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.

Structural basis for mechanical force regulation of the adhesin FimH via finger trap-like beta sheet twisting.

The Escherichia coli fimbrial adhesive protein, FimH, mediates shear-dependent binding to mannosylated surfaces via force-enhanced allosteric catch bonds, but the underlying structural mechanism was previously unknown. Here we present the crystal structure of FimH incorporated into the multiprotein fimbrial tip, where the anchoring (pilin) domain of FimH interacts with the mannose-binding (lectin) domain and causes a twist in the beta sandwich fold of the latter. This loosens the mannose-binding pocket on the opposite end of the lectin domain, resulting in an inactive low-affinity state of the adhesin. The autoinhibition effect of the pilin domain is removed by application of tensile force across the bond, which separates the domains and causes the lectin domain to untwist and clamp tightly around the ligand like a finger-trap toy. Thus, beta sandwich domains, which are common in multidomain proteins exposed to tensile force in vivo, can undergo drastic allosteric changes and be subjected to mechanical regulation.

Clonal Deletion Prunes but Does Not Eliminate Self-Specific αß CD8(+) T Lymphocytes.

It has long been thought that clonal deletion efficiently removes almost all self-specific T cells from the peripheral repertoire. We found that self-peptide MHC-specific CD8(+) T cells in the blood of healthy humans were present in frequencies similar to those specific for non-self antigens. For the Y chromosome-encoded SMCY antigen, self-specific T cells exhibited only a 3-fold lower average frequency in males versus females and were anergic with respect to peptide activation, although this inhibition could be overcome by a stronger stimulus. We conclude that clonal deletion prunes but does not eliminate self-specific T cells and suggest that to do so would create holes in the repertoire that pathogens could readily exploit. In support of this hypothesis, we detected T cells specific for all 20 amino acid variants at the p5 position of a hepatitis C virus epitope in a random group of blood donors.

Virus-specific CD4(+) memory-phenotype T cells are abundant in unexposed adults.

Although T cell memory is generally thought to require direct antigen exposure, we found an abundance of memory-phenotype cells (20%-90%, averaging over 50%) of CD4(+) T cells specific to viral antigens in adults who had never been infected. These cells express the appropriate memory markers and genes, rapidly produce cytokines, and have clonally expanded. In contrast, the same T cell receptor (TCR) specificities in newborns are almost entirely naïve, which might explain the vulnerability of young children to infections. One mechanism for this phenomenon is TCR cross-reactivity to environmental antigens, and in support of this, we found extensive cross-recognition by HIV-1 and influenza-reactive T lymphocytes to other microbial peptides and expansion of one of these after influenza vaccination. Thus, the presence of these memory-phenotype T cells has significant implications for immunity to novel pathogens, child and adult health, and the influence of pathogen-rich versus hygienic environments.

γδ T cells recognize a microbial encoded B cell antigen to initiate a rapid antigen-specific interleukin-17 response.

γδ T cells contribute uniquely to immune competence. Nevertheless, how they function remains an enigma. It is unclear what most γδ T cells recognize, what is required for them to mount an immune response, and how the γδ T cell response is integrated into host immune defense. Here, we report that a noted B cell antigen, the algae protein phycoerythrin (PE), is a murine and human γδ T cell antigen. Employing this specificity, we demonstrated that antigen recognition activated naive γδ T cells to make interleukin-17 and respond to cytokine signals that perpetuate the response. High frequencies of antigen-specific γδ T cells in naive animals and their ability to mount effector response without extensive clonal expansion allow γδ T cells to initiate a swift, substantial response. These results underscore the adaptability of lymphocyte antigen receptors and suggest an antigen-driven rapid response in protective immunity prior to the maturation of classical adaptive immunity.

Medication adherence, utilization of healthcare services, and mortality of patients with epilepsy on opiate replacement therapy: A retrospective cohort study.

