Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Monterrey

(1)

Instituto Tecnológico y de Estudios Superiores de Monterrey

Campus Monterrey

School of Engineering and Sciences

Development of a screening method for identifying potential aminoglycosides producers from a collection of environmental

Actinobacteria

A thesis presented by

Luz Ángela González Salazar

Submitted to

School of Engineering and Sciences

in partial fulfillment of the requirements for the degree of Master of Science

In Biotechnology

Monterrey Nuevo León, December 2^nd, 2020

(2)

iii

Thanks to my parents and friends for your support and encouragement. You are my inspiration to continue working in science.

(3)

iv

Acknowledgements

I would like to express my deepest gratitude to my parents, who encouraged me to finish this project even in the distance.

To July and her family, thanks for opening the doors of your home and your hearts.

To Luisa, for her friendship and strength. Friend, I admire you.

To Ulises, for always taking care of me. Thank you, brother.

To Héctor Castañeda, for giving me his support in the laboratory and being a good friend.

I will always be grateful.

I specially want to thank Cuauhtémoc Licona Cassani and Pablo Cruz Morales, advisors of this work, which always inspired me the curiosity, reflection, and love for science and allowed me to be part of the Industrial Genomics laboratory.

To David Melendez, Constantino Rodriguez, Mauricio Salazar, Raúl Piñero, Viridiana Perez, Monserrath Mora, Dany Cepeda and Caleb Damas for their inconditional support and comments of this work.

To Karina Verdel, Lorena Rodriguez, Paulina Mejia for their support during the sampling process of Calakmul isolates.

To my committee members, Dr. Adriana Pacheco for their valuable comments, advice, and contributions to this work.

To Dr. Susana De la Torre, for giving us access and considering us collaborators of the Cuatro Ciénegas project.

To Dr. Daniel Guajardo for giving us Tocosh samples from Peru.

To StrainBiotech for partial sponsorship of this work providing expertise.

To FEMSA Biotechnology Center and the Instituto Tecnológico y de Estudios Superiores de Monterrey, for enriching my professional and personal development and full sponsorship of the tuition fees. Likewise, to CONACyt for economical support providing me a full maintenance scholarship.

(4)

v

aminoglycosides producers from a collection of environmental Actinobacteria

by

Luz Ángela González Salazar

Abstract

Nature represents an important source of molecules with relevant applications in agriculture, industrial and health. Although many efforts have been performed in the identification of new natural products, target, and efficient strategies to select NPs producers are still needed. Previous studies have shown that the intrinsic resistance of Actinobacteria that produce antimicrobial agents could be used as a systematic approach for the detection antibiotics producers. Therefore, the aim of this work was to test different resistance, molecular and bioinformatic analysis as selection criteria to identify possible candidates to produce aminoglycoside antibiotics. This is the first approach developed for this class of antibiotics. we generated a collection of environmental strains and standardized some conditions using model strain producers. We could standardize the procedure for molecular screening in model producers, but we did not find evidence of aminoglycoside producers for the environmental collection, this fact supported by resistance and bioinformatic analysis. According to this, an improved strategy was proposed combining field-directed sampling, an improved molecular screening, as well as the selection of bioinformatic tools with high detection sensibility to AGs BGCs.

Additionally, a huge diversity of BGCs from other chemical families was observed according to the genome mining analysis. These results represent an opportunity to continue exploring the chemical diversity in Actinobacteria isolated from unique regions that can be studied as well as being the inspiration for the development of new screening protocols.

(5)

vi

List of Figures

Figure 1.1 Number of Natural products according to the Dictionary of Natural Products

database……… 2

Figure 1.2 Timeline of drug discovery and development ……….………….….3

Figure 1.3 Antibiotic discovery timeline……….4

Figure 1.4 Actinobacteria life cycle ....….………...………...6

Figure 1.5. Production of bioactive compounds in Actinobacteria………...7

Figure 1.6. One of the Biosynthetic gene clusters (BGCs) in the genome of Streptomyces coelicolor A3(2) ……….…8

Figure 1.7 Different strategies to identify and express cryptic biosynthetic gene clusters (BGCs) ………...………9

Figure 1.8 Percentage of Actinobacteria genera isolated from Cuatro Ciénegas………...11

Figure 1.9 Cultivation and screening strategies to improve isolation of new diversity….15 Figure 2.1. Classification of AGs according structural substitution………...18

Figure 2.2. Action mechanism of AGs………..………...19

Figure 2.3. Principal mechanism of AGs resistance.….………..…..21

Figure 2.4 General mechanism proposed mechanism of AG-induced codon read- through………..………...24

Figure 3.1 Actinobacteria selection strategy.….………27

Figure 3.2 Origin of Actinobacteria collection. …..……….………...28

Figure 3.3 Localization of study sites………..29

Figure 3.4 Samples from Peru (Tocosh) and Queensland island, Australia……….32

Figure 3.5 Steps for Actinobacteria identification………..…33

Figure 3.6 Transamination reaction catalyzed by L-glutamine:2-deoxy-scyllo-inosose aminotransferase………34

(6)

vii

Figure 3.8 Resistance growth test………..………..38

Figure 3.9 Actinobacteria DNA extraction protocol………..………..39

Figure 4.1. A summary of strategy and results obtain in this study ………...42

Figure 4.2. Collection composition………...43

Figure 4.3 Actinobacteria collection phenotype characteristics………..44

Figure 4.4 Step by step for AGs primer designed in this study………...46

Figure 4.5. Combination Fw_5 and Rv_5 in a theoretical annealing with a gene that codified an aminotransferase in the BGC of Tobramycin producer………46

Figure 4.6 Colony PCR standardization using 16S primers in Actinokineospora terrae B24049.………47

Figure 4.7 Colony PCR standardization for AGs primers combination FW_2 and RV_2 for model strains………...………48

Figure 4.8 Blast analysis from band sequenced………48

Figure 4.9. PCR screening of environmental strains. ………49

Figure 4.10. Summary of molecular screening in all the Actinobacteria collection……..50

Figure 4.11 Resistance test using different media with aminoglycoside at working solution concentrations. ………...51

Figure 4.12 Resistance test for environmental Actinobacteria ………..…………...52

Figure 4.13 Quality analysis of reads per one genome..………..……….……...55

Figure 4.14 AntiSMASH filtering for aminoglycoside BCG search. …………..………...56

Figure 4.15 Corason analysis………...58

Figure 4.16 Phylogenetic tree for strains identified as Streptomyces ………60

(7)

viii

Figure 4.17 Number of BCGs for the principal chemical families in genomes from

Actinobacteria collection………61

Figure 4.18 Number of BCGs per place of sampling……….62

Figure 4.19 BGCs with low percentage of identity (less 20%)……….62

Figure 4.19. BGC interaction between the most abundant genera in the collection …...67

