JAK-STAT signaling maintains homeostasis in T cells and macrophages.

Immune cells need to sustain a state of constant alertness over a lifetime. Yet, little is known about the regulatory processes that control the fluent and fragile balance that is called homeostasis. Here we demonstrate that JAK-STAT signaling, beyond its role in immune responses, is a major regulator of immune cell homeostasis. We investigated JAK-STAT-mediated transcription and chromatin accessibility across 12 mouse models, including knockouts of all STAT transcription factors and of the TYK2 kinase. Baseline JAK-STAT signaling was detected in CD8+ T cells and macrophages of unperturbed mice-but abrogated in the knockouts and in unstimulated immune cells deprived of their normal tissue context. We observed diverse gene-regulatory programs, including effects of STAT2 and IRF9 that were independent of STAT1. In summary, our large-scale dataset and integrative analysis of JAK-STAT mutant and wild-type mice uncovered a crucial role of JAK-STAT signaling in unstimulated immune cells, where it contributes to a poised epigenetic and transcriptional state and helps prepare these cells for rapid response to immune stimuli.

Cell-autonomous regulation of complement C3 by factor H limits macrophage efferocytosis and exacerbates atherosclerosis.

Complement factor H (CFH) negatively regulates consumption of complement component 3 (C3), thereby restricting complement activation. Genetic variants in CFH predispose to chronic inflammatory disease. Here, we examined the impact of CFH on atherosclerosis development. In a mouse model of atherosclerosis, CFH deficiency limited plaque necrosis in a C3-dependent manner. Deletion of CFH in monocyte-derived inflammatory macrophages propagated uncontrolled cell-autonomous C3 consumption without downstream C5 activation and heightened efferocytotic capacity. Among leukocytes, Cfh expression was restricted to monocytes and macrophages, increased during inflammation, and coincided with the accumulation of intracellular C3. Macrophage-derived CFH was sufficient to dampen resolution of inflammation, and hematopoietic deletion of CFH in atherosclerosis-prone mice promoted lesional efferocytosis and reduced plaque size. Furthermore, we identified monocyte-derived inflammatory macrophages expressing C3 and CFH in human atherosclerotic plaques. Our findings reveal a regulatory axis wherein CFH controls intracellular C3 levels of macrophages in a cell-autonomous manner, evidencing the importance of on-site complement regulation in the pathogenesis of inflammatory diseases.

Structural cells are key regulators of organ-specific immune responses.

The mammalian immune system implements a remarkably effective set of mechanisms for fighting pathogens1. Its main components are haematopoietic immune cells, including myeloid cells that control innate immunity, and lymphoid cells that constitute adaptive immunity2. However, immune functions are not unique to haematopoietic cells, and many other cell types display basic mechanisms of pathogen defence3-5. To advance our understanding of immunology outside the haematopoietic system, here we systematically investigate the regulation of immune genes in the three major types of structural cells: epithelium, endothelium and fibroblasts. We characterize these cell types across twelve organs in mice, using cellular phenotyping, transcriptome sequencing, chromatin accessibility profiling and epigenome mapping. This comprehensive dataset revealed complex immune gene activity and regulation in structural cells. The observed patterns were highly organ-specific and seem to modulate the extensive interactions between structural cells and haematopoietic immune cells. Moreover, we identified an epigenetically encoded immune potential in structural cells under tissue homeostasis, which was triggered in response to systemic viral infection. This study highlights the prevalence and organ-specific complexity of immune gene activity in non-haematopoietic structural cells, and it provides a high-resolution, multi-omics atlas of the epigenetic and transcriptional networks that regulate structural cells in the mouse.

The molecular and phenotypic makeup of fetal human skin T lymphocytes.

The adult human skin contains a vast number of T cells that are essential for skin homeostasis and pathogen defense. T cells are first observed in the skin at the early stages of gestation; however, our understanding of their contribution to early immunity has been limited by their low abundance and lack of comprehensive methodologies for their assessment. Here, we describe a new workflow for isolating and expanding significant amounts of T cells from fetal human skin. Using multiparametric flow cytometry and in situ immunofluorescence, we found a large population with a naive phenotype and small populations with a memory and regulatory phenotype. Their molecular state was characterized using single-cell transcriptomics and TCR repertoire profiling. Importantly, culture of total fetal skin biopsies facilitated T cell expansion without a substantial impact on their phenotype, a major prerequisite for subsequent functional assays. Collectively, our experimental approaches and data advance the understanding of fetal skin immunity and potential use in future therapeutic interventions.

Multi-omics profiling predicts allograft function after lung transplantation.

