Instrumental methods and challenges in quantifying polybrominated diphenyl ethers in environmental extracts: a review

Increased interest in the fate, transport and toxicity of polybrominated diphenyl ethers (PBDEs) over the past few years has led to a variety of studies reporting different methods of analysis for these persistent organic pollutants. Because PBDEs encompass a range of vapor pressures, molecular weights and degrees of bromine substitution, various analytical methods can lead to discrimination of some PBDE congeners. Recent improvements in injection techniques and mass spectrometer ionization methods have led to a variety of options to determine PBDEs in environmental samples. The purpose of this paper is therefore to review the available literature describing the advantages and disadvantages in choosing an injection technique, gas chromatography column and detector. Additional discussion is given to the challenges in measuring PBDEs, including potential chromatographic interferences and the lack of commercial standards for higher brominated congeners, which provides difficulties in examining degradation and debromination of BDE congeners, particularly for BDE 209.
Introduction
Over the past 5 years there has been a surge in the number of studies investigating polybrominated diphenyl ethers (PBDEs) in the environment. In 1981 the first report on PBDEs was made in biota samples from a Swedish river [1], which was followed by a handful of studies throughout the 1980s and 1990s. Then, in 2000 Noren and Meironyte [2] published a paper on organohalogen temporal trends in human breast milk from Sweden that generated rapid interest in the fate of PBDEs. This study demonstrated that PBDE levels appeared to be increasing at an exponential rate, and since then the number of studies investigating PBDEs has increased as well.
PBDEs are used as brominated flame retardant additives which are incorporated into a number of polymers and resins found in a majority of consumer products. PBDEs are the most widely used additive flame retardant and, until recently, almost 70,000 t was produced every year, half of which was used in products sold in the USA and Canada. In 2004 the European Union phased out the use of two of the three PBDE commercial mixtures, PentaBDE and OctaBDE. Following this action, and owing to growing concerns in some US state governments, the sole US chemical manufacturer voluntarily agreed to stop manufacturing these two PBDE commercial mixtures beginning in January 2005 [3]. Presently, the only remaining unregulated PBDE mixture in production is DecaBDE, with an annual global market demand of 56,000 t (http://www.bsef.com). DecaBDE is composed almost exclusively (more than 97%) of decabromodiphenyl ether (BDE 209), a fully brominated BDE congener.
Much of the concern regarding PBDEs has been focused on the elevated levels measured in human tissues within the US population [4]. The BDE congeners typically measured in human tissues are associated primarily with the PentaBDE mixture, and to some extent with the OctaBDE mixtures. While production of PentaBDE and OctaBDE has been halted, there are still a number of products left on the market and in use today that contain these mixtures. On the basis of their persistence and current reservoirs, the fate of PBDEs will most likely need to be monitored for years to come.
One of the greatest challenges to measuring PBDEs in environmental samples has been developing methods to accurately quantify BDE 209. While analytical methods are readily available for quantifying tribrominated through heptabrominated congeners found in the PentaBDE and OctaBDE mixtures, the analysis of higher brominated compounds (i.e., congeners with eight or more bromines) has proven to be difficult. In 1999–2000, the first worldwide interlaboratory comparison on PBDE measurements was conducted. Laboratory agreements were acceptable for most of the lower brominated congeners; however, reported values for BDE 209 varied over almost 2 orders of magnitude [5]. Over the past few years, improved methods have been developed to measure BDE 209 with increased accuracy and precision, and with the use of these methods, more data on BDE 209 have been generated.
A number of papers have described analytical methods for extracting and quantifying environmental samples for the determination of PBDEs [6–11]. In 2003 Covaci et al. [9] published an excellent review on extraction methods, cleanup techniques and analytical detection methods for brominated flame retardants. Since the publication of that review, several interesting papers have been published which highlight improvements in injection techniques, analyte resolution and selectivity, particularly for BDE 209. When choosing a method one must find the best compromise in considering cost, reproducibility, chromatographic and mass resolution, sensitivity, and analysis time. The aim of this paper is to review and disseminate the latest information available and discuss the advantages and disadvantages found when using different analytical techniques to determine PBDEs in environmental and human samples.
The physical-chemical properties of PBDEs are very similar to those of PCBs [12], and as such, PBDEs are typically extracted using nonpolar solvents (e.g., hexane, dichloromethane) in methods typically employed for PCB analysis. In this paper all PBDE congeners are labeled and numbered according to the same scheme developed by Ballschmitter and Zell [13], for PCB congeners. A variety of extraction and cleanup methods have been reported [6, 9, 14–16], but for the purpose of this paper, we will focus on instrumental methods of analysis for PBDE congeners.
Injection techniques
Owing to their vapor pressures and polarity, gas chromatography (GC) has become a standard analytical separation method employed for the analysis of PBDEs. The injection of samples into the GC system is an important and crucial step for the accurate and optimal determination of compounds with relatively high boiling points like PBDEs. Various injection methods have been reported which introduce sample analytes into the GC analytical column. The three most common injection techniques for PBDE analysis are split/splitless injection, on-column injection and programmable temperature vaporization (PTV) injection. All three methods possess advantages and disadvantages that are dictated primarily by availability, price, acceptable detection limits and discrimination of congeners on the basis of molecular weight. Split/splitless injection is the most routinely used, and is capable of analyzing dirty samples; however, this method can be limited by small injection volumes (typically 1–3 μl) and high injection temperatures (250–300 °C). During split/splitless injection the analytes are immediately vaporized in the inlet liner and are transferred to the column in the gas phase. In some cases, the high inlet temperature can lead to thermal degradation and discrimination of higher molecular weight PBDEs, particularly the fully brominated BDE 209. Evaporation of the sample can begin inside the injection needle or on the tip, and differences in evaporation rates can lead to discrimination of the higher molecular weight congeners. Inefficient transfer of the sample to the column can be difficult for analytes with high boiling points. Furthermore, active sites on dirty liners can result in thermal degradation; therefore, the temperature of the injection port and the time spent in the injection port prior to transfer are important mediating factors in the response of the PBDE congeners. A detailed study by Björklund et al. [17] found that the optimal injector temperature and splitless time should be kept as high as possible, in their case, 325 °C and 4 min, respectively (Fig. 1). Using these optimal setpoints, Björklund et al. demonstrated that the BDE 209 response increased by almost 25% when compared with average settings used in an interlaboratory comparison [5]. However, they also noted a significant decrease in the precision of higher molecular weight congeners, particularly BDE 209, compared with the case for on-column techniques, which are less prone to discrimination effects.
Fig. 1Detector response for BDE 209 as a function of splitless time and injector temperature, with pressure held constant at 2.3 bar. BDE brominated diphenyl ether. (Reprinted from Björklund et al. [17] with permission)
On-column injection has become a popular method for introducing samples into the gas chromatograph for PBDE analysis [18–20] and is comparable in cost to split/splitless injectors. The design is much simpler relative to a split/splitless injection port and involves direct injection of the sample, dissolved in a carrier solvent, onto the head of the column. This reduces the potential for thermal degradation and discrimination experienced in split/splitless injectors. In the same study mentioned previously, Björklund et al. [17] observed the highest precision in PBDE measurements, particularly for BDE 209, when using on-column injection compared with split/splitless and PTV injectors. However, when using on-column injection, care must be employed to ensure a clean sample, to prevent coextracted compounds from building up in the retention gap and column leading to increased noise, peak tailing, retention shifts and reduced lifetimes of the columns. On-column injections are also limited by injection volumes (typically 1–2 μl) to prevent excess solvent from overwhelming the detectors.
PTV inlets have become a more popular choice for injection over the past 5 years, particularly for the analysis of PBDEs [21–23]. The primary advantage in using PTV injectors is an increase in injection volumes (up to 125 μl), which can drastically improve detection limits, and can be useful in measuring low-level samples such as human serum [14, 24–26]. With this type of injection, multiple injections are made into a liner while the solvent is vented off, trapping the analytes and increasing the mass of analytes present in the liner. Transfer of the analytes to the head of the column occurs after the solvent vent is closed, and rapid heating of the injection port (200–700 °C/min) aids the carrier gas in transferring the analytes to the column. Several studies have demonstrated that the PTV injectors must be optimized prior to use as they are more complicated than the conventional split/splitless injector. Analyte discrimination can result if the following are not optimized: injection rate or flow, injection temperature, vent flow, solvent elimination time, injection volume, transfer time and transfer temperature.
Tollbäck et al. [25] published a review on optimization parameters for PTV injection and their optimal values are reported in Table 1. In general, it is important that the rate of injection is equivalent to the rate of solvent evaporation to avoid flooding the injector. Care must also be taken to avoid discriminating low molecular weight compounds through solvent vent losses. The rate of injection will influence the solvent introduction mode (i.e., drops versus spray), which can discriminate congeners of different masses on the basis of the point (vertical position) of solvent evaporation within the injection liner. BDE 209 responses can be particularly sensitive to injection flow rate. To balance the solvent removal from the injector, the temperature and vent flow must also be optimized. At elevated injector temperatures, low molecular weight compoundss can be lost through solvent venting and at high flow rates the risk of analyte loss increases. The type of liner used in the injector can also influence PBDE responses. Some liners contain activated sites, which can lead to irreversible adsorption or catalysis of thermal degradation of BDEs, particularly the high molecular weight congeners. Tollbäck et al. [25] recommended adding 0.1–0.35% (v/v) of dodecane with multibaffled liners to trap low molecular weight congeners and changing the liner every 200 injections. Transfer of analytes to the GC column is accomplished by rapid heating of the injector port after the solvent vent is closed. PBDE responses are not significantly affected by the analyte transfer temperature ramp; therefore, steep ramps (700 °C/min) should be used to increase the rate of transfer to GC columns. A final injector temperature of 325 °C was found to be a good compromise to balance transfer efficiency of high molecular weight analytes relative to the risk of their thermal degradation in the injector.
Table 1Optimized injector parameters for polybrominated diphenyl ether (PBDE) analysis using programmable temperature vaporization as reported by Tollbäck et al. [25]ParameterDomain Injection rate300 μl/minInjection temperature80 °CVent flow150 ml/minTemperature rate700 °C/minTransfer temperature325 °CTransfer time0.3–0.6 minSolvent elimination time0.1 minCarrier gas pressure650 kPa
While split/splitless, on-column and PTV injections are the most common methods used in PBDE analysis, other injection techniques have been reported. Large volume injections up to 500 μl have been reported using both loop-type [26] and automated rotary valves [27]. Loop-type injectors can easily be constructed and used but require day-to-day checks for stability and reproducibility owing to problems with the injection pressure and temperature which can result in peak distortion. Björklund et al. [27] used a stainless steel high-performance liquid chromatography (LC) automated rotary valve to inject PBDEs into a GC system. This technique reduces discrimination of higher molecular weight congeners and thermal degradation similar to on-column injection, but offers the advantage of injecting larger volumes (up to 50 μl). However, care must be taken to incorporate a solvent vent system for certain detector systems, particularly mass spectrometry (MS). Lastly, Sjodin [28] used a septum-equipped programmable injector (SPI), which resulted in reproducible results with minimal congener discrimination; however, these injectors are no longer produced by the manufacturer.
