circulating natural antibodies to inflammatory cytokines in type-2 diabetes and clinical significance

Background

Inflammatory cytokines have been demonstrated to be involved in developing insulin resistance and type-2 diabetes (T2D). Natural antibodies in the circulation have protective effects on common diseases in humans. The present study was thus designed to test the hypothesis that natural antibodies against inflammatory cytokines could be associated with T2D.

Methods

An enzyme-linked immunosorbent assay (ELISA) was developed in-house to detect plasma IgG against peptide antigens derived from interleukin 1α (IL1α), IL1β, IL6, IL8 and tumor necrosis factor-α (TNF-α) in 200 patients with T2D and 220 control subjects.

Results

Binary regression showed that compared with control subjects, T2D patients had a decreased level of plasma anti-IL6 IgG (adjusted r²=0.034, p=0.0001), anti-IL8 IgG (adjusted r²=0.021, p=0.002) and anti-TNF-α IgG (adjusted r²=0.017, p=0.003). Female patients mainly contributed to decreased levels of anti-IL6 IgG (adjusted r²=0.065, p=0.0008) and anti-IL8 IgG (adjusted r²=0.056, p=0.003), while male patients mainly contributed to decreased anti-TNF-α IgG levels (adjusted r²=0.024, p=0.005). ROC curve analysis revealed a sensitivity of 16.5% against specificity of 95.5% for anti-IL6 IgG assay and a sensitivity of 19.5% against specificity of 95.9% for anti-IL8 IgG assay. Glycated hemoglobin levels measured after 6-month glucose-lowering treatment appeared to be inversely correlated with plasma anti-IL1α IgG (r=-0.477, df=17, p=0.039) and anti-IL6 IgG (r=-0.519, df=17, p=0.023) although such correlation failed to survive the Bonferroni correction.

Conclusions

Deficiency of natural IgG against inflammatory cytokines is likely to be a risk factor for T2D development and detection of such antibodies may be useful for personalized treatment of the disease.

Electronic supplementary material

The online version of this article (10.1186/s12950-017-0171-6) contains supplementary material, which is available to authorized users.

Differential antibody responses to gliadin-derived indigestible peptides in patients with schizophrenia

Gluten consumption has previously been implicated in the development of schizophrenia while an immunological link between gluten and schizophrenia was established by the detection of circulating antibodies against gliadin, a major component of wheat gluten. Several studies have reported an increase in circulating antibodies against native gliadin molecules that are unlikely to survive degradation in the digestive system. In this study, therefore, we measured plasma immunoglobulin G (IgG) and IgA antibodies against indigestible gliadin-derived peptide antigens using an in-house enzyme-linked immunosorbent assay (ELISA) among 169 patients with schizophrenia and 236 control subjects. We also examined the plasma levels of IgG and IgA antibodies against the mixture of native gliadins using commercially available ELISA kits. The results showed that patients with schizophrenia had the increased levels of plasma IgG against the γ-gliadin-derived fragment, namely AAQ6C, but decreased levels of plasma IgG against the α- and γ3-gliadin-derived antigens, as compared with control subjects. This study also demonstrated a uniform decrease in plasma IgA antibodies against gliadin-derived antigens. There was no significant difference in the levels of plasma antibodies against native gliadins between the patient group and the control group. Of eight gliadin-derived antigens tested, four showed a sensitivity of >20% against the specificity of ⩾95% for detection of their corresponding antibodies in plasma. These four tests may thus have a potential to serve as biomarkers for the identification of schizophrenia subgroups that may need an alternative therapy or precision treatment. Further investigation with clinical trials should be carried out to explore this possibility.

