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. 2018 Dec 4;19:868. doi: 10.1186/s12864-018-5244-2

Do environmentally induced DNA variations mediate adaptation in Aspergillus flavus exposed to chromium stress in tannery sludge?

Akanksha Jaiswar 1, Deepti Varshney 1, Alok Adholeya 1, Pushplata Prasad 1,
PMCID: PMC6278149  PMID: 30509176

Abstract

Background

Environmental stress induced genetic polymorphisms have been suggested to arbitrate functional modifications influencing adaptations in microbes. The relationship between the genetic processes and concomitant functional adaptation can now be investigated at a genomic scale with the help of next generation sequencing (NGS) technologies. Using a NGS approach we identified genetic variations putatively underlying chromium tolerance in a strain of Aspergillus flavus isolated from a tannery sludge. Correlation of nsSNPs in the candidate genes (n = 493) were investigated for their influence on protein structure and possible function. Whole genome sequencing of chromium tolerant A. flavus strain (TERIBR1) was done (Illumina HiSeq2000). The alignment of quality trimmed data of TERIBR1 with reference NRRL3357 (accession number EQ963472) strain was performed using Bowtie2 version 2.2.8. SNP with a minimum read depth of 5 and not in vicinity (10 bp) of INDEL were filtered. Candidate genes conferring chromium resistance were selected and SNPs were identified. Protein structure modeling and interpretation for protein-ligand (CrO4− 2) docking for selected proteins harbouring non-synonymous substitutions were done using Phyre2 and PatchDock programs.

Results

High rate of nsSNPs (approximately 11/kb) occurred in selected candidate genes for chromium tolerance. Of the 16 candidate genes selected for studying effect of nsSNPs on protein structure and protein-ligand interaction, four proteins belonging to the Major Facilitator Superfamily (MFS) and recG protein families showed significant interaction with chromium ion only in the chromium tolerant A. flavus strain TERIBR1.

Conclusions

Presence of nsSNPs and subsequent amino-acid alterations evidently influenced the 3D structures of the candidate proteins, which could have led to improved interaction with (CrO4− 2) ion. Such structural modifications might have enhanced chromium efflux efficiency of A. flavus (TERIBR1) and thereby offered the adaptation benefits in counteracting chromate stress. Our findings are of fundamental importance to the field of heavy-metal bio-remediation.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5244-2) contains supplementary material, which is available to authorized users.

Keywords: Non synonymous SNPs (nsSNPs), Mutation, Protein structure and function, Protein-ligand interaction, Adaptation

Background

Bioremediation of heavy metals by microbial cells has been recognized as a potential alternative to the existing physico-chemical technologies for recovery of heavy metals from industrial effluents [1]. Metal uptake in microorganisms takes place either actively (bioaccumulation) or passively (biosorption) [26]. Several species of bacteria and fungi have been identified for their bioaccumulation or absorption potentials and reduced cost and toxicity achieved by microbial bioremediation approach are appreciated over the conventional methods [7]. Various bacterial species detoxify chromium by periplasmic absorption, intracellular bioaccumulation and biotransformation through direct enzymatic reaction or indirectly with metabolites. Filamentous fungi have been identified as a potential biomass for removal of heavy metals from solutions and species of Aspergillus, Rhizopus and Penicillium are reported useful in biological treatment of the sludge [811], Several reports support the prominent ability of Aspergillus flavus in detoxification of chromium and other heavy metals [12]. However, the molecular mechanisms underlying heavy metal detoxification in fungi are largely unknown. Understanding the genes and pathways involved in metal accumulation/tolerance in fungi has several biotechnological implications for bioremediation of heavy metal-contaminated sites.

The extensive use of chromium in diverse industrial processes has made it a significant environmental contaminant. Chromium is a Class A human carcinogen [13, 14] and exists in eleven valence states (from −IV to +VI), among which Cr (III) and Cr (VI) are the most stable forms in the environment. Due to high water solubility Cr (VI) is 100-folds more toxic over Cr (III). As per the United States Environmental Protection Agency (US EPA) the maximum contaminant level for Cr (VI) and total chromium content in domestic water supplies is 0.05 and 2 mg/l respectively [15]. Cr (VI) actively crosses biological membranes [16] and generates active intermediates Cr (V) and/or Cr (IV), free radicals, and Cr (III). Cellular accumulation of Cr (III) causes damage to DNA and alters the structure and activity of proteins [17, 18]. The existing physico-chemical processes for treating chromium-contaminated water bodies include precipitation, ion exchange, reverse osmosis, evaporation and electro dialysis, which are reported to display poor efficiency [14, 1924].

For survival in Cr (VI) contaminated environments, microorganisms must develop efficient systems to detoxify the effects of chromium. These mechanisms involve detoxification or repair strategies such as Cr (VI) efflux pumps, Cr (VI) reduction to Cr (III), and activation of enzymes involved in the detoxifying processes, repair of DNA lesions, sulfur metabolism, and iron homeostasis [16, 18, 25]. Additionally, alterations in gene function due to mutation have been suggested to support survival under chromium toxic conditions [26]. Biotransformation and biosorption are suggested as the putative fungal processes that help them transform or adsorb heavy metals [27]. The fungal cell walls predominantly consist of chitins, glucans, mannans and proteins in addition to other polysaccharides, lipids and pigments [28, 29]. The functional groups on these structural components enable binding of metal ions on the fungal cell walls [30]. Uptake and reduction of hexavalent chromium has been suggested as the mechanisms for chromium tolerance in Aspergillus sp. [27, 31].

Information on genes supporting survival under environmental stress in bacterial system has been recently curated in BacMet database (http://bacmet.biomedicine.gu.se) which primarily contains several experimentally verified Chromate ion transporter (CHR) genes [32] responsible for chromium efflux, transport or binding, and other enzymes involved in chromium uptake. However, very less knowledge is available on genetic mechanisms responsible for chromium tolerance in fungi. In the Neurospora crassa strain 74-A, chr-1 gene that encodes a putative CHR-1 protein and belongs to the CHR superfamily was identified [33]. However, contrary to the bacterial ChrA (chromate transport protein) homologues that confer chromate resistance by exporting chromate ions from the cell’s cytoplasm, the experimental data suggested that the N. crassa CHR-1 protein functions as a transporter that takes up chromate [34]. The presence of CHR-1 protein was reported to cause chromate sensitivity and chromium accumulation in N. crassa.

Experimental evidences in a recent study suggested that environmental stress could induce adaptation in a wide range of micro-organisms by extensive positive pleiotropy in a manner that multiple beneficial mutations dramatically enhance numerous fitness components simultaneously [35]. Environmentally induced mutations and polymorphisms in DNA and subsequently the alteration in proteins are hypothesized to offer a significant evolutionary advantage by enabling faster adaptation to toxic conditions [36]. We identified a high chromium tolerant Aspergillus flavus strain (TERIBR1) from a tannery sludge in Kanpur, Uttar Pradesh, India. TERIBR1 showed accumulation of Cr (III) in its biomass while growing in Cr containing media. It showed no toxic effect of Cr (VI) up to 250 mg/l. In order to identify the genetic factors underlying chromium tolerance in TERIBR1, we investigated effects of nonsynonymous variations (nsSNPs) in candidate genes on protein structure and their interaction with chromate ion.

Our study comprises whole genome sequencing of A. flavus strain TERIBR1 followed by single nucleotide polymorphism (SNPs) analysis in candidate genes for chromium-resistance. Protein modeling for candidate genes with nsSNPs was done and interactions between modeled proteins and the ligand (CrO4− 2) were assessed by protein-ligand docking. For all comparative genomics and genetics analyses the A. flavus strain TERIBR1 was considered as the “test” and previously sequenced strain NRRL3357 as the “reference” type.

Materials and methods

Fungal strain and DNA extraction

The protocol followed for isolation and characterization of fungi from a tannery sludge is previously described [37]. Briefly, the Cr-resistant fungi were isolated from a tannery sludge [containing 250 mg/l of Cr (III)] through an enrichment culture technique. The sludge sample was collected from a tannery waste disposal site in Kanpur, India. Pure culture of the isolated A. flavus strain (TERIBR1) was grown in potato dextrose broth (PDB) at 28 °C in a shaking incubator (100 rpm) for 72 h in dark condition. After incubation, culture was centrifuged at 5000 g for 10 min at room temperature. The pellet was washed thrice with sterile distilled water to remove any media components and was further used for DNA extraction. Genomic DNA was extracted using the DNeasy plant mini kit (QIAGEN, USA), according to the manufacturer’s instructions. Genetic characterization of isolated fungi was done using universal fungal ITS (nuclear ribosomal internal transcribed spacer) primer set [ITS1: 5’ TCCGTAGGTGAACCTGCGG, 3′ and ITS4: 5’ TCCTCCGCTTATTGATATGC 3′; [38] that amplified the ITS1, 5.8S and ITS2 regions of the nuclear ribosomal RNA genes.

Growth kinetics and sensitivity to Cr (VI)

The effect of different concentrations of chromium [Cr (VI)], 0 mg/l, 100 mg/l and 250 mg/l, on the growth of A. flavus strains TERIBR1 and NRRL3357 was compared. The strains were grown in PDB and mycelial biomass (dry weight) was measured at different time periods (0, 1, 2, 3, 4 and 5 days).

