UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma.

Although clonal neo-antigen burden is associated with improved response to immune therapy, the functional basis for this remains unclear. Here we study this question in a novel controlled mouse melanoma model that enables us to explore the effects of intra-tumor heterogeneity (ITH) on tumor aggressiveness and immunity independent of tumor mutational burden. Induction of UVB-derived mutations yields highly aggressive tumors with decreased anti-tumor activity. However, single-cell-derived tumors with reduced ITH are swiftly rejected. Their rejection is accompanied by increased T cell reactivity and a less suppressive microenvironment. Using phylogenetic analyses and mixing experiments of single-cell clones, we dissect two characteristics of ITH: the number of clones forming the tumor and their clonal diversity. Our analysis of melanoma patient tumor data recapitulates our results in terms of overall survival and response to immune checkpoint therapy. These findings highlight the importance of clonal mutations in robust immune surveillance and the need to quantify patient ITH to determine the response to checkpoint blockade.

Variation in the human immune system is largely driven by non-heritable influences.

There is considerable heterogeneity in immunological parameters between individuals, but its sources are largely unknown. To assess the relative contribution of heritable versus non-heritable factors, we have performed a systems-level analysis of 210 healthy twins between 8 and 82 years of age. We measured 204 different parameters, including cell population frequencies, cytokine responses, and serum proteins, and found that 77% of these are dominated (>50% of variance) and 58% almost completely determined (>80% of variance) by non-heritable influences. In addition, some of these parameters become more variable with age, suggesting the cumulative influence of environmental exposure. Similarly, the serological responses to seasonal influenza vaccination are also determined largely by non-heritable factors, likely due to repeated exposure to different strains. Lastly, in MZ twins discordant for cytomegalovirus infection, more than half of all parameters are affected. These results highlight the largely reactive and adaptive nature of the immune system in healthy individuals.

Interactions between immune cell types facilitate the evolution of immune traits.

An essential prerequisite for evolution by natural selection is variation among individuals in traits that affect fitness1. The ability of a system to produce selectable variation, known as evolvability2, thus markedly affects the rate of evolution. Although the immune system is among the fastest-evolving components in mammals3, the sources of variation in immune traits remain largely unknown4,5. Here we show that an important determinant of the immune system's evolvability is its organization into interacting modules represented by different immune cell types. By profiling immune cell variation in bone marrow of 54 genetically diverse mouse strains from the Collaborative Cross6, we found that variation in immune cell frequencies is polygenic and that many associated genes are involved in homeostatic balance through cell-intrinsic functions of proliferation, migration and cell death. However, we also found genes associated with the frequency of a particular cell type that are expressed in a different cell type, exerting their effect in what we term cyto-trans. The vertebrate evolutionary record shows that genes associated in cyto-trans have faced weaker negative selection, thus increasing the robustness and hence evolvability2,7,8 of the immune system. This phenomenon is similarly observable in human blood. Our findings suggest that interactions between different components of the immune system provide a phenotypic space in which mutations can produce variation with little detriment, underscoring the role of modularity in the evolution of complex systems9.

Social network architecture of human immune cells unveiled by quantitative proteomics.

The immune system is unique in its dynamic interplay between numerous cell types. However, a system-wide view of how immune cells communicate to protect against disease has not yet been established. We applied high-resolution mass-spectrometry-based proteomics to characterize 28 primary human hematopoietic cell populations in steady and activated states at a depth of >10,000 proteins in total. Protein copy numbers revealed a specialization of immune cells for ligand and receptor expression, thereby connecting distinct immune functions. By integrating total and secreted proteomes, we discovered fundamental intercellular communication structures and previously unknown connections between cell types. Our publicly accessible (http://www.immprot.org/) proteomic resource provides a framework for the orchestration of cellular interplay and a reference for altered communication associated with pathology.

UV-Protection Timer Controls Linkage between Stress and Pigmentation Skin Protection Systems.

Skin sun exposure induces two protection programs: stress responses and pigmentation, the former within minutes and the latter only hours afterward. Although serving the same physiological purpose, it is not known whether and how these programs are coordinated. Here, we report that UVB exposure every other day induces significantly more skin pigmentation than the higher frequency of daily exposure, without an associated increase in stress responses. Using mathematical modeling and empirical studies, we show that the melanocyte master regulator, MITF, serves to synchronize stress responses and pigmentation and, furthermore, functions as a UV-protection timer via damped oscillatory dynamics, thereby conferring a trade-off between the two programs. MITF oscillations are controlled by multiple negative regulatory loops, one at the transcriptional level involving HIF1α and another post-transcriptional loop involving microRNA-148a. These findings support trait linkage between the two skin protection programs, which, we speculate, arose during furless skin evolution to minimize skin damage.

