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1.
Nat Immunol ; 20(3): 373, 2019 Mar.
Article in English | MEDLINE | ID: mdl-30728493

ABSTRACT

In the version of this article initially published, three authors (Hui-Fern Kuoy, Adam P. Uldrich and Dale. I. Godfrey) and their affiliations, acknowledgments and contributions were not included. The correct information is as follows:Ayano C. Kohlgruber1,2, Shani T. Gal-Oz3, Nelson M. LaMarche1,2, Moto Shimazaki1, Danielle Duquette4, Hui-Fern Koay5,6, Hung N. Nguyen1, Amir I. Mina4, Tyler Paras1, Ali Tavakkoli7, Ulrich von Andrian2,8, Adam P. Uldrich5,6, Dale I. Godfrey5,6, Alexander S. Banks4, Tal Shay3, Michael B. Brenner1,10* and Lydia Lynch1,4,9,10*1Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA, USA. 2Division of Medical Sciences, Harvard Medical School, Boston, MA, USA. 3Department of Life Sciences, Ben-Gurion University of the Negev, Beersheba, Israel. 4Division of Endocrinology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. 5Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Australia. 6ARC Centre of Excellence in Advanced Molecular Imaging, University of Melbourne, Parkville, Australia. 7Department of General and Gastrointestinal Surgery, Brigham and Women's Hospital, Boston, MA, USA. 8Department of Microbiology and Immunology, Harvard Medical School, Boston, MA, USA. 9School of Biochemistry and Immunology, Trinity College, Dublin, Ireland. 10These authors jointly supervised this work: Michael B. Brenner, Lydia Lynch. *e-mail: mbrenner@research.bwh.harvard.edu; llynch@bwh.harvard.eduAcknowledgementsWe thank A.T. Chicoine, flow cytometry core manager at the Human Immunology Center at BWH, for flow cytometry sorting. We thank D. Sant'Angelo (Rutgers Cancer Institute) for providing Zbtb16-/- mice and R. O'Brien (National Jewish Health) for providing Vg4/6-/- mice. Supported by NIH grant R01 AI11304603 (to M.B.B.), ERC Starting Grant 679173 (to L.L.), the National Health and Medical Research Council of Australia (1013667), an Australian Research Council Future Fellowship (FT140100278 for A.P.U.) and a National Health and Medical Research Council of Australia Senior Principal Research Fellowship (1117766 for D.I.G.).Author contributionsA.C.K., L.L., and M.B.B. conceived and designed the experiments, and wrote the manuscript. A.C.K., N.M.L., L.L., H.N.N., M.S., T.P., and D.D. performed the experiments. S.T.G.-O. and T.S. performed the RNA-seq analysis. A.S.B. and A.I.M. provided advice and performed the CLAMS experiments. A.T. provided human bariatric patient samples. Parabiosis experiments were performed in the laboratory of U.v.A. H.-F.K., A.P.U. and D.I.G provided critical insight into the TCR chain usage of PLZF+ γδ T cells. M.B.B., N.M.L., and L.L. critically reviewed the manuscript.The errors have been corrected in the HTML and PDF version of the article.Correction to: Nature Immunology doi:10.1038/s41590-018-0094-2 (2018), published online 18 April 2018.

2.
Nat Immunol ; 19(5): 464-474, 2018 05.
Article in English | MEDLINE | ID: mdl-29670241

ABSTRACT

γδ T cells are situated at barrier sites and guard the body from infection and damage. However, little is known about their roles outside of host defense in nonbarrier tissues. Here, we characterize a highly enriched tissue-resident population of γδ T cells in adipose tissue that regulate age-dependent regulatory T cell (Treg) expansion and control core body temperature in response to environmental fluctuations. Mechanistically, innate PLZF+ γδ T cells produced tumor necrosis factor and interleukin (IL) 17 A and determined PDGFRα+ and Pdpn+ stromal-cell production of IL-33 in adipose tissue. Mice lacking γδ T cells or IL-17A exhibited decreases in both ST2+ Treg cells and IL-33 abundance in visceral adipose tissue. Remarkably, these mice also lacked the ability to regulate core body temperature at thermoneutrality and after cold challenge. Together, these findings uncover important physiological roles for resident γδ T cells in adipose tissue immune homeostasis and body-temperature control.


