Winfried R Leeman
email: winfried.leeman@tno.nl

Kor J Van Den Berg

Geert F Houben

Transfer of chemicals from feed to animal products -the use of transfer factors in risk assessment

Keywords: Feeding, Process contaminants, Residues, Toxic elements Food Types: Animal feed, Animal products -meat Transfer factor, carry-over, contaminants, residues, risk assessment, risk management

The human risk assessment of feed contaminants has often been hampered by a lack of knowledge concerning their behavior when consumed by livestock. To gain a better understanding of the transfer of contaminants from animal feed to animal products, a meta-analysis of published literature was made. Data concerning feed contaminant concentrations, feeding periods, residue levels in animal products, and other parameters, were gathered and recorded. For each case a "transfer factor", defined as the ratio of the concentration of a chemical in an animal product to the concentration of the chemical in animal feed, was calculated. Scientifically founded transfer factors were calculated and analyzed for groups of chemicals based on their contaminant classes or physico-chemical properties. These database-derived transfer factors enable a more accurate risk assessment in the case of a feed contamination, and enable rapid risk management decision-making and/or intervention.

Introduction

In recent years increasing attention has been paid to the risk to consumers posed by chemical contaminants or residues in animal feed. This was caused by various cases of milk, eggs or other animal products contaminated with environmental chemicals. The best-known examples include contamination of milk with dioxins and PCBs as a result of industrial activities (e.g. emission of dioxins and PCBs by waste incinerators). In addition, animal feed has been found adulterated with hormones, antibiotics, dioxins, and other chemicals either deliberately, or from malpractice, or from sloppy manufacturing practices. The current use of pesticides for crop protection is an example of controlled "contamination" of crops which may become available for human consumption via animal feed. Also, contamination of animal feed can occur in a more or less biological way as is the case with mycotoxins due to improper storage of feed or feed ingredients.

Cases like the Belgian PCB incident, as described by [START_REF] Bernard | The Belgian PCB/Dioxin incident: Analysis of the food chain contamination and health risk evaluation[END_REF], demonstrated that adequate risk management is indispensable.

As various kinetic processes determine the qualitative and quantitative transfer of contaminants from feed to edible commodities, it is noted that without detailed information on a specific contaminant, a worst case approach is the only way to perform a risk assessment. For most of the contaminants this will lead to an excessive overestimation of residue levels in animal commodities. However, for some accumulating compounds a worst case estimation may still be an underestimation of the actual residue levels after prolonged exposure. This might be the case for highly lipophilic compounds like DDT or for some (heavy) metals which are known to accumulate in edible offal. A risk assessment of a possible feed contamination using a worst-case approach might therefore lead to wrong decisions, costing effort and money in the case of an erroneous overestimation, or health risk to consumers in the case of an erroneous underestimation of the contamination levels of the edible products under evaluation. In order to respond promptly to questions concerning risk assessment of contaminated livestock feed, the availability of a comprehensive data set on the transfer from feed to animal products of various classes of contaminants was considered useful. Therefore, a meta-analysis of the literature was performed to gather data on the transfer from livestock feed to animal products covering various classes of chemicals. This paper will enable risk assessors to gain a better understanding on the transfer of feed contaminants to edible commodities.

Furthermore, the data presented can be used to perform a rapid and founded estimation of feed contaminant transfer if needed.

Methods

Data on the transfer of contaminants from animal feed to animal products included in the Several studies were published in languages other than English, including Czech, Dutch, German, Italian, Polish, Korean, and Japanese. Publications in other languages than English, Dutch and German were taken into account as far as data could be derived from English abstracts or are available in the tables provided in these publications. Only studies were selected in which the compound was administered via the feed or by alternative oral exposure (e.g. via capsules

Establishment of transfer factors

The term transfer factor is used throughout this paper to define the transfer of chemical compounds from animal feed to animal products as determined in animal feeding studies.

The transfer factor is expressed as the concentration of the compound in animal products (mg/kg) divided by the concentration of the compound in animal feed (mg/kg), in which the concentration in animal products is on a wet weight basis, and in feed on a dry weight basis.

For most of the transfer factors, the compound itself is analyzed both in the feed and in the animal commodity. However, for (heavy) metals, most of the analytical methods used are specific for the metal in the respective commodity (e.g. Ni), but not for the compound as present in the feed (e.g. NiCl 2 ). The transfer factors calculated are therefore based on the metal ion and included in the database as such. Furthermore, several studies are included in the database based on sludge contaminated feed where no specific compound is known apart from the total metal concentration. For several other compounds, the parent compound is metabolized after becoming systemically available. An example is the metabolism of the pesticide 2,4-D to 2,4-dichlorophenol, where the metabolite may be present at concentrations exceeding that of the parent compound in sheep with a factor of 35. For metabolites, the metabolite concentration in the animal commodity is divided by the parent compound concentration in feed to calculate the respective transfer factor.

In case the transfer factor is based on a metabolite, its identity is indicated in the database.

In case data are reported which are not in the appropriate format (e.g. residues in milk fat instead of whole milk, or residue in animal products based on a dry weight basis), standardized factors are used to convert these data accordingly. The following conversion factors were used in order to unify the data from the various available studies. An average multiplication factor of 4.0 was used for all matrices to convert dry animal product weight into wet tissue weight [START_REF] Boyer | Trace elements in tissues from cattle fed a sewage sludgeamended diet[END_REF]. Milk data were often found to be expressed on the basis of milk fat, especially dioxin and PCB data. Since dietary risk assessment procedures take whole milk into account, all milk data in the database are expressed on the basis of whole milk. On the assumption that whole milk may contain up to 4.3% fat [START_REF] Bluethgen | Investigations on the transfer of selected dibenzodioxins and -furans (PCDD/F) after oral supplementation into milk of lactating cows[END_REF] whole milk. Some egg data were expressed on the basis of egg fat or egg fatty acids. For the same reasons as for whole milk, the various egg data were converted to the whole egg using a conversion factor of 0.088, derived from the data of Schuler (1997). Data in egg white and yolk are converted to whole egg assuming a ratio of 65:35 (egg white: egg yolk). It is noted that in case actual data (e.g. percentage of milk fat) are specified in the publication, these data are used for the conversion.

In studies where the daily body dose, but not the feed concentration was specified, it was assumed that the dry weight feed consumption of dairy cattle was 20 kg/day, of pigs 3 kg/day, and of chickens 0.12 kg/day (EC working document 7031/VI/95, rev. 4).

If commodity levels are determined below the limit of determination (LOD), the LOD value was used for the calculation of the transfer factor. Transfer factors based on LOD values are marked in the database in case a compound specific analysis has to be performed. As a consequence, the calculated means are an overestimation of the means when calculated using the actual commodity concentrations (<LOD).