Genome sequencing and assembly

Genome sequencing was performed at MOgene LC, USA, using next generation sequencing technology Illumina as reported previously [39]. Two paired end libraries (insert sizes 180 bp and 500 bp) and one mate pair library (5 kb) were constructed. DNA libraries were purified using AMPure XP beads. KAPA was done to quantify the libraries, which were then normalized and pooled at 4 nM concentration.

A total of 8 GB raw data was subjected to adaptor- and quality-based trimming. Quality-passed data was assembled using the de novo genome assembler AllpathsLG [40]. Reads with overlaps were first combined to form contigs. The reads were mapped back to contigs. With paired-end reads, contigs from the same transcript, as well as the distances between these contigs, were detected. In order to generate scaffolds, contigs were connected using “N” to represent unknown sequences between two contigs. Mate-pair reads were used for gap filling of scaffolds in order to get sequences with minimal N’s and the longest length. The whole genome project has been deposited at https://submit.ncbi.nlm.nih.gov/subs/wgs/under Bioproject PRJNA362980.

Structural and functional annotation of A. flavus TERIBR1 genome was done using MAKER [41] pipeline, InterProScan [42] and nrBlast [39] as described previously.

Identification of single nucleotide polymorphisms (SNPs)

Genome and protein sequences for reference genome were retrieved from the Aspergillus flavus Database (http://fungidb.org/fungidb/app/record/organism/aflaNRRL3357). The alignment of quality trimmed data of TERIBR1 with NRRL3357 (assembly) was performed using Bowtie2 version 2.2.8 [43]. Samtools [http://samtools.sourceforge.net/] was used for SNP identification.

SNP analysis in candidate genes for chromium resistance

Genes conferring chromium resistance in bacterial system were selected from BacMet database [32]. BacMet is freely available antibacterial biocide and metal resistance genes database for bacteria. InterProScan analysis [42] was performed to identify A. flavus genes harbouring atleast one IPR domains that are present in the chromium resistance genes documented in the BacMet database. SNPs were identified in the selected candidate genes using variant calling format (VCF) file and Blastn tool. SNPs were further annotated as synonymous or non-synonymous (nsSNPs) using an in-house perl script.

Protein structure modeling

Protein modeling was done by fold recognition methods through Phyre2 server [44]. The amino acid sequences of candidate genes in both the reference (NRRL3357) and the test strains were modeled. The top model with highest confidence and coverage was selected for each protein. The predicted confidence score and coverage for all the final structures were recorded. To assess the reliability of all the predicted models, structural analysis and verification was exercised. The selected models were validated using the PROCHECK [45] and ERRAT [46] to estimate the stereo chemical figures, geometry, and hydrogen bonding energy, torsion angles and error rate of the predicted structures. In addition, energy minimization was performed with in vacuo GROMOS96 43B1 parameters set using GROMOS96 implementation in Swiss-Pdb Viewer [47]. The energy optimized protein structures were used for protein-small ligand docking.

Prediction of ligand binding sites

Prior to docking, a web based approach 3DLigandSite [48] was used to predict the ligand binding sites. 3DLigandSite utilizes protein-structure prediction to provide structural models for proteins that have not been solved. Ligands bound to structures similar to the query are superimposed onto the model and used to predict the binding site.

Protein- ligand docking

In order to investigate protein–ligand interactions, proteins were docked with the chromate ion (CrO4− 2) through a rigid docking protocol using PatchDock (http://bioinfo3d.cs.tau.ac.il/PatchDock/) [49, 50] which docks the ligand with the protein based on structure complementarity. Also, binding sites predicted by 3DLigandSite in the receptor/proteins were specified and uploaded in PatchDock analysis. The protein-ligand interactions were interpreted based on Atomic Contact Energy (ACE) and docking score. The pdb file of chromate ion was downloaded from the RCSB PDB (research collaborator fo structural Bioinformatics protein data bank) site [51]. The PDB structures of target proteins and protein-ligand interaction were visualized using the PyMOL [52].

Results

Growth kinetics and sensitivity to Cr (VI)

Dry weight of fungal biomass was recorded at different time periods (from 1 to 5 days) for both the strains under the conditions mentioned above. No significant difference in growth was observed between the two strains under the control condition (Fig. 1). However, stark difference in the mycelial biomass (dry weight) between the reference strain (NRRL3357) and the test strain (TERIBR1) was observed when potato dextrose broth was amended with chromium 100 mg/l and 250 mg/l. Growth kinetics of the TERIBR1 strain at chromium concentration of 100 mg/l were similar to that observed under control condition (no chromium). The reference strain exhibited delayed growth response with concomitant decrease in biomass in comparison to the test strain at different time intervals (between day 1 and day 5) when the growth media was amended with chromium at concentrations of 100 mg/l and 250 mg/l.

Fig. 1.

Fig. 1

Chromium [Cr (VI)] dose response exhibited by TERIBR1 and NRRL3357 strains of A. flavus. Chromium dose/growth response (measured by dry weight) exhibited by TERIBR1 and NRRL3357 strains of A. flavus grown up to 5 days in potato dextrose broth supplemented with Cr (VI): (a) 0 mg/l, (b) 100 mg/l and 250 mg/l

Global genome structure

The genome of A. flavus strain TERIBR1 was sequenced to 200x coverage and reads were assembled into 322 scaffolds. The sum of the scaffolds length is equal to 38.2 Mb. The three largest scaffolds are 2.76 kb, 2.64 kb, and 2.50 kb in size. The MAKER annotation pipeline predicted 13,587 protein coding genes as compared to 13,659 in NRRL3357. Gain or loss of unique genes, DNA duplication, gene family expansion, and translocation of transposon-like elements are often observed between different isolates of a fungal species [53]. This may suggest that some of the genes present in NRRL3357 could have been lost in TERIBR1, possibly during environmental adaptations.

Identification of candidate genes in A. flavus

No homologue of CHR-1 protein (XP_961667.3) coded by N. crassa was identified in both the A. flavus strains included in this study. A total of 34 InterProScan domains coding for transporter or regulator proteins responsible for chromium bio-accumulation or tolerance in bacteria were reported in the BacMet database. nrBlast was performed to identify genes containing at least one IPR domain associated with chromium tolerance in the genome of A.

Flavus strain TERIBR1, NRRL3357 (http://fungidb.org/fungidb/app/record/organism/aflaNRRL3357) and AF70 (https://www.ncbi.nlm.nih.gov/assembly/GCA_000952835.1). 23/34 bacterial IPR domains were not found in any of the three strains of A. flavus. A total of 493 candidate genes was identified to harbor one or more IPR domains of interest in TERIBR1(Table 1). IPR domains mdrL/yfmO (IPR011701; n = 334), recG (IPR001650; n = 71), ruvB (IPR003959; n = 45) and recG (IPR011545; n = 44) were among the maximally present protein domains related to chromium resistance.

Table 1.

Distribution of IPR domains important in chromium bio-accumulation in A. flavus strains TERIBR1, NRRL3357 & AF70

Gene Family (BacMet db) Description Interproscan Domain # of Genes containing IPR domains of interest
NRRL3357 TERIBR1 AF70
Chromate ion transporter (CHR) family (chrA) Efflux IPR003370 1 1 2
Rhodanese family (chrE) Enzyme IPR001763 9 6 10
NADH_dh2 family (chrR) Enzyme IPR005025 4 4 4
IPR000415 0 3 0
MFS superfamily (mdrL/yfmO) Efflux IPR011701 374 334 394
Contains 1 DEAD/DEAH box helicase domain (recG) Enzyme IPR011545 43 44 42
IPR001650 74 71 80
IPR004365 2 5 4
RuvB family (ruvB) Enzyme IPR003959 47 45 48
IPR012301 2 2 2

Identification of single nucleotide polymorphisms (SNPs)

The read alignment rate of TERIBR1 with NRRL3357 (assembly) was 78.62% (29,001,807 / 36,890,268) of which 78.23% (22,681,743) were uniquely mapped reads. A total of 201,145 SNPs (read depth > 5) was identified at a frequency of ~ 5 SNPs per Kb of the TERIBR1 genome. SNP mapping in n = 493 candidate genes, homologous among A. flavus NRRL3357 and TERIBR1 isolates was done using Samtools. No SNP was identified in 325/493 genes. SNPs identified in the remaining n = 168 genes were annotated as synonymous or non-synonymous (Additional file 1: Table S1). 28/168 candidate genes contained only synonymous polymorphisms whereas 16/168 candidate genes, belonging to MFS (n = 12), recG (n = 3) and chrE (n = 1) protein families, showed higher rate of nsSNP as compared to other candidate genes (Additional file 2: Table S2).

