Annotation and metabolic network construction of the Probiotic Strain Lactobacillus acidophilus ATCC 4356. Visualization in X-OMEQ platform

LEONARDO LUCIANA; GERMÁN F. BURGUENER; EZEQUIEL SOSA; MARIA MERCEDES PALOMINO; MARIANA ALLIEVI; SANDRA RUZAL; ADRIAN TURJANSKI; MARCELO MARTI; DARÍO FERNÁNDEZ DO PORTO

Bahía Blanca

Congreso; VI Congreso Argentino de Bioinformática y Biología Computacional; 2015

Asociación Argentina de Bioinformática y Biología Computacional

The original Lactobacillus acidophilus strain ATCC 4356 was isolated in 1900 from human infant feces [1]. L. acidophilus ATCC 4356 is an important inhabitant of the gastrointestinal tract with reported probiotic properties [2?4]. The genome sequence was obtained using a whole-genome shotgun strategy with a HiSeq 1500 Illumina at Instituto de Agrobiotecnolgía de Rosario (INDEAR), Argentina. Assembly was done with Stampy v1.0.27 [5], using L. acidophilus La-14 as a template. This assembly generated 1 scaffold with 1,02% of gaps. The draft genome is 1,991,579 bp in length, and the GC content is 34.64%. Genomic annotations were assigned automatically by our own Annotation Pipeline [6]. The annotation predicted 1,882 coding sequences (CDSs) and 69 structural RNAs (61 tRNAs). A total of 17 CDSs (0.9%) were classified as hypothetical proteins and 407 (21.6%) as uncharacterized proteins. Afterwards, we used Pathway tools [7] and manual revision to build a comprehensive metabolic network (MN) of the bacteria. The software automatically associates genes with reactions, based on enzyme code (EC) numbers and function descriptions contained in the GenBank file of the annotated genome. Curation includes deleting inappropriate pathways, adding missing pathways with experimental evidence, or filling existing pathways (by using the hole filler tool of pathway tools). After MN construction, a Python script was written to generate a list of all products and reactants involved in this network, and the metabolic network was represented as a graph with Cytoscape 2.8.3 4 [8]. These allow us to analyze the uniqueness (I.e being a choke point) and centrality of the predicted reactions. As reported [9], there is significant coincidence between essential genes and genes associated to choke points and central reactions. As a last step in our procedure, the annotated genome was deployed in our server: XOMEQ platform (http://www.biargentina.com.ar/xomeq/). X-ome-Q is an easy-to-use web-based platform which allows genome wide based data consolidation from diverse sources at different processing stages including assembly, annotation, comparative genomics, metabolic pathway recognition, and modeling of proteins? structure. All this information can be easily navigated by position or by using keywords and different gene based annotations, including ontology (Figure 1) and cog terms, protein family and metabolic pathways.