GC column selection
The next crucial step in the method development is the selection of an appropriate column system. The optimal chromatographic column is one that optimizes resolution and discrimination of congeners with the greatest sensitivity in the shortest amount of time. Of the three primary injection methods described earlier, both on-column and PTV injections require the use of a retention gap or guard column to reduce column deterioration and to aid in focusing the initial band of analytes. Most guard columns are either composed of untreated fused silica with active silanol groups or deactivated fused silica that ranges in polarity. Björklund et al. [17] recommends using a Siltek deactivated guard column, which the authors found had the best precision (measured by peak response) and displayed the least degradation of higher molecular weight congeners. In contrast, a nonpolar fused silica guard column resulted in a 50% reduction in sensitivity for the higher molecular weight PBDE congeners.
A variety of different columns have been used for the determination of PBDEs in environmental samples. When choosing a column one must consider the column stationary phase (polarity), column length, film thickness and inner diameter, all of which will influence the response towards PBDEs. Generally speaking, the most sensitive method for measuring the entire range (low to high bromine substitution) of PBDE congeners is found when using short (10–15-m) nonpolar DB columns with thin (0.1-μm) stationary phases [17]. Most GC/MS methods employed for PCB quantification use a 60-m column to increase the resolution power; however, longer columns are not well suited for PBDE analysis, particularly for the higher molecular weight congeners. Longer columns result in longer residence times for the analytes, initiating degradation of the higher brominated congeners (noticeably for heptaBDE, octaBDE, nonaBDE and decaBDE congeners) in the column. Björklund et al. [17] determined that the DB-5MS column (Agilent Technologies, Palo Alto, CA, USA) produced the lowest discrimination and the highest precision compared with five other columns tested (Fig. 2). Columns that contained similar stationary phases but that were produced by two different manufacturers also resulted in different column performance. An HP-1 column from Agilent Technologies significantly degraded nonaBDEs and BDE 209, whereas a comparable DB-1 column from J & W Scientific (Folsom, CA, USA) did not. Narrow-bore columns (inner diameters 0.1 mm or less) have been used recently in combination with PTV injectors, resulting in faster and more efficient separation of congeners [24, 25]. Narrow-bore columns produce extremely narrow peaks, with average peak widths at half peak height of less than 1 s, compared with almost 2 s using columns with 0.25-mm film thickness.
Fig. 2The relative response of five selected BDE congeners using different gas chromatography capillary columns. The response for DB-5MS was set to 1. Mean values are plotted (n=5) and the error bars correspond to the standard deviation. (Reprinted from Björklund et al. [17] with permission)
Another consideration in choosing a GC column is its resolving power. Korytár et al. [29] recently published a retention time database for 126 PBDE congeners using seven different capillary columns with varying film thicknesses (Table 2). This database is useful for determining the most suitable column for quantitative, congener-specific PBDE analysis. Of the seven columns tested, the DB-XLB column (30 m × 0.25 mm × 0.25 μm, J & W Scientific) was found to have the fewest number of coelutions with other BDE congeners and with other brominated flame retardants, followed closely by the DB-1 column (30 m × 0.25 mm × 0.25 μm, J & W Scientific). However, the DB-XLB column demonstrated greater degradation of the higher molecular weight congeners relative to the DB-1 column. Korytár et al. [29] compared the relative retention times (RRTs) from their study to a GC-RRT model developed by Rayne and Ikonomou [30] which was based on the number of ortho-, meta- and para-bromine substituents, dipole moments and the molecular weight of the compounds. In general the RRTs of lower molecular weight PBDE congeners compared very well between the two studies. In contrast, the higher molecular weight congeners, noticeably the heptaBDE and octaBDE congeners were not tightly correlated, most likely owing to a lack of commercially available standards for heptaBDE and octaBDE congeners when the GC-RRT model was developed. Additional standards are now available and it will be useful to update these types of models in the future.
Table 2An assessment of PBDE coelution from seven gas chromatography columns as tested and reported by Korytár et al. [63]ColumnDB-1DB-5HT-5DB-17DB-XLBHT-8CP-Sil 19Dimension (m × mm × μm)30 × 0.25 × 0.2530 × 0.25 × 0.2530 × 0.25 × 0.1030 × 0.25 × 0.2530 × 0.25 × 0.2525 × 0.22 × 0.2517 × 0.15 × 0.30Number of coeluting BDEs62636667566272Number of coelutions with flame retardants24262730222629Co-elution with major BDE congenersMajor BDE 2816, 3316, 3316, 33, 3816, 33, 3816, 33 47 4968, 8068686242, 48, 68, 716851, 75 85155114 99116127 100109101109, 120 138166HBCD166 153HBCD168 154MTBBP-A, BB-153MTBBP-A, BB 153105126BB 153 183BB 169BB 169MTBBP-A dimethylated tetrabromobisphenol-A, HBCD hexabromocyclododecane
Chromatographic interferences
Coelution of compounds can be an important consideration when using GC/electron capture detection (ECD) and GC/electron capture negative ionization (ECNI) MS techniques to quantify environmental samples, especially as more PBDE congeners become available for analysis. The database of Korytár et al. [29] found that at a minimum, 56 of the 126 PBDE congeners tested were coeluted on the GC columns. The majority of these coelutions were observed for congeners not typically observed in environmental samples; however, a few major congeners (i.e., BDE 28, BDE 49 and BDE 154) can often be composed of different BDE congeners and other brominated flame retardants. Because ECD and ECNI-MS techniques are not as selective as electron ionization (EI) MS for the analysis of lower brominated congeners, coextracted halogenated compounds can sometimes be mistaken for PBDE congeners. Several papers have reported the coelution of 2,2′,4,4′,5,5′-hexabromobiphenyl (BB 153) with BDE 154 and of tetrabromobisphenol-A with BDE 153 [6, 9, 29], typically on 15- and 30-m capillary columns. PCB congeners can also be coeluted if not specifically separated during the extraction process. Alaee et al. [31] reported ten possible interferences between PBDEs and organochlorines. Of particular notice was the interference of BDE 47 with CB-180, and that of BDE 99 with CB-205. The additional mass accuracy of high resolution MS (HRMS) may not be adequate to distinguish certain PCBs and BDEs. Alaee et al. [31] found that the isotopic cluster of [M-Cl2]+ from heptachlorinated biphenyls contains the same mass fragments found in tetrabrominated diphenyl ethers [M-Br2]+ and resolving powers of 25,000 (m/Δm) were required for discriminating the two.
Natural brominated compounds have recently been identified in some marine algae, mammals and birds [32–34]. Because GC/ECNI-MS methods rely upon selective ion monitoring (SIM) of Br− ions [79Br and 81Br], other brominated compounds can produce the same fragment ion and confound analysis of PBDEs. Methoxylated PBDEs (MeO-BDEs) are often extracted with PBDEs in environmental samples and can cause interferences when using both GC/EI-MS and GC/ECNI-MS methods. As seen in Fig. 3, the BDEs and MeO-BDEs are eluted very closely together using common techniques and differentiation must be based solely on retention time. Two MeO-BDEs, (6-methoxy-2,2′,4,4′-tetrabromodiphenyl ether and 2′-methoxy-2,3′,4,5′-tetrabromodiphenyl ether) have been identified in several marine species [32, 34] and are eluted between BDE 47 and BDE 100 with identical Br− ion ratios. Other studies have also identified a brominated bipyrrole known as 1,1′-dimethyl-tetrabromo-dichloro-2,2′-bipyrrole (DBP-Br4Cl2) in marine species [35–37]. DBP-Br4Cl2 is also eluted very closely to BDEs and MeO-BDEs using common methods of analysis (Fig. 4).
Fig. 3Comparison of elution order for methoxylated BDEs (MeO-BDE) and BDE congeners on a 30-m DB-5MS capillary column using gas chromatography (GC)/electron capture negative ionization (ECNI) mass spectrometry (MS). PDBE polyBDEFig. 4GC/ECNI-MS chromatogram of sea lion blubber containing both PBDEs and a marine natural product, DBP-Br4Cl2
Calibration and quantification standards
PBDE calibration standards are commercially available from a few suppliers, including Accustandard (New Haven, CT, USA), Cambridge Isotope Laboratories (Andover, MA, USA), Wellington Laboratories (Guelph, ON, Canada) and Chiron Co. (Trondheim, Norway). All of these suppliers provide the suite of major PBDE congeners typically identified in environmental samples. To the author’s knowledge, about 160 of the 209 possible BDE congeners are currently available commercially. There are still many hexaBDE, heptaBDE and octaBDE congeners that are not available and it can sometimes be difficult to identify unknown peaks in chromatograms that may be those of BDE congeners. For example, octaBDE congeners have been identified as degradation products of BDE 209 through apparent debromination mechanisms [38–40]; however, the bromine substitution of these congeners is unclear owing to a lack of commercially available octaBDE congeners for comparison. Hopefully in the future, more standards will be available for comparison and will provide more information on the fate of higher brominated congeners.
Another facet to consider in measuring PBDEs is the choice of an appropriate internal quantification standard. Ideally, isotopically labeled (13C) PBDE standards would be the best choice for quantifying PBDEs. Unfortunately, for some detection methods (see later), 13C PBDEs cannot be distinguished from native PBDEs and cannot be used. Several studies have used alternative internal standards such as 13C-labeled bromobiphenyls and chlorinated diphenyl ethers [41], polychlorinated biphenyls [42], or unlabeled BDE congeners [5, 20] that are not typically identified in samples such as BDE 15 and BDE 75. Chiron Co. recently announced the availability of fluorinated BDEs for use as internal standards. For these standards one fluorine atom is substituted for hydrogen on a BDE congener, resulting in a slightly different vapor pressure. These fluorinated BDEs fragment in a manner similar to that of native BDEs, but have slightly different retention times from their nonfluorinated analogues (Fig. 5), making them very useful as internal standards in GC/ECNI-MS methods when 13C-labeled BDE congeners cannot be used.
Fig. 5GC/ECNI-MS chromatogram (a) illustrating the elution order of 6-fluro-BDE 47 relative to the PBDE congeners and its comparable ion fragmentation pattern (b)
Detection techniques
Determination of PBDEs in environmental samples is sometimes conducted using ECD, but more commonly mass spectrometers are used, the latter being classified into low-resolution (LR) and HR instruments. ECD is advantageous because is relatively inexpensive, user-friendly and sensitive for measurements of halogenated organics; however, ECD is limited in selectivity because detection and identification is based solely on retention time and halogenated interferences can lead to misidentification [31]. Determination of PBDEs using LR instruments is typically made with the instruments operated in either EI-MS or ECNI-MS modes. In contrast, HR instruments are used almost exclusively in EI mode (EI-HRMS). HRMS offers the best selectivity for PBDE measurements, with a mass spectrometric resolution (m/Δm) of approximately 10,000, resulting in fewer coeluting interferences and facilitating the use of isotopically labeled internal standards. However, HR instruments are more expensive relative to LR instruments. In addition, HRMS is labor-intensive, requiring trained personnel to maintain the instruments and keep them actively running. A brief review of LRMS and HRMS techniques are given next.
