Screening Through Bioinformatic Studies of Secondary Metabolites from the Rare Alkaline-Halophilic Stachybotrys Microspora Compared to Related Fungi

Screening Through Bioinformatic Studies of Secondary Metabolites from the Rare Alkaline-Halophilic Stachybotrys Microspora Compared to Related Fungi

Salma Abdeljalil

Molecular Biotechnology of Eukaryotes Laboratory, Centre of Biotechnology of Sfax- University of Sfax, Tunisia Centre de Biotechnologie de Sfax, Route Sidi Mansour, BP «1177» 3018, Université de Sfax Tunisia.

Abstract – Screening for microbial secondary metabolites (SMs) has attracted the attention of the scientific community since 1940s. In fact, since the discovery of penicillin, intensive researches have been conducted worldwide in order to detect and identify novel microbial secondary metabolites.

However, the development and advances of omics-based techniques such as metabolomics and genomics have revealed the potential to discover novel SMs encoded in the DNA of microorganisms. However, we have not yet tested their production in the laboratory to determine the biosynthesis gene clusters (BGCs) actually associated with the biosynthesis of these secondary metabolites.In fact, Fungi produce a number of structural classes of secondary metabolites including polyketides (PKs), non-ribosomal peptides (NRPs), hybrid PK- NRPs, indole alkaloids, and terpenes. In almost all cases the genes responsible for the production of these classes of secondary metabolites are organized as a gene cluster. We report in this paper a large scale analysis of secondary metabolites produced by the rare fungus Stachybotrys microspora using different bioinfomatic tools.

Keywords: Stachybotrys microspora; metabolism; Secondary metabolites; Prism 4; OmicsBox platform; anti-SMASH database.

1. INTRODUCTION

   Numerous facets of society are impacted by fungi, which have incalculable effects on ecosystems and are crucial to ecology, agriculture, health, and biotechnology. Because of their typical haplobion life cycle, which promotes the phenotypic of mutations, and the fact that the majority of cells can differentiate throughout the organism, they are utilized as appropriate organisms for fundamental research. Numerous fungi are also beneficial for molecular, genetic, and microbiological methods and are simple to cultivate. As a result, fungi were among the earliest model organisms used in the study of cells, developmental biology, biochemistry, and genetics.

   We are working on a rare, locally isolated fungal strain belonging to the Stachybotrys microspora. Stachybotrys is related to the genus Memnoniella, most Stachybotrys species inhabit cellulose-rich substances [1]. This genus is widely distributed and contains about 50 species [2]. Our previous biochemical studies on S. microspora shows the production of various cell wall degrading enzymes. Interestingly, our strain is characterized by growing in a cellulosic-based medium over a wide pH range of 4-9. This is rarely reported in most known fungal species. In addition to its ability to grow at alkaline pH, S. microspora produces neutral and alkaline endoglucanases [3] and secretes several -glucosidases [4-9] whereas fungi generally have one or two beta-. Produces glucosidase.

   To take advantage of the potential of this strain, we have also conducted a molecular study of the cellulolytic genes to investigate the molecular expression profile of the corresponding gene. We succeeded in isolating three -glucosidase genes from the glycoside hydrolases 1 and 3 families, confirming the abundance of S. microspora in the production of -glucosidase, and showed different expression patterns [7,8].

   In addition to secreting cellulase, the fungus produces xylanase, protease, chitinase, -glucanase and pectinase, some of which share the same regulatory mechanism to induce or suppress their respective activities [10].

   Sequencing of the whole genome using next-generation sequencing can identify sequences that are potentially associated with toxin production and other yet unknown secondary metabolites.

   In fact, the most infamous species, S. chartarum (also known as S. atra) and S.chlorohalonata are known as “black mold” or “toxic black mold” and are frequently associated with poor indoor air quality that arises after fungal growth on water damaged building materials. S. chartarum produces several mycotoxins, highly toxic macrocyclic trichothecenes and related trichoverroids as well as immunosuppresants and endothelin receptor antagonists [11].

