Biology subjects

Abedpour, N.

Publications and source records attributed to Abedpour, N..

4 recordsLinked to original sources

bioRxiv2026

Combination of enhanced gene activation and cell death causes lethal inflammatory pulmonary fibrosis

The current paradigm of inflammatory disease etiology is that it is caused by an aberrant increase in either gene activation or cell death. Studying the linear ubiquitin (linUb) chain assembly complex (LUBAC) was instrumental in proving that immune-receptor-dependent cell death can cause inflammatory disease. As LUBAC regulates both, gene activation and cell death, it can, however, also serve to evaluate the inflammatory-disease-initiating role of gene activation. Investigating mice with two different gene-activation-enhancing point mutations in LUBAC components showed that aberrantly increased gene activation in Hoil 1C458A/C458A mice did not cause pathology. In contrast, in HoipN101A/N101A mice it led to untoward cell death which, unexpectedly, was required for pathological inflammation, culminating in lethal lung fibrosis resembling human idiopathic pulmonary fibrosis (IPF). Hence, we here uncover a new etiology of inflammation, whereby aberrantly enhanced gene activation can be root cause of pathological, fibrogenic inflammation, yet intriguingly via inducing untoward cell death.

cell biology↗

bioRxiv2024

T-cell development and activation in humanized mice lacking mouse major histocompatibility complexes

Humanized mice transplanted with CD34+ hematopoietic progenitor cells (HPCs) are used to study human immune responses in vivo. However, the mismatch between the mouse major histocompatibility complexes (MHCs) and the human leukocyte antigens (HLAs) is not optimal for T-cell development and can trigger xenograft reactivity. We evaluated human T-cell development in NOD.Scid.Gamma mice lacking expression of MHC class I and II (NSG-DKO). Human leukocyte engraftment was detectable at 8 weeks post-transplantation. Human CD4+ and CD8+ T-cells were detectable in blood, thymus, bone marrow and spleen of humanized NSG-DKO mice for up to 20 weeks post-transplantation. Further, we evaluated the effects of lentiviral vector (LV) systemic delivery of HLA-A*02:01, HLA-DRB1*04:01, human GM-CSF/IFN- and the human cytomegalovirus gB antigen. LV delivery promoted development and activation of human central memory, {beta} and {gamma}{delta} T-cells with amplifications of the T-cell repertoire. LV administration unleashed multiple reactome pathways such as type-I interferon responses, cell cycle and metabolic processes. In summary, development of human T-cells in humanized mice does not rely on mouse MHCs and can be boosted systemically via LV administration.

immunology↗

bioRxiv2019

Dissecting intratumor heterogeneity of nodal B cell lymphomas on the transcriptional, genetic, and drug response level

Tumor heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is well-known to affect the efficacy of anti-cancer drugs, most personalized treatment approaches do not account for intratumor heterogeneity. We addressed this issue by studying the heterogeneity of lymph node-derived B cell non-Hodgkin lymphoma (B-NHL) by single cell RNA-sequencing (scRNA-seq) and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subclones and compared their drug response and genomic profiles. Malignant subclones of the same patient responded strikingly different to anti-cancer drugs ex vivo, which recapitulated subclone-specific drug sensitivity during in vivo treatment. Tumor infiltrating T cells represented the majority of non-malignant cells, whose gene expression signatures were similar across all donors, whereas the frequencies of T cell subsets varied significantly between the donors. Our data provide new insights into the heterogeneity of B-NHL and highlight the relevance of intratumor heterogeneity for personalized cancer therapies.

cancer biology↗

bioRxiv2016

Predicting gene expression level in E. coli from mRNA sequence information

MotivationThe accurate characterization of the translational mechanism is crucial for enhancing our understanding of the relationship between genotype and phenotype. In particular, predicting the impact of the genetic variants on gene expression will allow to optimize specific pathways and functions for engineering new biological systems. In this context, the development of accurate methods for predicting translation efficiency from the nucleotide sequence is a key challenge in computational biology.\n\nMethodsIn this work we present PGExpress, a binary classifier to discriminate between mRNA sequences with low and high translation efficiency in E. coli. PGExpress algorithm takes as input 12 features corresponding to RNA folding and anti-Shine-Dalgarno hybridization free energies. The method was trained on a set of 1,772 sequence variants (WT-High) of 137 essential E. coli genes. For each gene, we considered 13 sequence variants of the first 33 nucleotides encoding for the same amino acids followed by the superfolder GFP. Each gene variant is represented sequence blocks that include the Ribosome Binding Site (RBS), the first 33 nucleotides of the coding region (C33), the remaining part of the coding region (CC), and their combinations.\n\nResultsOur logistic regression-based tool (PGExpress) was trained using a 20-fold gene-based cross-validation procedure on the WT-High dataset. In this test PGExpress achieved an overall accuracy of 74%, a Matthews correlation coefficient 0.49 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.81. Tested on 3 sets of sequences with different Ribosome Binding Sites, PGExpress reaches similar AUC. Finally, we validated our method by performing in-house experiments on five newly generated mRNA sequence variants. The predictions of the expression level of the new variants are in agreement with our experimental results in E. coli.\n\nAvailabilityhttp://folding.biofold.org/pgexpress\n\nContactmarkus.kollmann@hhu.de, emidio.capriotti@unibo.it

bioinformatics↗