TRESK channel contribution to nociceptive sensory neurons excitability: modulation by nerve injury

<p>Abstract</p> <p>Background</p> <p>Neuronal hyperexcitability is a crucial phenomenon underlying spontaneous and evoked pain. In invertebrate nociceptors, the S-type leak K⁺channel (analogous to TREK-1 in mammals) plays a critical role of in determining neuronal excitability following nerve injury. Few data are available on the role of leak K_2Pchannels after peripheral axotomy in mammals.</p> <p>Results</p> <p>Here we describe that rat sciatic nerve axotomy induces hyperexcitability of L4-L5 DRG sensory neurons and decreases TRESK (K2P18.1) expression, a channel with a major contribution to total leak current in DRGs. While the expression of other channels from the same family did not significantly change, injury markers ATF3 and Cacna2d1 were highly upregulated. Similarly, acute sensory neuron dissociation (<it>in vitro </it>axotomy) produced marked hyperexcitability and similar total background currents compared with neurons injured <it>in vivo</it>. In addition, the sanshool derivative IBA, which blocked TRESK currents in transfected HEK293 cells and DRGs, increased intracellular calcium in 49% of DRG neurons in culture. Most IBA-responding neurons (71%) also responded to the TRPV1 agonist capsaicin, indicating that they were nociceptors. Additional evidence of a biological role of TRESK channels was provided by behavioral evidence of pain (flinching and licking), in vivo electrophysiological evidence of C-nociceptor activation following IBA injection in the rat hindpaw, and increased sensitivity to painful pressure after TRESK knockdown in vivo.</p> <p>Conclusions</p> <p>In summary, our results clearly support an important role of TRESK channels in determining neuronal excitability in specific DRG neurons subpopulations, and show that axonal injury down-regulates TRESK channels, therefore contributing to neuronal hyperexcitability.</p

Machine learning can identify newly diagnosed patients with CLL at high risk of infection

Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors

Ancient and recent differences in the intrinsic susceptibility of Mycobacterium tuberculosis complex to pretomanid

Objectives To develop a robust phenotypic antimicrobial susceptibility testing (AST) method with a correctly set breakpoint for pretomanid (Pa), the most recently approved anti-tuberculosis drug. Methods The Becton Dickinson Mycobacterial Growth Indicator Tube™ (MGIT) system was used at six laboratories to determine the MICs of a phylogenetically diverse collection of 356 Mycobacterium tuberculosis complex (MTBC) strains to establish the epidemiological cut-off value for pretomanid. MICs were correlated with WGS data to study the genetic basis of differences in the susceptibility to pretomanid. Results We observed ancient differences in the susceptibility to pretomanid among various members of MTBC. Most notably, lineage 1 of M. tuberculosis, which is estimated to account for 28% of tuberculosis cases globally, was less susceptible than lineages 2, 3, 4 and 7 of M. tuberculosis, resulting in a 99th percentile of 2 mg/L for lineage 1 compared with 0.5 mg/L for the remaining M. tuberculosis lineages. Moreover, we observed that higher MICs (≥8 mg/L), which probably confer resistance, had recently evolved independently in six different M. tuberculosis strains. Unlike the aforementioned ancient differences in susceptibility, these recent differences were likely caused by mutations in the known pretomanid resistance genes. Conclusions In light of these findings, the provisional critical concentration of 1 mg/L for MGIT set by EMA must be re-evaluated. More broadly, these findings underline the importance of considering the global diversity of MTBC during clinical development of drugs and when defining breakpoints for AST

Mechanism of Heparin Acceleration of Tissue Inhibitor of Metalloproteases-1 (TIMP-1) Degradation by the Human Neutrophil Elastase

Heparin has been shown to regulate human neutrophil elastase (HNE) activity. We have assessed the regulatory effect of heparin on Tissue Inhibitor of Metalloproteases-1 [TIMP-1] hydrolysis by HNE employing the recombinant form of TIMP-1 and correlated FRET-peptides comprising the TIMP-1 cleavage site. Heparin accelerates 2.5-fold TIMP-1 hydrolysis by HNE. The kinetic parameters of this reaction were monitored with the aid of a FRET-peptide substrate that mimics the TIMP-1 cleavage site in pre-steady-state conditionsby using a stopped-flow fluorescence system. The hydrolysis of the FRET-peptide substrate by HNE exhibits a pre-steady-state burst phase followed by a linear, steady-state pseudo-first-order reaction. The HNE acylation step (k2 = 21±1 s−1) was much higher than the HNE deacylation step (k3 = 0.57±0.05 s−1). The presence of heparin induces a dramatic effect in the pre-steady-state behavior of HNE. Heparin induces transient lag phase kinetics in HNE cleavage of the FRET-peptide substrate. The pre-steady-state analysis revealed that heparin affects all steps of the reaction through enhancing the ES complex concentration, increasing k1 2.4-fold and reducing k−1 3.1-fold. Heparin also promotes a 7.8-fold decrease in the k2 value, whereas the k3 value in the presence of heparin was increased 58-fold. These results clearly show that heparin binding accelerates deacylation and slows down acylation. Heparin shifts the HNE pH activity profile to the right, allowing HNE to be active at alkaline pH. Molecular docking and kinetic analysis suggest that heparin induces conformational changes in HNE structure. Here, we are showing for the first time that heparin is able to accelerate the hydrolysis of TIMP-1 by HNE. The degradation of TIMP-1is associated to important physiopathological states involving excessive activation of MMPs

A standardised method for interpreting the association between mutations and phenotypic drug resistance inMycobacterium tuberculosis

A clear understanding of the genetic basis of antibiotic resistance in Mycobacterium tuberculosis is required to accelerate the development of rapid drug susceptibility testing methods based on genetic sequence. Raw genotype–phenotype correlation data were extracted as part of a comprehensive systematic review to develop a standardised analytical approach for interpreting resistance associated mutations for rifampicin, isoniazid, ofloxacin/levofloxacin, moxifloxacin, amikacin, kanamycin, capreomycin, streptomycin, ethionamide/prothionamide and pyrazinamide. Mutation frequencies in resistant and susceptible isolates were calculated, together with novel statistical measures to classify mutations as high, moderate, minimal or indeterminate confidence for predicting resistance. We identified 286 confidence-graded mutations associated with resistance. Compared to phenotypic methods, sensitivity (95% CI) for rifampicin was 90.3% (89.6–90.9%), while for isoniazid it was 78.2% (77.4–79.0%) and their specificities were 96.3% (95.7–96.8%) and 94.4% (93.1–95.5%), respectively. For second-line drugs, sensitivity varied from 67.4% (64.1–70.6%) for capreomycin to 88.2% (85.1–90.9%) for moxifloxacin, with specificity ranging from 90.0% (87.1–92.5%) for moxifloxacin to 99.5% (99.0–99.8%) for amikacin. This study provides a standardised and comprehensive approach for the interpretation of mutations as predictors of M. tuberculosis drug-resistant phenotypes. These data have implications for the clinical interpretation of molecular diagnostics and next-generation sequencing as well as efficient individualised therapy for patients with drug-resistant tuberculosis