The microbiome of the sponge Aplysina caissara in two sites with different levels of anthropogenic impact.

Despite the important roles that marine sponges play in ecosystem functioning and structuring, little is known about how the sponge holobiont responds to local anthropogenic impacts. Here we assess the influence of an impacted environment (Praia Preta) on the microbial community associated with the endemic sponge Aplysina caissara in comparison to a less-impacted area (Praia do Guaecá) from the coast of São Paulo state (Brazil, southwestern Atlantic coast). We hypothesized that the local anthropogenic impacts will change the microbiome of A. caissara and that the community assembly will be driven by a different process (i.e. deterministic versus stochastic) under distinct levels of impact. The microbiome at the amplicon sequence variants level was found to be statistically distinct between sponges from the different sites, and this was also seen for the microbial communities of the surrounding seawater and sediments. Microbial communities of A. caissara from both sites were found to be assembled by deterministic processes, even though the sites presented distinct anthropogenic impacts, showing a pivotal role of the sponge host in selecting its own microbiome. Overall, this study revealed that local anthropogenic impacts altered the microbiome of A. caissara; however, assembly processes are largely determined by the sponge host.

Prokaryotic, Fungal, and Unicellular Eukaryotic Core Communities Across Three Sympatric Marine Sponges From the Southwestern Atlantic Coast Are Dominated Largely by Deterministic Assemblage Processes.

Marine sponges are known to harbor a diverse and complex microbiota; however, a vast majority of surveys have been investigating the prokaryotic communities in the north hemisphere and Australia. In addition, the mechanisms of microbial community assembly are poorly understood in this pivotal player of the ecosystem. Thus, this survey addressed the holobiome of the sponge species in the São Paulo region (Brazil) for the first time and investigated the contribution of neutral and niche processes of prokaryotic, fungal, and unicellular eukaryotic assemblage in three sympatric species Aplysina caissara, Aplysina fulva, and Tedania ignis along with environmental samples. The compositions of the holobiome associated with the sponges and detected in environmental samples were strikingly different. Remarkably, between 47 and 88% of the assigned operational taxonomic units (OTUs) were specifically associated with sponge species. Moreover, around 77, 69, and 53% of the unclassified OTUs from prokaryotic, fungal, and unicellular eukaryotic communities, respectively, showed less than 97% similarity with well-known databases, suggesting that sponges from the southwestern Atlantic coast are an important source of microbial novelty. These values are even higher, around 80 and 61% of the unclassified OTUs, when excluding low abundance samples from fungal and unicellular eukaryotic datasets, respectively. Host species were the major driver shaping the sponge-associated microbial community. Deterministic processes were primarily responsible for the assembly of microbial communities in all sponge species, while neutral processes of prokaryotic and fungal community assembly were also detected in the sympatric A. caissara and T. ignis replicates, respectively. Most of the species-rich sponge-associated lineages from this region are also found in the Northern seas and many of them might play essential roles in the symbioses, such as biosynthesis of secondary metabolites that exhibit antimicrobial and antiviral activities, as well as provide protection against host predation. Overall, in this study the microbiota was assembled by interactions with the host sponge in a deterministic-based manner; closely related sponge species shared a strong phylogenetic signal in their associated prokaryotic and fungal community traits and Brazilian sponges were a reservoir of novel microbial species.

PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants.

Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (Saccharum spp.) and in maize (Zea mays). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms.