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Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 65,403 bioRxiv papers from 289,703 authors.

Most downloaded bioRxiv papers, all time

in category systems biology

1,810 results found. For more information, click each entry to expand.

41: Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells
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Posted to bioRxiv 07 Aug 2016

Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells
2,402 downloads systems biology

Stefan Semrau, Johanna Goldmann, Magali Soumillon, Tarjei S. Mikkelsen, Rudolf Jaenisch, Alexander van Oudenaarden

Gene expression heterogeneity in the pluripotent state of mouse embryonic stem cells (mESCs) has been increasingly well-characterized. In contrast, exit from pluripotency and lineage commitment have not been studied systematically at the single-cell level. Here we measured the gene expression dynamics of retinoic acid driven mESC differentiation using an unbiased single-cell transcriptomics approach. We found that the exit from pluripotency marks the start of a lineage bifurcation as well as a transient phase of susceptibility to lineage specifying signals. Our study revealed several transcriptional signatures of this phase, including a sharp increase of gene expression variability. Importantly, we observed a handover between two classes of transcription factors. The early-expressed class has potential roles in lineage biasing, the late-expressed class in lineage commitment. In summary, we provide a comprehensive analysis of lineage commitment at the single cell level, a potential stepping stone to improved lineage control through timing of differentiation cues.

42: Environmental gene regulatory influence networks in rice (Oryza sativa): response to water deficit, high temperature and agricultural environments
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Posted to bioRxiv 03 Mar 2016

Environmental gene regulatory influence networks in rice (Oryza sativa): response to water deficit, high temperature and agricultural environments
2,369 downloads systems biology

Olivia Wilkins, Christoph Hafemeister, Anne Plessis, Meisha-Marika Holloway-Phillips, Gina M Pham, Adrienne B Nicotra, Glenn B. Gregorio, S.V. Krishna Jagadish, Endang M. Septiningsih, Richard Bonneau, Michael Purugganan

Environmental Gene Regulatory Influence Networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental and developmental signals. EGRINs encompass many layers of regulation, which culminate in changes in the level of accumulated transcripts. Here we infer EGRINs for the response of five tropical Asian rice cultivars to high temperatures, water deficit, and agricultural field conditions, by systematically integrating time series transcriptome data (720 RNA-seq libraries), patterns of nucleosome-free chromatin (18 ATAC-seq libraries), and the occurrence of known cis-regulatory elements. First, we identify 5,447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes with known cis-regulatory motifs in nucleosome-free chromatin regions proximal to transcriptional start sites (TSS) of genes. We then use network component analysis to estimate the regulatory activity for these TFs from the expression of these putative target genes. Finally, we inferred an EGRIN using the estimated TFA as the regulator. The EGRIN included regulatory interactions between 4,052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of a large TF family, including a putative regulatory connection between abiotic stress and the circadian clock, as well as specific regulatory functions for TFs in the drought response. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference and that supplementing data from controlled experimental conditions with data from outdoor field conditions increases the resolution for EGRIN inference.

43: Subcellular localization of drug distribution by super-resolution ion beam imaging
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Posted to bioRxiv 22 Feb 2019

Subcellular localization of drug distribution by super-resolution ion beam imaging
2,367 downloads systems biology

Xavier Rovira-Clave, Sizun Jiang, Yunhao Bai, Graham Larson Barlow, Salil Bhate, Ahmet Coskun, Guojun Han, Bokai Zhu, Chin-Min Ho, Chuck Hitzman, Shih-Yu Chen, Felice-Alessio Bava, Garry Nolan

Technologies that visualize multiple biomolecules at the nanometer scale in cells will enable deeper understanding of biological processes that proceed at the molecular scale. Current fluorescence-based methods for microscopy are constrained by a combination of spatial resolution limitations, limited parameters per experiment, and detector systems for the wide variety of biomolecules found in cells. We present here super-resolution ion beam imaging (srIBI), a secondary ion mass spectrometry approach capable of high-parameter imaging in 3D of targeted biological entities and exogenously added small molecules. Uniquely, the atomic constituents of the biomolecules themselves can often be used in our system as the "tag". We visualized the subcellular localization of the chemotherapy drug cisplatin simultaneously with localization of five other nuclear structures, with further carbon elemental mapping and secondary electron visualization, down to ~30 nm lateral resolution. Cisplatin was preferentially enriched in nuclear speckles and excluded from closed-chromatin regions, indicative of a role for cisplatin in active regions of chromatin. These data highlight how multiplexed super-resolution techniques, such as srIBI, will enable studies of biomolecule distributions in biologically relevant subcellular microenvironments.

44: Optimization and uncertainty analysis of ODE models using second order adjoint sensitivity analysis
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Posted to bioRxiv 27 Feb 2018

Optimization and uncertainty analysis of ODE models using second order adjoint sensitivity analysis
2,319 downloads systems biology

Paul Stapor, Fabian Fröhlich, Jan Hasenauer

Motivation: Parameter estimation methods for ordinary differential equation (ODE) models of biological processes can exploit gradients and Hessians of objective functions to achieve convergence and computational efficiency. However, the computational complexity of established methods to evaluate the Hessian scales linearly with the number of state variables and quadratically with the number of parameters. This limits their application to low-dimensional problems. Results: We introduce second order adjoint sensitivity analysis for the computation of Hessians and a hybrid optimization-integration based approach for profile likelihood computation. Second order adjoint sensitivity analysis scales linearly with the number of parameters and state variables. The Hessians are effectively exploited by the proposed profile likelihood computation approach. We evaluate our approaches on published biological models with real measurement data. Our study reveals an improved computational efficiency and robustness of optimization compared to established approaches, when using Hessians computed with adjoint sensitivity analysis. The hybrid computation method was more than two-fold faster than the best competitor. Thus, the proposed methods and implemented algorithms allow for the improvement of parameter estimation for medium and large scale ODE models. Availability: The algorithms for second order adjoint sensitivity analysis are implemented in the Advance MATLAB Interface CVODES and IDAS (AMICI, https://github.com/ICB-DCM/AMICI/). The algorithm for hybrid profile likelihood computation is implemented in the parameter estimation toolbox (PESTO, https://github.com/ICB-DCM/PESTO/). Both toolboxes are freely available under the BSD license.

45: A generalizable experimental framework for automated cell growth and laboratory evolution
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Posted to bioRxiv 12 Mar 2018

A generalizable experimental framework for automated cell growth and laboratory evolution
2,318 downloads systems biology

Brandon G Wong, Christopher P Mancuso, Szilvia Kiriakov, Caleb J Bashor, Ahmad S. Khalil

In the post-genomics era, exploration of phenotypic adaptation is limited by our ability to experimentally control selection conditions, including multi-variable and dynamic pressure regimes. While automated cell culture systems offer real-time monitoring and fine control over liquid cultures, they are difficult to scale to high-throughput, or require cumbersome redesign to meet diverse experimental requirements. Here we describe eVOLVER, a multipurpose, scalable DIY framework that can be easily configured to conduct a wide variety of growth fitness experiments at scale and cost. We demonstrate eVOLVER's versatility by configuring it for diverse growth and selection experiments that would be otherwise challenging for other systems. We conduct high-throughput evolution of yeast across different population density niches. We perform growth selection on a yeast knockout library under temporally varying temperature regimes. Finally, inspired by large-scale integration in electronics and microfluidics, we develop novel millifluidic multiplexing modules that enable complex fluidic routines including multiplexed media routing, cleaning, vial-to-vial transfers, and automated yeast mating. We propose eVOLVER to be a versatile design framework in which to study, characterize, and evolve biological systems.

46: Massively parallel single cell lineage tracing using CRISPR/Cas9 induced genetic scars
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Posted to bioRxiv 19 Oct 2017

Massively parallel single cell lineage tracing using CRISPR/Cas9 induced genetic scars
2,308 downloads systems biology

Bastiaan Spanjaard, Bo Hu, Nina Mitic, Jan Philipp Junker

A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism consisting of many different cell types. Single-cell RNA- sequencing (scRNA-seq) has become a widely-used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However, a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here, we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses, LINNAEUS (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences) can be used as a systematic approach for identifying the lineage origin of novel cell types, or of known cell types under different conditions.