Subject(s)
Adipose Tissue/cytology , Homeostasis/physiology , Interleukin-17/metabolism , T-Lymphocytes, Regulatory/physiology , Thermogenesis/physiology , Adipose Tissue/physiology , Animals , Humans , Male , Mice , Mice, Inbred C57BL , Mice, Knockout , Receptors, Antigen, T-Cell, gamma-delta , T-Lymphocyte Subsets/physiology
3.
J Immunol ; 213(1): 96-104, 2024 07 01.
Article in English | MEDLINE | ID: mdl-38775402

ABSTRACT

The response to type I IFNs involves the rapid induction of prototypical IFN signature genes (ISGs). It is not known whether the tightly controlled ISG expression observed at the cell population level correctly represents the coherent responses of individual cells or whether it masks some heterogeneity in gene modules and/or responding cells. We performed a time-resolved single-cell analysis of the first 3 h after in vivo IFN stimulation in macrophages and CD4+ T and B lymphocytes from mice. All ISGs were generally induced in concert, with no clear cluster of faster- or slower-responding ISGs. Response kinetics differed between cell types: mostly homogeneous for macrophages, but with far more kinetic diversity among B and T lymphocytes, which included a distinct subset of nonresponsive cells. Velocity analysis confirmed the differences between macrophages in which the response progressed throughout the full 3 h, versus B and T lymphocytes in which it was rapidly curtailed by negative feedback and revealed differences in transcription rates between the lineages. In all cell types, female cells responded faster than their male counterparts. The ISG response thus seems to proceed as a homogeneous gene block, but with kinetics that vary between immune cell types and with sex differences that might underlie differential outcomes of viral infections.


Subject(s)
B-Lymphocytes , Interferon Type I , Macrophages , Mice, Inbred C57BL , Animals , Mice , Female , Interferon Type I/metabolism , Interferon Type I/immunology , Male , B-Lymphocytes/immunology , Macrophages/immunology , Kinetics , CD4-Positive T-Lymphocytes/immunology , Sex Factors , Single-Cell Analysis
4.
Cell ; 144(2): 296-309, 2011 Jan 21.
Article in English | MEDLINE | ID: mdl-21241896

ABSTRACT

Though many individual transcription factors are known to regulate hematopoietic differentiation, major aspects of the global architecture of hematopoiesis remain unknown. Here, we profiled gene expression in 38 distinct purified populations of human hematopoietic cells and used probabilistic models of gene expression and analysis of cis-elements in gene promoters to decipher the general organization of their regulatory circuitry. We identified modules of highly coexpressed genes, some of which are restricted to a single lineage but most of which are expressed at variable levels across multiple lineages. We found densely interconnected cis-regulatory circuits and a large number of transcription factors that are differentially expressed across hematopoietic states. These findings suggest a more complex regulatory system for hematopoiesis than previously assumed.


Subject(s)
Gene Expression Regulation , Gene Regulatory Networks , Hematopoiesis , Transcription Factors/metabolism , Gene Expression Profiling , Humans
5.
Nat Immunol ; 14(1): 90-9, 2013 Jan.
Article in English | MEDLINE | ID: mdl-23202270

ABSTRACT

Invariant natural killer T cells (iNKT cells) are innate-like T lymphocytes that act as critical regulators of the immune response. To better characterize this population, we profiled gene expression in iNKT cells during ontogeny and in peripheral subsets as part of the Immunological Genome Project. High-resolution comparative transcriptional analyses defined developmental and subset-specific programs of gene expression by iNKT cells. In addition, we found that iNKT cells shared an extensive transcriptional program with NK cells, similar in magnitude to that shared with major histocompatibility complex (MHC)-restricted T cells. Notably, the program shared by NK cells and iNKT cells also operated constitutively in γδ T cells and in adaptive T cells after activation. Together our findings highlight a core effector program regulated distinctly in innate and adaptive lymphocytes.


