Journal: Cell Systems

Abbreviation

Cell Syst

Publisher

Cell Press

Journal Volumes

ISSN

2405-4720

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2015 - 2019222020 - 202520
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Publications 1 - 10 of 42
  • A Proteomic Connectivity Map
    Item type: Other Publication
    Feller, Christian; Aebersold, Ruedi (2018)
    Cell Systems
  • Genomic footprinting uncovers global transcription factor responses to amino acids in Escherichia coli
    Item type: Journal Article
    Trouillon, Julian; Doubleday, Peter F.; Sauer, Uwe (2023)
    Cell Systems
    Our knowledge of transcriptional responses to changes in nutrient availability comes primarily from few well-studied transcription factors (TFs), often lacking an unbiased genome-wide perspective. Leveraging recent advances allowing bacterial genomic footprinting, we comprehensively mapped the genome-wide regulatory responses of Escherichia coli to exogenous leucine, methionine, alanine, and lysine. The global TF Lrp was found to individually sense three amino acids and mount three different target gene responses. Overall, 531 genes had altered RNA polymerase occupancy, and 32 TFs responded directly or indirectly to the presence of amino acids, including regulators of membrane and osmotic pressure homeostasis. About 70% of the detected TF-DNA interactions had not been reported before. We thus identified 682 previously unknown TF-binding locations, for a subset of which the involved TFs were identified by affinity purification. This comprehensive map of amino acid regulation illustrates the incompleteness of the known transcriptional regulation network, even in E. coli.
  • Tailoring synthetic receptors to specific ligands
    Item type: Other Journal Item
    Scheller, Leo; Fussenegger, Martin (2018)
    Cell Systems
  • Yeast Creates a Niche for Symbiotic Lactic Acid Bacteria through Nitrogen Overflow
    Item type: Journal Article
    Ponomarova, Olga; Gabrielli, Natalia; Sévin, Daniel C.; et al. (2017)
    Cell Systems
    Many microorganisms live in communities and depend on metabolites secreted by fellow community members for survival. Yet our knowledge of interspecies metabolic dependencies is limited to few communities with small number of exchanged metabolites, and even less is known about cellular regulation facilitating metabolic exchange. Here we show how yeast enables growth of lactic acid bacteria through endogenous, multi-component, crossfeeding in a readily established community. In nitrogen- rich environments, Saccharomyces cerevisiae adjusts its metabolism by secreting a pool of metabolites, especially amino acids, and thereby enables survival of Lactobacillus plantarum and Lactococcus lactis. Quantity of the available nitrogen sources and the status of nitrogen catabolite repression pathways jointly modulate this niche creation. We demonstrate how nitrogen overflow by yeast benefits L. plantarum in grape juice, and contributes to emergence of mutualism with L. lactis in a medium with lactose. Our results illustrate how metabolic decisions of an individual species can benefit others.
  • Systems Analyses Reveal Physiological Roles and Genetic Regulators of Liver Lipid Species
    Item type: Journal Article
    Jha, Pooja; McDevitt, Molly T.; Gupta, Rahul; et al. (2018)
    Cell Systems
    The genetics of individual lipid species and their relevance in disease is largely unresolved. We profiled a subset of storage, signaling, membrane, and mitochondrial liver lipids across 385 mice from 47 strains of the BXD mouse population fed chow or high-fat diet and integrated these data with complementary multi-omics datasets. We identified several lipid species and lipid clusters with specific phenotypic and molecular signatures and, in particular, cardiolipin species with signatures of healthy and fatty liver. Genetic analyses revealed quantitative trait loci for 68% of the lipids (lQTL). By multi-layered omics analyses, we show the reliability of lQTLs to uncover candidate genes that can regulate the levels of lipid species. Additionally, we identified lQTLs that mapped to genes associated with abnormal lipid metabolism in human GWASs. This work provides a foundation and resource for understanding the genetic regulation and physiological significance of lipid species.
  • Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering
    Item type: Journal Article
    Minot, Mason; Reddy, Sai T. (2024)
    Cell Systems
    Machine learning-guided protein engineering is rapidly progressing; however, collecting high-quality, large datasets remains a bottleneck. Directed evolution and protein engineering studies often require extensive experimental processes to eliminate noise and label protein sequence-function data. Meta learning has proven effective in other fields in learning from noisy data via bi-level optimization given the availability of a small dataset with trusted labels. Here, we leverage meta learning approaches to overcome noisy and under-labeled data and expedite workflows in antibody engineering. We generate yeast display antibody mutagenesis libraries and screen them for target antigen binding followed by deep sequencing. We then create representative learning tasks, including learning from noisy training data, positive and unlabeled learning, and learning out of distribution properties. We demonstrate that meta learning has the potential to reduce experimental screening time and improve the robustness of machine learning models by training with noisy and under-labeled training data.
  • Evaluating predictive patterns of antigen-specific B cells by single-cell transcriptome and antibody repertoire sequencing
    Item type: Journal Article
    Erlach, Lena; Kuhn, Raphael; Agrafiotis, Andreas; et al. (2024)
    Cell Systems
    The field of antibody discovery typically involves extensive experimental screening of B cells from immunized animals. Machine learning (ML)-guided prediction of antigen-specific B cells could accelerate this process but requires sufficient training data with antigen-specificity labeling. Here, we introduce a dataset of single-cell transcriptome and antibody repertoire sequencing of B cells from immunized mice, which are labeled as antigen specific or non-specific through experimental selections. We identify gene expression patterns associated with antigen specificity by differential gene expression analysis and assess their antibody sequence diversity. Subsequently, we benchmark various ML models, both linear and non-linear, trained on different combinations of gene expression and antibody repertoire features. Additionally, we assess transfer learning using features from general and antibody-specific protein language models (PLMs). Our findings show that gene expression-based models outperform sequence-based models for antigen-specificity predictions, highlighting a promising avenue for computationally guided antibody discovery.
  • Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens
    Item type: Journal Article
    Paysan, Daniel; Radhakrishnan, Adityanarayanan; Zhang, Xinyi; et al. (2025)
    Cell Systems
    Representation learning provides an opportunity to uncover the link between 3D genome organization and gene regulatory networks, thereby connecting the physical and the biochemical space of a cell. Our method, Image2Reg, uses chromatin images obtained in large-scale genetic and chemical perturbation screens. Through convolutional neural networks, Image2Reg generates gene embedding that represents the effect of gene perturbation on chromatin organization. In addition, combining protein-protein interaction data with cell-type-specific transcriptomic data through a graph convolutional network, we obtain a gene embedding that represents the regulatory effect of genes. Finally, Image2Reg learns a map between the resulting physical and biochemical representation of cells, allowing us to predict the perturbed gene modules based on chromatin images. Our results confirm the deep link between chromatin organization and gene regulation and demonstrate that it can be harnessed to identify drug targets and genes upstream of perturbed phenotypes from a simple and inexpensive chromatin staining.
  • SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework
    Item type: Journal Article
    Skums, Pavel; Mohebbi, Fatemeh; Tsyvina, Vyacheslav; et al. (2022)
    Cell Systems
    Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.
  • What are the keys to succeeding as a computational biologist in today's research climate?
    Item type: Other Journal Item
    Berger, Bonnie; Tian, Dechao; Li, Wei Vivian; et al. (2022)
    Cell Systems
Publications 1 - 10 of 42