Comparison of different quantitative biofilm assays
\r\n\tThe discussion will include between others topics: biodegradation processes of by-products coming from food-processing industries, tertiary treatment of wastewaters with microalgae coupled with the generation of potentially commercial interest compounds, biotechnological processes to improve lignocellulosic waste treatment or potential use of fungi in the degradation of environmental pollutants.
\r\n\tThis book intends to provide the reader with a comprehensive overview of the current state-of-the-art in biodegradation processes that contribute to a sustainable and circular bioeconomy giving added value compounds from waste and by-products, up to now hidden, and reducing waste generation at the same time.
Biofilms are defined as sessile communities of bacteria that form on surfaces and are entrapped in a matrix that they themselves produce. Biofilms cause severe problems in many natural (Ferris et al., 1989; Nyholm et al., 2002), clinical (Nicolle, 2005; Rice, 2006), and industrial settings (Brink et al., 1994; McLean et al., 2001; Wood et al., 2006), while being beneficial for waste water treatment and biofuel production (Wang and Chen, 2009). In addition, the bioremediation of crude oil spills involves a biofilm of oil degrading microbes, potentially supplemented by marine flagellates and ciliates (Gertler et al., 2010). Identifying the environmental conditions that prevent or support biofilm formation, as well as understanding the regulatory pathways that signal these conditions, is a pre-requisite to both, the solving of biofilm-associated problems and the use for beneficial purposes. In a previous study by our laboratory (Prüβ et al., 2010), it was determined that nutrition ranked among the more important environmental factors affecting biofilm-associated biomass in Escherichia coli K-12. The key to this study was a high-throughput experiment, where biofilm biomass was determined in a collection of cell surface organelle and global regulator mutants under a variety of combinations of environmental conditions. The cell surface organelles each represented a distinct phase of biofilm formation (Sauer et al., 2002). Flagella are required for reversible attachment (phase I), curli or type I fimbriae are characteristic of irreversible attachment (phase II), and a polymeric capsule forms the matrix that permits the maturation of the biofilm (phase III). Eventually, flagellated bacteria are released from the biofilm (phase IV). Phases III and IV are particularly problematic for the disease progression. Bacteria that are located deep within the mature biofilm are particularly resistant to antibiotics and dispersed bacteria tend to serve as a reservoir that continuously feed the infection. Please, see Figure 1 for the distinction of biofilm phases.
The global regulators included in our previous study (Prüβ et al., 2010) are involved in the co-ordinate expression and synthesis of biofilm-associated cell surface organelles. Many of them are components of two-component systems (2CSTS), each consisting of a histidine kinase and a response regulator (for reviews on 2CSTS signaling, please, see Galperin, 2004; Parkinson, 1993; West & Stock, 2001). In response to an environmental stimulus, the sensor kinase uses ATP as a phosphodonor to auto-phosphorylate at a conserved histidine, then transferring the phosphate to the response regulator at a conserved aspartate residue. In addition, many response regulators can be phosphorylated in a kinase independent manner by the activated acetate intermediate acetyl phosphate (for a review on acetyl phosphate as a signaling molecule, please, see Wolfe, 2005). One 2CSTS that is involved in the formation of biofilms is EnvZ/OmpR, regulating the synthesis of flagella (Shin and Park, 1995), type I fimbriae (Oshima et al., 2002), and curli (Jubelin et al., 2005). RcsCDB is involved in the formation of biofilms, serving as an activator of colanic acid production (Gottesman et al., 1985). RcsCDB constitutes a rare phosphorelay, consisting of three proteins and four signaling domains (Appleby et al., 1996). Much of the effect of EnvZ/OmpR, and RcsCDB upon biofilm formation involves FlhD/FlhC (Prüβ et al., 2006), which was initially described as a flagella master regulator (Bartlett et al., 1988) and later recognized as a global regulator of bacterial gene expression (Prüβ & Matsumura, 1996; Prüβ et al., 2001, 2003).
Time course of biofilm formation
An early review article (Prüβ et al., 2006) summarized the portion of the transcriptional network of regulation that centered around FlhD/FlhC. This partial network contained 16 global regulators, among them many 2CSTSs, such as EnvZ/OmpR, RcsCDB, and CpxR. The regulation of approximately 800 genes was affected by the network. Since many of these encoded components of the biofilm-associated cell surface organelles, it was hypothesized that the network may affect biofilm formation. This hypothesis was confirmed by the high-throughput study that led to the identification of nutrition as one of the more instrumental factors in determining biofilm biomass (Prüβ et al., 2010). The global regulators that were part of the network led to the mutant collection for the experiment. Among the tested environmental conditions were temperature, nutrition, inoculation density, and incubation time. Temperature and nutrition were more important in determining biofilm biomass than were inoculation density and incubation time. The mutant screen was consistent with the idea that acetate metabolism may act as a nutritional sensor, relaying information about the environment to the development of biofilms. This hypothesis was confirmed by scanning electron microscopy. A new 2CSTS, DcuS/DcuR, was identified as important in determining the amount of biofilm-associated biomass (Prüβ et al., 2010).
The high-throughput experiment merely determined that nutrient rich bacterial growth media are more supportive of biofilm formation than are nutrient poor media. Specific nutrients that are supportive or inhibitory to biofilm formation were not determined and are the next logical step. This will be dependent on an assay system that quantifies biofilm biomass in the presence of an array of single nutrients. With this study, we will introduce such a system that quantifies biofilm biomass formed by Escherichia coli mutants in the presence of single nutrients by combining the Phenotype MicroArrayTM technology from BioLog (Hayward, CA) with the ATP quantitative biofilm assay that was previously developed by our own lab (Sule et al., 2009), followed up by statistical analysis of the data, and metabolic modeling.
The BioLog Phenotype MicroArray (PM) technology has been developed for the determination of bacterial growth phenotypes (Bochner, 2009; Bochner et al., 2001, 2008). The PM technology consists of 96 well plates with 95 single nutrients dried to the base of each of 95 wells (the additional well constitutes the negative control). When used with the tetrazolium dye that is provided by the manufacturer and indicative of respiration, the PM system is used to determine growth of bacterial strains on single nutrients. Since the total system consists of 20 of such plates, the user is enabled to screen growth under close to 2,000 conditions. The plates are designated PM1 through PM20, with PM1 and PM2 containing carbon sources, PM3 containing nitrogen sources, and PM4 containing sulfur and phosphorous sources. The remaining plates can be used to determine the pH range of growth or resistance to antibiotics or other harsh conditions. Liquid growth media are supplied together with the respective plates.
With respect to bacterial growth, PMs have been used in numerous previous studies (Baba et al., 2008; Edwards et al., 2009; Mascher et al., 2007; Mukherjee et al., 2008; Zhou et al., 2003). However, use of this technology for the investigation of biofilms has been limited (Boehm et al., 2009). In E. coli, the use of PM technology for the quantification of biofilm biomass has not been reported. In addition, the previous use of PM technology in biofilm studies has been based on the use of the crystal violet assay for the quantification of biomass. There are, however, many more assays that have been developed for the quantification of biofilm-associated biomass, each of which serves a different purpose. The different quantitative biofilm assays are compared in Table 1.
Crystal violet is a non-specific protein dye that stains the bacterial cells and their exopolysaccharide matrix for dead and live bacteria alike. Biofilms are cultivated on 96 well plates and stained with 0.1% crystal violet in H2O. In a second step, crystal violet is solubilized with a mix of ethanol and acetone (80:20) and measured spectrophotometrically (O’Toole et al., 1999; Pratt & Kolter, 1998). The assay was developed as a high-throughput assay that is suitable for robotic instrumentation (Kugel et al., 2009; Stafslien et al., 2006, 2007). ATP (adenosine triphosphate) (Sule et al., 2008, 2009) and XTT (4-nitro-5-sulfophenyl-5-[(phenylamino) carbonyl]-2H-tetrazolium hydroxide) (Cerca et al., 2005) are both assays that quantify the energy metabolism of the bacteria. Therefore, only biomass of live bacteria is considered. ATP is converted by the enzyme luciferase into a bioluminescence signal, XTT is reduced by NADH to an orange colored water-soluble formazan derivative. Similar to crystal violet, fluoro-conjugated lectins quantify the biomass of live and dead bacteria alike (Burton et al., 2006). Lectins are highly-specific carbohydrate binding proteins that have been utilized to quantify different cell wall components, as well as extracellular matrix (Stoitsova et al., 2004). Specifically, wheat germ agglutinin (WGA) and soybean agglutinin (SBA) selectively complex lipooligosaccharides and colanic acid, respectively. For our experiments, we needed an assay that quantifies biofilm biomass in live bacteria that is also suitable for high-throughput experimentation, cost effective, and rapid. The ATP assay appeared as the most suitable assay among the five compared assays (Table 1).
