Genome-wide sequencing and metabolic annotation of Pythium irregulare CBS 494.86: understanding Eicosapentaenoic acid production

RESEARCH ARTICLE Open Access Genome-wide sequencing and metabolic annotation of Pythium irregulare CBS 494.86: understanding Eicosapentaenoic acid production Bruna S. Fernandes 1,2* , Oscar Dias 2 , Gisela Costa 2 , Antonio A. Kaupert Neto 3 , Tiago F. C. Resende 2 , Juliana V. C. Oliveira 3 , Diego M. Riaño-Pachón 4 , Marcelo Zaiat 5* , José G. C. Pradella 6 and Isabel Rocha 2* Abstract Background: Pythium irregulare is an oleaginous Oomycete able to accumulate large amounts of lipids, including Eicosapentaenoic acid (EPA). EPA is an important and expensive dietary supplement with a promising and very competitive market, which is dependent on fish-oil extraction. This has prompted several research groups to study biotechnological routes to obtain specific fatty acids rather than a mixture of various lipids. Moreover, microorganisms can use low cost carbon sources for lipid production, thus reducing production costs. Previous studies have highlighted the production of EPA by P. irregulare, exploiting diverse low cost carbon sources that are produced in large amounts, such as vinasse, glycerol, and food wastewater. However, there is still a lack of knowledge about its biosynthetic pathways, because no functional annotation of any Pythium sp. exists yet. The goal of this work was to identify key genes and pathways related to EPA biosynthesis, in P. irregulare CBS 494.86, by sequencing and performing an unprecedented annotation of its genome, considering the possibility of using wastewater as a carbon source. Results: Genome sequencing provided 17,727 candidate genes, with 3809 of them associated with enzyme code and 945 with membrane transporter proteins. The functional annotation was compared with curated information of oleaginous organisms, understanding amino acids and fatty acids production, and consumption of carbon and nitrogen sources, present in the wastewater. The main features include the presence of genes related to the consumption of several sugars and candidate genes of unsaturated fatty acids production. Conclusions: The whole metabolic genome presented, which is an unprecedented reconstruction of P. irregulare CBS 494.86, shows its potential to produce value-added products, in special EPA, for food and pharmaceutical industries, moreover it infers metabolic capabilities of the microorganism by incorporating information obtained from literature and genomic data, supplying information of great importance to future work. Keywords: Eicosapentaenoic acid, Metabolic annotation, Pythium irregulare, unsaturated fatty acids, whole-genome sequence © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. * Correspondence: [email protected];[email protected]; [email protected] 1 Department of Civil and Environmental Engineering, Federal University of Pernambuco, Recife, PE, Brazil 5 Biological Processes Laboratory, Center for Research, Development and Innovation in Environmental Engineering, São Carlos School of Engineering (EESC), University of São Paulo, São Carlos, SP, Brazil 2 Centre of Biological Engineering, Universidade do Minho, Braga, Portugal Full list of author information is available at the end of the article Fernandes et al. BMC Biotechnology (2019) 19:41 https://doi.org/10.1186/s12896-019-0529-3 Background Pythium irregulare is an oleaginous diploid Oomycete, a microscopic Stramenopiles [1,2] and pathogen of various crops [1], including Arabidopsis plants [3]. P. irregulare has the potential to be industrially used to produce lipids because it is able to accumulate a large amount of these compounds, including Eicosapentaenoic acid (EPA) [4]. EPA (C 20 H 30 O 2 ) is a 20-carbon polyunsaturated fatty acid with five cis double bonds, with the first double bond located at the third carbon from the omega end, which justifies its classification as an omega3 fatty acid. The Food and Agriculture Organization of the United Nations recommends ingestion up to 500 mg per day of EPA and DHA (Docosahexaenoic acid) in the early years of life and for prevention of cardiovascular diseases [5], as it is not naturally synthesized in humans. Omega-3 fatty acids are important dietary supplements, with high selling prices (US$ 600 –US$ 4000 per kg of omega-3) [6], and a promising and very competitive market [7]. The expected omega-3 revenue is estimated at US$ 2.7 billion by 2020, with a Compound Annual Growth Rate (CAGR) of 17.5% (2014–2020), just in the pharmaceutical market [8]. This scenario has prompted several groups to search for alternative ways to produce omega-3, particularly EPA. Microorganisms are very attractive sources of EPA, because they can be driven to produce specific fatty acids rather than a mixture of various lipids, using low cost carbon sources without presence of heavy metals in the cultivated medium. This can reduce the cost of lipid extraction and purification and help to reduce the dependence on fish-oil. Some microorganisms have been studied with this goal, such as Mortierella alpine,Mortierella elongate,Monochrysis luteri, Pseudopedinella sp.,Coccolithus huxleyi,Cricosphaera carterae,Monodus sub-terraneous,Nannochlorus sp.,Porphyrium cruentum,Cryptomonas muculata,Cryptomonas sp.,Rhodomonas leans, and Pythium irregulare [9–12]. Some studies have indicated the possibility of producing EPA using