Functional Prediction of Biological Profile During Eutrophication in Marine Environment

Abstract

In the marine environment, coastal nutrient pollution and algal blooms are increasing in many coral reefs and surface waters around the world, leading to higher concentrations of dissolved organic carbon (DOC), nitrogen (N), phosphate (P), and sulfur (S) compounds. The adaptation of the marine microbiota to this stress involves evolutionary processes through mutations that can provide selective phenotypes. The aim of this in silico analysis is to elucidate the potential candidate hub proteins, biological processes, and key metabolic pathways involved in the pathogenicity of bacterioplankton during excess of nutrients. The analysis was carried out on the model organism Escherichia coli K-12, by adopting an analysis pipeline consisting of a set of packages from the Cystoscape platform. The results obtained show that the metabolism of carbon and sugars generally are the 2 driving mechanisms for the expression of virulence factors.

Introduction

In recent decades, the emergence of molecular methods, especially the omics approach, has facilitated the study of microbial communities to understand their activities, compositions, interactions between taxa, and the use of nutrients. ¹ Transcriptomics has often been coupled with other methods to understand the response of microbes to ecological interactions, nutrient acquisition, membrane transport, and growth, generating a large number of results that require strong tools to derive useful information.^2,3

The marine coastal areas are increasingly subjected to anthropogenic and natural pollutants that affect the growth of macroorganisms and microorganisms. ⁴ Bacterioplankton has been linked to several types of pollution including wastewater,^5,6 chemicals, ⁷ organic or biological products, and waste. ⁸ During nutrient pollution (NP) caused by excess of nutrients specifically in coastal areas, the biota is negatively affected by algal blooms, increased growth of macroalgae, increased sedimentation and oxygen consumption, oxygen depletion in lower water layers and, sometimes, mortality of benthic animals and fish. ⁹ Through these negative effects, the bacterioplankton also undergoes several types of stress that act directly and indirectly on the functioning of the ecosystem and the microbiota. ¹⁰ This stress is caused by the higher concentrations of dissolved organic carbon (DOC), Nitrogen (N), Phosphate (P), and Sulfur (S) compounds,^11,12 to which the adaptation of bacterioplankton depends on the community structure, the physiology of the organisms, the variety of environmental conditions, and their interactions.^13,14

To survive changing environments, bacteria have evolved exquisite systems that not only sense stress but also trigger appropriate responses. ¹⁵ Their responses are related to an adaptation that involves a known resistance process especially in pathogenic bacteria such as the case of Listeria monocytogenes and a direction also of the expression of virulence genes at the appropriate time and place.^16,17 An appreciation of stress responses and their regulation is therefore essential to understand bacterial pathogenesis. Among the modules of understanding used is the analysis of changes at the molecular and cellular level regulated by highly complex signaling pathways. ¹⁸ The whole is modulated in the form of protein-protein interaction (PPI) networks and other resulting networks because the phenomenon of protection against stress strongly suggests the presence of central proteins that control the various responses to stress. ¹⁹

The study of PPI networks requires several open source or integrated software packages that allow the integration of biomolecular interaction networks with high-throughput expression data and other molecular states in a unified conceptual framework. ²⁰ Cytoscape is a powerful platform in this field, with its various plugins and its conjunction with large databases, it allows the extraction of central processes, central metabolic pathways (MPs), and hubs proteins during a particular stress in humans and model organisms.^21-23

The investigation of interactomes in model organisms such as Arabidopsis thaliana (L.), ²⁴ Saccharomyces cerevisiae (Meyen), and Escherichia coli K-12 ²⁵ has been involved in predicting and improving the understanding of cellular processes and biological interactions in other organisms.^26,27 Furthermore, the power of Cytoscape plugins in the analysis of microbiota has been documented in several works and in different microbiomes including intestinal, ²⁸ oral, ²⁹ vaginal, ³⁰ and marine.^31,32

The study of the behavior of bacterioplankton during nutrient excess as one of the environmental parameters that affect its capacity of pathogenesis is not well documented and has never been analyzed in silico. In this work, we want to study this capacity during eutrophication and algal blooming in the model organism Escherichia coli K12, through the analysis of a profile of differentially overexpressed genes (DEGs) collected from several bibliographic sources to predict hubs proteins, biological processes (BPs), and MPs involved in the selection of copiotrophic species and the virulence of bacterioplankton.

