Progress in Microbes and Molecular Biology Review Article 1 Taxonomic and Characterization Methods of Streptomyces: A Review Jodi Woan-Fei Law1, Kei-Xian Tan1, Sunny Hei Wong2, Nurul-Syakima Ab Mutalib3*, Learn-Han Lee1,4,5* 1Novel Bacteria and Drug Discovery Research Group, Microbiome and Bioresource Research Strength, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor Darul Ehsan, Malaysia 2 Li Ka Shing Institute of Health Sciences, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong 3UKM Medical Molecular Biology Institute (UMBI), UKM Medical Centre, University Kebangsaan Malaysia, Kuala Lum- pur, Malaysia 4Biofunctional Molecule Exploratory Research Group, Biomedicine Research Advancement Centre, School of Pharmacy, Monash University Malaysia, Selangor Darul Ehsan, Malaysia 5Center of Health Outcomes Research and Therapeutic Safety (Cohorts), School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand Abstract : The 16S ribosomal RNA gene is the gold standard for taxonomic identification and phylogenetic study of most of the bacteria. However, the resolution of 16S rRNA gene is found to be insufficient to distinguish closely related species within the genus Streptomyces due to large size of Streptomyces member. Thus, it is essential to find alternative phylogenetic gene markers with higher discriminatory power in addition to 16S rRNA gene for the phylogenetic analysis of Streptomyces species in order to effectively indicate the evolutionary relationships among Streptomyces members at intra- and inter- specific level. This article aims to discuss the resolution power of ribosomal genes (16S rRNA and 23S rRNA gene), of protein-coding genes (gyrB and trpB gene) and between ribosomal and protein-coding genes in order to identify gene that could provide better resolution for phylogenetic studies of Streptomyces. Keywords: Streptomyces; actinobacteria; taxonomic; review; method Received: 8th October 2018 Accepted: 15th November 2018 Published Online: 14th December 2018 *Correspondence to: Learn-Han Lee, Novel Bacteria and Drug Discovery Re- search Group, Microbiome and Bioresource Research Strength, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia; lee.learn.han@ monash.edu. Nurul-Syakima Ab Mutalib, UKM Medi- cal Molecular Biology Institute (UMBI), UKM Medical Centre, University Kebangsaan Malaysia, Kuala Lum- pur, Malaysia; syakima@ppukm.ukm.edu.my Citation: Law JWF, Tan KX, Wong SH, et al. Taxonomic and Characterization Methods of Streptomyces: A Review. Prog- Microbes Mol Biol 2018; 1(1): a0000009. INTRODUCTION Members of Actinobacteria consist of different meta- bolic and physiological characteristics while expressing different morphological characteristics such as coccoid and fragmenting hyphalor branched mycelium forms[1]. Actinobacteria possess various lifestyles, for instances, as soil inhabitants (example: Streptomyces), gastrointes- tinal commensals (example: Bifidobacterium) and patho- gens (example: Mycobacterium and Corynebacterium)[2]. Streptomyces is the largest genus located within the family Streptomycetaceae, belonging to the phylum Actinobac- teria [2]. Streptomyces bacteria are biologically active at which they can produce secondary metabolites that have various biological effects[3]. Currently, there are over 10 000 bioactive compounds have been derived from Strep- tomyces [3-5]. Prior to the late 1950s, it was believed that members of Streptomyces are eukaryotes instead of prokaryotes due to their distinctive life cycle which is similar to the life cycle of multicellular eukaryotes [6]. As a matter of fact, Streptomyces consists of multicellular, Gram posi- tive, filamentous aerobic, and mycelial bacteria that live mainly in soil as saprophytes[7]. These bacteria have special features such as complex life cycle involving the development of substrate mycelium with extensive branches containing LL-diaminopimelic acid as main diamino acid and carries aerial hyphae that are able to differentiate into spores or arthrospores[8,9]. Spores al- low extended survival of Streptomyces species in an inactive form in the soil because they are known to be resistant to both water and nutrients deficiencies as well as high temperature in unfavourable environments[7]. Another important feature of Streptomyces includes Copyright 2018 by Law JWF, et al. and HH Publisher. This work under licensed under the Creative Commons Attribution-NonCom- mercial 4.0 International Lisence (CC-BY-NC 4.0) 2 the generation of diverse pigments that form the colour of aerial and substrate mycelia[10]. Interestingly, some Streptomyces bacteria exist as marine symbionts, plant root symbionts in rhizophere, growing on gamma-irra- diated surfaces or thermal springs[11]. Meanwhile, some Streptomyces bacteria are human, animal or plant patho- gens such as Streptomyces scabies that stimulates potato scab[12]. The genus Streptomyces within the order Actinomyce- tales has spectacular diversity in morphology, genomic size, genomic G+C content, and amount of coding se- quences. Streptomyces bacteria are noted by their large and linearized chromosomes with approximately 8.5-12 Mb in size and high GC content of about 67-78%[3,9,13]. The large genome of Streptomyces could account for their ability to produce many secondary metabolites[14]. Several studies have reported the discovery of over 20 biosynthetic gene clusters in the genome of Strepto- myces which could be associated with the production of secondary metabolites[4]. For examples, the genome of Streptomyces coelicolor A3(2) was found to contain more than 20 gene clusters related to numerous metabo- lites[15]; genome of Streptomyces avermitilis was found to contain more than 30 gene clusters related to numerous metabolites[16]. IMPORTANCE OF Streptomyces Being an important soil microbial population, Strepto- myces has been extensively screened and isolated over the years due to its production of natural biologically ac- tive secondary metabolites that are medically and com- mercially important[4]. It has been an ongoing effort for the screening of novel Streptomyces since the discovery of streptomycin and actinomycin from Streptomyces griseus and Streptomyces antibioticus respectively[17,18]. There are over 75 % of useful antibiotics that are com- mercially available for the use in both human and veteri- nary medicines have been produced by various Strepto- myces species[19]. Many antibiotics belonging to different classes have been derived from Streptomyces. For in- stances: chloramphenicol (chloramphenicol) from Strep- tomyces venezuelae, neomycin (aminoglycoside) from Streptomyces fradiae, clavulanic acid (β-lactam) from Streptomyces clavuligerus, vancomycin (glycopeptide) from Streptomyces orientalis, tetracycline (tetracycline) from Streptomyces aureofaciens, and nystatin (polyene- polyol macrolide) from Streptomyces noursei[20-22]. Other than the capacity of producing antibiotics, Strep- tomyces bacteria are producers of many other bioactive compounds with high functional and structural diversity such as anti-parasitic, anticancer/antitumour, antifun- gal, biocatalysts, biopesticides, immunosuppressive, and herbicides biological control agents[23-26]. They are also producers of enzymes which are important in en- vironmental, food biotechnology and other industries[27]. Streptomyces