Human systems immunology: hypothesis-based modeling and unbiased data-driven approaches

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases

Background

COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures.

Aim

To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis.

Method

We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis.

Results

Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state.

Conclusion

This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

Mechanistic model for booster doses effectiveness in healthy, cancer, and immunosuppressed patients infected with SARS-CoV-2

SARS-CoV-2 vaccines are effective at limiting disease severity, but effectiveness is lower among patients with cancer or immunosuppression. Effectiveness wanes with time and varies by vaccine type. Moreover, previously prescribed vaccines were based on the ancestral SARS-CoV-2 spike-protein that emerging variants may evade. Here, we describe a mechanistic mathematical model for vaccination-induced immunity. We validate it with available clinical data and use it to simulate the effectiveness of vaccines against viral variants with lower antigenicity, increased virulence, or enhanced cell binding for various vaccine platforms. The analysis includes the omicron variant as well as hypothetical future variants with even greater immune evasion of vaccine-induced antibodies and addresses the potential benefits of the new bivalent vaccines. We further account for concurrent cancer or underlying immunosuppression. The model confirms enhanced immunogenicity following booster vaccination in immunosuppressed patients but predicts ongoing booster requirements for these individuals to maintain protection. We further studied the impact of variants on immunosuppressed individuals as a function of the interval between multiple booster doses. Our model suggests possible strategies for future vaccinations and suggests tailored strategies for high-risk groups.

Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis

Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This article proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. We demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to estimate the causal effect of medical countermeasures for severely ill patients.

Single cell immune profiling of dengue virus patients reveals intact immune responses to Zika virus with enrichment of innate immune signatures

The genus Flavivirus contains many mosquito-borne human pathogens of global epidemiological importance such as dengue virus, West Nile virus, and Zika virus, which has recently emerged at epidemic levels. Infections with these viruses result in divergent clinical outcomes ranging from asymptomatic to fatal. Myriad factors influence infection severity including exposure, immune status and pathogen/host genetics. Furthermore, pre-existing infection may skew immune pathways or divert immune resources. We profiled immune cells from dengue virus-infected individuals by multiparameter mass cytometry (CyTOF) to define functional status. Elevations in IFNβ were noted in acute patients across the majority of cell types and were statistically elevated in 31 of 36 cell subsets. We quantified response to in vitro (re)infection with dengue or Zika viruses and detected a striking pattern of upregulation of responses to Zika infection by innate cell types which was not noted in response to dengue virus. Significance was discovered by statistical analysis as well as a neural network-based clustering approach which identified unusual cell subsets overlooked by conventional manual gating. Of public health importance, patient cells showed significant enrichment of innate cell responses to Zika virus indicating an intact and robust anti-Zika response despite the concurrent dengue infection.

Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.

Simulation modelling for immunologists

The immune system is inordinately complex with many interacting components determining overall outcomes. Mathematical and computational modelling provides a useful way in which the various contributions of different immunological components can be probed in an integrated manner. Here, we provide an introductory overview and review of mechanistic simulation models. We start out by briefly defining these types of models and contrasting them to other model types that are relevant to the field of immunology. We follow with a few specific examples and then review the different ways one can use such models to answer immunological questions. While our examples focus on immune responses to infection, the overall ideas and descriptions of model uses can be applied to any area of immunology.

Development of data-driven models for the flow cytometric crossmatch

HLA laboratories use virtual crossmatching (VXM) to predict recipient and donor compatibility using HLA antibody data and donor HLA type. Increasingly, transplant centers are utilizing VXM as the final compatibility determination prior to transplant. However, the VXM interpretation is based on HLA experience of individual transplant centers. This study developed data-driven algorithms that predicted flow cytometric crossmatch (FCXM) outcomes using HLA antibody mean fluorescent intensity (MFI) data and donor HLA typing without the need for human interpretation.Two algorithms were evaluated; an MFI Optimal-Threshold model and a Least-Squares-Fitting model. The Optimal-Threshold model correctly determined between 81.5% and 85.5% of T or B-cell responses. A class I antibody MFI threshold of 4670 was optimal for predicting T-cell response while an antibody MFI threshold of 6180 was optimal for predicting B-cell responses. HLA class I antibodies had a 1.47-fold greater influence on FCXM outcomes than class II antibodies. HLA-B antibodies influenced T and B-cell responses more than HLA-A or -C (-B > -A > -C). The Least-Squares-Fitting model increased accuracy to 94.1% and 88.8% for T and B-cell responses, respectively. The algorithms described here provide enhanced FCXM prediction and novel insights into the influence of specific HLA antibodies on the crossmatch outcome.

