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Flood waters can be devastating, especially if proactive measures are not adequately taken ahead of time to mitigate the effects of the flood. In addition to the direct impact of flood water is the transmission of waterborne, foodborne, and airborne infection sequelae. Most of these infections are caused by pathogenic and opportunistic bacteria carried in the water from one location to another and include salmonellosis, leptospirosis, shigellosis, staphylococcus infections, burkholderiosis, vibriosis, and other infections [1, 2]. Different bacteria have been described in water from different sources worldwide, but there is paucity of data on bacteria in flood water during massive flood sessions.

Unexpected massive flood waters, that have defied meteorological forecasts, the worse in the history of Malaysia, hit the east coast of Peninsula Malaysia from 15th December 2014 to 3rd January, 2015 with Kelantan being the worst affected state. This great flood was estimated to have destroyed public property worth MYR (Malaysian Ringgit) 2.85 billion (about 814,285,714 USD); caused 25 deaths; affected 541,896 victims; with 2,076 houses destroyed, and a further 6,698 houses damaged; and 168 government healthcare facilities affected with an estimated MYR 380 million (108,571,429 USD) damage, and water levels rose 5-10 m above floodplain [3].

A study in Pahang state of Malaysia identified Shigella flexneri, Escherichia coli, and Salmonella typhimurium in the flood water [4], but generally there has not been any study to determine the bacterial biodiversity of the flood water and describe the antibacterial resistance pattern of flood bacteria in Malaysia. In Thailand, a study of flood water and tap water contaminated by floods in 2011 showed the presence of pathogenic bacteria such as Shigella sp., Leptospira sp., and Vibrio cholerae [5]. Mhuantong et al. [6] reported a predominance of proteobacteria in flood water and sediment samples collected from the great Thailand flood of 2011 with 21 different genera of bacteria found.

This study was conducted to elucidate the bacterial biodiversity of flood water, describe the antibiogram of some bacteria found in flood water, and postulate the possible public health impact of flood water using water samples taken from the Kelantan flood disaster in Malaysia.

Materials and methods
Study area and sampling procedure

Kota Bharu is the capital city of Kelantan state of Malaysia located on the east coast of Peninsula Malaysia at 6°8′N 102° 15′E and close to the Thailand border with a population of about 491,237.

During the unexpected massive floods that hit the city, water samples were taken from 6 locations in the city as follows:

Taman Bendahara (Universiti Malaysia Kelantan city campus hostel area) with Global Positioning System (GPS) coordinates N06°09.809′E102°17.070′ and elevation of 12 m above sea level.

KampungTok Sadang (along Airport Road) with GPS coordinates N06°10.560′E102°17.115′ and elevation of 10 m above sea level.

Jalan Gajah Mati (Clock Roundabout) with GPS coordinates N06°07.508′E102°14.222′ and elevation of 23 m above sea level.

Kota Bharu mall surroundings with GPS coordinates N06°07.116′E102° 14.396′ and elevation of 25 m above sea level.

Tesco Bus stop area with GPS coordinates N06°06.789′E102° 13.757′ and elevation of 21 m above sea level.

Jalan Kuala Krai with GPS coordinates N06°06.285′E102° 14.433′ and elevation of 26 m above sea level.

The GPS coordinates and elevation above sea level were measured using a Nuvi 255 WT receiver (Garmin, Lenexa, KS, USA). We collected 31 water samples in 50 mL sterile containers from the 6 different locations (5 samples from each of Taman Bendahara, Kampung Tok Sedang, Kota Bharu clock roundabout, Kota Bharu mall area, Tesco mall area, and 6 samples from Jalan Kuala Krai) for bacteriological analysis. Each water sample was inoculated into blood agar and nutrient agar using sterile swabs. The inoculated media were put in an incubator at 37°C for 24–48 hours. The various bacterial colonies were selected based on colony characteristics such as shape, color, hemolysis, and size. The selected colonies were subcultured on nutrient agar to obtain pure colonies. The pure colonies were subjected to the following biochemical tests: catalase, oxidase, triple sugar iron agar (TSI), citrate, urease, motility, indole, methyl red (MR), and Voges–Proskauer (VP) tests. Because biochemical tests were inadequate and not exhaustive, genomic DNA was further isolated from the pure colonies using a commercial genomic DNA extraction and purification kit (Vivantis, Selangor Darul Ehsan, Malaysia and Oceanside, CA, USA) following the manufacturer’s instructions. Suitable oligonucleotide universal primers were used in a conventional polymerase chain reactions (PCR) to target 16S rRNA sequences of the unknown bacteria isolates.