Assay | Live/dead cells | Detected material | High-throughput suitability | Reference |
Crystal violet | Live and dead cells | Exopolysaccharide | Yes | (Kugel et al., 2009; Stafslien et al., 2006, 2007) |
ATP | Live cells | Energy (ATP) | Yes | (Sule et al., 2008, 2009) |
XTT | Live cells | Energy (NADH) | Yes | (Cerca et al., 2005) |
WGA | Live and dead cells | Lipooligosaccharide | Not tested | (Burton et al., 2006; Stoitsova et al., 2004) |
SBA | Live and dead cells | Colanic acid | Not tested | (Burton et al., 2006; Stoitsova et al., 2004) |
Comparison of different quantitative biofilm assays
In the past, ATP has been used as a measure of biomass (Monzón et al., 2001; Romanova et al., 2007; Takahashi et al., 2007) because its concentration is relatively constant across many growth conditions (Schneider & Gourse, 2004). For the quantification of biofilms, the BacTiter GloTM assay from Promega (Madison WI) has been used for biomass determination in Pseudomonas aeruginosa (Junker & Clardy, 2007) and E. coli (Sule et al., 2008, 2009). In E. coli, we established that a two fold increase in bioluminescence did indeed relate to a two fold increase in the ATP concentration and a 2 fold increase in the number of bacteria (Sule et al., 2008). Across eight isogenic E. coli strains (one parent strain and seven mutants), differences in biofilm biomass that were determined with the ATP assay were paralleled by observations made with scanning electron microscopy (Sule et al., 2009).
The protocol involves the formation of the biofilms on 96 well micro titer plates, incubation at the desired temperature, and washing of the biofilms with phosphate buffered saline (PBS). Special attention is needed to distinguish the pellicle that forms at the air-liquid interface from the biofilm that forms at the bottom of the wells. In particular, the AJW678 derivatives that we are working with form a solid pellicle that covers the entire surface of the culture (Wolfe et al., 2003). For users who like to include the pellicle into their study, the growth medium and the PBS will be pipetted off carefully from each well. Users who wish to discard of the pellicle can flip the entire 96 well plate over and remove the liquid this way. Eventually, 100 µl of BacTiter Glo reagent are added to each well. After 5 min of incubation, bioluminescence is measured.
For this study, we will use the ATP assay to quantify biofilm biomass that forms on the PM1 plate of BioLog’s PM system. The PM1 plate contains 95 single carbon sources in addition to the negative control. Besides the fact that the use of PM technology for the determination of the nutritional requirements of biofilm has not been reported in E. coli yet, the combination of PM technology with the ATP assay is novel. The combination of both, PM technology and ATP assay, together with the statistical analysis and metabolic modeling, enables the rapid screening of thousands of nutrients for their ability to support or inhibit growth and biofilm formation in one experimental setup. The described technique is not only cost-efficient and easy to perform, but also high-throughput in nature, providing valuable insight into the nutritional requirements during biofilm formation.
The bacterial strains used in this study were the E. coli parental strain AJW678, which was characterized as an efficient biofilm former (Kumari et al., 2000) and its isogenic flhD, fliA, fimA, and fimH mutants. The flhD mutant was constructed by P1 transduction, using MC1000 flhD:kan (Malakooti, 1989) as a donor and AJW678 as a recipient. This resulted in strain BP1094. AJW2145 contained a fliA::Tn5 insertion, AJW2063 a fimA::Kn mutation, and AJW2061 a fimH::kn mutation, all in AJW678 (Wolfe et al., 2003). The mutations abolish expression of FlhD/FlhC, FliA, FimA, and FimH, respectively. As a consequence, mutants in flhD and fliA are non-motile, whereas mutants in fimA are lacking the major structural subunit and mutants in fimH the mannose specific adhesive tip of the type I fimbrium. Bacterial strains were stored at -80C in 8% dimethylsulfoxide, plated onto Luria Bertani plates (LB; 1% tryptone, 0.5% yeast extract, 0.5% NaCl, 1.5% agar) prior to use, and incubated overnight at 37C. Bacterial strains are summarized in Table 2.
Strain | Relevant genotype | Reference |
AJW678 | thi-1 thr-1(am) leuB6 metF159(am) rpsL136 ΔlacX74 | (Kumari et al., 2000) |
BP1094 | AJW678 flhD::kn | (Prüß et al., 2010) |
AJW2145 | AJW678 fliA::Tn5 | (Wolfe et al., 2003) |
AJW2063 | AJW678 ΔfimA::kn | (Wolfe et al., 2003) |
AJW2061 | AJW678 fimH::kn | (Wolfe et al., 2003) |
Bacterial strains used for this study
For this study, a mutation was needed that would abolish one of the early cell surface organelles that contribute to the biofilm, while still permitting the formation of biofilms. We performed scanning electron microscopy (SEM) to determine the ability of the five bacterial strains (parental strain, flhD mutant, fliA mutant, fimA mutant, fimH mutant) to form biofilms. Biofilms were grown for 38 h at 37oC on glass cover slips with tryptone broth (TB; 1% tryptone, 0.5% NaCl) as a growth medium. Biofilms were fixed in 2.5% glutaraldehyde and prepared for SEM as described (Sule et al., 2009). Images were obtained with a JEOL JSM-6490 LV scanning electron microscopy (SEOL Ltd., Tokyo, Japan) at 3,000 fold magnification. 10 to 15 images were obtained per bacterial strain from at least three independent biological samples. One representative image is shown per bacterial strain.
We used the PM1 plate of the BioLog PM system that contains 95 single carbon sources. When used with the tetrazolium dye that is provided by the manufacturer and indicative of respiration (Bochner et al., 2001), the PM system can be used for measuring growth of bacterial strains on single nutrients. We here describe a protocol for the determination of biofilm amounts (Figure 2).
As recommended by the manufacturer for the determination of growth phenotypes, the bacterial cultures were streaked from LB plates onto R2A plates (to deplete nutrient stores) and incubated at 37C for 48 hours. Bacteria were removed from the plates with a flocked swab (Copan, Murrieta CA), resuspended and then further diluted with IF-0a GN/GP Base (BioLog, Hayward CA) inoculation fluid to an optical density (OD600) of 0.1. Leucine, methionine, threonine and thiamine were added at a final concentration of 20 μg/ml, the redox dye that is used for the determination of growth phenotypes was omitted for biofilm quantification. 100 μl of the inoculum was then dispensed into each of the 96 wells of the PM1 plates. The inoculated plates were wrapped with parafilm to minimize evaporation and incubated at 37C for 48 hours. Biofilm amounts were quantified using the previously described ATP based technique (Sule et al., 2008, 2009). Briefly, the growth medium was carefully aspirated out of each well, minimizing loss of biofilm at the air liquid interface. The biofilms were then washed twice with phosphate buffered saline (PBS) in order to remove any residual media components. The biofilms were air dried and quantified using 100 μl BacTiter Glo™ reagent (Promega, Wisconsin, WI). The biofilms were incubated with the reagent for 10 min at room temperature and the bioluminescence was recorded using a TD 20/20 luminometer from Turner Design (Sunnyvale, CA). The bioluminescence was reported as relative lux units (RLU).
The determination of biofilm amounts in the presence of single nutrients was performed four times for each strain. In addition, growth on these carbon sources was determined in three independent replicate experiments, following the protocol that is described for the determination of growth phenotypes and including the redox dye (Bochner et al., 2001). Carbon sources on which both strains grew to an average OD600 of 0.5 or more were selected for the t-test analysis and carbon sources on which each strain grew to an average OD600 of 0.5 or more were selected for the ANOVA/Duncan analysis of biofilm amounts (see below).
Work flow for the determination of biofilm amounts on PM plates with the ATP assay
Prior to the statistical analysis, the biofilm amounts from each strain were normalized for experiment specific variation; total bioluminescence across each experiment was summed up and the fold variation was calculated, using the lowest experiment as a norm (1 fold). Data points in each experiment were divided by the respective fold variation. The normalized experimental data sets were subjected to two independent types of statistical analysis, all done using SAS software (SAS Institute Inc., 2009). First, we performed Student’s t-test on all those carbon sources on which both strains grew to an average OD600 of 0.5 or more to determine statistically significant differences between the amounts of biofilm that were formed on a given carbon source between the two strains. Since this analysis yielded more carbon sources than we could comprehend on a physiological level, we then analyzed each strain individually and then compared biofilm amounts on individual carbon sources for specific nutrient categories of structurally related carbon sources. For this analysis, the normalized biofilm data from each strain were subjected to separate one way ANOVAs, followed up with Duncan’s multiple range tests. The tests compared the means of the amount of biofilm formed in the presence of each carbon source to all the other carbon sources within each strain. Carbon sources whose mean was different from the means of all the other carbon sources with statistical significance formed their own group in the Duncan’s test. Carbon sources whose mean difference from the other carbon sources was not statistically significant formed overlapping groups.
