Search

Infection, autoimmunity, and cancer are principal human health challenges of the 21st century. Often regarded as distinct ends of the immunological spectrum, recent studies hint at potential overlap between these diseases. For example, inflammation can be pathogenic in infection and autoimmunity. T resident memory (TRM) cells can be beneficial in infection and cancer. However, these findings are limited by size and scope; exact immunological factors shared across diseases remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune/post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation and increased humoral immunity and that they resemble TRM cells. Our results suggest NKG2A+ biases as a cross-disease factor of protection, supporting suggestions of immunological overlap between infection, autoimmunity, and cancer.

Tuberculosis (TB) is caused by infection with the bacterial pathogen Mycobacterium tuberculosis (M.tb) in the respiratory tract. There was an estimated 10.6 million people newly diagnosed with TB, and there were approximately 1.3 million deaths caused by TB in 2022. Although the global prevalence of TB has remained high for decades and is an annual leading cause of death attributed to infectious diseases, only one vaccine, Bacillus Calmette--Gueérin (BCG), has been approved so far to prevent/attenuate TB disease. Correlates of protection or immunological mechanisms that are needed to control M.tb remain unknown. The protective role of antibodies after BCG vaccination has also remained largely unclear; however, recent studies have provided evidence for their involvement in protection against disease, as biomarkers for the state of infection, and as potential predictors of outcomes. Interestingly, the antibodies generated post-vaccination with BCG are linked to the activation of innate immune cascades, providing further evidence that antibody effector functions are critical for protection against respiratory pathogens such as M.tb. In this review, we aim to provide current knowledge of antibody application in TB diagnosis, prevention, and treatment. Particularly, this review will focus on 1) The role of antibodies in preventing M.tb infections through preventing Mtb adherence to epithelium, antibody-mediated phagocytosis, and antibody-mediated cellular cytotoxicity; 2) The M.tb-directed antibody response generated after vaccination and how humoral profiles with different glycosylation patterns of these antibodies are linked with protection against the disease state; and 3) How antibody-mediated immunity against M.tb can be further explored as early diagnosis biomarkers and different detection methods to combat the global M.tb burden. Broadening the paradigm of differentiated antibody profiling and antibody-based detection during TB disease progression offers new directions for diagnosis, treatment, and preventative strategies. This approach involves linking the aforementioned humoral responses with the disease state, progression, and clearance. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that tissue homeostasis and metabolic function are dependent on distinct tissue-resident immune cells that form functional cell circuits with structural cells. Within these cell circuits, immune cells integrate cues from dietary contents and commensal microbes in addition to endocrine and neuronal signals present in the tissue microenvironment to regulate structural cell metabolism. These tissue-resident immune circuits can become dysregulated during inflammation and dietary overnutrition, contributing to metabolic diseases. Here, we review the evidence describing key cellular networks within and between the liver, gastrointestinal tract, and adipose tissue that control systemic metabolism and how these cell circuits become dysregulated during certain metabolic diseases. We also identify open questions in the field that have the potential to enhance our understanding of metabolic health and disease.

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.

IntroductionPredicting TCR–peptide binding is a complex and significant computational problem in systems immunology. During the past decade, a series of computational methods have been developed for better predicting TCR–peptide binding from amino acid sequences. However, the performance of sequence-based methods appears to have hit a bottleneck. Considering the 3D structures of TCR–peptide complexes, which provide much more information, could potentially lead to better prediction outcomes.MethodsIn this study, we developed TCRcost, a deep learning method, to predict TCR–peptide binding by incorporating 3D structures. TCRcost overcomes two significant challenges: acquiring a sufficient number of high-quality TCR–peptide structures and effectively extracting information from these structures for binding prediction. TCRcost corrects TCR 3D structures generated by protein structure tools, significantly extending the available datasets. The main and side chains of a TCR structure are separately corrected using a long short-term memory (LSTM) model. This approach prevents interference between the chains and accurately extracts interactions among both adjacent and global atoms. A 3D convolutional neural network (CNN) is designed to extract the atomic features relevant to TCR–peptide binding. The spatial features extracted by the 3DCNN are then processed through a fully connected layer to estimate the probability of TCR–peptide binding.ResultsTest results demonstrated that predicting TCR–peptide binding from 3D TCR structures is both efficient and highly accurate with an average accuracy of 0.974 on precise structures. Furthermore, the average accuracy on corrected structures was 0.762, significantly higher than the average accuracy of 0.375 on uncorrected original structures. Additionally, the average root mean square distance (RMSD) to precise structures was significantly reduced from 12.753 Å for predicted structures to 8.785 Å for corrected structures.DiscussionThus, utilizing structural information of TCR–peptide complexes is a promising approach to improve the accuracy of binding predictions.

