Effects of Mitochondrial Nucleases on mtDNA Degradation

Mitochondria are unique to have a multicopy genome, resulting in a substantially different fate of damaged DNA molecules in comparison to nuclear DNA. Damaged DNA usually represents only a small fraction of total mitochondrial DNA (mtDNA) in a cell, which can be removed, through DNA degradation, without severe consequences and get replaced by replication of intact mtDNA. This idea of a "disposable genome" plays an essential role for modern gene therapy of mitochondrial diseases, which aim to eliminate pathogenic mtDNA mutations by selectively breaking down mutated mtDNA. Introducing mtDNA double-strand breaks (DSB), elimination of paternal mtDNA or virus-induced mtDNA depletion are described phenomena of eliminating mtDNA. The molecular machinery performing mtDNA degradation is still unknown. This work used the CRISPR/Cas9 technique to create stable knockout and knockin cell lines of selected mitochondrial nucleases in a cellular model to study degradation of linear mtDNA. An induced expression of restriction endonuclease mitoEagI introduced DSB into the mitochondrial genome of living cells, linearizing it in the process. Inactivation of the mitochondrial 5'–3' exonuclease MGME1 and the 3'–5' exonuclease activity of POLG (a subunit of the mitochondrial DNA polymerase gamma), through introducing the p.D274A point mutation severely impaired rapid linear mtDNA degradation. Additional knockout cell lines of other mitochondrial nucleases (APEX2, EXOG) showed no deficiencies on linear mtDNA degradation. Along with recent findings, that the mitochondrial DNA helicase Twinkle is also involved in linear mtDNA degradation (Peeva and Blei et al., 2018), this altogether proposes novel, additional roles for the mtDNA replication machinery

Replication fork rescue in mammalian mitochondria

Replication stalling has been associated with the formation of pathological mitochondrial DNA (mtDNA) rearrangements. Yet, almost nothing is known about the fate of stalled replication intermediates in mitochondria. We show here that replication stalling in mitochondria leads to replication fork regression and mtDNA double-strand breaks. The resulting mtDNA fragments are normally degraded by a mechanism involving the mitochondrial exonuclease MGME1, and the loss of this enzyme results in accumulation of linear and recombining mtDNA species. Additionally, replication stress promotes the initiation of alternative replication origins as an apparent means of rescue by fork convergence. Besides demonstrating an interplay between two major mechanisms rescuing stalled replication forks - mtDNA degradation and homology-dependent repair - our data provide evidence that mitochondria employ similar mechanisms to cope with replication stress as known from other genetic systems.Peer reviewe

Linear mitochondrial DNA is rapidly degraded by components of the replication machinery.

Emerging gene therapy approaches that aim to eliminate pathogenic mutations of mitochondrial DNA (mtDNA) rely on efficient degradation of linearized mtDNA, but the enzymatic machinery performing this task is presently unknown. Here, we show that, in cellular models of restriction endonuclease-induced mtDNA double-strand breaks, linear mtDNA is eliminated within hours by exonucleolytic activities. Inactivation of the mitochondrial 5'-3'exonuclease MGME1, elimination of the 3'-5'exonuclease activity of the mitochondrial DNA polymerase POLG by introducing the p.D274A mutation, or knockdown of the mitochondrial DNA helicase TWNK leads to severe impediment of mtDNA degradation. We do not observe similar effects when inactivating other known mitochondrial nucleases (EXOG, APEX2, ENDOG, FEN1, DNA2, MRE11, or RBBP8). Our data suggest that rapid degradation of linearized mtDNA is performed by the same machinery that is responsible for mtDNA replication, thus proposing novel roles for the participating enzymes POLG, TWNK, and MGME1

Near fatal posterior reversible encephalopathy syndrome complicating chronic liver failure and treated by induced hypothermia and dialysis: a case report

<p>Abstract</p> <p>Introduction</p> <p>Posterior reversible encephalopathy syndrome is a clinico-neuroradiological entity characterized by headache, vomiting, altered mental status, blurred vision and seizures with neuroimaging studies demonstrating white-gray matter edema involving predominantly the posterior region of the brain.</p> <p>Case presentation</p> <p>We report a 47-year-old Caucasian man with liver cirrhosis who developed posterior reversible encephalopathy syndrome following an upper gastrointestinal hemorrhage and who was managed with induced hypothermia for control of intracranial hypertension and continuous veno-venous hemodiafiltration for severe hyperammonemia.</p> <p>Conclusion</p> <p>We believe this is the first documented case report of posterior reversible encephalopathy syndrome associated with cirrhosis as well as the first report of the use of induced hypothermia and continuous veno-venous hemodiafiltration in this setting.</p

Joint Inference in Weakly-Annotated Image Datasets via Dense Correspondence

We present a principled framework for inferring pixel labels in weakly-annotated image datasets. Most previous, example-based approaches to computer vision rely on a large corpus of densely labeled images. However, for large, modern image datasets, such labels are expensive to obtain and are often unavailable. We establish a large-scale graphical model spanning all labeled and unlabeled images, then solve it to infer pixel labels jointly for all images in the dataset while enforcing consistent annotations over similar visual patterns. This model requires significantly less labeled data and assists in resolving ambiguities by propagating inferred annotations from images with stronger local visual evidences to images with weaker local evidences. We apply our proposed framework to two computer vision problems, namely image annotation with semantic segmentation, and object discovery and co-segmentation (segmenting multiple images containing a common object). Extensive numerical evaluations and comparisons show that our method consistently outperforms the state-of-the-art in automatic annotation and semantic labeling, while requiring significantly less labeled data. In contrast to previous co-segmentation techniques, our method manages to discover and segment objects well even in the presence of substantial amounts of noise images (images not containing the common object), as typical for datasets collected from Internet search

If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills

Interview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills - the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.Comment: Working draft. To appear in the ACM CHI Conference on Human Factors in Computing Systems (CHI 2020

Studying user income through language, behaviour and affect in social media

Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions