Proteomic and metabolic profiling reveals molecular phenotype associated with chemotrophic growth of Rubrivivax benzoatilyticus JA2 on L-tryptophan

Abstract

Rubrivivax benzoatilyticus strain JA2 is an anoxygenic phototrophic bacterium, able to grow under different growth modes. Particularly under chemotrophic conditions, it produces novel Trp-melanin, anthocyanin-like, and pyomelanin pigments. However, the underlying molecular adaptations of strain JA2 that lead to the formation of novel metabolites under chemotrophic conditions remain unexplored. The present study used iTRAQ-based global proteomic and metabolite profiling to unravel the biochemical processes operating under the L-tryptophan-fed chemotrophic state. Exometabolite profiling of L-tryptophan fed chemotrophic cultures revealed production of diverse indolic metabolites, many of which are hydroxyindole derivatives, along with unique pigmented metabolites. Proteomic profiling revealed a global shift in the proteome and detected 2411 proteins, corresponding to 61.8% proteins expressed. Proteins related to signalling, transcription-coupled translation, stress, membrane transport, and metabolism were highly differentially regulated. Extensive rewiring of amino acid, fatty acid, lipid, and energy metabolism was observed under L-tyrptophan fed chemotrophic conditions. Moreover, energy conservation and cell protection strategies such as efflux pumps involved in the efflux of aromatic compounds were activated. The study demonstrated a correlation between some of the detected indole derivatives and the up-regulation of proteins associated with L-tryptophan catabolism, indicating a possible role of aromatic mono/dioxygenases in the formation of hydroxyindole derivatives and pigments under chemotrophic conditions. The overall study revealed metabolic flexibility in utilizing aromatic compounds and molecular adaptations of strain JA2 under the chemotrophic state.

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1. Introduction

The genetic and metabolic diversity of microorganisms allows them to produce a wide range of natural compounds; thus they are often referred to as microbial chemical factories.^1–3 These diverse chemical compounds of microbial origin have applications in various fields, including the food, cosmetic, textile, and pharmaceutical industries.^4–8 There has been renewed interest in discovering new compounds of microbial origin as microorganisms are an invaluable resource of novel chemical compounds.^9–11 The increasing availability of bacterial genome sequences has greatly enhanced our understanding of microbial metabolism and the potential novel natural product biosynthesis.^5,8 Combining genome sequence analysis with metabolic simulation methods revealed the intricate metabolic pathways responsible for novel biomolecules production.^10,12 Though genome-centered approaches are promising for predicting the potential novel biomolecules, they fall short of covering the entire range of novel compounds, owing to their inherent homology-driven nature.^3,13

However, change in growth conditions and interactions with other biotic systems are some of the conditions that activate the genes involved in the biosynthesis of natural compounds.^9,14,15 A comprehensive approach such as the integration of powerful functional genomics tools and a modified growth configuration would identify novel compounds and help in elucidating their cellular mechanisms. Combining untargeted metabolomics and other omics techniques such as proteomics or transcriptomics has great potential to uncover the novel compounds and their underlying biochemical processes particularly in less explored bacterial groups.

Rubrivivax benzoatilyticus strain JA2 is one such metabolically versatile phototrophic bacterium. Studies on L-tryptophan metabolism in strain JA2 resulted in the discovery of the novel biomolecule, rubrivivaxin¹⁶ and a novel enzyme tryptophan ammonia lyase.¹⁷ Furthermore, a non-canonical pathway of L-tryptophan based melanin synthesis was discovered in strain JA2.¹⁸ The strain JA2 is capable of transforming L-phenylalanine and L-tyrosine into phenolic compounds, such as phenyllactic acid, pigmented metabolites, and pyomelanin.^19–22 Altered growth conditions and untargeted metabolic profiling revealed a previously unknown phenylalanine catabolic process in strain JA2 and anthocyanin-like pigments.^22,23 In line with this strategy, we recently reported the L-tryptophan-dependent catabolic phenotype in strain JA2 and the biosynthesis of L-tryptophan-dependent melanin under the chemotrophic state.¹⁸ Though the strain JA2 metabolizes organic compounds under phototrophic and chemotrophic growth modes, previous studies indicate that the chemotrophic state activates unique metabolic processes and our understanding of biochemical adaptations of strain JA2 under the chemotrophic state is limited. The present investigation employs global proteomic and metabolic profiling tools to uncover the cellular and biochemical adaptations under chemotrophic conditions and L-tryptophan catabolism leading to the production of secondary metabolites in strain JA2.

2. Materials and methods

2.1 Organism and growth conditions

Rubrivivax benzoatilyticus (strain JA2^T, =MTCC 7087^T = JCM 13220^T = ATCC BAA-35^T) was grown photoheterotrophically in a 100/250 ml culture bottle (at 30 ± 1 °C, and light 2400 Lux) containing mineral media (pH 6.8 to 6.9) with malate (22 mM) and ammonium chloride (7 mM) as the carbon and nitrogen source, respectively.^18,24,25 In the current study, photoheterotrophically grown log phase culture (0.3 OD. @ 660 nm) was used as an inoculum, and ammonium chloride was replaced with L-tryptophan (1 mM) as the sole source of nitrogen. Chemotrophic culture was grown in a 500 ml conical flask containing 100 ml of medium and incubated in a shaker incubator (Eppendorf Innova) operating at 180 rpm and 30 ± 1 °C. Experiments were performed in triplicate and chemotrophic cultures fed with L-tryptophan (1 mM) and control cultures without any L-tryptophan feeding were used for all the studies. Growth was measured turbidometrically at 660 nm and pigment formation was measured spectrophotometrically at 400 nm.

2.2 Estimation of total indoles

Total indole formation in L-tryptophan fed and control culture supernatants was measured using Salper's reagent;^26,27 1.0 ml of culture supernatant was mixed with 1.0 ml of ethyl acetate and to this 2 ml of freshly prepared Salper's reagent [1 ml of 0.5 M FeCl₃ in 50 ml of 35% (v/v) perchloric acid] was added and absorbance was read at 535 nm against reagent blank.

2.3 Metabolite extraction and TLC and HPLC profiling

Strain JA2 was grown with and without L-tryptophan feeding (control) in a 500 ml conical flask containing 100 ml of media to the stationary phase (for 48 hours) under chemotrophic conditions. Cultures were harvested by centrifugation (10 000 rpm at 4 °C, 10 min) and the collected supernatants were acidified (pH 2.0) with 5 N HCl. Acidified supernatants were stored for two to four days at 4 °C to allow pigment precipitation; the precipitated brown pigment was separated, and the remaining supernatant was used for further metabolite extraction (Fig. S1a, ESI†). Acidified supernatants (100 ml) of L-tryptophan fed and control cultures were used for metabolite extraction, and metabolites were extracted three times with an equal volume of ethyl acetate. Ethyl acetate extracts were then pooled and evaporated under vacuum using a rotary flash evaporator (Heidolph, Germany) at 35 °C. The dried samples were then dissolved in 0.5 ml of MS grade methanol and filtered through a membrane filter (0.22 μm, Icon pall-Scientifics) and the thus obtained extracts of control and L-tryptophan fed cultures were stored at −80 °C till analysis.

The exometabolites obtained from control and L-tryptophan fed cultures (ethyl acetate extracts dissolved in MS grade methanol) were subjected to TLC profiling (Silica gel 60 dimension, F254, 20 × 10 cm, 0.2 mm from Merck) using a solvent system consisting of chloroform : methanol : glacial acetic acid (9 : 0.95 : 0.05). HPLC analysis was performed according to previous reports.^28,29 In brief, HPLC analysis was carried out using the Shimadzu prominence HPLC system with a Phenomenex C-18 column (Luna, 5 μm, 250 × 4.6 mm). The absorption spectra of metabolites were recorded using a PDA detector and the concentration of metabolites was quantified based on the peak area of the known concentration of the authentic standard, procured from Sigma-Aldrich.

2.4 LC–MS-ESI analysis, data interpretation and identification of metabolites

The exometabolite samples (ethyl acetate extracts in triplicates) obtained from control and L-tryptophan fed chemotrophic conditions were subjected to LC–MS analysis. Mass analysis was performed on an Agilent Technologies Series 6520 LC–MS/MS TOF system equipped with a PDA detector. Metabolites were separated by reverse-phase column chromatography (Luna, C-18, Luna, 3.6 μm, 150 × 4.6 mm), and the parameters used for the chromatographic separation of metabolites, data acquisition, data analysis and metabolite identification were the same as those published in previous reports.^18,21,23 Metabolites were identified based on their absorption spectra and masses compared with the available databases HMDB (https://www.hmdb.ca), Massbank (https://www.massbank.jp), METLIN (https://metlin.scripps.edu), and KEGG (https://www.genome.jp/kegg/pathway) and authentic standards available.

2.5 Enzyme assay

Aminotransferase enzyme assay was performed using the cell free extract obtained from chemotrophic culture of strain JA2 fed with L-tryptophan. A clear cell free extract was prepared according to earlier studies. In brief, clear cell free extracts were used as a source of enzymes to study aminotransferase/transaminase activity, and the reaction was carried out in 4 ml of α-ketoglutarate (1 mM) and pyridoxal phosphate (PLP; 50 μM) as cofactors, with hydroxytryptophan (1 mM) as a substrate. The reaction was started with the addition of an appropriate volume of the enzyme source into the reaction mixture and the pre-denatured enzyme was used as control. The reaction mixture was incubated for 1 h at 37 °C and the reaction was terminated by adding 5 N HCl after 1.0 hour incubation. The reaction mixture was then centrifuged at 16 000 rpm (at 4 °C) for 20 min, and the supernatant obtained was used to extract reaction products; metabolites were extracted thrice with equal volumes of ethyl acetate. Ethyl acetate fractions were pooled and dried by using a rotary flash evaporator. Finally, the extracted fraction was dissolved in 300 μl of methanol (MS grade). The aminotransferase assay products OH-IPyA (hydroxyindole-pyruvate) or hydroxyindole-3-acetic acid were measured by HPLC and coelution with authentic standards.