A study of type-1 diabetes associated autoantibodies in schizophrenia

Epidemiological studies revealed an association between type-1 diabetes (T1D) and schizophrenia but the findings reported to date have been controversial. To clarify the inconsistency across studies, T1D-associated autoantibodies were examined in plasma samples collected from 272 patients with schizophrenia and 276 control subjects. An in-house enzyme-linked immunosorbent assay (ELISA) was developed using three linear peptide antigens, one of which was derived from glutamic acid decarboxylase (GAD) and two were derived from insulinoma-associated antigen 2 (IA2). Mann-Whitney U test showed a significant decrease in the levels of plasma IgG against the IA2b antigen in schizophrenia patients as compared to control subjects (Z=-3.54, p=0.0007), while no significant difference was found between these two groups either in anti-IA2a IgG levels (Z=-1.62, p=0.105) or in anti-GAD IgG levels (Z=-1.63, p=0.104). Linear regression analysis indicated no association of antipsychotic medication with the levels of plasma IgG against IA2a, IA2b or GAD, while the levels of plasma IgG for these 3 peptide antigens were significantly correlated with each other. Binary logistic regression showed that neither the DQ2.5 variant nor the DQ8 variant was associated with circulating levels of 3 T1D-associated autoantibodies in both the patient group and the control group. The coefficient of variation was 10.7% for anti-IA2a IgG assay, 10.1% for anti-IA2b IgG assay and 10.7% for anti-GAD IgG assay. The present work suggests that T1D-associated antibodies are unlikely to confer risk of schizophrenia and that the in-house ELISA developed with linear peptide antigens is highly reproducible.

FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses

FluKB is a knowledge-based system focusing on data and analytical tools for influenza vaccine discovery. The main goal of FluKB is to provide access to curated influenza sequence and epitope data and enhance the analysis of influenza sequence diversity and the analysis of targets of immune responses. FluKB consists of more than 400,000 influenza protein sequences, known epitope data (357 verified T-cell epitopes, 685 HLA binders, and 16 naturally processed MHC ligands), and a collection of 28 influenza antibodies and their structurally defined B-cell epitopes. FluKB was built using a modular framework allowing the implementation of analytical workflows and includes standard search tools, such as keyword search and sequence similarity queries, as well as advanced tools for the analysis of sequence variability. The advanced analytical tools for vaccine discovery include visual mapping of T- and B-cell vaccine targets and assessment of neutralizing antibody coverage. FluKB supports the discovery of vaccine targets and the analysis of viral diversity and its implications for vaccine discovery as well as potential T-cell breadth and antibody cross neutralization involving multiple strains. FluKB is representation of a new generation of databases that integrates data, analytical tools, and analytical workflows that enable comprehensive analysis and automatic generation of analysis reports.

Study of circulating IgG antibodies to peptide antigens derived from BIRC5 and MYC in cervical cancer

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Autoantibodies to BIRC5 and MYC were measured in cervical cancer patients and a control group.

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There was a significant difference in circulating levels of these autoantibodies in the patient and control groups.

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ROC analysis suggests that anti-BIRC5 IgG has a higher sensitivity than anti-MYC IgG.

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Anti-BIRC5 IgG could serve as a biomarker for the early diagnosis of cervical cancer.

The present study was undertaken to detect circulating IgG antibodies to peptide antigens derived from baculoviral IAP repeat-containing protein 5 isoform 2 (BIRC5) and myc proto-oncogene protein (MYC) in cervical cancer. A total of 107 female patients with cervical cancer of stages I and II, and 130 healthy female subjects were recruited for analysis of circulating IgG antibodies to BIRC5 and MYC. Student’s t-test showed significant differences in circulating levels of anti-BIRC5 IgG (t = −4.27, df = 235, P < 0.0001) and anti-MYC IgG (t = 3.51, df = 232, P = 0.0005) between the patient group and the control group. Receiver operating characteristic (ROC) analysis showed an area under the ROC curve (AUC) of 0.67 with sensitivity of 23.4% against specificity of 90% for the anti-BIRC5 IgG assay and an AUC of 0.66 with sensitivity of 9.4% against specificity of 90.6% for the anti-MYC IgG assay. Analysis of quality control samples gave an inter-assay deviation of 8.9% in the anti-BIRC5 IgG assay and 9.0% in the anti-MYC IgG assay. This work suggests that anti-BIRC5 IgG could serve as a biomarker for early diagnosis of cervical cancer although a panel of such tumor-associated antigens is needed to develop a highly sensitive test.

Computational peptide vaccinology

Immunoinformatics focuses on modeling immune responses for better understanding of the immune system and in many cases for proposing agents able to modify the immune system. The most classical of these agents are vaccines derived from living organisms such as smallpox or polio. More modern vaccines comprise recombinant proteins, protein domains, and in some cases peptides. Generating a vaccine from peptides however requires technologies and concepts very different from classical vaccinology. Immunoinformatics therefore provides the computational tools to propose peptides suitable for formulation into vaccines. This chapter introduces the essential biological concepts affecting design and efficacy of peptide vaccines and discusses current methods and workflows applied to design successful peptide vaccines using computers.