Statistics

The SAS/STAT software V8. In general, the distribution of the transfer factors displayed a log-normal distribution for each contaminant class. The main exception was the population of transfer factors for the PCBs/PBBs in fat, for which a bimodal distribution pattern was found. Overall, it is noted that (as was to be expected) the transfer factor is depending on the lipophilicity of the compound (determined by the octanol-water partition coefficient; log Po/w), the potential accumulation of the compound in animal matrices, and/or the feeding level and feeding period. The highest transfer factors determined in the animal matrices were in the order fat > edible offal's > meat > eggs > whole milk. The highest transfer factors found in animal fat are indeed found to be related to lipophilic compounds which tend to accumulate in the body fat ("old" pesticides, dioxins, furans, PCBs, and PBBs). It is noted that these fat accumulating contaminants also showed a higher potential for egg and edible offal's, but less for whole milk. Metal compounds with potential accumulation in edible offal's are cadmium, copper, mercury, selenium, and zinc. The inclusion of feeding levels and feeding periods in the database are of importance to refine the selection of relevant data within the dataset before a transfer factor is elaborated in case of e.g. accumulating compounds. As can be seen in figure I, the transfer factors of edible offal after intake of cadmium for ca. 50 days are rather comparable between the different feeding levels ranging from 0.09 to 48 mg/kg. After a further continuous exposure, the transfer factors will differentiate depending on the dose.

If tissue levels become too high, saturation will become apparent showing lower increase of the transfer factors (see the slope of the feeding level of 48 mg/kg in figure I), although the actual tissue concentration is still rising. At feeding levels of 300 and 600 mg/kg, respective concentrations of 668 mg/kg (TF= 2.2) and 667 mg/kg (TF= 1.1) in edible offal's are found at 70 days exposure [START_REF] Bokori | Complex study of the physiological role of cadmium. III. Cadmium loading trials on broiler chickens[END_REF], showing a ceiling cadmium level in edible offal at 70 days.

<Insert figure I>

The use of contaminant classes will not in each case be applicable or scientifically valid in case a contaminant in the feed is found. Therefore, the use of transfer factors related to the physico-chemical properties of the compound was examined. Statistical analysis was performed using the respective molecular weight (MW) and the respective but also limited to the contaminant, whereas information on comparable chemicals may be of value to estimate the transfer of the respective contaminant from the feed to the edible commodities. The use of database derived transfer factors in risk assessment, including a comparison using worst case assumptions, is illustrated in 3 case studies below.

Case 1: Nickel contamination.

To illustrate the value of the database, we used a case of a possible metal contamination of a raw material to be used in the production of animal feed. The question was raised about the consumer risks upon a possible presence of nickel in dairy cattle feed. The feed's contamination level was expected to be maximally 1.5 mg/kg feed (dry weight basis).

In order to perform a risk assessment, the maximum transfer factors of nickel were retrieved from the database for each commodity. As comparison, also the P 95 transfer factor of the contaminant class of metals (and the subgroup of non-accumulating metals), the maximum metal transfer factor, and the transfer factors based on the overall P 95 transfer factor were retrieved from the database to simulate an increasing level of uncertainty in case insufficient information on nickel would be present in the database. A comparison to a traditional risk assessment was made using worst case assumptions. For this we assumed complete absorption and retention of nickel by livestock animals and complete distribution towards (one of the) edible commodities. To bring some nuance into the worst case approach, we assumed a steady state after feeding for about 1 week (> 5 times the plasma half life). This would mean that only the cumulative nickel intake for 1 week would add to the ultimate residue levels in edible commodities. [START_REF] Baars | Re-evaluation of human-toxicological maximum permissible risk levels[END_REF]. Furthermore, a human consumption pattern is used, as assumed in the health risk assessment for residues of Veterinary Medicinal Products [START_REF] Ec | Notice to applicants and note for guidance: Establishment of maximum residue limits (MRL's) for residues of veterinary medicinal products[END_REF] i.e. daily consumption of 1.5 kg of milk and milk products, 100 g eggs and egg products, 300 g meat, 50 g fat, 100 g liver, and 50 g kidney. On the basis of these figures and the presumed worst case assumptions, consumption of the cattle commodities would lead to intake estimates which can be compared to its respective TDI.

Based on these transfer factors, the intake by the consumption of the respective commodities was calculated as a percentage of the TDI for each commodity (see noted that not kidney or liver, as estimated by the worst case approach or the maximum metal transfer factor, but meat may be the major source of nickel residue intake by consumers. The nickel intake via meat is in fact rather comparable to the calculations based on worst-case assumptions, the overall P 95 , and the metal P 95 transfer factors. Yet, it appears that the intake by whole milk and fat is expected to be much lower than assumed on the basis of the worst case assessment. Although differences in the intake calculations exist between the evaluations, it is noted that in case of lacking data for a specific metal, the P 95 transfer factor of the contaminant class of (non-) accumulating metals is a better alternative over the worst case approach, showing intake estimations rather near the actual data.

Case 2: Contamination based on physical chemical properties (log Po/w <3).

To When comparing the calculated and databases derived transfer factors after exposure to SMX, it is noted that the database derived transfer factors are about 10 times higher than calculated using the experimental data. This might be expected considering the short (5 day) exposure period to SMX. Although the database derived transfer factors are higher than might be expected form the experimental data, it is noted that when using worst case assumptions, considerable higher transfer factors are indicated (e.g. egg; 3.2, meat; 1.1, fat; 2.9, and kidney; 76). The worst case assumptions considered were a full absorption of SMX, followed by a complete distribution towards the matrix under consideration, no excretion of SMX, whereas an accumulation of SMX during 7 days is assumed for meat, fat, and kidney, taking into account a bodyweight of 1.9 kg, comprised of meat, fat and kidney, for 40%, 15%, and 0.6% of the bodyweight, respectively. An egg weight is considered of 0.053 kg (size S) with an egg production of 0.7 eggs/day. The database IV), the elaborated transfer factor to fat is about a factor 1.5 times higher than might be expected from the actual data. When elaborating the transfer factor based on the log Po/w class of 6-7 (table VI), the database derived transfer factor to fat is 30, which is about a factor 4.5 times higher than the calculated transfer factor based on the actual data. Considering the fat accumulating potential of the compounds present in the respective dioxins/furans class or the log Po/w class of 6-7, the P 95 value is highly related to long term (> 1 Year) exposure. In case a longer exposure period is considered, it is expected that an increase in the actual transfer factor will be apparent. It is noted that when worst case assumptions were made, a transfer factor of 20

for fat was calculated. The worst case assumptions considered were a full absorption of each congener, followed by a complete distribution towards the fat, no excretion of the congeners, whereas an accumulation during 7 days is assumed, taking into account a bodyweight of 550 kg and a slaughter weight of 7 kg of fat. A limited accumulation period was chosen taking the recovery period of 1 week into account. Again a refinement can be made by restriction of data to be included for the calculation of the database derived transfer factor, by selecting the respective feeding periods or exposure concentrations. The database derived transfer factors showed an approximate 1.5 to 4.5 fold higher transfer compared to the actual data. It is however noted that for the actual data, a one week recovery period is included, whereas the transfer database is based on a continuous exposure until slaughter which may indicate somewhat higher levels in the animal matrices. As the compounds considered in this case are stable and have a high affinity for fat, the one week recovery is not considered to have a major effect on the decline of the compounds during the recovery period. Furthermore, the highest residue levels reported were used for the calculation of the actual transfer which is considered to compensate for a possible decline of the mean contamination values in fat. Taking these data into account, the database derived transfer factors showed a somewhat higher prediction of the actual transfer, but are regarded to give a more accurate prediction than when considering the transfer using worst case assumptions. Therefore, also in this case the use of database derived transfer factors is considered favorable over calculations using worst case assumptions.

