Commonalities of optic nerve injury and glaucoma-induced neurodegeneration: Insights from transcriptome-wide studies.

Glaucoma is a collection of diseases that lead to an irreversible vision loss due to damage of retinal ganglion cells (RGCs). Although the underlying events leading to RGC death are not fully understood, recent research efforts are beginning to define the genetic changes that play a critical role in the initiation and progression of glaucomatous injury and RGC death. Several genetic and experimental animal models have been developed to mimic glaucomatous neurodegeneration. These models differ in many respects but all result in the loss of RGCs. Assessing transcriptional changes across different models could provide a more complete perspective on the molecular drivers of RGC degeneration. For the past several decades, changes in the retinal transcriptome during neurodegeneration process were defined using microarray methods, RNA sequencing and now single cell RNA sequencing. It is understood that these methods have strengths and weaknesses due to technical differences and variations in the analytical tools used. In this review, we focus on the use of transcriptome-wide expression profiling of the changes occurring as RGCs are lost across different glaucoma models. Commonalities of optic nerve crush and glaucoma-induced neurodegeneration are identified and discussed.

Derivation of notochordal cells from human embryonic stem cells reveals unique regulatory networks by single cell-transcriptomics.

Intervertebral disc degeneration (IDD) is a public health dilemma as it is associated with low back and neck pain, a frequent reason for patients to visit the physician. During IDD, nucleus pulposus (NP), the central compartment of intervertebral disc (IVD) undergo degeneration. Stem cells have been adopted as a promising biological source to regenerate the IVD and restore its function. Here, we describe a simple, two-step differentiation strategy using a cocktail of four factors (LDN, AGN, FGF, and CHIR) for efficient derivation of notochordal cells from human embryonic stem cells (hESCs). We employed a CRISPR/Cas9 based genome-editing approach to knock-in the mCherry reporter vector upstream of the 3' untranslated region of the Noto gene in H9-hESCs and monitored notochordal cell differentiation. Our data show that treatment of H9-hESCs with the above-mentioned four factors for 6 days successfully resulted in notochordal cells. These cells were characterized by morphology, immunostaining, and gene and protein expression analyses for established notochordal cell markers including FoxA2, SHH, and Brachyury. Additionally, pan-genomic high-throughput single cell RNA-sequencing revealed an efficient and robust notochordal differentiation. We further identified a key regulatory network consisting of eight candidate genes encoding transcription factors including PAX6, GDF3, FOXD3, TDGF1, and SOX5, which are considered as potential drivers of notochordal differentiation. This is the first single cell transcriptomic analysis of notochordal cells derived from hESCs. The ability to efficiently obtain notochordal cells from pluripotent stem cells provides an additional tool to develop new cell-based therapies for the treatment of IDD.

Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion.

BACKGROUND: Gene co-expression studies can provide important insights into molecular and cellular signaling pathways. The GeneNetwork database is a unique resource for co-expression analysis using data from a variety of tissues across genetically distinct inbred mice. However, extraction of biologically meaningful co-expressed gene sets is challenging due to variability in microarray platforms, probe quality, normalization methods, and confounding biological factors. In this study, we tested whether literature derived functional cohesion could be used as an objective metric in lieu of 'ground truth' to evaluate the quality of probes and microarray datasets. RESULTS: We examined Sirtuin-3 (Sirt3) co-expressed gene sets extracted from either liver or brain tissues of BXD recombinant inbred mice in the GeneNetwork database. Depending on the microarray platform, there were as many as 26 probes that targeted different regions of Sirt3 primary transcript. Co-expressed gene sets (ranging from 100-1000 genes) associated with each Sirt3 probe were evaluated using the previously developed literature-derived cohesion p-value (LPv) and benchmarked against 'gold standards' derived from proteomic studies or Gene Ontology classifications. We found that the maximal F-measure was obtained at an average window size of 535 genes. Using set size of 500 genes, the Pearson correlations between LPv and F-measure as well as between LPv and mitochondrial gene enrichment p-values were 0.90 and 0.93, respectively. Importantly, we found that the LPv approach can distinguish high quality Sirt3 probes. Analysis of the most functionally cohesive Sirt3 co-expressed gene set revealed core metabolic pathways that were shared between hippocampus and liver as well as distinct pathways which were unique to each tissue. These results are consistent with other studies that suggest Sirt3 is a key metabolic regulator and has distinct functions in energy-producing vs. energy-demanding tissues. CONCLUSIONS: Our results provide proof-of-concept that literature cohesion analysis is useful for evaluating the quality of probes and microarray datasets, particularly when experimentally derived gold standards are unavailable. Our approach would enable researchers to rapidly identify biologically meaningful co-expressed gene sets and facilitate discovery from high throughput genomic data.