Molecular analysis

The sequence of primers used was as follows: forward 5′-GGTGGAGCATGTGGTTTA-3′, reverse 5′-CCATTGTAGCACGTGTGT-3′ [7]. The expected product size was 287 bp. The PCR product was purified and sequenced for identification of isolated bacteria using DNA Sanger sequencing. Decipher software was used to check for any suspected chimeric sequences [8]. These sequences were compared with highly similar sequences at NCBI BLAST and SepsiTest BLAST for identification at up to species level. The threshold for identification was set at >97% for species identification. Species were not reported for any sequences below the threshold. Sequences were deposited at the GenBank, NCBI, USA, and accession numbers were obtained (isolates and sequences for the rest of this article are referred to by their initial identity without the characters preceding this e.g. SUB882316 UMK1a1, will be referred to as simply 1a1). Two isolates (2d1 and 3d1) of interest to the authors because of their characteristic violet-to-black pigmented colonies were also confirmed using species specific PCR primers (C. violaceum) with the sequence recA-Viol-f (5′-AAGACAAGAGCAAGGCGCTGGC-3′) and revA-Viol-r (5′-TCGAAGGCGTCGTCGGCGAAC-3′) and PCR product size of 1047bp [9]. A literature search indicated that little or no report has been made about the following bacterial species at any time in Malaysia: Acinetobacter ursingii, Curvibacter gracilis, Pseudomonas veronii, Wautersia numazuensis, Bacillus idriensis, Pectobacterium cypripedi, Bacillus luciferensis, Exiguobacterium mexicanum, and Pseudomonas vranovensis.

Metagenomics analysis through 16s rRNA sequencing data

All 16s rRNA sequencing data were subjected to the following preprocessing procedure:

The quality control of the sequences was conducted by analyzing the trace files using SeqScanner version 1.0 (Applied Biosystems, Foster City, CA, USA). The leading vector, tailing and poor-quality (trace score >20) sequences were removed accordingly (file available on request).

The remaining sequences were trimmed at the 3′ or 5′ ends to remove low quality ends of the sequences because of the noise introduced by low quality regions (in Geneious version R8.1 (Biomatters, http://www.geneious.com, Kearse et al. [10]) as shown following:

For each sample, the paired reads (forward and reverse) were assembled through assembly in Geneious as shown following:

Figure 1

Trimming using Geneious version R8.1 (Biomatters, http://www.geneious.com, Kearse et al. [10])

Figure 2

Capping using Geneious version R8.1 (Biomatters, http://www.geneious.com, Kearse et al. [10])