IntroductionDevelopment of an effective vaccine against tuberculosis is a critical step towards reducing the global burden of disease. A therapeutic vaccine might also reduce the high rate of TB recurrence and help address the challenges of drug-resistant strains. ID93+GLA-SE is a candidate subunit vaccine that will soon be evaluated in a phase 2b efficacy trial for prevention of recurrent TB among patients undergoing TB treatment. ID93+GLA-SE vaccination was shown to elicit robust CD4+ T cell and IgG antibody responses among recently treated TB patients in the TBVPX-203 Phase 2a study (NCT02465216), but the mechanisms underlying these responses are not well understood.MethodsIn this study we used specimens from TBVPX-203 participants to describe the changes in peripheral blood gene expression that occur after ID93+GLA-SE vaccination.ResultsAnalyses revealed several distinct modules of co-varying genes that were either up- or down-regulated after vaccination, including genes associated with innate immune pathways at 3 days post-vaccination and genes associated with lymphocyte expansion and B cell activation at 7 days post-vaccination. Notably, the regulation of these gene modules was affected by the dose schedule and by participant sex, and early innate gene signatures were correlated with the ID93-specific CD4+ T cell response.DiscussionThe results provide insight into the complex interplay of the innate and adaptive arms of the immune system in developing responses to vaccination with ID93+GLA-SE and demonstrate how dosing and schedule can affect vaccine responses.

RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses. [ABSTRACT FROM AUTHOR]

The interplay between the immune system and the metabolic state of a cell is intricate. In all phases of an immune response, the corresponding metabolic changes shall occur to support its modulation, in addition to the signalling through the cytokine environment and immune receptor stimulation. While autoimmune disorders may develop because of a metabolic imbalance that modulates switching between T‐cell phenotypes, the effects that the interaction between T and B cells have on one another's cellular metabolism are yet to be understood in disease context. Here, we propose a perspective which highlights the potential of targeting metabolism to modulate T‐ and B‐cell subtypes populations as well as T–B and B–T cell interactions to successfully treat autoimmune disorders. Specifically, we envision how metabolic changes can tip the balance of immune cells interactions, through definite mechanisms in both health and disease, to explain phenotype switches of B and T cells. Within this scenario, we highlight targeting metabolism that link inflammation, immunometabolism, epigenetics and ageing, is critical to understand inflammatory disorders. The combination of treatments targeting immune cells that cause (T/B) cell phenotype imbalances, and the metabolic pathways involved, may increase the effectiveness of treatment of autoimmune disorders, and/or ameliorate their symptoms to improve patients' quality of life. [ABSTRACT FROM AUTHOR]

Flow cytometry is the method of choice for immunophenotyping in the context of clinical, translational, and systems immunology studies. Among the latter, the Milieu Intérieur (MI) project aims at defining the boundaries of a healthy immune response to identify determinants of immune response variation. MI used immunophenotyping of a 1000 healthy donor cohort by flow cytometry as a principal outcome for immune variance at steady state. New generation spectral cytometers now enable high‐dimensional immune cell characterization from small sample volumes. Therefore, for the MI 10‐year follow up study, we have developed two high‐dimensional spectral flow cytometry panels for deep characterization of innate and adaptive whole blood immune cells (35 and 34 fluorescent markers, respectively). We have standardized the protocol for sample handling, staining, acquisition, and data analysis. This approach enables the reproducible quantification of over 182 immune cell phenotypes at a single site. We have applied the protocol to discern minor differences between healthy and patient samples and validated its value for application in immunomonitoring studies. Our protocol is currently used for characterization of the impact of age and environmental factors on peripheral blood immune phenotypes of >400 donors from the initial MI cohort. [ABSTRACT FROM AUTHOR]

Although generating high neutralizing antibody levels is a key component of protective immunity after acute viral infection or vaccination, little is known about why some individuals generate high versus low neutralizing antibody titers. Here, we leverage the high-dimensional single-cell profiling capacity of mass cytometry to characterize the longitudinal cellular immune response to Zika virus (ZIKV) infection in viremic blood donors in Puerto Rico. During acute ZIKV infection, we identify widely coordinated responses across innate and adaptive immune cell lineages. High frequencies of multiple activated cell types during acute infection are associated with high titers of ZIKV neutralizing antibodies 6 months post-infection, while stable immune features suggesting a cytotoxic-skewed immune set point are associated with low titers. Our study offers insight into the coordination of immune responses and identifies candidate cellular biomarkers that may offer predictive value in vaccine efficacy trials aimed at inducing high levels of antiviral neutralizing antibodies.

IL-6 and IL-17 are paradoxical cytokines that progress inflammatory states in chronic diseases, including cancer. In lung cancer, their role has been elucidated to favor cancer development by modulating signaling mechanisms critical to cellular growth. The intrinsic ability of these cytokines to influence macroautophagy is yet another reason to facilitate lung cancer. Here, we employed a systems immunology approach to discover the mechanistic role of these cytokines in cancer development. In a biological system, at later stages, the activation of NFkB stimulates immunosuppressive phenotypes to achieve tolerating effects in a transformed cell. We found that the upregulation of cytokines signaled M2 macrophages to modulate tumor responses through the activation of autophagic intermediates and inflammasome mediators. This caused immune perturbations in the tumor microenvironment, which were associated with cancer inflammation. To address these inflammatory states, we performed triggered event analysis to examine whether overexpressing immune effectors or downregulating immune suppressors may have an effect on cancer reversal. Interestingly, the inhibition of immune regulators opposed the model outcome to an increased immune response. Therefore, IL6-IL17-mediated regulation of lung cancer may address tumor malignancy and potentiate the development of newer therapeutics for NSCLC. [ABSTRACT FROM AUTHOR]

