• Users Online: 2910
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

 Table of Contents  
Year : 2023  |  Volume : 23  |  Issue : 1  |  Page : 10-16

Comparing 16S rRNA gene similarity with simple polar lipids profiling amongst Salmonella isolates

1 Department of Biomedical Sciences, College of Veterinary Medicine, Sudan University of Science and Technology, Khartoum, Sudan; Department of Biochemistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
2 Department of Microbiology, College of Medicine, King Khalid University, Abha, Saudi Arabia; Department of Biochemistry, Faculty of Veterinary Medicine, University of Khartoum, Khartoum, Sudan
3 Department of Biochemistry, Faculty of Veterinary Medicine, University of Khartoum, Khartoum, Sudan
4 Department of Microbiology, Animal Resources Research Corporation, Khartoum, Sudan

Date of Submission11-Dec-2022
Date of Decision13-Feb-2023
Date of Acceptance13-Apr-2023
Date of Web Publication17-Jul-2023

Correspondence Address:
Prof. I M T Fadlalla
College of Veterinary Medicine, Sudan University of Science and Technology, P.O. Box 204, Khartoum

Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/njhs.njhs_23_22

Rights and Permissions

Background and Objectives: Polar lipids and the 16S rRNA gene have a significant role in taxonomic characteristics. The study's objective is to determine the potential relationship between simple migration distances (mm) of polar lipids and Salmonella's 16S rRNA gene similarity.
Materials and Methods: Based on 16S RNA sequences and simple thin-layer chromatography migration distances (mm) of polar lipids, allowed to compare the various Salmonellae species, and apply it to examine the variability and estimate bacterial similarities.
Results: The chromatography migration distance analysis revealed 3–4 spots of polar lipids. The polar lipids revealed two denser spots, the most abundant lipids, between 10 and 28 mm. The other two low-density spots of migration distance ranged in size from 23 to 25 mm. Between 99.4% and 100% of the three Salmonella isolates and other Salmonella species exhibited 16S rRNA similarities. One strain had a similarity of 98.9%. These findings demonstrated the nearly identical 16S rRNA gene sequences and polar lipids profile of the isolates.
Conclusions: This study has concluded that all Salmonella species share similarities in both the polar lipid profiles and the 16S rRNA genes. The study validates the utility of coupling of polar lipids and 16S rRNA gene sequencing as useful tools for taxonomic differentiation and epidemiological tracing of Salmonella.

Keywords: 16S rRNA, polar lipids, Salmonella, similarities

How to cite this article:
Fadlalla I M, Hamid M E, Rahim A G, Elamin E D. Comparing 16S rRNA gene similarity with simple polar lipids profiling amongst Salmonella isolates. Niger J Health Sci 2023;23:10-6

How to cite this URL:
Fadlalla I M, Hamid M E, Rahim A G, Elamin E D. Comparing 16S rRNA gene similarity with simple polar lipids profiling amongst Salmonella isolates. Niger J Health Sci [serial online] 2023 [cited 2024 Feb 28];23:10-6. Available from: http://www.https://chs-journal.com//text.asp?2023/23/1/10/381739

  Introduction Top

 Salmonella More Detailse are a Gram-negative, facultatively anaerobic, zoonotic pathogens in both humans and animals.[1] The formation of the membrane bilayer permeability barrier of cells and organelles is one of lipids' fundamental biological functions.[2] Comprehensive lipid profiles are utilised as compelling evidence to support species classification.[3] Lipids, proteins and sugars are the main components of biological membranes.[4]

The three main phospholipid classes found in Salmonella typhimurium are cardiolipin, phosphatidylethanolamine (PE) and phosphatidylglycerol. The four minor molecules include Phosphatidylserine (PS), phosphatidic acid and two partially described lipids.[5] Polar lipids and fatty acid profiles of specific species could identify between gram-positive and gram-negative species.[6]

A molecular method based on 16S rRNA sequence analysis was used to identify Salmonella enterica strains. Bacterial 16S rRNA genes are now widely used for phylogenetic analysis and species identification.[7] The findings of Fadlalla et al.[8] showed that 16S rRNA gene sequencing is sufficient for characterising Salmonellae and determining their phylogenetic relatedness.

