High-level Plasmodium falciparum sulfadoxine-pyrimethamine resistance with the concomitant occurrence of septuple haplotype in Tanzania Open Access
2015. 2015

Vito Baraka email@example.com
Tanga Research Centre
National Institute for Medical Research
P. O. Box 5004TangaUnited Republic of Tanzania

Deus S Ishengoma
Tanga Research Centre
National Institute for Medical Research
P. O. Box 5004TangaUnited Republic of Tanzania

Filbert Fransis
Tanga Research Centre
National Institute for Medical Research
P. O. Box 5004TangaUnited Republic of Tanzania

Daniel T R Minja
Tanga Research Centre
National Institute for Medical Research
P. O. Box 5004TangaUnited Republic of Tanzania

Rashid A Madebe
Tanga Research Centre
National Institute for Medical Research
P. O. Box 5004TangaUnited Republic of Tanzania

Deogratius Ngatunga
Tanga Research Centre
National Institute for Medical Research
P. O. Box 5004TangaUnited Republic of Tanzania

Jean-Pierre Van Geertruyden
Tanga Research Centre
National Institute for Medical Research
P. O. Box 5004TangaUnited Republic of Tanzania

High-level Plasmodium falciparum sulfadoxine-pyrimethamine resistance with the concomitant occurrence of septuple haplotype in Tanzania Open Access

Malar J
144392015. 201510.1186/s12936-015-0977-8RESEARCH This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. *Correspondence: Full list of author information is available at the end of the article Page 2 of 9Plasmodium falciparumSingle nucleotide polymorphismsSulphadoxine-pyrimethamineMolecular markersSeptuple PfdhfrPfdhpsTanzania
Background: Tanzania abandoned sulfadoxine-pyrimethamine (SP) as the first-line treatment for uncomplicated malaria in 2006 due to high levels Plasmodium falciparum resistance. However, SP is still being used for intermittent preventive treatment during pregnancy (IPTp-SP). This study aimed to assess the pattern of P. falciparum dihydrofolate reductase (Pfdhfr) and dihydropteroate synthetase (Pfdhps) mutations and associated haplotypes in areas with different malaria transmission intensities in mainland Tanzania, 6 years after withdrawal of SP as a first-line treatment regimen for uncomplicated malaria.Methods: A total of 264 samples were collected during cross-sectional surveys in three districts of Muheza, Muleba and Nachingwea in Tanga, Kagera and Lindi regions, respectively. Parasite genomic DNA was extracted from P. falciparum positive samples. The Pfdhfr, Pfdhps single nucleotide polymorphisms (SNPs) were amplified using nested polymerase chain reaction and detected by sequence specific oligonucleotide probe-enzyme linked immunosorbent assay (SSOP-ELISA).Results:The prevalence of the mutant Pfdhfr-Pfdhps haplotypes was heterogenous and transmission dependent. The triple Pfdhfr mutant haplotypes (CIRNI) were predominant in all sites with significantly higher frequencies at Muheza (93.3 %) compared to Muleba (75.0 %) and Nachingwea districts (70.6 %), (p < 0.001). Overall, the prevalence of the wild-type Pfdhps (SAKAA) haplotype was lowest at Muheza (1.3 %), (p = 0.002). Double Pfdhps haplotype SGEAA was significantly high at Muheza (27.2 %) and Muleba (20.8 %) while none (0 %) was detected at Nachingwea (p < 0.001). The prevalence of triple Pfdhps SGEGA haplotype was significantly higher at Muheza compared to Muleba and Nachingwea (p < 0.001). In contrast, Nachingwea and Muleba had significantly higher prevalence of another triple Pfdhps AGEAA haplotype (χ 2 = 39.9, p < 0.001). Conversely, Pfdhfr-Pfdhps as quintuple and sextuple haplotypes were predominant including the emergence of a septuple mutant haplotype CIRNI-AGEGA (n = 11) observed at Muheza and Muleba.Conclusion:These results ascertain the high prevalence and saturation of Pfdhfr and Pfdhps haplotypes conferring SP resistance in areas with changing malaria epidemiology; and this could undermine the use of IPTp-SP in improving pregnancy outcomes. In these settings where high level SP resistance is documented, additional control efforts are needed and evaluation of an alternative drug for IPTp is an urgent priority.

Keywords: Plasmodium falciparum, Single nucleotide polymorphisms, Sulphadoxine-pyrimethamine, Molecular markers, Septuple Pfdhfr, Pfdhps, Tanzania dihydropteroate synthetase (DHPS) enzymes [13,14]. Notably, several single nucleotide polymorphisms (SNPs) in the Pfdhps gene at codons Serine 436 to Alanine (S436A), Alanine 437 to Glycine (A437G), Lysine 540 Glutamic acid (K540E), Alanine 581 to Glycine (A581G) and Alanine 613 to Serine (A613S) are associated with sulfadoxine resistance. Pyrimethamine resistance is conferred by mutations in the Pfdhfr gene resulting from amino acid substitution at codons Cysteine 50 to Arginine (C50R), Asparagine 51 to Isoleucine (N51I), Cysteine 59 to Arginine (C59R), Serine 108 to Asparagine/Threonine (S108 N/T), and Isoleucine 164 to Leucine (I164L) [15,16]. Emergence and subsequent accumulation of the mutations in both the Pfdhfr/Pfdhps genes is associated with SP clinical TF in several epidemiological settings [11,[17][18][19]. The major Pfdhfr haplotypes emerge as a result of combination of mutations of the wildtype cysteine-asparagine-cysteine-asparagine-isoleucine (CNCNI) followed by the gradual changes resulting to cysteine-isoleucinecysteine-asparagine-isoleucine (CICNI), cysteineasparagine-arginine-asparagine-isoleucine (CNRNI), cysteine-isoleucine-arginine-asparagine-isoleucine (CIRNI) and cysteine-isoleucine-arginine-asparagineleucine (CIRNL) as a single, double, triple and quadruple mutants, respectively (at amino acid positions C50R, N51I, C59R, S108N, and L164I) [20,21]. For Pfdhps, the wild type genotype serine-alanine-lysine-alanine-alanine (SAKAA) can change to a single alanine-alanine-lysinealanine-alanine or serine-glycine-lysine-alanine-alanine (AAKAA or SGKAA), double (AGKAA, SGKGA or SGEAA) or triple mutants (AGEAA or SGEGA, at amino acid positions S436A, A437G, K540E, A581G and A613S/T) [14,22,23]. Quintuple mutations as a result of the combination of Pfdhfr triple (CIRNI) and the Pfdhps double (A437G, K540E) mutations were reported in East Africa [24,25] and have been shown to confer high level SP resistance rendering SP ineffective for treatment of P. falciparum infections. In northeastern Tanzania, the rise in Pfdhps mutations at codons A581G has led to higher proportions of SGEGA haplotypes [26]. Combined Pfdhfr-Pfdhps SGEGA-CIRNI forming the sextuple haplotype has been associated with sub-optimal prophylactic effect and thus poor pregnancy outcomes particularly in areas where SP resistance is widespread in sub-Saharan Africa [17,18,27].