A variety of papers have reported PBDE measurements in environmental samples using both GC/LR-EI-MS [43–45] and GC/LR-ECNI-MS [19, 46, 47]. In general, LR-EI-MS is more selective than LR-ECNI-MS methods because molecular fragments (typically [M-Br2]+) are monitored for each homologue group, in contrast to the bromide ions ([Br−]−, m/z 79 and 81) monitored for all homologue groups in LR-ECNI-MS methods. However, LR-ECNI-MS is a much more sensitive method with lower limits of detection (LOD) relative to LR-EI-MS, even when using SIM mode. The LOD reported for LR-EI-MS range from 0.53 to 32.09 pg, whereas for LR-ECNI-MS the LOD is an order of magnitude lower, ranging from 30 fg to 1.72 pg [48], which is particularly useful for the analysis of low-concentration samples such as human serum and plasma [14, 33, 49]. Additionally, there is a large drop in sensitivity when measuring BDE congeners with more than six bromine atoms using quadrupoles in LR-EI-MS; therefore, one must prioritize selectivity versus sensitivity when choosing a reliable detection method. However, recent work conducted by Ackerman et al. [50] suggests that selectivity can be retained when using LR-ECNI-MS under optimized conditions. The authors reported that optimization of the electron energy, emission current, source temperature and system pressure increased the relative abundance of molecular fragment ions [M-xH-yBr]−, which can be monitored for each homologue group in place of the nonspecific bromide ions. With this method one could use isotopically labeled BDE congeners as internal standards, which cannot be used when monitoring bromide ions (79Br and 81Br) in typical LR-ECNI-MS methods. The exception is BDE 209, in which 13C-labeled BDE 209 can be used in all LR-ECNI-MS applications, since fragmentation produces a higher abundance of the [C6Br5O]− ion relative to bromide [51].
Different reagent gases have also been used to increase PBDE response in LR-ECNI-MS. Typical methods use methane gas as a buffering reagent in the ionization process, but ammonia and isobutane can also be used. Eljarrat et al. [48] found that ammonia and methane gas provided similar LOD for PBDEs, but required significantly different ion source temperatures and system pressure. Ackerman et al. [50] found that isobutane resulted in a 20% increase in the abundance of molecular fragments relative to methane; however, isobutane appeared to contaminate the ion source very quickly, resulting in decreased sensitivity after only 12 h of use and was therefore not recommended for use.
HRMS offers both good sensitivity and optimum selectivity. Alaee et al. [52] described a GC/HRMS method for the determination of PBDEs in fish. HRMS instruments have a dual magnetic and electrostatic sector, which increases sensitivity for higher molecular weight analytes such as the higher brominated congeners. This is particularly useful for quantifying heptaBDE through decaBDE congeners with greater confidence. Additionally, HRMS allows one to use isotope dilution with 13C-labeled BDE standards, which are ideal for accurate and precise measurements. In a comparison between GC/HR-EI-MS and GC/LR-ECNI-MS, Thomsen et al. [49] found that both methods provided similar LOD and repeatability. And, as stated previously, HRMS methods will limit the number of potential mass interferences. Therefore, if an instrument and funds are available, HRMS methods provide the best standards for selectivity and sensitivity.
Additional ionization methods and detectors have also been used to measure PBDEs in environmental samples. Ikonomou [53] used GC/HRMS with metastable atom bombardment with nitrogen gas as an alternative to electron impact ionization. This method provided less fragmentation of the parent BDE congeners and was useful for identifying the degree of bromination on unknown BDE congeners relative to conventional GC/HR-EI-MS methods. Tandem mass spectrometers using ion traps have also been reported for the analysis of PBDEs [16, 54]. Ion traps offer the advantage of increased selectivity at a low mass resolution because the analytes are fragmented twice. This type of detection minimizes the chance of isobaric interferences and significantly reduces background noise. Ion traps typically use a collision-induced dissociation (CID) cell to fragment a precursor ion, forming a secondary fragment ion. For PBDEs, the precursor ion is typically a molecular ion [M]+or [M-Br2]+ and the secondary fragment formed from the CID is a [M-COBr]+ ion. Wang et al. [54] optimized a GC/ion trap method for 20 BDE congeners and noted that the relative abundance of the fragment ions was dependent upon the number of ortho-substituted bromines on the congener. They also pointed out that coelution could be a potential problem for higher brominated compounds that could produce similar precursor and fragment ions and contribute to additional signals to the MS/MS channel monitored. More work is needed to determine if other brominated compounds would be isobaric using this method.
HR time-of-flight (TOF) mass spectrometers have also been used to determine PBDEs in environmental samples [55, 56], with detection limits comparable to those of most other MS methods. TOF methods offer the advantage of acquiring spectral data across a wide mass range without compromising sensitivity, which is often the case with quadrupole mass filters. TOF-MS can be operated in both electron impact and ECNI modes, similar to quadrupole methods. Operation of a TOF mass spectrometer in ECNI mode results in the same type of fragmentation pattern observed in quadrupole MS, and therefore offers little improvement and/or advantage in spectral data because the bromide ions, and not molecular fragments, are found in greatest abundance. HRTOF instruments offer increased mass selectivity and also permit the estimation of elemental composition; however, like most high mass resolution instruments, TOF-MS can be expensive and require high maintenance. Additionally, Cajka et al. [56] found that concentrated samples can result in inaccurate mass determination owing to saturation of the instrument’s time-to-digital converter. Therefore, TOF mass spectrometers have a limited linear range, and dilution and reanalysis would be necessary to quantify concentrated samples.
Alternate analytical techniques
While a gas chromatograph connected to a mass spectrometer is generally the method of choice for PBDE measurements, alternate methods have been investigated. Despite the limited chromatographic resolving power of LC, methods employing LC/MS and LC/MS-MS offer promise for PBDE determination. Debrauwer et al. [57] recently investigated the use of atmospheric pressure photoionization (APPI) using LC/MS-MS. Most traditional LC/MS-MS methods use electrospray ionization (ESI) or atmospheric chemical ionization; however, PBDEs do not ionize well with either of these two techniques. Using APPI however, PBDEs will ionize in both negative and positive modes, depending on the degree of bromine substitution. Positive ion mode was more sensitive towards diBDE through pentaBDE, whereas negative ion mode was more sensitive towards pentaBDE through decaBDE. APPI appears to be a softer ionization technique relative to EI as M+ ions are the most intense ions produced by the interaction of PBDEs with charged dopants (charged by photons), compared with the [M-Br2]+ ions formed in EI. Multiple reaction monitoring in the MS-MS system follows the M+ to [M-Br2]+ transition.
The use of ESI may be limiting for PBDEs, but the analysis of PBDE metabolites can be conducted using ESI-LC/MS methods. Hydroxylated BDEs (OH-BDEs) have been identified in environmental samples in several studies [58–60] and have been observed in serum samples of rats dosed with PBDEs [61], suggesting they are products of PBDE metabolism. A method for measuring OH-BDEs using LC/MS-MS has recently been reported [62]. LC/MS-MS holds much promise for investigating other potential metabolic and breakdown products of PBDEs and other brominated flame retardants in general.
The development of comprehensive two dimensional GC (GC × GC) has improved the GC resolving power for organohalogen determination and GC × GC can be used to sidestep some of the coelution problems encountered in standard GC/MS methods. The use of two columns with different separation characteristics increases the chromatographic resolving power and has been proven successful in separating PBDEs from other halogenated compounds. Korytár et al. [63] used a DB-1 column in combination with 65% phenylmethylspolysiloxane (007-65HT) to efficiently separate PBDEs from polychlorinated alkanes. Focant et al. [55] used GC×GC coupled to a TOF mass spectrometer to resolve 58 different halogenated compounds, including PBDEs, PCBs, bromobiphenyls and organochlorine pesticides.
Challenges in PBDE analyses
Increase in the fate and transport of PBDEs in the environment has led to the development of a number of different analytical methods for measuring this class of persistent halogenated contaminants. An interlaboratory comparison exercise for the determination of PBDEs in marine sediment conducted during 2004–2005 [64] found that laboratory agreement in measurements of PBDEs have improved since the first PBDE intercomparison exercise was conducted in 2001 [5]. Of particular note was the improvement of BDE 209 measurements among laboratories. The development of methods to limit degradation of BDE 209 in the injection port and column of GC systems has drastically improved the accuracy and precision of measurements. With this advance, an increasing number of studies are reporting BDE 209 measurements in environmental samples [65, 66], particularly in human samples [67, 68], suggesting that BDE 209 is more diffuse and ubiquitous in the environment than originally observed.
Theoretically, there are 209 different BDE congeners that can potentially be found in the environment. As stated previously, commercial standards are not currently available for all 209 different congeners and thus one of the major challenges in PBDE analysis is the lack of data on the prevalence of higher brominated BDE congeners in the environment. Several laboratory studies have demonstrated that PBDEs, particularly BDE 209, may debrominate both through abiotic [69, 70] and biotic [38, 39, 71–73] pathways, resulting in the formation of pentaBDE through nonaBDE congeners. The lack of standards has made it difficult to determine the products and elucidate the mechanisms of selective bromine loss. Very little is known about the potential for debromination in the ambient environment and few laboratories routinely measure octaBDE and nonaBDE congeners. The identification of octaBDE and nonaBDE congeners in human samples [40, 74] has led to many questions about the origin of these congeners, such as if they are a result of selective uptake of impurities in the technical mixtures, or if they are a result of debromination. Studies have attempted to identify all major and minor BDE congeners present in the commercial mixtures [29, 75]; however, the lack of standards has made congener identification difficult.
With the implementation of the European Union’s directive on waste electrical and electronic equipment and on the restriction of the use of certain hazardous substances (including PBDEs) in electrical and electronic equipment (RoHS; 2002/95/EC), fast and reliable methods for determining PBDE content in electrical equipment will be needed to ensure compliance. The use of X-ray fluorescence (XRF) portable analyzers is a promising method that would allow measurement of the total bromine content of a plastic component by scanning its surface with the handheld device. Obviously there will be difficulties in determining which type of brominated flame retardants are present in materials analyzed with this instrument. However, the XRF may be useful for determining if PBDE degradation occurs after exposure to light and it may be possible to determine the percentage of bromine lost through this pathway in an easy and fast method. Future directions in PBDE research may also wish to examine the fate of the bromine atoms that could be lost from PBDEs via the aforementioned degradation/debromination processes.
Conclusions
In conclusion, a variety of methods are now available to accurately and precisely measure a suite of PBDE congeners in environmental samples. The choice of method selection will involve a compromise between cost, selectivity and sensitivity, particularly since different congeners require different instrumental optimization techniques, especially BDE 209. The Environmental Protection Agency has outlined a protocol for measuring PBDEs (EPA method 1614), but to the author’s knowledge, this method is still in draft form.

