On-line optimization of glutamate production based on balanced metabolic control by RQ

In glutamate fermentations by Corynebacterium glutamicum, higher glutamate concentration could be achieved by constantly controlling dissolved oxygen concentration (DO) at a lower level; however, by-product lactate also severely accumulated. The results of analyzing activities changes of the two key enzymes, glutamate and lactate dehydrogenases involved with the fermentation, and the entire metabolic network flux analysis showed that the lactate overproduction was because the metabolic flux in TCA cycle was too low to balance the glucose glycolysis rate. As a result, the respiratory quotient (RQ) adaptive control based “balanced metabolic control” (BMC) strategy was proposed and used to regulate the TCA metabolic flux rate at an appropriate level to achieve the metabolic balance among glycolysis, glutamate synthesis, and TCA metabolic flux. Compared with the best results of various DO constant controls, the BMC strategy increased the maximal glutamate concentration by about 15% and almost completely repressed the lactate accumulation with competitively high glutamate productivity.
Introduction
For more than a few decades, the Corynebacterium spp. bacteria have been used for amino acids productions, including the commercially important products of glutamate, glutamine, and lysine. Among the amino acids mentioned earlier, L-glutamate is the largest fermentative product, which occupies about 53% of the world’s amino acids market [1]. It is also particularly important in food industries and widely used as an important starting substance for the synthesis of various and useful pharmaceutical and healthy products. The previous research works [2–4] have revealed that primary by-products, such as lactate, accumulated during L-glutamate fermentation if the operating condition was improperly controlled, which in turn deteriorated the fermentation performance in terms of both glutamate productivity and yield.
To cope with the problem, the metabolic reaction network (flux) model (MR model) or technique, as its appearance in the early 1990s, has been recognized as an useful system analysis tool and thus widely used in those areas such as metabolic flux distribution analysis [2, 5], determination of the bottleneck controlling the targeted metabolic product formation [6, 7], recognition of fermentation phases [8], and calculation of theoretical or maximum yields [9, 10], etc. However, the research reports of using MR model for on-line physiological state prediction or process control are very limited [3, 11, 12], and the study stayed on on-line recognition of different fermentation phases or physiological states so as to provide information for the subsequent process control, such as whether substrate should be added or whether the fermentation should be terminated [11], determination of glucose feeding rate to avoid the acetate or ethanol overproduction in Escherichia coli or Saccharomyces cerevisiae fed-batch cultivation [12], etc. Optimization of fermentation processes by MR model was mostly limited on using the feed-forward type’s off-line control strategy with the MR model as the constraint conditions [13–14].
On the other hand, analysis of key enzymes in metabolic network of cells was also very important, as those key enzyme generally play important roles in regulating and overproducing the targeted metabolic products. As a result, extensive research works with regard to the enzymes activities under different operating conditions have been carried out. Among them, the enzymes activities in fermentation by Corynebacterium glutamicum such as the influence of NH4+ concentration on C. glutamicum growth and glutamate dehydrogenase (GDH) [15], activity of phosphoenolpyruvate carboxylase responsible for the anaplerotic reactions in lysine continuous production by C. glutamicum under different dilution rates [16, 17], as well as lactate dehydrogenase (LDH) for pyruvate overproduction under different dilution rates with lactate as the sole carbon source [18], were investigated. The metabolic network models were also built for C. glutamicum under different operating conditions, aiming at achieving metabolic flux distribution information concerning the overproduction of targeted metabolites or substrate utilization [17–19], as well as on-line prediction of the targeted products formation and analysis of the entire metabolic fluxes [3].
In our previous study [3], we found that dissolved oxygen (DO) largely affected the metabolic flux distribution and the glutamate fermentation performance. The comprehensive analysis or evaluation of a fermentation process by the MR model-based metabolic flux analysis integrated with the intracellular enzymes activities monitoring, might gain a deeper insight into the entire fermentation process and the metabolic regulation mechanisms, so as to supply a more comprehensive and accurate information base for the subsequent process control and optimization of the fermentation process.
In this study, combining our previous study on on-line metabolic flux analysis of the glutamate fermentation by C. glutamicum, the two major enzymes (GDH and LDH), which possibly dominated the overall glutamate metabolism, were carefully investigated. Then, based on the comprehensive evaluation results of both the metabolic flux analysis and intracellular enzymes activities monitoring, a new optimization strategy of “balanced metabolic control (BMC)” was proposed and verified experimentally, aiming to increase the glutamate production yield, while completely repressing the by-product overproduction simultaneously.
Materials and methods
Microorganism and fermentation conditions
Corynebacterium glutamicum S9114, kept by the laboratory was used throughout this study. The same medium and seed culture conditions described in our previous study [3] were used.
Corynebacterium glutamicum S9114 was cultured for glutamate production at 32°C in a 5 L fermentor (BIOTECH-5BG, Baoxing Co., China) containing about 3.4 L medium. Concentrated glucose was fed based on requirement to ensure the substrate concentration above a suitable level (15 g/L) throughout the fermentation period. pH was controlled at 7.1 ± 0.1 by automatic addition of 25% (w/w) ammonia water which also supplied the nitrogen source required for glutamate synthesis. DO was controlled at various levels by automatically or manually controlling the agitation speed based on particular requirements. The air aeration rate and fermentor pressure were kept constantly at 1.60 vvm and 0.07 MPa, respectively.
Analytical methods
The concentrations of cells, glucose, glutamate, and lactate were measured with the same methods as reported in our previous study [3]. The CO2 and O2 concentrations (partial pressure) in the inlet and exhaust gas were on-line measured by a gas analyzer (LKM2000A, Lokas Co. Ltd, Korea). The collected on-line data were smoothly filtered, and then oxygen uptake rate (OUR) and CO2 evolution rate (CER) were on-line calculated based on the literature reported method [20]. Respiratory quotient (RQ) was determined by its definition (RQ = CER/OUR) using the on-line measured OUR and CER data.
Extraction and assay of the GDH and LDH
The two key enzymes dominating glutamate fermentation and metabolism, the NADPH and NADH dependent GDH and LDH were detected by spectrophotometric method, and the relevant stoichiometric reactions could be written as follows:
The enzymes in the reverse direction of glutamate and lactate consumption were not considered and assayed in this case. Cells were collected by centrifugation and then suspended in 25 mmol/L Tris–HCl buffer, pH 7.5. The suspension was sonicated by a sonifier (JY92-II, Scientz Biotechnology Co., China) at 0°C for a total period of 10 min (pulse on, 1 s; pulse off, 3 s). Cell debris was removed by centrifugation at 9,400g, 4°C, for 20 min, the supernatant was then used as the cell-free crude enzyme. GDH was assayed with a spectrophotometer (UV-2100, Unico, Shanghai, China) at 340 nm by measuring the optical variation within 1 min, with 0.05 mL supernatant and 2.95 ml GDH reaction mixture consisting of 13.3 mmol/L α-ketoglutaric acid, 15 mmol/L NH4Cl, and 1.67 mmol/L NADPH (Sigma Chemical Co., St Louis, MO, USA) in phosphate buffer (pH 7.5) at 37°C. LDH was assayed in the same condition but with the LDH reaction mixture containing 0.757 mmol/L pyruvic acid and 1.67 mmol/L NADH (Sigma Chemical Co., St Louis, MO, USA). Protein was measured by Bradford method with bovine serum albumin as the standard. Enzyme-specific activity (GDH or LDH) was expressed as units/mg-protein, where 1 U was defined as the quantity of enzyme that converted 1 μmol of NAD(P)H per minute.
