Omics of Brucella : Species-Specific sRNA-Mediated Gene Ontology Regulatory Networks Identified by Computational Biology

Abstract

Brucella is an intracellular bacterium that causes the zoonotic infectious disease, brucellosis. Brucella species are currently intensively studied with a view to developing novel global health diagnostics and therapeutics. In this context, small RNAs (sRNAs) are one of the emerging topical areas; they play significant roles in regulating gene expression and cellular processes in bacteria. In the present study, we forecast sRNAs in three Brucella species that infect humans, namely Brucella melitensis, Brucella abortus, and Brucella suis, using a computational biology analysis. We combined two bioinformatic algorithms, SIPHT and sRNAscanner. In B. melitensis 16M, 21 sRNA candidates were identified, of which 14 were novel. Similarly, 14 sRNAs were identified in B. abortus, of which four were novel. In B. suis, 16 sRNAs were identified, and five of them were novel. TargetRNA2 software predicted the putative target genes that could be regulated by the identified sRNAs. The identified mRNA targets are involved in carbohydrate, amino acid, lipid, nucleotide, and coenzyme metabolism and transport, energy production and conversion, replication, recombination, repair, and transcription. Additionally, the Gene Ontology (GO) network analysis revealed the species-specific, sRNA-based regulatory networks in B. melitensis, B. abortus, and B. suis. Taken together, although sRNAs are veritable modulators of gene expression in prokaryotes, there are few reports on the significance of sRNAs in Brucella. This report begins to address this literature gap by offering a series of initial observations based on computational biology to pave the way for future experimental analysis of sRNAs and their targets to explain the complex pathogenesis of Brucella.

Introduction

Brucellosis is a zoonotic infectious disease and a veritable global health burden. Brucella species are currently intensively studied with a view to developing novel global health diagnostics and therapeutics. In this context, small RNAs (sRNAs) are one of the emerging topical areas as they play significant roles in regulating gene expression and cellular processes in bacteria (Vishnu et al., 2015).

sRNAs are present in all life forms. Bacterial sRNAs are typically 50–500 nucleotides (nt) long and regulate diverse cellular functions, such as bacterial virulence, secretion, stress response, and quorum sensing. (Gottesman, 2005; Storz et al., 2005). sRNAs regulate the translation (inhibition/activation) by interacting with specific mRNA targets through base pairing. sRNAs downregulate the gene expression either by interfering with ribosome binding or degrading mRNA target (Storz et al., 2011). Very few sRNAs were shown to positively regulate the gene expression by exposing the ribosome binding site and stabilizing the mRNA (Papenfort et al., 2013). The majority of the sRNAs identified are trans-encoded, these sRNA genes are located far away from their mRNA targets, and their hybridization occurs through short imperfect base pairing (Waters and Storz, 2009). These regulatory RNA molecules are situated in the intergenic regions between protein-coding genes (Atluvia, 2007). Another type of sRNA is cis-encoded, which is cotranscribed with its mRNA targets to regulate its transcription or translation. As the number of available whole-genome sequences is increasing, many computational methods have been developed to identify sRNAs from genome sequences. To date, several sRNAs have been identified in pathogenic bacteria, such as Mycobacterium tuberculosis (Arnvig and Young, 2009), Salmonella spp. (Altier et al., 2000), Listeria monocytogenes (Behrens et al., 2014; Mraheil et al., 2011), and Staphylococcus spp. (Bohn et al., 2010; Pichon and Feldon, 2005). Several computational methods have been developed for the identification of sRNAs in bacterial genome sequences.

