Comparative studies of different adjuvants are sparse, and the mechanism of action is usually poorly comprehended (48)

Comparative studies of different adjuvants are sparse, and the mechanism of action is usually poorly comprehended (48). most proteins in the cell) and (which encodes the receptor for B-cell growth factor BLyS-BAFF and plays a role in the differentiation of plasma cells) (7). The authors were able to predict the immunogenicity of YF-17D with innate immune signatures. Thereby, the study laid the groundwork for using a systems biology approach to predict the magnitude of the adaptive immune response to vaccine early on. Trivalent Inactivated (TIV) and Live Attenuated (LAIV) Influenza Vaccine Nakaya et al. in 2011 extended a systems biology approach to investigate the innate and adaptive immune responses to the TIV and live attenuated influenza vaccines in humans. Their objective was to determine MBM-17 whether comparable signatures, which were predictive of the adaptive immune response in YF-17D were present with TIV and LAIV. They found that LAIV induced a strong type I IFN antiviral transcriptomic signatures. TIV also induced the expression of genes encoding type I IFNs as well as pro-inflammatory mediators and genes involved in the innate sensing of viruses 1C3 days after vaccination and then genes such as and others known to be involved in the differentiation of plasmablasts; these correlated well with the magnitude of hemagglutinin titers 28 days after immunization. Another gene, calmodulin-dependent protein kinase IV (was shown to have an expression profile inversely proportional to later antibody titers. LAIV did not induce as strong of an antibody response as TIV. Ultimately, the clinical effectiveness of these two vaccines is known to be similar despite the difference in antibody response. The authors suggested the comparable clinical effectiveness may be related to the hypothesized mechanism by which LAIV primes immune cells in the nasal mucosa, which then circulate in the blood to activate other immune cells (8). Delivery method may play an important role in vaccine efficacy. The Human Immunology Project Consortium (HIPC) and the Center for MBM-17 Human Immunology were able to identify transcriptional signatures predictive of response to influenza vaccination. They showed the presence of inflammatory gene signatures was associated with more robust antibody responses in more youthful individuals, but worse antibody responses in older individuals (9). Ultimately, these studies confirmed that predicting vaccine responses through a systems biology approach was possible in the context of influenza and that baseline immunological status is usually a potential mechanism by which to understand poor vaccination outcomes in older individuals. Meningococcal Quadrivalent Polysaccharide Vaccine (MPSV4) and Meningococcal Quadrivalent CLC Conjugate Vaccine (MCV4) Another study MBM-17 by Li et al. in 2014, utilized a systems vaccinology approach to investigate the immune response to meningococcal polysaccharide (MPSV4) and meningococcal conjugate vaccine (MCV4) as it compares with that of YF-17D, TIV, and LAIV. Both MPSV4 and MCV4 are capable of inducing high antibody titers post-vaccination, but MPSV4 is usually thought to induce T-cell impartial antibody responses, resulting in waning humoral immunity and memory. The authors analyzed data by merging 32,000 peripheral blood mononuclear cell (PBMC) gene expression profiles from 540 published studies and were able to identify 334 different blood transcriptome modules (BTMs) from existing transcriptomic data in public repositories. The study revealed three unique transcriptomic programs, which could potentially be used to predict vaccine efficacy. One transcriptomic program was a protein recall response that correlated with the antibody response to TIV and a portion of MCV4. Another MBM-17 transcriptomic program was a main viral response elicited by YF-17D. The final transcriptomic program was an anti-polysaccharide signature induced by the polysaccharide portions of MCV4 and MPSV4 (10). Hepatitis B Computer virus (HBV) Vaccine In 2016, Fourati et?al. recognized transcriptomic patterns associated with aging and correlated these transcriptomic modules with biological pathways after HBV vaccination. An aggregate score depicting age-related transcriptomic changes (BioAge signature), a surrogate for B-cell activation, was shown to predict the response to the HBV vaccine with a 60% accuracy. Higher levels of baseline memory B cells and CD4+ T cells were associated with a sufficient immune response to vaccination. Additionally, 15 gene expression patterns related to inflammation and interferon signaling pathways are significantly different between vaccine responders and non-responders (11). Such immunologic patterns may be used in addition.