Genomic loci modulating retinal ganglion cell death following elevated IOP in the mouse.

The present study was designed to identify genomic loci modulating the susceptibility of retinal ganglion cells (RGC) to elevated intraocular pressure (IOP) in the BXD recombinant inbred mouse strain set. IOP was elevated by injecting magnetic microspheres into the anterior chamber and blocking the trabecular meshwork using a handheld magnet to impede drainage. The IOP was then measured over the next 21 days. Only animals with IOP greater than 25 mmHg for two consecutive days or an IOP above 30 mmHg on a single day after microsphere-injection were used in this study. On day 21, mice were sacrificed and the optic nerve was processed for histology. Axons were counted for both the injected and the control eye in 49 BXD strains, totaling 181 normal counts and 191 counts associated with elevated IOP. The axon loss for each strain was calculated and the data were entered into genenetwork.org. The average number of normal axons in the optic nerve across all strains was 54,788 ±â€¯16% (SD), which dropped to 49,545 ±â€¯20% in animals with artificially elevated IOP. Interval mapping demonstrated a relatively similar genome-wide map for both conditions with a suggestive Quantitative Trait Locus (QTL) on proximal Chromosome 3. When the relative axon loss was used to generate a genome-wide interval map, we identified one significant QTL (p < 0.05) on Chromosome 18 between 53.6 and 57 Mb. Within this region, the best candidate gene for modulating axon loss was Aldh7a1. Immunohistochemistry demonstrated ALDH7A1 expression in mouse RGCs. ALDH7A1 variants were not significantly associated with glaucoma in the NEIGHBORHOOD GWAS dataset, but this enzyme was identified as part of the butanoate pathway previously associated with glaucoma risk. Our results suggest that genomic background influences susceptibility to RGC degeneration and death in an inducible glaucoma model.

Teaching with Big Data: Report from the 2016 Society for Neuroscience Teaching Workshop.

As part of a series of workshops on teaching neuroscience at the Society for Neuroscience annual meetings, William Grisham and Richard Olivo organized the 2016 workshop on "Teaching Neuroscience with Big Data." This article presents a summary of that workshop. Speakers provided overviews of open datasets that could be used in teaching undergraduate courses. These included resources that already appear in educational settings, including the Allen Brain Atlas (presented by Joshua Brumberg and Terri Gilbert), and the Mouse Brain Library and GeneNetwork (presented by Robert Williams). Other resources, such as NeuroData (presented by William R. Gray Roncal), and OpenFMRI, NeuroVault, and Neurosynth (presented by Russell Poldrack) have not been broadly utilized by the neuroscience education community but offer obvious potential. Finally, William Grisham discussed the iNeuro Project, an NSF-sponsored effort to develop the necessary curriculum for preparing students to handle Big Data. Linda Lanyon further elaborated on the current state and challenges in educating students to deal with Big Data and described some training resources provided by the International Neuroinformatics Coordinating Facility. Neuroinformatics is a subfield of neuroscience that deals with data utilizing analytical tools and computational models. The feasibility of offering neuroinformatics programs at primarily undergraduate institutions was also discussed.

A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions.