2.6 Protein extraction and iTRAQ labeling

Chemotrophic cultures of strain JA2 (biological triplicates) grown on L-tryptophan and control (without L-tryptophan) were used for proteomics studies. Control and L-tryptophan fed cultures were harvested after 48 h (centrifuged at 4 °C, 10 000 rpm for 10 min) and the cell pellet was collected. Control and L-tryptophan fed culture proteomes were extracted as per previous reports^28,29 and the thus obtained protein samples were lyophilized (dry freezer for moisture removal) and stored at −80 °C for further analysis. To check the quality of proteins, proteomic samples were analyzed on 10% SDS-PAGE. Further, iTRAQ based quantitative proteomic analysis (triplex control and L-tryptophan fed conditions) was outsourced to the University of California, San Diego, USA. Lyophilized protein samples (100 μg) were resuspended in 600 μl of buffer (100 mM Tris buffer pH 8.0, 8 M urea), vortexed for 5 to 10 min and TCEP [tris(2-carboxyethyl)] phosphine was added at a final concentration of 10 mM. Chloroacetamide solution (40 mM) was added and vortexed. Optimal conditions for protein estimation were chosen: an equal volume of 50 mM buffer (Tris pH 8) was added to dilute the urea concentration to 4 M, and the resulting solution was then subjected to enzymatic digestion. For LysC digestion, the LysC enzyme was added at 1 : 500 to the protein sample. The protein sample was incubated at 37 °C in a shaker incubator for a period ranging from 4 to 6 hours. Similarly, for trypsin digestion, another equal volume of 50 mM buffer (Tris pH 8) was added to reduce the urea concentration to 2 M. Subsequently, the trypsin enzyme was added at a ratio of 1 : 50 to the protein sample for the digestion. Trypsinized samples (6 samples) were labelled with isobaric tags (iTRAQ8, ABSCIEX³⁰), where each sample was labelled with a specific tag (113, 114, 115 for control and 116, 117, 118 for L-tryptophan fed conditions) according to the manufacturer's instructions. The iTRAQ8 (Sciex 4390812 used first 6 tags) labelled peptides from each set were pooled in the ratio of 1 : 1 : 1 : 1 : 1 : 1 and fractionated using high pH reverse phase chromatography as described previously.²⁸

2.6.1 Protein identification, quantitation and mass spectral data analysis. LC–MS/MS was performed with the fraction of iTRAQ labelled samples according to earlier reports³¹ with slight modifications.^28,29 The nanospray ionization experiments were performed using an Orbitrap fusion Lumos hybrid mass spectrometer (Thermo) interfaced with a nano-scale reversed-phase UPLC system (Thermo Dionex UltiMate 3000 RSLC nano System) using a 25 cm, 75 μm ID glass capillary packed with 1.7 μm C18, BEH beads (Waters Corporation). Peptides were identified using the Peak DB (ABSCIEX) algorithm with default parameters. The search and quantitation parameters were set: (i) Sample Type “iTRAQ 6-plex quantification”; (ii) Cysteine alkylation “iodoacetamide”; (iii) Enzyme digestion, “Trypsin”; (iv) Special factors “none”; (v) Species “none”; (vi) Sequence Database, search was accomplished against genome project strain JA2 database (Version AEWG00000000.1); (vii) Search effort “Thorough ID”; (viii) Search using False Discovery Rates. The fragment masses were analyzed in the Orbitrap mass analyzer with a mass resolution setting of 15000 (with ion trap scan rate of turbo, first mass m/z was 100, AGC Target 20000 and maximum injection time of 22 ms). Protein identification and quantification were carried out using Peaks Studio 8.5 (https://www.ncbi.nlm.nih.gov/pubmed/22186715) (Bioinformatics Solutions Inc.). The data were sorted based on the peptide score, calculated by the program for each of the reported protein ratios (https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btt274). Detected proteins, after filtering with the criteria of minimum two peptides and one unique peptide (99% confidence level) and false discovery rate < 1%, were considered for FC (fold change) analysis. Six FC expression ratios were calculated for each of the iTRAQ detected proteins in triplex style (3 control, 3 tryptophan replicates).^28,29 Proteins with iTRAQ fold change ratio < 0.8 (log₂, FC < −0.26) were considered as downregulated and those with fold change ratio >1.25 (log₂, FC > 0.26) were considered as upregulated (with p < 0.05/significance score threshold at 20, peptide score above 20 in at least two out of three expression ratios). Total differentially regulated proteins were further analysed with the Expasy tool to find out the GRAVY and the isoelectric point. Differentially expressed proteins (DEPs) were categorized based on their function according to the KEGG (Kyoto Encyclopedia of Genes and Genomes, https://www.genome.jp/kegg/) database. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD055514.

3. Results

3.1 Growth, pigment and indole production by strain JA2

Strain JA2 utilized L-tryptophan as the sole source of nitrogen for growth under chemotrophic conditions and concomitant pigment formation was observed only in the L-tryptophan-fed chemotrophic state but not under control conditions (Fig. 1A and B). Indole formation was found to be significantly higher under L-tryptophan fed conditions compared to the control (Fig. 1B). Further, chemotrophic conditions influence L-tryptophan biotransformation as evident from the indole derivatives and brown color pigment formation in the culture supernatant. The culture supernatants from L-tryptophan fed culture turned pigmented over a period of time (Fig. 1A) compared to the control indicating the possible pigmented exometabolite accumulation in the medium (Fig. S1A, ESI†). Furthermore, the TLC profiling of the ethyl acetate fraction obtained from the culture supernatants of the L-tryptophan fed chemotrophic cultures of strain JA2 showed an array of color bands (yellow, red, and pink band) and brown color metabolites as compared to control (Fig. 2A). Exometabolite HPLC profiling detected the array of indole derivatives under the L-tryptophan fed conditions but not under control conditions (Fig. S1C). The purified major pigments from preparative TLC were subjected to HPLC analysis and they exhibited significant absorbance in the UV-vis range (270 to 290 and 400 to 500 nm; Fig. 2B).

Fig. 1 Growth, pigment and indole production in strain JA2 under L-tryptophan fed and control conditions. (A) Control and tryptophan (Trp) fed cultures and their respective supernatants; (B) growth, pigment and indole formation in L-tryptophan fed culture and indole production in control (yellow triangles) in strain JA2.

Fig. 2 Pigmented metabolites’ TLC and HPLC analysis. (A) TLC profiling of ethyl acetate fractions of control and L-tryptophan (Trp) fed conditions; (B) HPLC chromatogram of pink and yellow pigments and the insert shows the absorbance spectrum of respective pigments.

3.2 HPLC based exometabolite profiling

HPLC-based exometabolite profiles of extracts obtained from L-tryptophan fed cultures were distinct from control, wherein more metabolites with specific absorbance at 270, 280, and 290 nm were found under L-tryptophan fed conditions (Table 1). Metabolites showing strong absorbance at 280–290 nm are indicative of the production of indole derivatives in L-tryptophan fed cultures, while they were not found in control (Fig. S1C, ESI†). The LC–MS analysis of ethyl acetate extracts of L-tryptophan fed conditions detected more numbers of metabolic features in negative ionization mode as compared to positive (Table 1). LC–MS/MS analysis revealed a total of thirty-six metabolic features from L-tryptophan fed cultures (Table 1), wherein thirteen metabolites were confirmed, three metabolites were putatively identified, and the remaining were unidentified (Table 1). Metabolites R_t, absorption and molecular ion masses are presented in Table 1 and some metabolites were identified based on mass fragmentation, absorption spectrum, and authentic standards. The metabolite peak at R_t 7.9 min had absorption maxima at 275 nm with an ion mass of 219 [M⁻] and was identified as a hydroxytryptophan. R_t at 10.9 min had absorption maxima of 260 and 290 nm and an ion mass of 207 [M⁻] and was identified as 5,6-dihydroxy indole-acetic acid (IAA). Peak R_t at 10.9 min had absorption maxima at 270, 280, and 290 nm with a molecular mass of 207 [M⁻], and was identified as dihydroxy methyl indole carboxylic acid. R_t at 15.6 min having absorption maxima at 270, 272, and 288 nm with a molecular mass of 175 [M⁻] was identified as 5-hydroxy indole acetaldehyde. Peak [R_t] at 14.3 min having absorption maxima at 275 and 288 nm with a molecular mass of 191 [M⁻] was identified as 5-hydroxy indole acetic acid, while R_t at 15.5 min having an absorption of 260 and 292 nm with a molecular mass of 219 [M⁻] was identified as hydroxy indole pyruvate. R_t at 16.3 min having absorption maxima of 290 nm with a molecular mass of 147 [M⁻] was identified as hydroxymethyl indole. R_t at 18 min had absorption maxima at 254 nm with a molecular mass of 137 [M⁻] and was identified as 4-hydroxybenzoic acid, R_t at 22 min with absorption maxima at 280 nm with a molecular mass of 175 [M⁻] was identified as hydroxy indole acetic acid, and R_t at 22 min with absorption maxima of 275 and 300 nm with a molecular mass of 175 [M⁻] was identified as 5-hydroxyindole acetaldehyde (Table 1). Some of the metabolites remained unidentified due to their complex molecular formulas, multiple hits, and due to lack of authentic standards (Table 1).

Table 1 Tryptophan derived metabolites produced by Rubrivivax benzoatilyticus JA2 under L-tryptophan fed chemotrophic conditions

S. no.	R t	Generated formula	Exact mass	Neg. mode	Pos. mode	ppm	Absorbance [nm]	Metabolites
a These metabolites are confirmed by the retention time, mass fragmentation through the authentic standards and by comparison with the available database. b These metabolites are putatively identified on the basis of molecular formula, absorption spectra, and mass in comparison with the database.UN, unidentified; no suitable match of molecular formula in the database; Rt, retention time; Neg., negative ionization mode; Pos., positive ionization mode. 5-OH-IAA, 5-hydroxyindole-3-acetic acids; OH-KYNA, hydroxyl-kyneuric acid; 5-OH IAA, 5-hydroxy indole-3-acetic acid; 5-OHIPyA, 5-hydroxy indole-3-pyruvic acids; IAA, indole-3-acetic acid; OH me-indole, hydroxy-methylindole.
1	5.36	C9H11NO3	181	180		−0.75	272, 280, 288	UN
2	7.22	C8H7NO3	165	164		−0.41	262, 290	UN
3^a	7.9	C11H12N2O3	220	219.77		−0.75	275	5-Hydroxytryptophan
4^a	10.9	C10H9NO4	207	206		−0.45	260, 290	1H-Indole-3-acetic acid, 5,7-dihydroxy
5^a	10.9	C11H9NO4	207	206		−0.86	270, 280, 288	5,6-Dihydroxy IAA/dihydroxy methyl indole carboxylic acid
6	14.3	C10H9NO3	191	190		−0.34	275, 288	UN
7	14.5	C10H7NO3	189	188		−4.25	240, 262, 305	Kynurenic acid
8	15	C10H9NO2	175	174		−5	256, 305	UN
9	15.7	C8H7N	118	117		−0.02	256, 300	UN
10^a	15.5	C11H9NO4	119	218		−1.42	260, 292	Hydroxyindole pyruvate
11^a	15.6	C10H9NO2	175	174		−3.23	272, 288, 290	5-Hydroxyindole acetaldehyde
12^a	16.3	C9H9NO	147	146		−1.36	290	OH methyl-indole
13	17.25	C10H7NO	157	156		−2.48	260, 290	UN
14^a	18	C7H6NO3	138	137		−1.9	254	Hydroxybenzoic acid
15	19.2	C10H7NO2	173	172		−1.08	300	UN
16	19.36	C21H17N3O4	376		377	−1.7	260, 270	UN
17	20.6	C27H19N3O3	433		434	−1.8	272, 278, 290	UN
18^b	20.6	C25H17N3	359		360	−2.39	270, 280, 290	3,3′-(3H-Indol-3-ylidenemethylene)bis(1H-indole)
19	21.8	C10H9N	175	174		−1.42	260, 290, 300	UN
20^a	22	C10H9NO3	191	190		−4.26	275, 295	5-Hydroxy IAA
21^a	22	C10H9NO2	175	174		0.36	260, 300	5-Hydroxyindole acetaldehyde
22^a	22.1	C11H11NO3	205	204		3.11	225, 260, 290	Indole lactic acid
23^b	23	C18H12N2O2	289	287		2.11	272, 280, 288	Di-indol-3-yl-ethanedione
24	23.8	C20H19N3O3	349	350	350	−2.5	280, 290, 300	UN
25	24.1	C10H9NO3	161	160		1.88	258, 286	UN
26^a	24.39	C9H7NO2	161	160		1.34	260, 290	Indole carboxylic acid
27	25.52	C9H7NO	145	144		0.39	260, 300	UN
28^a	26.22	C10H9NO2	175	174		3	270, 280, 290	IAA
29	29.5	C16H12N2O2	264	263		1.24	260, 290	UN
30	34.3	C21H17N3O3	359	358		0.84	258, 290	UN
31	35	C19H12N2O3	316	315		0.57	260, 290	UN
32	35.01	C22H19N2O2	358	358	359	−2.23	270, 280, 290	UN
33^b	48.36	C26H19N3O	389	388		0.72	290	Indole trimer
34	37.2	C18H12N2O2	288	289	289	−2.54	270, 280, 290	UN
35	37.3	C20H17N3O3	347	346		−1.17	260, 290	UN
36	40.14	C20H10N2O2	310	311	312	2.61	260, 270	UN

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3.3 Hydroxytryptophan catabolism by aminotransferase enzyme activity

Hydroxytryptophan catabolism was further validated by the key enzyme aminotransferase activity using cell-free extracts and the activity was confirmed by the detection of hydroxyindole-3-acetic and hydroxyindole-3-pyruvic acids through HPLC and coelution with the authentic standard (Fig. S2A and B, ESI†). Hydroxytryptophan aminotransferase assay can also lead to hydroxyindole-3-acetic acid formation through the IPyA pathway (Fig. S2A and B, ESI†). Collectively, our results revealed that strain JA2 transformed L-tryptophan to hydroxytryptophan which was further catabolized through aminotransferase to hydroxyindole derivatives under chemotrophic conditions.

