Showing papers in "Molecular Systems Biology in 2019"
TL;DR: The steps of a typical single‐cell RNA‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis, are detailed.
Abstract: Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.
1,180 citations
TL;DR: A quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs and that protein expression is often more stable across tissues than that of transcripts.
Abstract: Genome-, transcriptome- and proteome-wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they ar ...
466 citations
TL;DR: Plasma proteome profiling can identify potential biomarkers and drug targets in liver disease and strongly associated DPP4, ANPEP, TGFBI, PIGR, and APOE with NAFLD and cirrhosis.
Abstract: Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population and can progress to cirrhosis with limited treatment options. As the liver secretes most of the blood plasma proteins, liver disease may affect the plasma proteome. Plasma proteome profiling of 48 patients with and without cirrhosis or NAFLD revealed six statistically significantly changing proteins (ALDOB, APOM, LGALS3BP, PIGR, VTN, and AFM), two of which are already linked to liver disease. Polymeric immunoglobulin receptor (PIGR) was significantly elevated in both cohorts by 170% in NAFLD and 298% in cirrhosis and was further validated in mouse models. Furthermore, a global correlation map of clinical and proteomic data strongly associated DPP4, ANPEP, TGFBI, PIGR, and APOE with NAFLD and cirrhosis. The prominent diabetic drug target DPP4 is an aminopeptidase like ANPEP, ENPEP, and LAP3, all of which are up-regulated in the human or mouse data. Furthermore, ANPEP and TGFBI have potential roles in extracellular matrix remodeling in fibrosis. Thus, plasma proteome profiling can identify potential biomarkers and drug targets in liver disease.
156 citations
TL;DR: A simple 12‐fraction protocol for crosslinked multi‐protein complexes and cell lysates, quantitative analysis, and high‐density crosslinking, without requiring specific crosslinker features is demonstrated.
Abstract: We present a concise workflow to enhance the mass spectrometric detection of crosslinked peptides by introducing sequential digestion and the crosslink identification software xiSEARCH. Sequential digestion enhances peptide detection by selective shortening of long tryptic peptides. We demonstrate our simple 12-fraction protocol for crosslinked multi-protein complexes and cell lysates, quantitative analysis, and high-density crosslinking, without requiring specific crosslinker features. This overall approach reveals dynamic protein-protein interaction sites, which are accessible, have fundamental functional relevance and are therefore ideally suited for the development of small molecule inhibitors.
104 citations
TL;DR: An integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation and identifies novel sub‐complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them is described.
Abstract: Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error-controlled, complex-centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub-complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.
103 citations
TL;DR: It is concluded that TRAPP is a widely applicable tool for RBPome characterization and the precise sites of crosslinking for hundreds of RNA–peptide conjugates, using iTRAPP, providing insights into potential regulation.
Abstract: The RNA binding proteome (RBPome) was previously investigated using UV crosslinking and purification of poly(A)‐associated proteins. However, most cellular transcripts are not polyadenylated. We therefore developed total RNA‐associated protein purification (TRAPP) based on 254 nm UV crosslinking and purification of all RNA–protein complexes using silica beads. In a variant approach (PAR‐TRAPP), RNAs were labelled with 4‐thiouracil prior to 350 nm crosslinking. PAR‐TRAPP in yeast identified hundreds of RNA binding proteins, strongly enriched for canonical RBPs. In comparison, TRAPP identified many more proteins not expected to bind RNA, and this correlated strongly with protein abundance. Comparing TRAPP in yeast and E. coli showed apparent conservation of RNA binding by metabolic enzymes. Illustrating the value of total RBP purification, we discovered that the glycolytic enzyme enolase interacts with tRNAs. Exploiting PAR‐TRAPP to determine the effects of brief exposure to weak acid stress revealed specific changes in late 60S ribosome biogenesis. Furthermore, we identified the precise sites of crosslinking for hundreds of RNA–peptide conjugates, using iTRAPP, providing insights into potential regulation. We conclude that TRAPP is a widely applicable tool for RBPome characterization.
99 citations
TL;DR: This work found that each phase duration follows an Erlang distribution and is statistically independent from other phases, and provided an explanation for the historical observation that phase durations are both inherited and independent and suggests how cell cycle progression may be altered in disease states.