Salmonella can be epidemiologically tracked down to the species level using the 16S rRNA gene sequencing method. According to Xu et al.,[9] the 16S rRNA is highly conserved in structure and function. It can accurately reflect the differences between various bacteria and intragenomic sequence variability. Clustering bacteria and viruses into phylogenetic groups based on 16S rRNA gene sequences are important.[10]

The comparison of bacteria strains with genomes that shared >99% of the 16S rRNA gene revealed that each strain belonged to a separate species.[11] According to Yabe et al.,[12] there are very few similarities in terms of the 16S rRNA gene. Given the low degree of similarity in the 16S rRNA gene sequence and variations in the properties of polar lipids, they should be separated from other species at the genus level.

According to Selander et al.,[13] S. enterica serovar Typhi is more homogenous than the majority of Salmonella serovars. The percentage of similarity between strains investigated was very high (96.4%–99.2%), indicating a high degree of 16S rRNA gene sequence conservation within this gene.[14]

This study hopes that the results will be of use as a basis for future biochemical and genetics studies of Salmonella. The study's objective is to look at the potential relationship between simple migration distances (mm) of polar lipids and Salmonella's 16S rRNA gene similarity.

  Materials and Methods Top

Bacterial isolation

This is a cross-sectional study. Fecal samples were collected using the technique outlined by ElAmin and Mahmoud.[15] Selenite broth medium (oxoid) was used to culture faeces, and it was incubated at 43°C overnight. For subcultures, desoxycholate citrate agar (oxoid), MacConkey agar (oxoid), a triple sugar iron agar, urea agar (oxoid), Salmonella-Shigella agar (oxoid), were used, and then incubated at 37°C overnight. Pure strains are kept in nutrient agar (oxoid).

Biochemical and serological tests

Biochemical tests were done by utilising API 20E (Bio Merieux, France) identification kits. Serological analysis, which determines the presence or absence of the somatic (O Polysaccharide) and flagella (H) salmonella antigens, was used to further identify the Salmonella strains. The slide agglutination (Becton Dickinson and Co., Sparks, Md.) method was used to create stereotypes of the Salmonella cultures.

Total polar lipids extraction

According to the method described by Hamid,[16] nine salmonella strains were extracted using the chloroform/methanol technique. Dried biomes (50 mg) were treated in a test tube with 4 ml of chloroform methanol (2:1 V/V) from Sigma-Aldrich, the extracts were dried, and their polar lipid composition was examined.

Two-dimensional-thin-layer chromatography spots analysis

Polar lipid extractions are examined using thin-layer chromatography (TLC), system (D). According to Dobson et al.,[17] analytical two-dimensional TLC (2D-TLC) of the least polar class in the Salmonella polar lipid fractions was carried out. The dried free lipid extracts were dissolved in 100 μl chloroform/methanol mixture (2:1, v/v) (Sigma-Aldrich). TLC plates were dried, and detection was accomplished by spraying one plate with α-naphthol (15% solution in ethanol and molybdophosphoric acid (MPA) (5% MPA in ethanol-concentrated sulphuric acid). Yellow to brown spots denote the presence of lipids. All lipids appeared as dark blue-green spots in a light green background.

Genomic DNA isolation and amplification of the 16S rRNA gene

On the DSM medium (DSMZ-German, Germany),[18] the Salmonella isolates were cultured. As previously mentioned by Fadlalla et al.[8] genomic DNA was isolated from each Salmonella isolate. According to the manufacturer's instructions, the Prep-A-Gene kit (Bio-Rad, Hercules, CA) was used to recover and elute DNA in 50 μL of sterile distilled water. Using an Eppendorf biophotometer, the amount of DNA was measured. According to the instructions in the manual, the purity of the purified DNA was assessed spectrophotometrically by measuring absorbance at A260/280 and A260/230 nm. The DNA integrity was evaluated using agarose gel electrophoresis.