3.4 Proteomic inventory of strain JA2

To decipher the molecular response of strain JA2 grown on L-tryptophan as the sole source of nitrogen under chemotrophic conditions, an iTRAQ-based quantitative proteomic study was carried out. SDS-PAGE preliminary analysis of global proteomic samples obtained from control and L-tryptophan fed cells under chemotrophic conditions showed a distinct protein band pattern (Fig. S3A and B, ESI†). Further, the proteomic samples were subjected to iTRAQ analysis and the workflow of the study is depicted in Fig. S3C (ESI†). Based on the set parameters mentioned in the materials and methods section on iTRAQ analysis, global proteome iTRAQ analysis detected 2411 proteins against the reference database of strain JA2 (coding 3898 proteins in the genome). iTRAQ analysis could capture 61.86% of the total theoretical proteome of strain JA2 (Fig. S3D, ESI†).

Total proteins detected by iTRAQ analysis were subjected to principal component analysis (PCA), an unsupervised reductive statistical method to discriminate control and L-tryptophan fed conditions. PCA analysis could explain 92% of the variation between control and L-tryptophan-fed proteomes, wherein component one (PC1) resolved 81% of the variation and component two (PC2) 11%, respectively (Fig. 3A). The PCA score plot separated control and L-tryptophan fed samples into distinct clusters indicative of variations in proteomes (Fig. 3A).

Fig. 3 PCA analysis and functional classification of iTRAQ quantified proteins. (A) PCA scatter plot of iTRAQ quantified proteins from control and tryptophan-fed chemotrophic states; prediction ellipses indicate 0.95 probability; (B) functional classification of deferentially regulated proteins; proteins with significance threshold score ≥20 above and fold change >1.25 and <0.8 were considered as differentially regulated and these proteins were functionally classified according to the KEGG database.

3.4.1 Functional classification of differentially regulated proteins. Applying the criteria for quantitation of iTRAQ detected proteins as mentioned in the materials and methods section, a total of 210 proteins were identified as significantly differentially regulated, wherein 87 were upregulated and 123 were downregulated. The linear regression analysis of the log₂ fold change values of differentially regulated proteins from two biological replicates displayed a correlation coefficient value of R = 0.8, R₂ = 0.656 (Fig. S4, ESI†), showing that the data were reproducible with a linear equation of y = 0.7842X (slope = 0.78 and intercept = −0.0012, Fig. S4, ESI†). The differentially regulated proteins were further subjected to in silico analysis, to determine their physico-chemical properties (MW, isoelectric point (pI), and GRAVY) (https://www.expasy.org). The GRAVY value for a protein is calculated as the sum of the hydropathy values of all the amino acids divided by the number of residues. A negative GRAVY value indicates hydrophilicity and a positive value indicates hydrophobicity, and GRAVY analysis revealed that 64.8% of the proteins detected in the current study were hydrophilic (globular) and 35.2% were hydrophobic (membrane bound) (Fig. S5A, ESI†). The plots of molecular mass vs. pI of differentially regulated proteins indicated two clusters: pI range of 5.0 to 7.8 and 8 to 12.0 respectively (Fig. S5B, ESI†). A pI value <7 shows the protein to be acidic and >7 represents basic protein (Fig. S5B, ESI†).

A total of 210 differentially regulated proteins were classified into different categories, based on their molecular and cellular function, according to the KEGG pathway (Fig. 3B). These proteins were further grouped into eighteen functional categories including membrane transport, signal response, central carbon metabolism, aromatic amino acid metabolism, carbohydrate metabolism, vitamins and cofactors, folding and sorting, hypothetical proteins, metabolism of amino acids, energy metabolism, nucleotide metabolism, transcription, and translation (Fig. 3B). Furthermore, the highly upregulated proteins were grouped into eighteen categories including signaling sensors, membrane signal histidine kinases, and membrane transport proteins (Fig. 3B). These upregulated proteins were observed in conjunction with aromatic compound metabolism, carbohydrate metabolism, amino acid metabolism, vitamins and cofactors, nucleotide metabolism, energy-related processes, transcription, translation, as well as hypothetical proteins (Fig. 3B). Hypothetical proteins showed the highest differential regulation among all categories (Fig. 3B) under L-tryptophan-fed conditions. The percentage ratio of differentially upregulated proteins was as follows: membrane proteins related to signalling 10.4%, aromatic amino acid metabolism 3.44%, lipid metabolism 5.7%, carbohydrate metabolism 6.8%, metabolism 3.4%, stress 5.7%, vitamins and cofactors 4.6%, transcription 6.8%, translation 3.4%, membrane transport 11.5% and hypothetical proteins 25.2% (Fig. 4A). The percentage ratio of downregulated proteins was as follows: signal related 8.1%, amino acid metabolism 5.7%, fatty acid metabolism 6.5%, lipid metabolism 1.62%, carbohydrate metabolism 5.71%, metabolism 7.31%, DNA repair 2.3%, stress 1.62%, vitamins and cofactors 7.31%, photosystem 8.13%, transcription 1%, translation 5.7%, membrane transport 8.13%, electron transport chain (ETC) 8.1%, antioxidant 4%, shikimate pathway 0.83%, and hypothetical proteins 18.7% (Fig. 4B).

Fig. 4 Pie chart representing functional categories of upregulated proteins (A) and downregulated proteins (B). Proteins with significance threshold score ≥20 and fold change >1.25 and <0.8 were considered as differentially regulated and these proteins were functionally classified according to the KEGG database.

4. Discussion

Strain JA2 is unable grow at the expense of aromatic compounds as the sole source of carbon; it uses aromatic amino acids as a nitrogen source under phototrophic (anaerobic) as well as chemotrophic (aerobic) growth modes. However, much of the previous reports on aromatic compound metabolism explored the remarkable biotransformation potential of strain JA2 under phototrophic conditions.^{19,29,32–34} Recent studies on the chemotrophic metabolism of aromatic amino acids like L-phenylalanine and L-tryptophan by strain JA2 revealed hitherto unknown new metabolic processes and novel metabolite production. These studies suggest that strain JA2 uses different metabolic and molecular processes to metabolize aromatic amino acids under varying growth conditions. The present study elucidates the molecular responses of strain JA2 when grown on L-tryptophan under chemotrophic conditions using an integrated global proteomic and metabolic profiling approach. Proteomics and metabolomics are two powerful functional genomic tools that can capture different layers of cellular function and their integration thus reveals a true snapshot of the molecular phenotype of cells.

4.1 Molecular response of strain JA2 to L-tryptophan-fed chemotrophic conditions

We employed metabolite profiling to identify the metabolites that represent the final products of gene expression, thus providing a snapshot of the underlying molecular processes. It is widely acknowledged that alteration in growth conditions can alter the metabolic processes, and in agreement with this pigment formation was observed in strain JA2 cultures that were grown on L-tryptophan under chemotrophic conditions (Fig. 1A and B). Similarly, we reported previously the production of novel pigments from L-phenylalanine-fed chemotrophic cultures of strain JA2.^21,22 There is increasing evidence that the dynamic changes in environmental exposure to aromatic compounds, including aromatic amino acids like L-tryptophan, can modulate microbial physiology and potentially rewire the metabolism.^{19,21,22,29,32} Sensing the extracellular cues is pivotal for survival and bacteria sense and thereby modulate their metabolic process; in this regard we observed significant upregulation of proteins related to signal transduction such as histidine kinase-like protein, PAS/PAC sensor hybrid histidine kinase, and diguanylate cyclase/phosphodiesterase (Table 2 and Fig. 5), which are involved in the modulation of gene expression in response to changes in the external environment.^29,35 Several transcriptional factors, including the TetR family, YidE/YbjL duplication, Sel1 domain-containing protein, transcriptional activator domain-containing protein, silent information regulator protein Sir2, transcription regulator proteins, and FliA/WhiG family RNA polymerase sigma 28 subunits, were upregulated indicating the orchestration of gene expression required to adapt to the metabolic demands of cells under chemotrophic conditions (Table 2).

Table 2 Differentially regulated proteins detected by iTRAQ analysis in L-tryptophan fed cells of Rubrivivax benzoatilyticus JA2