Protein- ligand docking

For studying protein-chromate ion interaction, we predicted tertiary protein structures of homologous pairs of the 16 highly polymorphic proteins (Additional file 2: Table S2) using Phyre2 server (Additional file 3: Table S3). Prediction for Cr binding sites in the target proteins was done by 3DLigandSite (Table 2). Strength of protein-ligand interaction was measured based on the atomic contact energy (ACE) in the PatchDock score (Table 3). Also change in free energy (ΔG) of the amino acid residues present in the predicted binding and ligand docking sites was recorded (Fig. 2). Structures of 8 proteins in both the reference and test strains did not show any possible interaction between the ligand and the target proteins. Ligand docking was observed in both the strains for four proteins (g8975, g685, g6212, g9525; Additional file 4: Figure S1). Binding residues that showed a drop in free energy on chromate docking in PatchDock analysis are depicted on the 3D structures of these four proteins (Additional file 4: Figure S1).

Table 2.

Prediction of binding site and protein – ligand interaction using 3DLigandSite and PatchDock softwares respectively

Protein ID nsSNP Predicted binding sites Docking status and residues in recognition cavity # nsSNP Gene
Family
NRRL3357 TERI
BRI
NRRL
3357
TERI
BR1
NRRL3357 TERIBR1
AFL2G_00299 g652 A346D, D351N, M389 T A261, G262, I263 No B.S. N/A ≤2 mdrL/ yfmO
AFL2G_04853 g9548 P254L, K261I, K263E, A262D, M34 T T67, F68, V69, S70, P71, L72, A73, S74, S75, L104, Y107, V108, P111, G161, C164, L165, W188, P192, Y280, L283, Y284, T288, Y393, T416, A417, S420, L421, V422, A424, L425, L426 Y122, W203, P207, Y319, L322, Y323 N/A 2 to 5 mdrL/ yfmO
AFL2G_04391 g8975 P341A, D349E, H356Y, P373L, P374L Q119, F240, H403, T404, N405, V407, Q408, L454, F477, S481, Y485, V508, L511, Q512, V514, S515, R516, F518, V519, L520, P521, S524 Q115, F240, N405, A406, Q408, T409, L454, F477, S481, Y485, V508, L511, Q512, V514, S515, R516, F518, V519, L520, P521, S524, R552 Dock 2 to 5 mdrL/ yfmO
AFL2G_02473 g5755 K53 N, N59D, K213 M, V293I, K340R H97, W124, I125, L126, V127, M128, F129, F130, A131, L132, N133, I134, D135, I183, G184, P185, D186, R187, W188, I189, P190, I191, Q192, I193, I194, L195, S197, F226, D229, V253, S257, A288, S291, I292, G295, F296, S298, F299, L302 W115, I116, L117, V118, M119, F120, A122, I174, G175, P176, D177, R178, W179, I180, P181, I182, Q183, I184, I185, L186, F217, S282, G286, S289, F290, L293, V294 N/A 2 to 5 mdrL/ yfmO
AFL2G_00264 g685 S126G, T139A, G179E, Y112F T334, L335, G400, K401, S402, L403, E461, H465, F680, G681, R711 T321, L322, M386, G387, K388, S389, L390, E442, H446, F726, G727, R757 Dock 2 to 5 recG
AFL2G_05826 g6641 P307L, Q11P, V19G, E102D, V126A, A129V No B.S. S270, M273, I274, Q396 N/A > 5 mdrL/ yfmO
AFL2G_09247 g6212 R471H, R437Q, S837P, L1229 V, V192I, L233S K272, L273, L274, Q277, G309, L310, G311, K312, T313, V314, E380, K384, L919, G920, L921, N922, R947, R950, L951 L544, V546, K547, L548, L549, Q552, G584, L585, G586, K587, T588, V589, E655, K659, L1194, G1195, N1197, R1222, R1225, L1226 Dock > 5 recG
AFL2G_08767 g9986 F222I, A244P, Q270P, G340R, A342G, F431 L, S472I W290, L291, Y292, L294, M295, I353, L354, V355, M356, H357, L358, W359, T360, P362, P363, F401, I404, Y455, M458, N459, L462, T465, R466 K277, Y278, Q279, V281, E282, A283, T285, I288, A337, V338, M339, V340, G341, G342, A343, S344, P346, P347, F385, I388, N443, L446, L447, R450, L453, I454 N/A Dock
K277, Q279, T285, L446
> 5 mdrL/ yfmO
AFL2G_05032 g9401 N373D, S445 N, E503G, S535 L, V572G, F592Y, K610E, I50M H614, H616, L666, H668, H670 C97, A98, F100, L101, Y104, I107, M159, A160, I161, I162, Y164, S165, A168, I169, F198, A202, V205, S257, T260, H261, A264, N267, K268 N/A > 5 mdrL/ yfmO
AFL2G_06586 g3683 S517 N, Q324E, L899S, S57 L, D63G, T18I, K285R Q193, L194, K195, Q198, M221, G222, L223, G224, K225, T226, I227, E266, I643 L145, S147, Q148, L149, G179, K180, T181, I182, E221, K224, W225, E573, G574, R604 N/A Dock
L149, K180, T181, I182
> 5 recG
AFL2G_11779 g4359 G294D, K360E, V388I, F393 L, L468P, Q66H, R19K, V637I, A646T L582, V586, M589, N590, M593, A621, Y623, L631, H632, A635, H636, H640, W647, I659 R34, T36, A94, V95, Y100, S101, A178, I206, P207, L208, A209, V211 N/A > 5 chrE
AFL2G_09661 g4104 R80M, L110 V, V144 L, N150S, F191 L, G198R, Y199C, E407G, G5E, I25N No B.S. N/A Dock V144 L, N150S > 5 mdrL/ yfmO
AFL2G_04878 g9525 G210S, C363W, I368S, H438Y, M484, P106A, V131I, V146A, N156S, F163 L, L182F, Q59H S63, I66, F92 S141, I144, F170 Dock > 5 mdrL/ yfmO
AFL2G_00229 g712 R163L, G180C, S215C, S220Y, A226P, V693I, S765 N, F834I, Q854H, C938S, V121A G570, A571, N572, S573, G574, L575, V595, R596, S597, K600, L624, D625, M626, L627, N652, A653, G654, I655, V673, V704, G705, S706, Y745, K749, P780, G781, P782, T783, S785, G786, L787 G666, A667, N668, S669, G670, L671, V691, R692, K696, L720, D721, M722, L723, N748, A749, G750, I751, V769, V800, G801, S802, Y841, K845, P876, G877, P878, T879, S881, G882, L883 N/A > 5 mdrL/ yfmO
AFL2G_04255 g9088 L52 V, S101G, A212T, F214 L, T217A, S237 L, A250V, P252L, P271S, M280 T, K292 N, S297R, V303I A104, L105, P108, S110, L138, I139, V141, G142, M165, M169, A226, I256, F338, L341, N342, M367, Y477, G481, L483 A195, P198 N/A > 5 mdrL/ yfmO
AFL2G_11442 g4641 M254I, P321T, I433V, D661G, P675Q, F682 L, D110N, A111V, I114V, K143 T, T3A, H13Q, T24A, C46S, K826R A120, F121, V122, V123, S124, A125, A126, S127, S128, L156, F159, A160, S163, M187, P216, L217, Y240, S244, Y355, F359, D363, T513, V514, Y517, C518, A519, G521, G522, M523 S372, A492, V493, L494, P496, F603, F606, W628, V629, A630, M631, Y632, V633, G634, I635, M636, L637, L640, S724 N/A Dock
Y632, F606, W628, A492
> 5 mdrL/ yfmO

SNPs marked in bold were predicted binding site present in the predicted recognition cavity of the protein. B.S. stands for binding site

Table 3.

Docking analysis using PatchDock for selected proteins of A. flavus strain TERIBR1

Protein ID TERIBR1 Score Area ACE (kcal/mol)
g652 2764 330.5 −13.58
g9548 2496 304.5 −58.67
bg8975 2806 333.4 −29.90
g5755 2746 329.6 31.22
bg685 2576 321.4 −1.40
g641 2846 322.9 19.42
bg6212 2924 326.8 −62.95
a g9986 2644 296.6 −46.56
g9401 2594 324.6 −77.47
a g3683 2788 306.3 −30.21
g4359 2664 285.8 −60.63
a g4104 3034 335.4 −72.70
bg9525 2966 362.2 −83.82
g712 2454 299.4 −7.63
g9088 2368 258 −62.84
a g4641 2772 302.9 −66.10

aProtein - ligand interaction observed only in A. flavus strain TERIBR1

bProtein - ligand interaction observed in both the strains of A. flavus

The entries marked in bold indicate significant interaction of ligand with the protein

Fig. 2.

Fig. 2

Protein-chromate ion interaction observed with four MFS transporter proteins of A. flavus strain TERIBR1. Docking of chromate ion with MFS transporter proteins in occluded conformation. The chromate ion is depicted as a sphere model. The amino acids of the interacting protein showing negative energy are depicted as bright orange sticks and the interacting binding sites as green sticks. Presence of nsSNPs in the protein sequence is shown in magenta. Amino-acids present in the close vicinity of the binding sites are marked in black (sSNP) and magenta (nsSNP). Figure was produced using the PyMOL Molecular Graphics System

Interestingly, the presence of non-synonymous mutations correlated with change in bioactive conformation and drop in free energy (ΔG) of four proteins (g9986, g3683, g4104, g4641) belonging to three MFS and one recG (helicase) superfamilies in the test strain only (Fig. 2). The structural changes in these proteins lead to successful protein-ligand interactions.

