Babesia divergens-like organisms from free-ranging chamois (Rupicapra r. rupicapra) and roe deer (Capreolus c. capreolus) are distinct from B. divergens of cattle origin - an epidemiological and molecular genetic investigation

In 2005 and 2006, three adult female chamois (Rupicapra r. rupicapra) were found dead with signs of acute babesial infection in the eastern Swiss Alps. PCR on DNA extracted from blood or spleen of the carcasses revealed sequence identity of the amplified part of the 18S rRNA gene with GenBank entries attributed to Babesia divergens of cattle origin or B. capreoli of wild ruminant origin which have never been described before in this region. Examination of 424 blood samples from 314 head of cattle from this area by IFAT, microscopy and PCR provided no evidence for babesial infection. Six of 887 ticks collected from cattle were PCR-positive, and sequencing revealed Babesia sp. genotype EU1 in five and B. divergens/B. capreoli in one of them. A Babesia isolate of chamois, two isolates of roe deer from the same region and one isolate of a roe deer from the north-western Swiss Alps were genetically compared with two Swiss B. divergens isolates of cattle origin by analysing the genomic rDNA locus. Whereas the near full length sequences of the 18S rRNA gene were virtually identical among all six isolates (>99.4% identity), distinct differences between the two isolates from cattle on the one hand and the four isolates from free-ranging ruminants on the other hand were observed in the sequences of the internal transcribed spacers 1 and 2 (ITS1, ITS2) and part of the 28S rRNA gene. These results indicate that, albeit genetically very closely related, these babesial organisms from cattle and from free-ranging ruminants indeed are distinguishable organisms with different host specificities, and they support the use of the discrete species name B. capreoli for the B. divergens-like organisms from chamois and roe deer

The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data

Feature-Based Molecular Networking (FBMN) is a popular analysis approach for LC-MS/MS-based non-targeted metabolomics data. While processing LC-MS/MS data through FBMN is fairly streamlined, downstream data handling and statistical interrogation is often a key bottleneck. Especially, users new to statistical analysis struggle to effectively handle and analyze complex data matrices. In this protocol, we provide a comprehensive guide for the statistical analysis of FBMN results. We explain the data structure and principles of data clean-up and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. Additionally, the protocol is accompanied by a web application with a graphical user interface (https://fbmn-statsguide.gnps2.org/), to lower the barrier of entry for new users. Together, the protocol, code, and web app provide a complete guide and toolbox for FBMN data integration, clean-up, and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking (GNPS and GNPS2) and can be adapted to other MS feature detection, annotation, and networking tools

Feature-based molecular networking in the GNPS analysis environment

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry

Feature-based molecular networking in the GNPS analysis environment.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry