Bacterial Community Changes in Penaeus vannamei Boone, 1931 Surface and Rearing Water During Enterocytozoon hepatopenaei Infection

Bacterial Community Changes in Penaeus vannamei Boone, 1931 Surface and Rearing Water During Enterocytozoon hepatopenaei Infection PALLAVI BALIGA, PUNEETH THADOORU GOOLAPPA, MALATHI SHEKAR*, S.K. GIRISHA, RAMESH K.S., VILASINI UDYAVARA, M.N. VENUGOPAL Department of Aquatic Animal Health Management, College of Fisheries, Karnataka Veterinary, Animal and Fisheries Sciences University, Mangalore 575 002, Karnataka, India © Asian Fisheries Society *E-mail: malathishekar@rediffmail.com | Received: 25/03/2021; Accepted: 22/06/2021 Published under a Creative Commons license ISSN: 0116-6514 E-ISSN: 2073-3720 https://doi.org/10.33997/j.afs.2021.34.2.006 Abstract White faeces syndrome is one of the major disease problems in shrimp aquaculture, resulting in enormous economic losses to farmers. Although white faeces syndrome is usually associated with Enterocytozoon hepatopenaei (EHP) infections, it may not be the sole cause for the occurrence of white faecal strings on the pond water surface. There is limited information on the microbial dynamics in a pond affected by white faeces syndrome. Hence, this study aimed at the bacterial community changes occurring on the surface of shrimp Penaeus vannamei Boone, 1931 afflicted by the white faeces syndrome and the pond water in which it was reared. The pond water and the shrimp surface shared >45 % of the operational taxonomic units (OTUs), reflecting the influence of water quality on the bacterial community composition on the shrimp surface. Among these, the Proteobacteria formed the principal phyla and remained unaltered throughout the culture period. Bacteroidetes formed the second largest group across samples, followed by Cyanobacteria, Actinobacteria, Planctomycetes, Verrucomicrobia and Chloroflexi. The relative abundance levels of health indicator bacterial families such as Thiotrichaceae, Microbacteriaceae and Chitinophagaceae showed significant fluctuations on the shrimp surface. Disease indicators such as Rickettsiaceae, Mycobacteriaceae showed an increase in numbers on the shrimp surface. PICRUSt functional predictions revealed higher abundances of genes involved in metabolism and genetic information processing. The study provides valuable findings on the bacterial communities of rearing water and shrimp surface associated with white faeces syndrome. Keywords: 16s RNA, amplicon sequencing, operational taxonomic units, infected shrimp surface, white faeces syndrome Introduction Marine shrimps dominate the world aquaculture market and are an important foreign exchange commodity for several developing countries (FAO, 2019). The global production of farmed shrimp reached almost 4 million tonnes in 2018 (FAO, 2019). In recent years, microbiome studies involving cultured shrimp have been gaining importance. They have shown that associated microbial communities play an important role in influencing nutrient cycling, probiotic/pathogenic activity and nutrient acquisition besides acting as rapid biological indicators of critical chemical changes in the rearing water (Md Zoqratt et al., 2018). Studies have focused mainly on the gut microbiome and microbial communities associated with rearing waters of cultured shrimps (Zeng et al., 2017; Li et al., 2018; Yan et al., 2020). In India, studies on the microbiome of shrimps being cultured are lacking and therefore, this study aimed to investigate the microbiota of shrimp being cultured and the microbial communities associated with its rearing pond water at different stages of shrimp culture. However, during the study period, the cultured shrimps (40th day of culture) showed signs of hepatopancreatic microsporidiosis, a disease caused by the microsporidian parasite Enterocytozoon hepatopenaei (EHP) (Biju et al., 2016). The emergence of EHP disease is attributed to various factors such as complex interactions between the host and the surrounding water, water quality and most importantly, the activities of the resident microbial communities 168 Asian Fisheries Science 34 (2021):168–180 (Chen et al., 2017a). The shrimp exoskeleton acts as a primary, resisting the entry of opportunistic pathogens (Vogan et al., 2008). Thus, an investigation into the shrimp surface microbiome is essential in the context of a disease outbreak. Therefore, this report presents the changes in the microbial communities associated with the shrimp surface and its rearing water during EHP infection. Materials and Methods Sample collection A shrimp farm located in the Udupi district (Latitude: 13°25'632'' N; Longitude: 74°73'505'' E) of Karnataka involved in traditional P. vannamei, culture was selected for the present study. A pond measuring ~5000 m2 was subjected to thorough drying, sediment treatment and liming before being filled with filtered brackish water from a nearby creek. The pond was stocked with shrimp