Materials and Methods

Data set collection and the choice of the model organism

A list of 196 DEGs, during NP by excess of inorganic and organic nutrients in aquatic environments (NH⁴⁺, NO₂, NO³⁻, ${\mathrm{PO}}_4^{3-}$ PO34‒, H₂PO₄, ${\mathrm{HPO}}_4^{2-}\cdot {\mathrm{O}}_4{\mathrm{P}}^{3-}$ N₂O, CCaO₃ CO₃, CO₂, HCO³⁻, CH₄, S, ${\mathrm{SO}}_3^{2-}$ , SO, SO₃), with a fold change values >1 and adjusted P value <.05 has been collected from scientific publications (Table 1). And to avoid disambiguation, their ID has been verified in the UniProt database (https://www.uniprot.org/) and EcoCyc (the Encyclopedia of E coli K-12 genes and metabolism).

Table 1.

List of genes differentially overexpressed during nutrient excess (log FC > 1), collected from several bibliographical sources.^33-38.

Genes symbol	Uniprot accession ID	Protein name
AbfD	P55792	Vinylacetyl-CoA isomerase
AccA	P0ABD5	Acetyl-CoA carboxyltransferase subunit α
AccD	P0A9Q5	Acetyl-CoA carboxyltransferase subunit β
AcnB	P36683	Bifunctional aconitate hydratase B and 2-methylisocitrate dehydratase
AcnC	P0ACI6	DNA-binding transcriptional dual regulator
ACS	P27550	Acetyl-CoA synthetase
AdhE	P0A9Q7	Fused acetaldehyde-CoA dehydrogenase and iron-dependent alcohol dehydrogenase
Agaz	P0C8K0	Tagatose 6-phosphate aldolase 1, subunit Kbaz
AmoA	Q04507/A0A5E9SRA6	Ammonia monooxygenase alpha subunit
AppY	P05052	DLP12 prophage; DNA-binding transcriptional activator
AroD	P05194	3-dehydroquinate dehydratase
AstA	P0AE37	Arginine succinyltransferase
AtoB	P76461	Acetyl-CoA acetyltransferase
AtpF	P0ABA0	ATP synthase F0 complex—subunit b
Bcp	P0AE52	Thiol peroxidase, thioredoxin-dependent
BPSL3038	Q63QI4	Putative molybdopterin-containing oxidoreductase
BtuB	P06129	Cobalamin outer membrane transporter
Can	P61517	Carbonic anhydrase 2
ChaA	P31801	Sodium/calcium: proton antiporter (CaCA family)
CheY	P0AE67	Chemotaxis protein
CodA	P25524	Cytosine/isoguanine deaminase
CooS1	P59934	Carbon-monoxide dehydrogenase1
Cpc/ptrA	C5P1W9/P05458	Protease
CusR	P0ACZ8	DNA-binding transcriptional activator
CutA	P69488	Copper binding protein
CysH	P17854	Phosphoadenosine phosphosulfate reductase
DdpF	P77622	Putative dipeptide transport protein (ABC superfamily, atp_bind)
DgcZ	P31129	Enzyme diguanylate cyclase
DsbB	P0A6M2	Disulfide bond formation proteins (oxidoreductase) with quinone as electron acceptor, reoxidizes DsbA
DsrB	P0AEG8	Dissimilatory sulfate reductase
Edd	P0ADF6	Phosphogluconate dehydratase
Eno	P0A6P9	Enolase
EutC	P19636	Ethanolamine ammonia-lyase subunit β
EutG	P76553	Polypeptide putative alcohol dehydrogenase
FAZ83_23975	A0A6D2XMK9	Branched-chain amino acid ABC transporter permease
FbaA	P0AB71	Fructose-1,6-bisphosphate aldolase
Fbp	P0A993	Fructose-1,6-bisphosphatase
FccA	W1IBJ7	Flavocytochrome c sulfide dehydrogenase
FdhF	P07658	Formate dehydrogenase H
Fic	P20605	Possible cell filamentation protein, induced in stationary phase
FlhA	P76298	Flagellar biosynthesis protein
FocA	P0AC23	Formate transport protein (formate channel 1) (FNT family)
FolC	P08192	Bifunctional folylpolyglutamate synthase/dihydrofolate synthase
FolD	P24186	Methylene-tetrahydrofolate dehydrogenase
FolP1/Sul1	Q4GY13	Dihydropteroate synthase
FrpC	P55127	Iron-regulated protein
FtsH	P0AAI3	ATP-dependent zinc metalloprotease
FucR	P0ACK8	DNA-binding transcriptional activator
FumA	P0AC33	Fumarate hydratase class I
FumC	P05042	Fumarate hydratase class II
GapA	P0A9B2	Glyceraldehyde-3-phosphate dehydrogenase
GatC	P69831	Galactitol-specific PTS enzyme IIC component