Distributed Adaptive Search in T Cells: Lessons From Ants

There are striking similarities between the strategies ant colonies use to forage for food and immune systems use to search for pathogens. Searchers (ants and cells) use the appropriate combination of random and directed motion, direct and indirect agent-agent interactions, and traversal of physical structures to solve search problems in a variety of environments. An effective immune response requires immune cells to search efficiently and effectively for diverse types of pathogens in different tissues and organs, just as different species of ants have evolved diverse search strategies to forage effectively for a variety of resources in a variety of habitats. Successful T cell search is required to initiate the adaptive immune response in lymph nodes and to eradicate pathogens at sites of infection in peripheral tissue. Ant search strategies suggest novel predictions about T cell search. In both systems, the distribution of targets in time and space determines the most effective search strategy. We hypothesize that the ability of searchers to sense and adapt to dynamic targets and environmental conditions enhances search effectiveness through adjustments to movement and communication patterns. We also suggest that random motion is a more important component of search strategies than is generally recognized. The behavior we observe in ants reveals general design principles and constraints that govern distributed adaptive search in a wide variety of complex systems, particularly the immune system.

Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus that causes global epidemics of debilitating disease worldwide. To gain functional insight into the host cellular genes required for virus infection, we performed whole-blood RNA-seq, 37-plex mass cytometry of peripheral blood mononuclear cells (PBMCs), and serum cytokine measurements of acute- and convalescent-phase samples obtained from 42 children naturally infected with CHIKV Semi-supervised classification and clustering of single-cell events into 57 sub-communities of canonical leukocyte phenotypes revealed a monocyte-driven response to acute infection, with the greatest expansions in "intermediate" CD14++CD16+ monocytes and an activated subpopulation of CD14+ monocytes. Increases in acute-phase CHIKV envelope protein E2 expression were highest for monocytes and dendritic cells. Serum cytokine measurements confirmed significant acute-phase upregulation of monocyte chemoattractants. Distinct transcriptomic signatures were associated with infection timepoint, as well as convalescent-phase anti-CHIKV antibody titer, acute-phase viremia, and symptom severity. We present a multiscale network that summarizes all observed modulations across cellular and transcriptomic levels and their interactions with clinical outcomes, providing a uniquely global view of the biomolecular landscape of human CHIKV infection.

Systemic Inflammation-Associated Proteins and Retinopathy of Prematurity in Infants Born Before the 28th Week of Gestation

Purpose

To assess the association between systemic levels of inflammation-associated proteins and severe retinopathy of prematurity (ROP) in extremely preterm infants.

Methods

We collected whole blood on filter paper on postnatal days 1, 7, 14, 21, and 28 from 1205 infants born before the 28th week of gestation, and measured the concentrations of 27 inflammation-associated, angiogenic, and neurotrophic proteins. We calculated odds ratios with 95% confidence intervals for the association between top quartile concentrations of each protein and prethreshold ROP.

Results

During the first three weeks after birth, high concentrations of VEGF-R1, myeloperoxidase (MPO), IL-8, intercellular adhesion molecule (ICAM)-1, matrix metalloproteinase 9, erythropoietin, TNF-α, and basic fibroblast growth factor were associated with an increased risk for prethreshold ROP. On day 28, high levels of serum amyloid A, MPO, IL-6, TNF-α, TNF-R1/-R2, IL-8, and ICAM-1 were associated with an increased risk. Top quartile concentrations of the proinflammatory cytokines TNF-α and IL-6 were associated with increased risks of ROP when levels of neuroprotective proteins and growth factors, including BDNF, insulin-like growth factor 1, IGFBP-1, VEGFR-1 and -2, ANG-1 and PlGF, were not in the top quartile. In contrast, high concentrations of NT-4 and BDNF appeared protective only in infants without elevated inflammatory mediators.

Conclusions

Systemic inflammation during the first postnatal month was associated with an increased risk of prethreshold ROP. Elevated concentrations of growth factors, angiogenic proteins, and neurotrophins appeared to modulate this risk, and were capable of reducing the risk even in the absence of systemic inflammation.

Fighting Cancer with Mathematics and Viruses

After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex antitumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these research fields to increase the value of the preclinical development and the therapeutic efficacy of the resulting treatments.

Solving Immunology?