INTRODUCTION: Substance misuse is not uncommonly recognized in people with epilepsy (PWE). Mortality is significantly greater in those with comorbid substance misuse, but it remains unclear whether epilepsy care and management contribute to this. This cohort study aimed to compare the rates of mortality in PWE receiving opiate replacement therapy (ORT) and PWE alone, as well as evaluate their medication adherence, levels of engagement with epilepsy services as currently delivered, and utilization of unscheduled hospital care. MATERIAL AND METHODS: A 5-year historical cohort for PWE was identified and manually validated using electronic patient records registered with NHS Tayside. Overall incidence rates for mortality and contact with emergency health care services were calculated for PWE receiving ORT and PWE alone. Engagement with outpatient epilepsy services was also noted. Adherence to antiepileptic drugs (AEDs) was expressed in terms of medication possession ratio (MPR). RESULTS: Of the 1297 PWE attending a tertiary care epilepsy service, 68 (5.3%) PWE were receiving ORT. The mortality rate was significantly greater in PWE on ORT in comparison to PWE only (7.4% vs 1.7 %; P < 0.05; relative risk of death: 4.34, 95% CI 1.19-15.7), as well as the incidence of emergency healthcare services contact being higher (24.5% vs 17.7%; P < 0.05; incidence rate ratio: 1.39, 95% CI: 1.12-1.71). Poor adherence to AEDs was also more common in PWE on ORT (28.4% vs 23.5%; P = 0.02), as well as failure to engage with elective outpatient services (8.4% vs 3.0%; P < 0.05; rate ratio 2.77, 95% CI: 1.86-4.1). CONCLUSION: People with epilepsy on ORT are less likely to engage with elective epilepsy services as currently delivered or take AEDs as prescribed despite most of these patients having daily attendance at a community pharmacist. This may contribute to the significantly increased rates of mortality and unscheduled hospital care. Clinicians and policymakers should consider service redesign to meet the demands of this high-risk population in an attempt to reduce mortality and morbidity.

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning.

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.

Environments Tune and Select Cellular Diversity.

Technical advances in single-cell sequencing data and their application to greater samples is revealing substantial cell-to-cell variation in expression levels and propagation of this variation between molecules across cells. New quantitative approaches that apply mechanistic and statistical models in a systems-wide approach are illuminating the drivers of phenotypic diversity.

High-Throughput Characterization of Blood Serum Proteomics of IBD Patients with Respect to Aging and Genetic Factors.

To date, no large scale, systematic description of the blood serum proteome has been performed in inflammatory bowel disease (IBD) patients. By using microarray technology, a more complete description of the blood proteome of IBD patients is feasible. It may help to achieve a better understanding of the disease. We analyzed blood serum profiles of 1128 proteins in IBD patients of European descent (84 Crohn's Disease (CD) subjects and 88 Ulcerative Colitis (UC) subjects) as well as 15 healthy control subjects, and linked protein variability to patient age (all cohorts) and genetic components (genotype data generated from CD patients). We discovered new, previously unreported aging-associated proteomic traits (such as serum Albumin level), confirmed previously reported results from different tissues (i.e., upregulation of APOE with aging), and found loss of regulation of MMP7 in CD patients. In carrying out a genome wide genotype-protein association study (proteomic Quantitative Trait Loci, pQTL) within the CD patients, we identified 41 distinct proteomic traits influenced by cis pQTLs (underlying SNPs are referred to as pSNPs). Significant overlaps between pQTLs and cis eQTLs corresponding to the same gene were observed and in some cases the QTL were related to inflammatory disease susceptibility. Importantly, we discovered that serum protein levels of MST1 (Macrophage Stimulating 1) were regulated by SNP rs3197999 (p = 5.96E-10, FDR<5%), an accepted GWAS locus for IBD. Filling the knowledge gap of molecular mechanisms between GWAS hits and disease susceptibility requires systematically dissecting the impact of the locus at the cell, mRNA expression, and protein levels. The technology and analysis tools that are now available for large-scale molecular studies can elucidate how alterations in the proteome driven by genetic polymorphisms cause or provide protection against disease. Herein, we demonstrated this directly by integrating proteomic and pQTLs with existing GWAS, mRNA expression, and eQTL datasets to provide insights into the biological processes underlying IBD and pinpoint causal genetic variants along with their downstream molecular consequences.

Systematic Analysis of Environmental Chemicals That Dysregulate Critical Period Plasticity-Related Gene Expression Reveals Common Pathways That Mimic Immune Response to Pathogen.