P. irregulare, exploiting diverse abundant low-cost carbon sources including wastewaters such as vinasse from corn-meal ethanol production, glycerol, wastewater from the food industry, and several sugars [4,13,14]. However, there is still a lack of knowledge about the biosynthetic pathways for EPA in this microorganism, and Stramenopiles, in general. This taxonomical order covers very diverse ecological niches and lifestyles ranging from photosynthetic diatoms and brown algae to filamentous saprophytic and pathogenic oomycetes [15]. Hereto, Pythium irregulare DAOM BR486 is the only P. irregulare strain sequenced and annotated at the National Center for Biotechnology Information - NCBI database (Bioproject number: PRJNA169053). Its annotation was performed automatically using MAKER v.203 tool [16] and was based on Pythium ultimum Genome database [17][18]. However, it aimed at evaluating the pathogenicity of oomycetes, disregarding the annotation of metabolic functions. Moreover the automatic annotation can produce false positive and erroneous data [19]. The goal of this work was to identify the key genes and pathways related to EPA biosynthesis, as well as other metabolites of biotechnological importance, in P. irregulare strain CBS 494.86, including amino acids and fatty acids production, and consumption of carbon and nitrogen sources, present in the wastewater. As this strain was unexplored and unpublished, its genome was thus sequenced and annotated, with a special emphasis in metabolic functions that were thoroughly manually curated. Its possible application was examined through a biotechnological perspective, using as carbon sources vinasse from bioethanol production process a low cost wastewater produced globally in high amount, such as vinasse, from bioethanol process production, glycerol, from biodiesel wastewater (obtained in the biodiesel production), and several food and beverage wastewaters (Additional file 1: Figure S1). The genome-wide functional annotation, presented in this manuscript, was corroborated with evidences from literature, thus allowing its use as the basis for the reconstruction of a genome scale metabolic model. Results Whole-genome sequencing The species classification of the isolate selected for genome sequencing was confirmed by Sanger sequencing and analysis of the cytochrome oxidase I gene (COI) and internal transcribed spacer regions (ITS1 and ITS2), which were aligned, using the nucleotide Basic Local Alignment Search Tool (BLAST) [20], with the NCBI genomic database. The ITS sequence was 98% identical to P. irregulare CBS 250.28 (sequence ID: AY598702.2) with coverage of 86%, wheras the COI sequence was 99% identical to P. irregulare CBS 493.86 / CBS 250.28 (sequence ID: GU071821.1) with coverage of 99%. The genomic DNA from the cultivated P. irregulare strain CBS 494.86 was extracted and sequenced on a HiSeq2500 using a single paired-end library (2x100bp). The HiSeq2500 produced 58,990,406 sequenced fragments 2x100bp which were used for assembly; 43,436, 209 of these remained after quality control. Genome assembly resulted in 9658 scaffolds larger than 500 bp, with an N50 of 13.460 bp (6.653 scaffolds longer than 1 Kbp) and a total genome size of 47.121.789 bp (Table 1). The coverage evaluation of the gene space by our assembly was performed using BUSCÒ v3 [21] with two sets of conserved genes, one for all eukaryotes with 303 conserved genes and one for protists with 215 conserved Fernandes et al. BMC Biotechnology (2019) 19:41 Page 2 of 17 genes. For both datasets our assembly showed over 90% of coverage of complete BUSCÒs (Additional file 2). The gene space coverage observed in this project assembly is similar to that of the published genome sequence of P. irregulare strain DAOM BR486, and the size of their haploid genomes is also similar [21] (Additional file 2). Gene prediction was carried out with Augustus [22], which was trained by exploiting available data from the Buell lab of another strains of P. irregulare and P. ultimum [23][17], and resulted in the prediction of 17,008 protein-coding genes and 29 tRNA genes. The different copies of the ribosomal operon were collapsed into a single copy. In strain CBS 493.86, 95.4% of the predicted genes can be mapped to the genome of strain CBS 805.95. The sequenced genome was deposited in NCBI (Bioproject number: PRJNA371716). Metabolic annotation The merlin software (metabolic models reconstruction using genome-scale information) [24] was used for the functional annotation of proteins with metabolic functions encoded in the genome of P. irregulare. For the analysis and interpretation of the results from the semi-automatic annotation performed by merlin, each candidate metabolic gene was inspected and accepted, or rejected, according to a developed annotation pipeline, reported in the Methods section. The manual curation of merlin results began by inspecting the information in different databases for each candidate and identified homologues, prioritizing UniProt’s reviewed information [25]. As the functional annotation of P. irregulare is yet to be described and most annotations in the Stramenopiles lineage are not reviewed in UniProt, the annotation pipeline took into account phylogeny [26], to retrieve the closest organisms with reviewed information at Swiss-Prot. Arabidopsis thaliana was defined as the organism of reference in the annotation process, since