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1.
Trends Biochem Sci ; 43(1): 1-4, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-29239765

RESUMO

Diverse gene regulatory mechanisms impact on immune homeostasis, and a new model now emerges as fundamental in light of recent genome-wide studies. In this picture, transcriptional networks drive functional changes during immune activation, whereas autoregulatory feedback loops of post-transcriptional programs ensure the original cell lineage identity and subsequent immune resolution.


Assuntos
Redes Reguladoras de Genes/imunologia , Imunidade/genética , Transcrição Gênica/imunologia , Animais , Redes Reguladoras de Genes/genética , Humanos , Transcrição Gênica/genética
2.
Elife ; 122024 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-38775664

RESUMO

Cardiac macrophages are heterogenous in phenotype and functions, which has been associated with differences in their ontogeny. Despite extensive research, our understanding of the precise role of different subsets of macrophages in ischemia/reperfusion (I/R) injury remains incomplete. We here investigated macrophage lineages and ablated tissue macrophages in homeostasis and after I/R injury in a CSF1R-dependent manner. Genomic deletion of a fms-intronic regulatory element (FIRE) in the Csf1r locus resulted in specific absence of resident homeostatic and antigen-presenting macrophages, without affecting the recruitment of monocyte-derived macrophages to the infarcted heart. Specific absence of homeostatic, monocyte-independent macrophages altered the immune cell crosstalk in response to injury and induced proinflammatory neutrophil polarization, resulting in impaired cardiac remodeling without influencing infarct size. In contrast, continuous CSF1R inhibition led to depletion of both resident and recruited macrophage populations. This augmented adverse remodeling after I/R and led to an increased infarct size and deterioration of cardiac function. In summary, resident macrophages orchestrate inflammatory responses improving cardiac remodeling, while recruited macrophages determine infarct size after I/R injury. These findings attribute distinct beneficial effects to different macrophage populations in the context of myocardial infarction.


Assuntos
Macrófagos , Receptores de Fator Estimulador das Colônias de Granulócitos e Macrófagos , Animais , Macrófagos/imunologia , Camundongos , Receptores de Fator Estimulador das Colônias de Granulócitos e Macrófagos/metabolismo , Receptores de Fator Estimulador das Colônias de Granulócitos e Macrófagos/genética , Isquemia Miocárdica/imunologia , Infarto do Miocárdio/patologia , Infarto do Miocárdio/fisiopatologia , Infarto do Miocárdio/imunologia , Masculino , Traumatismo por Reperfusão Miocárdica/imunologia , Traumatismo por Reperfusão Miocárdica/patologia , Camundongos Endogâmicos C57BL , Miocárdio/patologia , Miocárdio/imunologia , Modelos Animais de Doenças
3.
J Exp Med ; 216(7): 1700-1723, 2019 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-31126966

RESUMO

The RNase Regnase-1 is a master RNA regulator in macrophages and T cells that degrades cellular and viral RNA upon NF-κB signaling. The roles of its family members, however, remain largely unknown. Here, we analyzed Regnase-3-deficient mice, which develop hypertrophic lymph nodes. We used various mice with immune cell-specific deletions of Regnase-3 to demonstrate that Regnase-3 acts specifically within myeloid cells. Regnase-3 deficiency systemically increased IFN signaling, which increased the proportion of immature B and innate immune cells, and suppressed follicle and germinal center formation. Expression analysis revealed that Regnase-3 and Regnase-1 share protein degradation pathways. Unlike Regnase-1, Regnase-3 expression is high specifically in macrophages and is transcriptionally controlled by IFN signaling. Although direct targets in macrophages remain unknown, Regnase-3 can bind, degrade, and regulate mRNAs, such as Zc3h12a (Regnase-1), in vitro. These data indicate that Regnase-3, like Regnase-1, is an RNase essential for immune homeostasis but has diverged as key regulator in the IFN pathway in macrophages.


Assuntos
Homeostase/imunologia , Imunidade Inata , Interferons/metabolismo , Células Mieloides/metabolismo , Ribonucleases/metabolismo , Regiões 3' não Traduzidas , Animais , Autoimunidade , Linfócitos B/metabolismo , Citometria de Fluxo , Regulação da Expressão Gênica , Macrófagos/metabolismo , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Knockout , Células Mieloides/enzimologia , Reação em Cadeia da Polimerase em Tempo Real , Ribonucleases/genética , Transdução de Sinais , Linfócitos T/metabolismo
4.
J Vis Exp ; (133)2018 03 07.
Artigo em Inglês | MEDLINE | ID: mdl-29578509

RESUMO

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action. Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes. The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.


Assuntos
Sequenciamento de Nucleotídeos em Larga Escala/métodos , Fatores de Transcrição/genética
5.
Cell Rep ; 19(3): 643-654, 2017 04 18.
Artigo em Inglês | MEDLINE | ID: mdl-28423325

RESUMO

Activation of immune cells results in rapid functional changes, but how such fast changes are accomplished remains enigmatic. By combining time courses of 4sU-seq, RNA-seq, ribosome profiling (RP), and RNA polymerase II (RNA Pol II) ChIP-seq during T cell activation, we illustrate genome-wide temporal dynamics for ∼10,000 genes. This approach reveals not only immediate-early and posttranscriptionally regulated genes but also coupled changes in transcription and translation for >90% of genes. Recruitment, rather than release of paused RNA Pol II, primarily mediates transcriptional changes. This coincides with a genome-wide temporary slowdown in cotranscriptional splicing, even for polyadenylated mRNAs that are localized at the chromatin. Subsequent splicing optimization correlates with increasing Ser-2 phosphorylation of the RNA Pol II carboxy-terminal domain (CTD) and activation of the positive transcription elongation factor (pTEFb). Thus, rapid de novo recruitment of RNA Pol II dictates the course of events during T cell activation, particularly transcription, splicing, and consequently translation.


Assuntos
Genoma , Biossíntese de Proteínas , RNA Polimerase II/metabolismo , Splicing de RNA/genética , Linfócitos T/metabolismo , Transcrição Gênica , Animais , Sistemas Computacionais , Camundongos Transgênicos , Fosforilação , Domínios Proteicos , RNA Polimerase II/química
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