BACKGROUND: We address the problem of integratively analyzing multiple gene expression, microarray datasets in order to reconstruct gene-gene interaction networks. Integrating multiple datasets is generally believed to provide increased statistical power and to lead to a better characterization of the system under study. However, the presence of systematic variation across different studies makes network reverse-engineering tasks particularly challenging. We contrast two approaches that have been frequently used in the literature for addressing systematic biases: meta-analysis methods, which first calculate opportune statistics on single datasets and successively summarize them, and data-merging methods, which directly analyze the pooled data after removing eventual biases. This comparative evaluation is performed on both synthetic and real data, the latter consisting of two manually curated microarray compendia comprising several E. coli and Yeast studies, respectively. Furthermore, the reconstruction of the regulatory network of the transcription factor Ikaros in human Peripheral Blood Mononuclear Cells (PBMCs) is presented as a case-study. RESULTS: The meta-analysis and data-merging methods included in our experimentations provided comparable performances on both synthetic and real data. Furthermore, both approaches outperformed (a) the naïve solution of merging data together ignoring possible biases, and (b) the results that are expected when only one dataset out of the available ones is analyzed in isolation. Using correlation statistics proved to be more effective than using p-values for correctly ranking candidate interactions. The results from the PBMC case-study indicate that the findings of the present study generalize to different types of network reconstruction algorithms. CONCLUSIONS: Ignoring the systematic variations that differentiate heterogeneous studies can produce results that are statistically indistinguishable from random guessing. Meta-analysis and data merging methods have proved equally effective in addressing this issue, and thus researchers may safely select the approach that best suit their specific application.

Systems genetics identifies methionine as a high risk factor for Alzheimer's disease.

As a dietary strategy, methionine restriction has been reported to promote longevity and regulate metabolic disorders. However, the role and possible regulatory mechanisms underlying methionine in neurodegenerative diseases such as Alzheimer's disease (AD), remain unexplored. This study utilized the data from BXD recombinant inbred (RI) mice to establish a correlation between the AD phenotype in mice and methionine level. Gene enrichment analysis indicated that the genes associated with the concentration of methionine in the midbrain are involved in the dopaminergic synaptic signaling pathway. Protein interaction network analysis revealed that glycogen synthase kinase 3 beta (GSK-3ß) was a key regulator of the dopaminergic synaptic pathway and its expression level was significantly correlated with the AD phenotype. Finally, in vitro experiments demonstrated that methionine deprivation could reduce the expression of Aß and phosphorylated Tau, suggesting that lowering methionine levels in humans may be a preventive or therapeutic strategy for AD. In conclusion, our findings support that methionine is a high risk factor for AD. These findings predict potential regulatory network, theoretically supporting methionine restriction to prevent AD.

Identifying genetic susceptibility to Aspergillus fumigatus infection using collaborative cross mice and RNA-Seq approach.

BACKGROUND: Aspergillus fumigatus (Af) is one of the most ubiquitous fungi and its infection potency is suggested to be strongly controlled by the host genetic background. The aim of this study was to search for candidate genes associated with host susceptibility to Aspergillus fumigatus (Af) using an RNAseq approach in CC lines and hepatic gene expression. METHODS: We studied 31 male mice from 25 CC lines at 8 weeks old; the mice were infected with Af. Liver tissues were extracted from these mice 5 days post-infection, and next-generation RNA-sequencing (RNAseq) was performed. The GENE-E analysis platform was used to generate a clustered heat map matrix. RESULTS: Significant variation in body weight changes between CC lines was observed. Hepatic gene expression revealed 12 top prioritized candidate genes differentially expressed in resistant versus susceptible mice based on body weight changes. Interestingly, three candidate genes are located within genomic intervals of the previously mapped quantitative trait loci (QTL), including Gm16270 and Stox1 on chromosome 10 and Gm11033 on chromosome 8. CONCLUSIONS: Our findings emphasize the CC mouse model's power in fine mapping the genetic components underlying susceptibility towards Af. As a next step, eQTL analysis will be performed for our RNA-Seq data. Suggested candidate genes from our study will be further assessed with a human cohort with aspergillosis.

Genome-Wide Screening of Broad-Spectrum Resistance to Leaf Rust (Puccinia triticina Eriks) in Spring Wheat (Triticum aestivum L.).

Wheat leaf rust (LR) causes significant yield losses worldwide. In Egypt, resistant cultivars began to lose their efficiency in leaf rust resistance. Therefore, a diverse spring wheat panel was evaluated at the seedling stage to identify new sources of broad-spectrum seedling resistance against the Egyptian Puccinia triticina (Pt) races. In three different experiments, seedling evaluation was done using Pt spores collected from different fields and growing seasons. Highly significant differences were found among experiments confirming the presence of different races population in each experiment. Highly significant differences were found among the tested genotypes confirming the ability to select superior genotypes. Genome-wide association study (GWAS) was conducted for each experiment and a set of 87 markers located within 48 gene models were identified. The identified gene models were associated with disease resistance in wheat. Five gene models were identified to resist all Pt races in at least two experiments and could be identified as stable genes under Egyptian conditions. Ten genotypes from five different countries were stable against all the tested Pt races but showed different degrees of resistance.