Direct sequencing of polymerase chain reaction products

Using primers specific to the 16S rRNA gene, the genomic DNA of Salmonella isolates S-117, S-118 and S-119 were each amplified separately by polymerase chain reaction (PCR) [Table 1]. According to the manufacturer's instructions, the purified PCR amplicons of each Salmonella isolate were sequenced independently using the ABI PRISM TM Dye Terminator Cycle Sequencing Reader kit (Applied Biosystems, Germany).
Table 1: The oligonucleotide primers targeting the 16S rRNA gene conserved positions 10–1510

Click here to view

A DNA sequencer from Applied Biosystems model 373A was used to electrophorese the sequenced amplicons. Salmonella isolates obtained 16S rRNA gene sequences were then manually aligned into the alignment editor ae2 and compared to one another. The 16S rRNA gene sequence of representative Enterobacteriaceae organisms was then compared to it.[8] We quantified and compared the genomic GC content of the isolates to further study the genetic interrelatedness amongst the isolates.[19]

The genomic DNA of each of Salmonella isolates S-117, S-118 and S-119 were used in a separate PCR to amplify the 16S rRNA gene using primers targeting the 16S rRNA gene (conserved position 10–1510,  Escherichia More Details coli numbering.

  Results Top

Epidemiological study

Salmonella typhi, Salmonella paratyphi A, S. paratyphi B, Salmonella enteritidis, Salmonella choleraesuis and additional unidentified S. enterica spp. were discovered in humans. S. enterica spp. S. enteritidis, Salmonella heidelberg, S. amersfoort and other species were shown to be present in infected poultry. Cattle were found to be infected with S. enteritidis and other S. enterica spp. Out of the 989 people evaluated, 87 people, or 8.79%, were affected. In terms of animals, the infection rate for all infected poultry was 4151 out of 9505, or 43.67% as infection rate, while for all infected cattle, it was 17 out of 164, or 10.36% as infection rate [Table 2].
Table 2: Epidemiological findings for some Salmonellae infections in Khartoum state

Click here to view

Two-dimensional-thin-layer chromatography polar lipids profiling

[Figure 1] shows the 2D TLC images displaying the separations of total lipid extracts of Salmonella strains. Individual polar lipid components were identified by comparison of their migration distance values (mm). Analysis of polar lipids spots revealed a close similar pattern of polar lipids of the different Salmonellae strains [Table 3]. The chromatography analysis of the extracts revealed 3–4 spots of polar lipids. Some plates present three spots, it is possible that the 4th spot (which is very low dense spot) it is present in undetectable amounts, or, that it is present mostly as the non-phosphorylated derivative (which would be part of the 'neutral fat' class). The migration distance values showed two denser spots (lower and higher distance), which are the most abundant lipids. The other two low dense spots of migration distance (median distance), which are the less abundant lipids, are common in all strains. The migration distance values indicated the antigenic diversity of different Salmonella strains to be identical or barely different [Table 3]. Two minor compounds and two partially described lipids have both been identified. The only difference is the absence or decreased level of one of the minor components in Salmonella. The migration distance values of polar lipids were as follows: The two denser spots were between 26 and 28 mm for the upper, highly dense areas, and between 10 and 11 mm for the lower ones (with two exceptions of 13 and 15 mm). The two low dense spots ranged between 23 and 25 mm. This reflects that most variation could be within the denser polar lipid's groups. On the other hand, S. paratyphi A had shown a relatively tight distance between the two low-density patches amongst all the examined species (26–27 mm). All species exhibited the higher dense spots; however, the two intermediate and lower denser spots may lose one.
Figure 1: 2D-TLC of total polar lipids profiles from Salmonella. 2D-TLC: Two-dimensional thin-layer chromatography

