Abdulshakour Mohammadnia

Predicting involvement of polycomb repressive complex 2 in direct conversion of mouse fibroblasts into induced neural stem cells

IntroductionMouse fibroblasts could be directly converted into induced neural stem cells (iNSCs), by introducing a set of known transcription factors (TFs). This process, known as direct reprogramming, is an alternative source of NSCs production for cell therapy applications, hence, more common sources for such cells including embryonic stem cell (ESCs) and induced pluripotent stem cell (iPSCs) are also in use. Despite their importance, the exact role of different TFs involved in the conversion of fibroblasts into iNSCs and the interactions between these factors has not been studied.MethodsHere, we have used available microarray data to construct a gene regulatory network to understand the dynamic of regulatory interactions during this conversion. We have implemented other types of data such as information regarding TFs binding sites and valid protein-protein interactions to improve the network reliability. The network contained 1857 differentially expressed (DE) genes, linked by11054 interactions. The most important TFs identified based on topology analysis of the network. Furthermore, in selecting such TFs, we have also considered their role in the regulation of nervous system development.ResultsBased on these analyses, we found that Ezh2, Jarid2, Mtf2, Nanog, Pou5f1, Sall4, Smarca4, Sox2, Suz12, and Tcf3 are the main regulators of direct conversion of mouse fibroblasts into iNSCs. Because, members of the polycomb repressive complex 2 (PRC2) were present in the most effective TFs’ list, we have concluded that this complex is one of the major factors in this conversion. Additionally, gene expression profiling of iNSCs, obtained from a different data sets, showed that Sox2 and Ezh2 are two main regulators of the direct reprogramming process.ConclusionsOur results provide an insight into molecular events that occur during direct reprogramming of fibroblasts into iNSCs. This information could be useful in simplifying the production of iNSCs, by reducing the number of required factors, for use in regenerative medicine.

Involvement of Polycomb Repressive Complex 2 in Maturation of Induced Pluripotent Stem Cells during Reprogramming of Mouse and Human Fibroblasts.

Induced pluripotent stem cells (iPSCs) provide a reliable source for the study of regenerative medicine, drug discovery, and developmental biology. Despite extensive studies on the reprogramming of mouse and human fibroblasts into iPSCs, the efficiency of reprogramming is still low. Here, we used a bioinformatics and systems biology approach to study the two gene regulatory waves governing the reprogramming of mouse and human fibroblasts into iPSCs. Our results revealed that the maturation phase of reprogramming was regulated by a more complex regulatory network of transcription factors compared to the initiation phase. Interestingly, in addition to pluripotency factors, the polycomb repressive complex 2 (PRC2) members Ezh2, Eed, Jarid2, Mtf2, and Suz12 are crucially recruited during the maturation phase of reprogramming. Moreover, we found that during the maturation phase of reprogramming, pluripotency factors, via the expression and induction of PRC2 complex members, could silence the lineage-specific gene expression program and maintain a ground state of pluripotency in human and mouse naïve iPSCs. The findings obtained here provide us a better understanding of the gene regulatory network (GRN) that governs reprogramming, and the maintenance of the naïve state of iPSCs.

Meta-Analysis of Transcriptome Regulation During Induction to Cardiac Myocyte Fate from Mouse and Human Fibroblasts.

Ectopic expression of a defined set of transcription factors (TFs) can directly convert fibroblasts into a cardiac myocyte cell fate. Beside inefficiency in generating induced cardiomyocytes (iCMs), the molecular mechanisms that regulate this process remained to be well defined. The main purpose of this study was to provide better insight on the transcriptome regulation and to introduce a new strategy for candidating TFs for the transdifferentiation process. Eight mouse and three human high quality microarray data sets were analyzed to find differentially expressed genes (DEGs), which we integrated with TF‐binding sites and protein–protein interactions to construct gene regulatory and protein–protein interaction networks. Topological and biological analyses of constructed gene networks revealed the main regulators and most affected biological processes. The DEGs could be categorized into two distinct groups, first, up‐regulated genes that are mainly involved in cardiac‐specific processes and second, down‐regulated genes that are mainly involved in fibroblast‐specific functions. Gata4, Mef2a, Tbx5, Tead4 TFs were identified as main regulators of cardiac‐specific gene expression program; and Trp53, E2f1, Myc, Sfpi1, Lmo2, and Meis1 were identified as TFs which mainly regulate the expression of fibroblast‐specific genes. Furthermore, we compared gene expression profiles and identified TFs between mouse and human to find the similarities and differences. In summary, our strategy of meta‐analyzing the data of high‐throughput techniques by computational approaches, besides revealing the mechanisms involved in the regulation of the gene expression program, also suggests a new approach for increasing the efficiency of the direct reprogramming of fibroblasts into iCMs. J. Cell. Physiol. 232: 2053–2062, 2017.

