Red blood cell phenotype effect.
\r\n\tThe emergence of novel prion strains in animals, which include the only evidenced zoonotic prion C-BSE causing vCJD in humans, has created an important public health concern. Currently, new threats to human and animals may develop because of the plausible zoonotic properties of scrapie, L-BSE and the recently emerging chronic wasting disease in Europe.
\r\n\tThis book will gather experts in prion diseases and present new scientific advances in the field and relations with other amyloid neuropathologies.
The Anti-D immunoglobulin (Anti-D IgG) produced from human plasma is used in the immunoprophylaxis of hemolytic disease of the newborn individuals who do not possess the red blood cells D antigen. Health authorities and manufacturers determine the concentration of antibody for each batch of medicine. Initially, the monitoring of Anti-D in the plasma of pregnant women and in finished products (Anti-D IgG) was performed by the anti-globulin test or Coombs test [1, 2] or by the continuous flow analysis of hemagglutination with a Technicon Autoanalyzer [3, 4, 5, 6, 7, 8]. The automatic hemagglutination method has been considered as a reference by the European Pharmacopeia (EP); despite this, some groups of experts and regulatory bodies have replaced such methodology by radioimmunoassay (RIA) [9, 10, 11] flow cytometry and a competitive enzyme immunoassay (EIA). These alternatives to the current reference method of European Pharmacopeia have been successfully investigated during international collaborative studies [11, 12, 13, 14, 15, 16, 17]. Competitive flow cytometry and EIA assays have been described previously [18, 19, 20].
\nWe describe the use of flow cytometry in the control of manufacturing processes of Anti-D globulin, establishing the validation parameters and compliance with the quality standards required by the GMP and established by the producer at the different stages of the industrial process (hyperimmune plasma, Fraction II of the Cohn-Oncley process, and finished product) [13, 14, 21, 22, 23].
\nThe rationale of the technique lies in the binding antigen antibodies, which are labeled with fluorescein detected by flow cytometry. The Anti-D immunoglobulin is quantified in comparison with the international reference preparation, calibrated in international units (IU), which allows to give a specification in IU/ml [20, 24]. In relation to flow cytometry, Expert Group No 6B of the NIBSC standardized a procedure to be applied in the evaluation of Anti-D in Anti-D IgG solutions [24]. In our laboratory, a flow cytometry technique was developed for the quantititation of Anti-D antibodies of the Gamma-Rho UNC, designed according to the procedures described in the literature [20, 24] and validated according to the criteria indicated by the NIBSC expert group [16, 24].
\nA laser hits the red fluorescein-labeled cells that cross the cytometer in a continuous flow. The light emission produced by the interaction is detected by the forward scatter (FS) and converted into a voltage pulse (FS). A limit of 25,000 events is determined at a flow rate of 600 cells per second with a power of 1300 V. The concentration of Anti-D will be proportional to the size and intensity of fluorescence emitted by the antigen-antibody fluorescein complex of each red blood cell. The mean fluorescence intensity is plotted according to the logarithm of the Anti-D concentration.
\nA Rho red cell suspension (Red Cell phenotypes R1R1/R1R2 and Rh-rr as control), were diluted to 0.2% in phosphate-buffered saline (PBS) pH 72 and plasma samples, Fraction II and finished product were diluted 1/5 to 1/100 with 1% PBS—pH 7.2.
\n0.2% red cell suspension was incubated with different test fractions (Anti-D standard IRP 68/419 OMS-NIBSC, human plasma, Fraction II of the Cohn process and gamma Anti-D globulin) in a 50/50 μl ratio. After stirring 30 min at 4°C, red blood cell suspension is washed with PBS-1% BSA and incubated with a 1/50 dilution of a goat anti-human IgG FITC (Figure 1). Red blood cells resuspended in 1 ml of PBS-1% BSA are measured with flow cytometry in Epics XL-MCL Cytometer-Coulter.
\nCalibration curve of the conjugate dilutions.
In a concentration range of 1500 ng/ml (7.5 IU/ml)–180 (0.9 IU/ml) ng/ml of international reference for human Anti-D immunoglobulin preparation (IRP 68/419 WHO), we establish the standard curve. No Anti-D sensitized cells were used, to set false positives and the minimum level of detection. Mean fluorescence intensity values were between 600 and 1900 and averaged around 800. The upper and lower limit values were determined using five standard curves. Nonparametric statistics was used and processed with Microsoft Excel 7.0 program and Method Validator 1.15 [25, 26, 27, 28].
\nEpics XL-MCL Cytometer (Coulter, Corp., Luston, UK) was checked and calibrated using the standard immunocheck particles (Becton Dickinson, Oxford, UK). The red cell samples automatically passed through the cytometer according to their FS and side scatter (SS). After defining the working conditions and the protocol to be used with the cytometer, it was determined that the most suitable conjugate dilutions were 1/50 and 1/100 (Figure 1). It was determined to work with 25,000 events at a flow rate of 600 events/s (Figure 2). The intensity of the signal observed in the negative controls is related to non-specific negative unions. There were no other phenomena related to false positives (Figure 3).
\nWork and flow rate events.
Forward (FS) and side (SS) scatter graphs of a homogeneous population of non-sensitized cells.
