Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.

Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high dimensionality data. COPD is characterized by lung destruction and inflammation with smoke exposure being a major risk factor. To define novel biological mechanisms in COPD, we utilized unsupervised and supervised interpretable machine learning analyses of single cell-RNA sequencing data from the gold standard mouse smoke exposure model to identify significant latent factors (context-specific co-expression modules) impacting pathophysiology. The machine learning transcriptomic signatures coupled to protein networks uncovered a reduction in network complexity and novel biological alterations in actin-associated gelsolin (GSN), which was transcriptionally linked to disease state. GSN was altered in airway epithelial cells in the mouse model and in human COPD. GSN was increased in plasma from COPD patients, and smoke exposure resulted in enhanced GSN release from airway cells from COPD patients. This method provides insights into rewiring of transcriptional networks that are associated with COPD pathogenesis and provide a novel analytical platform for other diseases.

Spatial microniches of IL-2 combine with IL-10 to drive lung migratory TH2 cells in response to inhaled allergen.

The mechanisms that guide T helper 2 (TH2) cell differentiation in barrier tissues are unclear. Here we describe the molecular pathways driving allergen-specific TH2 cells using temporal, spatial and single-cell transcriptomic tracking of house dust mite-specific T cells in mice. Differentiation and migration of lung allergen-specific TH2 cells requires early expression of the transcriptional repressor Blimp-1. Loss of Blimp-1 during priming in the lymph node ablated the formation of TH2 cells in the lung, indicating early Blimp-1 promotes TH2 cells with migratory capability. IL-2/STAT5 signals and autocrine/paracrine IL-10 from house dust mite-specific T cells were essential for Blimp-1 and subsequent GATA3 upregulation through repression of Bcl6 and Bach2. Spatial microniches of IL-2 in the lymph node supported the earliest Blimp-1+TH2 cells, demonstrating lymph node localization is a driver of TH2 initiation. Our findings identify an early requirement for IL-2-mediated spatial microniches that integrate with allergen-driven IL-10 from responding T cells to drive allergic asthma.

Tim-3 Is Required for Regulatory T Cell-Mediated Promotion of T Cell Exhaustion and Viral Persistence during Chronic Lymphocytic Choriomeningitis Virus Infection.

Expression of T cell Ig and mucin domain-containing protein 3 (Tim-3) is upregulated on regulatory T cells (Tregs) during chronic viral infections. In several murine and human chronic infections, the expression of Tim-3 is associated with poor control of viral burden and impaired antiviral immune responses. However, the role of Tim-3+ Tregs during persistent viral infections has not been fully defined. We employed an inducible Treg-specific Tim-3 loss-of-function (Tim-3 Treg knockout) murine model to dissect the role of Tim-3 on Tregs during chronic lymphocytic choriomeningitis virus infection. Tim-3 Treg knockout mice exhibited a decrease in morbidity, a more potent virus-specific T cell response, and a significant decrease in viral burden. These mice also had a reduction in the frequency of PD-1+Tim-3+ and PD-1+Tox+ gp33-specific exhausted CD8+ T cells. Our findings demonstrate that modulation of a single surface protein on Tregs can lead to a reduction in viral burden, limit T cell exhaustion, and enhance gp33-specific T cell response. These studies may help to identify Tim-3-directed therapies for the management of persistent infections and cancer.

Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis.

Schistosomiasis, a highly prevalent parasitic disease, affects more than 200 million people worldwide. Current diagnostics based on parasite egg detection in stool detect infection only at a late stage, and current antibody-based tests cannot distinguish past from current infection. Here, we developed and used a multiplexed antibody profiling platform to obtain a comprehensive repertoire of antihelminth humoral profiles including isotype, subclass, Fc receptor (FcR) binding, and glycosylation profiles of antigen-specific antibodies. Using Essential Regression (ER) and SLIDE, interpretable machine learning methods, we identified latent factors (context-specific groups) that move beyond biomarkers and provide insights into the pathophysiology of different stages of schistosome infection. By comparing profiles of infected and healthy individuals, we identified modules with unique humoral signatures of active disease, including hallmark signatures of parasitic infection such as elevated immunoglobulin G4 (IgG4). However, we also captured previously uncharacterized humoral responses including elevated FcR binding and specific antibody glycoforms in patients with active infection, helping distinguish them from those without active infection but with equivalent antibody titers. This signature was validated in an independent cohort. Our approach also uncovered two distinct endotypes, nonpatent infection and prior infection, in those who were not actively infected. Higher amounts of IgG1 and FcR1/FcR3A binding were also found to be likely protective of the transition from nonpatent to active infection. Overall, we unveiled markers for antibody-based diagnostics and latent factors underlying the pathogenesis of schistosome infection. Our results suggest that selective antigen targeting could be useful in early detection, thus controlling infection severity.

