Anisakis simplex sensu stricto and Anisakis pegreffii are sibling species of nematodes parasitic on marine mammals. Zoonotic human infection with third stage infective larvae causes anisakiasis, a debilitating and potentially fatal disease. These 2 species show evidence of hybridisation in geographical areas where they are sympatric. How the species and their hybrids differ is still poorly understood. Third stage larvae of Anisakis simplex s.s., Anisakis pegreffii and hybrids were sampled from Merluccius merluccius (Teleosti) hosts captured in waters of the FAO 27 geographical area. Specimens of each species and hybrids were distinguished with a diagnostic genetic marker (ITS). RNA was extracted from pools of 10 individuals of each taxon. Transcriptomes were generated using Illumina RNA-Seq, and assembled de novo. A joint assembly (here called merged transcriptome) of all 3 samples was also generated. The inferred transcript sets were functionally annotated and compared globally and also on subsets of secreted proteins and putative allergen families. While intermediary metabolism appeared to be typical for nematodes in the 3 evaluated taxa, their transcriptomes present strong levels of differential expression and enrichment, mainly of transcripts related to metabolic pathways and gene ontologies associated to energy metabolism and other pathways, with significant presence of excreted/secreted proteins, most of them allergens. The allergome of the 2 species and their hybrids has also been thoroughly studied; at least 74 different allergen families were identified in the transcriptomes. A. simplex s.s., A. pegreffi and their hybrids differ in gene expression patterns in the L3 stage. Strong parent-of-origin effects were observed: A. pegreffi alleles dominate in the expression patterns of hybrids albeit the latter, and A. pegreffii also display significant differences indicating that hybrids are intermediate biological entities among their parental species, and thus of outstanding interest in the study of speciation in nematodes. Analyses of differential expression based on genes coding for secreted proteins suggests that co-infections presents different repertoires of released protein to the host environment. Both species and their hybrids, share more allergen genes than previously thought and are likely to induce overlapping disease responses.

Populations of RNA viruses are composed of complex and dynamic mixtures of variant genomes that are termed mutant spectra or mutant clouds. This applies also to SARS-CoV-2, and mutations that are detected at low frequency in an infected individual can be dominant (represented in the consensus sequence) in subsequent variants of interest or variants of concern. Here we briefly review the main conclusions of our work on mutant spectrum characterization of hepatitis C virus (HCV) and SARS-CoV-2 at the nucleotide and amino acid levels and address the following two new questions derived from previous results: (i) how is the SARS-CoV-2 mutant and deletion spectrum composition in diagnostic samples, when examined at progressively lower cut-off mutant frequency values in ultra-deep sequencing; (ii) how the frequency distribution of minority amino acid substitutions in SARS-CoV-2 compares with that of HCV sampled also from infected patients. The main conclusions are the following: (i) the number of different mutations found at low frequency in SARS-CoV-2 mutant spectra increases dramatically (50- to 100-fold) as the cut-off frequency for mutation detection is lowered from 0.5% to 0.1%, and (ii) that, contrary to HCV, SARS-CoV-2 mutant spectra exhibit a deficit of intermediate frequency amino acid substitutions. The possible origin and implications of mutant spectrum differences among RNA viruses are discussed.

The argasid tickOrnithodoros moubatais the main vector of human relapsing fever (HRF) and African swine fever (ASF) in Africa. Salivary proteins are part of the host-tick interface and play vital roles in the tick feeding process and the host infection by tick-borne pathogens; they represent interesting targets for immune interventions aimed at tick control.The present work describes the transcriptome profile of salivary glands ofO.moubataand assesses the gene expression dynamics along the trophogonic cycle using Illumina sequencing.De novotranscriptome assembling resulted in 71,194 transcript clusters and 41,011 annotated transcripts, which represent 57.6% of the annotation success. Most salivary gene expression takes place during the first 7 days after feeding (6,287 upregulated transcripts), while a minority of genes (203 upregulated transcripts) are differentially expressed between 7 and 14 days after feeding. The functional protein groups more abundantly overrepresented after blood feeding were lipocalins, proteases (especially metalloproteases), protease inhibitors including the Kunitz/BPTI-family, proteins with phospholipase A2 activity, acid tail proteins, basic tail proteins, vitellogenins, the 7DB family and proteins involved in tick immunity and defence. The complexity and functional redundancy observed in the sialotranscriptome ofO.moubataare comparable to those of the sialomes of other argasid and ixodid ticks.This transcriptome provides a valuable reference database for ongoing proteomics studies of the salivary glands and saliva ofO.moubataaimed at confirming and expanding previous data on theO.moubatasialoproteome.

Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome–host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.

Replication of SARS-CoV-2 in the human population is defined by distributions of mutants that are present at different frequencies within the infected host and can be detected by ultra-deep sequencing techniques. In this study, we examined the SARS-CoV-2 mutant spectra of amplicons from the spike-coding (S-coding) region of 5 nasopharyngeal isolates derived from patients with vaccine breakthrough. Interestingly, all patients became infected with the Alpha variant, but amino acid substitutions that correspond to the Delta Plus, Iota, and Omicron variants were present in the mutant spectra of the resident virus. Deep sequencing analysis of SARS-CoV-2 from patients with vaccine breakthrough revealed a rich reservoir of mutant types and may also identify tolerated substitutions that can be represented in epidemiologically dominant variants.