Biology subjects

Bertolini, A.

Publications and source records attributed to Bertolini, A..

6 recordsLinked to original sources

bioRxiv2026

Clone-level multi-modal prediction of tumour drug response

Tumour heterogeneity presents a major challenge for precision oncology, as genetically and phenotypically distinct tumour clones may respond differently to therapy. To address this, we introduce scClone2DR, a probabilistic multi-modal framework that predicts drug responses at the level of individual tumour clones by integrating single-cell DNA and RNA sequencing with ex-vivo drug-screening data. In simulations, scClone2DR substantially outperforms alternatives in recovering true drug-effects and clonal sensitivities. Applied to 60 melanoma and 21 acute myeloid leukaemia patient samples, the method identifies heterogeneous clonal responses, yields biologically meaningful feature rankings, highlights clones that may be resistant to treatment, and improves the prediction of clinical outcomes compared to models ignoring clonal structure. These results demonstrate that modelling tumour evolution and clonal diversity is crucial for accurate drug-response prediction and provides a foundation for more effective, clone-aware precision oncology.

cancer biology↗

bioRxiv2024

Differential prolyl hydroxylation by six Physcomitrella prolyl-4 hydroxylases

The hydroxylation of proline residues to 4-trans-hydroxyproline (Hyp) is a common post-translational protein modification in plants, mediated by prolyl 4-hydroxylases (P4Hs). Hyps predominantly occur in a group of cell wall proteins, the Hydroxyproline-rich glycoproteins (HRGPs), where they are frequently O-glycosylated. While prolyl-hydroxylation and O-glycosylation are important, e.g. for cell wall stability, they are undesired on plant-made pharmaceuticals. Sequence motifs recognized for prolyl-hydroxylation derived from vascular plants were proposed but did not include data from mosses, such as Physcomitrella. Here, a phylogenetic reconstruction of plant P4Hs identified six P4Hs in four subfamilies in mosses. We analysed the amino acid sequences and structural environments around Hyps in Physcomitrella utilizing 73 Hyp sites in 24 secretory proteins from multiple MS/MS datasets, and found that prolines in close proximity to other prolines, alanine, serine, threonine and valine were preferentially hydroxylated. About 95% of the Hyp sites were predictable with a combination of previously defined motifs and methods. In our data, AOV (Ala-Hyp-Val) was the most frequent prolyl-hydroxylation pattern. Additionally, short arabinose chains were attached to Hyps in two cell-wall pectinesterases. A combination of 443 AlphaFold structure models and our MS data of peptides with nearly 3000 proline sites found Hyps predominantly on protein surfaces in disordered regions. Moss-produced human erythropoietin (EPO) exhibited plant-specific O-glycosylation with arabinose chains on two Hyps. This modification was significantly reduced in a P4H1 single knock-out (KO) Physcomitrella mutant. Quantitative proteomics after isotope labelling with different P4H-KO moss mutants revealed specific changes in the amount of proteins, including HRGPs, and modified prolyl-hydroxylation pattern from the mutants, suggesting a differential function of the six Physcomitrella P4Hs. Quantitative RT-PCR proved a differential effect of single P4H KOs on the expression of the other five p4h genes, suggesting a partial compensation of the mutation. AlphaFold-Multimer models for Physcomitrella P4H1 and its target EPO peptide superposed with the crystal structure of Chlamydomonas P4H1 and a peptide substrate suggested significant amino acids in the active centre of the enzyme that form H-bonds with the peptide substrate, and revealed differences between P4H1 and the other five Physcomitrella P4Hs. HighlightsMosses, such as Physcomitrella, encode six prolyl 4-hydroxylases in four subfamilies Computational analysis reveals specific environment for proline hydroxylation AlphaFold structure models and mass spectrometry reveal features of modification Quantitative proteomics and mutant analysis identify relevant modifying enzyme Modelling suggests relevant amino acids in active centre of the modifying enzyme

plant biology↗

bioRxiv2024

SMAC mimetics overcome apoptotic resistance in ovarian cancer through MSLN-TNF alpha axis

Resistance to chemotherapy and PARPi inhibitors remains a critical challenge in the treatment of epithelial ovarian cancer, mainly due to disabled apoptotic responses in tumor cells. Given mesothelins pivotal role in ovarian cancer and its restricted expression in healthy tissues, we conducted a drug-screening discovery analysis across a range of genetically modified cancer cells to unveil mesothelins therapeutic impact. We observed enhanced cell death in cancer cells with low mesothelin expression when exposed to a second mitochondria-derived activator of caspases (SMAC) mimetics, and demonstrated a compelling synergy when combined with chemotherapy in ex vivo patient-derived cultures and zebrafish tumor xenografts. Mechanistically, the addition of the SMAC mimetics drug birinapant to either carboplatin or paclitaxel triggered the activation of the Caspase 8-dependent apoptotic program facilitated by TNFLJ signaling. Multimodal analysis of neoadjuvant-treated patient samples further revealed an association between tumor-associated macrophages and the activation of TNFLJ-related pathways. Our proposed bimodal treatment shows promise in enhancing the clinical management of patients by harnessing the potential of SMAC mimetics alongside conventional chemotherapy.

cancer biology↗

bioRxiv2022

gExcite - A start-to-end framework for single-cell gene expression, hashing, and antibody analysis

SummarySingle-cell RNA sequencing (scRNA-seq) based gene expression analysis is now an established powerful technique to decipher tissues at a single-cell level. Recently, CITE-seq emerged as a multimodal single-cell technology capturing gene expression and surface protein information from the same single-cells, which allows unprecedented insights into disease mechanisms and heterogeneity, as well as immune cell profiling. Multiple single-cell profiling methods exist, but they are typically focussed on either gene expression or antibody analysis, not their combination. Moreover, existing software suites are not easily scalable to a multitude of samples. To this end, we designed gExcite, a start-to-end workflow that provides both gene expression and CITE-seq analysis, as well as hashing deconvolution. Embedded in the Snakemake workflow manager, gExcite facilitates reproducible and scalable analyses. We showcase the output of gExcite on a study of different dissociation protocols on PBMC samples. AvailabilitygExcite is open source available on github at https://github.com/ETH-NEXUS/gExcite_pipeline The software is distributed under the GNU General Public License 3 (GPL3). Contactsinger@nexus.ethz.ch Supplementary InformationSupplementary information is available at the journals web site.

bioinformatics↗

bioRxiv2022

scROSHI - robust supervised hierarchical identification of single cells

Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene sets and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.

bioinformatics↗

bioRxiv2021

scAmpi - A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.

bioinformatics↗