Click here to view
Table 3: Thin layer chromatographic migration distance (mm) values

Click here to view

16S rRNA gene sequence similarities analysis

The species with the highest similarity scores between their 16S rRNA gene sequences are listed in [Table 4] The similarity value amongst the three Salmonella isolates and members of the genus Salmonellae ranged between 99.4% and 100% (one strain shows 98.9% similarity). These findings demonstrated that Salmonella isolates have almost similar 16S rRNA gene sequences. The 16S rRNA gene sequencing revealed that the Salmonella isolate S-119's 16S rRNA gene sequences have three fewer base pairs (bp) than those of Salmonella isolates S-117 and S-118 [490 vs. 493 and 493 bp, respectively; [Table 4] and [Table 5]. The 16S rRNA gene sequences of Salmonella isolate S-117 also differed from those of Salmonella isolates S-118 and S-119 by having one more adenine base (127 vs. 126 and 126, respectively) and one fewer guanine base (159 vs. 160 and 160, respectively) [Table 5]. In addition, according to the examination of the three Salmonella isolates' 16S rRNA gene sequences indicated that the three Salmonella isolates belong to the medium GC content bacteria [55.3%–55.6% GC; [Table 5]].
Table 4: 16S rRNA gene similarity values for the Salmonella isolates S-117, S-118, S-119 and reference Salmonella species and subspecies based on partial 16S rRNA gene sequencing comparison

Click here to view
Table 5: Length and nucleotide identity of each 16S rRNA gene of Salmonella isolates S-117, S.118 and S-119

Click here to view

The 16S rRNA gene sequences of the three Salmonella isolates shared 100% sequence similarity with some reference Salmonellae. They also shared 99.8% gene similarity with some other reference Salmonella. In addition, some also exhibited 99.6% and 99.4% of their 16S rRNA gene sequences. Furthermore, there is 98.9% sequence identity with S. enterica subsps. enterica serovar paratyphi C. According to this investigation, strains ID 02–117, ID 02–118 and ID 02–199 appear to represent strains of the genus salmonella. High 16S rRNA similarity values (99%–100%), similar polar lipid profiles (3–4 spots), and nearly identical migration distances with only very tiny variances all supported Salmonella's high degree of DNA similarity. An analysis of the polar lipid migration distances between different Salmonella strains and the similarity of their 16S rRNA gene sequences showed a strong association. Salmonella has a lot in common while having only very slight differences when comparing the polar lipid migration distances and the 16S rRNA gene sequences. The polar lipids of Salmonella may reflect its genetic composition.

The similarity of 16S rRNA gene G+C content is about the same amongst the three Salmonella isolates.

The 16S rRNA gene sequences of the isolates S-117, S-118 and S 119 were aligned manually and compared with the 16S rRNA gene sequences of representative organisms belonging to Enterobacteriaceae.

  Discussion Top

[Figure 1] illustrates how the peak area versus sample amount produced a linear relationship when lipid spots were analysed. The retardation factor (Rf) value or migration distances influence TLC analysis. The larger spots are the higher permitted to migrate on the chromatophore. In general, the bigger the spots get determined the peak area only when the spot is migrated sufficiently to form its spot characteristics.[20] In this study, according to migration distances (mm) analysis, Salmonella strains produced lipids that follow the polar lipid pattern [Table 3]. The chromatography analysis of the extracts revealed 3–4 spots of polar lipids. The migration distance measurements revealed two denser spots and ranged from 10 mm to 28 mm. Most strains share two additional low-density regions of migration distance, with values ranging between 21 and 25 mm. The polar lipid composition of the nine organisms is quite similar or barely different and they migrate very closely to each other on 2D TLC.

