Biology subjects

Bild, A.

Publications and source records attributed to Bild, A..

5 recordsLinked to original sources

bioRxiv2021

Heterocomplexes between the Atypical Chemokine MIF and the CXC-Motif Chemokine CXCL4L1 Regulate Inflammation and Thrombus Formation

To fulfil their orchestrating function in immune cell trafficking in homeostasis and disease, a network of 49 chemokines and 23 receptors capitalizes on features of specificity, redundancy, and functional selectivity such as biased agonism. The discovery of the chemokine interactome, i.e. heteromeric chemokine-chemokine interactions, even across CC- and CXC-class borders, has further expanded the complexity within the network. Moreover, some inflammatory mediators, which are not structurally linked to classical CC-, CXC-, CX3C-, or C-chemokines, can bind to chemokine receptors and behave as atypical chemokines (ACKs). We identified the cytokine macrophage migration inhibitory factor (MIF) as an ACK that binds to the chemokine receptors CXCR2 and CXCR4 to promote atherogenic leukocyte recruitment. Here, we hypothesized that chemokine-chemokine interactions extend to ACKs and that MIF may form heterocomplexes with classical chemokines. We tested this hypothesis, applying an unbiased chemokine protein binding array. The platelet chemokine CXCL4L1, but not its variant CXCL4 or the CXCR2/CXCR4 ligands CXCL8 or CXCL12, was identified as a candidate interactor. MIF/CXCL4L1 complexation was verified by co-immunoprecipitation, surface plasmon-resonance analysis, and microscale thermophoresis, which also established high-affinity binding (KD{asymp}100-150 nM). The binding interface was predicted by peptide array-based mapping and molecular docking. We next determined whether heterocomplex formation modulates inflammatory and atherogenic activities of MIF. MIF-elicited T-cell chemotaxis as assessed in a 3D-matrix-based live cell-imaging set-up was abrogated, when cells were co-incubated with MIF and CXCL4L1. Heterocomplexation also blocked MIF-triggered migration of Egfp+ microglia in cortical cultures in situ. Of note, CXCL4L1 blocked the binding of Alexa-MIF to a soluble ectodomain mimic of CXCR4 and co-incubation with CXCL4L1 attenuated MIF-triggered dynamic mass redistribution in HEK293-CXCR4 transfectants, indicating that complex formation interferes with MIF/CXCR4 pathways. As MIF and CXCL4L1 are abundant platelet products, we finally tested their role in platelet activation. Multi-photon microscopy, FLIM- FRET, and proximity ligation assay visualized heterocomplexes in platelet aggregates and clinical human thrombus sections. Moreover, heterocomplex formation inhibited MIF- stimulated thrombus formation under flow and skewed the morphology of adhering platelets from a large to a small lamellipodia phenotype. Together, our study establishes a novel molecular interaction, adding to the complexity of the chemokine interactome and chemokine/receptor network. MIF/CXCL4L1, or more generally, ACK/CXC-motif chemokine heterocomplexes may be promising target structures to modulate inflammation and thrombosis.

immunology↗

bioRxiv2021

Convergent evolution of resistance pathways during early stage breast cancer treatment with combination cell cycle (CDK) and endocrine inhibitors

Combining cyclin-dependent kinase (CDK) inhibitors with endocrine therapy improves outcomes for metastatic estrogen receptor positive (ER+), HER2 negative, breast cancer patients. However, the value of this combination in potentially curable earlier stage patients is not clear. Using single cell transcriptomic profiling, we examined the evolutionary trajectories of early stage breast cancer tumors using serial tumor biopsies from a clinical trial of preoperative endocrine therapy (letrozole) alone or in combination with the cell cycle inhibitor ribociclib. Applying hierarchical regression and Gaussian process mathematical modelling, we classified each tumor by whether it shrinks or persists with therapy and determined cancer phenotypes related to evolution of resistance and cell cycle transcriptional rewiring. We found that all patients tumors undergo subclonal evolution during therapy, irrespective of the clinical response. However, tumors subjected to endocrine therapy alone showed reduced diversity over time, while those facing combination therapy exhibited increased diversity. Despite different subclonal diversity, single nuclei RNA sequencing uncovered common phenotypic changes in tumor cells that persist following treatment. In these tumors, cancer cells with accelerated loss of estrogen signaling have convergent up-regulation of the JNK pathway, while cells that maintain estrogen signaling during therapy show potentiation of CDK4/6 activation consistent with ERBB4 and ERK signaling up-regulation. These convergent phenotypes were associated with growing tumors resistant to combination therapy. Cell cycle reconstruction identified that these tumors can rebound during combination therapy treatment, indicating stronger selection and promotion of a proliferative state. These results indicate that combination therapy in early stage ER+ breast cancers with ER and CDK inhibition drives rapid evolution of resistance via a shift from estrogen signaling to alternative growth factor receptor mediated proliferation and JNK signaling activation, concordant with a bypass in the G1 checkpoint.

