Computational and Biological Approaches for Identification of Hedgehog Signaling Targets and Their Application to Intestinal Visceral Smooth Muscle Development in the Mouse.

The Hedgehog (Hh) pathway is an evolutionarily conserved cell-cell signaling pathway that controls organ development and homeostasis in embryos and adults. Hh signaling functions in cell fate choice, patterning, cell survival, proliferation and/or differentiation. Several birth defects are known to result from altered Hh signaling and aberrant Hh signaling is also responsible for several cancers. Despite its central role in development and disease, very little is known about the precise genetic targets of Hh signaling or the genomic enhancers that activate those genes. These target genes and associated Hh-responsive enhancers are themselves responsible for disease initiation and progression. A comprehensive effort to identify these signaling targets and to dissect the context specificity that underlies their expression is therefore a high priority. This work was driven by two Aims: 1) to explore novel computational approaches for the identification of Hh-responsive enhancers; and 2) to understand the contribution of Hh-driven gene expression in the context of a single Hh-responsive cell type, intestinal visceral smooth muscle (ISM). This work comprised a multi-pronged approach, integrating both computational and biological methods in parallel, to achieve these Aims. First, we explored the degree to which clustered binding sites for the Hh transcription factor, ci/GLI, would predict functional enhancers. While this method was somewhat successful in the fly, it could not be applied to the mouse, where Hh enhancers tend not to be homotypically clustered. Therefore, a machine learning strategy was explored with substantial success, resulting in the identification of seven new enhancers in genes encoding Hh pathway components. Finally, RNA-seq and ChIP-seq data were collected to generate a catalog of smooth muscle genes that are expressed in a specific layer of developing intestinal smooth muscle. Analysis of this data implicated cJUN as a regulatory component in ISM formation and established Hh as an upstream regulator of cJun expression in that tissue. Though this work has focused on Hh signaling, a similar approach could be applied to any transcription factor or signaling pathway to comprehensively analyze the gene regulatory networks governing many normal and disease-related cell states.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120838/1/gurdziel_1.pd

Computational prediction and experimental validation of novel Hedgehog-responsive enhancers linked to genes of the Hedgehog pathway

Abstract Background The Hedgehog (Hh) signaling pathway, acting through three homologous transcription factors (GLI1, GLI2, GLI3) in vertebrates, plays multiple roles in embryonic organ development and adult tissue homeostasis. At the level of the genome, GLI factors bind to specific motifs in enhancers, some of which are hundreds of kilobases removed from the gene promoter. These enhancers integrate the Hh signal in a context-specific manner to control the spatiotemporal pattern of target gene expression. Importantly, a number of genes that encode Hh pathway molecules are themselves targets of Hh signaling, allowing pathway regulation by an intricate balance of feed-back activation and inhibition. However, surprisingly few of the critical enhancer elements that control these pathway target genes have been identified despite the fact that such elements are central determinants of Hh signaling activity. Recently, ChIP studies have been carried out in multiple tissue contexts using mouse models carrying FLAG-tagged GLI proteins (GLIFLAG). Using these datasets, we tested whether a meta-analysis of GLI binding sites, coupled with a machine learning approach, could reveal genomic features that could be used to empirically identify Hh-regulated enhancers linked to loci of the Hh signaling pathway. Results A meta-analysis of four existing GLIFLAG datasets revealed a library of GLI binding motifs that was substantially more restricted than the potential sites predicted by previous in vitro binding studies. A machine learning method (kmer-SVM) was then applied to these datasets and enriched k-mers were identified that, when applied to the mouse genome, predicted as many as 37,000 potential Hh enhancers. For functional analysis, we selected nine regions which were annotated to putative Hh pathway molecules and found that seven exhibited GLI-dependent activity, indicating that they are directly regulated by Hh signaling (78 % success rate). Conclusions The results suggest that Hh enhancer regions share common sequence features. The kmer-SVM machine learning approach identifies those features and can successfully predict functional Hh regulatory regions in genomic DNA surrounding Hh pathway molecules and likely, other Hh targets. Additionally, the library of enriched GLI binding motifs that we have identified may allow improved identification of functional GLI binding sites.http://deepblue.lib.umich.edu/bitstream/2027.42/134520/1/12861_2016_Article_106.pd