Phospholipids (PLs), glycolipids, glycophospholipids, aminolipids and lipids containing sulphur are the most prevalent polar lipids found in bacteria.[21] The most prevalent element is PE, which makes up 75% of the total phospholipid according to Ames.[5] Phosphatidylglycerol, the second-most prevalent component, makes up 18% of S. typhimurium's total phospholipid.[22] S. typhimurium were examined for their fatty acid profiles and lipid content, there were quantitative differences in the levels of phospholipid in each strain. PLs were found to be the major band in the total lipid extracts, along with five minor bands made up of monoacylglycerols, cholesterol, diacylglycerols, free fatty acids, and triacylglycerols. PE was found in high concentrations, followed by PS and cardiolipin (C).[23]

There are eight spots of the PLs of S. typhimurium and E. coli K-12 known to exist. The four main classes of PLs, PE, phosphatidylglycerol and cardiolipin have been characterised. PS, phosphatidic acid, and two partially described lipids are amongst the four minor substances that have been found. The two organisms' phospholipid compositions are remarkably similar; the only difference is the absence of one of the minor components and a decreased level of all components in E. coli.[5]

The findings of this study are similar to Reinink et al.[24] findings, who reported four lipid spots for Salmonella. In comparison to S. paratyphi A, two spots with retardation factors (Rf) of 0.26 and 0.22 exhibited substantially denser spots in S. typhi. Two unknown lipids with Rf 0.22 and Rf 0.26.

Modern bacterial taxonomy is based on similarities in the 16S rRNA gene. The 95% (for genus) and 98.7% (for species) sequence similarity levels that are now used to categorise bacterial isolates may not apply to many genera. Any similarity value below 95% or above 98.7% is regarded as abnormal.[25] In 1996, Vandamme et al. suggested that polyphasic taxonomy should take into account differences in DNA G+C content, similarity in 16S rRNA gene sequences,[26] and chemotaxonomic criteria, in particular fatty acid analysis.[27] According to DNA hybridisation, all salmonella strains are at least 70% related to each other.[28] The majority of salmonella strains remain classified under the species S. enterica despite the effectiveness of employing the 16S rRNA sequencing for the classification and identification of bacterial strains.[29]

The type strains of three species of Salmonella were identified in this investigation using 16S rDNA sequencing similarities data. They share sequence similarities with several strains of S. enterica ssp. enterica, S. choleraesuis ssp. choleraesuis serovar typhimurium, S. typhimurium and S. typhi (serovar Typhimurium, Montevideo and Sofia). Although species association is not conceivable, the three strains appear to be Salmonella strains, according to Liebisch and Schwarz.[2] The detection of 16S rRNA genes proved to be helpful for the epidemiological typing of certain Salmonella serovars, such as S. typhimurium and S. berta.[30] It is highly beneficial to compare the 16S rDNA sequence information discovered in this study with the outcomes of the serological and biochemical testing. Bacteria's 16S ribosomal RNA is used in describing the diversity of microorganisms. Only a small percentage of bacterial genomes contain identical copies of the 16S rRNA gene and genome size data can be used to estimate the closest related taxon.[31]

For several reasons, the 16S rRNA is suitable for taxonomical purposes. At various taxonomic levels, the 16S rRNA copy counts per genome were taxon specific. The variation in 16S rRNA copy counts was less prominent at lower taxonomic levels, and differences at the level of families, genera and species were frequently considerable.[32] Genome size was well preserved within each particular bacterial species, with variation <3.8%. At a genome level, the average 16S rRNA similarity within a genome is 99.7+0.46%, with 97.6% of genomes showing average 16S rRNA similarity above 99%.According to Větrovský and Baldrian,[31] the average 16S rRNA genome similarity across taxa was 95.566. Average 16S rRNA similarity across all bacterial species was 99.30.