Stramenopiles are the closest relatives of Viridiplantae [27]. Moreover Arabidopsis thaliana has been widely used in genomic studies, for example by Arabidopsis Genome Initiative, AGI, since 1996 [28], affording high consistency in the P. irregulare annotation. From the 17,727 candidate genes provided by the genome sequencing, 5213 were found to have homologies with metabolic genes. From those, 2622 candidates (50.3%) had very high confidence level, meaning that these genes have a very high probability of being correctly classified, because there was consistency in the Enzyme Commission (EC) numbers found in the similarity search conducted. On the other hand, there were 1404 gene candidates with very low confidence level, which means that these might have been erroneously assigned with metabolic functions, weakening the classification confidence, and consequently they were rejected from the set of metabolic genes. There were also 1187 candidates with high, medium and low confidence level, which were manually curated, according to the developed pipeline described in the Methods section (Fig. 8). Figure 1displays the distribution of organisms with at least one homologous gene found during the enzymatic annotation, and its domain or kingdom of origin. A total of 9338 organisms were mapped in the homologous gene analysis, with 470 of them being reported in the enzymatic annotation, 154 of which were reported at least twice in the annotation. Among the 470 organisms, Table 1 Pythium irregulare CBS 494.86 genome statistics Assembly statistics for genome Estimated genome size 47.1 Mb Number of scaffolds 9658 Number of scaffolds (≥1000 bp) 6653 Number of scaffolds (≥5000 bp) 2204 Number of scaffolds (> = 10,000 bp) 1192 Number of scaffolds (≥25,000 bp) 334 Number of scaffolds (≥50,000 bp) 64 Total length 45,784,433 Total length (≥1000 bp) 43,603,753 Total length (≥5000 bp) 33,709,764 Total length (≥10,000 bp) 26,550,523 Total length (≥25,000 bp) 13,047,962 Total length (≥50,000 bp) 4,077,371 Largest scaffolds 191,247 GC (%) 53.43 Scaffolds N50 13,460 Scaffolds N75 4686 Scaffolds L50 878 Scaffolds L75 2334 #N’s per 100 kbp 262.38 Number of genes 17,758 Number of mRNAs 17,727 Number of tRNAs 29 Number of rRNAs 2 Total CDS length 23,740,968 Total Gene length 23,750,652 Average gene length 1337.54 Longest gene 26,310 Shortest gene 42 % of genome covered by genes 51.87 % of genome covered by CDS 51.85 Average number of exons 2.58 Max number of exons 42 Fernandes et al. BMC Biotechnology (2019) 19:41 Page 3 of 17 (a) (b) (c) (d) Fig. 1 Analysis of 470 organisms reported in the set of homologues of the candidate genes. Detailed legend: Analysis developed with merlin for the enzymatic annotation (a) Distribution by domains; (b) Distribution by the main Kingdoms of Domain Eukaryota; (c) The most frequent organisms and their Kingdoms in the analysis of homologous of candidate gene; and (d) Phylogenetic Tree of the organisms reported in the set of homologous of the candidate genes (reported at least twice –154 organisms). The figure was drawn using the iTOL tool [29] Fernandes et al. BMC Biotechnology (2019) 19:41 Page 4 of 17 87.5% were from Eukaryota Domain (Fig. 1.a), predominating the Viridiplantae Kingdom (Fig. 1.b), with 34.3% of homologue genes coming from Arabidopsis thaliana. Other organisms listed do not exceed 7% of frequency; for example, P. ultimum, a Stramenopiles microorganism, was only listed in 1.6% of the cases (Fig. 1.c). These results corroborated the choice of Arabidopsis thaliana in the pipeline development. Moreover, the Phylogenetic tree represented in Fig. 1.d shows high taxonomic similarity between Viridiplantae and Stramenopiles, according to NCBI taxonomy identifiers. According to the developed pipeline, 3852 EC numbers were manual and automatic assigned (Fig. 2). The enzyme class distribution is described in Fig. 2. Transferases and hydrolases are the biggest enzyme groups with 35 and 34% of annotated EC numbers, respectively. On the other hand, lyases, isomerases, and ligases, only encompass 4, 3, and 7% of the annotated EC numbers, respectively. Figure 2shows that 24.4% of the annotated EC numbers are partial, with hydrolases as the group having the largest amount of incomplete EC numbers (11.2%). Regarding the annotation of transporters, merlin’sTRIAGE [30] independent module identified candidate transporter proteins encoding genes and, for the genes that fulfilled certain conditions, automatically created transport reactions. From this annotation, 945 candidate genes were identified to encode membrane transporter proteins associated with the transport of 860 metabolites. These data are available in the Availability of data and materials (Additional file 4). TRIAGE classified 39.9% genes as Electrochemical Potential-driven Transporters (transporter classification (TC) TC2), 24.4% as Channel/Pores (TC1), 20.2% as Primary Active Transporters (TC3), 9% as Incomplete Characterized Transport Systems (TC9), 4% as Accessory Factors Involved in Transport (TC8), 1% as Group Translocators (TC4), and 1% Transmembrane Electron Carriers (TC15) (Table 2). Analysis of the functional annotation Carbon source metabolism The functional annotation of P. irregulare CBS 494.86 was compared with curated information for Arabidopsis thaliana,Saccharomyces cerevisiae,Yarrowia lipolytica, and Mortierella alpina [28,31–37], because there is no curated