In fact, WHO recommends stopping IPTp-SP in areas where the Pfdhfr K540E prevalence is >95 % and Pfdhps A581G is >10 % as SP is likely to be ineffective [28]. The

Background

Plasmodium falciparum malaria remains a major public health problem in the sub-Saharan Africa (SSA). Increased global efforts in malaria control and elimination have resulted into significant reduction in the disease burden through scaling up of control interventions such as use of insecticide-treated nets (ITNs), indoor residual spraying (IRS) and early case diagnosis and prompt treatment using effective anti-malarial drugs [1]. However, malaria control programmes are repeatedly challenged by rapid and widespread anti-malarial drug resistance [2][3][4]. Following the widespread drug resistance to sulfadoxine-pyrimethamine (SP) and chloroquine (CQ) [5,6], the World Health Organization (WHO) recommended a policy change from monotherapy to artemisinin-based combination therapy (ACT) [7]. Despite policy changes as a result of widespread drug resistance against SP, the drug is still being recommended for intermittent preventive treatment during pregnancy (IPTp-SP) whereby in areas of moderate to high transmission SP dose is given at each scheduled antenatal care (ANC) visit at least monthly to prevent pregnancy associated malaria (PAM) and improve pregnancy outcomes [8]. In addition, SP is still being used in the IPT in infants (IPTi-SP) to reduce malaria and anaemia among infants as well as seasonal malaria chemoprevention (SMC) programmes in some malaria endemic settings [9]. Nonetheless, the chemoprophylactic effectiveness of IPTp-SP, IPTi-SP and SMC-SP strategies against malaria control amongst the most vulnerable is increasingly being compromised due to the rapid and widespread SP-resistance.

In Tanzania, SP was introduced as the first-line antimalarial drug for treatment of uncomplicated falciparum malaria in 2001 as a result of high level chloroquine (CQ) resistance (CQR) and clinical treatment failures (TFs) [10]. However, 5 years after its introduction, the policy was again revised in November 2006 due to widespread resistance against SP and remarkable TFs [4,11]. Thus, artemether-lumefantrine (AL), an ACT, was introduced as the first-line treatment for uncomplicated falciparum malaria in the mainland Tanzania [12]. The changes in malaria treatment policies were supported by data from molecular epidemiological resistance surveillance against CQ and SP. The in vivo molecular surveillance ascertained the treatment failures (TFs) [4,11] with P. falciparum resistance against CQ and SP in clinical settings.

Sulfadoxine-pyrimethamine acts by inhibiting the P. falciparum dihydrofolate reductase (DHFR) and widely spread highly resistant haplotypes including quintuple (CIRNI-SGEAA) and sextuple (CIRNI-SGEGA) are likely to compromise effectiveness of IPTp-SP strategy. Continuous surveillance of SP effectiveness using molecular markers is therefore, critical and should be routinely implemented as recommended by WHO [29].

Determination of molecular markers in the Pfdhfr and Pfdhps genes offers an invaluable tool for epidemiological surveillance of SP resistance in malaria endemic settings and will generate important data to assist and inform malaria control programmes on the status of resistance particularly due to emergence and rapid spread of highly resistant mutations and haplotypes that may highly compromise the usefulness of IPT strategies. This study aimed to assess the status of Pfdhfr-Pfdhps mutations and haplotypes in areas with different malaria transmission intensities in mainland Tanzania, 6 years after withdrawal of SP as first line drug for treatment of uncomplicated falciparum malaria.

Methods

Study sites

The samples were collected from cross-sectional surveys at three sites with heterogeneous malaria transmission intensities in Tanzania from May to August 2013. The sites included Mkuzi health Centre in Muheza District in Tanga Region, Rubya Designated District Hospital (DDH), Muleba District in Kagera Region and Nachingwea District hospital in Lindi Region. The Regions are located in north-eastern, north-western and southern zones of mainland Tanzania, respectively (Fig. 1). The study districts were selected based on evidence from the Tanzania HIV and Malaria Indicator Survey of 2012 and represent areas with low, moderate and high malaria transmission intensities [30].

Study design and sampling methods

Cross-sectional studies were carried out in the period May-August 2013. Briefly, patients aged ≥6 months who presented at the health facilities with uncomplicated or complicated malaria were enrolled. Patients with history of fever in the past 24 h or fever at presentation (temperature ≥37.5 °C) were screened for malaria and their demographic, clinical and parasitological information were collected. Laboratory screening for malaria was done using rapid diagnostic tests (RDT) and confirmed using microscopic examination of blood smears. Venous blood samples (3-5 ml) were collected into EDTA tubes for genomic analysis of malaria parasites. Thick and thin blood smears were prepared for detection and quantification of malaria parasites. The smears were air-dried and the thin films were fixed with methanol prior to staining using 3 % Giemsa stain for 45 min. Slides were examined microscopically to count asexual or sexual parasites per 200 white blood cells (WBCs) or 500WBCs, respectively. A slide was considered negative if no parasite was detected after examining 200 fields. Parasite density was calculated by multiplying the number of asexual parasites by 40 and sexual parasites by 16 assuming that one microliter of blood contained 8000 WBCs. Haemoglobin measurement was done using a Hemocue ® machine (HemoCue, Ångelholm, Sweden). All patients who tested microscopically positive for malaria were treated as per national guidelines for treatment of malaria [31]. Global positioning system (GPS) coordinates were recorded using a handheld GPS device. In Muheza District, Tanga Region, samples were also collected from a clinical trial evaluating the efficacy of AL as per study protocol [32].

DNA extraction

Blood smear microscopically positive P. falciparum infected blood samples were used for DNA extraction. The samples were depleted of the white blood cells (WBCs) using a CF11 cellulose columns as previously described [33]. Genomic DNA was extracted using the QIAamp DNA Blood Midi (Qiagen GmbH, Hilden, Germany) as per manufacturer's instructions.

Genotyping for Pfdhps and Pfdhfr mutations

The SNPs in the Pfdhfr and Pfdhps genes were detected by a combination of nested PCR and sequence specific oligonucleotide probe (SSOP)-ELISA analysis as previously described [34]. The SNPs analysed were Pfdhfr at codons C50R, N51I, C59R, S108N, and L164I, and Pfdhps at codons S436A, A437G, K540E, A581G and A613S.

Ethical issues

The studies which contributed specimens were approved by the Tanzanian Medical Research Coordinating Committee of (MRCC) hosted by the National Institute for Medical Research, Tanzania (NIMR). Permission to conduct the study in the three districts was obtained from the relevant regional and district authorities while the heads of the health facilities gave the permission to conduct the study at the respective sites. Prior to enrolment, written informed consent was obtained from each individual or from parents/legally acceptable representative in case of children.

Data analysis

Microsoft Access version 2007 and Excel databases were utilized for data management with double entry, validation and cleaning of the field and laboratory data. Statistical analyses were done using STATA version 11 (STATA Corp Inc., TX, USA). Characteristics of the study population for the different sites were tested by analysis of variance (ANOVA). The prevalence of the genotypes was calculated as the proportion of wild type, mixed or mutants in the total analysable samples. Mixed infections with wild-type and mutant alleles were treated as mutant while generating the haplotype prevalence. Prevalence of mutations and haplotypes between sites were compared using Chi-square or Fisher's exact test as appropriate. Statistical analyses were performed at the 5 % significance level and 95 % Confidence Interval (CI).

