Ortiz, A. R. | Search bioRxiv

bioRxiv2023

Accumulation of m6A exhibits stronger correlation with MAPT than beta-amyloid pathology in an APP NL-G-F /MAPT P301S mouse model of Alzheimer's disease

The study for the pathophysiology study of Alzheimers disease (AD) has been hampered by lack animal models that recapitulate the major AD pathologies, including extracellular {beta}-amyloid (A{beta}) deposition, intracellular aggregation of microtubule associated protein tau (MAPT), inflammation and neurodegeneration. We now report on a double transgenic APPNL-G-F MAPTP301S mouse that at 6 months of age exhibits robust A{beta} plaque accumulation, intense MAPT pathology, strong inflammation and extensive neurodegeneration. The presence of A{beta} pathology potentiated the other major pathologies, including MAPT pathology, inflammation and neurodegeneration. However, MAPT pathology neither changed levels of amyloid precursor protein nor potentiated A{beta} accumulation. The APPNL-G-F/MAPTP301S mouse model also showed strong accumulation of N6-methyladenosine (m6A), which was recently shown to be elevated in the AD brain. M6A primarily accumulated in neuronal soma, but also co-localized with a subset of astrocytes and microglia. The accumulation of m6A corresponded with increases in METTL3 and decreases in ALKBH5, which are enzymes that add or remove m6A from mRNA, respectively. Thus, the APPNL- G-F/MAPTP301S mouse recapitulates many features of AD pathology beginning at 6 months of aging.

neuroscience↗

bioRxiv2021

Development of an exosomal biomarker signature to detect minimal residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning

Osteosarcoma has a guarded prognosis. A major hurdle in developing more effective osteosarcoma therapies is the lack of disease-specific biomarkers to predict risk, prognosis, or therapeutic response. Exosomes are secreted extracellular microvesicles emerging as powerful diagnostic tools. However, their clinical application is precluded by challenges in identifying disease-associated cargo from the vastly larger background of normal exosome cargo. We developed a method using canine osteosarcoma in mouse xenografts to distinguish tumor-derived from host-response exosomal mRNAs. The model allows for the identification of canine osteosarcoma-specific gene signatures by RNA sequencing and a species-differentiating bioinformatics pipeline. An osteosarcoma-associated signature consisting of five gene transcripts (SKA2, NEU1, PAF1, PSMG2, and NOB1) was validated in dogs with spontaneous osteosarcoma by qRT-PCR, while a machine learning model assigned dogs into healthy or disease groups. Serum/plasma exosomes were isolated from 53 dogs in distinct clinical groups ("healthy", "osteosarcoma", "other bone tumor", or "non-neoplastic disease"). Pre-treatment samples from osteosarcoma cases were used as the training set and a validation set from post-treatment samples was used for testing, classifying as "osteosarcoma-detected" or "osteosarcoma-NOT detected". Dogs in a validation set whose post-treatment samples were classified as "osteosarcoma-NOT detected" had longer remissions, up to 15 months after treatment. In conclusion, we identified a gene signature predictive of molecular remissions with potential applications in the early detection and minimal residual disease settings. These results provide proof-of-concept for our discovery platform and its utilization in future studies to inform cancer risk, diagnosis, prognosis, and therapeutic response. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/429432v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1b612c4org.highwire.dtl.DTLVardef@8a1847org.highwire.dtl.DTLVardef@3ffd64org.highwire.dtl.DTLVardef@114df8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