produces wood or fungal cell wall hydro- lytic enzymes which include hemicellulases, chitinases, glucanases and cellulases to decompose a variety of plant-based polysaccharides, insoluble polymers like chitin and cellulose[28]. They are also involved in biodeg- radation and bioremediation at which they can degrade lignin and lignin-related aromatic compounds[28,29]. It is important to continue searching for novel Strepto- myces from different habitats in hope to find novel sec- ondary metabolites such as new antibiotics to combat against pathogens which have gained resistance towards existing antibiotics via chromosomal mutation, vertical or horizontal gene transfer[30]. It is also essential to search for effective antimicrobial compounds to fight against infec- tious disease agents[31]. Studies indicated that researchers have only discovered a fraction of antibiotics generated by Streptomyces species. Hence, it is still possible to isolate novel antibiotics from terrestrial Streptomyces despite of being screened continually over the past half century[21]. Nevertheless, exploring novel Streptomyces from other areas could lead to the findings of other new/interesting compounds[32]. Many studies have shown the successful isolation of novel Streptomyces species from different environments including marine sediments[33], lakes[34], caves[35], mangroves[36], deserts[37], peat swamps[38], and plants[39]. Researchers also found that many novel Strep- tomyces species possess important bioactivities such as antioxidant, anticancer, and antifungal[40,41]. BIOACTIVE METABOLITES FROM Strepto- myces Secondary metabolites are different from primary me- tabolites as they are not essential for growth, reproduc- tion and other metabolisms in the cell[42]. They played an important role in the survival and defense mechanism for their producers to compete in a stressful environment which has a high amount of other metabolically active bacteria[32]. Hence, prolific production of secondary me- tabolites is an advantage trait of Streptomyces. Streptomyces produces secondary metabolites when en- countering nutritional stresses from the surrounding en- vironment and the production of secondary metabolites is referred as physiological differentiation[43]. There is a close association between morphological differentiation and physiological differentiation in the life cycle of Strep- tomyces[44]. It is apparent that morphological differentia- tion is related with the sensing of nutrient deficiencies and environmental stresses[8]. Therefore, the generation of various secondary metabolites such as antibiotics or anti- fungals are initiated during the morphological transition of Streptomyces from vegetative to aerial growth stage[44]. In most cases, production of antibiotics is depending on the spatial and temporal control of morphogenesis, me- tabolism, gene expression and metabolites flux[45]. In fact, bioactive secondary metabolites are produced by meta- bolic pathways that can be linked to primary metabolism; both secondary and primary metabolite pathways share intermediates, also, the intermediate or end products of primary metabolism are usually the precursors used to synthesize secondary metabolites[31,46]. Moreover, the composition of the culture medium will also affect the capacity of Streptomyces species in producing bioactive compounds[4,42]. Bioactive secondary metabolites are synthesized extra- Taxonomic and Characterization Methods... 3 cellularly, which can be isolated and purified with highest purity from fermentation broth by using a combination of different extraction and purification methods. Often, the methods utilized for extraction and purification of sec- ondary metabolites are dependent on their applications. These methods include simple processes such as solvent extraction, chemical precipitation, and processes utilizing advance instruments such as ion-exchange chromatogra- phy, high performance liquid chromatography (HPLC) and many more [4,31]. Sometimes, these secondary metabolites may be obtained in the form of crude extracts which can be further subjected to chemical profiling using liquid chro- matography-mass spectrometry (LC-MS) or gas chroma- tography-mass spectrometry (GC-MS) and thus allowing the prediction of compounds present in the extracts prior to purification [47,48]. TAXONOMIC AND CHARACTERIZATION METHODS OF Streptomyces Due to large-scale isolation and screening over the years, there were nearly 3000 species of Streptomyces named by year 1970 with most of them identified only on patent lit- erature in the past[49]. Most organisms were given a new name solely based on small variations in cultural and mor- phological characteristics or for the sake of meeting pub- lications or patent requirements. The issues arisen from this situation include several confusions in the criteria for Streptomyces speciation, risks of misclassificaiton as well as concerns on the competency of the individual who de- scribing the new organism as reported by Trejo (1970)[50]. Fortunately, amendments on their taxonomy have been car- ried out to address these issues [49,50]. To date, Streptomyces genus consists of 844 species and 38 subspecies that are validly described (www.bacterio.net). Taxonomic characterization of Streptomyces is undeniably more complicated and challenging as a result of the large amount of described species in the genus Streptomyces which is relatively greater as compared to that of other mi- crobial genera[51]. The techniques used for characterization of Streptomyces have improved over time; from classical morphological classifications such as spore chain morphol- ogy, colour of substrate and aerial mycelia to numerical taxonomic analyses that include phenotypic characteriza- tion based on standardized sets and, now, applying molecu- lar and phylogenetic analyses[51,52]. International Streptomyces Project (ISP), a tradi- tional approach International Streptomyces Project (ISP) is one of the ap- proaches for the classification of Streptomyces strains since 1964 with the objective of producing reliable characteriza- tion of existing type strains of both Streptomyces and Strep- toverticillium species. ISP was amended and enacted by the Subcommittee on Taxonomy of Actinomycetes of the Committee on Taxonomy of the American Society of Mi- crobiology and the Subcommittee on Taxonomy of Actino- mycetes of the International Committee on Bacteriological Nomenclature[51,53]. ISP works by sending type and neo- type strains of species under these genera to a minimum of three experts in different nations to be investigated under strict standardized experimental and media set- tings for the identification of morphological characteris- tics, pigmentation, and carbon utilization profiles of the strains[53,54]. The protocols and outcomes of ISP studies were previously published and the resulting identified type strains were deposited in a few service collections which are internationally recognized[53,55,56]. Although ISP has always been a standard method for the classification and characterization of Streptomyces, however, it is not the best approach since it is heavily de- pending on limited number of selected features or stan- dard phenotypic criteria that are highly based on mor- phological and pigmentation