Big data analytics in immunology: a knowledge-based approach

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology

High-risk human papillomaviruses (HPVs) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis and characterization of these cancers, it is necessary to make full use of the immunological data on HPV available through publications, technical reports and databases. These data vary in granularity, quality and complexity. The extraction of knowledge from the vast amount of immunological data using data mining techniques remains a challenging task. To support integration of data and knowledge in virology and vaccinology, we developed a framework called KB-builder to streamline the development and deployment of web-accessible immunological knowledge systems. The framework consists of seven major functional modules, each facilitating a specific aspect of the knowledgebase construction process. Using KB-builder, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2781 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. The HPVdb also catalogs 191 verified T cell epitopes and 45 verified human leukocyte antigen (HLA) ligands. Primary amino acid sequences of HPV antigens were collected and annotated from the UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature and databases. The data were subject to extensive quality control (redundancy elimination, error detection and vocabulary consolidation). A set of computational tools for an in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, classification of HPV types based on cancer risk, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis and sequence variability analysis, has been integrated within the HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a knowledge-based system that integrates curated data and information with tailored analysis tools to facilitate data mining for HPV vaccinology and immunology. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of HPV antigens and peptides. Database URL: http://cvc.dfci.harvard.edu/hpv/.

Detection of circulating antibodies to linear peptide antigens derived from ANXA1 and DDX53 in lung cancer

The EarlyCDT®-Lung test was the first autoantibody-based diagnostic tool for lung cancer, which was developed with a panel of recombinant protein antigens. To confirm whether the antibody test developed with linear peptide antigens has a similar power to that developed with the whole protein molecules, the present work was then undertaken to develop an in-house enzyme-linked immunosorbent assay with linear peptide antigens derived from annexin A1 (ANXA1) and DEAD box protein 53 (DDX53), which have been used to develop the EarlyCDT®-Lung test. A total of 272 patients with non-small cell lung cancer (NSCLC) and 227 control subjects matched in age and smoking history were recruited. Student's t test showed that the levels of circulating IgG to ANXA1-derived peptide antigens were significantly higher in patients with NSCLC than control subjects (t = 5.66, P < 0.0001), in which the increased anti-ANXA1 IgG levels were observed only in patients at stages I, II, or III, but not in those at stage IV. However, the levels of circulating IgG to DDX53-derived peptide antigens were not significantly altered in NSCLC (t = 1.78, P = 0.076). Receiver operating characteristic analysis showed that the sensitivity against specificity of >90% was 23.7% for ANXA1 IgG assay and 13.8% for DDX53 IgG assay. This work suggests that the linear peptide antigen derived from ANXA1 may be suitable for the development of diagnostic tool for lung cancer although further screening is needed to identify more such peptide antigens derived from tumor-associated antigens.

Circulating autoantibody to FOXP3 may be a potential biomarker for esophageal squamous cell carcinoma

The present study was undertaken to develop a relatively quantitative enzyme-linked immunosorbent assay (ELISA) in-house using human leukocyte antigen class II-restricted epitopes in order to test circulating autoantibodies to human forkhead/winged helix transcription factor (FOXP3) as a biomarker for esophageal cancer. A total of 97 patients with esophageal squamous cell carcinoma (ESCC) and 227 healthy subjects were recruited for this study, and their plasma samples were collected for antibody analysis with the ELISA approach. Student's t test showed that the anti-FOXP3 IgG antibody levels were significantly higher in the patient group than the control group (t=6.23, P<0.0001). Based on a cutoff value determined by the mean+3SD of control IgG levels, the positive rate was 5.15 % in patients with ESCC as compared to 0.88 % in control subjects (X (2) =6.53, P=0.019, OR=5.85, 95 % CI 1.12-30.67), in which patients at stage I had the highest positivity (11.54 %, X (2) =12.15, P=0.0005, OR=13.10, 95 % CI 2.09-82.04). The sensitivity against >95 % specificity was 22.7 % for the IgG assay with an inter-assay deviation of 13.35 %. This work suggests that circulating IgG autoantibody to FOXP3 may be a potential biomarker for early diagnosis of esophageal cancer.