Systems Genetics for Evolutionary Studies.

Systems genetics combines high-throughput genomic data with genetic analysis. In this chapter, we review and discuss application of systems genetics in the context of evolutionary studies, in which high-throughput molecular technologies are being combined with quantitative trait locus (QTL) analysis in segregating populations.The recent explosion of high-throughput data-measuring thousands of RNAs, proteins, and metabolites, using deep sequencing, mass spectrometry, chromatin, methyl-DNA immunoprecipitation, etc.-allows the dissection of causes of genetic variation underlying quantitative phenotypes of all types. To deal with the sheer amount of data, powerful statistical tools are needed to analyze multidimensional relationships and to extract valuable information and new modes and mechanisms of changes both within and between species. In the context of evolutionary computational biology, a well-designed experiment and the right population can help dissect complex traits likely to be under selection using proven statistical methods for associating phenotypic variation with chromosomal locations.Recent evolutionary expression QTL (eQTL) studies focus on gene expression adaptations, mapping the gene expression landscape, and, tentatively, define networks of transcripts and proteins that are jointly modulated sets of eQTL networks. Here, we discuss the possibility of introducing an evolutionary "prior" in the form of gene families displaying evidence of positive selection, and using that prior in the context of an eQTL experiment for elucidating host-pathogen protein-protein interactions.Here we review one exemplar evolutionairy eQTL experiment and discuss experimental design, choice of platforms, analysis methods, scope, and interpretation of results. In brief we highlight how eQTL are defined; how they are used to assemble interacting and causally connected networks of RNAs, proteins, and metabolites; and how some QTLs can be efficiently converted to reasonably well-defined sequence variants.

Novel Gene-Based Analysis of ASD GWAS: Insight Into the Biological Role of Associated Genes.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by its significant social impact and high heritability. The latest meta-analysis of ASD GWAS (genome-wide association studies) has revealed the association of several SNPs that were replicated in additional sets of independent samples. However, summary statistics from GWAS can be used to perform a gene-based analysis (GBA). GBA allows to combine all genetic information across the gene to create a single statistic (p-value for each gene). Thus, PASCAL (Pathway scoring algorithm), a novel GBA tool, has been applied to the summary statistics from the latest meta-analysis of ASD. GBA approach (testing the gene as a unit) provides an advantage to perform an accurate insight into the biological ASD mechanisms. Therefore, a gene-network analysis and an enrichment analysis for KEGG and GO terms were carried out. GENE2FUNC was used to create gene expression heatmaps and to carry out differential expression analysis (DEA) across GTEx v7 tissues and Brainspan data. dbMDEGA was employed to perform a DEG analysis between ASD and brain control samples for the associated genes and interactors. Results: PASCAL has identified the following loci associated with ASD: XRN2, NKX2-4, PLK1S1, KCNN2, NKX2-2, CRHR1-IT1, C8orf74 and LOC644172. While some of these genes were previously reported by MAGMA (XRN2, PLK1S1, and KCNN2), PASCAL has been useful to highlight additional genes. The biological characterization of the ASD-associated genes and their interactors have demonstrated the association of several GO and KEGG terms. Moreover, DEA analysis has revealed several up- and down-regulated clusters. In addition, many of the ASD-associated genes and their interactors have shown association with ASD expression datasets. Conclusions: This study identifies several associations at a gene level in ASD. Most of them were previously reported by MAGMA. This fact proves that PASCAL is an efficient GBA tool to extract additional information from previous GWAS. In addition, this study has characterized for the first time the biological role of the ASD-associated genes across brain regions, neurodevelopmental stages, and ASD gene-expression datasets.

Gene-based analysis of ADHD using PASCAL: a biological insight into the novel associated genes.