The tens of thousands of industrial and synthetic chemicals released into the environment have an unknown but potentially significant capacity to interfere with neurodevelopment. Consequently, there is an urgent need for systematic approaches that can identify disruptive chemicals. Little is known about the impact of environmental chemicals on critical periods of developmental neuroplasticity, in large part, due to the challenge of screening thousands of chemicals. Using an integrative bioinformatics approach, we systematically scanned 2001 environmental chemicals and identified 50 chemicals that consistently dysregulate two transcriptional signatures of critical period plasticity. These chemicals included pesticides (e.g., pyridaben), antimicrobials (e.g., bacitracin), metals (e.g., mercury), anesthetics (e.g., halothane), and other chemicals and mixtures (e.g., vehicle emissions). Application of a chemogenomic enrichment analysis and hierarchical clustering across these diverse chemicals identified two clusters of chemicals with one that mimicked an immune response to pathogen, implicating inflammatory pathways and microglia as a common chemically induced neuropathological process. Thus, we established an integrative bioinformatics approach to systematically scan thousands of environmental chemicals for their ability to dysregulate molecular signatures relevant to critical periods of development.

Automated cell type discovery and classification through knowledge transfer.

MOTIVATION: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis. RESULTS: We present a new algorithm called utomated ell-type iscovery and lassification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. AVAILABILITY AND IMPLEMENTATION: A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc . CONTACT: brian.kidd@mssm.edu or joel.dudley@mssm.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic Analysis of Kidney Allograft Injury Identifies Hematopoietic Cell Kinase as a Key Driver of Renal Fibrosis.

Renal fibrosis is the common pathway of progression for patients with CKD and chronic renal allograft injury (CAI), but the underlying mechanisms remain obscure. We performed a meta-analysis in human kidney biopsy specimens with CAI, incorporating data available publicly and from our Genomics of Chronic Renal Allograft Rejection study. We identified an Src family tyrosine kinase, hematopoietic cell kinase (Hck), as upregulated in allografts in CAI. Querying the Kinase Inhibitor Resource database revealed that dasatinib, a Food and Drug Administration-approved drug, potently binds Hck with high selectivity. In vitro, Hck overexpression activated the TGF-ß/Smad3 pathway, whereas HCK knockdown inhibited it. Treatment of tubular cells with dasatinib reduced the expression of Col1a1 Dasatinib also reduced proliferation and α-SMA expression in fibroblasts. In a murine model with unilateral ureteric obstruction, pretreatment with dasatinib significantly reduced the upregulation of profibrotic markers, phosphorylation of Smad3, and renal fibrosis observed in kidneys pretreated with vehicle alone. Dasatinib treatment also improved renal function, reduced albuminuria, and inhibited expression of profibrotic markers in animal models with lupus nephritis and folic acid nephropathy. These data suggest that Hck is a key mediator of renal fibrosis and dasatinib could be developed as an antifibrotic drug.

Variants in TRIM22 That Affect NOD2 Signaling Are Associated With Very-Early-Onset Inflammatory Bowel Disease.

BACKGROUND & AIMS: Severe forms of inflammatory bowel disease (IBD) that develop in very young children can be caused by variants in a single gene. We performed whole-exome sequence (WES) analysis to identify genetic factors that might cause granulomatous colitis and severe perianal disease, with recurrent bacterial and viral infections, in an infant of consanguineous parents. METHODS: We performed targeted WES analysis of DNA collected from the patient and her parents. We validated our findings by a similar analysis of DNA from 150 patients with very-early-onset IBD not associated with known genetic factors analyzed in Toronto, Oxford, and Munich. We compared gene expression signatures in inflamed vs noninflamed intestinal and rectal tissues collected from patients with treatment-resistant Crohn's disease who participated in a trial of ustekinumab. We performed functional studies of identified variants in primary cells from patients and cell culture. RESULTS: We identified a homozygous variant in the tripartite motif containing 22 gene (TRIM22) of the patient, as well as in 2 patients with a disease similar phenotype. Functional studies showed that the variant disrupted the ability of TRIM22 to regulate nucleotide binding oligomerization domain containing 2 (NOD2)-dependent activation of interferon-beta signaling and nuclear factor-κB. Computational studies demonstrated a correlation between the TRIM22-NOD2 network and signaling pathways and genetic factors associated very early onset and adult-onset IBD. TRIM22 is also associated with antiviral and mycobacterial effectors and markers of inflammation, such as fecal calprotectin, C-reactive protein, and Crohn's disease activity index scores. CONCLUSIONS: In WES and targeted exome sequence analyses of an infant with severe IBD characterized by granulomatous colitis and severe perianal disease, we identified a homozygous variant of TRIM22 that affects the ability of its product to regulate NOD2. Combined computational and functional studies showed that the TRIM22-NOD2 network regulates antiviral and antibacterial signaling pathways that contribute to inflammation. Further study of this network could lead to new disease markers and therapeutic targets for patients with very early and adult-onset IBD.

