TY  - JOUR
A1  - Schlecht, Anja
A1  - Wolf, Julian
A1  - Boneva, Stefaniya
A1  - Prinz, Gabriele
A1  - Braunger, Barbara M.
A1  - Wieghofer, Peter
A1  - Agostini, Hansjürgen
A1  - Schlunck, Günther
A1  - Lange, Clemens
T1  - Transcriptional and distributional profiling of microglia in retinal angiomatous proliferation
JF  - International Journal of Molecular Sciences
N2  - Macular neovascularization type 3, formerly known as retinal angiomatous proliferation (RAP), is a hallmark of age-related macular degeneration and is associated with an accumulation of myeloid cells, such as microglia (MG) and infiltrating blood-derived macrophages (MAC). However, the contribution of MG and MAC to the myeloid cell pool at RAP sites and their exact functions remain unknown. In this study, we combined a microglia-specific reporter mouse line with a mouse model for RAP to identify the contribution of MG and MAC to myeloid cell accumulation at RAP and determined the transcriptional profile of MG using RNA sequencing. We found that MG are the most abundant myeloid cell population around RAP, whereas MAC are rarely, if ever, associated with late stages of RAP. RNA sequencing of RAP-associated MG showed that differentially expressed genes mainly contribute to immune-associated processes, including chemotaxis and migration in early RAP and proliferative capacity in late RAP, which was confirmed by immunohistochemistry. Interestingly, MG upregulated only a few angiomodulatory factors, suggesting a rather low angiogenic potential. In summary, we showed that MG are the dominant myeloid cell population at RAP sites. Moreover, MG significantly altered their transcriptional profile during RAP formation, activating immune-associated processes and exhibiting enhanced proliferation, however, without showing substantial upregulation of angiomodulatory factors.
KW  - AMD
KW  - Mactel 2
KW  - macular neovascularization
KW  - MNV type 3
KW  - retinal angiomatous proliferation
KW  - RAP
KW  - microglia
KW  - RNA sequencing
KW  - Cx3cr1
KW  - CreERT2
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-284072
SN  - 1422-0067
VL  - 23
IS  - 7
ER  - 
TY  - JOUR
A1  - Caliskan, Aylin
A1  - Crouch, Samantha A. W.
A1  - Giddins, Sara
A1  - Dandekar, Thomas
A1  - Dangwal, Seema
T1  - Progeria and aging — Omics based comparative analysis
JF  - Biomedicines
N2  - Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare “normal aging” (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.
KW  - progeria
KW  - aging
KW  - omics
KW  - RNA sequencing
KW  - bioinformatics
KW  - sun exposure
KW  - HGPS
KW  - IGFBP2
KW  - ACKR4
KW  - WNT
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-289868
SN  - 2227-9059
VL  - 10
IS  - 10
ER  -