scholarly journals Benchmarking non-targeted metabolomics using yeast derived libraries

2020 ◽  
Author(s):  
Evelyn Rampler ◽  
Gerrit Hermann ◽  
Gerlinde Grabmann ◽  
Yasin El Abiead ◽  
Harald Schoeny ◽  
...  
Keyword(s):  
Large Scale ◽  
High Resolution Mass Spectrometry ◽  
Chemical Space ◽  
Large Fraction ◽  
Cost Effective ◽  
Regulatory Elements ◽  
Instrumental Performance ◽  
Compound Identification ◽  
Targeted Metabolomics ◽  
Targeted Analysis

AbstractNon-targeted analysis by high-resolution mass spectrometry (HRMS) is the essential discovery tool in metabolomics. Up to date, standardization and validation remain a challenge. Community wide accepted, cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts, derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of > 200 metabolites, reproducibly recovered in ethanolic extracts by orthogonal LCHRMS methods, different fermentations (over three years) and different laboratories. More specifically, compound identification was based on accurate mass, matching retention times, and MS/MS spectra as compared to authentic standards and internal databases. The library includes metabolites from the classes of 1) organic acids and derivatives (2) nucleosides, nucleotides and analogues, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds and (7) benzoids at expected concentrations ranges of sub-nM to µM. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the Human metabolome data base (HMDB). A large fraction of metabolites was found to be stable for several years when stored at −80°C. Thus, the yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments, but enabled in-house routines for instrumental performance tests. Finally, the benchmark material opens new avenues for batch to batch corrections in large scale non-targeted metabolomics studies.

scholarly journals Benchmarking Non-Targeted Metabolomics Using Yeast-Derived Libraries

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 160
Author(s):  
Evelyn Rampler ◽  
Gerrit Hermann ◽  
Gerlinde Grabmann ◽  
Yasin El Abiead ◽  
Harald Schoeny ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Protein Identification ◽  
Regulatory Elements ◽  
Yeast Fermentation ◽  
Compound Identification ◽  
Targeted Metabolomics ◽  
Targeted Analysis ◽  
Orthogonal State ◽  
Starting Point

Non-targeted analysis by high-resolution mass spectrometry (HRMS) is an essential discovery tool in metabolomics. To date, standardization and validation remain a challenge. Community-wide accepted cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of >200 identified metabolites based on compound identification by accurate mass, matching retention times, and MS/MS, as well as a comprehensive literature search. The library includes metabolites from the classes of (1) organic acids and derivatives (2) nucleosides, nucleotides, and analogs, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds, and (7) benzoids at expected concentrations ranges of sub-nM to µM. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the human metabolome database (HMDB). Orthogonal state-of-the-art reversed-phase (RP-) and hydrophilic interaction chromatography mass spectrometry (HILIC-MS) non-targeted analysis and authentic standards revealed that 104 out of the 206 confirmed metabolites were reproducibly recovered and stable over the course of three years when stored at −80 °C. Overall, 67 out of these 104 metabolites were identified with comparably stable areas over all three yeast fermentation and are the ideal starting point for benchmarking experiments. The provided yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments but also allowed in-house routines for instrumental performance tests. Transferring the quality control strategy of proteomics workflows based on the number of protein identification in HeLa extracts, metabolite IDs in the yeast benchmarking material can be used as metabolomics quality control. Finally, the benchmark material opens new avenues for batch-to-batch corrections in large-scale non-targeted metabolomics studies.

scholarly journals Systematic comparison of experimental assays and analytical pipelines for identification of active enhancers genome-wide

2021 ◽  
Author(s):  
Li Yao ◽  
Jin Liang ◽  
Abdullah Ozer ◽  
Alden King-Yung Leung ◽  
John T. Lis ◽  
...  
Keyword(s):  
Large Scale ◽  
K562 Cells ◽  
Cost Effective ◽  
Regulatory Elements ◽  
Nascent Transcript ◽  
Transcription Start Sites ◽  
The Road ◽  
Genome Wide ◽  
Active Enhancer ◽  
Effective Manner

