Biology subjects

De Noia, M.

Publications and source records attributed to De Noia, M..

3 recordsLinked to original sources

bioRxiv2020

Unpicking the mysterious symbiosis of mycoplasmas in salmonids

Lacking a peptidoglycan cell wall, mycoplasmas are the smallest self-replicating life forms. Members of this bacterial genus are known to parasitise a wide array of metazoans including vertebrates. Whilst much research has been significant targeted at parasitic mammalian mycoplasmas, very little is known about their role in other vertebrates. In the current study, we aim to explore the biology and evolution of Mycoplasma in salmonids, including cellular niche, genome size structure and gene content. Using Fluorescence in-situ hybridisation (FISH), mycoplasmas were identified in epithelial tissues across the digestive tract (stomach, pyloric caecum and midgut) during the developmental stages (eggs, parr, subadult) of farmed Atlantic salmon (Salmo salar), showing a high abundance in acidic compartments. With high throughput sequencing from subadults farmed Atlantic salmon, we assembled a nearly complete genome (~0.57 MB) via shotgun-metagenomics. The phylogenetic inference from the recovered genome revealed successful taxonomic proximity to Mycoplasma penetrans (~1.36 Mb) from the recovered genome. Although, no significant correlation between genome size and its phylogeny was observed, we recovered functional signatures, especially, riboflavin encoding genes pathway and sugars transporters, suggesting a symbiotic relationship between Mycoplasma and the host. Though 247 strains of Mycoplasma are available in public databases, to the best of our knowledge, this is the first study to demonstrate ecological and functional association between Mycoplasma and Salmo salar which delineates symbiotic reductive evolution and genome erosion primarily and also serves as a proxy for salmonid health in aquaculture processes (cell lines, in vitro gut models).

evolutionary biology↗

bioRxiv2020

In-situ non-lethal rapid test to accurately detect the presence of the nematode parasite, Anguillicoloides crassus, in European eel, Anguilla anguilla.

Anguillicoloides crassus is an invasive nematode parasite of the European eel, Anguilla anguilla, and one of the primary drivers of eel population collapse. The presence of the parasite has been shown to impact many features of eel physiology and life history. Early detection of the parasite is vital to limit the spread of A. crassus. However, until recently, accurate diagnosis of infection could only be achieved via terminal dissection. To support A. anguilla fisheries management in the context of A. crassus we developed a rapid non-lethal and non-invasive environmental DNA method to detect the presence of the parasite in the swim bladder. Screening of 131 wild eels was undertaken between 2017 and 2019 Ireland and UK to validate the procedure. DNA extractions and PCR were conducted using both a Qiagen Stool kit at Glasgow University and in situ using Whatman qualitative filter paper No. 1 and a miniPCR DNA Discovery System. Primers were specifically designed from the cytochrome oxidase mtDNA gene region. In situ extraction and amplification takes approx. 3h for up to 16 individuals with higher specificity and sensitivity compare to the laboratory Qiagen kit extraction. The local diagnostic procedure demonstrated Positive Predictive Values at 96% and Negative Predictive Values at 87%. Our method will be a powerful tool in the hands of fisheries managers to help protect this iconic but critically endangered species. It will allow a non-invasive monitoring of the A. crassus dispersion across the European waters.

ecology↗

bioRxiv2017

Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach

Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Lepeophtheirus salmonis is the predominant species that occurs in the Northern Hemisphere. Dispersal of sea lice between marine aquaculture sites and geographic regions is thought to occur rapidly via planktonic transport of larvae. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic L. salmonis, frustrating efforts to track louse populations, improve targeted control measures and understand local adaption to environmental conditions. The aim of this study was to test the power of reduced representation library sequencing (IIb-RAD sequencing) coupled with random forest machine learning algorithms to define markers for fine-scale discrimination of louse populations. We identified 1286 robustly supported SNPs among four L. salmonis populations from Ireland (N=2, 27 individuals), Scotland (N=1, 11 individuals) and North Norway (N=1, 12 individuals). Weak global structure (FSC = 0.018, p<0.0001) and only one significant pairwise FST comparison was observed (Scotland vs Kenmare Bay, (FST = 0.018, p<0.0001)) using all 1286 SNPs. The application of a random forest machine-learning algorithm identified 98 discriminatory SNPs that dramatically improved population assignment (DAPC assignment probability = 1), increased global Fsc = 0.098, (p<0.0001) and resulted in pairwise comparisons that all showed highly significant Fst-values (range = 0.081 - 0.096, p<0.0001). Out of 19 SNPs found to be under directional selection between populations, 12 corresponded to the discriminatory SNPs identified using random forest. Taken together our data suggest that L. salmonis SNP diversity exists with which it is possible to discriminate differences between nearby populations given suitable marker selection approaches, and that such differences might have an adaptive basis. We discuss these data in light of sea lice adaption to anthropogenic and environmental pressures as well as novel approaches to track and predict sea louse dispersal.

ecology↗