characteristics[51]. Hence, other methods have been established to further assist in the classification and characterization of bacteria. Molecular characterization of Streptomyces, a modern approach Identification and characterization methods have evolved to molecular and phylogenetic characteriza- tions with analysis of gene sequences which targets pre- dominantly linear 16S rRNA gene sequences due to the advent of polymerase chain reaction (PCR), DNA-DNA hybridization (DDH) and DNA sequencing approach- es[57]. Molecular revolution in the last 30 years allowed not only further insights into molecular cell biology but also brought huge improvement to the study of evolu- tion, conservation and ecology. In other words, genetic techniques enable taxonomic identification of bacteria which can hardly be delineated based on morphologi- cal characteristics alone, accompanied with higher ef- ficiency in terms of rapid and high-throughput identifi- cation[58]. Molecular characterization - polymerase chain reaction (PCR) PCR is invented in 1980s by the American biochemist, Kary Mullis, as a revolutionary method[59]. The inven- tion of PCR plays a crucial role in improving the knowl- edge of DNA evolution and phylogenetic relationships among Streptomyces species. An adequate amount of a particular gene or DNA fragment is needed to enable the detection and identification of gene sequence. To achieve this practical amount of DNA, the application of PCR is able to specifically and rapidly amplify any DNA stretch within size limits which is flanked by syn- thetic oligonucleotide known as primers to produce up to millions or billions of copies of a particular nucleo- tide sequence in approximately two hours[60]. These primers with approximately 20 base pairs in length will determine the 5’-3’ end of the target region of the double-stranded DNA which is the target of amplifica- tion. Primers are used to initiate DNA synthesis while heat-stable DNA polymerase such as Taq polymerase is used to produce a new DNA strand enzymatically from single-stranded DNA template by adding nucleotides. PCR amplification is operated in a thermal cycler that Law JWF et al. 4 consists of repeated cycles of heating and cooling reaction at a series of specific temperature to melt and replicate the target DNA sequence[60,61]. There are three major steps in a PCR amplification pro- cess: denaturation, annealing, and extension[61,62]. The PCR reaction begins when high temperature (E.g. 90-97 ºC) is used to melt and separate the double-stranded DNA mol- ecule for one to several minutes to form single-stranded DNA. Next, primers are used to anneal to the DNA tem- plate strands at a lower temperature of 50-65ºC. Basically, the annealing temperature is approximately 3-5 ºC lower than the lowest melting temperature of primers pair used. DNA polymerase then extents and synthesizes new com- plementary DNA strand at the end of the annealed primers at approximately 72 ºC for 2 to 5 minutes. Consequently, the original DNA is duplicated at which each of the new DNA molecules are made up of one new and one old DNA strand. These two new DNA strands can then be used as DNA template to produce more copies in coming cycle. This cycle of denaturing, annealing, extending and synthe- sizing is repeated up to 30 or 40 times to produce up to billions of identical copies of the original DNA sequence. PCR is widely used in laboratories for many applications including sequencing, genetic engineering and cloning[61]. The application of PCR is certainly useful for the rapid identification of an organism at genus or species level, de- pending on the primers used. Many studies have been uti- lizing PCR for the identification of the genus Streptomyces, especially via the detection of 16S rRNA gene[63-65]. Molecular characterization - BOX-PCR BOX-PCR is a repetitive element sequence-based PCR (rep-PCR) fingerprinting technique which possesses high discriminatory power and can be used for typing pur- pose[66]. It is based on the BOX dispersed-repeat motif, initially found in Streptococcus pneumonia but appears to be common in most of the bacterial species. This 154 base pair(bp)-box elements comprising 3 subunits (boxA 59bp, boxB 45bp, and boxC 50bp) represent the naturally occurring, highly conserved and repetitive sequence ele- ments present in multiple copies which are found in most Gram-negative and some Gram-positive bacterial ge- nomes[66,67]. BOX-PCR can simultaneously survey many DNA regions scattered in the genome of bacteria because the BOX repetitive sequences are interspersed throughout the bacterial genome[68]. The primer used in BOX-PCR (BOX A1R primer) targets and amplifies selectively the regions located between BOX elements, thereby resulting in DNA amplification products of different sizes which serve as DNA fingerprints of the bacteria. These amplified fragments will then be resolved using gel electrophoresis to produce BOX-PCR genomic fingerprinting profile for differentiation at species, subspecies, and strain level[67,68]. BOX-PCR is inexpensive, efficient and easy to perform, concurrently offering equivalent or superior discrimina- tory power as compared to other methods such as restric- tion fragment length polymorphism (RFLP) and amplified fragment length polymorphism (AFLP)[68]. Molecular characterization - DNA-DNA hy- bridization (DDH) DDH is a DNA-based molecular technique used to in- dicate genetic distance or genetic similarity between species, particularly between those closely related spe- cies, through genome-wide comparisons. In the 1960s, DDH has been employed by bacterial taxonomists and it is well known as the gold standard for species de- lineation and for taxonomic evaluation of strain [69-71]. DNA-DNA reassociation method is often conducted in conjunction with other tests such as morphological, physiological and phylogenetic tests [70]. DDH is a pro- cess where two DNA molecules from different biologi- cal sources or organisms are mixed and heated to form single-stranded DNA before cooling step. The DNA of one of these sources is label while another is unlabeled. These single-stranded DNA are then allowed to rean- neal to reform the hybrid double helices or DNA-DNA hybrids based on contingent sequence homology of the two organisms. Besides that, values of DDH are usually expressed in percentage to quantitatively estimate the genome-wide similarity of the DNA sequence. For in- stance, two strains that are from same species will have a high value of hybridization which is typically more than 70% while two strains of different species but same genus would have around 30-60% of hybridization val- ue. If the unlabeled DNA is from the same organism as the labelled DNA then the hybridization value would be 100% [72]. DDH analysis is one of the assays included in polyphasic taxonomy which is performed in taxonomic studies involving the delineation of taxa at all levels[73]. The application of DDH can be seen in many recent studies pertaining the discovery of novel Streptomyces, for instances, discovery of Streptomyces mangrovisoli sp. nov.[74], Streptomyces humi sp. nov.[75], and Strepto- myces gilvigriseus sp. nov.