Figure 5.1 Description of improved screening strategy in aminoglycosides………...72

(8)

ix

Table 2.1. Classification AGs antibiotics according structure composition………16

Table 3.1 Composition of media used for sample isolation……….………30

Table 3.2 Antibiotic used for sampling selection………...31

Table 3.3 Reference code and other characteristics for the aminotransferase sequences used in the primers design……….34

Table 4.1 Number of strains included in the Actinobacteria collection………..44

Table 4.2 Some characteristics of the primers design……….45

Table 4.3. Quality and integrity conditions for DNA extractions………..53

Table 4.4. Assembly characteristics of genomes………..55

Table 4.5. AntiSMASH result for a most similar cluster of AGs………..57

Table 4.6. Taxonomic identification based on MIGA analysis……….59

Table 4.7. BGCs with 70- 100% identity according to antiSMASH analysis……….63

(9)

x

Chapter 1 1. Introduction

1.1 Natural products: a source of chemical diversity with many challenges Nature represents an extraordinary source of diverse and complex molecules known as Natural Products (NPs), many of them with attractive biological activities (Katz & Baltz, 2016). Natural products have played a significant role in the industry, health and agriculture development (Bernardini et al., 2018; Sparks et al., 2019). For instance, 51%

of all newly approved drugs, 65% of all antimicrobial agents, and 73% of all anticancer compounds currently used are or derive from NPs (Newman & Cragg, 2016).

Chemical databases describe around 300,000 NPs (Figure 1.1), where the highest number belongs to plants (67%), followed by animal sources (13%), Fungi (10%) and Eubacteria (9%) (Chassagne et al., 2019) .

Figure 1.1 Number of Natural products according to the Dictionary of Natural Products database. A.

Distribution per kingdom of life. B. Main chemical classes in each kingdom of life (Chassagne et al., 2019).

Although NPs present a vital position as scaffolds for potential medicines, in the last 20 years, many pharmaceutical companies lost interest in research and development of new

(13)

Chapter 1. Introduction_________ __2

drugs from these compounds (Naman et al., 2017). The high costs, the intrinsic variability of biological material itself, the difficulty in the discovery of new molecules with novel action mechanisms (redundancy), the low economic return, extended times of testing programs, impracticality of scale-up, difficulties in the isolation or purification procedures, the lack of infrastructures and insufficient capital investments are the main reasons exposed by industry to decline the NPs research (Figure 1.2) (David et al., 2015; Lamottke et al., 2011; Thomas & Johannes, 2011).

Figure 1.2 Timeline of drug discovery and development. The current process to produce new drugs can take ~15 years. From thousands of compounds, discover only a few reaches the market. Adapted from (Matthews et al., 2016).

In this context, there is an urgent need to design strategies to improve the process of development of new therapeutic agents, as well as to recover the industry investment , specially by the appearance of antimicrobial resistance which causes around 700,000 deaths per year, and will let 10 million deaths by 2050 (WHO, 2019). On the other hand, the urgent necessity of treatments for cancer or rare diseases (disorders that affect 6–7%

of the developed world) (Igarashi, 2019), also represent a challenge for medical research and a global health concern.

(14)

3

Recently, interest in NPs has been revitalized, mostly because other processes to create chemical diversity, like chemical synthesis, did not satisfy the expectations (Bernardini et al., 2018). For example, from 1,135 new drugs approved between 1981 to 2010, only 36% were purely synthetic, whereas more than half were molecules from natural sources, derivatives or analogues (Newman & Cragg, 2016). Due to structural and biological potency, NPs still have an advantage over synthetic derivates that made them attractive for drug scaffolds (Lamottke et al., 2011). This new trend is destined to grow and lead a substantial number of NPs to reach the market shortly successfully (Bernardini et al., 2018; Lamottke et al., 2011).

1.2 Exploring NPs diversity from past to present: the role of microorganisms.

Microorganisms are a prolific source of natural products with approximately 23,000 described molecules and remarkable bioactivities, such as antibiotics, antitumor drugs, immune suppressants, and antiparasitic agents (Bérdy, 2012). Natural product biosynthetic pathways evolved through serial events of gene expansion and recruitment to assemble specialized metabolic pathways (Jenke-Kodama et al., 2008). It is known that specialized metabolism is the result of adaptation under adverse environmental conditions, such as nutrient depletion or competition, resulting in complex and diverse structures (Barka et al., 2016).

Since the discovery of Penicillin in 1928, microorganisms have been a reliable source to discover new chemical diversity (Mullis et al., 2019). Research was focused on the isolation from microbial sources with antibiotic activity, passing through different historical approaches that can be divided into three overlapping periods: the 1940s–1970s, 1970s–

2000s, and 2000 until present.

During the first period (1940-1970), the discovery of new NPs used traditional isolation methods from soil samples (Katz & Baltz, 2016). The collection of biological material includes strain fermentation, product isolation, testing of fermentation broths, compound

(15)

Chapter 1. Introduction_________ 4

4

purification and developed of bioassays to find bioactivity (Knight et al., 2003). This approach obtained more than 1,000 NPs, including the most relevant to clinic families of antibiotics (Figure 1.3). At the end of the twentieth century, every pharmaceutical company developed NPs discovery programs focused on the treatment of a wide variety of diseases (e.g., anti-bacterial, anti-fungal, infectious diseases) (Bernardini et al., 2018).

Figure 1.3 Antibiotic discovery timeline. most of the molecules discovered between 1940-1970 belong to the “Golden age of antibiotics” (González-Zorn & Escudero, 2012).

During the second period (1970-2000), the previous success obtained with the NPs discovery programs created enthusiasm and, at the same time, generated pressure to increase the number of new drugs (Knight et al., 2003). In consequence, pharmaceuticals companies started to use new techniques like combinatorial and computational chemistry as a faster way to increase compound chemical diversity (Cragg & Newman, 2013).

While pharmaceutical companies abandoned NPs research due to difficulty in finding new chemicals with high cost and long periods of; the advances in sequencing technologies and bioinformatics development were opening the possibilities to find new NP diversity (Cragg & Newman, 2013). Genomic studies made possible to predict at least partial structures of new NPs and novel metabolic pathways, and showed the potential of fungi

(16)

5

and bacteria (Bacillus, Pseudomonas, and Actinobacteria), resulting in a resurgence of discovery and plug in the innovation gaps in drug discovery (Fischbach & Waltz, 2009).

