Genome-wide screen of cell-cycle regulators in normal and tumor cells identifies a differential response to nucleosome depletion

PMID: 27929715Peer reviewe

Glutamine synthetase expression rescues human dendritic cell survival in a glutamine-deprived environment

Introduction: Glutamine deficiency is a well-known feature of the tumor environment. Here we analyzed the impact of glutamine deprivation on human myeloid cell survival and function. Methods: Different types of myeloid cells were cultured in the absence or presence of glutamine and/or with L-methionine-S-sulfoximine (MSO), an irreversible glutamine synthetase (GS) inhibitor. GS expression was analyzed on mRNA and protein level. GS activity and the conversion of glutamate to glutamine by myeloid cells was followed by 13C tracing analyses. Results: The absence of extracellular glutamine only slightly affected postmitotic human monocyte to dendritic cell (DC) differentiation, function and survival. Similar results were obtained for monocyte-derived macrophages. In contrast, proliferation of the monocytic leukemia cell line THP-1 was significantly suppressed. While macrophages exhibited high constitutive GS expression, glutamine deprivation induced GS in DC and THP-1. Accordingly, proliferation of THP-1 was rescued by addition of the GS substrate glutamate and 13C tracing analyses revealed conversion of glutamate to glutamine. Supplementation with the GS inhibitor MSO reduced the survival of DC and macrophages and counteracted the proliferation rescue of THP-1 by glutamate. Discussion: Our results show that GS supports myeloid cell survival in a glutamine poor environment. Notably, in addition to suppressing proliferation and survival of tumor cells, the blockade of GS also targets immune cells such as DCs and macrophages

Bitter taste signaling in tracheal epithelial brush cells elicits innate immune responses to bacterial infection

Constant exposure of the airways to inhaled pathogens requires efficient early immune responses protecting against infections. How bacteria on the epithelial surface are detected and first-line protective mechanisms are initiated are not well understood. We have recently shown that tracheal brush cells (BCs) express functional taste receptors. Here we report that bitter taste signaling in murine BCs induces neurogenic inflammation. We demonstrate that BC signaling stimulates adjacent sensory nerve endings in the trachea to release the neuropeptides CGRP and substance P that mediate plasma extravasation, neutrophil recruitment, and diapedesis. Moreover, we show that bitter tasting quorum-sensing molecules from Pseudomonas aeruginosa activate tracheal BCs. BC signaling depends on the key taste transduction gene Trpm5, triggers secretion of immune mediators, among them the most abundant member of the complement system, and is needed to combat P. aeruginosa infections. Our data provide functional insight into firstline defense mechanisms against bacterial infections of the lung

Theoretical Determination of the pK a Values of Betalamic Acid Related to the Free Radical Scavenger Capacity: Comparison Between Empirical and Quantum Chemical Methods

Health benefits of dietary phytochemicals have been suggested in recent years. Among 1000s of different compounds, Betalains, which occur in vegetables of the Cariophyllalae order (cactus pear fruits and red beet), have been considered because of reducing power and potential to affect redox-modulated cellular processes. The antioxidant power of Betalains is strictly due to the dissociation rate of the acid moieties present in all the molecules of this family of phytochemicals. Experimentally, only the pK a values of betanin were determined. Recently, it was evidenced it was evidenced as the acid dissociation, at different environmental pHs, affects on its electron-donating capacity, and further on its free radical scavenging power. The identical correlation was studied on another Betalains family compound, Betalamic Acid. Experimental evidences showed that the free radical scavenging capacity of this compound drastically decreases at pH > 5, but pK a values were experimentally not measured. With the aim to justify the Betalamic Acid behavior as free radical scavenger, in this paper we tried to predict in silico the pK a values by means different approaches. Starting from the known experimental pK as of acid compounds, both phytochemicals and small organic, two empirical approaches and quantum-mechanical calculation were compared to give reliable prediction of the pK as of Betalamic Acid. Results by means these computational approaches are consistent with the experimental evidences. As shown herein, in silico, the totally dissociated species, at the experimental pH > 5 in solution, is predominant, exploiting the higher electron-donating capability (HOMO energy). Therefore, the computational estimated pK a values of Betalamic Acid resulted very reliable

Different subcellular localisations of TRIM22 suggest species-specific function

The B30.2/SPRY domain is present in many proteins, including various members of the tripartite motif (TRIM) protein family such as TRIM5α, which mediates innate intracellular resistance to retroviruses in several primate species. This resistance is dependent on the integrity of the B30.2 domain that evolves rapidly in primates and exhibits species-specific anti-viral activity. TRIM22 is another positively selected TRIM gene. Particularly, the B30.2 domain shows rapid evolution in the primate lineage and recently published data indicate an anti-viral function of TRIM22. We show here that human and rhesus TRIM22 localise to different subcellular compartments and that this difference can be assigned to the positively selected B30.2 domain. Moreover, we could demonstrate that amino acid changes in two variable loops (VL1 and VL3) are responsible for the different subcellular localisations

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Bitter taste signaling in tracheal epithelial brush cells elicits innate immune responses to bacterial infection.

peer reviewedConstant exposure of the airways to inhaled pathogens requires efficient early immune responses protecting against infections. How bacteria on the epithelial surface are detected and first-line protective mechanisms are initiated are not well understood. We have recently shown that tracheal brush cells (BCs) express functional taste receptors. Here we report that bitter taste signaling in murine BCs induces neurogenic inflammation. We demonstrate that BC signaling stimulates adjacent sensory nerve endings in the trachea to release the neuropeptides CGRP and substance P that mediate plasma extravasation, neutrophil recruitment, and diapedesis. Moreover, we show that bitter tasting quorum-sensing molecules from Pseudomonas aeruginosa activate tracheal BCs. BC signaling depends on the key taste transduction gene Trpm5, triggers secretion of immune mediators, among them the most abundant member of the complement system, and is needed to combat P. aeruginosa infections. Our data provide functional insight into first-line defense mechanisms against bacterial infections of the lung

The human body at cellular resolution: the NIH Human Biomolecular Atlas Program

Abstract: Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery