ALBERT

Description: Asymmetric positive feedback loops reliably control biological responses Molecular Systems Biology 8, (2012). doi:10.1038/msb.2012.10 Authors: Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Schoggins, John W -- MacDuff, Donna A -- Imanaka, Naoko -- Gainey, Maria D -- Shrestha, Bimmi -- Eitson, Jennifer L -- Mar, Katrina B -- Richardson, R Blake -- Ratushny, Alexander V -- Litvak, Vladimir -- Dabelic, Rea -- Manicassamy, Balaji -- Aitchison, John D -- Aderem, Alan -- Elliott, Richard M -- Garcia-Sastre, Adolfo -- Racaniello, Vincent -- Snijder, Eric J -- Yokoyama, Wayne M -- Diamond, Michael S -- Virgin, Herbert W -- Rice, Charles M -- K01 DK095031/DK/NIDDK NIH HHS/ -- R00 AI095320/AI/NIAID NIH HHS/ -- R01 AI032972/AI/NIAID NIH HHS/ -- R01 AI091707/AI/NIAID NIH HHS/ -- R01 AI102597/AI/NIAID NIH HHS/ -- R01 AI104972/AI/NIAID NIH HHS/ -- England -- Nature. 2015 Sep 3;525(7567):144. doi: 10.1038/nature14555. Epub 2015 Jul 8.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26153856" target="_blank"〉PubMed〈/a〉

Description: Antiviral responses must be tightly regulated to defend rapidly against infection while minimizing inflammatory damage. Type 1 interferons (IFN-I) are crucial mediators of antiviral responses and their transcription is regulated by a variety of transcription factors; principal among these is the family of interferon regulatory factors (IRFs). The IRF gene regulatory networks are complex and contain multiple feedback loops. The tools of systems biology are well suited to elucidate the complex interactions that give rise to precise coordination of the interferon response. Here we have used an unbiased systems approach to predict that a member of the forkhead family of transcription factors, FOXO3, is a negative regulator of a subset of antiviral genes. This prediction was validated using macrophages isolated from Foxo3-null mice. Genome-wide location analysis combined with gene deletion studies identified the Irf7 gene as a critical target of FOXO3. FOXO3 was identified as a negative regulator of Irf7 transcription and we have further demonstrated that FOXO3, IRF7 and IFN-I form a coherent feed-forward regulatory circuit. Our data suggest that the FOXO3-IRF7 regulatory circuit represents a novel mechanism for establishing the requisite set points in the interferon pathway that balances the beneficial effects and deleterious sequelae of the antiviral response.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556990/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556990/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Litvak, Vladimir -- Ratushny, Alexander V -- Lampano, Aaron E -- Schmitz, Frank -- Huang, Albert C -- Raman, Ayush -- Rust, Alistair G -- Bergthaler, Andreas -- Aitchison, John D -- Aderem, Alan -- HHSN272200700038C/AI/NIAID NIH HHS/ -- HHSN272200700038C/PHS HHS/ -- HHSN272200800058C/AI/NIAID NIH HHS/ -- HSN272200800058C/PHS HHS/ -- R01 AI025032/AI/NIAID NIH HHS/ -- R01 AI032972/AI/NIAID NIH HHS/ -- R01AI025032/AI/NIAID NIH HHS/ -- R01AI032972/AI/NIAID NIH HHS/ -- U19 AI100627/AI/NIAID NIH HHS/ -- U54 GM103511/GM/NIGMS NIH HHS/ -- U54 RR022220/RR/NCRR NIH HHS/ -- U54GM103511/GM/NIGMS NIH HHS/ -- England -- Nature. 2012 Oct 18;490(7420):421-5. doi: 10.1038/nature11428. Epub 2012 Sep 16.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Seattle Biomedical Research Institute, Seattle, Washington 98109, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22982991" target="_blank"〉PubMed〈/a〉