Discussion

As expected for functional conservation, majority of candidate genes in the TERIBR1 genome showed the presence of a large number of sSNPs and a few nsSNPs. Notably, 28/168 candidate genes contained only synonymous polymorphisms. Synonymous codon positions, though do not alter amino acid sequences of the encoded proteins, they may determine secondary structure, stability and translation rate of the mRNA [54]. Presence of sSNPs in the chromium tolerance candidate genes in the test strain could have affected folding and post-translational modifications of the nascent polypeptides which could in turn affect candidate protein expression and function towards Cr tolerance.

The polymorphism rate in 16 candidate genes that showed a high frequency of nsSNPs as compared to synonymous changes (Table 2) was ~ 16 SNPs/Kb with a frequency of ~ 11 nsSNPs/Kb. The observed high rate of nsSNPs in chromium-tolerance candidate genes of TERIBR1 as compared to the housekeeping genes (0.4 nsSNPs/kb; Table 4) could mirror environmental stress induced DNA variations and might provide an advantage in counteracting chromate stress. These included genes from mdrL/yfmO (12), recG (3) and chrE (1) families. The mdrL/yfmO genes belonged to the major facilitator superfamily (MFS), which codes for a metal ion-specific efflux protein [55]. High frequency of nsSNPs observed in the mdrL/yfmO genes in TERIBR1 could have led to altered protein structure and subsequent chromium efflux efficacy under extreme environmental condition, which we discussed in detail under the protein-ligand docking section. recG is a conserved enzyme present in bacteria, archaea, and eukaryota. recG encodes for the ATP-dependent recG DNA helicase which plays a critical role in DNA recombination and repair [56]. In vivo experiments conducted in E. coli showed that chromium salt stimulates several stress promoters associated with different types of DNA damage, indicating that DNA is one of the main targets for Cr (III) inside the cell [57]. After being internalized in cells Cr (VI) is reduced to Cr (III); recG eliminates polymerase arresting lesions (PALs), caused by Cr (III). The observed high frequency of nsSNPs in recG genes observed in our study might have resulted in higher efficiency of the enzyme to remove PAL lesions, thus mediating chromium stress tolerance in the fungal strain. In congruence, a study in Pseudomonas corrugata suggested that recG helicase played a crucial role in chromium tolerance by dismissing PAL lesions caused by Cr (VI)/Cr (III) [58]. The chrE gene encodes a rhodanese type enzyme [59]. Rhodanese protein subfamilies are suggested to be involved in different biological functions including cyanide detoxification, arsenic resistance and chromate responsive DNA-binding regulator. In addition, UniProt database defines ChrE as proteins involved in the processing of chromium-glutathione-complexes. An abundance of nsSNPs in these candidate genes for chromium tolerance could be the result of environment induced variations, perhaps for achieving functional relevance in TERIBR1. Environmentally guided changes in DNA and subsequently the proteins could be advantageous and may enable functional adaptation to extreme environmental influences [36].

Table 4.

