A non-antibiotic-disrupted gut microbiome is associated with clinical responses to CD19-CAR-T cell cancer immunotherapy.

Increasing evidence suggests that the gut microbiome may modulate the efficacy of cancer immunotherapy. In a B cell lymphoma patient cohort from five centers in Germany and the United States (Germany, n = 66; United States, n = 106; total, n = 172), we demonstrate that wide-spectrum antibiotics treatment ('high-risk antibiotics') prior to CD19-targeted chimeric antigen receptor (CAR)-T cell therapy is associated with adverse outcomes, but this effect is likely to be confounded by an increased pretreatment tumor burden and systemic inflammation in patients pretreated with high-risk antibiotics. To resolve this confounding effect and gain insights into antibiotics-masked microbiome signals impacting CAR-T efficacy, we focused on the high-risk antibiotics non-exposed patient population. Indeed, in these patients, significant correlations were noted between pre-CAR-T infusion Bifidobacterium longum and microbiome-encoded peptidoglycan biosynthesis, and CAR-T treatment-associated 6-month survival or lymphoma progression. Furthermore, predictive pre-CAR-T treatment microbiome-based machine learning algorithms trained on the high-risk antibiotics non-exposed German cohort and validated by the respective US cohort robustly segregated long-term responders from non-responders. Bacteroides, Ruminococcus, Eubacterium and Akkermansia were most important in determining CAR-T responsiveness, with Akkermansia also being associated with pre-infusion peripheral T cell levels in these patients. Collectively, we identify conserved microbiome features across clinical and geographical variations, which may enable cross-cohort microbiome-based predictions of outcomes in CAR-T cell immunotherapy.

Alloreactive T cells deficient of the short-chain fatty acid receptor GPR109A induce less graft-versus-host disease.

The intestinal microbiota is essential for the fermentation of dietary fiber into short-chain fatty acids (SCFA) such as butyrate, acetate, and propionate. SCFAs can bind to the G-protein-coupled receptors GPR43 and GPR109A (HCAR2), with varying affinities to promote cellular effects in metabolism or changes in immune function. We explored the role of GPR109A as the main receptor for butyrate in mouse models of allogeneic hematopoietic cell transplantation (allo-HCT) and graft-versus-host disease (GVHD). Deletion of GPR109A in allo-HCT recipients did not affect GVHD, but transplantation of T cells from GPR109A knockout (KO) (Gpr109a-/-) mice into allo-HCT recipient mice significantly reduced GVHD morbidity and mortality compared with recipients of wild-type (WT) T cells. Recipients of Gpr109a-/- T cells exhibited less GVHD-associated target organ pathology and decreased proliferation and homing of alloreactive T cells to target tissues. Although Gpr109a-/- T cells did not exhibit immune deficits at a steady state, following allo-activation, Gpr109a-/- T cells underwent increased apoptosis and were impaired mitochondrial oxidative phosphorylation, which was reversible through antioxidant treatment with N-acetylcysteine (NAC). In conclusion, we found that GPR109A expression by allo-activated T cells is essential for metabolic homeostasis and expansion, which are necessary features to induce GVHD after allo-HCT.

Severe Dysbiosis and Specific Haemophilus and Neisseria Signatures as Hallmarks of the Oropharyngeal Microbiome in Critically Ill Coronavirus Disease 2019 (COVID-19) Patients.

BACKGROUND: At the entry site of respiratory virus infections, the oropharyngeal microbiome has been proposed as a major hub integrating viral and host immune signals. Early studies suggested that infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict coronavirus disease 2019 (COVID-19) illness. However, the results are not conclusive, as critical illness can drastically alter a patient's microbiome through multiple confounders. METHODS: To study oropharyngeal microbiome profiles in SARS-CoV-2 infection, clinical confounders, and prediction models in COVID-19, we performed a multicenter, cross-sectional clinical study analyzing oropharyngeal microbial metagenomes in healthy adults, patients with non-SARS-CoV-2 infections, or with mild, moderate, and severe COVID-19 (n = 322 participants). RESULTS: In contrast to mild infections, patients admitted to a hospital with moderate or severe COVID-19 showed dysbiotic microbial configurations, which were significantly pronounced in patients treated with broad-spectrum antibiotics, receiving invasive mechanical ventilation, or when sampling was performed during prolonged hospitalization. In contrast, specimens collected early after admission allowed us to segregate microbiome features predictive of hospital COVID-19 mortality utilizing machine learning models. Taxonomic signatures were found to perform better than models utilizing clinical variables with Neisseria and Haemophilus species abundances as most important features. CONCLUSIONS: In addition to the infection per se, several factors shape the oropharyngeal microbiome of severely affected COVID-19 patients and deserve consideration in the interpretation of the role of the microbiome in severe COVID-19. Nevertheless, we were able to extract microbial features that can help to predict clinical outcomes.