On-line control system
The on-line measured RQ data were sent to a PC in which a control program written with Visual-Basic (Microsoft Inc., USA) was embedded. Based on the RQ set-points and the measured RQ, the PC on-line regulated the agitation rate of the fermentor (AGT) with a discrete PI control manner described by Eq. 3 , which renewed the agitation rate at an interval of 5 min.
In Eq. 3, k represented the current control instant; RQset was the RQ set-point which might be subject to changes during the control; KC and τI were proportional and integral constants of the feedback controller, respectively. KC and τI were determined by Coon–Cohen method by observing the RQ response to a step change in the input (agitation rate) during a certain period of the glutamate production phase.
Results and discussion
The changing patterns of glutamate and lactate production, as well as the GDH and LDH activities at different DO control levels
Constant control of DO is generally considered as the simplest optimization control method for the aerobic fermentations with aeration. Fig. 1a, c, d showed the time courses of glutamate, lactate, cells, and residual glucose concentrations when DO was constantly controlled at 10 and 50% (saturation level), respectively. The results showed that, glutamate and lactate formation pattern strongly depended on the DO control level. A higher glutamate production rate could be achieved when the DO was controlled at a lower level of 10% and the final glutamate concentration reached about 91.5 g/L at 34 h, while the final glutamate concentration stopped at a lower level of 72.7 g/L (30 h) when controlling DO at 50%. On the other hand, lactate severely accumulated up to 28 g/L when DO was controlled at a lower level of 10%, while almost no lactate accumulation occurred when controlling DO at 50%. These results suggested that the enzymatic activities of GDH and LDH under lower and higher DO level might be quite different.Fig. 1Time courses of glutamate, lactate, cells and glucose concentrations, as well as the key enzymes activities at different DO control levels. a Glutamate concentration: (filled circle) DO 10%, (filled triangle) DO 50%; lactate concentration: (open circle) DO 10%, (open triangle) DO 50%. b GDH activity: (filled circle) DO 10%, (open circle) DO 50%; LDH activity: (filled triangle) DO 10%, (open triangle) DO 50%. c Cells concentration: (filled circle) DO 10%, (filled triangle) DO 50%. d Glucose concentration: (filled circle) DO 10%, (filled triangle) DO 50%
Figure 1b indicated the changing pattern of the GDH and LDH activities under the same DO control levels. The GDH activity for the lower DO case (10%) was much higher and declined slower than that of the higher DO case (50%) after experiencing the enzymatic activity peak of about 1.8 U/mg-protein around 12–13 h. This result could at least explain or account for the fact that, higher glutamate production rate and accumulation occurred at lower DO control level in the initial and middle production phases of 8–20 h. The slow down of glutamate production rate in the late production stage (20–30 h) and the final stoppage of glutamate production were due to the reduction of the co-enzyme NADPH regeneration rate, which will be further discussed in the following section. As for the LDH activities, from the result of severe lactate’s accumulation at lower DO level, it was speculated that the LDH activity under lower DO level (10%) should also be much higher than that of the higher DO case. However, the result did not agree with the expectation. Although the LDH activity under lower DO level was slightly higher than that of the higher DO for the majority of the fermentation period, this result could not stand for the large difference in the lactate accumulation under different DO levels.
It should be noted that the cell concentration could reach a constant and stabilized level of about 20 g/L prior to production phase (Fig. 1c) for all of the constant DO control cases, therefore using the unit of “U/mg-protein” could actually reflect the total enzyme activity.
The changing patterns of glutamate and lactate production, as well as the GDH and LDH activities at anaerobic fermentation condition
Generally, it is considered that the anaerobic condition is extremely harmful to glutamate production. To verify the above speculation, an experiment under extremely low DO level was conducted. In the fermentation, DO was initially controlled at 30%, and the agitation rate was manually reduced to bring DO down to 0% instantly at 12 h. Then, the same agitation rate was kept for the next 6 h. During this period, the fermentation could be considered as implemented under anaerobic condition, glutamate production stopped and lactate overflowed as shown in Fig. 2a. At 18 h, the automatic control of DO was resumed to quickly bring DO back to 30%, a partial recovery of glutamate production was observed. However, the final glutamate concentration ended at a very low level of about 45 g/L. As shown in Fig. 2b, during the anaerobic period, GDH activity sharply dropped to a level of 0.87 U/mg from the original 1.86 U/mg, and could not be recovered again even when DO was resumed back to 30% level. The results indicated that the occurrence of anaerobic condition even for a short period would be both fatal and irretrievable to glutamate fermentation.Fig. 2The changing pattern of the products formation and the key enzymes activities when DO control level was subject to a sudden change during 12–18 h, and maintained at extremely low level during the period. a The changing patterns of glutamate and lactate concentrations, as well as the DO control level, filled circle: glutamate concentration, open triangle: lactate concentration, solid line: DO. b The changing patterns of the key enzyme activities, filled circle: GDH activity, open triangle: LDH activity
The glucose consumption rates (glycolysis rates) at different DO control levels
Figure 3 showed the average glucose consumption rates under different DO control levels during the production phase. Apparently, under regular (aerobic) fermentation, the glycolysis rate did not depend on the DO control level, and the changing patterns of glycolysis rate at different DO levels were basically the same.Fig. 3The time courses of glucose consumption rate at different DO levels. Open circle: DO = 10%, batch no. 050331; open triangle: DO = 50%, batch no. 050407
The mechanism analysis of lactate overflow and the control strategy of BMC
For the purpose of easily interpreting the experimental results, the metabolic flow chart of glutamate fermentation by C. glutamicum as shown in our previous study [3] was simplified and re-depicted in Fig. 4. The metabolic fluxes changes at 10, 20, and 30 h of the fermentations with the DO constant controls (10, 50%) and BMC were also depicted. As shown in the map, glucose was converted into pyruvate through EMP pathway, and supplemented with PP pathway to produce NADPH for structuring glutamate synthesis precursors by cells. The pyruvate formed either entered TCA cycle via pyruvate dehydrogenase, generating the energy as well as co-enzyme substances such as ATP, NADH, etc. for the entire metabolic network operations, or was converted into lactate with LDH as the enzymatic catalyst (r5, Eq. 2). At α-KG (α-ketoglutaric acid) node of TCA cycle, the glutamate synthesis reaction (r6) with GDH as the enzymatic catalyst (Eq. 1) occurred. As shown in Fig. 4, with the constant DO control strategy, the decrease of the DO control level enhanced the metabolic fluxes of both glutamate and lactate formation; while the metabolic flux of TCA after the α-KG node (r4) sharply decreased, and the TCA cycle was almost completely shut down when controlling DO at 10% level (also refer to Fig. 5).Fig. 4Simplified metabolic flow chart and the relevant metabolic fluxes changes during the fermentations. Broken line: very low metabolic flux or channels shut down; solid line: moderate metabolic flux; bold solid line: enhanced metabolic flux. a: at 10 h; b: at 20 h; c: at 30 hFig. 5Comparison of RQ and calculated TCA metabolic flux rate at different DO control levels
A higher enzyme activity in vivo reflects a higher potential in reaction rate catalyzed by the enzyme. However, it never means that the relevant reaction rate must be high, as the reaction rate also depends on other factors, such as the coenzyme (NADPH, NADH) activity as well as the reactant concentration. As shown in Fig. 1a, b, at the end of the fermentation, the glutamate synthesis completely stopped even though GDH activity still remained at a higher level. The calculation results in our previous report [3] showed that at the end of the fermentation, the coenzyme NADPH regeneration rate had decreased to 5–20% of its peak level (figure not presented). Therefore, it could be concluded that, the glutamate synthesis stoppage was due to the significant reduction of NADPH regeneration rate instead of the reduction in GDH activity.