Introduction: People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers. Methods: Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study. Results: Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV. Discussion: These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes. [ABSTRACT FROM AUTHOR]

The recent development of advanced models of adaptive human immunity, such as immune organoids, provide new systems to better understand human germinal center biology and vaccine responses ex vivo. Immune organoids are validated as a robust high throughput platform to better understand these features. Recent utilization of these systems has begun to define the mechanisms and cellular signatures of influenza virus vaccines and the differences among distinct vaccine formats. Systems immunology approaches that integrate organoids and in vivo human data can provide opportunities for reverse translation. Influenza virus infections remain a global health issue because vaccine effectiveness is suboptimal and highly variable from year to year. Model systems that recapitulate new aspects of human adaptive immunity, such as human immune organoids, can aid to uncover the mechanistic underpinnings of effective vaccine responses and guide rational vaccine design. In the long term, with additional benchmarking studies for human in vivo data, organoids have the potential to contribute to clinical decision-making. Current influenza A and B virus (IABV) vaccines provide suboptimal protection and efforts are underway to develop a universal IABV vaccine. Blood neutralizing antibodies are the current gold standard for protection, but many processes that regulate human IABV-specific immunity occur in mucosal and lymphoid tissues. We need an improved mechanistic understanding of how immune cells respond within these tissues to advance our current (slow and expensive) vaccine testing model. We posit that advanced in vitro models of human adaptive immunity can bridge some of the gaps between vaccine design, animal models, and human clinical trials. Here, we highlight how they can be integrated into current practices and play a role in reverse translating the defined features of protective vaccines to rationally design new candidates. [ABSTRACT FROM AUTHOR]

It is now 60 years since Ian Mackay and Macfarlane Burnet published their seminal text "The Autoimmune Diseases" in which they examined the full scope of human inflammatory pathology as a manifestation of the underlying structure and function of the immune system. Here I revisit this approach to ask to what extent has the promise of Mackay and Burnet's work been exploited in clinical medicine as currently practiced. In other words, is immunology doing well? Despite spectacular headline contributions of immunology in clinical medicine, I present evidence suggesting a performance ceiling in our capacity to answer the relatively straightforward questions that patients frequently ask about their own diseases and find that this ceiling exists across almost all of the 100 immune‐mediated inflammatory diseases examined. I propose that these questions are difficult, not so much because the immune system is overwhelmingly complex but rather that we have more to learn about the relatively simple agents and rules that may underpin self‐organizing complex interacting systems as revealed in studies from other disciplines. The way that the immune system has evolved to exploit the ancient machinery determining three independent cell fate timers as described in this Journal would be a great place to start to decode the self‐organizing principles that underpin the emergent pathology that we observe in the clinic. [ABSTRACT FROM AUTHOR]

High‐dimensional immunoprofiling is essential for studying host response to immunotherapy, infection, and disease in murine model systems. However, the difficulty of multiparameter panel design combined with a lack of existing murine tools has prevented the comprehensive study of all major leukocyte phenotypes in a single assay. Herein, we present a 40‐color flow cytometry panel for deep immunophenotyping of murine lymphoid tissues, including the spleen, blood, Peyer's patches, inguinal lymph nodes, bone marrow, and thymus. This panel uses a robust set of surface markers capable of differentiating leukocyte subsets without the use of intracellular staining, thus allowing for the use of cells in downstream functional experiments or multiomic analyses. Our panel classifies T cells, B cells, natural killer cells, innate lymphoid cells, monocytes, macrophages, dendritic cells, basophils, neutrophils, eosinophils, progenitors, and their functional subsets by using a series of co‐stimulatory, checkpoint, activation, migration, and maturation markers. This tool has a multitude of systems immunology applications ranging from serial monitoring of circulating blood signatures to complex endpoint analysis, especially in pre‐clinical settings where treatments can modulate leukocyte abundance and/or function. Ultimately, this 40‐color panel resolves a diverse array of immune cells on the axes of time, tissue, and treatment, filling the niche for a modern tool dedicated to murine immunophenotyping. [ABSTRACT FROM AUTHOR]

Abstract The interplay between the immune system and the metabolic state of a cell is intricate. In all phases of an immune response, the corresponding metabolic changes shall occur to support its modulation, in addition to the signalling through the cytokine environment and immune receptor stimulation. While autoimmune disorders may develop because of a metabolic imbalance that modulates switching between T‐cell phenotypes, the effects that the interaction between T and B cells have on one another's cellular metabolism are yet to be understood in disease context. Here, we propose a perspective which highlights the potential of targeting metabolism to modulate T‐ and B‐cell subtypes populations as well as T–B and B–T cell interactions to successfully treat autoimmune disorders. Specifically, we envision how metabolic changes can tip the balance of immune cells interactions, through definite mechanisms in both health and disease, to explain phenotype switches of B and T cells. Within this scenario, we highlight targeting metabolism that link inflammation, immunometabolism, epigenetics and ageing, is critical to understand inflammatory disorders. The combination of treatments targeting immune cells that cause (T/B) cell phenotype imbalances, and the metabolic pathways involved, may increase the effectiveness of treatment of autoimmune disorders, and/or ameliorate their symptoms to improve patients’ quality of life.

