Nanopore guided annotation of transcriptome architectures.

Nanopore direct RNA sequencing (DRS) enables the capture and full-length sequencing of native RNAs, without recoding or amplification bias. Resulting data sets may be interrogated to define the identity and location of chemically modified ribonucleotides, as well as the length of poly(A) tails, on individual RNA molecules. The success of these analyses is highly dependent on the provision of high-resolution transcriptome annotations in combination with workflows that minimize misalignments and other analysis artifacts. Existing software solutions for generating high-resolution transcriptome annotations are poorly suited to small gene-dense genomes of viruses due to the challenge of identifying distinct transcript isoforms where alternative splicing and overlapping RNAs are prevalent. To resolve this, we identified key characteristics of DRS data sets that inform resulting read alignments and developed the nanopore guided annotation of transcriptome architectures (NAGATA) software package (https://github.com/DepledgeLab/NAGATA). We demonstrate, using a combination of synthetic and original DRS data sets derived from adenoviruses, herpesviruses, coronaviruses, and human cells, that NAGATA outperforms existing transcriptome annotation software and yields a consistently high level of precision and recall when reconstructing both gene sparse and gene-dense transcriptomes. Finally, we apply NAGATA to generate the first high-resolution transcriptome annotation of the neglected pathogen human adenovirus type F41 (HAdV-41) for which we identify 77 distinct transcripts encoding at least 23 different proteins. IMPORTANCE: The transcriptome of an organism denotes the full repertoire of encoded RNAs that may be expressed. This is critical to understanding the biology of an organism and for accurate transcriptomic and epitranscriptomic-based analyses. Annotating transcriptomes remains a complex task, particularly in small gene-dense organisms such as viruses which maximize their coding capacity through overlapping RNAs. To resolve this, we have developed a new software nanopore guided annotation of transcriptome architectures (NAGATA) which utilizes nanopore direct RNA sequencing (DRS) datasets to rapidly produce high-resolution transcriptome annotations for diverse viruses and other organisms.

Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning.

Purpose: Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, in intermediate AMD (iAMD) using high-precision artificial intelligence (AI) quantifications on optical coherence tomography imaging. Methods: In this longitudinal observational study, individuals with bilateral iAMD participating in a multicenter clinical trial were screened for OPL subsidence and RPE and outer retinal atrophy. OPL subsidence was segmented on an A-scan basis in optical coherence tomography volumes, obtained 6-monthly with 36 months follow-up. AI-based quantification of photoreceptor (PR) and outer nuclear layer (ONL) thickness, drusen height and choroidal hypertransmission (HT) was performed. Changes were compared between topographic areas of OPL subsidence (AS), drusen (AD), and reference (AR). Results: Of 280 eyes of 140 individuals, OPL subsidence occurred in 53 eyes from 43 individuals. Thirty-six eyes developed RPE and outer retinal atrophy subsequently. In the cohort of 53 eyes showing OPL subsidence, PR and ONL thicknesses were significantly decreased in AS compared with AD and AR 12 and 18 months before OPL subsidence occurred, respectively (PR: 20 µm vs. 23 µm and 27 µm [P < 0.009]; ONL, 84 µm vs. 94 µm and 98 µm [P < 0.008]). Accelerated thinning of PR (0.6 µm/month; P < 0.001) and ONL (0.8 µm/month; P < 0.001) was observed in AS compared with AD and AR. Concomitant drusen regression and hypertransmission increase at the occurrence of OPL subsidence underline the atrophic progress in areas affected by OPL subsidence. Conclusions: PR and ONL thinning are early subclinical features associated with subsequent OPL subsidence, an indicator of progression toward geographic atrophy. AI algorithms are able to predict and quantify morphological precursors of iAMD conversion and allow personalized risk stratification.

Deep Neural Networks for Automated Outer Plexiform Layer Subsidence Detection on Retinal OCT of Patients With Intermediate AMD.