Srinivasan et al.[33] found a genus-level agreement rate of 96% and a species-level concordance rate of 87.5% between 16S gene. 16S-based identification has high sensitivity and specificity, with extremely high confidence in genus-level identification. 87.5% of species matches the clinical identification, at the level of species. With exceptions in a few genera including Stenotrophomonas, Enterobacter, Citrobacter and Escherichia.[34] According to Srinivasan et al.,[33] a curated 16S rRNA gene database can be used to routinely identify bacteria with high confidence.

Enterobacter had (78.4%), Escherichia (80.0%), Mycobacterium (91.1%) and Nocardia (93.9%) 16S rRNA gene sequence similarities amongst the studied genera (43.0%). The Enterobacteriaceae family displayed 50% similarities. Intra-species 16S rRNA gene sequence differences may be significant, as is the case for Enterobacter agglomerans.[35] Pei et al.[36] have been reported differences in the inter-species 16S rRNA gene sequence similarity scores between genera.

Fournier and Raoult,[37] and Beye et al.[38] caution against using the 95% and 98.7% inter-species 16S rRNA gene sequence similarity levels as definite tools for the classification of bacterial strains, may only be used as indicators. The use of 16S has required several assumptions, such as the now historic assumption that sequences with >95% identity indicate the same genus while sequences with >97% identity represent the same species. Certain sub-regions will be more suited for discriminating closely related members of particular taxa since discriminating polymorphisms may only be present in certain variable regions.[39]

  Conclusions Top

The 16S rRNA sequences and polar lipids profiling can be used for the identification, categorisation and quantification of bacteria. The findings of this study may aid genomic researchers in selecting a more effective technique for microbial clustering that are linked to particular diseases. Our conclusion is that Salmonella species had similarities in their polar lipid profiles and 16S rRNA genes, as shown by our data. The study confirms the effectiveness of combining 16S rRNA gene sequencing and polar lipid profiles as helpful tools for taxonomic differentiation and the epidemiological tracing of Salmonella.


The authors would like to thank the DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany, for assistance with the molecular analysis. Before all, deepest thanks and gratitude are also extended to the British Council, Khartoum, Sudan, for their financial support.

Financial support and sponsorship

This work was funded by the British Council, Khartoum, Sudan [KHT/991/21/Vet], which was used for the samples collection, laboratory analysis, kits supply, agars and consumables.

Conflicts of interest

There are no conflicts of interest.