   Recently, a comparative genome sequencing study has revealed chemotype-specific gene clusters in the toxigenic black mold

   Stachybotrys, coding for satratoxins and other less-toxic atronomes [12].

   Triprenyl phenols (SMTPs), a novel family of small, advantageous compounds that improve plasminogen activation and fibrin- binding, are secreted by Stachybotrys microspora and are non-toxic. Little is known regarding their action in vivo with regard to plasminogen activation and blood clot clearance, despite the fact that their effects on fibrinolysis have been described in vitro [13]. Furthermore, at recommended dosages, enzymes are categorized as non-toxic. Ben Hmad et al. assessed the cytotoxicity effect of an enzyme cocktail produced by Stachybotrys microspora on human endothelial cells (HUVEC) in light of the growing consumer safety concerns related to enzymes in the food sector.

   Remarkably, the results showed that cell viability and replication remained between 120 and 150 percent across enzyme cocktail concentrations ranging from 1 to 10 percent. This suggests that the enzyme cocktail from Stachybotrys microspora does not exhibit cytotoxicity but rather promotes the growth of human endothelial cells [14].

2. MATERIALS AND METHODS
   1. Screening for secondary metabolites clusters using antiSMASH database

      Microbial secondary metabolites are a potent source of antibiotics and other pharmaceuticals. Genome mining of their biosynthetic gene clusters has become a key method to accelerate their identification and characterization. In 2011, antiSMASH was developed as a web-based analysis platform that automates this process available at http://antismash.secondarymetabolites.org/.

      In fact, many fungal secondary metabolites are made by products of genes that are found adjacent to one another in a single contiguous locus [15]. These genetic loci are known as secondary metabolite biosynthesis (SMB) clusters. SMB clusters were identified with the anti-SMASH cluster prediction software.

   2. Screening of secondary metabolites screening using OmicsBox platform

      Recently, we published the first draft of the genome sequence of S. microspora consisting of 3,715 contigs with a genome size of 35,343,854 bp and a GC content of 53.31 percent.Two bioinformatic tools, Omicsbox and the fungicompanion web server, were used for functional annotation [9].

      We report in this paper the screening of secondary metabolites using eggnog map annotation via Omicsbox platform.

   3. Screening of secondary metabolites screening using Prisma 4

      All kinds of antibiotics now used in clinical settings can have their chemical structures predicted using the full PRISM 4 platform. The development of machine-learning techniques to forecast the probable biological activity of encoded molecules is made possible by the precision of chemical structure prediction. Using PRISM 4, we map the production of secondary metabolitesin a population of more than 10,000 bacterial genomes from metagenomic and cultivated isolates, uncovering thousands of antibiotics that are encoded. The interactive web application PRISM 4 can be found for free from http://prism.adapsyn.com.

   4. Multiple alignment and phylogenetic analysis

      Amino acid sequence alignment was performed using the ClustalW-BioEdit program.

      The phylogenetic tree was performed with Cobalt Multiple Protein Alignment Tool of the NCBI Web BLAST service using the neighbour-joining method. COBALT is a multiple sequence alignment tool that finds a collection of pairwise constraints derived from conserved domain database, protein motif database, and sequence similarity, using RPS-BLAST, BLASTP, and PHI- BLAST. Pairwise constraints are then incorporated into a progressive multiple alignment.

3. RESULTS AND DISCUSSION:
   1. 1. Screening of secondary metabolites using the online database anti- SMASH

         The screening of secondary metabolites using the online database anti-SMASH gave a total number of 45 compounds classified as12 of T1PKS (Type I PKS (Polyketide synthase),8 of NRPS (Non-ribosomal peptide synthetase cluster),indole, 7 of terpene,NRPS-like, 3 of T3PKS (fig.1).

      2. Screening of secondary metabolites screening using OmicsBox platform

         Secondary metabolites were also studied using Omicsbox platform under eggnog map annotation.