Species classification and relative abundance measurement using high throughput 16S rRNA amplicon sequencing data from environmental samples were performed using the cloud-based 16S rRNA biodiversity tool (Geneious version R8.1, (Biomatters, http://www.geneious.com Kearse et al. [10])). The final verified sequences were submitted through the Geneious R8.1 bioinformatics platform to a distributed cloud compute resource. The data were then analyzed using the Ribosomal Database Project Database (RDP) Classifier [11]. The RDP Classifier assigns sequences derived from bacterial and archaeal 16S genes and fungal 28S genes to the corresponding taxonomy model using a ‘Naïve Bayesian Classifier’ for rapid assignment of rRNA sequences. The Geneious 16S biodiversity tool accurately assigned a taxonomy (in the range of domain to genus) along with a confidence-estimate for each sequence by comparing them to the RDP database and can only identify bacteria up to genus level to produce a chart [12]. The output was then displayed in a web browser using Krona [13], which produces an interactive html5 hierarchical graph of the bacterial diversity in the sample. Krona allows hierarchical data to be explored with zoomable pie charts. The difference between the total number of different bacteria identified for each location and sea level was calculated using a χ2 test at 95% confidence level with SPSS Statistics for Windows version 22 (IBM Corp, Armonk, NY, US). The difference between percentages of bacterial families was elucidated using a χ2 test at 95% confidence level, and the differences in the antibiotic susceptibility of the 17 bacterial isolates to 7 antibiotics was calculated using one-way ANOVA at 95% confidence level in SPSS version 22. Interpretation of zone of inhibition diameter (Table 2) was conducted according to standard procedures of the Clinical and Laboratory Standards Institute, 2007 (http://clsi.org/) and Benedict et al. [14]. Multiple antibiotic resistance was defined as resistance to 2 or more antibiotics tested. The effect size of differences observed in antibiotic sensitivity test was estimated using an η2 test according to the interpretation of Cohen [15].

Results

Charts, Tables, and Figures can be requested by email from: pwaveno.hb@umk.edu.my; pwaveno.bamaiyi@kiu.ac.ug. PCR results on gel electrophoresis are shown ( Figures 3-7). The chart (Figure 8)classified the samples into 3 bacterial domains, Proteobacteria (67%), Firmicutes (32%), and Bacteroidetes (1%). The chart reveals 12 families of bacteria: Moraxellaceae (10%), Aeromonadaceae (8%), Comamonadaceae (13%), Neisseriaceae (2%), Bacillaceae 1 (16%), Staphylococcaceae (8%), Bacillales Incertae Sedis XII (3%), Bacillaceae 2 (3%), Streptococcaceae (2%), Flavobacteriaceae (2%), Enterobacteriaceae (25%) and Pseudomonadaceae (10%) with P = 0.03 for the number of isolates belonging to the families. Please see: https://16s.geneious.com/16s/results/ cdee2e80-b6b7-4a5c-bf33-4e6760294758.html for a version that can be manupulated. A literature search indicates that 10 of the 12 families of bacteria (83%) contained bacteria pathogenic to man and animals. The isolate sequences that passed the criteria, were successfully submitted to the GenBank, their accession numbers and species identified are listed in Table 1. There was no significant difference (P > 0.05) between the number of different species of bacteria isolated from the 6 locations studied. The susceptibility of 17 selected bacteria isolates to 7 different antibiotics was found to be significant with P < 0.0001 (Tables 2 and 3) with the bacteria having the highest susceptibility to gentamycin followed by tetracycline, and the lowest susceptibility was to penicillin G followed by ampicillin (Figure 9). The size of this difference was large (η2 = 0.236). Some bacterial isolates in Table 2 (isolates 4d2 and 5d1; 4e2 and 6a1y; 6b2 and 6c) did not show the same antibiotic sensitivity pattern even though they were same bacterial species. Klebsiella pneumoniae showed the highest resistance to multiple antibiotics while Exiguobacterium mexicanum, Acinetobacter calcoaceticus, and Pseudomonas vranovensis did not show resistance to any of the antibiotics tested; 82% of bacteria isolated showed resistance to one or more antibiotics while 76% of bacteria isolates tested showed multiple antibiotic resistance. Readers can use the LINK and click on the different classes, families and/or species to find out more details about the abundance of each category among the samples. This will provide a comprehensive illustration of the samples.