  References Top

Groisman EA, Ochman H. The path to Salmonella. ASM News 2000;66:21-7.  Back to cited text no. 1
Hancock CR. Analysis of cell wall constituents of gram-positive bacteria. In: Good fellow M, O'Donnell AG, editors. Chemical Methods in Prokaryotic Systematic. Chichester: John Wiley and Sons; 1994. p. 68-84.  Back to cited text no. 2
Nguyen TM, Kim J. A rapid and simple method for identifying bacterial polar lipid components in wet biomass. J Microbiol 2017;55:635-9.  Back to cited text no. 3
Stieger B, Steiger J, Locher KP. Membrane lipids and transporter function. Biochim Biophys Acta Mol Basis Dis 2021;1867:166079.  Back to cited text no. 4
Ames GF. Lipids of Salmonella typhimurium and Escherichia coli: Structure and metabolism. J Bacteriol 1968;95:833-43.  Back to cited text no. 5
Bay DC, Booth SC, Turner RJ. Respiration and ecological niche influence bacterial membrane lipid compositions. Environ Microbiol 2015;17:1777-93.  Back to cited text no. 6
Harmsen D, Rothgänger J, Singer C, Albert J, Frosch M. Intuitive hypertext-based molecular identification of micro-organisms. Lancet 1999;353:291.  Back to cited text no. 7
Fadlalla IM, Hamid ME, ARahim AG, Osman MA. Efficacy of partial 16S rRNA gene sequencing for precise determination of phylogenetic relatedness among Salmonellae. Sci Afr 2021;14:e01004.  Back to cited text no. 8
Xu P, Wang H, Qin C, Li Z, Lin C, Liu W, et al. Analysis of the taxonomy and pathogenic factors of Pectobacterium aroidearum L6 using whole-genome sequencing and comparative genomics. Front Microbiol 2021;12:679102.  Back to cited text no. 9
Islam MM, Akter S, Hadiul Kabir M, Haque Mollah N. Phylogenetic clustering of microbial communities based on 16S rRNA sequences. Int J Stat Sci 2021;21:117-26.  Back to cited text no. 10
Hahn MW, Jezberová J, Koll U, Saueressig-Beck T, Schmidt J. Complete ecological isolation and cryptic diversity in polynucleobacter bacteria not resolved by 16S rRNA gene sequences. ISME J 2016;10:1642-55.  Back to cited text no. 11
Yabe S, Zheng Y, Wang CM, Sakai Y, Abe K, Yokota A, et al. Reticulibacter mediterranei gen. Nov., sp. nov., within the new family reticulibacteraceae fam. nov., and ktedonospora formicarum gen. nov., sp. nov., ktedonobacter robiniae sp. nov., dictyobacter formicarum sp. nov. and dictyobacter arantiisoli sp. nov., belonging to the class ktedonobacteria. International journal of systematic and evolutionary microbiology, 2021;71:004883. Available from: https://www.microbiologyresearch.org/content/journal/ijsem/10.1099/ijsem.0.004883.  Back to cited text no. 12
Selander RK, Beltran P, Smith NH, Helmuth R, Rubin FA, Kopecko DJ, et al. Evolutionary genetic relationships of clones of Salmonella serovars that cause human typhoid and other enteric fevers. Infect Immun 1990;58:2262-75.  Back to cited text no. 13
Subramaniam G, Puthucheary S, Yssin R, Pang T, Thong KL. Genomic analysis of salmonella species based on 16S rRNA gene sequences. Asia Pac J Mol Biol Biotechnol 2000;8:155-60.  Back to cited text no. 14
ElAmin ED, Mahmoud AA. Preliminary observation in Salmonella Dublin infection in nomadic cattle in Kordofan, Sudan. J Vet Sci Anim Husb 1986;25:87-91.  Back to cited text no. 15
Hamid ME. Classification and Identification of Actiomycetes Associated with Bovine Fancy. Ph.D. UK: Thesis, University of New Castle upon Tyne; 1994. p. 95-8.  Back to cited text no. 16
Dobson G, Minnikin DE, Minnikin SM, Parlett JH, Goodfellow M, Ridell M, et al. In: Goodfellow M, Minnikin DE, editors. Systematic Analysis of Complex Mycobacterial Lipids, in Chemical Methods in Bacterial Systematics. Vol. 1. London: Academic Press; 1985. p. 237-65.  Back to cited text no. 17
Edwards PR, Kauffmann F. A simplification of the Kauffmann-White schema. Am J Clin Pathol 1952;22:692-7.  Back to cited text no. 18
Muto A, Osawa S. The guanine and cytosine content of genomic DNA and bacterial evolution. Proc Natl Acad Sci U S A 1987;84:166-9.  Back to cited text no. 19
