3.2. Quantitative expression of biofilm and catabolic genes: Response surface analysis
The aromatic stress in the chemical surroundings influences phenotype transitions through differential gene expression (Jefferson et al. 2004; Flemming et al. 2016). To test this possibility in our strain, RSM was used to check the combined effects of factors, 2-4-D degradation (A), and 4-CCA accumulation (B) rates on expression rates of biofilm matrix (pelA, pgaA) and catabolic genes (arhd, tfdC) (responses) separately under static and shaking conditions. Real-time PCR analysis showed differential expression levels of the biofilm matrix and catabolic genes in biofilm and planktonic adapted phenotypes, as shown in the contour plots (Fig. 3), which were similar to the predicted values calculated by the Minitab software (Table: S2). The calculated R2 > 0.90 in all the genes indicated that the predictions of the response function were in accordance with the experimental ones at the confidence level of higher than 90% (ANOVA Table.2). The absolute coefficients of A and B were significantly higher in pelA, pgaA, arhd, and tfdC expression in biofilm adapted phenotype than its planktonic counterpart (Eg.3a-3h). The p-value for interaction terms is < 0.05 stipulating their significant effect on the regression model. The contour plots illustrating the interaction effect between independent variables on the responses, where the favorable regions for catabolic and biofilm matrix genes were significantly more in the biofilm phenotype than the planktonic counterpart (Fig. 3). It can be observed that expression rates of biofilm matrix genes (pelA and pgaA) had a maximum value of 0.075, 0.055 fold change/hr at the high levels (+ 1) of A1 (1.46) and B1 (0.00461), respectively (Table: S2). Likewise, the favorable regions for catabolic genes were comparatively different in both the phenotypes, with tfdC showing a noteworthy response in the biofilm phenotype with expression rate 0.023 fold change/hr. It appears that, as the 4-chlorocatechol accumulates rate is lowered, tfdC expression rate increases indicating its utlization in the biofilm adapted cells (Table S1; S2). For every 0.008 fold change/hr of arhd expression, tfdC expression rate was increased by 2.8 times, as that of pelA and pgaA by 9.3 and 6.8 times (Table: S2). On the contrary, this trend is reversed in planktonic associated cells. At high levels (+ 1) of A2 (1.36) and B2 (0.132), the expression rates for arhd, tfdC, pelA and pgaA were 0.027, 0.0102, 0.000369, 0.0208. It indicates the decline in tfdC expression rate by 0.2 fold change/hr. The declination of matrix genes expression rates were significant by 73 and 1.29 times comparative to arhd expression. Similarly at low levels (-1) of A2 and B2, the catabolic and biofilm matrix genes expression rates were significantly dissimilar in both the phenotypes of EGD-AQ6. It also corroborates to the greater participation and importance of the matrix protein pellicle than pgaA in building the biofilm at increasing chloroaromatic compound stress. It is similar to the results obtained by Meliani and Bensoltane (2014), indicates that pellicle synthesis was increased during xylene degradation and conferred an increased biofilm mass to P. fluorescens and P. aeruginosa. It indicates the combined impact of toxic metabolites, leading to phenotype switch of the strain EGD-AQ6 to combat the stress imposed, resulting in elevated biodegradation potential (Fig. 2a, 2b, 2e, 2f). In this way, we have managed to unravel the gene expression patterns, regulating the 2-4-D biodegradation in both the phenotypes. Similarly, Yong and Zhong. (2013) reported that a different central cleavage pathway was initiated on account of a phenotype switch during phenol degradation. Diverse catabolic, matrix synthesizing pathways and signal transduction mechanisms are involved in the phenotypic switch that allows a bacterium to attain physiological and metabolic heterogeneity. Furthermore, these pathways are also appear to be interconnected. Therefore, it is very likely that depending on the prevailing conditions, number of variants may appear simultaneously in the same strain (Allison et al. 2011; Amato et al. 2013). Also, this invulnerability of the underlying cells in the biofilm is due to diffusion limitations imparted by local variations in pH, nutrient and oxygen availability, and concentrations of bacterial metabolites leading to physiological heterogeneity, slow growth (Sauer et al. 2002: Jefferson et al. 2004; Flemming et al. 2016). It further influences the activity of dioxygenases through regulating the partial pressure of oxygen in the fluid surroundings (Ding et al. 2008), resulting in differential gene regulation leading to phenotype transition (Paliwal et al. 2014). The interactive effect of the factors on the two responses (Table: S2; Table 2) was statistically quantified, and the approximate functions for attached cells (Eq. 3a, 3c) and planktonic cells (Eq. 3b, Eq. 3d) under static and shaking conditions was obtained as follows.
