Special Issue "Vaccine Informatics"

A special issue of Microorganisms (ISSN 2076-2607).

Deadline for manuscript submissions: closed (31 December 2018)

Special Issue Editor

Guest Editor
Dr. Yongqun "Oliver" He

Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
Website | E-Mail
Interests: bioinformatics; systems biology; host-Brucella interaction; vaccine research

Special Issue Information

Dear Colleagues,

Vaccination is one of the most effective medical interventions ever introduced in modern medicine, and has prevented millions of cases of infectious diseases worldwide, every year. However, there are still no effective and safe vaccines against many infectious diseases (e.g., tuberculosis, HIV, malaria, and emerging antibiotics resistant diseases), in humans or animals. With newly-emerging technologies (e.g., next-generation sequencing and Omics), we have faced new opportunities and challenges of developing and using various informatics methods to standardize, integrate, and analyze ever-increasing amounts of vaccine-related data to support better vaccine research and development. This "Vaccine Informatics" Special Issue aims to collect state-of-the-art studies on various topics of vaccine informatics. The studies can cover different stages of vaccinology, including basic research, vaccine development and evaluation, clinical trial, and postlicensure vaccine safety. Diverse informatics topics, such as vaccine design, host-vaccine interaction, and vaccine adverse events, can be studied. Various bioinformatic, statistical, and mathematical approaches can be investigated and utilized. Related informatics technologies, such as reverse vaccinology, immunoinformatics, Omics data analysis, literature mining, and data standardization method, can be utilized. We expect that this Special Issue will become an influential platform and provide the most recent updates on the area of emerging vaccine informatics.

Dr. Yongqun "Oliver" He
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microorganisms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Reverse vaccinology
  • Vaccine antigen prediction
  • Immunoinformatics and vaccine epitope prediction
  • Structure informatics
  • Host-pathogen interaction
  • Omics data analysis
  • Vaccine database
  • Vaccine data standardization and integration
  • Vaccine adverse event and safety
  • Vaccine immunization registry
  • Literature mining
  • Vaccine-related ontology development and application

Published Papers (2 papers)

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Research

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Open AccessArticle Multi-Level Model to Predict Antibody Response to Influenza Vaccine Using Gene Expression Interaction Network Feature Selection
Microorganisms 2019, 7(3), 79; https://doi.org/10.3390/microorganisms7030079
Received: 29 December 2018 / Revised: 24 February 2019 / Accepted: 8 March 2019 / Published: 14 March 2019
PDF Full-text (2825 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Vaccination is an effective prevention of influenza infection. However, certain individuals develop a lower antibody response after vaccination, which may lead to susceptibility to subsequent infection. An important challenge in human health is to find baseline gene signatures to help identify individuals who [...] Read more.
Vaccination is an effective prevention of influenza infection. However, certain individuals develop a lower antibody response after vaccination, which may lead to susceptibility to subsequent infection. An important challenge in human health is to find baseline gene signatures to help identify individuals who are at higher risk for infection despite influenza vaccination. We developed a multi-level machine learning strategy to build a predictive model of vaccine response using pre−vaccination antibody titers and network interactions between pre−vaccination gene expression levels. The first-level baseline−antibody model explains a significant amount of variation in post-vaccination response, especially for subjects with large pre−existing antibody titers. In the second level, we clustered individuals based on pre−vaccination antibody titers to focus gene−based modeling on individuals with lower baseline HAI where additional response variation may be predicted by baseline gene expression levels. In the third level, we used a gene−association interaction network (GAIN) feature selection algorithm to find the best pairs of genes that interact to influence antibody response within each baseline titer cluster. We used ratios of the top interacting genes as predictors to stabilize machine learning model generalizability. We trained and tested the multi-level approach on data with young and older individuals immunized against influenza vaccine in multiple cohorts. Our results indicate that the GAIN feature selection approach improves model generalizability and identifies genes enriched for immunologically relevant pathways, including B Cell Receptor signaling and antigen processing. Using a multi-level approach, starting with a baseline HAI model and stratifying on baseline HAI, allows for more targeted gene−based modeling. We provide an interactive tool that may be extended to other vaccine studies. Full article
(This article belongs to the Special Issue Vaccine Informatics)
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Review

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Open AccessReview Intracellular Bacterial Infections: A Challenge for Developing Cellular Mediated Immunity Vaccines for Farmed Fish
Microorganisms 2018, 6(2), 33; https://doi.org/10.3390/microorganisms6020033
Received: 13 March 2018 / Revised: 15 April 2018 / Accepted: 20 April 2018 / Published: 22 April 2018
Cited by 1 | PDF Full-text (407 KB) | HTML Full-text | XML Full-text
Abstract
Aquaculture is one of the most rapidly expanding farming systems in the world. Its rapid expansion has brought with it several pathogens infecting different fish species. As a result, there has been a corresponding expansion in vaccine development to cope with the increasing [...] Read more.
Aquaculture is one of the most rapidly expanding farming systems in the world. Its rapid expansion has brought with it several pathogens infecting different fish species. As a result, there has been a corresponding expansion in vaccine development to cope with the increasing number of infectious diseases in aquaculture. The success of vaccine development for bacterial diseases in aquaculture is largely attributed to empirical vaccine designs based on inactivation of whole cell (WCI) bacteria vaccines. However, an upcoming challenge in vaccine design is the increase of intracellular bacterial pathogens that are not responsive to WCI vaccines. Intracellular bacterial vaccines evoke cellular mediated immune (CMI) responses that “kill” and eliminate infected cells, unlike WCI vaccines that induce humoral immune responses whose protective mechanism is neutralization of extracellular replicating pathogens by antibodies. In this synopsis, I provide an overview of the intracellular bacterial pathogens infecting different fish species in aquaculture, outlining their mechanisms of invasion, replication, and survival intracellularly based on existing data. I also bring into perspective the current state of CMI understanding in fish together with its potential application in vaccine development. Further, I highlight the immunological pitfalls that have derailed our ability to produce protective vaccines against intracellular pathogens for finfish. Overall, the synopsis put forth herein advocates for a shift in vaccine design to include CMI-based vaccines against intracellular pathogens currently adversely affecting the aquaculture industry. Full article
(This article belongs to the Special Issue Vaccine Informatics)
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