Signaling and Gene Regulatory Networks Governing Definitive Endoderm Derivation from Pluripotent Stem Cells

The generation of definitive endoderm (DE) from pluripotent stem cells (PSCs) is a fundamental stage in the formation of highly organized visceral organs, such as the liver and pancreas. Currently, there is a need for a comprehensive study that illustrates the involvement of different signaling pathways and their interactions in the derivation of DE cells from PSCs. This study aimed to identify signaling pathways that have the greatest influence on DE formation using analyses of transcriptional profiles, protein–protein interactions, protein–DNA interactions, and protein localization data. Using this approach, signaling networks involved in DE formation were constructed using systems biology and data mining tools, and the validity of the predicted networks was confirmed experimentally by measuring the mRNA levels of hub genes in several PSCs‐derived DE cell lines. Based on our analyses, seven signaling pathways, including the BMP, ERK1‐ERK2, FGF, TGF‐beta, MAPK, Wnt, and PIP signaling pathways and their interactions, were found to play a role in the derivation of DE cells from PSCs. Lastly, the core gene regulatory network governing this differentiation process was constructed. The results of this study could improve our understanding surrounding the efficient generation of DE cells for the regeneration of visceral organs. J. Cell. Physiol. 231: 1994–2006, 2016.

Role of Hepatic-Specific Transcription Factors and Polycomb Repressive Complex 2 during Induction of Fibroblasts to Hepatic Fate.

Direct reprogramming using defined sets of transcription factors (TFs) is a recent strategy for generating induced hepatocytes (iHeps) from fibroblasts for use in regenerative medicine and drug development. Comprehensive studies detailing the regulatory role of TFs during this reprogramming process could help increase its efficiency. This study aimed to find the TFs with the greatest influences on the generation of iHeps from fibroblasts, and to further understand their roles in the regulation of the gene expression program. Here, we used systems biology approaches to analyze high quality expression data sets in combination with TF-binding sites data and protein-protein interactions data during the direct reprogramming of fibroblasts to iHeps. Our results revealed two main patterns for differentially expressed genes (DEGs): up-regulated genes were categorized as hepatic-specific pattern, and down-regulated genes were categorized as mesoderm- and fibroblast-specific pattern. Interestingly, hepatic-specific genes co-expressed and were regulated by hepatic-specific TFs, specifically Hnf4a and Foxa2. Conversely, the mesoderm- and fibroblast-specific pattern was mainly silenced by polycomb repressive complex 2 (PRC2) members, including Suz12, Mtf2, Ezh2, and Jarid2. Independent analysis of both the gene and core regulatory network of DE-TFs showed significant roles for Hnf4a, Foxa2, and PRC2 members in the regulation of the gene expression program and in biological processes during the direct conversion process. Altogether, using systems biology approaches, we clarified the role of Hnf4a and Foxa2 as hepatic-specific TFs, and for the first time, introduced the PRC2 complex as the main regulator that favors the direct reprogramming process in cooperation with hepatic-specific factors.

Regulating the transcriptomes that mediate the conversion of fibroblasts to various nervous system neural cell types