methods
polybrominated diphenyl ethers
review
analysis
brominated flame retardants


Aspergillus niger genome-wide analysis reveals a large number of novel alpha-glucan acting enzymes with unexpected expression profiles

The filamentous ascomycete Aspergillus niger is well known for its ability to produce a large variety of enzymes for the degradation of plant polysaccharide material. A major carbon and energy source for this soil fungus is starch, which can be degraded by the concerted action of α-amylase, glucoamylase and α-glucosidase enzymes, members of the glycoside hydrolase (GH) families 13, 15 and 31, respectively. In this study we have combined analysis of the genome sequence of A. niger CBS 513.88 with microarray experiments to identify novel enzymes from these families and to predict their physiological functions. We have identified 17 previously unknown family GH13, 15 and 31 enzymes in the A. niger genome, all of which have orthologues in other aspergilli. Only two of the newly identified enzymes, a putative α-glucosidase (AgdB) and an α-amylase (AmyC), were predicted to play a role in starch degradation. The expression of the majority of the genes identified was not induced by maltose as carbon source, and not dependent on the presence of AmyR, the transcriptional regulator for starch degrading enzymes. The possible physiological functions of the other predicted family GH13, GH15 and GH31 enzymes, including intracellular enzymes and cell wall associated proteins, in alternative α-glucan modifying processes are discussed.
Introduction
Aspergillus niger is a saprophytic fungus well known for its production and secretion of a variety of hydrolytic enzymes contributing to its ability to degrade plant polysaccharides such as cellulose, hemicellulose, pectin, starch and inulin (De Vries and Visser 2001; Tsukagoshi et al. 2001; Yuan et al. 2006). Starch is the most abundant storage carbohydrate in the plant kingdom and is present in tubers, seeds and roots of a variety of crop plants including cereals, potatoes and manioc (Peters 2006). Starch is composed of two different molecules: (1) amylose, an unbranched, single chain of α-(1,4)-linked glucose residues and (2) amylopectin, consisting of a α-(1,4)-linked glucose chain with α-(1,6)-branches on every 12–25 glucose residues along the α-(1,4)-linked backbone (Robyt 1998). The degradation of starch is performed by a variety of enzymes, which are divided over three Glycoside Hydrolase (GH) families based on their sequence similarity (http://www.cazy.org) (Coutinho and Henrissat 1999). The first step in starch degradation is the endo-hydrolysis of the long polysaccharide chains into shorter maltooligosaccharides and α-limit dextrins by α-amylases (EC 3.2.1.1). α-Amylases belong to family GH13, a large family containing various hydrolysing and transglycosylating enzymes, mostly acting on α-(1,4) or α-(1,6)-glycosidic bonds. Members of family GH13 have a (β/α)8 barrel structure and can be recognized by four highly conserved amino acid regions containing three catalytic residues (MacGregor et al. 2001; Nakajima et al. 1986). After endo-hydrolysis, subsequent steps in starch degradation involve exo-acting enzymes releasing glucose. This reaction is performed by glucoamylase type enzymes of family GH15 (EC 3.2.1.3), a relatively confined family with regard to enzyme specificity, as all its studied members hydrolyse either α-(1,4) or α-(1,6)-bonds to release β-glucose from the non-reducing end of maltooligosaccharides. Most GH15 enzymes described thus far possess a starch-binding domain (SBD) (Sauer et al. 2000), a discrete C-terminal region of the protein that binds to starch and facilitates hydrolysis (Southall et al. 1999). Additionally, α-(1,4)-glucosidases of family GH31 may release α-glucose from the non-reducing end of starch (EC 3.2.1.20). This family also harbours other enzyme specificities such as α-xylosidase activity.
Several A. niger enzymes involved in starch degradation, and their corresponding genes, have been characterized and isolated. A. niger glucoamylase GlaA (family GH15) is an important enzyme for the modification of starch in the food industry (Boel et al. 1984; van Dijck et al. 2003). Additionally, one GH31 α-glucosidase (AglA, renamed AgdA) (Nakamura et al. 1997) has been characterized previously, as well as three family GH13 α-amylases: acid amylase AamA, and the almost identical AmyA and AmyB (Boel et al. 1990; Korman et al. 1990). The transcriptional regulation of the genes encoding starch-degrading enzymes has been studied in several aspergilli (Nakamura et al. 2006). In general, their expression is high on starch and induced by the presence of (iso)maltose (Tsukagoshi et al. 2001; Kato et al. 2002a). The presence of the inducer activates the Zn(II)2Cys6 transcription factor AmyR which binds to CGGN8(C/A)GG sequences in the promoter regions of AmyR target genes thereby activating their transcription (Petersen et al. 1999; Gomi et al. 2000; Tani et al. 2001; Ito et al. 2004).
Recent studies have indicated that some GH13 enzymes in fungi may be involved in the synthesis or modification of α-glucan in the fungal cell wall, rather than in starch degradation. The cell wall of aspergilli contains four major classes of polysaccharides: chitin, α-glucan, β-(1,3)-glucan and galactomannan (Fontaine et al. 2000; Beauvais and Latgé 2001). The α-glucan fraction identified in A. niger consists of two types of molecules: a linear polymer with alternating α-(1,3)/(1,4)-glycosidic bonds called nigeran (Barker and Carrington 1953) and pseudonigeran, a linear α-(1,3)-glucan molecule with some (3–10%) α-(1,4)-linkages (Johnston 1965; Horisberger et al. 1972). Synthesis of α-glucan is thought to be carried out by α-glucan synthase enzymes encoded by ags genes. The first putative α-d-glucan synthase encoding gene (ags1) was identified in Schizosaccharomyces pombe (Hochstenbach et al. 1998). The ags1 gene encodes a large, three-domain protein. In addition to the multi-pass transmembrane domain in the C-terminal part of the protein, two predicted catalytic domains are present. The middle domain shows strong similarity to glycogen and starch synthases in Glycosyltransferase family (GT) 5 and is predicted to be involved in the synthesis of α-glucan. The N-terminal part of the protein is similar to α-amylases and belongs to family GH13. This part of the protein is predicted to be localized extracellularly and might be involved in connecting two α-(1,3)-glucan chains (Grün et al. 2005). Apart from the α-glucan synthases, two types of family GH13 enzymes were recently identified in fungi to play a role in fungal cell wall biosynthesis. Marion et al. (2006) provided evidence for the involvement of a putative α-amylase (Amy1p) in the formation of α-(1,3)-glucan in the cell wall of Histoplasma capsulatum. An AMY1 knockout was unable to produce α-(1,3) glucan and showed reduced virulence. In dimorphic fungi like H. capsulatum, cell wall α-glucan is a known virulence factor (Rappleye et al. 2004; Rappleye and Goldman 2006). The second α-amylase-like enzyme Aah3p was first studied in S. pombe (Morita et al. 2006). Disruption of aah3 encoding a GPI-anchored protein resulted in hypersensitivity towards cell wall-degrading enzymes and an aberrant cell shape, indicating that normal cell wall biosynthesis was affected. Disruption of a homologous gene (agtA) in A. niger also affected cell wall stability (van der Kaaij et al. 2007a).
We previously surveyed the A. niger genome sequence to identify all GH13, GH15 and GH31 family members present in this important industrial source for amylolytic enzymes (Pel et al. 2007). This resulted in identification of a surprisingly large number of previously unknown enzymes. In this study, we have analysed their phylogeny, the presence of specific protein features and synteny with other Aspergillus species, which allowed the division of the members of each GH family into several groups. Additionally, we studied the transcriptional regulation of the genes encoding these proteins in a wild type A. niger strain, and in a derived amyR deletion strain, during their growth on xylose and maltose. Only few of the identified proteins were induced by maltose. Expression of many of the identified groups of enzymes, including the homologues of both S. pombe Aah3p and H. capsulatum Amy1p, was not induced by maltose and was not dependent on the presence of AmyR. The possible involvement of these enzymes in cell wall α-glucan synthesis and modification is discussed.
Material and methods
Database mining of A. niger genome and analysis of predicted proteins
The full genome sequence of A. niger strain CBS 513.88 has been deposited at the EMBL database with accession numbers AM270980–AM270998 (Pel et al. 2007) and was used for database mining. The nucleotide accession numbers of A. niger genes, as listed in Tables 1 and 2, refer to this database. Hidden Markov Model (HMM) profiles were built with the HMMER package (Durbin and Eddy 1998) (http://hmmer.wustl.edu/) based on the amino acid sequences of known members of GH13, GH15 and GH31. Proteins belonging to these families, originating from the different kingdoms of life, were retrieved from the CAZy website at http://www.cazy.org (Coutinho and Henrissat 1999), and the protein sequences were extracted from the GenBank/GenPept database at http://www.ncbi.nlm.nih.gov/entrez/ and Swiss-Prot database at http://www.expasy.org/sprot/. The A. niger genome was searched with the HMM profiles using the WISE2 package (Birney et al. 2004) (http://www.ebi.ac.uk/Wise2/).
