Biology subjects

Charmpi, K.

Publications and source records attributed to Charmpi, K..

5 recordsLinked to original sources

bioRxiv2024

Proteostasis dysregulation in p.A53T-α-Synuclein iPSC-derived astrocytes exacerbates neurodegeneration in a Parkinson's disease model with Lewy-like pathology

Alpha-Synuclein (Syn) plays a central role in Parkinsons disease (PD) and the p.A53T mutation causes an early-onset familial form of PD with severe manifestations. The pathological effects of the p.A53T-Syn mutation have been extensively investigated in neurons, yet the consequences on astrocytes and astrocytic contribution to PD pathology are understudied. Here, we differentiated induced pluripotent stem cells from PD patients carrying the p.A53T-Syn mutation to astrocytes, which uncovered cell-intrinsic phenotypes, including calcium dyshomeostasis and accumulation of protein aggregates. Proteomic profiling and functional analyses revealed perturbed protein catabolic processes, involving the proteasome and autophagy, associated with lysosomal malfunction. Dopamine neurons co-cultured with p.A53T-Syn astrocytes displayed exacerbated neurodegeneration with hallmark Lewy-like pathologies, reversed by control astrocytes at least due to their ability to resolve neuronal Syn aggregates by endocytic clearance. Our findings underscore a critical impact of p.A53T-Syn on astrocytic protein quality control mechanisms, positioning astrocytes as important contributors to PD neuropathology. HighlightsO_LIiPSC-derived astrocytes from PD patients with the p.A53T-Syn mutation display cell-autonomous pathological phenotypes and toxic Syn accumulation C_LIO_LIProteome and functional analyses reveal failure of major proteostasis mechanisms in p.A53T-Syn astrocytes, largely related to lysosomal malfunction C_LIO_LIInherent Lewy-like pathological features are identified in p.A53T-Syn astrocyte-neuron co-cultures C_LIO_LIp.A53T-Syn astrocytes induce PD-relevant neuropathology to healthy neurons C_LIO_LIControl but not p.A53T-Syn astrocytes alleviate neuropathology in co-cultured neurons C_LI

neuroscience↗

bioRxiv2021

Optimizing Network Propagation for Multi-Omics Data Integration

Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a topology bias caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand.

bioinformatics↗

bioRxiv2020

Proteogenomic heterogeneity of localized human prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high throughput genomic measurements. It remains unclear though, how complex genome alterations affect molecular networks through changing protein levels, and consequently biochemical states of tumor tissues. ResultsHere, we investigated the propagation of genomic effects along the axis of gene expression during prostate cancer progression. For that, we quantified genomic, transcriptomic and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis revealed convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devised a network-based approach that integrates perturbations across different molecular layers, which identified a sub-network consisting of nine genes whose joint activity positively correlated with increasingly aggressive tumor phenotypes and was predictive of recurrence-free survival. Further, our data revealed a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. ConclusionsThis study uncovered molecular networks with remarkably convergent alterations across tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single sub-network that was perturbed in all high-grade tumor regions.

systems biology↗

bioRxiv2020

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

Although genetic and epigenetic abnormalities in breast cancer have been extensively studied, it remains difficult to identify those patients who will respond to particular therapies. This is due in part to our lack of understanding of how the variability of cellular signaling affects drug sensitivity. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data - on more than 80 million single cells from 4,000 conditions - were used to fit mechanistic signaling network models that provide unprecedented insights into the biological principles of how cancer cells process information. Our dynamic single-cell-based models more accurately predicted drug sensitivity than static bulk measurements for drugs targeting the PI3K-MTOR signaling pathway. Finally, we identified genomic features associated with drug sensitivity by using signaling phenotypes as proxies, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. This provides proof of principle that single-cell measurements and modeling could inform matching of patients with appropriate treatments in the future. One-linerSingle-cell proteomics coupled to perturbations improves accuracy of breast tumor drug sensitivity predictions and reveals mechanisms of sensitivity and resistance. HIGHLIGHTSO_LIMass cytometry study of signaling responses of 62 breast cancer cell lines and five lines from healthy tissue to EGF stimulation with or without perturbation with five kinase inhibitors. C_LIO_LISingle-cell signaling features and mechanistic signaling network models predicted drug sensitivity. C_LIO_LIMechanistic signaling network models deepen the understanding of drug resistance and sensitivity mechanisms. C_LIO_LIWe identify drug sensitivity-predictive genomic features via proxy signaling phenotypes. C_LI

systems biology↗

bioRxiv2018

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

Many tumors are characterized by large genomic heterogeneity and it remains unclear to what extent this impacts on protein biomarker discovery. Here, we quantified proteome intra-tissue heterogeneity (ITH) based on a multi-region analysis of 30 biopsy-scale prostate tissues using pressure cycling technology and SWATH mass spectrometry. We quantified 8,248 proteins and analyzed the ITH of 3,700 proteins. The level of ITH varied significantly depending on proteins and tissue types. Benign tissues exhibited generally more complex ITH patterns than malignant tissues. Spatial variability of ten prostate biomarkers was further validated by immunohistochemistry in an independent cohort (n=83) using tissue microarrays. PSA was preferentially variable in benign prostatic hyperplasia, while GDF15 substantially varied in prostate adenocarcinomas. Further, we found that DNA repair pathways exhibited a high degree of variability in tumorous tissues, which may contribute to the genetic heterogeneity of tumors. This study conceptually adds a new perspective to protein biomarker discovery by quantifying spatial proteome variation and it demonstrates the feasibility by exploiting recent technological progress.

systems biology↗