Biology subjects

Kagan, D.

Publications and source records attributed to Kagan, D..

3 recordsLinked to original sources

bioRxiv2024

Modeling and Correction of Protein Conformational Disease in iPSC-derived Neurons through Personalized Base Editing

AbstractAltered protein conformation can cause incurable neurodegenerative disorders. Mutations in SERPINI1, the gene encoding neuroserpin, can alter protein conformation resulting in cytotoxic aggregation leading to neuronal death. Familial encephalopathy with neuroserpin inclusion bodies (FENIB) is a rare autosomal dominant progressive myoclonic epilepsy that progresses to dementia and premature death. We developed HEK293T and induced pluripotent stem cell (iPSC) models of FENIB, harboring a patient-specific pathogenic SERPINI1 variant or stably overexpressing mutant neuroserpin fused to GFP (MUT NS-GFP). Here, we utilized a personalized adenine base editor (ABE)-mediated approach to correct the pathogenic variant efficiently and precisely to restore neuronal dendritic morphology. ABE-treated MUT NS-GFP cells demonstrated reduced inclusion size and number. Using an inducible MUT NS-GFP neuron system, we identified early prevention of toxic protein expression allowed aggregate clearance, while late prevention halted further aggregation. To address several challenges for clinical applications of gene correction, we developed a neuron-specific engineered virus-like particle to optimize neuronal ABE delivery, resulting in higher correction efficiency. Our findings provide a targeted strategy which may treat FENIB and potentially other neurodegenerative diseases due to altered protein conformation such as Alzheimers and Huntingtons diseases.

molecular biology↗

bioRxiv2023

Network-based anomaly detection algorithm reveals proteins with major roles in human tissues

BackgroundAnomaly detection in graphs is critical in various domains, notably in medicine and biology, where anomalies often encapsulate pivotal information. Here, we focused on network analysis of molecular interactions between proteins, which is commonly used to study and infer the impact of proteins on health and disease. In such a network, an anomalous protein might indicate its impact on the organisms health. ResultsWe propose Weighted Graph Anomalous Node Detection (WGAND), a novel machine learning-based method for detecting anomalies in weighted graphs. WGAND is based on the observation that edge patterns of anomalous nodes tend to deviate significantly from expected patterns. We quantified these deviations to generate features, and utilized the resulting features to model the anomaly of nodes, resulting in node anomaly scores. We created four variants of the WGAND methods and compared them to two previously-published (baseline) methods. We evaluated WGAND on data of protein interactions in 17 human tissues, where anomalous nodes corresponded to proteins with major roles in tissue contexts. In 13 of the tissues, WGAND obtained higher AUC and P@K than baseline methods. We demonstrate that WGAND effectively identified proteins that participate in tissue-specific processes and diseases. ConclusionWe present WGAND, a new approach to anomaly detection in weighted graphs. Our results underscore its capability to highlight critical proteins within protein-protein interaction networks. WGAND holds the promise to enhance our understanding of intricate biological processes and might pave the way for novel therapeutic strategies targeting tissue-specific diseases. Its versatility ensures its applicability across diverse weighted graphs, making it a robust tool for detecting anomalous nodes.

bioinformatics↗

bioRxiv2020

Scientometric Trends for Coronaviruses and Other Emerging Viral Infections

COVID-19 is the most rapidly expanding coronavirus outbreak in the past two decades. To provide a swift response to a novel outbreak, prior knowledge from similar outbreaks is essential. Here, we study the volume of research conducted on previous coronavirus outbreaks, specifically SARS and MERS, relative to other infectious diseases by analyzing over 35 million papers from the last 20 years. Our results demonstrate that previous coronavirus outbreaks have been understudied compared to other viruses. We also show that the research volume of emerging infectious diseases is very high after an outbreak and drops drastically upon the containment of the disease. This can yield inadequate research and limited investment in gaining a full understanding of novel coronavirus management and prevention. Independent of the outcome of the current COVID-19 outbreak, we believe that measures should be taken to encourage sustained research in the field.

microbiology↗