Absence of the Filarial Endosymbiont Wolbachia in Seal Heartworm (Acanthocheilonema spirocauda) but Evidence of Ancient Lateral Gene Transfer

The symbiotic relationship of Wolbachia spp. was first observed in insects and subsequently in many parasitic filarial nematodes. This bacterium is believed to provide metabolic and developmental assistance to filarial parasitic nematodes, although the exact nature of this relationship remains to be fully elucidated. While Wolbachia is present in most filarial nematodes in the familyOnchocercidae, it is absent in several disparate species such as the human parasite Loa loa. All tested members of the genusAcanthocheilonema, such as Acanthocheilonema viteae, have been shown to lack Wolbachia. Consistent with this, we show thatWolbachia is absent from the seal heartworm (Acanthocheilonema spirocauda), but lateral gene transfer (LGT) of DNA sequences between Wolbachia and A. spirocauda has occurred, indicating a past evolutionary association. Seal heartworm is an important pathogen of phocid seals and understanding its basic biology is essential for conservation of the host. The findings presented here may allow for the development of future treatments or diagnostics for the disease and also aid in clarification of the complicated nematode–Wolbachia relationship

Dynamic consent: a possible solution to improve patient confidence and trust in how electronic patient records are used in medical research

With one million people treated every 36 hours, routinely collected UK National Health Service (NHS) health data has huge potential for medical research. Advances in data acquisition from electronic patient records (EPRs) means such data are increasingly digital and can be anonymised for research purposes. NHS England’s care.data initiative recently sought to increase the amount and availability of such data. However, controversy and uncertainty following the care.data public awareness campaign led to a delay in rollout, indicating that the success of EPR data for medical research may be threatened by a loss of patient and public trust. The sharing of sensitive health care data can only be done through maintaining such trust in a constantly evolving ethicolegal and political landscape. We propose that a dynamic consent model, whereby patients can electronically control consent through time and receive information about the uses of their data, provides a transparent, flexible, and user-friendly means to maintain public trust. This could leverage the huge potential of the EPR for medical research and, ultimately, patient and societal benefit

Dynamic consent: a possible solution to improve patient confidence and trust in how electronic patient records are used in medical research

With one million people treated every 36 hours, routinely collected UK National Health Service (NHS) health data has huge potential for medical research. Advances in data acquisition from electronic patient records (EPRs) means such data are increasingly digital and can be anonymised for research purposes. NHS England’s care.data initiative recently sought to increase the amount and availability of such data. However, controversy and uncertainty following the care.data public awareness campaign led to a delay in rollout, indicating that the success of EPR data for medical research may be threatened by a loss of patient and public trust. The sharing of sensitive health care data can only be done through maintaining such trust in a constantly evolving ethicolegal and political landscape. We propose that a dynamic consent model, whereby patients can electronically control consent through time and receive information about the uses of their data, provides a transparent, flexible, and user-friendly means to maintain public trust. This could leverage the huge potential of the EPR for medical research and, ultimately, patient and societal benefit

A caspase-3 'death-switch' in colorectal cancer cells for induced and synchronous tumor apoptosis in vitro and in vivo facilitates the development of minimally invasive cell death biomarkers