Bacteria Identified to species level from the flood and deposited at the GenBank

S/No.Submission IDAccession NumberBacteria Identified
1SUB882316 UMK1a1KR027927Staphylococcus xylosus
2SUB882316 UMK1a2KR027928Acinetobacter ursingii
3SUB882316 UMK1b1KR027929Aeromonas aquariorum (A. dhakensis)
4SUB882316 UMK1b2KR027930Bacillus pseudofirmus
5SUB882316 UMK1b3KR027931Bacillus altitudinis
6SUB882316 UMK1c2KR027932Acinetobacter radioresistens
7SUB882316 UMK1d1KR027933Acinetobacter radioresistens
8SUB882316 UMK1d2KR027934Lactococcus lactis subsp. Lactis
9SUB1092331 UMK1e1KT731961Acidovorax caeni
10SUB1092331 UMK1e2KT731962Acidovorax caeni
11SUB882316 UMK2a1KR027935Staphylococcus xylosus
12SUB1092331 UMK2a2KT731963Acidovorax caeni
13SUB882316 UMK2a3KR027936Aeromonas veronii
14SUB1092331 UMK2d1KT731964Chromobacterium violaceum
15SUB882316 UMK2e1KR027937Staphylococcus xylosus
16SUB882316 UMK2e2KR027938Aeromonas veronii
17SUB882316 UMK3a1KR027939Acinetobacter junii
18SUB882316 UMK3a2KR027940Klebsiella pneumoniae subsp. Rhinoscleromatis
19SUB882316 UMK3a3KR027941Raoultella terrigena
20SUB882316 UMK3b1KR027942Pseudomonas trivialis
21SUB1092331 UMK3b2KT731965Curvibacter gracilis
22SUB882316 UMK3b3KR027943Pseudomonas veronii
23SUB1092331 UMK3c3KT731966Rhodococcus equi
24SUB1092331 UMK3d2KT731967Chromobacterium violaceum
25SUB882316 UMK3d3KR027944Bacillus megaterium
26SUB1092331 UMK3d4KT731968Aquitalea magnusonii
27SUB1092331 UMK3d5KT731969Wautersia numazuensis (Cupriavidus numazuensis)
28SUB882316 UMK3e1KR027945Exiguobacterium acetylicum
29SUB882316 UMK3e2KR027946Chryseobacterium gambrini
30SUB883111 UMK4a1wKR048048Salmonella enterica subsp. Diarizonae
31SUB883111 UMK4a1yKR048049Bacillus idriensis
32SUB883111 UMK4a2KR048050Staphylococcus xylosus
33SUB882316 UMK4b1KR027947Aeromonas aquariorum (A dhakensis)
34SUB882316 UMK4b2KR027948Pectobacterium cypripedii (Pantoea cypripedii)
35SUB882316 UMK4b3KR027949Pseudomonas trivialis
36SUB882316 UMK4c2KR027950Bacillus luciferensis
37SUB882316 UMK4c3KR027951Enterobacter asburiae
38SUB882316 UMK4d2KR027952Bacillus luciferensis
39SUB882316 UMK4d3KR027953Aeromonas aquariorum (A dhakensis)
40SUB882316 UMK4e1KR027954Acinetobacter calcoaceticus
41SUB882316 UMK4e2KR027955Bacillus pseudofirmus
42SUB882316 UMK5a1KR027956Proteus mirabilis
43SUB882316 UMK5a2KR027957Escherichia coli
44SUB882316 UMK5b1KR027958Exiguobacterium mexicanum
45SUB882316 UMK5d1KR027959Bacillus luciferensis
46SUB883111 UMK6a1wKR048051Staphylococcus xylosus
47SUB883111 UMK6a1yKR048052Bacillus pseudofirmus
48SUB1092331 UMK6a2KT731970Acinetobacter calcoaceticus
49SUB1092331 UMK6b1KT731971Rubrivivax gelatinosus
50SUB1092331 UMK6b2KT731972Acidovorax caeni
51SUB1092331 UMK6cKT731973Acidovorax caeni
52SUB882316 UMK6dKR027960Raoultella terrigena
53SUB882316 UMK6eKR027961Pseudomonas vranovensis
54SUB1092331 UMK6x1KT731974Acidovorax caeni
55SUB1092331 UMK6x2KT731975Acidovorax caeni

