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.

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.

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.

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.

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.

A personalized network framework reveals predictive axis of anti-TNF response across diseases.

Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we use a computational approach termed Disruption Networks to generate a data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. This data type extends beyond the "feature level space", to the "relations space", by quantifying individual-level breaking or rewiring of cross-feature relations. Applying Disruption Networks to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.

Interferon-stimulated neutrophils as a predictor of immunotherapy response.

Despite the remarkable success of anti-cancer immunotherapy, its effectiveness remains confined to a subset of patients-emphasizing the importance of predictive biomarkers in clinical decision-making and further mechanistic understanding of treatment response. Current biomarkers, however, lack the power required to accurately stratify patients. Here, we identify interferon-stimulated, Ly6Ehi neutrophils as a blood-borne biomarker of anti-PD1 response in mice at baseline. Ly6Ehi neutrophils are induced by tumor-intrinsic activation of the STING (stimulator of interferon genes) signaling pathway and possess the ability to directly sensitize otherwise non-responsive tumors to anti-PD1 therapy, in part through IL12b-dependent activation of cytotoxic T cells. By translating our pre-clinical findings to a cohort of patients with non-small cell lung cancer and melanoma (n = 109), and to public data (n = 1440), we demonstrate the ability of Ly6Ehi neutrophils to predict immunotherapy response in humans with high accuracy (average AUC ≈ 0.9). Overall, our study identifies a functionally active biomarker for use in both mice and humans.

Cell type-specific gene expression differences in complex tissues.

We describe cell type-specific significance analysis of microarrays (csSAM) for analyzing differential gene expression for each cell type in a biological sample from microarray data and relative cell-type frequencies. First, we validated csSAM with predesigned mixtures and then applied it to whole-blood gene expression datasets from stable post-transplant kidney transplant recipients and those experiencing acute transplant rejection, which revealed hundreds of differentially expressed genes that were otherwise undetectable.

Network motifs in the transcriptional regulation network of Escherichia coli.

Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

Reconstructing disease dynamics for mechanistic insights and clinical benefit.

Diseases change over time, both phenotypically and in their underlying molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult due to their complexity and the high heterogeneity in disease development between individuals. We present TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional, short time-series data. We demonstrate the utility of TimeAx by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identify a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increased mortality. Moreover, the continuous TimeAx model differentiates between early and late tumors within the same tumor subtype, uncovering molecular transitions and potential targetable pathways. Overall, we present a powerful approach for studying disease progression dynamics-providing improved molecular interpretability and clinical benefits for patient stratification and outcome prediction.

Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation and is mediated by multiple immune cell types. In this work, we aimed to determine the relevance of changes in cell proportions in peripheral blood mononuclear cells (PBMCs) during the development of disease and following treatment. Samples from healthy blood donors, newly diagnosed RA patients, and established RA patients that had an inadequate response to MTX and were about to start tumor necrosis factor inhibitors (TNFi) treatment were collected before and after 3 months of treatment. We used in parallel a computational deconvolution approach based on RNA expression and flow cytometry to determine the relative cell-type frequencies. Cell-type frequencies from deconvolution of gene expression indicate that monocytes (both classical and non-classical) and CD4+ cells (Th1 and Th2) were increased in RA patients compared to controls, while NK cells and B cells (naïve and mature) were significantly decreased in RA patients. Treatment with MTX caused a decrease in B cells (memory and plasma cell), and a decrease in CD4 Th cells (Th1 and Th17), while treatment with TNFi resulted in a significant increase in the population of B cells. Characterization of the RNA expression patterns found that most of the differentially expressed genes in RA subjects after treatment can be explained by changes in cell frequencies (98% and 74% respectively for MTX and TNFi).