Accession no.	Description of proteins	Avg. FC (tryptophan fed/control)	log2_Fc (tryptophan fed/controls)	SD (standard dev.)
Protein ratios (L-tryptophan fed/control) identified under chemotrophic conditions were log2 transformed, and values are mean ± standard deviation of three independent experiments. Proteins discovered in at least two out of three replicates with two peptide and one unique peptide and P-value ≤0.05 (significance threshold of peptide score >20) are all presented in the table. Protein fold change (FC) ratios (tryptophan fed/control) of identified proteins were log 2 transformed and the values presented are average of log 2FC ratios obtained from biological triplicate experiments (as mentioned in the Materials and methods section). Protein fold change ≥1.25 (log2 FC ≥ 0.22) was considered upregulation and fold change ≥0.8 (log2 FC ≥ −0.22) was considered downregulation. Bold denotes up-regulated proteins and italic denotes down-regulated proteins.
Signal related proteins
giI332110811	Signal recognition particle protein	1.285062	0.250807	0.113059
giI332107759	Kinase-like protein	1.258988	0.230294	0.179213
giI332107765	Putative signal peptide protein	17.399429	2.856437	0.190705
giI332110892	Twitching motility signal transduction protein	1.530321	0.425476	0.147247
giI332110828	YidE/YbjL duplication	3.200660	1.163357	0.439744
giI332111620	Response regulator	2.259650	0.815210	0.101321
giI332112187	PAS/PAC sensor hybrid histidine kinase	1.553856	0.440741	0.060059
giI332107914	TetR family transcriptional regulator	4.816176	1.571980	0.552208
giI332109371	Diguanylate cyclase/phosphodiesterase	1.479512	0.391712	0.262764
giI332108263	Crp/FNR family transcriptional regulator	0.724264	−0.322613	0.013171
giI332108264	Cyclic glucan phosphoglycerol modification protein	1.219412	0.198369	0.030267
giI332111653	Multi-sensor hybrid histidine kinase	0.650858	−0.429461	0.163594
giI332110816	N-Acetyl-anhydromuranmyl-L-alanine amidase	0.729942	−0.314792	0.040106
giI332109370	Putative high potential iron–sulfur (hipip) signal peptide protein	0.743768	−0.296032	0.044503
giI332110893	Twitching motility protein	0.777192	−0.252071	0.271268
giI332111874	AraC family transcriptional regulator	0.769219	−0.262383	0.183645
giI332111446	Methyl-accepting chemotaxis sensory transducer	0.524714	−0.644931	0.240615
giI332110825	CheW protein	0.424778	−0.856192	0.065022
giI332111479	Response regulator receiver protein	0.444271	−0.811321	0.067809
giI332111651	Osmosensitive K^+ channel signal transduction histidine kinase	0.296164	−1.216841	0.018921
giI332111476	Methyl-accepting chemotaxis sensory transducer	0.688727	−0.372912	0.654661
giI332111446	Methyl-accepting chemotaxis sensory transducer	0.524712	−0.644921	0.240615
Amino acid/aromatic amino acid
giI332111318	Putative amidase	1.623396	0.484520	0.3009423
giI332107776	Phenylalanine 4-monooxygenase	1.619804	0.482305	0.0951972
giI332112186	Tryptophanase/L-cysteine desulfohydrase PLP-dependent	21.740121	3.079161	0.0774442
giI332108048	Carbamoyl-phosphate synthase L chain ATP-binding protein	0.428892	−0.846552	0.048883
giI332109992	Carboxyl transferase	0.604910	−0.502671	0.075871
giI332108280	Urease accessory protein UreD	0.756294	−0.279323	0.034407
giI332111194	Glycine cleavage system H protein	0.662492	−0.411752	0.252108
giI332109142	FAD linked oxidase domain-containing protein	0.786068	−0.240715	0.084512
giI332112222	S-Adenosyl-L-homocysteine hydrolase	0.530532	−0.633871	0.078485
Lipid metabolism
giI332109908	Geranyltranstransferase	1.524236	0.421493	0.218178
giI332110050	Putative phospholipase A1	1.421203	0.351504	0.042938
giI332112215	Lipid A biosynthesis acyltransferase	1.562746	0.446445	0.250501
giI332108133	Lipoprotein YaeC family	1.551964	0.439521	0.039473
giI332109922	Hydroxyneurosporene synthase	1.290276	0.254856	0.169613
giI332108566	Esterase/lipase/thioesterase family protein	1.206379	0.187623	0.198624
giI332110872	Acyltransferase WS/DGAT/MGAT	1.207434	0.188498	0.056543
giI332109204	Pullanase-associated protein	1.235948	0.211838	0.006892
giI332108038	Acetyl-CoA acetyltransferase	0.755801	−0.279983	0.006328
giI332108563	Modular polyketide synthase	0.795594	−0.228672	0.051645
giI332109100	Acyltransferase	0.755801	−0.279981	0.075156
giI332108035	Isovaleryl-CoA dehydrogenase	0.427782	−0.849143	0.023989
giI332111406	Acyl carrier protein ACP	0.738686	−0.302881	0.096934
giI332108046	Enoyl-CoA hydratase/isomerase	0.529848	−0.635163	0.067127
giI332108044	Propionyl-CoA carboxylase	0.420041	−0.867432	0.102905
giI 332108049	Hydroxymethylglutaryl-CoA lyase	0.791216	−0.234181	0.027697
giI332108049	Hydroxymethylglutaryl-CoA lyase	0.791216	−0.234183	0.027697
giI332108116	Propionyl-CoA carboxylase subunit alpha	0.592150	−0.524723	0.041921
giI332109504	Butyryl-CoA dehydrogenase	0.555028	−0.588741	0.338557
giI332109978	Metallophosphoesterase	0.798696	−0.224782	0.037705
Carbohydrate metabolism
giI332107698	Radical SAM family protein	1.489623	0.398523	0.158851
giI332112596	Blue (type 1) copper domain protein	2.501581	0.916923	0.211158
giI332108733	Methyltransferase FkbM family protein	1.750670	0.559998	0.145732
giI332112236	Dihydroneopterin aldolase	1.405922	0.340694	0.149005
giI332108628	Precorrin-2 C20-methyltransferase	1.944139	0.664819	0.066500
giI 332108584	Glycosyl transferase group 1	1.415214	0.347281	0.273457
giI332108769	Group 1 glycosyl transferase	1.452036	0.372966	0.163735
giI332108575	Galactosamine-containing minor teichoic acid biosynthesis	1.518870	0.417966	0.127898
giI332111130	Polyhydroxyalkanoate depolymerase intracellular	1.275586	0.243406	0.219222
giI332110443	Poly-beta-hydroxybutyrate polymerase-like protein	1.499534	0.405154	0.722681
giI332108738	Inositol monophosphatase	1.492906	0.400724	0.024237
giI332109093	6,7-Dimethyl-8-ribityllumazine synthase	1.208715	0.189558	0.063707
Metabolism related proteins
giI332107698	Radical SAM family protein	1.489623	0.398523	0.158851
giI332112596	Blue (type 1) copper domain protein	2.501581	0.916923	0.211158
giI332108733	Methyltransferase FkbM family protein	1.750670	0.559998	0.145732
giI332111446	Methyl-accepting chemotaxis sensory transducer	0.524712	−0.644912	0.240615
giI332110825	CheW protein	0.424778	−0.856193	0.065022
giI332111479	Response regulator receiver protein	0.444271	−0.811321	0.067809
giI332111651	Osmosensitive K^+ channel signal transduction histidine kinase	0.296164	−1.216843	0.018912
giI332111971	Bifunctional 3-demethylubiquinone-9 3-methyltransferase/2-octaprenyl-6-hydroxy phenol methylase	0.778571	−0.250292	0.011096
Stress related protein
giI332111778	Heat-inducible transcription repressor	1.700926	0.531172	0.255731
giI332108229	Cysteine proteinase	1.426278	0.355068	0.241143
giI332108269	Transthyretin	1.384174	0.325103	0.092155
giI332108410	NmrA-like protein	1.265308	0.235316	0.180224
giI332107928	CcmE/CycJ protein	0.726850	−0.319032	0.131878
giI332109993	Membrane ATPase/protein kinase	0.666412	−0.405853	0.034699
giI332108331	co-Chaperonin GroES	0.745458	−0.293762	0.192393
giI332108280	Urease accessory protein UreD	0.756294	−0.279321	0.034406
giI332110848	Alkyl hydroperoxide reductase/thiol specific antioxidant/Mal allergen	0.641842	−0.443413	0.010722
giI332110906	Alkyl hydroperoxide reductase/thiol specific antioxidant/Mal allergen	0.700231	−0.356342	0.144799
giI332111629	Multiple antibiotic resistance (MarC)-like protein	0.528754	−0.637231	0.254592
giI332108118	Glyoxalase/bleomycin resistance protein/dioxygenase	0.659913	−0.415654	0.142838
giI332109995	GntR family transcriptional regulator	0.699616	−0.357221	0.02108
Vitamins and cofactors metabolism
giI332108336	TMAO/DMSO reductase	0.711854	−0.339882	0.032776
giI332110824	Biotin carboxyl carrier protein	0.671958	−0.397561	0.182103
giI332108117	Biotin synthase	0.783324	−0.244213	0.063372
giI332112748	Adenosylcobinamide-phosphate synthase	0.780775	−0.247471	0.313741
giI332112223	5,10-Methylenetetrahydrofolate reductase	0.713350	−0.337781	0.009864
giI332109937	Coenzyme B12-binding aerobic repressor	0.786061	−0.240723	0.066481
Photosystem and electron transport chain related proteins
giI332112043	F0F1 ATP synthase subunit delta	1.260569	0.231556	0.113997
giI332109917	Photosynthetic reaction center subunit	1.259735	0.230901	0.078051
giI332108428	Pyruvate flavodoxin/ferredoxin oxidoreductase domain protein	1.302798	0.264514	0.067028
giI332112736	PUCC protein	1.329158	0.284546	0.203301
giI332109448	Oxidoreductase	1.790259	0.582360	0.416203
giI332111162	NADH dehydrogenase (quinone)	1.255974	0.227912	0.248718
giI332112043	F0F1 ATP synthase subunit delta	1.260561	0.231556	0.113997
giI332109917	Photosynthetic reaction center subunit L	1.259735	0.230901	0.078051
giI332111893	Cytochrome d1 heme region	0.795332	−0.228991	0.072877
giI332109128	Cytochrome b/b6 domain-containing protein	0.758833	−0.275972	0.170348
giI332111159	NADH-ubiquinone oxidoreductase subunit J	0.674932	−0.393143	0.113572
giI332107504	Oxidoreductase FAD/NAD(P)-binding protein	0.563364	−0.573831	0.203011
giI332109911	Chlorophyllide reductase iron protein subunit X	0.789292	−0.236621	0.096708
giI332109144	Ubiquinone/menaquinone biosynthesis methyltransferase	0.787392	−0.239031	0.032531
giI332109504	Butyryl-CoA dehydrogenase	0.555028	−0.588741	0.338557
giI332109142	FAD linked oxidase domain-containing protein	0.786068	−0.240712	0.084500
giI332107928	CcmE/CycJ protein	0.726850	−0.319032	0.131878
giI332111971	Bifunctional 3-demethylubiquinone-9 3-methyltransferase/2-octaprenyl-6-hydroxy phenol methylase	0.795332	−0.228991	0.011096
giI332111893	Cytochrome d1 heme region	0.758834	−0.275973	0.072877
giI332109128	Cytochrome b/b6 domain-containing protein	0.674932	−0.393141	0.170348
giI332111159	NADH-ubiquinone oxidoreductase subunit J	0.563364	−0.573832	0.113572
giI332107504	Oxidoreductase FAD/NAD(P)-binding protein	0.778571	−0.250291	0.011096
Transcription
giI332107591	FliA/WhiG family RNA polymerase sigma 28 subunit	1.670856	0.513336	0.236045
giI332108120	Transcription regulator protein	1.640899	0.495244	0.336495
giI332112137	Nucleoside diphosphate kinase regulator	1.609610	0.475992	0.182085
giI332112180	Sel1 domain-containing protein	1.542559	0.433442	0.216528
giI332109398	Transcriptional activator domain-containing protein	1.421898	0.351993	0.253212
giI332107624	Silent information regulator protein Sir2	1.901756	0.642777	0.808419
giI332110749	Chromosome segregation and condensation protein ScpA	0.725120	−0.321426	0.100932
giI332109844	Transcriptional regulatory protein	0.464472	−0.766855	0.167103
giI332111449	Putative two-component response-regulatory protein YehT	0.753769	−0.282673	0.080353
giI332108330	Transcriptional regulator NifA	0.723226	−0.324031	0.088084
giI332109946	Zinc-binding alcohol dehydrogenase	0.787379	−0.239043	0.133698
giI332107887	Preprotein translocase subunit SecY	0.760956	−0.273181	0.124596
giI332109483	Integration host factor alpha-subunit	0.766809	−0.265523	0.034022
giI332111850	Heavy metal translocating P-type ATPase	0.765950	−0.266645	0.081188
giI332107543	Nucleoid protein Hbs	0.785261	−0.241743	0.282477
giI332109092	Putative N utilization substance B	0.785261	−0.241741	0.161277
giI332109844	Transcriptional regulatory protein	0.464472	−0.766851	0.167103
giI332111449	Putative two-component response-regulatory protein YehT	0.753769	−0.282673	0.080353
Protein synthesis
giI32109861	50S ribosomal protein L33	1.271606	0.240281	0.551598
giI332108000	Sigma 54 modulation protein/ribosomal protein S30EA	1.556705	0.442571	0.047306
giI332111487	GTP-binding protein	1.556705	15.567050	0.214125
giI332109232	50S ribosomal protein L34	4.447049	1.492240	0.616053
giI332107870	30S ribosomal protein S19	0.707446	−0.34609	0.184761
giI332107880	30S ribosomal protein S14	0.780216	−0.24818	0.096944
giI332112450	Ribosome recycling factor	0.777953	−0.25109	0.099912
giI332108097	Ribosomal protein l11	0.787738	−0.23859	0.048772
giI332108090	30S ribosomal protein S12	0.798265	−0.22531	0.172108
giI332112098	ABC transporter-like protein	0.797229	−0.22661	0.070808
giI 332111866	TPR domain-containing protein	0.793412	−0.23141	0.165118
giI 332110456	HPr kinase	0.791272	−0.23411	0.103097
Transporter and membrane proteins
giI332109251	RND family efflux transporter MFP subunit	1.317720	0.275903	0.059969
giI332107915	Efflux transporter RND family MFP subunit	1.385910	0.326357	0.048904
giI332112091	Putative ABC transporter ATP-binding protein	1.417498	0.348893	0.222226
giI332109313	Ferrous iron transport protein B	1.279440	0.246422	0.109868
giI332111700	Putative transport system ATP-binding protein	1.264302	0.234520	0.041694
giI332111371	Putative transmembrane sensor histidine kinase transcription regulator protein	1.259109	0.222431	0.178917
giI332111172	TRAP dicarboxylate transporter subunit DctP	1.311781	0.271386	0.081982
giI332112665	Putative lipoprotein	1.365804	0.311743	0.128253
giI332109251	RND family efflux transporter MFP subunit	1.317720	0.275903	0.059968
giI332107915	Efflux transporter RND family MFP subunit	1.385910	0.326357	0.048904
giI332112091	Putative ABC transporter ATP-binding protein	1.417498	0.348893	0.222226
giI332109313	Ferrous iron transport protein B	1.279440	0.246422	0.109868
giI332107916	RND efflux system outer membrane lipoprotein	1.596582	0.467864	0.149292
giI332107926	Periplasmic protein thiol	0.707790	−0.345612	0.044628
giI332111814	Putative polar amino acid transport system ATP-binding protein	0.773599	−0.256731	0.176880
giI332107951	TonB-dependent siderophore receptor family protein 13	0.689158	−0.372283	0.069959
giI332108054	Acetate permease	0.779919	−0.248561	0.079658
giI332110494	Putative transmembrane protein	0.729962	−0.314762	0.072124
giI332110561	Potassium transporter	0.770433	−0.260832	0.042607
giI332107546	Anaerobic c4-dicarboxylate membrane transporter family protein	0.744580	−0.294931	0.103902
giI332108262	Molybdenum cofactor sulfurylase	0.702288	−0.353412	0.173396
giI332112098	ABC transporter-like protein	0.797229	−0.226613	0.070807
giI332109993	Membrane ATPase/protein kinase	0.666412	−0.405852	0.034699
Electron transport chain proteins
giI332111669	Cytochrome d ubiquinol oxidase subunit II	0.522634	−0.648871	0.159393
giI332109185	Formate dehydrogenase subunit alpha	0.680776	−0.384532	0.104479
giI332109907	Cytochrome c-552 precursor	0.670386	−0.399921	0.052807
Antioxidant
giI332110848	Alkyl hydroperoxide reductase/thiol specific antioxidant/Mal allergen	0.641842	−0.443412	0.010721
giI332110906	Alkyl hydroperoxide reductase/thiol specific antioxidant/Mal allergen	0.700231	−0.356341	0.144798
giI332111629	Multiple antibiotic resistance (MarC)-like protein	0.528753	−0.637232	0.254592
giI332110560	Benzoate transporter	0.655899	−0.421753	0.076028
giI332108118	Glyoxalase/bleomycin resistance protein/dioxygenase	0.659913	−0.415652	0.142837
DNA replication, repair and synthesis
giI332112171	DNA-directed DNA polymerase	1.360696	0.307996	0.150143
giI332111924	Holliday junction DNA helicase RuvA	1.286508	0.251932	0.273658
giI332110436	Anaerobic ribonucleoside triphosphate reductase	0.791772	−0.233481	0.217804
giI332108691	Phosphoribosylaminoimidazole carboxylase ATPase subunit	0.735638	−0.307022	0.114037
Shikimate pathway
giI332109290	Isochorismate synthase	0.732438	−0.311382	0.053731
giI332111971	Bifunctional 3-demethylubiquinone-9 3-methyltransferase/2-octaprenyl-6-hydroxy phenol methylase	0.778571	−0.250291	0.011096
Cell wall proteins
giI332109978	Metallophosphoesterase	0.798696	−0.224781	0.037705
giI332107758	Peptidyl-dipeptidase Dcp	0.790719	−0.234812	0.089857
giI332109465	Hydrolase	0.476220	−0.741871	0.165655
giI332111521	Phospho-N-acetylmuramoyl-pentapeptide-transferase	0.788817	−0.237223	0.108067
giI332108581	Polysaccharide deacetylase	0.550108	−0.597641	0.114778
giI332108759	Mannose-1-phosphate guanylyltransferase/mannose-6-phosphate isomerase	0.798645	−0.224843	0.071808
giI332110816	N-Acetyl-anhydromuranmyl-L-alanine amidase	0.729942	−0.314792	0.040107
giI332112579	HAD-superfamily hydrolase subfamily IA variant 3	0.762906	−0.270621	0.040375
giI332108541	Alpha/beta hydrolase fold protein	0.765301	−0.267492	0.079825
Hypothetical proteins
giI332109514	Hypothetical protein RBXJA2T_08925	1.279060	0.246126	0.104975
giI332112531	Hypothetical protein RBXJA2T_18423	1.307658	0.268238	0.085321
giI332111655	Hypothetical protein RBXJA2T_14968	1.380884	0.322724	0.083241
giI332110463	Hypothetical protein RBXJA2T_10691	1.331688	0.286447	0.054846
giI332111444	Hypothetical protein RBXJA2T_13904	1.670387	0.513056	0.227013
giI332108437	Hypothetical protein RBXJA2T_04998	1.357358	0.305541	0.215566
giI332111125	Hypothetical protein RBXJA2T_12552	1.353201	0.302472	0.175941
giI332109166	Hypothetical protein RBXJA2T_07160	1.350034	0.300131	0.169409
giI332107851	Hypothetical protein RBXJA2T_02040	1.328880	0.284336	0.176266
giI332111706	Hypothetical protein RBXJA2T_15223	1.346939	0.297828	0.239591
giI332109490	Hypothetical protein RBXJA2T_08805	1.317135	0.275412	0.224887
giI332108504	Hypothetical protein RBXJA2T_05333	1.720186	0.542432	0.246681
giI332112575	Hypothetical protein RBXJA2T_18643	1.774418	0.573472	0.210987
giI332110761	Hypothetical protein RBXJA2T_11453	2.892699	1.062191	0.312293
giI332107824	Hypothetical protein RBXJA2T_01905	2.426420	0.886418	0.329965
giI332111907	Hypothetical protein RBXJA2T_16222	1.609389	0.475848	0.123126
giI332108499	Hypothetical protein RBXJA2T_05308	1.536876	0.429752	0.226361
giI332112648	Hypothetical protein RBXJA2T_19014	1.312625	0.272029	0.086227
giI332112608	Hypothetical protein RBXJA2T_18814	1.285558	0.251192	0.161866
giI332109471	Hypothetical protein RBXJA2T_08710	1.302381	0.264194	0.086941
giI332111436	Hypothetical protein RBXJA2T_13864	1.256437	0.220289	0.120844
giI332107518	Hypothetical protein RBXJA2T_00345	1.199880	0.182221	0.047647
giI332107846	Hypothetical protein RBXJA2T_02015	0.800367	−0.222684	0.025080
giI332112597	Hypothetical protein RBXJA2T_18753	0.789716	−0.236082	0.082459
giI332108659	Hypothetical protein RBXJA2T_06120	0.786881	−0.239678	0.092913
giI332111328	Hypothetical protein RBXJA2T_13329	0.777793	−0.251293	0.097477
giI332107821	Hypothetical protein RBXJA2T_01890	0.776611	−0.252814	0.044973
giI332109359	Hypothetical protein RBXJA2T_08148	0.776455	−0.253016	0.069663
giI332111066	Hypothetical protein RBXJA2T_12257	0.773731	−0.256530	0.047320
giI332109904	Hypothetical protein RBXJA2T_09407	0.773582	−0.256723	0.082530
giI332108050	Hypothetical protein RBXJA2T_03051	0.773230	−0.257178	0.178172
giI332112705	Hypothetical protein RBXJA2T_19301	0.772483	−0.258145	0.112544
giI332108447	Hypothetical protein RBXJA2T_05048	0.762806	−0.270752	0.184806
giI332110026	Hypothetical protein RBXJA2T_10029	0.761009	−0.273109	0.036106
giI332107818	Hypothetical protein RBXJA2T_01875	0.743329	−0.296616	0.098524
giI332112191	Hypothetical protein RBXJA2T_17636	0.732628	−0.311118	0.135147
giI332111402	Hypothetical protein RBXJA2T_13694	0.725228	−0.321268	0.042410
giI332111415	Hypothetical protein RBXJA2T_13759	0.720756	−0.327454	0.017046
giI332111212	Hypothetical protein RBXJA2T_12989	0.717391	−0.332133	0.081103
giI332110876	Hypothetical protein RBXJA2T_12032	0.708705	−0.344316	0.116150
giI332111846	Hypothetical protein RBXJA2T_15917	0.708024	−0.345278	0.260682
giI332107981	Hypothetical protein RBXJA2T_02702	0.690276	−0.370663	0.078981
giI332110540	Hypothetical protein RBXJA2T_11076	0.676633	−0.390624	0.324398
giI332109437	Hypothetical protein RBXJA2T_08540	0.588054	−0.530936	0.085965
giI332110899	Hypothetical protein RBXJA2T_12147	0.578375	−0.547531	0.290891