Antibiotic sensitivity test of some bacteria isolates from the flood

No.Isolate (bacteria)TE 30-tetracyclineAML 10– amoxycillinS 25 – streptomycinCN 10– gentamycinE 15– erythromycinAMP 10– ampicillinP10– penicillin G% Total resistance to all antibiotics
12dl (Chromobacterium violaceum)31 (S)R15 (I)19 (S)20 (I)RR43%
23a2 (Klebsiella pneumoniae subsp. Rhinoscleromatis)25 (S)R11 (R)19 (S)12 (R)RR71%
33a3 (Raoultella terrigena)22 (S)12 (R)15 (I)19 (S)11 (R)RR57%
44b3 (Pseudomonas trivialis)16 (I)R20 (I)20 (S)24 (S)RR43%
54d2 (Bacillus luciferensis)28 (S)13 (R)23 (S)25 (S)30 (S)12 (I)R29%
64d3 (Aeromonas aquariorum)21 (S)27 (S)18 (I)22 (S)15 (I)RR29%
74e2 (Bacillus pseudofirmus)12 (R)20 (S)21 (S)25 (S)23 (S)R22 (S)29%
85a2 (Escherichia coli)14 (R)15 (I)12 (R)18 (S)12 (R)17 (S)R57%
95b 1 (Exiguobacterium mexicanum)26 (S)40 (S)22 (S)25 (S)28 (S)44 (S)30 (S)0%
105dl (Bacillus luciferensis)21 (S)22 (S)20 (I)25 (S)19 (I)20 (S)12 (R)14%
116a lw (Staphylococcus xvlosus)20 (S)13 (R)22 (S)24 (S)15 (I)12 (R)R43%
126a ly (Bacillus pseudofirmus)22 (S)R17 (I)18 (S)23 (S)RR43%
136a2 (Acinetobacter calcoaceticus)25 (S)31 (S)25 (S)31 (S)28 (S)31 (S)28 (S)0%
146b 1 (Rubrivivax gelatinosus)23 (S)R20 (I)33 (S)13 (R)RR57%
156b2 (Acidovorax caeni)11 (R)17 (I)18 (I)22 (S)25 (S)R18 (S)29%
166c (Acidovorax caeni)12 (R)12 (R)18 (I)25 (S)26 (S)12 (I)7 (R)43%
176e (Pseudomonas vranovensis)23 (S)32 (S)24 (S)25 (S)16 (I)32 (S)25 (S)0%

Key: R = Resistant; I = Intennediate; S = Susceptible

Figure 3

PCR results on gel electrophoresis are shown isolates 1a1 to 1d2

Figure 4

PCR results on gel electrophoresis are shown isolates 1e1 to 3d2

Figure 5

PCR results on gel electrophoresis are shown isolates 3d3 to 4d3

Figure 6

PCR results on gel electrophoresis are shown isolates 4e1 to 6x2

Figure 7

Chromobacterium violaceum confirmed by species specific PCR for isolates 2d1 and 3d1

Figure 8

Preprocessed 16S rRNAamplicon data submitted to the Ribosomal Database Project Database Classifier (Wang et al. [11]) and visualized using the Krona Interactive Hierarchical Browser (Ondov et al. [13]).

Figure 9

Antibiotic resistance plot showing the mean resistance of various antibiotics tested

Analysis of variance of antibiotics sensitivity (multiple comparisons) with Tukey honest significant difference post hoc test

(I) Antibiotic(J) AntibioticsMean Difference (I–J)Std. ErrorP95% Confidence Interval
LowerUpper
TE 30–tetracyclineAML 10–amoxycillin5.473.090.57–3.7914.73
S 25–streptomycin1.823.09>0.99–7.4411.09
CN 10–gentamycin–2.533.090.98–11.796.73
E 15–erythromycin0.713.09>0.99–8.569.97
AMP 10–ampicillin9.59

P<0.05;

3.090.040.3318.85
P 10–penicilling11.77

P<0.01;

