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Sophisticated immune evasion strategies enable Helicobacter pylori (H. pylori) to colonize the gastric mucosa of approximately half of the world’s population. Persistent infection and the resulting chronic inflammation are a major cause of gastric cancer. To understand the intricate interplay between H. pylori and host immunity, spatial profiling was used to monitor immune cells in H. pylori infected gastric tissue. Dendritic cell (DC) and T cell phenotypes were further investigated in gastric organoid/immune cell co-cultures and mechanistic insights were acquired by proteomics of human DCs. Here, we show that ADP-heptose, a bacterial metabolite originally reported to act as a bona fide PAMP, reduces H. pylori-induced DC maturation and subsequent T cell responses. Mechanistically, we report that H. pylori uptake and subsequent DC activation by an ADP-heptose deficient H. pylori strain depends on TLR2. Moreover, ADP-heptose attenuates full-fledged activation of primary human DCs in the context of H. pylori infection by impairing type I IFN signaling. This study reveals that ADP-heptose mitigates host immunity during H. pylori infection.

Abstract 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.

Abstract 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.

DNA methylation is involved in regulatory processes throughout the animal kingdom. Here, the authors map DNA methylation in 535 vertebrates and 45 invertebrates, establishing a reference dataset for cross-species analysis and exploring epigenetic variation across vertebrate evolution.

Abstract Background Deep learning has emerged as a versatile approach for predicting complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the biological mechanisms that underlie a successful prediction. Here we demonstrate deep learning on biological networks, where every node has a molecular equivalent, such as a protein or gene, and every edge has a mechanistic interpretation, such as a regulatory interaction along a signaling pathway. Results With knowledge-primed neural networks (KPNNs), we exploit the ability of deep learning algorithms to assign meaningful weights in multi-layered networks, resulting in a widely applicable approach for interpretable deep learning. We present a learning method that enhances the interpretability of trained KPNNs by stabilizing node weights in the presence of redundancy, enhancing the quantitative interpretability of node weights, and controlling for uneven connectivity in biological networks. We validate KPNNs on simulated data with known ground truth and demonstrate their practical use and utility in five biological applications with single-cell RNA-seq data for cancer and immune cells. Conclusions We introduce KPNNs as a method that combines the predictive power of deep learning with the interpretability of biological networks. While demonstrated here on single-cell sequencing data, this method is broadly relevant to other research areas where prior domain knowledge can be represented as networks.

Abstract Background Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool to dissect cell-specific effects of drug treatment in complex tissues. This application requires high levels of precision, robustness, and quantitative accuracy—beyond those achievable with existing methods for mainly qualitative single-cell analysis. Here, we establish the use of standardized reference cells as spike-in controls for accurate and robust dissection of single-cell drug responses. Results We find that contamination by cell-free RNA can constitute up to 20% of reads in human primary tissue samples, and we show that the ensuing biases can be removed effectively using a novel bioinformatics algorithm. Applying our method to both human and mouse pancreatic islets treated ex vivo, we obtain an accurate and quantitative assessment of cell-specific drug effects on the transcriptome. We observe that FOXO inhibition induces dedifferentiation of both alpha and beta cells, while artemether treatment upregulates insulin and other beta cell marker genes in a subset of alpha cells. In beta cells, dedifferentiation and insulin repression upon artemether treatment occurs predominantly in mouse but not in human samples. Conclusions This new method for quantitative, error-correcting, scRNA-seq data normalization using spike-in reference cells helps clarify complex cell-specific effects of pharmacological perturbations with single-cell resolution and high quantitative accuracy.

Ibrutinib, a Bruton tyrosine kinase inhibitor, provides effective treatment for chronic lymphocytic leukemia (CLL). Here, the authors describe time-dependent molecular changes to malignant cells and to the immune system in patients undergoing ibrutinib therapy, with can be used for therapy monitoring.

Atopic dermatitis (AD) typically starts in infancy or early childhood, showing spontaneous remission in a subset of patients, while others develop lifelong disease. Despite an increased understanding of AD, factors guiding its natural course are only insufficiently elucidated. We thus performed suction blistering in skin of adult patients with stable, spontaneous remission from previous moderate-to-severe AD during childhood. Samples were compared to healthy controls without personal or familial history of atopy, and to chronic, active AD lesions. Skin cells and tissue fluid obtained were used for single-cell RNA sequencing and proteomic multiplex assays, respectively. We found overall cell composition and proteomic profiles of spontaneously healed AD to be comparable to healthy control skin, without upregulation of typical AD activity markers (e.g., IL13, S100As, and KRT16). Among all cell types in spontaneously healed AD, melanocytes harbored the largest numbers of differentially expressed genes in comparison to healthy controls, with upregulation of potentially anti-inflammatory markers such as PLA2G7. Conventional T-cells also showed increases in regulatory markers, and a general skewing toward a more Th1-like phenotype. By contrast, gene expression of regulatory T-cells and keratinocytes was essentially indistinguishable from healthy skin. Melanocytes and conventional T-cells might thus contribute a specific regulatory milieu in spontaneously healed AD skin.

IFN-γ is central in inflammatory pathogenesis, response to infection and autoimmune diseases. Here the authors show that MMP12 expression is reduced in patients with SLE and that MMP12 post-translationally truncates IFN-y, inhibiting its function and affecting pathogenesis of mouse models of peritonitis, SLE and rheumatoid arthritis.

Abstract 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.

Deregulated cathepsin proteolysis occurs across numerous cancers, but in vivo substrates mediating tumorigenesis remain ill-defined. Applying 8-plex iTRAQ terminal amine isotopic labeling of substrates (TAILS), a systems-level N-terminome degradomics approach, we identified cathepsin B, H, L, S, and Z in vivo substrates and cleavage sites with the use of six different cathepsin knockout genotypes in the Rip1-Tag2 mouse model of pancreatic neuroendocrine tumorigenesis. Among 1,935 proteins and 1,114 N termini identified by TAILS, stable proteolytic products were identified in wild-type tumors compared with one or more different cathepsin knockouts (17%–44% of 139 cleavages). This suggests a lack of compensation at the substrate level by other cathepsins. The majority of neo-N termini (56%–83%) for all cathepsins was consistent with protein degradation. We validated substrates, including the glycolytic enzyme pyruvate kinase M2 associated with the Warburg effect, the ER chaperone GRP78, and the oncoprotein prothymosin-alpha. Thus, the identification of cathepsin substrates in tumorigenesis improves the understanding of cathepsin functions in normal physiology and cancer.

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.