Double deletion of PINK1 and Parkin impairs hepatic mitophagy and exacerbates acetaminophen-induced liver injury in mice

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Mitochondria damage plays a critical role in acetaminophen (APAP)-induced necrosis and liver injury. Cells can adapt and protect themselves by removing damaged mitochondria via mitophagy. PINK1-Parkin pathway is one of the major pathways that regulate mitophagy but its role in APAP-induced liver injury is still elusive. We investigated the role of PINK1-Parkin pathway in hepatocyte mitophagy in APAP-induced liver injury in mice. Wild-type (WT), PINK1 knockout (KO), Parkin KO, and PINK1 and Parkin double KO (DKO) mice were treated with APAP for different time points. Liver injury was determined by measuring serum alanine aminotransferase (ALT) activity, H&E staining as well as TUNEL staining of liver tissues. Tandem fluorescent-tagged inner mitochondrial membrane protein Cox8 (Cox8-GFP-mCherry) can be used to monitor mitophagy based on different pH stability of GFP and mCherry fluorescent proteins. We overexpressed Cox8-GFP-mCherry in mouse livers via tail vein injection of an adenovirus Cox8-GFP-mCherry. Mitophagy was assessed by confocal microscopy for Cox8-GFP-mCherry puncta, electron microscopy (EM) analysis for mitophagosomes and western blot analysis for mitochondrial proteins. Parkin KO and PINK1 KO mice improved the survival after treatment with APAP although the serum levels of ALT were not significantly different among PINK1 KO, Parkin KO and WT mice. We only found mild defects of mitophagy in PINK1 KO or Parkin KO mice after APAP, and improved survival in PINK1 KO and Parkin KO mice could be due to other functions of PINK1 and Parkin independent of mitophagy. In contrast, APAP-induced mitophagy was significantly impaired in PINK1-Parkin DKO mice. PINK1-Parkin DKO mice had further elevated serum levels of ALT and increased mortality after APAP administration. In conclusion, our results demonstrated that PINK1-Parkin signaling pathway plays a critical role in APAP-induced mitophagy and liver injury.NIH R01 AA 020518NIH R01 DK 102142NIH U01 AA 024733NIH P20 GM 103549NIH P30 GM 118247NIH COBRE grant 9P20GM104936NIH S10RR02756

Wireless Communication Networks for Gas Turbine Engine Testing

A new trend in the field of Aeronautical Engine Health Monitoring is the implementation of wireless sensor networks (WSNs) for data acquisition and condition monitoring to partially replace heavy and complex wiring harnesses, which limit the versatility of the monitoring process as well as creating practical deployment issues. Using wireless technologies instead of fixed wiring will fuel opportunities for reduced cabling, faster sensor and network deployment, increased data acquisition flexibility and reduced cable maintenance costs. However, embedding wireless technology into an aero engine (even in the ground testing application considered here) presents some very significant challenges, e.g. a harsh environment with a complex RF transmission environment, high sensor density and high data-rate. In this paper we discuss the results of the Wireless Data Acquisition in Gas Turbine Engine Testing (WIDAGATE) project, which aimed to design and simulate such a network to estimate network performance and de-risk the wireless techniques before the deployment

A Novel Flow Cytometric HTS Assay Reveals Functional Modulators of ATP Binding Cassette Transporter ABCB6

ABCB6 is a member of the adenosine triphosphate (ATP)-binding cassette family of transporter proteins that is increasingly recognized as a relevant physiological and therapeutic target. Evaluation of modulators of ABCB6 activity would pave the way toward a more complete understanding of the significance of this transport process in tumor cell growth, proliferation and therapy-related drug resistance. In addition, this effort would improve our understanding of the function of ABCB6 in normal physiology with respect to heme biosynthesis, and cellular adaptation to metabolic demand and stress responses. To search for modulators of ABCB6, we developed a novel cell-based approach that, in combination with flow cytometric high-throughput screening (HTS), can be used to identify functional modulators of ABCB6. Accumulation of protoporphyrin, a fluorescent molecule, in wild-type ABCB6 expressing K562 cells, forms the basis of the HTS assay. Screening the Prestwick Chemical Library employing the HTS assay identified four compounds, benzethonium chloride, verteporfin, tomatine hydrochloride and piperlongumine, that reduced ABCB6 mediated cellular porphyrin levels. Validation of the identified compounds employing the hemin-agarose affinity chromatography and mitochondrial transport assays demonstrated that three out of the four compounds were capable of inhibiting ABCB6 mediated hemin transport into isolated mitochondria. However, only verteporfin and tomatine hydrochloride inhibited ABCB6’s ability to compete with hemin as an ABCB6 substrate. This assay is therefore sensitive, robust, and suitable for automation in a high-throughput environment as demonstrated by our identification of selective functional modulators of ABCB6. Application of this assay to other libraries of synthetic compounds and natural products is expected to identify novel modulators of ABCB6 activity

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

What I Thought I Wanted? Miswanting and Regret for a Standard Good in a Mass-Customized World

How can a standardized product survive in a mass-customized world? This requires understanding that consumers often experience problems predicting their future hedonic reactions to new experiences (such as custom products), leading to feelings of regret. This form of regret occurs not because the custom product differs from specifications, but because consumers miswanted the design they ordered. Our analytic model shows that regret aversion induces consumers to design custom products to reflect the attributes of the available standard products. Consequently, regret-averse consumers may choose the standard product rather than place a custom order. The number of available standard products, however, moderates both these effects. Two experiments empirically substantiate the key predictions of the analytical model: (a) the custom product's resemblance to the standard product grows with regret aversion associated with miswanting, (b) there exists a segment of “regretfully loyal consumers” for the standard product in a mass-customized world and it expands with regret aversion, (c) both the above effects are weakened by the presence of a second standard product, and (d) the custom product can increase its market share when the number of standard products increases.customization, standard goods, preference uncertainty, regret

Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases

Much work in recent years has gone into the construction of large knowledge bases (KBs), such as Freebase, DBPedia, NELL, and YAGO. While these KBs are very large, they are still very incomplete, necessitating the use of inference to fill in gaps. Prior work has shown how to make use of a large text corpus to augment random walk inference over KBs. We present two improvements to the use of such large corpora to augment KB inference. First, we present a new technique for combining KB relations and surface text into a single graph representation that is much more compact than graphs used in prior work. Second, we describe how to incorporate vector space similarity into random walk inference over KBs, reducing the feature sparsity inherent in using surface text. This allows us to combine distributional similarity with symbolic logical inference in novel and effective ways. With experiments on many relations from two separate KBs, we show that our methods significantly outperform prior work on KB inference, both in the size of problem our methods can handle and in the quality of predictions made.</p