Mapping pediatric brain tumors to their origins in the developing cerebellum

Understanding the cellular origins of childhood brain tumors is key for discovering novel tumor-specific therapeutic targets. Previous strategies mapping cellular origins typically involved comparing human tumors to murine embryonal tissues, a potentially imperfect approach due to spatio-temporal gene expression differences between species. Here we use an unprecedented single-nucleus atlas of the developing human cerebellum (Sepp, Leiss, et al) and extensive bulk and single-cell transcriptome tumor data to map their cellular origins with focus on three most common pediatric brain tumors - pilocytic astrocytoma, ependymoma, and medulloblastoma. Using custom bioinformatics approaches, we postulate the astroglial and glial lineages as the origins for posterior fossa ependymomas and radiation-induced gliomas (secondary tumors after medulloblastoma treatment), respectively. Moreover, we confirm that SHH, Group3 and Group4 medulloblastomas stem from granule cell/unipolar brush cell lineages, whereas we propose pilocytic astrocytoma to originate from the oligodendrocyte lineage. We also identify genes shared between the cerebellar lineage of origin and corresponding tumors, and genes that are tumor specific; both gene sets represent promising therapeutic targets. As a common feature among most cerebellar tumors, we observed compositional heterogeneity in terms of similarity to normal cells, suggesting that tumors arise from or differentiate into multiple points along the cerebellar "lineage of origin".

Fission of megamitochondria into multiple smaller well-defined mitochondria in the ageing zebrafish retina

Mitochondria are essential adenosine triphosphate (ATP)-generating cellular organelles. In the retina, they are highly numerous in the photoreceptors and retinal pigment epithelium (RPE) due to their high energetic requirements. Fission and fusion of the mitochondria within these cells allow them to adapt to changing demands over the lifespan of the organism. Using transmission electron microscopy, we examined the mitochondrial ultrastructure of zebrafish photoreceptors and RPE from 5 days post fertilisation (dpf) through to late adulthood (3 years). Notably, mitochondria in the youngest animals were large and irregular shaped with a loose cristae architecture, but by 8 dpf they had reduced in size and expanded in number with more defined cristae. When investigating temporal gene expression of several mitochondrial-related markers, they indicated fission as the dominant mechanism contributing to these changes observed over time. This is likely to be due to continued mitochondrial stress resulting from the oxidative environment of the retina and prolonged light exposure. We have characterised retinal mitochondrial ageing in a key vertebrate model organism, that provides a basis for future studies of retinal diseases that are linked to mitochondrial dysfunction.

Transcriptomic Analyses of Differential Host Responses to Red Rot Pathogen Colletotrichum Falcatum in Sugarcane Through Subtractive Library and NGS Approach

The fungal pathogen Colletotrichum falcatum causes the stalks, the economically important for sugar extraction. Although, disease management is achieved by cultivating resistant cultivars, the complex polyploidy of sugarcane genome complicates understanding the inheritance of disease resistance. Earlier attempts of using resistant and susceptible varieties to understand host-pathogen interaction resulted in cultivar specific expression of genes due to different genomic background of the varieties. To avoid host background variation in the interaction, suppression subtractive hybridization (SSH) based next generation sequencing technology was utilized in the same cv Co 7805 which behaves differently as incompatible and compatible to two different C. falcatum pathotypes. In the incompatible interaction (ICI) with C. falcatum pathotype Cf87012 (Less virulent, LVir) 10,038 contigs were assembled from ~54,699,263 raw reads. In the compatible interaction (CI) to the C. falcatum pathotype Cf94012 (Virulent, Vir) 4022 contigs were assembled from ~52,509,239 raw reads. The transcripts homologous to CEBiP receptor and transcripts involved in the signals ROS, Ca2+, BR, JA and ABA were exhibited in both the responses. Additionally, MAPK, ET, PI signals and JA amino conjugation related transcripts were found only in ICI. Finally, the temporal gene expression of a total number of 16 transcripts was monitored in qRT-PCR. Most of the transcripts exhibited highest induction in ICI in comparison with CI. Further, more than 17 transcripts specific to the pathogen were found only in CI, indicating that the pathogen colonizes the host tissue whereas it failed to to do so in ICI. Overall, this study has identified for the first time, the differential responses of a single sugarcane host to two different C. falcatum pathotypes and PAMP triggered immunity (PTI) is exhibited in both the responses, but the more efficient effector triggered immunity (ETI) was found only in ICI at the molecular level.