Description: The type I interferon (IFN) response protects cells from viral infection by inducing hundreds of interferon-stimulated genes (ISGs), some of which encode direct antiviral effectors. Recent screening studies have begun to catalogue ISGs with antiviral activity against several RNA and DNA viruses. However, antiviral ISG specificity across multiple distinct classes of viruses remains largely unexplored. Here we used an ectopic expression assay to screen a library of more than 350 human ISGs for effects on 14 viruses representing 7 families and 11 genera. We show that 47 genes inhibit one or more viruses, and 25 genes enhance virus infectivity. Comparative analysis reveals that the screened ISGs target positive-sense single-stranded RNA viruses more effectively than negative-sense single-stranded RNA viruses. Gene clustering highlights the cytosolic DNA sensor cyclic GMP-AMP synthase (cGAS, also known as MB21D1) as a gene whose expression also broadly inhibits several RNA viruses. In vitro, lentiviral delivery of enzymatically active cGAS triggers a STING-dependent, IRF3-mediated antiviral program that functions independently of canonical IFN/STAT1 signalling. In vivo, genetic ablation of murine cGAS reveals its requirement in the antiviral response to two DNA viruses, and an unappreciated contribution to the innate control of an RNA virus. These studies uncover new paradigms for the preferential specificity of IFN-mediated antiviral pathways spanning several virus families.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077721/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077721/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Schoggins, John W -- MacDuff, Donna A -- Imanaka, Naoko -- Gainey, Maria D -- Shrestha, Bimmi -- Eitson, Jennifer L -- Mar, Katrina B -- Richardson, R Blake -- Ratushny, Alexander V -- Litvak, Vladimir -- Dabelic, Rea -- Manicassamy, Balaji -- Aitchison, John D -- Aderem, Alan -- Elliott, Richard M -- Garcia-Sastre, Adolfo -- Racaniello, Vincent -- Snijder, Eric J -- Yokoyama, Wayne M -- Diamond, Michael S -- Virgin, Herbert W -- Rice, Charles M -- 099220/Wellcome Trust/United Kingdom -- AI057158/AI/NIAID NIH HHS/ -- AI057160/AI/NIAID NIH HHS/ -- AI083025/AI/NIAID NIH HHS/ -- AI091707/AI/NIAID NIH HHS/ -- AI095611/AI/NIAID NIH HHS/ -- AI104972/AI/NIAID NIH HHS/ -- DK095031/DK/NIDDK NIH HHS/ -- G0801822/Medical Research Council/United Kingdom -- GM076547/GM/NIGMS NIH HHS/ -- GM103511/GM/NIGMS NIH HHS/ -- HHSN266200700010C/PHS HHS/ -- HHSN272200900041CU19/CU/CSP VA/ -- K01 DK095031/DK/NIDDK NIH HHS/ -- R00 AI095320/AI/NIAID NIH HHS/ -- R01 AI032972/AI/NIAID NIH HHS/ -- R01 AI091707/AI/NIAID NIH HHS/ -- R01 AI102597/AI/NIAID NIH HHS/ -- R01 AI104972/AI/NIAID NIH HHS/ -- T32 AI005284/AI/NIAID NIH HHS/ -- T32 AR007279/AR/NIAMS NIH HHS/ -- Howard Hughes Medical Institute/ -- England -- Nature. 2014 Jan 30;505(7485):691-5. doi: 10.1038/nature12862. Epub 2013 Nov 27.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, New York 10065, USA [2] Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA (J.W.S.); MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland G61 1QH, UK (R.M.E.). ; Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri 63110, USA. ; Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, New York 10065, USA. ; Rheumatology Division, Department of Medicine, and Howard Hughes Medical Institute, Washington University School of Medicine, St Louis, Missouri 63110, USA. ; Infectious Diseases Division, Department of Medicine and Department of Molecular Microbiology, Washington University School of Medicine, St Louis, Missouri 63110, USA. ; Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. ; 1] Seattle Biomedical Research Institute, Seattle, Washington 98109, USA [2] Institute for Systems Biology, Seattle, Washington 98109, USA. ; Seattle Biomedical Research Institute, Seattle, Washington 98109, USA. ; Department of Microbiology and Immunology, Columbia University, New York, New York 10032, USA. ; Department of Microbiology, University of Chicago, Chicago, Illinois 60637, USA. ; 1] School of Biology, University of St Andrews, St Andrews, Scotland KY16 9ST, UK [2] Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA (J.W.S.); MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland G61 1QH, UK (R.M.E.). ; 1] Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA [2] Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA [3] Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA. ; Department of Medical Microbiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands. ; 1] Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri 63110, USA [2] Infectious Diseases Division, Department of Medicine and Department of Molecular Microbiology, Washington University School of Medicine, St Louis, Missouri 63110, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24284630" target="_blank"〉PubMed〈/a〉