Mounting evidence supports the idea that transcriptional patterns serve as more specific identifiers of active enhancers than histone marks; however, the optimal strategy to identify active enhancers both experimentally and computationally has not been determined. In this study, we compared 13 genome-wide RNA sequencing assays in K562 cells and showed that the nuclear run-on followed by cap-selection assay (namely, GRO/PRO-cap) has significant advantages in eRNA detection and active enhancer identification. We also introduced a new analytical tool, Peak Identifier for Nascent-Transcript Sequencing (PINTS), to identify active promoters and enhancers genome-wide and pinpoint the precise location of the 5′ transcription start sites (TSSs) within these regulatory elements. Finally, we compiled a comprehensive enhancer candidate compendium based on the detected eRNA TSSs available in 120 cell and tissue types. To facilitate the exploration and prioritization of these enhancer candidates, we also built a user-friendly web server (https://pints.yulab.org) for the compendium with various additional genomic and epigenomic annotations. With the knowledge of the best available assays and pipelines, this large-scale annotation of candidate enhancers will pave the road for selection and characterization of their functions in a time-, labor-, and cost-effective manner in future.

scholarly journals Revisiting Five Years of CASMI Contests with EPA Identification Tools

Metabolites ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 260
Author(s):  
Andrew D. McEachran ◽  
Alex Chao ◽  
Hussein Al-Ghoul ◽  
Charles Lowe ◽  
Christopher Grulke ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution Mass Spectrometry ◽  
Critical Assessment ◽  
Tandem Mass ◽  
Chemical Identification ◽  
Compound Identification ◽  
Software Applications ◽  
Targeted Analysis ◽  
The Us ◽  
Fit For Purpose

Software applications for high resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) continue to enhance chemical identification capabilities. Given the variety of available applications, determining the most fit-for-purpose tools and workflows can be difficult. The Critical Assessment of Small Molecule Identification (CASMI) contests were initiated in 2012 to provide a means to evaluate compound identification tools on a standardized set of blinded tandem mass spectrometry (MS/MS) data. Five CASMI contests have resulted in recommendations, publications, and invaluable datasets for practitioners of HRMS-based screening studies. The US Environmental Protection Agency’s (EPA) CompTox Chemicals Dashboard is now recognized as a valuable resource for compound identification in NTA studies. However, this application was too new and immature in functionality to participate in the five previous CASMI contests. In this work, we performed compound identification on all five CASMI contest datasets using Dashboard tools and data in order to critically evaluate Dashboard performance relative to that of other applications. CASMI data was accessed via the CASMI webpage and processed for use in our spectral matching and identification workflow. Relative to applications used by former contest participants, our tools, data, and workflow performed well, placing more challenge compounds in the top five of ranked candidates than did the winners of three contest years and tying in a fourth. In addition, we conducted an in-depth review of the CASMI structure sets and made these reviewed sets available via the Dashboard. Our results suggest that Dashboard data and tools would enhance chemical identification capabilities for practitioners of HRMS-based NTA.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

scholarly journals Serum Metabolomics Identifies Altered Bioenergetics, Signaling Cascades in Parallel with Exposome Markers in Crohn’s Disease

Molecules ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 449 ◽  
Author(s):  
Yunjia Lai ◽  
Jingchuan Xue ◽  
Chih-Wei Liu ◽  
Bei Gao ◽  
Liang Chi ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
High Resolution Mass Spectrometry ◽  
Signaling Cascades ◽  
Compound Identification ◽  
Serum Samples ◽  
Therapy Assessment ◽  
Integral Role ◽  
Important Pathway ◽  
Biomarker Research

: Inflammatory bowel disease (IBD) has stimulated much interest due to its surging incidences and health impacts in the U.S. and worldwide. However, the exact cause of IBD remains incompletely understood, and biomarker is lacking towards early diagnostics and effective therapy assessment. To tackle these, the emerging high-resolution mass spectrometry (HRMS)-based metabolomics shows promise. Here, we conducted a pilot untargeted LC/MS metabolomic profiling in Crohn’s disease, for which serum samples of both active and inactive cases were collected, extracted, and profiled by a state-of-the-art compound identification workflow. Results show a distinct metabolic profile of Crohn’s from control, with most metabolites downregulated. The identified compounds are structurally diverse, pointing to important pathway perturbations ranging from energy metabolism (e.g., β-oxidation of fatty acids) to signaling cascades of lipids (e.g., DHA) and amino acid (e.g., L-tryptophan). Importantly, an integral role of gut microbiota in the pathogenesis of Crohn’s disease is highlighted. Xenobiotics and their biotransformants were widely detected, calling for massive exposomic profiling for future cohort studies as such. This study endorses the analytical capacity of untargeted metabolomics for biomarker development, cohort stratification, and mechanistic interpretation; the findings might be valuable for advancing biomarker research and etiologic inquiry in IBD.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

scholarly journals Multi-Country Scale Assessment of Available Energy Recovery Potential Using Micro-Hydropower in Drinking, Pressurised Irrigation and Wastewater Networks, Covering Part of the EU