Conclusion

It is not feasible to generate chemical-specific information for every compound for every situation at any moment, especially considering possible (differences in) metabolism, feed concentrations and exposure periods for each livestock animal. In this respect, the use of database derived transfer factors showed to be a powerful tool for the evaluation of contaminants and enables rapid risk management decision making and/or intervention.

Three cases of repeated exposure showed that the use of database derived transfer factors, transfer, as observed in most of the cases, is most likely related to the limited contaminant feeding period. Deriving transfer factors from the database restricted to actual feeding periods, feeding levels and/or relevant animals, will provide a more accurate prediction of the transfer than when all data in the respective group, including long term feeding periods, are used. Future studies with the database will be aimed at this refinement.

When the risk assessment using database derived transfer factors is compared to the risk assessment using worst case assumptions, the database derived transfer factors provide a far more accurate indication of the contaminant levels and its concurrent risk estimation, than when using the traditional worst-case approach. Further studies will also be aimed at a further validation of this approach. Great care should be taken when worst-case assumptions are used for assumed accumulating contaminants like highly lipophilic compounds (log Po/w of 5 to 8) or cadmium, copper, mercury, selenium or zinc containing compounds. Especially for these compounds, database derived transfer factors based on physical chemical properties or structural related compounds may provide a more rapid and accurate prediction than in case of using worst case assumptions.

Using database derived transfer factors, an evaluation can be performed using data of the specific compound, structural related compounds, compounds with comparable physicochemical properties, or contaminant classes, if needed at specified feeding levels and/or feeding periods. Furthermore, the relative vulnerability of animal matrices to a feed contaminant can be evaluated. Instead of using worst-case assumptions, a generic approach in risk assessment, by using the overall transfer factors of the respective edible commodity, is preferable in case limited data on the identity or properties of the contaminant involved are available. When compared to a risk assessment using worstcase assumptions, a better understanding of the transfer of feed contaminants and residues to animal products resulting in a more refined risk assessment is possible using data base derived transfer factors.

  Transfer of chemicals from feed to animal products; the use of transfer factors in risk assessment.

  database were mainly collected from the open literature. The literature databases AGRIS, AGRICOLA, Food & Human Nutrition, and Toxline, were searched covering the period of 1970 to 2005. The data on the transfer of pesticides were also obtained from publicly available evaluations by the FAO/JMPR, the Advisory Committee on Pesticides of the UK-PSD, and from pesticide dossiers filed in the archives of the TNO Quality of Life.

  estimated log Po/w. Considering the transfer factors based on the molecular weight, no clear discrimination could be made between the groups defined. Main reason is the non-homogeneous distribution of the compounds over the molecular weight groups. Furthermore, the presence of accumulating metal compounds is highly affecting the outcome of the transfer factors of meat and edible offal at a MW below 100, as a large amount of transfer factors of metal contaminants are present without information on the respective anion (only metal levels reported in the respective studies). For these reasons the transfer factors based on the MW were not considered a feasible discriminator based on the available dataset. Establishing log Po/w categories between 0 and 8, using an increment of 1 between each category, showed a rather homogeneous distribution of the transfer data per group. The respective transfer factors and the amount of data available at the 95 percentile for each of the matrices are given in

  is therefore considered to be of value for a more refined estimation of transfer compared to worst case assumptions.Case 3: Contamination based on physical chemical properties (log Po/w ≥ 3).Equivalent to case 2, a comparison is made based on the physical chemical properties of compounds with a relative high log Po/w, for which accumulation might be suspected in one or more edible matrices. Experimental data on the transfer of 3 dioxins and 1 furan from feed to fat are compared to the database derived transfer factor based on the 95 thpercentile of the respective log Po/w class. The compounds used for the comparison were not (yet) included in the database when the comparison was made.Thorpe et al. (2001) exposed cattle for 4 weeks to a defined mixture of PCDD/F congeners, corresponding to feed levels of 7.5 ng/kg of each congener. After a recovery period of 1 week the animals were slaughtered. Analysis of the individual congeners was performed in fat, and the fat fraction of liver and meat. As no information was provided on the fat fraction of liver and meat, only the transfer of feed to fat was considered (see

  based on the P 95 values of contaminant or log Po/w classes, results in a rather accurate prediction of the presence of the respective contaminant in the edible commodity when compared to the actual experimental transfer factors. A slight overestimation of the

  Nakamoto K, Hirota K, Katagiri K. 1977. Clearance of sulfamethoxazole in eggs and tissues of chickens. Poult.Sci. 56:813-821 Schuler F, Schmid P, Schlatter C. 1997. Transfer of airborne polychlorinated dibenzopdioxins and dibenzofurans into dairy milk. J. Agric. Food. Chem. 45:4162-4167. Thorpe S, Kelly M, Startin J, Harrison N, Rose M. 2001. Concentration changes for 5 PCDD/F congeners after administration in beef cattle. Chemosphere 43:869-879

  Figure I Transfer factor curves of cadmium in poultry edible offal's 199x121mm (600 x 600 DPI)

<Insert table IV> <Insert table V>

	calculate the mean, median, 95th percentiles, and maximum values of the transfer factors. A further analysis of the values in table III was achieved for each animal product by
	calculating the statistical parameters also for each contaminant class. These values are
	Results given in table IV (meat, fat and edible offal's), and table V (eggs and whole milk).
	F o r The meta-analysis of the literature evaluated up to 2005, covering about 250 references, F o resulted in a total of 3624 transfer factors, most of which were found on edible offal's r
	P (31%) and meat (25%), followed in about equal numbers by eggs (12%), whole milk P
	e e r R e v i e w (15%) and fat (17%). Animals included are cattle, poultry, pig, sheep, goat, rabbit and several birds like pheasant, turkey, duck and quail. An overview of the amounts of e e r transfer factors found in each animal commodity, as well as for each of the various contaminant classes are given in table II. <Insert table II> R e v i e w
	O n Statistical parameters covering the geometric mean, geometric standard deviation, median, 95 th percentile and maximum transfer factors were calculated using all transfer O n
	l y factors for each commodity, and are listed in table III. It appears that the highest transfer factors are found for fat and edible offal's. The transfer factors for eggs, meat and whole l y
	2, 1999-2001 (Cary, NC. SAS Institute Inc.), statistical milk are generally lower compared with fat and edible offal's.
	package was used to determine the distribution pattern of the transfer factors. The
	<Insert table III>

  It is remarkable that the group with a log Po/w of 4-5 has much lower transfer factors compared to the adjoining groups for all matrices. It is noted that this observation seems not to be biased due to a small number of observations. Based on the current dataset, transfer factors based on the 95 th percentile (P 95 ) can be used as a first step for assessing the transfer of specific contaminants. These P 95 values can be applied in a tiered approach of risk assessment. In case chemical specific transfer data are available in the dataset, the actual data can be used in the risk assessment. If no chemical specific transfer data are available, the P 95 value of the respective log Po/w

	6 and 7. Use of the transfer database in risk assessment
	F o F o
	r r
	P P
	e e r e e r
	R e v i e w R e v i e w
	O n O n
	l y l y

table VI. As no log Po/w can be calculated for inorganic compounds, it was decided to form separate groups for the metal compounds. P 95 transfer factors are calculated covering the whole group of metals, accumulating metals (cadmium, copper, mercury, selenium and zinc), and non-accumulating metals. These figures are also included in table

VI

.

