scholarly journals Focused natural product elucidation by prioritizing high-throughput metabolomic studies with machine learning

2019 ◽  
Author(s):  
Nicholas J. Tobias ◽  
César Parra-Rojas ◽  
Yan-Ni Shi ◽  
Yi-Ming Shi ◽  
Svenja Simonyi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Product ◽  
High Throughput ◽  
Abiotic Factors ◽  
Future Research ◽  
Natural Product Research ◽  
Strain Collection ◽  
Metabolic Variation ◽  
Metabolic Screening ◽  
Different Strains

AbstractBacteria of the genera Photorhabdus and Xenorhabdus produce a plethora of natural products to support their similar symbiotic lifecycles. For many of these compounds, the specific bioactivities are unknown. One common challenge in natural product research when trying to prioritize research efforts is the rediscovery of identical (or highly similar) compounds from different strains. Linking genome sequence to metabolite production can help in overcoming this problem. However, sequences are typically not available for entire collections of organisms. Here we perform a comprehensive metabolic screening using HPLC-MS data associated with a 114-strain collection (58 Photorhabdus and 56 Xenorhabdus) from across Thailand and explore the metabolic variation among the strains, matched with several abiotic factors. We utilize machine learning in order to rank the importance of individual metabolites in determining all given metadata. With this approach, we were able to prioritize metabolites in the context of natural product investigations, leading to the identification of previously unknown compounds. The top three highest-ranking features were associated with Xenorhabdus and attributed to the same chemical entity, cyclo(tetrahydroxybutyrate). This work addresses the need for prioritization in high-throughput metabolomic studies and demonstrates the viability of such an approach in future research.

scholarly journals Development of an agar-plug cultivation system for bioactivity assays of actinomycete strain collections

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258934
Author(s):  
Nico Ortlieb ◽  
Elke Klenk ◽  
Andreas Kulik ◽  
Timo Horst Johannes Niedermeyer
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Microtiter Plate ◽  
Growth Media ◽  
Natural Product Research ◽  
Solid Media ◽  
Agar Plug ◽  
Strain Collection ◽  
Actinomycete Strain ◽  
Initial Medium

Natural products are an important source of lead compounds for the development of drug substances. Actinomycetes have been valuable especially for the discovery of antibiotics. Increasing occurrence of antibiotic resistance among bacterial pathogens has revived the interest in actinomycete natural product research. Actinobacteria produce a different set of natural products when cultivated on solid growth media compared with submersed culture. Bioactivity assays involving solid media (e.g. agar-plug assays) require manual manipulation of the strains and agar plugs. This is less convenient for the screening of larger strain collections of several hundred or thousand strains. Thus, the aim of this study was to develop a 96-well microplate-based system suitable for the screening of actinomycete strain collections in agar-plug assays. We developed a medium-throughput cultivation and agar-plug assay workflow that allows the convenient inoculation of solid agar plugs with actinomycete spore suspensions from a strain collection, and the transfer of the agar plugs to petri dishes to conduct agar-plug bioactivity assays. The development steps as well as the challenges that were overcome during the development (e.g. system sterility, handling of the agar plugs) are described. We present the results from one exemplary screening campaign targeted to identify compounds inhibiting Agr-based quorum sensing where the workflow was used successfully. We present a novel and convenient workflow to combine agar diffusion assays with microtiter-plate-based cultivation systems in which strains can grow on a solid surface. This workflow facilitates and speeds up the initial medium throughput screening of natural product-producing actinomycete strain collections against monitor strains in agar-plug assays.

New Antileishmanial Drug Leads from a Library of Natural Product Fractions Prepared by a High Throughput Fractionation Paradigm*

Planta Medica ◽  
2012 ◽  
Vol 78 (05) ◽  
Author(s):  
SK Jain ◽  
R Sahu ◽  
J Zhang ◽  
MR Jacob ◽  
XC Li ◽  
...  
Keyword(s):  
Natural Product ◽  
High Throughput ◽  
Antileishmanial Drug ◽  
Drug Leads

High throughput huaman pancreactic lipase screening of natural product library for lipase inhibition activity

Planta Medica ◽  
2012 ◽  
Vol 78 (11) ◽  
Author(s):  
P Jiao ◽  
J Zhao ◽  
J Yeop Lee ◽  
J Tseng-Crank ◽  
B Corneliusen ◽  
...  
Keyword(s):  
Natural Product ◽  
High Throughput ◽  
Inhibition Activity ◽  
Natural Product Library ◽  
Lipase Screening ◽  
Lipase Inhibition

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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