Brucella spp. are the etiological agents of the zoonotic disease, brucellosis. Brucella is gram-negative, facultative intracellular bacterium and belongs to the class Alphaproteobacteria (Whatmore, 2009). The genus Brucella is classified into 10 species: Brucella melitensis (sheep and goat), Brucella abortus (cattle), Brucella suis (pig), Brucella canis (dog), Brucella ovis (sheep), Brucella pinnipedialis (seal), Brucella microti (common vole), Brucella neotomae (desert wood rat), Brucella ceti (dolphin, whale), and Brucella inopinata (human), based on the host preference and antigenic and biochemical characteristics (O'Callaghan and Whatmore, 2011). Brucellosis is characterized by sterility, infections, and abortion in animals (Corbel, 1997) and, in humans, it causes undulant fever, osteoarthritis, and several neurological disorders (Young, 1995). Brucella spp. are highly conserved at the nucleotide level and do not show many genetic variations among different species (Paulsen et al., 2002). Brucella does not have classical virulence factors, such as fimbria, toxins, and plasmids. (Moreno and Moriyon, 2002). The virulence of Brucella relies on its ability to survive and replicate in the host phagocytes (Kaufmann, 2011). However, the complex mechanism of intracellular survival of Brucella remains poorly understood. Recently, few sRNAs were identified in Brucella and their roles in intracellular survival and pathogenicity were reported (Caswell et al., 2012; Wang et al., 2015). In this study, we report the computational prediction of sRNAs and their mRNA targets in B. melitensis, B. abortus, and B. suis. We also report the functional enrichment analysis of the predicted mRNA targets and the associated pathways.

Materials and Methods

Prediction of sRNAs in three Brucella species

sRNA candidates were predicted by integrating the outputs of two programs, SIPHT and sRNAscanner. SIPHT (sRNA Identification Protocol using High-throughput Technologies) identifies candidate sRNAs by searching for putative Rho-independent terminators downstream of conserved intergenic sequences. Each predicted locus is then annotated with several features, including its conservation in other species, its association with one of several transcription factor binding sites, its position relative to flanking genes, and its homology and conserved genomic position with previously identified sRNAs (Livny et al., 2008). SIPHT searches on Web server (http://newbio.cs.wisc.edu/sRNA/) were used to detect sRNAs with following parameters: sRNA length −50–550 nt, maximum blast E value –1e-15, minimum TransTerm confidence value −87, maximum FindTerm score −10, and maximum RNAMotif score—9. Other parameters were kept at their default values. To ensure the accuracy of computational prediction, we considered only the candidates denoted as RNA by the QRNA (Rivas and Eddy, 2001) analyses of SIPHT.

sRNAscanner (Sridhar et al., 2010) was the other program used to screen sRNA candidates in three Brucella genomes. sRNAscanner identifies intergenic sRNA transcriptional units based on the transcriptional signals. This computational program uses position weight matrices of sRNA promoter and rho-independent terminator signals derived from experimentally defined Escherichia coli K-12 MG1655 to identify sRNAs through sliding window-based genome scans. The searches were restricted to intergenic regions, and the sRNA length for prediction was set to 50–550 nt. All other parameters were kept at default values.

Target prediction

The mRNA targets of the sRNA candidates were predicted using TargetRNA2 (Kery et al., 2014) (http://cs.wellesley.edu/∼btjaden/TargetRNA2) with default parameters.

Functional enrichment analysis

Functional categorization of predicted target genes was done using COG (cluster of orthologous groups) analysis and Gene Ontology (GO) annotations. COG classification was done using COG database (Tatusov et al., 2003) and Comparative GO was used for GO annotations (Fruzangohar et al., 2013). Pathway annotations were done using KEGG pathway database with default parameters.

GO regulatory network

Regulatory relationships among the biological processes were analyzed using the Comparative GO Web server (Fruzangohar et al., 2013). GO regulatory network (GRN) provides information on (1) regulatory relationships between GO terms and their associated genes, (2) enrichment levels of GO terms, and (3) genes associated with each GO term. We constructed GRN for the predicted sRNA target genes in three Brucella species.

Results

In-silico prediction of sRNA candidates in three Brucella species

sRNAs in three genomes of Brucella were predicted using SIPHT and sRNAscanner. We considered only the candidates predicted as RNA by QRNA algorithm of SIPHT as sRNAs. The number of sRNAs predicted for each Brucella genome is shown in Figure 1 and the details of predicted sRNAs are given in Tables 1 –3. sRNAs predicted by both methods were considered as potential sRNA candidates. In B. melitensis, 21 sRNAs were consistently predicted by both QRNA and sRNAscanner. Similarly, in B. abortus and B. suis, 13 and 16 consensus RNAs, respectively, were predicted. sRNA candidates identified varied in length between 62 and 487 nt (Fig. 2). The majority of the candidates were between 51 and 150 nt in length. GC content of sRNA candidates ranged from 34% to 65%. The consensus sRNAs predicted were searched against Rfam database (http://rfam.sanger.ac.uk/) and Bacterial Small Regulatory RNA Database (http://bac-srna.org/BSRD/index.jsp#) to eliminate the duplicates if present. Two sRNA candidates in B. melitensis, one in B. abortus, and two in B. suis were showing homology with already identified sRNAs in Rfam database (Table 4).