The most suitable conjugate was determined by processing 26 tubes in duplicate and 1/100 dilutions of the conjugate (Sigma Anti Human IgG -Fc Conjugate-F9512 specific and Kallestad FITC Conjugate #30446). Serial dilutions of commercial gamma globulin (250 μg/ml) were used as control. Figure 4 shows that the Sigma conjugate presents a greater fluorescence signal. After 10 assays using different fluorescence particles, the fluorescence intensity (MESF) and the fluorescence signal emitted by the cytometer presented good correlation. Figure 5 shows the linear relationship between the emitted MFI and the MESF. Peak’s SD values in a range of 1.4–7.6 represent points with higher and lower intensity.
\nComparative curve of the conjugates.
Calibration curve of the flow cytometer, correlation between MFI and MESF.
The histograms of the fluorescence parameters that were plotted according to the anti-Rho IgG concentrations can be seen in Figure 6. Figure 7a shows the working area determined with the forward and lateral dispersion of a homogenous population of non-sensitized cells. We observed no significant agglutination of auto aggregated red cells for anti-Rho IgG concentrations less than or equal to 960 ng/ml (4.8 IU/ml). We worked in intervals of ≤960 ng/ml to avoid increments of FS caused by auto aggregation (Figure 7b).
\nFL1 histogram of fluorescence stockings obtained from seven dilutions of the WHO 68/419 standard using GR Rho + R1R1, \n\n\n 960 ng/ml AD, \n\n\n 480 ng/ml AD,
(a) Working region selected for Rho + R1R1 red blood cells according to the FS and SS of washed cells not sensitized in a concentration of 0.2% in PBS. (b) Increase of FS of GR R1R1 sensitized with anti-Rho IgG standard 68/419 960 ng/ml.
Phenotypes R1R1, R1R2, R1r´, and R1r were tested to optimize the assay. The values of estimated concentrations (means-standard deviations-CV%) suggest that the tests are reproducible for each sample and each phenotype (Table 1). The R1R1 and R1R2 phenotypes presented a greater fluorescence signal, and, therefore, a greater amount of Anti-D was adsorbed than the rest of the phenotypes (Figure 8).
\nPhenotype | \nR1R1 | \nR1R2 | \n||||
---|---|---|---|---|---|---|
ng STD | \n480 | \n120 | \n60 | \n480 | \n120 | \n60 | \n
ng Rec. | \n480.45 | \n119.70 | \n60.30 | \n478.75 | \n118.00 | \n60.10 | \n
475.80 | \n118.98 | \n60.00 | \n477.30 | \n119.00 | \n56.90 | \n|
481.06 | \n120.00 | \n59.45 | \n476.89 | \n118.5 | \n59.89 | \n|
480.03 | \n121.00 | \n60.10 | \n477.00 | \n116.78 | \n60.05 | \n|
Media | \n479.33 | \n119.92 | \n59.96 | \n477.48 | \n118.07 | \n59.235 | \n
SD | \n2.39 | \n0.84 | \n0.36 | \n0.86 | \n0.95 | \n1.56 | \n
VC% | \n0.5 | \n0.7 | \n0.6 | \n0.2 | \n0.8 | \n2.6 | \n
SD + 2 | \n484.12 | \n121.59 | \n60.69 | \n479.21 | \n119.97 | \n62.35 | \n
SD − 2 | \n474.54 | \n118.24 | \n59.23 | \n475.76 | \n116.17 | \n56.12 | \n
Phenotype | \nR1r´ | \nR1r | \n||||
ng STD | \n480 | \n120 | \n60 | \n480 | \n120 | \n60 | \n
ng Rec. | \n476.35 | \n117.45 | \n60.10 | \n465.98 | \n120.21 | \n60.10 | \n
469.40 | \n117.67 | \n56.90 | \n475.35 | \n114.78 | \n56.90 | \n|
474.45 | \n118.25 | \n57.98 | \n474.95 | \n118.25 | \n57.98 | \n|
475.60 | \n117.33 | \n58.87 | \n477.60 | \n117.33 | \n56.89 | \n|
Media | \n473.95 | \n117.675 | \n58.46 | \n473.47 | \n117.64 | \n57.96 | \n
SD | \n3.13 | \n0.40 | \n1.35 | \n5.12 | \n2254 | \n1.51 | \n
VC% | \n0.7 | \n0.3 | \n2.3 | \n1.08 | \n1.9 | \n2.6 | \n
SD + 2 | \n480.21 | \n118.49 | \n61.17 | \n483.72 | \n122.15 | \n60.98 | \n
SD − 2 | \n467.68 | \n116.85 | \n55.74 | \n463.21 | \n113.13 | \n54.94 | \n
Red blood cell phenotype effect.
Standard curves using red blood cells of phenotype: R1R1 (\n\n\n), R1R2 (\n), R1r´ (\n\n\n), and R1r (\n\n\n).
To establish the standard curve, dilutions of WHO reference preparation were used in a concentration range of 30–480 ng/ml. The corresponding values of MFI were between 1.4 and 4.5. To establish the minimum level of detection, non-sensitized cells were run, with MFI values averaging 1.2 (Figure 9).
\nConstruction of the standard curve. A total of 18 curves was run and its linear regression was r = 0.963.