Aberrant mitochondrial DNA synthesis in macrophages exacerbates inflammation and atherosclerosis.

There is a large body of evidence that cellular metabolism governs inflammation, and that inflammation contributes to the progression of atherosclerosis. However, whether mitochondrial DNA synthesis affects macrophage function and atherosclerosis pathology is not fully understood. Here we show, by transcriptomic analyzes of plaque macrophages, spatial single cell transcriptomics of atherosclerotic plaques, and functional experiments, that mitochondrial DNA (mtDNA) synthesis in atherosclerotic plaque macrophages are triggered by vascular cell adhesion molecule 1 (VCAM-1) under inflammatory conditions in both humans and mice. Mechanistically, VCAM-1 activates C/EBPα, which binds to the promoters of key mitochondrial biogenesis genes - Cmpk2 and Pgc1a. Increased CMPK2 and PGC-1α expression triggers mtDNA synthesis, which activates STING-mediated inflammation. Consistently, atherosclerosis and inflammation are less severe in Apoe-/- mice lacking Vcam1 in macrophages. Downregulation of macrophage-specific VCAM-1 in vivo leads to decreased expression of LYZ1 and FCOR, involved in STING signalling. Finally, VCAM-1 expression in human carotid plaque macrophages correlates with necrotic core area, mitochondrial volume, and oxidative damage to DNA. Collectively, our study highlights the importance of macrophage VCAM-1 in inflammation and atherogenesis pathology and proposes a self-acerbating pathway involving increased mtDNA synthesis.

Regulatory T Cell Insufficiency in Autoimmune Diabetes Is Driven by Selective Loss of Neuropilin-1 on Intraislet Regulatory T Cells.

Approaches to reverse or limit regulatory T cell (Treg) insufficiency are of great interest for development of immunotherapeutic treatments for autoimmune patients, including type 1 diabetes. Treg insufficiency is heavily implicated in the progression of autoimmune diabetes in the NOD mouse model and is characterized by defects in Treg numbers, development, and/or function. Utilizing a Treg-centric screen, we show that intraislet Tregs have a uniquely dysfunctional phenotype, hallmarked by an almost complete lack of neuropilin-1 (Nrp1), a cell surface receptor required to maintain Treg stability. Intraislet Nrp1- Tregs exhibit hallmark features of fragility, including reduced suppressive capacity, decreased CD73 and Helios, and increased Rorγt and Tbet. Intraislet Nrp1- Tregs also exhibit decreased Foxp3 expression on a per cell basis, suggesting that Nrp1 may also be required for long-term Treg stability. Mechanistically, Treg-restricted augmentation of Nrp1 expression limited the onset of autoimmune diabetes in NOD mice suggesting that Nrp1 critically impacts intraislet Treg function. Transcriptional analysis showed that Nrp1 restoration led to an increase in markers and pathways of TCR signaling, survival, and suppression, and when Nrp1 protein expression is examined by cellular indexing of transcriptomes and epitopes by sequencing, significant differences were observed between Nrp1+ and Nrp1- Tregs in all tissues, particularly in markers of Treg fragility. This translated into substantive differences between Nrp1+ and Nrp1- Tregs that afforded the former with a competitive advantage in the islets. Taken together, these data suggest that maintenance of Nrp1 expression and signaling on Tregs limits diabetes onset and may serve as a strategy to combat Treg insufficiency in autoimmune disease.

The RNA binding protein Arid5a drives IL-17-dependent autoantibody-induced glomerulonephritis.