Allosteric coupling in the bacterial adhesive protein FimH.

The protein FimH is expressed by the majority of commensal and uropathogenic strains of Escherichia coli on the tips of type 1 fimbriae and mediates adhesion via a catch bond to its ligand mannose. Crystal structures of FimH show an allosteric conformational change, but it remains unclear whether all of the observed structural differences are part of the allosteric mechanism. Here we use the protein structural analysis tool RosettaDesign combined with human insight to identify and synthesize 10 mutations in four regions that we predicted would stabilize one of the conformations of that region. The function of each variant was characterized by measuring binding to the ligand mannose, whereas the allosteric state was determined using a conformation-specific monoclonal antibody. These studies demonstrated that each region investigated was indeed part of the FimH allosteric mechanism. However, the studies strongly suggested that some regions were more tightly coupled to mannose binding and others to antibody binding. In addition, we identified many FimH variants that appear locked in the low affinity state. Knowledge of regulatory sites outside the active and effector sites as well as the ability to make FimH variants locked in the low affinity state may be crucial to the future development of novel antiadhesive and antimicrobial therapies using allosteric regulation to inhibit FimH.

Apoptosis and other immune biomarkers predict influenza vaccine responsiveness.

Despite the importance of the immune system in many diseases, there are currently no objective benchmarks of immunological health. In an effort to identifying such markers, we used influenza vaccination in 30 young (20-30 years) and 59 older subjects (60 to >89 years) as models for strong and weak immune responses, respectively, and assayed their serological responses to influenza strains as well as a wide variety of other parameters, including gene expression, antibodies to hemagglutinin peptides, serum cytokines, cell subset phenotypes and in vitro cytokine stimulation. Using machine learning, we identified nine variables that predict the antibody response with 84% accuracy. Two of these variables are involved in apoptosis, which positively associated with the response to vaccination and was confirmed to be a contributor to vaccine responsiveness in mice. The identification of these biomarkers provides new insights into what immune features may be most important for immune health.

The bacterial fimbrial tip acts as a mechanical force sensor.

There is increasing evidence that the catch bond mechanism, where binding becomes stronger under tensile force, is a common property among non-covalent interactions between biological molecules that are exposed to mechanical force in vivo. Here, by using the multi-protein tip complex of the mannose-binding type 1 fimbriae of Escherichia coli, we show how the entire quaternary structure of the adhesive organella is adapted to facilitate binding under mechanically dynamic conditions induced by flow. The fimbrial tip mediates shear-dependent adhesion of bacteria to uroepithelial cells and demonstrates force-enhanced interaction with mannose in single molecule force spectroscopy experiments. The mannose-binding, lectin domain of the apex-positioned adhesive protein FimH is docked to the anchoring pilin domain in a distinct hooked manner. The hooked conformation is highly stable in molecular dynamics simulations under no force conditions but permits an easy separation of the domains upon application of an external tensile force, allowing the lectin domain to switch from a low- to a high-affinity state. The conformation between the FimH pilin domain and the following FimG subunit of the tip is open and stable even when tensile force is applied, providing an extended lever arm for the hook unhinging under shear. Finally, the conformation between FimG and FimF subunits is highly flexible even in the absence of tensile force, conferring to the FimH adhesin an exploratory function and high binding rates. The fimbrial tip of type 1 Escherichia coli is optimized to have a dual functionality: flexible exploration and force sensing. Comparison to other structures suggests that this property is common in unrelated bacterial and eukaryotic adhesive complexes that must function in dynamic conditions.

Acute Effects of Growth Hormone on the Cellular Immunologic Landscape in Pediatric Patients.

