Biology subjects

Rigo, M. M.

Publications and source records attributed to Rigo, M. M..

4 recordsLinked to original sources

bioRxiv2024

Epigenetic changes regulating the epithelial-mesenchymal transition in human trophoblast differentiation

The phenotype of human placental extravillous trophoblast (EVT) at the end of pregnancy reflects both first trimester differentiation from villous cytotrophoblast (CTB) and later gestational changes, including loss of proliferative and invasive capacity. Invasion abnormalities are central to two major placental pathologies, preeclampsia and placenta accreta spectrum, so characterization of the corresponding normal processes is crucial. In this report, our gene expression analysis, using purified human CTB and EVT cells, highlights an epithelial- mesenchymal transition (EMT) mechanism underlying CTB-EVT differentiation and provides a trophoblast-specific EMT signature. In parallel, DNA methylation profiling shows that CTB cells, already hypomethylated relative to non-trophoblast cell lineages, show further genome- wide hypomethylation in the transition to EVT. However, a small subgroup of genes undergoes gains of methylation (GOM) in their regulatory regions or gene bodies, associated with differential mRNA expression (DE). Prominent in this GOM-DE group are genes involved in the EMT, including multiple canonical EMT markers and the EMT-linked transcription factor RUNX1, for which we demonstrate a functional role in modulating the migratory and invasive capacities of JEG3 trophoblast cells. This analysis of DE associated with locus-specific GOM, together with functional studies of an important GOM-DE gene, highlights epigenetically regulated genes and pathways acting in human EVT differentiation and invasion, with implications for obstetric disorders in which these processes are dysregulated.

developmental biology↗

bioRxiv2023

Innate extracellular Hsp70 inflammatory properties are mediated by the interaction of Siglec-E and LOX-1 receptors

Innate immune responses to cell damage-associated molecular patterns induce a controlled degree of inflammation, ideally avoiding the promotion of intense unwanted inflammatory adverse events. When released by damaged cells, Hsp70 can stimulate different responses that range from immune activation to immune suppression. The effects of Hsp70 are mediated through innate receptors expressed primarily by myeloid cells, such as dendritic cells (DCs). The regulatory innate receptors that bind to extracellular mouse Hsp70 (mHsp70) are not fully characterized, and neither are their potential interactions with activating innate receptors. Here, we describe that extracellular mHsp70 interacts with a receptor complex formed by inhibitory Siglec-E and activating LOX-1 on DCs. We also find that this interaction takes place within lipid microdomains, and Siglec-E acts as a negative regulator of LOX-1-mediated innate activation upon mHsp70 or oxidized LDL binding. Thus, HSP70 can both bind to and modulate the interaction of inhibitory and activating innate receptors on the cell surface. These findings add another dimension of regulatory mechanism to how self-molecules contribute to dampening of exacerbated inflammatory responses.

immunology↗

bioRxiv2023

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conformational ensembles. In this work we: (1) provide an overview of existing methods and tools for protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples found in the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

bioinformatics↗

bioRxiv2021

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

MotivationRecent efforts to computationally identify inhibitors for SARS-CoV-2 proteins have largely ignored the issue of receptor flexibility. We have implemented a computational tool for ensemble docking with the SARS-CoV-2 proteins, including the main protease (Mpro), papain-like protease (PLpro) and RNA-dependent RNA polymerase (RdRp). ResultsEnsembles of other SARS-CoV-2 proteins are being prepared and made available through a user-friendly docking interface. Plausible binding modes between conformations of a selected ensemble and an uploaded ligand are generated by DINC, our parallelized meta-docking tool. Binding modes are scored with three scoring functions, and account for the flexibility of both the ligand and receptor. Additional details on our methods are provided in the supplementary material. Availabilitydinc-covid.kavrakilab.org Supplementary informationDetails on methods for ensemble generation and docking are provided as supplementary data online. Contactgeancarlo.zanatta@ufc.br, kavraki@rice.edu

bioinformatics↗