scholarly journals Inborn errors of metabolism: Lessons from iPSC models

Author(s):  
Rubén Escribá ◽  
Raquel Ferrer-Lorente ◽  
Ángel Raya

AbstractThe possibility of reprogramming human somatic cells to pluripotency has opened unprecedented opportunities for creating genuinely human experimental models of disease. Inborn errors of metabolism (IEMs) constitute a greatly heterogeneous class of diseases that appear, in principle, especially suited to be modeled by iPSC-based technology. Indeed, dozens of IEMs have already been modeled to some extent using patient-specific iPSCs. Here, we review the advantages and disadvantages of iPSC-based disease modeling in the context of IEMs, as well as particular challenges associated to this approach, together with solutions researchers have proposed to tackle them. We have structured this review around six lessons that we have learnt from those previous modeling efforts, and that we believe should be carefully considered by researchers wishing to embark in future iPSC-based models of IEMs.

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 483
Author(s):  
Kathrin Haake ◽  
Anna-Lena Neehus ◽  
Theresa Buchegger ◽  
Mark Philipp Kühnel ◽  
Patrick Blank ◽  
...  

Interferon γ (IFN-γ) was shown to be a macrophage activating factor already in 1984. Consistently, inborn errors of IFN-γ immunity underlie Mendelian Susceptibility to Mycobacterial Disease (MSMD). MSMD is characterized by genetic predisposition to disease caused by weakly virulent mycobacterial species. Paradoxically, macrophages from patients with MSMD were little tested. Here, we report a disease modeling platform for studying IFN-γ related pathologies using macrophages derived from patient specific induced pluripotent stem cells (iPSCs). We used iPSCs from patients with autosomal recessive complete- and partial IFN-γR2 deficiency, partial IFN-γR1 deficiency and complete STAT1 deficiency. Macrophages from all patient iPSCs showed normal morphology and IFN-γ-independent functionality like phagocytic uptake of bioparticles and internalization of cytokines. For the IFN-γ-dependent functionalities, we observed that the deficiencies played out at various stages of the IFN-γ pathway, with the complete IFN-γR2 and complete STAT1 deficient cells showing the most severe phenotypes, in terms of upregulation of surface markers and induction of downstream targets. Although iPSC-derived macrophages with partial IFN-γR1 and IFN-γR2 deficiency still showed residual induction of downstream targets, they did not reduce the mycobacterial growth when challenged with Bacillus Calmette–Guérin. Taken together, we report a disease modeling platform to study the role of macrophages in patients with inborn errors of IFN-γ immunity.


2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Heidrun Steinle ◽  
Marbod Weber ◽  
Andreas Behring ◽  
Ulrike Mau-Holzmann ◽  
Christian Schlensak ◽  
...  

The reprogramming of somatic cells into induced pluripotent stem cells (iPSCs) is gaining in importance in the fields of regenerative medicine, tissue engineering, and disease modeling. Patient-specific iPSCs have as an unlimited cell source a tremendous potential for generating various types of autologous cells. For the future clinical applicability of these iPSC-derived cells, the generation of iPSCs via nongenome integrating methods and the efficient reprogramming of patients’ somatic cells are required. In this study, 2 different RNA-based footprint-free methods for the generation of iPSCs were compared: the use of synthetic modified messenger RNAs (mRNAs) or self-replicating RNAs (srRNAs) encoding the reprogramming factors and GFP. Using both RNA-based methods, integration-free iPSCs without genomic alterations were obtained. The pluripotency characteristics identified by specific marker detection and the in vitro and in vivo trilineage differentiation capacity were comparable. Moreover, the incorporation of a GFP encoding sequence into the srRNA enabled a direct and convenient monitoring of the reprogramming procedure and the successful detection of srRNA translation in the transfected cells. Nevertheless, the use of a single srRNA to induce pluripotency was less time consuming, faster, and more efficient than the daily transfection of cells with synthetic mRNAs. Therefore, we believe that the srRNA-based approach might be more appropriate and efficient for the reprogramming of different types of somatic cells for clinical applications.


2020 ◽  
Author(s):  
Immacolata Belviso ◽  
Veronica Romano ◽  
Daria Nurzynska ◽  
Clotilde Castaldo ◽  
Franca Di Meglio

Induced Pluripotent Stem cells (iPSC) are adult somatic cells genetically reprogrammed to an embryonic stem cell-like state. Due to their autologous origin from adult somatic cells, iPSCs are considered a tremendously valuable tool for regenerative medicine, disease modeling, drug discovery and testing. iPSCs were first obtained by introducing specific transcription factors through retroviral transfection. However, cell reprogramming obtained by integrating methods prevent clinical application of iPSC because of potential risk for infection, teratomas and genomic instability. Therefore, several integration-free alternate methods have been developed and tested thus far to overcome safety issues. The present chapter provides an overview and a critical analysis of advantages and disadvantages of non-integrating methods used to generate iPSCs.


2016 ◽  
Vol 5 (01) ◽  
pp. 4723 ◽  
Author(s):  
Bhusnure O. G.* ◽  
Gholve V. S. ◽  
Sugave B. K. ◽  
Dongre R. C. ◽  
Gore S. A. ◽  
...  

Many researchers have attempted to use computer-aided design (C.A.D) and computer-aided manufacturing (CAM) to realize a scaffold that provides a three-dimensional (3D) environment for regeneration of tissues and organs. As a result, several 3D printing technologies, including stereolithography, deposition modeling, inkjet-based printing and selective laser sintering have been developed. Because these 3D printing technologies use computers for design and fabrication, and they can fabricate 3D scaffolds as designed; as a consequence, they can be standardized. Growth of target tissues and organs requires the presence of appropriate growth factors, so fabrication of 3Dscaffold systems that release these biomolecules has been explored. A drug delivery system (D.D.S) that administrates a pharmaceutical compound to achieve a therapeutic effect in cells, animals and humans is a key technology that delivers biomolecules without side effects caused by excessive doses. 3D printing technologies and D. D. Ss have been assembled successfully, so new possibilities for improved tissue regeneration have been suggested. If the interaction between cells and scaffold system with biomolecules can be understood and controlled, and if an optimal 3D tissue regenerating environment is realized, 3D printing technologies will become an important aspect of tissue engineering research in the near future. 3D Printing promises to produce complex biomedical devices according to computer design using patient-specific anatomical data. Since its initial use as pre-surgical visualization models and tooling molds, 3D Printing has slowly evolved to create one-of-a-kind devices, implants, scaffolds for tissue engineering, diagnostic platforms, and drug delivery systems. Fuelled by the recent explosion in public interest and access to affordable printers, there is renewed interest to combine stem cells with custom 3D scaffolds for personalized regenerative medicine. Before 3D Printing can be used routinely for the regeneration of complex tissues (e.g. bone, cartilage, muscles, vessels, nerves in the craniomaxillofacial complex), and complex organs with intricate 3D microarchitecture (e.g. liver, lymphoid organs), several technological limitations must be addressed. Until recently, tablet designs had been restricted to the relatively small number of shapes that are easily achievable using traditional manufacturing methods. As 3D printing capabilities develop further, safety and regulatory concerns are addressed and the cost of the technology falls, contract manufacturers and pharmaceutical companies that experiment with these 3D printing innovations are likely to gain a competitive edge. This review compose the basics, types & techniques used, advantages and disadvantages of 3D printing


Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Michiel Bongaerts ◽  
Ramon Bonte ◽  
Serwet Demirdas ◽  
Edwin H. Jacobs ◽  
Esmee Oussoren ◽  
...  

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.


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