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

Embracing Complexity beyond Systems Medicine: A New Approach to Chronic Immune Disorders

In order to combat chronic immune disorders (CIDs), it is an absolute necessity to understand the bigger picture, one that goes beyond insights at a one-disease, molecular, cellular, and static level. To unravel this bigger picture we advocate an integral, cross-disciplinary approach capable of embracing the complexity of the field. This paper discusses the current knowledge on common pathways in CIDs including general psychosocial and lifestyle factors associated with immune functioning. We demonstrate the lack of more in-depth psychosocial and lifestyle factors in current research cohorts and most importantly the need for an all-encompassing analysis of these factors. The second part of the paper discusses the challenges of understanding immune system dynamics and effectively integrating all key perspectives on immune functioning, including the patient’s perspective itself. This paper suggests the use of techniques from complex systems science in describing and simulating healthy or deviating behavior of the immune system in its biopsychosocial surroundings. The patient’s perspective data are suggested to be generated by using specific narrative techniques. We conclude that to gain more insight into the behavior of the whole system and to acquire new ways of combatting CIDs, we need to construct and apply new techniques in the field of computational and complexity science, to an even wider variety of dynamic data than used in today’s systems medicine.

Computational immuno-biology for organ transplantation and regenerative medicine

Organ transplantation and regenerative medicine are adopted platforms that provide replacement tissues and organs from natural or engineered sources. Acceptance, tolerance and rejection depend greatly on the proper control of the immune response against graft antigens, motivating the development of immunological and genetical therapies that prevent organ failure. They rely on a complete, or partial, understanding of the immune system. Ultimately, they are innovative technologies that ensure permanent graft tolerance and indefinite graft survival through the modulation of the immune system. Computational immunology has arisen as a tool towards a mechanistic understanding of the biological and physicochemical processes surrounding an immune response. It comprehends theoretical and computational frameworks that simulate immuno-biological systems. The challenge is centered on the multi-scale character of the immune system that spans from atomistic scales, during peptide-epitope and protein interactions, to macroscopic scales, for lymph transport and organ-organ reactions. In this paper, we discuss, from an engineering perspective, the biological processes that are involved during the immune response of organ transplantation. Previous computational efforts, including their characteristics and visible limitations, are described. Finally, future perspectives and challenges are listed to motivate further developments.

A Simple Screening Approach To Prioritize Genes for Functional Analysis Identifies a Role for Interferon Regulatory Factor 7 in the Control of Respiratory Syncytial Virus Disease

Greater understanding of the functions of host gene products in response to infection is required. While many of these genes enable pathogen clearance, some enhance pathogen growth or contribute to disease symptoms. Many studies have profiled transcriptomic and proteomic responses to infection, generating large data sets, but selecting targets for further study is challenging. Here we propose a novel data-mining approach combining multiple heterogeneous data sets to prioritize genes for further study by using respiratory syncytial virus (RSV) infection as a model pathogen with a significant health care impact. The assumption was that the more frequently a gene is detected across multiple studies, the more important its role is. A literature search was performed to find data sets of genes and proteins that change after RSV infection. The data sets were standardized, collated into a single database, and then panned to determine which genes occurred in multiple data sets, generating a candidate gene list. This candidate gene list was validated by using both a clinical cohort and in vitro screening. We identified several genes that were frequently expressed following RSV infection with no assigned function in RSV control, including IFI27, IFIT3, IFI44L, GBP1, OAS3, IFI44, and IRF7. Drilling down into the function of these genes, we demonstrate a role in disease for the gene for interferon regulatory factor 7, which was highly ranked on the list, but not for IRF1, which was not. Thus, we have developed and validated an approach for collating published data sets into a manageable list of candidates, identifying novel targets for future analysis. IMPORTANCE Making the most of "big data" is one of the core challenges of current biology. There is a large array of heterogeneous data sets of host gene responses to infection, but these data sets do not inform us about gene function and require specialized skill sets and training for their utilization. Here we describe an approach that combines and simplifies these data sets, distilling this information into a single list of genes commonly upregulated in response to infection with RSV as a model pathogen. Many of the genes on the list have unknown functions in RSV disease. We validated the gene list with new clinical, in vitro, and in vivo data. This approach allows the rapid selection of genes of interest for further, more-detailed studies, thus reducing time and costs. Furthermore, the approach is simple to use and widely applicable to a range of diseases.