Purpose: The subsidence of the outer plexiform layer (OPL) is an important imaging biomarker on optical coherence tomography (OCT) associated with early outer retinal atrophy and a risk factor for progression to geographic atrophy in patients with intermediate age-related macular degeneration (AMD). Deep neural networks (DNNs) for OCT can support automated detection and localization of this biomarker. Methods: The method predicts potential OPL subsidence locations on retinal OCTs. A detection module (DM) infers bounding boxes around subsidences with a likelihood score, and a classification module (CM) assesses subsidence presence at the B-scan level. Overlapping boxes between B-scans are combined and scored by the product of the DM and CM predictions. The volume-wise score is the maximum prediction across all B-scans. One development and one independent external data set were used with 140 and 26 patients with AMD, respectively. Results: The system detected more than 85% of OPL subsidences with less than one false-positive (FP)/scan. The average area under the curve was 0.94 ± 0.03 for volume-level detection. Similar or better performance was achieved on the independent external data set. Conclusions: DNN systems can efficiently perform automated retinal layer subsidence detection in retinal OCT images. In particular, the proposed DNN system detects OPL subsidence with high sensitivity and a very limited number of FP detections. Translational Relevance: DNNs enable objective identification of early signs associated with high risk of progression to the atrophic late stage of AMD, ideally suited for screening and assessing the efficacy of the interventions aiming to slow disease progression.

Arsenic trioxide impacts hepatitis B virus core nuclear localization and efficiently interferes with HBV infection.

The key to a curative treatment of hepatitis B virus (HBV) infection is the eradication of the intranuclear episomal covalently closed circular DNA (cccDNA), the stable persistence reservoir of HBV. Currently, established therapies can only limit HBV replication but fail to tackle the cccDNA. Thus, novel therapeutic approaches toward curative treatment are urgently needed. Recent publications indicated a strong association between the HBV core protein SUMOylation and the association with promyelocytic leukemia nuclear bodies (PML-NBs) on relaxed circular DNA to cccDNA conversion. We propose that interference with the cellular SUMOylation system and PML-NB integrity using arsenic trioxide provides a useful tool in the treatment of HBV infection. Our study showed a significant reduction in HBV-infected cells, core protein levels, HBV mRNA, and total DNA. Additionally, a reduction, albeit to a limited extent, of HBV cccDNA could be observed. Furthermore, this interference was also applied for the treatment of an established HBV infection, characterized by a stably present nuclear pool of cccDNA. Arsenic trioxide (ATO) treatment not only changed the amount of expressed HBV core protein but also induced a distinct relocalization to an extranuclear phenotype during infection. Moreover, ATO treatment resulted in the redistribution of transfected HBV core protein away from PML-NBs, a phenotype similar to that previously observed with SUMOylation-deficient HBV core. Taken together, these findings revealed the inhibition of HBV replication by ATO treatment during several steps of the viral replication cycle, including viral entry into the nucleus as well as cccDNA formation and maintenance. We propose ATO as a novel prospective treatment option for further pre-clinical and clinical studies against HBV infection. IMPORTANCE: The main challenge for the achievement of a functional cure for hepatitis B virus (HBV) is the covalently closed circular DNA (cccDNA), the highly stable persistence reservoir of HBV, which is maintained by further rounds of infection with newly generated progeny viruses or by intracellular recycling of mature nucleocapsids. Eradication of the cccDNA is considered to be the holy grail for HBV curative treatment; however, current therapeutic approaches fail to directly tackle this HBV persistence reservoir. The molecular effect of arsenic trioxide (ATO) on HBV infection, protein expression, and cccDNA formation and maintenance, however, has not been characterized and understood until now. In this study, we reveal ATO treatment as a novel and innovative therapeutic approach against HBV infections, repressing viral gene expression and replication as well as the stable cccDNA pool at low micromolar concentrations by affecting the cellular function of promyelocytic leukemia nuclear bodies.

Nanopore Guided Annotation of Transcriptome Architectures.