Systems Analysis of Immunity to Influenza Vaccination across Multiple Years and in Diverse Populations Reveals Shared Molecular Signatures.

Systems approaches have been used to describe molecular signatures driving immunity to influenza vaccination in humans. Whether such signatures are similar across multiple seasons and in diverse populations is unknown. We applied systems approaches to study immune responses in young, elderly, and diabetic subjects vaccinated with the seasonal influenza vaccine across five consecutive seasons. Signatures of innate immunity and plasmablasts correlated with and predicted influenza antibody titers at 1 month after vaccination with >80% accuracy across multiple seasons but were not associated with the longevity of the response. Baseline signatures of lymphocyte and monocyte inflammation were positively and negatively correlated, respectively, with antibody responses at 1 month. Finally, integrative analysis of microRNAs and transcriptomic profiling revealed potential regulators of vaccine immunity. These results identify shared vaccine-induced signatures across multiple seasons and in diverse populations and might help guide the development of next-generation vaccines that provide persistent immunity against influenza.

Antibiotic use differentially affects the risk of anti-drug antibody formation during anti-TNFα therapy in inflammatory bowel disease patients: a report from the epi-IIRN.

OBJECTIVE: Anti-drug antibodies (ADA) to anti-tumour necrosis factor (anti-TNF) therapy drive treatment loss of response. An association between intestinal microbial composition and response to anti-TNF therapy was noted. We therefore aimed to assess the implications of antibiotic treatments on ADA formation in patients with inflammatory bowel disease (IBD). DESIGN: We analysed data from the epi-IIRN (epidemiology group of the Israeli IBD research nucleus), a nationwide registry of all patients with IBD in Israel. We included all patients treated with anti-TNF who had available ADA levels. Survival analysis with drug use as time varying covariates were used to assess the association between antibiotic use and ADA development. Next, specific pathogen and germ-free C57BL mice were treated with respective antibiotics and challenged with infliximab. ADA were assessed after 14 days. RESULTS: Among 1946 eligible patients, with a median follow-up of 651 days from initiation of therapy, 363 had positive ADA. Cox proportional hazard model demonstrated an increased risk of ADA development in patients who used cephalosporins (HR=1.97, 95% CI 1.58 to 2.44), or penicillins with ß-lactamase inhibitors (penicillin-BLI, HR=1.4, 95% CI 1.13 to 1.74), whereas a reduced risk was noted in patients treated with macrolides (HR=0.38, 95% CI 0.16 to 0.86) or fluoroquinolones (HR=0.20, 95% CI 0.12 to 0.35). In mice exposed to infliximab, significantly increased ADA production was observed in cephalosporin as compared with macrolide pretreated mice. Germ-free mice produced no ADA. CONCLUSION: ADA production is associated with the microbial composition. The risk of ADA development during anti-TNF therapy can possibly be reduced by avoidance of cephalosporins and penicillin-BLIs, or by treatment with fluoroquinolones or macrolides.

Investigating the potential of a prematurely aged immune phenotype in severely injured patients as predictor of risk of sepsis.

BACKGROUND: Traumatic injury elicits a hyperinflammatory response and remodelling of the immune system leading to immuneparesis. This study aimed to evaluate whether traumatic injury results in a state of prematurely aged immune phenotype to relate this to clinical outcomes and a greater risk of developing additional morbidities post-injury. METHODS AND FINDINGS: Blood samples were collected from 57 critically injured patients with a mean Injury Severity Score (ISS) of 26 (range 15-75 years), mean age of 39.67 years (range 20-84 years), and 80.7% males, at days 3, 14, 28 and 60 post-hospital admission. 55 healthy controls (HC), mean age 40.57 years (range 20-85 years), 89.7% males were also recruited. The phenotype and frequency of adaptive immune cells were used to calculate the IMM-AGE score, an indicator of the degree of phenotypic ageing of the immune system. IMM-AGE was elevated in trauma patients at an early timepoint (day 3) in comparison with healthy controls (p < 0.001), driven by an increase in senescent CD8 T cells (p < 0.0001), memory CD8 T cells (p < 0.0001) and regulatory T cells (p < 0.0001) and a reduction in naïve CD8 T cells (p < 0.001) and overall T cell lymphopenia (p < 0 .0001). These changes persisted to day 60. Furthermore, the IMM-AGE scores were significantly higher in trauma patients (mean score 0.72) that developed sepsis (p = 0.05) in comparison with those (mean score 0.61) that did not. CONCLUSIONS: The profoundly altered peripheral adaptive immune compartment after critical injury can be used as a potential biomarker to identify individuals at a high risk of developing sepsis and this state of prematurely aged immune phenotype in biologically young individuals persists for up to two months post-hospitalisation, compromising the host immune response to infections. Reversing this aged immune system is likely to have a beneficial impact on short- and longer-term outcomes of trauma survivors.

