Advances in Microbiology
Vol.05 No.06(2015), Article ID:57166,15 pages

Identification of Bacterial Fish Pathogens in Brazil by Direct Colony PCR and 16S rRNA Gene Sequencing

F. A. Sebastião, L. R. Furlan, D. T. Hashimoto, F. Pilarski*

Aquaculture Center of São Paulo State University―CAUNESP, Universidade Estadual Paulista, Jaboticabal, Brazil

Email: *

Copyright © 2015 by authors and Scientific Research Publishing Inc.

This work is licensed under the Creative Commons Attribution-NonCommercial International License (CC BY-NC).

Received 14 May 2015; accepted 11 June 2015; published 16 June 2015


Intensive fish farming systems in Brazil have increased the disease incidence, mainly of bacterial origin, due to higher stocking density, high organic matter levels and poor quality of the aquatic environment that causes high mortality rates during outbreaks. The identification of pathogenic species using a fast and reliable method of diagnosis is essential for successful epidemiological studies and disease control. The present study evaluated the use of direct colony PCR in combination with 16S rRNA gene sequencing to diagnose fish bacterial diseases, with the goal of reducing the costs and time necessary for bacterial identification. The method was successful for all 178 isolates tested and produced bands with the same intensity as the standard PCR performed using pure DNA. In conclusion, the genetics methods allowed detecting the most common and important pathogens in Aquaculture, including 12 species of occurrence in Brazilian fish farms. The results of the present study constitute an advance in the available diagnostic methods for bacterial pathogens in fish farms.


Direct Colony PCR, 16S rRNA Sequencing, Bacterial Fish Pathogens

1. Introduction

Due to its high water availability and favorable climate conditions, Brazil displays high potential for the development of fish farming, which is an activity that has been growing substantially over the last few years. According to the Food and Agriculture Organization of the United Nations [1] , Brazil is the second largest aquaculture producer in Latin America and the Caribbean, and freshwater aquaculture (tilapia, carp, and native fish) represented 87% (545,300 ton) of the total aquaculture production in 2011.

The growing interest in this activity and, consequently, the search for higher profitability, have been leading producers to adopt super-intensive production systems. However, the high density of confined fish, inadequate farming management practices, and water contamination by toxic products cause chronic stress and immunosuppression in farmed animals. These effects lead to the occurrence of diseases and epizootic outbreaks caused by pathogens that would not have high expression in natural environments [2] . Knowledge about the etiological agents, pathogenesis, biochemistry, antigenicity, epizootiology, and inter-relationship of stress and environmental factors of bacterial infections affecting fish is essential to avoid and control diseases. However, these factors have not been well studied, especially because fish farming is a recent activity, with its intensification beginning in the 1990s [3] .

Gram-negative bacteria such as Aeromonas, Flavobacterium, Pseudomonas, and Francisella and gram-posi- tive bacteria from the genera Streptococcus and Lactococcus [4] - [11] are some of the pathogens responsible for economic losses in Brazil. They can cause high fish mortality rates up to 72 h after infection [12] .

Although the number of studies focusing on the diagnosis of bacterial etiological agents has increased over the last few years, there are still few available alternatives for the control of fish bacterial infections in Brazil. Therefore, quicker and more effective diagnostic alternatives are necessary, which would help control diseases before they lead to irreversible clinical consequences and high mortality rates. Molecular diagnostic methods use reduced volumes of sample material and exhibit high sensitivity, specificity, and accuracy in pathogen detection [13] .

Methods that do not require purified DNA extraction, such as direct colony PCR, are quicker and less expensive and may greatly aid in the early detection of fish pathogens [14] . In addition, because not all microorganism sequences are catalogued in current databases, the use of universal and degenerate primers is a wise strategy. For this reason, methods based on 16S rRNA ribosomal gene amplification and sequencing have been widely explored [13] .

The use of universal PCR primers is based on the hypothesis that the primers used are complementary to conserved regions of genes in the environment, resulting in amplification; in turn, heterogeneity is found inside of the fragments flanked by the primers, in hypervariable regions [15] [16] . This method has been revolutionizing microbial ecology, from studies of non-cultivable bacteria to the correct identification of pathogens for accurate diagnoses.