High-resolution annotations of transcriptomes from all domains of life are essential for many sequencing-based RNA analyses, including Nanopore direct RNA sequencing (DRS), which would otherwise be hindered by misalignments and other analysis artefacts. DRS allows the capture and full-length sequencing of native RNAs, without recoding or amplification bias, and resulting data may be interrogated to define the identity and location of chemically modified ribonucleotides, as well as the length of poly(A) tails on individual RNA molecules. Existing software solutions for generating high-resolution transcriptome annotations are poorly suited to small gene dense organisms such as viruses due to the challenge of identifying distinct transcript isoforms where alternative splicing and overlapping RNAs are prevalent. To resolve this, we identified key characteristics of DRS datasets and developed a novel approach to transcriptome. We demonstrate, using a combination of synthetic and original datasets, that our novel approach yields a high level of precision and recall when reconstructing both gene sparse and gene dense transcriptomes from DRS datasets. We further apply this approach to generate a new high resolution transcriptome annotation of the neglected pathogen human adenovirus type F 41 for which we identify 77 distinct transcripts encoding at least 23 different proteins.

Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCT.

Objective: To identify the individual progression of geographic atrophy (GA) lesions from baseline OCT images of patients in routine clinical care. Design: Clinical evaluation of a deep learning-based algorithm. Subjects: One hundred eighty-four eyes of 100 consecutively enrolled patients. Methods: OCT and fundus autofluorescence (FAF) images (both Spectralis, Heidelberg Engineering) of patients with GA secondary to age-related macular degeneration in routine clinical care were used for model validation. Fundus autofluorescence images were annotated manually by delineating the GA area by certified readers of the Vienna Reading Center. The annotated FAF images were anatomically registered in an automated manner to the corresponding OCT scans, resulting in 2-dimensional en face OCT annotations, which were taken as a reference for the model performance. A deep learning-based method for modeling the GA lesion growth over time from a single baseline OCT was evaluated. In addition, the ability of the algorithm to identify fast progressors for the top 10%, 15%, and 20% of GA growth rates was analyzed. Main Outcome Measures: Dice similarity coefficient (DSC) and mean absolute error (MAE) between manual and predicted GA growth. Results: The deep learning-based tool was able to reliably identify disease activity in GA using a standard OCT image taken at a single baseline time point. The mean DSC for the total GA region increased for the first 2 years of prediction (0.80-0.82). With increasing time intervals beyond 3 years, the DSC decreased slightly to a mean of 0.70. The MAE was low over the first year and with advancing time slowly increased, with mean values ranging from 0.25 mm to 0.69 mm for the total GA region prediction. The model achieved an area under the curve of 0.81, 0.79, and 0.77 for the identification of the top 10%, 15%, and 20% growth rates, respectively. Conclusions: The proposed algorithm is capable of fully automated GA lesion growth prediction from a single baseline OCT in a time-continuous fashion in the form of en face maps. The results are a promising step toward clinical decision support tools for therapeutic dosing and guidance of patient management because the first treatment for GA has recently become available. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs.

Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six-month interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans.

Morph-SSL: Self-Supervision with Longitudinal Morphing for Forecasting AMD Progression from OCT Volumes.

The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan. Although eye clinics generate vast amounts of longitudinal OCT scans to monitor AMD progression, only a small subset can be manually labeled for supervised DL. To address this issue, we propose Morph-SSL, a novel Self-supervised Learning (SSL) method for longitudinal data. It uses pairs of unlabelled OCT scans from different visits and involves morphing the scan from the previous visit to the next. The Decoder predicts the transformation for morphing and ensures a smooth feature manifold that can generate intermediate scans between visits through linear interpolation. Next, the Morph-SSL trained features are input to a Classifier which is trained in a supervised manner to model the cumulative probability distribution of the time to conversion with a sigmoidal function. Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). The Classifier was evaluated with a five-fold cross-validation on 2418 scans from 343 eyes with clinical labels of the conversion date. The Morph-SSL features achieved an AUC of 0.779 in predicting the conversion to nAMD within the next 6 months, outperforming the same network when trained end-to-end from scratch or pre-trained with popular SSL methods. Automated prediction of the future risk of nAMD onset can enable timely treatment and individualized AMD management.

DMSO and Its Role in Differentiation Impact Efficacy of Human Adenovirus (HAdV) Infection in HepaRG Cells.