Mathematics in modern immunology

Mathematical and statistical methods enable multidisciplinary approaches that catalyse discovery. Together with experimental methods, they identify key hypotheses, define measurable observables and reconcile disparate results. We collect a representative sample of studies in T-cell biology that illustrate the benefits of modelling–experimental collaborations and that have proven valuable or even groundbreaking. We conclude that it is possible to find excellent examples of synergy between mathematical modelling and experiment in immunology, which have brought significant insight that would not be available without these collaborations, but that much remains to be discovered.

The study of severe cutaneous drug hypersensitivity reactions from a systems biology perspective

PURPOSE OF REVIEW

Stevens-Johnson syndrome and toxic epidermal necrolysis are severe hypersensitivity reactions, the majority of which are drug induced. The underlying mechanisms are not fully understood. Here, we review recent findings concerning both mechanistic and genetic factors related to these diseases and propose future approaches to unravel their complexity.

RECENT FINDINGS

Genome-wide association study studies have identified several variants in the human leukocyte antigen region associated with these reactions. These are highly dependent on the population studied and the triggering drug. The T-cell receptor repertoire of the patient is also key. Fas-Fas ligand interactions, perforin and granulysin have also been identified as important players. Furthermore, a high-throughput gene expression study has identified a number of genes that increase in expression in patients during the acute phase of these reactions.

SUMMARY

We review recent high-throughput studies on these diseases and suggest ways in which the data can be combined and reanalyzed using integrative systems biology techniques. We also suggest future lines of research using recent technology that could shed further light on their underlying mechanisms.

Unravelling adverse reactions to NSAIDs using systems biology

We introduce the reader to systems biology, using adverse drug reactions (ADRs), specifically hypersensitivity reactions to multiple non-steroidal anti-inflammatory drugs (NSAIDs), as a model. To disentangle the different processes that contribute to these reactions - from drug intake to the appearance of symptoms - it will be necessary to create high-throughput datasets. Just as crucial will be the use of systems biology to integrate and make sense of them. We review previous work using systems biology to study related pathologies such as asthma/allergy, and NSAID metabolism. We show examples of their application to NSAIDs-hypersensitivity using current datasets. We describe breakthroughs in high-throughput technology and speculate on their use to improve our understanding of this and other drug-induced pathologies.

Predictive models of type 1 diabetes progression: understanding T-cell cycles and their implications on autoantibody release

Defining the role of T-cell avidity and killing efficacy in forming immunological response(s), leading to relapse-remission and autoantibody release in autoimmune type 1 diabetes (T1D), remains incompletely understood. Using competition-based population models of T- and B-cells, we provide a predictive tool to determine how these two parametric quantities, namely, avidity and killing efficacy, affect disease outcomes. We show that, in the presence of T-cell competition, successive waves along with cyclic fluctuations in the number of T-cells are exhibited by the model, with the former induced by transient bistability and the latter by transient periodic orbits. We hypothesize that these two immunological processes are responsible for making T1D a relapsing-remitting disease within prolonged but limited durations. The period and the number of peaks of these two processes differ, making them potential candidates to determine how plausible waves and cyclic fluctuations are in producing such effects. By assuming that T-cell and B-cell avidities are correlated, we demonstrate that autoantibodies associated with the higher avidity T-cell clones are first to be detected, and they reach their detectability level faster than those associated with the low avidity clones, independent of what T-cell killing efficacies are. Such outcomes are consistent with experimental observations in humans and they provide a rationale for observing rapid and slow progressors of T1D in high risk subjects. Our analysis of the models also reveals that it is possible to improve disease outcomes by unexpectedly increasing the avidity of certain subclones of T-cells. The decline in the number of β-cells in these cases still occurs, but it terminates early, leaving sufficient number of functioning β-cells in operation and the affected individual asymptomatic. These results indicate that the models presented here are of clinical relevance because of their potential use in developing predictive algorithms of rapid and slow progression to clinical T1D.

Unifying immunology with informatics and multiscale biology

The immune system is a highly complex and dynamic system. Historically, the most common scientific and clinical practice has been to evaluate its individual components. This kind of approach cannot always expose the interconnecting pathways that control immune-system responses and does not reveal how the immune system works across multiple biological systems and scales. High-throughput technologies can be used to measure thousands of parameters of the immune system at a genome-wide scale. These system-wide surveys yield massive amounts of quantitative data that provide a means to monitor and probe immune-system function. New integrative analyses can help synthesize and transform these data into valuable biological insight. Here we review some of the computational analysis tools for high-dimensional data and how they can be applied to immunology.