A linear and proportional to the log of the Anti-D concentration fluorescence response was obtained in the measured range. The linear regression of 270 points was r = 0.963. The linear measurement range was set between 120 and 240 ng/ml and was the most acceptable for our purpose.
\nSeven similar curves were run with each other (Figure 10a), and the average curve and two SD values were calculated at each point, and the upper and lower limits of each reading were calculated and plotted. To determine the appropriate limits for our purpose, 19 standard curves were tested. As shown in Figure 10, at high concentrations of Anti-D, there are no significant deviations, and in the diluted samples, three showed deviations from the upper limit (0.24 μg/ml) and one below the lower limit (120 ng/ml). The limits of 120–240 ng/ml were taken as validation criteria of the standard curve.
\n(a) Determination of the lower and upper limits (------) and the values of ±2DS for the mean standard curve. The limit lines were used for the approval of the standard curves. (b) From 19 standard curves, 15 were found completely within the lower and upper limit lines.
The cutoff value set at 10 ng/ml (0.05 IU/ml) corresponds to a 1:1024 Anti-D standard (NIBS) dilution. After 135 plasma assays (55 Anti-D positive—80 negative), it was determined.
\nTests in different matrices repeated 10 times simultaneously (intra-assay) during different days (inter-assay) evaluated the reproducibility and repeatability of flow cytometry. The sources of variation found were linked to the preparation of samples and variations in the cytometer (Table 2). The test showed good repeatability between tests, indicated by the coefficient of variation in representative samples (CV 4.95%, high concentration; 3.36%, intermediate concentration; and 4.78%, low concentration) (Table 3). These results coincide with Thorpe and Schäffner in the collaborative study of the European Union [13].
\nIntra-assay | \n(a) Reference standard 68/419 WHO in ng/ml | \n|||
---|---|---|---|---|
Concentration | \n480 | \n120 | \n30 | \n\n | \n
n | \n10 | \n10 | \n10 | \n\n | \n
Average MFI | \n4.79 | \n1.98 | \n1.25 | \n\n | \n
SD | \n0.24 | \n0.07 | \n0.06 | \n\n | \n
VC % | \n4.95 | \n3.36 | \n4.78 | \n\n | \n
Intra-assay | \n(b) Sample control μg/ml | \n|||
Sample | \nPool of plasma* | \nCohn Fraction II * | \nγ1* | \nγ2* | \n
Concentration | \n12.5 | \n30.2 | \n197.2 | \n179.5 | \n
n | \n10 | \n10 | \n10 | \n10 | \n
Average MFI | \n2.08 | \n11.26 | \n3.12 | \n3.49 | \n
SD | \n0.06 | \n0.05 | \n0.16 | \n0.17 | \n
VC% | \n3.06 | \n4.32 | \n5.16 | \n4.93 | \n
% Recovery | \n113.6 | \n91.56 | \n98.33 | \n109.46 | \n
Accuracy and reproducibility of the standard (a) and control samples (b).
Commercial gamma globulins: concentration 1 = 204 μg/ml, 2 = 164 μg/ml; pool = 11 μg/ml, Cohn’s Fraction II = 33 μg/ml.
Inter-assay: (a) reference standard 68/419 WHO in ng/ml | \n|||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
ng/ml | \nMFI | \nSD | \nVC% | \n+2SD | \n−2SD | \n||||||
M 1 | \nM 2 | \nM 3 | \nM 4 | \nM 5 | \nM 6 | \n_ X | \n|||||
480 | \n5.95 | \n5.12 | \n5.06 | \n5.44 | \n4.76 | \n5.22 | \n5.26 | \n0.40 | \n7.69 | \n8.38 | \n4.45 | \n
240 | \n3.11 | \n3.17 | \n3.21 | \n2.95 | \n3.01 | \n3.34 | \n3.13 | \n0.14 | \n4.49 | \n5.57 | \n2.85 | \n
120 | \n1.8 | \n1.97 | \n1.88 | \n1.84 | \n2.11 | \n2.01 | \n1.93 | \n0.12 | \n6.01 | \n3.97 | \n1.70 | \n
60 | \n1.31 | \n1.53 | \n1.39 | \n1.31 | \n1.63 | \n1.59 | \n1.46 | \n0.14 | \n9.71 | \n1.46 | \n1.18 | \n
30 | \n1.11 | \n1.23 | \n1.27 | \n1.22 | \n1.3 | \n1.2 | \n1.22 | \n0.06 | \n5.36 | \n1.22 | \n1.09 | \n
Inter-assay: (b) Control samples | \n|||||||||
---|---|---|---|---|---|---|---|---|---|
Product | \nGamma H* | \nGamma P* | \nGamma B* | \n||||||
Dilution | \n1/250 | \n1/500 | \n1/750 | \n1/350 | \n1/700 | \n1/1000 | \n1/250 | \n1/500 | \n1/1000 | \n
MFI | \n1.21 | \n1.16 | \n1.05 | \n1.37 | \n1.24 | \n1.06 | \n1.21 | \n1.15 | \n1.02 | \n
1.18 | \n1.16 | \n1.04 | \n1.39 | \n1.24 | \n1.06 | \n1.23 | \n1.15 | \n1.03 | \n|
1.19 | \n1.17 | \n1.04 | \n1.39 | \n1.25 | \n1.06 | \n1.19 | \n1.13 | \n0.98 | \n|
1.22 | \n1.19 | \n1.04 | \n1.38 | \n1.23 | \n1.07 | \n1.22 | \n1.14 | \n1.00 | \n|
1.20 | \n1.14 | \n1.06 | \n1.3 | \n1.15 | \n1.05 | \n1.21 | \n1.16 | \n1.01 | \n|
1.18 | \n1.15 | \n1.05 | \n1.27 | \n1.22 | \n1.04 | \n1.23 | \n1.13 | \n1.02 | \n|