Subject(s)
Natural Killer T-Cells/immunology , T-Lymphocyte Subsets/immunology , Thymus Gland/immunology , Transcriptome , Adaptive Immunity/genetics , Animals , Cell Differentiation , Cell Lineage , Genome, Human/immunology , Humans , Immunity, Innate/genetics , Immunologic Memory/genetics , Mice , Microarray Analysis , Thymus Gland/growth & development
6.
Nat Immunol ; 14(6): 633-43, 2013 Jun.
Article in English | MEDLINE | ID: mdl-23624555

ABSTRACT

The differentiation of hematopoietic stem cells into cells of the immune system has been studied extensively in mammals, but the transcriptional circuitry that controls it is still only partially understood. Here, the Immunological Genome Project gene-expression profiles across mouse immune lineages allowed us to systematically analyze these circuits. To analyze this data set we developed Ontogenet, an algorithm for reconstructing lineage-specific regulation from gene-expression profiles across lineages. Using Ontogenet, we found differentiation stage-specific regulators of mouse hematopoiesis and identified many known hematopoietic regulators and 175 previously unknown candidate regulators, as well as their target genes and the cell types in which they act. Among the previously unknown regulators, we emphasize the role of ETV5 in the differentiation of γδ T cells. As the transcriptional programs of human and mouse cells are highly conserved, it is likely that many lessons learned from the mouse model apply to humans.


Subject(s)
Algorithms , Gene Expression Regulation/immunology , Immune System/metabolism , Transcription, Genetic/immunology , Animals , Cell Differentiation/genetics , Cell Differentiation/immunology , Cell Lineage/genetics , Cell Lineage/immunology , DNA-Binding Proteins/genetics , DNA-Binding Proteins/immunology , Gene Expression Profiling , Gene Regulatory Networks/immunology , Humans , Immune System/cytology , Mice , Oligonucleotide Array Sequence Analysis , Receptors, Antigen, T-Cell, gamma-delta/immunology , Receptors, Antigen, T-Cell, gamma-delta/metabolism , Repressor Proteins/genetics , Repressor Proteins/immunology , T-Lymphocytes/immunology , T-Lymphocytes/metabolism , Trans-Activators/genetics , Trans-Activators/immunology , Transcription Factors/genetics , Transcription Factors/immunology , Transcriptome/genetics , Transcriptome/immunology
7.
Nat Immunol ; 14(6): 619-32, 2013 Jun.
Article in English | MEDLINE | ID: mdl-23644507

ABSTRACT

The differentiation of αßT cells from thymic precursors is a complex process essential for adaptive immunity. Here we exploited the breadth of expression data sets from the Immunological Genome Project to analyze how the differentiation of thymic precursors gives rise to mature T cell transcriptomes. We found that early T cell commitment was driven by unexpectedly gradual changes. In contrast, transit through the CD4(+)CD8(+) stage involved a global shutdown of housekeeping genes that is rare among cells of the immune system and correlated tightly with expression of the transcription factor c-Myc. Selection driven by major histocompatibility complex (MHC) molecules promoted a large-scale transcriptional reactivation. We identified distinct signatures that marked cells destined for positive selection versus apoptotic deletion. Differences in the expression of unexpectedly few genes accompanied commitment to the CD4(+) or CD8(+) lineage, a similarity that carried through to peripheral T cells and their activation, demonstrated by mass cytometry phosphoproteomics. The transcripts newly identified as encoding candidate mediators of key transitions help define the 'known unknowns' of thymocyte differentiation.