Likewise, improvements in technology for DNA synthesis, cloning, and heterologous expression allows rapid transplantation of novel NP clusters from the sequenced host into well-characterized genetically tractable engineered hosts to enable high probability production of novel NPs (Cole, 2014).

1.3 The potential of Actinobacteria in the NPs discovery.

The phylum Actinobacteria is one of the most important bacterial NPs producers, with 32 % of all microbial NPs. Actinobacteria are recognized for producing classic antibiotics like streptomycin, gentamicin, and tetracyclines (Bérdy, 2012). Genome sequencing has revealed an exceptional abundance of information associated with uncharacterized natural products in this group, being the central target for discovering bioactive molecules.

1.3.1 Actinobacteria: Life cycle and specialized metabolites production

Actinobacteria are gram-positive organisms abundant in soils rich in organic matter.

These organisms are aerobic and can use various nutritional sources, including different complex polysaccharides (Barka et al., 2016). Actinobacteria can also colonize aquatic environments (e.g., Streptomyces, Micromonospora, Rhodococcus, and Salinispora species), plants (e.g., Frankia spp.), plant or animal pathogens (e.g., Corynebacterium, Mycobacterium, or Nocardia species) or gastrointestinal surfaces (e.g., Bifidobacterium spp.) (Gao & Gupta, 2012).

One of most remarkable characteristics of some Actinobacteria is their fungal-like life cycle (Figure 1.4). In general, during the growth of a colony, colonial complexity increases. After a suitable metabolic switch, a germ tube emerges from a spore, and then the formation of branching hyphae occurs to finally form a vegetative mycelium (Van Wezel & McDowall, 2011). When colonies grow, the older mycelium becomes densely piled up, and some changes product of the nutrient limitation or other physiological stresses occur. Consequently, aerial hyphae grows up, finalizing in the formation and liberation of a new spore (Wink et al., 2017).

(17)

6

Figure 1.4 Actinobacteria life cycle.The process from spore germination until aerial hyphae formation (Modified from (Wink et al., 2017).

The production of most bioactive molecules like antibiotics is considered specie’s specific and occurs when the vegetative mycelium differentiates to aerial hyphae. In this process, the vegetative mycelium is degraded by a programmed cell death (PCD)-like mechanism to acquire the building blocks needed in the aerial hyphae formation (Flärdh & Buttner, 2009). As a result of this, accumulated amino acids, amino sugars, nucleotides, and lipids are released around the substrate mycelium, attracting competing microbes in the habitat.

Like a response for protecting their resources, Actinobacteria produce different kind of molecules, specially antimicrobial agents as antibiotics (van Bergeijk et al., 2020). This process, is regulated for the sensor DasR, which controls early development and antibiotic production (Figure 1.5), and responds to the accumulation of cell wall-derived N- acetylglucosamine (Flärdh & Buttner, 2009) .

(18)

7

Figure 1.5. Production of bioactive compounds in Actinobacteria. Due to environmental stress, programmed cell death (PCD) leads to the degradation of the old mycelia, resulting in the derepression of pathway specific activators of antibiotic biosynthetic gene clusters (BGCs) (van Bergeijk et al., 2020).

1.3.2 Actinobacteria genomics

Actinobacteria's genomes show interesting characteristics like high G+C content (70% for some genus such as Streptomyces. Additionally, most actinobacterial genomes are large and linear, such as Actinomyces, Amycolatopsis, Actinoplanes, Streptoverticillium, and Micromonospora, with sizes ranging from 7.7 Mb (e.g., Micromonospora chalcea) to 9.7 Mb (Streptoverticillium abikoense) (Ventura et al., 2007). One common characteristic in Actinobacteria genomes is the localization of the genes responsible for production of individual specialized metabolites. They are almost always distributed together in the genome forming biosynthetic gene clusters (BGCs) (Figure 1.6) and regulated by pathway-specific transcriptional activators (Pinjari & Bramhachari, 2018).

(19)

8

Figure 1.6. One of the Biosynthetic gene clusters (BGCs) in the genome of Streptomyces coelicolor A3(2). The subdivisions in the yellow circle represents clusters for different chemical families. The chromosome’s circular representation maximize one cluster region to visualized the common parts of a biosynthetic gene cluster (BGC) (Nett et al., 2009).

The sequencing of the first two Actinobacteria genomes in the early 2000s Streptomyces coelicolor (Bentley et al., 2002) and Streptomyces avermitilis (Ikeda et al., 2003), revealed more than the BCGs expressed in laboratory conditions were encoded in these large genomes. This fact can be generalized to other genomes from Actinobacteria (Aigle et al., 2014; Nett et al., 2009; Wang et al., 2010).

Now is estimated that less than <10 % of BGCs are expressed in sufficient quantities to be observed under routine fermentation analyses and are “cryptic or silent” and require special conditions or genetic manipulations to reveal their products (Baltz & Bleomycin, 2015). Likewise, different isolates of the same species can encode additional and unique NPs. For instance, Streptomyces albus strains which collectively encode 48 BGCs, of which 18 are common to all strains, 14 are observed in more than one strain, and 16 are strain specific (Seipke, 2015). Other recent example is described by Martinet et al., (2020)

(20)

9

where 18 genomes from taxonomically clustered S. lunaelactis isolated from the same environment in moonmilk deposits (creamy substance found inside some types of caves), revealed 54% of the BGCs were strain specific, and some of them were not present in comparison to the reference strain; Additionally, the compounds production differ between strains according results obtained during two compounds production:

bagremycin and ferroverdin.

In this context, the interaction of enzymology, structural biology, bioinformatics, transcriptomics, proteomics, metabolomics as well as advances in mass spectrometry analysis have allowed the discovery of novel gene clusters through different approaches:

1. prioritization by comparison with known BGCs (Figure 1.7-A); 2. predicting, annotating and expressing transcription factors (Figure 1.7-B) or by quantifying differential gene expression of BGCs by RNA- sequencing of cocultures or microbiomes with or without a pathogen of interest (Figure 1.7-C) (Nguyen et al., 2020).

Figure 1.7 Different strategies to identify and express cryptic biosynthetic gene clusters (BGCs). A.

Prioritization using bioinformatics. B. Detection of regulatory genes. C. Differential expression using co- cultures or pathogen interactions. Adapted from (van Bergeijk et al., 2020).