metabolic annotation available for any Pythium strain. The reason for choosing A. thaliana was already mentioned above, while S. cerevisiae,Y. lipolytica, and M. alpina are relevant producers of lipids of commercial interest, are well characterized in the literature and are considered promising EPA producers [9]. Additionally, Oomycetes are generally not employed for lipids production, except P. irregulare. As this is the first curated functional annotation developed for Pythium irregulare, all metabolic pathways presented in the present article are mostly based on the findings obtained through the metabolic annotation and crossed with evidence from the literature. In general, there were high similarity between glycolysis, pentose phosphate, and tricarboxylic acid (TCA) cycle pathways for P. irregulare,A. thaliana,S. cerevisiae,Y. lipolytica, and M. alpina. None of these organisms are able to perform the Entner-Doudoroff pathway, although this has been described for some Stramenopiles microorganism [38]. According to the illustration of the metabolic annotation shown in Fig. 3, the process of fatty acids biosynthesis starts with transport of some carbon source, such as sucrose, glucose, fructose, cellulose or glycerol. In the sucrose metabolism, for example, sucrose is degraded extracellularly by an irreversible reaction into D-fructose and D-glucose (by maltase-glucoamylase; EC:3.2.1.20 or beta-fructofuranosidase; EC:3.2.1.26) which will then be transported into the cell. Cellulose, as carbon source, is metabolized in cellobiose (by cellulose 1,4-beta-cellobiosidase; EC:3.2.1.91) and converted into D-glucose (by 11% 30% 23% 4% 3% 3% 4.9% 4.4% 11.2% 0.1% 0.3% 3.5% Oxidoreductases (1.-) Transferases (2.-) Hydrolases (3.-) Lyases (4.-) Isomerases (5.-) Ligases (6.-) Enzymatic family Complete Incomplete Fig. 2 Classification of genes according to the enzymatic family obtained in the P. irregulare metabolic annotation Fernandes et al. BMC Biotechnology (2019) 19:41 Page 5 of 17 Table 2 Transporters level and classification (TC) associated with candidate genes identified by merlin’s TRIAGE TC level Classification Number of TCG a Percentage (%) TC1 Channels/Pores 231 24.4 TC2 Electrochemical Potential-driven Transporters 377 39.9 TC3 Primary Active Transporters 191 20.2 TC4 Group Translocators 8 0.9 TC5 Transmembrane Electron Carriers 13 1.4 TC8 Accessory Factors Involved in Transport 36 3.8 TC9 Incompletely characterized Transport Systems 89 9.4 Total 945 100 a TCG Transporter Candidate Genes Fig. 3 Carbon source consumption and Eicosapentaenoic acid biosynthesis simplified pathway based on the metabolic annotation. Detailed legend: The figure represents metabolites, reactions and enzyme or transporter annotated for each reaction. The figure is divided by pathways, and the pathways marked with A (Fatty acids biosynthesis) and B (Unsaturated Fatty acids biosynthesis) are described in Figura 7. The dashed blue lines (pathway B) highlights the end of fatty acids production by Saccharomyces cerevisiae,Yarrowia lipolytica, and Mortierella alpina Fernandes et al. BMC Biotechnology (2019) 19:41 Page 6 of 17 beta-glucosidase; EC:3.2.1.21), which is then phosphorylated to D-glucose-6P (by glucokinase; EC:2.7.1.2), entering into the Glycolysis pathway. Cellulose and cellobiose are not consumed by Saccharomyces cerevisiae or Yarrowia lipolytica. Glycerol is an example of a carbon source that comes from the glycolipid metabolism pathway (by glycerol kinase, EC: 2.7.1.30), which is converted by some reactions into 3-Phospho-D-glycerate to enter Glycolysis. P. irregulare is a microorganism that could transport and use carbon sources available in different waste streams, namely, vinasse, from first or second generation ethanol production, composed by sucrose, D-glucose, Dfructose, glycerol, acetate, cellulose and others (Additional file 1: Figure S1) [4,39–41]; glycerol, a byproduct in biodiesel production process; and wastewaters from several food and beverage industries [42–44]. The results regarding the enzymes and transporters annotations are in agreement with the carbon sources reported in the literature [4,13,14,29,38] and presented in Table 3, with the exception of D-Galactose, Lrhamnose, and D-mannose. Though there is experimental evidence [14] regarding the use of these carbon sources by P. irregulare, and transporter proteins were identified for these sugars, no further consuming enzymes have been found in the metabolic annotation (Table 3). Cellulose consumption is associated with the pathogenicity of P. irregulare, which is able to degrade plant cell walls of a wide range of plants (corn, soybeans, wheat, fruit trees, vegetables, cereals, and others). P. irregulare invades and forms haustoria within living plant cells, consuming its nutrients [23,29,45,46]. The mapped candidate genes for cellulose consumption are important to understand the genetic basis of its pathogenicity. In summary, most carbon sources (Fig. 3) are converted and guided to the Glycolysis pathway providing pyruvate, and subsequently acetyl-CoA, which, together with NADPH produced in the pentose phosphate pathway are critical precursors of fatty acid synthesis. Furthermore, other pathways can participate in acetyl-CoA’s availability, mainly consumption of amino acids from the host. Those pathways, together with the biosynthetic pathways for amino acids are reported in the