Subject(s)
CD4-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/immunology , Cell Differentiation/immunology , Receptors, Antigen, T-Cell, alpha-beta/immunology , Animals , Antigens, CD/immunology , Antigens, CD/metabolism , Antigens, Differentiation, T-Lymphocyte/immunology , Antigens, Differentiation, T-Lymphocyte/metabolism , CD4-Positive T-Lymphocytes/metabolism , CD8-Positive T-Lymphocytes/metabolism , Cell Differentiation/genetics , Cell Lineage/genetics , Cell Lineage/immunology , Cell Proliferation , Cells, Cultured , Cluster Analysis , Flow Cytometry , Histocompatibility Antigens/genetics , Histocompatibility Antigens/immunology , Histocompatibility Antigens/metabolism , Lectins, C-Type/immunology , Lectins, C-Type/metabolism , Male , Mice , Mice, Inbred C57BL , Oligonucleotide Array Sequence Analysis , Phosphorylation/immunology , Receptors, Antigen, T-Cell, alpha-beta/genetics , Receptors, Antigen, T-Cell, alpha-beta/metabolism , Thymocytes/cytology , Thymocytes/immunology , Thymocytes/metabolism , Transcriptome/genetics , Transcriptome/immunology
8.
Nat Immunol ; 13(11): 1118-28, 2012 Nov.
Article in English | MEDLINE | ID: mdl-23023392

ABSTRACT

We assessed gene expression in tissue macrophages from various mouse organs. The diversity in gene expression among different populations of macrophages was considerable. Only a few hundred mRNA transcripts were selectively expressed by macrophages rather than dendritic cells, and many of these were not present in all macrophages. Nonetheless, well-characterized surface markers, including MerTK and FcγR1 (CD64), along with a cluster of previously unidentified transcripts, were distinctly and universally associated with mature tissue macrophages. TCEF3, C/EBP-α, Bach1 and CREG-1 were among the transcriptional regulators predicted to regulate these core macrophage-associated genes. The mRNA encoding other transcription factors, such as Gata6, was associated with single macrophage populations. We further identified how these transcripts and the proteins they encode facilitated distinguishing macrophages from dendritic cells.


Subject(s)
Antigens, CD/genetics , Macrophages/metabolism , RNA, Messenger/genetics , Transcription Factors/genetics , Transcription, Genetic , Animals , Antigens, CD/immunology , Cell Differentiation , Dendritic Cells/cytology , Dendritic Cells/immunology , Dendritic Cells/metabolism , Gene Expression Profiling , Gene Expression Regulation , Genetic Variation , Liver/cytology , Liver/immunology , Liver/metabolism , Lung/cytology , Lung/immunology , Lung/metabolism , Macrophages/cytology , Macrophages/immunology , Mice , Microglia/cytology , Microglia/immunology , Microglia/metabolism , Oligonucleotide Array Sequence Analysis , Organ Specificity , RNA, Messenger/immunology , Spleen/cytology , Spleen/immunology , Spleen/metabolism , Transcription Factors/immunology
9.
Nat Immunol ; 13(9): 888-99, 2012 Sep.
Article in English | MEDLINE | ID: mdl-22797772

ABSTRACT

Although much progress has been made in the understanding of the ontogeny and function of dendritic cells (DCs), the transcriptional regulation of the lineage commitment and functional specialization of DCs in vivo remains poorly understood. We made a comprehensive comparative analysis of CD8(+), CD103(+), CD11b(+) and plasmacytoid DC subsets, as well as macrophage DC precursors and common DC precursors, across the entire immune system. Here we characterized candidate transcriptional activators involved in the commitment of myeloid progenitor cells to the DC lineage and predicted regulators of DC functional diversity in tissues. We identified a molecular signature that distinguished tissue DCs from macrophages. We also identified a transcriptional program expressed specifically during the steady-state migration of tissue DCs to the draining lymph nodes that may control tolerance to self tissue antigens.