Average MFI | \n1.19 | \n1.161 | \n1.05 | \n1.35 | \n1.22 | \n1.06 | \n1.215 | \n1.14 | \n1.01 | \n
SD | \n0.016 | \n0,017 | \n0,008 | \n0.05 | \n0.04 | \n0.01 | \n0.015 | \n0.012 | \n0.018 | \n
VC% | \n1.36 | \n1.48 | \n0.78 | \n3.83 | \n2.99 | \n0.98 | \n1.25 | \n1.06 | \n1.77 | \n
μg/ml | \n149.50 | \n150.25 | \n142.64 | \n236.25 | \n220.86 | \n192.00 | \n151.87 | \n147.41 | \n183.64 | \n
% Recovery | \n99.66 | \n100.16 | \n95.09 | \n107.39 | \n100.39 | \n87.27 | \n92.61 | \n89,89 | \n111.97 | \n
+2SD | \n1.23 | \n1.20 | \n1.06 | \n1.45 | \n1.29 | \n1.08 | \n1.24 | \n1.17 | \n1.04 | \n
−2SD | \n1.16 | \n1.13 | \n1.03 | \n1.25 | \n1.15 | \n1.04 | \n1.18 | \n1.12 | \n0.97 | \n
Accuracy and repeatability of standard (a) control samples (b).
Declared concentration for H: 150 μg/vial, P: 220 μg/vial, B: 164 μg/vial.
The estimated concentration of Anti-D for each measured point in three commercial products presented a %CV in the range of 1–7.5% (Table 4). Two-thirds of the powers estimated by the manufacturers were within 95% of the acceptance limit of flow cytometry.
\nProduct | \nn | \nAverage μg/ml | \nLimit 95% | \nVC% | \nDeclared concentration μg/ml | \n% Recovery | \n
---|---|---|---|---|---|---|
H | \n6 | \n149.50 | \n145–154 | \n2.6 | \n150 | \n99.7 | \n
6 | \n150.25 | \n146–155 | \n3.3 | \n100.2 | \n||
6 | \n142.64 | \n140–144 | \n1.3 | \n95.1 | \n||
P | \n6 | \n236.25 | \n218–254 | \n7.2 | \n220 | \n107.4 | \n
6 | \n220.86 | \n208–233 | \n5.7 | \n100.4 | \n||
6 | \n192.00 | \n189–196 | \n2.1 | \n87.3 | \n||
B | \n6 | \n151.87 | \n147–155 | \n1.9 | \n164 | \n92.6 | \n
6 | \n147.1 | \n145–151 | \n2.7 | \n89.9 | \n||
6 | \n183.64 | \n176–189 | \n2.7 | \n111.9 | \n
Comparison between anti-D concentration declared by the producer and concentration obtained by flow cytometry.
Only 1 of the 30 tests performed on 3 known concentration of commercial products showed deviations in the cut of the determined concentrations (87.3–111.9). The assay presented good recoveries of concentration in the samples tested (Table 4). The concentrations were estimated using the Wilcoxon test.
\nThe availability of a quantitation method for human plasma, Cohn fractions, and Anti-D gammaglobulin has a relevant importance for the process control of the Anti-D IgG production. Pharmaceutical companies are employing sensitive EIA, RIA, and flow cytometry techniques [10, 11, 15, 20, 24] for the quantitation of Anti-D level in finished products, the use in Anti-D gammaglobulin manufacturing process control have no be reported.
\nA rapid and low-cost flow cytometry test applied in the control of the industrial process of large-scale production obtaining Anti-D gammaglobulin has been described. This responded to the need of an alternative method to EIA and RIA in the quantitation of Anti-D in different process samples (human plasma, Chon fractions, gammaglobulin) [29].
\nAccording to this, the threshold of the assay was adjusted to select the level of antibody content, which will be accepted, to produce an immunoglobulin with a potency of 250 μg/vial (1250 IU/vial), according to the regulatory requirements [13, 14, 28]. To validate the assay, chemical and physical-chemical factors that affect the binding of antigen-antibody influence in the red blood cells were determined. The spatial conditions of cell packing did not affect the assay. The adsorption of antibodies to the cell surface was 1000 times greater when the incubation temperature was 37°C without this affecting the dissociation rate [9]. The different fractions behave similarly in the assay conditions, and there was no interference by autoagglutination at the concentration of red blood cells used (Figure 7). The analytical quality assurance of the trial showed that the method presents good reproducibility, repeatability (1–7.5 VC%), and correlation with the standard curve (r: 0.9267). The assay is specific and recovery is 95% of the known Anti-D concentration values. For validation our strategy was based on an international guide accepted by manufacturers and control laboratories [13, 30]. The validation method meets the requirements according to the good manufacturing practices in the pharmaceutical industry. A validation methodology was followed to meet the special requirements of standardization of the good manufacturing practices in the pharmaceutical industry [30, 31].