Table 1All members of family GH13, GH15 and GH31 identified in the genome sequence of A. niger CBS 513.88 using HMM profiles Accession no.GeneFamilyEnzyme activityFeaturesaAmyR binding motifbProposed biological functionRefcAn11g03340aamAGH13Acid α-amylaseSS Starch degradation1An12g06930amyAGH13α-AmylaseSS+970; +252Starch degradation2An05g02100amyBGH13α-AmylaseSS+252Starch degradation2An04g06930amyCGH13α-AmylaseSS+787; +664; −531Starch degradation3An09g03100agtAGH13α-GlucanotransferaseSS, GPICell wall α-glucan synthesis4An12g02460agtBGH13α-GlucanotransferaseSS, GPI+810Cell wall α-glucan synthesis4An15g07800agtCGH13Putative α-glucanotransferaseSS, GPICell wall α-glucan synthesis4An02g13240agdCGH13Putative α-glucosidase+368UnknownAn13g03710agdDGH13Putative α-glucosidaseUnknownAn01g13610amyDGH13Putative α-amylase+504; −32Cell wall α-glucan synthesisAn09g03110amyEGH13Putative α-amylase−76Cell wall α-glucan synthesisAn01g06120gdbAGH13Glycogen debranching enzyme−487; +393Glycogen metabolismAn14g04190gbeAGH13Glycogen branching enzymeGlycogen metabolismAn04g09890agsAGH13Putative α-glucan synthaseSSCell wall α-glucan synthesis5An15g07810agsBGH13Putative α-glucan synthaseSS+287Cell wall α-glucan synthesis5An12g02450agsCGH13Putative α-glucan synthaseSS−973; +622, −185Cell wall α-glucan synthesis5An02g03260agsDGH13Putative α-glucan synthaseSSCell wall α-glucan synthesis5An09g03070agsEGH13Putative α-glucan synthaseSSCell wall α-glucan synthesis5An03g06550glaAGH15GlucoamylaseSS, SBD−792; −669; +423; −301Starch degradation6An12g03070glaBGH15Putative glucoamylase−878UnknownAn04g06920agdAGH31α-GlucosidaseSS+574; +191, Starch degradation7An01g10930agdBGH31Putative α-glucosidaseSS+904; −334Starch degradationAn09g05880agdEGH31Putative α-glucosidase IISSProtein glycosylationAn18g05620agdFGH31UnknownUnknownAn07g00350agdGGH31UnknownSS+402UnknownAn09g03300axlAGH31Putative α-xylosidaseSS+126Xyloglucan degradationAn01g04880axlBGH31Putative α-xylosidase+430; +138, Xyloglucan degradationThe newly identified proteins are indicated in boldaSS predicted N-terminal Signal Sequence; GPI  predicted Glycosylphosphatidylinositol anchor signal; SBD  predicted starch-binding domainbThe presence of consensus AmyR binding motif (CGGN8(A/C)GG) was analysed in the promoter region up to 1 kb upstream of the start codoncReferences: 1 (Boel et al. 1990); 2 (Korman et al. 1990); 3 (R. M. Van der Kaaij and X. L. Yuan, unpublished). ; 4 (van der Kaaij et al. 2007); 5 (Damveld et al. 2005); 6 (Boel et al. 1984); 7 (Nakamura et al. 1997)Table 2Functionally described family GH13 and GH31 members from other organisms, used for the multiple sequence alignments in Fig. 1Accession no.NameFamilyEnzyme activityFeaturesaBiological functionOrganismRefbBAA78714 AndGbe1GH13Glycogen branching enzymeGlycogen metabolismA. nidulans1BAA34996ScGdb1GH13Glycogen debranching enzymeGlycogen metabolismS. cerevisiae2P19571BsAmyAGH13α-AmylaseSSStarch degradationBacillus sp.3CAA54266BsAglAGH13α-GlucosidaseStarch degradationBacillus sp.4CAA21237 SpAah1GH13UnknownSS, GPIα-Glucan biosynthesisS. pombe5CAA91249SpAah2GH13UnknownSS, GPIα-Glucan biosynthesisS. pombe5CAB40006SpAah3GH13UnknownSS, GPIα-Glucan biosynthesisS. pombe5CAA16864SpAah4GH13UnknownSS, GPIα-Glucan biosynthesisS. pombe5ABK62854HcAmy1GH13Unknownα-Glucan biosynthesisH. capsulatum6ABF50883AN7345.2GH31α/β-GlucosidaseSSStarch/cellulose degradationA. nidulans7ABF50846AN7505.2GH31α-XylosidaseXylan degradationA. nidulans7BAB39856AndAgdBGH31α-GlucosidaseSSStarch degradationA. nidulans8AAU87580 TrAguIIGH31α-Glucosidase IISSProtein glycosylationT. reesei9A45249CAMAL2GH31MaltaseMaltose degradationC. albicans10For each A. niger protein identified, a functionally or biochemically characterized protein with the highest similarity was used in the phylogenetic analysis aSS  predicted N-terminal Signal Sequence; GPI  predicted Glycosylphosphatidylinositol anchor signalbReferences: 1 (Sasangka et al. 2002); 2 (Teste et al. 2000); 3 (Tsukamoto et al. 1988); 4 (Nakao et al. 1994); 5 (Morita et al. 2006); 6 (Marion et al. 2006); 7 (Bauer et al. 2006); 8 (Kato et al. 2002b); 9 (Geysens et al. 2005); 10 (Geber et al. 1992)
The presence of signal peptidase cleavage sites, glycosylphosphatidylinositol (GPI-) attachment sites and SBD in the obtained sequences were predicted by Web-based tools at URL: http://www.cbs.dtu.dk/services/SignalP/ (Bendtsen et al. 2004), URL: http://mendel.imp.ac.at/sat/gpi/gpi_server (Eisenhaber et al. 2004), and URL: http://www.ncbi.nlm.nih.gov/BLAST/ (Marchler-Bauer and Bryant 2004), respectively.
Multiple sequence alignments of GH13, 15 and 31 family members were performed using DNAMAN version 4.0 (Lynnon BioSoft, Canada). The alignments were based on the full length of the predicted proteins, except in case of predicted α-glucan synthases for which only the N-terminal part, encoding the family GH13 domain, was used for the alignment. The phylogenetic relationship was calculated by using Optimal Alignment (Thompson et al. 1994) with gap opening penalty and gap extension penalty of 10 and 0.05, respectively. A bootstrapped test of phylogeny was performed by the Neighbour-Joining method using 1,000 replicates. Wherever possible, one protein with described activity was included for each of the groups identified based on phylogenetic analysis.
Strains and transformations
The wild type A. niger strain used in this study is N402, a cpsA1 derivative of A. niger van Tieghem (CBS 120.49, ATCC 9029) (Bos et al. 1988). Strain AB4.1 is a pyrG negative derivative of N402 (van Hartingsveldt et al. 1987) and was used to construct the amyR disruption strain. A. niger strains were grown in Aspergillus minimal medium (MM) (Bennett and Lasure 1991), or Aspergillus complete medium (CM) consisting of MM with the addition of 0.5% (w/v) yeast extract and 0.1% (w/v) casamino acids. Growth medium was supplemented with 10 mM uridine (Serva, Germany) when required. Transformation of A. niger AB4.1 was performed as described earlier (Punt and van den Hondel 1992) using lysing enzymes (L1412, Sigma, USA) for protoplastation. The bacterial strain used for transformation and amplification of recombinant DNA was Escherichia coli XL1-Blue (Stratagene, USA). Transformation of XL1-Blue was performed according to the heat shock protocol (Inoue et al. 1990).
Disruption of the maltose utilization activator amyR in A. niger
Plasmid pJG01 containing the A. nigeramyR gene as a 4.3 kb NsiI fragment in pGEM11 was kindly provided by P. van Kuyk (Wageningen University, the Netherlands) and was used to disrupt the amyR gene. The construction of the amyR deletion cassette was performed as follows. The BamHI–EcoRI fragment and NsiI–SalI fragment flanking the amyR ORF at the 5′ and 3′ region, respectively, were isolated from pJG01. The isolated NsiI–SalI fragment was cloned into pUC19 to obtain plasmid pAmyRF3. Subsequently, a BamHI–SalI fragment carrying the A. oryzaepyrG gene, obtained from plasmid pAO4-13 (de Ruiter-Jacobs et al. 1989) was inserted into pAmyRF3 which resulted in plasmid pAmyRF3-pyrG. The BamHI–EcoRI fragment isolated from pJG10 was ligated into pAmyRF3-pyrG resulting in the amyR deletion plasmid (pΔamyR). Prior to transformation to AB4.1, pΔamyR was linearized with EcoRI. Uridine prototrophic transformants were selected by their ability to grow on MM without uridine. After two rounds of purification, transformants were tested for their ability to grow on starch. Approximately 10% of the pyrG+ transformants showed defective growth on MM agar plates containing starch as sole carbon source. Six independent putative amyR deletion strains (YvdM1.1-1.6) with identical phenotypes were obtained. Southern blot analysis confirmed proper deletion and a single integration of the amyR disruption cassette at the amyR locus. Strain YvdM1.1 was used for further analysis and we will refer to this strain as the ΔamyR strain in the remaining of this paper.
Culture conditions, RNA preparation, microarray experiments and data analysis
RNA extracted from the A. niger ΔamyR strain and its parental strain (N402) grown on different carbon sources were used for microarray experiments using custom-made ‘dsmM_ANIGERa_coll’ Affymetrix GeneChip® Microarrays kindly provided by DSM Food Specialties (Delft, The Netherlands). All experiments for each growth condition (culturing the mycelia, RNA extractions and microarray hybridizations) were performed twice as independent biological experiments.
Aspergillus niger spores (2 × 106 spores ml−1) were inoculated in 250 ml MM supplemented with 2% (w/v) xylose (Sigma) and 0.1% (w/v) casamino acids and grown for 18 h at 30°C on a rotary shaker at 300 rpm. The mycelium was harvested by suction over a nylon membrane and washed with MM without carbon source. Aliquots of 1.6 g wet weight of mycelium were transferred to 300 ml Erlenmeyer flasks containing 70 ml MM supplemented with 1% (w/v) carbon source [maltose (Sigma) or xylose] and incubated at 30°C for a further 2 or 8 h. The pH of all cultures grown for 2 h was equal to the pH at the time of transfer (pH 6.2). Cultures grown for 8 h were buffered at pH 4 by the addition of 100 mM of citric acid/sodium citrate to allow comparison between the N402 and the ΔamyR strain at this time point. The mycelium was harvested over Miracloth filter, frozen in liquid nitrogen and stored at −80°C. Total RNA was isolated from mycelia using TRIzol reagent (Invitrogen) and RNA quality was verified by analyzing aliquots with glyoxal/DMSO gel electrophoresis and Agilent Bioanalyzer “Lab on chip” system (Agilent Technologies, USA). Processing, labeling and hybridization of cRNA to A. niger Affymetrix GeneChips were performed according to the corresponding Affymetrix protocols for “Eukaryotic Target Preparation” and “Eukaryotic Target hybridization”. For probe array washing and staining, the protocol “Antibody Amplification for Eukaryotic Targets” was followed. Hybridized probe array slides were scanned with Agilent technologies G2500A Gene Array Scanner at a 3 μm resolution and a wavelength of 570 nm. Affymetrix Microarray Suite software MAS5.0 was used to calculate the signal and P-values and to set the algorithm’s absolute call flag, which indicates the reliability of the data points according to P (present), M (marginal) and A (absent). The data on each chip were globally scaled to an arbitrary target gene intensity of 500. The complete microarray data were deposited into ArrayExpress with an accession E-TABM-324 at http://www.ebi.ac.uk/miamexpress.
The pre-scaled data from each hybridization experiment was then normalized for statistical analysis using Genespring 7.0 software (Silicon Genetics, USA). The per chip normalization was performed to ensure that the overall characteristic of the expression distribution such as median should be the same for all the chips. For the genome-wide analysis, we focused on maltose-induced genes and therefore a pre-filtering of data was performed to select for genes whose detection calls are present in both maltose duplicate samples in the wild-type strain (N402). The selected dataset was used to perform a one-way ANOVA analysis under the test type of “parametric test, don’t assume variances equal”. Fold changes in expression between two different conditions were then computed for genes with P < 0.08 based on one-way ANOVA analysis.
Results
Identification of glycoside hydrolase family 13, 15 and 31 genes in the A. niger CBS 513.88 genome sequence
α-Amylases, glucoamylases and α-glucosidases, members of families GH13, 15 and 31, respectively, are the three main types of enzymes involved in breakdown of starch by aspergilli (Tsukagoshi et al. 2001). To identify all genes encoding enzymes that might play a role in starch utilization, or other α-glucan modifying processes in A. niger, the genome of A. niger CBS 513.88 was searched with HMM profiles based on known enzymes from families GH13, 15 and 31. This resulted in the retrieval of a total of 27 protein sequences including 17 previously unknown proteins (Pel et al. 2007), as listed in Table 1. The predicted proteins were annotated based on their similarity to known enzymes. Gene names were assigned based on this annotation and do not necessarily match the activity of the enzymes, as this is often unknown.