Our understanding of the mechanism of cell fate transition during the direct reprogramming of fibroblasts into various central nervous system (CNS) neural cell types has been limited by the lack of a comprehensive analysis on generated cells, independently and in comparison with other CNS neural cell types. Here, we applied an integrative approach on 18 independent high throughput expression data sets to gain insight into the regulation of the transcriptome during the conversion of fibroblasts into induced neural stem cells, induced neurons (iNs), induced astrocytes, and induced oligodendrocyte progenitor cells (iOPCs). We found common down‐regulated genes to be mostly related to fibroblast‐specific functions, and suggest their potential as markers for screening of the silencing of the fibroblast‐specific program. For example, Tagln was significantly down‐regulated across all considered data sets. In addition, we identified specific profiles of up‐regulated genes for each CNS neural cell types, which could be potential markers for maturation and efficiency screenings. Furthermore, we identified the main TFs involved in the regulation of the gene expression program during direct reprogramming. For example, in the generation of iNs from fibroblasts, the Rest TF was the main regulator of this reprogramming. In summary, our computational approach for meta‐analyzing independent expression data sets provides significant details regarding the molecular mechanisms underlying the regulation of the gene expression program, and also suggests potentially useful candidate genes for screening down‐regulation of fibroblast gene expression profile, maturation, and efficiency, as well as candidate TFs for increasing the efficiency of the reprogramming process.

Common gene expression patterns in the generation of induced neurons from fibroblasts

In the current study, we analyzed ten gene expression data sets including RNA-sequencing and microarray experiment data during the direct reprogramming of mouse and human fibroblasts to induced neurons and found common gene expression pattern across all data sets for this conversion.

Concerns on “Dissection of Regulatory Elements During Direct Conversion of Somatic Cells Into Neurons” Paper: CONCERNS ON DISSECTION OF REGULATORY ELEMENTS

Direct Conversion of Somatic Cells Into Neurons” Paper Moein Yaqubi,* Abdulshakour Mohammadnia ,** and Ping Wee Department of Psychiatry, Sackler Program for Epigenetics and Psychobiology at McGill University, Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada Department of Medical Genetics and Signal Transduction Research Group, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada

Comprehensive transcriptome mining of the direct conversion of mesodermal cells

The direct reprogramming of somatic cells is a promising approach for regenerative medicine, especially in the production of mesoderm layer-derived cells. Meta-analysis studies provide precise insight into the undergoing processes and help increase the efficiency of reprogramming. Here, using 27 high-throughput expression data sets, we analyzed the direct reprogramming of mesodermal cells in humans and mice. Fibroblast-derived cells showed a common expression pattern of up- and down-regulated genes that were mainly involved in the suppression of the fibroblast-specific gene expression program, and may be used as markers of the initiation of reprogramming. Furthermore, we found a specific gene expression profile for each fibroblast-derived cell studied, and each gene set appeared to play specific functional roles in its cell type, suggesting their use as markers for their mature state. Furthermore, using data from protein-DNA interactions, we identified the main transcription factors (TFs) involved in the conversion process and ranked them based on their importance in their gene regulatory networks. In summary, our meta-analysis approach provides new insights on the direct conversion of mesodermal somatic cells, introduces a list of genes as markers for initiation and maturation, and identifies TFs for which manipulating their expression may increase the efficiency of direct conversion.

Comprehensive Dissection of Transcriptome Data and Regulatory Factors in Pancreatic Cancer Cells

Features of pancreatic cancers include high mortality rates caused by rapid tumor progression and a lack of effective therapy. Underpinning the molecular mechanisms involved in the alteration of the gene expression program in the pancreatic cancer remains to be understood. In the current study, we performed a comprehensive analysis using 282 pancreatic tumor and normal samples from seven independent expression data sets to provide a better view on the interactions between different transcription factors (TFs) and the most affected biological pathways in pancreatic cancer. We highlighted common differentially expressed genes (DEGs) and common affected processes within pancreatic cancer samples. We revealed 16 main DE‐TFs that regulated gene expression alterations as well as the most significant processes in pancreatic cancer compared to normal cells. For example, we found the upregulated FOXM1 to be a top regulator of pancreatic cellular transformation based on results from different analyses, including from its regulation of gene regulatory networks, its presence in protein complex, its significant regulation of genes related to cancer pathways, and its regulation of most of the identified DE‐TFs. Furthermore, we provided a model and assessed the role of different DE‐TFs in the regulation of the most affected pancreatic‐ and cancer‐specific processes. In conclusion, our bioinformatics meta‐analysis of high throughput expression data sets, besides clarifying common affected genes and pathways, also showed the mechanisms involved in regulating these common profiles. Our results, especially for DE‐TFs, could potentially be useful for screening for pancreatic cancer, and for confirming or determining novel pharmacological targets. J. Cell. Biochem. 118: 3976–3985, 2017.