Genetically Engineered Mice: Tools To Understand Craniofacial Development

1995 ◽  
Vol 6 (3) ◽  
pp. 181-201 ◽  
Author(s):  
Michael A. Ignelzi ◽  
Yi-Hsin Liu ◽  
Robert E. Maxson ◽  
Malcolm L. Snead

In this review, we provide a survey of the experimental approaches used to generate genetically engineered mice. Two specific examples are presented that demonstrate the applicability of these approaches to craniofacial development. In the first, a promoter analysis of the Msx2 gene is presented which illustrates the cis regulatory interactions that define cell-specific gene expression. In the second, a mouse model of the human disease craniosynostosis, Boston type, has been created by misregulation of the Msx2 gene product. Finally, we present a formulary of spontaneously occurring and genetically engineered mice that exhibit defects in developmental processes affecting the craniofacial complex. The purpose of this review is to provide insight into the experimental approaches that are used to create genetically engineered mice and to impress upon the reader that genetically engineered mice are well-suited to address fundamental questions pertaining to the development, maintenance, and regeneration of tissues and organs.

2021 ◽  
Vol 11 ◽  
Author(s):  
Pin Zhao ◽  
Samiullah Malik ◽  
Shaojun Xing

Hepatocellular carcinoma (HCC), is the third leading cause of cancer-related deaths, which is largely caused by virus infection. About 80% of the virus-infected people develop a chronic infection that eventually leads to liver cirrhosis and hepatocellular carcinoma (HCC). With approximately 71 million HCV chronic infected patients worldwide, they still have a high risk of HCC in the near future. However, the mechanisms of carcinogenesis in chronic HCV infection have not been still fully understood, which involve a complex epigenetic regulation and cellular signaling pathways. Here, we summarize 18 specific gene targets and different signaling pathways involved in recent findings. With these epigenetic alterations requiring histone modifications and DNA hyper or hypo-methylation of these specific genes, the dysregulation of gene expression is also associated with different signaling pathways for the HCV life cycle and HCC. These findings provide a novel insight into a correlation between HCV infection and HCC tumorigenesis, as well as potentially preventable approaches. Hepatitis C virus (HCV) infection largely causes hepatocellular carcinoma (HCC) worldwide with 3 to 4 million newly infected cases diagnosed each year. It is urgent to explore its underlying molecular mechanisms for therapeutic treatment and biomarker discovery. However, the mechanisms of carcinogenesis in chronic HCV infection have not been still fully understood, which involve a complex epigenetic regulation and cellular signaling pathways. Here, we summarize 18 specific gene targets and different signaling pathways involved in recent findings. With these epigenetic alterations requiring histone modifications and DNA hyper or hypo-methylation of these specific genes, the dysregulation of gene expression is also associated with different signaling pathways for the HCV life cycle and HCC. These findings provide a novel insight into a correlation between HCV infection and HCC tumorigenesis, as well as potentially preventable approaches.


2021 ◽  
Author(s):  
Yu Xu ◽  
Jiaxing Chen ◽  
Aiping Lyu ◽  
William K Cheung ◽  
Lu Zhang

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.


2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Mehran Aflakparast ◽  
Geert Geeven ◽  
Mathisca C.M. de Gunst

Abstract Background Observed levels of gene expression strongly depend on both activity of DNA binding transcription factors (TFs) and chromatin state through different histone modifications (HMs). In order to recover the functional relationship between local chromatin state, TF binding and observed levels of gene expression, regression methods have proven to be useful tools. They have been successfully applied to predict mRNA levels from genome-wide experimental data and they provide insight into context-dependent gene regulatory mechanisms. However, heterogeneity arising from gene-set specific regulatory interactions is often overlooked. Results We show that regression models that predict gene expression by using experimentally derived ChIP-seq profiles of TFs can be significantly improved by mixture modelling. In order to find biologically relevant gene clusters, we employ a Bayesian allocation procedure which allows us to integrate additional biological information such as three-dimensional nuclear organization of chromosomes and gene function. The data integration procedure involves transforming the additional data into gene similarity values. We propose a generic similarity measure that is especially suitable for situations where the additional data are of both continuous and discrete type, and compare its performance with similar measures in the context of mixture modelling. Conclusions We applied the proposed method on a data from mouse embryonic stem cells (ESC). We find that including additional data results in mixture components that exhibit biologically meaningful gene clusters, and provides valuable insight into the heterogeneity of the regulatory interactions.


2019 ◽  
Author(s):  
Linjun Chen ◽  
Yi Wang ◽  
Lei Chen ◽  
Fangyuan wang ◽  
Fei Ji ◽  
...  

Abstract Background: Waardenburg syndrome is a common syndromic hereditary deafness disease caused by stria vascularis dysfunction. However, the genetic pathway affecting stria vascularis development is still not clear. In this paper, the transcript profile of stria vascularis of Waardenburg syndrome was studied using Mitf-M mutant pigs and mice models. GO analysis was performed to identify the differential gene expression caused by Mitf-M mutation. Results: There were over than one hundred genes mainly found in tyrosine metabolism, melanin formation and ion transportations showed significant changes in both models. In addition, there were some spiced specific gene changes in the stria vascularis in the mouse and porcine models. The expression of tight junction-associated genes, including Cadm1, Cldn11, Pcdh1, Pcdh19 and Cdh24 genes , were significantly higher in porcine models compared to mouse models. Vascular-related and ion channel-related genes in the stria vascularis were also shown significantly difference between the two species. The expression of Col2a1, Col3a1, Col11a1 and Col11a2 genes were higher and the expression of Col8a2, Cd34 and Ncam genes were lower in the porcine model compared to mouse model. In both models, Trpm1, Kcnj13, and Slc45a2 genes were both affected by the Mitf-M mutation. In the pig models, the expression of Kcnn1, Clcn2 and Trpm4 genes were higher than the mouse model; whereas the expression of Trpm7, Kcnq1 and Kcnj8 genes were higher in the mouse models than the pig models. However, there was no significant difference in the morphology of the stria vascularis between these two models. Conclusions: Our data suggests that there is a significant difference on the gene expression and function between these two models.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1581-1581
Author(s):  
Danielle C Croucher ◽  
Laura M Richards ◽  
Zhihua Li ◽  
Ellen nong Wei ◽  
Xian Fang Huang ◽  
...  