Mite Diet Sequences Obtained by High Throughput Sequencing of Gut Contents of Freshly Collected Water Mites

Cytochrome oxidase subunit I (COI) barcode sequences in this file were obtained from gut DNA extracted from 54 freshly collected water mites, comprising 21 Lebertia quinquemaculosa , 30 Lebertia davidcooki , 1 Limnesia , and 2 Arrenurus specimens. Methods and other details about these sequences are described in a paper by the same authors in a submitted publication (2021: URL to be given here when published). Data on collection locations, primers (mLep and LCOI), amino acid translations, etc. are included in corresponding sequences uploaded to GenBank. The right column below contains additional notes on naming the taxa of the sequences that were not included in the GenBank annotation. These notes include the highest percentage identity to a previous sequence in GenBank as determined by BLASTN in June 2018. The FASTA file name given here includes the Accession ID, followed by the best match taxon (at an appropriate taxonomic level, dependent on the percent identity, as described in the notes in the right-hand column), the phrase water mite diet isolate , a specific RamLab sequence identifier of the sequence, and then the COI gene description. Accession IDs of sequences uploaded to GenBank begin with MW; other sequences begin with RL and a RamLab sequence identifier. The RamLab sequence identifier in the FASTA name includes information as follows: RamLab ID number-location and date of collection with three location letters (e.g., BHL stands for Blue Heron Lagoon) and the date usually in a 6-character format of MMDDYY-information on the location of sequence on the Illunina sequencing plate-and a 4- to 6-character identifier of the mite species (Lq=L. quinquemaculosa ; Ldc=L. davidcooki ; Lim=Limnesia ; Arr=Arrenurus ) and the animal number in that series of experiments (2 digits)

Mammalian Models of Traumatic Brain Injury and a Place for Drosophila in TBI Research

Traumatic brain injury (TBI), caused by a sudden blow or jolt to the brain that disrupts normal function, is an emerging health epidemic with ∼2.5 million cases occurring annually in the United States that are severe enough to cause hospitalization or death. Most common causes of TBI include contact sports, vehicle crashes and domestic violence or war injuries. Injury to the central nervous system is one of the most consistent candidates for initiating the molecular and cellular cascades that result in Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Not every TBI event is alike with effects varying from person to person. The majority of people recover from mild TBI within a short period of time, but repeated incidents can have deleterious long-lasting effects which depend on factors such as the number of TBIs sustained, time till medical attention, age, gender and genetics of the individual. Despite extensive research, many questions still remain regarding diagnosis, treatment, and prevention of long-term effects from TBI as well as recovery of brain function. In this review, we present an overview of TBI pathology, discuss mammalian models for TBI and focus on current methods using Drosophila melanogaster as a model for TBI study. The relatively small brain size (∼100,000 neurons and glia), conserved neurotransmitter signaling mechanisms and sophisticated genetics of Drosophila allows for cell biological, molecular and genetic analyses that are impractical in mammalian models of TBI

Lead Modulates trans- and cis-Expression Quantitative Trait Loci (eQTLs) in Drosophila melanogaster Heads