GcX	P39366	Polypeptide KpLE2 phage-like element; putative endoglucanase with Zn-dependent exopeptidase domain
GlpX	P0A9C9	Fructose-1,6-bisphosphatase 1 class 2
GltA	P0ABH7	Citrate synthase
GpmB	P0A7A2	Putative phosphoglyceromutase 2
GuaC	P60560	GMP reductase
HcaF	Q47140	Putative 3-phenylpropionate/cinnamate dioxygenase subunit β
Hcp	P75825	Hydroxylamine oxidoreductase-like protein
HemH	P23871	Ferrochelatase
HemW	P52062	Heme chaperone
Hha	P0ACE3	Hemolysin expression-modulating protein
HlpA	P0AEU7	Periplasmic molecular chaperone for outer membrane proteins
HlyB	P15492	Alpha-hemolysin translocation ATP-binding protein
HlyE	P77335	Hemolysin E, chromosomal
Hns	P0ACF8	DNA-binding protein
HpcD	Q05354	5-carboxymethyl-2-hydroxymuconate Delta-isomerase
HtgA	P28697	Transcriptional activator for sigma H (sigma 32) promoters, permitting growth at high temperature
HybF	P0A703	Hydrogenase maturation protein
IbpA	P0C054	Small heat shock protein
IdnD	P39346	l-idonate 5-dehydrogenase
KorB	P07674	Transcriptional repressor protein
LtxA	P16462	Leukotoxin
LtxB	A0A2G8ZPA1	RTX toxin hemolysin A
MaeA	P26616	NAD-dependent malic enzyme
Mdh	P61889	Malate dehydrogenase
Mfd	P30958	Transcription-repair ATP-dependent coupling factor
MhpT	P77589	3-(3-Hydroxy-phenyl) propionate transporter
MoaD	P30749	Molybdopterin-containing oxidoreductase
Nac	Q47005	Nitrogen assimilation transcription factor
NapA	P33938	Nitrate reductase, periplasmic, large subunit
NapF	P0AAL0	Polypeptide ferredoxin-type protein
Nar	P11350	Nitrate reductase
NemA	P77258	N-ethylmaleimide reductase
NfsA	P17117	Oxygen-insensitive NADPH nitroreductase, also anaerobic azo reductase
NifH	P00459	Nitrogenase iron protein
NirB	P08201	Nitrite reductase (NADH) large subunit
NirD	P0A9I8	Nitrite reductase (NADH) small subunit
NirK	P38501	Copper containing nitrite reductase
NirS	P24474	Nitrite reductase
NmpC	P21420	DLP12 prophage; putative outer membrane porin
NorV	Q46877	Nitric oxide reductase
NosZ	P19573	Nitrous oxide reductase
NuoH	P0AFD4	NADH: quinone oxidoreductase subunit H
NuoJ	P0AFE0	NADH: quinone oxidoreductase subunit J
PaaZ	P77455	Crotonyl-CoA hydratase
ParC	P0AFI2	Dimer of DNA topoisomerase IV subunit A
PckA	P22259	Phosphoenolpyruvate carboxykinase
PfkA	P0A796	6-Phosphofructokinase
Pgi	P0A6T1	Glucose-6-phosphate isomerase
Pgk	P0A799	3-phosphoglycerate kinase
PheA	P0A9J8	Bifunctional: chorismate mutase P (N-terminal); prephenate dehydratase (C-terminal)
PheL	P0AD72	Phe operon leader peptide
PheT	P07395	Phenylalanine tRNA synthetase, beta-subunit
PhoU	P0A9K7	Phosphate-specific transport system accessory protein
PKS	B2HIL7	Polyketide synthase
PotH	P31135	Putrescine transport protein (ABC superfamily, membrane)
PpsA	P23538	Phosphoenolpyruvate synthase
Ppx	P0AFL6	Exopolyphosphatase
PRK1	A0A4S5AZM1	Phosphoribulokinase
ProA	P07004	Gamma-glutamyl phosphate reductase
PurL	P15254	Phosphoribosylformylglycinamidine synthase II
PurU	P37051	Formyltetrahydrofolate synthetase
PuuA	P78061	Gamma-glutamylputrescine synthetase
PuuD	P76038	Gamma-glutamyl-gamma-aminobutyrate hydrolase
Pyc	Q58626	Pyruvate carboxylase
PykA	P21599	Pyruvate kinase II
RavA	P31473	Regulatory ATPase
RbcL	A0A2J1D642	Ribulose bisphosphate carboxylase
Rbn	P0A8V0	Ribonuclease BN
RbsR	P0ACQ0	DNA-binding transcriptional dual regulator
RfbD	P37760	TDP-rhamnose synthetase, NAD(P)-binding
Rpe	P0AG07	Ribulose-5-phosphate 3-epimerase
RpiA	P0A7Z0	Ribose 5-phosphate isomerase
RpmJ	P0A7Q6	50S ribosomal subunit protein L36