FIG. 1.

sRNA candidates predicted in (A) Brucella melitensis 16M, (B) Brucella abortus 2308, and (C) Brucella suis 1330. sRNA, small RNA.

FIG. 2.

Length distribution of sRNAs identified in B. melitensis, B. abortus, and B. suis.

Table 1.

sRNAs Identified in Brucella melitensis

sRNA	Up gene name	Distance up gene	sRNA start	sRNA end	sRNA length	Distance down gene	Down gene name
BmsRNA1^a	Hypothetical protein	637	116541	116636	95	0	Preprotein translocase subunit, SecA
BmsRNA2	Dihydrolipoamide dehydrogenase	30	143419	143455	36	136	Hypothetical_protein
BmsRNA3	Isopropylmalate isomerase large subunit	20	153291	153523	232	3	Acetyltransferase
BmsRNA4	Acetyltransferase	1	210870	211313	443	77	Immunoglobulin-binding protein, EIBE
BmsRNA5	Hypothetical protein	23	330394	330579	185	18	50S ribosomal protein L31
BmsRNA6^b	OPGC	110	340403	340476	73	282	Hypothetical protein
BmsRNA7	50S ribosomal protein L25/general stress protein C	79	502689	502991	302	427	ATP-binding protein
BmsRNA8^a	tRNA-Trp	2	769451	769580	129	319	Preprotein translocase subunit SecE
BmsRNA9^a	Single-strand-binding protein	1	912498	912648	150	533	GntR family transcriptional regulator
BmsRNA10^a	Acetate CoA-transferase alpha subunit	221	961305	961367	62	307	Diguanylate cyclase/phosphodiesterase
BmsRNA11^a	Malic enzyme	41	1007409	1007511	102	673	Glutamyl-tRNA synthetase
BmsRNA12^a	Hypothetical protein	19	1051288	1051361	73	0	tRNA-Arg
BmsRNA13	Hypothetical protein	157	1969792	1970045	253	6	30S ribosomal protein S1
BmsRNA14	ATP-dependent protease ATP-binding subunit HslU	12	2107841	2107940	99	124	Hypothetical protein
BmsRNA15^a	Outer membrane protein, OprF	75	36012	36098	86	277	Putative cytoplasmic protein
(BmsRNA16)ctRNA_p42d_NC_003318^b	Replication initiation protein, RepC	47	91982	92110	128	221	Replication protein B
BmsRNA17^a	Putative cytoplasmic protein	21	572273	572414	141	367	Glycine betaine/L-proline transport ATP-binding protein
BmsRNA18^a	Glycine dehydrogenase	83	588986	589111	125	578	Hypothetical protein
BmsRNA19	Glycine dehydrogenase	431	589334	589472	138	217	Hypothetical protein
BmsRNA20^a	Cell division protein, FTSK	20	781571	781750	179	578	Surfeit locus protein 1
BmsRNA21^a	Xylose transport system permease protein, XylH	17	376822	376938	116	7	D-amino acid aminotransferase

Novel sRNAs in the major range (51 to 150 nt) identified in this study.

sRNAs having homologs in Rfam.

sRNAs, small RNAs.