In general, the enzymes catalyzing interaction assures an intracellular carbon fluxes balance. This means that in glutamate fermentation, glycolysis rate (r1) should balance with glutamate synthesis (r6), lactate formation (r5), as well as the TCA metabolic flux (r4) that was involved with CO2 release. As a result, from Figs. 1, 4, and 5, we could speculate that the severe lactate accumulation at lower DO control (DO = 10%) was due to the carbon metabolic balance rather than the higher LDH activity by the following facts. First, the changing patterns of glycolysis rate (r1) at different DO levels were almost the same; second, no significant differences in LDH activities were shown under different DO level; Third, the metabolic flux of TCA significantly decreased with the decrease in DO control level. Under the lower DO level, even though GDH activity was higher, the higher glutamate synthesis rate (r6) still could not completely balance with the glycolysis rate (r1), as TCA cycle was almost closed completely and the TCA metabolic flux (r4) was very low. Under this circumstance, lactate had to be overflowed or excreted (r5) into the broth to achieve the entire intracellular carbon balance. On the other hand, under the higher DO level, GDH activity was relatively low, but the TCA cycle was nearly open for a complete oxidation. Under this condition, lactate was not necessarily overflowed, as the glutamate synthesis rate (r6) plus the higher TCA metabolic flux (r4) were big enough to balance with the glycolysis rate (r1), even though LDH exhibited almost equivalent activity as compared with that of the lower DO case.
Is there an effective way to solve the problem of simultaneous accumulation of the main product (glutamate) and by-product (lactate)? Two ways of using genetic engineering technique could be considered as the solution: (1) to use genetic engineering technique to knock-out the genes coding for LDH; (2) to use certain LDH inhibitors to eliminate the LDH action without affecting GDH and entire fermentation activities at the same time. These are direct and straightforward solutions, but might be very complicated and difficult to realize. Based on the metabolic analysis mentioned earlier, an alternative solution, the “BMC” strategy as shown in Fig. 4, which is adaptively regulating of the TCA metabolic fluxes at an appropriate level to achieve the metabolic balance among glycolysis, glutamate synthesis, and TCA metabolic flux for enhancement of the glutamate synthesis and repression of lactate overflow, seemed to be easier to implement and more realistic.
Possibility of using and realizing the BMC strategy
The necessary conditions for realization of BMC existed in the following two aspects: (1) the rates of the glutamate synthesis (r6) plus the TCA metabolic flux (r4) should just balance with the glycolysis rate (r1); (2) DO should be controlled at a reasonably lower level to assure a higher GDH activity. To achieve the target, an on-line measurable or predictable state variable that could actually reflect the TCA metabolic flux changes, must be available. Based on our experimental results and the on-line metabolic flux calculation in the previous study, we found RQ are related with the TCA metabolic flux (r4) closely, and could be considered as the on-line feedback index for realization of BMC.
Figure 5 depicted the time course of RQ and the TCA metabolic flux (r4) for the cases of constantly controlling DO at 10 and 50%, respectively. For both cases, RQ almost followed the same changing patterns during the growth phase. However, during the glutamate production phase, the changing patterns of RQ and TCA metabolic flux under different DO levels were apparently different. RQ almost stayed constantly around 0.80 in the case of controlling DO at 50%, while RQ gradually declined into the range of 0.5–0.6 for the case of controlling DO at 10%. Theoretically, RQ = 1.0 means that TCA cycle is fully opened and glucose is completely converted into CO2 and H2O via TCA cycle. In glutamate fermentation, a lower RQ (for example, RQ = 0.5–0.6) reflected the fact that, TCA cycle was almost completely shut down in order to direct the carbon flux to the glutamate synthesis at α-KG node. On the other hand, a higher RQ meant that the TCA metabolic flux was too high that a large portion of carbon was oxidased into CO2 and H2O accompanyied with large quantities of NADH and ATP formation, which actually caused the “inefficient carbon cycle‘’. Based on Fig. 5, If we could carefully and adaptively control RQ at a suitable level, maintaining the TCA metabolic flux at a moderate level in between the fluxes of lower and higher DO levels, while still directing major carbon flux to the glutamate synthesis, then the BMC might be realized and lactate overflow be relieved.
The performance and experimental result of the BMC strategy
Figure 6 showed one complete data set of the BMC control results. The BMC was activated at 11 h after the fermentation entered into production phase, where the two controller parameters, KC and τI were set at 20 and 3, respectively. As shown in Fig. 6, the set-point of RQ was changed three times during the fermentation (RQset: 0.80, 11–15 h, initial stage of production phase; 0.70, 15–28 h, middle stage; 0.60, 28–35 h, final stage). The principle of changing RQset was to assure that DO does not subject to either a higher (≥40%) or a very low level (≤5%) for a relatively longer time (1 h) in order to keep a higher GDH activity: if DO stayed at higher level for relatively long time, then it initiatively reduced the RQset down; if DO stayed at very low level for relatively long time, then it initiatively brings the RQset up. By using DO as the complementary indicator in this way, RQ could be controlled around its set-point, with the agitation rate stably staying in the range of 450–750 rpm without reaching its upper limit (950 rpm) or low limit (450 rpm) even for a short time. With this control strategy, DO fluctuated at a lower range of 5–40% so that a higher GDH activity could be maintained. The BMC control led to an excellent fermentation performance as compared with those of the DO constant controls: the maximal glutamate concentration reached 101.6 g/L at 34 h which was about an 11% increase over the best result of the DO constant controls (91.5 g/L, DO = 10%); the lactate concentration at the corresponding time was only 0.11 g/L which was only 14% of the best result of the DO constant controls (0.8 g/L, DO = 50%). To verify the repeatability of BMC control’s performance, the same experiment was repeated once. Again, a maximal glutamate concentration of 98.8 g/L was obtained at 38 h, with the lactate accumulation of only 1.04 g/L at the same time.Fig. 6The experimental results and control performance using the BMC optimization strategy. Filled circle: glutamate concentration; open triangle: lactate concentration; open circle: cells concentration; filled square: glucose concentration
It should be noted that constant RQ control strategy could not lead to a good fermentation performance. We did three constant RQ control experiments setting RQ at 0.6, 0.7, and 0.8, respectively. The glutamate production stopped at very low level (40–60 g/L) for all of the three cases. In the first two cases, constantly controlling RQ at 0.6 and 0.7 caused DO to drop to zero level, or the anaerobic environmental condition in other word, in the initial production stage. The cells metabolic and GDH activities were largely damaged as suggested by Fig. 2 and relevant discussion. In the third case, the high RQ set-point (RQ = 0.8) caused a continuous rise-up and high level of DO (from 20 to 70%), which reduced the GDH activity and deteriorated glutamate production in turn. As a result, the adaptive RQ control (step-wise changing RQ set-points) in simultaneous consideration of DO change has to be adopted to satisfy the two conditions of obtaining an appropriate r4 flux and a reasonable lower DO level for realizing the BMC, as described in the previous section.