Subject(s)
Cell Differentiation/immunology , Cell Lineage/immunology , Dendritic Cells/immunology , Transcription, Genetic , Cell Differentiation/genetics , Dendritic Cells/cytology , Gene Expression Profiling , Humans
10.
Curr Top Microbiol Immunol ; 441: 1-19, 2023.
Article in English | MEDLINE | ID: mdl-37695423

ABSTRACT

Women have a stronger immune response and a higher frequency of most autoimmune diseases than men. While much of the difference between men and women is due to the effect of gonadal hormones, genetic differences play a major role in the difference between the immune response and disease frequencies in women and men. Here, we focus on the immune differences between the sexes that are not downstream of the gonadal hormones. These differences include the gene content of the sex chromosomes, the inactivation of chromosome X in women, the consequences of non-random X inactivation and escape from inactivation, and the states that are uniquely met by the immune system of women-pregnancy, birth, and breast feeding. While these female-specific states are temporary and involve gonadal hormonal changes, they may leave a long-lasting footprint on the health of women, for example, by fetal cells that remain in the mother's body for decades. We also briefly discuss the immune phenotype of congenital sex chromosomal aberrations and experimental models that enable hormonal and the non-hormonal effects of the sex chromosomes to be disentangled. The increasing human life expectancy lengthens the period during which gonadal hormones levels are reduced in both sexes. A better understanding of the non-hormonal effects of sex chromosomes thus becomes more important for improving the life quality during that period.


Subject(s)
Autoimmune Diseases , Sex Characteristics , Pregnancy , Female , Humans , Male , Autoimmune Diseases/genetics , Phenotype , Quality of Life
11.
Physiology (Bethesda) ; 37(2): 55-68, 2022 03 01.
Article in English | MEDLINE | ID: mdl-34514870

ABSTRACT

Despite numerous studies of immune sexual dimorphism, sexual differences are not rigorously mapped and dimorphic mechanisms are incompletely understood. Current immune research typically studies sex differences in specific cells, tissues, or diseases but without providing an integrated picture. To connect the dots, we suggest comprehensive research approaches to better our understanding of immune sexual dimorphism and its mechanisms.


Subject(s)
Sex Characteristics , Female , Humans , Male
12.
Nucleic Acids Res ; 49(W1): W162-W168, 2021 07 02.
Article in English | MEDLINE | ID: mdl-33988713

ABSTRACT

Alternative splicing results in multiple transcripts of the same gene, possibly encoding for different protein isoforms with different domains. Whereas it is possible to manually determine the effect of alternative splicing on the domain composition for a single event, the process requires the tedious integration of several data sources; it is error prone and not feasible for genome-wide characterization of domains affected by differential splicing. To fulfill the need for an automated solution, we developed the Domain Change Presenter (DoChaP, https://dochap.bgu.ac.il/), a web server for the visualization of exon-domain associations. DoChaP visualizes all transcripts of a given gene, the encoded proteins and their domains, and enables a comparison between the transcripts and between their protein products. The colors and organization make the structural effect of alternative splicing events on protein structures easily identified. To enable the study of the conservation of exons structure, alternative splicing, and the effect of alternative splicing on protein domains, DoChaP also provides a two-species comparison of exon-domain associations. DoChaP thus provides a unique and easy-to-use visualization of the exon-domain association and conservation, and will facilitate the study of the structural effects of alternative splicing in health and disease.


Subject(s)
Alternative Splicing , Exons , Protein Domains , Software , Animals , Genomics , Humans , Mice , Protein Isoforms/chemistry , Protein Isoforms/genetics , Protein Isoforms/metabolism , Rats , Xenopus Proteins/chemistry , Zebrafish Proteins/chemistry
13.
J Immunol ; 198(9): 3375-3379, 2017 05 01.
Article in English | MEDLINE | ID: mdl-28416714