Table 2.

sRNAs Identified in Brucella abortus

sRNA	Up gene name	Distance up gene	sRNA start	sRNA end	sRNA length	Distance down gene	Down gene name
BasRNA1	Zinc-dependent metallopeptidase	1	271515	271841	326	56	Hypothetical protein
BasRNA2	Ribosomal protein S4:RNA-binding S4	40	826744	827231	487	0	Hypothetical protein
BasRNA3^a	Aminoacyl tRNA synthetase	1	940249	940321	72	727	Membrane protein involved in aromatic
BasRNA4	Prokaryotic sulfate/thiosulfate binding	1	1308731	1308979	248	15	Hypothetical protein
BasRNA5	Phosphoglycerate kinase G-protein beta WD-40	22	1684166	1684354	188	79	Hypothetical protein
BasRNA6	NUDIX hydrolase	30	1782405	1782563	158	16	PDZ/DHR/GLGF domain:Tail specific
BasRNA7	H+-transporting two-sector ATPase, alpha/beta	1	2005699	2006063	364	15	Conserved hypothetical protein 701
BasRNA8	Initiation factor 2, Elongation factor	1	2102735	2102937	202	61	Ribosome-binding factor A
BasRNA9	Ferrochelatase	1	75638	75764	126	9	Lipoprotein Omp10
BasRNA10	Hypothetical protein	1	190681	190850	169	173	RNA binding region RNP-1 (RNA recognition
BasRNA11	DNA mismatch repair protein, ATP binding region	100	208406	208488	82	0	Binding protein-dependent transport systems
BasRNA12^a	Glycine cleavage system P-protein	431	508672	508810	138	217	Hypothetical protein
BasRNA13^b	Glycine cleavage system P-protein	597	508838	509027	189	0	Hypothetical protein

Novel sRNAs in the major range (51 to 150 nt) identified in this study.

sRNAs having homologs in Rfam.

Table 3.

sRNAs Identified in Brucella suis

sRNA	Up gene name	Distance up gene	sRNA start	sRNA end	sRNA length	Distance down gene	Down gene name
BssRNA1^a	Hypothetical protein	118	28213	28315	102	3	trRNA
BssRNA2	30S ribosomal protein S1	23	33333	33726	393	0	Hypothetical protein
BssRNA3^a	30S ribosomal protein S4	1	808746	808821	75	431	Hypothetical protein
BssRNA4	GGDEF domain protein	326	1027030	1027267	237	0	Acetyl-CoA hydrolase/transferase family protein
BssRNA5	Sulfate ABC transporter, sulfate binding	1	1293614	1293862	248	15	Hypothetical protein
BssRNA6^b	Pseudogene	1	1408355	1408783	428	4	5,10-methylenetetrahydrofolate reductase
BssRNA7	Phosphoglycerate kinase	3	1670067	1670353	286	0	Hypothetical protein
BssRNA8	Dinucleoside polyphosphate hydrolase	30	1768260	1768418	158	16	Carboxyl terminal protease
BssRNA9	Transcription termination factor Rho	1	1991918	1992282	364	15	Hypothetical protein
BssRNA10	Ferrochelatase	1	75815	75941	126	9	Lipoprotein Omp10
BssRNA11	Hypothetical protein	1	187976	188145	169	173	Cytochrome b561, putative
BssRNA12	DNA mismatch repair protein	100	205671	205753	82	0	cis,cis-muconate transport protein, MucK
BssRNA13^a	Hypothetical protein	1	297885	298009	124	67	Glutaredoxin-like protein NrdH
BssRNA14	Hypothetical protein	149	708105	708345	240	545	Glycine dehydrogenase
BssRNA15^b	Hypothetical protein	366	708322	708461	139	429	Glycine dehydrogenase
BssRNA16^a	chromosomal replication initiation protein	1	2276	2357	81	145	DNA polymerase III subunit beta

Novel sRNAs in the major range (51 to 150 nt) identified in this study.

sRNAs having homologs in Rfam.

Table 4.

sRNA Candidates Having Homologs in Rfam, BSRD, and Other Organisms

Genome	sRNA	Homolog identified	Rfam accession
B. melitensis 16M	BmsRNA6	BjrC1505	RF02356
	BmsRNA16	ctRNA_p42d	RF00489
	BmsRNA5	Ochrobactrum anthropi strain OAB	—
	BmsRNA7	Ochrobactrum anthropi strain OAB	—
	BmsRNA13	Ochrobactrum anthropi strain OAB	—
	BmsRNA14	Ochrobactrum anthropi strain OAB	—
B. abortus 2308	BasRNA13	ar15	RF02345
B. suis 1330	BssRNA6	Atu_C9	RF02503
	BssRNA15	suhB	RF00519
	BssRNA14	Ochrobactrum anthropi strain OAB	—