RATIONALE: Lung transplantation is the ultimate treatment option for patients with end-stage respiratory diseases but bears the highest mortality rate among all solid organ transplantations due to chronic lung allograft dysfunction (CLAD). The mechanisms leading to CLAD remain elusive due to an insufficient understanding of the complex post-transplant adaptation processes. OBJECTIVES: To better understand these lung adaptation processes after transplantation and to investigate their association with future changes in allograft function. METHODS: We performed an exploratory cohort study of bronchoalveolar lavage samples from 78 lung recipients and donors. We analysed the alveolar microbiome using 16S rRNA sequencing, the cellular composition using flow cytometry, as well as metabolome and lipidome profiling. MEASUREMENTS AND MAIN RESULTS: We established distinct temporal dynamics for each of the analysed data sets. Comparing matched donor and recipient samples, we revealed that recipient-specific as well as environmental factors, rather than the donor microbiome, shape the long-term lung microbiome. We further discovered that the abundance of certain bacterial strains correlated with underlying lung diseases even after transplantation. A decline in forced expiratory volume during the first second (FEV1) is a major characteristic of lung allograft dysfunction in transplant recipients. By using a machine learning approach, we could accurately predict future changes in FEV1 from our multi-omics data, whereby microbial profiles showed a particularly high predictive power. CONCLUSION: Bronchoalveolar microbiome, cellular composition, metabolome and lipidome show specific temporal dynamics after lung transplantation. The lung microbiome can predict future changes in lung function with high precision.

Single-Cell RNA Sequencing Analysis Reveals a Crucial Role for CTHRC1 (Collagen Triple Helix Repeat Containing 1) Cardiac Fibroblasts After Myocardial Infarction.

BACKGROUND: Cardiac fibroblasts (CFs) have a central role in the ventricular remodeling process associated with different types of fibrosis. Recent studies have shown that fibroblasts do not respond homogeneously to heart injury. Because of the limited set of bona fide fibroblast markers, a proper characterization of fibroblast population heterogeneity in response to cardiac damage is lacking. The purpose of this study was to define CF heterogeneity during ventricular remodeling and the underlying mechanisms that regulate CF function. METHODS: Collagen1α1-GFP (green fluorescent protein)-positive CFs were characterized after myocardial infarction (MI) by single-cell and bulk RNA sequencing, assay for transposase-accessible chromatin sequencing, and functional assays. Swine and patient samples were studied using bulk RNA sequencing. RESULTS: We identified and characterized a unique CF subpopulation that emerges after MI in mice. These activated fibroblasts exhibit a clear profibrotic signature, express high levels of Cthrc1 (collagen triple helix repeat containing 1), and localize into the scar. Noncanonical transforming growth factor-ß signaling and different transcription factors including SOX9 are important regulators mediating their response to cardiac injury. Absence of CTHRC1 results in pronounced lethality attributable to ventricular rupture. A population of CFs with a similar transcriptome was identified in a swine model of MI and in heart tissue from patients with MI and dilated cardiomyopathy. CONCLUSIONS: We report CF heterogeneity and their dynamics during the course of MI and redefine the CFs that respond to cardiac injury and participate in myocardial remodeling. Our study identifies CTHRC1 as a novel regulator of the healing scar process and a target for future translational studies.

Single-cell transcriptomics combined with interstitial fluid proteomics defines cell type-specific immune regulation in atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease, but its complex pathogenesis is only insufficiently understood, resulting in still limited treatment options. OBJECTIVE: We sought to characterize AD on both transcriptomic and proteomic levels in humans. METHODS: We used skin suction blistering, a painless and nonscarring procedure that can simultaneously sample skin cells and interstitial fluid. We then compared results with conventional biopsies. RESULTS: Suction blistering captured epidermal and most immune cells equally well as biopsies, except for mast cells and nonmigratory CD163+ macrophages that were only present in biopsy isolates. Using single-cell RNA sequencing, we found comparable transcriptional profiles of key inflammatory pathways between blister and biopsy AD, but suction blistering was superior in cell-specific resolution for high-abundance transcripts (KRT1/KRT10, KRT16/KRT6A, S100A8/S100A9), which showed some background signals in biopsy isolates. Compared with healthy controls, we found characteristic upregulation of AD-typical cytokines such as IL13 and IL22 in Th2 and Th22 cells, respectively, but we also discovered these mediators in proliferating T cells and natural killer T cells, that also expressed the antimicrobial cytokine IL26. Overall, not T cells, but myeloid cells were most strongly enriched in AD, and we found dendritic cell (CLEC7A, amphiregulin/AREG, EREG) and macrophage products (CCL13) among the top upregulated proteins in AD blister fluid proteomic analyses. CONCLUSION: These data show that by using cutting-edge technology, suction blistering offers several advantages over conventional biopsies, including better transcriptomic resolution of skin cells, combined with proteomic information from interstitial fluid, unraveling novel inflammatory players that shape the cellular and proteomic microenvironment of AD.