---

Fig. 5 Model illustrating the molecular phenotype of strain JA2 grown on L-tryptophan under the chemotrophic state. Solid up arrows in red indicate upregulation and down arrows in green indicate downregulation of the metabolic process. Proteins in red indicate upregulation, green indicates downregulation and black indicates proteins whose levels were unchanged. Dotted arrows denote multiple steps in the pathway; metabolites which are in high levels are indicated in red and those in low levels in green. SucC, SucD, succinate synthase subunits; SdhC, D, succinate dehydrogenase subunits; Fdh, fumarate dehydrogenase; ICS, isochorismate synthase; 5,6 bp methylase, bifunctional 3-demethylubiquinone-9 3-methyltransferase/2-octaprenyl-6-hydroxy phenol methylase; THF, tetrahydrobiopterine; 5,6 THF, 5,10-methylenetetrahydrofolate reductase; 5-M THF, 5-methyltetrahydrobiopterine; SAM, S-adenosyl methionine; NER, nucleotide excision repair; BER, base excision repair; In-6-p, inositol-6-phosphate; NAG, N-A-acetyl galactosamine; PHB, parahydroxybenzoic acid; UQ, ubiquinone; Mk6, menaquinone; ANT, anthraquinone; Phap, polyhydroxyalkanoate polymerization; phad, polyhydroxyalkanoate depolymerisation; Plpase, phospholipase; Mut A & B, methylmelonyl A and methylmelonyl B; pphA, phenylalanine 4-monooxygenase; Tna, tryptophanase; AAT, aromatic amino transferase; amd, amidase.