Performing Duncan’s test on the parent strain, two carbon sources formed groups A and B. Among the remaining carbon sources, we determined those that were structurally related to group A and B carbon sources. This was done after a determination of the respective chemical structures with the Kyoto Encyclopedia of Genes and Genomes (KEGG; Kanehisa & Goto, 2000; KEGG, 2006). Biofilm amounts formed by the flhD mutant were compared to the parent strain for all these carbon sources. In a second analysis, one carbon source formed group A in the Duncan’s test for the flhD mutant. Among the remaining carbon sources, we identified two carbon sources that were structurally related. Biofilm amounts for these three carbon sources were compared between the two strains. For both analyses, data were summarized in a Table (3 and 4).
Metabolic pathways that lead to the degradation of all the carbon sources that are discussed in this study were determined with KEGG. Metabolic intermediates that were common between different pathways were used to construct metabolic maps. Pathways for both strains were combined in Figures 5 and 6.
To determine the ability to form biofilm, electron microscopy was performed with the five strains that were listed in Materials and Methods. Figure 3 depicts one representative illustration of the 10 to 15 images that were obtained per bacterial strain. Most of these strains formed biofilm despite mutations affecting cell surface organelles of either reversible (flagella) or irreversible (type I fimbriae) attachment. The sole exception was the fimH mutant which only showed a small number of scattered bacteria attached across the slide. The fimA mutant exhibited a large number of filamentous appendages. We are currently unable to explain these appendages.
Electron micrographs at 3,000 fold magnification for the AJW678 parent strain, and its isogenic mutants in flhD, fliA, fimA, and fimH
We wanted a strain for the phenotype microarray experiment that was able to form biofilm on complex media, while lacking one of the cell surface organelles. Since the amount of biofilm formed by the flhD mutant was similar to that of the parental strain in the electron micrographs, the flhD mutant was selected for further testing using the PM1 plates. The flhD mutant has as an additional advantage that much of the regulation by FlhD/FlhC has been previously described. This vast amount of information will help us to analyze the complex metabolic data.
Biofilms that formed on the PM1 plates were quantified with the ATP assay and compared between the two strains with the t-test. The analysis did not yield any carbon sources that supported more biofilm in the parent strain than in the mutant. The 25 carbon sources that yielded significantly higher amounts of biofilm in the flhD mutant are demonstrated in Figure 4. Since the carbon sources that supported biofilm formation by the mutant more so than by the parent are numerous, we decided to analyze each strain statistically first and focus the comparison between the strains to specific structural categories of carbon sources. These are designated ‘nutrient categories’ throughout this manuscript.
The normalized data set from the parent strain was subjected to Duncan’s multiple range test. According to this test, the two carbon sources that were the best biofilm supporters for the parent E. coli strain, maltotriose and maltose, formed exclusive groups A and B. Without
Biofilm formation in the parent strain and the flhD mutant were compared using a t-test. The dark shaded bars resemble the parent strain, the lighter bars the mutant. The error bars in the graph indicate the standard deviation. Note that only carbon sources were included in this analysis that supported growth to at least 0.5 OD600 in both strains.
forming its own Duncan group, ribose was the carbon source that supported the smallest amount of biofilm among all carbon sources tested, while still supporting growth. The parent strain also formed good amounts of biofilm on the remaining C6-sugars. Interestingly, the amount of biofilm that formed on maltotriose (trisaccharide of glucose) was roughly three times the amount of biofilm that formed on glucose. The amount of biofilm that formed on maltose (disaccharide of glucose) was about twice the amount that formed on glucose. The C5-sugars xylose and lyxose did not support growth of the parental strain to the cutoff of 0.5 OD600. For all these carbon sources, biofilm amounts formed by the flhD mutant were compared to the parent strain (Table 3). In contrast to the parental strain, the flhD mutant did not grow well on C6-sugars and their oligosaccharides. Unlike the parental strain, the mutant did not grow well on ribose, but grew to the cut off of 0.5 OD600 on lyxose and xylose. Still, the amount of biofilm formed by this strain on C5-sugars was low (<1,000 RLU). An interesting phenomenon was observed for sugar phosphates and sugar acids. Sugar phosphates supported biofilm production by the mutant more so (>1,200 RLU) than for the parent strain (<600 RLU). Likewise, sugar acids were found to be good supporters of biofilm for the flhD mutant strain (1,500 to 2,500 RLU), but not for the parent (500 to 800 RLU). This was even more remarkable, considering the fact that the parental strain (OD600 ~ 1.0) grew better on sugar acids than the flhD mutant (OD600 of 0.2 to 0.8).
Nutrient category | Nutrients | AJW678 flhD mutant | |
Biofilm Amount (RLU) | Biofilm Amount (RLU) | ||
Trisaccharide | Maltotriose | 4,935 | NA* |
Disaccharide | Maltose | 2,928 | NA* |
C6-sugars | Glucose Fructose Mannose Rhamnose | 1,615 1,500 1,745 873 | NA* NA* NA* NA* |
C5-sugars | Ribose Lyxose Xylose | 147 NA NA | NA* 650 544 |
Sugar phosphates | Glucose 6-P Fructose 6-P | 614 338 | 1,722 1,258 |
Sugar acids | D-galacturonic acid D-gluconic acid D-glucuronic acid | 668 532 852 | 2,358 1,679 2,110 |
Biofilm amounts on carbon sources which formed their own Duncan’s grouping for the parent strain and structurally related carbon sources. Columns 1 and 2 indicate the nutrient categories and single carbon sources for which data are included. Columns 3 and 4 represent biofilm amounts for the parent strain and the mutant on carbon sources that permitted growth to more than 0.5 OD600. NA denotes ‘not applicable’, where the strain grew to an OD600 below 0.5.
The amount of biofilm formed on each carbon source by the flhD mutant was quantified and subjected to Duncan’s multiple range test. According to the Duncan’s grouping, the sole carbon source that formed its own group A for the flhD mutant was N-acetyl-D-glucosamine. Structurally related carbon sources that were included in the PM1 plate are D-glucosaminic acid and N-acetyl-β-D-mannosamine. Biofilm amounts formed on these three carbon sources were compared between the two strains (Table 4).
Nutrient category | Nutrients | flhD mutant AJW678 | |
Biofilm Amount (RLU) | Biofilm Amount (RLU) | ||
Sugar amines | N-acetyl-D-glucosamine | 4,911 | 1,285 |
D-glucosaminic acid | 660 | NA | |
N-acetyl-β-D-mannosamine | 1,368 | 559 |
Biofilm amounts on carbon sources which formed their own Duncan’s grouping for the flhD strain and structurally related carbon sources. Columns 1 and 2 indicate the nutrient category and single carbon sources for which data are included. Columns 3 and 4 represent biofilm amounts for the flhD mutant and its parent strain on carbon sources that permitted growth to more than 0.5 OD600. NA denotes ‘not applicable’, where the strain grew to an OD600 below 0.5.
On N-acetyl-D-glucosamine, the flhD mutant (4,900 RLU) formed a significantly larger amount of biofilm than the parent strain (1,300 RLU), while both strains grew to approximately 1 OD600. On D-glucosaminic acid, the parent strain did not grow to the cutoff OD of 0.5. The flhD mutant grew well, but the amount of biofilm biomass was poor (~600 RLU). For N-acetyl-β-D-mannosamine, both strains grew well, the flhD mutant expressed more than twice the ability to form biofilm than its isogenic parent.
Metabolic pathways were drawn for the degradation of all those carbon sources that supported amounts of biofilm larger than 1,000 RLU for one of the tested strains. These are carbon sources of the nutrient categories C6-sugars, sugar phosphates, sugar acids, and sugar amines. C6-sugars all have pathways that feed into the Embden-Meyerhof pathway, sugar phosphates are intermediates of this pathway. As shown in Figure 5, mannose, fructose, and N-acetyl D-glucosamine feed into fructose 6-phosphate. Gluconate, glucuronate, galacturonate, and rhamnose feed into glyceraldehyde 3-phosphate. This leads to the production of acetyl-CoA, acetyl phosphate and acetate (Figure 6).
Metabolic pathways from the top biofilm producing carbon sources for both E. coli strains, feeding into the Embden-Meyerhof pathway.
Altogether, we present an assay that builds upon two previous assays, the PM technology and the ATP assay. Both assays have been used in much different contexts previously. PM plates have been commonly used to discover various bacterial characteristics based on phenotypic changes (Bochner et al., 2008). Studies involving PM plates include the evaluation of the alkaline stress response induced changes in the metabolism of Desulfovibrio vulgaris (Stolyar et al., 2007). PMs have also been used for the identification of bacterial species (Al-Khaldi & Mossoba, 2004). The use of PM technology in biofilm research is
Metabolic pathways from the top biofilm producing carbon sources for both strains to the production of acetate. Carbon sources that are printed in bold were top biofilm supporters for the parent strain. Carbon sources that are underlined were top biofilm supporters for the flhD mutant. The effect of acetyl phosphate on RcsB and OmpR on the synthesis of flagella, curli, fimbriae, and capsule is indicated.
limited to a study of the ability of E. coli to form biofilm upon ribosomal stress (Boehm et al., 2009). That study used the crystal violet assay as a detection tool for the amount of biofilm.