(21)

10

1.4 The necessity to improve the selection of new potential producers Although many advances in genome identification and expression of metabolites exist, it is still necessary to generate new ways for screening potential producers of bioactive compounds efficiently. In this sense, three approaches have been applied to maximize the producer's search: exploring unique environments, isolating new strains, and improving the mining of strains with the large genome in the actual collections (Hug et al., 2018; Rebets et al., 2013; Subramani & Sipkema, 2019; Tiwari & Gupta, 2012b).

1.4.1 Study Actinobacteria communities in special environments

Historically, an unprecedented global effort was undertaken to amass extensive collections from diverse geographic locations, which result in the selection of endemic microorganisms, and thus the presence of divergent NP biosynthetic pathways (Hernandez et al., 2020). This approach has resulted in the discovery of many new molecules and strains (Subramani & Sipkema, 2019; Tiwari & Gupta, 2012a). However, it is necessary to improve some aspects related to improving the efficiency in search of new potential strains that can offer new chemical diversity, avoiding the reliant on a high degree of uncertainty (serendipity, a lucky discovery made by accident). In many cases, it is necessary to define the available microbial and NP diversity understanding the dynamics of regions and populations over time instead of visiting many places (Patin et al., 2016).

1.4.1.1 Exploring microbial chemical diversity in Oligotrophic environments One of the targets of unique environments is to explore oligotrophic environments. This definition includes ecosystems with deficient nutrient levels. For example, deep oceanic sediments, caves, glacial and polar ice, deep subsurface soil, aquifers, ocean waters, and leached soils. In recent years, oligotrophic environments raised interest mainly because of the strategies stablish by many organisms to survive (Souza et al., 2018).

Recent reports show the efforts for describing the extent of the diversity of culturable actinomycetes on different conditions and extreme environments, including deserts, marine sediments, and vents, coral reefs, and glaciers as well as in symbiotic relationships (Qin et al., 2019).

(22)

11

In Mexico, one of the places more studied due to its unique characteristics is the Cuatro Ciénegas lagoon localized in the Chihuahuan Desert (State of Coahuila, Mexico). This ecosystem presents a Ca²⁺ rich ecosystem by the deposition of carbonate in mineralized waters and a Phosphor limitation of primary production (Elser et al., 2005). These environmental conditions allow a high morphological and unique taxonomic diversity of Actinobacteria, making this place relevant as a source of new producers and new chemical diversity (Souza et al., 2018). Previous studies like the one performed by Arocha-Garza et al., (2017) have described a total of 105 members of the Phylum Actinobacteria belong to 11 genera being the most abundant the genus Streptomyces (Figure 1.8) recognized as an important source for antibiotics with broad applicationsand make Cuatro Ciénegas an interesting place for bioprospecting approaches.

Figure 1.8 Percentage of Actinobacteria genera isolated from Cuatro Ciénegas. A. Distribution of Actinobacteria isolates found in Cuatro Ciénegas. Genera of Actinobacteria according to B. Sampling site.

C. Isolation media (Arocha-Garza et al., 2017).

Research using “traditional” approaches that are based on selective isolation strategy on a wide array of culture media, have found relevant bioactive compounds in rare o little explored Actinobacteria from Cuatro Ciénegas from a collection of around 500 strains (Arocha-Garza et al., 2018). From this collection, 20% of the isolates were antagonists against of human pathogens such as Candida albicans, Staphylococcus aureus, and Escherichia coli and relevant activity over cancer cells (Tavares-Carreón et al., 2020) ;

(23)

12

Other promising Streptomyces, have been reported to produce a large amount of extracellular hydrolytic enzymes such endoglucanases, β-glucosidases, endoxylanases, and β-xylosidases (Archna et al., 2015) and other Actinobacteria strains have presented a potential role as plant growth promoters phosphorus solubilizers, phytohormone and siderophore producers, and phytopathogen controllers (Souza et al., 2018). Likewise, BGC analysis from selected strain genomes have demonstrated a subset of unconventional natural product biosynthetic pathways as non-ribosomal peptide synthetase (NRPS), polyketide synthase (PKS), and terpene clusters for finding novel activities of biomedical interest (Gallegos-López et al., 2020).

1.4.2 Genome mining strategy

As mentioned before genome sequencing from Actinobacteria shows that most biosynthetic gene clusters are expressed poorly or not at all under laboratory culture conditions (Nguyen et al., 2020). Genome mining is a powerful paradigm for the discovery and characterization of natural product biosynthetic genes mainly because is possible to obtain a massive amount of genomic data including cryptic gene clusters information (Choi et al., 2015). For instance, some studies have tested the power of bacterial genomics and bioinformatics over 1000 new bacterial genomes from prolific producers by genome mining and mass spectrometry, identifying ‘a strong phylogenetic signal’ in the pattern of secondary metabolites (Pylayeva-Gupta, 2011).

The identification of BGCs, is essentially based on homology comparisons with known secondary metabolite gene clusters and then classification of the cluster according pathway types like polyketides synthetase -PKS I, PKS II, non-ribosomal peptide synthetase NRPS, or ribosomally synthesized and post-translationally modified peptides (RiPPs). In this sense, a range of computational tools, for example, the Antibiotics &

Secondary Metabolite Analysis Shell antiSMASH (Blin et al., 2019) the Antibiotic Resistance Target Seeker ARTS (Mungan et al., 2020) , the PRediction Informatics for Secondary Metabolomes PRISM (Wenger et al., 2013) among others (Blin et al., 2018), have been developed in the past decade to automate the identification of BGCs and facilitate the prediction of chemical structures (Ju et al., 2015; Nishijima et al., 2016).

(24)

13

However, identifying tailoring enzymes with diverse structures, additional primary metabolic enzymes and defining the extent of BGCs still represent a challenge for these kinds of bioinformatic platforms (Ward & Allenby, 2018).

1.4.3 Direct selection strategies

Although traditional microbiological methods have successfully isolated numerous producers strains of interest, this approach require substantial amounts of time and patience to succeed (Lewis et al., 2020). To overcome or at least minimize these potential limitations, some strategies, or modifications to the currently process have been developed to optimize the discovery of new diversity (Lewis & Ettema, 2019). Two main strategies have been broadly followed: 1. Isolate selective organisms with specific functional characteristics or taxonomic group (targeted isolation). 2. Increase the chance of isolating species of interest scaling- up the number of cell isolations (high- throughput isolation and cultivation).

1.4.3.1 Isolate selective organisms with specific functional characteristics or taxonomic group (targeted isolation)

Mostly NP discovery efforts have been focused on cultivating only understudied taxa.