post-larvae at a density of 30 m-2. The shrimps were cultured for 100 days. The prestocking and post-stocking pond water were measured for pH using calibrated pH meter (Equiptronics, India), dissolved oxygen levels by the Winkler’s method (Winkler et al., 1888), salinity using the hand held refractometer (Erma, Japan) and temperature using thermometer (N.S. Dimple thermometers, India). Pond water sampling The water samples were collected from three random sampling sites from the shrimp grow-out pond in sterile bottles to determine the microbial communities associated with pond water. For each pond water sampling, 1 L of water sample was collected randomly from three different sites in duplicate from the same pond using sterile bottles. Onsite, 200 mL of the collected water samples were drawn using sterile syringes and filtered by passing it through 0.45 µm Whatman cyclopore polycarbonate membranes (Sigma Aldrich, USA) fitted onto a filter holder with Luer-slip connector (Cole Parmer, USA). The filters were immediately stored in moleculargrade 100 % ethanol and kept at -20 °C until further use. The water samples collected were Prestocking water-PS1; Pond water at the 40th day of stockingPW1; 55th day-PW2; 70th day-PW3; and at 95th day of stocking-PW4. Shrimp surface sampling To assess the microbial communities associated with shrimp surface, 10 shrimps from the pond were collected and kept in sterile autoclaved distilled water (checked for sterility by plating on nutrient agar) for 20 min, after which the water was processed in the same manner as for the pond water samples. The samples collected were, infected shrimp surface at 40th day of stocking–IS1; 55th day-IS2; 70th day-IS3; and 95th day of stocking-IS4 and the corresponding length and weight Asian Fisheries Science 34 (2021):168–180 169 were 5.6 cm / 3 g; 7.5 cm / 5 g; 8 cm / 9 g and 10 cm / 13 g, respectively. Molecular surveillance for pathogens During each sampling, the shrimps were also routinely monitored for OIE listed shrimp pathogens which include white spot syndrome virus, infectious hypodermal and hematopoietic necrosis virus, monodon baculovirus, hepatopancreatic parvovirus, yellow head virus, Taura syndrome virus, infectious myonecrosis virus, Enterocytozoon hepatopenaei and Vibrios responsible for acute hepatopancreatic necrosis disease DNA was extracted as per (Otta et al., 2003) and was tested for the presence of infection by polymerase chain reaction (PCR) using the OIE listed primers (Office International des Epizooties, 2003). The primers have been listed in Supplementary Table 1. DNA extraction, amplification, purification and sequencing The pond water and shrimp surface samples were subjected to DNA extraction and sequencing. The filters were vacuum-dried to remove ethanol, followed by the addition of lysis buffer [30 mM Tris, 30 mM ethylenediaminetetraacetic acid (EDTA), pH 8] to ensure complete lysis of the cells. The filters were stored in lysis buffer at -80 °C until the next use. For DNA extraction, the filters were thawed and incubated with lysozyme (50 mg.mL-1) at 37 °C for 30 min. Following that, 10 % (sodium dodecyl sulfate) SDS and proteinase K (20 mg.mL-1) was added and incubated at 55 °C for one hour. The filter was then incubated with 5M NaCl and 10 % CTAB (cetyltrimethyl ammonium bromide) at 65 °C for 10 min. The next step involved the addition of chloroform:isoamyl alcohol (24:1) followed by centrifugation at 14000 ×g at 4 °C. The aqueous layer was collected in a fresh tube and the chloroform:isoamyl alcohol (24:1) wash was repeated to obtain a cleaner extract. A 0.1 volume of sodium acetate (7M) was added to the aqueous extract to aid the precipitation of the nucleic acids. DNA was precipitated by the addition of 0.7 volume of isopropanol to each tube at room temperature for 1–2 h. The DNA pellet obtained by centrifuging at 21000 ×g for 30 min at room temperature was washed with 70 % ice-cold ethanol, air-dried and finally dissolved in TE buffer and stored at -20 °C until further use. The DNA extracted was aliquoted and subjected to amplification of the hypervariable region V3-V4 of the 16rRNA gene by outsourcing to the DNA sequencing facility at Clevergene Pvt. Ltd., Bengaluru, India. The sequencing was done on the Illumina MiSeq platform (2 × 300 bp) using the primers V3V4F: CCTACGGGNGGCWGCAG and V3V4R: GACTACHVGGG TATCTAATCC. Data analysis Raw sequence reads were checked for their quality using FastQC and MultiQC software. The generated reads were trimmed to remove the degenerate primers, adapter sequences and low-quality bases using the program Trimgalore (Krueger, 2015). The paired sequence reads were aligned to form contigs using Mothur, an open-source software package (Schloss et al., 2009). The contig sequences shorter than 300 bp, duplicates, chimeric sequences having chimaeras and ambiguous nucleotides were further filtered out to obtain quality reads. The filtered contigs were processed and