Differentiated HepaRG cells are popular in vitro cell models for hepatotoxicity studies. Their differentiation is usually supported by the addition of dimethyl sulfoxide (DMSO), an amphipathic solvent widely used in biomedicine, for example, in potential novel therapeutic drugs and cryopreservation of oocytes. Recent studies have demonstrated drastic effects, especially on epigenetics and extracellular matrix composition, induced by DMSO, making its postulated inert character doubtful. In this work, the influence of DMSO and DMSO-mediated modulation of differentiation on human adenovirus (HAdV) infection of HepaRG cells was investigated. We observed an increase in infectivity of HepaRG cells by HAdVs in the presence of 1% DMSO. However, this effect was dependent on the type of medium used for cell cultivation, as cells in William's E medium showed significantly stronger effects compared with those cultivated in DMEM. Using different DMSO concentrations, we proved that the impact of DMSO on infectability was dose-dependent. Infection of cells with a replication-deficient HAdV type demonstrated that the mode of action of DMSO was based on viral entry rather than on viral replication. Taken together, these results highlight the strong influence of the used cell-culture medium on the performed experiments as well as the impact of DMSO on infectivity of HepaRG cells by HAdVs. As this solvent is widely used in cell culture, those effects must be considered, especially in screening of new antiviral compounds.

Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection.

Automated lesion detection in retinal optical coherence tomography (OCT) scans has shown promise for several clinical applications, including diagnosis, monitoring and guidance of treatment decisions. However, segmentation models still struggle to achieve the desired results for some complex lesions or datasets that commonly occur in real-world, e.g. due to variability of lesion phenotypes, image quality or disease appearance. While several techniques have been proposed to improve them, one line of research that has not yet been investigated is the incorporation of additional semantic context through the application of anomaly detection models. In this study we experimentally show that incorporating weak anomaly labels to standard segmentation models consistently improves lesion segmentation results. This can be done relatively easy by detecting anomalies with a separate model and then adding these output masks as an extra class for training the segmentation model. This provides additional semantic context without requiring extra manual labels. We empirically validated this strategy using two in-house and two publicly available retinal OCT datasets for multiple lesion targets, demonstrating the potential of this generic anomaly guided segmentation approach to be used as an extra tool for improving lesion detection models.

Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).

Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis. We have developed a deep learning-based age-related macular degeneration (AMD) stage classifier, to efficiently identify the first onset of early/intermediate (iAMD), atrophic (GA), and neovascular (nAMD) stage of AMD in retrospective data. We trained a two-stage convolutional neural network to classify macula-centered 3D volumes from Topcon OCT images into 4 classes: Normal, iAMD, GA and nAMD. In the first stage, a 2D ResNet50 is trained to identify the disease categories on the individual OCT B-scans while in the second stage, four smaller models (ResNets) use the concatenated B-scan-wise output from the first stage to classify the entire OCT volume. Classification uncertainty estimates are generated with Monte-Carlo dropout at inference time. The model was trained on a real-world OCT dataset, 3765 scans of 1849 eyes, and extensively evaluated, where it reached an average ROC-AUC of 0.94 in a real-world test set.

Comparison of Novel Volumetric Microperimetry Metrics in Intermediate Age-Related Macular Degeneration: PINNACLE Study Report 3.

Purpose: To investigate and compare novel volumetric microperimetry (MP)-derived metrics in intermediate age-related macular degeneration (iAMD), as current MP metrics show high variability and low sensitivity. Methods: This is a cross-sectional analysis of microperimetry baseline data from the multicenter, prospective PINNACLE study (ClinicalTrials.gov NCT04269304). The Visual Field Modeling and Analysis (VFMA) software and an open-source implementation (OSI) were applied to calculate MP-derived hill-of-vison (HOV) surface plots and the total volume (VTOT) beneath the plots. Bland-Altman plots were used for methodologic comparison, and the association of retinal sensitivity metrics with explanatory variables was tested with mixed-effects models. Results: In total, 247 eyes of 189 participants (75 ± 7.3 years) were included in the analysis. The VTOT output of VFMA and OSI exhibited a significant difference (P < 0.0001). VFMA yielded slightly higher coefficients of determination than OSI and mean sensitivity (MS) in univariable and multivariable modeling, for example, in association with low-luminance visual acuity (LLVA) (marginal R2/conditional R2: VFMA 0.171/0.771, OSI 0.162/0.765, MS 0.133/0.755). In the multivariable analysis, LLVA was the only demonstrable predictor of VFMA VTOT (t-value, P-value: -7.5, <0.001) and MS (-6.5, <0.001). Conclusions: The HOV-derived metric of VTOT exhibits favorable characteristics compared to MS in evaluating retinal sensitivity. The output of VFMA and OSI is not exactly interchangeable in this cross-sectional analysis. Longitudinal analysis is necessary to assess their performance in ability-to-detect change. Translational Relevance: This study explores new volumetric MP endpoints for future application in therapeutic trials in iAMD and reports specific characteristics of the available HOV software applications.

