Biology subjects

Rendeiro, A.

Publications and source records attributed to Rendeiro, A..

6 recordsLinked to original sources

bioRxiv2026

Mapping human natural killer cell development in tonsil

Secondary lymphoid tissue, including tonsil, supports human NK cell development, but the spatial organization and tissue niches that drive this differentiation remain undefined. Here, we used single cell analysis of cyclic immunofluorescence to generate a comprehensive atlas of human NK cell development in tonsil. By integrating regional localization, chemokine signaling, cytokine availability, and cell phenotype, we show that NK cell differentiation follows a reproducible spatial trajectory defined by stage-specific cell-cell interactions. CD34+ NK cell progenitors are found in the interfollicular domain in proximity to high endothelial venules, consistent with this route of progenitor entry into tissue. Mature NK cells are primarily found in the T cell-rich parafollicular domain, where they interact with other NK cells and T cell subsets. Local inflammation increases NK cell frequency in tissue through both proliferation of NK progenitors and increased frequency of mature NK cells. Finally, we identify a subset of tonsil stromal cells that support differentiation of NK cells in vitro and proliferation of NK precursors in situ. Together, these findings demonstrate that spatial localization defines human NK cell development and provide an in situ definition of niches that support human NK cell differentiation in tonsil.

immunology↗

bioRxiv2023

Mapping human natural killer cell development in pediatric tonsil by imaging mass cytometry & high-resolution microscopy

Natural killer (NK) cells develop from CD34+ progenitors in a stage-specific manner defined by changes in cell surface receptor expression and function. Secondary lymphoid tissues, including tonsil, are sites of human NK cell development. Here we present new insights into human NK cell development in pediatric tonsil using cyclic immunofluorescence and imaging mass cytometry. We show that NK cell subset localization and interactions are dependent on NK cell developmental stage and tissue residency. NK cell progenitors are found in the interfollicular domain in proximity to cytokine-expressing stromal cells that promote proliferation and maturation. Mature NK cells are primarily found in the T-cell rich parafollicular domain engaging in cell-cell interactions that differ depending on their stage and tissue residency. The presence of local inflammation results in changes in NK cell interactions, abundance, and localization. This study provides the first comprehensive atlas of human NK cell development in secondary lymphoid tissue.

immunology↗

bioRxiv2023

Transcriptomic signatures of progressive and regressive liver fibrosis and portal hypertension

Persistent liver injury triggers a fibrogenic program that causes pathologic remodelling of the hepatic microenvironment (i.e., liver fibrosis) and portal hypertension. The dynamics of gene regulation during liver disease progression and regression remain understudied. Here, we generated hepatic transcriptome profiles in two well-established liver disease models at peak fibrosis and during spontaneous regression after the removal of the inducing agents. We linked the dynamics of key liver disease readouts, such as portal pressure, collagen proportionate area, and transaminase serum levels, to most differentially expressed genes, enabling the identification of transcriptomic signatures of progressive vs. regressive liver fibrosis and portal hypertension. These candidate biomarkers (e.g., Scube1, Tcf4, Src, Hmga1, Trem2, Mafk, Mmp7) were also validated in RNA-seq datasets of patients with cirrhosis and portal hypertension. Finally, deconvolution analysis identified major cell types and suggested an association of macrophage and portal hepatocyte signatures with portal hypertension and fibrosis area in both models.

systems biology↗

bioRxiv2020

Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects

Organizing and annotating biological sample data is critical in data-intensive bioinformatics. Unfortunately, metadata formats from a data provider are often incompatible with requirements of a processing tool. There is no broadly accepted standard to organize metadata across biological projects and bioinformatics tools, restricting the portability and reusability of both annotated datasets and analysis software. To address this, we present Portable Encapsulated Projects (PEP), a formal specification for biological sample metadata structure. The PEP specification accommodates typical features of data-intensive bioinformatics projects with many samples, whether from individual experiments, organisms, or single cells. In addition to standardization, the PEP specification provides descriptors and modifiers for different organizational layers of a project, which improve portability among computing environments and facilitate use of different processing tools. PEP includes a schema validator framework, allowing formal definition of required metadata attributes for any type of biomedical data analysis. We have implemented packages for reading PEPs in both Python and R to provide a language-agnostic interface for organizing project metadata. PEP therefore presents an important step toward unifying data annotation and processing tools in data-intensive biological research projects.

bioinformatics↗

bioRxiv2018

MTHFD1 is a genetic interactor of BRD4 and links folate metabolism to transcriptional regulation

The histone acetyl-reader BRD4 is an important regulator of chromatin structure and transcription, yet factors modulating its activity have remained elusive. Here we describe two complementary screens for genetic and physical interactors of BRD4, which converge on the folate pathway enzyme MTHFD1. We show that a fraction of MTHFD1 resides in the nucleus, where it is recruited to distinct genomic loci by direct interaction with BRD4. Inhibition of either BRD4 or MTHFD1 results in similar changes in nuclear metabolite composition and gene expression, and pharmacologic inhibitors of the two pathways synergize to impair cancer cell viability in vitro and in vivo. Our finding that MTHFD1 and other metabolic enzymes are chromatin-associated suggests a direct role for nuclear metabolism in the control of gene expression.

cancer biology↗

bioRxiv2018

STAT3 promotes melanoma metastasis by CEBP-induced repression of the MITF pigmentation pathway

Metastatic melanoma is hallmarked by its ability to switch oncogenic MITF expression. Here we tested the impact of STAT3 on melanoma onset and progression in association with MITF expression levels. We established a mouse melanoma model for deleting Stat3 specifically in melanocytes with specific expression of human hyperactive NRASQ61K in an Ink4a deficient background. Mice with tissue specific Stat3 deletion showed an early onset of disease, but displayed significantly diminished lung metastases. Whole genome expression profiling also revealed a reduced invasion phenotype, which was functionally confirmed in 3D melanoma model systems. Notably, loss or knockdown of STAT3 in mouse or human cells resulted in up-regulation of MITF and induction of cell proliferation. Mechanistically we show that STAT3 induced CEBPa/b expression was sufficient to suppress MITF transcription. Epigenetic analysis by ATAC-seq confirmed that STAT3 enabled CEBPa/b binding to the MITF enhancer region thereby silencing it. We conclude that STAT3 is a metastasis driver in melanoma able to antagonize the MITF oncogene via direct induction of CEBP family member transcription facilitating RAS-RAF-driven melanoma metastasis.\n\nList of Abbreviations\n\nATAC-seq, Assay for Transposase-Accessible Chromatin using sequencing; CEBP, CAAT Box Enhancer Binding Protein; CRE, Cre recombinase; EGF, Epidermal Growth Factor; GEO, Gene Expression Omnibus; GSEA, Gene Set Enrichment Analysis; HSC70, Heat Shock 70 kDa protein; IHC, Immunohistochemistry; IL-6, Interleukin-6; JAK, Janus Kinase; MITF, Microphthalmia-Associated Transcription Factor; NSG, NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ mice; OSM, Oncostatin M; PDGF, Platelet-Derived Growth Factor; pS, phosphoserine; pY, phosphotyrosine; RAS, Rat Sarcoma; RAF, Rapidly Accelerated Fibrosarcoma; RTK, Receptor Tyrosine Kinase; RT-PCR, Reverse Transcription Polymerase Chain Reaction; S100b, Calcium Binding Protein S100 beta; shRNA, short hairpin RNA; SOX10, Sex Determining Region Y-10; STAT, Signal Transducer and Activator of Transcription; TCGA, The Cancer Genome Atlas; TMA, Tissue Micro Array

cancer biology↗