Abstract: The cell cycle is canonically described as a series of four consecutive phases: G1, S, G2, and M. In single cells, the duration of each phase varies, but the quantitative laws that govern phase durations are not well understood. Using time-lapse microscopy, we found that each phase duration follows an Erlang distribution and is statistically independent from other phases. We challenged this observation by perturbing phase durations through oncogene activation, inhibition of DNA synthesis, reduced temperature, and DNA damage. Despite large changes in durations in cell populations, phase durations remained uncoupled in individual cells. These results suggested that the independence of phase durations may arise from a large number of molecular factors that each exerts a minor influence on the rate of cell cycle progression. We tested this model by experimentally forcing phase coupling through inhibition of cyclin-dependent kinase 2 (CDK2) or overexpression of cyclin D. Our work provides an explanation for the historical observation that phase durations are both inherited and independent and suggests how cell cycle progression may be altered in disease states.
87 citations
TL;DR: R2‐P2 is an end‐to‐end automated method that uses magnetic particles to process protein extracts to deliver mass spectrometry‐ready phosphopeptides that facilitates large‐scale signaling studies involving hundreds of perturbations opening the door to systems‐level studies aiming to capture signaling complexity.
Abstract: Recent developments in proteomics have enabled signaling studies where > 10,000 phosphosites can be routinely identified and quantified. Yet, current analyses are limited in throughput, reproducibility, and robustness, hampering experiments that involve multiple perturbations, such as those needed to map kinase-substrate relationships, capture pathway crosstalks, and network inference analysis. To address these challenges, we introduce rapid-robotic phosphoproteomics (R2-P2), an end-to-end automated method that uses magnetic particles to process protein extracts to deliver mass spectrometry-ready phosphopeptides. R2-P2 is rapid, robust, versatile, and high-throughput. To showcase the method, we applied it, in combination with data-independent acquisition mass spectrometry, to study signaling dynamics in the mitogen-activated protein kinase (MAPK) pathway in yeast. Our results reveal broad and specific signaling events along the mating, the high-osmolarity glycerol, and the invasive growth branches of the MAPK pathway, with robust phosphorylation of downstream regulatory proteins and transcription factors. Our method facilitates large-scale signaling studies involving hundreds of perturbations opening the door to systems-level studies aiming to capture signaling complexity.
84 citations
TL;DR: Integration of RanSEPs predictions with transcriptomics data showed that some annotated non‐coding RNAs could in fact encode for SEPs, and envision Ran SEPs as a tool to unmask the hidden universe of small bacterial proteins.
Abstract: We thank Dr. Luca Cozzuto from the Bioinformatics Unit at CRG for providing valuable guidance for the conservation studies. Also, we would like to thank Dr. Carolina Gallo, Dr. Eva Yus, and Dr. Raul Burgos for providing MS data. Finally, we thank Dr. Marc Weber for his recommendation about how to analyze Ribo-Seq data. We acknowledge support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Spanish Ministry of Economy and Competitiveness, “Centro de Excelencia Severo Ochoa”, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under agreement No 670216 (MYCOCHASSIS), the CERCA Programme/Generalitat de Catalunya, the European Regional Development Fund (ERDF) project from Instituto Carlos III (ISCIII, Accion Estrategica en Salud 2016; reference CP16/00094), and “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya” (2014SGR678). The CRG/UPF Proteomics Unit is part of the “Plataforma de Recursos Biomoleculares y Bioinformaticos (ProteoRed)” supported by grant PT13/0001 of Instituto de Salud Carlos III from the Spanish Government.
81 citations
TL;DR: ScHPF revealed an expression signature that was spatially biased toward the glioma‐infiltrated margins and associated with inferior survival in glioblastoma, and scHFP does not require prior normalization and captures statistical properties of single‐cell data better than other methods in benchmark datasets.
Abstract: Common approaches to gene signature discovery in single-cell RNA-sequencing (scRNA-seq) depend upon predefined structures like clusters or pseudo-temporal order, require prior normalization, or do not account for the sparsity of single-cell data. We present single-cell hierarchical Poisson factorization (scHPF), a Bayesian factorization method that adapts hierarchical Poisson factorization (Gopalan et al, 2015, Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, 326) for de novo discovery of both continuous and discrete expression patterns from scRNA-seq. scHPF does not require prior normalization and captures statistical properties of single-cell data better than other methods in benchmark datasets. Applied to scRNA-seq of the core and margin of a high-grade glioma, scHPF uncovers marked differences in the abundance of glioma subpopulations across tumor regions and regionally associated expression biases within glioma subpopulations. scHFP revealed an expression signature that was spatially biased toward the glioma-infiltrated margins and associated with inferior survival in glioblastoma.