Immune responses in human tissues rely on the concerted action of different cell types. Inter-cellular communication shapes both the function of the multicellular interaction networks and the fate of the individual cells that comprise them. With the advent of new methods to profile and experimentally perturb primary human tissues, we are now in a position to systematically identify and mechanistically dissect these cell-cell interactions and their modulators. Here, we introduce the concept of multicellular hubs, functional modules of immune responses in tissues. We outline a roadmap to discover multicellular hubs in human tissues and discuss how emerging technologies may further accelerate progress in this field.

BackgroundThe great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients.MethodsLeveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial.ResultsWe identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2-7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset.ConclusionsEarly immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models.FundingSupport for the study was provided from National Institute of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) (U01 AI150741-01S1 and T32-AI052073), the Stanford's Innovative Medicines Accelerator, National Institutes of Health/National Institute on Drug Abuse (NIH/NIDA) DP1DA046089, and anonymous donors to Stanford University. Peginterferon lambda provided by Eiger BioPharmaceuticals.

ABSTRACTA significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

In the field of immunology, a systems biology approach is crucial to understanding the immune response to infection and vaccination considering the complex interplay between genetic, epigenetic, and environmental factors. Significant progress has been made in understanding the innate immune response, including cell players and critical signaling pathways, but many questions remain unanswered, including how the innate immune response dictates host/pathogen responses and responses to vaccines. To complicate things further, it is becoming increasingly clear that the innate immune response is not a linear pathway but is formed from complex networks and interactions. To further our understanding of the crosstalk and complexities, systems-level analyses and expanded experimental technologies are now needed. In this review, we discuss the most recent immunoprofiling techniques and discuss systems approaches to studying the global innate immune landscape which will inform on the development of personalized medicine and innovative vaccine strategies.

IntroductionPeople living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers.MethodsBulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study.ResultsSingle-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV.DiscussionThese scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.

Regulatory T cells (Tregs) interact with conventional T (Tconv) cells and antigen-presenting cells (APCs) in a complex circuit involving both feedforward and feedback regulation. This circuit can operate in multiple dynamical regimes and is key to Treg function in homeostasis, infection responses, and inflammation resolution. The Treg-Tconv-APC circuit, together with the T cell receptor repertoire of Tregs being biased toward the recognition of self-antigens, restricts the activation of self-reactive Tconv cells under homeostatic conditions while allowing a robust infection response from pathogen-specific Tconv cells. Recent experimental advances characterizing the Treg-Tconv-APC interplay in space and time have advanced our understanding of Treg biology in health and disease. Combining quantitative experiments with multiscale mathematical models and machine learning approaches might be key to advancing our understanding of Treg function across diverse biological contexts and for developing putative Treg-targeting therapies. Regulatory T cells perform critical functions in diverse biological contexts in health and disease. Here, a unifying framework is proposed that interprets the different facets of Treg function as distinct dynamical operating regimes of a circuit involving feedforward and feedback interactions among regulatory T cells, conventional T cells, and antigen-presenting cells. Regulatory T (Treg) cells play vital roles in immune homeostasis and response, including discrimination between self- and non-self-antigens, containment of immunopathology, and inflammation resolution. These diverse functions are orchestrated by cellular circuits involving Tregs and other cell types across space and time. Despite dramatic progress in our understanding of Treg biology, a quantitative framework capturing how Treg-containing circuits give rise to these diverse functions is lacking. Here, we propose that different facets of Treg function can be interpreted as distinct operating regimes of the same underlying circuit. We discuss how a systems immunology approach, involving quantitative experiments, computational modeling, and machine learning, can advance our understanding of Treg function, and help identify general operating and design principles underlying immune regulation. [ABSTRACT FROM AUTHOR]

The human immune system is a distributed system of specialized cell populations with unique functions that collectively give rise to immune responses to infections and during immune-mediated diseases. Cell composition, plasma proteins, and functional responses vary among individuals, making the system difficult to interpret, but this variation is nonrandom. With careful analyses using novel experimental and computational tools, human immune system composition and function carry interpretable information. Here, we propose that systems-level analyses offer an opportunity to make human immune responses more interpretable in the future, and we discuss herein important considerations and lessons learned to this end. Predictable human immunology holds implications for better diagnostic and curative precision in patients with infectious and immune-associated diseases. Systems immunology is the study of cell–cell interactions in the immune system, their regulatory functions, and the emergent properties of immune responses. We posit that, despite large interindividual variation, cell composition is nonrandom, and understanding its rules will make human immune systems more predictable in the future. A growing number of computational tools exists for the inference of cell–cell relationships, but no 'gold standard' methods exist, and statistical power is often limiting. Minimizing the technical sources of variance by optimal experimental design and sampling procedures allows for more biological 'signals' to be discovered. A systems approach is necessary to comprehend the complexity of an organism's immune system since there is an underlying shared mechanism of cell–cell relationships. Better inferences of cell–cell interactions are essential to increase our understanding of immune responses for various health conditions. A well-planned longitudinal sampling of whole blood is vital in translating in vitro findings to in vivo biology, allowing the systematic tracking of immune system responses over time, and facilitating the development of more accurate models with improved predictability for immune system behaviors. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME) is a complex and dynamic ecosystem that includes a variety of immune cells mutually interacting with tumor cells, structural/stromal cells, and each other. The immune cells in the TME can have dual functions as pro-tumorigenic and anti-tumorigenic. To understand such paradoxical functions, the reductionistic approach classifies the immune cells into pro- and anti-tumor cells and suggests the therapeutic blockade of the pro-tumor and induction of the anti-tumor immune cells. This strategy has proven to be partially effective in prolonging patients' survival only in a fraction of patients without offering a cancer cure. Recent advances in multi-omics allow taking systems immunology approach. This essay discusses how a systems immunology approach could revolutionize our understanding of the TME by suggesting that internetwork interactions of the immune cell types create distinct collective functions independent of the function of each cellular constituent. Such collective function can be understood by the discovery of the immunological patterns in the TME and may be modulated as a therapeutic means for immunotherapy of cancer. [ABSTRACT FROM AUTHOR]