classified into taxonomical outlines and clustered into OTUs (operational taxonomic unit) based on the Greengenes v.13.8-99 database (DeSantis et al., 2006). PICRUSt (Langille et al., 2013) was used to predict gene family abundance. The rarefaction curve was generated using vegan R package (Oksanen et al., 2018). Phyloseq R package was used for alpha diversity calculations. PCoA plot was generated using STAMP software (Parks et al., 2014). Alpha diversity was measured using seven different metrics (absolute number of Observed OTUs, Chao, ACE, Shannon, Simpson, InvSimpson, Fisher). The observed species index measures the count of unique OTUs in each sample. The species richness indices in the microbiome were estimated using Chao (Chao, 1984) and ACE indices (Colwell and Coddington, 1994). The “evenness” or homogeneity of the samples was estimated using Shannon, Fisher, Simpson and InvSimpson indices (Jost, 2007). To evaluate the differences in OTU abundance between sample groups, the White's non-parametric t-test was performed. A calculated P < 0.05 was considered statistically significant. Results Shrimps and EHP infection Molecular screening showed the shrimp samples to be negative for major shrimp pathogens, namely white spot syndrome virus (WSSV), infectious hypodermal hematopoietic necrosis virus (IHHNV), monodon baculovirus (MBV), hepatopancreatic parvovirus (HPV) throughout the study period. Shrimps exhibited size variation from the 40th day of culture with floating white faecal strings evident from the 55th day of culture (Fig. 1a), typical of EHP infection. A nested PCR test for EHP further confirmed the shrimps to be infected by the EHP disease (Fig. 1b). The average dissolved oxygen, pH, temperature and salinity of the rearing pond water during the culture period were 5.7 mg.L-1, 7.3, 27 °C and 28 ppt, respectively. Analysis of sequence reads To characterise the microbial consortia associated with the EHP infection in shrimp, the Illumina MiSeq based amplicon sequencing of the 16srRNA gene was used for pond water as well as shrimp samples obtained at different days of the culture period. The Fig. 1. (a) White faecal strings noticed on pond surface. (b) Molecular diagnosis of EHP. M-100 bp marker, P-positive control, N-negative control, S-EHP positive shrimp sample (176 bp). reads generated by Illumina sequencing were filtered to obtain high-quality sequences that could be classified into OTUs. Sequence analysis revealed that a majority of the sequences (88.24 %) could be classified into different phyla, while the remaining were unclassified (Table 1). Rarefaction curves generated for each sample tended to reach a plateau, indicating that data obtained was reliable, reflecting the microbial diversity in each sample (Supplementary Fig. 1). Alpha diversity indices values showed that the microbiome associated with shrimp surface was more diverse in comparison to the microbiota associated with pre-stocking water and culture pond water (Table 2). The principal coordinate analysis (PCoA) analysis grouped the bacterial OTUs obtained for the nine samples into four clusters PC1-PC4 (Supplementary Fig. 2). The five pond water samples were observed to significantly cluster into two groups PC1 (PS1, PW1 and PW2) and PC2 (PW3 and PW4) while the OTUs obtained for shrimp surface clustered into two groups PC3 (IS1 and IS2) and PC4 (IS3 and IS4). The OTUs obtained across all samples were used in calculating the percentage relative abundance. A histogram predicting the relative abundance for operational taxonomic units in each sample is presented in Figure 2. Microbiota associated with pond water At the phylum level, the pre-stocking pond water (PS1) was dominated by phyla Cyanobacteria (29.9 %), Proteobacteria (26.82 %), Bacteroidetes (17.86 %) and Actinobacteria (7.4 %). However, as the culture progressed, the dominant phyla observed in PS1 were seen to marginally alter at different time points of the culture. Overall, the phylum Proteobacteria was seen to dominate the pond waters (PW1–PW4) from the 40th day of culture and remained the most dominant phyla throughout the culture period. Similarly, the levels of phyla Cyanobacteria (20.8 %), Bacteroidetes (17.4 %), 170 Asian Fisheries Science 34 (2021):168–180 Table 1. Sequence reads and number of operational taxonomic units obtained and their classification. Sample SampleID (DOC) Reads OTUs Phyla Class Order Family Genus Bioproject number (GenBank) Prestocking water PS1 (0) 231322 59532 40 120 223 360 595 SRX7343914 PW1(45) 205904 52607 46 133 249 382 586 SRX7343915 PW2(55) 229750 45616 47 126 234 360 550 SRX7343916 PW3(70) 176042 49773 37 106 187 298 427 SRX7343917 PW4(95) 263244 38989 44 116 207 317 463 SRX7343918 IS1(45) 176740 37701 42 131 258 412 655 SRX7343919 IS2(55) 291650 60565 49 136 258 431 710 SRX7343920 IS3(70) 217792 49745 47 129 247 391 639 SRX7343921 IS4(95) 212772 44511 45 124 239 398 658 SRX7343922 Pond water Shrimp surface Table 2. Alpha