Acquisition of functional neurons by direct conversion: Switching the developmental clock directly

Chemical transdifferentiation of somatic cells to neural cells: a systematic review

INTRODUCTION

Transdifferentiation is the conversion of a specific somatic cell into another cell type, bypassing a transient pluripotent state. This implies a faster method to generate cells of interest with the additional benefit of reduced tumorigenic risk for clinical use.

OBJECTIVE

We describe protocols that use small molecules as direct conversion inducers, without the need for exogenous factors, to evaluate the potential of cell transdifferentiation for pharmacological and clinical applications.

METHODS

In this systematic review, using PRISMA guidelines, we conducted a personalized search strategy in four databases (PubMed, Scopus, Embase, and Web Of Science), looking for experimental works that used exclusively small molecules for transdifferentiation of non-neural cell types into neural lineage cells.

RESULTS

We explored the main biological mechanisms involved in direct cell conversion induced by different small molecules used in 33 experimental in vitro and in vitro transdifferentiation protocols. We also summarize the main characteristics of these protocols, such as the chemical cocktails used, time for transdifferentiation, and conversion efficiency.

CONCLUSION

Small molecules-based protocols for neuronal transdifferentiation are reasonably safe, economical, accessible, and are a promising alternative for future use in regenerative medicine and pharmacology.

Reprogramming Glioblastoma Cells into Non-Cancerous Neuronal Cells as a Novel Anti-Cancer Strategy

Glioblastoma Multiforme (GBM) is an aggressive brain tumor with a high mortality rate. Direct reprogramming of glial cells to different cell lineages, such as induced neural stem cells (iNSCs) and induced neurons (iNeurons), provides genetic tools to manipulate a cell's fate as a potential therapy for neurological diseases. NeuroD1 (ND1) is a master transcriptional factor for neurogenesis and it promotes neuronal differentiation. In the present study, we tested the hypothesis that the expression of ND1 in GBM cells can force them to differentiate toward post-mitotic neurons and halt GBM tumor progression. In cultured human GBM cell lines, including LN229, U87, and U373 as temozolomide (TMZ)-sensitive and T98G as TMZ-resistant cells, the neuronal lineage conversion was induced by an adeno-associated virus (AAV) package carrying ND1. Twenty-one days after AAV-ND1 transduction, ND1-expressing cells displayed neuronal markers MAP2, TUJ1, and NeuN. The ND1-induced transdifferentiation was regulated by Wnt signaling and markedly enhanced under a hypoxic condition (2% O2 vs. 21% O2). ND1-expressing GBM cultures had fewer BrdU-positive proliferating cells compared to vector control cultures. Increased cell death was visualized by TUNEL staining, and reduced migrative activity was demonstrated in the wound-healing test after ND1 reprogramming in both TMZ-sensitive and -resistant GBM cells. In a striking contrast to cancer cells, converted cells expressed the anti-tumor gene p53. In an orthotopical GBM mouse model, AAV-ND1-reprogrammed U373 cells were transplanted into the fornix of the cyclosporine-immunocompromised C57BL/6 mouse brain. Compared to control GBM cell-formed tumors, cells from ND1-reprogrammed cultures formed smaller tumors and expressed neuronal markers such as TUJ1 in the brain. Thus, reprogramming using a single-factor ND1 overcame drug resistance, converting malignant cells of heterogeneous GBM cells to normal neuron-like cells in vitro and in vivo. These novel observations warrant further research using patient-derived GBM cells and patient-derived xenograft (PDX) models as a potentially effective treatment for a deadly brain cancer and likely other astrocytoma tumors.

Moving CNS axon growth and regeneration research into human model systems

Axon regeneration is limited in the adult mammalian central nervous system (CNS) due to both intrinsic and extrinsic factors. Rodent studies have shown that developmental age can drive differences in intrinsic axon growth ability, such that embryonic rodent CNS neurons extend long axons while postnatal and adult CNS neurons do not. In recent decades, scientists have identified several intrinsic developmental regulators in rodents that modulate growth. However, whether this developmentally programmed decline in CNS axon growth is conserved in humans is not yet known. Until recently, there have been limited human neuronal model systems, and even fewer age-specific human models. Human in vitro models range from pluripotent stem cell-derived neurons to directly reprogrammed (transdifferentiated) neurons derived from human somatic cells. In this review, we discuss the advantages and disadvantages of each system, and how studying axon growth in human neurons can provide species-specific knowledge in the field of CNS axon regeneration with the goal of bridging basic science studies to clinical trials. Additionally, with the increased availability and quality of 'omics datasets of human cortical tissue across development and lifespan, scientists can mine these datasets for developmentally regulated pathways and genes. As there has been little research performed in human neurons to study modulators of axon growth, here we provide a summary of approaches to begin to shift the field of CNS axon growth and regeneration into human model systems to uncover novel drivers of axon growth.

Genetic and Epigenetic Interplay Define Disease Onset and Severity in Repeat Diseases

Repeat diseases, such as fragile X syndrome, myotonic dystrophy, Friedreich ataxia, Huntington disease, spinocerebellar ataxias, and some forms of amyotrophic lateral sclerosis, are caused by repetitive DNA sequences that are expanded in affected individuals. The age at which an individual begins to experience symptoms, and the severity of disease, are partially determined by the size of the repeat. However, the epigenetic state of the area in and around the repeat also plays an important role in determining the age of disease onset and the rate of disease progression. Many repeat diseases share a common epigenetic pattern of increased methylation at CpG islands near the repeat region. CpG islands are CG-rich sequences that are tightly regulated by methylation and are often found at gene enhancer or insulator elements in the genome. Methylation of CpG islands can inhibit binding of the transcriptional regulator CTCF, resulting in a closed chromatin state and gene down regulation. The downregulation of these genes leads to some disease-specific symptoms. Additionally, a genetic and epigenetic interplay is suggested by an effect of methylation on repeat instability, a hallmark of large repeat expansions that leads to increasing disease severity in successive generations. In this review, we will discuss the common epigenetic patterns shared across repeat diseases, how the genetics and epigenetics interact, and how this could be involved in disease manifestation. We also discuss the currently available stem cell and mouse models, which frequently do not recapitulate epigenetic patterns observed in human disease, and propose alternative strategies to study the role of epigenetics in repeat diseases.

LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion

Recent studies uncover that subcellular location of long non-coding RNAs (lncRNAs) can provide significant information on its function. Due to the lack of experimental data, the number of lncRNAs is very limited, experimentally verified subcellular localization, and the numbers of lncRNAs located in different organelle are wildly imbalanced. The prediction of subcellular location of lncRNAs is actually a multi-classification small sample imbalance problem. The imbalance of data results in the poor recognition effect of machine learning models on small data subsets, which is a puzzling and challenging problem in the existing research. In this study, we integrate multi-source features to construct a sequence-based computational tool, lncLocation, to predict the subcellular location of lncRNAs. Autoencoder is used to enhance part of the features, and the binomial distribution-based filtering method and recursive feature elimination (RFE) are used to filter some of the features. It improves the representation ability of data and reduces the problem of unbalanced multi-classification data. By comprehensive experiments on different feature combinations and machine learning models, we select the optimal features and classifier model scheme to construct a subcellular location prediction tool, lncLocation. LncLocation can obtain an 87.78% accuracy using 5-fold cross validation on the benchmark data, which is higher than the state-of-the-art tools, and the classification performance, especially for small class sets, is improved significantly.