ABSTRACT

Recent advances in single-cell RNA-sequencing (scRNA-seq) technology increase the understanding of immune differentiation and activation processes, as well as the heterogeneity of immune cell types. Although the number of available immune-related scRNA-seq datasets increases rapidly, their large size and various formats render them hard for the wider immunology community to use, and read-level data are practically inaccessible to the non-computational immunologist. To facilitate datasets reuse, we created the JingleBells repository for immune-related scRNA-seq datasets ready for analysis and visualization of reads at the single-cell level (http://jinglebells.bgu.ac.il/). To this end, we collected the raw data of publicly available immune-related scRNA-seq datasets, aligned the reads to the relevant genome, and saved aligned reads in a uniform format, annotated for cell of origin. We also added scripts and a step-by-step tutorial for visualizing each dataset at the single-cell level, through the commonly used Integrated Genome Viewer (www.broadinstitute.org/igv/). The uniform scRNA-seq format used in JingleBells can facilitate reuse of scRNA-seq data by computational biologists. It also enables immunologists who are interested in a specific gene to visualize the reads aligned to this gene to estimate cell-specific preferences for splicing, mutation load, or alleles. Thus JingleBells is a resource that will extend the usefulness of scRNA-seq datasets outside the programming aficionado realm.


Subject(s)
RNA/genetics , Sequence Analysis, RNA/methods , Software , Animals , Computational Biology , Datasets as Topic , Gene Expression Profiling , Genome/immunology , High-Throughput Nucleotide Sequencing , Humans , Immunity/genetics , Mice , Single-Cell Analysis , Zebrafish
14.
Mol Cell ; 42(4): 524-35, 2011 May 20.
Article in English | MEDLINE | ID: mdl-21596316

ABSTRACT

Normal cells require continuous exposure to growth factors in order to cross a restriction point and commit to cell-cycle progression. This can be replaced by two short, appropriately spaced pulses of growth factors, where the first pulse primes a process, which is completed by the second pulse, and enables restriction point crossing. Through integration of comprehensive proteomic and transcriptomic analyses of each pulse, we identified three processes that regulate restriction point crossing: (1) The first pulse induces essential metabolic enzymes and activates p53-dependent restraining processes. (2) The second pulse eliminates, via the PI3K/AKT pathway, the suppressive action of p53, as well as (3) sets an ERK-EGR1 threshold mechanism, which digitizes graded external signals into an all-or-none decision obligatory for S phase entry. Together, our findings uncover two gating mechanisms, which ensure that cells ignore fortuitous growth factors and undergo proliferation only in response to consistent mitogenic signals.


Subject(s)
Breast/cytology , Epidermal Growth Factor/physiology , Epithelial Cells/cytology , Mitosis , Repressor Proteins/metabolism , Tumor Suppressor Protein p53/metabolism , Breast/drug effects , Cell Line , Epidermal Growth Factor/pharmacology , Epithelial Cells/drug effects , Epithelial Cells/metabolism , Female , Gene Expression Profiling , Humans , Mitosis/genetics , Phosphatidylinositol 3-Kinases/metabolism , Proteomics , Proto-Oncogene Proteins c-akt/metabolism , Signal Transduction , Transcription, Genetic
15.
Trends Immunol ; 34(12): 602-9, 2013 Dec.
Article in English | MEDLINE | ID: mdl-23631936

ABSTRACT

Immunological studies of single proteins in a single cell type have been complemented in recent years by larger studies, enabled by emerging high-throughput technologies. This trend has recently been exemplified by the discovery of gene networks controlling regulatory and effector αß T cell subset development and human hematopoiesis. The Immunological Genome Project (ImmGen) aims to decipher the gene networks underpinning mouse hematopoiesis. The first phase, completed in 2012, profiled the transcriptome of 249 immune cell types. We discuss the utilities of the datasets in high-resolution mapping of the hematopoietic system. The immune transcriptome compendium has revealed unsuspected cell lineage relations and the network reconstruction has identified novel regulatory factors of hematopoiesis.


Subject(s)
Gene Regulatory Networks/genetics , Gene Regulatory Networks/immunology , Signal Transduction/genetics , Signal Transduction/immunology , Transcriptome/genetics , Transcriptome/immunology , Animals , Cell Lineage/genetics , Cell Lineage/immunology , Hematopoiesis/genetics , Hematopoiesis/immunology , Humans
16.
Proc Natl Acad Sci U S A ; 110(8): 2946-51, 2013 Feb 19.
Article in English | MEDLINE | ID: mdl-23382184

