Sepsis-trained macrophages promote antitumoral tissue-resident T cells.

Sepsis induces immune alterations, which last for months after the resolution of illness. The effect of this immunological reprogramming on the risk of developing cancer is unclear. Here we use a national claims database to show that sepsis survivors had a lower cumulative incidence of cancers than matched nonsevere infection survivors. We identify a chemokine network released from sepsis-trained resident macrophages that triggers tissue residency of T cells via CCR2 and CXCR6 stimulations as the immune mechanism responsible for this decreased risk of de novo tumor development after sepsis cure. While nonseptic inflammation does not provoke this network, laminarin injection could therapeutically reproduce the protective sepsis effect. This chemokine network and CXCR6 tissue-resident T cell accumulation were detected in humans with sepsis and were associated with prolonged survival in humans with cancer. These findings identify a therapeutically relevant antitumor consequence of sepsis-induced trained immunity.

Progression toward Vertebral Collapse of Vertebral Metastases Treated with Percutaneous Vertebroplasty: Rate and Risk Factors.

PURPOSE: To evaluate of the rate of and risks for progression toward collapse in vertebral metastases (VMs) treated with percutaneous vertebroplasty (PV). MATERIALS AND METHODS: A total of 151 PVs were performed in 81 patients with vertebral metastases and were retrospectively analyzed. Follow-up imaging was performed at 12 months to measure vertebral body height and to report vertebral collapse at the level of the treated vertebrae. Vertebral characteristics (spine instability neoplastic score [SINS], number of lysed cortices, and prior radiotherapy) and procedural parameters (Saliou score, cortical contact with cement, and intradiscal cement leakage) were compared between the group of patients with and without collapse of the treated vertebrae. RESULTS: Of the vertebrae treated with PV, 41 of 151 (27%) progressed toward collapse. Vertebral collapse was influenced by a high SINS (odds ratio [OR] = 1.27, P = .004), SINS value > 9 (OR = 2.96, P = .004), intradiscal cement leakage (OR = 2.18, P = .048), pre-existing spinal deformity (OR = 2.65, P = .020), and pre-existing vertebral fracture (OR = 3.93, P = .045). A high Saliou score (OR = 0.82, P = .011), more than 3 cortices in contact with the cement (OR = 0.38, P = .014), and preserved spinal alignment (OR = 0.38, P = .020) were associated with a lower incidence of collapse. CONCLUSIONS: Rate of vertebral collapse despite PV was influenced by vertebra-specific characteristics and by cement injection quality. Vertebrae with a SINS of ≤9 and with homogeneous cement filling had a lower incidence of collapse.

Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.

BACKGROUND: The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to demonstrate that intensive care unit (ICU) variables are not efficient to predict 6-month functional outcome in survivors with moderate to severe TBI (msTBI) but are mostly associated with mortality, which leads to a mortality bias for models predicting a composite outcome of mortality and severe disability. METHODS: We analyzed the data from the multicenter randomized controlled Continuous Hyperosmolar Therapy in Traumatic Brain-Injured Patients trial and developed predictive models using machine learning methods and baseline characteristics and predictors collected during ICU stay. We compared our models' predictions of 6-month binary Glasgow Outcome Scale extended (GOS-E) score in all patients with msTBI (unfavorable GOS-E 1-4 vs. favorable GOS-E 5-8) with mortality (GOS-E 1 vs. GOS-E 2-8) and binary functional outcome in survivors with msTBI (severe disability GOS-E 2-4 vs. moderate to no disability GOS-E 5-8). We investigated the link between ICU variables and long-term functional outcomes in survivors with msTBI using predictive modeling and factor analysis of mixed data and validated our hypotheses on the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model. RESULTS: Based on data from 370 patients with msTBI and classically used ICU variables, the prediction of the 6-month outcome in survivors was inefficient (mean area under the receiver operating characteristic 0.52). Using factor analysis of mixed data graph, we demonstrated that high-variance ICU variables were not associated with outcome in survivors with msTBI (p = 0.15 for dimension 1, p = 0.53 for dimension 2) but mostly with mortality (p < 0.001 for dimension 1), leading to a mortality bias for models predicting a composite outcome of mortality and severe disability. We finally identified this mortality bias in the IMPACT model. CONCLUSIONS: We demonstrated using machine learning-based predictive models that classically used ICU variables are strongly associated with mortality but not with 6-month outcome in survivors with msTBI, leading to a mortality bias when predicting a composite outcome of mortality and severe disability.