Two approaches were combined to predict putative functions in cellular processes for this surprisingly large number of newly identified proteins. First, phylogenetic trees were constructed using the GH13, GH15 and GH31 family members identified in the A. niger genome, as well as functionally characterized proteins from other organisms with similarity to the identified A. niger proteins (Fig. 1). Second, using DNA microarrays, the expression of all the A. niger genes encoding GH13, GH15 and GH31 enzymes was examined in both the A. niger wild type strain N402 and the ΔamyR strain derived, after growth on different carbon sources (Fig. 2; Table S1). Both the N402 and the ΔamyR strains were pregrown in xylose for 18 h, and mycelia were transferred to either xylose or maltose media and grown further for 2 or 8 h. Expression levels were determined based on geometric mean data of biological duplicate samples. We will discuss each enzyme family in detail and combine the findings in A. niger with the predicted proteins present in the genomes of A. fumigatus (Nierman et al. 2005), A. nidulans (Galagan et al. 2005) and A. oryzae (Machida et al. 2005).
Fig. 1Bootstrapped phylogenetic tree of A. niger GH13 (a) and GH31 (b) enzymes and several closest homologues from other species. Newly identified proteins in the genome of A. niger are shown in bold. A description of each protein is listed in Tables 1 and 2. Bootstrap values are indicated on the node of each branch. The tree was created with DNAMAN 4.0 using gap and extension penalties of 10 and 0.5, respectively. The scale bar corresponds to a genetic distance of 0.05 substitution per positionFig. 2Expression profiles of A. niger family GH13, 15 and 31 enzymes. Accession numbers of the gene names are given in Tables 1 and 2. Strain and time points after transfer from the preculture are indicated on the right-hand side. The numeric values and Present/Absent calls from the expression data are provided as Supplementary Tables 1, 2
Identification and transcriptional regulation of GH13 family members
The HMMer search for family GH13 enzymes in the A. niger genome resulted in the identification of 18 protein sequences of which 10 had not been identified previously (Table 1). Table 3 displays the four conserved regions typical for family GH13 proteins as identified in these enzymes. A phylogenetic tree was produced combining the A. niger family GH13 enzymes with several functionally characterized GH13 family proteins from other organisms (Fig. 1). The combination of this phylogenetic analysis with a functional annotation of the proteins revealed six recognizable subgroups.
Table 3Alignment of the four conserved regions of all family GH13 enzymes identified in A. niger, as well as in four Aah proteins from S. pombe and Amy1p from H. capsulatumThe seven residues generally conserved in family GH13 are indicated in bold and the three catalytic residues are additionally underlined. The group to which the proteins are assigned, as described in this paper, is indicated
Group I consists of four extracellular α-amylases. Three of these are the previously characterized extracellular α-amylases acid-amylase (AamA) and α-amylases AmyA and AmyB (Korman et al. 1990). One new extracellular α-amylase was identified and named AmyC. This protein displays high similarity with the known A. niger α-amylases (74% identity to AmyA and AmyB, 65% identity to AamA). The amyC gene is located in a gene cluster also containing an α-glucosidase gene (agdA) and the amyR gene encoding the AmyR transcription factor. Expression of aamA in A. niger N402 was not detectable in xylose media, but was strongly induced in the presence of maltose (Fig. 2). The expression of aamA was reduced to a non-detectable level in the ΔamyR strain. Expression of amyA and amyB was not detected in A. niger N402 in any of the conditions tested (Fig. 2) (see Discussion). The expression of the newly identified amyC gene was relatively low compared to the aamA gene. At 2 h after transfer from the preculture, the expression level of amyC was independent of the carbon source, while after 8 h the expression level was reduced 3-fold on xylose compared to maltose. Additionally, the expression on maltose was reduced 2- to 3-fold in the ΔamyR strain (Table S1). The presence of three putative AmyR-binding elements in the promoter region of amyC further suggests that its expression is controlled by AmyR (Table 1).
The A. niger acid amylase (encoded by aamA) in the CBS 513.88 strain does not contain a predicted SBD, whereas its homologues in A. nidulans, A. fumigatus and other aspergilli contain a full-length SBD (Table S2). In addition, the presence of a functional SBD in the AamA protein of A. niger N402 was suggested because of the purification of AamA via its SBD from culture fluid of a N402 ΔglaA strain, and subsequent demonstration of the SBD with specific antibodies (M. F. Coeffet-Le Gal and D. Archer, personal communication, University of Nottingham, UK). We therefore PCR amplified the aamA gene and its 3′ flanking regions using N402 genomic DNA as a template, determined its DNA sequence and compared it to the aamA gene and its 3′ flanking region from CBS 513.88. The comparison revealed that the aamA gene of A. niger strain N402 does include an SBD and that the CBS513.88 strain harbours a deletion of 230 nucleotides right after the part encoding the (β/α)8 barrel, causing a frame shift and the introduction of a stop codon resulting in a truncated protein (Fig. S2). After the deletion, the DNA sequence continues by encoding part of a predicted SBD, but this part is not translated (Fig. S2). The SBD in AamA found in the A. niger N402 strain is also predicted to be present in A. niger strain ATCC 1015 (Baker 2006).
Group II contains three putative GPI-anchored enzymes, recently identified as α-glucanotransferases, and named AgtA, AgtB and AgtC, respectively (van der Kaaij et al. 2007a). This subgroup of proteins is characterized by the presence of two hydrophobic signal sequences. The N-terminal signal sequence is predicted to serve for translocation to the endoplasmic reticulum (ER), whereas the C-terminal sequence is predicted to be replaced by a preassembled glycosylphosphatidylinositol (GPI) anchor in the ER (Orlean 1997). The three enzymes cluster together with the α-amylases in the phylogenetic tree (Fig. 1a), but can be distinguished from the α-amylases by their catalytic domains which are clearly different from the consensus sequence for the α-amylase family. In all three proteins, one or two highly conserved histidines in conserved regions I and IV are replaced by other hydrophilic residues (Table 3). Family GH13 members without these histidine residues, which are part of the active site, are very rare (Uitdehaag et al. 1999). Mutation of these residues generally results in reduced activity or altered reaction specificity of the enzymes (Chang et al. 2003; Leemhuis et al. 2004). Interestingly, both the conserved His-residues in Regions I and IV are also missing in all (putative) α-glucan synthases (Table 3). Other aspergillus genomes harbour two or three Agt homologues (Fig. S1A; Table S2), all sharing the aberrant conserved regions and predicted GPI-anchoring. In the A. niger genome, both agtB and agtC are located next to genes encoding putative α-glucan synthases, and this arrangement of genes is conserved in other aspergilli. The agtA gene is constitutively expressed in both the wild type strain N402 and the ΔamyR strain under all growth conditions examined (Fig. 2). Expression of agtB was only detected 8 h after transfer, regardless of the carbon source and independent of AmyR, while expression of agtC was not detected.
Group III consists of two putative, intracellular α-glucosidases, named AgdC (An02g13240) and AgdD (An13g03710). The protein sequences contained all residues commonly conserved in the α-amylase superfamily (Table 3). The predicted intracellular proteins lack clear similarity to any previously characterized fungal protein, although similar enzymes are predicted in A. oryzae, A. nidulans and A. fumigatus (Fig. S1A). Their most related functionally characterized homologue is an α-glucosidase from Bacillus sp. SAM1606 (Nakao et al. 1994). Expression of agdC was low and not induced on maltose, and expression of agdD was not observed (Fig. 2).
Group IV contains two putative intracellular α-amylases, named AmyD (An01g13610) and AmyE (An09g03110), which share 55% identity. AmyD was recently characterized as an α-amylase with low-hydrolyzing activity on starch and related substrates (Van der Kaaij et al. 2007b). AmyD and AmyE are similar to a recently identified protein Amy1p from H. capsulatum, which was shown to be involved in cell wall α-glucan synthesis. The latter protein has not been characterized biochemically. Functionally characterized enzymes with similarity to this cluster therefore included only bacterial enzymes, of which maltohexaose-forming α-amylase of alkalophilic Bacillus sp. #707 (Tsukamoto et al. 1988) had the highest similarity. No significant expression of amyD or amyE was detected in our experiments (Fig. 2). Predicted enzymes highly similar to A. niger AmyD and AmyE were also present in other Aspergillus species (Fig. S1A; Table S2). Like the A. niger amyE gene, the orthologues in A. nidulans (AN3309.3) and in A. oryzae (AO003001497) are clustered in the genome with genes encoding α-glucanotransferases and α-glucan synthases (Table S2).
The two A. niger proteins in Group V could be reliably annotated as enzymes involved in glycogen metabolism: glycogen branching enzyme (GbeA) and glycogen debranching enzyme (GdbA). A homologue for each of these enzymes is present in the other Aspergillus genome sequences. Transcriptional analysis in A. niger showed that both genes were expressed both on xylose and maltose, and that their expression was unaffected in the ΔamyR strain (Fig. 2).
Group VI contains the five predicted α-glucan synthase genes (Damveld et al. 2005). The derived proteins are highly similar (66–83%) to each other, and all contain the two catalytic domains (GT5 and GH13) characteristic for these proteins. Both agsB and agsC are clustered in the genome with genes encoding the aforementioned α-glucanotransferases agtC and agtB, respectively. In both cases, the direction of transcription of the pair of genes is such that they can be transcribed from their intergenic region. Expression analysis in our microarray data collection, which includes data from various time points on several carbon sources (glucose, maltose, xylose, inulin, sucrose) indicate that agsC and agtB are co-expressed at a later growth stage, independent of the carbon source (data not shown). Expression of both agsB and its neighbouring gene agtC was not detected in any of the growth conditions. The expression levels of agsA and agsD were very low, or not detectable. AgsE was highly expressed in all experiments independent of AmyR (Fig. 2). As expected, several proteins with high similarity to the A. niger α-glucan synthases are predicted from the other Aspergillus genomes, although the number of homologues present is highest in A. niger (Fig. S1A; Table S2).
Identification and transcriptional regulation of GH15 family members
An HMMer search for GH15 family members in the A. niger genome returned two predicted proteins: the previously described glucoamylase GlaA (Boel et al. 1984) and an unknown predicted protein named GlaB (An12g03070). The GlaB protein lacks both an N-terminal signal sequence and an SBD, and displays a low similarity of 26% with GlaA. These two types of family GH15 enzymes are also recognized in other aspergilli studied (Fig. S1B). All predicted proteins similar to GlaB lack both a signal for secretion and an SBD, features typically present in the fungal GH15 enzymes described to date. A. niger glaA was expressed on xylose and strongly induced on maltose, as described previously (Fowler et al. 1990). The induction of glaA was AmyR dependent. Expression of glaB was not detected in any of the conditions tested (Fig. 2).