ABSTRACT

Much of the knowledge about cell differentiation and function in the immune system has come from studies in mice, but the relevance to human immunology, diseases, and therapy has been challenged, perhaps more from anecdotal than comprehensive evidence. To this end, we compare two large compendia of transcriptional profiles of human and mouse immune cell types. Global transcription profiles are conserved between corresponding cell lineages. The expression patterns of most orthologous genes are conserved, particularly for lineage-specific genes. However, several hundred genes show clearly divergent expression across the examined cell lineages, and among them, 169 genes did so even with highly stringent criteria. Finally, regulatory mechanisms--reflected by regulators' differential expression or enriched cis-elements--are conserved between the species but to a lower degree, suggesting that distinct regulation may underlie some of the conserved transcriptional responses.


Subject(s)
Gene Expression Profiling , Immune System/metabolism , Transcription, Genetic , Animals , Humans , Lymphocyte Activation , Mice , T-Lymphocytes/immunology
17.
Nat Genet ; 39(4): 503-12, 2007 Apr.
Article in English | MEDLINE | ID: mdl-17322878

ABSTRACT

Signaling pathways invoke interplays between forward signaling and feedback to drive robust cellular response. In this study, we address the dynamics of growth factor signaling through profiling of protein phosphorylation and gene expression, demonstrating the presence of a kinetically defined cluster of delayed early genes that function to attenuate the early events of growth factor signaling. Using epidermal growth factor receptor signaling as the major model system and concentrating on regulation of transcription and mRNA stability, we demonstrate that a number of genes within the delayed early gene cluster function as feedback regulators of immediate early genes. Consistent with their role in negative regulation of cell signaling, genes within this cluster are downregulated in diverse tumor types, in correlation with clinical outcome. More generally, our study proposes a mechanistic description of the cellular response to growth factors by defining architectural motifs that underlie the function of signaling networks.


Subject(s)
Feedback, Physiological/genetics , Intercellular Signaling Peptides and Proteins/physiology , Signal Transduction/genetics , Transcription Factors/physiology , Acid Sensing Ion Channels , Cells, Cultured , Cluster Analysis , Degenerin Sodium Channels , Epidermal Growth Factor/physiology , Epithelial Sodium Channels/physiology , Extracellular Signal-Regulated MAP Kinases/physiology , Gene Expression Regulation , HeLa Cells , Humans , Kruppel-Like Transcription Factors/physiology , MafF Transcription Factor/physiology , Models, Biological , Nerve Tissue Proteins/physiology , Nuclear Proteins/physiology , Transcription Factors/genetics , Tristetraprolin/physiology
18.
Nat Cell Biol ; 9(8): 961-9, 2007 Aug.
Article in English | MEDLINE | ID: mdl-17643115

ABSTRACT

Cell migration driven by the epidermal growth factor receptor (EGFR) propels morphogenesis and involves reorganization of the actin cytoskeleton. Although de novo transcription precedes migration, transcript identity remains largely unknown. Through their actin-binding domains, tensins link the cytoskeleton to integrin-based adhesion sites. Here we report that EGF downregulates tensin-3 expression, and concomitantly upregulates cten, a tensin family member that lacks the actin-binding domain. Knockdown of cten or tensin-3, respectively, impairs or enhances mammary cell migration. Furthermore, cten displaces tensin-3 from the cytoplasmic tail of integrin beta1, thereby instigating actin fibre disassembly. In invasive breast cancer, cten expression correlates not only with high EGFR and HER2, but also with metastasis to lymph nodes. Moreover, treatment of inflammatory breast cancer patients with an EGFR/HER2 dual-specificity kinase inhibitor significantly downregulated cten expression. In conclusion, a transcriptional tensin-3-cten switch may contribute to the metastasis of mammary cancer.