Results

A total of 264 samples from the three sites (Muheza, Muleba and Nachingwea) were included in the analysis with each site contributing 88 samples. The demographic and parasitological characteristics of the study population between the study sites were comparable ( Table 1). The study populations were similar with respect to age (χ 2 = 3.71, p = 0.16), gender (χ 2 = 1.92, p = 0.38) and the mean haemoglobin levels (g/dL) (F = 2.5, p = 0.08). However, the geometric mean parasite density was significantly lower at Muleba compared to Muheza and Nachingwea (F = 10.9, p < 0.001), (Table 1). Fever at health facility presentation (temperature ≥ 37.5 °C) was significantly higher at Muheza (χ 2 = 8.37, p = 0.02) compared to Muleba and Nachingwea. Only a few patients reported prior history of AL (n = 13) or SP (n = 2) usage 2 weeks prior to enrolment into the study.

Prevalence of SNPs associated with SP resistance in Plasmodium falciparum dhfr and dhps genes

Significantly higher prevalence of Pfdhfr mutations at codons C50/N51I was detected at Muheza (100 %) and Muleba (98.8 %) compared to Nachingwea (67.5 %), (p < 0.001). At Muheza, the Pfdhfr mutation at codon C59R had reached almost saturation level (96.6 %) while other sites, Muleba (73.1 %) and Nachingwea (81.8 %), had significantly lower prevalence (p < 0.001). For codon S108N, the prevalence was significantly higher at Muheza (98.7 %) and Nachingwea (95.5 %) compared to Muleba (83.3 %) (p = 0.003). The Pfdhfr mutation at codon I164L was not detected at any site.

For the Pfdhps, significantly high prevalence of parasite carrying mutant at codons S436A/A437G was detected (79.8 %) at Muheza as compared to Muleba (22.2 %) while Nachingwea had none (p < 0.001). However, Nachingwea had significantly higher prevalence (61.4 %) of double mutants S436A/A437G (p < 0.001). The prevalence of Pfdhps K540E at Muheza was significantly higher (95.4 %) compared to the other sites (p < 0.001). Another Pfdhps mutation also associated with high level resistance at codon A581G was significantly higher at Muheza (p < 0.001) reaching 48.9 % in comparison to Muleba (3.9 %) and Nachingwea at which the mutations was not detected. Low prevalence of Pfdhps A613S was detected at Muleba (2/77, 2.6 %) and Nachingwea (1/82, 1.2 %).

Prevalence of the major Plasmodium falciparum dhfr and dhps haplotypes

Similarly, the prevalence of double mutant haplotypes (CICNI, CNRNI and CIRSI) was low except for Muleba where a significantly higher prevalence (23.6 %) of double haplotype CICNI was reported compared to the other sites (p = 0.008). Overall, the triple Pfdhfr mutants (CIRNI) were predominant at all sites and almost near saturation at Muheza (93.3 %) but with significantly lower prevalence of and at Muleba (75 %) and Nachingwea (70.6 %), (p < 0.001).

The prevalence of wildtype Pfdhps haplotype (SAKAA) was significantly low at Muheza (1.3 %) compared to other sites (p = 0.003). Similarly, the three major single mutant haplotypes were also low ( Table 2). The double Pfdhps haplotype SGEAA was higher at Muheza (27.2 %) and Muleba (20.8 %) compared to Nachingwea (p < 0.001) while AAKAS was low at both site (p = 0.76). About 56 % of all isolates were triple mutants with Muheza having the highest prevalence of SGEGA haplotype compared to AGEAA which was more predominant at Muleba and Nachingwea (p < 0.001), ( Table 2). The Pfdhps quadruple haplotypes AGEGA were generally low and varied significantly between the sites, whereby eight isolates (10.4 %) were detected at Muheza, two at Muleba (5.6 %) and none was detected at Nachingwea (p = 0.003) ( Table 2).

Upon combination of the Pfdhfr-Pfdhps haplotypes (Table 3), quadruple mutant haplotypes with single Pfdhps and triple Pfdhfr mutation (AAKAA-CIRNI) was lowly distributed (n = 16) across all sites while the quintuple mutant haplotype, CIRNI-SGEAA was observed in only 31 isolates, at Muheza and Muleba. Sextuple CIRNI-SGEGA haplotypes (n = 32) were more predominant in Muheza whereas other sextuple haplotype combinations, (CIRNI-AGEAA (n = 69)) were mainly observed in Nachingwea. Interestingly, the emergence of the new septuple mutant haplotypes with three Pfdhfr and four Pfdhps mutant combination (CIRNI-AGEGA) was observed for the first time albeit in few samples (n = 11) and these were mainly from Muheza (n = 8) and (n = 3) were from Muleba whereas none was noted at Nachingwea. The occurrences of other Pfdhps-Pfdhfr haplotypes were generally low (Table 3).

Discussion

The rapid and widespread anti-malarial drug resistance has necessitated frequent revisions of malaria treatment guidelines in P. falciparum malaria endemic regions. The emergence of super resistant mutations, such as the sextuple Pfdhfr/Pfdhps haplotypes has not only compromised malaria case management and treatment outcomes but also affected the effectiveness of the IPT strategies.