Identification and transcriptional regulation of GH31 family members
HMMer searches in the genome of A. niger revealed the presence of seven GH31 family members, of which only one was previously identified as an α-glucosidase (aglA) (Nakamura et al. 1997). We propose to name all (putative) α-glucosidases in A. niger Agd enzymes, similar to the nomenclature in A. nidulans, and to rename AglA as AgdA, to prevent confusion with α-galactosidases (den Herder et al. 1992). The phylogenetic tree (Fig. 1b) shows the presence of (at least) four subgroups within family GH31. In the group of the α-glucosidases, AgdA clusters with AgdB, a predicted extracellular protein. AgdB has some similarity to two A. nidulans enzymes: AN7345.2 (62% identity) with both α- and β-glucosidase activity (Bauer et al. 2006) and α-glucosidase B (AndAgdB/ AN8953.3, 53% identity), with strong transglycosylation activity (Kato et al. 2002b). However, none of the true orthologues of AgdB has been characterized (Fig. S1C). Transcript analysis revealed that agdB was regulated similar to agdA, as the strong induction of both genes in the presence of maltose was dependent on the presence of amyR. However, where most AmyR-regulated genes (aamA, glaA and agdA) reached their highest level of induction after 8 h growth on maltose, the expression level of agdB was decreased after 8 h compared to 2 h (Fig. 2).
From the remaining five GH31 family members, AgdE (An09g05880) shows similarity to Trichoderma reesei glucosidase II (TrAguII) (Geysens et al. 2005). This type of α-glucosidases is located in the ER where it is involved in the trimming of α-(1,3)-linked glucose residues from N-glycan core structure Glc3Man9GlcNAc2, which may be attached to proteins designated to be secreted (Geysens et al. 2005). AgdE contains a predicted signal sequence which might serve to direct the protein to the ER. The agdE gene was constitutively expressed in all conditions tested (Fig. 2), consistent with its predicted function as an α-glucosidase II. Two additional GH31 family members, AgdF (An18g05620) and AgdG (An07g0350), lack similarity to any functionally described proteins. Expression of both genes was not detected in any of the tested growth conditions.
The two final family GH31 members, named AxlA and AxlB, are highly similar to AN7505.2 from A. nidulans, which was recently characterized as an α-xylosidase (Bauer et al. 2006). Gene axlA was highly expressed in the presence of xylose, whereas no expression was detected in maltose grown cultures (Fig. 2). The high expression of axlA on xylose further supports its putative function as a xylanolytic enzyme. The gene encoding the putatively intracellular AxlB was expressed at a very low level independent of the carbon source (Fig. 2).
A BLASTP search in the genomes of A. fumigatus, A. nidulans and A. oryzae for family GH31 enzymes resulted in a similar collection of enzymes as identified in A. niger (Fig. S1C). Several clusters of orthologous proteins are distinguishable, but the assignment of an enzymatic activity to these clusters is not yet possible due to a lack of well studied homologues for these enzymes, thus requiring biochemical studies in our future work.
Genome-wide analysis of AmyR dependent maltose induced genes using microarrays
The expression analysis of genes encoding family GH13, GH15 and GH31 enzymes in the A. niger genome revealed that the expression of only a limited number of genes was induced by maltose in an AmyR dependent way. In fact, only four genes (aamA, glaA, agdA, and agdB) showed the predicted expression pattern for genes encoding enzymes involved in the breakdown of starch (Fig. 2). To identify additional genes with a possible role in starch metabolism, the expression of all 14,509 predicted open reading frames on the Affymetrix microarray chips was analysed. Comparison of the transcriptome of the wild type strain (N402), grown for 2 h after transfer on either xylose or maltose, identified 634 genes that were significantly induced by maltose (One-way ANOVA analysis P < 0.08 and >2-fold change in expression level). A set of 12 genes was expressed >2-fold higher in N402 compared to the ΔamyR strain (One-way ANOVA analysis P < 0.08) after the transfer of these strains from xylose to maltose. Combining the two gene sets resulted in a collection of six genes that were >2-fold induced on maltose in an AmyR-dependent manner (Fig. 3, Tables 4, 5). Five of these were assigned to the category of carbohydrate transport and metabolism according to FunCat (Ruepp et al. 2004), including the three genes encoding known extracellular starch degrading enzymes (aamA, glaA and agdA) and a putative α-glucosidase (agdB) (Table 4). Gene An15g03940, encoding a protein with high similarity (68%) to a Candida intermedia glucose/xylose symporter (Leandro et al. 2006), was 2.3-fold induced by maltose; this induction was not observed in the ΔamyR strain. The sixth induced gene (An11g02550) encodes a protein highly similar (72%) to Kluyveromyces lactis phosphoenolpyruvate carboxykinase (Kitamoto et al. 1998) which functions in gluconeogenesis by catalyzing the conversion of oxaloacetate into phosphoenolpyruvate. It should be noted that the pH of the growth medium remained 6.2 during the 2 h growth period after transfer.
Fig. 3Results of microarray analysis for maltose induced and AmyR dependent genes. Venn-diagram showing the number of genes induced on maltose compared to xylose in A. niger N402, and the number of genes induced in N402 compared to the ΔamyR deletion strain. The number of genes both induced by maltose and dependent on AmyR is indicated in bold. The maltose induced and AmyR dependent genes which are present in both 2 and 8 h after transfer from a preculture are shown in the boxTable 4Overview of six maltose induced and AmyR dependent genes in A. niger at 2 h (pH 6.2) after transfer from precultureORF no.aN402 maltosebCallcN402 xyloseCallΔamyR maltoseCallFold inductionP-valuePutative functionMaltose vs xyloseN402 vs ΔamyRCarbohydrate transport and metabolismAn11g033403.15 ± 0.12P0.08 ± 0.04A0.13 ± 0.09A38.624.20.080Acid alpha-amylase AamAAn04g0692085.96 ± 22.54P6.24 ± 0.98P5.03 ± 0.03P13.817.10.064Extracellular alpha-glucosidase AgdAAn01g1093096.50 ± 31.50P10.47 ± 2.59P8.35 ± 2.13P9.211.50.062Extracellular alpha-glucosidase AgdBAn03g06550117.80 ± 43.16P13.78 ± 4.14P5.85 ± 0.47P8.520.10.072Glucoamylase GlaA An15g03940152.30 ± 19.00P65.74 ± 3.04P70.59 ± 2.88P2.32.20.074Putative monosaccharide transporterEnergyAn11g0255058.87 ± 3.86P10.13 ± 1.41P25.27 ± 5.35P5.82.30.033Putative phosphoenolpyruvate carboxykinase aGene name in bold indicates that the gene is >2-fold induced on maltose in an AmyR dependant way after both 2 and 8 hbThe expression level was based on the geometric mean value of the duplicate samples, and the deviation values between the duplicate samples are indicated. The P-value was based on 1-way ANOVA analysiscP, M or A represent detection calls for present, marginal or absent, respectively. Genes were divided over different functional groups according to FunCat (Ruepp et al. 2004)Table 5Overview of the 18 maltose induced and AmyR dependent genes in A. niger at 8 h (pH4.0) after transfer from precultureORF no.aN402 maltosebCallcN402 xyloseCallΔamyR maltoseCallFold inductionP-valuePutative functionMaltose vs xyloseN402 vs ΔamyRCarbohydrate transport and metabolismAn11g0334065.26 ± 7.48P0.58 ± 0.57A0.08 ± 0.01A113.0808.40.004Acid alpha-amylase AamAAn01g1093049.46 ± 3.42P2.37 ± 0.08P6.84 ± 1.99P20.97.20.006Extracellular alpha-glucosidase AgdBAn04g06920179.10 ± 13.90P9.89 ± 3.63P3.36 ± 0.46P17.651.70.012Extracellular alpha-glucosidase AgdAAn02g0354062.79 ± 8.36P8.81 ± 0.61P0.96 ± 0.82P, A6.761.90.012Putative hexose transport protein MstCAn15g03940144.40 ± 6.80P35.93 ± 2.72P3.04 ± 0.03P, M4.047.60.003Putative monosaccharide transporterAn03g06550239.30 ± 2.05P83.03 ± 4.35P19.03 ± 1.75P2.912.60.012Glucoamylase GlaA An04g069304.36 ± 0.50P1.50 ± 0.16P1.40 ± 0.03P2.93.10.062Extracellular alpha-amylase AmyCAn09g0481018.47 ± 0.44P6.46 ± 0.35P2.52 ± 0.02P2.97.40.003Putative hexose transporterAn12g07450137.60 ± 13.00P51.57 ± 11.82P52.50 ± 6.31P2.72.60.064Sugar/H+ symporter MstAEnergyAn03g062704.72 ± 0.29P1.59 ± 0.14A0.20 ± 0.25A3.325.60.035Putative isoamyl alcohol oxidase An16g060105.18 ± 1.55P1.80 ± 0.39P0.22 ± 0.02A2.923.80.032Putative phosphoglycerate mutaseAmino acid transport and metabolismAn03g002808.35 ± 0.90P3.57 ± 0.74P, A0.12 ± 0.06A2.368.00.050Similarity to tyrosinase protein Cell rescue, defence and virulence An14g057303.99 ± 0.59P0.23 ± 0.03A1.37 ± 0.73P, A18.83.20.037Similarity to integral membrane protein An03g002905.96 ± 0.89P1.68 ± 0.47P0.64 ± 0.21P3.59.30.068Similarity to integral membrane protein An06g004908.14 ± 0.89P3.81 ± 0.30P, A2.23 ± 0.44A2.13.70.066Similarity to integral membrane proteinProtein fateAn18g042605.44 ± 0.40P2.57 ± 0.07P1.77 ± 0.28M, A2.13.10.056Similarity to UDP-galactose transporterUnclassifiedAn16g0129015.04 ± 0.28P5.22 ± 1.69P, A1.97 ± 0.26A2.66.90.027Unknown An09g0613012.62 ± 2.51P5.41 ± 0.93P2.69 ± 0.30P, A2.34.70.059Unknowna,b,cSee footnotes of Table 4
A similar comparative analysis was performed for samples taken for cultures that had grown for 8 h after the transfer. To prevent differences in pH between the N402 and the ΔamyR strain after 8 h of growth, the pH of the medium was buffered to 4.0 using 100 mM of citric acid/sodium citrate to allow comparison between the N402 and the ΔamyR strain at this time point, as the medium of the ΔamyR strain did not acidify as quickly as the medium of the N402 strain. A total of 28 genes were significantly induced by maltose in comparison with xylose, and 161 genes were 2-fold higher expressed in N402 compared with the ΔamyR strain (one-way ANOVA analysis P < 0.08). By combining the two sets, we identified 18 genes which were induced by maltose and whose induction was AmyR dependent (Fig. 3, Table 5). Nine of these genes encode proteins involved in carbohydrate transport and metabolism, from which five were also identified as differentially expressed after 2 h (aamA, glaA, agdA, agdB, and An15g03940). Additionally identified genes included amyC, encoding an extracellular α-amylase, three genes encoding putative sugar transporters, and several other genes belonging to various functional categories (Tables 4, 5). At this stage we cannot exclude that additional overlapping genes have escaped our attention, because of the different pH values of the medium after transfer, which also could affect gene expression. The results from the microarray experiments were validated using Northern blot analysis for a selected number of amylolytic genes. These genes include glaA, aamA, agdB and amyC. As shown in supplementary Fig. 3, the results of the Northern hybridizations are in good agreement with the microarray data (Supplementary Table 1). The Northern analysis also revealed an additional mRNA of a larger size for the amyC gene, 8 h after transfer. The detection of two different-sized mRNAs suggests two different mRNA start sites or different polyadenylation sites, but the exact sequence of the different mRNAs and possible consequences for the gene model have not been addressed so far.
