Biology subjects

Muller, A.

Publications and source records attributed to Muller, A..

6 recordsLinked to original sources

bioRxiv2018

Independent component analysis provides clinically relevant insights into the biology of melanoma patients

The integration of publicly available and new patient-derived transcriptomic datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Here we present a methodology that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large reference datasets. The approach is based on independent component analysis (ICA) - an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes merged new and reference datasets into signals with minimal mutual dependency. By applying the method to a small cohort of primary melanoma and control samples combined with a large public melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. Cancer subtypes, patient survival and activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation were characterized. Additionally, through integration of transcriptomes and miRNomes, the method identified biological functions of miRNAs, which would otherwise not be possible.

genomics↗

bioRxiv2017

Protein Interaction Screen on Peptide Matrix (PRISMA) reveals interaction footprints and the PTM-dependent interactome of intrinsically disordered C/EBPβ

CCAAT enhancer binding protein beta (C/EBP{beta}) is a pioneer transcription factor that specifies cell differentiation. C/EBP{beta} is intrinsically unstructured, a molecular feature common to many proteins involved in signal processing and epigenetics. The structure of C/EBP{beta} differs depending on alternative translation start site usage and multiple post-translational modifications (PTM). Mutation of distinct PTM sites in C/EBP{beta} alters designated protein interactions and cell differentiation, suggesting a C/EBP{beta} PTM indexing code determines epigenetic outcomes. Herein, we systematically explored the interactome of C/EBP{beta} using an array of spot-synthesised C/EBP{beta}-derived linear tiling peptides with and without PTM, combined with mass spectrometric proteomic analysis of protein interactions. We identified interaction footprints of ~1300 proteins in nuclear cell extracts, many with chromatin modifying, remodelling and RNA processing functions. The results suggest C/EBP{beta} acts as a multi-tasking molecular switchboard, integrating signal-dependent modifications and structural plasticity to orchestrate interactions with numerous protein complexes directing cell fate and function.\n\nHighlightsO_LIPeptide array based interaction proteomics map SLiM and PTM dependent C/EBP{beta} interactome\nC_LIO_LINovel links between C/EBP{beta}, RNA processing, transcription elongation, MLL, NuRD were revealed\nC_LIO_LIC/EBP{beta} structure organizes modular hub function for gene regulatory machinery\nC_LIO_LIPRISMA is suitable to resolve protein interactions and networks based on intrinsically disordered proteins\nC_LI

biochemistry↗

bioRxiv2017

Dr.Paso: Drug response prediction and analysis system for oncology research

The prediction of anticancer drug response is crucial for achieving a more effective and precise treatment of patients. Models based on the analysis of large cell line collections have shown potential for investigating drug efficacy in a clinically-meaningful, cost-effective manner. Using data from thousands of cancer cell lines and drug response experiments, we propose a drug sensitivity prediction system based on a 47-gene expression profile, which was derived from an unbiased transcriptomic network analysis approach. The profile reflects the molecular activity of a diverse range of cancer-relevant processes and pathways. We validated our model using independent datasets and comparisons with published models. A high concordance between predicted and observed drug sensitivities was obtained, including additional validated predictions for four glioblastoma cell lines and four drugs. Our approach can accurately predict anti-cancer drug sensitivity and will enable further pre-clinical research. In the longer-term, it may benefit patient-oriented investigations and interventions.

bioinformatics↗

bioRxiv2017

The circadian hippocampus and its reprogramming in epilepsy: impact for chronotherapeutics

Gene and protein expression displays circadian oscillations in numerous body organs. These oscillations can be disrupted in diseases, thus contributing to the disease pathology. Whether the molecular architecture of cortical brain regions oscillates daily and whether these oscillations are modified in brain disorders is less understood. We identified 1200 daily oscillating transcripts in the hippocampus of control mice. More transcripts (1600) were oscillating in experimental epilepsy, with only one fourth oscillating in both conditions. Proteomics confirmed these results. Metabolic activity and targets of antiepileptic drugs displayed different circadian regulation in control and epilepsy. Hence, the hippocampus, and perhaps other cortical regions, shows a daily remapping of its molecular landscape, which would enable different functioning modes during the night/day cycle. The impact of this remapping in brain pathologies needs to be taken into account not only to study their mechanisms, but also to design drug treatments and time their delivery.

neuroscience↗

bioRxiv2017

Accumulation of sensory evidence is impaired in Parkinson’s disease with visual hallucinations

Models of hallucinations across disorders emphasise an imbalance between sensory input and top-down influences over perception. However, the psychological and mechanistic correlates of this imbalance remain underspecified. Visual hallucinations in Parkinsons disease (PD) are associated with impairments in lower level visual processes and attention, accompanied by over activity and connectivity in higher-order association brain networks. PD therefore provides an attractive framework to explore the relative contributions of bottom-up versus top-down disturbances in hallucinations. Here, we characterised sensory processing in PD patients with and without visual hallucinations, and in healthy controls, by fitting a hierarchical drift diffusion model (hDDM) to an attentional task. The hDDM uses Bayesian estimates to decompose reaction time and response output into parameters reflecting drift rates of evidence accumulation, decision thresholds and non-decision time. We observed slower drift rates in PD patients with hallucinations, which were insensitive to changes in task demand. In contrast, wider decision boundaries and shorter non-decision times relative to controls were found in PD regardless of hallucinator status. Inefficient and less flexible sensory evidence accumulation emerge as unique features of PD hallucinators. We integrate these results with current models of hallucinations, suggesting that slow and inefficient sensory input in PD is less informative, and may therefore be down-weighted leading to an over reliance on top-down influences. Our findings provide a novel computational framework to better specify the impairments in dynamic sensory processing that are a risk factor for visual hallucinations.

neuroscience↗

bioRxiv2017

Decoding sequence-level information to predict membrane protein expression

The expression of integral membrane proteins (IMPs) remains a major bottleneck in the characterization of this important protein class. IMP expression levels are currently unpredictable, which renders the pursuit of IMPs for structural and biophysical characterization challenging and inefficient. Experimental evidence demonstrates that changes within the nucleotide or amino-acid sequence for a given IMP can dramatically affect expression; yet these observations have not resulted in generalizable approaches to improved expression. Here, we develop a data-driven statistical predictor named IMProve, that, using only sequence information, increases the likelihood of selecting an IMP that expresses in E. coli. The IMProve model, trained on experimental data, combines a set of sequence-derived features resulting in an IMProve score, where higher values have a higher probability of success. The model is rigorously validated against a variety of independent datasets that contain a wide range of experimental outcomes from various IMP expression trials. The results demonstrate that use of the model can more than double the number of successfully expressed targets at any experimental scale. IMProve can immediately be used to identify favorable targets for characterization.

biophysics↗