Background: The study of human lacrimal gland biology and development is limited. Lacrimal gland tissue is damaged or poorly functional in a number of disease states including dry eye disease. Development of cell based therapies for lacrimal gland diseases requires a better understanding of the gene expression and signaling pathways in lacrimal gland. Differential gene expression analysis between lacrimal gland and other embryologically similar tissues may be helpful in furthering our understanding of lacrimal gland development. Methods: We performed global gene expression analysis of human lacrimal gland tissue using Affymetrix gene expression arrays. Primary data from our laboratory was compared with datasets available in the NLM GEO database for other surface ectodermal tissues including salivary gland, skin, conjunctiva and corneal epithelium. Results: The analysis revealed statistically significant difference in the gene expression of lacrimal gland tissue compared to other ectodermal tissues. The lacrimal gland specific, cell surface secretory protein encoding genes and critical signaling pathways which distinguish lacrimal gland from other ectodermal tissues are described. Conclusions: Differential gene expression in human lacrimal gland compared with other ectodermal tissue types revealed interesting patterns which may serve as the basis for future studies in directed differentiation among other areas.
Human Lacrimal Gland Gene Expression.
Specimen part
View SamplesTranscriptional fingerprint of hypomyelination in Zfp191null and Shiverer (Mbpshi) mice
Transcriptional Fingerprint of Hypomyelination in Zfp191null and Shiverer (Mbpshi) Mice.
Sex, Specimen part, Cell line
View SamplesOligodendrocytes (OLs) and myelin are critical for normal brain function and they have been implicated in neurodegeneration. Human neuroimaging studies have demonstrated that alterations in axons and myelin occur early in Alzheimer's Disease (AD) course. However, the molecular mechanism underlying the role of OLs in AD remains largely unknown. In this study, we systematically interrogated OL-enriched gene networks constructed from large-scale genomic, transcriptomic, and proteomic data in human AD postmortem brain samples. These robust OL networks were highly enriched for genes associated with AD risk variants, including BIN1. We corroborated the structure of the AD OL coexpression and gene-gene interaction networks through ablation of genes identified as key drivers of the networks, including UGT8, CNP, MYRF, PLP1, NPC1, and NDGR1. Perturbations of these key drivers not only caused dysregulation in their associated network neighborhoods, but also mimicked pathways of gene expression dysregulation seen in human AD postmortem brain samples. In particular, the OL subnetwork controlled by the AD risk gene PSEN1 was strongly dysregulated in AD, suggesting a potential role of PSEN1 in disrupting the myelination pathway towards the onset of AD. In summary, this study built and systematically validated the first comprehensive molecular blueprint of OL dysregulation in AD, and identified key OL- and myelination-related genes and networks as potential candidate targets for the future development of AD therapies. Overall design: The mouse knockout models have been previously described for each of Ugt8 (Coetzee et al., 1996), Cnp (Lappe-Siefke et al., 2003), and Plp1 (Klugmann et al., 1997). For each of the two conditions studied (control and homozygous knockout mice), five mice of either sex were sacrificed at postnatal day 20 and brains were flashed-frozen until analysis. The frontal cortex (FC) and cerebellum (CBM) were dissected out and individually processed. RNA was isolated using Trizol reagent and processed using Ribo-Zero rRNA removal. RNA-sequencing was performed using the Illumina HiSeq2000 with 100 nucleotide paired-end reads. RNA-sequencing reads were mapped to the mouse genome (mm10, UCSC assembly) using Bowtie (version 2.2.3.0), TopHat (version 2.0.11), and SamTools (version 0.1.19.0) using a read length of 100. Reads were converted to counts at the gene level using HTSeq on the BAM files from TopHat2 using the UCSC known genes data set.
Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease.
Specimen part, Subject
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