[Monitoring of the progression of geographic atrophy with optical coherence tomography]. / Monitoring der Progression von geografischer Atrophie in der optischen Kohärenztomographie.

With the prospect of available therapy for geographic atrophy in the near future and consequently increasing patient numbers, appropriate management strategies for the clinical practice are needed. Optical coherence tomography (OCT) as well as automated OCT analysis using artificial intelligence algorithms provide optimal conditions for assessing disease activity as well as the treatment response for geographic atrophy through a rapid, precise and resource-efficient evaluation.

Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment.

Geographic atrophy (GA) represents a late stage of age-related macular degeneration, which leads to irreversible vision loss. With the first successful therapeutic approach, namely complement inhibition, huge numbers of patients will have to be monitored regularly. Given these perspectives, a strong need for automated GA segmentation has evolved. The main purpose of this study was the clinical validation of an artificial intelligence (AI)-based algorithm to segment a topographic 2D GA area on a 3D optical coherence tomography (OCT) volume, and to evaluate its potential for AI-based monitoring of GA progression under complement-targeted treatment. 100 GA patients from routine clinical care at the Medical University of Vienna for internal validation and 113 patients from the FILLY phase 2 clinical trial for external validation were included. Mean Dice Similarity Coefficient (DSC) was 0.86 ± 0.12 and 0.91 ± 0.05 for total GA area on the internal and external validation, respectively. Mean DSC for the GA growth area at month 12 on the external test set was 0.46 ± 0.16. Importantly, the automated segmentation by the algorithm corresponded to the outcome of the original FILLY trial measured manually on fundus autofluorescence. The proposed AI approach can reliably segment GA area on OCT with high accuracy. The availability of such tools represents an important step towards AI-based monitoring of GA progression under treatment on OCT for clinical management as well as regulatory trials.

Segmentation of Bruch's Membrane in Retinal OCT With AMD Using Anatomical Priors and Uncertainty Quantification.

Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world. Automated BM segmentation methods exist, but they usually do not account for the anatomical coherence of the results, neither provide feedback on the confidence of the prediction. These factors limit the applicability of these systems in real-world scenarios. With this in mind, we propose an end-to-end deep learning method for automated BM segmentation in AMD patients. An Attention U-Net is trained to output a probability density function of the BM position, while taking into account the natural curvature of the surface. Besides the surface position, the method also estimates an A-scan wise uncertainty measure of the segmentation output. Subsequently, the A-scans with high uncertainty are interpolated using thin plate splines (TPS). We tested our method with ablation studies on an internal dataset with 138 patients covering all three AMD stages, and achieved a mean absolute localization error of 4.10 µm. In addition, the proposed segmentation method was compared against the state-of-the-art methods and showed a superior performance on an external publicly available dataset from a different patient cohort and OCT device, demonstrating strong generalization ability.

Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning.