         Some strains from the fungal genus Stachybotrys strains comprise two mutually-exclusive toxin chemotypes, one producing satratoxins, which are a subclass of trichothecenes, and the other producing the less-toxic atranones [12].

         The whole annotation of Stachybotrys microspora toxins has indicated the presence of 6 Satratoxin biosynthesis SC1 cluster protein 4 with two of them were incomplele, Satratoxin biosynthesis SC1 cluster transcription factor SAT9, Satratoxin biosynthesis SC3 cluster transcription factor SAT20, putative HC-toxin efflux carrier TOXA, killer toxin subunits alpha/beta, host-specific AK-toxin Akt2, toxin efflux pump (AflT), structural toxin protein RtxA, killer toxin resistance protein, zeta toxin family protein, Ras-like C3 botulinum toxin substrate 1, gliotoxin biosynthesis but no atranones.

         Compared to other Stachybotrys species which the largest named the core atranone cluster, which encodes 14 proteins that may suffice to produce all observed atranone compounds via reactions that include an unusual Baeyer-Villiger oxidation. Satratoxins are suggested to be made by products of multiple gene clusters that encode 21 proteins in all, including polyketide synthases, acetyltransferases, and other enzymes expected to modify the trichothecene skeleton. One such satratoxin chemotype-specific cluster is adjacent to the core trichothecene cluster, which has diverged from those of other trichothecene producers to contain a unique polyketide synthase [12].

         The eggnog annotation has shown 458 secondary metabolites using OmicsBoxEggNOG Annotation (Table 1) and a report of all eggonog annotation is illustrated as supplementary file (supplementary file 1).

         Regarding the biosynthesis of aflatoxin, we must state that among the known mycotoxins, aflatoxinsa class of furanocoumarins generated from polyketidesare the most harmful and carcinogenic substances. However, only four main aflatoxinsB1, B2, G1, and G2 (AFB1, AFG1, AFB2, and AFG2)contaminate agricultural commodities and may be harmful to animals out of the at least 16 structurally related aflatoxins that have been identified [16].

         Interestingly, the annotation of S.microspora genome revealed the presence of aflatoxin reductases : aflatoxin B1 aldehyde reductase member 2, 3 and 4 , aflatoxin biosynthesis ketoreductase nor-1, aflatoxin biosynthesis regulatory protein, probable aflatoxin efflux pump AFLT.

         According to [17] , these enzymes serve as detoxifying agents against aflatoxins, which are secondary metabolites mostly generated by Aspergillus flavus and Aspergillus parasiticus. There are currently more than 20 known aflatoxin derivatives. The International Agency for Research on Cancer has designated aflatoxins B1 (AFB1) as a Group I carcinogen, making it the most hazardous of them [18,19]. Many crops, including rice, peanuts, wheat, and maize, naturally become contaminated with AFB1 [20]. Consuming tainted food or feed can result in serious financial losses as well as acute and chronic illnesses in both people and farm animals [21].

      3. Screening of secondary metabolites using Prism 4

         Due to the limitation of the input file size which was up to 20 Mb we were obliged to divide the whole genomic Fasta file in two distinct files.The first one gives an annotation of about 30 cluster genes mostly are polyketide and non ribosomal peptide wich are sometimes in the direct or oppositedirection in the clusters (Fig.2). RIPP (The ribosomally synthesized and post-translationally modified peptide) and trichodiene-derived terpene are also predicted (Fig.3)

         The second file predict only 4 clusters of polyketides (data not shown).

         Terpenes and terpenoids have well-known pharmaceutical properties, including anti-cancer, anti-viral, anti-bacterial and anti- inflammatory activity [22].

      4. Multiple alignment and phylogenetic analysis of aflatoxin B1 aldehyde reductase

   To more investigate the aflatoxin B1 aldehyde reductase member 2, 3 and 4 produced by our strain we performed a multiple alignment of the corresponding sequences and we found some region identities (Fig.4).

   Moreover, to understand the relationship with other fungi regarding the production of this class of enzymes, we analyzed the sequences blasted (the top 30 sequences obtained after blast protein) by phylogenetic analysis.