\nBiological variability, red blood cells, and matrices were considered in a validation protocol according to international guidelines [14, 30].
\nThis flow cytometry method is a sensitive and specific method that allows reproducible results. The concentration values are comparable with those estimated with other methods such as RIA and EIA used by commercial manufacturers of gamma globulins, and the method presents accuracy and precision. The test is completed in 3 h and is easy to perform, allowing quantitative assessment of Anti-D antibodies from plasma, fractions of the Cohn process, and finished products. The flow cytometry method presented shorter processing times (3 h) than the EIA or RIA methods (5 h).
\nThe potential of the flow cytometry method described here represents an alternative to quantify Anti-D in different matrices and meets the criteria of good laboratory practices (simple, fast, and reliable as well as being sensitive and accurate). In the control of industrial processes, this method has shown reproducibility and reliability for this purpose.
\nThe cost of materials is slightly lower for flow cytometry ($ 15/test) than in EIA or RIA ($ 25/test).
\nHighly significant correlations were observed between the flow cytometry test Anti-D values and EIA values determined in different matrices (Serum, Fraction II, Semi-elaborated, gamma globulin).
\nFor the case of process samples (Fraction II, semi-processed), strong correlations were observed between the flow cytometry test and the EIA values. A potential advantage of the flow cytometry assay could be the higher sensitivity presented than the EIA assay.
\nIt is concluded that the flow cytometry method has advantages over the EIA and RIA method as a substitute for the present standard method.
\nAnorexia nervosa (AN) is a serious and often enduring psychiatric condition. The hallmark features of AN are a phobia for weight gain, and for intake of fattening food, disturbance in body image, and often compensatory behaviors such as excessive exercise and purging, which overall leads to a reduction of energy intake relative to energy expenditure leading to low body weight. An increased risk of suicide and frequent potential life-threatening medical complications of several body organs contribute to AN having a high standardized mortality ratio of 5.2 [3.7–7.5] [1]. This is coupled with a high risk of enduring disease [2].
The weight loss is in some patients preceded by a depression, a trauma, gastrointestinal symptoms or an infection. But in a majority of patients there is no detectable psychiatric or somatic disorder preceding the weight loss. In children and adolescents with AN, family-based treatment as described by Lock and LeGrange is recommended [3] and if treatment is started shortly after debut of the disorder, the prognosis is fairly good. However, if treatment is delayed, the prognosis becomes worse [4]. In adults, individual eating-disorder-focused therapy (CBT-ED) is recommended [5]. With this treatment, drop-out rates are high and even with optimal treatment by well-trained therapists only 50% of the patients who start CBT-ED have good effect of the therapy [6, 7].
Considering the high mortality, high chronicity and lack of knowledge on the etiology of AN, there is an immense need for an improved understanding of the etiology and pathophysiology of the disease in order to find ways to better treatments. This knowledge would preferably explain both the routes into developing the disorder and mechanisms that serve to maintain it, and proposedly involve both biological and psychological factors, such that measures and biomarkers to follow the development and recovery from of the disease could be identified. Potential further benefits with biomarkers for AN may be guidance for risk stratification, treatment and target identification for novel treatments. The last few years have seen an increase in studies on the gut microbiota and its associated microbiome which might harbor trait biomarkers for AN.
The “microbiota” refers to the cumulative microorganisms, including Bacteria, Viruses, Archaea, Protists and Fungi, which populate a number of human tissues and biofluids including the skin, lungs, roal mucosa, saliva, and gastrointestinal tract, and the “microbiome” refers to the collective genomes of the present microorganisms [8]. There are more than 1000 ‘species-level’ phylotypes that coexist in a human [9], and the majority of these phylotypes are Bacteria, with Faecalibacterium prausnitzii, Roseburia intestinalis, and Bacteroides uniformis dominating in the adult microbiota found in feces samples [10]. The composition of the phylotypes is mostly consistent across individuals, albeit there may be a large variability with regard to relative composition and diversity of the included microorganisms, intra-individually depending on anatomical site and inter-individually at the same anatomical location. In addition, there are inter-individual variations at the same anatomical site.
The gut microbiota is critical for the development of the gut mucosal immunity [11, 12], and it is also involved in the regulation of the hypothalamic-pituitary-adrenal (HPA) axis [13], serotonergic neurotransmission [14], and signaling mechanisms affecting neuronal circuits involved in motor control and anxiety in mice [15]. This pathway has been named the gut-brain axis [16].
The existence of the gut-brain axis is exemplified by irritable bowel syndrome (IBS) where more than half of the patients also suffer from mood disorders and for which antidepressants is one of the more common pharmaceutical treatments [17]. In IBS and other potential gut-brain axis disorders, cognitive alterations seem to be key features of the disorders [18]. These cognitive alterations might be induced by signal transduction from gut to the brain [18]. In addition, the existence is also shown by the effects of antibiotic exposure, which may lead to altered brain function such as anxiety, panic disorder, major depression, psychosis, and delirium which are usually described as side effects of antibiotic treatment [19]. Support for the latter comes also from studies in mice which have shown that an altered composition of the gut microbiota in adult mice, and an increased exploratory behavioral including hippocampal expression of Brain Derived Nerve growth Factor (BDNF) has been found after oral administration of non-absorbable antimicrobials [20], in contrast to intraperitoneal administration, which had no effect on behavior or BDNF levels.
