Molecular alterations in human milk in simulated maternal nasal mucosal infection with live attenuated influenza vaccination.

Breastfeeding protects against mucosal infections in infants. The underlying mechanisms through which immunity develops in human milk following maternal infection with mucosal pathogens are not well understood. We simulated nasal mucosal influenza infection through live attenuated influenza vaccination (LAIV) and compared immune responses in milk to inactivated influenza vaccination (IIV). Transcriptomic analysis was performed on RNA extracted from human milk cells to evaluate differentially expressed genes and pathways on days 1 and 7 post-vaccination. Both LAIV and IIV vaccines induced influenza-specific IgA that persisted for at least 6 months. Regulation of type I interferon production, toll-like receptor, and pattern recognition receptor signaling pathways were highly upregulated in milk on day 1 following LAIV but not IIV at any time point. Upregulation of innate immunity in human milk may provide timely protection against mucosal infections until antigen-specific immunity develops in the human milk-fed infant.

Network vaccinology.

The structure and function of the immune system is governed by complex networks of interactions between cells and molecular components. Vaccination perturbs these networks, triggering specific pathways to induce cellular and humoral immunity. Systems vaccinology studies have generated vast data sets describing the genes related to vaccination, motivating the use of new approaches to identify patterns within the data. Here, we describe a framework called Network Vaccinology to explore the structure and function of biological networks responsible for vaccine-induced immunity. We demonstrate how the principles of graph theory can be used to identify modules of genes, proteins, and metabolites that are associated with innate and adaptive immune responses. Network vaccinology can be used to assess specific and shared molecular mechanisms of different types of vaccines, adjuvants, and routes of administration to direct rational design of the next generation of vaccines.

The Protein-Protein Interaction Network Reveals a Novel Role of the Signal Transduction Protein PII in the Control of c-di-GMP Homeostasis in Azospirillum brasilense.

The PII family comprises a group of widely distributed signal transduction proteins ubiquitous in prokaryotes and in the chloroplasts of plants. PII proteins sense the levels of key metabolites ATP, ADP, and 2-oxoglutarate, which affect the PII protein structure and thereby the ability of PII to interact with a range of target proteins. Here, we performed multiple ligand fishing assays with the PII protein orthologue GlnZ from the plant growth-promoting nitrogen-fixing bacterium Azospirillum brasilense to identify 37 proteins that are likely to be part of the PII protein-protein interaction network. Among the PII targets identified were enzymes related to nitrogen and fatty acid metabolism, signaling, coenzyme synthesis, RNA catabolism, and transcription. Direct binary PII-target complex was confirmed for 15 protein complexes using pulldown assays with recombinant proteins. Untargeted metabolome analysis showed that PII is required for proper homeostasis of important metabolites. Two enzymes involved in c-di-GMP metabolism were among the identified PII targets. A PII-deficient strain showed reduced c-di-GMP levels and altered aerotaxis and flocculation behavior. These data support that PII acts as a major metabolic hub controlling important enzymes and the homeostasis of key metabolites such as c-di-GMP in response to the prevailing nutritional status.IMPORTANCE The PII proteins sense and integrate important metabolic signals which reflect the cellular nutrition and energy status. Such extraordinary ability was capitalized by nature in such a way that the various PII proteins regulate different facets of metabolism by controlling the activity of a range of target proteins by protein-protein interactions. Here, we determined the PII protein interaction network in the plant growth-promoting nitrogen-fixing bacterium Azospirillum brasilense The interactome data along with metabolome analysis suggest that PII functions as a master metabolic regulator hub. We provide evidence that PII proteins act to regulate c-di-GMP levels in vivo and cell motility and adherence behaviors.

Methods for predicting vaccine immunogenicity and reactogenicity.

Subjects receiving the same vaccine often show different levels of immune responses and some may even present adverse side effects to the vaccine. Systems vaccinology can combine omics data and machine learning techniques to obtain highly predictive signatures of vaccine immunogenicity and reactogenicity. Currently, several machine learning methods are already available to researchers with no background in bioinformatics. Here we described the four main steps to discover markers of vaccine immunogenicity and reactogenicity: (1) Preparing the data; (2) Selecting the vaccinees and relevant genes; (3) Choosing the algorithm; (4) Blind testing your model. With the increasing number of Systems Vaccinology datasets being generated, we expect that the accuracy and robustness of signatures of vaccine reactogenicity and immunogenicity will significantly improve.

Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool.

Transcriptome analyses have increased our understanding of the molecular mechanisms underlying human diseases. Most approaches aim to identify significant genes by comparing their expression values between healthy subjects and a group of patients with a certain disease. Given that studies normally contain few samples, the heterogeneity among individuals caused by environmental factors or undetected illnesses can impact gene expression analyses. We present a systematic analysis of sample heterogeneity in a variety of gene expression studies relating to inflammatory and infectious diseases and show that novel immunological insights may arise once heterogeneity is addressed. The perturbation score of samples is quantified using nonperturbed subjects (i.e., healthy subjects) as a reference group. Such a score allows us to detect outlying samples and subgroups of diseased patients and even assess the molecular perturbation of single cells infected with viruses. We also show how removal of outlying samples can improve the "signal" of the disease and impact detection of differentially expressed genes. The method is made available via the mdp Bioconductor R package and as a user-friendly webtool, webMDP, available at http://mdp.sysbio.tools.