Antidotes, antibody-mediated immunity and the future of pharmaceutical product development

If new scientific knowledge is to be more efficiently generated and applied toward the advancement of health, human safety must be more effectively addressed in the conduct of research. Given the present difficulties of accurately predicting biological outcomes of novel interventions in vivo, the imperative of human safety suggests the development of novel pharmaceutical products in tandem with their prospective antidotes in anticipation of possible adverse events, to render the risks of initial clinical trials more acceptable from a regulatory standpoint. Antibody-mediated immunity provides a generally applicable mechanistic basis for developing antidotes to both biologicals and small-molecule drugs (such that antibodies may serve as antidotes to pharmaceutical agents as a class including other antibodies) and also for the control and prevention of both infectious and noninfectious diseases via passive or active immunization. Accordingly, the development of prophylactic or therapeutic passive-immunization strategies using antipeptide antibodies is a plausible prelude to the development of corresponding active-immunization strategies using peptide-based vaccines. In line with this scheme, global proliferation of antibody- and vaccine-production technologies, especially those that obviate dependence on the cold chain for storage and transport of finished products, could provide geographically distributed breakout capability against emerging and future health challenges.

A study of circulating anti-CD25 antibodies in non-small cell lung cancer

PURPOSE

Tumors can trigger specific immune response to tumor-associated antigens but the precise mechanism remains unclear. Since regulatory T-lymphocytes (Treg) play a crucial role in controlling autoimmune responses, the present work was undertaken to test whether dysfunction of Treg cells could be involved in developing autoimmunity in patients with lung cancer.

METHODS

In this study, we developed an in-house enzyme-linked immunosorbent assay to test circulating anti-CD25 autoantibodies among 272 patients with non-small cell lung cancer (NSCLC) and 226 control subjects matched in age, gender and smoking history.

RESULTS

Mann-Whitney U test showed that the anti-CD25 IgG level was significantly higher in patients with NSCLC than control subjects (Z = -7.48, P < 0.001) while the anti-CD25 IgA level was not significantly changed in the patient group as compared with the control group (Z = -1.34, P = 0.181). Spearman correlation analysis failed to reveal a significant correlation between the levels of anti-CD25 IgG and IgA either in patients with NSCLC (r = -0.034, P = 0.578) or in control subjects (r = 0.055, P = 0.429). ROC analysis showed an AUC of 0.70 for anti-CD25 IgG, in which NSCLC at stage III had the highest AUC (0.75). The sensitivity against a specificity of >90 % was 35.0 % for anti-CD25 IgG assay with an inter-assay deviation of 9.4 %, and 4.0 % for anti-CD25 IgA assay with an inter-assay deviation of 13.0 %.

CONCLUSIONS

Circulating anti-CD25 IgG antibody may be a useful biomarker for prognosis of lung cancer.

A study of circulating anti-CD25 antibodies in non-small cell lung cancer

PURPOSE

Tumors can trigger specific immune response to tumor-associated antigens but the precise mechanism remains unclear. Since regulatory T-lymphocytes (Treg) play a crucial role in controlling autoimmune responses, the present work was undertaken to test whether dysfunction of Treg cells could be involved in developing autoimmunity in patients with lung cancer.

METHODS

In this study, we developed an in-house enzyme-linked immunosorbent assay to test circulating anti-CD25 autoantibodies among 272 patients with non-small cell lung cancer (NSCLC) and 226 control subjects matched in age, gender and smoking history.

RESULTS

Mann-Whitney U test showed that the anti-CD25 IgG level was significantly higher in patients with NSCLC than control subjects (Z = -7.48, P < 0.001) while the anti-CD25 IgA level was not significantly changed in the patient group as compared with the control group (Z = -1.34, P = 0.181). Spearman correlation analysis failed to reveal a significant correlation between the levels of anti-CD25 IgG and IgA either in patients with NSCLC (r = -0.034, P = 0.578) or in control subjects (r = 0.055, P = 0.429). ROC analysis showed an AUC of 0.70 for anti-CD25 IgG, in which NSCLC at stage III had the highest AUC (0.75). The sensitivity against a specificity of >90 % was 35.0 % for anti-CD25 IgG assay with an inter-assay deviation of 9.4 %, and 4.0 % for anti-CD25 IgA assay with an inter-assay deviation of 13.0 %.