Another area of evidence for the gut-brain-axis stems from dietary induction of changes in gut microbiota and linked psychopathological outcomes. For example, a high fat diet has been found associated with an altered microbial diversity and diminished synaptic plasticity [21, 22] but also increased vulnerability and anxiety-like behavior in the mice [23]. In addition, a diet high in sucrose also led to an altered microbial diversity associated with impaired development of spatial bias for long term memory, short term memory, and reversal trainings [24]. Another strong evidence for the gut-brain axis comes from a study in mice exposed to a microbiome depletion and/or transplantation paradigm where microbiota, in a first step, was isolated from donors who were provided with either in high fat diet or a controlled diet, and thereafter in a second step, transfused to mice who developed significant and selective disruptions in exploratory, cognitive, and also developed the stereotypical behavior following the high fat diet [25]. However, there are also evidence from studies where alcohol exposure, smoking habits, and disruptions in diurnal rhythm all have been shown to affect the microbiota composition.
There are also other evidence pointing to a reciprocal interaction from a study where a second generation antipsychotics, olanzapine, was exposed to rats and found to affect the composition of the microbiota, which also triggered an inflammatory response and weight gain [26, 27]. Furthermore, the exposure to antibiotics seemed to attenuate these physiological effects [28].
The microbiome has also been found to have been altered in various psychiatric conditions, or to affect its clinical expression, as well altered in rodent models for these disorders [29]. One example is major depressive disorder (MDD) where, for example, in germ-free mice (mice completely void of bacterial microbiota or derived molecules), there are both changes from comparable normal mice in the hypothalamic, pituitary, adrenal stress response, as well as altered levels of monoamines concentrations or their receptors [13, 14, 15, 20, 30]. Indirect evidence in MDD also comes from an increased serum antibody level to lipopolysaccharides that stems from Gram-negative enterobacteria, which are higher in MDD compared to controls [31], and which is associated with stress associated increased gut permeability and bacterial translocation in animal models [32, 33]. In addition, depression also altered the gut microbiota in a mouse model, in which chronic depression and anxiety-like behaviors were induced by olfactory bulbectomy [34], suggesting a feedback loop between depressive states and dysbiosis.
Furthermore, a similar type of relation between dysbiosis and psychopathogenesis is found in schizophrenia [35, 36]. For example, elevated levels of serological markers of bacterial translocation have been found to be highly correlated with systemic inflammatory markers in schizophrenia [37], and, cytokine levels in schizophrenia are correlated with the severity of symptomatology [38]. From a genetic point of view, several of the strongest associations identified between genetic risk and schizophrenia stems from genes that are linked to immunological function [57, 58]. This is particularly interesting in view of the genetic association between AN and schizophrenia [39].
The mechanism behind the gut brain axis may be multifaceted involving neural signal transduction in nervus vagus, neurotransmitters, immunological mechanisms, and mechanisms related to metabolism and energy utilization [40]. One of the strongest links from a mechanistic point of view, stems from research on serotonin and the microbiota. Enterochromaffin (EC) cells provides approximately 95% of the total body content of serotonin [41] of which the majority exists in plasma. Multiple levels of evidence links disturbances in the serotonergic system and several psychiatric disorder such as depression, anxiety, and borderline personality disorder. For example, the metabolism of tryptophan, a precursor of serotonin, is potentially regulated by the gut microbiota thereby enabling it to influence brain function [42]. Tryptophan is an essential amino acid derived from the diet [43], and tryptophan that is absorbed from the gut into the bloodstream passes the blood-brain barrier to contribute to serotonin synthesis in situ [43]. The availability of tryptophan is strongly affected by the gut microbiota, and several studies have indicated that bacteria such as streptococcus, Escherichia, enterococcus species and Bifidobacterium infantis, and especially indigenous spore-forming bacteria may modulate serotonin levels by increasing plasma tryptophan [44]. An example of this is studies in germ free mice that have found that they exhibit an increased plasma tryptophan concentration [14, 15], which after post weaning colonization can be normalized [14]. The serotonergic neurotransmission may thereby be influenced by the availability of tryptophan for serotonin production [45]. There are studies have found that a depletion of tryptophan influences mood, anxiety and borderline personality traits, for example, in AN and bulimia nervosa [46, 47, 48, 49].
There are also other evidences that link the gut microbiota with psychiatric conditions such as MDD. For examples, a recent publication by Seng et al. [50] provides three additional levels of evidences: (a) that germ free mice lacks gut microbiota and display depression like features in forced swimming test compared to conventionally raised healthy control mice; (b) that the gut microbiota composition of MDD patients differ from that of healthy controls; and (c) and that transplantation of MDD microbiota to germ free mice led to the development of depression like behaviors. In addition, Seng et al. found that mice that were harboring the microbiota from MDD patients primarily exhibited disturbances of microbiome genes and host metabolism which thereby suggests that the depression-like behavior was mediated through the host metabolism [50].