following subsections. Amino acids This subsection aims to present the metabolic annotation obtained for amino acid production and highlight the main pathways involved in the fatty acid production. General view Figure 4represents the possible pathways of amino acid production in P. irregulare according to its functional annotation developed in this research. In general, there are only a few differences in the amino acid production pathways reported for A. thaliana [28], some diatoms (Thalassiosira pseudonana and Phaeodactylum tricornutum), Stramenopiles microorganisms [47], and S. cerevisiae [35]. The divergences are in the pathways B – Glycine and Serine, C - Tyrosine, and E –Arginine, reported in this item (Fig. 4); and B –Cysteine and F –Lysine pathways, both described in item 2.3.2.1.1. Finally, a reflection regarding the pathways associated with fatty acids metabolism such as Leucine, Isoleucine, and Lysine degradation and Cysteine and Lysine biosynthesis is described in item 2.3.2.2 Amino acids associated with metabolism of fatty acids (FAs) . B–Glycine and Serine pathways A. thaliana and Diatoms have glycine and serine synthesis as part of photorespiration and non-photorespiration [47]. Only the non-photorespiratory pathway of serine synthesis is observed in P. irregulare, based on the functional annotation, similarly to what is observed in S. cerevisiae [35] and Y. lipolytica [34],as well as other Stramenopiles [47] (Fig. 4). C - Tyrosine pathway Phenylalanine, tyrosine, and tryptophan are aromatic amino acids and central molecules in Arabidopsis thaliana metabolism, serving as precursors for a variety of hormones, but they are not classified as essential in plants [48]. In this functional annotation, the shikimate pathway from erythrose4-phosphate and phosphoenolpyruvate to chorismic acid is a common pathway adopted by S. cerevisiae [49]andA. thaliana [48]and Pythium irregulare (Fig. 4). Tyrosine in P. irregulare comes from phenylalanine as found in Diatoms [47]and M. alpine [37], instead of 4-Hydroxyphenylpyruvate pathways found in S. cerevisiae [49]andY. lipolytica [34]and from L-Arogenate pathway or Phenylalanine in A. thaliana [28].AccordingtoWangetal.[50], this degradation reaction by phenylalanine hydroxylase (EC:1.14.16.1) from phenylalanine to pyrosine is functionally relevant in lipid metabolism, including the sequential reactions to acetylCoA (Fig. 4). E–Arginine pathway According to this functional annotation, glutamate, glutamine, proline, and arginine pathways are derived from 2-oxoglutarate, a product of the TCA cycle. In P. irregulare, these pathways can use ammonia, nitrate, or nitrite as a nitrogen source Fig. 4). This provides a great versatility for this microorganism, unlike S. cerevisiae and Y. lipolitica which cannot consume nitrate and nitrite as a nitrogen sources [34,35]. Nitrate is present in great amounts in some wastewaters such as vinasse [51]. Besides this difference, the biosynthetic pathways for these amino acids are similar in all studied organisms, Fernandes et al. BMC Biotechnology (2019) 19:41 Page 7 of 17 except for reaction 16, in pathway E –L-Glutamate, LGlutamine, Proline, and Arginine described in Fig. 4, which catalyzes the oxidation of Arginine into Citrulline in the presence of NADPH and O 2 by the family of enzymes named nitric-oxide synthases (NOSs, EC: 1.14.13.39) These are found in P. irregulare’s metabolic annotation and A. thaliana [28,52], but not in S. cerevisiae [35] and Y. lipolytica Arginine is a major storage and transport form of organic nitrogen in plants. Additionally, it has a role in protein synthesis, as a precursor of nitric oxide, polyamines, besides its importance as a pathway in pathogen resistance mechanism [52]. P. irregulare, using A. thaliana as a host, could develop invasion strategies, including enzyme production to interfere in the metabolic targets common to its host [3], in this case, probably reducing Arginine availability in plants by converting it into Citrulline (by nitric oxide synthase, EC:1.14.13.39) (Fig. 4). Amino acids associated with metabolism of fatty acids (FAs) Amino acid biosynthesis and degradation play an important role in the biomass development and fatty acids (FAs) biosynthesis, by providing Acetyl-CoA [53], obtained in the degradation of branched-chain Table 3 Comparison betwwen carbon source consumption based on the Metabolic Annotation and experimental data Fernandes et al. BMC Biotechnology (2019) 19:41 Page 8 of 17 amino acid such as leucine (EC:2.3.1.9, EC:4.1.3.4), isoleucine (EC:2.3.1.16), and lysine (EC:1.5.1.8, EC:2.3.1.9) (Fig. 4), as well as in the lysine [54] and L-cysteine biosynthesis [55]. The leucine, isoleucine, and lysine degradation pathways (Fig. 4) are identified in Y. lipolytica, an oleaginous microorganism [36], but not in S. cerevisiae [35]. Such pathways can be associated to the pathogen’s resistance mechanism, maximizing the energy storage through the accumulation of lipids. Lysine biosynthesis, in P. irregulare, comes from the diaminopimelate (DAP) pathway, observed in plants [56] and in other Stramenopiles [47], instead of the alphaaminoadipate (AAA) pathway found in S. cerevisiae and Fig. 4 Prediction of amino acid biosynthesis according to the metabolic annotation of P. irregulare CBS 494.86. The amino acid pathways are listed from A to F. The reactions are enumerated inside each specific pathway, followed by