Alignment of single-cell trajectories to compare cellular expression dynamics.

Single-cell RNA sequencing and high-dimensional cytometry can be used to generate detailed trajectories of dynamic biological processes such as differentiation or development. Here we present cellAlign, a quantitative framework for comparing expression dynamics within and between single-cell trajectories. By applying cellAlign to mouse and human embryonic developmental trajectories, we systematically delineate differences in the temporal regulation of gene expression programs that would otherwise be masked.

Found In Translation: a machine learning model for mouse-to-human inference.

Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we present Found In Translation (FIT; http://www.mouse2man.org ), a statistical methodology that leverages public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition. We applied FIT to data from mouse models of 28 different human diseases and identified experimental conditions in which FIT predictions outperformed direct cross-species extrapolation from mouse results, increasing the overlap of differentially expressed genes by 20-50%. FIT predicted novel disease-associated genes, an example of which we validated experimentally. FIT highlights signals that may otherwise be missed and reduces false leads, with no experimental cost.

Infliximab-Tumor Necrosis Factor Complexes Elicit Formation of Anti-Drug Antibodies.

BACKGROUND & AIMS: Some patients develop anti-drug antibodies (ADAs), which reduce the efficacy of infliximab, a monoclonal antibody against tumor necrosis factor (TNF), in the treatment of immune-mediated diseases, including inflammatory bowel diseases. ADAs arise inconsistently, and it is not clear what factors determine their formation. We investigated features of the immune system, the infliximab antibody, and its complex with TNF that might contribute to ADA generation. METHODS: C57BL/6 mice were given injections of infliximab and recombinant human TNF or infliximab F(ab')2 fragments. Blood samples were collected every 2-3 days for 2 weeks and weekly thereafter for up to 6 weeks; infliximab-TNF complexes and ADAs were measured by enzyme-linked immunosorbent assay (ELISA). Intestinal biopsy and blood samples were obtained from patients having endoscopy who had received infliximab therapy for inflammatory bowel diseases; infliximab-TNF complexes were measured with ELISA. Infliximab-specific plasma cells were detected in patient tissue samples by using mass cytometry. We studied activation of innate immune cells in peripheral blood mononuclear cells (PBMCs) from healthy donors incubated with infliximab or infliximab-TNF complexes; toll-like receptors (TLRs) were blocked with antibodies, endocytosis was blocked with the inhibitor PitStop2, and cytokine expression was measured by real-time polymerase chain reaction and ELISAs. Uptake of infliximab and infliximab-TNF complexes by THP-1 cells was measured with confocal microscopy. RESULTS: Mice given increasing doses of infliximab produced increasing levels of ADAs. Blood samples from mice given injections of human TNF and infliximab contained infliximab-TNF complexes; complex formation was associated with ADA formation with an area under the curve of 0.944 (95% confidence interval, 0.851-1.000; P = .003). Intestinal tissues from patients, but not blood samples, contained infliximab-TNF complexes and infliximab-specific plasma cells. Incubation of PBMCs with infliximab-TNF complexes resulted in a 4.74-fold increase in level of interleukin (IL) 1ß (IL1B) messenger RNA (P for comparison = .005), increased IL1B protein secretion, and a 2.69-fold increase in the expression of TNF messenger RNA (P for comparison = 0.013) compared with control PBMCs. Infliximab reduced only IL1B and TNF expression. Antibodies against TLR2 or TLR4 did not block the increases in IL1B or TNF expression, but endocytosis was required. THP-1 cells endocytosed higher levels of infliximab-TNF complexes than infliximab alone. CONCLUSIONS: In mice, we found ADA formation to increase with dose of infliximab given and concentration of infliximab-TNF complexes detected in blood. Based on studies of human intestinal tissues and blood samples, we propose that infliximab-TNF complexes formed in the intestine are endocytosed by and activate innate immune cells, which increase expression of IL1B and TNF and production of antibodies against the drug complex. It is therefore important to optimize the infliximab dose to a level that is effective but does not activate an innate immune response against the drug-TNF complex.

Simulation of single-protein nanopore sensing shows feasibility for whole-proteome identification.