Few years after its adoption as the first-line treatment drug for uncomplicated malaria in Tanzania, several efficacy studies detected unacceptably high levels of molecular markers of parasite resistance to SP [4,35]. Despite its replacement by ACT, SP continued to be used in the IPT strategies which is likely to provide sub-optimal effect and, therefore, monitoring the spread of resistance using molecular markers (Pfdhfr/Pfdhps) is still recommended [36][37][38]. In this study, the prevalence of A581G was almost 50 % in Muheza and this was significantly higher compared to Muleba (~4 %) and Nachingwea (0 %). This could confirm the earlier suggestions that this "super resistant" mutation may have originated in the northeastern part of Tanzania and spread to other areas albeit at low prevalence [37]. A remarkable difference was observed in the prevalence of Pfdhps SNPs between the study sites with Muheza having the highest levels of Pfdhps 436/437SG, A581G, A540E SNPs and haplotypes compared to  Muleba and Nachingwea. As expected areas with high malaria transmission intensities in Muleba and Nachingwea had higher prevalence of Pfdhps wild types and single mutant 436/437AG as compared to Muheza in north eastern where malaria endemicity has declined remarkably in recent years [39,40]. The current WHO recommendations suggest that SP-IPTp should be discontinued if the prevalence of this double Pfdhps mutant, K540E is more than 95 % and the A581G is more than 10 % as it is considered to be ineffective [29]. Obviously, these criteria are still met in Muheza confirming the findings of previous studies conducted in north Eastern Tanzania [26]. In a cohort study conducted at Muheza, it was shown that IPTp-SP was associated with increased prevalence of parasites with mutations at codon A581G and that use of IPTp-SP conferred no benefit in improvement of pregnancy outcomes [17,37]. Of note, this area is known to be the major focus of Pfdhps A581G mutations in East Africa which is believed to occur almost exclusively with Pfdhfr K540E leading to double mutant haplotypes [41]. The high frequency of Pfdhps A581G at alarming level in this area clearly suggests for no beneficial protective effect from the IPTp-SP [17,18,42]. This higher prevalence in north eastern Tanzania could be explained by the sustained drug pressure due to self-medication that could have elevated levels of SP resistance, Ringsted et al. [43] reported 76 % volume sales of SP in private drug shops in this areas. Additionally, the high prevalence levels could be maintained due to selective pressure on Pfdhfr and Pfdhps as a result of co-trimoxazole (trimethoprimsulfamethoxazole), another antifolate, used to prevent opportunistic infections in HIV infected individuals as cross-resistance might also occur [44,45]. The prevalence of the Pfdhfr-Pfdhps wild type haplotypes was low in all sites. The prevalence of mutant genotypes C51I, was at saturation in Muheza (100 %) and equally at Muleba (98.8 %) with significantly lower prevalence (67.5 %) at Nachingwea (p < 0.001). Similarly, almost complete saturation (>96 %) of other mutations in Pfdhfr (N51I, C59R, and S108 N) was observed at Muheza with marked differences between Muleba and Nachingwea ( Table 2). In contrast, some previous studies have shown that other major resistance mutations in Pfdhfr are well established throughout the country where the Pfdhfr triple mutations (51I, 59R and 108N) were above 84 % and close to saturation in six regions of Tanzania [46]. Elsewhere, these data are in consistent with studies in West Africa where Pfdhfr N51I, N59R, and S108N have been shown to rise [19]. The prevalence of I164L mutation conferring high pyrimethamine resistance was not detected at all the three sites which is in accordance to previous reports elsewhere [47]. However, in other parts of Africa, only a few studies have reported occurrence of this high level mutation [15,16,48]. Of note, the Pfdhfr I164L mutation was first documented at low prevalence in Muheza in 1999 before the deployment of IPTp-SP [49] and later one isolate (mixed allele) was reported in Rufiji [50], but to date its presence has rarely been reported in the country.

The CIRNI haplotype with triple mutations was present and highly prevalent at all study sites regardless of the transmission intensity. This is in line with several studies in Tanzania and elsewhere in the SSA [51,52]. The CIRNI haplotype is associated with high level resistance to pyrimethamine and increases the risk of SP resistance if it occurs concurrently with Pfdhps mutations [21]. The increased double mutant Pfdhps SGEAA haplotype was observed in Muheza (28 %) and Muleba (22 %), but not in Nachingwea (0 %). In Muheza, the highly resistant triple SGEGA Pfdhps haplotypes was observed, (38.7 %).

The combinations of Pfdhfr-Pfdhps were detected at higher numbers including quintuple mutant CIRNI-SGEAA, sextuple haplotype which comprise triple mutations in both genes CIRNI-SGEGA and CIRNI-AGEGA have been highly associated with sub-optimal IPTp-SP effectiveness in previous studies [17]. Interestingly, a septuple mutant haplotype CIRNI-AGEGA was observed which had not been previously reported in the study areas. From these observations, it is apparent that these mutant haplotypes associated with poor IPTp-SP are expanding in different epidemiological settings. Of worth noting, this study was not designed to correlate the clinical data with observed resistance pattern. Nonetheless previous literature showing the association of these haplotypes with clinical or treatment outcomes have been noted.

Ideally, in order to reduce the sustained drug pressure, efforts are required to limit the use of SP for IPTp purposes only through limiting over the counter SP prescriptions. Also the availability of ACT for the treatment of uncomplicated malaria and proper implementation of the national malaria treatment guidelines would also contribute in the reducing the selection pressure. However, alternative drugs for IPTp are urgently needed to replace the failing SP due to the saturation of the parasite population with Pfdhps-Pfdhfr mutations and haplotypes highly associated with SP resistance. Deployment of the suboptimal IPTp-SP strategy is therefore unlikely to confer the anticipated effect on improving pregnancy outcomes.

Conclusion

These results ascertain the high prevalence of Pfdhfr and Pfdhps haplotypes conferring SP resistance in areas with changing malaria epidemiology. The high prevalence of Pfdhfr-Pfdhps sextuple mutant haplotypes and the occurrence of the new septuple mutant haplotype undermine the usefulness of IPTp-SP. Thus, additional control efforts are needed to contain the spread of SP resistance and suitable alternative drugs for IPTp are urgently needed.

Fig. 1
1The map of Tanzania showing the study site and neighbouring countries

Table 1
1Demographic and parasitological characteristics of the study population in TanzaniaSignificant values are in boldface IQR inter-quartile range, GMPD geometric mean parasite density, asexual parasites/µL of blood a One way ANOVA was used to test the differences between the study sitesParameters
Study site

Table 2
2P. falciparum dihydrofolate reductase (Pfdhfr) and dihydropteroate synthetase (Pfdhps) haplotypes by study sitesBold underline indicates the amino acid changes n number of haplotypes excluding the mixed infections/genotypes * Pearson Chi-square test was applied (χ2 = 39.9, p < 0.001), all others were compared using Fisher's exact testGene haplotype
Prevalence of haplotypes by sites

Muheza (n = 77)
Muleba (n = 72)
Nachingwea (n = 85)
P value

Pfdhfr
CNCSI
0 (0)
0 (0)
3 (3.5)
0.109

CICNI
4 (5.2)
17 (23.6)
7 (8.4)
0.002

CIRSI
1 (1.3)
0 (0)
0 (0)
0.642

CNRNI
0 (0)
1 (1.4)
15 (17.7)
<0.001

CIRNI
72 (93.3)
54 (75.0)
60 (70.6)
<0.001

n = 77
n = 72
n = 82

Pfdhps
SAKAA
1 (1.3)
11 (15.3)
11 (16.5)
0.003

AAKAA
4 (5.2)
4 (5.6)
16 (18.8)
0.008

SAKAS
0 (0)
1 (1.4)
0 (0)
0.308

SAEAA
0 (0)
4 (5.6)
0 (0)
0.008

AAKAS
0 (0)
1 (1.4)
1 (1.2)
0.76

SGEAA
21 (27.2)
15 (20.8)
0 (0)
<0.001

SGEGA
31 (40.3)
2 (2.8)
0 (0)
<0.001

AGEAA
12 (15.6)
30 (41.7)
54 (63.5)
<0.001*

AGEGA
8 (10.4)
4 (5.6)
0 (0)
0.003

Table 3
3The pattern of combined P. falciparum dhfr-dhps haplotypesThe overall numbers (absolute) parasite haplotypes on the Pfdhfr-Pfdhps genes in the samples successfully analysed (n = 234). Of all the combined haplotypes, the
quintuple CIRNI-SGEAA, sextuple CIRNI-AGEAA, CIRNI-SGEGA were more frequently detected

Bold underline indicates the amino acid changes, while SAKAA and CNCSI represent the wildtype haplotype for Pfdhps and Pfdhfr, respectively

