Arginase-1 deficiency in humans is a rare genetic disorder of metabolism resulting from a loss of arginase-1, leading to impaired ureagenesis, hyperargininemia and neurological deficits. Previously, we generated a tamoxifen-inducible arginase-1 deficient mouse model harboring a deletion of Arg1 exons 7 and 8 that leads to similar biochemical defects, along with a wasting phenotype and death within two weeks. Here, we report a strategy utilizing the Clustered, Regularly Interspaced, Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system in conjunction with piggyBac technology to target and reincorporate exons 7 and 8 at the specific Arg1 locus in attempts to restore the function of arginase-1 in induced pluripotent stem cell (iPSC)-derived hepatocyte-like cells (iHLCs) and macrophages in vitro. While successful gene targeted repair was achieved, minimal urea cycle function was observed in the targeted iHLCs compared to adult hepatocytes likely due to inadequate maturation of the cells. On the other hand, iPSC-derived macrophages expressed substantial amounts of “repaired” arginase. Our studies provide proof-of-concept for gene-editing at the Arg1 locus and highlight the challenges that lie ahead to restore sufficient liver-based urea cycle function in patients with urea cycle disorders.
Immunohistochemistry (IHC) is a testing methodology that is widely used for large number of diagnostic, prognostic, and predictive biomarkers. Although IHC is a qualitative methodology, in addition to threshold-based stratification (positive vs. negative), the increasing levels of expression of some of these biomarkers often lead to more intense staining, which published evidence linked to specific diagnosis, prognosis, and responses to therapy. It is essential that the descriptive thresholds between positive and negative staining, as well as between frequently used graded categories of staining intensity (eg, 1+, 2+, 3+) are standardized and reproducible. Histo-score (H-score) is a frequently used scoring system that utilizes these categories. Our study introduces categorization of the cutoff points between positive and negative results and graded categories of staining intensity for nuclear IHC biomarker assays based on color interaction between hematoxylin and diaminobenzidine (DAB); the Blue-brown Color H-score (BBC-HS). Six cases of diffuse large B-cell lymphoma were stained for a nuclear marker MUM1. The staining was assessed by H-score by 12 readers. Short tutorial and illustrated instructions were provided to readers. The novel scoring system in this study uses the interaction between DAB (DAB, brown stain) and hematoxylin (blue counterstain) to set thresholds between “0” (negative nuclei), “1+” (weakly positive nuclei), “2+” (moderately positive nuclei), and “3+” (strongly positive nuclei). The readers recorded scores for 300 cells. Krippendorff alpha (K-alpha) and intraclass correlation coefficient (ICC) were calculated. We have also assessed if reliability improved when counting the first 100 cells, first 200 cells, and for the total 300 cells using K-alpha and ICC. To assess the performance of each individual reader, the mean H-score and percent positive score (PPS) for each case was calculated, and the bias was calculated between each reader’s score and the mean. K-alpha was 0.86 for H-score and 0.76 for PPS. ICC was 0.96 for H-score and 0.92 for PPS. The biases for H-score ranged from −58 to 41, whereas for PPS it ranged from −27% to 33%. Overall, most readers showed very low bias. Two readers were consistently underscoring and 2 were consistently overscoring compared with the mean. For nuclear IHC biomarker assays, our newly proposed cutoffs provide highly reliable/reproducible results between readers for positive and negative results and graded categories of staining intensity using existing morphologic parameters. BBC-HS is easy to teach and is applicable to both human eye and image analysis. BBC-HS application should facilitate the development of new reliable/reproducible scoring schemes for IHC biomarkers.
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