Proteins involved in translation initiation and fidelity, such as 30S ribosomal proteins S21 (RBXJA2T_12532), S1 (RBXJA2T_16502), S8 (RBXJA2T_02190), S4 (RBXJA2T_02245), S5 (RBXJA2T_02205), and S3 (RBXJA2T_02145), remained unaffected. However, 50S ribosomal protein L34, sigma 54 modulation protein/ribosomal protein S30EA, and GTP-binding protein, which play an important role in the functional translation machinery, particularly in response to various environmental signals including stress, were upregulated.^3,36,37

Membrane transport-related proteins were highly differentially regulated indicating the dynamic metabolite/ion transport facilitating the cell survival (Table 2) particularly upon exposure to aromatic compounds. Bacteria upregulate proteins related to efflux pumps in response to aromatic compound exposure to prevent cell damage. In line with this, membrane efflux pump proteins such as ABC transporters, and related proteins, as well as proteins related to RND pumps (Table 2) were upregulated, strongly suggesting the involvement of an active efflux system in tryptophan exposure. This correlates with the detection of an array of indole/hydroxyindole derivatives in the culture supernatant (Table 1) and indole derivatives are known to cause cellular damage at high concentrations;^38,39 thus strain JA2 activates the efflux system to avoid toxicity. Detection of 35% of transmembrane proteins (hydrophobic) in GRAVY analysis (Fig. S5A, ESI†) may further supports the active membrane transport system.

DNA gyrase A and DNA topoisomerase IV subunit A, which play a role in the unwinding and decatenation of DNA, showed no significant change; on the other hand, DNA repair proteins RadA and Holliday junction DNA helicase RuvB were upregulated (Table 2). These results imply a possible DNA damage in cells grown on L-tryptophan and this is because the high concentrations of indolic metabolites derived from L-tryptophan may cause DNA damage as reported earlier.^38–41 Further downregulation of nucleotide biosynthesis proteins phosphoribosylaminoimidazole carboxylase ATPase subunit, chromosome segregation and condensation protein ScpA, and anaerobic ribonucleoside triphosphate reductases in L-tryptophan fed cells (Table 2) possibly aimed at minimizing the cellular energy burden as these mechanisms temporally may not offer fitness benefit to the cell.^28,32,34,42

Strain JA2 shows a remarkable ability to survive under different growth modes; surprisingly in the present study photosynthetic bacterial PUCC protein, necessary for the formation of the light-harvesting (LH) complex, and photosynthetic proteins reaction center subunit L (Fig. 4A) were up-regulated (Table 2), indicating active assembly of photosystems. This observation is consistent with the findings that anoxygenic photosynthetic bacteria display light-harvesting complex assembly under aerobic cultures possibly as an energy-adaptive mechanism. Further major proteins related to the electron transport chain (ETC) were largely unaffected and NADH dehydrogenase, the alternate flavoprotein (α/β-subunit), and FoF1 ATP synthase were highly upregulated (Fig. 4A) indicating sustained energy generation to meet the cellular demands.

Furthermore, upregulation of the heat-inducible transcription repressor which negatively controls the expression of heat shock proteins^28,43 is expected as there is no heat stress, and repressing heat shock proteins may be an energy conservation mechanism (Fig. 4B). Proteins related to stress response, such as cysteine proteinase, transthyretin, and NmrA-like protein, were significantly upregulated under L-tryptophan fed conditions, indicating that external L-tryptophan or its derived metabolites may act as stressors (Table 2).

4.2 Proteomics reveals extensive rewiring of metabolism

Proteins related to vitamin and cofactor biosynthesis, such as radical SAM family protein, blue (type 1) copper domain protein, methyltransferase FkbM family protein, and dihydroneopterin aldolase, showed significant upregulation (Table 2). These proteins are characterized by their roles in iron–sulfur cluster protein and play diverse roles in processes such as SAM synthesis, enzyme activation, peptide modification, transcription, and post-translational modification. Additionally, the blue (type 1) copper domain protein⁴⁴ is involved in regulating various processes including lipid metabolism, metabolite biosynthesis, antibiotic production, and coenzyme/cofactor biosynthesis.⁴⁴ Considering their role in various cellular processes, upregulation of these proteins suggests that they may play a pivotal role in cell survival under L-tryptophan-fed conditions.

The majority of proteins related to central carbon metabolism in strain JA2 such as glycolysis (EMP), Entner–Doudoroff (ED) pathway⁴⁵ and pentose phosphate pathway (PPP)⁴⁶ remained unaltered in the present study (Table 2). However, protein-related oxidoreductases such as NADH dehydrogenase protein was highly upregulated along with the protein pyruvate flavodoxin/ferredoxin oxidoreductase domain protein in L-tryptophan fed cells (Table 2), indicating that pyruvate to acetyl CoA conversion may provide the substrate for the TCA cycle or PHA granular formation. Additionally, galactosamine-containing proteins, implicated in cell wall formation, were upregulated (Fig. 4), which is consistent with the observation that proteins related to cell wall biogenesis, including the glycosyl transferase group, group 1 glycosyl transferase, and CDP-glycerol glycerophosphotransferase, were upregulated (Table 2), highlighting their involvement in cell wall biogenesis. These findings strongly suggest that aromatic compound (L-tryptophan) stress may have triggered the cell well reinforcement machinery of strain JA2; a similar kind of cell wall reinforcement under aromatic compound stress was reported in bacteria.^{28,34,47–49}

4.2.1 L-Tryptophan-fed cells displayed altered amino acid metabolism. Largely proteins related to aliphatic amino acid metabolism were unaltered, while proteins involved in the formation of reducing amino acids such as methionine and cysteine were downregulated, suggesting that the synthesis of sulfur-containing amino acids is downregulated (Table 2 and Fig. 5). Further, the majority of aromatic amino acid biosynthesis genes were unaffected, while isochorismate synthase which is involved in the conversion of de novo chorismate to isochorismate (Table 2) was downregulated, implying reduced chorismate to isochorismate conversion and suggesting that chorismate is fluxed to pHBA (parahydroxybenzoic acid) formation (Fig. 5). This is evident from accumulation of 4-hydroxybenzoic acid in the culture supernatant in L-tryptophan fed cells indicating the increased de novo synthesis of hydroxybenzoic acid (Table 1 and Fig. 5). Similarly, previous reports suggest the formation of 4-hydroxybenzoic acid via de novo synthesis in strain JA2 when fed with L-phenylalanine.^21,50 Further downregulation of isochorismate synthase (Fig. 5) may have resulted in low levels of isochorismate, a precursor for menaquinone synthesis, resulting in the downregulation of menaquinone synthesis (Fig. 5). Further this is supported by downregulation of bifunctional 3-demethylubiquinone-9 3-methyltransferase/2-octaprenyl-6-hydroxy phenol methylase involved in menaquinone synthesis (Table 2 and Fig. 5). Similarly, our group previously reported⁴² that cells grown in the presence of glucose showed down-regulation of quinones under the chemotrophic state.

4.2.2 Active fatty acid/lipid oxidation and PHA synthesis under tryptophan-fed conditions. Fatty acid biosynthesis is downregulated under L-tryptophan-fed conditions evident from the downregulation of proteins such as acetyl-CoA acetyltransferase, acyl carrier protein ACP, carboxyl transferase and modular polyketide synthase (Table 2). Similarly, proteins related to lipid biosynthesis such as acyltransferase and metallophosphoesterase were downregulated (Table 2). On the other hand, proteins of lipid catabolism, such as putative phospholipase A1 (PldA) and lipid A biosynthesis acyltransferase, were upregulated (Table 2 and Fig. 4A). These results indicate that lipid and fatty acid catabolism leads to the production of acetyl-CoA and possibly redirects the acetyl-CoA towards PHA formation (Fig. 5). This is evident from the fact that proteins involved in PHA (polyhydroxyalkanoate) formation such as poly-beta-hydroxybutyrate polymerase-like protein are upregulated under L-tryptophan fed chemotrophic conditions (Table 2 and Fig. 5). In a previous study a similar finding is reported wherein acetyl-CoA fluxed toward PHA biosynthesis and the cell accumulated PHAs under aromatic compound stress²⁹ and glucose exposure in strain JA2.⁴² Similarly, L-tryptophan-fed chemotrophic conditions may have triggered stress and activated PHA synthesis to cope with the stress in strain JA2. Furthermore, similar metabolic adaptation was observed in Escherichia coli and Vibrio sp., wherein cells depended on lipid and fatty acid catabolism to survive under energy-deficient conditions.^42,51–53 Detection of 67% globular proteins (hydrophilic) by GRAVY analysis (Fig. S5A, ESI†) further supports the active metabolic processes in strain JA2 grown on L-tryptophan under the chemotrophic state.

4.2.3 Proteo-metabolic profiling captured the diverse L-tryptophan catabolic processes. Proteins related to aromatic amino acid catabolism such as tryptophanase/L-cysteine desulfohydrase PLP-dependent, phenylalanine 4-monooxygenase and putative amidase (Table 2) were highly upregulated indicating the active aromatic amino acid catabolism (Fig. 5). This corroborates well with the detection of various structurally distinct indole derivatives in metabolic profiling (Table 1). Most notably upregulation of tryptophanase/L-cysteine desulfohydrase PLP-dependent involved in indole formation from L-tryptophan (Fig. 5) strongly suggests L-tryptophan catabolism via the indole route. Furthermore, upregulation of phenylalanine 4-monooxygenase, involved in aromatic ring hydroxylation, and putative amidase, involved in the removal of the amino group, plausibly plays a key role in L-tryptophan catabolism as reported previously.^21,24 Though phenylalanine 4-monooxygenase (phhA) catalyzes the conversion of phenylalanine to tyrosine through aromatic ring hydroxylation, it also has an affinity for L-tryptophan thus leading to the formation of hydroxytryptophan as reported earlier.^54–56

In our study, upregulation of phenylalanine 4-monooxygenase correlates with the detection of hydroxytryptophan and other hydroxyindole derivatives and this suggests the role of phenylalanine 4-monooxygenase in the synthesis of hydroxytryptophan (Fig. 5), and subsequent catabolism of hydroxytryptophan may have resulted in the formation of hydroxy indole derivatives (Fig. 5). This is further supported by identification of similar types of hydroxyindole derivatives from both L-tryptophan and hydroxytryptophan fed chemotrophic cultures of strain JA2 (unpublished data). Moreover, the demonstration of aromatic aminotransferase enzyme activity wherein the substrate, hydroxytryptophan, is catabolized through the IPA pathway, leading to the formation of hydroxyindole acetic acid (Fig. S2A and B, ESI†), strongly supports the view of tryptophan conversion to hydroxytryptophan and subsequently to hydroxyindoles. Phenylalanine 4-monooxygenase is known to express in aerobic cultures and upregulation of phenylalanine 4-monooxygenase and detection of hydroxyindoles suggest oxidative catabolism of tryptophan in chemotrophic state.