SNP frequency in housekeeping genes in A. flavus

Gene ID NRRL3357 Gene ID TERIBR1 Annotation Gene length (nucl) change in nucl change in aa status of SNPs
AFL2T_10032 g899 Calmodulin 4047 0 0 0
AFL2T_10117 g962 RPL5 (ribosomal protein) 1071 0 0 0
AFL2T_03358 g2143 Polyketide Synthase Acetate 1692 0 0 0
AFL2T_03019 g2421 Chitin Synthase 1 2655 0 0 0
AFL2T_08232 g3003 cyclophilin 522 0 0 0
AFL2T_08160 g3065 Ubiquitin-conjugating enzyme 450 0 0 0
AFL2T_01340 g5399 Vacuolar protein sorting association protein 324 0 0 0
AFL2T_01191 g5533 Cytochrome oxidase 348 0 0 0
AFL2T_12005 g6415 Ubiquitin-conjugating enzyme 510 0 0 0
AFL2T_02547 g6955 Ubiquitin-conjugating enzyme 513 0 0 0
AFL2T_02762 g7132 L- Asparaginase 690 0 0 0
AFL2T_09767 g7323 ATP_D (ATP synthase subunit beta) 1821 0 0 0
AFL2T_06390 g8072 Polyketide Synthase Acetate 2529 0 0 0
AFL2T_09983 g10276 Ubiquitin-conjugating enzyme 501 0 0 0
AFL2T_09876 g10370 L- Asparaginase 1677 0 0 0
AFL2T_07389 g10698 Elongation Factor Alpha like protein 1185 0 0 0
AFL2T_06969 g3347 ATP_D (Atp synthase subunit beta) 1539 0 0 0
AFL2T_06937 g3377 Chitin Synthase 1 5283 0 0 0
AFL2T_05991 g6789 GAPDH/Glyceraldehyde 3-phosphate dehydrogenase 1524 0 0 0
AFL2T_02677 g7061 Vacuolar protein sorting association protein 1563 0 0 0
AFL2T_05240 g3927 cyclophilin 1122 0 0 0
AFL2T_02454 g5775 TBPI (tata box binding protein) 690 0 0 0
AFL2T_03769 g1786 Actin interacting protein 3 2931 0 0 0
AFL2T_05664 g6503 Histone 807 0 0 0
AFL2T_11201 g8361 Ubiquitin-conjugating enzyme 333 0 0 0
AFL2T_12447 g9127 Ubiquitin-conjugating enzyme 837 0 0 0
AFL2T_12048 g11077 DNA Topoisomerase II 1032 0 0 0
AFL2T_04711 g9681 Ubiquitin-conjugating enzyme 450 0 0 0
AFL2T_07021 g3301 Ubiquitin-conjugating enzyme 474 0 0 0
AFL2T_07052 g3271 Lactate Dehydrogenase A 1065 0 0 0
AFL2T_11998 g6422 Ubiquitin-conjugating enzyme 456 0 0 0
AFL2T_05713 g6542 Vacuolar protein sorting association protein 387 0 0 0
AFL2T_03033 g2409 Chitin Synthase 1 1671 0 0 0
AFL2T_08388 g2865 Vacuolar protein sorting association protein 2022 0 0 0
AFL2T_08078 g3130 Histone 429 0 0 0
AFL2T_05673 g6508 28 s rRNA 450 0 0 0
AFL2T_04621 g9757 cyclophilin 630 0 0 0
AFL2T_07907 g4829 Vacuolar protein sorting association protein 351 0 0 0
AFL2T_01105 g5607 Ubiquitin-conjugating enzyme 1167 0 0 0
AFL2T_09240 g6218 Ubiquitin-conjugating enzyme 558 0 0 0
AFL2T_09015 g9269 Polyketide Synthase Acetate 6828 0 0 0
AFL2T_10236 g1076 Vacuolar protein sorting association protein 2313 0 0 0
AFL2T_05795 g6613 28 s rRNA 1815 0 0 0
AFL2T_03329 g2169 Ubiquitin-conjugating enzyme 2631 0 0 0
AFL2T_09350 g6131 18 s rRNA 2382 0 0 0
AFL2T_00575 g419 Chitin Synthase 1 3588 C150T H50H sSNPs
AFL2T_00433 g530 Vacuolar protein sorting association protein 2562 C1483G P496A nsSNPs
AFL2T_02076 g8000 Elongation Factor Alpha like protein 3222 A890C K297Q nsSNPs
AFL2T_09781 g7310 Vacuolar protein sorting association protein 2925 T1645C S548S sSNPs
AFL2T_09150 g8774 Polyketide Synthase Acetate 7425 T5820C L1938 L sSNPs
AFL2T_06936 g3378 Chitin Synthase 1 5574 A315G, C2100T, A2841T G105G, D700D, I947I sSNPs
AFL2T_06204 g8236 Chitin Synthase 1 3315 C1668T L556 L sSNPs
AFL2T_02195 g6007 Vacuolar protein sorting association protein 4545 A2925G, C4044T E975E, F1348F sSNPs
AFL2T_08239 g2996 Calmodulin 5103 A260G, T2232C, C2757G D87G, T744 T, L919 L nsSNPs, sSNPs, sSNPs
AFL2T_07518 g5174 Polyketide Synthase Acetate 6366 G1575A, G3053A S525 N, S1018 N nsSNPs
AFL2T_00612 g388 ATP_D (Atp synthase subunit beta) 1671 A1515C A505A sSNPs
AFL2T_05167 g3861 Vacuolar protein sorting association protein 3528 G456 T, G807A G152G, T269 T sSNPs
AFL2T_04317 g9038 Vacuolar protein sorting association protein 1920 T1383C I461I sSNPs
AFL2T_12048 g6382 DNA Topoisomerase II 3183 T1871C V624A nsSNPs
AFL2T_12403 g9165 Polyketide Synthase Acetate 4944 T1265C, C2231T, Y2271G M422 T, T744I, X757K nsSNPs
AFL2T_08114 g3103 Elongation Factor Alpha like protein 1383 T273A I91I sSNPs
AFL2T_02416 g5810 Vacuolar protein sorting association protein 2124 C1606T L536 L sSNPs
AFL2T_06144 g8287 aflatoxin regulatory protein 945 C46T L16F nsSNPs
AFL2T_07648 g5058 Ubiquitin-conjugating enzyme 1278 T570C G190G sSNPs
AFL2T_03037 g2405 secretory lipase 1353 W1271A X424N nsSNPs
AFL2T_05603 g4255 Vacuolar protein sorting association protein 2757 C1164T, G1491 T, C1884T I388I, T497 T, I628I sSNPs
AFL2T_09157 g8767 Ras protein 6435 C1509T, A2193G, G2304A, T2358C, G3018C, G4056A S503S, S731S, L768 L, N786 N, T1006 T, P1352P sSNPs
AFL2T_12399 g9169 Chitin Synthase 1 3222 C1317G V439 V sSNPs
AFL2T_06989 g3329 Elongation Factor Alpha like protein 2583 T1461C T487 T sSNPs
AFL2T_11104 g8446 Polyketide Synthase Acetate 7482 T3962C, A5970G, A5981G M1320 T, I1990M, D1994G nsSNPs
AFL2T_01971 g7909 Vacuolar protein sorting association protein 5862 G281A, A2246T, A2316G, C2766T, T3135A R94K, N749I, S772S, I922I, D1045E nsSNPs, nsSNPs, sSNPs, sSNPs, nsSNPs
AFL2T_01302 g5433 Vacuolar protein sorting association protein 2457 C704T, T828C A235V, G276G nsSNPs
AFL2T_11645 g4481 ATP_D (Atp synthase subunit beta) 1116 T972C I324I sSNPs
AFL2T_04569 g9796 Elongation Factor Alpha like protein 2400 C546T, T1785C, T2253C D182D, L595 L, F751F sSNPs
AFL2T_05904 g6711 Elongation Factor Alpha like protein 2730 T146C, C1836T, G2002A, A2435G F49S, P612P, V668I, D812G nsSNPs, sSNPs, nsSNPs, nsSNPs
AFL2T_02030 g7958 Ubiquitin-conjugating enzyme 1176 T641C V214A nsSNPs
AFL2T_09952 g10303 TBPI (tata box binding protein) 1338 T789G Y263Y sSNPs
AFL2T_02696 g7079 Elongation Factor Alpha like protein 2169 T576C, A825T, G1332A, T1557C D192D, I275I, E444E, F519F sSNPs
AFL2T_12346 g8605 Elongation Factor Alpha like protein 2874 A1914G E638E sSNPs
AFL2T_10814 g1537 Ubiquitin-conjugating enzyme 708 C507T D169D sSNPs
AFL2T_07094 g3237 Polyketide Synthase Acetate 6606 T3478A, T4780C, G4927A, A5446G, G5677A, T5862C,T6264C C1160S, Y1594H, E1643K, N1816D, A1893T, S1954S, C2088C nsSNPs, nsSNPs, nsSNPs, nsSNPs, nsSNPs, sSNPs, sSNPs
AFL2T_02027 g7956 Vacuolar protein sorting association protein 1044 T663C Y221Y sSNPs
AFL2T_06635 g3639 L- Asparaginase 1074 A257G, C663A D86G, N221 K nsSNPs
AFL2T_09646 g7430 Ubiquitin-conjugating enzyme 921 T6C, G36C S2S, A12A sSNPs
AFL2T_01296 g5440 Vacuolar protein sorting association protein 876 C93T, G369A T31 T, L123 L sSNPs
AFL2T_05777 g6597 Vacuolar protein sorting association protein 2058 T538C, G1317A, G1438A, G1938C Y180H, S439S, E480K, K646 N nsSNPs, sSNPs, nsSNPs, nsSNPs
AFL2T_08606 g2683 Chitin Synthase 1 5172 G3339A, G3618A, A3831G, G4764C, G4952A T1113 T, L1206 L, L1277 L, P1588P, R1651Q sSNPs, sSNPs, sSNPs, sSNPs, nsSNPs
AFL2T_09101 g9340 Elongation Factor Alpha like protein 2859 C1173T, G1275A, T1895C, G1947A, A2172G, C2670T D391D, L425 L, V632A, R649R, V724 V sSNPs, sSNPs, nsSNPs, sSNPs, sSNPs
AFL2T_08131 g3088 cyclophilin 486 C158T A53V nsSNPs
AFL2T_00781 g238 Polyketide Synthase Acetate 6951 G2689A, C3072T, A3121G, A3864G V897I, F1024F, F1041A, K1288 K nsSNPs, sSNPs, nsSNPs, nsSNPs
AFL2T_01027 g12 Vacuolar protein sorting association protein 732 K401C X134A nsSNPs
AFL2T_00198 g739 Ubiquitin-conjugating enzyme 3240 C849A, T852A, G1475C, T1905C, A2459G, G2659A F283 L, I284I, R492P, G635G, N820S, A887T nsSNPs, sSNPs, nsSNPs, sSNPs, nsSNPs, nsSNPs
AFL2T_11313 g4750 aflatoxin regulatory protein 1164 T208C, C889A, G922A S70P, E297E, G308R nsSNPs, sSNPs, nsSNPs
AFL2T_08488 g2771 Elongation Factor Alpha like protein 3249 G1398A, A1683A, G2161A, G2328A E466E, V561 V, A721T, Q776Q sSNPs, sSNPs, nsSNPs, sSNPs
AFL2T_07094 g10842 Polyketide Synthase Acetate 1455 G526A, T711C, T1113C, A295G A176T, S237S, L371 L nsSNPs, sSNPs, sSNPs
AFL2T_06011 g6804 Ubiquitin-conjugating enzyme 504 A210G P70P sSNPs
AFL2T_01283 g5448 Chitin Synthase 1 2589 T1072C, A1536G N358 N, K512 K sSNPs
AFL2T_02416 g10910 Vacuolar protein sorting association protein 370 A55T L18 L sSNPs
AFL2T_05917 g6723 Ubiquitin-conjugating enzyme 741 A567G K189 K sSNPs
AFL2T_11034 g8506 GAPDH/Glyceraldehyde 3-phosphate dehydrogenase 1077 C237T, C357A H79H, G119G sSNPs
AFL2T_02787 g7154 Cytochrome oxidase 1482 A849G, C857T, A1003T, E283E, T286I, T335F sSNPs, nsSNPs, nsSNPs
AFL2T_03260 g2222 secretory lipase 1365 C936T, G985A, T990C, G1286A, T1291C N312 N, G329R, T330 T, G429D, L431 L sSNPs, nsSNPs, sSNPs, nsSNPs, sSNPs
AFL2T_07361 g10719 Lactate Dehydrogenase A 933 G702C, C753T, G879A G234G, F251F, V293 V sSNPs
AFL2T_09556 g7507 Ras protein 1458 C565T, G681A, T771C, T1047A L189 L, T227 T, T257 T, P349P sSNPs
AFL2T_03516 g2002 Vacuolar protein sorting association protein 2853 A1176C, C1180T G392G, L394 L sSNPs
AFL2T_04629 g9750 Elongation Factor Alpha like protein 1443 T475G, C543T, G1185C, T1302C T181 T, V395 V, A434A sSNPs
AFL2T_01738 g8814 cyclophilin 1638 A501C, A513G, A522G, A624G, T1011C, A1043G, T1176C V167 V, E171E, V174 V, E208E, A337A, K348R, L392 L sSNPs, sSNPs, sSNPs, sSNPs, sSNPs, nsSNPs, sSNPs
AFL2T_04801 g9604 Cytochrome oxidase 555 T207A, T463G G69G, F155C sSNPs, nsSNPs
AFL2T_12397 g9171 Vacuolar protein sorting association protein 1758 T1659C G553G sSNPs
AFL2T_08911 g9862 Polyketide Synthase Acetate 7170 T3097C, A3519C, G3689A, A3761T W1033R, T1173 T, R1230Q, Y1254F nsSNPs, sSNPs, nsSNPs, nsSNPs
AFL2T_00897 g134 cyclophilin 1893 G616 T, C855T, T1191C, A1222G V206 L, F285F, Y397Y, T408A nsSNPs, sSNPs, sSNPs, nsSNPs
AFL2T_04106 g10177 cyclophilin 642 A72T T24 T sSNPs
AFL2T_06925 g3387 Cytochrome oxidase 348 C63A V21 V sSNPs
AFL2T_07038 g3286 Chitin Synthase 1 2076 G1794A T598 T sSNPs
AFL2T_08473 g2782 cyclophilin 498 C320G T107 T sSNPs
AFL2T_01646 g2579 secretory lipase 909 C10T, T309C, G509C, C646T L4L, H103H, R170P, L216 L sSNPs, sSNPs, nsSNPs, sSNPs
AFL2T_03998 g10190 Histone 768 C96T, A255G, C391T F32F, S85S, P131S sSNPs, sSNPs, nsSNPs
AFL2T_07224 g5682 aflatoxin regulatory protein 1218 C318G, G408C, G552 T, C581T, A794G, A979G, G1075A, C1137T T106 T, P136P, S184S, A194V, Y265C, S327G, V359 M, S379S sSNPs, sSNPs, sSNPs, nsSNPs, nsSNPs, nsSNPs, nsSNPs, sSNPs
AFL2T_00797 g223 L- Asparaginase 1137 T426C, T693C, G831C, C855T, T858C G142G, G231G, Q277H, I285I, D286D sSNPs, sSNPs, nsSNPs, sSNPs, sSNPs
AFL2T_08030 g3169 secretory lipase 1269 A642G, A795G, T903C, A904G, A913G, T927C, T933C, T963C, C1140T A214A, L265 L, Y301Y, N302D, I305V, D309D, F311F, N321 N, G380G sSNPs, sSNPs, sSNPs, sSNPs, nsSNPs, sSNPs, sSNPs, sSNPs, sSNPs
AFL2T_08467 g2788 cyclophilin 1641 T83C, C408A, G610A, G655A, T666C, C690T, T750A, A789G V28A, L136 L, A204T, A219T, F222F, Y230Y, T250 T, E263E nsSNPs, sSNPs, nsSNPs, nsSNPs, sSNPs, sSNPs, sSNPs, sSNPs
AFL2T_04948 g9453 Polyketide Synthase Acetate 1977 C1314G, T1444C, G1519A, A1590G, C1767A, C1854T, G1962A R438R, W482R, V507I, L530 L, I589I, N618 N, R654R sSNPs, nsSNPs, nsSNPs, sSNPs, sSNPs, sSNPs, sSNPs
AFL2T_07791 g4925 Vacuolar protein sorting association protein 3813 G39A, C213G, G234A, A291G, C354T L13 L, S71S, Q78Q, E97E, H118H sSNPs
AFL2T_12205 g8728 secretory lipase 939 T25C, G182A, T380C, C435T, T699C, T714G, G768A, G828A L8L, S61 N, I127T, S145S, I233I, L238 L, P256P, A276A sSNPs, nsSNPs, nsSNPs, sSNPs, sSNPs, sSNPs, sSNPs, sSNPs
AFL2T_01987 g7921 cyclophilin 537 A504G ,K168 K sSNPs
AFL2T_05263 g3947 Vacuolar protein sorting association protein 891 G301A, G401A, C558T, C654T, T749G, A750G, A775G A101T, G134D, D186D, I218I, I250R, I250R, M259 V nsSNPs, nsSNPs, sSNPs, sSNPs, nsSNPs, nsSNPs, nsSNPs
AFL2T_01745 g8808 GAPDH/Glyceraldehyde 3-phosphate dehydrogenase 1041 C192G, T222C, C345T, C348T, C393T, T474C D64E, I74I,G115G, A116A, F113F, A158A nsSNPs, sSNPs, sSNPs, sSNPs, sSNPs, sSNPs
AFL2T_04609 g9767 RPL5 (ribosomal protein) 531 C153T Y51Y sSNPs

