Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0

Microbial genomes are available at an ever-increasing pace, as cultivation and sequencing become cheaper and obtaining metagenome-assembled genomes (MAGs) becomes more effective. Phylogenetic placement methods to contextualize hundreds of thousands of genomes must thus be efficiently scalable and sensitive from closely related strains to divergent phyla. We present PhyloPhlAn 3.0, an accurate, rapid, and easy-to-use method for large-scale microbial genome characterization and phylogenetic analysis at multiple levels of resolution. PhyloPhlAn 3.0 can assign genomes from isolate sequencing or MAGs to species-level genome bins built from >230,000 publically available sequences. For individual clades of interest, it reconstructs strain-level phylogenies from among the closest species using clade-specific maximally informative markers. At the other extreme of resolution, it scales to large phylogenies comprising >17,000 microbial species. Examples including Staphylococcus aureus isolates, gut metagenomes, and meta-analyses demonstrate the ability of PhyloPhlAn 3.0 to support genomic and metagenomic analyses

Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations

Funding This work was supported by NIH NHGRI grant R01HG005220, NIDDK grant R24DK110499, NIDDK grant U54DE023798, and CMIT grant 6935956 to C.H., and by the European Research Council (ERC-STG project MetaPG-716575), MIUR “Futuro in Ricerca” RBFR13EWWI_001, the European Union (H2020-SFS-2018-1 project MASTER-818368 and H2020-SC1-BHC project ONCOBIOME-825410), and the National Cancer Institute of the National Institutes of Health (1U01CA230551) to N.S. Further support was provided by the Programma Ricerca Budget prestazioni Eurac 2017 of the Province of Bolzano, Italy to F.M., and by the EU-H2020 (DiMeTrack-707345) to E.P. and N.S. D.B., S.H.D., P.L., A.W.W. and The Rowett Institute received core funding support from the Scottish Government Rural and Environmental Sciences and Analytical Services (SG-RESAS). Availability of data and materials All datasets used in this study are publicly available and matched with their respective PMID (Additional file 5). The high-quality E. rectale MAGs in fasta format and a metadata file are available at http://segatalab.cibio.unitn.it/data/Erectale_Karcher_et_al.html and in the following Zenodo repository: https://doi.org/10.5281/zenodo.3763191 [80]. The two new isolate genomes L2–21 and T3BWe13 have been uploaded to NCBI and can be found in RefSeq under the accession numbers GCF_008122485.1 [81] and GCF_008123415.1 [82], respectively.Peer reviewedPublisher PD

Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery

The Rowett Institute and SRUC are core funded by the Rural and Environment Science and Analytical Services Division (RESAS) of the Scottish Government. The Roslin Institute forms part of the Royal (Dick) School of Veterinary Studies, University of Edinburgh. This project was supported by the Biotechnology and Biological Sciences Research Council (BBSRC; BB/N016742/1, BB/N01720X/1), including institute strategic programme and national capability awards to The Roslin Institute (BBSRC: BB/P013759/1, BB/P013732/1, BB/J004235/1, BB/J004243/1); and by the Scottish Government as part of the 2016–2021 commission.Peer reviewedPublisher PD

CART Peptide Is a Potential Endogenous Antioxidant and Preferentially Localized in Mitochondria

The multifunctional neuropeptide Cocaine and Amphetamine Regulated Transcript (CART) is secreted from hypothalamus, pituitary, adrenal gland and pancreas. It also can be found in circulatory system. This feature suggests a general role for CART in different cells. In the present study, we demonstrate that CART protects mitochondrial DNA (mtDNA), cellular proteins and lipids against the oxidative action of hydrogen peroxide, a widely used oxidant. Using cis-parinaric acid as a sensitive reporting probe for peroxidation in membranes, and a lipid-soluble azo initiator of peroxyl radicals, 2,2′-Azobis(2,4-dimethylvaleronitrile) we found that CART is an antioxidant. Furthermore, we found that CART localized to mitochondria in cultured cells and mouse brain neuronal cells. More importantly, pretreatment with CART by systemic injection protects against a mouse oxidative stress model, which mimics the main features of Parkinson's disease. Given the unique molecular structure and biological features of CART, we conclude that CART is an antioxidant peptide (or antioxidant hormone). We further propose that it may have strong therapeutic properties for human diseases in which oxidative stress is strongly involved such as Parkinson's disease