47: An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
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Posted to bioRxiv 20 Jun 2018

An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
2,242 downloads systems biology

Ilias Angelidis, Lukas M Simon, Isis E Fernandez, Maximilian Strunz, Christoph H Mayr, Flavia R Greiffo, George Tsitsiridis, Elisabeth Graf, Tim-Matthias Strom, Oliver Eickelberg, Matthias Mann, Fabian J. Theis, Herbert B Schiller

Aging promotes lung function decline and susceptibility to chronic lung diseases, which are the third leading cause of death worldwide. We used single cell transcriptomics and mass spectrometry to quantify changes in cellular activity states of 30 cell types and the tissue proteome from lungs of young and old mice. Aging led to increased transcriptional noise, indicating deregulated epigenetic control. We observed highly distinct effects of aging on cell type level, uncovering increased cholesterol biosynthesis in type-2 pneumocytes and lipofibroblasts as a novel hallmark of lung aging. Proteomic profiling revealed extracellular matrix remodeling in old mice, including increased collagen IV and XVI and decreased Fraser syndrome complex proteins and Collagen XIV. Computational integration of the aging proteome and single cell transcriptomes predicted the cellular source of regulated proteins and created a first unbiased reference of the aging lung. The lung aging atlas can be accessed via an interactive user-friendly webtool at: https://theislab.github.io/LungAgingAtlas

48: Cell-Type Selective Markers Represented in Whole-Kidney RNA-Seq Data
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Posted to bioRxiv 15 Jun 2018

Cell-Type Selective Markers Represented in Whole-Kidney RNA-Seq Data
2,219 downloads systems biology

Jevin Z Clark, Lihe Chen, Chung-Lin Chou, Hyun Jun Jung, Jae Wook Lee, Mark A. Knepper

Bulk-tissue RNA-Seq is seeing increasing use in the study of physiological and pathophysiological processes in the kidney. However, the presence of multiple cell types in kidney complicates the interpretation of the data. Here we address the question, What cell types are represented in whole-kidney RNA-Seq data? to identify circumstances in which bulk-kidney RNA-Seq can successfully be interpreted. We carried out RNA-Seq in mouse whole kidneys and microdissected proximal S2 segments. To aid in the interpretation of the data, we compiled a database of cell-type selective protein markers for 43 cell types believed to be present in kidney tissue. The whole-kidney RNA-Seq analysis identified transcripts corresponding to 17742 genes, distributed over 5 orders of magnitude of expression level. Markers for all 43 curated cell types were detectable. Analysis of the cellular makeup of a mouse kidney, calculated from published literature, suggests that proximal tubule cells likely account for more than half of the mRNA in a kidney. Comparison of RNA-Seq data from microdissected proximal tubules with whole-kidney data supports this view. RNA-Seq data for cell-type selective markers in bulk-kidney samples provide a valid means to identify changes in minority-cell abundances in kidney tissue. Although proximal tubules make up a substantial fraction of whole-kidney samples, changes in proximal tubule gene expression could be obscured by the presence of mRNA from other cell types.

49: Generation of human neural retina transcriptome atlas by single cell RNA sequencing
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Posted to bioRxiv 24 Sep 2018

Generation of human neural retina transcriptome atlas by single cell RNA sequencing
2,218 downloads systems biology

Samuel W Lukowski, Camden Y. Lo, Alexei Sharov, Quan H. Nguyen, Lyujie Fang, Sandy SC Hung, Ling Zhu, Ting Zhang, Tu Nguyen, Anne Senabouth, Jafar S Jabbari, Emily Welby, Jane C. Sowden, Hayley S. Waugh, Adrienne Mackey, Graeme Pollock, Trevor D. Lamb, Peng-Yuan Wang, Alex W Hewitt, Mark Gillies, Joseph E Powell, Raymond CB Wong

The retina is a highly specialized neural tissue that senses light and initiates image processing. Although the functional organisation of specific cells within the retina has been well-studied, the molecular profile of many cell types remains unclear in humans. To comprehensively profile cell types in the human retina, we performed single cell RNA-sequencing on 20,009 cells obtained post-mortem from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct clusters representing all known retinal cells: rod photoreceptors, cone photoreceptors, Muller glia cells, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, retinal astrocytes and microglia. Notably, our data captured molecular profiles for healthy and early degenerating rod photoreceptors, and revealed a novel role of MALAT1 in putative rod degeneration. We also demonstrated the use of this retina transcriptome atlas to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Muller glia cell line. This work provides an important reference with unprecedented insights into the transcriptional landscape of human retinal cells, which is fundamental to our understanding of retinal biology and disease.