<Insert table VI>

Based on the data in table VI, relative low transfer factors are found with compounds having a log Po/w below 3, and for whole milk. High transfer factors are found in the log Po/w categories of 3-4 and 5-8, with highest transfer factors in fat at a log Po/w between category or the respective chemical group may be used. It is noted that in case transfer factors of a specific chemical group are considered, one should think about the surplus value of the chemical group over the respective log Po/w data, since assigning chemical groups is in general relatively arbitrary taken the biological processes influencing the transfer factors into account. At last a generic approach, by using the overall transfer factors of the respective edible commodity (see table

III

), might be considered for components for which little to no information is present, e.g. unidentified components.

It is noted that without the use of database derived transfer factors, only a literature search, which should include the fate and behavior of the contaminant, or, in case no compound specific information could be retrieved from the public literature, a worst case scenario can be used in the risk assessment. This approach is not only time consuming,

  The respective transfer factors are given in table VII, in order of the assumed highest to lowest uncertainty.

<Insert table VII>

We used the figures of table VII as a basis for the risk assessment. For health risk assessment, a Tolerable Daily Intake (TDI) for nickel of 0.05 mg nickel per kg body weight per day (or 3 mg/person/day, assuming a body weight of 60 kg) can be used, as proposed by the Dutch National Institute of Public Health and the Environment

table VIII

 VIII The results presented in table VIII indicate that in general with a growing level of uncertainty, an increased consumer risk is indicated. One should keep in mind that the nickel data are based upon actual (experimental) data, and as such can serve as comparison for the other evaluations. Looking at the P 95 transfer factors for nickel it is

	<Insert table VIII>
	F o
	r
	P
	e e r
	R e v i e w
	O n
	l y
	).

  demonstrate the use of transfer factors based on physical chemical properties of a compound, experimental transfer data of a veterinary medicinal product were compared to database derived transfer factors based on the 95 th -percentile of the respective log Po/w class. Oikawa et al. (1977) exposed chicken for 5 successive days to 2000 and 4000 mg/kg of sulfamethoxazole (SMX) via feed. The log Po/w of SMX is 0.89(chemfinder.com; experimental data of Hansch, 1995). Residue levels of free SMX in meat, fat, kidney, and egg including their respective calculated transfer factors, and the database derived transfer factors (P 95 ) are given in table IX. It is noted that SMX was not included in the database.

	F o
	r
	<Insert table IX>	P
		e e r
		R e v i e w
		O n
		l y

Table I List of chemicals present in the chemical classes

 I

	1,2,3,6,7,8-HCDD Decoquinate		Mercury Zinc sulphate 57653-85-7 PCB128 18507-89-6	7733-02-0 38380-07-3
	Pesticides (new) Pesticides (old) 1,2,3,4,7,8-HCDF Dimetridazole		CAS no. (Heavy) metals CAS no. Mercury actetate Zinc lysine 70648-26-9 PCB138 551-92-8	CAS no. 1600-27-7 35065-28-2
	2,4-D Aldrin 1,2,3,4,7,8-HCDD Dinitolmide		94-75-7 Aluminum 309-00-2 Methyl-mercury dicyandiamide 39227-28-6 PCB141 148-01-6 Other	502-39-6 52712-04-6 CAS no.
	2-Aminobutane a-BHC Mycotoxins 1,2,3,4,7,8,9-HpCDF Doxycycline		13952-84-6 Aluminum chloride 319-84-6 Phenylmercuric acetate CAS no. PCB's/PBB's 55673-89-7 PCB149 564-25-0 Citrinin	16603-84-2 62-38-4 CAS no. 38380-04-0 518-75-2
	Acephate Chlordane 1,2,3,4,6,8-HCDD Erythromycin thiocyanate	30560-19-1 Antimony 57-74-9 Phenylmercuric hydroxide PCB151 114-07-8 Sodium chlorate	100-57-2 52663-63-5 7775-09-9
	Anilazine DDE 1,2,3,4,6,7,9-HpCDD Flubendazole Atrazin Azinfos-methyl F o DDT Dieldrin F o F o 1,2,3,4,6,7,8-HpCDF 1,2,3,4,6,7,8-HpCDD F o Flumequin Furaltadone F o F o Benomyl r Endrin r r r Furazolidone r r	3547-04-4 Methoxyethyl mercury hydroxide 58200-70-7 PCB153 31430-15-6 Acrylamide 101-05-31 Arsenic 1912-24-9 Arsenic trioxide 50-29-3 Methylmercuric hydroxide 60-57-1 Mercury nitrate 67562-39-4 PCB156 35822-46-9 PCB157 42835-25-6 Linoleic acid (Conjugated) 139-91-3 Fattyacid 20:4 n-6 86-50-0 Cadmium 17804-35-2 Cadmium chloride 72-20-8 Methylmercury PCB167 67-45-8 Fattyacid 20:5 n-3	35065-27-1 79-06-1 1184-57-2 10045-94-0 38380-08-4 69782-90-7 60-33-3 10108-64-2 52663-72-6
	Chlorpyrifos HCB Halofuginone	P P P P P P	2921-88-2 Cadmium acetate 118-74-1 Phenylmercury PCB169 55837-20-2 Fattyacid 22:6 n-3	543-90-8 32774-16-6
	Cryomazine Cyfluthrin Deltamethrin a-HCH b-HCH b-Hepo Lasalocid Monensin Narasin	66215-27-8 Cadmium sulphate 68359-37-5 Cadmium (Metallothionein) 52918-63-5 Chromium e e r 319-84-6 Ethylmercury chloride 319-85-7 Acetato fenylmercury Lead e e r e e r PCB170 PCB180 PCB183 e e r 11054-70-9 Eicosapentaenoic acid (20:5 n-3) 17090-79-8 Docosahexaenoic acid (22:6 n-3) 55134-13-9 e e r e e r	10124-36-4 107-27-7 62-38-4 35065-30-6 35065-39-3 52663-69-1 10417-94-4 6217-54-5
	Dimethoate Famoxadone Fenbuconazole Fenthion Fluquinconazole Imidacloprid Heptachlor Lindane (g-BHC) Methoxychlor Mirex PCP Neomycin Nicarbazin Nifursol Nitrofurazone Olaquindox Ormetoprim		60-51-5 Chromium picolineate 131807-57-3 Chromium chloride 114369-43-6 Chromium 3+ (potassium chromate) 55-38-9 Chromium 6+ (potassium chromate) 136426-54-5 Chromium 3+ (chromium sulphate) 105827-78-9 Chromium rutile R e v i e w 76-44-8 Lead acetate 58-89-9 Lead oxide 72-43-5 Lead sulphate 2385-85-5 Manganese 87-86-5 Manganese chloride Molybdene R e v i e w R e v i e w PCB187 PCB189 PCB194 PCB198 PCB206 R e v i e w 1404-04-2 330-95-0 16915-70-1 59-87-0 23696-28-8 6981-18-6 R e v i e w R e v i e w	10025-73-7 39322-04-8 7789-00-6 10101-53-8 301-04-2 1317-36-8 15739-80-7 7773-01-5 52663-68-0 39635-31-9 35694-08-7 40186-72-9
	Kresoxim-methyl Lambda-cyhalothrin Methamidophos Pirimicarb Veterinary medicines Aminosidine Amprolium Avermectin B1a Oxolinic acid Oxytetracycline Pyrimethamine Robenidine		143390-89-0 Sodium chromate 91465-08-6 Iron 10265-91-6 Ferric chloride 023103-98-2 Cobalt O n l y Nickel Nickel chloride Nickel rutile Rubidium O n l y O n l y CAS no. Nitrosamins 7542-37-2 N-nitrosodiethylamine (DENA) 121-25-5 N-nitrosodimethylamine (DMNA) 71751-41-2 N-nitrosodipropylamine (DPNA) O n l y 14698-29-4 79-57-2 O n O n 58-14-0 25875-51-8 l y l y	7775-11-3 7705-08-0 7718-54-9 CAS no. 55-18-5 62-75-9 621-54-7
	Pirimiphos-methyl Avilamycin Salinomycin		29232-93-7 Cobalt carbonate Sodium selenite 11051-71-1 53003-10-4	7542-09-8 26970-82-1
	Tebuconazole Bacitracin Salinomycin sodium salt	107534-96-3 Cobalt (II) chloride Selenium 1405-87-4 55721-31-8	7646-79-9
	Chloroamphenicol Spiramycin		Copper Tin 56-75-7 Hormones 8025-81-8	CAS no.
	Chlorotetracycline Spiramycin embonate		Copper sulphate Vanadium 57-62-5 Melengestrol acetate 67724-08-7	7758-98-7 2919-66-6
	Diclazuril Sulfachlorpyrazine		Zinc 101831-37-2 Estradiol 1672-91-9	50-28-2

