Biology subjects

Tzankov, A.

Publications and source records attributed to Tzankov, A..

7 recordsLinked to original sources

bioRxiv2026

The brain-meningeal interface functions as a reservoir and entry site for brain parenchymal macrophages

Microglia are regarded as self-maintaining brain parenchymal macrophages without contribution from adult hematopoiesis. Nevertheless, peripheral macrophage engraftment into the brain has been reported, but the biological variables governing central nervous system (CNS) macrophage niche access remain unclear. We show that CNS macrophage engraftment is determined by the balance between tissue-resident macrophage (TRM) self-renewal and the temporal alignment of niche opening with the availability of engraftment-competent cells and proximity to the vacated niche, rather than prolonged niche vacancy. Fate mapping revealed that monocytes entering the subdural space undergo border-associated macrophage (BAM)-like differentiation, clonal expansion, and transpial migration into the parenchyma, whereas mature BAMs directly repopulate selectively vacated parenchymal niches. We further identify a parenchymal macrophage population with a peripheral BAM-like transcriptional program in aged and neurodegenerative human brains, challenging the dogma of MG-exclusivity. Together, these findings establish a predictive framework for interpreting macrophage maintenance and replacement and for developing macrophage-based therapies.

neuroscience↗

bioRxiv2026

Coordinated immune-epithelial dynamics in the nasal epithelium protect against respiratory virus infection

Epithelial remodeling is a hallmark of host antiviral responses that strengthen barrier defenses against infection. Our current understanding of viral pathophysiology within the nasal epithelium, the initial site of most respiratory viral infections, remains incomplete due to limited understanding of the native tissue architecture and protective epithelial remodeling during immune events. Leveraging coronavirus disease 2019 (COVID-19) as a model, we applied spatial multi-omics on nasal cross-sectional tissues to characterize the immune-epithelial landscape during infection, identifying a coordinated increase in goblet cells and suppressive macrophages associated with elevated IL13 in the immune compartment. Our results further reveal that goblet cell and suppressive macrophage enrichment are spatially linked in proximity to IL13-expressing CD4 T cells, consistent with IL13-driven remodeling in situ. Using a primary human nasal air-liquid interface model, we demonstrate that IL13 alone is sufficient to remodel epithelial composition and morphology, subsequently restricting viral infection by reshaping the apical mucus barrier of the nasal epithelium. Our findings uncover a spatially organized, IL13-driven circuit for immune-epithelial remodeling as a protective barrier against respiratory viral infections.

immunology↗

bioRxiv2023

Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning

The classification of B cell lymphomas - mainly based on light microscopy evaluation by a pathologist - requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphomas - nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL) - as well as 291 control tissues. With regard to DLBCL and CLL, the models demonstrated optimal performance when utilizing only the most prevalent clonotype for classification, while in NLPBL - that has a dominant background of non-malignant bystander cells - a broader array of clonotypes enhanced model accuracy. Surprisingly, the straightforward logistic regression model performed best in this seemingly complex classification problem, suggesting linear separability in our chosen dimensions. It achieved a weighted F1-score of 0.84 on a test cohort including 125 cases from all three lymphoma entities and 58 healthy individuals. Together, we provide proof-of-concept that at least the 3 studied lymphoma entities can be differentiated from each other using BCR repertoire NGS on lymphoma-infiltrated tissues by a trained machine learning model. Author SummaryLymphoma, a complex group of malignant blood cancers, poses a significant diagnostic challenge due to its diverse subtypes. Yet, precise classification is crucial for tailored treatment. In our research, we developed a machine learning algorithm and conducted comprehensive validation to discern distinct B cell lymphoma subtypes. We therefore leveraged B cell repertoires of lymphoma-infiltrated tissue, as ascertained through next-generation sequencing. Our data offers three key insights: We detail the creation and training of our machine learning algorithm, explaining how we selected features and designed the model. We demonstrate the algorithms diagnostic precision using sequencing data from a test-set of patients. Moreover, through a deep dive into the most distinguishing aspects of our algorithm, we unveil distinctive disease-related patterns present within the malignant B cell and its surrounding environment. This analysis showed that both the malignant lymphoma cell, but also healthy bystander immune cells contribute to the distinctive architecture that characterizes a specific lymphoma subtype. We hope our work will contribute towards creating tools to diagnose lymphoma more easily and accurately ultimately leading to better outcomes for patients with this type of cancer.

bioinformatics↗

bioRxiv2023

ZEB1 shapes AML immunological niches suppressing CD8 T-cell activity while fostering Th17 cell expansion.