Frequency of SNPs = 0.9 SNPs/kb

Frequency of sSNPs = 0.7 SNPs/kb

Frequency of nsSNP = 0.4 SNPs/kb

Several studies have shown that non-synonymous substitutions are likely to affect protein structure [60]. Mapping of nsSNPs to a known 3D structure reveals whether the replacement is likely to destroy the hydrophobic property of a protein, electrostatic interactions or interactions with ligands. Many nsSNPs have been found near or inside the protein-protein interaction interfaces that alter the protein function [61]. Sequence-based structure predictions help in identifying the positions of a protein that are located in the active site. Protein – ligand docking analysis further helps in identifying crucial amino-acids that are involved in ligand binding.

Non-synonymous mutations mediated change in free energy (ΔG) and concomitant bioactive conformation of four proteins (g9986, g3683, g4104, g4641) belonging to the MFS and recG helicase super families were noteworthy. A decrease in free energy and atomic contact energy (ACE) putatively resulted in target-ligand interaction with a significant PatchDock score in the case of the proteins coded by the chromium tolerant strain, TERIBR1 (Table 2); whereas no ligand interaction was observed in the corresponding proteins coded by reference strain. Figure 2 shows the results of the molecular docking studies of the four proteins (g9986, g4104, g4641, g3683) coded by TERIBR1 strain. Ligand binding free energy estimates (ACE) indicated a significant decrease in free energy of these proteins (Table 3). The nsSNPs in the candidate genes of the chromium tolerant A. flavus strain TERIBR1 seemed to have influenced protein structure that could have mediated protein and chromium interaction. However, not much overlapping between the predicted binding sites (by 3DLigandSite) and the ligand docking position was observed for these proteins. The multidrug transporters of the MFS superfamily are polyspecific and can extrude a remarkably diverse range of substrates. However, discussions pertaining to multi-substrate recognition and transport by members of the MFS are still open and it is not clear if the same amino acid residues are involved in substrate recognition and binding in varying conformations of the protein [62]. Biochemical studies on the Escherichia coli MFS drug/H+ antiporter concluded that the structural basis of substrate promiscuity is governed by a large, flexible and complex substrate recognition cavity within the protein, which enables different substrates to interact with different amino acid residues of the cavity, and to form different interactions with MFS transporter [63, 64]. The putative correlation between the influence of genetic polymorphisms on the structure and function of MFS transporters and chromium tolerance in A. flavus suggested the importance of efflux mechanism in microbial chromium tolerance. Our results supported previous reports of heavy metal efflux as one of the primary mechanisms of tolerance in microbial systems [65, 66]. Furthermore, ligand docking was observed in four proteins (g8975, g685, g6212, g9525) and their homologs coded by the test and the reference strains respectively. The non-synonymous amino acid changes in these cases seemed to have no influence on protein-ligand interaction.

In a recent study four populations of yeast, exposed to arsenic in its most toxic form, As (III), accumulated changes in DNA, adapted faster and went from poor to optimal performance for fitness components (length of lag phase, population doubling time and efficiency of growth) within just a few mitotic divisions. The study concluded that fitness component enhancements in yeast populations were adaptive responses to arsenic and not to other selective pressures [35]. The observed high rate of variations in the DNA of A. flavus strain TERIBR1 in our study, especially nsSNP polymorphisms, highlights the scope for additional research on genetic mechanisms operating in A. flavus in order to conclude on the role of stress mediated alterations in DNA on adaptation in micro-organisms.

Conclusions

Changes in DNA, guided by extreme environmental conditions, could influence the structure of proteins important in chromium stress tolerance in Aspergillus flavus. The structural changes in transporter proteins and enzymes are expected to have potential influence on their functional efficacy. Our study provided insights into the genetic factors governing heavy metal tolerance, which may aid in the development of future heavy metal bio-remediation technologies. Further, to ensure that the genes presenting nsSNPs are involved in the tolerance to chromium of the TERIBR1 strain, the results obtained in the present study demand cross validation by a proteome analysis.

Additional files

Additional file 1: (97KB, xlsx)

Table S1: Representation of candidate genes for chromium tolerance in A. flavus. (XLSX 97 kb)

Additional file 2: (15.9KB, docx)

Table S2: 16 genes coded by A. flavus strain TERIBR1 with high frequency of non-synonymous substitutions. (DOCX 15 kb)

Additional file 3: (18.8KB, docx)

Table S3: Phyre2 prediction and analysis of secondary structure. (DOCX 18 kb)

Additional file 4: (415KB, pdf)

Figure S1. Protein-ligand interaction observed with homologous pairs of protein of A. flavus strains TERIBR1 and NRRL3357. (PDF 414 kb)

Acknowledgements

The authors are thankful to the TERI-Deakin Nanobiotechnology Research Centre, India for providing necessary infrastructure to carry out the required research work.

Funding

The manuscript consists of research work carried out in-house at the TERI-Deakin Nanobiotechnology Research Centre, India and was not supported by a particular funding source.

Availability of data and materials

The whole genome project has been deposited at https://submit.ncbi.nlm.nih.gov/subs/wgs/under Bioproject PRJNA362980.

Abbreviations

ACE

Atomic Contact Energy

BacMetdbs

Antibacterial Biocide & Metal Resistance Genes Database

CHR

Chromate Transport Protein

MFS

Major Facilitator Superfamily

NrBlast

Non Redundant Basic Local Alignment Search Tool

nsSNPs

Non Synonymous Single Nucleotide Polymorphisms

Phyre2

Protein Homology/AnalogY Recognition server

RCSB PDB

Research Collaboratory for Structural Bioinformatics Protein Data Bank

sSNPs

Synonymous Single Nucleotide Polymorphisms

Authors’ contributions

All authors have read and approved the final manuscript. AJ was involved in bio-informatics and proteomics data analyses, data compilation and manuscript writing. DV supported genomics data analysis. AA isolated the A. flavus strain TERIBR1 and supervised wet lab assays for chromium resistance. PP was the coordinator of the project, involved in conceptualization of the project, study design, data analyses, data compilation, manuscript writing, critical inputs and finalization of the manuscript.

Ethics approval and consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Competing interests

The authors declare that they have no competing interests.

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Contributor Information

Akanksha Jaiswar, Email: akanksha.jaiswar@teri.res.in.

Deepti Varshney, Email: deepti.varshney12@gmail.com.

Alok Adholeya, Email: aloka@teri.res.in.

Pushplata Prasad, Phone: +91 11 24682100, Email: pushplata.singh@teri.res.in.

