Mycobacterium tuberculosis Type VII Secreted Effector EsxH Targets Host ESCRT to Impair Trafficking

Mycobacterium tuberculosis (Mtb) disrupts anti-microbial pathways of macrophages, cells that normally kill bacteria. Over 40 years ago, D'Arcy Hart showed that Mtb avoids delivery to lysosomes, but the molecular mechanisms that allow Mtb to elude lysosomal degradation are poorly understood. Specialized secretion systems are often used by bacterial pathogens to translocate effectors that target the host, and Mtb encodes type VII secretion systems (TSSSs) that enable mycobacteria to secrete proteins across their complex cell envelope; however, their cellular targets are unknown. Here, we describe a systematic strategy to identify bacterial virulence factors by looking for interactions between the Mtb secretome and host proteins using a high throughput, high stringency, yeast two-hybrid (Y2H) platform. Using this approach we identified an interaction between EsxH, which is secreted by the Esx-3 TSSS, and human hepatocyte growth factor-regulated tyrosine kinase substrate (Hgs/Hrs), a component of the endosomal sorting complex required for transport (ESCRT). ESCRT has a well-described role in directing proteins destined for lysosomal degradation into intraluminal vesicles (ILVs) of multivesicular bodies (MVBs), ensuring degradation of the sorted cargo upon MVB-lysosome fusion. Here, we show that ESCRT is required to deliver Mtb to the lysosome and to restrict intracellular bacterial growth. Further, EsxH, in complex with EsxG, disrupts ESCRT function and impairs phagosome maturation. Thus, we demonstrate a role for a TSSS and the host ESCRT machinery in one of the central features of tuberculosis pathogenesis

Context-dependent compensation among phosphatidylserine-recognition receptors

Phagocytes express multiple phosphatidylserine (PtdSer) receptors that recognize apoptotic cells. It is unknown whether these receptors are interchangeable or if they play unique roles during cell clearance. Loss of the PtdSer receptor Mertk is associated with apoptotic corpse accumulation in the testes and degeneration of photoreceptors in the eye. Both phenotypes are linked to impaired phagocytosis by specialized phagocytes: Sertoli cells and the retinal pigmented epithelium (RPE). Here, we overexpressed the PtdSer receptor BAI1 in mice lacking MerTK (Mertk(-/-) Bai1(Tg)) to evaluate PtdSer receptor compensation in vivo. While Bai1 overexpression rescues clearance of apoptotic germ cells in the testes of Mertk(-/-) mice it fails to enhance RPE phagocytosis or prevent photoreceptor degeneration. To determine why MerTK is critical to RPE function, we examined visual cycle intermediates and performed unbiased RNAseq analysis of RPE from Mertk(+/+) and Mertk(-/-) mice. Prior to the onset of photoreceptor degeneration, Mertk(-/-) mice had less accumulation of retinyl esters and dysregulation of a striking array of genes, including genes related to phagocytosis, metabolism, and retinal disease in humans. Collectively, these experiments establish that not all phagocytic receptors are functionally equal, and that compensation among specific engulfment receptors is context and tissue dependent

A high throughput live transparent animal bioassay to identify non-toxic small molecules or genes that regulate vertebrate fat metabolism for obesity drug development