the encoded EC number. The EC numbers with a question mark (?) are cases of reactions not predicted by the metabolic annotation. Some metabolites and reactions are marked with M (metabolites) and/or numbers decoded as follows: in DL-Alanine, L-Valine, Leucine & Iso-Leucine pathway, M.1 = (R)-3-Hydro 3-methyl 2-oxopentanoate, M.2 = (S)-2,3 Dihydroxy 3-methypentanoate, M.3 = (S)-3-Methyl 2-oxopentanoate, M.4 = 2-Isopropylmaleate, M.5 = (2R,3S)-3-Isopropylmalate, M.6 = (2S)-2-Isopropyl 3-oxopentanoate, M.7 = 4-Methyl 2-oxopentanoate; 3 = EC:1.1.1.86; 4 = EC: 1.1.1.86; 5 = EC: 4.2.1.9; 13 = EC: 2.3.3.13, 14 = EC: 4.2.1.33; 15 = EC: 1.1.1.85; 16 = Spontaneous; in E –LGlutamate, L-Glutamine, Proline & Arginine pathway, M.8 = N-Acetyl-glutamate, M.9 = N-Acetyl-glutamyl-P, M.10 = N-Acetylglutamate semialdehyde,M.11=NAcetyl-ornithine, 5 = EC: 2.3.1.1; 6 = EC: 2.7.2.8; 7 = EC: 1.2.1.38; 8 = EC: 2.6.1.11; 9 = EC: 3.5.1.14; in E –L-Glutamine, L-Glutamate, Proline & Arginine pathway, M.12 = L-Glutamate 5-semialdehyde; M13 = (S)-1-Pyrroline-5-carboxylate;11 = non-enzymatic; 12 = EC:1.5.1.2; in F –L-Aspartate, L-Asparagine, Lysine, Threonine & L-Methionine pathway, M.14 = (2S,4S)-4-hydroxy 2,3,4,5 tetrahydro-dipicolinate, M.15 = L-2,3,4,5Tetrahydro-dipicolinate, M.16 = L-L-2,6 Diamino-pimelate, M.17 = meso2,6 Diamino-pimelate, 13 = EC: 4.3.3.7;14 = EC: 1.17.1.8; 15 = EC: 2.6.1.83 (?); 16 = EC: 5.1.1.7 (?); 17 = EC: 4.1.1.20 Fernandes et al. BMC Biotechnology (2019) 19:41 Page 9 of 17 Received: 10 December 2018 Accepted: 28 May 2019 References 1. Harvey PR, Butterworth PJ, Hawke BG, Pankhurst CE. Genetic variation among populations of Pythium irregulare in southern Australia. Plant Pathol. 2000;49(5):619–27. 2. Spies CFJ, Mazzola M, Botha WJ, Langenhoven SD, Mostert L, Mcleod A. Molecular analyses of Pythium irregulare isolates from grapevines in South Africa suggest a single variable species. Fungal Biol. 2011;115(12):1210–24. 3. de León IP, Montesano M. Activation of defense mechanisms against pathogens in mosses and flowering plants. Int J Mol Sci. 2013;14(2): 3178–200. 4. Liang Y, Zhao X, Strait M, Wen Z. Use of dry-milling derived thin stillage for producing eicosapentaenoic acid (EPA) by the fungus Pythium irregulare. Bioresour Technol. 2012;111:404–9. 5. FAO Food and agriculture organization of the united nations. Fats and fatty acids in human nutrition report of an expert consultation. Rome: Food and agriculture organization of the united nations; 2010. 6. Albert BB, Derraik JGB, Cameron-Smith D, Hofman PL, Tumanov S, VillasBoas SG, et al. Fish oil supplements in New Zealand are highly oxidised and do not meet label content of n-3 PUFA. Sci Rep. 2015;5(7928). https://doi. org/10.1038/srep07928. 7. Sitepu IR, Garay LA, Sestric R, Levin D, Block DE, German JB, et al. Oleaginous yeasts for biodiesel: current and future trends in biology and production. Biotechnol Adv. 2014;32(7):1336–60. 8. Grand View Research. Omega 3 Market Analysis And Segment Forecasts To 2020 [Internet]. 2014 [cited 2015 May 17]. Available from: http://www. grandviewresearch.com/industry-analysis/omega-3-market 9. Bajpai P, Bajpai PK. Eicosapentaenoic acid (EPA) production from microorganisms: a review. J Biotechnol. 1993;30:161–83. 10. Lee JM, Lee H, Kang SB, Park WJ. Fatty acid desaturases, polyunsaturated fatty acid regulation, and biotechnological advances. Nutrients. 2016;8(1):1–13. 11. Bajpai P, Bajpai P, Ward O. Eicosapentaenoic acid (EPA) formation_ comparative studies with Mortierella strains and production by Mortierella elongata. Mycol Res. 1991;95(11):1294–8. 12. Abedi E, Sahari MA. Long-chain polyunsaturated fatty acid sources and evaluation of their nutritional and functional properties. Food Sci Nutr. 2014; 2(5):443–63. 13. Athalye SK, Garcia RA, Wen Z. Use of biodiesel-derived crude glycerol for producing eicosapentaenoic acid (EPA) by the fungus pythium irregulare. J Agric Food Chem. 2009;57(7):2739–44. 14. Zerillo MM, Adhikari BN, Hamilton JP, Buell CR, Lévesque CA, Tisserat N. Carbohydrate-active enzymes in Pythium and their role in plant Cell Wall and storage polysaccharide degradation. PLoS One. 2013;8(9). 15. Seidl MF, Van Den Ackerveken G, Govers F, Snel B. Reconstruction of oomycete genome evolution identifies differences in evolutionary trajectories leading to present-day large gene families. Genome Biol Evol. 2012;4(3):199–211. 16. Cantarel BL, Korf I, Robb SMC, Parra G, Ross E, Moore B, et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 2008;18(1):188–96. 17. Pythium Genome Database [Internet]. 2017 [cited 2017 Feb 10]. Available from: http://pythium.plantbiology.msu.edu 18. Adhikari BN, Hamilton JP, Zerillo MM, Tisserat N, Lévesque CA, Buell CR. Comparative genomics reveals insight into virulence strategies of plant pathogenic oomycetes. PLoS One. 2013;8(10). 19. Koonin EV, Galperin MY. Genome annotation and analysis. In: Sequence - evolution - function: computational approaches in comparative genomics. Boston: Kluwer Aca; 2003. 20. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10. 21. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31(19):3210–2. 22. Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern BAUGUSTUS. A b initio prediction of alternative transcripts. Nucleic Acids Res. 2006; 34(WEB. SERV. ISS):435–9. 23. Lévesque CA, Brouwer H, Cano L, Hamilton JP, Holt C, Huitema E, et al. Genome sequence of the necrotrophic plant pathogen Pythium ultimum reveals original pathogenicity mechanisms and effector repertoire. Genome Biol. 2010;11(7):R73. 