Blank ML, Schmit JA, Privett OS. Quantitative analysis of lipids by thin-layer chromatography. J Am Oil Chem Soc 1964;41:371-6.  Back to cited text no. 20
da Costa MS, Albuquerque L, Nobre MF, Wait R. The identification of polar lipids in prokaryotes. In: Rainey FA, Oren A, editors. Taxonomy of Prokaryotes. Chennai: Academic Press; 2011. p. 165-81.  Back to cited text no. 21
Chang YY, Kennedy EP. Pathways for the synthesis of glycerophosphatides in Escherichia coli. J Biol Chem 1967;242:516-9.  Back to cited text no. 22
Oyofo BA, Spates GE, Beier RC, De Loach JR. Lipid content of salmonella typhmurium. J Gen Appl Microbiol 1989;35:333-42.  Back to cited text no. 23
Reinink P, Buter J, Mishra VK, Ishikawa E, Cheng TY, Willemsen PT, et al. Discovery of Salmonella trehalose phospholipids reveals functional convergence with mycobacteria. J Exp Med 2019;216:757-71.  Back to cited text no. 24
Rossi-Tamisier M, Benamar S, Raoult D, Fournier PE. Cautionary tale of using 16S rRNA gene sequence similarity values in identification of human-associated bacterial species. Int J Syst Evol Microbiol 2015;65:1929-34.  Back to cited text no. 25
Ludwig W, Klenk HP. Overview: A phylogenetic backbone and taxonomic framework for prokaryotic systematics. In: Boone DR, Castenholz RW, Garrity GM, editors. Bergey's Manual of Systematic Bacteriology. 2nd ed., Vol. 1. New York: John Wiley & Sons; 2001. p. 49-65.  Back to cited text no. 26
Vandamme P, Pot B, Gillis M, de Vos P, Kersters K, Swings J. Polyphasic taxonomy, a consensus approach to bacterial systematics. Microbiol Rev 1996;60:407-38.  Back to cited text no. 27
Crosa JH, Brenner DJ, Ewing WH, Falkow S. Molecular relationships among the Salmonelleae. J Bacteriol 1973;115:307-15.  Back to cited text no. 28
Brenner FW, Villar RG, Angulo FJ, Tauxe R, Swaminathan B. Salmonella nomenclature. J Clin Microbiol 2000;38:2465-7.  Back to cited text no. 29
Stanley J, Baquar N, Threlfall EJ. Genotypes and phylogenetic relationships of Salmonella typhimurium are defined by molecular fingerprinting of IS200 and 16S rrn loci. J Gen Microbiol 1993;139 Pt 6:1133-40.  Back to cited text no. 30
Větrovský T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One 2013;8:e57923.  Back to cited text no. 31
Head IM, Saunders JR, Pickup RW. Microbial evolution, diversity, and ecology: A decade of ribosomal RNA analysis of uncultivated microorganisms. Microb Ecol 1998;35:1-21.  Back to cited text no. 32
Srinivasan R, Karaoz U, Volegova M, MacKichan J, Kato-Maeda M, Miller S, et al. Use of 16S rRNA gene for identification of a broad range of clinically relevant bacterial pathogens. PLoS One 2015;10:e0117617.  Back to cited text no. 33
Clarridge JE 3rd. Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin Microbiol Rev 2004;17:840-62.  Back to cited text no. 34
Wang Y, Zhang Z, Ramanan N. The actinomycete Thermobispora bispora contains two distinct types of transcriptionally active 16S rRNA genes. J Bacteriol 1997;179:3270-6.  Back to cited text no. 35
Pei AY, Oberdorf WE, Nossa CW, Agarwal A, Chokshi P, Gerz EA, et al. Diversity of 16S rRNA genes within individual prokaryotic genomes. Appl Environ Microbiol 2010;76:3886-97.  Back to cited text no. 36
Fournier PE, Raoult D. Current knowledge on phylogeny and taxonomy of Rickettsia spp. Ann N Y Acad Sci 2009;1166:1-11.  Back to cited text no. 37
Beye M, Fahsi N, Raoult D, Fournier PE. Careful use of 16S rRNA gene sequence similarity values for the identification of Mycobacterium species. New Microbes New Infect 2018;22:24-9.  Back to cited text no. 38
Johnson JS, Spakowicz DJ, Hong BY, Petersen LM, Demkowicz P, Chen L, et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun 2019;10:5029.  Back to cited text no. 39


  [Figure 1]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  In this article
Materials and Me...
Article Figures
Article Tables

 Article Access Statistics
    PDF Downloaded336    
    Comments [Add]    

Recommend this journal