diversity indices for the microbial communities in pond water and shrimp surface samples. Samples PS1 PW1 PW2 PW3 PW4 IS1 IS2 IS3 IS4 OTUs 52681 45947 49939 39127 59638 37792 60705 49891 44603 Observed species 870 840 743 573 657 964 1127 951 979 Chao1 1161.28 1111.36 1034.7 838.46 860.03 1237.04 1392.39 1202.57 1269.52 ACE 1151.8 1123.92 1031.98 825.44 877.47 1235.58 1385.72 1213.38 1241.14 Shannon 3.78 4.13 3.61 3.66 3.67 4.37 4.71 4.17 4.57 Simpson 0.91 0.96 0.93 0.94 0.93 0.96 0.97 0.96 0.97 InvSimpson 11.68 25.7 14.64 17.03 13.38 24.46 36.23 23.31 33.71 Fisher 148.02 145.96 123.78 95.16 103.3 180.16 196.46 166.7 176.91 Fig. 2. Relative abundance of different phyla at different culture stages during Enterocytozoon hepatopenaei infection in a shrimp grow-out pond. IS1-Infected shrimp surface (40 dps); IS2-Infected shrimp surface (55 dps); IS3-Infected shrimp surface (70 dps); IS4-Infected shrimp surface (95 dps). PS1-Prestocking pond water sample; PW1-Pond water sample (40 dps); PW2-Pond water sample (55 dps); PW3-Pond water sample (70 dps); PW4-Pond water sample (95 dps). Asian Fisheries Science 34 (2021):168–180 171 Actinobacteria (8 %) and Planctomycetes (5.4 %) were seen fluctuating marginally. At the class level, the Proteobacterial class was dominated by Alphaproteobacteria (47 %), followed by Gammaproteobacteria (22 %), Deltaproteobacteria (6 %) and Betaproteobacteria (5 %). Within the class Alphaproteobacteria, the families Rhodobacteraceae, Rhodospirillaceae and Pelagibacteraceae showed the highest mean relative frequencies (P <0.05) and were the most significant groups that primarily dominated the pond water throughout the culture period. Similarly, the families Alteromonadaceae, Xanthomonadaceae, Pseudomonadaceae, Pseudoalteromonadaceae and Vibrionaceae within the class Gammaproteobacteria were found to be the most statistically significant (P < 0.05) groups. The family Bacteriovoracaceae within the class Deltaproteobacteria and Methylophilaceae and Comamonadaceae within Betaproteobacteria were also found to be enriched in pond water (P < 0.05). The other significant bacterial families affiliated to the Phylum Bacteroidetes which dominated the pond water, were the Flavobacteriaceae (Class: Flavobacteriia), Sphingobacteriaceae (Class: Sphingobacteriia), Saprospiraceae and Chitinophagaceae (Class: Saprospirae) (P < 0.05). Further, in the pond water the abundance of family Synechococcaceae (Phylum Cyanobacteria, Class: Synechococcophycideae) was significantly higher (P < 0.05). At the class level, the major representatives of the phylum Actinobacteria included Actinobacteria and Acidimicrobiia. Microbacteriaceae was the major group among class Actinobacteria and C111 among Acidimicrobiia (P < 0.05). Microbiota associated with shrimp surface The Proteobacteria and Bacteroidetes formed the most dominant phyla on the shrimp surface throughout the sampling period with relative abundance of 31 % and 23 %, respectively. Planctomycetes, Cyanobacteria, Verrucomicrobia, Actinobacteria and Chloroflexi were the other major phyla with relative abundance levels >4 % (Fig. 2). At the class level, the phylum Proteobacteria was dominated by Alphaproteobacteria (39 %), followed by Gammaproteobacteria (36 %), Deltaproteobacteria (9 %) and the Betaproteobacteria (4 %). Among the Class Alphaproteobacteria, families like Rhodobacteraceae and Rickettsiaceae presented the highest mean relative frequencies (P < 0.05) and dominated the shrimp surface. Alteromonadaceae, Xanthomonadaceae, Vibrionaceae and Pseudoalteromonadaceae were the most significant dominant groups among Gammaproteobacteria on the shrimp surface (P < 0.05). Levels of family Thiotrichaceae from Gammaproteobacteria dipped on day 55. Bacteriovoracaceae, the dominant family among Deltaproteobacteria kept fluctuating on the shrimp surface. Oxalobacteraceae and Comamonadaceae among Class Betaproteobacteria dominated the shrimp surface (P < 0.05). Among the phylum Bacteroidetes the dominant classes were Flavobacteria (21 %), followed by Sphingobacteriia (13 %) and Saprospirae (12 %). The shrimp surface harboured significant levels of the families Flavobacteriaceae (Class: Flavobacteria), Sphingobacteriaceae (Class: Sphingobacteriia), Saprospiraceae and Chitinophagaceae (Class: Saprospirae) (P < 0.05). The phylum Planctomycetes was the next most abundant phylum with the class Planctomycetia (78 %) and family Pirellulaceae being the major representative. Their levels increased throughout the culture period. Similarly, family Synechococcaceae (Phylum: Cyanobacteria, Class: Synechococcophycideae) and Verrucomicrobiaceae (Phylum: Verrucomicrobia, Class Verrucomicrobiae) was dominant throughout the infection period (P < 0.05). The other dominant lineage on the shrimp surface was Actinobacteria (81 %) with the families Mycobacteriaceae and Micrococcaceae present in higher levels in comparison to pond water (P < 0.05). Further, Class Anaerolineae (62 %) from the phylum Chloroflexi dominated the shrimp surface with major representation from Anaerolinaceae and Caldilineaceae (P < 0.05). Unique and shared bacterial groups among pond water and shrimp surface A Venn plot for the unique and shared OTUs between the pre-stocking water (PS1) and all pond water samples showed that out of the total 1307 OTUs identified, 341 OTUs were shared across samples PS1PW4 (26.09 %) (Fig. 3A). Out of the total 1528 OTUs identified for the shrimp surface, 589 OTUs (38.55 %) were shared across the samples IS1–IS4 (Fig. 3B). The pond water (PW1) and shrimp surface samples (IS1) from the 40 th day of culture shared 669 of 1135 OTUs (58.94 %), 171 OTUs were unique to PW1 and 295 were unique to IS1. Similarly, the pond water (PW2) and shrimp surface samples (IS2) from the 55th day of culture shared 635 of 1235 OTUs (51.42 %), 108 OTUs were unique to PW2 and 492 OTUs were unique to IS2. Further, the pond water (PW3) and shrimp surface samples (IS3) from the 70th day of culture shared 477 of 1047 (45.56 %) of the OTUs, 96 OTUs were unique to PW3 and 474 OTUs were unique to IS3. The pond water (PW4) and shrimp surface samples (IS4) from the 95th day of culture shared 539 of 1097 OTUs (49.13 %), 118 OTUs were unique to PW4 and 440 OTUs were unique to IS4 (Fig. 3C). The dominant families that were shared between the pond water and shrimp surface included Rhodobacteraceae, Commamonadaceae, Oxalobacteraceae, Bacteriovoracaceae, Polyangiaceae, 172 Asian Fisheries Science 34 (2021):168–180 Fig. 3. Venn diagram illustrating the overlap of operational taxonomic units for (a) prestocking and pond water; (b) Infected shrimp surface samples; (c) Pond water and shrimp surface at four different time points. PS: Prestocking water; PW: Pond water; IS: Infected shrimp surface. Alteromonadaceae, Flavobacteriaceae, Sphingobacteriaceae, Saprospiraceae, Chitinophagaceae, Cytophagaceae, Bacteroidaceae, Synechococcaceae, Actinomycetaceae, Micrococcaceae, C111 (P < 0.05) (Supplementary Table 2). Functional category prediction suggested that metabolism (e.g. carbohydrate metabolism, amino acid metabolism); genetic information processing (e.g., transcription, translation, replication and repair) were the most dominant functional categories across the groups. Discussion The objective of this study was to apply cultureindependent methods to characterise the microbial dynamics in pond water and shrimp surface in a traditional shrimp culture pond. However, by the 40 th day of culture, the shrimps displayed growth variation as evidenced by their size and confirmed molecular diagnosis of EHP. White faecal strings could be seen on the pond water surface on the 55th day of culture. Shrimp specimens infected with EHP exhibited the characteristic white faeces syndrome (WFS), with the appearance of white faecal strings floating on the surface of the shrimp ponds (Tang et al., 2016). Analysis of the microbiome associated with shrimp surface and its rearing water during the infection period showed that the Proteobacterial group dominated both in pond water as well as on shrimp surface, with their levels being constant throughout the sampling period. The results are in accordance with earlier studies wherein the Proteobacterial group associated with shrimp has been reported to be the Asian Fisheries Science 34 (2021):168–180 173 most stable phyla with their abundance remaining unaltered by changes to salinity or diet compositions (Li et al., 2018). Bacteroidetes recorded the secondlargest levels across all samples. A shift towards Bacteroidetes has been previously reported for the white faeces syndrome-associated intestinal microbiome of shrimp (Huang et al., 2020). Several of the bacteria could not be classified into any taxonomic level which probably implicates the association of some novel microbes with the onset of the white faeces syndrome. The other phyla that were enriched were Cyanobacteria, Actinobacteria, Planctomycetes, Verrucomicrobia and Chloroflexi. The occurrence of Cyanobacteria in pond water is influenced by environmental factors such as light, salinity, temperature, and nutrient levels, contributing to the pond water quality (Chen et al., 2017 b). While Actinomycetales and Planctomycetes have been reported to be disease indicators of shrimp (Zheng et al., 2017), Verrucomicrobia were enriched in the sediment samples of P. vannamei culture pond affected by the AHPND/EMS disease (CornejoGranados et al., 2017). Earlier reports suggested the dominance of Chloroflexi in a microbiome associated with white faeces (Huang et al., 2020). The OTUs corresponding to the phylum Firmicutes was seen to be relatively low in abundance in all the samples. Such studies wherein decreased Firmicutes/Bacteroidetes ratio in shrimps affected by slow growth syndrome has been reported implicating underlying disease condition (Fan and Li, 2019). At the class level, an abundance of Alphaproteobacteria and Gammaproteobacteria