Author Correction: Elucidating causative gene variants in hereditary Parkinson’s disease in the Global Parkinson’s Genetics Program (GP2)

Correction to: npj Parkinson’s Disease, published online 27 June 2023 In this article the Global Parkinson’s Genetics Program (GP2) members names and affiliations were missing in the main author list of the Original article which are listed in the below

BIOAUGMENTATION STRATEGIES TO ENHANCE METHANE PRODUCTION FROM LIGNOCELLULOSIC SUBSTRATES: DYNAMICS OF THE PROKARYOTIC COMMUNITY STRUCTURE

Optimization of anaerobic digestion (AD) of lignocellulosic substrates via bioaugmentation for enhanced biomethane production is imperative. Here, eight different screened bacterial isolates were used to augment different batch anaerobic digesters that comprised freshly chopped water hyacinth (WH) and cow dung (CD), at 2% total solids (w/v). Methane production was monitored at intervals and DNA metabarcoding of the 16S rRNA genes revealed the dynamics of the prokaryotic community structure of the anaerobic digesters. Obtained results suggest bioaugmentation improved the abundance and diversity of the prokaryotic community. The treatment that was inoculated with Serratia marcescens produced the highest cumulative methane of 0.68 L, 45.6% methane more than the consortium treatment. A shift from Pseudomonas to Bacteroides was observed in the bacterial community at the genus taxonomic level after AD while Methanosarcina dominated the archaeal genera. Furthermore, independent bioaugmentation of AD of different substrates such as water hyacinth, cow dung, and cellulose powder with identified model bioaugmentation agent, Serratia marcescens portrayed a positive effect with regards to methane production when compared with the control treatment. This study represents an economic and environmentally friendly approach of single isolate inoculation (bioaugmentation) in optimizing methane production from lignocellulosic substrates such as water hyacinth

Dynamics and Insights into the Unique Ecological Guild of Fungi in Bacteria-Bioaugmented Anaerobic Digesters

Anaerobic digesters host a variety of microorganisms, and they work together to produce biogas. While bacterial and archaeal communities have been well explored using molecular techniques, fungal community structures remain relatively understudied. The present study aims to investigate the dynamics and potential ecological functions of the predominant fungi in bacteria-bioaugmented anaerobic digesters. Eight different anaerobic digesters that contained chopped water hyacinth and cow dung as feedstock at 2% total solids were respectively inoculated with eight different bacterial strains and digested anaerobically in controlled conditions. The diversity and dynamics of the fungal community of the digesters before and after digestion were monitored using high-throughput sequencing of the fungal ITS2 sub-region of the ribosomal gene. The functional potential of the fungal community was predicted using ecological guild analysis. The dominant fungal phyla were (with relative abundance ≥1%) Ascomycota and Neocallimastigomycota. Ascomycota exhibited over 90% dominance in all treatments after anaerobic digestion (AD). Aspergillus sp. was consistently dominant across treatments during AD, while prominent anaerobic fungal genera Anaeromyces, Cyllamyces, and Caeomyces decreased. Ecological guild analysis at genus level showed that the majority of the identified fungi were saprophytes, and diversity indices indicated decreased richness and diversity after AD, suggesting a negative impact of AD on fungal communities in the anaerobic digesters. The multivariate structure of the fungal communities showed clustering of treatments with similar fungal taxa. The findings from this study provide insights into the fungal ecological guild of different bacteria-bioaugmented anaerobic digesters, highlighting their potentials in bacteria-augmented systems. Identification of an anaerobic fungal group within the phylum Ascomycota, beyond the well-known fungal phylum Neocallimastigomycota, offers a new perspective in optimizing the AD processes in specialized ecosystems

Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study

The advent of metagenomics in animal breeding poses the challenge of statistically modelling the relationship between the microbiome, the host genetics and relevant complex traits. A set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed here to jointly analyse the host–metagenome–phenotype relationship. A non‐recursive bivariate model was set as benchmark to compare the recursive model. The relative abundance of rumen microbes (RA), methane concentration (CH4) and the host genetics was used as a case of study. Data were from 337 Holstein cows from 12 herds in the north and north‐west of Spain. Microbial composition from each cow was obtained from whole metagenome sequencing of ruminal content samples using a MinION device from Oxford Nanopore Technologies. Methane concentration was measured with Guardian® NG infrared gas monitor from Edinburgh Sensors during cow's visits to the milking automated system. A quarterly average from the methane eructation peaks for each cow was computed and used as phenotype for CH4. Heritability of CH4 was estimated at 0.12 ± 0.01 in both the recursive and bivariate models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48. Genetic correlations between the microbial composition and CH4 ranged from −0.76 to 0.65 in the non‐recursive bivariate model and from −0.68 to 0.69 in the recursive model. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the seven genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota (Methanobrevibacter sp.), Chytridiomycota (Neocallimastix sp.) and Fibrobacteres (Fibrobacter sp.) phyla. These results suggest that rumen's whole metagenome recursively regulates methane emissions in dairy cows and that both CH4 and the microbiota compositions are partially controlled by the host genotype.National plan of research, development and innovation 2013‐2020, Grant/ Award Number: RTA2015‐0022‐CO3 (METALGEN)UCR::Vicerrectoría de Docencia::Ciencias Agroalimentarias::Facultad de Ciencias Agroalimentarias::Escuela de ZootecniaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Agroalimentarias::Centro de Investigación en Nutrición Animal (CINA

Assessment of arbuscular mycorrhizal fungal spore density and viability in soil stockpiles of South African opencast coal mines

The symbioses between arbuscular mycorrhizal (AM) fungi and plant roots is essential for nutrient uptake and growth of most vascular plants. Soil condition and management influence the density and viability of arbuscular mycorrhizal fungal (AMF) spores. In this study, AMF spore density and viability in soil stockpiles obtained from three opencast coal mines were assessed. Soil samples were randomly collected from stockpiles and an unmined site at depths of ≤20 cm (topsoil) and >20 cm (subsoil) for enumeration of AMF spores and a mycorrhizal trap culture experiment using maize (Zea mays). Roots of trap-plants were assessed for mycorrhization by classical staining as well as detection of nuclear rRNA gene of the Glomeromycota phylum. Number of AMF spores was highest in topsoil from the unmined site and was significantly (p Paraglomus were identified; however, some of the OTUs were phylogenetically distant from defined Paraglomus species on the basis of sequences in the GenBank database. Overall, results suggest that AMF spore density in stockpiles do not differ from an unmined site, but viability may be influenced by soil depth.</p

Elucidating causative gene variants in hereditary Parkinson’s disease in the Global Parkinson’s Genetics Program (GP2)

The Monogenic Network of the Global Parkinson’s Genetics Program (GP2) aims to create an efficient infrastructure to accelerate the identification of novel genetic causes of Parkinson’s disease (PD) and to improve our understanding of already identified genetic causes, such as reduced penetrance and variable clinical expressivity of known disease-causing variants. We aim to perform short- and long-read whole-genome sequencing for up to 10,000 patients with parkinsonism. Important features of this project are global involvement and focusing on historically underrepresented populations