Figure 7 showed the changing patterns of the TCA metabolic flux (r4) when using the BMC control and the DO constant controls. Apparently, the BMC strategy controlled the TCA metabolic flux at an appropriate level just in between those of higher and lower DO levels. This realized the target of “balanced metabolism”, which led to a higher glutamate production and almost completely repressed the lactate accumulation.Fig. 7The calculated TCA metabolic fluxes of the BMC strategy and the DO constant controls
Table 1 indicated that CO2 yield (total CO2 released quantity versus total glucose consumed quantity) of the BMC was also just in between those of higher and lower DO constant control cases, which supported the BMC idea from the other side. However, it should be noted that, besides channel r4, the CO2 release also occurred at the other routes in the entire metabolic network, the shutdown of TCA cycle never meant the total CO2 production would cease. The effectiveness of the BMC strategy was also supported by the metabolic fluxes changes results shown in Fig. 4. The moderate TCA metabolic flux (r4) simultaneously ensured a higher glutamate synthetic flux and an almost nil lactate formation flux.Table 1The materials and carbon balances of the balanced metabolic control (BMC) and dissolved oxygen (DO) constant controlsBatch no.Glucose consumed (g)Glutamate produced (g)Lactate produced (g)Cellsa (g)CO2 released (g)Carbon balanceb (%)CO2/Glucose (−) (%)DO = 10%839.2384.0102.090.8352.9C-content335.6156.740.846.996.2101.528.6DO = 50%530.2265.23.277.1262.9C-content212.1108.21.339.871.7104.233.9RQ-BMC716.7406.40.460.7335.9C-content286.7165.90.1831.491.6100.831.9aThe dry cell weight, using the composition of strain C. glutamicum AJ-3462 (C4.71H8.02O1.92N, Ref.#11) as the calculation referencebRatio of the total production (glutamate, lactate, cell, and CO2) to total consumption (glucose)
Table 2 summarized the results and performance of the BMC and the DO constant controls. In consideration of the repeatability of the different control strategies, the experiments of constantly controlling DO at 10 and 50%, as well as BMC were repeated at least twice and then the averaged values were calculated for performance evaluation. The table evaluated the two different control strategies in terms of the four major fermentation performance index: maximal glutamate concentration, lactate accumulation quantity, glutamate productivity, and conversion rate from glucose to glutamate. The BMC showed an equivalent performance in the terms of productivity and conversion rate as compared with the best results of the DO constant controls. However, BMC strategy greatly improved the other two performance index: maximal glutamate concentration was increased by about 15% (with respect to DO = 10% case) and lactate accumulation was decreased by about 36% (with respect to DO = 50% case).Table 2The summarized results of the BMC and DO constant controlBatch no.Conversion rate (%)Glutamate concentration (g/L)Lactate concentration (g/L)Productivity (g/L/h)Constant DO controls 050331 (DO = 10%)49.3883.0027.902.86 050526 (DO = 10%)43.6291.5025.502.69 DO = 10%, average46.5087.2526.452.77 050407 (DO = 50%)44.5174.201.002.56 050512 (DO = 50%)53.4072.800.802.43 DO = 50%, average48.9673.500.902.49 050427 (DO = 30%)55.5583.2018.602.19Balanced metabolic control by RQ (RQ-BMC) 050509 (RQ-BMC)56.71101.600.112.99 050516 (RQ-BMC)49.8098.801.042.60 RQ-BMC, average53.26100.200.582.80
Summary
A novel fermentation optimization method—the “balanced metabolic control” (BMC) strategy was proposed and successfully used by feedback controlling RQ to regulate the TCA metabolic flux rate at an appropriate level to achieve the metabolic balance among glycolysis, glutamate synthesis, and TCA metabolic flux for glutamate fermentation. The proposed BMC strategy greatly improved the fermentation performance in the two terms of maximal glutamate concentration and the lactate overflow repression. The maximal glutamate concentration increased by about 15% compared with the best results of the DO constant controls, and furthermore, the lactate overproduction occurred in the DO constant control cases could be completely relieved when using the BMC strategy. As a result, the proposed BMC strategy is expected to be applicable for optimization of other aerobic amino acids fermentations in potential.

on-line optimization
glutamate fermentation
metabolic balancing
metabolic flux analysis
intracellular enzymes activities


Involvement of (pro)renin receptor in the glomerular filtration barrier

(Pro)renin receptor-bound prorenin not only causes the generation of angiotensin II via the nonproteolytic activation of prorenin, it also activates the receptor’s own intracellular signaling pathways independent of the generated angiotensin II. Within the kidneys, the (pro)renin receptor is not only present in the glomerular mesangium, it is also abundant in podocytes, which play an important role in the maintenance of the glomerular filtration barrier. Recent in vivo studies have demonstrated that the overexpression of the (pro)renin receptor to a degree similar to that observed in hypertensive rat kidneys leads to slowly progressive nephropathy with proteinuria. In addition, the handle region peptide, which acts as a decoy peptide and competitively inhibits the binding of prorenin to the receptor, is more beneficial than an angiotensin-converting enzyme inhibitor with regard to alleviating proteinuria and glomerulosclerosis in experimental animal models of diabetes and essential hypertension. Thus, the (pro)renin receptor may be upregulated in podocytes under hypertensive conditions and may contribute to the breakdown of the glomerular filtration barrier.
Introduction
When the (pro)renin receptor binds to the “handle” region of inactive prorenin, the receptor-bound prorenin gains its enzyme activity (ability to generate angiotensin I) without the proteolytic cleavage of the prosegment of prorenin in COS-7 cells [1, 2], presumably as a result of a conformational change. On the other hand, receptor-bound prorenin also triggers its own intracellular signaling pathways independent of the generated angiotensin II. Studies have shown that the stimulation of the (pro)renin receptor by renin/prorenin activates tyrosine phosphorylation leading to the activation of extracellular signal-related protein kinases (ERK) [3] and upregulates transforming growth factor-β1 (TGF-β1) and matrix proteins without involving angiotensin II generation in human and rat mesangial cells [4]. In this review, we focus on the latest progress in elucidating the localization, regulation, and pathophysiological roles of the (pro)renin receptor in the kidneys.
Localization in the kidney
Studies have demonstrated that the (pro)renin receptor protein and messenger ribonucleic acid (mRNA) are expressed in the mesangium cells of human kidneys [3, 4]. However, double immunohistochemical analyses using a polyclonal antirat (pro)renin receptor antibody demonstrated that the (pro)renin receptor was colocalized with a podocyte marker, podocalyxin, but not with a mesangium marker, Thy1.1, in rat kidneys [5] and electron microscopic analyses appeared to indicate the predominant presence of the rat (pro)renin receptor in podocytes, excluding the foot processes, and its absence in mesangial cells [5]. In addition, we recently detected (pro)renin receptor mRNA in cultured human podocytes (unpublished data). Within the glomerulus, podocytes play an important role in the maintenance of the glomerular filtration barrier and podocyte injury leads to proteinuria and initiates glomerulosclerosis resulting in the progressive loss of renal function [6]. Therefore, (pro)renin receptor in the podocytes may contribute to proteinuria and renal injury through an angiotensin-II-dependent pathway, an angiotensin-II-independent pathway, or both pathways in chronic kidney diseases.