were noted in this study. Similar observations were reported in earlier studies (Zheng et al., 2017). In shrimp aquaculture, the classification of bacterial communities at the family level generates maximum ecological cohesion as health indicators (Xiong et al., 2015). So this taxonomic level has been used in this study to understand the temporal dynamics of the bacterial communities. Rhodobacteraceae dominated both in rearing water as well as shrimp surface throughout the culture period. It may serve as the keystone species in rearing water and probably interact with shrimp at various stages of growth (Zheng et al., 2017). Pelagibacteraceae, which dominated the rearing water in this study, is the most common clade reported in aquatic 16s libraries (Campbell et al, 2015). In comparison to the prestocking levels, elevations in Rhodospirillaceae were noted. It has been previously associated with the diseased tissues of Platygyra carnosus Veron, 2000 corals (Ng et al., 2015) as well as Crassostrea gigas (Thunberg, 1793) oysters susceptible to Pacific Oyster Mortality Syndrome (Clerissi et al., 2020). Burkholderiaceae whose levels dipped in comparison to pre-stocking levels, has been reported to be enriched in healthy shrimps (Zheng et al., 2017). In this study, Rickettsiaceae, Mycobacteriaceae and Synechococcaceae families were observed to dominate the shrimp surface. Rickettsiaceae could be a parasitic inhabitant as reported earlier and responsible for severe diseases (Xiong et al., 2014). This family was also exclusively enriched in shrimps afflicted with “cotton shrimp-like” disease (Zhou et al., 2019). Within the family Bacteroidetes, the Sphingobacteriaceae formed a dominant group in pond water and shrimp surface throughout the culture period. This assumes significance as the previous report suggests the dominance of this group in diseased shrimps (Zheng et al., 2017). Mycobacteriaceae have been reported as potentially infectious for penaeid shrimp (Pedrosa et al., 2018). Synechococcus marine strains have been reported to contain compounds toxic to marine invertebrates (Martins et al., 2007). Their presence on the shrimp surface could negatively impact the health status of the shrimp. The other families dominating the shrimp surface were Vibrionaceae, Alteromonadaceae and Pseudoalteromonadaceae. A recent study has correlated these families to be a responsible cause of white faecal syndrome in shrimps (Alfiansah et al., 2020). Vibrio spp. secrete chitinolytic enzymes which can lead to adverse effects on the carapace of the shrimp resulting in tail necrosis, red disease and loose shell syndrome (Holt et al., 2020). The shrimp exoskeleton is known to act as a primary barrier, restricting the entry of opportunistic pathogens (Vogan et al., 2008). On the other hand, Thiotrichaceae, Microbacteriaceae as well as Chitinophagaceae have been identified as symbiotic microbes with a positive impact on aquatic animals (Chen et al., 2017b). In this study, a fluctuation in the levels of these health indicators was observed which probably could have been responsible for the detrimental effect on the shrimp health. The set of OTUs shared by all the samples constitutes the core microbiota (Zheng et al., 2017). The prestocking water shared only 26 % of the OTUs with the pond water samples at four different time points, indicating the high temporal turnover of the bacterial communities in the pond water. The pond water and the shrimp shared >45 % of the OTUs. This reflects the influence of the water quality on the shrimp surface microbiome. The shrimp surface shared only 38 % of OTUs, indicating temporal turnover of the bacterial communities. PICRUSt functional predictions revealed higher abundances of genes involved in metabolism and genetic information processing. A similar observation was reported in the case of the intestinal gut microbiome of shrimp with white faeces syndrome (Hou et al., 2018). The present study reveals the bacterial communities associated with the shrimp surface and rearing pond water to be altered by the white faeces syndrome. A recent study of microbial communities associated with healthy shrimp grown in a biofloc system reported phylum Proteobacteria, Bacteroidetes and Planctomycetes as the most dominant indigenous bacterial communities (Pallavi et al., 2021). However, the microbiome of shrimp and rearing waters could be greatly influenced by various environmental factors and farming practices (Rajeev et al., 2021) or even biases from specific laboratory procedures such as the sequencing platform and the various partial 16S sequence targets (Md Zoqratt et al., 2018). The present study provides valuable information on microbiome associated with rearing water and shrimp surface in relation to EHP infection, which could be exploited for maintaining a healthy shrimp microbiome for healthy production. Conclusion Microbiome studies involving cultured shrimp have shown that associated microbial