3.090.0042.5021.03
AML 10–amoxycillinTE 30–tetracycline–5.473.090.57–14.733.79
S 25–streptomycin–3.653.090.90–12.915.62
CN 10–gentamycin–8.003.090.14–17.261.26
E 15–erythromycin–4.773.090.72–14.034.50
AMP 10–ampicillin4.123.090.83–5.1413.38
P 10–penicilling6.293.090.40–2.9715.56
S 25–streptomycinTE 30–tetracycline–1.823.09>0.99–11.097.44
AML 10–amoxycillin3.653.090.90–5.6212.91
CN 10–gentamycin–4.353.090.80–13.624.91
E 15–erythromycin–1.123.09>0.99–10.388.14
AMP 10–ampicillin7.773.090.16–1.5017.03
P 10–penicilling9.94

P<0.05;

3.090.030.6819.20
CN 10–gentamycinTE 30–tetracycline2.533.090.98–6.7311.79
AML 10–amoxycillin8.003.090.14–1.2617.26
S 25–streptomycin4.353.090.80–4.9113.62
E 15–erythromycin3.243.090.94–6.0312.50
AMP 10–ampicillin12.12

P<0.01;

3.090.0032.8621.38
P 10–penicilling14 29

P<0.001.F = 5.875; η = 0.239

3.090.0015.0323.56
E 15–erythromycinTE 30–tetracycline–0.713.090.99–9.978.56
AML 10–amoxycillin4.763.090.72–4.5014.03
S 25–streptomycin1.123.090.99–8.1410.38
CN 10–gentamycin–3.243.090.94–12.506.03
AMP 10–ampicillin8.883.090.070.3818.14
P 10–penicilling11.06

P<0.05;

3.090.0091.8020.32
AMP 10–ampicillinTE 30–tetracycline–9.59

P<0.05;

3.090.04–18.85–0.33
AML 10–amoxycillin–4.123.090.83–13.385.14
S 25–streptomycin–7.763.090.16–17.031.50
CN 10–gentamycin–12.12

P<0.01;

3.090.003–21.38–2.86
E 15–erythromycin–8.883.090.07–18.140.38
P 10– penicillin G2.183.09>0.99–7.0911.44
P 10–penicillin GTE 30–tetracycline–11.76

P<0.01;

3.090.004–21.03–2.50
AML 10–amoxycillin–6.293.090.40–15.562.97
S 25–streptomycin–9.94

P<0.05;

3.090.03–19.20–0.68
CN 10–gentamycin–14 29

P<0.001.F = 5.875; η = 0.239

3.090.001–23.56–5.03
E 15–erythromycin–11.06

P<0.01;

3.090.009–20.32–1.80
AMP 10–ampicillin–2.183.090.99–11.447.09
Discussion

The unexpected nature of this study, which was conducted during the course of a devastating flood, may have affected some parameters that could have improved this study. The use of only culturing of water samples and looking for only bacterial colonies that grew within 48 hours may have excluded other bacteria such as Leptospira spp, Vibrio spp, and Burkholderiapseudomallei from being documented. Nevertheless, the isolation of several species of bacteria and genera of bacteria indicates a rich bacterial biodiversity of pathogenic or potentially pathogenic bacteria in the flood water during the great flood of December 2014 to January 2015 in Kelantan, Malaysia.

After the flooding, the prevalence of bacterial infections usually increases triggering episodes of intestinal symptoms such as diarrhea, vomiting, stomach aches, and other gastrointestinal disease symptoms as contaminated water moves from one geographical location to another, carrying a “cocktail” of bacteria along with it [16-18]. The distribution of the bacteria isolated did not show any remarkable difference between the different locations from which water samples were taken. This is probably because there was no large difference in the elevation between the locations, and during flood there is a massive movement of water from one location to the other, which can carry and distribute bacteria almost uniformly from one flood water location to the other. Different water depths would be expected to produce a variety of bacteria species [19]. This has potential public health implications because it implies waterborne infections can easily be carried from one flood location to another.