CONCLUSIONS

Circulating anti-CD25 IgG antibody may be a useful biomarker for prognosis of lung cancer.

On the meaning of affinity limits in B-cell epitope prediction for antipeptide antibody-mediated immunity

B-cell epitope prediction aims to aid the design of peptide-based immunogens (e.g., vaccines) for eliciting antipeptide antibodies that protect against disease, but such antibodies fail to confer protection and even promote disease if they bind with low affinity. Hence, the Immune Epitope Database (IEDB) was searched to obtain published thermodynamic and kinetic data on binding interactions of antipeptide antibodies. The data suggest that the affinity of the antibodies for their immunizing peptides appears to be limited in a manner consistent with previously proposed kinetic constraints on affinity maturation in vivo and that cross-reaction of the antibodies with proteins tends to occur with lower affinity than the corresponding reaction of the antibodies with their immunizing peptides. These observations better inform B-cell epitope prediction to avoid overestimating the affinity for both active and passive immunization; whereas active immunization is subject to limitations of affinity maturation in vivo and of the capacity to accumulate endogenous antibodies, passive immunization may transcend such limitations, possibly with the aid of artificial affinity-selection processes and of protein engineering. Additionally, protein disorder warrants further investigation as a possible supplementary criterion for B-cell epitope prediction, where such disorder obviates thermodynamically unfavorable protein structural adjustments in cross-reactions between antipeptide antibodies and proteins.

PAAQD: Predicting immunogenicity of MHC class I binding peptides using amino acid pairwise contact potentials and quantum topological molecular similarity descriptors

Prediction of peptide immunogenicity is a promising approach for novel vaccine discovery. Conventionally, epitope prediction methods have been developed to accelerate the process of vaccine production by searching for candidate peptides from pathogenic proteins. However, recent studies revealed that peptides with high binding affinity to major histocompatibility complex molecules (MHCs) do not always result in high immunogenicity. Therefore, it is promising to predict the peptide immunogenicity rather than epitopes in order to discover new vaccines more effectively. To this end, we developed a novel T-cell reactivity predictor which we call PAAQD. Nonapeptides were encoded numerically, using combining information of amino acid pairwise contact potentials (AAPPs) and quantum topological molecular similarity (QTMS) descriptors. Encoded data were used in the construction of our classification model. Our numerical experiments suggested that the predictive performance of PAAQD is at least comparable with POPISK, one of the pioneering techniques for T-cell reactivity prediction. Also, our experiment suggested that the first and eighth positions of nonapeptides are the most important for immunogenicity and most of the anchor residues in epitope prediction were not important in T-cell reactivity prediction. The R implementation of PAAQD is available at http://pirun.ku.ac.th/~fsciiok/PAAQD.rar.

Circulating autoantibody to ABCC3 may be a potential biomarker for esophageal squamous cell carcinoma

PURPOSE

There is an urgent need to identify biomarkers for early diagnosis and prognosis of esophageal cancer. The present study was undertaken to test whether circulating autoantibodies to ATP-binding cassette C3 (ABCC3) transporter could serve as a biomarker for the malignant tumor.

METHODS

An enzyme-linked immunosorbent assay approach was developed in-house to test circulating autoantibodies to ABCC3 in 114 patients with esophageal squamous cell carcinoma (ESCC) and 226 healthy subjects well matched in age and smoking history.

RESULTS

Mann-Whitney U test showed that the IgA antibody levels were significantly higher in patients with ESCC than control subjects (Z = -4.226, P < 0.001) while the IgG antibody levels were not significantly different between the two groups (Z = -1.072, P = 0.284). The sensitivity against >95 % specificity was 13.2 % for the IgA assay with an inter-assay deviation of 13.0 and 7.9 % for the IgG assay with an inter-assay deviation of 9.4 %.

CONCLUSIONS

This work suggests that circulating IgA autoantibody to ABCC3 may be a potential biomarker for ESCC, which could be used for early diagnosis and prognosis of the malignant tumor.