Abstract Introduction: Immune checkpoint receptor (ICR) blockade has emerged as an effective anti-tumour modality, but only in a subset of cancer patients. Moreover, in Multiple myeloma (MM), single-agent activity has not been observed, highlighting the need to better understand the mechanism of action of this class of drugs. We recently showed that combinatorial ICR blockade using αLAG3 and αPD-1 delays disease progression and improves survival in the transplantable Vκ*MYC model of MM (Croucher et al. ASH 2018). However, despite this being a controlled study with genetically-homogeneous tumours, anti-tumour immune responses were heterogeneous, with only a subset of mice demonstrating a delay in tumour progression (17/29 mice, response rate = 58.6%). Thus, using this model, we set out to define mechanisms underlying variability in response to ICR blockade. Methods: We established a cohort of mice by engrafting 5-week-old C57BL/6 mice with Vκ12598 cells via tail vein injection. Treatment with αLAG3/αPD-1 or Ig-control was initiated 1-week post-engraftment and bone marrow (BM) samples were collected 3 weeks after the start of treatment. Following FACS-enrichment of T cells and plasma cells (PCs), single cell suspensions were subjected to matched single-cell gene expression (5' scRNA-seq) and T cell receptor (TCR)/B cell receptor (BCR) profiling (10x Genomics). Results: Samples were selected for profiling based on response to treatment, with responders (n=4) defined by significantly lower disease burden compared to non-responders (n=3) and control-treated mice (n=5), as measured by serum M-protein and %PCs in BM/spleen at sacrifice. Unsupervised clustering of scRNA-seq data from PCs (n=3,318 cells) identified no gene expression or BCR repertoire differences between control and treated, or between responder and non-responder samples, supporting that variability in response was not related to malignant Vκ12598 cells themselves. Across all samples, a statistically significant difference was not detected between the total number of unique TCR sequences (clonotypes) comparing control-treated (351-2369), non-responders (1185-2327) and responders (1378-1698), with no overlapping TCR sequences between top clonotypes. Evaluation of TCR repertoire diversity revealed that αLAG3/αPD-1 treatment induces clonal T cell expansion in control versus treated mice, but this was not significantly different between responders and non-responders. Analysis of paired scRNA-seq data (n=21,520 cells) revealed that expanded T cells from αLAG3/αPD-1-treated mice occupy a different cell state in responder vs. non-responder mice. We speculate that underlying differences in the TCR repertoire may dictate the downstream phenotype of expanded, anti-tumour T cells in mice treated with combinatorial αLAG3/αPD-1. Tumour control following treatment was associated with clonal expansion of T cells expressing genes related to cytoxicity and activation (Ccl5, Ifng, Fasl, Gzmb), whereas tumour progression was associated with clonal expansion of proliferative T cells (Cdkn3, Birc5, Ccna2, Aurka, Mki67). Although T cell proliferation is typically a phenotype ascribed to effector T cells, recent studies have similarly observed this proliferative cell state in dysfunctional T cells within melanoma tumours. Moreover, emerging evidence supports suppression of T cell proliferation by CDK4/6 inhibitors as a means to augment anti-tumour activity of ICR-based therapy. Thus, studies exploring whether reversal of the observed proliferative T cell state can restore response to αLAG3/αPD-1 treatment in non-responding Vκ12598 mice are ongoing and will be reported. Conclusions: ICR inhibitors demonstrate significant activity in some cancers, however many patients fail to respond and a similarly promising level of efficacy has not been achieved in MM. Studies aimed at unraveling the mechanisms of response and resistance to ICR inhibitors are therefore needed to improve the utility of this class of drugs for all patients. Our approach of using paired single-cell gene expression and TCR repertoire profiling has enabled identification of molecular cell states specifically in expanded T cells of responder vs. non-responder mice. In turn, our work nominates novel mechanisms that may be used as potential biomarkers for anti-tumour immune responses as well as potential targets to augment responses to ICR blockade therapy. Disclosures Chesi: Abcuro: Patents & Royalties: Genetically engineered mouse model of myeloma; Novartis: Consultancy, Patents & Royalties: human CRBN transgenic mouse; Pfizer: Consultancy; Pi Therapeutics: Patents & Royalties: Genetically engineered mouse model of myeloma; Palleon Pharmaceuticals: Patents & Royalties: Genetically engineered mouse model of myeloma. Bergsagel: GSK: Consultancy, Honoraria; Genetech: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Sebag: Janssen: Research Funding; Bristol Myers-Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Karyopharm Therapeutics: Consultancy, Honoraria. Trudel: BMS/Celgene: Consultancy, Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Roche: Consultancy; Sanofi: Honoraria; Pfizer: Honoraria, Research Funding; Genentech: Research Funding.


Cell Reports ◽  
2013 ◽  
Vol 4 (2) ◽  
pp. 385-401 ◽  
Author(s):  
Isaac M. Chiu ◽  
Emiko T.A. Morimoto ◽  
Hani Goodarzi ◽  
Jennifer T. Liao ◽  
Sean O’Keeffe ◽  
...  

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