Autoantibody-mediated glomerulonephritis (AGN) arises from dysregulated renal inflammation, with urgent need for improved treatments. IL-17 is implicated in AGN and drives pathology in a kidney-intrinsic manner via renal tubular epithelial cells (RTECs). Nonetheless, downstream signaling mechanisms provoking kidney pathology are poorly understood. A noncanonical RNA binding protein (RBP), Arid5a, was upregulated in human and mouse AGN. Arid5a-/- mice were refractory to AGN, with attenuated myeloid infiltration and impaired expression of IL-17-dependent cytokines and transcription factors (C/EBPß, C/EBPδ). Transcriptome-wide RIP-Seq revealed that Arid5a inducibly interacts with conventional IL-17 target mRNAs, including CEBPB and CEBPD. Unexpectedly, many Arid5a RNA targets corresponded to translational regulation and RNA processing pathways, including rRNAs. Indeed, global protein synthesis was repressed in Arid5a-deficient cells, and C/EBPs were controlled at the level of protein rather than RNA accumulation. IL-17 prompted Arid5a nuclear export and association with 18S rRNA, a 40S ribosome constituent. Accordingly, IL-17-dependent renal autoimmunity is driven by Arid5a at the level of ribosome interactions and translation.

Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.

New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike position or the stochastic nature of their appearance. Here we show that SARS-CoV-2 lineages have distinct evolutionary spaces (a probabilistic definition of the sequence states that can be occupied by expanding virus subpopulations). This space can be accurately inferred from the patterns of amino acid variability at the whole-protein level. Robust networks of co-variable sites identify the highest-likelihood mutations in new VOC sublineages and predict remarkably well the emergence of subvariants with resistance mutations to COVID-19 therapeutics. Our studies reveal the contribution of low frequency variant patterns at heterologous sites across the protein to accurate prediction of the changes at each position of interest.

The medical competence of health care providers in sub-Saharan Africa: Evidence from 16 127 providers across 11 countries.

Despite a consensus that quality of care is critically deficient in low-income countries, few nationally representative studies provide comparable measures of quality of care across countries. To address this gap, we used nationally representative data from in-person administrations of clinical vignettes to measure the competence of 16 127 health care providers across 11 sub-Saharan African countries. Rather than large variations across countries, we found that 81% of the variation in competence is within countries and the characteristics of health care providers do not explain most of this variation. Professional qualifications-including cadre and education-are only weakly associated with competence: across our sample, one-third of nurses are more competent than the average doctor in the same country and one-quarter of doctors are less competent than the average nurse. Finally, while younger cohorts do tend to be more competent, perhaps reflecting improvements in medical education, it would take 25 decades of turnover to improve care by 10 percentage points, on average, if we were to rely on such improvements alone. These patterns necessitate a fundamentally different approach to health care human resource management, calling into question typical staffing policies based on qualifications and seniority rather than directly measured quality.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions.

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

Temporal dynamics and genomic programming of plasma cell fates.

Affinity-matured plasma cells (PCs) of varying lifespans are generated through a germinal center (GC) response. The developmental dynamics and genomic programs of antigen-specific PC precursors remain to be elucidated. Here, using a model antigen in mice, we demonstrate biphasic generation of PC precursors, with those generating long-lived bone marrow PCs preferentially produced in the late phase of GC response. Clonal tracing using single-cell RNA sequencing and B cell antigen receptor sequencing in spleen and bone marrow compartments, coupled with adoptive transfer experiments, reveals a new PC transition state that gives rise to functionally competent PC precursors. The latter undergo clonal expansion, dependent on inducible expression of TIGIT. We propose a model for the proliferation and programming of precursors of long-lived PCs, based on extended antigen encounters in the GC.

Prevalence of long COVID symptoms in Haryana, India: a cross-sectional follow-up study.