Protease-Inhibitor Interaction Predictions: Lessons on the Complexity of Protein-Protein Interactions.

Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data, including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features, thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of nonproteolytic and noninhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task and thereby highlight limitations of computational interaction prediction methods.

Interactome disassembly during apoptosis occurs independent of caspase cleavage.

Protein-protein interaction networks (interactomes) define the functionality of all biological systems. In apoptosis, proteolysis by caspases is thought to initiate disassembly of protein complexes and cell death. Here we used a quantitative proteomics approach, protein correlation profiling (PCP), to explore changes in cytoplasmic and mitochondrial interactomes in response to apoptosis initiation as a function of caspase activity. We measured the response to initiation of Fas-mediated apoptosis in 17,991 interactions among 2,779 proteins, comprising the largest dynamic interactome to date. The majority of interactions were unaffected early in apoptosis, but multiple complexes containing known caspase targets were disassembled. Nonetheless, proteome-wide analysis of proteolytic processing by terminal amine isotopic labeling of substrates (TAILS) revealed little correlation between proteolytic and interactome changes. Our findings show that, in apoptosis, significant interactome alterations occur before and independently of caspase activity. Thus, apoptosis initiation includes a tight program of interactome rearrangement, leading to disassembly of relatively few, select complexes. These early interactome alterations occur independently of cleavage of these protein by caspases.

Uncovering a Dual Regulatory Role for Caspases During Endoplasmic Reticulum Stress-induced Cell Death.

Many diseases are associated with endoplasmic reticulum (ER) stress, which results from an accumulation of misfolded proteins. This triggers an adaptive response called the "unfolded protein response" (UPR), and prolonged exposure to ER stress leads to cell death. Caspases are reported to play a critical role in ER stress-induced cell death but the underlying mechanisms by which they exert their effect continue to remain elusive. To understand the role caspases play during ER stress, a systems level approach integrating analysis of the transcriptome, proteome, and proteolytic substrate profile was employed. This quantitative analysis revealed transcriptional profiles for most human genes, provided information on protein abundance for 4476 proteins, and identified 445 caspase substrates. Based on these data sets many caspase substrates were shown to be downregulated at the protein level during ER stress suggesting caspase activity inhibits their cellular function. Additionally, RNA sequencing revealed a role for caspases in regulation of ER stress-induced transcriptional pathways and gene set enrichment analysis showed expression of multiple gene targets of essential transcription factors to be upregulated during ER stress upon inhibition of caspases. Furthermore, these transcription factors were degraded in a caspase-dependent manner during ER stress. These results indicate that caspases play a dual role in regulating the cellular response to ER stress through both post-translational and transcriptional regulatory mechanisms. Moreover, this study provides unique insight into progression of the unfolded protein response into cell death, which may help identify therapeutic strategies to treat ER stress-related diseases.

Network analyses reveal pervasive functional regulation between proteases in the human protease web.

Proteolytic processing is an irreversible posttranslational modification affecting a large portion of the proteome. Protease-cleaved mediators frequently exhibit altered activity, and biological pathways are often regulated by proteolytic processing. Many of these mechanisms have not been appreciated as being protease-dependent, and the potential in unraveling a complex new dimension of biological control is increasingly recognized. Proteases are currently believed to act individually or in isolated cascades. However, conclusive but scattered biochemical evidence indicates broader regulation of proteases by protease and inhibitor interactions. Therefore, to systematically study such interactions, we assembled curated protease cleavage and inhibition data into a global, computational representation, termed the protease web. This revealed that proteases pervasively influence the activity of other proteases directly or by cleaving intermediate proteases or protease inhibitors. The protease web spans four classes of proteases and inhibitors and so links both recently and classically described protease groups and cascades, which can no longer be viewed as operating in isolation in vivo. We demonstrated that this observation, termed reachability, is robust to alterations in the data and will only increase in the future as additional data are added. We further show how subnetworks of the web are operational in 23 different tissues reflecting different phenotypes. We applied our network to develop novel insights into biologically relevant protease interactions using cell-specific proteases of the polymorphonuclear leukocyte as a system. Predictions from the protease web on the activity of matrix metalloproteinase 8 (MMP8) and neutrophil elastase being linked by an inactivating cleavage of serpinA1 by MMP8 were validated and explain perplexing Mmp8-/- versus wild-type polymorphonuclear chemokine cleavages in vivo. Our findings supply systematically derived and validated evidence for the existence of the protease web, a network that affects the activity of most proteases and thereby influences the functional state of the proteome and cell activity.