The predominance of proteobacteria making up 67% of bacteria in this study is similar to a study conducted in Thailand, which reported that majority of bacteria from the 2011 Thailand flood were from the phylum proteobacteria, which made up 56.5% to 91.4% of bacteria in different water samples in Thailand [6]. However, the majority of families and genera of bacteria reported from Thailand differ from the ones in this study, demonstrating the heterogeneity of bacterial communities across different flooded environments. Enterobacteriaceae from the phylum proteobacteria were the most common bacteria encountered in water samples from this study. Enterobacteriaceae also constitute the majority of waterborne and foodborne infections known in man and animals. Their ubiquitous nature and ability to thrive of the environment, helps them to be widespread in nature. They have been reported in various kinds of water, including flood water, by other investigators worldwide [16, 17, 20]. They are usually opportunistic bacteria, but can enter animals and human hosts, where some cause illnesses such as salmonellosis, shigellosis, and other intestinal infections. The high prevalence of this family in our study is a cause for concern because several members are known to be resistant to many antibiotics including those considered the current last line of antibiotic defense [21]. The present study has revealed Klebsiella pneumoniae subsp. Rhinoscleromatis as most resistant to the antibiotics tested. This is consistent with findings worldwide [22-24]. Three bacteria, Exiguobacterium mexicanum, Acinetobacter calcoaceticus, and Pseudomonas vranovensis did not show resistance to any of the antibiotics tested. This is consistent with other findings, but little is known about the antibiotic sensitivities of the relatively new bacterial species Pseudomonas vranovensis [25-27]. These bacteria species seem not to pose threats to health as do Klebsiella pneumoniae.

Flood water is a conglomeration from different sources including overflowing seas, rivers, streams, springs, wells, and other water. Humankind′s activities during floods including bathing, swimming, washing, and excretion of waste into flood water affects its bacterial composition [28]. Some of the pollution may also have come from industrial waste, sewage water contamination, and admixtures of water from all manner of unhealthy sources during the course of the flooding [29, 30]. The study from Thailand showed pathogenic bacteria and high cross-contamination between flood water and other water sources [5]. The degree of pollution of soil surface and the metallic components of the soil also determine the richness and diversity of the bacteria present with presence of zinc decreasing both diversity and levels of species richness [31]. The rich bacterial diversity of our flood water showed bacteria from human and animal sources, and bacteria from the environment. A study in Brazil found that usual environmental water is a rich source of many species of bacteria with varying degrees of antibiotic resistance, showing some bacterial communities tolerating up to 600 times the clinical treatment levels of common antibiotics [32].

There appeared to be some strain variation in pathogenicity of some of the isolates as indicated by the same species showing different antibiotic sensitivities. Further studies that go beyond species identification to identification of different strains and genes coding for resistance are required to establish if indeed these are different strains of the same bacteria with varying pathogenicity.

There were 9 bacteria in the present study not previously reported from any source in Malaysia based on our literature search. All of them were bacteria recently reported and classified within the last 20 years. Wautersia numazuensis (Cupriavidus numazuensis) was first reported in 2011 from Mexico in soils and agricultural plants [25, 33-37]. The epidemiological and public health impact of these water microbes and their ecological roles require future study.

Conclusion

During this massive flood session, there was a rich bacterial biodiversity including some species of potentially pathogenic bacteria that could endanger public health. This altered bacterial composition of normal water outside of flooding, and may explain why there are outbreaks of various infectious diseases during and after flood disasters. During the flood disaster period the only functional tertiary hospital (Hospital Universiti Sains Malaysia) in Kelantan handled 180 cases/day in the emergency department [38]. In adjacent Pahang state, 1,220 flood-related cases were handled within the first 6 days of the Kelantan flood disaster [39].

Most of the bacteria isolated from this study were resistant to one or more commonly used antibiotics. This is of interest to health practitioners and health policy makers because the presence of multidrug resistant bacteria should guide clinicians in the choice of antibiotics during flood disasters for effective treatment and control of waterborne infections. Gentamycin and tetracycline antibiotic classes appeared to be the best antibiotics to consider, but this may be an ever-changing picture. During flooding human and animal contact with flood water should be minimized, if not avoided completely, and adequate provisions should be made for provision of clean water to avoid outbreaks of waterborne diseases.

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