Visualizing microbiome-immune system interplay

Exploring the complex relationship between the lung microbiome and ventilator-associated pneumonia

INTRODUCTION

Understanding the presence and function of a diverse lung microbiome in acute lung infections, particularly ventilator-associated pneumonia (VAP), is still limited, evidencing significant gaps in our knowledge.

AREAS COVERED

In this comprehensive narrative review, we aim to elucidate the contribution of the respiratory microbiome in the development of VAP by examining the current knowledge on the interactions among microorganisms. By exploring these intricate connections, we endeavor to enhance our understanding of the disease's pathophysiology and pave the way for novel ideas and interventions in studying the respiratory tract microbiome.

EXPERT OPINION

The conventional perception of lungs as sterile is deprecated since it is currently recognized the existence of a diverse microbial community within them. However, despite extensive research on the role of the respiratory microbiome in healthy lungs, respiratory chronic diseases and acute lung infections such as pneumonia are not fully understood. It is crucial to investigate further the relationship between the pathophysiology of VAP and the pulmonary microbiome, elucidating the mechanisms underlying the interactions between the microbiome, host immune response and mechanical ventilation for the development of VAP.

Microbiome analysis, the immune response and transplantation in the era of next generation sequencing

The human gastrointestinal tract, skin and mucosal surfaces are inhabited by a complex system of bacteria, viruses, fungi, archaea, protists, and eukaryotic parasites with predominance of bacteria and bacterial viruses (bacteriophages). Collectively these microbes form the microbiota of the microecosystem of humans. Recent advancement in technologies for nucleic acid isolation from various environmental samples, feces and body secretions and advancements in shotgun throughput massive parallel DNA and RNA sequencing along with 16S ribosomal gene sequencing have unraveled the identity of otherwise unknown microbial entities constituting the human microecosystem. The improved transcriptome analysis, technological developments in biochemical analytical methods and availability of complex bioinformatics tools have allowed us to begin to understand the metabolome of the microbiome and the biochemical pathways and potential signal transduction pathways in human cells in response to microbial infections and their products. Also, developments in human whole genome sequencing, targeted gene sequencing of histocompatibility genes and other immune response associated genes by Next Generation Sequencing (NGS) have allowed us to have a better conceptualization of immune responses, and alloimmune responses. These modern technologies have enabled us to dive into the intricate relationship between commensal symbiotic and pathogenic microbiome and immune system. For the most part, the commensal symbiotic microbiota helps to maintain normal immune homeostasis besides providing healthy nutrients, facilitating digestion, and protecting the skin, mucosal and intestinal barriers. However, changes in diets, administration of therapeutic agents like antibiotics, chemotherapeutic agents, immunosuppressants etc. along with certain host factors including human histocompatibility antigens may alter the microbial ecosystem balance by causing changes in microbial constituents, hierarchy of microbial species and even dysbiosis. Such alterations may cause immune dysregulation, breach of barrier protection and lead to immunopathogenesis rather than immune homeostasis. The effects of human microbiome on immunity, health and disease are currently under intense research with cutting edge technologies in molecular biology, biochemistry, and bioinformatics along with tremendous ability to characterize immune response at single cell level. This review will discuss the contemporary status on human microbiome immune system interactions and their potential effects on health, immune homeostasis and allograft transplantation.

CANTARE: finding and visualizing network-based multi-omic predictive models

BACKGROUND

One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be "ome aware." Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs.

METHODS

We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting "top table" of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements).

RESULTS

We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10-5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User's Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/ .

CONCLUSION

CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.