A Multiscale Map of the Stem Cell State in Pancreatic Adenocarcinoma.

Drug resistance and relapse remain key challenges in pancreatic cancer. Here, we have used RNA sequencing (RNA-seq), chromatin immunoprecipitation (ChIP)-seq, and genome-wide CRISPR analysis to map the molecular dependencies of pancreatic cancer stem cells, highly therapy-resistant cells that preferentially drive tumorigenesis and progression. This integrated genomic approach revealed an unexpected utilization of immuno-regulatory signals by pancreatic cancer epithelial cells. In particular, the nuclear hormone receptor retinoic-acid-receptor-related orphan receptor gamma (RORγ), known to drive inflammation and T cell differentiation, was upregulated during pancreatic cancer progression, and its genetic or pharmacologic inhibition led to a striking defect in pancreatic cancer growth and a marked improvement in survival. Further, a large-scale retrospective analysis in patients revealed that RORγ expression may predict pancreatic cancer aggressiveness, as it positively correlated with advanced disease and metastasis. Collectively, these data identify an orthogonal co-option of immuno-regulatory signals by pancreatic cancer stem cells, suggesting that autoimmune drugs should be evaluated as novel treatment strategies for pancreatic cancer patients.

Ptpn6 inhibits caspase-8- and Ripk3/Mlkl-dependent inflammation.

Ptpn6 is a cytoplasmic phosphatase that functions to prevent autoimmune and interleukin-1 (IL-1) receptor-dependent, caspase-1-independent inflammatory disease. Conditional deletion of Ptpn6 in neutrophils (Ptpn6∆PMN) is sufficient to initiate IL-1 receptor-dependent cutaneous inflammatory disease, but the source of IL-1 and the mechanisms behind IL-1 release remain unclear. Here, we investigate the mechanisms controlling IL-1α/ß release from neutrophils by inhibiting caspase-8-dependent apoptosis and Ripk1-Ripk3-Mlkl-regulated necroptosis. Loss of Ripk1 accelerated disease onset, whereas combined deletion of caspase-8 and either Ripk3 or Mlkl strongly protected Ptpn6∆PMN mice. Ptpn6∆PMN neutrophils displayed increased p38 mitogen-activated protein kinase-dependent Ripk1-independent IL-1 and tumor necrosis factor production, and were prone to cell death. Together, these data emphasize dual functions for Ptpn6 in the negative regulation of p38 mitogen-activated protein kinase activation to control tumor necrosis factor and IL-1α/ß expression, and in maintaining Ripk1 function to prevent caspase-8- and Ripk3-Mlkl-dependent cell death and concomitant IL-1α/ß release.

The diversification of methods for studying cell-cell interactions and communication.

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

Deciphering cell-cell interactions and communication from gene expression.

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.

Variant STAT4 and Response to Ruxolitinib in an Autoinflammatory Syndrome.

BACKGROUND: Disabling pansclerotic morphea (DPM) is a rare systemic inflammatory disorder, characterized by poor wound healing, fibrosis, cytopenias, hypogammaglobulinemia, and squamous-cell carcinoma. The cause is unknown, and mortality is high. METHODS: We evaluated four patients from three unrelated families with an autosomal dominant pattern of inheritance of DPM. Genomic sequencing independently identified three heterozygous variants in a specific region of the gene that encodes signal transducer and activator of transcription 4 (STAT4). Primary skin fibroblast and cell-line assays were used to define the functional nature of the genetic defect. We also assayed gene expression using single-cell RNA sequencing of peripheral-blood mononuclear cells to identify inflammatory pathways that may be affected in DPM and that may respond to therapy. RESULTS: Genome sequencing revealed three novel heterozygous missense gain-of-function variants in STAT4. In vitro, primary skin fibroblasts showed enhanced interleukin-6 secretion, with impaired wound healing, contraction of the collagen matrix, and matrix secretion. Inhibition of Janus kinase (JAK)-STAT signaling with ruxolitinib led to improvement in the hyperinflammatory fibroblast phenotype in vitro and resolution of inflammatory markers and clinical symptoms in treated patients, without adverse effects. Single-cell RNA sequencing revealed expression patterns consistent with an immunodysregulatory phenotype that were appropriately modified through JAK inhibition. CONCLUSIONS: Gain-of-function variants in STAT4 caused DPM in the families that we studied. The JAK inhibitor ruxolitinib attenuated the dermatologic and inflammatory phenotype in vitro and in the affected family members. (Funded by the American Academy of Allergy, Asthma, and Immunology Foundation and others.).

Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication.

Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.

Boltzmann Model Predicts Glycan Structures from Lectin Binding.

Glycans are complex oligosaccharides that are involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese hamster ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling.

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks.

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

Inferring secretory and metabolic pathway activity from omic data with secCellFie.

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework.

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years.

Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85-89% and AUC-PR scores of 84-92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.

Predicting pathways for old and new metabolites through clustering.

The diverse metabolic pathways are fundamental to all living organisms, as they harvest energy, synthesize biomass components, produce molecules to interact with the microenvironment, and neutralize toxins. While the discovery of new metabolites and pathways continues, the prediction of pathways for new metabolites can be challenging. It can take vast amounts of time to elucidate pathways for new metabolites; thus, according to HMDB (Human Metabolome Database), only 60% of metabolites get assigned to pathways. Here, we present an approach to identify pathways based on metabolite structure. We extracted 201 features from SMILES annotations and identified new metabolites from PubMed abstracts and HMDB. After applying clustering algorithms to both groups of features, we quantified correlations between metabolites, and found the clusters accurately linked 92% of known metabolites to their respective pathways. Thus, this approach could be valuable for predicting metabolic pathways for new metabolites.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

CHOGlycoNET: Comprehensive glycosylation reaction network for CHO cells.

Chinese hamster ovary (CHO) cells are extensively used for the production of glycoprotein therapeutics proteins, for which N-linked glycans are a critical quality attribute due to their influence on activity and immunogenicity. Manipulation of protein glycosylation is commonly achieved through cell or process engineering, which are often guided by mathematical models. However, each study considers a unique glycosylation reaction network that is tailored around the cell line and product at hand. Herein, we use 200 glycan datasets for both recombinantly produced and native proteins from different CHO cell lines to reconstruct a comprehensive reaction network, CHOGlycoNET, based on the individual minimal reaction networks describing each dataset. CHOGlycoNET is used to investigate the distribution of mannosidase and glycosyltransferase enzymes in the Golgi apparatus and identify key network reactions using machine learning and dimensionality reduction techniques. CHOGlycoNET can be used for accelerating glycomodel development and predicting the effect of glycoengineering strategies. Finally, CHOGlycoNET is wrapped in a SBML file to be used as a standalone model or in combination with CHO cell genome scale models.

Identification of hyperosmotic stress-responsive genes in Chinese hamster ovary cells via genome-wide virus-free CRISPR/Cas9 screening.

Chinese hamster ovary (CHO) cells are the preferred mammalian host cells for therapeutic protein production that have been extensively engineered to possess the desired attributes for high-yield protein production. However, empirical approaches for identifying novel engineering targets are laborious and time-consuming. Here, we established a genome-wide CRISPR/Cas9 screening platform for CHO-K1 cells with 111,651 guide RNAs (gRNAs) targeting 21,585 genes using a virus-free recombinase-mediated cassette exchange-based gRNA integration method. Using this platform, we performed a positive selection screening under hyperosmotic stress conditions and identified 180 genes whose perturbations conferred resistance to hyperosmotic stress in CHO cells. Functional enrichment analysis identified hyperosmotic stress responsive gene clusters, such as tRNA wobble uridine modification and signaling pathways associated with cell cycle arrest. Furthermore, we validated 32 top-scoring candidates and observed a high rate of hit confirmation, demonstrating the potential of the screening platform. Knockout of the novel target genes, Zfr and Pnp, in monoclonal antibody (mAb)-producing recombinant CHO (rCHO) cells and bispecific antibody (bsAb)-producing rCHO cells enhanced their resistance to hyperosmotic stress, thereby improving mAb and bsAb production. Overall, the collective findings demonstrate the value of the screening platform as a powerful tool to investigate the functions of genes associated with hyperosmotic stress and to discover novel targets for rational cell engineering on a genome-wide scale in CHO cells.