Single-molecule techniques for protein sequencing are making headway towards single-cell proteomics and are projected to propel our understanding of cellular biology and disease. Yet, single cell proteomics presents a substantial unmet challenge due to the unavailability of protein amplification techniques, and the vast dynamic-range of protein expression in cells. Here, we describe and computationally investigate the feasibility of a novel approach for single-protein identification using tri-color fluorescence and plasmonic-nanopore devices. Comprehensive computer simulations of denatured protein translocation processes through the nanopores show that the tri-color fluorescence time-traces retain sufficient information to permit pattern-recognition algorithms to correctly identify the vast majority of proteins in the human proteome. Importantly, even when taking into account realistic experimental conditions, which restrict the spatial and temporal resolutions as well as the labeling efficiency, and add substantial noise, a deep-learning protein classifier achieves 97% whole-proteome accuracies. Applying our approach for protein datasets of clinical relevancy, such as the plasma proteome or cytokine panels, we obtain ~98% correct protein identification. This study suggests the feasibility of a method for accurate and high-throughput protein identification, which is highly versatile and applicable.

Autologous Hematological Stem Cell Transplantation for Systemic Sclerosis in Israel.

BACKGROUND: Autologous hematological stem cell transplantation (HSCT) is a novel therapy for systemic sclerosis (SSc) that has been validated in three randomized controlled trials. OBJECTIVES: To report the first Israeli experience with HSCT for progressive SSc and review the current literature. METHODS: Five SSc patients who were evaluated in our department and were treated by HSCT were included. Medical records were evaluated retrospectively. Demographic, clinical, and laboratory data were recorded. Continuous data are presented as the mean ± standard deviation. Categorical variables are presented as frequencies and percentages. RESULTS: Five SSc patients were treated with HSCT. Four patients were adults (mean age 53 ± 12 years) and one was a 12-year-old pediatric patient. All patients were female. HSCT was initiated 1.4 ± 0.8 years after diagnosis. Two patients were RNA POLIII positive, two were anti-topoisomerase 1 positive, and one only antinuclear antibodies positive. All patients had skin and lung involvement. The mean modified Rodnan Skin Score was 29 ± 4.7 before HSCT, which improved to 10.4 ± 9.6 after HSCT. The forced vital capacity improved from 68 ± 13% to 90 ± 28%. Diffusing capacity of the lungs for carbon monoxide increased by 6%. Among severe adverse events were cyclophosphamide-related congestive heart failure, antithymocyte globulin-related capillary leak syndrome, and scleroderma renal crisis. All symptoms completely resolved with treatment without sequela. No treatment related mortality was recorded. CONCLUSIONS: HSCT is an important step in the treatment of progressive SSc in Israel. Careful patient selection reduces treatment related morbidity and mortality.

Cell-centred meta-analysis reveals baseline predictors of anti-TNFα non-response in biopsy and blood of patients with IBD.

OBJECTIVE: Although anti-tumour necrosis factor alpha (anti-TNFα) therapies represent a major breakthrough in IBD therapy, their cost-benefit ratio is hampered by an overall 30% non-response rate, adverse side effects and high costs. Thus, finding predictive biomarkers of non-response prior to commencing anti-TNFα therapy is of high value. DESIGN: We analysed publicly available whole-genome expression profiles of colon biopsies obtained from multiple cohorts of patients with IBD using a combined computational deconvolution-meta-analysis paradigm which allows to estimate immune cell contribution to the measured expression and capture differential regulatory programmes otherwise masked due to variation in cellular composition. Insights from this in silico approach were experimentally validated in biopsies and blood samples of three independent test cohorts. RESULTS: We found the proportion of plasma cells as a robust pretreatment biomarker of non-response to therapy, which we validated in two independent cohorts of immune-stained colon biopsies, where a plasma cellular score from inflamed biopsies was predictive of non-response with an area under the curve (AUC) of 82%. Meta-analysis of the cell proportion-adjusted gene expression data suggested that an increase in inflammatory macrophages in anti-TNFα non-responding individuals is associated with the upregulation of the triggering receptor expressed on myeloid cells 1 (TREM-1) and chemokine receptor type 2 (CCR2)-chemokine ligand 7 (CCL7) -axes. Blood gene expression analysis of an independent cohort, identified TREM-1 downregulation in non-responders at baseline, which was predictive of response with an AUC of 94%. CONCLUSIONS: Our study proposes two clinically feasible assays, one in biopsy and one in blood, for predicting non-response to anti-TNFα therapy prior to initiation of treatment. Moreover, it suggests that mechanism-driven novel drugs for non-responders should be developed.

Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset.