50: Fundamental limits on dynamic inference from single cell snapshots
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Posted to bioRxiv 30 Jul 2017

Fundamental limits on dynamic inference from single cell snapshots
2,200 downloads systems biology

Caleb Weinreb, Samuel Wolock, Betsabeh K. Tusi, Merav Socolovsky, Allon M. Klein

Single cell profiling methods are powerful tools for dissecting the molecular states of cells, but the destructive nature of these methods has made it difficult to measure single cell expression over time. When cell dynamics are asynchronous, they can form a continuous manifold in gene expression space whose structure is thought to encode the trajectory of a typical cell. This insight has spurred a proliferation of methods for single cell trajectory discovery that have successfully ordered cell states and identified differentiation branch-points. However, all attempts to infer dynamics from static snapshots of cell state face a common limitation: for any measured distribution of cells in high dimensional state space, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we enumerate from first principles the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone, but nonetheless have a profound influence on temporal ordering and fate probabilities of cells. On the basis of these unknowns, we identify assumptions necessary to constrain a unique solution for the dynamics and translate these constraints into a practical algorithmic approach, called Population Balance Analysis (PBA). At its core, PBA invokes a new method based on spectral graph theory for solving a certain class of high dimensional differential equation. We show the strengths and limitations of PBA using simulations and validate its accuracy on single cell profiles of hematopoietic progenitor cells. Altogether, these results provide a rigorous basis for dynamic interpretation of a gene expression continuum, and the pitfalls facing any method of dynamic inference. In doing so they clarify experimental designs to minimize these shortfalls.

51: Dynamic post-transcriptional regulation during embryonic stem cell differentiation
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Posted to bioRxiv 03 Apr 2017

Dynamic post-transcriptional regulation during embryonic stem cell differentiation
2,159 downloads systems biology

Patrick R. van den Berg, Bogdan Budnik, Nikolai Slavov, Stefan Semrau

During in vitro differentiation, pluripotent stem cells undergo extensive remodeling of their gene expression profile. While studied extensively at the transcriptome level, much less is known about protein dynamics. Here, we measured mRNA and protein levels of 7459 genes during differentiation of embryonic stem cells (ESCs). This comprehensive data set revealed pervasive discordance between mRNA and protein. The high temporal resolution of the data made it possible to determine protein turnover rates genome-wide by fitting a kinetic model. This model further enabled us to systematically identify dynamic post-transcriptional regulation. Moreover, we linked different modes of regulation to the function of specific gene sets. Finally, we showed that the kinetic model can be applied to single-cell transcriptomics data to predict protein levels in differentiated cell types. In conclusion, our comprehensive data set, easily accessible through a web application, is a valuable resource for the discovery of post-transcriptional regulation in ESC differentiation.

52: Sample Preparation by Easy Extraction and Digestion (SPEED) - A Universal, Rapid, and Detergent-free Protocol for Proteomics based on Acid Extraction
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Posted to bioRxiv 16 Aug 2018

Sample Preparation by Easy Extraction and Digestion (SPEED) - A Universal, Rapid, and Detergent-free Protocol for Proteomics based on Acid Extraction
2,120 downloads systems biology