However, in nature exist underexplored microorganisms occupying extraordinary habitats or specific ecological niches with successful strategies few explored (Losee et al., 2018;

Mahler et al., 2018) . For example Hoffmann et al., (2018) performed a systematic metabolite survey of ~2300 bacterial strains of the order Myxococcales, using approaches as metagenomics, genome mining and mass spectrometry, demonstrating that few currently available myxobacterial genomes have an enormous potential for the production of presently unknown metabolites.

Regarding culture methods, some approaches have been performed, trying to imitate environmental in situ growth conditions, avoiding excessive quantities of nutrients typically provided by classical media. Membranes that separated the cells but maintaining

(25)

14

the diffusion of growth factors (Ferrari et al., 2005); fibers (porous tubes) connected to syringes that function as cell isolation chambers where water provides the growth factors (Zhao et al., 2017) or chips with arrays that function as microchambers to capture and isolate single cells (Nichols et al., 2010) are some examples of these new technologies.

Other strategies take into account the habitat of isolation (e.g., thermophilic, halophilic, alkalophilic, acidophilic) and the characteristics of the strains (rare genera) to combine antibiotics, chemicals and selective culture media to make an efficient isolation of strains (Subia, 2010)

1.4.3.2 Increase the chance of isolating species of interest (high- throughput isolation and cultivation)

Several methods are able to increase the number of species in small volumes and large numbers of replicates, for example microfluidic systems; where cells are incubated in thousands of microcompartments, which can contain various media and substrates and sorting cells (Ma et al., 2014). Other can include fluorescence technologies like Fluorescence- activated cell sorting (FACS) where the intrinsic fluorescence properties such as DNA and phospholipid membranes, making discriminations between groups of cells (Ferrari et al., 2005).

1.4.3.3 Improvements on screening strategies

After isolation of the strains a way to identify organisms of interest for specific studies are the screening methods of selection that could contain optical observation, molecular approaches, and mass spectroscopy. Screenings by visualizations are based on photo spectrometer plate- readers used to detect and count cells based on physical or chemical properties (Fiedler et al., 2018). The molecular screening consists of performing PCR with primers specific for target species or molecules especially from the most relevant chemical families to filter taxa or producers (Huitu et al., 2009; H. Wang et al., 2011). The technique of MALDI- TOF mass spectrometry also have been used as an alternative

(26)

15

method of taxonomic identification, proven fast and cost- effective and highly sensitive way to record a mass profile for taxonomic identification (Santos et al., 2016).

• A resistance-based selection strategy

A resistance-guided antibiotic discovery platform developed by Thaker et al., (2014) is a variant of Waksman’s classic Actinobacteria isolation protocol which follows the idea that every producer of an antibacterial compound invariably requires a self-resistance mechanism to avoid committing suicide from the biosynthesis of its own molecule. In this sense, the platform includes experiments of intrinsic resistance, molecular screening and phylogenomics to identify producing strains of antimicrobial agents like antibiotics. This platform has successfully increased 15,000-fold the frequency of glycopeptides (GPA) and identified producers of novel, pekiskomycin, by obtaining putative producers, compared with the estimated frequency in a conventional screen. They have also applied the protocol to screen for producers of other therapeutics. For example, in a screen for ansamycins, we used a rifamycin antibiotic as a selective filter and identified a producer of the anticancer compound geldanamycin, as well as other putative producers.

Figure 1.9 Cultivation and screening strategies to improve isolation of new diversity. A. Targeted isolation B. High-throughput cultivation. C. Directed screening based on resistance (Lewis et al., 2020;

Thaker et al., 2013).

(27)

Chapter 2. Theoretical framework_________ __16

Chapter 2 2. Theoretical framework

2.1 Exploring aminoglycoside chemical diversity

Aminoglycosides (AGs) are one of the most representative categories of antibiotics commonly prescribed due to their antimicrobial efficacy and broad-spectrum capacity (Krause et al., 2016). The first aminoglycoside discovered, streptomycin, was isolated from Streptomyces griseus in a well-planned search for antibacterial substances, stimulated by the discovery of penicillin (Jackson et al., 2013). Eventually other aminoglycosides (Table 2.1) like neomycin from Streptomyces fradiae (1949), kanamycin from Streptomyces kanamyceticus (1957), gentamicin from Micromonospora purpurea (1963) and tobramycin from Streptomyces tenebrarius (1967) were available as chemotherapeutic agents (Krause et al., 2016). The introduction of semisynthetic derivatives as netilmicin from sisomicin (1976) and amikacin from kanamycin (1972), increases chemotherapeutic agents against resistant bacteria.

Other AGs like hygromycin B and daptomycin A have applications as animal anthelmintics, while kasugamycin and validamycin A has a role in the prevention of plant diseases. Geneticin (G-418) and a few other AGs are available as biochemical reagents (Kondo & Hotta, 1999).

Table 2.1. Classification of useful aminoglycosides antibiotics according structure composition.

Adapted from (Kondo & Hotta, 1999).

DIAMINOCYCLITOL GLYCOSIDIC SUBSTITUTION

ANTIBIOTIC GROUP

NATURALLY OCCURRING

SEMISYNTHETIC

Streptidine 4- Streptomycin Streptomycin^a

Actinamine 4,5- Spectinomycin Spectinomycin^a

(28)

17 2- Deoxystreptamine 4-

5-

4,5-

4,6-

Apramycin Destomycin

Neomycin

Kanamycin

Gentamicin

Apramycin Destomycin A^b Hygromycin B^b Neomycinsâ Paromomycinsâ Lividomycins Ribostamycinâ Butirosins Kanamycinâ Bekanamycinâ Tobramycinâ Gentamicin Câ Gentamicin B Micronomicinâ Sisomicinâ Geneticin^c

Dibekacinâ Amikacinâ Arbekacinâ Netilmicinâ Isepamicinâ

Fortamine 6- Fortimicin Astromicin^a

a Clinical chemotherapeutic

b Veterinary anthelmintic agent

c Biochemical reagent

The aminoglycoside chemical structure contain an aminocyclitol ring (core) that can be streptidine or 2-deoxystreptamine, and two or more amino sugars linked by glycosidic bonds (Vakulenko & Mobashery, 2003). The AGs classification depends on the substitution pattern of the highly-conserved aminocyclitol ring (Figure 2.1) (the most common is the 2-DOS, 2-deoxystreptamine): 4-6 monosubstituted (Neamine, Paromamine, apramycin), 4,5 disubstituted (Ribostamycin, butirosin B, neomycin B, paromomycin, lividomycin) or 4,6-disubstituted (kanamycin A, kanamycin B, kanamycin C, tobramycin, amikacin, gentamicin C1, gentamicin C2, gentamicin C1A) (Krause et al., 2016).