Pfdhps haplotypes
Pfdhfr haplotypes
Total

Wild-type
Double
Triple

CNCSI
CICNI
CIRSI
CNRNI
CIRNI

Wildtype
SAKAA
0
4
0
2
20
26

Single
AAKAA
1
3
0
4
16
24

SAEAA
0
0
0
0
4
4

SAKAS
0
0
0
0
1
1

Double
SGEAA
0
5
0
0
31
36

AAKAS
0
0
0
0
2
2

Triple
SGEGA
0
1
0
0
32
33

AGEAA
2
14
1
10
69
96

Quadruple
AGEGA
0
1
0
0
11
12

Total
3
28
1
16
186
234

AcknowledgementsWe thank Lydia Lugomora (NIMR), Gwen Lemey and Jan Vervoort (Universiteit Antwerpen, Belgium), Michael Alifrangis, Ulla Abildtrup and Christentze Schmiegelow (CMP-Denmark) for their logistical assistance. Our thanks also extend to the staff at AMBRELA laboratory who participated in field data collection and data management. The study was supported by the South Initiative Grant (Code ZAIN2014Z168) and ICP PhD Grant by the VLIR UOS, Brussels, Belgium (for the laboratory component) while the sample and data collection was supported by the UK Medical research Council (MRC) (G0600718-Centennial award).Authors' contributions VB, RAM, DSI and JPG designed the study; VB, RAM, DN, DSI and JPG performed the research; VB, RAM, DTRM and FF analysed data; all authors contributed to the writing of the paper. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.
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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. G Mombo-Ngoma, S Oyakhirome, R Ord, J J Gabor, K C Greutélaers, K Profanter, Malar J. 10123High prevalence of dhfr triple mutant and correlation with high rates of sulphadoxine-pyrimethamine treatment failures in vivo in Gabonese children. Submit your manuscript at www.biomedcentral.com/submitMombo-Ngoma G, Oyakhirome S, Ord R, Gabor JJ, Greutélaers KC, Pro- fanter K, et al. High prevalence of dhfr triple mutant and correlation with high rates of sulphadoxine-pyrimethamine treatment failures in vivo in Gabonese children. Malar J. 2011;10:123. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit


Reliable Metabolic Flux Estimation in Escherichia coli Central Carbon Metabolism Using Intracellular Free Amino Acids
2014

Nobuyuki Okahashi
Department of Bioinformatic Engineering
Graduate School of Information Science and Technology
Osaka University
1-5 Yamadaoka565-0871SuitaOsakaJapan

Shuichi Kajihata email@example.com.
Department of Bioinformatic Engineering
Graduate School of Information Science and Technology
Osaka University
1-5 Yamadaoka565-0871SuitaOsakaJapan

Chikara Furusawa email@example.com.
Department of Bioinformatic Engineering
Graduate School of Information Science and Technology
Osaka University
1-5 Yamadaoka565-0871SuitaOsakaJapan

Quantitative Biology Center
RIKEN
6-2-3 Furuedai565-0874SuitaJapan

Hiroshi Shimizu firstname.lastname@example.com
Department of Bioinformatic Engineering
Graduate School of Information Science and Technology
Osaka University
1-5 Yamadaoka565-0871SuitaOsakaJapan

Reliable Metabolic Flux Estimation in Escherichia coli Central Carbon Metabolism Using Intracellular Free Amino Acids

Metabolites
4201410.3390/metabo4020408Received: 17 March 2014; in revised form: 10 May 2014 / Accepted: 20 May 2014 /* Author to whom correspondence should be addressed; OPEN ACCESS Metabolites 2014, 4 409metabolic flux analysisintracellular free amino acidsEscherichia coliGC-MS
13 C metabolic flux analysis (MFA) is a tool of metabolic engineering for investigation of in vivo flux distribution. A direct 13 C enrichment analysis of intracellular free amino acids (FAAs) is expected to reduce time for labeling experiments of the MFA. Measurable FAAs should, however, vary among the MFA experiments since the pool sizes of intracellular free metabolites depend on cellular metabolic conditions. In this study, minimal 13 C enrichment data of FAAs was investigated to perform the FAAs-based MFA. An examination of a continuous culture of Escherichia coli using 13 C-labeled glucose showed that the time required to reach an isotopically steady state for FAAs is rather faster than that for conventional method using proteinogenic amino acids (PAAs). Considering 95% confidence intervals, it was found that the metabolic flux distribution estimated using FAAs has a similar reliability to that of the PAAs-based method. The comparative analysis identified glutamate, aspartate, alanine and phenylalanine as the common amino acids observed in E. coli under different culture conditions. The results of MFA also demonstrated that the 13 C enrichment data of the four amino acids is required for a reliable analysis of the flux distribution.

Introduction

Metabolic flux analysis (MFA) is a tool driving metabolic engineering through a detailed understanding of intracellular carbon flux distributions in various organisms [1][2][3][4][5]. The level of flux in each reaction is estimated by a tracer labeling experiment using, such as 13 C-labeled glucose, as the labeling patterns of the intracellular metabolites reflect the flux distribution inside the cells [6][7][8][9]. In conventional MFA, the flux distribution is estimated from the 13 C enrichment of proteinogenic amino acids (PAAs) determined by gas chromatography-mass spectrometry (GC-MS) or nuclear magnetic resonance (NMR) [10,11]. Whereas a large amount of PAAs in the cells is preferable for a precise determination of the 13 C enrichment, the slow turnover of PAAs has restricted MFA applications, since a relatively long experimental time, for example, 5 residence time in continuous culture, is required for the complete labeling of the PAAs.

For the MFA study of batch and fed-batch culture, intracellular free amino acids (FAAs) with faster turnover rates are more promising targets for a 13 C enrichment measurement [12][13][14][15][16]. The time course analysis of 13 C enrichment of FAAs demonstrated that the experimental time required to reach an isotopic steady state of the FAAs is 2 to 10 times faster than that for PAAs [17]. However, the smaller pool sizes and large compositional variations of FAAs are expected to affect the design of an MFA experiment [18]. This is because a confidence interval of flux estimation depends on a set of measureable FAAs. The relationship between the set of measurable data and resulting confidence intervals are not well investigated from the aspect of experimental data. The large sampling volume of broth required for broader amino acid coverage would disturb a constant culture condition. Understanding the required dataset for reliable MFA will be a useful guideline for the design of MFA experiments using FAAs.

In this study, minimal 13 C enrichment data of FAAs to perform the FAAs-based MFA was investigated by the analysis of Escherichia coli. It was realized that four amino acids, i.e., glutamate (Glu), aspartate (Asp), alanine (Ala), and phenylalanine (Phe), are the most commonly observed in E. coli at distinct culture conditions. The results of MFA also demonstrated that the 13 C enrichment data of the four amino acids is required for a reliable analysis of the flux distribution.