Proteome TopFIND 3.0 with TopFINDer and PathFINDer: database and analysis tools for the association of protein termini to pre- and post-translational events.

The knowledgebase TopFIND is an analysis platform focussed on protein termini, their origin, modification and hence their role on protein structure and function. Here, we present a major update to TopFIND, version 3, which includes a 70% increase in the underlying data to now cover a 90,696 proteins, 165,044 N-termini, 130,182 C-termini, 14,382 cleavage sites and 33,209 substrate cleavages in H. sapiens, M. musculus, A. thaliana, S. cerevisiae and E. coli. New features include the mapping of protein termini and cleavage entries across protein isoforms and significantly, the mapping of protein termini originating from alternative transcription and alternative translation start sites. Furthermore, two analysis tools for complex data analysis based on the TopFIND resource are now available online: TopFINDer, the TopFIND ExploRer, characterizes and annotates proteomics-derived N- or C-termini sets for their origin, sequence context and implications for protein structure and function. Neo-termini are also linked to associated proteases. PathFINDer identifies indirect connections between a protease and list of substrates or termini thus supporting the evaluation of complex proteolytic processes in vivo. To demonstrate the utility of the tools, a recent N-terminomics data set of inflamed murine skin has been re-analyzed. In re-capitulating the major findings originally performed manually, this validates the utility of these new resources. The point of entry for the resource is http://clipserve.clip.ubc.ca/topfind from where the graphical interface, all application programming interfaces (API) and the analysis tools are freely accessible.

TAILS N-terminomics of human platelets reveals pervasive metalloproteinase-dependent proteolytic processing in storage.

Proteases, and specifically metalloproteinases, have been linked to the loss of platelet function during storage before transfusion, but the underlying mechanisms remain unknown. We used a dedicated N-terminomics technique, iTRAQ terminal amine isotopic labeling of substrates (TAILS), to characterize the human platelet N-terminome, proteome, and posttranslational modifications throughout platelet storage over 9 days under blood-banking conditions. From the identified 2938 proteins and 7503 unique peptides, we characterized N-terminal methionine excision, co- and posttranslational Nα acetylation, protein maturation, and proteolytic processing of proteins in human platelets. We also identified for the first time 10 proteins previously classified by the Human Proteome Organization as "missing" in the human proteome. Most N termini (77%) were internal neo-N termini (105 were novel potential alternative translation start sites, and 2180 represented stable proteolytic products), thus highlighting a prominent yet previously uncharacterized role of proteolytic processing during platelet storage. Protease inhibitor studies revealed metalloproteinases as being primarily responsible for proteolytic processing (as opposed to degradation) during storage. System-wide identification of metalloproteinase and other proteinase substrates and their respective cleavage sites suggests novel mechanisms of the effect of proteases on protein activity and platelet function during storage. All data sets and metadata are available through ProteomeXchange with the data set identifier PXD000906.

The path of no return--Truncated protein N-termini and current ignorance of their genesis.

Almost all regulatory processes in biology ultimately lead to or originate from modifications of protein function. However, it is unclear to which extent each mechanism of regulation actually affects proteins and thus phenotypes. We assessed the extent of N-terminal protein truncation in a global analysis of N-terminomics data and find that most proteins have N-terminally truncated proteoforms. Because N-terminomics analyses do not identify the process generating the identified N-termini, we compared identified termini to the three N-termini generating events: protein cleavage, alternative translation, and alternative splicing. Of these, we sought to identify the most likely cause of N-terminal protein truncations in the human proteome. We found that protease cleavage and alternative protein translation are the likely cause for most shortened proteoforms. However, the vast majority (about 90%) of N-termini remain unexplained by any of these processes identified to date, so revealing large gaps in our knowledge of protein termini and their genesis. Further analysis and annotation of terminomics data is required, to which end we have created the TopFIND database, a major systematic annotation effort for protein termini. We outline the new features in version 3.0 of the updated database and the new bioinformatics tools available and encourage submission of generated data to fill current knowledge gaps.

Reliable interpretability of biology-inspired deep neural networks.

Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where real-world concepts are encoded in hidden nodes, which can be ranked by importance and thereby interpreted. In such models trained on single-cell transcriptomes, we previously demonstrated that node-level interpretations lack robustness upon repeated training and are influenced by biases in biological knowledge. Similar studies are missing for related models. Here, we test and extend our methodology for reliable interpretability in P-NET, a biology-inspired model trained on patient mutation data. We observe variability of interpretations and susceptibility to knowledge biases, and identify the network properties that drive interpretation biases. We further present an approach to control the robustness and biases of interpretations, which leads to more specific interpretations. In summary, our study reveals the broad importance of methods to ensure robust and bias-aware interpretability in biology-inspired deep learning.

Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis.

Metastasis is the major cause of cancer-related deaths. Neuroblastoma (NB), a childhood tumor has been molecularly defined at the primary cancer site, however, the bone marrow (BM) as the metastatic niche of NB is poorly characterized. Here we perform single-cell transcriptomic and epigenomic profiling of BM aspirates from 11 subjects spanning three major NB subtypes and compare these to five age-matched and metastasis-free BM, followed by in-depth single cell analyses of tissue diversity and cell-cell interactions, as well as functional validation. We show that cellular plasticity of NB tumor cells is conserved upon metastasis and tumor cell type composition is NB subtype-dependent. NB cells signal to the BM microenvironment, rewiring via macrophage mgration inhibitory factor and midkine signaling specifically monocytes, which exhibit M1 and M2 features, are marked by activation of pro- and anti-inflammatory programs, and express tumor-promoting factors, reminiscent of tumor-associated macrophages. The interactions and pathways characterized in our study provide the basis for therapeutic approaches that target tumor-to-microenvironment interactions.

In vivo screening characterizes chromatin factor functions during normal and malignant hematopoiesis.

Cellular differentiation requires extensive alterations in chromatin structure and function, which is elicited by the coordinated action of chromatin and transcription factors. By contrast with transcription factors, the roles of chromatin factors in differentiation have not been systematically characterized. Here, we combine bulk ex vivo and single-cell in vivo CRISPR screens to characterize the role of chromatin factor families in hematopoiesis. We uncover marked lineage specificities for 142 chromatin factors, revealing functional diversity among related chromatin factors (i.e. barrier-to-autointegration factor subcomplexes) as well as shared roles for unrelated repressive complexes that restrain excessive myeloid differentiation. Using epigenetic profiling, we identify functional interactions between lineage-determining transcription factors and several chromatin factors that explain their lineage dependencies. Studying chromatin factor functions in leukemia, we show that leukemia cells engage homeostatic chromatin factor functions to block differentiation, generating specific chromatin factor-transcription factor interactions that might be therapeutically targeted. Together, our work elucidates the lineage-determining properties of chromatin factors across normal and malignant hematopoiesis.

Comparative analysis of genome-scale, base-resolution DNA methylation profiles across 580 animal species.

Methylation of cytosines is a prototypic epigenetic modification of the DNA. It has been implicated in various regulatory mechanisms across the animal kingdom and particularly in vertebrates. We mapped DNA methylation in 580 animal species (535 vertebrates, 45 invertebrates), resulting in 2443 genome-scale DNA methylation profiles of multiple organs. Bioinformatic analysis of this large dataset quantified the association of DNA methylation with the underlying genomic DNA sequence throughout vertebrate evolution. We observed a broadly conserved link with two major transitions-once in the first vertebrates and again with the emergence of reptiles. Cross-species comparisons focusing on individual organs supported a deeply conserved association of DNA methylation with tissue type, and cross-mapping analysis of DNA methylation at gene promoters revealed evolutionary changes for orthologous genes. In summary, this study establishes a large resource of vertebrate and invertebrate DNA methylomes, it showcases the power of reference-free epigenome analysis in species for which no reference genomes are available, and it contributes an epigenetic perspective to the study of vertebrate evolution.

Stress signaling boosts interferon-induced gene transcription in macrophages.

Promoters of antimicrobial genes function as logic boards, integrating signals of innate immune responses. One such set of genes is stimulated by interferon (IFN) signaling, and the expression of these genes [IFN-stimulated genes (ISGs)] can be further modulated by cell stress-induced pathways. Here, we investigated the global effect of stress-induced p38 mitogen-activated protein kinase (MAPK) signaling on the response of macrophages to IFN. In response to cell stress that coincided with IFN exposure, the p38 MAPK-activated transcription factors CREB and c-Jun, in addition to the IFN-activated STAT family of transcription factors, bound to ISGs. In addition, p38 MAPK signaling induced activating histone modifications at the loci of ISGs and stimulated nuclear translocation of the CREB coactivator CRTC3. These actions synergistically enhanced ISG expression. Disrupting this synergy with p38 MAPK inhibitors improved the viability of macrophages infected with Listeria monocytogenes. Our findings uncover a mechanism of transcriptional synergism and highlight the biological consequences of coincident stress-induced p38 MAPK and IFN-stimulated signal transduction.