RpoN	P24255	RNA polymerase, sigma 54 (sigma N) factor
RseP	P0AEH1	Intramembrane zinc metalloprotease
RtxA	A0A3L0W7I6	Multifunctional-autoprocessing repeats-in-toxin
ScpA	P27253	Methylmalonyl-coa epimerase
SdhA	P0AC41	Succinate dehydrogenase
SdhA	P0AC41	Succinate: quinone oxidoreductase, FAD binding protein
SeqA	P0AFY8	Negative modulator of initiation of replication
SoxR	P0ACS2	DNA-binding transcriptional dual regulator
Sqr	P0AC41	Sulfide: quinone reductase
StfR	P76072	Rac prophage; putative tail fiber protein
Suc	P0AGE9	Succinyl-CoA synthetase
SucA	P0AFG3	2-Oxoglutarate dehydrogenase E1 component
SulA	P0AFZ5	Suppressor of ion; inhibitor of cell division and FtsZ ring formation on DNA damage/inhibition
TktA	P27302	Transketolase 1
TorC	P33226	Cytochrome c-type protein in TMAO respiration; with TorA, also negative regulator of tor operon
TpiA	P0A858	Triosephosphate isomerase
Tsr	P02942	Protein methyl-accepting chemotaxis protein—serine-sensing
UgpA	P10905	Sn-glycerol-3-phosphate transport system permease protein
UmuD	P0AG11	Component of DNA polymerase V, signal peptidase with UmuC
Ung	P12295	Uracil-DNA-glycosylase
UraA	P0AGM7	Uracil: H+ symporter
UspD	P0AAB8	Universal stress protein D
XanP	P0AGM9	Xanthine: H+ symporter
XdhA	Q46799	Putative xanthine dehydrogenase molybdenum-binding subunit
YacG	P0A8H8	DNA gyrase inhibitor
YadC	P31058	Uncharacterized fimbrial-like adhesion protein
YafO	Q47157	mRNA interferase toxin YafO
YagZ	P0AAA3	Common pilus major subunit
YaiL	A0A376JCL2	Nucleoprotein/polynucleotide-associated enzyme
YbcJ	P0AAS7	Putative RNA-binding protein
YbdO	P77746	Putative LysR family DNA-binding transcriptional regulator
YbeV	P77359	Putative chaperone with DnaJ-like domain
YbgG	P54746	Putative sugar hydrolase with alpha-mannosidase domain
YcaP	P75839	DUF421 domain-containing protein
YcdG	P75892	Putative uracil transport protein (NCS2 family)
YcfJ	P0AB35	Putative membrane protein
YcgR	P76010	Flagellar brake protein
YdeP	P77561	Putative formate dehydrogenase, related to acid resistance with formate dehydrogenase/DMSO reductase
YdgF	P69212	Multidrug/spermidine efflux pump membrane subunit
YdiJ	P77748	Putative FAD-linked oxidoreductase
YdiL	P76196	Conserved hypothetical protein
YdjX	P76219	DedA family protein
YehT	P0AFT5	DNA-binding transcriptional dual regulator
yfaL	P45508	Serine protease autotransporter
YfbQ,	P0A959	Glutamate—pyruvate aminotransferase
YfdM	P76509	Prophage; putative methyltransferase
YfiF	P0AGJ5	Putative methyltransferase
YfjR	P52133	CP4-57 prophage; putative DNA-binding transcriptional regulator
YgeF	Q46786	Conserved hypothetical protein
YgeP	Q46796	Unknown CDS
YhbH	P0AFX0	Putative sigma N (sigma 54) modulator
YhbP	P67762	Putative FMN binding protein
YhcE	P45421	Putative uncharacterized protein
YheO	P64624	DNA-binding transcriptional regulator
YhjB	P37640	Putative response regulator in 2-component regulatory system
YhjC	P37641	Putative DNA-binding transcriptional regulator
YiaK	P37672	2,3-Diketo-L-gulonate reductase
YidE	P60872	Putative transport protein
YieM	P0ADN0	Conserved protein with Integrin A (or I) domain
YjaG	P0A9V5	Uncharacterized HTH-type transcriptional regulator
YjcE	P32703	Putative transporter
YjeK	P39280	Lysine 2,3-aminomutase
YjiD	P39375	Anti-adapter protein
YjiH	P39379	Gate family protein
YkfF	P75677	CP4-6 prophage; protein
YmdC	P75919	Putative synthase with phospholipase D/nuclease domain
YnbE	P64448	Lipoprotein
YnfD	P76172	DUF1161 domain-containing protein
YtfJ	P39187	Conserved hypothetical protein