The aim of the present study was to evaluate the direct colony PCR combined with 16S rRNA gene sequencing as a faster and less expensive method to identify fish bacterial pathogens, compared to the classic PCR protocol. Moreover, we have used these methods to demonstrate the efficiency of genetic approaches for the practical evaluation of the diagnosis of aquaculture diseases in Brazilian fish farms.

2. Materials and Methods

2.1. Bacterial Strains and Culture Conditions

178 bacterial isolates were obtained between 2010 and 2014 from the following hosts (n = number of assessed fish): tilapia (Oreochromis niloticus n = 93), tambaqui (Colossoma macropomum n = 10), carp (Cyprinus carpio n = 3), cachara (Pseudoplatystoma reticulatum n = 34), and pacu (Piaractus mesopotamicus n = 8).

The hosts exhibited clinical signs of bacterial diseases, such as skin ulcerative lesions, hemorrhagic septicemia, meningoencephalitis, fin rot, exophthalmia, and were collected at fish farms in different regions of Brazil:Dourados (Mato Grosso do Sul State, 22˚13'16"S, 54˚48'20"W, n = 38), Rio de Janeiro (Rio de Janeiro State, 22˚54'S, 43˚10'W, n = 4), Itambaracá (Paraná State, 23˚0'49"S, 50˚24'7"W, n = 10), Itaju (22˚25'37''S, 45˚27'11''W), Arealva (22˚1'38''S, 48˚54'36''W), Porto Ferreira (21˚51’18’’S, 47˚28'45''W), Guaíra (20˚19'5''S, 48˚18'42''W), Santa Fé do Sul (20˚12'43''S, 50˚55'38''W), Palmital (22˚47'30''S, 50˚12'18''W) and Jaboticabal (21˚15'19''S, 48˚19'21''W―São Paulo State, n = 123).

For the isolation of bacteria, scrapings were performed using sterile swabs on fish kidneys and brain. Gram- negative colonies were plated on TSA (Tryptic Soy Agar-Biolife), and TSB (Tryptic Soy Broth-Biolife) and incubated for 24 h in bacteriological incubator adjusted to 28˚C. While gram-positive colonies were seeded in Columbia blood agar (Difco) incubated for 24 - 72 h at 30˚C and subcultured in BHI (Brain Heart Infusion Broth, Himedia).

The strains of Palmital (SP) were obtained directly from the Laboratory of Aquatic Animal Disease, APTA, Votuporanga, SP.

2.2. Molecular Identification of Isolates

Two methods of molecular diagnosis were compared in this study aiming to evaluate the efficiency of direct colony PCR (time and cost effectiveness) in relation to the PCR amplification of purified DNA by extraction, both combined with gene sequencing (Table 1).

The standard PCR of purified DNA method followed the steps below.

2.2.1. DNA Extraction

One colony of each isolated was transferred to a tube containing appropriate liquid culture medium (TSB for gram-negative and BHI for gram-positive) and incubated at 28˚C until the OD600 was between 1 and 1.5. Following incubation, 1.0 mL of the bacteria culture was centrifuged at 12,000×g for 1 min, the supernatant was discarded, and the pellet was frozen at −20˚C until DNA extraction. The Axyprep® miniprep kit for bacterial genomic DNA was used according to the manufacturer’s instructions (Axygen Biosciences, Union City, CA, USA). DNA was quantified by fluorometry using a Qubit 2.0 fluorometer (Life Technologies, NY, USA).

2.2.2. Standard PCR

PCR was performed in a 25 µL final volume, containing 2.5 µL of 10X buffer (10 mM Tris-HCl, 50 mM KCl), 0.2 µL of 25 mM dNTP, 1.0 µL of 50 mM MgSO4, 0.2 µL of Taq High Fidelity (Platinum®Taq DNA Polymerase, Life Technologies, NY, USA), 2.0 µL of each primer (10 pmol), 25 ng of DNA template, and Milli-Q water up to the final volume. The PCR program consisted of 94˚C for 2 min; 35 cycles of 94˚C for 30 seconds, 55˚C for 30 s, and 68˚C for 1.5 min; and final extension at 68˚C for 10 min. We used the primers 8F/907R (Table 2), specific for the 16S rRNA bacterial gene [15] [17] [18] . The resulting amplicons of approximately 900 bp (base pair) were analyzed by electrophoresis in 1.5% agarose gel stained with ethidium bromide, according to Sambrook et at. [19] .