Under static conditions
Yarhd = 0.004912 + 0.000049 A + 0.003207 B − 0.000430 A*A + 0.000185 B*B − 0.000254 A*B……. Eq. 3a.
YtfdC = 0.01303 + 0.00021 A + 0.00485 B + 0.00181 A*A − 0.00392 B*B − 0.00058 A*B……. Eq. 3b.
YpelA = 0.04255 + 0.00638 A + 0.02480 B + 0.00184 A*A + 0.00181 B*B − 0.01256 A*B……. Eq. 3c.
YpgaA = 0.032250 − 0.000327 A + 0.021895 B − 0.001448 A*A − 0.001363 B*B − 0.002148 A*B……. Eq. 3d.
Under shaking conditions
Yarhd = 0.001389 − 0.000059 A + 0.000840 B − 0.000064 A*A − 0.000024 B*B − 0.000043 A*B……. Eq. 3e.
YtfdC = 0.030408 + 0.001127 A + 0.002967 B − 0.026625 A*A − 0.026187 B*B − 0.003062 A*B……. Eq. 3f.
YpelA = 0.000022 + 0.000001 A + 0.000015 B − 0.000001 A*A − 0.000013 B*B + 0.000011 A*B……. Eq. 3g
YpgaA = 0.001389 − 0.000059 A + 0.000840 B − 0.000064 A*A − 0.000024 B*B − 0.000043 A*B……. Eq. 3h
Taken together, the gradient-based physical and chemical microenvironments imparted by the matrix to the inhabitants (Palmer et al., 2007; Ghosh et al., 2017b) may have regulated the catabolic activity through oxygen fluctuations and additional alterations in gene expression in the biofilm.
3.3. Whole-genome sequence (WGS) analysis
Pseudomonas simiae EGD-AQ6 genome sequence unraveled variable catabolic capacities (NCBI under accession no: AVQG00000000). However, in this study, the 2-4-D degrading gene clusters were characterized, scattered in the two contigs. The gene clusters (tfd genes) showed up to 99% functional homology with the reported degraders (Table: 3) viz; P.simiae WCS417, P.simiae PICF7, P.simiae K-hf-19, P.simiae PCL175.
Another gene cluster showed aromatic ring hydroxylating dioxygenase (arhd) of P. simiae EGD-AQ6 62% sequence homology with tfdA of R. eutropha JMP134 as determined using pBLAST and CDD (Table;S5), which is responsible for α-ketoglutarate-dependent dioxygenation, hence regarded as the first enzyme of the pathway. 2-4-D degradation occurs via; the formation of 4-CCA as the central metabolic intermediate (Balajee and Mahadevan, 1993; Zharikova et al., 2018). The degradation proceeds via; further oxidations and hydroxylations through tfdCDEF genes to TCA cycle intermediates similar in R. eutropha and Azotobacter chroococcum. (Balajee and Mahadevan, 1993; Camara et al., 2009). Apart from chloro-aromatic degrading genes, the region in NCBI (O204_02005-O203_02035) and (0204_26495-0204_26465) encompasses gene clusters in a single operon, which exhibited significant similarity (80–99%) with the reported biofilm-forming Pseudomonas species (Table S3; S4). They are principal components of biofilm, involved in the building of carbohydrate-rich extracellular matrices such as poly-glucosamine (pga) and pellicles (pel) (Itoh et al., 2008; Franklin et al., 2011).