Joerg Doellinger, Andy Schneider, Marcell Hoeller, Peter Lasch

Mass spectrometry is the method of choice for deep and comprehensive analysis of proteomes and has become a key technology to support the progress in life science and biomedicine. However, sample preparation in proteomics is not standardized and contributes to a lack of reproducibility. The main challenge is to extract all proteins in a manner that enables efficient digestion into peptides and is compatible with subsequent mass spectrometric analysis. Current methods are based on the idea of removing detergents or chaotropic agents during sample processing, which are essential for protein extraction but interfere with digestion and LC-MS. These multi-step preparations are prone to losses, biases and contaminations, while being time-consuming and labor-intensive. We report a universal detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which is based on a simple three-step procedure, acidification, neutralization and digestion. SPEED is a one-pot method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction. SPEED-based sample processing is highly reproducible, provides exceptional peptide yields and enables preparation even of tissue samples with less than 15 min hands-on time and without any special equipment. Evaluation of SPEED performance revealed, that the number of quantified proteins and the quantitative reproducibility are superior compared to the well-established sample processing protocols FASP, ISD-Urea and SP3 for various sample types, including human cells, bacteria and tissue, even at low protein starting amounts.

53: Defining the anti-Shine-Dalgarno sequence interaction and quantifying its functional role in regulating translation efficiency
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Posted to bioRxiv 06 Feb 2016

Defining the anti-Shine-Dalgarno sequence interaction and quantifying its functional role in regulating translation efficiency
2,049 downloads systems biology

Adam J Hockenberry, Adam R Pah, Michael C Jewett, Luís A. Nunes Amaral

Studies dating back to the 1970s established that binding between the anti-Shine-Dalgarno (aSD) sequence on prokaryotic ribosomes and mRNA helps to facilitate translation initiation. The location of aSD binding relative to the start codon, the full extents of the aSD sequence, and the functional form of the relationship between aSD binding and translation efficiency are important parameters that remain ill defined in the literature. Here, we leverage genome-wide estimates of translation efficiency to determine these parameters and show that anti-Shine-Dalgarno sequence binding increases the translation of endogenous mRNAs on the order of 50%. Our findings highlight the non-linearity of this relationship, showing that translation efficiency is maximized for sequences with intermediate aSD binding strengths. These mechanistic insights are highly robust; we find nearly identical results in ribosome profiling datasets from 3 highly diverged bacteria, as well as independent genome-scale estimates and controlled experimental data using recombinant GFP expression.

54: Open science resources for the discovery and analysis of Tara Oceans Data
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Posted to bioRxiv 08 May 2015

Open science resources for the discovery and analysis of Tara Oceans Data
2,017 downloads systems biology

Stéphane Pesant, Fabrice Not, Marc Picheral, Stefanie Kandels-Lewis, Noan Le Bescot, Gabriel Gorsky, Daniele Iudicone, Eric Karsenti, Sabrina Speich, Romain Troublé, Céline Dimier, Sarah Searson, Tara Oceans Consortium Coordinators

The Tara Oceans expedition (2009-2013) sampled contrasting ecosystems of the world oceans, collecting environmental data and plankton, from viruses to metazoans, for later analysis using modern sequencing and state-of-the-art imaging technologies. It surveyed 210 ecosystems in 20 biogeographic provinces, collecting over 35000 samples of seawater and plankton. The interpretation of such an extensive collection of samples in their ecological context requires means to explore, assess and access raw and validated data sets. To address this challenge, the Tara Oceans Consortium offers open science resources, including the use of open access archives for nucleotides (ENA) and for environmental, biogeochemical, taxonomic and morphological data (PANGAEA), and the development of on line discovery tools and collaborative annotation tools for sequences and images. Here, we present an overview of Tara Oceans Data, and we provide detailed registries (data sets) of all campaigns (from port-to-port), stations and sampling events.

55: A CRISPR-Cas9 Genome Engineering Platform in Primary CD4+ T Cells for the Interrogation of HIV Host Factors
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Posted to bioRxiv 19 Oct 2017

A CRISPR-Cas9 Genome Engineering Platform in Primary CD4+ T Cells for the Interrogation of HIV Host Factors
1,990 downloads systems biology

Judd F. Hultquist, Joseph Hiatt, Kathrin Schumann, Michael J. McGregor, Theodore L. Roth, Paige Haas, Jennifer Doudna, Alexander Marson, Nevan J. Krogan