Possible upregulation of (pro)renin receptor expression under hypertensive conditions
In the kidneys of young hypertensive stroke-prone spontaneously hypertensive rats (SHRsp), an increase by two- or threefold in the mRNA expression of the (pro)renin receptor has been observed [7]. However, the kidneys of older hypertensive SHRsp did not show an elevation in (pro)renin receptor mRNA expression. Because the binding of renin to the (pro)renin receptor lowers the (pro)renin receptor mRNA level through a promyelocytic zinc-finger-protein-mediated negative feedback mechanism [8], a further increase in the plasma renin levels in old hypertensive SHRsp might inhibit the increase in (pro)renin receptor mRNA levels through a negative feedback loop. More recently, enhanced mRNA expression of the (pro)renin receptor was also observed in the clipped kidneys of Goldblatt hypertensive rats [9]. These results seem to provide evidence of the importance of hypertensive conditions in the regulation of (pro)renin receptor expression in the kidneys.
Effects of (pro)renin receptor overexpression on the kidney
Recent studies in transgenic rats overexpressing the human (pro)renin receptor gene nonspecifically by three to seven times demonstrated that glomerulosclerosis with proteinuria developed at 5–6months of age even in the absence of an elevation in blood pressure [10]. In the kidneys of 5- to 6-month-old transgenic rats, mitogen-activated protein kinase(s) (MAPK(s)) were activated without recognizable tyrosine phosphorylation of the epidermal growth factor receptor and the expression of TGF-β1 was enhanced. The in vivo administration of angiotensin-converting enzyme (ACE) inhibitor did not inhibit the development of glomerulosclerosis, proteinuria, MAPK activation, or TGF-β1 expression in the kidneys despite a significant decrease in the renal angiotensin II level. As shown in Fig. 1, recombinant rat prorenin stimulated MAPK activation in human-receptor-expressed cultured cells but human receptor was unable to evoke the enzyme activity of rat prorenin. Thus, the overexpression of the human (pro)renin receptor elicits slowly progressive nephropathy via angiotensin-II-independent MAPK activation but not through the stimulation of angiotensin II generation. However, hypertension developed at 7months of age in transgenic rats overexpressing the human (pro)renin receptor gene exclusively in smooth muscle cells [11]. Because young transgenic rats of this strain show an enhanced expression of macula densa cyclooxygenase-2 that suppresses the tubuloglomerular feedback system and contributes to the inhibition of hypertension development [12], hypertension may not yet have developed at 6months of age or earlier. Therefore, we can interpret these observations as indicating that the hypertension may have occurred as a result of the nephropathy. However, further studies are needed to elucidate the involvement of the (pro)renin receptor in the development of hypertension.
Fig. 1Both rat and human prorenin stimulate human (pro)renin receptor (h(P)RR)-dependent intracellular signals, but the h(P)RR activates human prorenin but does not activate rat prorenin
Receptor-bound prorenin in diabetic kidneys
Recombinant prorenin binds to recombinant (pro)renin receptor expressed compulsorily on the cell surfaces of COS-7 cells and prorenin bound to the receptor on the cell surfaces exerts renin activity without undergoing any change in its molecular weight. However, synthetic peptides containing the amino acid sequence corresponding to the “handle” region of the prorenin prosegment competitively inhibit prorenin from binding to cell membrane receptors [13]. In the absence of experimental data showing the blockade of renin signaling by the handle region peptide, we believe that the possible inhibition of receptor signals and angiotensin II generation by the handle region peptide should be interpreted as indicating the blockade of prorenin binding.
Diabetic animals or patients have lower renin levels and higher prorenin levels than normal healthy controls [14, 15]. The handle region peptide was administered to rats with streptozotosin-induced type I diabetes and the tissue angiotensin I and II levels in their kidneys, and the development of nephropathy were followed [16]. The administration of the handle region peptide for 6months significantly inhibited the increase in renal angiotensin II levels and the development of proteinuria and glomerulosclerosis, suggesting that the nonproteolytic activation of prorenin bound to the (pro)renin receptor plays an important role in the development of nephropathy. Although renal mRNA expression of the (pro)renin receptor was similar in the control and diabetic rats [17], nonproteolytically activated prorenin increased in the kidneys of the diabetic rats, and this increase was inhibited by the handle region peptide. In addition, the renal total renin content remained unchanged during the treatment with the handle region peptide. These results suggested that the handle region peptide inhibits the conformational change in prorenin that leads to prorenin’s enzymatic activation but does not alter the prorenin levels. Because the mRNA expression of cathepsin B, which processes the conversion of prorenin to renin, simultaneously decreased in the kidneys of the diabetic rats, prorenin may have accumulated in their kidneys. Also, the amount of prorenin released to the circulatory system may be elevated in diabetes leading to an elevation in receptor-bound prorenin as a result of the increased prorenin levels with no change in (pro)renin receptor expression.
To investigate the contribution of angiotensin-II-independent, (pro)renin-receptor-dependent signal pathways to diabetic end-organ damage, the therapeutic effects of infusion were compared with the effect of the deletion of angiotensin II type 1a receptor (ATKO) and ACE inhibition on streptozotosin-induced type I diabetes [18]. Deletion of the angiotensin II type 1 gene attenuated the development of glomerulosclerosis and proteinuria but did not prevent these developments. ACE inhibitor also failed to prevent diabetic nephropathy despite the normalization of renal angiotensin II. On the other hand, the handle region peptide almost completely prevented the development of these conditions despite the absence of an increase in renal angiotensin levels. Of note, MAPKs were markedly activated in diabetic ATKO animals and, while the handle region peptide almost completely inhibited the activation, ACE inhibition failed to even ameliorate it. These results indicate that inhibition of the angiotensin-II-mediated pathway had a little therapeutic effect on nephropathy in the diabetic rats, whereas the blockade of receptor-bound prorenin seemed to suppress the two major pathways for diabetic nephropathy: excessive production of angiotensin II in the kidneys and renal MAPK activation. In this experiment, the renal angiotensin II levels of diabetic mice treated with the handle region peptide were similar to those of untreated wild-type mice. Because the handle region peptide did not seem to inhibit the binding of renin to the (pro)renin receptor, the physiological production of renal angiotensin II induced by renin or receptor-bound renin was probably unaffected by the handle-region-peptide-induced inhibition of receptor-bound prorenin. In addition, the handle region peptide did not influence the plasma levels of renin and prorenin (receptor-unbound free renin and receptor-unbound free prorenin) or blood pressure. Thus, the handle region peptide also had no effect on the physiology of the circulating renin–angiotensin system.
We recently found that the handle region peptide also leads to the regression of nephropathy in rats with streptozotosin-induced type I diabetes [17]. Diabetes was induced by streptozotosin injection in 4-week-old rats. Increased urinary protein excretion and glomerulosclerosis were observed 12weeks later and the administration of the handle region peptide or vehicle was started. While urinary protein excretion and glomerulosclerosis had progressed in vehicle-treated 28-week-old diabetic rats, treatment with the handle region peptide significantly reduced the proteinuria and glomerulosclerosis from the levels observed at the start of treatment. This reversal of the glomerulosclerosis that had already developed in the diabetic rats suggested that receptor-bound prorenin contributes not only to the onset of nephropathy but also to its progression.
The increased plasma prorenin level might be supplied by the kidneys in which high glucose levels might inhibit the processing of prorenin to renin as described above. In addition, extrarenal tissues might also be sources of the increased prorenin level under high glucose conditions because plasma prorenin is detectable even after a bilateral nephrectomy [15]. Thus, high glucose levels may contribute to an increase in the plasma prorenin level via both renal and extrarenal mechanisms, and the elevated plasma prorenin level might trigger an increase in receptor-bound prorenin leading to nephropathy. These mechanisms may explain why the handle region peptide inhibited the development and progression of nephropathy in animal models of diabetes without affecting the high blood glucose level. Further study is needed to clarify these points.
