Biology subjects

Pedrioli, P. G. A.

Publications and source records attributed to Pedrioli, P. G. A..

4 recordsLinked to original sources

bioRxiv2024

MultiOmicsAgent: Guided extreme gradient-boosted decision trees-based approaches for biomarker-candidate discovery in multi-omics data

MultiOmicsAgent (MOAgent) is an innovative, Python based open-source tool for biomarker discovery, utilizing machine learning techniques specifically extreme gradient-boosted decision trees to process multi-omics data. With its cross-platform compatibility, user-oriented graphical interface and a well-documented API, MOAgent not only meets the needs of both coding professionals and those new to machine learning but also addresses common data analysis challenges like data incompleteness, class imbalances and data leakage between disjoint data splits. MOAgents guided data analysis strategy opens up data-driven insights from digitized clinical biospecimen cohorts and makes advanced data analysis accessible and reliable for a wide audience. Biographical NoteJens Settelmeier, Julia Boshart, Martin Gesell are Ph.D. candidates, Jianbo Fu, Sebastian N. Steiner are Post Doc candidates and Sandra Goetze, Patrick Pedrioli senior scientists at the Institute of Translational Medicine at Health Sciences and Technology department at ETH Zurich, Switzerland, within Professor Bernd Wollscheids research group who has been working in the fields of bioinformatics, clinical multi-omics with a focus on spatial cell surface proteomics. Peter J. Schuffler is professor at the institute of Pathology at the TU Munich, Germany and has been working in the field of digital pathology and clinical multi-modal studies. Diyora Salimova is junior professor at the department of Applied Mathematics at the Albert-Ludwigs-University of Freibug, Germany and has been working in the field of stochastic processes, approximation theory and machine learning related topics. Key PointsO_LIMOAgent enables a guided biomarker-candidate discovery in multi-omics studies, providing a graphical interface and well-documented API. C_LIO_LIA user can run MOAgent on a personal computer without the requirement of coding a single line. C_LIO_LIMOAgent is a Python-based solution for biomarker-candidate discovery, using machine learning to analyze multi-omics data. C_LIO_LIMOAgent can address challenges like data incompleteness and class imbalances, ensuring reliable analysis. C_LIO_LIMOAgent makes advanced data analysis accessible, enhancing insights from clinical data. C_LI

bioinformatics↗

bioRxiv2022

PRAMEL7/CUL2 axis regulates NuRD stability to establish ground-state pluripotency in embryonic stem cells

Pluripotency is established in E4.5 preimplantation epiblast. Embryonic stem cells (ESCs) represent the immortalization of pluripotency, however, they only partially resemble the gene expression signature of developmental ground-state. Induced PRAMEL7 expression, a protein highly expressed in the ICM but lowly expressed in ESCs, reprograms developmentally advanced ESC+serum into ground-state pluripotency by causing DNA hypomethylation and gene expression signature close to developmental ground-state. However, how PRAMEL7 reprograms gene expression remains elusive. Here we show that PRAMEL7 associates with Cullin2 (CUL2) and this interaction is required to establish ground-state gene expression. PRAMEL7 recruits CUL2 to chromatin and targets for proteasomal degradation regulators of repressive chromatin, including NuRD complex. PRAMEL7 antagonizes NuRD-mediated repression of genes implicated in pluripotency by decreasing NuRD stability and promoter association in a CUL2-dependent manner. Our data link proteasome degradation pathways to ground-state gene expression, offering insights to generate in vitro models to reproduce the in vivo ground-state pluripotency.

developmental biology↗

bioRxiv2021

Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework

It is a general assumption of molecular biology that the ensemble of expressed molecules, their activities and interactions determine biological processes, cellular states and phenotypes. Quantitative abundance of transcripts, proteins and metabolites are now routinely measured with considerable depth via an array of "OMICS" technologies, and recently a number of methods have also been introduced for the parallel analysis of the abundance, subunit composition and cell state specific changes of protein complexes. In comparison to the measurement of the molecular entities in a cell, the determination of their function remains experimentally challenging and labor-intensive. This holds particularly true for determining the function of protein complexes, which constitute the core functional assemblies of the cell. Therefore, the tremendous progress in multi-layer molecular profiling has been slow to translate into increased functional understanding of biological processes, cellular states and phenotypes. In this study we describe PCfun, a computational framework for the systematic annotation of protein complex function using Gene Ontology (GO) terms. This work is built upon the use of word embedding-- natural language text embedded into continuous vector space that preserves semantic relationships-- generated from the machine reading of 1 million open access PubMed Central articles. PCfun leverages the embedding for rapid annotation of protein complex function by integrating two approaches: (1) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector, and (2) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing the statistical test for the enrichment of the top NN GO terms within the child terms of the predicted GO terms by RF models. Thus, PCfun amalgamates information learned from the gold-standard protein-complex database, CORUM, with the unbiased predictions obtained directly from the word embedding, thereby enabling PCfun to identify the potential functions of putative protein complexes. The documentation and examples of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

bioinformatics↗

bioRxiv2019

Surface proteome dynamics during neuronal development and synaptic plasticity

Neurons are highly compartmentalized cells with tightly controlled subcellular protein organization. While broad brain transcriptome, connectome and global proteome maps are being generated, system-wide analysis of temporal protein dynamics at the subcellular level are currently lacking for neuronal development and synapse formation. We performed a temporally-resolved surfaceome analysis of developing primary neuron cultures to a depth of 1000 bona fide surface proteins and reveal dynamic surface protein clusters that reflect the functional requirements during distinct stages of neuronal development. Moreover, our data shows that synaptic proteins are globally trafficked to the surface prior to synapse formation. Direct comparison of surface and total protein pools demonstrates that, depending on the time scale, surface abundance changes can correlate or differ from total protein abundance. The uncoupling of surface and total abundance changes has direct functional implications as shown in the context of synaptic vesicle transport. To demonstrate the utility of our approach we analyzed the surfaceome modulation in response to homeostatic synaptic scaling and found dynamic remodeling of the neuronal surface, which was largely independent of global proteostasis, indicative of wide-spread regulation on the level of surface trafficking. Finally, we present a quantitative analysis of the neuronal surface during early-phase long-term potentiation (LTP) and reveal fast externalization of diverse classes of surface proteins beyond the AMPA receptor, providing new insights into the requirement of exocytosis for LTP. Our resource and finding of organizational principles highlight the importance of subcellular resolution for systems-level understanding of cellular processes, which are typically masked by broad omics-style approaches.

neuroscience↗