<p>Abstract</p> <p>Background</p> <p>The alarming rise in the obesity epidemic and growing concern for the pathologic consequences of the metabolic syndrome warrant great need for development of obesity-related pharmacotherapeutics. The search for such therapeutics is severely limited by the slow throughput of animal models of obesity. Amenable to placement into a 96 well plate, zebrafish larvae have emerged as one of the highest throughput vertebrate model organisms for performing small molecule screens. A method for visually identifying non-toxic molecular effectors of fat metabolism using a live transparent vertebrate was developed. Given that increased levels of nicotinamide adenine dinucleotide (NAD) via deletion of CD38 have been shown to prevent high fat diet induced obesity in mice in a SIRT-1 dependent fashion we explored the possibility of directly applying NAD to zebrafish.</p> <p>Methods</p> <p>Zebrafish larvae were incubated with daily refreshing of nile red containing media starting from a developmental stage of equivalent fat content among siblings (3 days post-fertilization, dpf) and continuing with daily refreshing until 7 dpf.</p> <p>Results</p> <p>PPAR activators, beta-adrenergic agonists, SIRT-1 activators, and nicotinic acid treatment all caused predicted changes in fat, cholesterol, and gene expression consistent with a high degree of evolutionary conservation of fat metabolism signal transduction extending from man to zebrafish larvae. All changes in fat content were visually quantifiable in a relative fashion using live zebrafish larvae nile red fluorescence microscopy. Resveratrol treatment caused the greatest and most consistent loss of fat content. The resveratrol tetramer Vaticanol B caused loss of fat equivalent in potency to resveratrol alone. Significantly, the direct administration of NAD decreased fat content in zebrafish. Results from knockdown of a zebrafish G-PCR ortholog previously determined to decrease fat content in <it>C. elegans </it>support that future GPR142 antagonists may be effective non-toxic anti-obesity therapeutics.</p> <p>Conclusion</p> <p>Owing to the apparently high level of evolutionary conservation of signal transduction pathways regulating lipid metabolism, the zebrafish can be useful for identifying non-toxic small molecules or pharmacological target gene products for developing molecular therapeutics for treating clinical obesity. Our results support the promising potential in applying NAD or resveratrol where the underlying target protein likely involves Sirtuin family member proteins. Furthermore data supports future studies focused on determining whether there is a high concentration window for resveratrol that is effective and non-toxic in high fat obesity murine models.</p

Lymphoma incidence, survival and prevalence 2004–2014 : sub-type analyses from the UK’s Haematological Malignancy Research Network

Background: Population-based information about cancer occurrence and survival are required to inform clinical practice and research; but for most lymphomas data are lacking. Methods: Set within a socio-demographically representative UK population of nearly 4 million, lymphoma data (N ¼ 5796) are from an established patient cohort. Results: Incidence, survival (overall and relative) and prevalence estimates for 420 subtypes are presented. With few exceptions, males tended to be diagnosed at younger ages and have significantly (Po0.05) higher incidence rates. Differences were greatest at younger ages: the o15 year male/female rate ratio for all subtypes combined being 2.2 (95% CI 1.3–3.4). These gender differences impacted on prevalence; most subtype estimates being significantly (Po0.05) higher in males than females. Outcome varied widely by subtype; survival of patients with nodular lymphocyte predominant Hodgkin lymphoma approached that of the general population, whereas less than a third of those with other B-cell (e.g., mantle cell) or T-cell (e.g., peripheral-T) lymphomas survived for Z5 years. No males/female survival differences were detected. Conclusions: Major strengths of our study include completeness of ascertainment, world-class diagnostics and generalisability. The marked variations demonstrated confirm the requirement for ‘real-world’ data to inform aetiological hypotheses, health-care planning and the future monitoring of therapeutic changes

Development of message passing-based graph convolutional networks for classifying cancer pathology reports

Background: Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results: We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions: Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive

The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

Summary Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Funding Bill & Melinda Gates Foundation.Bill & Melinda Gates Foundation.publishedVersio

A Keyword-enhanced Approach To Handle Class Imbalance In Clinical Text Classification

Recent applications ofdeep learning have shown promising results for classifying unstructured text in the healthcare domain. However, the reliability of models in production settings has been hindered by imbalanced data sets in which a small subset of the classes dominate. In the absence of adequate training data, rare classes necessitate additional model constraints for robust performance. Here, we present a strategy for incorporating short sequences of text (i.e. keywords) into training to boost model accuracy on rare classes. In our approach, we assemble a set of keywords, including short phrases, associated with each class. The keywords are then used as additional data during each batch of model training, resulting in a training loss that has contributions from both raw data and keywords. We evaluate our approach on classification of cancer pathology reports, which shows a substantial increase in model performance for rare classes. Furthermore, we analyze the impact of keywords on model output probabilities for bigrams, providing a straightforward method to identify model difficulties for limited training data