Description: Systems scale models provide the foundation for an effective iterative cycle between hypothesis generation, experiment and model refinement. Such models also enable predictions facilitating the understanding of biological complexity and the control of biological systems. Here, we demonstrate the reconstruction of a globally predictive gene regulatory model from public data: a model that can drive rational experiment design and reveal new regulatory mechanisms underlying responses to novel environments. Specifically, using ~1500 publically available genome-wide transcriptome data sets from Saccharomyces cerevisiae , we have reconstructed an environment and gene regulatory influence network that accurately predicts regulatory mechanisms and gene expression changes on exposure of cells to completely novel environments. Focusing on transcriptional networks that induce peroxisomes biogenesis, the model-guided experiments allow us to expand a core regulatory network to include novel transcriptional influences and linkage across signaling and transcription. Thus, the approach and model provides a multi-scalar picture of gene dynamics and are powerful resources for exploiting extant data to rationally guide experimentation. The techniques outlined here are generally applicable to any biological system, which is especially important when experimental systems are challenging and samples are difficult and expensive to obtain—a common problem in laboratory animal and human studies.

Description: The nuclear lamina is a filamentous structure subtending the nuclear envelope and required for chromatin organization, transcriptional regulation and maintaining nuclear structure. The trypanosomatid coiled-coil NUP-1 protein is a lamina component functionally analogous to lamins, the major lamina proteins of metazoa. There is little evidence for shared ancestry, suggesting the presence of a distinct lamina system in trypanosomes. To find additional trypanosomatid lamina components we identified NUP-1 interacting proteins by affinity capture and mass-spectrometry. Multiple components of the nuclear pore complex (NPC) and a second coiled-coil protein, which we termed NUP-2, were found. NUP-2 has a punctate distribution at the nuclear periphery throughout the cell cycle and is in close proximity to NUP-1, the NPCs and telomeric chromosomal regions. RNAi-mediated silencing of NUP-2 leads to severe proliferation defects, gross alterations to nuclear structure, chromosomal organization and nuclear envelope architecture. Further, transcription is altered at telomere-proximal variant surface glycoprotein (VSG) expression sites (ESs), suggesting a role in controlling ES expression, although NUP-2 silencing does not increase VSG switching. Transcriptome analysis suggests specific alterations to Pol I-dependent transcription. NUP-1 is mislocalized in NUP-2 knockdown cells and vice versa , implying that NUP-1 and NUP-2 form a co-dependent network and identifying NUP-2 as a second trypanosomatid nuclear lamina component.

Description: Cell growth is a complex phenotype widely used in systems biology to gauge the impact of genetic and environmental perturbations. Due to the magnitude of genome-wide studies, resolution is often sacrificed in favor of throughput, creating a demand for scalable, time-resolved, quantitative methods of growth assessment. We present ODELAY (One-cell Doubling Evaluation by Living Arrays of Yeast), an automated and scalable growth analysis platform. High measurement density and single-cell resolution provide a powerful tool for large-scale multiparameter growth analysis based on the modeling of microcolony expansion on solid media. Pioneered in yeast but applicable to other colony forming organisms, ODELAY extracts the three key growth parameters (lag time, doubling time, and carrying capacity) that define microcolony expansion from single cells, simultaneously permitting the assessment of population heterogeneity. The utility of ODELAY is illustrated using yeast mutants, revealing a spectrum of phenotypes arising from single and combinatorial growth parameter perturbations.

Description: Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

Description: Introduction The multistep progression of multiple myeloma from a normal plasma cell to a system with the features of invasive cancer provides a unique opportunity to understand the co-evolution of the malignant clone within its microenvironment. Understanding these changes is becoming increasingly important as we attempt to design early intervention strategies and to precisely leverage emerging immunotherapeutic modalities to prevent and treat disease progression. In this work, we used mass cytometry (CyTOF) to generate a high-resolution map of the BM microenvironment and how it changes during the transition from health through pre-malignancy to disease. This approach allows us to both understand microenvironmental patterns that correlate with rapid disease progression as well as to generate new hypotheses about permissive and protective immune-phenotypes that might reveal novel immunologic drug targets. Methods To understand the immunologic characteristics of monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), newly diagnosed multiple myeloma (NDMM) and relapsed-refractory multiple myeloma (RRMM), we profiled BM aspirates from 79 patients using mass cytometry by time of flight (CyTOF). Furthermore, we compared the BM compartment of pre-malignant, malignant, and relapsed disease states to the BM of healthy donors using a 37-marker pan-immune panel. In this panel, we used antibodies against several immune lineages, tumor antigens, and functional surface markers, including co-stimulatory and co-inhibitory receptors. Cell clusters defined by Citrus analysis of CyTOF data were combined into an evolutionarily optimized decision tree by evtree to identify cluster interactions that strongly partition patient samples. Results During MGUS, when the tumor plasma cells are