[76]. Henceforth, emphasizing the importance of DDH as a criterion for the delineation of bacterial species despite being time-consuming and labor-intensive[69]. Molecular characterization – next generation sequencing (NGS) The rapid evolution of new molecular technologies lead- ing to the development of DNA sequencing technologies has revolutionized biological and biomedical related re- search. These studies assist scientists in many applica- tions including molecular cloning, search of pathogenic genes, comparative and evolutionary studies[77]. In this context, the sequencing of bacterial genome has be- coming more common and being routinely applied in many microbial studies. During the early 1990s, DNA sequencing is based on the Sanger’s method, also identi- fied as dideoxy chain termination sequencing chemis- tries. However, the Sanger’s “first generation sequenc- ing” method has eventually reached a plateau in terms of technical development, along with several limitations faced by this technique when comes to whole genome sequencing such as time consuming and resource inten- sive[78-80]. In 2005, the introduction of “next generation sequenc- Taxonomic and Characterization Methods... 5 ing” (NGS) technologies has offer more advantages than Sanger’s method in the aspects of producing massive vol- ume of data at rapid high-throughput and cost-effective manners[77,78]. Besides, the latest NGS technologies oper- ate differently than the Sanger’s method as they employ various massively parallel sequencing instruments that are commercially available, for examples, 454 (Roche), Solexa (Illumina), and SOLiD (Applied Biosystems) plat- forms[81]. All of these instruments have their respective work flows, but they generally share some similar features: (1) simplified initial preparatory steps, (2) library prepa- ration by DNA fragmentation and amplification, and (3) sequencing reaction is conducted followed by automated detection using imaging systems[78,81,82]. With the availabil- ity of numerous massively parallel sequencing platforms and the dramatic decrease in the costs for sequencing, it is anticipated that NGS will be routinely used for the whole genome sequencing of bacterial genome, hence, enables the extraction of biologically or clinically useful insights from the genome sequence[32,81]. TARGET GENES FOR Streptomyces CHARACTER- IZATION Ribosomal RNA genes The taxonomic classification of Streptomyces species based on morphological, biochemical, nutritional and physiologi- cal characterizations are challenging and often problematic [51,83,84]. Therefore, molecular approaches including phylo- genetic analysis can be an improvement in evolutionary and diversity studies. The rRNA gene sequences which code for the ribosomal subunits such as 16S, 23S and 5S rRNA are the target of phylogenetic analysis due to the highly conserved rRNA structure in all cells throughout evolution[51]. Majority of the rRNA folding have impor- tant functions regardless of the divergence in primary se- quence. Precise spatial relationships are needed to produce functional ribosomes, resulting in some regions of rRNA genes which are linked with other components in ribo- some are conserved[85,86]. The rRNA gene sequences are universally distributed across distantly related strains, and therefore these sequences can be aligned precisely to make measurement of true differences between them easier [87,88]. However, these rRNA genes also consist of hypervariable regions such as gamma region that diverged over evolu- tionary time which can be used for species discrimination. Ribosome is defined as large ribonucleoprotein which syn- thesizes protein and highly conserved among the three life kingdoms. For prokaryotes, ribosome is made up of two subunits which are the small 30S subunit and large 50S subunit. The small 30S subunit consists of a 16S rRNA and 20 proteins while large 50S subunit consists of a 5S rRNA, a 23S rRNA and over 30 proteins. Thus, the con- served regions in rRNA genes are used as tools to study distant phylogenetic relationships while regions between the conserved regions with higher mutation rates are used to discriminate closely related bacteria. It is also impor- tant to note that within the highly constrained rRNA genes that are essential for survival of organisms, horizontal gene transfer events are usually implausible to happen[86]. Ribosomal RNA genes - 16S rRNA gene In general, the 16S rRNA gene is the gold standard in the identification of taxonomic and phylogenetic relation- ships among different bacteria[83]. The 16S rRNA gene is an essential constituent found in all bacteria, contain- ing highly conserved regions and variable regions with approximately 1600 nucleotides long (bp)[88]. The 16S rRNA gene can clearly distinguish the three main king- doms which include Archaea, Bacteria and Eukarya. The hypervariable regions of 16S rRNA gene in a single genome provides sufficient sequence variation for phy- logenetic discrimination[88]. Generally, 16S rRNA gene is still sensitive enough for other genera but unfortunately seems to be less sensitive for Streptomyces. Previously, many studies attempted to utilize sequences from variable regions of 16S rRNA gene to form taxonomic structure within the Streptomy- ces genus. However, the variation was too low to dis- tinguish between closely related Streptomyces species/ strains as they might exhibit highly similar or identical 16S rRNA gene sequences, with the addition of overspe- ciation issue within the genus[89,90]. Alternately, Strepto- myces strains with highly similar or identical 16S rRNA gene sequences appear to be of different species[49]. Fur- thermore, there are multiple copies of 16S rRNA gene or heterogeneous rRNA operons within a single genome have been found in some bacteria[87]. This might suggest that there is a potential for the occurrence of horizon- tal gene transfer events in the rRNA genes[87,90]. Hence, phylogenetic construction based on 16S rRNA gene sequences could not assure well-resolved phylogenetic trees that represent relationship between bacterial spe- cies particularly within genus Streptomyces[9,12,51]. The 16S rRNA gene sequences provide low resolution as phylogenetic marker for Streptomyces species. Further- more, insignificant phenotypic differences, limitations of DNA fingerprinting and DNA-DNA hybridization appear to ineffectively elaborate taxonomic grouping of Streptomyces species within the genus Streptomyces[91]. This suggests that it is challenging for Streptomyces tax- onomic classification, thus, methods with higher resolu- tion are crucial to evaluate taxonomic grouping among closely related Streptomyces species. Ribosomal RNA genes - 23S rRNA gene The 23S rRNA gene is formerly less preferred to be used for phylogenetic study as compared to 16S rRNA gene due to costing consideration[9]. However, the use of 23S rRNA gene in phylogenetic analysis is eventually being highlighted because of several factors including reduced sequencing costs due to the advancement of technology such as next generation DNA sequencing[92]. The 23S rRNA gene offers similar advantages as 16S rRNA gene with additional properties owing to its longer sequence length of approximately 3000 bp such as better resolu- tion due to greater sequence variation, unique insertions and/or deletions, and more characteristic sequence re- gions[9,86,92,93]. Recent study conducted by Chaves et al. (2018)[94] revealed that the application