(29)

Chapter 2. Theoretical framework_________ _18

18

Figure 2.1. Classification of AGs according structural substitution. In Orange 4,5 substitution. In blue 4,6 substitutions. In green other scaffolds for AGs.

2.1.1 Aminoglycoside action mechanism

Aminoglycosides are potent bactericidal antibiotics used to combat different infections caused by Gram-positive and Gram-negative organisms (Krause et al., 2016). Typically, they are used to treat infections for Acinetobacter baumannii, Enterobacteriaceae spp, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa and as second-line of defense treatment for multidrug-resistant (MDR) tuberculosis (Mycobacterium tuberculosis) (Garneau-Tsodikova & Labby, 2016).

Aminoglycosides can be considered polycationic species, which allow them to have a binding affinity for negatively charged residues in the outer membrane of gram-negative bacilli and nucleic acids (Jana & Deb, 2006). AGs attack the 30S subunit in the bacterial ribosome at the molecular level, which plays a crucial role in providing a high-fidelity

(30)

19

translation of genetic material. Then, they bind to the A-site and disrupt protein synthesis, interfering with the accurate recognition of tRNA by rRNA during translation (Magnet &

Blanchard, 2005), causing the incorporation of incorrect amino acids into the peptide. In other cases, AGs can affect protein synthesis by blocking elongation or inhibiting initiation (Figure 2.2).

Other molecular effects have been described but it is not clear if some of them are secondary to protein mistranslation. Some examples include the inhibition of 30S ribosomal subunit assembly, induction of RNA cleavage, or interference with the action of RNase P (Belousoff et al., 2009). The length and type of misreading vary with the aminoglycoside's interaction with the proteins. For example, Streptomycin acts on a single site, but other aminoglycosides act on several (Wilson, 2014).

Figure 2.2. Action mechanism of aminoglycosides. A. AGs binds to the 30S ribosomal subunit and interferes with the initiation of protein. B. Premature termination of translation with a detachment of the ribosomal complex and incompletely synthesized protein. C. Incorporation of incorrect amino acids, resulting in the production of abnormal or nonfunctional proteins (Krause et al., 2016).

2.2 Challenges and opportunities with AGs

Aminoglycosides are potent tools against infections, but unfortunately, the levels of resistance are growing, and in consequence, failure of treatments with aminoglycosides is becoming more common (Avent et al., 2011; Ramirez et al., 2013). Despite their

(31)

20

inherent toxicity and the acquired bacterial resistance that continuously threaten their long term clinical use, aminoglycosides (AGs) still remain valuable medical treatments (Ramirez et al., 2013). Recent literature shows that the AGs' role has been further expanded as multi-tasking players in different study areas, giving them a new remarkable value (Fosso et al., 2014).

2.2.1 AGs resistance

The mechanisms for bacterial resistance in AGs are diverse (Figure 2.3). The most common mechanism is the inactivation by a family of enzymes named AMEs (Perry et al., 2014). AMEs are highly mobile and the genes that encode them can be transfered on plasmids, integrons, transposons, and other transposable gene elements, often along with other resistance genes. Based on the type of chemical modification they apply to their AG substrates AMEs can be classified in three subclasses : AG N-acetyltransferases (AACs), AG O-nucleotidyltransferases (ANTs) and AG O-phosphotransferases (APHs) (Ramirez & Tolmasky, 2010) .

Although the native functions of AMEs remain unclear; they likely have roles in normal cellular metabolism, but have since evolved from their original “proto-resistance genes”

to modify AGs upon selective pressure from exposure to these antibiotics (Perry et al., 2014).

Figure 2.3 Principal mechanism of AGs resistance (Garneau-Tsodikova & Labby, 2016).

(32)

21

Another AG resistance mechanism are the mutations of the ribosome. For instance, mutations in some important genes like rrs gene, which codes for 16S rRNA, hinder AG- binding or the overexpression of a 16S rRNA fragment resembling helix, which decreases its binding to the ribosome (Galimand et al., 2003). In addition, the modifications of the ribosome by a family of ribosomal methyltransferase named RMTases are acquired by other bacterial species by uptake of a plasmid containing the RMTase gene, and potentially other resistance genes enzymes (Wilson, 2014). Additionally, the bacterial cell wall serves as an intrinsic barrier, and its impermeability increased by lipid modifications can cause repulsion of AGs. Furthermore, even if AGs enter the bacterial cell, intercellular concentrations may remain low due to the active expulsion of AGs out of the cell by efflux pumps (Fernández & Hancock, 2012).

2.2.2 AGs new perspectives Other applications of aminoglycosides include:

• RNA binders; AGs bind to the rRNAs in the ribosomes and interact with other types of RNA structures (Sunil Kumar, Liang Xue, 2011).

• Riboswitches; AGs can serve as tools for discovering new artificial and natural riboswitches (Wang et al., 2019). Riboswitches are mRNA domains in the 50 - leader sequences that control the expression of the downstream genes through binding of various ligands, ranging from metabolites, secondary messengers to xenobiotics, among others (Park, 2014). Naturally occurring riboswitches are essential gene regulators in pathogenic bacteria, becoming in targets for understanding resistance mechanisms and developing antibiotics.

• Fungal activity; paromomycin, neomycin, ribostamycin, and streptomycin were evaluated against six crop pathogenic oomycetes (Phytophthora and Pythium species) showing modest to excellent antioomycete activity (Loreto et al., (2014).

• In vitro growth inhibition studies have showed some AGs exhibit antileishmanial activity against antimony-resistant strains. For instance, in a combination with

(33)

22

other drugs, paromomycin has efficacy against visceral leishmaniasis (the most aggressive form of leishmaniasis) (Salah et al., 2013)

Perhaps the most recognized applications of AGs in eucaryotes are those related to codon-readthrough (Figure 2.4). In nature, codon-readthrough occurs when a stop codon is misread by the translational apparatus, allowing the synthesis of an extended polypeptide (Dabrowski et al., 2015). This feature can cause the initiation of efforts to developed them as drugs to treat nonsense mutation related genetic disorders such as cystic fibrosis, nephrogenic diabetes insipidus, APC-mediated colon cancer and Duchenne muscular dystrophy (Belakhov & Baasov, 2013; Zingman et al., 2007).

Duchenne muscular dystrophy (DMD) shows lethal X-linked pathology that lacks the protein dystrophin in the muscle leading to progressive muscle degeneration. Nonsense mutations in the gene encoding for dystrophin account for about 15% of all DMD cases.