Technological Pathways to Produce Compressed and Highly Pure Hydrogen from Solar Power.

Hydrogen (H2 ) produced from renewables will have a growing impact on the global energy dynamics towards sustainable and carbon-neutral standards. The share of green H2 is still too low to meet the net-zero target, while the demand for high-quality hydrogen continues to rise. These factors amplify the need for economically viable H2 generation technologies. The present article aims at evaluating the existing technologies for high-quality H2 production based on solar energy. Technologies such as water electrolysis, photoelectrochemical and solar thermochemical water splitting, liquid metal reactors and plasma conversion utilize solar power directly or indirectly (as carbon-neutral electrons) and are reviewed from the perspective of their current development level, technical limitations and future potential.

Translational Modeling of Chloroquine and Hydroxychloroquine Dosimetry in Human Airways for Treating Viral Respiratory Infections.

PURPOSE: Chloroquine and hydroxychloroquine are effective against respiratory viruses in vitro. However, they lack antiviral efficacy upon oral administration. Translation of in vitro to in vivo exposure is necessary for understanding the disconnect between the two to develop effective therapeutic strategies. METHODS: We employed an in vitro ion-trapping kinetic model to predict the changes in the cytosolic and lysosomal concentrations of chloroquine and hydroxychloroquine in cell lines and primary human airway cultures. A physiologically based pharmacokinetic model with detailed respiratory physiology was used to predict regional airway exposure and optimize dosing regimens. RESULTS: At their reported in vitro effective concentrations in cell lines, chloroquine and hydroxychloroquine cause a significant increase in their cytosolic and lysosomal concentrations by altering the lysosomal pH. Higher concentrations of the compounds are required to achieve similar levels of cytosolic and lysosomal changes in primary human airway cells in vitro. The predicted cellular and lysosomal concentrations in the respiratory tract for in vivo oral doses are lower than the in vitro effective levels. Pulmonary administration of aerosolized chloroquine or hydroxychloroquine is predicted to achieve high bound in vitro-effective concentrations in the respiratory tract, with low systemic exposure. Achieving effective cytosolic concentrations for activating immunomodulatory effects and adequate lysosomal levels for inhibiting viral replication could be key drivers for treating viral respiratory infections. CONCLUSION: Our analysis provides a framework for extrapolating in vitro effective concentrations of chloroquine and hydroxychloroquine to in vivo dosing regimens for treating viral respiratory infections.

In Vivo Profiling of a Natural Alkaloid, Anatabine, in Rodents: Pharmacokinetics and Anti-Inflammatory Efficacy.

Natural alkaloids, a large class of plant-derived substances, have attracted considerable interest because of their pharmacological activities. In this study, the in vivo pharmacokinetics and anti-inflammatory profile of anatabine, a naturally occurring alkaloid, were characterized in rodents. Anatabine was found to be bioavailable and brain-penetrant following systemic administration. Following intraperitoneal (i.p.) administration (1, 2, and 5 mg/kg), anatabine caused a dose-dependent reduction in carrageenan-induced paw edema in rats; in mice, it inhibited the production of pro-inflammatory cytokines and simultaneously elevated the levels of an anti-inflammatory cytokine in a dose-dependent manner 2 h after lipopolysaccharide challenge. Furthermore, anatabine (∼10 and ∼20 mg/kg/day for 4 weeks; inhalation exposure) had effects in a murine model of multiple sclerosis, reducing neurological deficits and bodyweight loss. Comparative studies of the pharmacokinetics and anti-inflammatory activity of anatabine demonstrated its bioequivalence in rats following i.p. administration and inhalation exposure. This study not only provides the first detailed profile of anatabine pharmacokinetics in rodents but also comprehensively characterizes the anti-inflammatory activities of anatabine in acute and chronic inflammatory models. These findings provide a basis for further characterizing and optimizing the anti-inflammatory properties of anatabine.

Effect of Continuous Infusion of Hypertonic Saline vs Standard Care on 6-Month Neurological Outcomes in Patients With Traumatic Brain Injury: The COBI Randomized Clinical Trial.

Importance: Fluid therapy is an important component of care for patients with traumatic brain injury, but whether it modulates clinical outcomes remains unclear. Objective: To determine whether continuous infusion of hypertonic saline solution improves neurological outcome at 6 months in patients with traumatic brain injury. Design, Setting, and Participants: Multicenter randomized clinical trial conducted in 9 intensive care units in France, including 370 patients with moderate to severe traumatic brain injury who were recruited from October 2017 to August 2019. Follow-up was completed in February 2020. Interventions: Adult patients with moderate to severe traumatic brain injury were randomly assigned to receive continuous infusion of 20% hypertonic saline solution plus standard care (n = 185) or standard care alone (controls; n = 185). The 20% hypertonic saline solution was administered for 48 hours or longer if patients remained at risk of intracranial hypertension. Main Outcomes and Measures: The primary outcome was Extended Glasgow Outcome Scale (GOS-E) score (range, 1-8, with lower scores indicating worse functional outcome) at 6 months, obtained centrally by blinded assessors and analyzed with ordinal logistic regression adjusted for prespecified prognostic factors (with a common odds ratio [OR] >1.0 favoring intervention). There were 12 secondary outcomes measured at multiple time points, including development of intracranial hypertension and 6-month mortality. Results: Among 370 patients who were randomized (median age, 44 [interquartile range, 27-59] years; 77 [20.2%] women), 359 (97%) completed the trial. The adjusted common OR for the GOS-E score at 6 months was 1.02 (95% CI, 0.71-1.47; P = .92). Of the 12 secondary outcomes, 10 were not significantly different. Intracranial hypertension developed in 62 (33.7%) patients in the intervention group and 66 (36.3%) patients in the control group (absolute difference, -2.6% [95% CI, -12.3% to 7.2%]; OR, 0.80 [95% CI, 0.51-1.26]). There was no significant difference in 6-month mortality (29 [15.9%] in the intervention group vs 37 [20.8%] in the control group; absolute difference, -4.9% [95% CI, -12.8% to 3.1%]; hazard ratio, 0.79 [95% CI, 0.48-1.28]). Conclusions and Relevance: Among patients with moderate to severe traumatic brain injury, treatment with continuous infusion of 20% hypertonic saline compared with standard care did not result in a significantly better neurological status at 6 months. However, confidence intervals for the findings were wide, and the study may have had limited power to detect a clinically important difference. Trial Registration: ClinicalTrials.gov Identifier: NCT03143751.

Lung transcriptomic clock predicts premature aging in cigarette smoke-exposed mice.

BACKGROUND: Lung aging is characterized by a number of structural alterations including fibrosis, chronic inflammation and the alteration of inflammatory cell composition. Chronic exposure to cigarette smoke (CS) is known to induce similar alterations and may contribute to premature lung aging. Additionally, aging and CS exposure are associated with transcriptional alterations in the lung. The current work aims to explore the interaction between age- and CS- associated transcriptomic perturbations and develop a transcriptomic clock able to predict the biological age and the impact of external factors on lung aging. RESULTS: Our investigations revealed a substantial overlap between transcriptomic response to CS exposure and age-related transcriptomic alterations in the murine lung. Of particular interest is the strong upregulation of immunoglobulin genes with increased age and in response to CS exposure, indicating an important implication of B-cells in lung inflammation associated with aging and smoking. Furthermore, we used a machine learning approach based on Lasso regression to build a transcriptomic age model that can accurately predict chronological age in untreated mice and the deviations associated with certain exposures. Interestingly, CS-exposed-mice were predicted to be prematurely aged in contrast to mice exposed to fresh air or to heated tobacco products (HTPs). The accelerated aging rate associated with CS was reversed upon smoking cessation or switching to HTPs. Additionally, our model was able to predict premature aging associated with thoracic irradiation from an independent public dataset. CONCLUSIONS: Aging and CS exposure share common transcriptional alteration patterns in the murine lung. The massive upregulation of B-cell restricted genes during these processes shed light on the contribution of cell composition and particularly immune cells to the measured transcriptomic signal. Through machine learning approach, we show that gene expression changes can be used to accurately monitor the biological age and the modulations associated with certain exposures. Our findings also suggest that the premature lung aging is reversible upon the reduction of harmful exposures.

A 6-month systems toxicology inhalation study in ApoE-/- mice demonstrates reduced cardiovascular effects of E-vapor aerosols compared with cigarette smoke.