Here we report for the first time a combination of the established ATP assay along with the PM technology to assess nutritional dependence of E. coli during biofilm formation. Since the statistics approach alone (t-test) yielded no more than a list of data that were difficult to interpret, we decided for a combined statistics/metabolism approach to analyze the complex data. The combination of the two experimental parts of the assay together with the two analysis parts enables the user to rapidly screen hundreds and thousands of single nutrients for their ability to inhibit growth and biofilm formation in one experimental setup. Integrating different mutants into the study will yield valuable insight into the regulatory mechanisms that are involved in the signaling of these nutrients. The described technique is not only cost-efficient and easy to perform, but also high-throughput in nature. It is ideally suited to provide valuable insight into the nutritional requirements that determine biofilm biomass, as well as the respective signaling pathways.
In the described study, we observed that the FlhD mutants made quantitatively higher amounts of biofilms on numerous carbon sources. Interestingly, the parental strain did not form higher quantities of biofilm than the mutant on any of the tested carbon sources. These observations shed light into the ongoing controversial debate, elucidating the role of motility in biofilm formation. In certain bacterial species including Yersinia enterocolitica, the presence of motility has been shown to be beneficial for biofilm formation (Wang et al., 2007). Several previous studies from our lab demonstrate that the absence of motility enhances the ability of E. coli to form substantial amounts of biofilm. As one example, strains transformed with the FlhD expressing plasmid pXL27 showed diminished biofilm forming capabilities (Prüß et al., 2010). Additionally, ongoing studies carried out in the lab with E. coli O157:H7 and the E. coli K-12 strains MC1000 and AJW678 point in the same direction, exemplifying our belief that FlhD and motility are detrimental to biofilm formation for our bacterial strains and under the conditions of our experiments (Sule et al., unpublished data).
As a second observation, carbon sources that supported maximal biofilm formation by either strain all fed into glycolysis eventually, and produced actetate. Although the carbon sources that promoted the highest biofilm amounts were different for the two strains, they still were in the same pathway. The previous high-throughput experiment that had pointed towards nutriition as instrumental in determining biofilm associated biomass had also postulated acetate metabolism as one of the key players in biofilm formation (Prüß et al., 2010). Phosphorylation of OmpR and RcsB by the activated acetate intermediate acetyl phosphate (Kenney et al., 1995) and acetylation of RcsB by acetyl-CoA (Thao et al., 2010) have been described in the past. These activated 2CSTS response regulators then affect the expression level of biofilm associated cell surface organelles, such as flagella, type I fimbriae, curli, and capsule (Ferrieres & Clarke, 2003; Francez-Charlot et al., 2003; Oshima et al., 2002; Prüß, 1998; Shin & Park, 1995) (Figure 6). The positive effect on biofilm amounts of carbon sources that lead to the production of acetate can be explained with the combined inhibitory effect of acetyl phosphate and acetyl-CoA on flagella through OmpR and RcsB and the above described disadvantage of flagella and motility during biofilm formation. We however do not state that acetate is the sole controlling mechanism as the complexity of the bacterial system cannot be explained based on a small number of signaling molecules.
The most striking observation obtained from our studies pertains to the pattern of growth and biofilm formation on sugar acids. It was observed that the FlhD mutants grew to lower optical densities on sugar acids, but formed much higher amounts of biofilm as compared to the parental strain. Previous work from the Prüß lab had shown similar defects in growth of flhD mutants on sugar acids (Prüß et al., 2003), biofilm formation was not tested in that study. The inverse effect of sugar acids on growth and biofilm amounts may have implications in the intestine. Mutants in flhD have an early disadvantage in colonization, but recover after prolonged incubation (Horne et al., 2009). They even take over the population after more than two weeks (Leatham et al., 2005). The initial lack of colonization could be explained by the inability of the flhD mutant to degrade the numerous sugar acids present in the intestine (Peekhaus & Conway, 1998). On the other hand, the ability to take over the bacterial population at a later stage may have to do with the lack of the flagellin, which is a potent cytokine inducer (McDermott et al., 2000). The here discovered ability to make an increased amount of biofilm may add to the long term survival of flhD mutants in the intestine. Bacteria deep within the biofilm will be protected from the immune system, while metabolizing very slowly and not needing much nutrition.
Among the carbon sources that were the least supportive of biofilm formation, the inability of the C5-sugars to support growth and/or biofilm formation was the most striking. Ribose supported growth by the parent strain, but yielded the lowest biofilm amount of all tested carbon sources. The flhD mutant did not even grow on ribose. According to Fabich and coworkers (Fabich et al., 2008), ribose is not among the carbon sources that the E. coli K-12 strain MG1655 utilizes when bacteria colonize the intestine. Our data are consistent with this observation. Since E. coli O157:H7 EDL933 does actually utilize ribose in the intestine, ribose utilization may constitute a mechanism by which pathogenic E. coli can find a niche in the intestine to co-exist with the commensal E. coli strains.
The inability to grow on lyxose is also consistent with previous observations, where only a mutation in the rha locus enabled the bacteria to grow on lyxose via the rhamnose pathway (Badia et al., 1991). Normally, E. coli are unable to grow on lyxose. Most interesting is the behavior of the two strains on xylose. The parent E. coli strain was unable to grow on xylose. The flhD mutant did grow, while producing moderately low amounts of biofilm. Co-utilization of glucose and xylose by E. coli strains is of upmost importance during the production of biofuels, since the fermented plant material contains both, cellulose (polymer of glucose) and hemicellulose (polymer of glucose and xylose), in addition to lignin. Much research is currently dedicated to the genetic modification of E. coli that enables the bacteria to utilize xylose more efficiently (Balderas-Hernandez et al., 2010; Hanly & Henson, 2010). It would be interesting to see whether a mixture of our parent strain and its isogenic flhD mutant would be able to co-utilize glucose and xylose, particularly since the mutant produced a moderate amount of biofilm which can also be beneficial to the production of biofuels.
In summary, we developed an assay system that quantifies biofilm biomass in the presence of distinct nutrients. The assay enables the user to screen a large number of such nutrients for their effect on biofilm amounts. Examples of metabolic analysis relate back to previous literature, as well as giving raise to new hypotheses. Yielding further evidence for the previous hypothesis that acetate metabolism was important in determining biofilm amounts can serve as a positive control that the assay actually yields data of biological significance. Particularly with respect to life in the intestine and the production of biofuels, the data open new avenues of research by providing testable hypotheses. Overall, there is no limit to extensions of the assay into different bacterial species or serving the development of high-throughput data mining algorithms that will computerize the statistic/metabolic analysis that we started in this study.
The authors like to thank Dr. Alan J. Wolfe (Loyola University Chicago, Maywood IL) for providing the bacterial strains that were used for this study, Dr. Jayma Moore (Electron Microscopy Lab, NDSU) for help with the scanning electron microscopy, Dr. Barry Bochner (BioLog, Hayward CA) for helpful discussions during the development of the combination assay, and Curt Doetkott (Department of Statistics, NDSU) for performing the statistical analyses of our data and helping us with their interpretation. The work was funded by an earmark grant on Agrosecurity: Disease Surveillance and Public Health through USDA/APHIS and the North Dakota State Board of Agricultural Research and Education. Figure 2 was created using Motifolio (Motifolio Inc., Ellicott MD).
The emerging high-throughput omics such as genomics, transcriptomics, proteomics, and metabolomics have been used in the search of new biomarkers in several diseases. Proteomic analyses or metabolomics and lipidomics as well as complementary technologies such as mass spectrometry (MS) (i.e., LC-MS-MS and MALDI-TOF/TOF), nuclear magnetic resonance spectroscopy, and other omics technologies, are being widely used in the search of new sources of markers, candidates for vaccine, and alteration of expression patterns in response to environmental changes and signaling pathways in different diseases. The search for proteins in the dynamic system of a proteome requires various proteomics approaches and the use of proteomics is crucial for the early disease diagnosis, prognosis, and to monitor the disease development. The proteomics is essential for the understanding of complex biochemical processes, and the high-throughput proteomics increases the depth of proteome coverage.
\nLipoproteins are macromolecular complex particles of lipids and proteins, which are related to the extracellular transport of lipids in many organisms [1]. Besides, lipoproteins have been also implicated as important host defense mediators and in the initiation of immune responses [2, 3, 4]. Lipid content has long been recognized as a critical factor in lipoprotein metabolism that acts as an important determinant of human health [5]. However, in last years, apolipoproteins and lipoprotein-associated proteins have been taken on relevance regarding lipoprotein metabolism since they serve as a frame for their assembly, maintain their structure, and interact with the membrane receptors and enzymes. Therefore, these proteins could be crucial in the identification of biomarkers related to diseases due to the fact that they are being studied under a global approach, denominated lipoproteome [6, 7].