communities play an important role in influencing probiotic/pathogenic activity and act as rapid biological indicators of chemical changes in the rearing water. The present study indicated that shifts in bacterial communities might trigger the onset of the white faeces syndrome along with the EHP infection. There was a fluctuation in the relative abundance levels of health indicator bacterial families such as Thiotrichaceae, Microbacteriaceae and Chitinophagaceae on the shrimp surface. Disease indicators such as Rickettsiaceae, Mycobacteriaceae were elevated on the shrimp surface. The results of this study provide valuable findings on the microbiome of rearing water and shrimp surface associated with the white faeces syndrome. Acknowledgements This study was financially supported by the DBTINDO-UK BBSRC project (BT/IN/Indo-UK/BBSRCAqua/37/RJK/2015-16). The DBT Bioinformatics 174 Asian Fisheries Science 34 (2021):168–180 Centre, College of Fisheries, Mangalore is gratefully acknowledged for carrying out the sequence analysis work. FAO. 2019. A quarterly update on world seafood markets. Globefish Highlights no. 2-2019. http://www.fao.org/3/ca5307en /ca5307en.pdf Holt, C.C., Bass, D., Stentiford, G.D., Giezen, M.V. 2020. 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C., Erchao and pathways of their transmission to healthy shrimp. Journal of L. 2019. Intestinal bacterial signatures of the "cotton shrimp-like" https://doi.org/10.1016 disease explain the change of growth performance and immune Invertebrate Pathology 140:1–7. /j.jip.2016.08.004 Veron, J.E.N., 2000 Corals of the world. Volume 1–3. Stafford-Smith, M. (Ed.) Australian Institute of Marine Science, Townsville, Australia. responses in Pacific white shrimp (Litopenaeus vannamei). Fish & Shellfish Immunology 92:629–636. https://doi.org/10.1016 /j.fsi.2019.06.054 1382 pp. 176 Asian Fisheries Science 34 (2021):168–180 Supplementary Fig. 1. Rarefaction curves of the microbiota associated with the pond water as well as shrimp surface in a white feces syndrome affected pond. IS1-Infected shrimp surface (40 dps); IS2-Infected shrimp surface (55 dps); IS3Infected shrimp surface (70 dps); IS4-Infected shrimp surface (95 dps); PS1-Prestocking pond water sample; PW1-Pond water sample (40 dps); PW2-Pond water sample (55 dps); PW3-Pond water sample (70 dps); PW4-Pond water sample (95 dps). Supplementary Fig. 2. Principal-coordinate analysis (PCoA) plot showing the relationship among the microbiota in nine samples associated with the white feces syndrome. IS1-Infected shrimp surface (40 dps); IS2-Infected shrimp surface (55 dps); IS3-Infected shrimp surface (70 dps); IS4-Infected shrimp surface (95 dps); PS1-Prestocking pond water sample; PW1Pond water sample (40 dps); PW2-Pond water sample (55 dps); PW3-Pond water sample (70 dps); PW4-Pond water sample (95 dps). Asian Fisheries Science 34 (2021):168–180 177 Supplementary Table 1. OIE listed primers used in the routine surveillance of shrimp pathogens. Sl. no. 1 2 3 4 5 6 7 8 9 Product size (bp) Pathogen Primer name Primer sequence 5’-3’ WSSV (White spot syndrome virus) IK1 TGGCATGACAACGGCAGGAG IK2 GGCTTCTGAGATGAGGACGG IHHNV (Infectious hypodermal and haematopoetic necrosis virus) MBV (Monodon baculovirus) IHHNV309F TCCAATCGCGTCTGCGATACT IHHNV309R TGTCTGCTACGATGATTATCCA MBV1.4NF ATAGAACGCATAGAAAACGCT MBV1.4NR CAGCGATTCATTCCAGCGCCACC HPV (Hepatopancreatic parvovirus) H441F GCATTACAAGAGCCAAGCAG H441R ACACTCAGCCTCTACCTTGT YHV (Yellow head virus) 10F CCGCTAATTTCAAAAACTACG 144R AAGGTGTTATGTCGAGGAAGT TSV (Taura syndrome virus) 9992F AAGTAGACAGCCGCGCTT 9195R TCAATGAGAGCTTGGTCC IMNV (Infectious myonecrosis virus) 4587F CGACGCTGCTAACCATACAA 4914R ACTCGGCTGTTCGATCAAGT EHP (Enterocytozoon hepatopenaei) ENF176F CAACGCGGGAAAACTTACCA ENF176R ACCTGTTATTGCCTTCTCCCTCC AHPND (Acute hepatopancreatic necrosis disease ) AP4-F1 ATGAGTAACAATATAAAACATGAAAC AP4-R1 ACGATTTCGACGTTCCCCAA 486 309 361 441 135 231 328 176 1269 Supplementary Table 2. Microbiome relative abundance percentage of operational taxonomic units at class level in pond water and shrimp samples at different stages of growth. Pond water samples PS1* PW1* Phylum - Proteobacteria; Class-Alphaproteobacteria, Family: Rhodobacteraceae 31 23 Pelagibacteraceae 18 23 Rhizobiaceae 6 4 Rhodospirillaceae 5 19 Sphingomonadaceae 4 3 Hyphomicrobiaceae 2 1 Bradyrhizobiaceae 2 1 Caulobacteraceae 1 1 Erythrobacteraceae 1 1 Rickettsiaceae 0 0 Others 6 1 Unclassified 25 24 Phylum - Proteobacteria; Class- Betaproteobacteria, Family: Burkholderiaceae 69 2 Comamonadaceae 11 32 Methylophilaceae 4 20 Oxalobacteraceae 4 11 Others 1 3 Unclassified 11 32 Phylum - Proteobacteria; Class- Deltaproteobacteria, Family: Bacteriovoracaceae 5 8 Polyangiaceae 10 16 Desulfobulbaceae 1 3 Desulfobacteraceae 2 5 Others 64 46 Unclassified 18 21 Taxon PW2* PW3* PW4* Shrimp samples IS1* IS2* IS3* IS4* 47 22 1 8 1 0 0 0 0 0 1 18 33 17 3 18 1 0 0 1 1 0 1 23 40 5 4 7 1 0 0 0 1 1 10 30 64 1 4 2 3 2 