The predictive analysis has been performed using the bacterium strain E coli K12 as model organism for the aquatic bacterioplankton; E coli K12 serves as the best characterized and good leader model organism for bacterial genetics and molecular biology studies.

Cytoscape pipeline analysis

To start the analysis, the DEGs’ profile was annotated in multiple Cytoscape packages (Version: 3.8.2 https://cytoscape.org/) following the pipeline (Figure 1). The raw list (196 DEGs) was queried in StringApp to obtain the PPI networks. The tab-delimited ranking list txt file generated from String was analyzed to generate a subnetwork with the hub proteins reflected by the network analyzer plugin to show a topological mapping. The subnetwork was analyzed by the ClueGO to identify BPs and MPs related to excess of nutrients.

Figure 1.

Graphical abstract of the analysis pipeline (step by step), from data collection to hub proteins and metabolic pathway identification with the various packages and databases used.

String analysis

The input list of 196 DEGs was analyzed by StringApp (Version: 11.0 https://string-db.org/) for a fixed search parameter with a confidence score cutoff to 0.4 without additional interactors. The resulting networks were customized by the layout and visual style in the control panel.

Subselection and topological mapping of hub proteins analysis

Three networks obtained by StringApp were subselected based on degree and filtered to obtain the hub proteins. The highlighted hub proteins and their first neighbors obtained were filtered to select the most significant terms. The results were mapped by Network analyzer plugin “http://apps.cytoscape.org/media/networkanalyzer.”

ClueGO analysis

The subnetwork resulting from the subselection has been analyzed by ClueGO (Version: 1.5 http://www.ici.upmc.fr/) to select representative GO processes and pathways and visualizing them in functionally organized networks. Statistical analysis of ClueGO enrichment was defined using a hypergeometric test with P ⩽ .05, corrected by the Benjamin-Hochberg method, and kappa scores ⩾0.4 as primary endpoint.

Results and Discussion

String results

The list of 196 collected proteins (Table 1) was imported and analyzed by the StringApp. This latter has mapped and annotated all the genes right away. The results were performed in the format of a network with different evidence indexes (Figure 2), and the PPI networks obtained have identified 7 associated networks with a total of 165 out of 196 nodes, 442 edges, and a P value <10⁻¹⁶. The 165 annotated proteins in the principal network are linked either directly or indirectly through one or more interacting proteins, which enhances the existence of functional links between them. These results suggest that the proteins are at least partially biologically connected as a group, maybe participate together in the same process and have the same phenotype, which has given great importance to co-expression and high weight to genetic and protein interactions.

Figure 2.