Another neurotransmitter that is produced by the microbiota and that may influence host behavior is gamma aminobutyric acid (GABA) which is the main inhibitory neurotransmitter in the CNS. GABA produced by the probiotic Lactobacillus rhamnosus was administered to mice and led to an alteration in the expression of GABA receptors in different CNS regions, associated with reduced anxiety and depression-like behaviors [51].
Another mechanism for interaction between the microbiome and the CNS is at the level of the blood-brain barrier (BBB). The vascular BBB is comprised of specialized brain endothelial cells acts as a regulatory interface between brain and blood that prevent the unrestricted transfer of molecules into the CNS. Disruption of the tight junctions of the BBB can expose the CNS, and has also been linked to CNS disorders [52]. A dysbiotic microbiome could possibly interact with the BBB in several ways: bacterial factors and immune-active molecules released from peripheral sites influenced by the microbiome can cross the BBB, alter BBB integrity or change BBB transport [53]. In germ-free mice, it has been shown that the BBB has increased permeability compared to pathogen-free mice with a normal gut flora. The increased permeability was associated with reduced expression of the tight junction proteins. Exposure of germ-free adult mice to a normal gut microbiota decreased BBB permeability and up-regulated the expression of tight junction proteins [54]. Metabolic products such as short-chain fatty acids (SCFAs) are produced through the fermentation of dietary fibers by the gut microbiota [55] and can cross the BBB to affect brain function. A low production of SCFAs could lead to increased BBB permeability and SCFAs has been shown to be able to improve a dysfunctional BBB in germ-free mice [54]. Another example is that antibiotics are able to modify barrier integrity and alter behavior in mice [56] and alterations to the microbiome composition in mice in favor of, for example, probiotic bifidobacteria spp. through food supplement with prebiotics showed impact on neuroinflammation and were accompanied with changes in the expression of tight junction proteins [57]. Furthermore, leptin, a key hormone for the control of appetite and weight gain, is normally restricted by the BBB but has been shown in mice with a deficit in leptin transport to the brain to enhance the sense of food reward [58].
Dysbiosis has been proposed in AN and through the long periods of starvation associated with the core psychopathology of AN, a considerable adaptation in gut microbiota could occur in individuals with AN. A systematic review by Schwennsen et al. [59] found some evidence of dysbiosis in AN, such as the abundance of the gut microbiota in AN, which was described as either normal [60, 61], reduced [62] or altered in AN [63]. In addition, the diversity of the gut microbiota in AN was described as normal [61, 63], or reduced (alpha, i.e., within-sample diversity) [64] both in the acute stage and after weight restoration.
Common microbiota findings in the acute stages of AN were low levels of phylum Bacteroidetes [61, 64], while the phylum Firmicutes was increased in AN in three studies [61, 64, 65] however decreased in a fourth [63]. Furthermore, the genus Methanobrevibacter and specifically, the species M. smithii, has been found increased in AN patients in several studies [60, 61, 63, 65]. It is important to remember the presence or lack of a specific bacterial spp. identified by their 16rRNA gene is not the same as the presences or lack of certain metabolic functions or microbiota steady-state dynamic. The state of knowledge of the microbiota in AN is in its infancy and more studies are needed.
This systematic review [59] identified two studies describing an association between the microbiota and clinical symptoms in AN. In one study, ClpB protein concentrations were significantly correlated with several subscales on the Eating Disorder Inventory-2 (EDI-2) for patients with eating disorders and the Montgomery-Åsberg Depression Rating Scale (MADRS) total score and specifically the anhedonia score for AN patients (p < 0.05) [66]. In another study, an association between alpha diversity and depression and eating disorder psychopathology was found in AN [64]. Should further studies find further support for that the microbiota drives the symptoms of AN, this would strengthen targeting the microbiota as a primary level of treatment of AN.
A potential mechanism through which the microbiota indirectly influences the pathophysiology and symptoms of AN is through the breakdown of organic material in the gut and the transfer of metabolites into the blood stream. One of the microbiota that has been described in AN is M. smithii, which is involved in the breakdown of polysaccharides from vegetable sources and the finding of this specific Archaeon could illustrate an adaptation to a typical diet rich in vegetables and fruits in persons with AN. In addition, methanogenic Archaea, such as M. smithii, have also been linked to constipation, a common complaint in patients with AN, which statins have been shown to alleviate by suppressing the growth of methanogens [65, 67, 68, 69]. The evidence of M. smithii in feces from constipated patients necessitate further investigation of whether this finding in AN patients is only related to constipation or also related to AN psychopathology as a potential biomarker.
The gut microbiota is involved in both weight gain and weight loss as well as with energy extraction from the diet in both humans and animals [70, 71]. Differences in the composition of the gut microbiota between obese and lean individuals have been consistently described, potentially illustrating differences in energy extraction efficiency between obese and lean individuals [72, 73], and specific gut dysbiosis could predispose to the drive toward negative energy balance in AN. With regard to the effect of weight gain on the fecal microbiota, Firmicutes has been found increased after weight restoration in two studies in AN [61, 64].
Intestinal dysbiosis has previously been associated with psychological function and mental health including depression and anxiety, both of which are commonly comorbid with AN [40]. AN patients often present with comorbid anxiety (75% lifetime prevalence of anxiety disorder) [74] and depression (more than 34% lifetime prevalence of depression) [75, 76]. These findings provide further support for a role of dysbiosis in the pathophysiology of AN.