Table II Contents of the database on transfer factors in animal commodities

 II Table III Overall transfer of all contaminants from feed to eggs, whole milk, meat, fat and edible offal's

	Page 31 of 41			Food Additives and Contaminants
	Contaminant class Animal	Number of transfer factors in each animal commodity Overall transfer factor
	product	GM	Eggs GSD Median Whole milk P 95	Meat Max	Fat	Edible	Total
								offal's
	Eggs	0.18	0.49	0.007		1.14	5.5
	All		433	532		920	632	1107	3624
	Pesticides ("new") Pesticides ("old") (Heavy) metals F o r 85 44 34 Whole milk 0.10 0.18 Meat 0.09 0.34 Fat 3.0 10 F o r	133 66 113 0.013 0.008 0.046	0.50 0.33 15	222 5 409 1.4 6 180	210 146 17	227 12 519	877 273 1092
	Mycotoxins Dioxins/Furans PCBs/PBBs Edible offal's 0.77 GM = geometric mean, GSD = geometric standard deviation, 66 46 1 P e 20 88 77 e r 2.5 0.04 3.7 P P 95 = 95 percentile value, e e r	126 34 32 52	62 91 56	184 37 35	458 296 201
	Nitrosamines Hormones Vet. medicines Other Max = maximum value	2 1 142 12	15 0 5 15	2 3 81 6 R e v i 0 9 38 3 e w R e v i e w	2 0 86 5	21 13 352 41
								O n O n
								l y l y

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Table IV Transfer factors for various contaminants into meat, edible offal's and fat

 IV

			Food Additives and Contaminants			Page 34 of 41
	Commodity Contaminant class				Transfer factor
	Commodity Contaminant class	N	GM	GSD	Transfer factor Median	P 95	Max
		Dioxins/Furans	N 37	GM 1.3	GSD 4.0		Median 0.070	P 95 6.5	Max 18
	Meat	Pesticides ("new") PCBs/PBBs	222 35	0.006 0.7	0.013 1.0		0.0024 0.2	0.02 2.7	0.17 3.9
		Pesticides ("old") Nitrosamines	5 2	0.032 0.023	0.026 0.00028		0.029 0.023	0.07 0.023	0.07 0.023
	(Heavy) metals Vet. medicines Mycotoxins Dioxins/Furans F o Other N = total amount of transfer factors, GM = geometric mean, GSD = geometric standard deviation, P 95 = 409 0.17 0.50 0.023 0.8 86 0.048 0.12 0.0091 0.19 0.99 6.1 126 0.0060 0.023 0.0004 0.021 0.24 34 0.11 0.10 0.08 0.33 5 0.001 0.001 0.001 0.001 0.001 F o 0.33 PCBs/PBBs 32 0.14 0.10 0.12 0.32 r r 95 percentile value 0.36 Nitrosamines 2 0.022 0.004 0.022 0.024 P P 0.025 Hormones 3 0.01 0.0041 0.0090 0.012 0.012 Vet. medicines 81 0.009 0.026 0.0022 0.027 0.18 e e r e e r
	Fat	Other Pesticides ("new") Pesticides ("old") (Heavy) metals Mycotoxins Dioxins/Furans	6 210 146 17 62 91	0.018 0.025 10 0.10 0.0051 1.5 R e v 0.0081 0.040 0.067 19 0.30 3.1 i e w R e v i e w	0.001 0.0033 5 0.011 0.0020 0.39	0.077 0.13 30 0.35 0.021 9	0.10 0.50 180 1.3 0.042 18
		PCBs/PBBs Hormones Vet. medicines Other	56 9 38 3	3.9 0.53 0.022 0.00011	5.2 0.31 0.054 0.00006	1.7 0.55 0.0021 O n O n 0.00013 l 16 1.0 0.15 0.00014 y l y	18 1.2 0.22 0.00014
	Edible	Pesticides ("new")	227	0.017	0.04		0.005	0.08	0.25
	offal's	Pesticides ("old")	12	0.76	0.98		0.38	2.7	3.0
		(Heavy) metals	519	1.5	3.4		0.33	6.6	52
		Mycotoxins	184	0.024	0.20		0.0022	0.047	2.76

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Table V Transfer factors for various contaminants into eggs and whole milk

 V

	Page 35 of 41		Food Additives and Contaminants
	Commodity Contaminant class				Transfer factor
			N		GM	GSD	Median	P 95	Max
	Eggs	Pesticides ("new")	85		0.010	0.020	0.0049	0.03	0.17
		Pesticides ("old")	44		1.2	0.96	1.2	2.5	5.5
	(Heavy) metals Mycotoxins Dioxins/Furans F o r	34 66 46		0.038 0.0068 0.28	0.050 0.021 0.27	0.016 0.0006 0.17	0.17 0.018 0.84	0.17 0.11 1.0
		PCBs/PBBs Nitrosamines P Vet. medicines Other e 1 2 142 12 e r	0.92 0.051 0.028 0.09	0.0155 0.082 0.071	0.92 0.051 0.0050 0.11	0.061 0.12 0.17	0.92 0.062 0.81 0.18
	Whole milk	Pesticides ("new") Pesticides ("old") (Heavy) metals Mycotoxins Dioxins/Furans PCBs/PBBs	133 66 113 20 88 77	0.0052 0.25 0.027 0.0018 0.12 0.26 R e v 0.0080 0.16 0.062 0.0015 0.14 0.32 i e w	0.0020 0.25 0.0050 0.0016 0.079 0.13	0.020 0.52 0.12 0.0046 0.42 0.87	0.044 0.62 0.50 0.005 0.57 1.4
	Nitrosamines Vet. medicines Other N = total amount of transfer factors, GM = geometric mean, GSD = geometric standard deviation, P 95 = 15 0.012 0.013 0.008 0.034 0.042 5 0.006 0.008 0.005 0.017 0.020 15 0.17 0.20 0.041 0.51 O n 0.53 l y
	95 percentile value