Receptor-bound prorenin in hypertensive kidneys
SHRsp rats have high plasma levels of prorenin and renin and are used as a model of primary hypertension [19]. In addition to the increased mRNA expression of the (pro)renin receptor as described above, elevated angiotensin I and II levels and glomerulosclerosis with proteinuria have been observed in the kidneys of young hypertensive SHRsp; administration of the handle region peptide significantly suppressed these changes without affecting the development of hypertension [7]. In addition, profibrotic changes in morphology were observed in the clipped kidneys of Goldblatt hypertensive rats with enhanced mRNA expression of the (pro)renin receptor [9]. Therefore, the increased expression of the (pro)renin receptor may play an important role in the development of renal tissues showing characteristic signs of hypertensive damage. Because recent studies have demonstrated the presence of angiotensin-II-independent, (pro)renin-receptor-dependent MAPK pathways in mesangial cells [20] and vascular smooth muscle cells [21], the beneficial effects of the handle region peptide may be mediated by both of these two major pathways that are stimulated by receptor-bound prorenin. Thus, receptor-bound prorenin also contributes to the development of hypertensive end-organ damage. This was recently supported, in part, by Susic et al., who reported that the handle region peptide significantly reduced the left ventricular mass even though the myocardial collagen content remained unchanged [22].
Possible inhibition of receptor-bound prorenin by high levels of renin
Figure 2 shows the binding of renin and prorenin to the (pro)renin receptor. Because both renin and prorenin bind to the (pro)renin receptor [3], increased renin levels may increase the amount of renin and decrease the amount of prorenin that binds to the receptor. A recent review reported that the handle region peptide offered no benefit to renin-transgenic models or to a model with renovascular hypertension [23]. Because the ratio of renin to prorenin must be higher in these models than in control models, renin—but not prorenin—that has bound to the receptor must contribute to the pathogenesis that occurs in these models. Because no data showed the blockade of renin signaling by the handle region peptide, the handle region peptide did not benefit the renin-transgenic models or the model with renovascular hypertension [23]. In contrast, the handle region peptide greatly benefited diabetic animals [16–18]. Because diabetic animals are known to have a low ratio of renin to prorenin [15], they have increased levels of prorenin bound to the receptor and decreased levels of renin bound to the receptor. Thus, the handle region peptide was thought to benefit in vivo diabetic animals by reducing the amount of prorenin that bound to the receptor. In addition, the handle region peptide had a partial benefit on SHRsp [7, 24] and SHR [22] animals fed a high-salt diet. This benefit was reduced by changing from a high-salt diet to a low-salt diet (unpublished preliminary data), suggesting that dietary salt might alter the levels of prorenin bound to the receptor by regulating the renin levels. Thus, the efficiency of the handle region peptide may depend on the ratio of renin to prorenin.
Fig. 2Renin and prorenin competitively bind to the (pro)renin receptor in vivo and to the (pro)renin receptor expressed on the cell surfaces of COS-7 cells
(Pro)renin receptors in cultured cells
Using vascular smooth muscle cells (VSMC) harvested from human-(pro)renin-receptor-transgenic rats, Batenburg et al. recently demonstrated that prorenin binds to the (pro)renin receptor and that receptor-bound prorenin exerts a catalytic activity but that renin does not bind to the receptor or increase its own catalytic activity [25], consistent with the results of a previous study using human VSMC [21]. The structural difference between renin and prorenin arises from 43 amino acids in the prorenin prosegment and prorenin acquires its catalytic activity without changing its molecular weight when a specific protein binds to the “handle” region of the prorenin prosegment [1]. Nevertheless, the handle region decoy peptide, which inhibited the binding of prorenin to the receptor in a test tube and to receptors expressed on the cell surfaces of COS-7 cells [13], failed to inhibit prorenin’s acquisition of catalytic activity by binding to the receptor [25]. A recent study using a human cultured cell line clearly demonstrated that the 39-kDa full-length (pro)renin receptor was predominantly present in the intracellular endoplasmic reticulum [8]. Because both renin and prorenin can permeate the cell membrane [8] but the handle region peptide cannot, the handle region peptide might be incapable of inhibiting intracellular binding between the (pro)renin receptor and its ligands (Fig. 3).
Fig. 3Possible fragmentation of (pro)renin receptor in cultured cells
Although (pro)renin receptors of VSMC harvested from human-(pro)renin-receptor-transgenic rats were not bound to renin [25], the same authors also showed that both renin and prorenin bind to the (pro)renin receptor and stimulate receptor-dependent ERK pathways in U937 monocytes [26]. In addition, a renin-stimulated, (pro)renin-receptor-dependent ERK pathway was observed in rat and human mesangial cells [4, 20], whereas the stimulation of the receptor by prorenin in cultured cardiomyocytes resulted in the activation of p-38 MAPK, but not ERK [27], and renin stimulation caused the interaction between the receptor and the transcription factor promyelocytic zinc finger protein in HeLa-S3 cells and HEK293 cells [8]. These results suggest that the ability to bind renin and prorenin and the intracellular pathways involving these proteins may be influenced by the species and condition of the cultured cells. In addition, the presence of (pro)renin receptors with molecular weights smaller than the 39kDa of the full-length receptor in cultured cells has been reported in abstracts presented at several meetings. Based on the results of all these studies regarding the (pro)renin receptor in cultured cells, we propose the possible fragmentation of the (pro)renin receptor (Fig. 3). Namely, when the 39-kDa full-length (pro)renin receptor in the endoplasmic reticulum is released to the cytoplasm, it may divide into a renin-binding part and a remnant part. In the cytoplasm of VSMC and cardiomyocytes, the remaining smaller (pro)renin receptor without the renin-binding part would be capable of binding prorenin but would be incapable of binding renin. However, in the cytoplasm of the mesangium and monocytes, the smaller receptor might readhere to the renin-binding part and resume its ability to bind renin. When the smaller receptor in the cytoplasm reaches the cell membrane, it might further divide into the prorenin-binding part, recognizing the “handle” region, and a remnant part. The prorenin-binding part would be released to the extracellular area and the smallest remnant receptor would remain at the cell membrane. Because the smallest receptor does not contain either the renin-binding part or the prorenin-binding part, it would no longer be capable of binding renin or prorenin. When the prorenin bound to the smaller receptor also reaches the cell membrane, it would divide into three parts: the prorenin prosegment adhered to the prorenin-binding part, renin, and the remaining smallest receptor. Because the former two proteins are released to the extracellular area, angiotensin I generation from external angiotensinogen would occur there. Thus, under cultured cell conditions, the (pro)renin receptor may be fragmented depending on the cell species. Further studies are needed to verify the possible fragmentation of the (pro)renin receptor.
Conclusion
In the glomerulus of the kidneys, the (pro)renin receptor is not only present in the mesangium but is also abundant in the podocytes and its expression was increased by two- to threefold in the kidneys of some animal models of hypertension. A transgene-induced overexpression of human (pro)renin receptor in rats resulted in the development of slowly progressive nephropathy with proteinuria. In addition, the handle-region-peptide-induced inhibition of receptor-bound prorenin significantly inhibited the development and progression of proteinuria in experimental diabetic and hypertensive animal models. Thus, the glomerular (pro)renin receptor appears to play an important role in the regulation of the glomerular filtration barrier.