Modeling and simulating networks of interdependent protein interactions.

Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, as generated by allosteric effects or mutual exclusion due to steric hindrance. Therefore, formal models for integrating and utilizing information about interaction dependencies are of high interest. Here, we describe an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks ("constrained networks"). The construction of these networks is based on public interaction databases as well as text-mined information about interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows fast simulation and facilitates the analysis of many proteins in large networks. In addition, this approach enables the simulation of perturbation effects, such as knockout of single or multiple proteins and changes of protein concentrations. We illustrate how our model can be used to analyze a constrained human adhesome protein network, which is responsible for the formation of diverse and dynamic cell-matrix adhesion sites. By comparing protein complex formation under known interaction dependencies versus without dependencies, we investigate how these dependencies shape the resulting repertoire of protein complexes. Furthermore, our model enables investigating how the interplay of network topology with interaction dependencies influences the propagation of perturbation effects across a large biochemical system. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsim and as a Bioconda package (https://bioconda.github.io).

Diverse patterns of molecular changes in the mechano-responsiveness of focal adhesions.

Focal adhesions anchor contractile actin fibers with the extracellular matrix, sense the generated tension and respond to it by changing their morphology and composition. Here we ask how this mechanosensing is enabled at the protein-network level, given the modular assembly and multitasking of focal adhesions. To address this, we applied a sensitive 4-color live cell imaging approach, enabling monitoring patterns of molecular changes in single focal adhesions. Co-imaging zyxin, FAK, vinculin and paxillin revealed heterogeneities in their responses to Rho-associated kinase (ROCK)-mediated perturbations of actomyosin contractility. These responses were rather weakly correlated between the proteins, reflecting diverse compositional changes in different focal adhesions. This diversity is partially attributable to the location of focal adhesions, their area, molecular content and previous contractility perturbations, suggesting that integration of multiple local cues shapes differentially focal adhesion mechano-responsiveness. Importantly, the compositional changes upon ROCK perturbations exhibited distinct paths in different focal adhesions. Moreover, the protein exhibiting the strongest response to ROCK perturbations varied among different focal adhesions. The diversity in response patterns is plausibly enabled by the modular mode of focal adhesions assembly and can provide them the needed flexibility to perform multiple tasks by combining optimally a common set of multifunctional components.

Reconceptualizing Fluorescence Correlation Spectroscopy for Monitoring and Analyzing Periodically Passing Objects.

Fluorescence correlation spectroscopy (FCS) is a sensitive technique commonly applied for studying the dynamics of nanoscale-labeled objects in solution. Current analysis of FCS data is largely based on the assumption that the labeled objects are stochastically displaced due to Brownian motion. However, this assumption is often invalid for microscale objects, since the motion of these objects is dominated by Stokes drag and settling or rising effects, rather than stochastic Brownian motion. To utilize the power of FCS for systems with nonstochastic displacements of objects, the collection and analysis of FCS data have to be reconceptualized. Here, we extended the applicability of FCS for the detection and analysis of periodically passing objects. Toward this end, we implemented droplet-based microfluidics, in which monodispersed droplets containing fluorescent marker are flowing equally spaced within microchannels. We show by simulations and experiments that FCS can sensitively quantify the flow-rates, variability, and content of rapidly passing droplets. This information can be derived at high temporal resolution, based on the intensity fluctuations generated by only 5-10 passing droplets. Moreover, by utilizing the periodicity of the flowing droplets for noise reduction by averaging, FCS can monitor accurately the droplets flow even if their fluorescence intensity is negligible. Hence, extending FCS for periodically passing objects converts it into a powerful analytical tool for high-throughput droplet-based microfluidics. Moreover, based on the principles described here, FCS can be straightforwardly applied for a variety of systems in which the passing of objects is periodic rather than stochastic.

Highly Multiplexed Imaging Uncovers Changes in Compositional Noise within Assembling Focal Adhesions.

Integrin adhesome proteins bind each other in alternative manners, forming within the cell diverse cell-matrix adhesion sites with distinct properties. An intriguing question is how such modular assembly of adhesion sites is achieved correctly solely by self-organization of their components. Here we address this question using high-throughput multiplexed imaging of eight proteins and two phosphorylation sites in a large number of single focal adhesions. We found that during the assembly of focal adhesions the variances of protein densities decrease while the correlations between them increase, suggesting reduction in the noise levels within these structures. These changes correlate independently with the area and internal density of focal adhesions, but not with their age or shape. Artificial neural network analysis indicates that a joint consideration of multiple components improves the predictability of paxillin and zyxin levels in internally dense focal adhesions. This suggests that paxillin and zyxin densities in focal adhesions are fine-tuned by integrating the levels of multiple other components, thus averaging-out stochastic fluctuations. Based on these results we propose that increase in internal protein densities facilitates noise suppression in focal adhesions, while noise suppression enables their stable growth and further density increase-hence forming a feedback loop giving rise to a quality-controlled assembly.

Multiplexed imaging of intracellular protein networks.

Cellular functions emerge from the collective action of a large number of different proteins. Understanding how these protein networks operate requires monitoring their components in intact cells. Due to intercellular and intracellular molecular variability, it is important to monitor simultaneously multiple components at high spatiotemporal resolution. However, inherent trade-offs narrow the boundaries of achievable multiplexed imaging. Pushing these boundaries is essential for a better understanding of cellular processes. Here the motivations, challenges and approaches for multiplexed imaging of intracellular protein networks are discussed. © 2016 International Society for Advancement of Cytometry.

Integrative systems and synthetic biology of cell-matrix adhesion sites.

The complexity of cell-matrix adhesion convolves its roles in the development and functioning of multicellular organisms and their evolutionary tinkering. Cell-matrix adhesion is mediated by sites along the plasma membrane that anchor the actin cytoskeleton to the matrix via a large number of proteins, collectively called the integrin adhesome. Fundamental challenges for understanding how cell-matrix adhesion sites assemble and function arise from their multi-functionality, rapid dynamics, large number of components and molecular diversity. Systems biology faces these challenges in its strive to understand how the integrin adhesome gives rise to functional adhesion sites. Synthetic biology enables engineering intracellular modules and circuits with properties of interest. In this review I discuss some of the fundamental questions in systems biology of cell-matrix adhesion and how synthetic biology can help addressing them.

Uncovering distinct protein-network topologies in heterogeneous cell populations.

BACKGROUND: Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation. RESULTS: Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins. CONCLUSIONS: Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.

Symmetric exchange of multi-protein building blocks between stationary focal adhesions and the cytosol.

How can the integrin adhesome get self-assembled locally, rapidly, and correctly as diverse cell-matrix adhesion sites? Here, we investigate this question by exploring the cytosolic state of integrin-adhesome components and their dynamic exchange between adhesion sites and cytosol. Using fluorescence cross-correlation spectroscopy (FCCS) and fluorescence recovery after photobleaching (FRAP) we found that the integrin adhesome is extensively pre-assembled already in the cytosol as multi-protein building blocks for adhesion sites. Stationary focal adhesions release symmetrically the same types of protein complexes that they recruit, thereby keeping the cytosolic pool of building blocks spatiotemporally uniform. We conclude a model in which multi-protein building blocks enable rapid and modular self-assembly of adhesion sites and symmetric exchange of these building blocks preserves their specifications and thus the assembly logic of the system.DOI: http://dx.doi.org/10.7554/eLife.02257.001.

Efficiently mining protein interaction dependencies from large text corpora.

