Enterocin AS-48 is a cyclic peptide produced by Enterococcus faecalis S-48 whose genetic determinants have been identified in the conjugative plasmid pMB2. A region of 7.8 kb, carrying the minimum information required for production of and immunity against AS-48, had been previously cloned and sequenced in pAM401 (pAM401-52). In this region, the as-48A structural gene and as-48B, as-48C, as-48C 1 , as-48D, and as-48D 1 genes and open reading frame 6 (ORF6) and ORF7 had been identified. The sequence analysis carried out in this work in the BglII B fragment (6.6-kb) from pMB2 cloned downstream from the last ORF identified (ORF7) revealed the existence of two new ORFs, as-48G and as-48H, necessary for full AS-48 expression. Thus, JH2-2 transformants obtained with the pAM401-81 plasmid became producers and resistant at the wild-type level. Tn5 disruption experiments in the last genes, as-48EFGH, were not able to reproduce these expression levels, confirming that expression of these genes is necessary to get the phenotype conferred by the wild-type pMB2 plasmid. The as-48EFGH operon encodes a new ABC transporter that could be involved in producer selfprotection. On the basis of the observed similarities, As-48G would be the ATP-binding domain, the deduced amino acid sequences of As-48E and As48-H could be assigned as transmembrane subunits, and As-48F, with an N-terminal transmembrane segment and a coiled-coil domain, strongly resembles the structure of some known ABC transporter accessory proteins whose localization in the cell is discussed. This cluster of genes is expressed by two polycistronic mRNAs, T 2 and T 3 , in JH2-2(pAM401-81) in coordinate expression. Our results also suggest that expression of T 3 could be regulated, because in JH2-2(pAM401 EH ) transformants, T 3 was not detected, suggesting that these genes do not by themselves confer immunity, in accordance with the requirement for the as-48D

BackgroundBiologically data-driven networks have become powerful analytical tools that handle massive, heterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the most relevant structures directly tied to biological functions. Functional enrichments can then be performed based on these structural aspects of gene relationships for the study of channelopathies. Channelopathies refer to a complex group of disorders resulting from dysfunctional ion channels with distinct polygenic manifestations. This study presents a semi-automatic workflow using protein-protein interaction networks that can identify the most relevant genes and their biological processes and pathways in channelopathies to better understand their etiopathogenesis. In addition, the clinical manifestations that are strongly associated with these genes are also identified as the most characteristic in this complex group of diseases.ResultsIn particular, a set of nine representative disease-related genes was detected, these being the most significant genes in relation to their roles in channelopathies. In this way we attested the implication of some voltage-gated sodium (SCN1A, SCN2A, SCN4A, SCN4B, SCN5A, SCN9A) and potassium (KCNQ2, KCNH2) channels in cardiovascular diseases, epilepsies, febrile seizures, headache disorders, neuromuscular, neurodegenerative diseases or neurobehavioral manifestations. We also revealed the role of Ankyrin-G (ANK3) in the neurodegenerative and neurobehavioral disorders as well as the implication of these genes in other systems, such as the immunological or endocrine systems.ConclusionsThis research provides a systems biology approach to extract information from interaction networks of gene expression. We show how large-scale computational integration of heterogeneous datasets, PPI network analyses, functional databases and published literature may support the detection and assessment of possible potential therapeutic targets in the disease. Applying our workflow makes it feasible to spot the most relevant genes and unknown relationships in channelopathies and shows its potential as a first-step approach to identify both genes and functional interactions in clinical-knowledge scenarios of target diseases.MethodsAn initial gene pool is previously defined by searching general databases under a specific semantic framework. From the resulting interaction network, a subset of genes are identified as the most relevant through the workflow that includes centrality measures and other filtering and enrichment databases.