Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Tick species from cattle in the Adama Region of Ethiopia and pathogens detected - Tafese Beyene Tufa; Silke Wölfel; Dana Zubriková; Bronislava Víchová; Martin Andersson; Ramona Rieß; Liliana Rutaihwa; André Fuchs; Hans Martin Orth; Dieter Häussinger; Torsten Feldt; Sven Poppert; et al. (Experimental and Applied Acarology, 2021)
Ticks will diminish productivity among farm animals and transmit zoonotic diseases. We conducted a study to identify tick species infesting slaughter bulls from Adama City and to screen them for tick-borne pathogens. In 2016, 291 ticks were collected from 37 bulls in Adama, which were ready for slaughter. Ticks were identified morphologically. Total genomic DNA was extracted from ticks and used to test for Rickettsia spp. with real-time PCR. Species identification was done by phylogenetic analysis using sequencing that targeted the 23S-5S intergenic spacer region and ompA genes. Four tick species from two genera, Amblyomma and Rhipicephalus , were identified. Amblyomma cohaerens was the dominant species (n = 241, 82.8%), followed by Amblyomma variegatum (n = 22, 7.5%), Rhipicephalus pulchellus (n = 19, 6.5%), and Rhipicephalus decoloratus (n = 9, 3.0%). Among all ticks, 32 (11%) were positive for Rickettsia spp. and 15 (5.2%) of these were identified as R. africae comprising at least two genetic clades, occurring in A. variegatum (n = 10) and A. cohaerens (n = 5). The remainder of Rickettsia -positive samples could not be amplified due to low DNA yield. Furthermore, another 15 (5.2%) samples carried other pathogenic bacteria: Ehrlichia ruminantium (n = 9; 3.1%) in A. cohaerens , Ehrlichia sp. (n = 3; 1%) in Rh. pulchellus and A. cohaerens , Anaplasma sp. (n = 1; 0.5%) in A. cohaerens , and Neoehrlichia mikurensis (n = 2; 0.7%) in A. cohaerens. All ticks were negative for Bartonella spp., Babesia spp., Theileria spp., and Hepatozoon spp. We reported for the first time E. ruminatium , N. mikurensis , Ehrlichia sp., and Anaplasma sp. in A. cohaerens. Medically and veterinarily important pathogens were mostly detected from A. variegatum and A. cohaerens. These data are relevant for a One-health approach for monitoring and prevention of tick-borne disease transmission.

Pilot study on the internal exposure to heavy metals of informal-level electronic waste workers in Agbogbloshie, Accra, Ghana - Jürgen Wittsiepe; Torsten Feldt; Holger Till; Gerd Burchard; Michael Wilhelm; Julius N. Fobil (Environmental Science and Pollution Research, 2017)