References

  • 1.Pérez Silva RM, Ábalos Rodríguez A, Gómez Montes De Oca JM, Cantero Moreno D. Biosorption of chromium, copper, manganese and zinc by Pseudomonas aeruginosa AT18 isolated from a site contaminated with petroleum. Bioresour Technol. 2009;100:1533–1538. doi: 10.1016/j.biortech.2008.06.057. [DOI] [PubMed] [Google Scholar]
  • 2.Shumate ES, Strandberg WG. Accumulation of metals by microbial cells. Comprehensive Biotechnology. 1985;13:235–247. [Google Scholar]
  • 3.Andres Y, MacCordick HJ, Hubert JC. Bacterial biosorption and retention of thorium and uranyl cations by mycobacterium smegmatis. J Radioanal Nucl Chem. 1992;166:431–440. doi: 10.1007/BF02167788. [DOI] [Google Scholar]
  • 4.Fourest E, Roux CJ. Heavy metal biosorption by fungal mycilial by- products: mechanisms and influence of pH. Appl Microbiol Biotechnol. 1992;37:399–403. doi: 10.1007/BF00211001. [DOI] [Google Scholar]
  • 5.Hussein H, Krull R, SI AEL-ELA, Hempel DC. Conference Proceedings. Berlin, Germany: International Water Association World Water Conference; 2001. Interaction of the different heavy metal ions with immobilized bacterial culture degrading xenobiotic wastewater compounds. [Google Scholar]
  • 6.Hussein H, Farag S, Moawad H. Isolation and characterisation of Pseudomonas resistant to heavy metals contaminants. Arab Journal of Biotechnology. 2003;7:13–22. [Google Scholar]
  • 7.Hansda A, Kumar V, Anshumali A comparative review towards potential of microbial cells for heavy metal removal with emphasis on biosorption and bioaccumulation. World J Microbiol Biotechnol. 2016;32(10):170. doi: 10.1007/s11274-016-2117-1. [DOI] [PubMed] [Google Scholar]
  • 8.Lacina C, Germain G, Spiros NA. Utilization of fungi for bio treatment of raw wastewaters. Afr J Biotechnol. 2003;2(12):620–630. doi: 10.5897/AJB2003.000-1116. [DOI] [Google Scholar]
  • 9.Volesky B, Holan ZR. Biosorption of heavy metals. Biotechnol. Progr. 1995;11:235–250. doi: 10.1021/bp00033a001. [DOI] [PubMed] [Google Scholar]
  • 10.Huang CP, Huang CP. Application of Aspergillus oryzae and Rhizopus oryzae. Wat. Res. 1996;30:1985–1990. doi: 10.1016/0043-1354(96)00020-6. [DOI] [Google Scholar]
  • 11.Zafar S, Aqil F, Ahmad I. Metal tolerance and biosorption potential of filamentous fungi isolated from metal contaminated agriculture soil. Biores Technol. 2007;98:2557–2561. doi: 10.1016/j.biortech.2006.09.051. [DOI] [PubMed] [Google Scholar]
  • 12.Das S, Santra SC. Bio-detoxification of chromium from industrial wastewater by fungal strains. BIOLOGIJA. 2012;58:1–6. doi: 10.6001/biologija.v58i1.2316. [DOI] [Google Scholar]
  • 13.Li ZH, Li P, Randak T. Evaluating the toxicity of environmental concentrations of waterborne chromium (VI) to a model teleost, Oncorhynchus mykiss: A comparative study of in vivo and in vitro. Comp Biochem Physiol - C Toxicol Pharmacol. 2011;153:402–407. doi: 10.1016/j.cbpc.2011.01.005. [DOI] [PubMed] [Google Scholar]
  • 14.Bansal M, Singh D, Garg VK. A comparative study for the removal of hexavalent chromium from aqueous solution by agriculture wastes’ carbons. J Hazard Mater. 2009;171:83–92. doi: 10.1016/j.jhazmat.2009.05.124. [DOI] [PubMed] [Google Scholar]
  • 15.Baral A, Engelken RD. Chromium-based regulations and greening in metal finishing industries in the USA. Environ Sci Pol. 2002;5:121–133. doi: 10.1016/S1462-9011(02)00028-X. [DOI] [Google Scholar]
  • 16.Viti C, Marchi E, Decorosi F, Giovannetti L. Molecular mechanisms of Cr (VI) resistance in bacteria and fungi. FEMS Microbiol Rev. 2014;38:633–659. doi: 10.1111/1574-6976.12051. [DOI] [PubMed] [Google Scholar]
  • 17.Levina A, Codd R, Dillon CT, Lay PA. Chromium in Biology: Toxicology and Nutritional Aspects. Prog Inorg Chem. 2003;51:145–250. [Google Scholar]
  • 18.Samuel MS, Abigail ME, Ramalingam C. Biosorption of Cr (VI) by Ceratocystis paradoxa MSR2 Using isotherm modelling, kinetic study and optimization of batch parameters using response surface methodology. PLoS One. 2015;10:1–23. doi: 10.1371/journal.pone.0118999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Memon JR, Memon SQ, Bhanger MI, El-Turki A, Hallam KR, Allen GC. Banana peel. A green and economical sorbent for the selective removal of Cr (VI) from industrial wastewater. Colloids Surfaces B Biointerfaces. 2009;70:232–237. doi: 10.1016/j.colsurfb.2008.12.032. [DOI] [PubMed] [Google Scholar]
  • 20.Moussavi G, Mahmoudi M. Removal of azo and anthraquinone reactive dyes from industrial wastewaters using MgO nanoparticles. J Hazard Mater. 2009;168:806–812. doi: 10.1016/j.jhazmat.2009.02.097. [DOI] [PubMed] [Google Scholar]
  • 21.Anwar J, Shafique U, Waheed-uz-Zaman, Salman M, Dar A, Anwar S. Removal of Pb (II) and Cd (II) from water by adsorption on peels of banana. Bioresour Technol. 2010;101:1752–1755. doi: 10.1016/j.biortech.2009.10.021. [DOI] [PubMed] [Google Scholar]
  • 22.Gupta VK, Rastogi A, Nayak A. Adsorption studies on the removal of hexavalent chromium from aqueous solution using a low cost fertilizer industry waste material. J Colloid Interface Sci. 2010;342:135–141. doi: 10.1016/j.jcis.2009.09.065. [DOI] [PubMed] [Google Scholar]
  • 23.Wahab MA, Jellali S, Jedidi N. Ammonium biosorption onto sawdust. FTIR analysis, kinetics and adsorption isotherms modeling. Bioresour Technol. 2010;101:5070–5075. doi: 10.1016/j.biortech.2010.01.121. [DOI] [PubMed] [Google Scholar]
  • 24.Hamdi Karaoğlu M, Zor Ş, Uğurlu M. Biosorption of Cr (III) from solutions using vineyard pruning waste. Chem Eng. 2010;159(1–3):98–106. doi: 10.1016/j.cej.2010.02.047. [DOI] [Google Scholar]
  • 25.Gutiérrez-Corona JF, Romo-Rodríguez P, Santos-Escobar F, Espino-Saldaña AE, Hernández-Escoto H. Microbial interactions with chromium: basic biological processes and applications in environmental biotechnology. World J Microbiol Biotechnol. 2016;32:191. doi: 10.1007/s11274-016-2150-0. [DOI] [PubMed] [Google Scholar]
  • 26.Cervantes C, Campos-García J, Devars S, Gutiérrez-Corona F, Loza-Tavera H, Torres-Guzmán JC, Moreno-Sánchez R. Interactions of chromium with microorganisms and plants. FEMS Microbiol Rev. 2001;25(3):335–347. doi: 10.1111/j.1574-6976.2001.tb00581.x. [DOI] [PubMed] [Google Scholar]
  • 27.Srivastava S, Thakur IS. Isolation and process parameter optimization of Aspergillus sp. for removal of chromium from tannery effluent. Bioresour. Technol. 2006;97:1167–1173. doi: 10.1016/j.biortech.2005.05.012. [DOI] [PubMed] [Google Scholar]
  • 28.Gadd GM. Interactions of fungi with toxic metals. New Phytol. 1993;124:25–60. doi: 10.1111/j.1469-8137.1993.tb03796.x. [DOI] [Google Scholar]
  • 29.Gadd GM, Mowll JL. Copper uptake by yeast-like cells, hyphae and chlamydospores of Aureobasidium pullulans. Exp Mycol. 1985;9:230–240. doi: 10.1016/0147-5975(85)90019-2. [DOI] [Google Scholar]
  • 30.Gadd GM. Biosorption: critical review of scientific rationale, environmental importance and significance for pollution treatment. J Chem Technol Biotechnol. 2009;84:13–28. doi: 10.1002/jctb.1999. [DOI] [Google Scholar]
  • 31.Shugaba A, Buba F. Uptake and Reduction of Hexavalent Chromium by Aspergillus Niger and Aspergillus Parasiticus. J Phylogenetics Evol Biol. 2012;3:119. [Google Scholar]
  • 32.Pal C, Bengtsson-Palme J, Rensing C, Kristiansson E, Larsson DG. BacMet: antibacterial biocide and metal resistance genes database. Nucleic Acids Res. 2014;42(Database issue):D737–D743. doi: 10.1093/nar/gkt1252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Roberts RK, Marzluf AG. The specific interaction of chromate with the dual sulfate permease systems of Neurospora crassa. Arch Biochem Biophys. 1971;142:651–659. doi: 10.1016/0003-9861(71)90531-5. [DOI] [PubMed] [Google Scholar]