Background: Emerging research indicates growing concern over long COVID globally, although there have been limited studies that estimate population burden. We aimed to estimate the burden of long COVID in three districts of Haryana, India, using an opportunity to link a seroprevalence study to follow-up survey of symptoms associated with long COVID. Methods: We used a population-based seroprevalence survey for COVID-19 conducted in September 2021 across Haryana, India. Adults from three purposively selected districts (Rohtak, Gurugram, and Jhajjar) were eligible to participate; 2205 of 3213 consented to participate in a survey on health status. Trained investigators administered a structured questionnaire that included demographic characteristics, self-reported symptoms of illness in the last six months before the survey, mental health, and history of COVID-19. Findings: Unadjusted regression estimates indicated positive correlations between symptomatic complaints and COVID-19 exposure, suggesting lingering effects of COVID-19 in this population. The overall physical morbidity index was higher among those who tested positive for COVID-19, as was the incidence of new cases. However, both morbidity and incidence became statistically insignificant after adjustment for multiple comparisons. Cough emerged as the only statistically significant individual persistent symptom. Sex-stratified analyses indicated significant estimates only for physical morbidity in women. Interpretation: This study is one of the first from India that uses a large population-based sample to examine longer term repercussions of COVID infections. The burden of long COVID should primarily be addressed in clinical settings, where specialised treatment for individual cases continues to evolve. Our analyses also provide insight into the size and nature of studies required to assess the population-level burden of long COVID. Funding: This paper was produced under the auspices of the Lancet COVID 19 Commission India Task Force, which was supported financially by the Reliance Foundation. The Lancet COVID 19 Commission was set up in July 2020 and submitted its final report by October 2022. This report by the India Task Force was prepared during the same period.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.

Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.

Care pathways of individuals with tuberculosis before and during the COVID-19 pandemic in Bandung, Indonesia.

The COVID-19 pandemic is thought to have undone years' worth of progress in the fight against tuberculosis (TB). For instance, in Indonesia, a high TB burden country, TB case notifications decreased by 14% and treatment coverage decreased by 47% during COVID-19. We sought to better understand the impact of COVID-19 on TB case detection using two cross-sectional surveys conducted before (2018) and after the onset of the pandemic (2021). These surveys allowed us to quantify the delays that individuals with TB who eventually received treatment at private providers faced while trying to access care for their illness, their journey to obtain a diagnosis, the encounters individuals had with healthcare providers before a TB diagnosis, and the factors associated with patient delay and the total number of provider encounters. We found some worsening of care seeking pathways on multiple dimensions. Median patient delay increased from 28 days (IQR: 10, 31) to 32 days (IQR: 14, 90) and the median number of encounters increased from 5 (IQR: 4, 8) to 7 (IQR: 5, 10), but doctor and treatment delays remained relatively unchanged. Employed individuals experienced shorter delays compared to unemployed individuals (adjusted medians: -20.13, CI -39.14, -1.12) while individuals whose initial consult was in the private hospitals experienced less encounters compared to those visiting public providers, private primary care providers, and informal providers (-4.29 encounters, CI -6.76, -1.81). Patients who visited the healthcare providers >6 times experienced longer total delay compared to those with less than 6 visits (adjusted medians: 59.40, 95% CI: 35.04, 83.77). Our findings suggest the need to ramp up awareness programs to reduce patient delay and strengthen private provide engagement in the country, particularly in the primary care sector.

Impact of the COVID-19 pandemic on quality of tuberculosis care in private facilities in Bandung, Indonesia: a repeated cross-sectional standardized patients study.

BACKGROUND: Indonesia has the second highest incidence of tuberculosis in the world. While 74% of people with tuberculosis in Indonesia first accessed the private health sector when seeking care for their symptoms, only 18% of tuberculosis notifications originate in the private sector. Little is known about the impact of the COVID-19 pandemic on the private sector. Using unannounced standardized patient visits to private providers, we aimed to measure quality of tuberculosis care during the COVID-19 pandemic. METHODS: A cross-sectional study was conducted using standardized patients in Bandung City, West Java, Indonesia. Ten standardized patients completed 292 visits with private providers between 9 July 2021 and 21 January 2022, wherein standardized patients presented a presumptive tuberculosis case. Results were compared to standardized patients surveys conducted in the same geographical area before the onset of COVID-19. RESULTS: Overall, 35% (95% confidence interval (CI): 29.2-40.4%) of visits were managed correctly according to national tuberculosis guidelines. There were no significant differences in the clinical management of presumptive tuberculosis patients before and during the COVID-19 pandemic, apart from an increase in temperature checks (adjusted odds ratio (aOR): 8.05, 95% CI: 2.96-21.9, p < 0.001) and a decrease in throat examinations (aOR 0.16, 95% CI: 0.06-0.41, p = 0.002) conducted during the pandemic. CONCLUSIONS: Results indicate that providers successfully identify tuberculosis in their patients yet do not manage them according to national guidelines. There were no major changes found in quality of tuberculosis care due to the COVID-19 pandemic. As tuberculosis notifications have declined in Indonesia due to the COVID-19 pandemic, there remains an urgent need to increase private provider engagement in Indonesia and improve quality of care.