72 citations
TL;DR: These studies indicate that STEN positive feedback is mediated by mutual inhibition between Ras/Rap and PIP2, while negative feedback depends on delayed PKB activation; PKBs link STEN to CEN; CEN includes positive feedback between Rac and F‐actin, and exerts fast positive and slow negative feedbacks to STEN.
Abstract: Cellular protrusions are typically considered as distinct structures associated with specific regulators. However, we found that these regulators coordinately localize as propagating cortical waves, suggesting a common underlying mechanism. These molecular events fell into two excitable networks, the signal transduction network STEN and the cytoskeletal network CEN with different wave substructures. Computational studies using a coupled‐network model reproduced these features and showed that the morphology and kinetics of the waves depended on strengths of feedback loops. Chemically induced dimerization at multiple nodes produced distinct, coordinated alterations in patterns of other network components. Taken together, these studies indicate: STEN positive feedback is mediated by mutual inhibition between Ras/Rap and PIP2, while negative feedback depends on delayed PKB activation; PKBs link STEN to CEN; CEN includes positive feedback between Rac and F‐actin, and exerts fast positive and slow negative feedbacks to STEN. The alterations produced protrusions resembling filopodia, ruffles, pseudopodia, or lamellipodia, suggesting that these structures arise from a common regulatory mechanism and that the overall state of the STEN‐CEN system determines cellular morphology.
TL;DR: Compared to traditional machine‐learning‐based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space and identifying biological pathways important for outcome prediction.
Abstract: Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine-learning approaches across most cancer types. Compared to traditional machine-learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows.
TL;DR: These analyses show superior performance of INKA over its components and substrate‐based single‐sample tool KARP, and underscore target potential of high‐ranking kinases, encouraging further exploration of InKA's functional and clinical value.
Abstract: Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/- drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value.
TL;DR: It is shown that a large fraction of PTR ratio variation in human tissues can be predicted from sequence, and it identifies many new candidate post‐transcriptional regulatory elements.
Abstract: Despite their importance in determining protein abundance, a comprehensive catalogue of sequence features controlling protein-to-mRNA (PTR) ratios and a quantification of their effects are still la ...
TL;DR: This work introduces COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single‐cell RNA‐seq data, and shows that COMET outperforms other methods for the Identification of single‐gene panels and enables, for the first time, prediction of multi-gene marker panels ranked by relevance.
Abstract: Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene marker panels for such populations remains a challenge. In this work, we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. We show that COMET outperforms other methods for the identification of single-gene panels and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow cytometry assay confirmed the accuracy of COMET's predictions in identifying marker panels for cellular subtypes, at both the single- and multi-gene levels, validating COMET's applicability and accuracy in predicting favorable marker panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data. COMET is available for use via a web interface (http://www.cometsc.com/) or a stand-alone software package (https://github.com/MSingerLab/COMETSC).
TL;DR: A computational model of underground metabolism and laboratory evolution experiments are employed to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non‐native substrates in Escherichia coli K‐12 MG1655 and altered promiscuous activities were shown to be directly involved in establishing high‐efficiency pathways.
Abstract: Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in Escherichia coli K-12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non-native substrates-D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high-efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity.
TL;DR: By analyzing the transcriptional and translational response, it is discovered that sequestered ribosomes at the pseudoknot contribute to a σ32‐mediated stress response, codon‐specific pausing, and a drop in translation initiation rates across the cell.
Abstract: Translation of mRNAs into proteins is a key cellular process. Ribosome binding sites and stop codons provide signals to initiate and terminate translation, while stable secondary mRNA structures can induce translational recoding events. Fluorescent proteins are commonly used to characterize such elements but require the modification of a part's natural context and allow only a few parameters to be monitored concurrently. Here, we combine Ribo-seq with quantitative RNA-seq to measure at nucleotide resolution and in absolute units the performance of elements controlling transcriptional and translational processes during protein synthesis. We simultaneously measure 779 translation initiation rates and 750 translation termination efficiencies across the Escherichia coli transcriptome, in addition to translational frameshifting induced at a stable RNA pseudoknot structure. By analyzing the transcriptional and translational response, we discover that sequestered ribosomes at the pseudoknot contribute to a σ32-mediated stress response, codon-specific pausing, and a drop in translation initiation rates across the cell. Our work demonstrates the power of integrating global approaches toward a comprehensive and quantitative understanding of gene regulation and burden in living cells.