2.2.3. Purification of PCR Products and Gene Sequencing

PCR products were purified using a MinElute Kit (Qiagen, Crawley, West Sussex, UK) according to the manufacturer’s instructions. Purified PCR products were quantified using a Qubit 2.0 fluorometer, and gene sequencing was performed using 50 ng/µL per sample. Sequencing was performed according to Sanger [20] . PCR products were amplified using AmpliTaq polymerase and BigDye Terminator (Applied Biosystems) according to the manufacturer’s instructions, using the primer 907R. Sequencing was performed using an ABI PRISM 3730 DNA analyzer (Applied Biosystems).

Table 1. Steps of the two methods compared in this study: Standard PCR of purified DNA and direct colony PCR, both combined with the 16S rRNA gene sequencing.

Table 2. Sequence of primers used for amplification of the 16S rRNA gene.

2.3. Direct Colony PCR

This method allows PCR to be performed on colonies isolated from Petri dishes, without the step of DNA extraction. Colonies (1 - 2 mm diameter) were inoculated by placing a sterile tooth pick at the bottom of a PCR tube (0.2 mL) and incubated at −20˚C overnight. The following solution was then added in the PCR tube: 2.0 µL of 10× buffer (10 mM Tris-HCl, 50 mM KCl), 1.2 µL of 50 mM MgCl2, 0.2 µL of 25 mM dNTP, 0.7 µL of each primer 8F/907R (10 pmol/µL), 0.2 µL of Taq DNA polymerase (2.5 U), and Milli-Q water up to 20 µL. The PCR program consisted of 95˚C for 5 min; 30 cycles of 95˚C for 1 min, 54˚C for 1.5 min, and 72˚C for 1 min; and final extension at 72˚C for 5 min. The amplified PCR products, at 50 ng/µL mean concentration, were analyzed by electrophoresis in a 1.5% agarose gel stained with ethidium bromide [19] . The gels were visualized under UV light, using a ChemiDoc MP imaging system (Bio-Rad). Samples were quantified by fluorometry using a Qubit 2.0 fluorometer and sequenced as described above.

After sequencing, samples of both methods had their nucleotides analyzed.

2.4. Analysis of Nucleotide Sequences

The obtained sequences were visualized using the Bio Edit Sequence Alignment Editor software (v. 7.1.11). Phred quality of sequences was determined. The initial and final portions of the sequences were then removed, keeping only the high-quality fragment.

After trim, sequences were exported in FASTA format and compared with the GenBank database ( using the Eztaxon algorithm ( 100% coverage and identity ≥ 98% were considered for specific identification. Sequences were also submitted to Ribosomal Database Project II ( for comparison and identification.

The sequences obtained in the present study were deposited at NCBI GenBank under accession numbers KJ560937 to KJ561113. The complete list of species identified, accession numbers, place of origin, fish species, season and year of collection, and size of amplified PCR products were included as Supplementary Material.

The Brazilian isolates tested were S. agalactiae (n = 23), S. iniae (n = 4), Lactococcus lactis (n = 11), L. raffinolactis (n = 2), L. garvieae (n = 16), Enterococcus casseliflavus (n = 16), E. durans (n = 2), E. faecalis (n = 11), Edwardsiella tarda (n = 5), Aeromonas hydrophila (n = 16), A. jandaei (n = 2), A. veronii (n = 15), Pseudomonas sp. (n = 15).

A phylogenetic diagram was constructed for validation of the sequencing data, using the 138 isolates listed above from the 178 of the present study. In addition, we used as reference 16 sequences originated from different countries (Table 3), obtained from GenBank database.

The 154 FASTA sequences were aligned using the ClustalW Multiple Alignment tool (BioEdit Sequence Alignment Editor software, v. 7.1.11). The data were then entered in Mega software (v. 5.05) to determine the best substitution model. As a result of the preliminary analysis, a maximum-likelihood phylogenetic diagram was constructed, using the Kimura 2-parameter model, with a gamma-shape parameter with 5 categories, the nearest-neighbor-interchange tree inference option. The stability of internal nodes was assessed by bootstrap analysis with 1000 replicates.