Predicted protein-protein interaction networks. Parameters: Score (0.4), no additional nodes; interaction sources used: experimentation, databases, co-expression, co-occurrence, gene fusion, and neighborhood. In the interaction networks, separate lines of different colors are used to show the type of evidence that supports each interaction.

The obtained PPI network was accompanied by a global functional enrichment analysis where BP, hub proteins, and MPs were exported. The results of the most 5 representative terms are shown in Table 2, where GO terms are generation of precursor metabolites and energy (GO.0006091), monocarboxylic process (GO:0032787), nicotinamide and metabolic process (GO:0046496), antibiotic metabolic process (GO:0016999), and small molecule biosynthetic process (GO:0044283), and the most significant MP are carbon metabolism (eco01200), pyruvate metabolism (eco00620), glycolysis/gluconeogenesis (eco00010), pentose phosphate pathway (eco00030), and methane metabolism (eco00680). These BPs and MPs involve biochemical reactions and pathways that ultimately lead to the formation of precursor metabolites and substances from which energy is derived.^39-44 This energy production is essential for the regulation of nutrient content during stress, to persist long enough, continue its cycle, and invade a new host. ⁴⁵

Table 2.

Most representative GO terms of biological processes and their associated pathways.

Category	GO term	Description	FDR value	Number of genes
GO Process	GO:0036091	Generation of precursor metabolites and energy	9.48E-36	44
	GO:0032787	Monocarboxylic process	3.12E-21	34
	GO:0046496	Nicotinamide and metabolic process	1.1E-20	21
	GO:0016999	Antibiotic metabolic process	9.2E-20	23
	GO:0044283	Small molecule biosynthetic process	8.82E-15	32
	GO:0036006	Glucose metabolic process	3.45E-14	14
KEGG Pathway	eco01200	Carbon metabolism	1.14E-57	51
	eco00620	Pyruvate metabolism	3.57E-26	25
	eco00010	Glycolysis/gluconeogenesis	1.16E-20	20
	eco00030	Pentose phosphate pathway	1.52E-18	17
	eco00680	Methane metabolism	1.79E-16	15
	eco00020	Citrate cycle (TCA cycle)	2.48E-16	15

Simultaneously, the 10 genes chosen as hub proteins (Table 3) based on their combined score and their connectivity in Figure 2, which shows a co-expression profile, neighborhood, and appearance links between them and between (eno, ftsH, ravA, codA, hemN/yggW, puuD, codA, mngB, norV, can) that encoded for virulence factors such as ferrochatalases, metalloenzymes, enolases, hydrolases, and cytotoxic chemotherapeutic agents. These factors are often linked to MPs for nutrients and toxins such as lipopolysaccharides, proteases (zinc metalloproteases), and virulence factors induced by sugar metabolism in bacteria.^46,47

Table 3.

List of top 10 hub proteins with their betweenness centrality (BC) and degree values.

Row	Gene name	Protein name	BC	Degree
1	pfo	Probable pyruvate-flavodoxin oxidoreductase	0.16	36
2	pykF	Pyruvate kinase I (formerly F)	0.03	29
3	gltA	Citrate synthase	0.05	28
4	glcB	Malate synthase G	0.02	27
5	pgi	Glucose-6-phosphate isomerase	0.04	26
6	maeB	NADP-dependent malic enzyme	0.02	25
7	aceE	Pyruvate dehydrogenase E1 component	0.02	23
8	ptA	Phosphate acetyltransferase	0.02	22
9	tktB	Transketolase 2	0.02	21
10	aceF	Dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex	0.008	20

Kennelly and Potts (1996) have stated that during stress conditions, microorganisms develop signal transduction systems from the outside to the inside of the cell. ⁴⁸ These signals include degradative enzymes such as proteases, lipases, and substrate capture enzymes such as glutamine synthetase and alkaline phosphatase to detect environmental stresses and to control the coordinated expression of genes involved in cellular defense mechanisms.^49-51 Their response to these signals will enable their survival; enhance their resistance to a number of environmental stresses such as low pH, heat, and oxidative stress;^52,53 and/or enhance their virulence.