Comparative analysis

The novel sRNAs identified were searched against the nonredundant database of NCBI using blastn algorithm with the parameter, exclude organism Brucella (taxid: 234), to identify the conservation of sRNA sequences in other organisms. In B. melitensis 16M, 15 sRNA candidates were found to be Brucella specific. Similarly, 12 and 13 candidate sRNAs of B. abortus 2308 and B. suis 1330, respectively, were Brucella specific. sRNA candidates were also searched against all other Brucella species to see the conservation in the genus. sRNA sequences were conserved in the genus except in nonzoonotic species B. neotomae and B. inopinata. In addition, we compared the sRNAs predicted in one species with other species to identify the homologous sRNAs in three Brucella species (Table 5). One sRNA of B. melitensis (BmsRNAs19) was found in other two species also. Similarly, seven sRNAs of B. abortus were found in B. suis, but not in B. melitensis, and one sRNA of B. melitensis was found in B. suis, but not in B. abortus (Table 5). In total, 14 sRNA candidates in B. melitensis were identified as novel and Brucella specific. Similarly, three sRNAs in B. abortus and five sRNAs in B. suis were identified as novel and Brucella specific.

Table 5.

Homologous sRNAs in Three Species of Brucella

B. melitensis	B. abortus	B. suis
BmsRNA13^a	—	BssRNA2
BmsRNA19	BasRNA12	BssRNA14^a
	BasRNA4	BssRNA5
	BasRNA5	BssRNA7
	BasRNA6	BssRNA8
	BasRNA7	BssRNA9
	BasRNA9	BssRNA10
	BasRNA10	BssRNA11
	BasRNA11	BssRNA12

sRNAs having homologs in other organisms also (Table 4).

Target prediction

To understand the role of sRNAs in biological functions, an in silico analysis was done for the prediction of mRNA targets of the predicted sRNAs, using the Web server TargetRNA2. Target genes predicted for each sRNA of all three species are listed in Supplementary Tables S1–S3. In B. suis, no targets were predicted for BssRNA7. Five candidate targets with high scores for each candidate sRNA were considered for further analysis.

Functional categorization of target genes

To gain more insights on the role of mRNA targets predicted, target genes were functionally classified based on COG analysis and GO annotations. In all the three species, mRNA targets were enriched in COG categories of transport and metabolism of carbohydrates, amino acids, lipids, nucleotides, and coenzyme, energy production and conversion, replication, recombination and repair, transcription, etc. (Fig. 3). Enriched GO terms for the mRNA targets are described in Supplementary Tables S4–S6. GO terms were distributed widely with regard to their respective biological processes. The target genes were enriched in GO terms, such as metabolic pathways, transport pathways, transcription, response to stress, DNA replication, and pathogenesis (Fig. 4A, D, G) (Supplementary Tables S4–S6). When the targets are categorized as molecular function, a majority of the genes were related to GO terms, catalytic activity and binding. Particularly, the genes were enriched in nucleic acid binding, DNA binding, ATP binding, GTP binding, ion binding, and transporter activity, etc. (Fig. 4B, E, H) (Supplementary Tables S4–S6).

FIG. 3.

COG classification of target genes in B. melitensis, B. abortus, and B. suis. The COG (cluster of orthologous groups) categories are coded as follows: C, energy production and conversion; D, cell division and chromosome partitioning; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; G, carbohydrate transport and metabolism; H, coenzyme metabolism; I, lipid metabolism; J, translation; K, transcription; L, DNA replication, recombination, and repair; M, cell wall/membrane biogenesis; O, post-translational modification, protein turnover, and chaperones; P, inorganic ion transport and metabolism; Q, secondary metabolite biosynthesis, transport, and catabolism; R, general functional prediction only; S, function-unassigned conserved proteins; T, signal transduction; and V, defense mechanisms.

FIG. 4.

Gene Ontology (GO) analyses of predicted target genes in B. melitensis, B. abortus, and B. suis. GO analysis of target genes that are predicted to be involved in (A) biological processes, (B) molecular functions, and (C) cellular components of B. melitensis; (D) biological processes, (E) molecular functions, and (F) cellular components of B. abortus; and (G) biological processes, (H) molecular functions, and (I) cellular components of B. suis.