PURPOSE: To identify disease activity and effects of intravitreal pegcetacoplan treatment on the topographic progression of geographic atrophy (GA) secondary to age-related macular degeneration quantified in spectral-domain OCT (SD-OCT) by automated deep learning assessment. DESIGN: Retrospective analysis of a phase II clinical trial study evaluating pegcetacoplan in GA patients (FILLY, NCT02503332). SUBJECTS: SD-OCT scans of 57 eyes with monthly treatment, 46 eyes with every-other-month (EOM) treatment, and 53 eyes with sham injection from baseline and 12-month follow-ups were included, in a total of 312 scans. METHODS: Retinal pigment epithelium loss, photoreceptor (PR) integrity, and hyperreflective foci (HRF) were automatically segmented using validated deep learning algorithms. Local progression rate (LPR) was determined from a growth model measuring the local expansion of GA margins between baseline and 1 year. For each individual margin point, the eccentricity to the foveal center, the progression direction, mean PR thickness, and HRF concentration in the junctional zone were computed. Mean LPR in disease activity and treatment effect conditioned on these properties were estimated by spatial generalized additive mixed-effect models. MAIN OUTCOME MEASURES: LPR of GA, PR thickness, and HRF concentration in µm. RESULTS: A total of 31,527 local GA margin locations were analyzed. LPR was higher for areas with low eccentricity to the fovea, thinner PR layer thickness, or higher HRF concentration in the GA junctional zone. When controlling for topographic and structural risk factors, we report on average a significantly lower LPR by -28.0% (95% confidence interval [CI], -42.8 to -9.4; P = 0.0051) and -23.9% (95% CI, -40.2 to -3.0; P = 0.027) for monthly and EOM-treated eyes, respectively, compared with sham. CONCLUSIONS: Assessing GA progression on a topographic level is essential to capture the pathognomonic heterogeneity in individual lesion growth and therapeutic response. Pegcetacoplan-treated eyes showed a significantly slower GA lesion progression rate compared with sham, and an even slower growth rate toward the fovea. This study may help to identify patient cohorts with faster progressing lesions, in which pegcetacoplan treatment would be particularly beneficial. Automated artificial intelligence-based tools will provide reliable guidance for the management of GA in clinical practice.

Comparison of Fundus Autofluorescence Versus Optical Coherence Tomography-based Evaluation of the Therapeutic Response to Pegcetacoplan in Geographic Atrophy.

PURPOSE: To perform an optical coherence tomography (OCT)-based analysis of geographic atrophy (GA) progression in patients treated with pegcetacoplan. DESIGN: Post hoc analysis of a phase 2 multicenter, randomized, sham-controlled trial. METHODS: Manual annotation of retinal pigment epithelium (RPE), ellipsoid zone (EZ), and external limiting membrane (ELM) loss was performed on OCT volumes from baseline and month 12 from the phase 2 FILLY trial of intravitreal pegcetacoplan for the treatment of GA secondary to age-related macular degeneration. MAIN OUTCOME MEASURES: Correlation of GA areas measured on fundus autofluorescence and OCT. Difference in square root transformed growth rates of RPE, EZ, and ELM loss between treatment groups (monthly injection [AM], injection every other month [AEOM], and sham [SM]). RESULTS: OCT volumes from 113 eyes of 113 patients (38 AM, 36 AEOM, and 39 SM) were included, resulting in 11 074 B-scans. The median growth of RPE loss was significantly slower in the AM group (0.158 [0.057-0.296]) than the SM group (0.255 [0.188-0.359], P = .014). Importantly, the growth of EZ loss was also significantly slower in the AM group (0.127 [0.041-0.247]) than the SM group (0.232 [0.130-0.349], P = .017). There was no significant difference in the growth of ELM loss between the treatment groups (P = .114). CONCLUSIONS: OCT imaging provided consistent results for GA growth compared with fundus autofluorescence. In addition to slower RPE atrophy progression in patients treated with pegcetacoplan, a significant reduction in EZ impairment was also identified by OCT, suggesting the use of OCT as a potentially more sensitive monitoring tool in GA therapy.

The Effect of Pegcetacoplan Treatment on Photoreceptor Maintenance in Geographic Atrophy Monitored by Artificial Intelligence-Based OCT Analysis.