This is relatively true because Gram-positive bacteria especially Actinobacteria and Firmicutes present a diverse collection of regulatory proteins (CcpA, CodY, and Rex) of central metabolic capacities and virulence, which have been shaped by reductive evolution.^45,54,55 Among these Gram-positive bacteria is Staphylococcus aureus (S aureus), a strain indigenous to aquatic environments and thus transferred by discharges. In the presence of excess carbon, the regulatory protein CcpA stimulates transcription of ilvBoperon, making CodY more active as a repressor of many pathways that remove intermediates from glycolysis and gluconeogenesis to be fully pathogenic. ⁴⁵ And in Gram-negative bacteria, regulation is stimulated by FNR which is influenced by the histone-like protein H-NS; nevertheless, FNR has been shown to be important for virulence and survival of Salmonella.^15,56

In the light of the above discussed results, we suggest that the metabolic behavior and central BPs are highly correlated with nutrient metabolism, contributing toward the progression of complications that can affect cell behavior and bacterioplankton phenotype, because as it has been mentioned, the growth of microorganisms in a non-optimal environment suggests evolutionary adaptations through specific mutations responsible for a physical form. ⁵⁷ In addition, the involvement of hub proteins related to carbohydrate metabolism, proteins, nucleic acids, and membrane transport have been reported in the selection of copiotrophic and pathogenic species,^34,58 but these results require further studies because the existing research to date has not thoroughly evaluated the 4 nutrients (C, N, P, and S) together.

Subselection and network analyzer results

The network generated by string software was imported as a pre-existing unformatted array in Cytoscape software. The network analyzer plugin function was used for providing network filtration and customization. The principal subnetwork obtained (Figure 3) provides 72/165 nodes with a confidence score of 0.8 and a PPI enrichment P value <10⁻¹⁶. The list of 72 genes was filtered and 10 hub proteins were subselected (Table 3). All of these genes exhibit the highest interactions between them to regulate some cellular functions. Indeed, several studies have demonstrated the key role of these enzymes in microbial metabolism such as glycolysis/gluconeogenesis, ⁵⁹ pyruvate metabolism, ⁶⁰ secondary metabolite biosynthesis, carbon metabolism, ⁶¹ and other fundamental intracellular processes. These results would be linked to the virulence of bacteria in the presence of an excess of nutrient. ⁵⁹ According to this work, other studies have suggested that these enzymes are considered moonlight proteins and are involved in microbial virulence.^46,47,62,63

Figure 3.

Topological mapping of the hub proteins obtained in the subselection analysis based on the cutoff value BC <0.02 and node degree >20. The larger circles correspond to the higher degrees and brown to blue color refers to increment of betweenness; the thickness of the lines represents the confidence score of the associations and different colors are used to show the type of evidence that supports each interaction .

ClueGO results

ClueGOapp was launched by an ontological and metabolic analyses to evaluate over-represented GO terms and MP by annotating subselected proteins and their first neighbors in biological terms hierarchically (parent-child relation) and to assign them to functional MP pathways. The results are presented as a pie chart (Figure 4) for BP and a functionally grouped network (Figure 5) for MP, and 80 terms were associated with the 72 proteins. The major representative terms for GO processes are the metabolic process of small molecules, the catabolic process of organic substances, the metabolic process of carbohydrates, the metabolic process of alpha-amino acids, and the positive regulation of biological process; the major representative terms for MP are glycolysis/glycogenesis, pyruvate metabolism, the 2-component system, purine metabolism, and oxidative phosphorylation for MPs.

Figure 4.

Predicted functional enrichment pie chart for the GO BPs by ClueGO.

Figure 5.

In ClueGO, metabolic pathways were predicted from KEGG as a network with the terms of the enriched pathways visualized using Cytoscape’s ClueGo/CluePedia plugin where several proteins share common functions. The size of the nodes corresponds to the importance of the metabolic pathway.

The ClueGO results are consistent with those provided by StringApp, which also involve biochemical reactions and pathways that ultimately lead to the formation of precursor metabolites and substances from which energy is derived and most of them refers to the MPs of the purine and citrate cycle (tricarboxylic acid [TCA] cycle). The metabolic process of purine seems to be a widespread phenomenon. ⁶⁴ It has been found to be a key modulator in virulence of pathogens. ⁶⁵ The TCA cycle, also known as the citric acid cycle or Krebs cycle, produces energy by the complete oxidation of acetate, derived from carbohydrates, fats and proteins, to carbon dioxide. ⁶⁶