Smoking cigarettes is harmful to the cardiovascular system. Considerable attention has been paid to the reduced harm potential of alternative nicotine-containing inhalable products such as e-cigarettes. We investigated the effects of E-vapor aerosols or cigarette smoke (CS) on atherosclerosis progression, cardiovascular function, and molecular changes in the heart and aorta of female apolipoprotein E-deficient (ApoE-/-) mice. The mice were exposed to aerosols from three different E-vapor formulations: 1) carrier (propylene glycol and vegetable glycerol), 2) base (carrier and nicotine), or 3) test (base and flavor) or to CS from 3R4F reference cigarettes for up to 6 mo. Concentrations of CS and base or test aerosols were matched at 35 µg nicotine/L. Exposure to CS, compared with sham-exposed fresh air controls, accelerated atherosclerotic plaque formation, whereas no such effect was seen for any of the three E-vapor aerosols. Molecular changes indicated disease mechanisms related to oxidative stress and inflammation in general, plus changes in calcium regulation, and altered cytoskeletal organization and microtubule dynamics in the left ventricle. While ejection fraction, fractional shortening, cardiac output, and isovolumic contraction time remained unchanged following E-vapor aerosols exposure, the nicotine-containing base and test aerosols caused an increase in isovolumic relaxation time similar to CS. A nicotine-related increase in pulse wave velocity and arterial stiffness was also observed, but it was significantly lower for base and test aerosols than for CS. These results demonstrate that in comparison with CS, E-vapor aerosols induce substantially lower biological responses associated with smoking-related cardiovascular diseases.NEW & NOTEWORTHY Analysis of key urinary oxidative stress markers and proinflammatory cytokines showed an absence of oxidative stress and inflammation in the animals exposed to E-vapor aerosols. Conversely, animals exposed to conventional cigarette smoke had high urinary levels of these markers. When compared with conventional cigarette smoke, E-vapor aerosols induced smaller atherosclerotic plaque surface area and volume. Systolic and diastolic cardiac function, as well as endothelial function, were further significantly less affected by electronic cigarette aerosols than conventional cigarette smoke. Molecular analysis demonstrated that E-vapor aerosols induce significantly smaller transcriptomic dysregulation in the heart and aorta compared with conventional cigarette smoke.

GladiaTOX: GLobal Assessment of Dose-IndicAtor in TOXicology.

SUMMARY: GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the 'tcpl' core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate PDF reports for quality control and data processing. AVAILABILITY AND IMPLEMENTATION: GladiaTOX R package (bioconductor). Also available via: git clone https://github.com/philipmorrisintl/GladiaTOX.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepCEST 3T: Robust MRI parameter determination and uncertainty quantification with neural networks-application to CEST imaging of the human brain at 3T.

PURPOSE: Calculation of sophisticated MR contrasts often requires complex mathematical modeling. Data evaluation is computationally expensive, vulnerable to artifacts, and often sensitive to fit algorithm parameters. In this work, we investigate whether neural networks can provide not only fast model fitting results, but also a quality metric for the predicted values, so called uncertainty quantification, investigated here in the context of multi-pool Lorentzian fitting of CEST MRI spectra at 3T. METHODS: A deep feed-forward neural network including a probabilistic output layer allowing for uncertainty quantification was set up to take uncorrected CEST-spectra as input and predict 3T Lorentzian parameters of a 4-pool model (water, semisolid MT, amide CEST, NOE CEST), including the B0 inhomogeneity. Networks were trained on data from 3 subjects with and without data augmentation, and applied to untrained data from 1 additional subject and 1 brain tumor patient. Comparison to conventional Lorentzian fitting was performed on different perturbations of input data. RESULTS: The deepCEST 3T networks provided fast and accurate predictions of all Lorentzian parameters and were robust to input perturbations because of noise or B0 artifacts. The uncertainty quantification detected fluctuations in input data by increase of the uncertainty intervals. The method generalized to unseen brain tumor patient CEST data. CONCLUSIONS: The deepCEST 3T neural network provides fast and robust estimation of CEST parameters, enabling online reconstruction of sophisticated CEST contrast images without the typical computational cost. Moreover, the uncertainty quantification indicates if the predictions are trustworthy, enabling confident interpretation of contrast changes.

Systems Toxicology Approach for Testing Chemical Cardiotoxicity in Larval Zebrafish.

