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- Jantti, H, et al.
(author)
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Human PSEN1 Mutant Glia Improve Spatial Learning and Memory in Aged Mice
- 2022
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In: Cells. - : MDPI AG. - 2073-4409. ; 11:24
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Journal article (peer-reviewed)abstract
- The PSEN1 ΔE9 mutation causes a familial form of Alzheimer’s disease (AD) by shifting the processing of amyloid precursor protein (APP) towards the generation of highly amyloidogenic Aβ42 peptide. We have previously shown that the PSEN1 ΔE9 mutation in human-induced pluripotent stem cell (iPSC)-derived astrocytes increases Aβ42 production and impairs cellular responses. Here, we injected PSEN1 ΔE9 mutant astrosphere-derived glial progenitors into newborn mice and investigated mouse behavior at the ages of 8, 12, and 16 months. While we did not find significant behavioral changes in younger mice, spatial learning and memory were paradoxically improved in 16-month-old PSEN1 ΔE9 glia-transplanted male mice as compared to age-matched isogenic control-transplanted animals. Memory improvement was associated with lower levels of soluble, but not insoluble, human Aβ42 in the mouse brain. We also found a decreased engraftment of PSEN1 ΔE9 mutant cells in the cingulate cortex and significant transcriptional changes in both human and mouse genes in the hippocampus, including the extracellular matrix-related genes. Overall, the presence of PSEN1 ΔE9 mutant glia exerted a more beneficial effect on aged mouse brain than the isogenic control human cells likely as a combination of several factors.
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- Kjellman, M., et al.
(author)
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A Plasma Protein Biomarker Strategy for Detection of Small Intestinal Neuroendocrine Tumors
- 2021
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In: Neuroendocrinology. - : S. Karger AG. - 0028-3835 .- 1423-0194. ; 111:9, s. 840-849
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Journal article (peer-reviewed)abstract
- Background: Small intestinal neuroendocrine tumors (SI-NETs) are difficult to diagnose in the early stage of disease. Current blood biomarkers such as chromogranin A (CgA) and 5-hydroxyindolacetic acid have low sensitivity (SEN) and specificity (SPE). This is a first preplanned interim analysis (Nordic non-interventional, prospective, exploratory, EXPLAIN study [NCT02630654]). Its objective is to investigate if a plasma protein multi-biomarker strategy can improve diagnostic accuracy (ACC) in SI-NETs. Methods: At the time of diagnosis, before any disease-specific treatment was initiated, blood was collected from patients with advanced SI-NETs and 92 putative cancer-related plasma proteins from 135 patients were analyzed and compared with the results of age- and sex-matched controls (n = 143), using multiplex proximity extension assay and machine learning techniques. Results: Using a random forest model including 12 top ranked plasma proteins in patients with SI-NETs, the multi-biomarker strategy showed SEN and SPE of 89 and 91%, respectively, with negative predictive value (NPV) and positive predictive value (PPV) of 90 and 91%, respectively, to identify patients with regional or metastatic disease with an area under the receiver operator characteristic curve (AUROC) of 99%. In 30 patients with normal CgA concentrations, the model provided a diagnostic SPE of 98%, SEN of 56%, and NPV 90%, PPV of 90%, and AUROC 97%, regardless of proton pump inhibitor intake. Conclusion: This interim analysis demonstrates that a multi-biomarker/machine learning strategy improves diagnostic ACC of patients with SI-NET at the time of diagnosis, especially in patients with normal CgA levels. The results indicate that this multi-biomarker strategy can be useful for early detection of SI-NETs at presentation and conceivably detect recurrence after radical primary resection.
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