CRISPR-Cas9 gene editing strategies have revolutionized our ability to engineer the human genome for robust functional interrogation of complex biological processes. We have recently adapted this technology to primary human T cells to generate a high-throughput platform for analyzing the role of host factors in pathogen infection and lifecycle. Here, we describe applications of this system to investigate HIV pathogenesis in CD4+ T cells. Briefly, CRISPR-Cas9 ribonucleoproteins (crRNPs) are synthesized in vitro and delivered to activated primary human CD4+ T cells by nucleofection. These edited cells are then validated and expanded for use in downstream cellular, genetic, or protein-based assays. Our platform supports the arrayed generation of several gene manipulations in only a few hours' time and is widely adaptable across culture conditions, infection protocols, and downstream applications. We present detailed protocols for crRNP synthesis, primary T cell culture, 96-well nucleofection, molecular validation, and HIV infection with additional considerations for guide and screen design as well as crRNP multiplexing.

56: Gene Expression-Based Drug Repurposing to Target Ageing
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Posted to bioRxiv 24 Jan 2018

Gene Expression-Based Drug Repurposing to Target Ageing
1,977 downloads systems biology

Handan Melike Donertas, Matías Fuentealba Valenzuela, Linda Partridge, Janet M. Thornton

Ageing is the largest risk factor for a variety of non-communicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both life- and health-span. Ageing is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target-centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat ageing in human brain. Using multiple gene expression datasets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterise ageing. Then, we compared these changes in gene expression with drug perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as anti-ageing drugs by reversing the detrimental changes that occur during ageing, others by mimicking the cellular defense mechanisms. The drugs that we identified included significant number of already identified pro-longevity drugs, indicating that the method can discover de novo drugs that meliorate ageing. The approach has the advantages that, by using data from human brain ageing data it focuses on processes relevant in human ageing and that it is unbiased, making it possible to discover new targets for ageing studies.

57: Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results
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Posted to bioRxiv 30 Apr 2018

Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results
1,952 downloads systems biology

Yansheng Liu, Yang Mi, Torsten Mueller, Saskia Kreibich, Evan G. Williams, Audrey Van Drogen, Christelle Borel, Pierre-Luc Germain, Max Frank, Isabell Bludau, Martin Mehnert, Michael Seifert, Mario Emmenlauer, Isabel Sorg, Fedor Bezrukov, Frederique Sloan Bena, Hu Zhou, Christoph Dehio, Giuseppe Testa, Julio Saez-Rodriguez, Stylianos E. Antonarakis, Wolf-Dietrich Hardt, Ruedi Aebersold

The independent reproduction of research results is a cornerstone of experimental research, yet it is beset by numerous challenges, including the quality and veracity of reagents and materials. Much of life science research depends on life materials, including human tissue culture cells. In this study we aimed at determining the degree of variability in the molecular makeup and the ensuing phenotypic consequences in commonly used human tissue culture cells. We collected 14 stock HeLa aliquots from 13 different laboratories across the globe, cultured them in uniform conditions and profiled the genome-wide copy numbers, mRNAs, proteins and protein turnover rates via genomic techniques and SWATH mass spectrometry, respectively. We also phenotyped each cell line with respect to the ability of transfected Let7 mimics to modulate Salmonella infection. We discovered significant heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto variety. We also observed progressive divergence within a specific cell line over 50 successive passages. From the aggregate multi-omic datasets we quantified the response of the cells to genomic variability across the transcriptome and proteome. We discovered organelle-specific proteome remodeling and buffering of protein abundance by protein complex stoichiometry, mediated by the adaptation of protein turnover rates. By associating quantitative proteotype and phenotype measurements we identified protein patterns that explained the varying response of the different cell lines to Salmonella infection. Altogether the results indicate a striking degree of genomic variability, the rapid evolution of genomic variability in culture and its complex translation into distinctive expressed molecular and phenotypic patterns. The results have broad implications for the interpretation and reproducibility of research results obtained from HeLa cells and provide important basis for a general discussion of the value and requirements for communicating research results obtained from human tissue culture cells.