\nLipoproteins are complex protein particles of an amphipathic nature, structurally are formed by an outer layer of phospholipids, free cholesterol, and apolipoproteins, and inside contain a nucleus of cholesterol esters and triglycerides [8]. When the lipoprotein complex is formed, the orientation of the hydrophilic proportions is toward the outside and the lipophilic proportions toward the interior; this structural characteristic allows the complex to have the ability to emulsify fats in extracellular fluids [8]. Based on their density defined by the protein to lipids ratios, lipoproteins are grouped into six classes: chylomicrons, very low density lipoproteins (VLDL), intermediate density lipoproteins (IDL), low density lipoproteins (LDL), high density lipoproteins (HDL), and lipoprotein (a) [Lp (a)] [8, 9].
\nTriglycerides, derived from dietary fat absorption by the small intestine, are carried by the chylomicrons into blood. After triglyceride digestion to free fatty acids (FFA) by lipases in the peripheral tissues, the size of these particles is reduced, which leads to the formation of chylomicron remnants. These latter are cleared by liver uptake via LDL receptor-related protein (LRP). To synthesize VLDL, newly synthesized triglyceride and cholesterol by the liver are incorporated into chylomicron remnants [8, 10]. These large triglyceride-rich VLDLs are released to the circulation and travel to peripheral tissues, where the lipase digestion of triglycerides occurs resulting in the IDL formation (VLDL remnants). These IDL particles can be either cleared from the circulation by the liver in a similar manner to that described for chylomicrons remnants or can be digested by hepatic lipase to generate cholesterol-rich LDL particles, which is taken up by the peripheral tissues via LDL receptor to supply their cholesterol requirements [10].
\nThe HDL lipoproteins have the highest relative density as compared to other lipoproteins despite being the smallest in size and heterogeneous in terms of composition. These HDL particles play an important role in the transport of reverse cholesterol as a carrier in the movement of cholesterol from peripheral tissues back into the liver [11]. The liver and intestine, practically in response to the lipolysis of triglyceride-rich lipoproteins, synthesize and secrete the nascent discoid HDLs that consist primarily of phospholipids and free cholesterol. Then, these particles reach the plasma where additional exchangeable apolipoproteins are picked up and excess free cholesterol is removed from both extrahepatic cells and other circulating lipoproteins, forming mature spherical HDL particles. Finally, these cholesterol-rich HDL particles can be delivered back to the liver [10, 12].
\nThe protein cargo related to lipoproteins consists of apolipoproteins and lipoprotein-associated proteins. These proteins play key roles in lipoprotein metabolism such as structural component, enzyme interaction, and receptors recognition, among other functions [7]. Changes in the quantity and type of these proteins could be involved in the outcome of diseases related to lipid metabolism. Therefore, the proteomic analysis of lipoproteins is an important notion for the present revision.
\nThe apolipoproteins can be classified as integral or peripheral, either they act as constituent components of the plasma membrane of the lipoproteins or they bind to the membrane and can be exchanged between one complex and another, respectively. Apolipoproteins are distributed throughout all lipoproteins and subfractions, with the proportion of them varying in each family. These variations in the amount of proteins present in the lipoprotein families in their membrane gives them their capacity to interact with different tissues [7, 10].
\nWithin the apolipoprotein A (ApoA) group is the apolipoprotein A-I (ApoA-I), the main protein component of HDL, which plays multiple roles in the transport of cholesterol, in addition to having been linked to the regulation of some functions of the inflammatory and immune response [13]. ApoA-II, also found in HDL, acts as an enzymatic inhibitor of lipoproteins and liver lipases. The apolipoproteins are capable of binding to each other to modify the interaction of the lipoprotein complex, as is the case with apoA-I and apoA-II in LDL [10].
\nApoA-IV is mainly synthesized in the small intestine where it is attached by enterocytes to the chylomicrons and secreted during a high-fat meal intake. ApoA-IV is associated with HDL and chylomicron remnants in circulation, but a significant portion is free [14]. On the other hand, ApoA-V participates in the regulation of triglyceride levels in plasma [15]. ApoA-V is expressed only in the liver and circulates at low concentrations. Despite this, apoA-V can be recovered in association with plasma lipoproteins [16].
\nThe apolipoprotein B (ApoB), the main protein component of chylomicrons, LDL, VLDL, IDL, and Lp (a), is encoded by a single gene that gives rises to two isoforms: ApoB-48 and ApoB-100. ApoB-48 is produced exclusively in the intestine and is the major structural protein of chylomicrons and chylomicron remnants. ApoB-100 is expressed in the liver and is found only in VLDL, IDL, LDL, and Lp (a). ApoB-containing lipoproteins are characterized by a spherical shape and contain one single apoB-48 or apoB-100 molecule per lipoprotein [17].
\nApolipoprotein C (ApoC) works as an inhibitor of certain processes and modulators of catabolism. ApoC is mainly synthesized in the liver and easily transferred between lipoprotein particles and therefore are found associated with chylomicrons, VLDL, and HDL [10]. ApoC-I is responsible for inhibiting the binding of lipoprotein to its receptor as well as the function of the esterified cholesterol transfer protein (CETP). ApoC-II is an essential cofactor of the lipoprotein lipase involved in triglyceride hydrolysis. Both apoC-III and apoC-IV inhibit triglyceride hydrolysis. In addition, apoC-III decreases the clearance of VLDL [10, 18].
\nSeveral tissues synthesize apolipoprotein E (ApoE), an exchangeable apolipoprotein associated with chylomicrons, chylomicron remnants, VLDL, IDL, and a subgroup of HDL particles, but the liver and intestine are the principal producers of circulating ApoE [10]. ApoE functions as a lipoprotein ligator with hepatocytes (clearance of apoE-containing lipoproteins) and peripheral cells related to the LDL receptor. ApoCs and apoEs are interchangeable between complexes during the conversion of VLDL to LDL. ApoEs may function as a lecithin-cholesterol acyltransferase (LCAT) activator and influence the activity of hepatic lipase and CETP [19, 20].
\nProteins belonging to the lipocalins family such as apoD, apoM, the orosomucoid protein, and retinol-binding protein (RBP) interact with the surface receptors of the cells, intervening in the formation of macromolecular complexes. It has been described that apoD binds to apoA-II and apoB-100 by means of disulfide bridges; it is associated with LCAT, so it is involved in the transport of lipids in the blood system. ApoJ is involved in a wide variety of processes, acting as a chaperone protein and its main role is the inhibition of lipid transfer and is related to cell death. There are apoproteins such as apoF whose function is unknown; however, there is a theory of their role in abnormal lipid composition inhibiting CETP in the small and dense LDL particles (sdLDL) [21, 22].
\nIn addition to apolipoproteins, lipoproteins require other proteins with specific activities to interact with the environment, which are denominated as lipoprotein-associated proteins. Between these proteins are included phospholipase A2, whose activity indicates the presence of sdLDL in plasma [23]. Serum amyloid A protein (SAA) is associated with several lipoproteins, especially in LDL [24]. Some proteins such as albumin and prenyl cysteine oxidase (PCYOX1) are responsible for protecting against oxidation by lipoproteins such as LDL and splitting the thioether bond of the prenyl cysteine generating H2O2, respectively. Also, proteomic studies have identified the apoL-I, PON1, and PAF-AH in small amounts linked to the complexes [25, 26].
\nThe function of many of the proteins that interact with lipoproteins is still unknown. The proteomic analyses have shown that the lipoprotein-associated proteins are involved in cardiovascular risk, immune system, inflammatory processes, among others [7, 26]. However, there is no guide for the analysis of lipoproteins at the proteomic level basically because it depends on the biological question to be investigated, and this determines the approach and the methods or tools to be used as well as the technological platform to perform it.
\nBefore describing the methodologies used for lipoproteomic studies, it is important to mention some general characteristics of lipoprotein-associated proteins and the importance of the research question to establish a good methodological flow chart to address this question. We will start by mentioning that must first consider the lipoprotein obtention by using methods for separation, concentration, and protein stability due to the heterogeneity of these particles.
\nDiverse chromatographic techniques—prior to proteomic techniques—have been used for the separation of plasma lipoproteins as well as the protein content, such as capillary electrophoresis, size exclusion, cation exchange, gel filtration, fast protein liquid, among other chromatographic techniques [7, 27, 28, 29, 30, 31, 32].
\nThe two-dimensional electrophoresis (2-DE) is one of the most usual methods that have been applied to separate the protein cargo related to lipoproteins. Although usually, the first-dimensional separation applied for the proteins in the 2-DE is on base of their charge (isoelectrofocusing), some lipoproteomic analyses have substituted this step by either one of the abovementioned methods to separate lipoproteins or electrophoresis on native gels, followed by SDS-PAGE, which could be denominated as gel-based lipoproteomics [27, 33, 34, 35].