Transcriptomic approaches can give insight into molecular mechanisms underlying chemical toxicity and are increasingly being used as part of toxicological assessments. To aid the interpretation of transcriptomic data, we have developed a systems toxicology method that relies on a computable biological network model. We created the first network model describing cardiotoxicity in zebrafish larvae-a valuable emerging model species in testing cardiotoxicity associated with drugs and chemicals. The network is based on scientific literature and represents hierarchical molecular pathways that lead from receptor activation to cardiac pathologies. To test the ability of our approach to detect cardiotoxic outcomes from transcriptomic data, we have selected three publicly available data sets that reported chemically induced heart pathologies in zebrafish larvae for five different chemicals. Network-based analysis detected cardiac perturbations for four out of five chemicals tested, for two of them using transcriptomic data collected up to 3 days before the onset of a visible phenotype. Additionally, we identified distinct molecular pathways that were activated by the different chemicals. The results demonstrate that the proposed integrational method can be used for evaluating the effects of chemicals on the zebrafish cardiac function and, together with observed cardiac apical end points, can provide a comprehensive method for connecting molecular events to organ toxicity. The computable network model is freely available and may be used to generate mechanistic hypotheses and quantifiable perturbation values from any zebrafish transcriptomic data.

NPA: an R package for computing network perturbation amplitudes using gene expression data and two-layer networks.

BACKGROUND: High-throughput gene expression technologies provide complex datasets reflecting mechanisms perturbed in an experiment, typically in a treatment versus control design. Analysis of these information-rich data can be guided based on a priori knowledge, such as networks of related proteins or genes. Assessing the response of a specific mechanism and investigating its biological basis is extremely important in systems toxicology; as compounds or treatment need to be assessed with respect to a predefined set of key mechanisms that could lead to toxicity. Two-layer networks are suitable for this task, and a robust computational methodology specifically addressing those needs was previously published. The NPA package ( https://github.com/philipmorrisintl/NPA ) implements the algorithm, and a data package of eight two-layer networks representing key mechanisms, such as xenobiotic metabolism, apoptosis, or epithelial immune innate activation, is provided. RESULTS: Gene expression data from an animal study are analyzed using the package and its network models. The functionalities are implemented using R6 classes, making the use of the package seamless and intuitive. The various network responses are analyzed using the leading node analysis, and an overall perturbation, called the Biological Impact Factor, is computed. CONCLUSIONS: The NPA package implements the published network perturbation amplitude methodology and provides a set of two-layer networks encoded in the Biological Expression Language.

Application of a multi-layer systems toxicology framework for in vitro assessment of the biological effects of Classic Tobacco e-liquid and its corresponding aerosol using an e-cigarette device with MESH™ technology.

We previously proposed a systems toxicology framework for in vitro assessment of e-liquids. The framework starts with the first layer aimed at screening the potential toxicity of e-liquids, followed by the second layer aimed at investigating the toxicity-related mechanism of e-liquids, and finally, the third layer aimed at evaluating the toxicity-related mechanism of the corresponding aerosols. In this work, we applied this framework to assess the impact of the e-liquid MESH Classic Tobacco and its aerosol compared with that of cigarette smoke (CS) from the 3R4F reference cigarette. In the first layer, we evaluated the cytotoxicity profile of the MESH Classic Tobacco e-liquid (containing humectants, nicotine, and flavors) and its Base e-liquid (containing humectant and nicotine only) in comparison with total particulate matter (TPM) of 3R4F CS using primary bronchial epithelial cell cultures. In the second layer, the same culture model was used to explore changes in specific markers using high-content screening assays to identify potential toxicity-related mechanisms induced by the MESH Classic Tobacco and Base e-liquids beyond cell viability in comparison with the 3R4F CS TPM-induced effects. Finally, in the third layer, we compared the impact of exposure to the MESH Classic Tobacco or Base aerosols with 3R4F CS using human organotypic air-liquid interface buccal and small airway epithelial cultures. The results showed that the cytotoxicity of the MESH Classic Tobacco liquid was similar to the Base liquid but lower than 3R4F CS TPM at comparable nicotine concentrations. Relative to 3R4F CS exposure, MESH Classic Tobacco aerosol exposure did not cause tissue damage and elicited lower changes in the mRNA, microRNA, and protein markers. In the context of tobacco harm reduction strategy, the framework is suitable to assess the potential-reduced impact of electronic cigarette aerosol relative to CS.

Biological changes in C57BL/6 mice following 3 weeks of inhalation exposure to cigarette smoke or e-vapor aerosols.