Biochemical research has yielded an extensive amount of information about dependencies between protein interactions, as generated by allosteric regulations, steric hindrance and other mechanisms. Collectively, this information is valuable for understanding large intracellular protein networks. However, this information is sparsely distributed among millions of publications and documented as freely styled text meant for manual reading. Here we develop a computational approach for extracting information about interaction dependencies from large numbers of publications. First, keyword-based tokenization reduces full papers to short strings, facilitating an efficient search for patterns that are likely to indicate descriptions of interaction dependencies. Sentences that match such patterns are extracted, thereby reducing the amount of text to be read by human curators. Application of this approach to the integrin adhesome network extracted from 59,933 papers 208 short statements, close to half of which indeed describe interaction dependencies. We visualize the obtained hypernetwork of dependencies and illustrate that these dependencies confine the feasible mechanisms of adhesion sites assembly and generate testable hypotheses about their switchability.

Fluorescence fluctuations of quantum-dot sensors capture intracellular protein interaction dynamics.

We extend the in vitro principle of co-immunoprecipitation to quantify dynamic protein interactions in living cells. Using a multiresolution implementation of fluorescence correlation spectroscopy to achieve maximal temporal resolution, we monitored the interactions of endogenous bait proteins, recruited by quantum dots, with fluorescently tagged prey. With this approach, we analyzed the rapid physiological regulation of protein kinase A.

Quantitative multicolor compositional imaging resolves molecular domains in cell-matrix adhesions.

BACKGROUND: Cellular processes occur within dynamic and multi-molecular compartments whose characterization requires analysis at high spatio-temporal resolution. Notable examples for such complexes are cell-matrix adhesion sites, consisting of numerous cytoskeletal and signaling proteins. These adhesions are highly variable in their morphology, dynamics, and apparent function, yet their molecular diversity is poorly defined. METHODOLOGY/PRINCIPAL FINDINGS: We present here a compositional imaging approach for the analysis and display of multi-component compositions. This methodology is based on microscopy-acquired multicolor data, multi-dimensional clustering of pixels according to their composition similarity and display of the cellular distribution of these composition clusters. We apply this approach for resolving the molecular complexes associated with focal-adhesions, and the time-dependent effects of Rho-kinase inhibition. We show here compositional variations between adhesion sites, as well as ordered variations along the axis of individual focal-adhesions. The multicolor clustering approach also reveals distinct sensitivities of different focal-adhesion-associated complexes to Rho-kinase inhibition. CONCLUSIONS/SIGNIFICANCE: Multicolor compositional imaging resolves "molecular signatures" characteristic to focal-adhesions and related structures, as well as sub-domains within these adhesion sites. This analysis enhances the spatial information with additional "contents-resolved" dimensions. We propose that compositional imaging can serve as a powerful tool for studying complex multi-molecular assemblies in cells and for mapping their distribution at sub-micron resolution.

Resolving and classifying haematopoietic bone-marrow cell populations by multi-dimensional analysis of flow-cytometry data.

The study of normal or malignant haematopoiesis requires the analysis of heterogeneous cell populations using multiple morphological and molecular criteria. Flow cytometry has the capacity to acquire multi-parameter information of large haematopoietic cell populations, utilizing various combinations of >200 molecular markers (clusters of differentiation, CD). However, current flow cytometry analyses are based on serial gating of two-parametric scatter plots--a process that is inherently incapable to discriminate all subgroups of cells in the data. Here we studied the cellular diversity of normal bone marrows (BM) using multi-dimensional cluster analysis of six-parametric flow cytometry data (four CD, forward scatter and side scatter), focusing mainly on the myeloid lineage. Twenty-three subclasses of cells were resolved, many of them inseparable even when examined in all possible two-parametric scatter plots. The multi-dimensional analysis could distinguish the haematopoietic progenitors according to International Society of Haematotherapy and Graft Engineering criteria from other types of immature cells. Based on the defined clusters, we designed a classifier that assigns BM cells in samples to subclasses based on robust six-dimensional position and extended shape. The analysis presented here can manage successfully both the increasing numbers of haematopoietic cellular markers and sample heterogeneity. This should enhance the ability to study normal haematopoiesis, and to identify and monitor haematopoietic disorders.

Induction of apoptosis in cultured endothelial cells by a cadherin antagonist peptide: involvement of fibroblast growth factor receptor-mediated signalling.