Interestingly, the transcription factor gene amyR was also induced 2 and 8 h after transfer to maltose compared to the transfer to xylose (2.6-fold, P-value 0.014; 2.8-fold, P-value 0.027, respectively), indicating transcriptional regulation of the amyR gene itself. As the AmyR transcription factor is per se missing in the deletion strain, it is not appropriate to include the amyR gene in the group of maltose induced, AmyR dependent genes. However, the 1-kb promoter region of the amyR gene contains two AmyR binding motifs, allowing for the possibility that AmyR can induce its own expression.
Discussion
The full inventory of glycoside hydrolases belonging to families GH13, GH15 and GH31 in A. niger strain CBS 513.88 has recently been described (Pel et al. 2007). Members of these protein families in aspergilli, including A. niger, have been studied extensively mainly because of their industrial relevance. Nevertheless, several groups of new alpha-glucan acting enzymes that had not been identified previously were identified. These novel enzymes are conserved among several Aspergillus species as well as other Ascomycetes, indicating that they may play an important role in fungal metabolism. In the present study, we have combined transcriptional analysis and more detailed phylogenetic analysis to further understand their function.
The GH13 family in A. niger contains six separate groups of amylase-type enzymes (Fig. 1), of which three groups had not been described thus far. The best-described group comprises the extracellular α-amylases, and is now extended with AmyC in addition to the three known α-amylases. Overexpression of the amyC gene in A. niger resulted in increased levels of endo-α-amylase activity in the medium, indicating that this gene indeed encodes an extracellular α-amylase (R. M. Van der Kaaij and X. L. Yuan, unpublished results). The relatively low expression of amyC compared to other starch degrading enzymes may explain why this protein has not been identified previously. The localization of the amyC gene in the genome is noteworthy, as it is part of a small cluster of amylolytic genes (with agdA) and their transcriptional regulator gene amyR. A similar organization is observed in the genomes of other aspergilli such as A. nidulans and A. oryzae RIB40 (Gomi et al. 2000). In A. fumigatus the same cluster is extended with a glucoamylase (similar to GlaA). Gene clusters of transcriptionally co-regulated genes in filamentous fungi are often involved in the same process, e.g. secondary metabolite production (Woloshuk et al. 1995) or catabolism of amino acids (Hull et al. 1989). A possible function of AmyC might be to act as a scouting enzyme for the presence of starch, resulting in the subsequent activation of AmyR by starch-derived molecules (maltose or isomaltose). Alternatively, AmyC might be regulated by another system additional to AmyR, e.g. pH-regulated expression, or its expression might be upregulated locally, e.g. in hyphal tips.
The lack of detectable expression of amyA and amyB in A. niger N402 is puzzling. Both genes were expressed in A. niger strain CBS 513.88 in a fed-batch fermentation using glucose as a carbon source (Pel et al. 2007), but batch cultivation of A. niger N402 on glucose did not result in detectable expression in microarray studies (E. Martens, H. Kools, P. Schaap, personal communication). These results indicate a difference between both strains with regard to either the presence of amyA/B in A. niger strain N402 or in the transcriptional regulation of amyA/B. In the recently released genome sequence of A. niger strain ATCC 1015 from the Joint Genome Institute (Baker, 2006) we could not identify orthologues of the AmyA/AmyB proteins suggesting that these genes might be actually absent from the N402 strain. The absence, or lack of expression of amyA/amyB, is consistent with the observation that the A. niger MGG029ΔaamA strain (N402 background) with gene disruptions for both aamA and glaA grows very poorly on starch (Weenink et al. 2006), confirming that AmyA and AmyB do not contribute significantly to the amylolytic potential of N402. The microarray analysis revealed that in addition to the amyA/B genes, five more amylolytic genes (agtC, amyD, agdD, agdG and glaB) were not detected (Absent calls) in any of the conditions tested. However, orthologous genes were identified in the genome of A. niger strain ATCC1015, suggesting that these genes are not absent from the N402 genome, but rather not expressed under the tested conditions.
In this study we have identified three new groups within the GH13 family that had not been described before in aspergilli: α-amylase-like proteins predicted to be GPI-anchored enzymes (group II) (subfamily GH13_1, as defined by Stam et al. 2006), intracellular α-glucosidases (group III) (unknown subfamily) and intracellular α-amylases (group IV) (subfamily GH13_5). Detailed biochemical studies of two GPI-anchored proteins revealed that they are α-glucanotransferases, acting mainly on maltooligosaccharides (van der Kaaij et al. 2007a). The expression of these genes was not regulated in response to maltose metabolism or AmyR. The Agt enzymes are homologues of a group of four GPI-anchored amylase-like proteins recently identified in S. pombe (Aah1-4p), which have similarly aberrant conserved regions (Table 3). A deletion of one of these homologues resulted in aberrant morphology of the cell and an increased sensitivity towards cell wall degrading enzymes (Morita et al. 2006). A similar phenotype was observed for an A. niger knockout of agtA (van der Kaaij et al. 2007a). Such a phenotype can be explained if α-glucan synthesis is affected. The clustering of agt genes with genes encoding α-glucan synthases in all four Aspergillus species studied, and the expression pattern of the A. niger agt genes both support the hypothesis that Agt enzymes play a role in α-glucan synthesis.
Aspergillus niger contains two putative intracellular α-amylases, AmyD and AmyE, that belong to the recently identified subfamily GH13_5 (Stam et al. 2006). A homologous gene in H. capsulatum, AMY1, was shown to be essential for the formation of α-(1,3)-glucan in the cell wall (Marion et al. 2006). The possibility of a role for subfamily GH13_5 enzymes in the cell wall of Aspergilli is strengthened by their location in the genome: A. niger, A. nidulans and A. oryzae, all contain a cluster of genes, containing a homologue of AMY1 as well as ags and agt genes (Table S2). Both the GPI-anchored α-glucanotransferases and the family GH13_5 α-amylase-like proteins are only present in fungi with cell wall α-glucan and not in the true yeasts, which lack this type of cell wall glucans. However, a study with heterologously expressed AmyD did not confirm a role for this enzyme in α-(1,3)-glucan formation, as the enzyme only showed a (low) hydrolyzing activity on substrates with α-(1,4) glycosidic bonds in vitro (van der Kaaij et al. 2007b). The expression levels of amyD and amyE were below detection level, and thus could not confirm nor refute the possible involvement of these proteins in cell wall α-glucan synthesis in A. niger.
At this moment, no function can be assigned to the predicted intracellular α-glucosidases AgdC and AgdD, and their homologues identified in other fungi. The enzymes show similarity to a Bacillus α-glucosidase as well as to a maltase from C. albicans, and might therefore be involved in the intracellular hydrolysis of maltose. Alternatively, these enzymes could play a role in trehalose metabolism, as they show some similarity to bacterial trehalose-6-phosphate hydrolases. Trehalose is known to be a common reserve carbohydrate in fungi including A. niger (Wolschek and Kubicek 1997; Arguelles 2000).
A surprising finding from the genome mining of A. niger was the high number of GH enzymes with a predicted intracellular location. This group included not only the starch-acting enzymes described here, but also other predicted intracellular glycoside hydrolases such as β-glucosidase, chitinase, β-mannosidase, β-xylosidase, rhamnosidase and invertase, present in various Aspergillus genomes (Pel et al. 2007; Goosen et al. 2007). Most of these proteins were predicted to be exo-acting, i.e. releasing terminal mono- or disaccharide residues from the non-reducing end of the substrate. In A. niger, the genes encoding these proteins are expressed at a relatively low level compared to the extracellular starch degrading enzymes, sometimes even below the detection limit. In a recent publication, expression of intracellular chitinases in A. nidulans was found to be induced during the autolysis phase of the culture (Yamazaki et al. 2007). A role for these and other predicted intracellular proteins during the autolysis phase would explain their low expression levels observed in A. niger during vegetative growth, as well as their lack of secretion signals.
The genome-wide expression analysis identified only five genes that were >2-fold induced by maltose in an AmyR dependent way, 2 and 8 h after the transfer to maltose. Among them are genes encoding known starch degrading enzymes (aamA, glaA), two (putative) α-glucosidases (agdA, agdB), and a putative sugar transporter protein (An15g03940). As described in the Results section, the samples taken after 2 and 8 h after transfer were grown at different pH values (pH 6.2 and 4.0, respectively), which might influence our results and lead to an underestimation of the number of genes that are induced by maltose in an AmyR dependant way at both 2 and 8 h as additional pH-controlled gene expression might also occur. To fully explore this, both strains should be cultivated at controlled conditions at identical pH.
The specificity of the putative sugar transporter encoded by the An15g03940 gene has not been examined, but its expression profile suggests that it might function as a glucose or a maltose transporter. Three additional sugar transporters were expressed higher on maltose than on xylose after 8 h (Table 5). One of them, An12g07450/MstA has been functionally characterized as a high-affinity glucose transporter (Vankuyk et al. 2004) which is in-line with its expression profile. The two additional sugar transporters encoding genes (An02g03540/mstC and An09g04810) showed an interesting expression pattern, with induced expression on maltose but also relatively high expression levels on xylose (Table 5), suggesting that these transporters might have a broad sugar transporting activity. In the ΔamyR strain both their expression levels were strongly reduced, also relative to the xylose conditions, indicating that the deletion of AmyR might have had an indirect effect. A possible explanation is that disruption of the AmyR transcription factor resulted in low levels of extracellular enzymes converting maltose to glucose, and consequently stress due to low glucose levels. The low availability of glucose might be a signal to downregulate mstC and An09g04810 if these genes encode low-affinity sugar transporters. To further address these observations additional array analysis should be performed using the ΔamyR strain grown on xylose. Interestingly, the amyR gene itself was also induced by the presence of maltose suggesting that its regulation takes place, at least partly, at the transcriptional level. Furthermore, conserved AmyR binding motifs are present in the promoters of all co-regulated genes, including the 1-kb amyR promoter region, with the exception of aamA (Table 1). Possibly the aamA promoter contains a currently unknown AmyR binding motif. Alternatively, an AmyR binding site might be present in the promoter region of aamA in the N402 background.
The results presented in this paper suggest that A. niger can metabolize maltose by inducing the expression of a limited amount of enzymes and sugar transporters. Other members of the GH13, GH15 and GH31 protein families might function in starch metabolism in different conditions (solid state growth, or influenced by pH, nitrogen metabolism, etc.), or they may be involved in other processes such as the synthesis of cell wall α-glucan, or the glycosylation of proteins. To determine the exact function of these enzymes will require further biochemical characterization in combination with the detailed analysis of gene deletion mutants.
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aspergillus niger
alpha-glucan
starch
glucoamylase
maltose
amyr
cell wall
starch-binding domain
alpha-amylase
alpha-glucosidase