The Missing Link: Cre Pigs for Cancer Research

The Cre/loxP system is a powerful tool for the generation of animal models with precise spatial and temporal gene expression. It has proven indispensable in the generation of cancer models with tissue specific expression of oncogenes or the inactivation of tumor suppressor genes. Consequently, Cre-transgenic mice have become an essential prerequisite in basic cancer research. While it is unlikely that pigs will ever replace mice in basic research they are already providing powerful complementary resources for translational studies. But, although conditionally targeted onco-pigs have been generated, no Cre-driver lines exist for any of the major human cancers. To model human pancreatic cancer in pigs, Cre-driver lines were generated by CRISPR/Cas9-mediated insertion of codon-improved Cre (iCre) into the porcine PTF1A gene, thus guaranteeing tissue and cell type specific function which was proven using dual fluorescent reporter pigs. The method used can easily be adapted for the generation of other porcine Cre-driver lines, providing a missing tool for modeling human cancers in large animals.

Long Non-coding RNA T-uc.189 Modulates Neural Progenitor Cell Fate by Regulating Srsf3 During Mouse Cerebral Cortex Development

Neurogenesis is a complex process that depends on the delicate regulation of spatial and temporal gene expression. In our previous study, we found that transcribed ultra-conserved regions (T-UCRs), a class of long non-coding RNAs that contain UCRs, are expressed in the developing nervous systems of mice, rhesus monkeys, and humans. In this study, we first detected the full-length sequence of T-uc.189, revealing that it was mainly concentrated in the ventricular zone (VZ) and that its expression decreased as the brain matured. Moreover, we demonstrated that knockdown of T-uc.189 inhibited neurogenesis. In addition, we found that T-uc.189 positively regulated the expression of serine-arginine-rich splicing factor 3 (Srsf3). Taken together, our results are the first to demonstrate that T-uc.189 regulates the expression of Srsf3 to maintain normal neurogenesis during cortical development.

Neuroplastin in human cognition: review of literature and future perspectives

Synaptic glycoprotein neuroplastin is involved in synaptic plasticity and complex molecular events underlying learning and memory. Studies in mice and rats suggest that neuroplastin is essential for cognition, as it is needed for long-term potentiation and associative memory formation. Recently, it was found that some of the effects of neuroplastin are related to regulation of calcium homeostasis through interactions with plasma membrane calcium ATPases. Neuroplastin is increasingly seen as a key factor in complex brain functions, but studies in humans remain scarce. Here we summarize present knowledge about neuroplastin in human tissues and argue its genetic association with cortical thickness, intelligence, schizophrenia, and autism; specific immunolocalization depicting hippocampal trisynaptic pathway; potential role in tissue compensatory response in neurodegeneration; and high, almost housekeeping, level of spatio-temporal gene expression in the human brain. We also propose that neuroplastin acts as a housekeeper of neuroplasticity, and that it may be considered as an important novel cognition-related molecule in humans. Several promising directions for future investigations are suggested, which may complete our understanding of neuroplastin actions in molecular basis of human cognition.

Machine learning algorithms for the identification of potential non-syndromic intellectual disability associated miRNAs

Background: Intellectual disabilities (IDs) are a group of developmental disorders with high phenotypic and genotypic heterogeneity. Association of genetic elements to IDs has typically been empirically accomplished, however recently, machine learning (ML) has proved to be an excellent instrument to elucidate these associations. miRNAs are short non-coding molecules that participate in spatiotemporal gene regulation, making them relevant for the understanding ID causality. Methods: In this study we used the BrainSpan spatio-temporal expression database to develop a series of machine learning predictors: SVM, RF, FF-ANN, and Stochastic Gradient Descent Classifier. These models were capable of recognizing gene expression profiles. The best classifier was used to label miRNAs associated with NS-IDs using the BrainSpan expression profiles. Results: The model with the best performance was a FF-ANN with 0.78 of F1-score, 0.78 of weighted recall and 0.78 of weighted precision. We used this model to identify miRNAs with high probability to be associated with NS-IDs using the spatio-temporal gene expression profile in the human brain. Labeled miRNAs that were annotated were associated with processes related to either IDs and-or neurodevelopmental processes. Conclusions: The development of a machine learning framework that identified potential NS-ID miRNAs represents an interesting approach for the identification of a potential list of on genes that could be subject for further experimental validation. This study also reinforces the potential of machine learning frameworks in their discovery of potential biomarkers that could improve disease detection and management.