\nLipoprotein-associated proteins are not easy to study by proteomic methods, mainly the embedded in the lipoprotein membranes. These latter have rigid transmembrane domains that contain α-helices or β-barrels, which stabilize the protein by strong secondary structural characteristics and these regions can resist proteolytic digestion [6, 36]. Thus, the protein identification for lipoproteomes has been mainly performed by two methods: mass spectrometry and resonance magnetic nuclear.
\nModern mass spectrometry (MS) techniques have allowed for thorough characterizations of the lipoproteins [37]. However, different experimental conditions have been reported to avoid contamination of the biological samples as well as selective and optimized methods to detect lipoproteins, usually liquid-phase separation techniques, prior to spectrometric techniques [36, 37]. Thus, different spectrometric experimentation conditions have been reported for the study of lipoproteins and apoproteins, respectively (Table 1).
\nPre-analysis | \nChromatography | \nMass spectrometry | \nAnalysis | \nPost-analysis | \n\n | |
---|---|---|---|---|---|---|
Source | \nMethods | \nchromatographic technique = Instrumentation (method) | \nMass analyzer = Instrumentation (method) | \nDatabase search | \nNumber of lipoproteins and apoproteins | \nReferences | \n
Mice C57BL/6 J: plasma | \nGel filtration/size exclusion chromatography, phospholipid-containing particles using CSH | \nLC-ESI = C18 reverse phase column (GRACE; 150 × 0.500 mm) | \nQuadrupole/TOF = 4800 scans, mass range: 300–1800 m/z, charge states:2–5, excluded target ions: 300 s | \nSwiss-Prot protein knowledge base for Mus musculus, PeptideProphet algorithm | \nVLDL/LDL: 32, HDL: 104; lipid poor lipoproteins: 55 | \n[37] | \n
Human serum (HDL) | \nTrypsin, delipidation, gold nanoparticles and LDI-MS, EDX, SPE, FT-IR | \n\n | MALDI-MS = nr | \nSwiss-Prot, Lipidmaps database | \nHDL (delipidation): 10 (prior), 6 (after). HDL (anion exchangers): 23 | \n[38] | \n
Mice C57BL/6 J: apoA-I KO and apoA-II KO, apoA-I KO and WT: plasma | \nParticles of CSH. Plasma separation by size exclusion chromatography | \nLC-ESI = column (C18 reverse phase (150 × 0.500 mm)) | \nQuadrupole/TOF = MS/MS. tolerance were set to ±35 PPM, and up to 3 missed tryptic cleavage sites were allowed | \nUniProtKB/Swiss-Prot Protein Knowledgebase, Peptide Prophet algorithm | \nHDL: WT:25 vs. ApoA-I KO: 21; ApoA-II KO: 11 vs. WT: 14; and ApoA-IV: 6 KO vs. WT: 7 | \n[28] | \n
17 Subjects (exposure to organic pollutants): plasma (HDL) | \nUltracentrifugation (290,000 g, 15°C, 4 h) | \nnLC: Column C18, ESI | \nLinear ion trap-Orbitrap: CID. ms/ms: 0.6 Da | \nMaxQuant v1.5.0, human Uniprot/Swiss-prot database | \nLCMS permit identified the pathway of interaction between HDL-proteins | \n[39] | \n
34 Men (2 dietary: weight loss/high car): plasma (LDL, ApoC-III) | \nELISA assay | \n\n | MALDI-TOF: 500 laser shots mass spectra | \n\n | Detection and concentrations values of 12 apoC-III glycoforms | \n[40] | \n
Mice (female, LDLr−/−, 8 week old): plasma (VLDL, LDL, HDL) | \nFPLC, ELISA assay | \nLC | \nOrbitrap: mass tolerance: 0.8 Da | \nSwiss-Prot database | \nHDL: 91 LDL: 49 VLDL: 39 | \n[41] | \n
1000 Children (6–8 years, Nepal rural zone): plasma | \nCation exchange chromatography | \nLC | \nOrbitrap | \nRefseq 40 database | \nApo-AI, Apo-AII, Apo-CIII | \n[42] | \n
458 Children (6–11 years, exposure to environmental tobacco smoke): serum (HDL, LDL) | \n\n | SPE-HPLC-TIS-MS/MS | \n\n | \n | LC detected various polyfluoroalkyl substances in serum A negative association PFOS and non-HDL | \n[43] | \n
57 Males (exposed to arsenicum): plasma, urinary (LDL, HDL, Lp(a), Apo-A1, Apo-B) | \nCentrifuged at 3500 rpm for 10 min | \n\n | ICP-MS: extraction voltage −100 V, Rf power 1400 W, focus voltage 12 V, and nebulizer gas flow rate (using a Burgener Miramist nebulizer) 0.83 L/min. Dwell times were 50 ms for 75As and 10 ms for internal standard (72 Ge) | \n\n | ICP-MS: Potential risk of the arsenic on lipoproteins and apolipoproteins | \n[44] | \n
Human healthy, normolipidemic males: plasma (LDL) | \nGel filtration chromatography, ultracentrifugation. | \nnLC: column (IntePepMap 100, C18, particle size 3 um), flow rate = 300 nL/min ESI: 2.5 kV, 150°C | \nTriple quadrupole TOF: mass tolerance: 50 mDa, 350–1800 m/z window, MSscan type: 0.25 s | \nUniProtKB/Swiss-Prot Protein Knowledgebase, Peptide Prophet algorithm | \nLC-MS permit the abundance protein as well as antioxidant activity | \n[30] | \n
110 Samples (purchased) | \n\n | Phospholipids: UHPLC: column (2.1 × 100 mm, 1.7 uM particle), flow rate: 0.7 mL/min, ESI | \nQtrap: MRM scanning, negative and positive mode | \nMultiquant software functions, JMP (SAS Institute) | \nLC-MS analysis of serum/lipoproteins | \n[45] | \n
23 Healthy volunteers: serum | \nGel filtration chromatography, sequential ultracentrifugation, immunoassay | \nUPLC: column (UPLCR BEH C8), floe rate: 450 uL/min., 60°C, autosampler: 4°C ESI | \nTriple-quadrupole: positive ion mode. Quantification of plasmalogens: Capillary voltage: 3500 V, source temperature 80°C, desolvation: 400°C, cone voltage: 35 V. CE: 20–32 eV | \n\n | LCMS: distribution of each molecular species in plasmalogen and choline plasmalogen | \n[46] | \n
Healthy volunteers: plasma (VLDL, LDL, HDL) | \nUltracentrifugation, SDS-PAGE, size exclusion chromatography, circular dichroism, spectroscopy, spectropolarimeter | \n\n | MALDI-TOF: 337 nm nitrogen laser, positive ion: 20 kV | \n\n | Identification of apolipoproteins released from VLDL by mass spectrometry | \n[47] | \n
20 Patients with lipoproteins (a) (18–70 years): plasma (LDL, HDL) | \nUltracentrifugation, ELISA | \nLC: solid-phase extraction | \nTriple Quadrupole-linear ion tramp: SRM. (energy collision: 34 V, Q1: 786–788; Q2: 1069–1072 m/z)\n | \n\n | LC-MS system: concentrations of lipid, lipoprotein and apolipoprotein | \n[48] | \n
Healthy donors (nonlipidemic, 24–65 years, purchased samples): plasma (HDL, LDL) | \nUltracentrifugation (330,000 g, 6 h), SDS-PAGE and Western blotting, Negative stain electron microscopy | \nUPLC: column (Kinetex EVOC18), ESI | \nTriple-quadrupole: lipid species were analyzed by selected reaction monitoring (from 141 to 369 m/z, from 0 to 50 eV) | \nExtraction and ionization efficacy by calculating analyte/ISTD ratios (AU) and expressed as AU/mg protein | \nLCS permit the separation of a mixture in HDL protein | \n[49] | \n
12 Healthy male (36–67 years): plasma (HDL, LDL) | \nUltracentrifugation (40,000 rpm, 44 h, 15°C), fractioned, apoA-I was detected by Western blotting, internal standars | \nHILIC-UHPLC-FLD: Glycan chromatography column, 150 × 2.1 mm i.d., 1.7 μm BEH particles, flow rate of 0.56 mL/min | \nMALDI-TOF-MS: 25 kV, acceleration voltage: 140 ns extraction delay, mass window: 1000–5000 m/z. For each spectrum: 10,000 laser shots, laser frequency of 2000 Hz | \n\n | LDL: 18 HDL: 22 N-glycome of human plasma lipoproteins | \n[50] | \n
16 Healthy adults: plasma (HDL, apoA-I, apoB) | \nUltracentrifugation. PRM analysis (shotgun proteomics experiments). | \nUPLC: flow rate: 0.6 uL, column (Xbridge BEH C18) ESI | \nOrbitrap: PRM mode, isolation window: 2 Th, HCD: 27%, orbitrap analyzer: 15,000 resolution, AGC: 5 × 104, maximum ion time 30 ms | \nPeptideAtlas mass spectral database | \nMeal macronutrient content HDL composition in the postprandial state | \n[51] | \n
47 Volunteers: serum (HDL, non-HDL). | \nAnti-apoAI magnetic nanoparticles (10 mg) and serum (5 μl) were mixed FTIR, X-ray diffraction | \nID/LC/MS system: LC: column (waters symmetry C18), flow rate: 0.3 mL/min. APCI: corona current: 5 uA, source temperature: 450°C | \nAPI 4000 tandem mass spectrometer (triple-quadrupole): Collision energy: 26 eV, collision exit potential: 6 V | \n\n | ID/LC-MS permit the monitoring serum in clinical settings to dyslipidemia and atherosclerosis | \n[52] | \n
Chromatographic and spectrometric methodologies used recently in lipoproteomic analysis.