In Table 2, 51 out of 165 proteins were assigned to carbon metabolism, which suggests it as the central metabolic process and the main nutrient during eutrophication. Deutscher et al and Görke and Stülke reported the binding of carbon catabolism to microbial virulence.^67,68 Excessive carbon sources and DOC were documented as enhancers of bacterial growth, oxygen removal, and selector for copiotrophs and opportunistic pathogens in both seawater and coral holobiota^69,70 using their preferred carbon substrate through ATP-binding cassette (ABC) transporters.^71,72 The ABC transporters were reported in studies involving genes related to virulence and symbiotic interactions ⁷³ and highly reported in copiotrophs to the opposites of oligotrophs. ⁷⁴ Haas et al ⁷⁵ reported the abundance of Gammaproteobacteria and Alphaproteobacteria in enriched and algal-dominated waters in contrast to coral-dominated oligotrophic waters, and this suggests the possible adaptation of the studied bacterioplankton in case of existence in such an environment, but all this needs further study and discussion to draw strong conclusions.

In Figure 5, many proteins are multitasking and provide at least 2 MPs, which reminds us of moonlighting proteins. The existence of moonlighting proteins in microorganisms is a known, but still poorly understood phenomenon. ⁷⁶ Most of these proteins exercise their role in the cytoplasm and outside the cell. Their existence has been linked to virulence and they are often domestic enzymes, especially those of the glycolytic pathway, such as enolase, aldolase, dehydrogenase, heat shock proteins, and transcription factors, and they may perform non-catalytic roles with different functions depending on their cellular localization and the concentration of substrates. ⁶² In the analyzed differential gene expression (DGE) profile, pyruvate metabolism, ⁶⁰ carbon metabolism, ⁶¹ and glycolysis/gluconeogenesis ⁵⁹ (Figure 5) are central glycolytic MP that involved moonlight proteins and are related to virulence in bacteria. Taken together, the analyses of BP and MP (Figures 4 and 5) reveal that the interconnected proteins during the nutrient excess and the bloom proliferation phase in the model organism E Coli K12 are involved in chemical reactions and cellular metabolism involving carbohydrates and organic acids. Thus, several studies have reported the relationship between moonlight proteins, carbon catabolism, and microbial virulence factors.^67,68 In addition, the involvement of hub proteins related to carbohydrate metabolism, proteins, nucleic acids, and membrane transport has been reported in the selection of copiotrophic and pathogenic species.^34,58

Conclusions

Transcriptomic data are increasingly numerous and varied, facilitating data mining at a system level. A large number of approaches/tools have been developed to detect pathways and processes that are significantly altered between different experimental conditions during stress by pollutants or other substances. The objective of this work is to study the capacity of bacterioplankton during eutrophication and algal blooms in the model organism E coli K12, through the analysis of a profile of DEGs collected from several bibliographic sources to predict hub proteins, BP and MP involved in copiotrophic species selection, and bacterioplankton virulence.

The obtained results suggested that the metabolic behavior and central BPs are strongly correlated with carbon and carbohydrate metabolism, contributing to the progression of complications that can affect the cellular behavior and phenotype of bacterioplankton. The involvement of hub proteins related to carbohydrate, protein, nucleic acid metabolism, and membrane transport has been reported in the selection of copiotrophic and pathogenic species during excess of nutrients, but these findings require further study.

The bacterial stress adaptation of E coli to excess nutrients and the possibility of increased virulence associated with stress need to be studied in more detail to prevent potential risks of host-microbiota interactions. This is important because understanding the mechanisms and regulation of bacterioplankton stress adaptation will provide information for pathogen control and enhance the effective design of new control methods. Furthermore, the identification of moonlight proteins is clearly not an easy process as most of the currently identified bacterial moonlight proteins were discovered by chance.

Today, researchers are using antimicrobial susceptibility testing to address the problem of multidrug resistance by Gram-positive and Gram-negative commensal and pathogenic bacteria. But questions arise as to their use in the treatment of pathogenesis in aquatic habitats. In aquatic environments, the use of such strategy has often been associated with aquaculture. Moreover, with the mechanisms of microbial evolution, their adaptations, the poor practices of treatment, and discharge of microbes in some laboratories in developing countries and the discharge of wastewater into aquatic environments, such a process suggests the development and diffusion of resistance genes to biomolecules (phenolic compounds) through horizontal and vertical transfers while creating a new problem to be solved but in the long term.