Gentamicin efficacy was tested in dystrophin-deficient mice (mdx mice) showing an increase in dystrophin expression around 20% and a decrease of contraction-induced damage (Wagner et al., 2001). This concept has been studied with other aminoglycosides. Arbekacin is an aminoglycoside with a similar mechanism of action that is currently being studied in a phase II trial for this disease (Reinig et al., 2017)

Other important experiments have been performed in Cystic fibrosis (CF) when mutations in the transmembrane conductance regulator (CFTR) gene affecting the cAMP-gated chloride channel in the cells epithelium. Additionally, several parent AGs like amikamicin and gentamicin, promote read-through of the PTC and increase the level of functional CFTR in mouse models and patient samples (Xue et al., 2014). Furthermore, co- administration of AGs with poly-L-aspartic acid showed enhancement of the PTC suppression of GEN by 40% (Du et al., 2009). In addition to parent AGs, their derivatives have also been exploited for higher efficacy and lower toxicity. Several AG derivatives, such as NB30, NB54, NB74, NB84, and NB124, have been reported to suppress PTC at higher potency compared to parent AGs (Fosso et al., 2014).

(34)

23

However, part of the problems of AGs applications as a solution for these diseases are related to ototoxicity and nephrotoxicity (Prayle et al., 2010). Several studies in CF patients with regular courses of aminoglycoside antibiotics presented subclinical kidney damage leading to chronic kidney disease (CKD) (Al-Aloul et al., 2005; Smyth et al., 2008). Although toxicity is an aminoglycoside class effect, experimental data suggest that gentamicin is more toxic than tobramycin and amikacin (Feldman et al., 2007).

Although some strategies have already adopted, such as decrease the daily doses, the use of tobramycin rather than gentamicin and the careful monitoring of drug levels in blood, these efforts continue being insufficient (Prayle et al., 2010). In this sense, exist a necessity for finding and developing new AGs congeners with equal or better bioactivity, trying to reduce their negative effects. For example, gentamicin C2, one of the congeners recently discovered from gentamicin was found to maintain the antimicrobial activity while no toxicity effects were observed (Sandoval et al., 2006).

.

Figure 2.4 General mechanism proposed mechanism of AG-induced codon read-through. A.

Eukaryotic ribosome complex during normal protein translation elongation. B. Translation during nonsense mutations results in a truncated protein product. C. Binding of AG allows random incorporation of an amino acid from a near-cognate tRNA and read-through at the PTC site (Fosso et al., 2014)

2.3 Methods for exploring the chemical diversity of AGs

According to the above, the extensive knowledge of new applications for aminoglycosides has led to renewed interest in developing novel variants. In this sense, the design of

(35)

24

different strategies to explore the AGs chemical diversity is necessary to continue searching for scaffolds for new bioactivities and functions. Other methods for selecting candidates were carried out in other molecules using classical methods like bioactivity test against other organisms and fermentations (Hozzein et al., 2011; N. Kumar et al., 2019), or by means of molecular detection (Huitu et al., 2009; Ningthoujam et al., 2009;

Ouyang et al., 2011; H. Wang et al., 2011).

However, in the case of antibiotics, one more efficient method has been studied for selecting producers. Thaker et al., (2013) used the current knowledge of antibiotic resistance mechanisms as a discriminatory criterion to increase the discovery of producers of both glycopeptide and ansamycin antibacterial compounds. In this study, from a collection of 1000 Actinobacteria strains, GPA resistance was tested through a selective isolation with vancomycin, found a subset of 38 resistance candidates. Then, the selected GPA producers was chosen according to amplification of sequences related to biosynthetic enzymes of GPA pathway (oxyB, oxyC and dpgC). Finally, a new scaffold of GPA was found using phylogenomic analysis. The compound was isolated and has been called Pekiskomycin (Thaker et al., 2013). This strategy provides an efficient way for screening producers of antimicrobial agents while reduce the efforts for searching and identify new chemical diversity.

Actinobacteria, being the original producers of aminoglycosides, present a natural resistance to protect their ribosomes from inhibition by the AGs they produce (Kirst &

Allen, 2006). In this sense, a selection strategy based on resistance and genomic methods could be explored to find AGs chemical diversity, particularly in environmental Actinobacteria isolated from unique environments. In this way, new applications for these antibiotics can be studied, becoming innovative treatments for diseases. Then, a feasible solution for screening this type of molecules must combine rational screening criteria with efficient analytical tools and an efficient search of BGCs (Genilloud, 2017).

(36)

25 2.4 Objectives

2.4.1 General Objective

To develop a screening method for detection of aminoglycoside producers from environmental samples through resistance phenotype, molecular tools, and genome mining.

2.4.2 Specific Objectives

1. To generate a collection of environmental Actinobacteria strains mainly isolated from two endemic regions of México: Cuatro Ciénegas, Coahuila, and Calakmul Reserve, Campeche and previously isolated strains from Peru and Australia.

2. To design a molecular method for detecting conserved aminotransferase core enzyme regions of aminoglycoside biosynthetic pathway in the Actinobacteria collection.

3. To design a resistance-based phenotypic assay for the selection of AGs producing strains.

4. To design a genome mining platform for the characterization of AGs BGCs found in this study.

(37)

Chapter 3 3. Methodology

3.1 Screening selection outline

The general strategy followed for selecting aminoglycoside producers is described in Figure 3.1. The first step began with the isolation of Actinobacteria from different environments to generate a collection of strains. Then, the potential producers pass through a filter created with degenerated primers to detect specific aminoglycoside enzyme conserved regions. A test for resistance would confirm the capacity of the strains to grow in medium with aminoglycosides. Finally, some bioinformatics for searching aminoglycoside BGCs would be performed for selecting the possible candidates.

Figure 3.1 Actinobacteria selection strategy. A. Creation of Actinobacteria collection. B. Molecular screening using colony PCR. C. Whole genome sequenging and Bioinformatic analysis. D. Final comprobation for resistance.

(38)

27 3.2 Generation of an Actinobacteria collection

Previously isolated strains were integrated through various sampling efforts in different regions of Mexico (CuatroCiénegas, Coahuila and Calakmul, Campeche), as strains previously isolated from Peru and Australia.

During the samplings carried out in Mexico, some of the strains were isolated using media with different families of antibiotics. During this process, we performed a specific selection using aminoglycosides antibiotics (Figure 3.2).

Figure 3.2 Origin of Actinobacteria collection. Inside the sampling of Actinobacteria in different Mexican regions, some media with AGs were part of this study's selection strategy.