24. Dias O, Rocha M, Ferreira EC, Rocha I. Reconstructing genome-scale metabolic models with merlin. Nucleic Acids Res. 2015 Apr;43(8):3899–910. 25. Chen C, Huang H, Wu C. Protein bioinformatics databases and resources. Methods Mol Biol. 2017;1558:3–39. 26. Sierra R, Canas-Duarte SJ, Burki F, Schwelm A, Fogelqvist J, Dixelius C, et al. Evolutionary origins of rhizarian parasites. Mol Biol Evol. 2016;33(4):980–3. 27. Restrepo S, Enciso J, Tabima J, Riaño-Pachón DM. Evolutionary history of the group formerly known as protists using a phylogenomics approach. Rev la Acad Colomb Ciencias Exactas, Fis y Nat. 2016;40(154):147–60. 28. Plant Metabolic Network (PMN) [Internet]. 2016 [cited 2016 Feb 1]. Available from: www.plantcyc.org 29. Matsumoto C, Kageyama K, Suga H, Hyakumachi M. Intraspecific DNA polymorphisms of Pythium irregulare. Mycol Res. 2000;104(11):1333–41. 30. Dias O, Gomes DG, Vilaça P, Cardoso JJ, Rocha M, Ferreira EEC, et al. Genome-wide semi-automated annotation of transporter systems. IEEE/ACM Trans Comput Biol Bioinforma. 2014;XX(X):1–14. 31. Yu Y, Li T, Wu N, Ren L, Jiang L, Ji X, et al. Mechanism of arachidonic acid accumulation during aging in Mortierella alpina : a large-scale label-free comparative proteomics study. J Agric Food Chem. 2016;64(47):9124–34. 32. Santos S, Liu F., Costa C, Vilaça P, Rocha M, Rocha I. MIYeastK: The Metabolic Integrated Yeast Knowledgebase. In: SPB2016 - Book of Abstracts XIX National Congress of Biochemistry No O5/03. Guimarães, Portugal; 2016. 33. Ye C, Xu N, Chen H, Chen YQ, Chen W, Liu L. Reconstruction and analysis of a genome-scale metabolic model of the oleaginous fungus Mortierella alpina. BMC Syst Biol. 2015;9(1). 34. Pan P, Hua Q. Reconstruction and in silico analysis of metabolic Network for an oleaginous yeast, Yarrowia lipolytica. PLoS One. 2012;7(12):1–11. 35. Saccharomyces Genome Database (SGD) [Internet]. 2017 [cited 2017 Jan 10]. Available from: http://www.yeastgenome.org 36. Kyoto Encyclopedia of Genes and Genomes(KEGG) [Internet]. 2017 [cited 2017 Jan 10]. Available from: http://www.genome.jp/kegg/ 37. Wang L, Chen W, Feng Y, Ren Y, Gu Z, Chen H, et al. Genome characterization of the oleaginous fungus mortierella alpina. PLoS One. 2011;6(12):e28319. 38. Hildebrand M, Abbriano RM, Polle JEW, Traller JC, Trentacoste EM, Smith SR, et al. Metabolic and cellular organization in evolutionarily diverse microalgae as related to biofuels production. Curr Opin Chem Biol. 2013; 17(3):506–14. 39. Doelsch E, Masion A, Cazevieille P, Condom N. Spectroscopic characterization of organic matter of a soil and vinasse mixture during aerobic or anaerobic incubation. Waste Manag. 2009;29(6):1929–35. 40. Santos SC, Ferreira Rosa PR, Sakamoto IK, Amâncio Varesche MB, Silva EL. Continuous thermophilic hydrogen production and microbial community analysis from anaerobic digestion of diluted sugar cane stillage. Int J Hydrog Energy. 2014;39(17):9000–11. 41. Aditiya HB, Mahlia TMI, Chong WT, Nur H, Sebayang AH. Second generation bioethanol production: a critical review. Renew Sust Energ Rev. 2016;66: 631–53. 42. Cheng M, Walher T, Hulbert G, Raman D. Fungal production of eicosapentaenoic and arachidonic acids from industrial waste streams and crude soybean oil. pdf Bioresour Technol. 1999;67:101–10. 43. Lio J, Wang T. Pythium irregulare fermentation to produce arachidonic acid (ARA) and Eicosapentaenoic acid (EPA) using soybean processing coproducts as substrates. Appl Biochem Biotechnol. 2013;169(2):595–611. 44. Dong M, Walker TH. Production and recovery of polyunsaturated fatty acids-added lipids from fermented canola. Bioresour Technol. 2008; 99(17):8504–6. 45. Wu L, Roe C, Wen Z. The safety assessment of Pythium irregulare as a producer of biomass and eicosapentaenoic acid for use in dietary supplements and food ingredients. Appl Microbiol Biotechnol. 2013;97: 7579–85. 46. Caballero JRI, Tisserat NA. Transcriptome and secretome of two Pythiumspecies during infection and saprophytic growth. Physiol Mol Plant Pathol. 2017;99:41-54. 47. Bromke MA. Amino acid biosynthesis pathways in diatoms. Metabolites. 2013;3(2):294–311. 48. Tzin V, Galili G. The biosynthetic pathways for shikimate and aromatic amino acids in Arabidopsis thaliana. Arab book; Am Soc Plant Biol. 2010: e0132. Fernandes et al. BMC Biotechnology (2019) 19:41 Page 16 of 17 49. Braus GH. Aromatic amino acid biosynthesis in the yeast Saccharomyces cerevisiae: a model system for the regulation of a eukaryotic biosynthetic pathway. Microbiol Rev. 1991;55(3):349–70. 50. Wang H, Chen H, Hao G, Yang B, Feng Y, Wang Y, et al. Role of the phenylalanine-hydroxylating system in aromatic substance degradation and lipid metabolism in the oleaginous fungus mortierella alpina. Appl Environ Microbiol. 2013;79(10):3225–33. 51. Francisco JP, Folegatti MV, Silva LBD, Silva JBG, Diotto AV. Variations in the chemical composition of the solution extracted from a latosol under fertigation with vinasse. Rev Cienc Agron. 2016;47(2):229–39. 52. Winter G, Todd CD, Trovato M, Forlani G, Funck D. Physiological implications of arginine metabolism in plants. Front Plant Sci. 2015;6(July):1–14. 53. Danne JC, Gornik SG, MacRae JI, McConville MJ, Waller RF. Alveolate mitochondrial metabolic evolution: dinoflagellates force reassessment of the role of parasitism as a driver of change in apicomplexans. Mol Biol Evol. 2013;30(1):123–39. 