Monocytes are crucial regulators of inflammation, and are characterized by three distinct subsets in humans, of which classical and non-classical are the most abundant. Different subsets carry out different functions and have been previously associated with multiple inflammatory conditions. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and poor reproducibility. Publicly available transcriptome profiles provide an alternative source of data characterized by high statistical power and real-world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in cell levels. Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures maintain their accuracy regardless of disease state in an independent cohort profiled by RNA-sequencing and are specific to their respective subset when compared to other immune cells from both myeloid and lymphoid lineages profiled across 6160 transcriptome profiles. Consequently, we show that these signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.

Over the last several years, there has been increased interest from academia and the pharmaceutical/biotech industry in the development of vaccine adjuvants for new and emerging vaccine modalities. Despite this, vaccine adjuvant development still has some of the longest timelines in the pharmaceutical space, from discovery to clinical approval. The reasons for this are manyfold and range from complexities in translation from animal to human models, concerns about safety or reactogenicity, to challenges in sourcing the necessary raw materials at scale. In this review, we will describe the current state of the art for many adjuvant technologies and how they should be approached or applied in the development of new vaccine products. We postulate that there are many factors to be considered and tools to be applied earlier on in the vaccine development pipeline to improve the likelihood of clinical success. These recommendations may require a modified approach to some of the common practices in new product development but would result in more accessible and practical adjuvant-containing products. [ABSTRACT FROM AUTHOR]

Background: Sepsis is a dysfunctional host response to infection. The syndrome leads to millions of deaths annually (19.7% of all deaths in 2017) and is the cause of most deaths from severe Covid infections. High throughput sequencing or 'omics' experiments in molecular and clinical sepsis research have been widely utilized to identify new diagnostics and therapies. Transcriptomics, quantifying gene expression, has dominated these studies, due to the efficiency of measuring gene expression in tissues and the technical accuracy of technologies like RNA-Seq. Objective: Most of these studies seek to uncover novel mechanistic insights into sepsis pathogenesis and diagnostic gene signatures by identifying genes differentially expressed between two or more relevant conditions. However, little effort has been made, to date, to aggregate this knowledge from such studies. In this study we sought to build a compendium of previously described gene sets that combines knowledge gained from sepsis-associated studies. This would enable the identification of genes most associated with sepsis pathogenesis, and the description of the molecular pathways commonly associated with sepsis. Methods: PubMed was searched for studies using transcriptomics to characterize acute infection/sepsis and severe sepsis (i.e., sepsis combined with organ failure). Several studies were identified that used transcriptomics to identify differentially expressed (DE) genes, predictive/prognostic signatures, and underlying molecular responses and pathways. The molecules included in each gene set were collected, in addition to the relevant study metadata (e.g., patient groups used for comparison, sample collection time point, tissue type, etc.). Results: After performing extensive literature curation of 74 sepsis-related publications involving transcriptomics, 103 unique gene sets (comprising 20,899 unique genes) from thousands of patients were collated together with associated metadata. Frequently described genes included in gene sets as well as the molecular mechanisms they were involved in were identified. These mechanisms included neutrophil degranulation, generation of second messenger molecules, IL-4 and -13 signaling, and IL-10 signaling among many others. The database, which we named SeptiSearch, is made available in a web application created using the Shiny framework in R, (available at https://septisearch.ca). Conclusions: SeptiSearch provides members of the sepsis community the bioinformatic tools needed to leverage and explore the gene sets contained in the database. This will allow the gene sets to be further scrutinized and analyzed for their enrichment in user-submitted gene expression data and used for validation of in-house gene sets/signatures. [ABSTRACT FROM AUTHOR]