3.2.1 Actinobacteria sampling and isolation from Mexican regions.

• Study site

Part of the strains isolated in this study came from two native environments in Mexico:

The Churince hydrological system, Cuatro cienegas, Coahuila (26°50´25.1” N, 102°08´01.7” W), sampled from June 2017 to October 2018, and The Calakmul Biosphere Reserve, Campeche (18°36′43″N, 89°32′53″O) sampled during June 2019 (Figure 3.3).

(39)

Chapter 3. Methodology_________________ _28

28

Figure 3.3 Localization of study sites for A. Cuatro Ciénegas (1) and Calakmul (2).

The Churince environment is a system of lagoons (spring, Intermediate, and desiccation lagoon) connected by small water causeways. This ecosystem is extremely poor in phosphorus concentration (lower than 0.1 µmol) but is rich in nitrates, sulfates, and magnesium, making it a unique ecosystem for metabolic traits of diverse microbial communities (H. F. Arocha-Garza et al., 2017; Elser et al., 2005).

In contrast to the Churince system, Calakmul Reserve is part of one of the largest rainforest ecosystems in Mexico, considering a privileged environment because of the richness and diversity with communities of microorganisms few explored (Bohn et al., 2014).

• Sampling

For Cuatro Ciénegas isolation, were obtained 28 samples from water, layer sediment, and stromatolite scraped next to the Intermediate Lagoon (SEMARNAT scientific sampling permit No. SGPA/DGVS/03121/15). All the samples were put in conical tubes (25 ml) and transported at room temperature to a nearby laboratory for processing. Then, the samples were suspended in saline solution before streaking out in primary plates and a 1:10 dilution from every sample. As isolation media, we utilized the International

(40)

29

Streptomyces Project-2 medium (ISP-2), and Streptomyces isolation medium (SIM) supplemented with Nalidixic acid (30mg/mL) and Cycloheximide (100mg/mL) to inhibit Gram-negative and fungi growth.

For Calakmul isolation, 15g of soil and sediments were collected in canonical tubes (50 ml) from three areas inside the forest: 1,2 km (8°21'34,52N'; 89°54'15,22''), 800m (18°22'0,57'';89°53'7,45''), and 200m (18°24'24,93''; 89°53'54,65'') at 5cm, 15cm and 21cm of deep (SEMARNAT scientific sampling permit No. F00.9. D-RBC-037/2018). The conditions for transporting and streaking were the same as in Cuatro Ciénegas but using Phosphate-buffered saline (PBS) for sample suspension.We employed the same media used in Cuatro Ciénegas plus other Oligotrophic, glycerol asparagine (YIM 17), and histidine–raffinose (YIM 212) complex isolation media (Table 3.1).

Table 3.1 Composition of media used by isolation. All the reagents were purchased from Merck, Darmstadt, Germany.

Medium (Preparation for 1L)

ISP-2

Yeast extract 4.0 g Malt extract 10.0 g Dextrose 4.0 g Agar 20.0 g

Distilled water 1000.0 ml pH 7.2

SIM

Casein 0,4 g Starch 1g KNO3 0,5g K2HPO4 0,2g MgSO4 0,1g CaCO3 0,1g Agar 15g pH 7.2-7.4 peptone 1 g

yeast extracts 0.5 g

(41)

30 Oligotrophic

K2HPO4 H2O 1 g MgSO4 7H2O 0.5 g CaCO3 0.3 g NaCl5 g

vitamin mixtures*

agar 15 g pH 7.5

YIM 17

L-asparagine 1 g, glycerol 10 g, K2HPO4 1 g,

Vitamin mixtures 3.7 mg, trace salt* 1 ml,

agar 20 g pH 7.2–7.4.

YIM 212

histidine 1 g raffinose 5 g K2HP4 3H2O 1 g MgSO4 7H2O 0.5 g agar 20 g

pH 7.2

SFM

20 g Mannitol 20 g Soya Flour 20 g Agar tap water pH 7.2

*Vitamin mixtures were added for maceration of one vitamin pill for medium.

• Antibiotic selection and isolation with aminoglycosides

Some media with different antibiotics were tested during the process of colleting in the two sampling sites (Cuatro Ciénegas and Calakmul) in a randomly way. The type and name of each antibiotic are mentioned in Table 3.2

.

All the antibiotics were purchase to Sigma-Aldrich, Misuri, USA. The testing process with AGs media occurred randomly using kanamycin, gentamicin or both combinations.

(42)

31

Table 3.2 Antibiotic used for sampling selection.

Sampling place Type name Working concentration

(ug/mL)

Cuatro Ciénegas

Nucleoside Tunicamycin 10

Macrolides Josamycin 5

Aminoglycoside Gentamicin 10

Calakmul Phenicol Cloranfenicol 35

Macrolides Josamycin 5

Ripps Thiostrepton 25

Aminoglycoside Kanamycin-Gentamicin 50 - 10

3.2.2 Strains isolated from other sources

Samples from the Tocosh medicinal plant collected in Peru (Figure 3.4-A), were also processed and added to the collection.Tocosh roots are an essential natural antibiotic employed for indigenous populations located in South America's Andean regions. These plants are related to penicillin production during pulp fermentation process (Mayta- Tovalino et al., 2019). For Tocosh samples, resuspension and strike followed the same Calakmul isolation procedure using only ISP-2 medium with antibiotic supplementation.

Other strains from Hamilton Island, Queensland, Australia (20°21′S 148°57′E) also were incorporated into the collection (Figure 3.4-B).

Figure 3.4 Samples from A. Peru (Tocosh) and B. Queensland island, Australia.

(43)

32 3.2.3 Isolation conditions in laboratory

All the different isolates were incubated at 28°C-30°C for 5 to 15 days. Actinobacteria colonies identification was based on colony morphology using the criteria described by kieser & Hopwood, (2000) and were restreaked several times on Soya Flour Mannitol (SFM) agar, for microbiological verification and purification (Figure 3.5). The purified strains were finally storage making a spore's suspension in Glycerol 50% (v/v).

Figure 3.5 Steps for Actinobacteria identification.

3.3 Degenerated primers design for Aminoglycoside screening

• Enzyme selection

We employed PUBMED-NCBI for searching core enzymes associated with aminoglycoside biosynthetic routes. According to the information presented by Kudo &

Eguchi (2009) the core enzyme selected was: L-glutamine:2-DOI aminotransferase for the design of the primers. This enzyme catalyzes the transamination of the core ring 2- deoxy-scyllo-inosose (2-DOI) to 2-deoxy-scyllo-inosamine (2-DOIA) being a common step in the biosynthesis of aminoglycosides reference code PF01041 (Figure 3.6).

Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Monterrey