1 2 1 13 2 5 49 2 7 3 6 3 2 2 1 11 3 12 41 2 8 7 6 5 2 2 1 1 4 22 29 0 6 2 7 4 1 1 2 17 3 27 1 17 45 12 5 20 1 18 47 11 4 19 2 22 42 11 3 21 1 42 3 26 10 17 2 35 1 35 11 16 2 36 0 36 10 16 2 36 1 31 14 16 35 9 6 5 35 10 49 4 3 5 15 24 27 1 2 2 19 49 26 13 7 7 32 15 15 10 12 7 44 13 35 9 6 3 27 18 10 11 2 1 26 50 178 Asian Fisheries Science 34 (2021):168–180 Supplementary Table 2. Continued. Pond water samples PS1* PW1* PW2* Phylum - Proteobacteria; Class- Gammaproteobacteria, Family: Alteromonadaceae 11 11 7 Moraxellaceae 8 0 0 Aeromonadaceae 7 0 0 Xanthomonadaceae 6 9 8 Halomonadaceae 4 5 3 Pseudomonadaceae 3 5 2 Oceanospirillaceae 3 0 0 Pseudoalteromonadaceae 2 2 2 Vibrionaceae 1 3 7 Sinobacteraceae 1 1 1 Enterobacteriaceae 1 1 1 Francisellaceae 0 0 10 Shewanellaceae 0 0 0 Chromatiaceae 0 1 1 Thiotrichaceae 0 0 0 Colwelliaceae 0 0 0 Marinicellaceae 0 0 0 Others 3 17 11 Unclassified 50 45 47 Phylum-Bacteroidetes; Class-Flavobacteriia;Family: Flavobacteriaceae 96 82 93 Cryomorphaceae 3 2 6 Others 0 0 0 Phylum-Bacteroidetes; Class-Sphingobacteriia, Family: Sphingobacteriaceae 100 96 99 NS11-12 0 4 1 Phylum-Bacteroidetes; Class- Saprospirae, Family: Saprospiraceae 63 29 80 Chitinophagaceae 37 71 20 Phylum-Bacteroidetes; Class- Cytophagia, Family: Cytophagaceae 66 90 93 Others 34 10 7 Phylum-Bacteroidetes; Class-Bacteroidia; Family: Bacteroidaceae 40 27 31 Others 60 73 69 Phylum- Cyanobacteria; Class- Synechococcophycideae; Family: Synechococcaceae 93 96 91 Pseudanabaenaceae 2 2 2 Unclassified 5 2 7 Phylum- Cyanobacteria; Class- Chloroplast; Family: Mamiellaceae 0 3 3 Others 0 0 1 Unclassified 100 97 96 Phylum-Actinobacteria ; Class- Actinobacteria; Family: Microbacteriaceae 68 77 76 Actinomycetaceae 14 14 16 Mycobacteriaceae 6 2 2 Micrococcaceae 5 2 2 Nocardioidaceae 3 2 2 Micromonosporaceae 1 1 1 Streptomycetaceae 1 1 1 Promicromonosporaceae 1 0 1 Others 1 1 1 Unclassified 0 0 0 Phylum-Actinobacteria ; Class-Acidimicrobiia; Family: C111 84 35 41 OCS155 14 62 56 Others 1 0 2 Unclassified 1 4 1 Taxon Asian Fisheries Science 34 (2021):168–180 179 PW3* PW4* Shrimp samples IS1* IS2* IS3* IS4* 4 0 0 4 5 2 0 1 1 3 0 0 0 0 0 0 0 45 33 4 0 0 3 0 1 0 1 1 3 0 2 0 0 0 0 0 24 59 6 4 0 5 1 6 0 15 16 1 1 0 1 1 13 2 0 1 28 15 2 0 10 0 5 0 7 11 1 1 3 1 3 4 9 2 3 22 11 2 0 9 0 4 1 12 18 2 4 1 5 1 2 2 1 2 24 13 5 0 11 0 5 5 4 10 2 1 8 0 1 9 1 3 3 19 68 32 0 53 47 0 85 14 1 97 2 1 97 2 1 96 4 0 32 68 34 66 99 1 99 1 97 3 99 1 95 5 93 7 57 43 37 63 72 28 60 40 94 6 78 22 91 9 88 12 98 2 93 7 34 66 37 63 25 75 26 74 38 63 33 67 95 2 4 73 24 3 48 45 7 70 25 5 95 2 3 71 26 3 2 0 98 2 0 98 0 1 98 0 13 87 0 1 99 0 3 97 63 24 3 3 3 1 1 1 1 0 41 45 4 4 2 2 1 1 1 0 9 39 14 14 7 5 4 3 6 0 3 42 15 16 7 5 3 3 5 0 5 36 16 18 8 5 3 4 6 0 4 34 19 18 7 5 4 4 7 0 49 43 4 4 92 5 2 1 69 14 15 2 76 1 16 8 94 3 2 2 85 0 11 4 Supplementary Table 2. Continued. Pond water samples PS1* PW1* Phylum -Planctomycetes ; Class- Planctomycetia; Family: Pirellulaceae 42 52 Isosphaeraceae 38 4 Gemmataceae 10 26 Planctomycetaceae 9 16 Unclassified 0 2 Phylum -Planctomycetes ; Class- Phycisphaerae; Family: Phycisphaeraceae 44 18 Unclassified 56 82 Phylum- Verrucomicrobia ; Class- Verrucomicrobiae; Family: Verrucomicrobiaceae 100 100 Phylum- Verrucomicrobia ; Class- Opitutae; Family: Opitutaceae 96 97 Others 4 3 Unclassified 0 0 Phylum- Verrucomicrobia ; Class-Spartobacteria; Family: Chthoniobacteraceae 100 100 01D2Z36 0 0 Phylum- Verrucomicrobia ; Class-Pedosphaerae;Family: Others 20 66 Unclassified 80 34 Phylum-Chloroflexi ; Class-Anaerolineae; Family: Anaerolinaceae 17 20 Caldilineaceae 12 11 Taxon PW2* PW3* PW4* Shrimp samples IS1* IS2* IS3* IS4* 49 3 12 36 1 39 0 1 60 0 40 0 2 58 0 60 5 18 16 1 64 4 13 19 1 79 0 2 19 0 70 1 5 24 0 16 84 36 64 63 37 17 83 22 78 51 49 55 45 100 100 100 100 100 100 100 90 10 0 79 21 0 77 22 1 97 3 0 100 0 0 100 0 0 98 2 0 100 0 100 0 100 0 99 1 100 0 100 0 99 1 71 29 62 38 65 35 68 32 57 43 63 37 73 27 11 8 6 56 5 39 16 16 16 19 13 34 12 21 A4b 12 5 42 18 41 7 5 7 11 Others 2 3 1 0 0 3 3 3 3 Unclassified 57 62 38 19 15 59 57 44 52 Phylum -Acidobacteria; Class- Chloracidobacteria; Family: Ellin6075 67 76 69 79 71 72 72 73 63 Unclassified 33 24 31 21 29 28 28 27 37 Phylum -Acidobacteria; Class-Acidobacteria-6; Family: Others 20 19 17 31 26 24 24 28 21 Unclassified 80 81 83 69 74 76 76 72 79 Phylum- Firmicutes; Class-Bacilli; Family: Bacillaceae 75 75 76 66 69 60 93 24 68 Others 25 25 24 34 31 40 7 76 32 Phylum- Firmicutes; Class-Clostridia; Family: Ruminococcaceae 26 27 35 15 45 26 32 28 30 Others 38 40 29 35 21 32 36 24 31 Unclassified 36 32 35 50 34 42 33 49 39 *IS1-Infected shrimp surface (40 dps); IS2-Infected shrimp surface (55 dps); IS3-Infected shrimp surface (70 dps); IS4-Infected shrimp surface (95 dps); PS1-Prestocking pond water sample; PW1-Pond water sample (40 dps); PW2-Pond water sample (55 dps); PW3-Pond water sample (70 dps); PW4-Pond water sample (95 dps). 180 Asian Fisheries Science 34 (2021):168–180