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Table VI Transfer factors categorized by log Po/w

 VI

			Food Additives and Contaminants
	Log Po/w				Transfer factor
			Egg	Whole milk	Meat	Fat	Edible	All Matrices
			P 95 (N)	P 95 (N)	P 95 (N)	P 95 (N)	Offal's 1	P 95 (N)
							P 95 (N)
	<0 0 to 1	0.03 (66) 0.05 (37) o F r	0.02 (25) 0.03 (15)	0.02 (80) 0.04 (18)	0.01 (57) 0.01 (13)	0.02 (94) 0.30 (18)	0.02 (322) 0.04 (101)
	1 to 2		0.04 (96) P	0.02 (30)	0.01 (117)	0.01 (64)	0.02 (144)	0.02 (451)
	2 to 3 3 to 4		0.13 (38) 0.92 (38) e 0.01 (29) 0.33 (48) e r	0.02 (71) 0.01 (51)	0.02 (45) 14.1 (77)	0.04 (81) 0.21 (65)	0.03 (264) 2.00 (279)
	4 to 5 5 to 6 6 to 7 7 to 8 >8		0.11 (16) 2.43 (26) 1.60 (44) 0.75 (23) 0.21 (13)	0.03 (19) 0.43 (45) 0.52 (108) 0.90 (51) 0.32 (30) R e 0.05 (69) 0.03 (35) 0.33 (32) 0.33 (28) 0.04 (8) v i e w 0.58 (58) 17.0 (81) 30.0 (137) 16.3 (48) 0.74 (27)	0.08 (68) 1.50 (36) 2.62 (39) 2.79 (28) 0.08 (8)	0.25 (230) 14.0 (223) 14.0 (360) 2.73 (178) 0.38 (86)
	Metals total Accumulating	0.17 (34) 0.17 (30)	0.12 (112) 0.15 (54)	0.82 (408) 1.47 (219)	0.35 (17) 0.74 (10) O n 6.61 (516) 9.62 (290)	3.54 (1087) 5.03 (603)
	metals					l y
	Non-accumulating	0.00 (4)	0.06 (58)	0.30 (189)	0.11 (7)	0.72 (226)	0.52 (484)
	metals
	P 95 = 95 percentile value, N = total amount of transfer factors in the respective subgroup

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Table VII Case study of nickel contamination, transfer factors

 VII

					Food Additives and Contaminants
					Transfer factor
		Worst	P 95		Max.	P 95 metal	P 95 Nickel
		case	overall	metal	(non-acc a )
	Whole	0.80	0.50		0.50	0.12	0.024
	milk					(0.06)	(0.025)
	Meat	0.60 F o 0.33		6.1	0.82 (0.30)	0.58 (0.66)
	Fat	20	15 r	1.3	0.35	0.12
			P	(0.11)	(0.13)
	Liver Kidney	20 74	3.7 3.7	52 52 e e r 6.6 (0.72) 6.6	0.70 (0.72) 0.70
						(0.72) R e v (0.72) i e w
							O n
							l y

a P 95 of non-accumulating metals

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Table VIII Case study of nickel contamination: percentage of the tolerable daily intake (TDI)

 VIII

					Food Additives and Contaminants
						% TDI
		Worst	P 95		Max.	P 95 metal	P 95 Nickel
		case	overall	metal	(non-acc a )
	Whole	60	38			38	9.0	1.8
	milk Meat	9.0 F o 5.0			92	(4.5) 12	8.7
			r			(4.4)
	Fat	50	38 P	3.3	0.88	0.30
	Liver	100	19	(0.28) 33 (3.6) r 259 e e	3.5
	Kidney	185	9.3		129	17 (1.8) R e v 1.8 i e w
							O n
							l y

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Table IX Transfer data on sulfamethoxazole in poultry

 IX

				Food Additives and Contaminants
		Free SMX	Calculated	Worst	Database
		(mg/kg)	Transfer Factor	case	derived
		2000	4000	2000	4000	Transfer	Transfer
		mg/kg	mg/kg	mg/kg	mg/kg	Factor	Factor
		in feed	in feed	in feed	in feed
	Egg a	13.6 F 26.3 o	0.007	0.007	3.2	(P 95 ) b 0.05
	Meat	9.74	34.9 r 0.005	0.009	1.1	0.04
	Fat	1.50	4.95 P 0.001	0.001	2.9	0.01
	Kidney	51.7	118	0.026 e 0.029 e r	76	0.30
					R e v i e w
							O n
							l y

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Table X Transfer data of PCDD and PCDF in cattle

 X

		Log	Levels in	Calculated	Database
		Po/w	fat	Transfer	derived Transfer
			(mg/kg)	Factor	Factor (P 95 )
	2,3,7,8-TCDD	6.08	5.6 x 10 -5	7.5
	1,2,3,7,8-PeCDD	6.05	5.7 x 10 -5	7.6
	1,2,3,6,7,8-HxCDD	6.77	4.3 x 10 -5	5.7
	2,3,4,7,8-PeCDF	6.92	4.5 x 10 -5	6.7

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Acknowledgments

The authors wish to thank Ms Monique Rennen and Jos Bessems for a critical review of and suggestions for the manuscript, and Ms Carina Rubingh for statistical analysis of the data.


Antonia Susca
email: antonia.susca@ispa.cnr.it

Gaetano Stea

Giancarlo Perrone

A rapid PCR-SSCP screening method for identification of Aspergillus 1 Sect. Nigri species by the detection of calmodulin nucleotide variations 2 3 4

Keywords: Aspergillus Section Nigri, SSCP, species detection, calmodulin gene, 16 rapid method

Single-stranded conformational polymorphism (SSCP) analysis for genetic diversity 3 studies has been widely applied to indirectly detect sequence differences up to a single 4 base in amplified DNA fragments of the same length, representing an alterative to 5 gene-sequencing. In this study SSCP analysis was used to detect sequence variations 6 contained in an about 180 bp-region of the calmodulin gene in order to identify 7 Aspergillus Sect. Nigri species. The method described shows that fluorescence-based 8 SSCP analysis by capillary electrophoresis (SSCP-CE) is cheaper and faster than direct 9 sequencing, and suitable for computer-assisted analyses allowing discrimination 10 between the Aspergillus species belonging to the Nigri Section: A. aculeatus, 11