TL;DR: Using transcriptomic approaches, gene expression variability between individual Arabidopsis thaliana plants growing in identical conditions over a 24‐h time course is analysed to shed new light on the impact of transcriptional variability in gene expression regulation in plants.
Abstract: A fundamental question in biology is how gene expression is regulated to give rise to a phenotype. However, transcriptional variability is rarely considered although it could influence the relationship between genotype and phenotype. It is known in unicellular organisms that gene expression is often noisy rather than uniform, and this has been proposed to be beneficial when environmental conditions are unpredictable. However, little is known about inter-individual transcriptional variability in multicellular organisms. Using transcriptomic approaches, we analysed gene expression variability between individual Arabidopsis thaliana plants growing in identical conditions over a 24-h time course. We identified hundreds of genes that exhibit high inter-individual variability and found that many are involved in environmental responses, with different classes of genes variable between the day and night. We also identified factors that might facilitate gene expression variability, such as gene length, the number of transcription factors regulating the genes and the chromatin environment. These results shed new light on the impact of transcriptional variability in gene expression regulation in plants.
TL;DR: DNA Regulatory element Analysis by cell‐Free Transcription and Sequencing (DRAFTS) is described, a rapid and robust in vitro approach for multiplexed measurement of transcriptional activities from thousands of regulatory sequences in a single reaction.
Abstract: Cell-free expression systems enable rapid prototyping of genetic programs in vitro. However, current throughput of cell-free measurements is limited by the use of channel-limited fluorescent readouts. Here, we describe DNA Regulatory element Analysis by cell-Free Transcription and Sequencing (DRAFTS), a rapid and robust in vitro approach for multiplexed measurement of transcriptional activities from thousands of regulatory sequences in a single reaction. We employ this method in active cell lysates developed from ten diverse bacterial species. Interspecies analysis of transcriptional profiles from > 1,000 diverse regulatory sequences reveals functional differences in promoter activity that can be quantitatively modeled, providing a rich resource for tuning gene expression in diverse bacterial species. Finally, we examine the transcriptional capacities of dual-species hybrid lysates that can simultaneously harness gene expression properties of multiple organisms. We expect that this cell-free multiplex transcriptional measurement approach will improve genetic part prototyping in new bacterial chassis for synthetic biology.
TL;DR: Assessment of changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions shows a high degree of genetic background dependencies for LoF phenotypes.
Abstract: Loss-of-function (LoF) mutations associated with disease do not manifest equally in different individuals. The impact of the genetic background on the consequences of LoF mutations remains poorly characterized. Here, we systematically assessed the changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions. We observed 18.5% of deletion phenotypes changing between pairs of strains on average with a small fraction conserved in all four strains. Conditions causing higher wild-type growth differences and the deletion of pleiotropic genes showed above-average changes in phenotypes. In addition, we performed a genome-wide association study (GWAS) for growth under the same conditions for a panel of 925 yeast isolates. Gene-condition associations derived from GWAS were not enriched for genes with deletion phenotypes under the same conditions. However, cases where the results were congruent indicate the most likely mechanism underlying the GWAS signal. Overall, these results show a high degree of genetic background dependencies for LoF phenotypes.
TL;DR: The robustness landscape of gene loss‐of‐function by CRISPR‐Cas9 is examined and it is suggested that physical dependency may contribute to the deleteriousness upon loss of function as revealed by the correlation between the strength of interactions between paralogs and their higher deleteriouslyness upon lost function.
Abstract: The protective redundancy of paralogous genes partly relies on the fact that they carry their functions independently. However, a significant fraction of paralogous proteins may form functionally dependent pairs, for instance, through heteromerization. As a consequence, one could expect these heteromeric paralogs to be less protective against deleterious mutations. To test this hypothesis, we examined the robustness landscape of gene loss-of-function by CRISPR-Cas9 in more than 450 human cell lines. This landscape shows regions of greater deleteriousness to gene inactivation as a function of key paralog properties. Heteromeric paralogs are more likely to occupy such regions owing to their high expression and large number of protein-protein interaction partners. Further investigation revealed that heteromers may also be under stricter dosage balance, which may also contribute to the higher deleteriousness upon gene inactivation. Finally, we suggest that physical dependency may contribute to the deleteriousness upon loss-of-function as revealed by the correlation between the strength of interactions between paralogs and their higher deleteriousness upon loss of function.