In addition, the identification of kynurenic acid (Table 1) in metabolic profiling indicates that L-tryptophan is catabolized through the kynurenine pathway. Under aerobic conditions, bacteria catabolize tryptophan via the kynurenine pathway²⁴ and the detection of kynurenic acids hints at L-tryptophan catabolism through this pathway in strain JA2. These findings strongly suggest differential catabolism of L-tryptophan by strain JA2 under chemo and phototrophic states, suggesting that metabolic adaptation of strain JA2 to utilizes L-tryptophan under varying growth modes. Interestingly, the present study showed an accumulation of L-tryptophan-derived pigments (Fig. 2A and B) and these pigments remained unidentified. L-Tryptophan-derived pigments were also reported in fungi Cryptococcus neoformans and C. glabrata in the presence of oxygen (chemotrophic state).⁵⁷ So far identified L-tryptophan-derived pigments from microorganisms are dimers or trimers of indole derivatives, and we speculate that some of these indolic derivatives formed under L-tryptophan-fed conditions may have reacted and converted into pigments. These pigmented metabolites were not formed under anaerobic (photoheterotrophic) conditions, indicating the role of the oxidative enzyme machinery in pigment formation. Similarly, chemotrophic metabolism of phenylalanine in strain JA2 led to anthocyanin-like pigment²³ formation and these results suggest that strain JA2 may harbor hitherto unknown aromatic oxidative metabolism. In the present study, metabolic profiling revealed an array of indolic metabolites many of which remain unidentified, indicating possible diverse L-tryptophan catabolic processes; based on proteomic and metabolite profiling we propose L-tryptophan chemotrophic catabolism (Fig. 5) wherein some amount of L-tryptophan is converted to hydroxytryptophan which acts as a central metabolite from which catabolic routes branch off into (1) the Ehrlich pathway leading to hydroxyindole acids and (2) pigment formation. Some of the tryptophan directly converts to indole through tryptophanase activity, undergoes oxidative catabolism via the kynurenine pathway, or follows the unidentified catabolic route(s). Although metabolites of the Ehrlich and kynurenine pathway were found, corresponding proteins were not detected possibly due to their temporal expression and a time series study may provide more details. However, we could not correlate any up-regulated proteins to pigment synthesis possibly due to a combination of reasons such as lack of structural confirmation of pigments, some housekeeping/non-specific enzyme involvement or our study did not capture the entire expressed proteome due to technical limitations. Moreover, because of the diverse array of indolic metabolites detected and limited genomic information available on aromatic catabolism in strain JA2, the role of possible cryptic metabolic processes in tryptophan catabolism is suggested which needs further investigation. Changing growth conditions in microorganisms are known to activate such cryptic or silent pathways which are otherwise not detected under normal growth conditions.^{10,12,21,58–60} Interestingly, a large number of hypothetical proteins are upregulated in the current study and we speculate that some of these may have a role in tryptophan metabolism which needs further investigation. Here in the present study the growth conditions were altered (chemotrophic) against the preferred growth mode (phototrophic) and thus may have induced alternative tryptophan processes leading to the formation of a diverse array of tryptophan-derived metabolites. The present study highlights the importance of integrating functional omics such as metabolite and proteomic profiling to decipher the molecular responses of living systems. The current study through proteomic profiling captured the underlying molecular adaptations to the chemotrophic state while metabolite profiling revealed tryptophan catabolic diversity in strain JA2. Integration of proteomic and metabolic profiling captured the molecular phenotype of strain JA2 under tryptophan fed conditions which is otherwise difficult to capture from a single omics study. Since adaptation to changing environmental conditions involves a complex interplay between different functional layers such as genome, transcriptome, proteome and metabolome, understanding these processes also requires a more comprehensive approach such as integrated omics. From a generalistic point of view our study highlights that organisms may possess a rich biochemical repertoire to thrive under ever changing environmental conditions and understanding these processes enhances our knowledge about the functioning of biological systems.

5. Conclusions

Anoxygenic photosynthetic bacteria grow under various growth modes using diverse organic substrates, including aromatic compounds. Despite considerable interest in aromatic metabolism in phototrophic growth mode, chemotrophic metabolism and the underlying molecular adaptations remain largely unexplored. The present investigation revealed strategies of strain JA2 to thrive under L-tryptophan fed chemotrophic conditions. Strain JA2 displayed active signal transductions that control gene expression and membrane transport, which is important for survival in a changing environment. These indicate adaptive strategies of cell survival against possible DNA and envelope damage under tryptophan-fed conditions. Strain JA2 also showed extensive rewiring of metabolic pathways, including certain amino acids, fatty acid/lipid oxidation, the TCA cycle, reduced menaquinone synthesis, and possible metabolic adaptations. The metabolic flux towards PHA synthesis and active energy conservation by downregulation of certain obsolete molecular processes suggest a general stress repose strategy and that L-tryptophan-fed chemotrophic conditions may be inducing stress. Furthermore, the study revealed versatile L-tryptophan catabolism evident from a few catabolic routes predicted such as Ehrlich, kynurenine, indole, and hydroxyindole formation, which is distinct from the phototrophic catabolic footprint. The hydroxylation of L-tryptophan by the up-regulated phenylalanine 4-monooxygenase is a key biotransformation step that results in the formation of hydroxy indoles, and their subsequent reactions may result in the formation of pigments. Due to the limitations in identifying unknown metabolites and capturing possible cryptic metabolic processes, the current study provided a partial picture of molecular and metabolic adaptations of L-tryptophan catabolism. Nevertheless, our study suggests that an alerted growth environment combined with integrated metabolomics and proteomics can capture novel biochemical processes. Renewed future efforts, such as time-series transcriptional studies, may provide new insights into the catabolic processes in strain JA2.

Author contributions

S. A., M. M., L. P. M., and V. R. C. conceived and designed the research. S. A. performed the experiments. S. A., M. M., L. P. M., and V. R. C. discussed and analyzed the data. S. A., M. M., and M. L. P. drafted the manuscript. S. A., M. M., M. L. P., V. R. C., and S. C. edited the manuscript. All authors have read and approved the manuscript.

Data availability

The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD055514. Data supporting this article have been included as an ESI† – separate PDF file.

Conflicts of interest

The authors declare no conflict of interest.

Acknowledgements

Shabbir Ahmad acknowledges the University Grants Commission, Govt. of India, for the JRF/SRF fellowship (File No. F1-17.1/201415/MANF-2014-15-MUS-JHA-35979) and SERB NPDF 2022 (PDF/2022/000700).