To further understand the role of the target genes in biological pathways, the predicted target genes were mapped to KEGG database using KEGG mapper. In B. melitensis, the target genes were mapped onto 43 known pathways with a p-value <0.05. The targets were predominantly enriched in metabolic pathways, microbial metabolism in diverse environments, ABC transporters, and biosynthesis of secondary metabolites. In B. abortus, the target genes were mapped onto 27 known pathways with a majority of the genes mapped on to metabolic pathways, ABC transporters, biosynthesis of secondary metabolites, and microbial metabolism in diverse environments. In B. suis, a majority of the genes mapped on to metabolic pathways, biosynthesis of secondary metabolites, and carbon metabolism.

GO regulatory network

We determined GO term enrichments for the mRNA targets identified for all the sRNAs from B. melitensis 16M, B. abortus 2308, and B. suis 1330. We describe the implementation of GO-based gene selection and GO network discovery. We constructed a GO interaction network between biological process GO terms for the mRNA targets identified in all the three genomes. The GO network of mRNA targets of B. melitensis 16M sRNAs is shown in Figure 5. Transcription antitermination (GO ID: 31564) was the central node in the GRN. Transcription antitermination protein NusG (BMEI0744) showed interaction with other GO terms, such as response to stress, transport, DNA replication, transcription DNA-templated, cell redox homeostasis, DNA repair, protein folding, cytolysis, cell cycle, nitrogen compound metabolic process, and ATP-coupled electron transport. (Fig. 5). In the case of B. abortus 2308, DNA-templated negative regulation of transcription (GO ID: 45892) was the central node in the network (Fig. 6) governed by BAB10578, which codes for a transcriptional regulator, betI. BAB10578 governed GO has interactions with many other GO terms, such as response to stress, pathogenesis, gluconeogenesis, transport, nucleoside metabolic process, DNA replication, lipopolysaccharide biosynthesis process, DNA repair, and protein folding. (Fig. 6). Cell redox homeostasis (GO ID: 45892) was the central node of the GRN of B. suis 1330 with the involvement of dihydrolipoamide dehydrogenase (BR1126). Cell redox homeostasis showed interaction with pathogenesis, DNA repair, transport, DNA recombination, DNA template transcription, metabolic process, and rRNA processing, etc. (Fig. 7).

FIG. 5.

GO regulatory network (GRN) based on the mRNA target genes of B. melitensis 16M.

FIG. 6.

GRN based on the mRNA target genes of B. abortus 2308.

FIG. 7.

GRN based on the mRNA target genes of B. suis 1330.

Discussion

sRNAs are known modulators of gene expression in prokaryotes (Papenfort et al., 2015; Prevost et al., 2011; Sakurai et al., 2012). However, very few reports are available on the roles of sRNAs in Brucella. Caswell et al. (2012) have reported two sRNAs and their roles in virulence of Brucella. Recently, sRNA responsible for Brucella adaptation to stress conditions and intracellular survival has been reported (Wang et al., 2015). In this study, we have used the computational approach to predict the sRNA candidates in three Brucella species.

Bioinformatic software for sRNA prediction are based on four major principles: (1) comparative genomics, (2) secondary structure and thermodynamic stability, (3) transcriptional signals, and (4) ab initio methods (Sridhar and Gunasekaran, 2013). A common practice to improve the accuracy is to combine several bioinformatic tools for the prediction of sRNAs (Dong et al., 2014; Khoo et al., 2012; Tesorero et al., 2013). In this study, we combined SIPHT and sRNAscanner to improve accuracy. We also made use of the QRNA analysis feature of SIPHT to predict sRNAs.