   At first, we have to say that all the enzymes share 100 % identity with the fungus Stachybotrys chlorohalonata.

   All the generated phylogenetic trees (Supplementary file 2, supplementary file 3 and supplementary file 4) showed that they are more closely related to Trichoderma species.

   Multiple alignment and phylogenetic analysis of satratoxin clusters

   To more investigate the Satratoxins production by our strain we performed multiple alignment of the corresponding sequences. We should note that they do not share any significant homology between each other (data not shown).

   Moreover, to understand the relationship with other fungi regarding the production of this class of toxins, we analyzed the sequences blasted (the top 30 sequences obtained after blast protein) by phylogenetic analysis for one Satratoxin biosynthesis SC1 cluster protein 4.

   At first, we have to say that all toxins share 100 % identity with the fungus Stachybotrys chlorohalonata.

   The analysis of the generated phylogenetic tree (Supplementary file 5) showed that they are more closely related to Verticillium and Colletotrichum species (sexual stage: Glomerella), which is a genus of fungi that establishes a relationship with plants as endophytes or phytopathogens. Many of the species in this genus are plant pathogens, but some species may have a mutualistic relationship with hosts [23].

   These results could suggest that S. microspora can act also as a phytopathogen for some plants.

4. CONCLUSIONS

Since the 1940s, the scientific community has been interested in screening for microbial secondary metabolites (SMs). Actually, since the discovery of penicillin, much research has been carried out all over the world to find and identify new secondary metabolites produced by microorganisms. As a result, employing conventional trials has greatly reduced the finding of novel SMs. Consequently, one of the top priorities was to find new methods for finding novel SMs. The possibility to find novel SMs that were encoded in the microorganisms’ DNA but were either not expressed in he lab media or might be created in undetectable amounts has been made clear by the invention and advancement of omics-based approaches like metabolomics and genomics.

Moreover, the identification of some gene in the gene clusters (and not all the cluster) doesnt mean necessary that we will detect the corresponding products in the media after

In actuality, a large number of microorganisms possess genes that could potentially code for particular tasks but do not operate during the organism’s typical lifetime. These genes have been dubbed “cryptic genes,” and a mutational event is typically necessary to activate them. They vary from pseudogenes, which are created when a functional gene is duplicated but stay inactive due to the accumulation of many mutations.

Moreover, the identification of our isolated strain as Stachybotrys microspora has shown that it is safe for humans but the presence of these toxin genes could suggest that it is only pathogen for plants and insects as it is the most case for the majority of fungal strains. Therefore and again, further laboratory experiments and research must be carried out to confirm or refute all these hypotheses.

The analysis of secondary metabolites through bioinformatics studies have shown the richness of our studied strain regarding secondary metabolites using the tool the EggNOG Annotation (the number of Secondary metabolites biosynthesis, transport and catabolism (Q) is around 458 and also the prediction of interesting detoxification enzymes like aflatoxin B1 aldehyde reductases.

ACKNOWLEDGMENTS

This work received financial support from the Ministry of Higher Education and Scientific Research, Tunisia, granted to the “Laboratory of Molecular Biotechnology of Eukaryotes”, Biotechnology Center of Sfax, Tunisia.

Author contributions

SA and AG were responsible for the study design. SA and AG supervised the draft of the manuscript. All of the authors approved the final version of the manuscript submitted for publication.

Data Availability

The data obtained or analyzed in the present study have been incorporated in this manuscript.

Declarations Conflict of interests

The authors declare that they have no conflicts of interest.

Ethical statement

No ethical approval is required for this current study.

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FIGURES LEGENDS

Figure 1. Prediction of secondary metabolites using anti-SMASH database. Colors show different clusters with percentages of similarity.

Figure 2. An example of the output generated by Prism 4 annotated tool for secondary metabolites.