The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and antiinflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment. To quantify this process, we used a previous regulatory network reconstructed by our group and transformed Boolean Network attractors of macrophage polarization to an ODE scheme, it enables us to quantify the activation of their genes in a continuous fashion. The transformation was developed using the interaction rules with a fuzzy logic approach. By implementing this approach, we analyzed different aspects that cannot be visualized in the Boolean setting. For example, this approach allows us to explore the dynamic behavior at different concentrations of cytokines and transcription factors in the microenvironment. One important aspect to assess is the evaluation of the transitions between phenotypes, some of them characterized by an abrupt or a gradual transition depending on specific concentrations of exogenous cytokines in the tumor microenvironment. For instance, IL-10 can induce a hybrid state that transits between an M2c and an M2b macrophage. Interferon- g can induce a hybrid between M1 and M1a macrophage. We further demonstrated the plasticity of macrophages based on a combination of cytokines and the existence of hybrid phenotypes or partial polarization. This mathematical model allows us to unravel the patterns of macrophage differentiation based on the competition of expression of transcriptional factors. Finally, we survey how macrophages may respond to a continuously changing immunological response in a tumor microenvironment. [ABSTRACT FROM AUTHOR]

Graves' disease (GD) and Hashimoto's thyroiditis (HT) are common human thyroid autoimmune diseases with opposite clinical manifestations. GD and HT originate from T cells that are autoreactive to thyroid-stimulating hormone (TSH) receptor antigens and to thyroglobulin (Tg)/thyroid peroxidase (TPO) antigens, respectively. The two diseases exhibit shared risk factors and incidence curves, and can interconvert. The autoimmune surveillance theory provides a rationale for a putative shared etiology between the two diseases based on a physiological circuit of a mutant removal mechanism. Autoreactive T cells are suggested to weed out hypersecreting mutants in the thyroid, with the cost of triggering a humoral response, leading to GD or HT in susceptible individuals. The origin of the two autoimmune diseases of the thyroid (Graves' disease and Hashimoto's thyroiditis) remains insufficiently understood. Despite their opposite clinical manifestations of hyper- and hypothyroidism, we propose a shared origin based on a physiological autoimmune surveillance mechanism against hypersecreting mutant cells, which may explain observed links between these diseases. Graves' disease (GD) and Hashimoto's thyroiditis (HT) are common autoimmune diseases of the thyroid gland, causing hyperthyroidism and hypothyroidism, respectively. Despite their opposing clinical manifestation, they have several enigmatic links. Here, we propose that GD and HT have the same fundamental origin: both diseases are the cost of a beneficial physiological process called autoimmune surveillance of hypersecreting mutants. Autoreactive T cells selectively eliminate mutant cells that hypersecrete the hormones and threaten to become toxic nodules. These T cells can trigger a humoral response in susceptible individuals, leading to the production of antibodies against thyroid antigens. This shared origin can explain similarities in incidence and risk factors between HT and GD, despite their opposite clinical phenotypes. [ABSTRACT FROM AUTHOR]

Immune responses in diverse tissue sites are critical for protective immunity and homeostasis. Here, we investigate how tissue localization regulates the development and function of human natural killer (NK) cells, innate lymphocytes important for anti-viral and tumor immunity. Integrating high-dimensional analysis of NK cells from blood, lymphoid organs, and mucosal tissue sites from 60 individuals, we identify tissue-specific patterns of NK cell subset distribution, maturation, and function maintained across age and between individuals. Mature and terminally differentiated NK cells with enhanced effector function predominate in blood, bone marrow, spleen, and lungs and exhibit shared transcriptional programs across sites. By contrast, precursor and immature NK cells with reduced effector capacity populate lymph nodes and intestines and exhibit tissue-resident signatures and site-specific adaptations. Together, our results reveal anatomic control of NK cell development and maintenance as tissue-resident populations, whereas mature, terminally differentiated subsets mediate immunosurveillance through diverse peripheral sites. VIDEO ABSTRACT.

The immune system, whose nature lies in being a complex network of interactions, lends itself well to being represented and studied using graph theory. However, it should be noted that although the formalization of models of the immune system is relatively recent, the medical use of its signaling network structure has been carried out empirically for centuries in vaccinology, immunopathology, and clinical immunology, as evidenced by the development of effective vaccines, the management of transplant rejection, the management of allergies, and the treatment of certain types of cancer and autoimmune diseases. A network optimization analogy is proposed through the employment of the system dynamic formalism of causal loop diagrams (CLDs), where current network operations (also known as NetOps) in information technology (IT), are interpreted as immune NetOps in coronavirus disease 2019 (COVID-19) treatment. Traffic shaping corresponds to signaling pathway modulation by immunosuppressors. Data caching corresponds to the activation of innate immunity by application of Bacillus Calmette-Guerin (BCG) and other vaccines. Data compression corresponds with the activation of adaptative immune response by vaccination with the actual approved COVID-19 vaccines. Buffer tuning corresponds with concurrent activation of innate and adaptative or specialized immune cells and antibodies that attack and destroy foreign invaders by trained immunity-based vaccines to develop. The present study delineates some experimental extensions and future developments. Given the complex communication architecture of signal transduction in the immune system, it is apparent that multiple parallel pathways influencing and regulating each other are not the exception but the norm. Thus, the transition from empirical immune NetOps to analytical immune NetOps is a goal for the near future in biomedicine.