of 23S rRNA gene can be utilized as an alternative to 16S rRNA gene for phylogenetic study of Streptomyces species which also appeared to be more discriminative than the 16S Law JWF et al. 6 rRNA gene. In addition, previous study showed that broad- range primers for 23S rRNA genes with a similar univer- sality degree to that of 16S rRNA genes can be generated based on the conserved regions of 23S rRNA genes[92]. Protein-coding genes The use of protein-coding genes as phylogenetic markers has become more common as they could determine ge- nome relatedness with higher accuracy and might replace DDH for species taxonomy in the future[70,83,95,96]. Cur- rently, multilocus sequence analysis (MLSA) technique that involves the use of a combination of a few protein- coding genes (also referred as housekeeping genes) has been widely applied to deduce bacterial phylogeny and aims to buffer phylogeny distortions caused by recombi- nation[97]. This approach could be an alternative to DDH technique since it is reproducible with comparable reso- lution to DDH[89]. Studies have reported that there was a high correlation determined between DDH and MLSA at which MLSA evolutionary distance of 0.007 based on five- gene schemes corresponds to DDH value of 70% species cut-off point for species delineation of the genus Strepto- myces[91,97,98]. Protein-coding genes are proven to improve the resolution and robustness at Streptomyces species level and can be used as an acceptable approach for species dif- ferentiation. Especially the gyrase B (gyrB) and tryptophan B (trpB) housekeeping genes can provide greater discrimi- natory power of closely related strains due to their higher percentages of variable regions and higher molecular evo- lution rates as compared to 16S rRNA gene[98]. The higher evolutionary rate could be explained by the synonymous substitutions predominantly at the third position of codons in these protein coding genes that occur without affecting the amino acid sequences[9]. Numerous Streptomyces phy- logenetic analyses were conducted using protein-coding genes due to their higher resolution power, for examples, taxonomic studies of Streptomyces griseus[83,98] and Strep- tomyces hygroscopicus [91], and diversity studies on the family Streptomycetaceae[9,51]. Protein-coding genes - DNA gyrase subunit B, topoisomerase type II (gyrB) The gyrB gene is one of the structural genes of DNA topoi- somerases; the DNA topoisomerases are responsible for DNA replication, recombination, transcription and repair, also controlling supercoiling level[99]. The gyrB gene en- codes for the subunit B protein of bacterial DNA gyrase (DNA topoisomerase type II) that is responsible for DNA replication and involves in direct DNA repair mecha- nism[96,100]. The specific functions of DNA gyrase include supercoiling relaxed closed circular double-stranded DNA negatively with ATP hydrolysis, whilst relaxing supercoiled DNA molecule in the absence of ATP hydrolysis[100,101]. The gyrB gene is found universally distributed among different species of bacteria. As compared to ribosomal 16S rRNA gene, gyrB gene has faster molecular evolution rate and also rarely undergo horizontal gene transfer[96,101]. Many studies have proven that the consistency between the re- sults of DDH and gyrB-based phylogenetic analysis[70,96,101], whereby approximately 98.5 % gyrB gene sequence simi- larity could corresponds to DNA-DNA relatedness thresh- old value of 70 % for species delineation[70,102]. Protein-coding genes - tryptophan synthase subunit B (trpB) The trpB gene encodes tryptophan synthase subunit B which is responsible for amino acid biosynthesis. It cat- alyzes the last two steps of tryptophan biosynthesis by causing indole and serine to be condensed irreversibly to produce tryptophan in the pyridoxal phosphate-depen- dent reaction[100,103]. Protein-coding genes such as trpB gene is often used for phylogenetic analysis because it is selectively neutral and is not affected by selection pressure for amino acid changes. This implied that trpB gene locus is independent as it underwent evolution[83]. The trpB gene has been used individually or in combina- tion with other protein-coding genes for the taxonomic characterization of Streptomyces in previous studies and had successfully resolved the phylogenetic relationships between closely related subspecies and species of Acti- nobacteria due to the high genetic variation within these genes as compared to ribosomal genes[9,51,83,89,90,104]. TOOLS AND DATABASES TO FETCH GENES Basic Local Alignment Search Tool (BLAST) BLAST stands for ‘Basic Local Alignment Search Tool’ and it is a sequence similarity search program, acces- sible as an independent tool or via a web interface. It works by comparing the query sequences either nucleo- tide or protein sequences to an appropriate nucleotide or protein sequence database. This is to determine type sequences that resemble the query sequence above a particular threshold level. Evolutionary relationships between sequences can then be indicated based on the sequence similarity found between query and type strains[105]. EzBioCloud database EzBioCloud is the latest improved version of open access database developed by ChunLab Inc., prospers the for- mer versions namely EzTaxon and EzTaxon-e (https:// www.ezbiocloud.net/). It is an integrated database that contains comprehensive hierarchical taxonomy of both Bacteria and Archaea domains from phylum to species level, represented by quality controlled 16S rRNA gene and genome sequences[106]. In the initial version of data- base, Eztaxon, contains 16S rRNA gene sequences of all validly described prokaryotic type strains which serves as a web-based tool for the analysis of 16S rRNA gene sequences including the determination of pairwise nu- cleotide similarity values of the gene sequences[107]. Ez- taxon was later evolved to the next generation database known as the Eztaxon-e. Extaxon-e database was en- hanced by including species with non-validly published names, uncultured phylotypes, and the incorporation of a new function involves the estimation of degree of completeness in sequencing. Besides, sequences of all species and phylotypes within this database have under- gone comprehensive phylogenetic analysis based on 16S rRNA gene sequences to produce a complete hierarchi- cal taxonomic system[108]. Despite the identification and comparison of 16S rRNA genes being a reliable method Taxonomic and Characterization Methods... 