Figure 3. Triterpenoid Biosynthesis prediction pathway using OmicsBox platfor. Figure 4. Multiple alignment of aflatoxin B1 aldehyde reductase member 2, 3 and 4 Supplementary figure 1.a report of all eggonog annotation using OmicsBox platform.

Supplementary figure 2, 3, 4 and 5: phylogenetic trees with COBALT:Multiple Protein Alignment Tool for aflatoxin B1 aldehyde reductase member 2, 3, 4 and Satratoxin biosynthesis SC1 cluster protein 4.

Overview 28.1 32.1 -·

41.1– i-·M – I

To Most similar known cluster Similarity Compact view O

82,504

57,177

I

Region 28.1 NRPS r! 15,170 57,150 monascorubrin ct Polyketide 100%

, T1PKS ct

• , indole r1

Region 32.1 T1PKSrd’ 13,221 55,423 ascochlorin r1 Terpene + Polyketide 25%

IDl·lll1lfl

NRPS rd’ 1 49,983

Region 50.1 NRPS r! 3,338 49,080

Region 56 1

T1PKSrd’ 7,427 45,402

Region 147.1

terpene r1 18,476 33,809 acetyldeoxynivalenol / neosolaniol / calonectrin I apotrichodiol / Terpene 8% isotrichotriol / 15-decalonectrinI T-2 toxin I 3-acetyl T-2 toxin I trichodiene P

ACtt>au ,.JX p; o a ..3teur pOl,

rd’

Region 152.1 terpene r1 1 12,561

Figure 1

Figure 2

Figure 2

Figure 3

Figure 4

Supplementary figure 1

EggNOG Annotation Report

General Information

COG Categories Distribution

Information Storage and Processing:

Top 10 Orthologous Groups Distribution

Supplementary figure 2

Supplementary figure 3

– Penicillim11 samsonianm11

. Verticillimn nonalfalfae

——-1o11Rhizodiscina lignyota

. 1111Tiichodenna arnndinacemn

T1ichode1ma harzianum Trichodenna silmnonsii Trichodenna lentifonne Trichodenna breve

T1ichodenna aggressivmn f. europaemn

– T1ichode1ma citrinovil·ide

Ilyonectria destrnctans

Ilyonectria sp. MPI-CAGE-AT-0026

1ichodenna virens FT-333 Ttichode1ma virens Gv29-8

Trichodenna aggressivum f. europaeum Trichodenna semiorbis

Ttichoderma harzianum richodem1a breve richodem1a lentiforme Tlichodemia guizhouense

Trichodenna asperelloides richodenna asperellmn

‘ T1ichodenna arm1dinaceum

‘ Fusarium mm1dagmra

,———uThozetella sp. PMI_491

“1111, lilStachybotrys chaitarmn IBT 40288

. aIJyonectria robusta

‘ ,Stachybottys microspor

Stachybottys chlorohalonata IBT 40285

Hyaloscypha bicolor E

Supplementary figure 4

, Penicillium cf. viridicatum

——-..g:Rhizodiscina lignyota

TI1ozetella sp. PMI_491 Trichode1ma guizhouense

richodenna harzianum richodenna simmonsii richode1111a lentifonne richodenna breve

Trichodenna aggressivurn f. europaeurn T1ichodenna virens FT-333

Trichodenna citiinovi1ide T1ichodenna gracile

:r1ichodenna parareesei

—-..verticilliurn nonalfalfae

– Thelonecti·ia olida

Tiichodenna asperellurn CBS 433.97 T1ichodenna asperelloides T1ichodenna asperellum

1ichode1ma atroviride IMI 206040 1ichodenna sp. CBMAI-0020

Trichoderma garnsii Trichodenna guizhouense Trichodenna semiorbis

richodenna breve richodenna lentifonne

Trichode1111a aggressivum f. europaeum

– mfusariurnoxysporum

, IIyonectI·ia robusta

Stacl1yboti·ys chartarum IBT 40288 Stachybotrys microsporai

Stachyboti·ys chlorohalonata IBT 40285

Supplementary figure 5

Table 1. Eggnog pathways results