INTRODUCTION: Growth hormone (GH) and the immune system have multiple bidirectional interactions. Data about the acute effects of GH on the immune system are lacking. The objective of our study was to evaluate the acute effects of GH on the immune system using time-of-flight mass cytometry. METHODS: This was a prospective study of pediatric patients who were being evaluated for short stature and underwent a GH stimulation test at a tertiary care center. Blood samples for immunologic markers, i.e., complete blood count (CBC) and time of flight mass cytometry (CyTOF), were collected at baseline (T0) and over the course of three hours (T3) of the test. Differences in immune profiling in patients by timepoint (T0, T3) and GH response (growth hormone sufficient (GHS) versus growth hormone deficient (GHD)) were calculated using a two-way ANOVA test.  Results: A total of 54 patients (39 boys and 15 girls) aged five to 18 years were recruited. Twenty-two participants tested GHD (peak GH <10 ng/ml). The CyTOF analysis showed a significant increase from T0 to T3 in granulocyte percentage, monocyte count, and dendritic cell (DC) count; in contrast, a significant decrease was seen in T lymphocytes (helper and cytotoxic) and IgD+ B lymphocytes. The CBC analysis supported these findings: an increase in total white blood cell count, absolute neutrophil count, and neutrophil percentage; a decrease in absolute lymphocyte count, lymphocyte percentage, absolute eosinophil count, and absolute monocyte count. No significant differences were found between CBC/CyTOF measurements and GH status at either time. CONCLUSIONS: This study provides the first high-resolution map of acute changes in the immune system with GH stimulation. This implies a key role for GH in immunomodulatory function.

Serum autoantibodies to myelin peptides distinguish acute disseminated encephalomyelitis from relapsing-remitting multiple sclerosis.

BACKGROUND AND OBJECTIVE: Acute disseminated encephalomyelitis (ADEM) and relapsing-remitting multiple sclerosis (RRMS) share overlapping clinical, radiologic and laboratory features at onset. Because autoantibodies may contribute to the pathogenesis of both diseases, we sought to identify autoantibody biomarkers that are capable of distinguishing them. METHODS: We used custom antigen arrays to profile anti-myelin-peptide autoantibodies in sera derived from individuals with pediatric ADEM (n = 15), pediatric multiple sclerosis (Ped MS; n = 11) and adult MS (n = 15). Using isotype-specific secondary antibodies, we profiled both IgG and IgM reactivities. We used Statistical Analysis of Microarrays software to confirm the differences in autoantibody reactivity profiles between ADEM and MS samples. We used Prediction Analysis of Microarrays software to generate and validate prediction algorithms, based on the autoantibody reactivity profiles. RESULTS: ADEM was characterized by IgG autoantibodies targeting epitopes derived from myelin basic protein, proteolipid protein, myelin-associated oligodendrocyte basic glycoprotein, and alpha-B-crystallin. In contrast, MS was characterized by IgM autoantibodies targeting myelin basic protein, proteolipid protein, myelin-associated oligodendrocyte basic glycoprotein and oligodendrocyte-specific protein. We generated and validated prediction algorithms that distinguish ADEM serum (sensitivity 62-86%; specificity 56-79%) from MS serum (sensitivity 40-87%; specificity 62-86%) on the basis of combined IgG and IgM anti-myelin autoantibody reactivity to a small number of myelin peptides. CONCLUSIONS: Combined profiles of serum IgG and IgM autoantibodies identified myelin antigens that may be useful for distinguishing MS from ADEM. Further studies are required to establish clinical utility. Further biological assays are required to delineate the pathogenic potential of these antibodies.

Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics.

Federated learning is becoming increasingly more popular as the concern of privacy breaches rises across disciplines including the biological and biomedical fields. The main idea is to train models locally on each server using data that are only available to that server and aggregate the model (not data) information at the global level. While federated learning has made significant advancements for machine learning methods such as deep neural networks, to the best of our knowledge, its development in sparse Bayesian models is still lacking. Sparse Bayesian models are highly interpretable with natural uncertain quantification, a desirable property for many scientific problems. However, without a federated learning algorithm, their applicability to sensitive biological/biomedical data from multiple sources is limited. Therefore, to fill this gap in the literature, we propose a new Bayesian federated learning framework that is capable of pooling information from different data sources without breaching privacy. The proposed method is conceptually simple to understand and implement, accommodates sampling heterogeneity (i.e., non-iid observations) across data sources, and allows for principled uncertainty quantification. We illustrate the proposed framework with three concrete sparse Bayesian models, namely, sparse regression, Markov random field, and directed graphical models. The application of these three models is demonstrated through three real data examples including a multi-hospital COVID-19 study, breast cancer protein-protein interaction networks, and gene regulatory networks.