7 to determine the identity of a strain of interest, however, it comes with a limitation where it does not guarantee that two strains with nearly identical 16S rRNA gene sequences belong to the same species, alternatively they could be of different species[109]. Hence, analyzing bacteria genome se- quences has become a method to overcome this issue. The latter EzBioCloud united database contains information on both 16S rRNA gene and genome sequences as well as sev- eral bioinformatics tools such OrthoANI. Furthermore, all genomes in this database were quality filtered where the taxonomic identification at genus, species or subspecies levels was performed according to the algorithm compris- ing a combination of gene-based search and OrthoANI[106]. Currently, EzBioCloud holds up to 81,151 taxa and 99,418 qualified genomes (available at https://www.ezbiocloud. net/dashboard, accessed on August 2018). National Center for Biotechnology Information (NCBI) GenBank database NCBI is a national resource for molecular biology infor- mation established in year 1988, which involves in the de- velopment of computational biology and software for ge- nome analysis[110]. The NCBI is the host of the GenBank - a comprehensive public online database that contains DNA sequences, supporting bibliographic and biological anno- tation (http://www.ncbi.nlm.nih.gov/genbank/)[111]. Newly discovered nucleotide sequences such as the non-coding DNA region, a particular gene region or coding region of a DNA sequence, and whole genome shotgun as well as other sequence data will be submitted to this database often by individual laboratories/authors and bulk submis- sions from high-throughput sequencing projects. Direct submissions to GenBank can be done via a web-based form named as BankIt or stand-alone submission program known as Sequin. GenBank is established as part of the In- ternational Nucleotide Sequence Database Collaboration, thus, it contains data from other databases such as the Eu- ropean Molecular Biology Laboratory (EMBL), and DNA DataBank of Japan (DDBJ). Consequently, GenBank holds DNA nucleotide sequences for more than 300,000 validly described species[111,112]. PHYLOGENETIC TREES RECONSTRUC- TION Phylogenetic is the study of evolutionary relationship while phylogenetic analysis is the estimation of these evo- lutionary relationships. Phylogenetic indicates changes caused by evolution and understand relationships between ancestor and its descendants to estimate divergent time and the evolution in a family[113,114]. Phylogenetic trees can be built by choosing an appropriate phylogenetic information marker (E.g. a particular DNA gene sequence, RNA) of prokaryotes or eukaryotes to determine the degree of re- latedness between species, family, genus or order and their hypothetical common ancestors[1,51,115]. Phylogenetic trees consist of nodes and branches, the node is the taxonomic unit or evolutionary event while branch is link between two adjacent nodes. The length of a branch represents the diver- gence. Branching presents an estimated pedigree of evolu- tionary relationships among various organisms[115]. Clade is a monophyletic taxon with a set of descendants which are originated from a single most common ancestor, at which the members of a clade are more closely related to one another than members of other clades. Members of the same clade possess a common evolutionary his- tory and share unique features which could not be found in distant ancestors[1,114]. There are two types of phyloge- netic trees which are: rooted tree that consists of a com- mon ancestor and unrooted tree which has no common ancestor. An outgroup is the least related operational taxanomic unit to the group of taxa being studied. It is used to root a tree and as a reference to determine evo- lutionary distance[115]. Bootstrapping analysis Bootstrapping is one of the statistical methods to deter- mine the reliability of tree branch arrangement/topol- ogy, in other words, to estimate the confidence intervals on phylogenies. The phylogenetic tree built is not the actual representation of the evolutionary relationships, instead, it is an estimation of these relationships. Hence, bootstrapping analysis is requested to calculate the reli- ability of that estimate. Columns in a multiple aligned sequences are resampled during bootstrapping process to produce many new alignments sets and replace the original dataset. The bootstrapping process is continued for at least 100 times while phylogenetic trees are pro- duced from all these alignments sets. The final outcomes determine the number of time a particular branch point is generated out of the total number of the built phylo- genetic trees. The branching point will be more valid when the number of occurring time is larger. Bootstrap values are statistically significant if they are ranged be- tween 90 to 100 %. Phylogenetic trees are produced multiple times and the trees produced are not always identical during every production. For instance, a node with a particular cluster descending from it might pro- duce bootstrap value of 90 % when a thousand of trees are built randomly, thereby inferring that node with that identical cluster descending from it appears for nine hundred times out of the one thousand times the tree were rebuilt[115-117]. BioEdit sequence alignment editor software BioEdit is a free of charge biological sequence alignment editor and sequence analysis software program available online for Windows. BioEdit is commonly used in many molecular biology studies, it provides auto-integration with Clustal W multiple alignments, manipulation, trim- ming and editing of aligned sequences. The sequences of type strains are aligned with query sequences to de- termine differences between them. Besides consisting options for sequence analysis, it also provides links to external analysis programs such as BLAST[118,119]. Clustal W alignments Alignment process is the crucial key for the construction of phylogenetic trees at which poor alignment would result in incorrect phylogenetic tree[120]. An alignment is considered as good if it consists of no gaps. Regions which do not align between sequences should be deleted before building of phylogenetic trees. Clustal W meth- od is a computational multiple alignment methods that Law JWF et al. 8 align sequences based on an explicitly phylogenetic criteri- on or known as a ‘guide tree’. The generation of guide tree is based on the initial pairwise sequence alignments while its guide tree is formatted as a PHYLIP tree file. Clustal W tool aligns all the sequences according to the matching identical or similar regions found between these sequences. The ‘W’ stands for ‘Weighted’ which refers to the differ- ent weights that are applied to parameters and sequences in different regions of the alignment. During the pairwise alignments, distance matrix is calculated among each se- quence. Distance is defined as amount of identical matches divided by the length of sequences regardless of gaps[121]. MEGA software Molecular Evolutionary Genetics Analysis (MEGA) soft- ware provides various tools for analyzing DNA and protein sequences according to evolutionary perspective. MEGA is widely utilized for assembling sequence alignments, constructing phylogenetic trees, mining online databases, testing selection, estimating molecular evolution rate and divergence times to study molecular evolutionary histo- ries of genes, genomes and species[122,123]. Several versions of MEGA have been developed, where the initial version MEGA 1 had made available since early 1990s[124]. MEGA version 1 has been further evolved up till MEGA version 6, in which the progress involved multiple upgrades for the use in Microsoft Windows including improved computa- tional algorithms and statistical methods[122,123]. MEGA 7 was later introduced as the latest 64-bit version with great- er computational power that capable of handling memory- intensive analysis of large datasets[125]. Recently, MEGA X has been developed as cross-platform version that is able to run natively on Linux and Microsoft Windows with addi- tional upgrade includes the