54. Vorapreeda T, Thammarongtham C, Cheevadhanarak S, Laoteng K. Alternative routes of acetyl-CoA synthesis identified by comparative genomic analysis: involvement in the lipid production of oleaginous yeast and fungi. Microbiology. 2012;158(1):217–28. 55. Niu X. Gene regulatory machinery and proteomics of sexual reproduction in Phytophthora infestans. University of California Riverside; 2010. 56. Morris PF, Schlosser LR, Onasch KD, Wittenschlaeger T, Austin R, Provart N. Multiple horizontal gene transfer events and domain fusions have created novel regulatory and metabolic networks in the oomycete genome. PLoS One. 2009;4(7). 57. Koprivova A, Michael M, von Ballmoos P, Mandel T, Brunold C, Kopriva S. Assimilatory sulfate reduction in C3, C3-C4, and C4 species of Flaveria. Plant Physiol. 2001;127(2):543–50. 58. Tehlivets O, Scheuringer K, Kohlwein SD. Fatty acid synthesis and elongation in yeast. Biochim Biophys Acta - Mol Cell Biol Lipids. 2007;1771(3):255–70. 59. Griffiths RG, Dancer J, O’Neill E, Harwood JL. Effect of culture conditions on the lipid composition of Phytophthora infestans. New Phytol. 2003;158(2): 337–44. 60. Uttaro AD. Biosynthesis of polyunsaturated fatty acids in lower eukaryotes. IUBMB Life. 2006;58(10):563–71. 61. Klug L, Daum G. Yeast lipid metabolism at a glance. FEMS Yeast Res. 2014; 14(3):369–88. 62. Schweizer E, Hofmann J. Microbial type I fatty acid synthases ( FAS ): major players in a Network of cellular FAS systems. Microbiol Mol Biol Rev. 2004; 68(3):501–17. 63. Xia EH, Jiang JJ, Huang H, Zhang LP, Bin ZH, Gao LZ. Transcriptome analysis of the oil-rich tea plant, Camellia oleifera, reveals candidate genes related to lipid metabolism. PLoS One. 2014;9(8):e104150. 64. Shpilka T, Welter E, Borovsky N, Amar N, Shimron F, Peleg Y, et al. Fatty acid synthase is preferentially degraded by autophagy upon nitrogen starvation in yeast. Proc Natl Acad Sci U S A. 2015;112(5):1434–9. 65. Xie D, Jackson EN, Zhu Q. Sustainable source of omega-3 eicosapentaenoic acid from metabolically engineered Yarrowia lipolytica: from fundamental research to commercial production. Appl Microbiol Biotechnol. 2015;99(4): 1599–610. 66. Haslam RP, Sayanova O, Kim HJ, Cahoon EB, Napier JA. Synthetic redesign of plant lipid metabolism. Plant J. 2016;87(1):76–86. 67. Xue Z, He H, Hollerbach D, MacOol DJ, Yadav NS, Zhang H, et al. Identification and characterization of new Δ-17 fatty acid desaturases. Appl Microbiol Biotechnol. 2013;97(5):1973–85. 68. Hong H, Datla N, Reed DW, Covello PS, MacKenzie SL, Qiu X. High-level production of γ-linolenic acid in Brassica juncea using a Δ6 desaturase from Pythium irregulare. Plant Physiol. 2002;129(1):354–62. 69. Hong H, Datla N, MacKenzie S, Qiu X. Isolation and characterization of a delta5 FA desaturase from Pythium irregulare by heterologous expression in Saccharomyces cerevisiae and oilseed crops. Lipids. 2002;37(9):863–8. 70. Raffaele S, Kamoun S. Genome evolution in filamentous plant pathogens: why bigger can be better. Nat Rev Microbiol. 2012;10(6):417–30. 71. Lévesque CA, de Cock AWAM. Molecular phylogeny and taxonomy of the genus Pythium. Mycol Res. 2004;108(12):1363–83. 72. de Graaff L, van den Broek H, Visser J. Isolation and characterization of the Aspergillus nidulans pyruvate kinase gene. Curt Genet. 1988;13:315–21. 73. Robideau GP, De Cock AWAM, Coffey MD, Voglmayr H, Brouwer H, Bala K, et al. DNA barcoding of oomycetes with cytochrome c oxidase subunit I and internal transcribed spacer. Mol Ecol Resour. 2011;11(6):1002–11. 74. Chikhi R, Medvedev P. Informed and automated k-mer size selection for genome assembly. Bioinformatics. 2014;30(1):31–7. 75. Nurk S, Bankevich A, Antipov D, Gurevich AA, Korobeynikov A, Lapidus A, et al. Assembling single-cell genomes and mini-metagenomes from chimeric MDA products. J Comput Biol. 2013;20(10):714–37. 76. Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A, Sakthikumar S, et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One. 2014;9(11). 77. Corrêa dos Santos RA, Goldman GH, Riaño-Pachon DM. ploidyNGS: Visually exploring ploidy with next generation sequencing data. Not Peer-Reviewed. 2016:0–5. 78. Palsson B. Metabolic systems biology. FEBS Lett. 2009;583(24):3900–4. 79. Médigue C, Moszer I. Annotation, comparison and databases for hundreds of bacterial genomes. Res Microbiol. 2007;158(10):724–36. 80. Rocha I, Förster J, Nielsen J. Design and application of genome-scale reconstructed metabolic models. In: Inc HP, editor. Methods in molecular biology, vol 416: gene essentiality; 2007. p. 409–33. 81. Dias O, Gombert AK, Ferreira EC, Rocha I. Genome-wide metabolic (re-) annotation of Kluyveromyces lactis. BMC Genomics. 2012;13:517. 82. Käll L, Krogh A, Sonnhammer EL. L. a combined transmembrane topology and signal peptide prediction method. J Mol Biol. 2004 May;338(5):1027–36. 83. Kall L, Krogh A, Sonnhammer ELL. Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server. Nucleic Acids Res. 2007 May;35(Web Server):W429–32. 84. Smith TF, Waterman MS. Identification of common molecular subsequences. J Mol Biol. 1981;147(1):195–7. 85. Goldberg T, Hecht M, Hamp T, Karl T, Yachdav G, Ahmed N, et al. LocTree3 prediction of localization. Nucleic Acids Res. 2014;42(W1):350–5. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Fernandes et al. BMC Biotechnology (2019) 19:41 Page 17 of 17