Biology subjects

Derakhshandeh, F.

Publications and source records attributed to Derakhshandeh, F..

2 recordsLinked to original sources

bioRxiv2025

Modulation of collagen-binding integrins affects fibroblast activation and inhibits fibrosis

The extracellular matrix contributes to the progression of several diseases, sometimes by disrupting organ function such as in lung and liver fibrosis. Because integrin receptors mediate cell-matrix interactions, we conditionally deleted {beta}1 integrin in murine hepatocytes in vivo. Increased TGF-{beta} and matrix deposition ensued. Application of a cyclic peptide (GLQGE) that binds to both 10{beta}1 and 11{beta}1 diminished fibrosis in two murine models. In liver fibrosis, TGF-{beta} production was reduced. In lung fibrosis, however, the effect was exclusively due to suppressing fibroblast activation and hence collagen production without TGF-{beta} involvement. In summary, integrin manipulation successfully changed cell behavior, with effects differing depending on the cell type. Importantly, it is possible to directly suppress fibroblast activation and consequently diminish matrix production independent of disease pathogenesis. This underscores the importance of matrix composition in modifying the behavior of embedded cells and hence disease severity. Key findingsO_LILoss of 11{beta}1 integrin-mediated signal in hepatocytes increases TGF-{beta} and leads to fibrosis C_LIO_LIEngaging 10{beta}1 and/or 11{beta}1 with a cyclic peptide (GLQGE) inhibits fibroblast activation directly and, consequently matrix production and progression of fibrosis C_LI

cell biology↗

bioRxiv2023

Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments

Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the phylogenetic tree reconstruction based on the Maximum Likelihood method. In this study, we explored the utility of neural networks to predict the correct model of sequence evolution and the correct topology for four sequence alignments. We trained neural networks with different architectures using simulated nucleotide and amino acid sequence alignments for a wide range of evolutionary models, model parameters and branch lengths. By comparing the accuracy of model and topology prediction of the trained neural networks with Maximum Likelihood and Neighbour Joining methods, we show that for quartet trees, the neural network classifier outperforms the Neighbour Joining method and is in most cases as good as the Maximum Likelihood method to infer the best model of sequence evolution and the best tree topology. These results are consistent for nucleotide and amino acid sequence data. Furthermore, we found that neural network classifiers are much faster than the IQ-Tree implementation of the Maximum Likelihood method. Our results show that neural networks could become a true competitor for the Maximum Likelihood method in phylogenetic reconstructions.

evolutionary biology↗