application of multiple comput- ing cores for various molecular evolutionary analyses[123]. TREE-BUILDING METHODS Tree building methods can be divided into two differ- ent methods: (1) distance-based, and (2) character-based. Character-based methods generate trees that optimize the distribution of actual data for each character. Examples of character-based methods are maximum likelihood and maximum parsimony algorithms. Distance methods calcu- late pairwise distances based on certain measure, followed by elimination of the actual character data while only uti- lize the fixed distances to obtain trees. Hence, pairwise dis- tances are not fixed because they are identified by the tree topology. Neighbour-joining is one of the commonly used algorithm in distance-based methods[114,126]. Character-based method This method works by using character data in every step of phylogenetic analysis at which aligned sequences such as DNA sequences are used directly during tree analysis. It also enables the reliability of the position of each base in an alignment to be assessed based on the positions of all other bases. Character-based methods examine every alignment column individually and determine the tree which best ac- commodates all of the information[114,126]. Character-based method - maximum likelihood (ML) algorithm ML algorithm builds a tree based on mathematical mod- els - the probability models to deduce the evolutionary distances. It determines the evolutionary model and tree which consist of the highest likelihood of generating the observed data. It searches the most likely tree from all the trees of the given dataset with the help of a tree model for nucleotide substitutions. ML is calculated for each base position in the multiple alignment by deter- mining the likelihood that a certain pattern of variation at a particular site would be generated by a particular substitution reaction when a particular tree and the over- all observed base frequencies are provided. Likelihood is the total of the probability of each particular variation pattern produced by a particular substitution process. A tree that best accounts for the high number of variations of the dataset given based on likelihood calculations will be generated[1,114,127]. Character-based method - maximum parsimo- ny (MP) algorithm The MP algorithm builds shortest phylogenetic tree that minimizes the amount of evolutionary change or con- sists of the fewest amount of evolutionary changes such as nucleotide substitution to explain the evolutionary dif- ferences found among different taxa. The MP algorithm- tree contain of the least parallel changes as compared to NJ and ML algorithms. Ancestral relationship can be determined as the MP algorithm uses all the evolution- ary information in every nucleotide bases[1,114,127,128]. Distance-based method This method build tree according to the evolutionary distance or total of dissimilarity between two aligned sequences. A true tree will be generated based on this method if all the event of genetic divergence in the se- quence were recorded accurately. The sequence data will first be converted into pairwise distance in order to build tree using the matrix of pairwise distances between gene sequences in an alignment regardless of the character data. Moreover, the tree is generated according to the distance values and clustering algorithm[128]. Distance-based method - neighbour joining (NJ) algorithm NJ algorithm is a commonly used distance-based meth- od for the reconstruction of phylogenetic trees[113]. It calculates the amount of differences between two se- quences and the actual tree is then generated based on the matrix of distance values[113,127]. The tree construc- tion begins when the most similar sequences or pairs of closest neighbours are joined. Then, a node (common ancestor) is successively inserted between them before next closest neighbours are chosen and added with node. The process is repeated until a tree with a combination of nodes that gives the smallest total branch lengths is produced. NJ algorithm works by trying to locate all neighbours in correct positions before correcting the length of branches[129]. It is frequently used by users due Taxonomic and Characterization Methods... 9 to its fast computation speed and high accuracy which is equivalent or better than other computationally intensive algorithms[114]. Substitution model Substitution model is used to determine the probability or rate and how one nucleotide replaces or substitutes another. They calculate evolutionary distance between sequences based on the observed differences found between sequenc- es. This evolutionary model works based on the measure- ment of normalized distance which is the mean degree of change per length of aligned DNA sequences. Thus, calcu- lations of distance between sequences are improved. Selec- tion of substitution model is important as it influences the alignment and tree building and thus the right substitution model could result in most accurate phylogenetic relation- ship[1,114]. Substitution model - Kimura-2 parameter Kimura-2 parameter is used to calculate evolutionary dis- tances and similarity values in NJ tree-building algorithm. It is an evolutionary model which states that transitions which is a change between pyrimidines or between purines (C to T or A to G) occur more frequently as compared to transversions which is a changed between purines to py- rimidines or vice versa (A to T or C to G) when the sub- stitution rate is unequal. Hence, the rate of transitional nu- cleotide substitutions is greater than rate of transversional nucleotide substitutions[130,131]. Substitution model - Tamura-Nei This is used to discriminate the rates of two types of transi- tional substitutions, at which transitional changes between A↔G (purine ↔purine) may occur at different rate as compared to that between C↔T (pyrimidine ↔ pyrimi- dine) while transversions happen at same rate and this rate can be different from both transitional substitutions rates. Therefore, two different types of transitional substitutions rates and transversional substitutions rate are estimated individually with the consideration of base composition bias which refers to unequal frequency of all four nucleo- tides[132,133]. GENERAL DISCUSSION The identification and taxonomic characterization of Strep- tomyces species could be relatively challenging mainly be- cause of the large amount of species present in this genus. Several techniques are often required to be conducted si- multaneously to produce a more reliable result for classifi- cation of bacterial species. Fortunately, the rapid progress in molecular techniques has greatly improve the efficiency of bacterial species identification and characterization. Traditional phenotypic approach may not be sufficient for describing or differentiating taxa as certain pheno- types changes could be caused by horizontal gene transfer event[57,73]. The incorporation of both molecular genotyping and phenotyping methods could assist in determining the correlations between both traits and thus lead to a better understanding of the Streptomyces taxonomy[57]. The ap- plication of PCR, DDH, and NGS have govern modern taxonomic studies and these techniques are important to generate genotypic data for classification and