The sRNAs identified here vary in length and GC content. The variation in GC content is expected to attain different requirements of stability as sRNAs are diverse in both functions and mechanisms of action. Our results show that the primary sequences of sRNAs identified are conserved only within the genus Brucella. We found two sRNAs in B. melitensis16M to have homologs in Rfam. BmsRNA6 and BmsRNA16 were showing homology with BjrC1505 and ctRNA_p42d, respectively. ctRNA (counter-transcribed RNA) is a noncoding RNA encoded by plasmids, which has roles in rolling circle replication. ctRNA binds to repB and causes translational inhibition (Venkova-Canova et al., 2003). BasRNA13 of B. abortus 2308 showed homology with ar15, which is a noncoding sRNA identified in Sinorhizobium meliloti (del val et al., 2007). BssRNA6 and BssRNA 16 of B. suis 1330 showed homology with Atu_C9 and suhB, respectively.

sRNAs regulate the gene expression by binding to the mRNA targets either by perfect or imperfect sequence complementarity. One sRNA might regulate several mRNA targets, which may result in upregulation or downregulation of a set of genes. By regulating the activities of different genes at one time, sRNAs play a fundamental role in organism's biological and cellular functions (Johansen et al., 2008; Lenz et al., 2004; Wang et al., 2015; Wassarman, 2002). In this study, we have identified potential targets of sRNAs, using the bioinformatic approach. The majority of the target genes were enriched in metabolic and transport pathways. KEGG analysis revealed that the target genes may play a significant role in Brucella biological process, replication, and intracellular survival. These sRNAs could regulate several transcriptional factors. GntR family transcriptional regulator is one such transcriptional regulator, which plays a major role in Brucella virulence (Delrue et al., 2004). An sRNA candidate of B. suis was identified to regulate type IV secretion system protein, VirB7, which is required for intracellular survival and pathogenesis of Brucella (Boschiroli et al., 2002; O'Callaghan et al., 1999). In addition, we could see that the identified sRNAs may regulate several hypothetical proteins. Identifying the functions of these hypothetical proteins may give more insights into the complex intracellular survival and pathogenesis of Brucella.

We further constructed the GRN of predicted mRNA targets using the biological process GO terms. Transcription antitermination protein, NusG, constitutes the central node in B. melitensis 16M. It interacts with the termination factor Rho and RNA polymerase and thereby influences transcription termination and antitermination. In B. abortus 2308, the betI gene was the central node in the regulatory network. This gene acts as a transcriptional repressor of bet genes. These genes are required for regulating the osmotic balance of the bacteria and needed to overcome osmotic stress (Lamark et al., 1996). GO network analysis showed that negative regulation of transcription, DNA-templated (GO ID: 45892) governed by the betI gene has influence in the suppression of genes with which it interacts, opens a new avenue for the treatment of brucellosis. In B. suis 1330, dihydrolipoamide dehydrogenase (lpdA-1) was the central node in the network. Dihydrolipoamide dehydrogenase is involved in several pathways, including glycolysis/gluconeogenesis, citrate cycle (TCA cycle), glycine, serine and threonine metabolism, valine, leucine and isoleucine degradation, pyruvate metabolism, glyoxylate and dicarboxylate metabolism, metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, biosynthesis of antibiotics, and carbon metabolism.

We have used a bioinformatic strategy to predict sRNA candidates in B. melitensis 16M, B. abortus 2308, and B. suis 1330 and predicted 21, 13, and 16 sRNAs, respectively. Most of these sRNAs were predicted for the first time. The functional categorization and pathway analysis of the target genes revealed that sRNAs are involved in various metabolic pathways. GO network analysis in B. melitensis 16M, B. abortus 2308, and B. suis 1330 revealed new biological insights.

Taken together, we would like to reemphasize that although sRNAs are veritable modulators of gene expression in prokaryotes, the reports on the significance of sRNAs in Brucella are quite limited. This work begins to address this literature gap by offering a series of initial observations based on a genome-wide computational biology analysis for future experimental analysis of sRNAs and their targets to explain the most complex multifactorial basis of Brucella pathogenesis and its intracellular survival.

Footnotes

Acknowledgments

This work was supported by the Department of Biotechnology, New Delhi, through the DBT Network Project on Brucellosis. The UGC-CAS, CEGS, NRCBS, DBT-IPLS, DST-PURSE Programs of School of Biological Sciences, Madurai Kamaraj University, are gratefully acknowledged.

Author Disclosure Statement

The authors declare that no competing financial interests exist.

Abbreviations Used

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