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research. [ABSTRACT FROM AUTHOR]

Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively. [ABSTRACT FROM AUTHOR]

Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies. [ABSTRACT FROM AUTHOR]

Several perturbations in the number of peripheral blood leukocytes, such as neutrophilia and lymphopenia associated with Coronavirus disease 2019 (COVID‐19) severity, point to systemic molecular cell cycle alterations during severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) infection. However, the landscape of cell cycle alterations in COVID‐19 remains primarily unexplored. Here, we performed an integrative systems immunology analysis of publicly available proteome and transcriptome data to characterize global changes in the cell cycle signature of COVID‐19 patients. We found significantly enriched cell cycle‐associated gene co‐expression modules and an interconnected network of cell cycle‐associated differentially expressed proteins (DEPs) and genes (DEGs) by integrating the molecular data of 1469 individuals (981 SARS‐CoV‐2 infected patients and 488 controls [either healthy controls or individuals with other respiratory illnesses]). Among these DEPs and DEGs are several cyclins, cell division cycles, cyclin‐dependent kinases, and mini‐chromosome maintenance proteins. COVID‐19 patients partially shared the expression pattern of some cell cycle‐associated genes with other respiratory illnesses but exhibited some specific differential features. Notably, the cell cycle signature predominated in the patients' blood leukocytes (B, T, and natural killer cells) and was associated with COVID‐19 severity and disease trajectories. These results provide a unique global understanding of distinct alterations in cell cycle‐associated molecules in COVID‐19 patients, suggesting new putative pathways for therapeutic intervention. [ABSTRACT FROM AUTHOR]

The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment. To quantify this process, we used a previous regulatory network reconstructed by our group and transformed Boolean Network attractors of macrophage polarization to an ODE scheme, it enables us to quantify the activation of their genes in a continuous fashion. The transformation was developed using the interaction rules with a fuzzy logic approach. By implementing this approach, we analyzed different aspects that cannot be visualized in the Boolean setting. For example, this approach allows us to explore the dynamic behavior at different concentrations of cytokines and transcription factors in the microenvironment. One important aspect to assess is the evaluation of the transitions between phenotypes, some of them characterized by an abrupt or a gradual transition depending on specific concentrations of exogenous cytokines in the tumor microenvironment. For instance, IL-10 can induce a hybrid state that transits between an M2c and an M2b macrophage. Interferon- γ can induce a hybrid between M1 and M1a macrophage. We further demonstrated the plasticity of macrophages based on a combination of cytokines and the existence of hybrid phenotypes or partial polarization. This mathematical model allows us to unravel the patterns of macrophage differentiation based on the competition of expression of transcriptional factors. Finally, we survey how macrophages may respond to a continuously changing immunological response in a tumor microenvironment.

IL-6 and IL-17 are paradoxical cytokines that progress inflammatory states in chronic diseases, including cancer. In lung cancer, their role has been elucidated to favor cancer development by modulating signaling mechanisms critical to cellular growth. The intrinsic ability of these cytokines to influence macroautophagy is yet another reason to facilitate lung cancer. Here, we employed a systems immunology approach to discover the mechanistic role of these cytokines in cancer development. In a biological system, at later stages, the activation of NFkB stimulates immunosuppressive phenotypes to achieve tolerating effects in a transformed cell. We found that the upregulation of cytokines signaled M2 macrophages to modulate tumor responses through the activation of autophagic intermediates and inflammasome mediators. This caused immune perturbations in the tumor microenvironment, which were associated with cancer inflammation. To address these inflammatory states, we performed triggered event analysis to examine whether overexpressing immune effectors or downregulating immune suppressors may have an effect on cancer reversal. Interestingly, the inhibition of immune regulators opposed the model outcome to an increased immune response. Therefore, IL6-IL17-mediated regulation of lung cancer may address tumor malignancy and potentiate the development of newer therapeutics for NSCLC.

The use of mathematical and computational tools in investigating Natural Killer (NK) cell biology and in general the immune system has increased steadily in the last few decades. However, unlike the physical sciences, there is a persistent ambivalence, which however is increasingly diminishing, in the biology community toward appreciating the utility of quantitative tools in addressing questions of biological importance. We survey some of the recent developments in the application of quantitative approaches for investigating different problems in NK cell biology and evaluate opportunities and challenges of using quantitative methods in providing biological insights in NK cell biology.

To understand how the interaction between an intracellular bacterium and the host immune system contributes to outcome at the site of infection, we studied leprosy, a disease that forms a clinical spectrum, in which progressive infection by the intracellular bacterium Mycobacterium leprae is characterized by the production of type I IFNs and antibody production. Dual RNA-seq on patient lesions identifies two independent molecular measures of M. leprae, each of which correlates with distinct aspects of the host immune response. The fraction of bacterial transcripts, reflecting bacterial burden, correlates with a host type I IFN gene signature, known to inhibit antimicrobial responses. Second, the bacterial mRNA:rRNA ratio, reflecting bacterial viability, links bacterial heat shock proteins with the BAFF-BCMA host antibody response pathway. Our findings provide a platform for the interrogation of host and pathogen transcriptomes at the site of infection, allowing insight into mechanisms of inflammation in human disease.