58: Towards an Integrated Map of Genetic Interactions in Cancer Cells
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Posted to bioRxiv 27 Mar 2017

Towards an Integrated Map of Genetic Interactions in Cancer Cells
1,939 downloads systems biology

Benedikt Rauscher, Florian Heigwer, Luisa Henkel, Thomas Hielscher, Oksana Voloshanenko, Michael Boutros

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype- dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/β-catenin signaling identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

59: Systematic detection of amino acid substitutions in proteome reveals a mechanistic basis of ribosome errors
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Posted to bioRxiv 29 Jan 2018

Systematic detection of amino acid substitutions in proteome reveals a mechanistic basis of ribosome errors
1,929 downloads systems biology

Ernest Mordret, Avia Yehonadav, Georgina D Barnabas, Jürgen Cox, Orna Dahan, Tamar Geiger, Ariel Lindner, Yitzhak Pilpel

Translation errors limit the accuracy of information transmission from DNA to proteins. Selective pressures shape the way cells produce their proteins: the translation machinery and the mRNA sequences it decodes co-evolved to ensure that translation proceeds fast and accurately in a wide range of environmental conditions. Our understanding of the causes of amino acid misincorporations and of their effect on the evolution of protein sequences is largely hindered by the lack of experimental methods to observe errors at the full proteome level. Here, we systematically detect and quantify errors in entire proteomes from mass spectrometry data. Following HPLC MS-MS data acquisition, we identify E. coli and S. cerevisiae peptides whose mass and fragment ion spectrum are consistent with that of a peptide bearing a single amino acid substitution, and verify that such spectrum cannot result from a post-translational modification. Our analyses confirm that most substitutions occur due to codon-to-anticodon mispairing within the ribosome. Patterns of errors due to mispairing were similar in bacteria and yeast, suggesting that the error spectrum is chemically constrained. Treating E. coli cells with a drug known to affect ribosomal proofreading increased the error rates due to mispairing at the wobble codon position. Starving bacteria for serine resulted in specific patterns of substitutions reflecting the amino acid deficiency. Overall, translation errors tend to occur at positions that are less evolutionarily conserved, and that minimally affect protein energetic stability, indicating that they are selected against. Genome wide ribosome density data suggest that errors occur at sites where ribosome velocity is relatively high, supporting the notion of a trade-off between speed and accuracy as predicted by proofreading theories. Together our results reveal a mechanistic basis for ribosome errors in translation.

60: Network-based prediction of protein interactions
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Posted to bioRxiv 02 Mar 2018

Network-based prediction of protein interactions
1,927 downloads systems biology

István A. Kovács, Katja Luck, Kerstin Spirohn, Yang Wang, Carl Pollis, Sadie Schlabach, Wenting Bian, Dae-Kyum Kim, Nishka Kishore, Tong Hao, Michael A. Calderwood, Marc Vidal, Albert-László Barabási

As biological function emerges through interactions between a cell's molecular constituents, understanding cellular mechanisms requires us to catalogue all physical interactions between proteins. Despite spectacular advances in high-throughput mapping, the number of missing human protein-protein interactions (PPIs) continues to exceed the experimentally documented interactions. Computational tools that exploit structural, sequence or network topology information are increasingly used to fill in the gap, using the patterns of the already known interactome to predict undetected, yet biologically relevant interactions. Such network-based link prediction tools rely on the Triadic Closure Principle (TCP), stating that two proteins likely interact if they share multiple interaction partners. TCP is rooted in social network analysis, namely the observation that the more common friends two individuals have, the more likely that they know each other. Here, we offer direct empirical evidence across multiple datasets and organisms that, despite its dominant use in biological link prediction, TCP is not valid for most protein pairs. We show that this failure is fundamental - TCP violates both structural constraints and evolutionary processes. This understanding allows us to propose a link prediction principle, consistent with both structural and evolutionary arguments, that predicts yet uncovered protein interactions based on paths of length three (L3). A systematic computational cross-validation shows that the L3 principle significantly outperforms existing link prediction methods. To experimentally test the L3 predictions, we perform both large-scale high-throughput and pairwise tests, finding that the predicted links test positively at the same rate as previously known interactions, suggesting that most (if not all) predicted interactions are real. Combining L3 predictions with experimental tests provided new interaction partners of FAM161A, a protein linked to retinitis pigmentosa, offering novel insights into the molecular mechanisms that lead to the disease. Because L3 is rooted in a fundamental biological principle, we expect it to have a broad applicability, enabling us to better understand the emergence of biological function under both healthy and pathological conditions.

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