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 899
Author(s):  
Djordje Mitrovic ◽  
Miguel Crespo Chacón ◽  
Aida Mérida García ◽  
Jorge García Morillo ◽  
Juan Antonio Rodríguez Diaz ◽  
...  
Keyword(s):  
Drinking Water ◽  
Northern Ireland ◽  
Energy Recovery ◽  
Large Scale ◽  
Significant Proportion ◽  
Value Added ◽  
Cost Effective ◽  
Net Energy ◽  
Water Networks ◽  
Scale Assessment

Studies have shown micro-hydropower (MHP) opportunities for energy recovery and CO2 reductions in the water sector. This paper conducts a large-scale assessment of this potential using a dataset amassed across six EU countries (Ireland, Northern Ireland, Scotland, Wales, Spain, and Portugal) for the drinking water, irrigation, and wastewater sectors. Extrapolating the collected data, the total annual MHP potential was estimated between 482.3 and 821.6 GWh, depending on the assumptions, divided among Ireland (15.5–32.2 GWh), Scotland (17.8–139.7 GWh), Northern Ireland (5.9–8.2 GWh), Wales (10.2–8.1 GWh), Spain (375.3–539.9 GWh), and Portugal (57.6–93.5 GWh) and distributed across the drinking water (43–67%), irrigation (51–30%), and wastewater (6–3%) sectors. The findings demonstrated reductions in energy consumption in water networks between 1.7 and 13.0%. Forty-five percent of the energy estimated from the analysed sites was associated with just 3% of their number, having a power output capacity >15 kW. This demonstrated that a significant proportion of energy could be exploited at a small number of sites, with a valuable contribution to net energy efficiency gains and CO2 emission reductions. This also demonstrates cost-effective, value-added, multi-country benefits to policy makers, establishing the case to incentivise MHP in water networks to help achieve the desired CO2 emissions reductions targets.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Anti-meningococcal B vaccination in Italian adolescents: a cost-effective health opportunity

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Panatto ◽  
P Landa ◽  
D Amicizia ◽  
P L Lai ◽  
E Lecini ◽  
...  
Keyword(s):  
Large Scale ◽  
Public Health Problem ◽  
Vaccination Strategy ◽  
Cost Effective ◽  
Dose Schedule ◽  
Developed Countries ◽  
Invasive Disease ◽  
International Setting ◽  
The Impact

Abstract Background Invasive disease due to Neisseria meningitidis (Nm) is a serious public health problem even in developed countries, owing to its high lethality rate (8-15%) and the invalidating sequelae suffered by many (up to 60%) survivors. As the microorganism is transmitted via the airborne route, the only available weapon in the fight against Nm invasive disease is vaccination. Our aim was to carry out an HTA to evaluate the costs and benefits of anti-meningococcal B (MenB) vaccination with Trumenba® in adolescents in Italy, while also considering the impact of this new vaccination strategy on organizational and ethics aspects. Methods A lifetime Markov model was developed. MenB vaccination with the two-dose schedule of Trumenba® in adolescents was compared with 'non-vaccination'. Two perspectives were considered: the National Health Service (NHS) and society. Three disease phases were defined: acute, post-acute and long-term. Epidemiological, economic and health utilities data were taken from Italian and international literature. The analysis was conducted by means of Microsoft Excel 2010®. Results Our study indicated that vaccinating adolescents (11th year of life) with Trumenba® was cost-effective with an ICER = € 7,912/QALY from the NHS perspective and € 7,758/QALY from the perspective of society. Vaccinating adolescents reduces the number of cases of disease due to meningococcus B in one of the periods of highest incidence of the disease, resulting in significant economic and health savings. Conclusions This is the first study to evaluate the overall impact of free MenB vaccination in adolescents both in Italy and in the international setting. Although cases of invasive disease due to meningococcus B are few, if the overall impact of the disease is adequately considered, it becomes clear that including anti-meningococcal B vaccination into the immunization program for adolescents is strongly recommended from the health and economic standpoints. Key messages Free, large-scale MenB vaccination is key to strengthening the global fight against invasive meningococcal disease. Anti-meningococcal B vaccination in adolescents is a cost-effective health opportunity.

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