We compared early biological changes in mice after inhalation exposures to cigarette smoke or e-vapor aerosols (MarkTen® cartridge with Carrier, Test-1, or Test-2 formulations; 4% nicotine). Female C57BL/6 mice were exposed to 3R4F cigarette smoke or e-vapor aerosols by nose-only inhalation for up to 4 hours/day, 5 days/week, for 3 weeks. The 3R4F and e-vapor exposures were set to match the target nose port aerosol nicotine concentration (∼41 µg/L). Only the 3R4F group showed postexposure clinical signs such as tremors and lethargy. At necropsy, the 3R4F group had significant increases in lung weight and changes in bronchoalveolar lavage parameters, as well as microscopic findings in the respiratory tract. The e-vapor groups had minimal microscopic changes, including squamous metaplasia in laryngeal epiglottis, and histiocytic infiltrates in the lung (Test-2 group only). The 3R4F group had a higher incidence and severity of microscopic findings compared to any e-vapor group. Transcriptomic analysis also showed that the 3R4F group had the highest number of differentially expressed genes compared to Sham Control. Among e-vapor groups, Test-2 group had more differentially expressed genes but the magnitude of gene expression-based network perturbations in all e-vapor groups was ∼94% less than the 3R4F group. On proteome analysis in the lung, differentially regulated proteins were detected in the 3R4F group only. In conclusion, 3-weeks of 3R4F exposure induced molecular and microscopic changes associated with smoking-related diseases in the respiratory tract, while e-vapor exposures showed substantially reduced biological activities.

Pressure-driven collective growth mechanism of planar cell colonies.

The growth of cell colonies is determined by the migration and proliferation of the individual cells. This is often modeled with the Fisher-Kolmogorov (FK) equation, which assumes that cells diffuse independently from each other, but stop to proliferate when their density reaches a critial limit. However, when using measured, cell-line specific parameters, we find that the FK equation drastically underestimates the experimentally observed increase of colony radius with time. Moreover, cells in real colonies migrate radially outward with superdiffusive trajectories, in contrast to the assumption of random diffusion. We demonstrate that both dicrepancies can be resolved by assuming that cells in dense colonies are driven apart by repulsive, pressure-like forces. Using this model of proliferating repelling particles (PRP), we find that colony growth exhibits different dynamical regimes, depending on the ratio between a pressure-related equilibrium cell density and the critial density of proliferation arrest.

Quantifying the risk-reduction potential of new Modified Risk Tobacco Products.

Quantitative risk assessment of novel Modified Risk Tobacco Products (MRTP) must rest on indirect measurements that are indicative of disease development prior to epidemiological data becoming available. For this purpose, a Population Health Impact Model (PHIM) has been developed to estimate the reduction in the number of deaths from smoking-related diseases following the introduction of an MRTP. One key parameter of the model, the F-factor, describes the effective dose upon switching from cigarette smoking to using an MRTP. Biomarker data, collected in clinical studies, can be analyzed to estimate the effects of switching to an MRTP as compared to quitting smoking. Based on transparent assumptions, a link function is formulated that translates these effects into the F-factor. The concepts of 'lack of sufficiency' and 'necessity' are introduced, allowing for a parametrization of a family of link functions. These can be uniformly sampled, thus providing different 'scenarios' on how biomarker-based evidence can be translated into the F-factor to inform the PHIM.

Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product.

Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants' predictions was done using predefined metrics. The top 3 performers' methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd's results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology.

Biosynthesis of the Enterotoxic Pyrrolobenzodiazepine Natural Product Tilivalline.

The nonribosomal enterotoxin tilivalline was the first naturally occurring pyrrolobenzodiazepine to be linked to disease in the human intestine. Since the producing organism Klebsiella oxytoca is part of the intestinal microbiota and the pyrrolobenzodiazepine causes the pathogenesis of colitis it is important to understand the biosynthesis and regulation of tilivalline activity. Here we report the biosynthesis of tilivalline and show that this nonribosomal peptide assembly pathway initially generates tilimycin, a simple pyrrolobenzodiazepine with cytotoxic properties. Tilivalline results from the non-enzymatic spontaneous reaction of tilimycin with biogenetically generated indole. Through a chemical total synthesis of tilimycin we could corroborate the predictions made about the biosynthesis. Production of two cytotoxic pyrrolobenzodiazepines with distinct functionalities by human gut resident Klebsiella oxytoca has important implications for intestinal disease.