allocation of taxa on phylogenetic tree[73]. Ribosomal RNA genes and protein-coding genes are commonly used molecular markers for phylogenetic studies of Streptomyces (Figure 1). The rRNA genes are typically considered as best phylogenetic markers as they are universally distributed across living organisms and they made up of a combination of highly conserved, variable, and hypervariable regions that evolve at vary- ing rates, thereby facilitate the differentiation among distinct bacterial groups[73,134,135]. The rRNA genes have slower evolutionary rate as compared to the protein cod- ing genes, hence, rRNA genes are important phyloge- netic markers for analyzing the phylogenies of distantly related species[134]. Among the rRNA genes, 16S rRNA gene acts as a very powerful discriminatory tool in resolving phylogenetic relationships and this gene has been the most sequence phylogenetic marker[135]. However, it is arguable wheth- er the gene sequence of 16S rRNA gene should be the gold standard for Streptomyces phylogeny. Many stud- ies have suggested that 16S rRNA gene sequence alone is not sufficient enough to discriminate between closely related species, notably species within Streptomyces ge- nus due to its low resolution at species level[51,98]. This issue is because of many species in the genus Strepto- myces share identical or highly similar 16S rRNA gene sequence in addition to highly similar phenotypes[9,51]. Henceforth, the implication of other molecular targets with higher resolution in addition to the 16S rRNA gene is recommended for the phylogenetic analysis within the genus Streptomyces to observe their actual relationships. As an alternative, the 23S rRNA gene is employed in taxonomic classification of Streptomyces species. The 23S rRNA gene offers better resolution than 16S rRNA gene as it has more variable regions. In the early days, it appears that 23S rRNA gene has somewhat lost favor to 16S rRNA gene as phylogenetic marker in taxonomic classification which might due to difficulties in devel- oping broad-range sequencing primers and sequencing of larger genes. Though the use of 23S rRNA gene has now eventually increased with time and advances in se- quencing technology[86]. Nevertheless, it is important to acknowledge that in- tragenomic or intraspecific diversity of rRNA genes is a common phenomenon in Streptomyces that could lead to false identification regarding phylogenetic relatedness and ancestry when there is only a single gene sequence used[86,95,136]. Besides, rRNA genes may not be able to provide high resolution when it comes to distinguish- ing Streptomyces species as the functional constraints on both 16S and 23S rRNA genes for them to be highly conserved to maintain the precise spatial relationships for producing functional ribosomes. Conserved regions are suitable for investigating organisms with distant relationships, whilst variable regions with faster evo- lutionary rate are suitable for differentiating organisms that are closely related[86,91]. Ribosomal genes were demonstrated to be insufficient to discriminate between Law JWF et al. 10 closely related Streptomyces species by a number of stud- ies despite of being a powerful tool in elucidating inter- and intrageneric evolutionary relationships[83,91,137]. Protein-coding genes (housekeeping genes) such as gyrB and trpB genes have exhibit potential for taxonomic clas- sification of Streptomyces at species level and have been shown to be effective discriminatory tools for phylogenetic analysis. Also, the utilization of multiple protein-coding genes in MLSA has been conducted in several biosyste- matics studies of Streptomyces species[51,83,91]. Protein- coding genes produce higher resolution in discriminating Streptomyces at species level than the rRNA genes because of the presence of more variable regions in the gene se- quences[9,91]. Additionally, protein-coding genes such as gyrB gene tend to exhibit higher number of base substitu- tions as compared to rRNA genes. Ochman et al. (1987)[138] reported that 16S rRNA gene had average base substitution rate of 1 % in 50 million years whereas the average base substitution rate of gyrB gene at the synonymous sites was found to be about 0.7 % to 0.8 % in one million years. As a result, the higher percentage of base substitution rate accounts for higher rate of evolution of protein-coding genes than rRNA genes and thus improve their resolu- tion as phylogenetic markers that capable of determin- ing the relationships between closely related Streptomy- ces species with the generation of stable phylogenetic trees[9,91]. The advent of NGS technology has increase the avail- ability of whole genome sequences of many prokary- otes, along with the rapid progression of bioinformatic tools which are important for analyzing these sequences. The inclusion of whole genome sequencing data could further enrich the prokaryotic systematics by overcom- ing the limitations of current approaches (E.g. DDH, 16S rRNA gene sequences etc.)[139]. There is an in- creasing evidence that phylogenetic and taxogenomic approach offers robust classification as it is capable of resolving complex taxa below family level (intrageneric and intrafamily)[12,139-141]. Therefore, providing reliable information on the relationship and differentiation of prokaryotes at species and genera level. The taxonomic classification of prokaryotes utilizing genomic data is likely becoming the norm in near future. Figure 1. Framework of ribosomal genes and protein-coding genes as molecular markers for phylogenetic analysis based on different algorithms. CONCLUSION Streptomyces bacteria have been a focus in systematic re- search due to the various beneficial properties they can of- fer towards mankind. However, there are taxonomic chaos and overspeciation problem occurred within the genus Streptomyces. Thus, it is important to search for solutions that could ease the taxonomic classification this complex taxon. Several important molecular makers including ri- bosomal genes and protein-coding genes where each has their own advantages and limitations in determining the phylogenetic relationships of Streptomyces species. The availability of various molecular techniques, bioinformatic tools, databases, sequence editing and phylogenetic tree reconstruction software have greatly assist in taxonomic studies of Streptomyces. Advances of NGS and associated bioinformatic tools will revolutionize taxonomic practice through the integration of genomic approach. It is an- ticipated that genome-based phylogenetic analysis will further improve the identification and taxonomic clas- sification of members in the genus Streptomyces. Authors Contributions The literature review and manuscript writing were per- formed by JW-FL, K-XT, SHW, N-SAM, and L-HL. L-HL founded the research project. Conflict of Interest The authors declare that the research was conducted in Taxonomic and Characterization Methods... 11 the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgments This work was supported by the MOSTI eScience Fund (02-02-10-SF0215) and External Industry Grants from Biotek Abadi Sdn Bhd [vote no. GBA-808138 and GBA- 808813] awarded to L-HL. Reference 1. Reddy BN. Basics for the construction of phylogenetic trees. 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