Results and Discussion

Continuous Culture of E. coli MG1655

In this study, FAAs-based MFA was developed by using E. coli MG1655 strain. E. coli is one of the most widely-used model organisms in the fields of metabolic engineering and systems biology. In order to compare the PAAs-and FAAs-based methods, 13 C enrichment of PAAs and FAAs were determined for E. coli cells obtained from a same culture. The continuous culture of E. coli cells was performed at a dilution rate of 0.2 h −1 using a synthetic medium containing glucose. After 8 residence time, the metabolic steady state was confirmed from constant levels of OD 600 , concentration of O 2 and CO 2 in exhausted gas and concentration of organic acids in medium ( Figure S1). Specific uptake rate of glucose and the specific production rate of acetate, formate, lactate, and ethanol in the metabolic steady state were 3.41 ± 0.01, 1.13 ± 0.06, 0.98 ± 0.05, 0.09 ± 0.01, and 0.01 ± 0.01 mmol/g-DCW/h, respectively. After attaining the metabolic steady state, the carbon source in the feeding medium was changed from 100% natural glucose to a mixture of glucose containing 1.0% non-labeled glucose, 49.2% [1-13 C] glucose and 49.8% [U-13 C] glucose. The E. coli cells were repeatedly collected from the culture, and the 13 C enrichment of PAAs and FAAs were determined using the GC-MS analysis. Time course of 13 C enrichment of representative PAAs and FAAs are shown in Figure 1. While an isotopic steady state was attained at 25 h (5 residence time) after the start of 13 C labeling for the case of PAAs, the 13 C enrichment of FAAs reached plateau in 10 h (2 residence time). This result confirms that the FAAs-based method can reduce labeling time for MFA. Although the faster turnover rate for FAAs is preferable for an MFA study of batch and fed-batch culture [12][13][14][15], it remains unclear whether a reliable result can be produced by FAAs-based MFA. It is not also obvious which FAAs are reproducibly observed from E. coli cells in various culture conditions, and whether a precise metabolic flux can be estimated by using the observed amino acids.

Metabolic Flux Analysis

Performance of FAAs-based MFA method was investigated by comparison with that of PAAs-based method. For this purpose, a reference metabolic flux distribution was determined by the PAAs-based MFA. From the E. coli cells obtained at 25 h after the start of the tracer experiment, PAAs were prepared via acid hydrolysis of the proteins. The GC-MS analysis of the derivatized samples successfully determined the 13 C enrichment of 25 fragments derived from a total of 11 amino acids (Table 1). Using all of the 13 C enrichment data (PAAs_fullset), a reference metabolic flux distribution was estimated by a non-linear fitting to a metabolic model (Tables S1-1 and S1-2). The estimated and measured 13 C enrichment were almost similar to each other, indicating that a reasonable  Figure 3 shows the estimated fluxes in their representative reactions. The results of PAAs_fullset (a in Figure 3) show that 75% of glucose is catabolized via the glycolysis (PGI net flux) and 25% via the pentose phosphate (PP) pathway (G6PDH flux). The Entner-Doudoroff (ED) pathway is inactive in this condition. While a significant amount of pyruvate (Pyr) and acetyl-CoA (AcCoA) are secreted to the medium as formate and acetate, the remaining carbon flows into the TCA cycle. The flux distribution is essentially comparable to a previously reported result [17]. In this study, 95% confidence intervals of each flux were estimated by the grid search method (represented as error bars in Figure 3). The 95% confidence intervals of glycolysis/PP pathway branch point were deduced to be ± 3, and the flux intervals of upper glycolysis, PP pathway and ED pathway were estimated to be within ± 6. Relatively large 95% confidence intervals (± 13) were observed for fluxes in lower glycolysis, TCA cycle, glyoxylate shunt and anaplerosis.   Flux values are normalized to a glucose uptake rate of 100. Fluxes and 95% confidence intervals in representative reactions are shown. Full data is available from Tables S1-1, S2-1, S3-1, S4-1 and S5-1.

The arrow width represents the flux levels estimated by using the reference (PAAs_fullset) dataset.  From the same E. coli culture, a flux distribution was estimated using FAAs. Intracellular FAAs were extracted from the E. coli cells at 15, 20, and 25 h after the start of the tracer labeling. The 13 C enrichment of 19 fragments derived from 11 amino acids was determined with signal to noise ratio > 10 by GC-MS analysis (FAAs_fullset). A flux distribution was successfully estimated by a non-linear fitting of the metabolic model to the mean 13 C enrichment data (Figure 2b, Tables S2-1 and S2-2). As shown in Figure 3, the metabolic flux distribution estimated from FAAs data (FAAs_fullset, b in Figure 3) was quite similar to the reference data determined by PAAs-based MFA (PAAs_fullset, a in Figure 3).

An investigation of the continuous culture of E. coli using a simplified metabolic model demonstrated that PAAs-and FAAs-based MFA methods can produce compatible flux distributions [17]. The MFA performed in this study using the generally accepted metabolic model confirmed that essentially identical flux distributions are estimated from the 13 C enrichment data of PAAs (PAAs_fullset) and FAAs (FAAs_fullset), with the 95% confidence intervals of the two sets overlapping each other (Figures 2 and 3).

A comparison of the confidence intervals also reveals that a flux distribution estimated from the FAAs_fullset shows wider confidence intervals ( Figure 3). It is because the number of measurable fragments, amino acids and independent measurements of the FAAs_fullset (n = 66) is significantly less than that for the PAAs_fullset (n = 92, Table 1). The smaller amount of 13 C enrichment data cannot be overlooked in the FAAs-based method when directly analyzing FAAs with low concentrations.

Combination of Amino Acids for Reliable FAAs-Based MFA

Measurable FAAs should vary among the FAA-based MFA experiments since the pool sizes of intracellular free metabolites depend on cellular metabolic conditions. For instance, it was reported that the pool size of FAAs in E. coli ranged from 2 × 10 −2 to 80 µmol/g-DCW [18]. Indeed, previous studies demonstrated that distinct sets of FAAs were observed in the FAAs-based MFA at different culture conditions (Table 2) [14,17,19]. For the amino acids shown in Table 2, Glu, Asp, Ala, and Phe are the four amino acids commonly observed in the four studies, suggesting that these amino acids are likely to be measurable from E. coli cells at various culture conditions (Table 2). These amino acids were also commonly observed in various single gene knock-out E. coli strains [20]. In the GC-MS data obtained in this study, the signal of glutamate is rather intense than for the other amino acids (Figure 4). The second, third and fourth most intense signals are from Asp, Ala, and Phe, respectively. A similar tendency was also reported [20], indicating that glutamate is a useful amino acid to investigate for the 13 C enrichment of α-ketoglutarate (αKG) in FAAs-based MFA.

