Dumitru, A. | bioRxiv Science

bioRxiv2020

High-affinity agonist binding to C5aR results from a cooperative two-site binding mechanism

A current challenge in the field of life sciences is to decipher, in their native environment, the functional activation of cell surface receptors upon binding of complex ligands. Lack of suitable nanoscopic methods has hampered our ability to meet this challenge in an experimental manner. Here, we use for the first time the interplay between atomic force microscopy, steered molecular dynamics and functional assays to elucidate the complex ligand-binding mechanism of C5a with the human G protein-coupled C5a receptor (C5aR). We have identified two independent binding sites acting in concert where the N-terminal C5aR serves as kinetic trap and the transmembrane domain as functional site. Our results corroborate the two-site binding model and clearly identify a cooperative effect between two binding sites within the C5aR. We anticipate that our methodology could be used for development and design of new therapeutic agents to negatively modulate C5aR activity.

biochemistry↗

bioRxiv2019

Investigating and Assessing the Dermoepidermal Junction with Multiphoton Microscopy and Deep Learning

Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard in the case of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton Microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both in-vivo or ex-vivo. Here we show that MPM imaging of the dermoepidermal junction (DEJ) in unstained tissues provides useful cues for a histopathologist to identify the onset of non-melanoma skin cancers. Furthermore, we show that MPM images collected on the DEJ, besides being easy to interpret by a trained specialist, can be automatically classified into healthy and dysplastic classes with high precision using a Deep Learning method and existing pre-trained Convolutional Neural Networks. Our results suggest that Deep Learning enhanced MPM for in-vivo skin cancer screening could facilitate timely diagnosis and intervention, enabling thus more optimal therapeutic approaches.

pathology↗