TL;DR: It is demonstrated that disrupting MadR program is lethal to diverse mycobacteria making this evolutionarily conserved regulator a prime antitubercular target for both early and late stages of infection.
Abstract: The success of Mycobacterium tuberculosis (MTB) stems from its ability to remain hidden from the immune system within macrophages. Here, we report a new technology (Path‐seq) to sequence miniscule amounts of MTB transcripts within up to million‐fold excess host RNA. Using Path‐seq and regulatory network analyses, we have discovered a novel transcriptional program for in vivo mycobacterial cell wall remodeling when the pathogen infects alveolar macrophages in mice. We have discovered that MadR transcriptionally modulates two mycolic acid desaturases desA1 / desA2 to initially promote cell wall remodeling upon in vitro macrophage infection and, subsequently, reduces mycolate biosynthesis upon entering dormancy. We demonstrate that disrupting MadR program is lethal to diverse mycobacteria making this evolutionarily conserved regulator a prime antitubercular target for both early and late stages of infection.
TL;DR: It is found that small differences in SOX2 and OCT4 levels impact cell fate commitment in G1 but not in S phase, which suggests that small endogenous fluctuations of pioneer transcription factors can bias cell fate decisions by concentration‐dependent priming of differentiation‐associated enhancers.
Abstract: SOX2 and OCT4 are pioneer transcription factors playing a key role in embryonic stem (ES) cell self-renewal and differentiation. How temporal fluctuations in their expression levels bias lineage commitment is unknown. Here, we generated knock-in reporter fusion ES cell lines allowing to monitor endogenous SOX2 and OCT4 protein fluctuations in living cells and to determine their impact on mesendodermal and neuroectodermal commitment. We found that small differences in SOX2 and OCT4 levels impact cell fate commitment in G1 but not in S phase. Elevated SOX2 levels modestly increased neuroectodermal commitment and decreased mesendodermal commitment upon directed differentiation. In contrast, elevated OCT4 levels strongly biased ES cells towards both neuroectodermal and mesendodermal fates in undirected differentiation. Using ATAC-seq on ES cells gated for different endogenous SOX2 and OCT4 levels, we found that high OCT4 levels increased chromatin accessibility at differentiation-associated enhancers. This suggests that small endogenous fluctuations of pioneer transcription factors can bias cell fate decisions by concentration-dependent priming of differentiation-associated enhancers.
TL;DR: The authors recently noticed that the affiliations for Valentina Vurchio, Andrea Bertotti, and Livio Trusolino were listed incorrectly and this error has been corrected.
Abstract: The authors recently noticed that the affiliations for Valentina Vurchio, Andrea Bertotti, and Livio Trusolino were listed incorrectly. The correct author affiliation is shown here and has been corrected in the original article. The authors apologize for this error.
TL;DR: This is the first systematic study of interactions between water‐soluble proteins and polar metabolites in an entire biological subnetwork and essentially doubled the number of known interactions, indicating that presently available information about protein–metabolite interactions may only be the tip of the iceberg.
Abstract: Metabolite binding to proteins regulates nearly all cellular processes, but our knowledge of these interactions originates primarily from empirical in vitro studies. Here, we report the first systematic study of interactions between water-soluble proteins and polar metabolites in an entire biological subnetwork. To test the depth of our current knowledge, we chose to investigate the well-characterized Escherichia coli central metabolism. Using ligand-detected NMR, we assayed 29 enzymes towards binding events with 55 intracellular metabolites. Focusing on high-confidence interactions at a false-positive rate of 5%, we detected 98 interactions, among which purine nucleotides accounted for one-third, while 50% of all metabolites did not interact with any enzyme. In contrast, only five enzymes did not exhibit any metabolite binding and some interacted with up to 11 metabolites. About 40% of the interacting metabolites were predicted to be allosteric effectors based on low chemical similarity to their target's reactants. For five of the eight tested interactions, in vitro assays confirmed novel regulatory functions, including ATP and GTP inhibition of the first pentose phosphate pathway enzyme. With 76 new candidate regulatory interactions that have not been reported previously, we essentially doubled the number of known interactions, indicating that the presently available information about protein-metabolite interactions may only be the tip of the iceberg.