In order to test the performance of FAA-based MFA experiment using Glu, Asp, Ala, and Phe, flux distributions were estimated using the 13 C enrichment data of 7 fragments of glutamate and aspartate (FAAs_Glu+Asp), 9 fragments of glutamate, aspartate, and alanine (FAAs_Glu+Asp+Ala), and 11 fragments of glutamate, aspartate, alanine and phenylalanine (FAAs_Glu+Asp+Ala+Phe), respectively ( Table 1). The results obtained from each dataset are shown in Figures 2c-e and 3, Tables S3-1, S3-2, S4-1, S4-2, S5-1 and S5-2. The comparison of the results with the reference data (PAAs_fullset) revealed that, in the case of the FAAs_Glu+Asp (c in Figure 3) and FAAs_Glu+Asp+Ala (d in Figure 3) datasets, very large 95% confidence intervals are observed especially for flux of the malic enzyme reaction and phosphoenolpyruvate carboxylase (PPC) and phosphoenolpyruvate carboxykinase (PCK) net flux (Figure 3). On the other hand, the FAAs_Glu+Asp+Ala+Phe (e in Figure 3) dataset produced a rather precise metabolic flux distribution with narrower confidence intervals. For example, the 95% confidence interval of PPC and PCK net flux and malic enzyme flux determined by FAAs_Glu+Asp+Ala+Phe were 38% and 51% of that of FAAs_Glu+Asp+Ala, respectively. This result indicates that Glu, Asp, Ala, and Phe constitute a practical minimal set for FAAs-based MFA.
+ + Leucine + + + - Isoleucine - - + - Lysine - - + - Aspartate + + + + Asparagine - - - + Threonine + - + + Methionine - - - - Glutamate + + + + Glutamine - - - + Arginine - - + - Proline - - + - Glycine + - + + Serine - + + + Cysteine - - - - Histidine - - + - Tyrosine + - + + Phenylalanine + + + + Tryptophan - - - - +: detected; -: not detected.
According to the study of flux ratio analysis reported by Fischer and Sauer [21], labeling patterns of 13 C enrichment of Pyr and oxaloacetate (Oxa) are essential to elucidate the branch ratio of glycolysis, PP pathway and ED pathway or TCA cycle and glyoxylate shunt. This indicates that 13 C-labeling of Ala and Asp synthesized from Pyr and Oxa, respectively, is necessary to estimate flux in various flux distributions in the central carbon metabolism. The metabolic flux analysis using FAAs_Glu+Asp and FAAs_Glu+Asp+Ala shows that, whereas levels of PGI and GAPDH net fluxes are similar to that of the reference data (PAA_fullset), large confidence intervals are observed than for the case of the other metabolic fluxes (Figure 3). The result indicates that, in addition to the 13 C enrichment information of αKG, Oxa, and Pyr derived from Glu, Asp, and Ala, respectively, an analysis of other amino acids is required for a more precise estimation of the metabolic flux distribution. The confidence intervals of metabolic flux analysis were drastically improved by employing the FAAs_Glu+Asp+Ala+Phe dataset, indicating a requirement for 13 C enrichment information of PEP and E4P in Phe (Figure 3). Indeed, it has been reported that levels of metabolic flux in anaplerotic pathways can be well estimated from PEP in addition to Pyr, Oxa, and αKG [21]. These results suggest that a combination of Ala, Asp, Glu, and Phe synthesized from Pyr, Oxa, αKG, PEP, and E4P is a requirement for FAAs-based MFA using [1-13 C] and [U- 13 C] glucose. This mixture of 13 C-labeled glucose was generally used in various MFA studies [1,[12][13][14]17,19,21,22]. Since there are significant fluxes in each reaction in the metabolic network shown in Figure 3, the four amino acids should contain enough information to determine metabolic fluxes in other culture conditions. The additional analysis of other amino acids (Table 2) is expected to improve the confidence intervals of the estimated metabolic fluxes. The findings in this study are most applicable for GC-MS analysis and further experiments would be needed to show that this knowledge is also applicable to MFA based on intermediate metabolites using LC-MS. Although additional confirmations are required, our approach also would be available to other microorganisms with similar metabolic pathway, such as Bacillus subtilis and Corynebacterium glutamicum.

Experimental Section

Strain and Medium

Culture Condition

A frozen stock of E. coli cells was inoculated in 40 mL M9 medium and incubated for 14 h at 37 °C with reciprocal shaking. Continuous culture was performed in a 1 L bioreactor (ABLE, Tokyo, Japan) equipped with temperature, pH, dissolved oxygen, and exhaust gas (O 2 and CO 2 ) sensors. The working volume and aeration rate were 400 mL and 400 mL/min, respectively. The temperature was controlled at 37 °C, and pH was set at 7.0 using an NH 3 solution. Five hours after inoculation, the continuous culture was started at a dilution rate of 0.2 h −1 . The feeding medium containing natural glucose was replaced with one containing 13 C-labeled glucose after the continuous culture reached a metabolic steady state. Labeling of glucose in the medium was determined by gas chromatography-mass spectrometry (GC-MS) analysis by a previously described method [22].

Off-Line Measurements

Sample Preparation for GC-MS Analysis

For analysis of the PAAs, 10 mL broth culture was taken directly from the reactor and centrifuged at 9800× g for 10 min at 4 °C. The cell pellet was washed twice with 0.9% NaCl and hydrolyzed in 2 mL 6 N HCl at 105 °C for 18 h. After filtration (Cosmonice filter W, pore size 0.45 µm, Filter diameter 13 mm, Nacalai Tesque, Kyoto, Japan), 10 µL of internal standard (600 µM cycloleucine) was added to 10 µL of the hydrolysate and evaporated to dryness. The dried residue was dissolved in 50 µL acetonitrile and 50 µL N-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide containing 1% tert-butyldimethylchlorosilane, and incubated at 105 °C for 1 h. After 1 h cooling, a supernatant was injected for GC-MS analysis. Intercellular FAAs were extracted by the following procedure. Cells were collected by filtration of 5 mL of culture broth (PTFE type membrane, 0.5 µm pore size, diameter 90 mm, ADVANTEC, Tokyo,Japan) [23,24]. Cells on the filter were immediately immersed in 1.6 mL methanol (−80 °C) and preserved at −80 °C. After the frozen cell sample was suspended in 1.6 mL of chloroform (−30 °C), 630 µL Milli-Q water (4 °C) and 10 µL 600 µM cycloleucine dissolved in water were added. After vortexing and sonication for 1 min, the mixture was centrifuged at 3700× g for 40 min at 4 °C. A 2 mL aqueous layer was evaporated to dryness (Speed Vac, Thermo Schientific, Waltham, Japan).

GC-MS Analysis of PAAs and FAAs

The mass isotopic distributions of four type of ion clusters at mass to charge (m/z) rations of (Agilent Technologies, Santa Clara, USA); carrier gas, Helium; flow rate, 1.0 mL/min; detection mode, selected ion monitoring; ion source temperature, 230 °C; electron impact ionization, 70 eV). PAAs were analyzed under the following conditions: injection volume, 1 μL; split ratio, 1:10; oven temperature, 150 °C for 2 min, increased by 3 °C/min to 270 °C, then increased at a rate of 10 °C/min to 300 °C, and maintained at that temperature for 5 min [17]. For the analysis of FAAs, splitless mode was employed to measure small amount of FAAs. Detailed conditions are as follows: injection volume, 1 μL; splitless mode; oven temperature, 60 °C for 2 min, increased by 20 °C/min to 150 °C, then increased at a rate of 3 °C/min to 270 °C, further increased to 300 °C at 10 °C/min and maintained at that temperature for 5 min.