During starvation, some of the gut bacteria will have insufficient nutrient supply for survival. Slowly growing bacteria or bacteria able to feed on the mucus lining the gut wall will survive for a longer period of time [77]. The competition between bacteria with different growth capacities to survive and proliferate in the gut has probably taken place for millions of years. Thus, it is reasonable to expect that various mechanisms for survival and proliferation have emerged among gut bacteria including the capacity to release of substances inhibiting food intake of the host. Alterations in gut permeability has been linked to a number of intestinal diseases, such as irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), but also to extraintestinal disease as depression, anxiety and autism specter disorders [78, 79]. Increased gut permeability may also facilitate signal transduction from the gut to the brain via the vagus nerve and blood [80], possibly in synergy with interaction with increased BBB permeability. In addition, in animal and human studies, the experience of stress is also linked to an increase in permeability of the intestinal barrier. This increase in permeability seems to be mediated through, among other factors, hypothalamic hormones, especially corticotropi-releasing hormone (CRH) [77]. Increased mucin degrading bacteria has been demonstrated in AN [81] indicating that decreased food intake induce overgrowth of bacteria able to feed on the mucus layer and thereby increase gut permeability.
An example of a possible biomarker species is the bacterium Akkermansia muciniphila which is abundant in humans and rodents and has been inversely correlates with body weight and is associated with metabolic syndromes and auto-immune diseases [82]. A. municiphilia is a symbiotic bacterium of the mucus layer, can utilize mucin as its sole carbon, nitrogen, and energy source and is able to produce certain SCFAs [83, 84]. In mice, it has been shown that the abundance of A. muciniphila decreased in obese and type 2 diabetic mice and that administration of the bacterium increased the intestinal levels of endocannabinoids that control inflammation, the gut barrier, and gut peptide secretion [82]. In a single AN patient case story, it has been shown that one treatment with a fecal matter transplant from a healthy donor led to weight gain and an increase in A. municiphilia and SCFAs blood levels [85]. A. municiphilia is an example of a complex interaction where the bacterium simultaneously degrade the mucin for energy, but also at the same time induces higher mucus production from the host. This could in turn improve protection of the gut wall from interaction with harmful molecules from other gut bacteria and leakage into the blood.
Furthermore, in an activity based mouse model of AN Jésus et al. demonstrated increased permeability in the colon, that is, “gut leakiness”, in anorexic mice, however the authors also found that the gut leakiness was more related to malnutrition than exercise [86]. Although there may be conflicting studies [87], yet another study examining the role of exercise on gut permeability, found that exercise increases intestinal permeability measured with the lactulose and rhamnose differential urinary excretion test [88].
Another support for a leaking gut wall in AN comes from a study by Breton et al. [66], who found an increase in ClpB protein concentrations in plasma in eating disorder patients compared to plasma of controls, and furthermore, that ClpB protein concentrations correlated positively with alpha-Melanocyte Stimulating Hormone-(alpha-MSH)-reactive IgG for all patients with eating disorders. ClpB protein is produced by Enterobacteriae such as Escherichia coli and has been found as a conformational mimetic of alpha-MSH, which is thought to be involved in satiety and anxiety [89]. The study adds evidence to the potential role of ClpB protein produced by Enterobacteriae in the gut and its impact on the brain and psychopathology in eating disorders.
The potentially altered gut permeability in AN may underlie the low-grade inflammation and increased risk of autoimmune diseases found in eating disorders [90]. Moreover, starvation has a significant impact on the gut microbiota, and a diet based on animal products used for re-nutrition, may stimulate the growth of bacteria that trigger inflammation [91].
The initial reduction of food intake induces alterations in the gut microbiota. These alterations in gut microbiota induce increased gut permeability. Due to this altered microbiota and increased gut and in addition, increased blood-brain barrier permeability, neurohormonal signals interfering with food intake are transferred to the brain, influencing brain functions, for example, cognition. This contributes in creating a vicious circle which subserves in maintaining the mechanisms associated with AN (Figure 1).
The initial reduction of food intake induces alterations in the gut microbiota. These alterations in gut microbiota induce increased gut permeability. Due to this altered microbiota and increased gut and in addition, increased blood-brain barrier permeability, neurohormonal signals interfering with food intake are transferred to the brain, influencing brain functions, for example, cognition. This contributes in creating a vicious circle which subserves maintaining in the mechanisms associated with AN.
There are a lot of evidence linking dysbiosis and inflammatory and psychiatric disorders and although there are only a few studies that have examined the microbiota in AN, several of these point to a dysbiosis also in AN. The effects of this dysbiosis is mediated through the gut-brain axis, and leakage through the gut and potentially also the BBB, provide pathways for neurohormonal signals to induce and maintain psychiatric disorders such as AN. The evidence in AN will need confirmation and further clarification in larger, randomized and controlled studies. We propose a model for disease development and maintenance in AN where a dysbiosis is a key component. Future studies will need to clarify the pathophysiology of AN.
We are thankful to Psychiatric Center Ballerup and the Capitol Region of Denmark, for providing support for this study.
The authors declare no conflict of interest.
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