Cadherins are a family of transmembrane glycoproteins mediating calcium-dependent, homophilic cell-cell adhesion. In addition, these molecules are involved in signaling events, regulating such processes as cell motility, proliferation, and apoptosis. Members of the cadherin subfamily, called either classical or type I cadherins, contain a highly conserved sequence at their homophilic binding site consisting of the three amino acids--histidine-alanine-valine (HAV). Previous studies have shown that peptides containing the HAV motif inhibit cadherin-dependent events such as cell aggregation, compaction, and neurite outgrowth. We report here that a cyclic peptide, N-Ac-CHAVC-NH2 can perturb cadherin-mediated endothelial cell interactions, resulting in a progressive apoptotic cell death. This effect depends on cell density, as it is only observed when dense cultures are treated with the peptide. Adherens junction (AJ)-associated cadherin and catenins are differentially affected by the N-Ac-CHAVC-NH2 treatment, as judged by double immunofluorescence labeling followed by immunofluorescence-ratio imaging. However, cell-cell adhesions are largely retained during the first few hours after addition of the peptide. It was also observed that following treatment, actin filaments partially lose their plasma membrane anchorage at AJs and translocate towards the cell center. Interestingly, addition of basic fibroblast growth factor to confluent, peptide-treated, endothelial cell cultures, completely blocks apoptosis and the inhibitory peptide reduce the phosphorylation of the FGF receptor target protein FRS2, suggesting that the peptide exerts its effect by inhibiting cadherin-mediated activation of fibroblast growth factor receptor signaling. We propose that cadherin-mediated signaling is essential for maintaining viability of confluent endothelial cells, and that its perturbation by N-Ac-CHAVC-NH2 drives these cells to apoptosis.

Human heparanase is localized within lysosomes in a stable form.

Heparanase is an endo-beta-D-glucuronidase involved in degradation of heparan sulfate (HS) and extracellular matrix (ECM) of a wide range of cells of vertebrate and invertebrate tissues. The enzymatic activity of heparanase is characterized by specific intrachain cleavage of glycosidic bonds with a hydrolase mechanism. This enzyme facilitates cell invasion and hence plays a role in tumor metastasis, angiogenesis, inflammation, and autoimmunity. Although the expression pattern and molecular properties of heparanase have been characterized, its subcellular localization has not been unequivocally determined. We have previously suggested that heparanase subcellular localization is a major determinant in regulating the enzyme's biological functions. In the present study we examined heparanase localization in three different cell types, utilizing immunofluorescent staining and electron microscopy. Our results indicate that heparanase is localized primarily within lysosomes and the Golgi apparatus. A construct composed of heparanase cDNA fused to green fluorescent protein, utilized in order to visualize the enzyme within living cells, confirmed its localization in acidic vesicles. We suggest that following synthesis, heparanase is transported into the Golgi apparatus and subsequently accumulates in a stable form within the lysosomes, where it functions in HS turnover. The lysosomal compartment may also serve as a site for heparanase confinement within the cells, limiting its secretion and uncontrolled extracellular activities associated with tumor metastasis and angiogenesis.

Activation, processing and trafficking of extracellular heparanase by primary human fibroblasts.

Heparanase is a heparan-sulfate-degrading endoglycosidase that has important roles in various biological processes, including angiogenesis, wound healing and metastatsis. Human heparanase is synthesized as a 65 kDa latent precursor, which is proteolytically processed into a highly active 50 kDa form. Extracellular heparanase is found in various tissues and is utilized by both normal cells and metastatic cancer cells to degrade heparan sulfate moieties in basement membranes and extracellular matrices. This study characterizes the processing and trafficking events associated with cellular activation of extracellular heparanase. We show that primary human fibroblasts are capable of binding and converting the 65 kDa heparanase precursor into its highly active 50 kDa form, concomitantly with its cytoplasmic accumulation. Heparanase uptake depends on the actin cytoskeleton integrity, resulting in a prolonged storage of the enzyme, mainly in endosomal structures. Heparanase endocytosis and its proteolytic activation are independent processes, indicating that heparanase cleavage is a cell surface event. Heparin completely inhibits heparanase endocytosis but only partially inhibits its association with the cells, suggesting that cell surface heparan sulfate moieties play a specific role in its endocytosis. Cellular binding and uptake of extracellular heparanase control its activation, clearance rate and storage within the cells.