  • 34.Flores-Alvarez LJ, Corrales-Escobosa AR, Cortés-Penagos C, Martínez-Pacheco M, Wrobel-Zasada K, Wrobel-Kaczmarczyk K, Cervantes C, Gutiérrez-Corona F. The Neurospora crassa chr-1 gene is up-regulated by chromate and its encoded CHR-1 protein causes chromate sensitivity and chromium accumulation. Curr Genet. 2012;58(5–6):281–290. doi: 10.1007/s00294-012-0383-5. [DOI] [PubMed] [Google Scholar]
  • 35.Gjuvsland AB, Zörgö E, Samy JK, Stenberg S, Demirsoy IH, Roque F, Maciaszczyk Dziubinska E, Migocka M, Alonso Perez E, Zackrisson M, Wysocki R, Tamás MJ, Jonassen I, Omholt SW, Warringer J. Disentangling genetic and epigenetic determinants of ultrafast adaptation. Mol Syst Biol. 2016;12:892. doi: 10.15252/msb.20166951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Richards EJ. Inherited epigenetic variation-revisiting soft inheritance. Mycorrhiza. 2006;7:395–401. doi: 10.1038/nrg1834. [DOI] [PubMed] [Google Scholar]
  • 37.Sharma S, Adholeya A. Detoxification and accumulation of chromium from tannery effluent and spent chrome effluent by Paecilomyces lilacinus fungi. Int Biodeterior Biodegrad. 2011;65:309–317. doi: 10.1016/j.ibiod.2010.12.003. [DOI] [Google Scholar]
  • 38.White TJ, Bruns TD, Lee SB, Taylor JW. PCR - Protocols and Applications - A Laboratory Manual. 1990. Amplification and direct sequencing of fungal ribosomal RNA Genes for phylogenetics. [Google Scholar]
  • 39.Prasad P, Varshney D, Adholeya A. Whole genome annotation and comparative genomic analyses of bio-control fungus Purpureocillium lilacinum. BMC Genomics. 2015;16:1004. doi: 10.1186/s12864-015-2229-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Allpaths LG: High quality genome assembly from low cost data:http://www.broadinstitute.org/software/allpaths-lg/blog/) Acceesed 9-oct-2016.
  • 41.MAKER: http://www.yandell-lab.org/software/maker.html. Accessed 20-oct-2016.
  • 42.InterProScan: protein sequence analysis & classification. (http://www.ebi.ac.uk/interpro/). Accessed 16-Nov-2016.
  • 43.Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357–359. doi: 10.1038/nmeth.1923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Kelley LA, Mezulis S, Yates CM, Wass MN, MJE S. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–858. doi: 10.1038/nprot.2015.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Laskowski RA, MA MA, Moss DS, Thornton JM. PROCHECK - a program to check the stereochemical quality of protein structures. J. App. Cryst. 1993;26:283–291. doi: 10.1107/S0021889892009944. [DOI] [Google Scholar]
  • 46.ERRAT: http://services.mbi.ucla.edu/ERRAT/ Accessed 10-Jan-2018.
  • 47.Guex N, Peitsch MC. SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis. 1997;18:2714–2723. doi: 10.1002/elps.1150181505. [DOI] [PubMed] [Google Scholar]
  • 48.Wass MN, Kelley LA, MJE S. 3DLigandSite: Predicting ligand-binding sites using similar structures. Nucleic Acids Res. 2010;38(2):W469–W473. doi: 10.1093/nar/gkq406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res. 2005;33:W363–W7.D. doi: 10.1093/nar/gki481. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Prabhu S, Rajeswari VD. In silico docking analysis of bioactive compounds from Chinese medicine Jinqi Jiangtang tablet (JQJTT) using patch dock. J Chem Pharm Res. 2016;5(8):15–21. [Google Scholar]
  • 51.Berman J, Westbrook Z, Feng G, Gilliland TN, Bhat H, Weissig IN, PEB S. The Protein Data Bank. Nucleic Acids Res. 2000;28:235–242. doi: 10.1093/nar/28.1.235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.PyMOL Molecular Graphics System: https://www.pymol.org/. Accessed: 21-Jan-2018.
  • 53.Xue M, Yang J, Li Z, Hu S, Yao N, Dean RA, Zhao W, Shen M, Zhang H, Li C, Liu L, Cao L, Xu X, Xing Y, Hsiang T, Zhang Z, Xu JR, Peng YL. Comparative analysis of the genomes of two field isolates of the rice blast fungus Magnaporthe oryzae. PLoS Genet. 2012;8(8):e1002869. doi: 10.1371/journal.pgen.1002869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Shabalina SA, Spiridonov NA, Kashina A. Sounds of silence: synonymous nucleotides as a key to biological regulation and complexity. Nucleic Acids Res. 2013;41(4):2073–2094. doi: 10.1093/nar/gks1205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Romanova NA, Wolffs PF, Brovko LY, Griffiths MW. Role of efflux pumps in adaptation and resistance of listeria monocytogenes to benzalkonium chloride. Appl Environ Microbiol. 2006;72(5):3498–3503. doi: 10.1128/AEM.72.5.3498-3503.2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.McGlynn P, Lloyd RG. Rescue of stalled replication forks by RecG: simultaneous translocation on the leading and lagging strand templates supports an active DNA unwinding model of fork reversal and Holliday junction formation. Proc Natl Acad Sci U S A. 2001;98:8227–8234. doi: 10.1073/pnas.111008698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Plaper A, Jenko-Brinovec S, Premzl A, Kos J, Raspor P. Genotoxicity of trivalent chromium in bacterial cells: Possible effects on DNA topology. Chem Res Toxicol. 2002;15:943–949. doi: 10.1021/tx010096q. [DOI] [PubMed] [Google Scholar]
  • 58.Decorosi F, Tatti E, Mini A, Giovannetti L, Viti C. Characterization of two genes involved in chromate resistance in a Cr (VI)-hyper-resistant bacterium. Extremophiles. 2009;13:917–923. doi: 10.1007/s00792-009-0279-6. [DOI] [PubMed] [Google Scholar]
  • 59.Chihomvu P, Stegmann P, Pillay M. Characterization and structure prediction of partial length protein sequences of pcoA, pcoR and chrB genes from heavy metal resistant bacteria from the Klip River, South Africa. Int J Mol Sci. 2015;16:7352–7374. doi: 10.3390/ijms16047352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Wu J, Jiang R. Prediction of deleterious nonsynonymous single-nucleotide polymorphism for human diseases. Sci World J. 2013;2013:675851. doi: 10.1155/2013/675851. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Zhao N, Han JG, Shyu C-R, Korkin D. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning. PLoS Comput Biol. 2014;10:e1003592. doi: 10.1371/journal.pcbi.1003592. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Lewinson O, Adler J, Sigal N, Bibi E. Promiscuity in multidrug recognition and transport: the bacterial MFS Mdr transporters. Mol Microbiol. 2006;61(2):277–284. doi: 10.1111/j.1365-2958.2006.05254.x. [DOI] [PubMed] [Google Scholar]
  • 63.Neyfakh AA. Mystery of multidrug transporters: the answer can be simple. Mol Microbiol. 2002;44(5):1123–1130. doi: 10.1046/j.1365-2958.2002.02965.x. [DOI] [PubMed] [Google Scholar]
  • 64.Adler J, Bibi E. Determinants of Substrate Recognition by the Escherichia coli Multidrug Transporter MdfA Identified on Both Sides of the Membrane. J Biol Chem. 2004;279:8957–8965. doi: 10.1074/jbc.M313422200. [DOI] [PubMed] [Google Scholar]
  • 65.Ianeva OD. Mechanisms of bacteria resistance to heavy metals. Mikrobiol. 2009;71(6):54–65. [PubMed] [Google Scholar]
  • 66.Kaur S, Kamli MR, Ali A. Role of arsenic and its resistance in nature. Can J Microbiol. 2011;57(10):769–774. doi: 10.1139/w11-062. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional file 1: (97KB, xlsx)

Table S1: Representation of candidate genes for chromium tolerance in A. flavus. (XLSX 97 kb)

Additional file 2: (15.9KB, docx)

Table S2: 16 genes coded by A. flavus strain TERIBR1 with high frequency of non-synonymous substitutions. (DOCX 15 kb)

Additional file 3: (18.8KB, docx)

Table S3: Phyre2 prediction and analysis of secondary structure. (DOCX 18 kb)

Additional file 4: (415KB, pdf)

Figure S1. Protein-ligand interaction observed with homologous pairs of protein of A. flavus strains TERIBR1 and NRRL3357. (PDF 414 kb)

Data Availability Statement

The whole genome project has been deposited at https://submit.ncbi.nlm.nih.gov/subs/wgs/under Bioproject PRJNA362980.


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