prorenin
angiotensin
nonproteolytic activation
podocytes
mitogen-activated protein kinases


Ocular blood flow in patients with obstructive sleep apnea syndrome (OSAS)

Background Sleep-related disorders are among the important risk factors for neurovascular diseases. Obstructive sleep apnea syndrome (OSAS) is characterized by snoring, excessive daytime sleepiness, and insomnia. Our aim was to investigate the presence of glaucoma in patients with OSAS and to reveal vascular pathology related to the pathogenesis of glaucoma in those patients.
Obstructive sleep apnea syndrome (OSAS) is characterized by snoring, excessive daytime sleepiness, and insomnia. Epidemiological studies revealed a prevalence between 2 and 20%. Among the risk factors, obesity, male gender, upper respiratory tract abnormality, consumption of alcohol, snoring, and thick neck are worth mentioning [5, 11, 12].
In previous studies, retinal vascular tortuosity and congestion [7], floppy eyelid syndrome [3, 18, 22, 27], keratoconus [22], papilledema, and optic neuropathy [2, 14, 20] have been described in patients with OSAS. It has been stressed that OSAS may be a predisposing condition for anterior optic neuropathy [14]. In a study, Mojon et al., investigating 69 patients with OSAS, found the incidence of glaucoma to be 7.2% [21]. Onen et al. found a high incidence (14.6–47.6%) of primary open-angle glaucoma (POAG) in patients with sleep-disordered breathing [24]. Similarly, in another study by Mojon et al., the prevalence of low-tension glaucoma (LTG) in patients with OSAS was found to be 50% [23]. Recently, in a study from Turkey, it was stated that OSAS is correlated with a proportional decrease in retinal nerve fiber layer (NFL) [15].
The study’s aim was to investigate the ocular blood flow in patients with OSAS since pathological ocular blood flow has been suggested as a major mechanism in the etiology of glaucoma and also to detect the glaucoma prevalence in the study group.
Patients and methods
Patients were recruited from those who were referred for suspected OSAS to the “Sleep Unit” of the Neurology Clinic (Erciyes University Faculty of Medicine, Kayseri, Turkey) between December 2003 and July 2004. These were consecutively all patients referred to the Sleep Unit during the 7-month period. A total of 42 patients were evaluated during this period. Among these, 31 patients fulfilled the inclusion criteria. The remaining 11 patients were excluded from the study because they had at least one of the exclusion criteria (such as those who had previous eye surgery or laser treatment, who had any anterior or posterior segment disease or a history of ocular trauma, those with secondary glaucoma, history of chronic steroid use, history of shock, and diabetes mellitus).
The study included 31 patients with OSAS and 25 healthy controls. Diagnosis of OSAS was made by the Sleep Unit of the Neurology Clinic.
Atherosclerotic risk factors likely to affect ocular blood flow such as hypertension, cigarette smoking, and high blood cholesterol level were investigated in all subjects; since it was impossible to completely eliminate these factors in both groups, the distribution of these risk factors was investigated and it was found to be similar between the groups (chi-square: 1.97; p = 0.16).
Criteria for the diagnosis of POAG were as follows (all four of the criteria listed had to be present for inclusion in the study):
A cup to disc ratio (c/d) over 0.5 or difference of c/d between two eyes >0.2 with thinning of the neuroretinal rim; a careful search was made to detect the presence of any disc hemorrhages to strongly support the diagnosis. The c/d ratio and the optic disc evaluation were derived with a +90 diopter aspherical Volk lens by two independent examiners who were masked to the condition of the patient.Any of the detected visual field defects such as localized defects, paracentral scotoma, Bjerrum scotoma, nasal step, temporal sector defect, and diffuse defect which cannot be explained by any neurologic or fundus lesion.Open iridocorneal angle.An intraocular pressure (IOP) >21 mmHg without treatment.
Diagnosis of normotensive glaucoma was made with criteria similar to those stated for POAG except for the fact that IOP was <21 mmHg without treatment.
The control group consisted of 25 age- and sex-matched healthy subjects who were recruited from those attending the eye clinic for conditions such as refractive errors, etc. Control subjects had to meet all of the following ophthalmological criteria for inclusion in the study:
No IOP elevation over 21 mmHgNo evidence of glaucomatous optic nerve appearance (no disc hemorrhages) and a c/d ratio less than 0.5Normal anterior chamber angle on slit lamp and gonioscopic examinationNormal visual field test resultsNo previous history of antiglaucomatous drug usage, ocular trauma, ocular surgery, or laser therapy
Patients underwent orbital color Doppler ultrasonography (Toshiba Power Vision 6000, Osaka, Japan) at the Radiology Unit of the University. An 11-MHz linear surface probe was used and the measurements were performed between 12:00 a.m. and 13:00 p.m. The patient was supine; eyes were closed and aimed at the ceiling under the closed eyelids. Systolic blood velocity (PSV) and end-diastolic blood velocity (EDV) were calculated in the ophthalmic artery and central retinal artery; resistivity index (RI) was calculated for the central retinal artery (CRARI) and ophthalmic artery (OARI; RI=PSV-EDV/PSV).
Polysomnography was performed at the Sleep Research Unit of the University; it was constituted by two EEG channels, two EOG channels, one EMG channel recording from the submental muscle, one nasal current channel, one thoracic motion channel, one abdominal motion channel, one oximeter channel, a microphone, two leg movement channels from the right and left anterior tibialis muscles, and video recordings made between 2300 and 0700 hours. The parameter used was the apnea-hypopnea index (AHI). Diagnosis of OSAS was made when the AHI was over 5. When the AHI was between 5 and 15, OSAS was regarded as “mild,” between 16 and 30 as “moderate,” and when it was over 30, it was regarded as “severe.”
All the subjects in the study underwent a complete ophthalmological examination including visual acuity, slit lamp biomicroscopy, IOP measurement, gonioscopy, and fundus examination. Perimetric examination was made with the Octopus 500 EZ perimeter (Interzeag AG, Schlieren, Switzerland); stimulus size: white, III, full threshold strategy was used. Only phases 1 and phase 3 of the G1 program were used. The Octopus perimeter has a prior education (preparation) program for possibly naive patients, which was used for all patients who were naive to the examination.
A factor based on the number of false-positive and false-negative replies to the stimuli, and their ratio (<13%), was used to determine reliability. Unreliable results were retested on another day.
Disc analysis was made with the Heidelberg Retinal Tomograph II (Heidelberg Engineering GmbH 2001, software version 3.0, Heidelberg, Germany). Mean nerve fiber layer thickness, rim area, rim volume, and linear c/d ratio were noted.
All statistical analyses were carried out using statistical software (SPSS, version 10.0 for Windows, SPSS Inc., Chicago, IL, USA). Differences were considered significant at p < 0.05. Student’s t-test was used for intergroup analysis; chi-square, and Kruskal-Wallis, and Mann-Whitney U tests for nonparametric tests; analysis of variance (ANOVA) was used for the correlation analysis.
The study was conducted in accordance with the Declaration of Helsinki and was approved by an Institutional Ethics Committee.
Results
This study included 31 patients (7 female, 24 male) and 25 controls (7 female, 18 male). There was no significant difference among the groups in terms of sex distribution (Yates corrected chi-square = 0.02; p = 0.88). While the mean age of the patient group was 52.07 ± 9.35 years, it was 50.44 ± 6.38 years in the control group; there was no statistically significant difference between age distributions among the groups (p = 0.13, unpaired Student’s t-test). There was no statistically significant difference between the two groups with respect to the distribution of arteriosclerotic risk factors and hypertension (Yates corrected chi-square = 1.97; p = 0.16).
