120 research outputs found

    RhoG regulates endothelial apical cup assembly downstream from ICAM1 engagement and is involved in leukocyte trans-endothelial migration

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    During trans-endothelial migration (TEM), leukocytes use adhesion receptors such as intercellular adhesion molecule-1 (ICAM1) to adhere to the endothelium. In response to this interaction, the endothelium throws up dynamic membrane protrusions, forming a cup that partially surrounds the adherent leukocyte. Little is known about the signaling pathways that regulate cup formation. In this study, we show that RhoG is activated downstream from ICAM1 engagement. This activation requires the intracellular domain of ICAM1. ICAM1 colocalizes with RhoG and binds to the RhoG-specific SH3-containing guanine-nucleotide exchange factor (SGEF). The SH3 domain of SGEF mediates this interaction. Depletion of endothelial RhoG by small interfering RNA does not affect leukocyte adhesion but decreases cup formation and inhibits leukocyte TEM. Silencing SGEF also results in a substantial reduction in RhoG activity, cup formation, and TEM. Together, these results identify a new signaling pathway involving RhoG and its exchange factor SGEF downstream from ICAM1 that is critical for leukocyte TEM

    A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography

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    IMPORTANCE: The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans. OBJECTIVE: To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023. EXPOSURE: A position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3). MAIN OUTCOMES AND MEASURES: Prediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. RESULTS: The study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3). CONCLUSIONS AND RELEVANCE: The fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation

    Characterization of the Poly-T Variant in the TOMM40 Gene in Diverse Populations

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    We previously discovered that a polymorphic, deoxythymidine-homopolymer (poly-T, rs10524523) in intron 6 of the TOMM40 gene is associated with age-of-onset of Alzheimer's disease and with cognitive performance in elderly. Three allele groups were defined for rs10524523, hereafter ‘523’, based on the number of ‘T’-residues: ‘Short’ (S, T≤19), ‘Long’ (L, 20≤T≤29) and ‘Very Long’ (VL, T≥30). Homopolymers, particularly long homopolymers like ‘523’, are difficult to genotype because ‘slippage’ occurs during PCR-amplification. We initially genotyped this locus by PCR-amplification followed by Sanger-sequencing. However, we recognized the need to develop a higher-throughput genotyping method that is also accurate and reliable. Here we describe a new ‘523’ genotyping assay that is simple and inexpensive to perform in a standard molecular genetics laboratory. The assay is based on the detection of differences in PCR-fragment length using capillary electrophoresis. We discuss technical problems, solutions, and the steps taken for validation. We employed the novel assay to investigate the ‘523’ allele frequencies in different ethnicities. Whites and Hispanics have similar frequencies of S/L/VL alleles (0.45/0.11/0.44 and 0.43/0.09/0.48, respectively). In African-Americans, the frequency of the L-allele (0.10) is similar to Whites and Hispanics; however, the S-allele is more prevalent (0.65) and the VL-allele is concomitantly less frequent (0.25). The allele frequencies determined using the new methodology are compared to previous reports for Ghanaian, Japanese, Korean and Han Chinese cohorts. Finally, we studied the linkage pattern between TOMM40-‘523’ and APOE alleles. In Whites and Hispanics, consistent with previous reports, the L is primarily linked to ε4, while the majority of the VL and S are linked to ε3. Interestingly, in African-Americans, Ghanaians and Japanese, there is an increased frequency of the ‘523’S-APOEε4 haplotype. These data may be used as references for ‘523’ allele and ‘523’-APOE haplotype frequencies in diverse populations for the design of research studies and clinical trials

    Population-based estimates of the prevalence of FMR1 expansion mutations in women with early menopause and primary ovarian insufficiency

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    PURPOSE: Primary ovarian insufficiency before the age of 40 years affects 1% of the female population and is characterized by permanent cessation of menstruation. Genetic causes include FMR1 expansion mutations. Previous studies have estimated mutation prevalence in clinical referrals for primary ovarian insufficiency, but these are likely to be biased as compared with cases in the general population. The prevalence of FMR1 expansion mutations in early menopause (between the ages of 40 and 45 years) has not been published. METHODS: We studied FMR1 CGG repeat number in more than 2,000 women from the Breakthrough Generations Study who underwent menopause before the age of 46 years. We determined the prevalence of premutation (55–200 CGG repeats) and intermediate (45–54 CGG repeats) alleles in women with primary ovarian insufficiency (n = 254) and early menopause (n = 1,881). RESULTS: The prevalence of the premutation was 2.0% in primary ovarian insufficiency, 0.7% in early menopause, and 0.4% in controls, corresponding to odds ratios of 5.4 (95% confidence interval = 1.7–17.4; P = 0.004) for primary ovarian insufficiency and 2.0 (95% confidence interval = 0.8–5.1; P = 0.12) for early menopause. Combining primary ovarian insufficiency and early menopause gave an odds ratio of 2.4 (95% confidence interval = 1.02–5.8; P = 0.04). Intermediate alleles were not significant risk factors for either early menopause or primary ovarian insufficiency. CONCLUSION: FMR1 premutations are not as prevalent in women with ovarian insufficiency as previous estimates have suggested, but they still represent a substantial cause of primary ovarian insufficiency and early menopause
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