Are circulating autoantibodies to ABCC3 transporter a potential biomarker for lung cancer?

PURPOSE

The present study was undertaken to test circulating autoantibody to ATP-binding cassette C3 (ABCC3) transporter in order to confirm whether anti-ABCC3 antibody could serve as a biomarker for early diagnosis of lung cancer.

METHODS

This study recruited 275 patients (178 males and 97 females) with non-small cell lung cancer (either squamous carcinoma or adenocarcinoma) and 226 control subjects (134 males and 92 females) well matched in age and smoking history. Anti-ABCC3 IgA and IgG were determined using an enzyme-linked immunosorbent assay (ELISA) approach that was developed in house with the human leukocyte antigen class II (HLA-II) restricted antigens.

RESULTS

Mann-Whitney U test showed that the IgG antibody level was significantly higher in female patients with adenocarcinoma than female controls (Z = -4.34, P < 0.001) and that the IgA antibody level was significantly higher in male patients with squamous carcinoma than male controls (Z = -3.12, P = 0.002). Pearson's Chi-square (χ(2)) test showed that female patients with adenocarcinoma had a significantly higher positive rate for IgG autoantibody than female controls (χ ( 2 ) = 8.73, P = 0.003). The ELISA sensitivity against a specificity of >95 % was 18.1 % for IgG assay in female patients and 18.0 % for IgA assay in male patients. The inter-assay deviation was 10.6 % for IgG assay and 14.5 % for IgA assay.

CONCLUSIONS

Circulating autoantibodies to ABCC3 transporter may be a potential biomarker that can be added to a panel of existing biomarkers for early diagnosis and prognosis of lung cancer although the gender differences should be taken into account.

Predictions versus high-throughput experiments in T-cell epitope discovery: competition or synergy?

Prediction methods as well as experimental methods for T-cell epitope discovery have developed significantly in recent years. High-throughput experimental methods have made it possible to perform full-length protein scans for epitopes restricted to a limited number of MHC alleles. The high costs and limitations regarding the number of proteins and MHC alleles that are feasibly handled by such experimental methods have made in silico prediction models of high interest. MHC binding prediction methods are today of a very high quality and can predict MHC binding peptides with high accuracy. This is possible for a large range of MHC alleles and relevant length of binding peptides. The predictions can easily be performed for complete proteomes of any size. Prediction methods are still, however, dependent on good experimental methods for validation, and should merely be used as a guide for rational epitope discovery. We expect prediction methods as well as experimental validation methods to continue to develop and that we will soon see clinical trials of products whose development has been guided by prediction methods.

Emerging vaccine informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

FLAVIdB: A data mining system for knowledge discovery in flaviviruses with direct applications in immunology and vaccinology

BACKGROUND

The flavivirus genus is unusually large, comprising more than 70 species, of which more than half are known human pathogens. It includes a set of clinically relevant infectious agents such as dengue, West Nile, yellow fever, and Japanese encephalitis viruses. Although these pathogens have been studied extensively, safe and efficient vaccines lack for the majority of the flaviviruses.

RESULTS

We have assembled a database that combines antigenic data of flaviviruses, specialized analysis tools, and workflows for automated complex analyses focusing on applications in immunology and vaccinology. FLAVIdB contains 12,858 entries of flavivirus antigen sequences, 184 verified T-cell epitopes, 201 verified B-cell epitopes, and 4 representative molecular structures of the dengue virus envelope protein. FLAVIdB was assembled by collection, annotation, and integration of data from GenBank, GenPept, UniProt, IEDB, and PDB. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). Further annotation of selected functionally relevant features was performed by organizing information extracted from the literature. The database was incorporated into a web-accessible data mining system, combining specialized data analysis tools for integrated analysis of relevant data categories (protein sequences, macromolecular structures, and immune epitopes). The data mining system includes tools for variability and conservation analysis, T-cell epitope prediction, and characterization of neutralizing components of B-cell epitopes. FLAVIdB is accessible at cvc.dfci.harvard.edu/flavi/

CONCLUSION

FLAVIdB represents a new generation of databases in which data and tools are integrated into a data mining infrastructures specifically designed to aid rational vaccine design by discovery of vaccine targets.