3. Results and Discussion

3.1. The Comparison of the Two Methods: Direct Colony PCR and Classic PCR Protocol

We found no difference in the band size in agarose electrophoresis, nor in the peaks pattern of electrophero- grams in the two methods evaluated.

Bands resulting from the direct colony PCR exhibited the same intensity as those of the standard PCR of purified DNA, for all 178 isolates tested (Figure 1).

Figure 1. Electropherogram of 1.5% agarose gel stained with ethidium bromide, showing amplification of 16S rRNA gene (primers 8F/907R). Lane 1, Marker 1kb. Lane 2, direct colony PCR. Lane 3, standard PCR. Lane 4, negative control.

Table 3. Reference strains used for the maximum-likelihood phylogenetic analysis and their places of origin.

Electropherograms resulting from the sequencing of both methods exhibited Phred quality scores ≥20. All the isolates had the same results of bacterial identification for both techniques (direct colony PCR and standard PCR of purified DNA). Thus, direct colony PCR was a less expensive and faster diagnostic method, as shown on Table 4. There were 51% savings in cost analysis per sample for direct colony PCR compared to Standard PCR of purified DNA. Moreover, direct colony PCR reduces 2 days in time to issue the final report. After the installation of a bacterial outbreak, fish shoals can be decimated by up to 72 hours. Therefore, rapid diagnosis in aquaculture is a critical point in the production chain, which can be assessed by the genetic tools of the present study.

A faster diagnosis is important, since the one based on classical microbiology techniques (isolation, platting and biochemical tests) can exceed the time for treatment in seven to 15 days and, in many cases, ending up inconclusive. The molecular diagnosis, on the other hand, can provide a faster, low cost, conclusive diagnosis, which is essential to determine the best treatment in fish farming (Table 5).

Besides, in an attempt to control disease outbreaks, in classical scenery in Brazil, producers use multiple antibiotics indiscriminately, selecting resistant strains, contaminating fish, water and raising the risks to consumer health, endorsing the need for rapid and effective diagnosis [21] .

A maximum-likelihood phylogenetic tree was built to validate the sequencing data (Figure 2). The bacterial isolates of the same species or phylogenetic related were correctly grouped into a common branch, as expected. The principle of maximum likelihood for phylogenetic inference evaluates the probability of a given model of evolutionary changes explaining the origin of the data observed. In this method, the initial tree is constructed using the neighbor-joining method, and the length of each branch is adjusted to maximize the likelihood that the information will produce the topology of the tree for the desired evolutionary model [22] .

These results confirm and validate the direct colony PCR method to be applied as a reliable tool for the identification of bacterial fish pathogens in aquaculture. Although this method has already been used in previous studies for different purposes [14] [23] , the present study represents the first practical application for the diagnosis of aquaculture diseases, a field lacking in terms of technological advancement.

Table 4. Cost analysis per sample for bacterial identification, performed in university laboratory already equipped.

*Isolation times vary depending on the species being cultured.

Table 5. Advantages and disadvantages of each method for aquaculture diagnosis.

Figure 2. Relationship among different bacteria species using 16S rRNA gene sequences, inferred by maximum-likelihood method. The phylogenetic diagram shows the correct clustering of related fish bacteria isolated in the present study.

3.2. The Analysis of the Common Bacterial Fish Pathogens

Direct colony PCR, combined with gene sequencing, was able to detect the most common and important pathogens in aquaculture, such as Aeromonas hydrophila, Aeromonas veronii, Aeromonas jandaei, Streptococcus agalactiae, Streptococcus iniae, Streptococcus dysgalactiae, Edwardsiella tarda, Pseudomonas sp., Lactococcus garvieae, Citrobacter freundii, Plesiomonas shigelloides, and Enterococcus sp.

As shown in Figure 3, genera related to pathogenic bacteria and with higher frequency among 178 bacterial isolates of this study were Aeromonas (31%), Lactococcus (23%), Enterococcus (22%), Streptococcus (20%), Pseudomonas (11%), Citrobacter (6%), Edwardsiella (5%), Acinetobacter (3%), Enterobacter (2%), Plesiomonas (1%) and Weissela (1%).