SRM: selected reaction monitoring. HPLC: high performance liquid chromatography. HILIC-UHPLC-FLD: hydrophilic-interaction ultra-high-performance liquid chromatography with fluorescence detection. LC: liquid Chromatography. UPLC: ultra-performance liquid chromatography. MALDI: matrix-assisted laser desorption/ionization. TOF: time of flight. Qtrap or LTQ linear trap: triple quadrupole-linear ion trap. CE: collision energy. MS/MS: tandem mass spectrometry. PRM: parallel reaction monitoring. AGC: automatic gain control. ID/LC/MS: isotope dilution liquid chromatography mass spectrometry. ESI: electrospray. nESI: nano-electrospray. FPLC: fast protein liquid chromatography. MRM: multiple reaction monitoring. HILIC: silica-based and solid-core reverse phase after hydrophilic interaction. ICP-MS: Inductively coupled plasma-mass spectrometer. SPE: solid phase extraction. SPE-HPLC-TIS-MS/MS: solid phase extraction coupled to high performance liquid chromatography-turbo ion spray ionization-tandem mass spectrometry. SEC-FPLC: size exclusion chromatography by fast protein liquid chromatography. APCI: atmospheric pressure chemical ionization. CID: collision-induced dissociation. LDI-MS: laser desorption/ionization mass spectrometry, EDX: dispersive X-Ray Spectroscopy. CSH: calcium silica hydrate. WT: wild-type. KO: knockout. FTIR: Fourier transformation infrared.
There are several challenges in the lipoproteomic analyses performed with the most advanced mass spectrometry methods. Some of them are the abundance of proteins from the biological source and the lipoprotein(s) purification steps, which conditioned the protein content and constitution. However, if this obstacle can be overcome, the mass spectrometry analysis has been demonstrated to be a useful tool to identify a diverse array of proteins related to lipoproteins, avoiding aberrant integration of unexpected proteins by reducing the suppression of ionization at high peptide resolution [53].
\nOn the other hand, the nuclear magnetic resonance (NMR) technique permit, besides protein identification, can provide information of the lipoproteins at both molecule and atomic levels under physiological or ‘near-physiological’ conditions. The signal most used to quantify lipoproteins is the methyl signal because give a specific response in the lipids that travel inside the lipoproteins [54, 55]. In this way, proton spectroscopy has been the most used nuclear magnetic resonance technique to quantify lipoproteins, but it is not the only one as will be seen later (Table 2).
\nPre-analysis | | \nSpectroscopy experiment NMR type | \nPost-analysis | \nReferences | \n|
---|---|---|---|---|
Source | \nisolation method | \n|||
133 Caucasian participants (T2DM, >18 years): serum (VLDL, LDL, and HDL) | \n\n | 1H: FT, 400 MHz | \nDeterminate the lipoprotein subtraction characteristics | \n[56] | \n
98 people (T2DM/ nor T2DM): plasma (GlycA vs. Lp-PLA2) | \nCentrifugation (1400 g, 15 min) | \n\n1H: 400 MHz, 47°C, CaEDTA resonance at 2.519 ppm was used as the internal chemical | \nGlycA is correlated with LP-PLA2 in plasma (person without T2DM/MetS) | \n[57] | \n
23 patients with primary aldosteronism: plasma (HDL, VLDL, LDL, ApoB, and ApoA-I) | \nImmunoturbidimetric assay | \n\n1H: 400 MHz, 47°C. (LipoProfile-3 algorithm) | \nCirculating LDL may contribute to adrenal steroidogenesis in humans | \n[58] | \n
115 nondiabetic women (35–55 years, mediterranean diet, physical exercise, 2 years): plasma (HDL. LDL) | \n\n | \n1H | \nLipoprotein size, particle and subclass concentrations | \n[54] | \n
Human serum (purchased) spiked into phlebotomy tubes (LDL, HDL) | \nCentrifugation (3000 g, 5 min, 4 h) | \n\n1H: 600 MHz | \nLipoprotein subclass analysis standardized by tube type and tube size to prevent risk of analytical interference. | \n[59] | \n
Patients with HFrEF (782), HFpEF (1004), and no HF (4742): plasma (HDL) | \n\n | NMR LipoProfile-3 algorithm | \nQuantify concentrations of HDL. Phenotyped cohorts of HFrEF, HFpEF, and patients without HF | \n[60] | \n
4897 subjects: plasma (LDL) | \n\n | \n1H: 400 MHz | \nDifferentiate in the size of particle in LDL profile | \n[61] | \n
309 patients (MACE): plasma (HDL, LDL, and VLDL). Control:902 | \n\n | \n1H | \nNeither baseline HDL nor the change in HDL on treatment with dalcetrapib or placebo was associated with risk of MACE after ACS | \n[62] | \n
Normal volunteer: plasma (HDL, LDL, andVLDL) | \nSequential ultracentrifuge | \n1H, 13C, 15N: 600.55 MHz, 47°C, different pressures | \nShow the spatial arrangement, phase behavior and molecular dynamics in the particle core | \n[55] | \n
3446 participants (HDL, LDL, and VLDL) | \n\n | \n1H: LipoProfile-3 algorithm | \nAssociation between FGF21 and NAFLD | \n[63] | \n
NMR methodology applied recently in lipoprotein-based analyses.
MHz: megahertz (106). MetS or MS: metabolic syndrome. T2DM: type 2 Diabetes mellitus. FT: Fourier transforms. CaEDTA: EDTA mono calcium. GlycA: glycoprotein acetylation. Lp-PLA2: lipoprotein-associated phospholipase A2. HFrEF: reduced ejection fractions. HFpEF: preserved ejection fraction. HF: heart failure. MACE: major adverse cardiovascular events. FGF21: fibroplast growth factor21. NAFLD: nonalcoholic fatty liver disease.
Furthermore, not only 1H NMR has been the technique used for the study of lipoproteomics but also the two-dimensional NMR techniques have been used. The two-dimensional heteronuclear 13C▬1H chemical-shift made it possible to analyze macromolecular complexes like HDL, but with a limited resolution in reduced peaks above 50 ppm and the limited resolution in the 29–33 ppm region, inclusive with artifacts that would later be discarded by the spectra of one dimension (1H and 13C) [64].
\nIn addition, the material to be used for the spectroscopic analysis should be the optimal one to avoid contamination, as in the case of a tube used for the collection of biological material and used in clinical research, which should be specifically for the analysis of lipoproteins [59].
\nAlso, the experimental conditions do not always favor the use of NMR for the analysis of lipoproteins. Thus, mass spectrometry permits the particle identification via LC-MS system in contrast to NMR spectroscopy, which failed. Due to that, the NMR spectroscopy makes HDL particle quantification only in a physiological setting: full serum or plasma but not in HDL-containing suspensions [49]. Therefore, the technician must consider the biological and technical variables for an assertive lipoproteomic analysis.
\nThe lipoproteomic analyses have been focused on understanding the functional mechanisms underlying apolipoproteins and lipoprotein-associated proteins that can be used to develop new diagnostic and/or prognostic biomarkers for many lipoproteins’ metabolism-related illness.
\nThe HDL lipoprotein fraction has been the most studied according to lipoproteomic relationships with different diseases. Among the HDL-associated proteins, ApoC-III levels have been seen increased in the patients with either a lupus nephritis (lupus erythematosus) or with a cerebral lacunar infarction, which could be related with a reduced anti-inflammatory activity of HDL particles [65, 66].
\nHowever, HDL-carried ApoC-III has been more implicated in cardiovascular disease (CVD) risk. One of the first evaluations was performed with coronary artery disease patients, which exhibit increased levels of ApoC-III [67]. Recently, in a cohort study, it was demonstrated that HDL-carried ApoC-III is related to a higher risk for coronary heart disease [68].
\nIn addition to ApoC-III and ApoC-II, other HDL-associated apolipoproteins, were proposed as biomarkers for CVD risk in patients with chronic hemodialysis [69]. The ApoC-III/ApoC-II/ApoE levels in VLDL lipoproteins, independent of HDL, were associated with incident CVD, which supports the concept of targeting triacylglycerol-rich lipoproteins to reduce the CVD risk [70].