cancer biology↗

bioRxiv2021

Reconstructing tumor trajectories during therapy through integration of multiple measurement modalities.

BackgroundAccurately determining changes in tumor size during therapy is essential to evaluating response or progression. However, individual imaging methodologies often poorly reflect pathologic response and long-term treatment efficacy in patients with estrogen receptor positive (ER+) early-stage breast cancer. Mathematical models that measure tumor progression over time by integrating diverse imaging and tumor measurement modalities are not currently used but could increase accuracy in measuring response and provide biological insights into cancer evolution. MethodsFor ER+ breast cancer patients enrolled on a neoadjuvant clinical trial, we reconstructed their tumor size trajectories during therapy by combining all available information on tumor size, including different imaging modalities, physical examinations and pathological assessment data. Tumor trajectories during six months of treatment were generated, using a Gaussian process and the most probable trajectories were evaluated, based on clinical data, using measurement models that account for biases and differences in precision between tumor measurement methods, such as MRI, ultrasound and mammograms. ResultsReconstruction of tumor trajectories during treatment identified five distinct patterns of tumor size changes, including rebound growth not evident from any single modality. These results increase specificity to distinguish innate or acquired resistance compared to using any single measurement alone. The speed of therapeutic response and extent of subsequent rebound tumor growth quantify sensitivity or resistance in this patient population. ConclusionsTumor trajectory reconstruction integrating multiple modalities of tumor measurement accurately describes tumor progression on therapy and reveals various patterns of patient responses. Mathematical models can integrate diverse response assessments and account for biases in tumor measurement, thereby providing insights into the timing and rate at which resistance emerges.

systems biology↗

bioRxiv2020

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data with ImmClassifier

Single-cell RNA sequencing is an emerging strategy for characterizing the immune cell population in diverse environments including blood, tumor or healthy tissues. While this has traditionally been done with flow or mass cytometry targeting protein expression, scRNA-Seq has several established and potential advantages in that it can profile immune cells and non-immune cells (e.g. cancer cells) in the same sample, identify cell types that lack precise markers for flow cytometry, or identify a potentially larger number of immune cell types and activation states than is achievable in a single flow assay. However, scRNA-Seq is currently limited due to the need to identify the types of each immune cell from its transcriptional profile, which is not only time-consuming but also requires a significant knowledge of immunology. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells vs macrophages), making fine distinctions has turned out to be a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that ImmClassifier outperforms other tools (+20% recall, +14% precision) in distinguishing fine-grained cell types (e.g. CD8+ effector memory T cells) with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

bioinformatics↗

bioRxiv2019

Phenotypic Switching of Naïve T cells to Immune-suppressive Treg-like Cells by Mutant KRAS

Oncogenic (mutant) Ras protein Kirsten rat sarcoma viral oncogene homolog (KRAS) promotes uncontrolled proliferation, altered metabolism, and loss of genome integrity in a cell-intrinsic manner. Here, we demonstrate that CD4+ T cells when incubated with tumor-derived exosomes from mutant (MT) KRAS non-small cell lung cancer (NSCLC) cells, patient sera, or a mouse xenograft model, induce phenotypic conversion to FOXP3+ Treg-like cells that are immune-suppressive. Furthermore, transfecting T cells with MT KRAS cDNA alone induced phenotypic switching and mathematical modeling supported this conclusion. Single-cell sequencing identified the interferon pathway as the mechanism underlying the phenotypic switch. These observations highlight a novel cytokine-independent, cell-extrinsic role for KRAS in T cell phenotypic switching. Thus, targeting this new class of Tregs represents a unique therapeutic approach for NSCLC. Since KRAS is the most frequently mutated oncogene in a wide variety of cancers, the findings of this investigation are likely to be of broad interest and have a large scientific impact.

cancer biology↗