Acute myeloid leukemia (AML) development and progression is favored by immune suppression directly triggered by leukemia cells. ZEB1 is a key transcription factor in epithelial-to-mesenchymal transition which, we show here, is capable immune regulation in AML. Leukemic cells which had ZEB1 knocked down have reduced engraftment and extramedullary disease when transplanted into immune competent mice due to concomitant activation of CD8 T lymphocytes and reduced expansion of Th17 cells. Differently, in ZEB1 competent AML, IL-17 sustains the development of a pro-invasive and self-maintaining loop inducing MMPs and SOCS2. In humans, AML patients show, in situ on bone marrow biopsies, a direct correlation between ZEB1 and Th17 and, in gene expression profile when divided according to the median value of ZEB1 expression, a different overall survival and relapse along with the expression of MMPs, SOCS2 and Th17 cells enrichment. Overall, our data shed new light into the role of ZEB1 in AML that entwines both pro-tumoral and immune regulatory capacity in AML blasts.

immunology↗

bioRxiv2022

COVID-19 patients have increased levels of membrane-associated and soluble CD48

COVID-19 is a respiratory-centered systemic disorder caused by SARS-CoV-2. The disease can progress into a severe form causing acute lung injury. CD48 is a co-signaling receptor, existing as both membrane-bound and soluble forms reported to be dysregulated in several inflammatory conditions. Therefore, we reasoned that CD48 could be deregulated in COVID-19 as well. Here we analyzed CD48 expression in autoptic sections and peripheral blood leukocytes and sera of COVID-19 patients by gene expression profiling (HTG(R) autoimmune panel), immunohistochemistry, flow cytometry and ELISA. Lung tissue of COVID-19 patients showed increased CD48 mRNA expression and infiltration of CD48+ lymphocytes. In the peripheral blood, mCD48 was considerably increased on all evaluated cells, and additionally, sCD48 levels were significantly higher in COVID-19 patients independently of disease severity. Considering the alterations of mCD48 and sCD48, a specific role for CD48 in COVID-19 can be assumed, suggesting it as a potential target for therapy.

immunology↗

bioRxiv2021

SARS-CoV-2 infects human adipose tissue and elicits an inflammatory response consistent with severe COVID-19

The COVID-19 pandemic, caused by the viral pathogen SARS-CoV-2, has taken the lives of millions of individuals around the world. Obesity is associated with adverse COVID-19 outcomes, but the underlying mechanism is unknown. In this report, we demonstrate that human adipose tissue from multiple depots is permissive to SARS-CoV-2 infection and that infection elicits an inflammatory response, including the secretion of known inflammatory mediators of severe COVID-19. We identify two cellular targets of SARS-CoV-2 infection in adipose tissue: mature adipocytes and adipose tissue macrophages. Adipose tissue macrophage infection is largely restricted to a highly inflammatory subpopulation of macrophages, present at baseline, that is further activated in response to SARS-CoV-2 infection. Preadipocytes, while not infected, adopt a proinflammatory phenotype. We further demonstrate that SARS-CoV-2 RNA is detectable in adipocytes in COVID-19 autopsy cases and is associated with an inflammatory infiltrate. Collectively, our findings indicate that adipose tissue supports SARS-CoV-2 infection and pathogenic inflammation and may explain the link between obesity and severe COVID-19. One sentence summaryOur work provides the first in vivo evidence of SARS-CoV-2 infection in human adipose tissue and describes the associated inflammation.

immunology↗

bioRxiv2020

Myeloid derived Oncostatin M reprogrammes cancer-associated fibroblasts and tumour cells promoting breast cancer progression

Cancer cells are constantly communicating with the surrounding tumour microenvironment (TME) and they hijack physiological cell interactions to overcome immune system surveillance and promote cancer progression1,2. However, the contribution of stromal cells to the reprogramming of the TME is not well understood. In this study we provide unprecedented evidence of the role of the cytokine Oncostatin M (OSM) as central node for multicellular interactions between immune and non-immune stroma and the epithelial compartment. We show that stromal expression of the OSM:Oncostatin M Receptor (OSMR) axis plays a key role in breast cancer progression. OSMR deletion in a multistage breast cancer model delays tumour onset, tumour growth and reduces metastatic burden. We ascribed causality to the stromal function of OSM axis by demonstrating reduced tumour burden of syngeneic tumours implanted in mice. Single-cell and bioinformatic analysis of murine and human breast tumours revealed that the expression of OSM signalling components is compartmentalized in the tumour stroma. OSM expression is restricted to myeloid cells, whereas OSMR expression is detected predominantly in fibroblasts and, to a lower extent, cancer cells. Myeloid-derived OSM reprograms fibroblasts to a more contractile and pro-tumorigenic phenotype, elicits the secretion of VEGF and pro-inflammatory chemokines (e.g. CXCL1 and CXCL16), leading to increased neutrophil and macrophage recruitment. In summary, our work sheds light on the mechanism of immune regulation by the tumour microenvironment, and supports that targeting OSM:OSMR interactions is a potential therapeutic strategy to inhibit tumour-promoting inflammation and breast cancer progression.

cancer biology↗