Aspergillus species belonging to the Nigri section, commonly known as "black 2 aspergilli" are distributed worldwide, many species cause food spoilage, and several are 3 used in the fermentation industry [START_REF] Bennett | Aspergillus: biology and industrial applications[END_REF]. Species within this Section 4 have been widely studied in recent years since they were identified as the main species 5 responsible for ochratoxin A (OTA) accumulation in grapes and wine (Cabanes et al. [START_REF] Battilani | Ochratoxin A in grapes and wine[END_REF]. However, the taxonomy 7 of this Section is still not completely resolved and the species distribution in food is 8 often not clearly reported, as it is mainly based on morphological traits. This is more 9 evident within the A. niger "aggregate" in which molecular studies support the division 10 of this "aggregate" into two to four morphologically indistinguishable species: A. niger, Although the exact identity of the polymorphism cannot be determined, SSCP can be 1 employed to detect and map unknown polymorphisms as they are co-dominant and can 2 be as robust as other sequence specific markers. SSCP indirectly detects sequence 3 differences to a single base in amplified DNA fragments of the same length. The SSCP 4 method requires no a priori knowledge of SNPs and can be applied to identify 5 polymorphisms in unknown strains. Polymorphisms are detected as alterations of 6 mobility induced by nucleotide differences that cause stable changes in the 7 conformation of the ssDNA. Therefore a single nucleotide change could dramatically 8 affect the strand's mobility through a gel by altering the intra-strand base pairing and its 9 resulting 3D conformation (Melcher, 2000). 10 11 The use of a fluorescence-based capillary electrophoresis system for SSCP analysis has 12 also contributed to the efficiency of the methodology and reliability of analysis.

13

The usefulness of the SSCP analysis for the identification of Aspergillus sect. Nigri The aim of this study is to provide a rapid, computer assisted, and relatively low cost Nigri (Table 1) amplified with SSCPCL1 and SSCPCL2. The x-axis shows peak 2 mobility and the y-axis relative fluorescent intensity. A single peak for 6 of 11 species, 3 2 peaks for 4 species and 3 peaks for Aspergillus "atypic uniseriate" were observed in 4 the forward strand; a single peak for 10 of 11 species and 3 peaks for Aspergillus 5 "atypic uniseriate" were observed in the reverse strand. Results were similar for other 6 strains analyzed, therefore electropherograms of the remaining 39 strains are not shown. Using this protocol all 39 genotypes belonging to 11 of the 16 Aspergillus Sect Nigri In conclusion, the SSCP protocol offers a sensitive, inexpensive, rapid, and convenient 22 method to determine differences in sequences in a high number of samples, so that only

6 2002;

 6 Da Rocha Rosa et al., 2002;

11 A

 11 . tubingensis, A. foetidus and A. brasiliensis (Kuster van Someren et al., 1991; Accensi 12 et al., 1999; Parenicova et al., 2000; Abarca et al., 2004). As A. tubingensis and A. 13 niger, together with A. carbonarius, are the three species within the "black aspergilli" 14 known to produce OTA, their exact identification is very important to avoid 15 overestimation of specific contamination and toxicological risk problems (Perrone et 16 al., 2006). Molecular analysis on fungal DNA is indispensable to identify species 17 within this Section clearly. In this respect, Single-Stranded Conformational 18 Polymorphism (SSCP) analysis has been widely applied to detect Single-Nucleotide 19 Polymorphisms (SNPs), including point variability, and consider useful polymorphic 20 markers for genetic studies. First announced in 1989 as a new means of detecting DNA 21 polymorphisms, or sequence variations, SSCP analysis offers an inexpensive, 22 convenient, and sensitive method for determining genetic variation (Orita et al., 1989; 23 Rath and Ansorg, 2000; Sunnucks et al., 2000).

14 occurring

 14 on food has not been investigated until now, even if it was set up and 15 described by Rath and Ansorg (2000) for other medically important Aspergillus 16 species, analyzing the PCR-amplified intergenic spacer region.

17

 17

  18

8 9 Sequences

 9 were aligned using the Clustal-W method. A generic primer pair, able to 10 amplify about 180 bp fragments in all Aspergillus spp. belonging to the Nigri section, 11 was designed with Primer Express software version 2.0 (Applied Biosystems, Foster 12 City, CA, USA): SSCPCL1 (5'-FAM-AGTCTGAGCTKCAGGACATGATCAA-3') 13 and SSCPCL2 (5'-JOE-CGGAGTCGGTYTCCTTCATCTT-3'). The primer pair 14 amplifies a species-specific variable region (figure 1).15 16 SSCP conditions. In this study 50 isolates were analysed: 11 type-strains (tab.1) as 17 reference and 39 Aspergillus spp. strains to test the method (3 A. aculeatus, 6 18 Aspergillus "atypic uniseriate", 2 A. brasiliensis, 8 A. carbonarius, 2 A. foetidus, 2 A. 19 ibericus, 2 A. japonicus, 7 A. niger, and 7 A. tubingensis).

20 21A

 20 single PCR reaction was performed for each strain using DNA purified from a pure 22 fungal culture as template, with the EZNA Fungal DNA Miniprep Kit. PCR reactions 23 were carried out on a 9700 thermalcycler (Applied Biosystems, Foster City, CA, USA) volume. Each mixture contained 5 pmol of each labelled primer, 0.5 U of DNA 1 Taq-Gold polymerase (Applied Biosystems, Foster City, CA, USA), 1X PCR buffer, 2 0.1 mM for each dNTP, approximately 3 ng of fungal template DNA. Amplification by 3 PCR was optimized under standard reaction conditions: denaturation at 95 °C for 5 min; 4 35 cycles of denaturation at 94 °C for 50 s; annealing at 60 °C for 50 s; extension at 72 5 °C for 1 min; final extension at 72 °C for 7 min.

6 7

 6 Samples were analyzed on an ABI 310 Genetic Analyzer (Applied Biosystems, Foster 8 City, CA, USA). All runs were conducted on a 36 cm capillary, containing 5% 9 GeneScan polymer, with 10% glycerol in Tris-borate-EDTA (TBE), and TBE as 10 running buffer. Injection was for 22 s at 1 kV and electrophoresis was at a constant 11 voltage of 15 kV. The electrophoretic runs were conducted at 35°C. Each amplification 12 product was diluted 1:20 with distilled water, denatured at 95°C for 5 min and snap-13 cooled on ice.

14 15(

 14 Data analysis.16 GeneScan Analysis software version 2.5 (Applied Biosystems, Foster City, CA, USA) 17 was used to align peaks to the internal standard, ROX 500 (Applied Biosystems, Foster 18 City, CA, USA), included in each run. Mobility values were assigned to internal 19 standard fragments, which encompassed the peak region of interest. Analysis 20 parameters were set to 4400-5500 scan numbers. The software then analyzed the 21 unknown sample peaks and assigned them values based on the internal standard 22 fragment migration rates. Eleven species type-strains were run 3 times each, to ensure 23 run-to-run reproducibility. SSCP analysis patterns were exported to Genotyper software 24 Applied Biosystems, Foster City, CA, USA), used to automate the data analysis of 1 SSCP. The SSCP patterns for each species, which consisted of at least one retention 2 time representative of each primer-labelled PCR product, were entered into a database.