Genome-wide screens uncover KDM2B as a modifier of protein binding to heparan sulfate.

Heparan sulfate (HS) proteoglycans bind extracellular proteins that participate in cell signaling, attachment and endocytosis. These interactions depend on the arrangement of sulfated sugars in the HS chains generated by well-characterized biosynthetic enzymes; however, the regulation of these enzymes is largely unknown. We conducted genome-wide CRISPR-Cas9 screens with a small-molecule ligand that binds to HS. Screening of A375 melanoma cells uncovered additional genes and pathways impacting HS formation. The top hit was the epigenetic factor KDM2B, a histone demethylase. KDM2B inactivation suppressed multiple HS sulfotransferases and upregulated the sulfatase SULF1. These changes differentially affected the interaction of HS-binding proteins. KDM2B-deficient cells displayed decreased growth rates, which was rescued by SULF1 inactivation. In addition, KDM2B deficiency altered the expression of many extracellular matrix genes. Thus, KDM2B controls proliferation of A375 cells through the regulation of HS structure and serves as a master regulator of the extracellular matrix.

Reconstructing the cell-cell interaction network among mouse immune cells.

Intercellular interactions and cell-cell communication are critical to regulating cell functions, especially in normal immune cells and immunotherapies. Ligand-receptor pairs mediating these cell-cell interactions can be identified using diverse experimental and computational approaches. Here, we reconstructed the intercellular interaction network between Mus musculus immune cells using publicly available receptor-ligand interaction databases and gene expression data from the immunological genome project. This reconstructed network accounts for 50,317 unique interactions between 16 cell types between 731 receptor-ligand pairs. Analysis of this network shows that cells of hematopoietic lineages use fewer communication pathways for interacting with each other, while nonhematopoietic stromal cells use the most network communications. We further observe that the WNT, BMP, and LAMININ pathways are the most significant contributors to the overall number of cell-cell interactions among the various pathways in the reconstructed communication network. This resource will enable the systematic analysis of normal and pathologic immune cell interactions, along with the study of emerging immunotherapies.

Enhancing CHO cell productivity through a dual selection system using Aspg and Gs in glutamine free medium.

The dominant method for generating Chinese hamster ovary (CHO) cell lines that produce high titers of biotherapeutic proteins utilizes selectable markers such as dihydrofolate reductase (Dhfr) or glutamine synthetase (Gs), alongside inhibitory compounds like methotrexate or methionine sulfoximine, respectively. Recent work has shown the importance of asparaginase (Aspg) for growth in media lacking glutamine-the selection medium for Gs-based selection systems. We generated a Gs/Aspg double knockout CHO cell line and evaluated its utility as a novel dual selectable system via co-transfection of Gs-Enbrel and Aspg-Enbrel plasmids. Using the same selection conditions as the standard Gs system, the resulting cells from the Gs/Aspg dual selection showed substantially improved specific productivity and titer compared to the standard Gs selection method, however, with reduced growth rate and viability. Following adaptation in the selection medium, the cells improved viability and growth while still achieving ~5-fold higher specific productivity and ~3-fold higher titer than Gs selection alone. We anticipate that with further optimization of culture medium and selection conditions, this approach would serve as an effective addition to workflows for the industrial production of recombinant biotherapeutics.

Correction: StanDep: Capturing transcriptomic variability improves context-specific metabolic models.

[This corrects the article DOI: 10.1371/journal.pcbi.1007764.].