The prevalence of glaucoma was 4 of 31 OSAS patients (12.9%). In two of these four patients, both eyes were affected. One of these patients had normotensive glaucoma and one had POAG. The other two patients had normotensive glaucoma only in one eye. The other eyes had large cupping without any visual field defect. All of these four patients belonged to the severe OSAS group (AHI > 30).
There was no significant difference between mild, moderate, or severe OSAS patients in terms of OARI or CRARI values (Kruskal-Wallis test p = 0.961, p = 0.774).
The patient and control groups did not differ significantly in terms of CRARI and OARI or IOP (p > 0.05; Table 1).
Table 1Comparison of patients and controls with respect to intraocular pressure (IOP), ophthalmic artery resistivity index (OARI), and central retinal artery resistivity index (CRARI)ParametersPatient eyes (mean ± SD) (n = 62)Control eyes (mean ± SD) (n = 50)tpIOP (mmHg)13.37 ± 4.3714.00 ± 4.31−1.000.32OARI0.72 ± 0.060.71 ± 0.060.7690.44CRARI0.69 ± 0.060.70 ± 0.05−0.8030.42
There was a statistically significant difference between median IOP values of mild (10.5 mmHg; range: 9–18 mmHg) and severe (14.5 mm Hg; range: 9–27 mmHg) OSAS patients (p = 0.031; Mann-Whitney U test).
When correlation was analyzed between variables, a statistically significant correlation was found between OARI and MD values (r = 0.339, p = 0.007, Fig. 1), CRARI and MD values (r = 0.246, p = 0.05, Fig. 2), CRARI and LV values (r = 0.253, p = 0.048, Fig. 3), and AHI and IOP values (r = 0.426, p = 0.001, Fig. 4).
Fig. 1The positive correlation between ophthalmic artery resistivity index (OARI) and mean defect (MD) in the patient group is statistically significant (r = 0.339, p = 0.007)Fig. 2The positive correlation between central retinal artery resistivity index (CRARI) and mean defect (MD) in the patient group is statistically significant (r = 0.246, p = 0.05)Fig. 3The positive correlation between central retinal artery resistivity index (CRARI) and loss variance (LV) in the patient group is statistically significant (r = 0.253, p = 0.048)Fig. 4The positive correlation between apnea-hypopnea index (AHI) and intraocular pressure (IOP) in the patient group is statistically significant (r = 0.426, p = 0.001)
Discussion
This study aimed to investigate the prevalence of glaucoma in patients with OSAS and also to investigate the association of ocular blood flow with OSAS and glaucoma. In previous studies, while the prevalence of glaucoma was found to be 2% [8], no correlation was obtained between respiratory disturbance index (RDI) and IOP. However, many other studies found higher incidences and a positive correlation between IOP and RDI. For instance, Mojon et al. stated that there was a positive correlation between IOP and RDI in 114 glaucoma suspects [21]. In our study, similar to Mojon et al.’s study, there was a significantly positive correlation between IOP and AHI (r = 0.426, p = 0.001). In another study by Robert et al., which was carried out with 69 patients with OSAS, prevalence was found to be 7.2% [27]. At the same time, a strong positive correlation was found between OSAS and lid hyperlaxity [27]. In that study, incidence was 8.7% and six patients were referred for glaucoma treatment [27]. In the study of McNab et al. in which 8 patients with floppy eyelid syndrome were investigated for OSAS and 20 other patients with known OSAS were examined for floppy eyelid syndrome and other possibly associated ocular features, it was found that 1 of 8 patients with floppy eyelids had normotensive glaucoma [18]. Onen et al. found a high prevalence of sleep-disordered breathing (SDB) in POAG patients [24]. When only snoring was taken into consideration, the incidence of “only snoring” was 47.6%, “snoring and excessive daytime sleeping” was 27.3%, and “snoring, daytime sleepiness, and insomnia” was 14.6% [24]. Marcus et al. also stressed that SDB may be a risk factor for low-tension glaucoma (LTG) [17]. Mojon et al. found that the prevalence of OSAS was 50% in LTG patients aged between 45 and 64 years and 63% in those over 64 years of age [23]. However, Girkin et al. [9] reported an odds ratio of 2.02 for OSAS patients to develop glaucoma.
Similar to most of the above-mentioned studies, the authors of the present study also found a high prevalence of glaucoma (12.9%) in the study group of 31 patients suggesting that OSAS in conjunction with a vessel disease is an important risk factor for glaucoma.
The correlation between RDI and IOP was also investigated by Geyer et al. [8]; the authors stated that there was no statistically significant correlation [8]. On the contrary, Mojon et al. found a positive correlation between two variables [21]. We found a statistically significant difference between AHI and IOP in mild and severe OSAS patients. In our study, all four glaucomatous patients were in the severe OSAS group with an AHI > 30. In the patient group, there was a positive correlation between OARI and MD as well as CRARI and MD, and LV. Tsang et al. also found that moderate to severe OSAS patients were associated with a higher incidence of visual field defects [29]. These results suggest that visual field defects may be caused by optic nerve perfusion insufficiency and that when RI increases, field defects also increase. However, there was no significant difference between OARI and CRARI values of patients and healthy subjects. This may suggest that the effects of OSAS on body systems are seen not only during sleep but also during daytime; pathological changes may be more clearly documented if recordings and ultrasonography can be made during the sleeping period with a suitable technique to be developed in the future. Hypoxia in OSAS is of intermittent character and the thinning may occur in the progression of the disease in such patients. Long-term follow-up of these patients in terms of NFL thickness changes over time may clarify this issue. Purvin et al. suggested that intracranial pressure increases as a result of episodic nocturnal hypoxemia and hypercapnia, thereby leading to papilledema and increasing the risk of visual loss [25]. Since intracranial pressure increase in these patients is of intermittent character, cerebrospinal fluid pressure may be normal. In our group of patients, visual field defects were detected in ten patients despite normal ophthalmological examination. This may be due to optic nerve damage caused by cerebral ischemia and intermittent intracranial pressure increase in such patients.
Low diastolic pressure and systemic hypertension are reported to be strongly associated with glaucoma [4, 6, 25, 26]. Some authors suggested that physiological nocturnal hypotension, in the presence of other vascular risk factors, can bring the optic nerve head circulation under the critical levels and thus play a role in the pathogenesis of anterior ischemic neuropathy and glaucoma [1, 10, 16, 28].
The pathogenesis of LTG is not clear; however, optic nerve head hypoperfusion has been suggested as the cause [30]. Hayreh stated that many systemic and ocular paralyses occur at nighttime and may be realized when the patients awaken early in the morning [13]. Furthermore, Hayreh [13] and Meyer [19] suggested that nocturnal hypotension might play a role in the pathogenesis of LTG.
In conclusion, in our group of patients with OSAS, a high prevalence was found (12.9% = 4 glaucoma patients of 31 OSAS patients) and it is interesting to note that all four of the glaucoma patients were in the severe OSAS group. The positive correlation detected between OARI and MD, and also between CRARI and MD as well as LV, suggests that visual field defects may be due to optic nerve perfusion defects and that these field defects also increase as the RI increases. The presence and progression of glaucoma should be investigated particularly in patients with severe OSAS in the long-term follow-up, and changes in retinal nerve fiber layer thickness and ocular Doppler ultrasonographic findings should also be monitored in such patients.

ocular blood flow
obstructive sleep apnea syndrome
glaucoma
polysomnography
apnea-hypopnea index