3

  Type-strains (table 1) belonging to 11 species within the Aspergillus sect. Nigri were 4 used as reference samples in identification. The type-strain SSCP patterns were defined 5 as the average retention times relative to the 3 runs. Peaks with similar retention times 6 were placed in the same category on the basis of a 0.1% position tolerance, as standard 7 deviation within a species ranged from 0.3 to 2. This allows the retention times for an 8 unknown organism to be classified into similar categories. Because of overlapping 9 confidence intervals, some organisms could be classified together in the same 10 categories, but this problem was resolved by considering matches of both strands which 11 consist of unique combinations: e.g. The A. carbonartius and A. ellipticus reverse 12 strands have very close retention times, and the A. carbonartius forward strand is more 13 than 0.1% position tolerance from A. ellipticus (figure2).

Figure 2

 2 Figure2shows the electropherograms for 11 Aspergillus species belonging to sect.

7

  Identifications were made by visually and computer assisted comparing peak mobility. 8 Data analysis was performed with Genotyper software which allowed fully automated 9 comparison of blind sample mobility to reference samples (type-strains). To this 10 purpose a macro in Genotyper software was set up on analysis of the mobility of both

14 were 20 theFood(

 1420 correctly identified, referring to ITEM collection related species information and 15 to their previous molecular identification (Perrone et al., 2006). application of a single-stranded conformation polymorphism/capillary 19 electrophoresis (SSCP/CE) assay for the rapid identification of Aspergillus species of Nigri Section. 21 22 Until now the species identification in the Nigri Section has been still essentially based 23 on observation of macroscopic and microscopic features, which is time-consuming and 24 Page 8 of 18 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk expertise. Some molecular tools for PCR-based diagnosis have been developed 1 to detect and identify isolates of A. ochraceus (Schmidt et al., 2003), A. carbonarius 2 Schmidt et al., 2004, Pellegrinelli-Fungaro et al., 2004; Mulè et al., 2006), A.

3( 17 Finally

 17 carbonarius, A. japonicus/aculeatus (Perrone et al., 2004), A. carbonarius/A. ochraceus 4 Patino et al., 2005; Gonzales-Salgado et al., 2005). Since they consist of species-5 specific assays, which should be performed individually for each species under different 6 PCR conditions, they are not always suitable for investigation of multiple species 7 contamination. Species-specific assays have the advantage that they can be utilized to 8 directly analyze environmental samples, but they are not yet available for all Aspergillus 9 sect. Nigri species. In contrast, the SSCP protocol set up in this study can only be 10 utilised to analyze pure cultures, but is rapid tool to identify closely related species that 11 are morphologically indistinguishable. The results of SSCP analysis can be obtained 12 within 30 min of PCR, and with the new capillary electrophoresis systems on the 13 market, 8 or 16 samples can be run at one time, further increasing the turnaround time. 14 Moreover some preliminary tests, made starting from environmental samples, were 15 successful (data not shown), but PCR condition optimization is needed. 16 , the automated sequencer enables quick and easy computer analysis, allowing 18 the generation of a database for comparison. SSCP analysis, excluding common initial 19 purchase of the equipment, is a cheaper and faster alternative to sequencing in order to 20 estimate sequence variation useful for genotype idenfication. It consists of a simple 21 PCR reaction with labelled generic primers amplifying DNA from all target information from the mobility of single stranded DNA in gel 1 electrophoresis which is dependent on strand size and length and is noticeably affected 2 by very small changes in nucleotide sequences, possibly with a resolution of one 3 changed nucleotide out of several hundred.

4 5

 4 The success of this method was demonstrated by its ability to identify organisms 6 comparing SSCP patterns of blind samples to others previously entered in the reference 7 database and its ability to recognize new patterns, further investigable by sequencing 8 only few representative samples. Therefore most DNA sequence variations can be 9 detected with relatively little sequencing.

10 11The

 10 CE-SSCP protocol set up in this study allows the rapid and reliable identification of 12 the different Aspergillus spp., based on analysis of the calmodulin gene sequence. 13 The SSCP assay set up in this study offers a potential diagnostic test able to identify 14 11/16 of the Aspergillus Sect Nigri species by a single reaction with a single primer pair 15 under the same PCR conditions. The 5 species (A. costaricaensis, A. homomorphus, A. 16 lacticoffeatus, A. sclerotioniger, A. vadensis) recently identified by Samson et al. (2004) 17 inside the Section Nigri rarely occur in food and therefore have not been included in this 18 study. However, a species-specific variability for them has also been found in the 19 calmodulin nucleotide sequence (Varga et al., 2007).

Food 3 ACKNOWLEDGMENTS4

 3 23 a few informative samples need be sequenced. Moreover it should be a useful tool for 24 Page 10 of 18 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk of a large number of Aspergillus spp. strains and for the assessment 1 of their potential ochratoxigenic risk in food. 2 This research was supported by the Italian Ministry of Education, University and 5 Research (MIUR) Project 12818 -SIVINA-and by the European Union project FP6-6 IST1-508774-IP -GoodFood. We thank A. Logrieco for critical suggestions in study Patino B, Gonzalez-Salgado A, Gonzalez-Jaen MT, Vazquez C. 2005. PCR detection 1 assays for the ochratoxin-producing Aspergillus carbonarius and Aspergillus 2 ochraceus species. International Journal of Food Microbiology 104(2):207-14.

3 Pelegrinelli- 17 of

 317 Fungaro MH, Vissoto PC, Sartori D, Vioas-Boas LA, Furlanetto MC, 4 Taniwaki MH. 2004. A molecular method for detection of Aspergillus 5 carbonarius in coffee beans. Current Microbiology 49:123-127. 6 Perrone G, Susca A, Stea G, Mule G. 2004. PCR assay for identification of Aspergillus 7 carbonarius and Aspergillus japonicus. European Journal of Plant Pathology 8 110:641-649. 9 Perrone G, Mulè G, Susca A, Battilani P, Pietri A, Logrieco A. 2006. Ochratoxin A 10 Production and AFLP Analysis of Aspergillus carbonarius, Aspergillus 11 tubingensis, and Aspergillus niger strains isolated from grapes in Italy. Applied 12 and Environmental Microbiology 72:680-685. 13 Rath P.M and Ansorg R. 2000. Identification of medically important Aspergillus species 14 by the single strand conformational polymorphism (SSCP) of the PCR-amplified 15 intergenic spacer region. Mycoses 43, 381-386. 16 Schmidt H, Ehrmann M, Vogel RF, Taniwaki MH, Niessen L. 2003. Molecular typing Aspergillus ochraceus and construction of species specific SCAR-primers 18 based on AFLP. Systematic and Applied Microbiology 26:434-438.

19FoodFig. 2

 2 Fig. 2 Panels show SSCP-profiles of the 11 Aspergillus species identifiable by the SSCP approach. Strains evidencing clearly different migrations due to variations in sequence. The profiles were respectively of A. aculeatus (A), A. atypic "uniseriate" (B), A. brasiliensis (C), A. carbonarius (D), A. ellipticus (E), A. foetidus (G), A. heteromorphus (G), A. ibericus (H), A. japonicus (I), A. niger (J), A. tubingensis (K). Values on the right indicate retention time of discriminant peaks..

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