Genomic characterization of Nontuberculous Mycobacteria

YesMycobacterium tuberculosis and Mycobacterium leprae have remained, for many years, the primary species of the genus Mycobacterium of clinical and microbiological interest. The other members of the genus, referred to as nontuberculous mycobacteria (NTM), have long been underinvestigated. In the last decades, however, the number of reports linking various NTM species with human diseases has steadily increased and treatment difficulties have emerged. Despite the availability of whole genome sequencing technologies, limited effort has been devoted to the genetic characterization of NTM species. As a consequence, the taxonomic and phylogenetic structure of the genus remains unsettled and genomic information is lacking to support the identification of these organisms in a clinical setting. In this work, we widen the knowledge of NTMs by reconstructing and analyzing the genomes of 41 previously uncharacterized NTM species. We provide the first comprehensive characterization of the genomic diversity of NTMs and open new venues for the clinical identification of opportunistic pathogens from this genus

Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

The Rowett Institute and SRUC are core funded by the Rural and Environment Science and Analytical Services Division (RESAS) of the Scottish Government. The Roslin Institute forms part of the Royal (Dick) School of Veterinary Studies, University of Edinburgh. This project was supported by the Biotechnology and Biological Sciences Research Council (BBSRC; BB/N016742/1, BB/N01720X/1), including institute strategic programme and national capability awards to The Roslin Institute (BBSRC: BB/P013759/1, BB/P013732/1, BB/J004235/1, BB/J004243/1); and by the Scottish Government as part of the 2016–2021 commission.Peer reviewedPublisher PD

A Framework to Improve the Accuracy of Process Simulation Models

Business process simulation is a methodology that enables analysts to run the process in different scenarios, compare the performances and consequently provide indications into how to improve a business process. Process simulation requires one to provide a simulation model, which should accurately reflect reality to ensure the reliability of the simulation findings. This paper proposes a framework to assess the extent to which a simulation model reflects reality and to pinpoint how to reduce the distance. The starting point is a business simulation model, along with a real event log that records actual executions of the business process being simulated and analyzed. In a nutshell, the idea is to simulate the process, thus obtaining a simulation log, which is subsequently compared with the real event log. A decision tree is built, using the vector of features that represent the behavioral characteristics of log traces. The tree aims to classify traces as belonging to the real and simulated event logs, and the discriminating features encode the difference between reality, represented in the real event log, and the simulation model, represented in the simulated event logs. These features provide actionable insights into how to repair simulation models to become closer to reality. The technique has been assessed on a real-life process for which the literature provides a real event log and a simulation model. The results of the evaluation show that our framework increases the accuracy of the given initial simulation model to better reflect reality

Estimating Activity Start Timestamps in the Presence of Waiting Times via Process Simulation

Process Mining aims to analyze and improve processes to enable organizations to provide better services or products. The starting point of Process Mining is an event log that is extracted from the organization’s information systems that support the process’ executions. Several techniques require event logs to record the timestamp when process’ activities have started and been completed. Unfortunately, information systems do not always record the timestamps when process activities start, preventing the application of these techniques. This paper reports on a technique based on process simulation that aims to estimate the start event timestamps when missing. In a nutshell, the idea is to build an accurate process model from the initial event log without start timestamps, to simulate it with alternative activity-duration profiles, and to select the model with the profile that generates the runs that are the closest to the initial log. This activity-duration profile is used to add the missing, start timestamps to the initial log. Experiments were conducted with two event logs with start timestamps, and aimed at their rediscovery: the results show our estimation of the start event timestamps is more accurate than the state of the art