Search bioRxiv⌕ Search

Biology subjects

Adetunji, M.

Publications and source records attributed to Adetunji, M..

2 recordsLinked to original sources

GLP-1R agonist NLY01 reduces retinal inflammation, astrocyte reactivity, and retinal ganglion cell death secondary to ocular hypertension.

Glaucoma is the leading cause of irreversible blindness worldwide and is characterized by the death of retinal ganglion cells. Reduction of intraocular pressure (IOP) is the only therapeutic mechanism available to slow disease progression. However, glaucoma can continue to progress despite normalization of IOP. New treatments are needed to reduce vision loss and improve outcomes for patients who have exhausted existing therapeutic avenues. Recent studies have implicated neuroinflammation in the pathogenesis of neurodegenerative diseases of both the retina and the brain, including glaucoma and Parkinsons disease. Pro-inflammatory A1 astrocytes contribute to neuronal cell death in multiple disease processes and have been targeted therapeutically in mouse models of Parkinsons disease. Microglial release of pro-inflammatory cytokines C1q, IL-1, and TNF- is sufficient to drive the formation of A1 astrocytes. The role of A1 astrocytes in glaucoma pathogenesis has not been explored. Using a mouse model of glaucoma, we demonstrated that IOP elevation was sufficient to trigger production of C1q, IL-1, and TNF- by infiltrating macrophages followed by resident microglia. These three cytokines drove the formation of A1 astrocytes in the retina. Furthermore, cytokine production and A1 astrocyte transformation persisted following IOP normalization. Ablation of this pathway, by either genetic deletions of C1q, IL-1, and TNF-, or treatment with glucagon-like peptide-1 receptor agonist NLY01, reduced A1 astrocyte transformation and RGC death. Together, these results highlight a new neuroinflammatory mechanism behind glaucomatous neurodegeneration that can be therapeutically targeted by NLY01 administration.

neuroscience↗

VARIANT ANALYSIS PIPELINE FOR ACCURATE DETECTION OF GENOMIC VARIANTS FROM TRANSCRIPTOME SEQUENCING DATA.

The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. Given the ability of RNA-seq to reveal active regions of the genome, detection of RNA-seq SNPs can prove valuable in understanding the phenotypic diversity between populations. Thus, we present a novel computational workflow named VAP (Variant Analysis Pipeline) that takes advantage of multiple RNA-seq splice aware aligners to call SNPs in non-human models using RNA-seq data only. We applied VAP to RNA-seq from a highly inbred chicken line and achieved >97% precision and >99% sensitivity when compared with the matching whole genome sequencing (WGS) data. Over 65% of WGS coding variants were identified from RNA-seq. Further, our results discovered SNPs resulting from post translational modifications, such as RNA editing, which may reveal potentially functional variation that would have otherwise been missed in genomic data. Even with the limitation in detecting variants in expressed regions only, our method proves to be a reliable alternative for SNP identification using RNA-seq data.

bioinformatics↗