Metabolic Flux Analysis

A metabolic model including glycolysis, the tricarboxylic acid (TCA) cycle, glyoxylate shunt, anaplerosis, the pentose phosphate (PP) pathway, the Entner-Doudoroff (ED) pathway, CO 2 exchange and C1 metabolism was employed for flux analysis [21]. The number of free flux or degree of freedom of the model was 21 (Table S1). The fluxes for biomass synthesis of E. coli were calculated from the precursor requirement [25]. The effect of naturally occurring isotopes was removed from the raw mass spectrometry data to obtain corrected 13 C labeling patterns of the carbons in the amino acids [26]. The fragments of amino acids were chosen according to the report of Antoniewicz et al. [27]. Additionally, the appropriate fragments were carefully screened by comparing the natural and theoretical isotope abundance ratios. The calculation of fluxes was performed using the in-house software OpenMebius [28], which is a flux estimation tool using the elementary metabolite units (EMU) framework in Matlab 2011b [29]. In short, the fluxes were estimated by minimizing the residual sum of squares (RSS) between the experimentally measured 13 C enrichment and the estimated value using the fmincon optimization solver in the Matlab toolbox. The optimizing function is described as:
RSS= (MID i measured -MID i estimated ) N i=1
where MID i measured is the mass isotopomer distribution (MID) of the ith measured metabolite, MID i estimated is the estimated MID of the corresponding metabolite, and N is the number of metabolites used for flux estimation. Optimization was started from 20 sets of random flux distributions. Confidence intervals were calculated by a grid search method as described previously [30][31][32]. The metabolic flux of reaction r is fixed to v opt,r + d and the objective function is re-optimized. Here, v opt,r is the optimized metabolic flux of reaction r and d is the perturbation level. The procedure is iterated with increased or decreased d. The range of fixed metabolic flux whose RSS is less than the threshold level is the confidence interval. The threshold level is determined by:

RSS min_fixed ≤ RSS min + RSS min n -p F α (1, n-p)

where RSS min_fixed is the minimized RSS with one fixed flux, RSS min is the original minimized RSS, n is the number of independent data points used in the fitting, p is the degrees of freedom in the original flux fit, F is the F-distribution, and α is the confidence level.

Conclusions

In this study, a performance of the metabolic flux analysis using free amino acids (FAAs) was directly compared with that using proteinogenic amino acids (PAAs). An estimation of 95% confidence intervals for the first time demonstrated that the FAAs-based MFA has essentially identical reliability with that obtained from the PAAs-based method ( Figure 3). It was also experimentally demonstrated that the amino acids derived from Pyr, Oxa, αKG, PEP, and E4P are at least required for FAAs-based MFA. The minimal set of amino acids for precise MFA can be a practical guide for the design of MFA experiments using FAAs.

Figure 1 .
1Time course of 13 C enrichment of proteinogenic and free amino acids. Fractional labeling of proteinogenic amino acids (PAAs, Closed circle) and free amino acids (FAAs, Closed square) are represented. Fractional labeling is a ratio of labeled carbons in the metabolite. Other data are shown inFigure S2

.

was estimated from the best-fitted results(Figure 2a).

Figure 2 .
2Comparison of measured and estimated 13 C enrichment of amino acids. The X axis and Y axis indicate measured and estimated 13 C enrichment, respectively, of the (a) PAAs_fullset; (b) FAAs_fullset; (c) FAAs_Glu+Asp; (

Figure 3 .
3Comparison of metabolic flux distributions estimated using different datasets.

Figure 4 .
4Signal intensities of free amino acids determined by GC-MS. Total signal intensities of Glu ([M-57], [M-85], [M-159]), Asp ([M-57], [M-85], [M-159], [f302]), Ala ([Ala-57], [Ala-85]) and Phe ([M-85], [f302]) are shown in the figure.

Cl, 123 mg/L MgSO 4 • 7H 2 O, 2.78 mg/L FeSO 4 • 7H 2 O, 14.7 mg/L CaCl 2 • 2H 2 O, 10 mg/L thiamine-hydrochloride and 5 g/L glucose.

[M-57], [M-85], [M-159], and [f302] derived from each amino acid were determined by using GC-MS (Agilent 7890A GC and 5975C Mass Selective Detector (Agilent Technologies, Santa Clara, USA); column, DB-5MS+DG; 30 m × 0.25 mm ID × 0.25 μm;

Table 1 .
1Data points used for metabolic flux analysis and residual sum of squares (RSS) of best-fitted results.Number of
independent
measurements
(=n)

Number of
metabolites
used for
fitting

Number of
fragments
used for
fitting

n-p 1
RSS
RSS/(n-p)

PAAs_fullset
92
11
25
71
0.0013
0.00002
FAAs_fullset
66
9
19
45
0.0037
0.00008
FAAs_Glu+Asp
25
2
7
9
0.0007
0.00017
FAAs_Glu+Asp+Ala
30
3
9
19
0.0012
0.00014
FAAs_Glu+Asp+Ala+Phe
40
4
11
4
0.0015
0.00008

1 p: Degree of freedom of metabolic model (=21).

Abbreviations: PGI: phosphoglucose isomerase, PPC: phosphoenolpyruvate carboxylase, PCK: phosphoenolpyruvate carboxykinase, G6PDH: glucose 6-phosphate dehydrogenase, GAPDH: glyceraldehyde 3-phosphate dehydrogenase, IDH: isocitrate dehydrogenase, G6P: glucose 6-phosphate, F6P: fructose 6-phosphate, FBP: fructose 1,6-bisphosphate, DHAP: dihydroxyacetone phosphate, GAP: glyceraldehyde 3-phosphate, PGA: 3-phosphoglycerate, PEP: phosphoenolpyruvate, Pyr: pyruvate, AcCoA: acetyl-coenzyme A, Cit: citrate, αKG: α-ketoglutarate, Suc: succinate, Fum: fumarate, Mal: malate, Oxa: oxaloacetate, 6PG: 6-phosphogluconate, Ru5P: ribulose 5-phosphate, R5P: ribose 5-phosphate, Xu5P: xylulose 5-phosphate, S7P: sedoheptulose 7-phosphate, E4P: erythrose 4-phosphate, Ala: alanine, Phe: phenylalanine, Asp: aspartate, Glu: glutamate.

Table 2 .
2Free amino acids measured in 13 C-metabolic flux analysis studies using E. coli.This study
Mori et al.
(2011) [17]

Toya et al.
(2010) [19]

Iwatani et al.
(2007) [14]
Experimental conditions
Analysis
GC-MS
GC-MS
CE-TOFMS
LC-MS/MS

Culture
Chemostat
culture

Chemostat
culture

Batch
culture

Fed-batch
culture
Amino acids
Alanine
+
+
+
+
Valine
+
-

© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
AcknowledgmentsWe thank Yoshihiro Toya and Fumio Matsuda for helpful comments. This research was partially supported by JST, Strategic International Collaborative Research Program, SICORP for JP-US metabolomics.Author ContributionsNobuyuki Okahashi, Chikara Furusawa, and Hiroshi Shimizu designed research. Nobuyuki Okahashi, Shuichi Kajihata, and Chikara Furusawa performed research. Nobuyuki Okahashi and Shuichi Kajihata analyzed data. Nobuyuki Okahashi, Chikara Furusawa, and Hiroshi Shimizu wrote the manuscript.Conflicts of InterestThe authors declare no conflict of interest.
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