PURPOSE: To investigate the therapeutic effect of intravitreal pegcetacoplan on the inhibition of photoreceptor (PR) loss and thinning in geographic atrophy (GA) on conventional spectral-domain OCT (SD-OCT) imaging by deep learning-based automated PR quantification. DESIGN: Post hoc analysis of a prospective, multicenter, randomized, sham (SM)-controlled, masked phase II trial investigating the safety and efficacy of pegcetacoplan for the treatment of GA because of age-related macular degeneration. PARTICIPANTS: Study eyes of 246 patients, randomized 1:1:1 to monthly (AM), bimonthly (AEOM), and SM treatment. METHODS: We performed fully automated, deep learning-based segmentation of retinal pigment epithelium (RPE) loss and PR thickness on SD-OCT volumes acquired at baseline and months 2, 6, and 12. The difference in the change of PR loss area was compared among the treatment arms. Change in PR thickness adjacent to the GA borders and the entire 20° scanning area was compared between treatment arms. MAIN OUTCOME MEASURES: Square-root transformed PR loss area in µm or mm, PR thickness in µm, and PR loss/RPE loss ratio. RESULTS: A total of 31 556 B-scans of 644 SD-OCT volumes of 161 study eyes (AM 52, AEOM 54, SM 56) were evaluated from baseline to month 12. Comparison of the mean change in PR loss area revealed statistically significantly less growth in the AM group at months 2, 6, and 12 than in the SM group (-41 µm ± 219 vs. 77 µm ± 126; P = 0.0004; -5 µm ± 221 vs. 156 µm ± 139; P < 0.0001; 106 µm ± 400 vs. 283 µm ± 226; P = 0.0014). Photoreceptor thinning was significantly reduced under AM treatment compared with SM within the GA junctional zone, as well as throughout the 20° area. A trend toward greater inhibition of PR loss than RPE loss was observed under therapy. CONCLUSIONS: Distinct and reliable quantification of PR loss using deep learning-based algorithms offers an essential tool to evaluate therapeutic efficacy in slowing disease progression. Photoreceptor loss and thinning are reduced by intravitreal complement C3 inhibition. Automated quantification of PR loss/maintenance based on OCT images is an ideal approach to reliably monitor disease activity and therapeutic efficacy in GA management in clinical routine and regulatory trials.

PML Alternative Splice Products Differentially Regulate HAdV Productive Infection.

Promyelocytic leukemia nuclear bodies (PML-NBs) were considered to maintain antiviral capacity, as these spherical complexes are antagonized by viruses. Actual work provides evidence, that PML-NB-associated factors might also be beneficial for distinct viral processes indicating why genomes and replication centers of nuclear replicating viruses are often found juxtaposed to PML-NBs. Several early HAdV proteins target PML-NBs, such as E4orf3 that promotes redistribution into track-like structures. PML-associated dependency factors that enhance viral gene expression, such as Sp100A remain in the nuclear tracks while restrictive factors, such as Daxx, are inhibited by either proteasomal degradation or relocalization to repress antiviral functions. Here, we did a comprehensive analysis of nuclear PML isoforms during HAdV infection. Our results show cell line specific differences as PML isoforms differentially regulate productive HAdV replication and progeny production. Here, we identified PML-II as a dependency factor that supports viral progeny production, while PML-III and PML-IV suppress viral replication. In contrast, we identified PML-I as a positive regulator and PML-V as a restrictive factor during HAdV infection. Solely PML-VI was shown to repress adenoviral progeny production in both model systems. We showed for the first time, that HAdV can reorganize PML-NBs that contain PML isoforms other then PML-II. Intriguingly, HAdV was not able to fully disrupt PML-NBs composed out of the PML isoforms that inhibit viral replication, while PML-NBs composed out of PML isoforms with beneficial influence on the virus formed tracks in all examined cells. In sum, our findings clearly illustrate the crucial role of PML-track formation in efficient viral replication. IMPORTANCE Actual work provides evidence that PML-NB-associated factors might also be beneficial for distinct viral processes indicating why genomes and replication centers of nuclear replicating viruses are often found juxtaposed to PML-NBs. Alternatively spliced PML isoforms I-VII are expressed from one single pml gene containing nine exons and their transcription is tightly controlled and stimulated by interferons and p53. Several early HAdV proteins target PML-NBs, such as E4orf3, promoting redistribution into track-like structures. Our comprehensive studies indicate a diverging role of PML isoforms throughout the course of productive HAdV infection in either stably transformed human lung (H1299) or liver (HepG2) cells, in which we observed a multivalent regulation of HAdV by all six PML isoforms. PML-I and PML-II support HAdV-mediated track formation and efficient formation of viral replication centers, thus promoting HAdV productive infection. Simultaneously, PML-III, -IV,-V, and -VI antagonize viral gene expression and particle production.