BACKGROUND: Correcting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task. Attempting to remove these batch effects will result in some biologically meaningful signals being lost. Thus, a central challenge is assessing if the removal of unwanted technical variation harms the biological signal that is of interest to the researcher. RESULTS: We describe a novel framework, B-CeF, to evaluate the effectiveness of batch correction methods and their tendency toward over or under correction. The approach is based on comparing co-expression of adjusted gene-gene pairs to a-priori knowledge of highly confident gene-gene associations based on thousands of unrelated experiments derived from an external reference. Our framework includes three steps: (1) data adjustment with the desired methods (2) calculating gene-gene co-expression measurements for adjusted datasets (3) evaluating the performance of the co-expression measurements against a gold standard. Using the framework, we evaluated five batch correction methods applied to RNA-seq data of six representative tissue datasets derived from the GTEx project. CONCLUSIONS: Our framework enables the evaluation of batch correction methods to better preserve the original biological signal. We show that using a multiple linear regression model to correct for known confounders outperforms factor analysis-based methods that estimate hidden confounders. The code is publicly available as an R package.

Antigen-dependent integration of opposing proximal TCR-signaling cascades determines the functional fate of T lymphocytes.

T cell anergy is a key tolerance mechanism to mitigate unwanted T cell activation against self by rendering lymphocytes functionally inactive following Ag encounter. Ag plays an important role in anergy induction where high supraoptimal doses lead to the unresponsive phenotype. How T cells "measure" Ag dose and how this determines functional output to a given antigenic dose remain unclear. Using multiparametric phospho-flow and mass cytometry, we measured the intracellular phosphorylation-dependent signaling events at a single-cell resolution and studied the phosphorylation levels of key proximal human TCR activation- and inhibition-signaling molecules. We show that the intracellular balance and signal integration between these opposing signaling cascades serve as the molecular switch gauging Ag dose. An Ag density of 100 peptide-MHC complexes/cell was found to be the transition point between dominant activation and inhibition cascades, whereas higher Ag doses induced an anergic functional state. Finally, the neutralization of key inhibitory molecules reversed T cell unresponsiveness and enabled maximal T cell functions, even in the presence of very high Ag doses. This mechanism permits T cells to make integrated "measurements" of Ag dose that determine subsequent functional outcomes.

Significance analysis of xMap cytokine bead arrays.

Highly multiplexed assays using antibody coated, fluorescent (xMap) beads are widely used to measure quantities of soluble analytes, such as cytokines and antibodies in clinical and other studies. Current analyses of these assays use methods based on standard curves that have limitations in detecting low or high abundance analytes. Here we describe SAxCyB (Significance Analysis of xMap Cytokine Beads), a method that uses fluorescence measurements of individual beads to find significant differences between experimental conditions. We show that SAxCyB outperforms conventional analysis schemes in both sensitivity (low fluorescence) and robustness (high variability) and has enabled us to find many new differentially expressed cytokines in published studies.

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.

Reconstructing the genomic content of microbiome taxa through shotgun metagenomic deconvolution.

Metagenomics has transformed our understanding of the microbial world, allowing researchers to bypass the need to isolate and culture individual taxa and to directly characterize both the taxonomic and gene compositions of environmental samples. However, associating the genes found in a metagenomic sample with the specific taxa of origin remains a critical challenge. Existing binning methods, based on nucleotide composition or alignment to reference genomes allow only a coarse-grained classification and rely heavily on the availability of sequenced genomes from closely related taxa. Here, we introduce a novel computational framework, integrating variation in gene abundances across multiple samples with taxonomic abundance data to deconvolve metagenomic samples into taxa-specific gene profiles and to reconstruct the genomic content of community members. This assembly-free method is not bounded by various factors limiting previously described methods of metagenomic binning or metagenomic assembly and represents a fundamentally different approach to metagenomic-based genome reconstruction. An implementation of this framework is available at http://elbo.gs.washington.edu/software.html. We first describe the mathematical foundations of our framework and discuss considerations for implementing its various components. We demonstrate the ability of this framework to accurately deconvolve a set of metagenomic samples and to recover the gene content of individual taxa using synthetic metagenomic samples. We specifically characterize determinants of prediction accuracy and examine the impact of annotation errors on the reconstructed genomes. We finally apply metagenomic deconvolution to samples from the Human Microbiome Project, successfully reconstructing genus-level genomic content of various microbial genera, based solely on variation in gene count. These reconstructed genera are shown to correctly capture genus-specific properties. With the accumulation of metagenomic data, this deconvolution framework provides an essential tool for characterizing microbial taxa never before seen, laying the foundation for addressing fundamental questions concerning the taxa comprising diverse microbial communities.