Lead exposure has long been one of the most important topics in global public health because it is a potent developmental neurotoxin. Here, an eQTL analysis, which is the genome-wide association analysis of genetic variants with gene expression, was performed. In this analysis, the male heads of 79 Drosophila melanogaster inbred lines from Drosophila Synthetic Population Resource (DSPR) were treated with or without developmental exposure, from hatching to adults, to 250 μM lead acetate [Pb(C2H3O2)2]. The goal was to identify genomic intervals that influence the gene-expression response to lead. After detecting 1798 cis-eQTLs and performing an initial trans-eQTL analysis, we focused our analysis on lead-sensitive “trans-eQTL hotspots,” defined as genomic regions that are associated with a cluster of genes in a lead-dependent manner. We noticed that the genes associated with one of the 14 detected trans-eQTL hotspots, Chr 2L: 6,250,000 could be roughly divided into two groups based on their differential expression profile patterns and different categories of function. This trans-eQTL hotspot validates one identified in a previous study using different recombinant inbred lines. The expression of all the associated genes in the trans-eQTL hotspot was visualized with hierarchical clustering analysis. Besides the overall expression profile patterns, the heatmap displayed the segregation of differential parental genetic contributions. This suggested that trans-regulatory regions with different genetic contributions from the parental lines have significantly different expression changes after lead exposure. We believe this study confirms our earlier study, and provides important insights to unravel the genetic variation in lead susceptibility in Drosophila model

Sex-Differences in Traumatic Brain Injury in the Absence of Tau in Drosophila

Traumatic brain injuries, a leading cause of death and disability worldwide, are caused by a severe impact to the head that impairs physiological and psychological function. In addition to severity, type and brain area affected, brain injury outcome is also influenced by the biological sex of the patient. Traumatic brain injury triggers accumulation of Tau protein and the subsequent development of Tauopathies, including Alzheimer’s disease and Chronic traumatic encephalopathy. Recent studies report differences in Tau network connections between healthy males and females, but the possible role of Tau in sex-dependent outcome to brain injury is unclear. Thus, we aimed to determine if Tau ablation would alleviate sex dependent outcomes in injured flies. We first assessed motor function and survival in tau knock-out flies and observed sex-differences in climbing ability, but no change in locomotor activity in either sex post-injury. Sex differences in survival time were also observed in injured tau deficient flies with a dramatically higher percent of female death within 24 h than males. Additionally, 3′mRNA-Seq studies in isolated fly brains found that tau deficient males show more gene transcript changes than females post-injury. Our results suggest that sex differences in TBI outcome and recovery are not dependent on the presence of Tau in Drosophila

DNA methylation and exposure to violence among African American young adult males

Exposure to violence (ETV) has been linked to epigenomics mechanisms such as DNA methylation (DNAm). We used epigenetic profiling of blood collected from 32 African American young adult males who lived in Washington DC to determine if changes in DNAm at CpG sites affiliated with nervous and immune system were associated with exposure to violence. Pathway analysis of differentially methylated regions comparing high and low ETV groups revealed an enrichment of gene sets annotated to nervous system and immune ontologies. Many of these genes are known to interact with each other which suggests DNAm alters gene function in the nervous and immune system in response to ETV. Using data from a unique age group, young African American adult males, we provide evidence that lifetime ETV could impact DNA methylation in genes impacted at Central Nervous System and Immune Function sites. Method: Methylation analysis was performed on DNA collected from the blood of participants classified with either high or low lifetime ETV. Illumina®MethylationEPIC Beadchips (~850k CpG sites) were processed on the iScan System to examine whole-genome methylation differences. Differentially methylated CpG-sites between high (n ​= ​19) and low (n ​= ​13) groups were identified using linear regression with violence and substance abuse as model covariates. Gene ontology analysis was used to identify enrichment categories from probes annotated to the nearest gene. Results: A total of 595 probes (279 hypermethylated; 316 hypomethylated) annotated to 383 genes were considered differentially methylated in association with ETV. Males with high ETV showed elevated methylation in several signaling pathways but were most impacted at Central Nervous System and Immune Function affiliated sites. Eight candidate genes were identified that play important biological roles in stress response to violence with HDAC4 (10%), NR4A3 (11%), NR4A2 (12%), DSCAML1(12%), and ELAVL3 (13%) exhibiting higher levels in the low ETV group and DLGAP1 (10%), SHANK2 (10%), and NRG1(11%) having increased methylation in the high ETV group. These findings suggest that individuals subjected to high ETV may be at risk for poor health outcomes that have not been reported previously