\nOther HDL-associated apolipoproteins have been associated with cardiac pathologies. For example, ApoA-I, ApoA-IV, ApoE, and ApoL1 levels have been seen enriched in HDL3 fraction from patients with acute coronary syndrome (ACV), with a concomitant reduction of these apolipoproteins in the HDL2 fraction [71]. Furthermore, ApoC-I was significantly decreased in the HDL particles of coronary heart disease (CAD) patients in comparison to normal individuals [72]. The existence of CAD has recently been correlated with an HDL apolipoproteomic score, independent of circulating ApoA-I and ApoB rates and other typical cardiovascular risk factors [73].
\nRegarding HDL-associated proteins, many of them have been related to heart illness. For example, serotransferrin, haptoglobin, hemopexin, complement factor B, ras-related protein Rab-7b, and paraoxonase-3 (PON3) levels have been seen reduced in HDL from patients with some cardiac pathology. Meanwhile, PON1, alpha-1B-glycoprotein, vitamin D-binding protein, alpha-1-antitrypsin (A1AT), acid ceramidase, serum amyloid A and P proteins, sphingosine-1-phosphate, filamin A, and pulmonary surfactant-associated protein B are increased in HDL fractions from patients with cardiometabolic disorders [69, 71, 72, 74, 75].
\nDiabetes is, perhaps, the major controllable risk factor for CVD. In particular, related to the HDL fraction, ApoA-I, ApoA-II, ApoA-IV, ApoE, ApoJ, as well as PON1, transthyretin, complement C3, and vitamin D-binding protein have shown changes in patients with type 2 diabetes (T2D) [76, 77]. In contrast, individuals with type 1 diabetes (T1D) had proteomic alterations of their HDL particles. For example, the complement factor H-related protein 2 was elevated, independent of glucose control, in T1D patients in comparison to healthy controls. Also, the optimal glucose control corrected the elevated levels of the alpha-1-beta glycoprotein and inter alpha trypsin inhibitor 4. Furthermore, the HDL particles in T1DM individuals, independent of glucose control, exhibit a higher abundance of irreversible post-translational modifications of HDL-associated apolipoproteins [78, 79].
\nAlso, in psoriatic patients, the levels of HDL-associated ApoA-I exhibit a significant reduction, whereas levels of apoA-II, serum amyloid A, C3, and A1AT, among other proteins were increased [80]. Besides C3 and C9, complement factor B, as well as ApoJ, fibrinogen, haptoglobin, and serum amyloid A have been also increased in HDL fraction from patients with rheumatoid arthritis [81]. Taken together, these results suggest that HDL-associated proteins could be involved in anti-inflammatory properties in chronic illness.
\nConcerning other diseases, the proteome of HDL particles has been used to identify protein markers. In nonalcoholic fatty liver disease, including individuals with nonalcoholic steatohepatitis, changes in the abundance of HDL-associated proteins such as antithrombin III and plasminogen have been identified [82]. Although not directly to HDL particles, proteomic analysis of some HDL-related apolipoproteins has been associated with viral diseases. For example, a change in the expression level of ApoA-I was suggested as a specific and appropriate alternative to conventional HIV diagnosis and progression measurements in clinical research settings [83]. Increased concentration of ApoM in sera patients with HBV infection have been detected [84]. Recently, a downregulation of ApoA-I and ApoM levels have been associated with the severity of COVID-19 infection [85].
\nIn addition to HDL, only other few lipoproteomic studies have been developed to discover changes in lipoproteins-associated proteins in diverse pathologies. Proteomic studies of LDL have reported that carry apolipoproteins AI, A-II, CI, C-II, C-III, C-IV, D, E, and F, in addition to apoB, as well as clusterin, C3, C4a, and C4b, and PON1 that are also associated to this particles [86]. Serum amyloid A levels were found to increase in all lipoprotein fractions, especially in LDL from atherosclerotic patients [24]. An enriched content of all the apoC-III isoforms and a lower content of apoA-I, apoC-I, and apoE were detected in the sdLDL from subjects with metabolic syndrome and subclinical peripheral atherosclerosis [87]. VLDL, IDL, and LDL fractions from Alzheimer patients exhibit low levels of complement C3 [88].
\nTarget discovery, which is an important step in the drug development process, includes discovering and validating targets associated with the diseases. It is increasingly recognized that, in many instances, metabolomics is used to identify novel biomarkers, which can help in the discovery of therapeutic strategies for many diseases [89].
\nUntil now, lipoproteomics has proved to be an effective method for identifying candidate cardiovascular disease markers. Studying the profiles of protein expression in drug-treated patients contributes to the discovery of multiple drug-specific targets. Traditionally, statin therapy has proven efficacy in reducing cardiovascular events, but as aforementioned, the identification of HDL-associated proteins is variable. The statin therapy has a notorious effect on the increment of A1AT associated to the large fraction of HDL particles enhancing its anti-inflammatory functionality, which may interfere with the statins outcome on reducing cholesterol levels [37]. In addition, a relationship between the CAD treatment and the HDL proteome was demonstrated using statin and niacin therapy, observing that ApoE and ApoC-II levels are enriched in the HDL3 fraction, which contains less ApoJ levels [90].
\nThe increment in the HDL levels by CETP inhibitors is another biomarker that has continued to be disappointing in clinical research. In fact, in patients with deficiency of CETP (CETP-D), the HDL particles are enriched with ApoE, angiopoietin-like3 protein, and complement regulatory proteins such as C3, C4a, C4b, and C9 that could be associated to the increased atherogenic profile in CETP-D patients [91].
\nThus, it is important to consider that among the diverse HDL populations not all possess a cardioprotective effect [92]. Therefore, we need a better knowledge of the protein cargo of the HDL populations with anti-atherogenic actions. A comprehensive understanding of the HDL proteomics can lead to the design of more effective anti-atherogenic drugs based on the activity of HDL-associated proteins that will provide new therapeutic strategies at the molecular level.
\nIn the dynamic drug discovery process, the different proteomic methods, which include MS-proteomics, expand beyond the general aim of target drug discovery. It must consider the drug-protein interactions as well as elucidate the mode of action of candidate drug molecules [93]. Thus, novel HDL-based therapeutic agents, besides the traditional statins, will need consider to the functional HDL lipoproteomics to characterize the interaction of the drug with the HDL-associated proteins. This is necessary as an attempt either to elucidate the mechanism of action by direct drug-protein interaction or as a biomarker for drug validation by monitoring the pharmacological effect through an increase of HDL populations with an anti-atherogenic protein cargo.
\nThe lipoproteins can be involved in different pathologies related to lipid metabolism such as atherosclerosis, cardiovascular risk, obesity, metabolic síndrome, and diabetes, among others. However, the protein cargo of these particles has been associated with several functions, which differ from the amply recognized as structural composition and receptors recognition during their function as lipid transporters. For this reason, the identification of variants of apolipoproteins and lipoprotein-associated proteins has been an important referent to detect new biomarkers. In fact, as we described here, several methodologies have been developed to improve the lipoproteomic profile.
\nDespite these studies, the majority concord that both biological source and lipoprotein purification are important steps to avoid protein contamination, principally from samples that are used directly as serum or plasma. Also, the most used method to identify the lipoprotein-associated proteins is the mass spectrometry, but some limits are presented that depend on the platforms to apply this methodology. Also, it is important to highlight that not everyone has the facility to use this methodology and it is necessary to develop new methodologies to apply in clinical fields to ensure discoveries about these proteins both in new lipoproteins’ fractions and new diseases.
\nAlso, the lipoproteomic analyses could be a new clinical area to evaluate the therapy of the pathologies described here as prognostic analytes. In this sense, the HDL lipoproteomics is, perhaps, the more advanced field considering the evaluations of populations with statins’ treatment. However, novel HDL therapeutic agents must consider the functional lipoproteomics of these particles. Finally, these lipoproteomes can help us to describe the molecular mechanism to understand the interaction of apolipoproteins as well as the lipoprotein-carried proteins to support the other omics such as lipidomics and metabolomics.
\nThis work was supported by the Autonomous University of Mexico City (UACM) awarded to MEAS.
\nThe authors declare no conflict of interest.
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',metaTitle:"Terms and Conditions",metaDescription:"These terms and conditions outline the rules and regulations for the use of IntechOpen Website at https://intechopen.com and all its subdomains owned by Intech Limited located at 7th floor, 10 Lower Thames Street, London, EC3R 6AF, UK.",metaKeywords:null,canonicalURL:"/page/terms-and-conditions",contentRaw:'[{"type":"htmlEditorComponent","content":"By accessing the website at www.intechopen.com you are agreeing to be bound by these Terms of Service, all applicable laws and regulations, and agree that you are responsible for compliance with any applicable local laws. Use and/or access to this site is based on full agreement and compliance of these Terms. All materials contained on this website are protected by applicable copyright and trademark laws.
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