Subject(s)
Breast Neoplasms/metabolism , Cell Movement/physiology , Epidermal Growth Factor/metabolism , Microfilament Proteins/metabolism , Breast Neoplasms/pathology , Cell Line, Tumor , Enzyme Inhibitors/metabolism , ErbB Receptors , Female , Humans , Microfilament Proteins/genetics , Oligonucleotide Array Sequence Analysis , RNA, Small Interfering/genetics , RNA, Small Interfering/metabolism , Receptor, ErbB-2/genetics , Receptor, ErbB-2/metabolism , Recombinant Fusion Proteins/genetics , Recombinant Fusion Proteins/metabolism , Tensins
19.
PLoS One ; 19(9): e0307997, 2024.
Article in English | MEDLINE | ID: mdl-39255285

ABSTRACT

Performing joint analysis of gene expression datasets from different experiments can present challenges brought on by multiple factors-differences in equipment, protocols, climate etc. "Cross-study normalization" is a general term for transformations aimed at eliminating such effects, thus making datasets more comparable. However, joint analysis of datasets from different species is rarely done, and there are no dedicated normalization methods for such inter-species analysis. In order to test the usefulness of cross-studies normalization methods for inter-species analysis, we first applied three cross-study normalization methods, EB, DWD and XPN, to RNA sequencing datasets from different species. We then developed a new approach to evaluate the performance of cross-study normalization in eliminating experimental effects, while also maintaining the biologically significant differences between species and conditions. Our results indicate that all normalization methods performed relatively well in the cross-species setting. We found XPN to be better at reducing experimental differences, and found EB to be better at preserving biological differences. Still, according to our in-silico experiments, in all methods it is not possible to enforce the preservation of the biological differences in the normalization process. In addition to the study above, in this work we propose a new dedicated cross-studies and cross-species normalization method. Our aim is to address the shortcoming mentioned above: in the normalization process, we wish to reduce the experimental differences while preserving the biological differences. We term our method as CSN, and base it on the performance evaluation criteria mentioned above. Repeating the same experiments, the CSN method obtained a better and more balanced conservation of biological differences within the datasets compared to existing methods. To summarize, we demonstrate the usefulness of cross-study normalization methods in the inter-species settings, and suggest a dedicated cross-study cross-species normalization method that will hopefully open the way to the development of improved normalization methods for the inter-species settings.


Subject(s)
Species Specificity , Animals , Gene Expression Profiling/methods , Sequence Analysis, RNA/methods , Humans , Computational Biology/methods
20.
FASEB J ; 26(4): 1582-92, 2012 Apr.
Article in English | MEDLINE | ID: mdl-22198386

ABSTRACT

The signaling pathways that commit cells to migration are incompletely understood. We employed human mammary cells and two stimuli: epidermal growth factor (EGF), which induced cellular migration, and serum factors, which stimulated cell growth. In addition to strong activation of ERK by EGF, and AKT by serum, early transcription remarkably differed: while EGF induced early growth response-1 (EGR1), and this was required for migration, serum induced c-Fos and FosB to enhance proliferation. We demonstrate that induction of EGR1 involves ERK-mediated down-regulation of microRNA-191 and phosphorylation of the ETS2 repressor factor (ERF) repressor, which subsequently leaves the nucleus. Unexpectedly, knockdown of ERF inhibited migration, which implies migratory roles for exported ERF molecules. On the other hand, chromatin immunoprecipitation identified a subset of direct EGR1 targets, including EGR1 autostimulation and SERPINB2, whose transcription is essential for EGF-induced cell migration. In summary, EGR1 and the EGF-ERK-ERF axis emerge from our study as major drivers of growth factor-induced mammary cell migration.


Subject(s)
Cell Movement/drug effects , Early Growth Response Protein 1/metabolism , Epidermal Growth Factor/pharmacology , Extracellular Signal-Regulated MAP Kinases/metabolism , Mammary Glands, Human/cytology , Repressor Proteins/metabolism , Signal Transduction/drug effects , Cell Line , Cell Proliferation/drug effects , Early Growth Response Protein 1/genetics , Female , Gene Expression Profiling , Humans , MicroRNAs/genetics , MicroRNAs/metabolism , Microarray Analysis , Proteome/analysis , Repressor Proteins/genetics , Signal Transduction/physiology , Two-Hybrid System Techniques
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