Novel anticancer drugs targeting key apoptosis regulators have been developed and are undergoing clinical trials. Pharmacodynamic biomarkers to define the optimum dose of drug that provokes tumor apoptosis are in demand; acquisition of longitudinal tumor biopsies is a significant challenge and minimally invasive biomarkers are required. Considering this, we have developed and validated a preclinical 'death-switch' model for the discovery of secreted biomarkers of tumour apoptosis using in vitro proteomics and in vivo evaluation of the novel imaging probe [ 18 F]ML-10 for non-invasive detection of apoptosis using positron emission tomography (PET). The 'death-switch' is a constitutively active mutant caspase-3 that is robustly induced by doxycycline to drive synchronous apoptosis in human colorectal cancer cells in vitro or grown as tumor xenografts. Deathswitch induction caused caspase-dependent apoptosis between 3 and 24 hours in vitro and regression of 'death-switched' xenografts occurred within 24 h correlating with the percentage of apoptotic cells in tumor and levels of an established cell death biomarker (cleaved cytokeratin-18) in the blood. We sought to define secreted biomarkers of tumor apoptosis from cultured cells using Discovery Isobaric Tag proteomics, which may provide candidates to validate in blood. Early after caspase-3 activation, levels of normally secreted proteins were decreased (e.g. Gelsolin and Midkine) and proteins including CD44 and High Mobility Group protein B1 (HMGB1) that were released into cell culture media in vitro were also identified in the bloodstream of mice bearing death-switched tumors. We also exemplify the utility of the death-switch model for the validation of apoptotic imaging probes using [ 18 F]ML-10, a PET tracer currently in clinical trials. Results showed increased tracer uptake of [ 18 F]ML-10 in tumours undergoing apoptosis, compared with matched tumour controls imaged in the same animal. Overall, the death-switch model represents a robust and versatile tool for the discovery and validation of apoptosis biomarkers. © 2013 Macmillan Publishers Limited. All rights reserved

Severity of Depression Predicts Remission Rates Using Transcranial Magnetic Stimulation

Background: Multiple factors likely impact response and remission rates in the treatment of depression with repetitive transcranial magnetic stimulation (rTMS). Notably the role of symptom severity in outcomes with rTMS is poorly understood.Objective/Hypothesis: This study investigated the predictors of achieving remission in patients suffering from depression who receive ≥3 rTMS treatments per week. Methods: Available data on 41 patients treated at Walter Reed National Military Medical Center from 2009 to 2014 were included for analysis. Patients received a range of pulse sequences from 3,000 to 5,000 with left sided or bilateral coil placement. Primary outcome measures were total score on the Patient Health Questionnaire (PHQ-9) or the Quick Inventory of Depressive Symptomatology—Self Rated (QIDS-SR). Remission was defined as a total score less than five, and response was defined as a 50% decrease in the total score on both outcome metrics. Outcomes in patients diagnosed as suffering from mild or moderate depression were compared to those suffering from severe depression. Results: Of the 41 patients receiving treatment, 16 reached remission by the end of treatment. Remission rate was associated with the initial severity of depression, with patients with mild or moderate depression reaching remission at a significantly higher rate than those with severe depression. Total number of rTMS sessions or length of treatment were not predictors of remission. Conclusion: Patients with a baseline level of depression characterized as mild or moderate had significantly better outcomes following rTMS compared to patients with severe depression

Reversal of DNA damage induced Topoisomerase 2 DNA–protein crosslinks by Tdp2

Mammalian Tyrosyl-DNA phosphodiesterase 2 (Tdp2) reverses Topoisomerase 2 (Top2) DNA–protein crosslinks triggered by Top2 engagement of DNA damage or poisoning by anticancer drugs. Tdp2 deficiencies are linked to neurological disease and cellular sensitivity to Top2 poisons. Herein, we report X-ray crystal structures of ligand-free Tdp2 and Tdp2-DNA complexes with alkylated and abasic DNA that unveil a dynamic Tdp2 active site lid and deep substrate binding trench well-suited for engaging the diverse DNA damage triggers of abortive Top2 reactions. Modeling of a proposed Tdp2 reaction coordinate, combined with mutagenesis and biochemical studies support a single Mg2+-ion mechanism assisted by a phosphotyrosyl-arginine cation-π interface. We further identify a Tdp2 active site SNP that ablates Tdp2 Mg2+ binding and catalytic activity, impairs Tdp2 mediated NHEJ of tyrosine blocked termini, and renders cells sensitive to the anticancer agent etoposide. Collectively, our results provide a structural mechanism for Tdp2 engagement of heterogeneous DNA damage that causes Top2 poisoning, and indicate that evaluation of Tdp2 status may be an important personalized medicine biomarker informing on individual sensitivities to chemotherapeutic Top2 poisons

Optimal model complexity for terrestrial carbon cycle prediction

The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.</p