SARS-CoV-2 mRNA Vaccines Induce Greater Complement Activation and Decreased Viremia and Nef Antibodies in Men With HIV-1.

BACKGROUND: Immune dysregulation in people with human immunodeficiency virus-1 (PWH) persists despite potent antiretroviral therapy and, consequently, PWH tend to have lower immune responses to licensed vaccines. However, limited information is available about the impact of mRNA vaccines in PWH. This study details the immunologic responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccines in PWH and their impact on HIV-1. METHODS: We quantified anti-S immunoglobulin G (IgG) binding and neutralization of 3 SARS-CoV-2 variants of concern and complement activation in blood from virally suppressed men with HIV-1 (MWH) and men without HIV-1 (MWOH), and the characteristics that may impact the vaccine immune responses. We also studied antibody levels against HIV-1 proteins and HIV-1 plasma RNA. RESULTS: MWH had lower anti-S IgG binding and neutralizing antibodies against the 3 variants compared to MWOH. MWH also produced anti-S1 antibodies with a 10-fold greater ability to activate complement and exhibited higher C3a blood levels than MWOH. MWH had decreased residual HIV-1 plasma viremia and anti-Nef IgG approximately 100 days after immunization. CONCLUSIONS: MWH respond to SARS-CoV-2 mRNA vaccines with lower antibody titers and with greater activation of complement, while exhibiting a decrease in HIV-1 viremia and anti-Nef antibodies. These results suggest an important role of complement activation mediating protection in MWH.

Transcriptomic and Proteomic Characterization of the Immune Response to Elective Spinal Reconstructive Surgery: Impact of Aging and Comparison with Traumatic Injury Response.

BACKGROUND: Major surgery triggers trauma-like stress responses linked to age, surgery duration, and blood loss, resembling polytrauma. This similarity suggests elective surgery as a surrogate model for studying polytrauma immune responses. We investigated stress responses across age groups and compared them with those of polytrauma patients. STUDY DESIGN: Patients undergoing major spinal reconstruction surgery were divided into older (age >65 years, n = 5) and young (age 18 to 39 years, n = 6) groups. A comparison group consisted of matched trauma patients (n = 8). Blood samples were collected before, during, and after surgery. Bone marrow mononuclear cells and peripheral blood mononuclear cells were analyzed using cellular indexing of transcriptomes and epitopes sequencing or single-cell RNA sequencing. Plasma was subjected to dual-platform proteomic analysis (SomaLogic and O-link). RESULTS: Response to polytrauma was highest within 4 hours. By comparison, the response to surgery was highest at 24 hours. Both insults triggered significant changes in cluster of differentiation 14 monocytes, with increased inflammation and lower major histocompatibility complex-class 2 expression. Older patient's cluster of differentiation 14 monocytes displayed higher inflammation and less major histocompatibility complex-class 2 suppression; a trend was also seen in bone marrow mononuclear cells. Although natural killer cells were markedly activated after polytrauma, they were suppressed after surgery, especially in older patients. In plasma, innate immunity proteins dominated at 24 hours, shifting to adaptive immunity proteins by 6 weeks with heightened inflammation in older patients. Senescence-associated secretory phenotype proteins were higher in older patients at baseline and further elevated during and after surgery. CONCLUSIONS: Although both major surgery and polytrauma initiate immune and stress responses, substantial differences exist in timing and cellular profiles, suggesting major elective surgery is not a suitable surrogate for the polytrauma response. Nonetheless, distinct responses in young vs older patients highlight the utility of elective spinal in studying patient-specific factors affecting outcomes after major elective surgery.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches.

A 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.