Of the 43 Aeromonas isolates, 53% were identified as A. hydrophila by 16S rRNA gene sequencing. This result is in accordance with previous reports that found this species to be predominant [24] . In turn, A. veronii corresponded to 40% of the isolates. The seasonality was also observed in the present study: at higher temperatures (Spring/Summer) there were higher isolation rates of these pathogens [25] , which causes hemorrhagic septicemia, characterized by small superficial lesions, focal hemorrhages, ulcers, abscesses, and abdominal distension. Internally, there can be ascitic fluid accumulation, anemia, and lesions in the liver and kidneys [26] .

For the genus Lactococcus, the emerging species L. garvieae corresponded to 52% of the total 29 isolates of this genus, followed by L. lactis with 41% incidence in fish originating from the states included in the present study, with higher incidence in P. reticulatum. The species L. garvieae has been isolated from several fish species worldwide, namely in Japan [27] , South Africa [28] , Europe [29] , and Brazil. Its first outbreak was reported in 2009 [7] . Fish with lactococcal infection exhibit lethargy, anorexia, skin darkening and swim closer to the water surface [30] , resulting in considerable economic losses, especially during the summer months when the water temperature increases [4] . Few studies report L. lactis as an opportunistic pathogen. However, L. lactis subsp. lactis has been responsible for a 100% loss of hybrid sturgeons (Huso huso × Acipenser ruthenus) in a fish farm in Taiwan, China [31] .

Figure 3. Percentage of bacterial genera identified by 16S rRNA gene of the 178 isolates of this study distributed in the states of Mato Grosso do Sul, São Paulo, Paraná and Rio de Janeiro.

In the present study, 31 Enterococcus strains were isolated from skin and kidney samples. Of these, 55% were E. casseliflavus, 36% E. faecalis, 6% E.durans, and 3% E. sulfureus. The predominance of E. casseliflavus has also been observed among isolates from water and sediment, accounting for 66.7% of a total of 410 Enterococcus sp. isolates in Thailand [32] .

Of the 27 Streptococcus strains originating from the states of Mato Grosso do Sul, Paraná, and São Paulo, 89% corresponded to S. agalactiae; this was previously observed by Netto et al. [33] and Figueiredo et al. [10] . Although infection by S. agalactiae is the main cause of losses in tilapia farming worldwide, this pathogen has also been isolated from “cachara” originating from Mato Grosso do Sul. S. agalactiae has been identified in several other fish species, such as Sparus auratus, Liza klunzingeri [34] , and Pampusargenteus [35] . Infected fish have meningoencephalitis, exophthalmia, erratic swimming, mainly.

The species P. putida (27%) and P. fulva (20%) were the predominant Pseudomonas species observed (n = 15). Eissa et al. [36] observed an incidence of 30.83% of Pseudomonas species in Nile tilapia in Egypt. Hussain [37] and Zorrilla et al. [38] reported 13.5%, and 9.7% incidence, respectively, of Pseudomonas species in marine fish, values that are similar to the 11% incidence found in the present study. P. fluorescens, P. angulliseptica, P. aeruginosa and P. putida were identified in various species of fish as causative agents of Pseudomonas septicemia. The disease is characterized by petechial hemorrhage, darkness of the skin, detached scales, abdominal ascitis and exophthalmia [39] .

As the number of isolates from each region was dissimilar and low, it would not be advisable to determine a frequency profile of pathogens by location, neither the prevalence of bacterial genera by fish species, but we emphasize the importance of drawing a regional profile in aquaculture health monitoring programs and preventive management, therefore, in case of disease outbreak, treatment measures are different in each region, since factors such as light, water quality and soil contamination, quantity of parasites, management, etc are also peculiar to each locality.

4. Conclusion

Direct colony PCR combined with 16S rRNA gene sequencing constitutes an efficient alternative for diagnosing bacterial fish diseases, with decreased cost and time compared with the classical methods used in Brazil, such as isolation, biochemical tests, and conventional PCR.


The author wishes to thank the State of São Paulo Research Foundation (FAPESP-Process 2011/07951-5) for the financial support; the Aquaculture Center (CAUNESP/UNESP, Jaboticabal) and the Laboratory of Microbial and Plant Biochemistry, Technology Department (Laboratório de Bioquímica de Microrganismos e Plantas, FCAV/UNESP Jaboticabal), UNESP, for technical support; and Dr. Fabiana Garcia for donating the bacterial strains.


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