TL;DR: This work transplanted the B. subtilis FBP‐binding transcription factor CggR into yeast and developed a biosensor to measure glycolytic flux in single yeast cells, anticipating that this biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.
Abstract: Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single-cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), we transplanted the B. subtilis FBP-binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.
TL;DR: Brown et al report good performance of genome‐scale screens in TP53 wild‐type cells and reiterate best practices for CRISPR screening.
Abstract: A recent study by Haapaniemi et al (2018) reported that intact p53 signaling hampers CRISPR-based functional genomic screens. Brown et al report good performance of genome-scale screens in TP53 wild-type cells and reiterate best practices for CRISPR screening.
TL;DR: This study predicts novel molecular links to targets of CLL therapies and provides a valuable resource for further studies on the epigenetic contribution to the disease.
Abstract: In chronic lymphocytic leukemia (CLL), a diverse set of genetic mutations is embedded in a deregulated epigenetic landscape that drives cancerogenesis. To elucidate the role of aberrant chromatin features, we mapped DNA methylation, seven histone modifications, nucleosome positions, chromatin accessibility, binding of EBF1 and CTCF, as well as the transcriptome of B cells from CLL patients and healthy donors. A globally increased histone deacetylase activity was detected and half of the genome comprised transcriptionally downregulated partially DNA methylated domains demarcated by CTCF. CLL samples displayed a H3K4me3 redistribution and nucleosome gain at promoters as well as changes of enhancer activity and enhancer linkage to target genes. A DNA binding motif analysis identified transcription factors that gained or lost binding in CLL at sites with aberrant chromatin features. These findings were integrated into a gene regulatory enhancer containing network enriched for B‐cell receptor signaling pathway components. Our study predicts novel molecular links to targets of CLL therapies and provides a valuable resource for further studies on the epigenetic contribution to the disease.
TL;DR: A simple digitalizer module is modelled and implemented that completely suppresses the basal level of otherwise strong promoters in such a way that expression in the absence of induction is entirely impeded, suggesting that mobile elements are a major source of circuit inactivation in vivo.
Abstract: While prokaryotic promoters controlled by signal-responding regulators typically display a range of input/output ratios when exposed to cognate inducers, virtually no naturally occurring cases are known to have an OFF state of zero transcription-as ideally needed for synthetic circuits. To overcome this problem, we have modelled and implemented a simple digitalizer module that completely suppresses the basal level of otherwise strong promoters in such a way that expression in the absence of induction is entirely impeded. The circuit involves the interplay of a translation-inhibitory sRNA with the translational coupling of the gene of interest to a repressor such as LacI. The digitalizer module was validated with the strong inducible promoters Pm (induced by XylS in the presence of benzoate) and PalkB (induced by AlkS/dicyclopropyl ketone) and shown to perform effectively in both Escherichia coli and the soil bacterium Pseudomonas putida. The distinct expression architecture allowed cloning and conditional expression of, e.g. colicin E3, one molecule of which per cell suffices to kill the host bacterium. Revertants that escaped ColE3 killing were not found in hosts devoid of insertion sequences, suggesting that mobile elements are a major source of circuit inactivation in vivo.
TL;DR: It is reported that fibroblast GF (FGF2) evokes a distinct behavior that consists of a gradually changing population distribution of transient/sustained ERK signaling states in response to increasing inputs in a dose response.
Abstract: Stimulation of PC-12 cells with epidermal (EGF) versus nerve (NGF) growth factors (GFs) biases the distribution between transient and sustained single-cell ERK activity states, and between proliferation and differentiation fates within a cell population. We report that fibroblast GF (FGF2) evokes a distinct behavior that consists of a gradually changing population distribution of transient/sustained ERK signaling states in response to increasing inputs in a dose response. Temporally controlled GF perturbations of MAPK signaling dynamics applied using microfluidics reveal that this wider mix of ERK states emerges through the combination of an intracellular feedback, and competition of FGF2 binding to FGF receptors (FGFRs) and heparan sulfate proteoglycan (HSPG) co-receptors. We show that the latter experimental modality is instructive for model selection using a Bayesian parameter inference. Our results provide novel insights into how different receptor tyrosine kinase (RTK) systems differentially wire the MAPK network to fine-tune fate decisions at the cell population level.