References

1. M. D. Lebar, J. L. Heimbegner and B. J. Baker, Cold-water marine natural products, Nat. Prod. Rep., 2007, 24, 774–797.
2. A. P. Lipton, V. S. Pramitha and J. J. Jose, Marine secondary metabolites (MSM) from macro algae enhance bacterial clearance in hemolymph of Penaeus monodon, Isr. J. Aquac. – Bamidgeh., 2009, 61(1), 42–47.
3. S. A. Jackson, L. Crossman, E. L. Almeida, L. M. Margassery, J. Kennedy and A. D. W. Dobson, Diverse and abundant secondary metabolism biosynthetic gene clusters in the genomes of marine sponge derived Streptomyces spp. Isolates, Mar. Drugs, 2018, 16(2), 67–83.
4. C. Kulandaisamy Venil, P. Lakshmanaperumalsamy, C. Kulandaisamy Venil and P. Lakshmanaperumalsamy, An Insightful Overview on Microbial Pigment, Prodigiosin Solid state fermentation of microorganism for pigment production View project An Insightful Overview on Microbial Pigment, Prodigiosin, Electron. J. Biol., 2009, 5(3), 49–61.
5. N. Sutthiwong, M. Fouillaud, A. Valla, Y. Caro and L. Dufossé, Bacteria belonging to the extremely versatile genus Arthrobacter as novel source of natural pigments with extended hue range, Food Res. Int., 2014, 65(PB), 156–162.
6. S. R. Pombeiro-Sponchiado, G. S. Sousa, J. C. R. Andrade, H. F. Lisboa and R. C. R. Gonçalves, Production of Melanin Pigment by Fungi and Its Biotechnological Applications, in Melanin, InTech, 2017 DOI:10.5772/67375.
7. M. Dieser, M. Greenwood and C. M. Foreman, Carotenoid pigmentation in Antarctic heterotrophic bacteria as a strategy to withstand environmental stresses, Arctic, Antarct Alp Res., 2010, 42(4), 396–405.
8. R. S. Celedón and L. B. Díaz, Natural pigments of bacterial origin and their possible biomedical applications, Microorganisms, 2021, 9, 739–748.
9. L.-E. Petersen, M. Y. Kellermann and P. J. Schupp, Secondary Metabolites of Marine Microbes: From Natural Products Chemistry to Chemical Ecology, in YOUMARES 9 - The Oceans: Our Research, Our Future, ed. S. Jungblutt, V. Liebich and M. Bode-Dalby, Springer, 2020, pp. 159–180 DOI:10.1007/978-3-030-20389-4_8.
10. A. K. R. Abadio, E. S. Kioshima, N. F. Martins, B. Maigret, M. S. S. Felipe and S. M. Freitas, et al., A pharmacophore-based virtual screening approach for the discovery of Trypanosoma cruzi GAPDH inhibitors, J. Chem. Inf. Model., 2015, 5, 2019–2039.
11. R. Srinivasan, A. Kannappan, C. Shi and X. Lin, Marine bacterial secondary metabolites: a treasure house for structurally unique and effective antimicrobial compounds, Mar. Drugs, 2021, 19, 530–539.
12. A. P. Tyurin, V. A. Alferova and V. A. Korshun, Chemical elicitors of antibiotic biosynthesis in actinomycetes, Microorganisms, 2018, 6, 52–59,  DOI:10.3390/microorganisms6020052.
13. E. Zazopoulos, K. Huang, A. Staffa, W. Liu, B. O. Bachmann and K. Nonaka, et al., A genomics-guided approach for discovering and expressing cryptic metabolic pathways, Nat. Biotechnol., 2003, 21(2), 187–190.
14. E. Pichersky and D. R. Gang, Genetics and biochemistry of secondary metabolites in plants: an evolutionary perspective, Trends Plant Sci., 2000, 5, 439–445.
15. N. Gurnani, D. Mehta, M. Gupta and B. K. Mehta, Natural Products: Source of Potential Drugs Natural Products Lab, School of Studies in Chemistry & Bio-Chemistry, Afr. J. Basic Appl. Sci., 2014, 6(6), 171–176.
16. N. K. Ranjith, C. V. Ramana and C. Sasikala, Rhodethrin: a novel indole Activities, ether produced by Rhodobacter sphaeroides has cytotoxic and phytohormonal activities, Biotechnol. Lett., 2007, 29, 1399–1402.
17. R. Kumavath, C. V. Ramana, C. Sasikala, D. Barh, A. Kumar and V. Azevedo, Isolation and Characterization of L-Tryptophan Ammonia Lyase from Rubrivivax benzoatilyticus Strain JA2, Curr. Protein Pept. Sci., 2015, 16(8), 775–781.
18. S. Ahmad, M. Mohammed, L. P. Mekala, S. Chintalapati and V. R. Chintalapati, Tryptophan, a non-canonical melanin precursor: new L-tryptophan based melanin production by Rubrivivax benzoatilyticus JA2, Sci. Rep., 2020, 10(1), 8925.
19. R. N. Kumavath, C. V. Ramana and C. Sasikala, L-Tryptophan catabolism by Rubrivivax benzoatilyticus JA2 occurs through indole 3-pyruvic acid pathway, Biodegradation, 2010, 21(5), 825–832.
20. M. Lakshmi Prasuna, M. Mujahid, C. Sasikala and C. V. Ramana, L-Phenylalanine catabolism and L-phenyllactic acid production by a phototrophic bacterium, Rubrivivax benzoatilyticus JA2, Microbiol. Res., 2012, 167(9), 526–531.
21. L. P. Mekala, M. Mohammed, S. Chintalapati and V. R. Chintalapati, Stable Isotope-Assisted Metabolic Profiling Reveals Growth Mode Dependent Differential Metabolism and Multiple Catabolic Pathways of L-Phenylalanine in Rubrivivax benzoatilyticus JA2, J. Proteome Res., 2018, 17(1), 189–202.
22. L. P. Mekala, M. Mohammed, S. Chinthalapati and V. R. Chinthalapati, Pyomelanin production: insights into the incomplete aerobic L-phenylalanine catabolism of a photosynthetic bacterium, Rubrivivax benzoatilyticus JA2, Int. J. Biol. Macromol., 2019, 126, 755–764.
23. L. P. Mekala, M. Mohammed, S. Chintalapati and V. R. Chintalapati, Precursor-feeding and altered-growth conditions reveal novel blue pigment production by Rubrivivax benzoatilyticus JA2, Biotechnol. Lett., 2019, 41(6–7), 813–822.
24. S. Ahmad, M. Mohammed, L. P. Mekala, R. Anusha, C. Sasikala and C. V. Ramana, Stable isotope-assisted metabolite profiling reveals new insights into L-tryptophan chemotrophic metabolism of Rubrivivax benzoatilyticus, World J. Microbiol. Biotechnol., 2023, 39(4), 98–103.
25. M. Mohammed, A. Isukapatla, L. P. Mekala, R. P. E. V. Venkata, S. Chintalapati and V. R. Chintalapati, Genome sequence of the phototrophic betaproteobacterium Rubrivivax benzoatilyticus strain JA2 T, J. Bacteriol., 2011, 193, 2898–2899.
26. S. Ahmad, M. Mohammed, L. P. Mekala, S. Chintalapati and V. R. Chintalapati, Tryptophan, a non-canonical melanin precursor: new L-tryptophan based melanin production by Rubrivivax benzoatilyticus JA2, Sci. Rep., 2020, 10(1), 8925.
27. S. A. Gordon and R. P. Weber, Colorimetric estimation of indoleacetic acid, Plant Physiol., 1951, 26(1), 192–195.
28. D. Gupta, M. Mohammed, L. P. Mekala, S. Chintalapati and V. R. Chintalapati, iTRAQ-based quantitative proteomics reveals insights into metabolic and molecular responses of glucose-grown cells of Rubrivivax benzoatilyticus JA2, J. Proteomics, 2019, 194, 59.
29. M. Mujahid, M. L. Prasuna, C. Sasikala and C. V. Ramana, Integrated metabolomic and proteomic analysis reveals systemic responses of rubrivivax benzoatilyticus JA2 to aniline stress, J. Proteome Res., 2015, 14(2), 711–727.
30. P. L. Ross, Y. N. Huang, J. N. Marchese, B. Williamson, K. Parker and S. Hattan, et al., Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents, Mol. Cell. Proteomics, 2004, 3(12), 1154–1169.
31. D. Povero, A. Eguchi, H. Li, C. D. Johnson, B. G. Papouchado and A. Wree, et al., Circulating extracellular vesicles with specific proteome and liver microRNAs are potential biomarkers for liver injury in experimental fatty liver disease, PLoS One, 2014, 9(12), e113651.
32. M. Mohammed, L. P. Mekala, S. Chintalapati and V. R. Chintalapati, New insights into aniline toxicity: aniline exposure triggers envelope stress and extracellular polymeric substance formation in Rubrivivax benzoatilyticus JA2, J. Hazard. Mater., 2020, 385, e87503.
33. M. Mujahid, C. Sasikala and C. V. Ramana, Production of indole-3-acetic acid and related indole derivatives from L-tryptophan by Rubrivivax benzoatilyticus JA2, Appl. Microbiol. Biotechnol., 2011, 89(4), 1001–1008.
34. M. Mujahid, C. Sasikala and V. C. Ramana, Aniline is an inducer, and not a precursor, for indole derivatives in Rubrivivax benzoatilyticus JA2, PLoS One, 2014, 9(2), 121571.
35. T. Mascher, J. D. Helmann and G. Unden, Stimulus Perception in Bacterial Signal-Transducing Histidine Kinases, Microbiol. Mol. Biol. Rev., 2006, 70(4), 910–938.
36. M. Theunis, H. Kobayashi, W. J. Broughton and E. Prinsen, Flavonoids, NodD1, NodD2, and Nod-Box NB15 modulate expression of the y4wEFG locus that is required for indole-3-acetic acid synthesis in Rhizobium sp. strain NGR234, Mol. Plant-Microbe Interact., 2004, 17(10), 1153–1161.
37. D. Dodd, M. H. Spitzer, W. Van Treuren, B. D. Merrill, A. J. Hryckowian and S. K. Higginbottom, et al., A gut bacterial pathway metabolizes aromatic amino acids into nine circulating metabolites, Nature, 2017, 551, 648–652.
38. R. Sonowal, A. Swimm, A. Sahoo, L. Luo, Y. Matsunaga and Z. Wu, et al., Indoles from commensal bacteria extend healthspan, Proc. Natl. Acad. Sci. U. S. A., 2017, 114(36), e7506.
39. R. Sonowal, A. I. Swimm, F. Cingolani, N. Parulekar, T. L. Cleverley and A. Sahoo, et al., A microbiota and dietary metabolite integrates DNA repair and cell death to regulate embryo viability and aneuploidy during aging, Sci. Adv., 2023, 9(8), eade8653.
40. B. Bommarius, A. Anyanful, Y. Izrayelit, S. Bhatt, E. Cartwright and W. Wang, et al., A Family of Indoles Regulate Virulence and Shiga Toxin Production in Pathogenic E. coli, PLoS One, 2013, 8(1), e54456.
41. A. Swimm, C. R. Giver, Z. DeFilipp, S. Rangaraju, A. Sharma and A. U. Antonova, et al., Indoles derived from intestinal microbiota act via type I interferon signaling to limit graft-versus-host disease, Blood, 2018, 132(23), 2506–2519.
42. D. Gupta, C. Sasikala and C. V. Ramana, Dynamics of proteo-metabolome from Rubrivivax benzoatilyticus JA2 reveals a programmed switch-off of phototrophic growth, leading to a non-cultivable state as a hyperglycemic effect, J. Proteomics, 2022, 260, 104569.
43. A. Segura, L. Molina, S. Fillet, T. Krell, P. Bernal and J. Muñoz-Rojas, et al., Solvent tolerance in Gram-negative bacteria, Curr. Opin. Biotechnol, 2012, 23, 415–421.
44. L. Xie, L. Zhang, C. Wang, X. Wang, Y. M. Xu and H. Yu, et al., Methylglucosylation of aromatic amino and phenolic moieties of drug-like biosynthons by combinatorial biosynthesis, Proc. Natl. Acad. Sci. U. S. A., 2018, 115(22), 4980–4989.
45. X. Chen, K. Schreiber, J. Appel, A. Makowka, B. Fähnrich and M. Roettger, et al., The Entner-Doudoroff pathway is an overlooked glycolytic route in cyanobacteria and plants, Proc. Natl. Acad. Sci. U. S. A., 2016, 113(19), 5441–5446.
46. M. Mohammed, A. Isukapatla and L. P. Mekala, Venkata RPEV, Chintalapati S, Chintalapati VR. Genome sequence of the phototrophic betaproteobacterium Rubrivivax benzoatilyticus strain JA2 T, J. Bacteriol., 2011, 193, 2898–2899.
47. K. Ghattavi, A. Homaei, E. Kamrani and S. K. Kim, Melanin pigment derived from marine organisms and its industrial applications, Dyes Pigm., 2022, 201, 110214.
48. C. S. Kim, J. H. Li, B. Barco, H. B. Park, A. Gatsios and A. Damania, et al., Cellular Stress Upregulates Indole Signaling Metabolites in Escherichia coli, Cell Chem. Biol., 2020, 27(6), 698–707.
49. A. Luna-Bulbarela, M. T. Romero-Gutiérrez, R. Tinoco-Valencia, E. Ortiz, M. E. Martínez-Romero and E. Galindo, et al., Response of Bacillus velezensis 83 to interaction with Colletotrichum gloeosporioides resembles a Greek phalanx-style formation: a stress resistant phenotype with antibiosis capacity, Microbiol. Res., 2024, 280, 127592.
50. R. Breitling, D. Vitkup and M. P. Barrett, New surveyor tools for charting microbial metabolic maps, Nat. Rev. Microbiol., 2008, 6(2), 156–161.
51. K. Sasikala, C. V. Ramana, P. R. Rao and K. L. Kovacs, Anoxygenic Phototrophic Bacteria: Physiology and Advances in Hydrogen Production Technology, Adv. Appl. Microbiol., 1993, 38(C), 211–295.
52. M. Bergkessel, D. W. Basta and D. K. Newman, The physiology of growth arrest: uniting molecular and environmental microbiology, Nat. Rev. Microbiol., 2016, 14, 549–562.
53. S. J. Krebs and R. K. Taylor, Nutrient-dependent, rapid transition of Vibrio cholerae to coccoid morphology and expression of the toxin co-regulated pilus in this form, Microbiology, 2011, 157(10), 2942–2953.
54. B. Muszyńska, K. Sułkowska-Ziaja and H. Ekiert, Analysis of indole compounds in methanolic extracts from the fruiting bodies of Cantharellus cibarius (the Chanterelle) and from the mycelium of this species cultured in vitro, J. Food Sci. Technol., 2013, 50(6), 1233–1237.
55. J. A. Mora-Villalobos and A. P. Zeng, Synthetic pathways and processes for effective production of 5-hydroxytryptophan and serotonin from glucose in Escherichia coli, J. Biol. Eng., 2018, 12(1), 2018.
56. Y. Chen, J. Tang, L. Wang, Z. Tian, A. Cardenas and X. Fang, et al., Creation of Bacterial Cells with 5-Hydroxytryptophan as a 21st Amino Acid Building Block, Chem, 2020, 6(10), 2717–2727.
57. S. Brunke, K. Seider, R. S. Almeida, A. Heyken, C. B. Fleck and M. Brock, et al., Candida glabrata tryptophan-based pigment production via the Ehrlich pathway, Mol. Microbiol., 2010, 76(1), 25–47.
58. M. R. Seyedsayamdost, High-Throughput platform for the discovery of elicitors of silent bacterial gene clusters, Proc. Natl. Acad. Sci. U. S. A., 2014, 111(20), 7266–7271.
59. J. S. Zarins-Tutt, T. T. Barberi, H. Gao, A. Mearns-Spragg, L. Zhang and D. J. Newman, et al., Prospecting for new bacterial metabolites: a glossary of approaches for inducing, activating and upregulating the biosynthesis of bacterial cryptic or silent natural products, Nat. Prod. Rep., 2016, 33, 54–72.
60. D. B. B. Trivella and R. de Felicio, The Tripod for Bacterial Natural Product Discovery: Genome Mining, Silent Pathway Induction, and Mass Spectrometry-Based Molecular Networking, mSystems, 2018, 3(2), e00160-17.

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Footnotes

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4mo00170b

‡ Current address: DBT – The Institute for Stem Cell Science and Regenerative Medicine (DBT-InStem), Bangalore 560065, Karnataka, India.

§ Current address: Department of Botany, Bharathidasan Government College for Women, Puducherry U.T. – 605003, India.

¶ Current address: Department of Botany, Avvaiyar Government College for Women, Karaikal, Puducherry- U.T 609 602, India.

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