BACKGROUND: Attention-Deficit Hyperactivity Disorder (ADHD) is a complex neurodevelopmental disorder (NDD) which may significantly impact on the affected individual's life. ADHD is acknowledged to have a high heritability component (70-80%). Recently, a meta-analysis of GWAS (Genome Wide Association Studies) has demonstrated the association of several independent loci. Our main aim here, is to apply PASCAL (pathway scoring algorithm), a new gene-based analysis (GBA) method, to the summary statistics obtained in this meta-analysis. PASCAL will take into account the linkage disequilibrium (LD) across genomic regions in a different way than the most commonly employed GBA methods (MAGMA or VEGAS (Versatile Gene-based Association Study)). In addition to PASCAL analysis a gene network and an enrichment analysis for KEGG and GO terms were carried out. Moreover, GENE2FUNC tool was employed to create gene expression heatmaps and to carry out a (DEG) (Differentially Expressed Gene) analysis using GTEX v7 and BrainSpan data. RESULTS: PASCAL results have revealed the association of new loci with ADHD and it has also highlighted other genes previously reported by MAGMA analysis. PASCAL was able to discover new associations at a gene level for ADHD: FEZF1 (p-value: 2.2 × 10- 7) and FEZF1-AS1 (p-value: 4.58 × 10- 7). In addition, PASCAL has been able to highlight association of other genes that share the same LD block with some previously reported ADHD susceptibility genes. Gene network analysis has revealed several interactors with the associated ADHD genes and different GO and KEGG terms have been associated. In addition, GENE2FUNC has demonstrated the existence of several up and down regulated expression clusters when the associated genes and their interactors were considered. CONCLUSIONS: PASCAL has been revealed as an efficient tool to extract additional information from previous GWAS using their summary statistics. This study has identified novel ADHD associated genes that were not previously reported when other GBA methods were employed. Moreover, a biological insight into the biological function of the ADHD associated genes across brain regions and neurodevelopmental stages is provided.

Systems Genetic Analysis in GeneNetwork.org.

Genome-wide association studies (GWAS) have emerged as a powerful tool to identify alleles and molecular pathways that influence susceptibility to psychiatric disorders and other diseases. Forward genetics using mouse mapping populations allows for a complementary approach that provides rigorous genetic and environmental control. In this unit, we describe techniques and tools that reduce the technical burden traditionally associated with genetic mapping in mice and enhance their translational utility to human psychiatric disorders. We provide guidance on choosing the appropriate mapping population, discuss the importance of phenotype, and offer detailed instructions on using the Web-based resource GeneNetwork to aid neuroscientists in better understanding the mechanisms through which genes influence behavior. We believe that the continued development of mouse mapping populations, genetic tools, bioinformatics resources, and statistical methodologies should remain a parallel strategy by which to investigate the genetic and environmental underpinnings of psychiatric disorders and other diseases in humans. © 2017 by John Wiley & Sons, Inc.

Systems Genetics of Liver Fibrosis.

This systems genetics analysis comprises quantitative measurements of hepatic fibrogenesis in mouse models and mapping of quantitative traits in mouse genetic reference populations. It is part of a large mapping project of fibrogenic genes including the analyses of experimental crosses from different inbred mouse strains. Extensive quantitative trait loci (QTL) mapping of fibrosis phenotypes and liver expression profiling in combination with in silico mapping facilitated the identification of QTL regions and underlying candidate genes that confer fibrosis susceptibility also in humans. Moreover, the approach led to the identification of interacting QTLs and gene networks in liver fibrosis, providing a key experimental platform for the development of novel, more precise therapeutic interventions. Here, we provide a use case for the application of different analysis tools and the integration of multiple datasets determined in F2 intercrosses and BXD recombinant inbred lines to identify, finemap and affirm fibrosis susceptibility loci.

Systems Genetics of Obesity.

Obesity is a complex trait, determined by many genes and influenced by environmental factors. Mapping genomic loci contributing to obesity helps to identify gene variants responsible for differences in the phenotype. However, measuring fat content alone is often not sufficient to identify the underlying gene or genes. Besides in-depth phenotyping, well-designed genetic populations and the combined analysis of data of different origins are necessary to detect one of several genetic determinants. Structured mouse populations and linking information from different experiments help to simplify the complexity in the search for direct genetic effects or factors that are hidden in the genome. In this chapter we present an example of how the physicochemical characterization of adipose tissue in BXD recombinant inbred lines contributes to enlighten the obese phenotype of mice. We describe the search for gene(s) contributing to collagen content in adipose tissue of BXD strains using the GeneNetwork platform.