Abstract “The Primate Malarias” book has been a uniquely important resource for multiple generations of scientists, since its debut in 1971, and remains pertinent to the present day. Indeed, nonhuman primates (NHPs) have been instrumental for major breakthroughs in basic and pre-clinical research on malaria for over 50 years. Research involving NHPs have provided critical insights and data that have been essential for malaria research on many parasite species, drugs, vaccines, pathogenesis, and transmission, leading to improved clinical care and advancing research goals for malaria control, elimination, and eradication. Whilst most malaria scientists over the decades have been studying Plasmodium falciparum, with NHP infections, in clinical studies with humans, or using in vitro culture or rodent model systems, others have been dedicated to advancing research on Plasmodium vivax, as well as on phylogenetically related simian species, including Plasmodium cynomolgi, Plasmodium coatneyi, and Plasmodium knowlesi. In-depth study of these four phylogenetically related species over the years has spawned the design of NHP longitudinal infection strategies for gathering information about ongoing infections, which can be related to human infections. These Plasmodium-NHP infection model systems are reviewed here, with emphasis on modern systems biological approaches to studying longitudinal infections, pathogenesis, immunity, and vaccines. Recent discoveries capitalizing on NHP longitudinal infections include an advanced understanding of chronic infections, relapses, anaemia, and immune memory. With quickly emerging new technological advances, more in-depth research and mechanistic discoveries can be anticipated on these and additional critical topics, including hypnozoite biology, antigenic variation, gametocyte transmission, bone marrow dysfunction, and loss of uninfected RBCs. New strategies and insights published by the Malaria Host–Pathogen Interaction Center (MaHPIC) are recapped here along with a vision that stresses the importance of educating future experts well trained in utilizing NHP infection model systems for the pursuit of innovative, effective interventions against malaria.

Systems characterization of immune landscapes in health, disease and clinical intervention cases is a priority in modern medicine. High-throughput transcriptomes accumulated from gene-knockout (KO) experiments are crucial for deciphering target KO signaling pathways that are impaired by KO genes at the systems-level. There is a demand for integrative platforms. This article describes the PathwayKO platform, which has integrated state-of-the-art methods of pathway enrichment analysis, statistics analysis, and visualizing analysis to conduct cutting-edge integrative pathway analysis in a pipeline fashion and decipher target KO signaling pathways at the systems-level. We focus on describing the methodology, principles and application features of PathwayKO. First, we demonstrate that the PathwayKO platform can be utilized to comprehensively analyze real-world mouse KO transcriptomes (GSE22873 and GSE24327), which reveal systemic mechanisms underlying the innate immune responses triggered by non-infectious extensive hepatectomy (2 hours after 85% liver resection surgery) and infectious CASP-model sepsis (12 hours after CASP-model surgery). Strikingly, our results indicate that both cases hit the same core set of 21 KO MyD88-associated signaling pathways, including the Toll-like receptor signaling pathway, the NFκB signaling pathway, the MAPK signaling pathway, and the PD-L1 expression and PD-1 checkpoint pathway in cancer, alongside the pathways of bacterial, viral and parasitic infections. These findings suggest common fundamental mechanisms between these immune responses and offer informative cues that warrant future experimental validation. Such mechanisms in mice may serve as models for humans and ultimately guide formulating the research paradigms and composite strategies to reduce the high mortality rates of patients in intensive care units who have undergone successful traumatic surgical treatments. Second, we demonstrate that the PathwayKO platform model-based assessments can effectively evaluate the performance difference of pathway analysis methods when benchmarked with a collection of proper transcriptomes. Together, such advances in methods for deciphering biological insights at the systems-level may benefit the fields of bioinformatics, systems immunology and beyond.

Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model’s components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.

The systemic bio-organization of humans and other mammals is essentially “preprogrammed”, and the basic interacting units, the cells, can be crudely mapped into discrete sets of developmental lineages and maturation states. Over several decades, however, and focusing on the immune system, we and others invoked evidence – now overwhelming – suggesting dynamic acquisition of cellular properties and functions, through tuning, re-networking, chromatin remodeling, and adaptive differentiation. The genetically encoded “algorithms” that govern the integration of signals and the computation of new states are not fully understood but are believed to be “smart”, designed to enable the cells and the system to discriminate meaningful perturbations from each other and from “noise”. Cellular sensory and response properties are shaped in part by recurring temporal patterns, or features, of the signaling environment. We compared this phenomenon to associative brain learning. We proposed that interactive cell learning is subject to selective pressures geared to performance, allowing the response of immune cells to injury or infection to be progressively coordinated with that of other cell types across tissues and organs. This in turn is comparable to supervised brain learning. Guided by feedback from both the tissue itself and the neural system, resident or recruited antigen-specific and innate immune cells can eradicate a pathogen while simultaneously sustaining functional homeostasis. As informative memories of immune responses are imprinted both systemically and within the targeted tissues, it is desirable to enhance tissue preparedness by incorporating attenuated-pathogen vaccines and informed choice of tissue centered immunomodulators in vaccination schemes. Fortunately, much of the “training” that a living system requires to survive and function in the face of disturbances from outside or within is already incorporated into its design, so it does not need to deep-learn how to face a new challenge each time from scratch. Instead, the system learns from experience how to efficiently select a built-in strategy, or a combination of those, and can then use tuning to refine its organization and responses. Efforts to identify and therapeutically augment such strategies can take advantage of existing integrative modeling approaches. One recently explored strategy is boosting the flux of uninfected cells into and throughout an infected tissue to rinse and replace the infected cells. [ABSTRACT FROM AUTHOR]