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INTRODUCTION

  1. Top of page
  2. INTRODUCTION
  3. Shortcomings of Current Approach to Lipid Normalization
  4. Improving Assessments of Bioaccumulative Potential
  5. REFERENCES

Lipid normalization is commonly used to express concentrations of nonpolar compounds in organic matrices and describe their equilibrium distribution between phases. Based on the principles of equilibrium partitioning, chemical activity and the resulting distribution between matrices and organisms should be equivalent once normalized to lipid content (Di Toro et al. 1991). This approach theoretically reduces the variability observed in total tissue burdens that are simply an artifact of varying proportions of total relative lipid content. This can be important for: between trophic level comparisons in which upper trophic level organisms rely more extensively on greater proportions of storage lipids as compared to lower trophic level organisms; within organism comparisons of various tissue types that have inherently differing lipid content; and, within species comparisons of individuals of differing nutritional status.

A number of predictive relationships are based on lipid normalized exposure calculations. For instance, knowledge of lipid content allows for direct comparison and prediction of contaminant concentrations across tissues within an organism and is required for fugacity-based multimedia modeling used to predict contaminant concentrations and partitioning between organisms and various environmental phases. Additionally, measures of bioaccumulation including the following are typically expressed on the basis of lipid-normalized concentrations of contaminant residues: bioconcentration factors (BCFs); bioaccumulation factors (BAFs); biomagnification factors (BMFs); biota-sediment accumulation factors (BSAFs); and, trophic magnification factors (TMFs).

Shortcomings of Current Approach to Lipid Normalization

  1. Top of page
  2. INTRODUCTION
  3. Shortcomings of Current Approach to Lipid Normalization
  4. Improving Assessments of Bioaccumulative Potential
  5. REFERENCES

Ongoing research in the area of lipid normalization and the predictive relationships based on lipid normalized chemical concentrations indicates a number of measurement parameters with great potential for improvement: quantification of the lipid pool; characterization of differing lipid classes within the lipid pool; and, utilization of lipid-specific chemical partitioning coefficients. Quantification of specific chemical residues in environmental matrices is generally conducted using standardized methods that typically include rigorous quality control programs to enable accuracy and precision of their results. Conversely, the accuracy with which the general parameter of “lipid” is quantified or characterized is not held to the same level of rigor. The accuracy of any lipid normalized contaminant concentration is determined by the precision and bias associated with both the chemical residue and the lipid pool; thus, both should be quantified with comparable certainty.

In addition to improving the determination of lipid content in biological samples, greater attention should be given to characterizing the composition of the lipid pool. All organisms contain structurally differing lipid classes comprising the lipid pool, including membrane phospholipids (polar) and glycerides in storage lipids (nonpolar). Given the structural differences between these classes of lipids, it is unsurprising that different lipid quantification methods using differing solvents result in varying extraction efficiencies and potentially differing ratios of lipid classes being extracted from the same biological tissues. In support of guideline revisions for the standardized determination of BCF values, Schlechtriem et al. (2012) outlined more than ten methods used to quantify lipid content. Yields of total lipid between extraction methods appeared to be comparable for lipid-rich tissues in which nonpolar storage lipids were predominant; however, substantial differences in lipid yield were observed between extraction methods for lean tissues or matrices containing a larger relative proportion of polar membrane lipids (van der Heijden and Jonker 2011). For example, Ewald et al. (1998) observed relative differences in lipid yield between two different extraction methods that ranged from less than 10% for lipid-rich fish to nearly 100% for lean fish.

The relative contribution that each lipid class makes to the lipid pool can vary significantly among species and between tissues from the same species. For example, membrane lipids comprise approximately 80% of the lipid pool found in chironomid larvae whereas storage lipids represent approximately 80% of the lipid pool found in fish (Bremle and Ewald 1995; Ewald et al. 1998). If a lipid extraction technique was used that did not efficiently extract membrane lipids, lipid-normalized contaminant concentrations at the base of the food chain could be artificially inflated and might significantly impact metrics such as the BMF or TMF. Similarly, lipid-normalized contaminant concentrations in a lean tissue, such as an axial muscle, are not likely to be representative of the more lipid-rich whole body homogenate due to differences in composition of the lipid pool.

In addition to influencing results of lipid quantification, lipid class also plays a large role in chemical partitioning behavior. For purposes of modeling and predicting bioaccumulative potential of a hydrophobic chemical, the accumulation capacity of “lipid” (i.e., the fugacity capacity; Zlipid) is generally assumed to be equal to that of n-octanol (Zoctanol) and the lipid-octanol partition coefficient (Klipid/octanol = Zlipid/Zoctanol) equal to 1.0. When quantitatively described using linear solvation energy relationships (LSER), the solute–solvent interactions of octanol, membrane lipids, and storage lipids can all be differentiated (Endo et al. 2013). Theoretical partition coefficients between octanol and both membrane and storage lipids (Klipid/octanol) for representative compounds from several different chemical classes are shown in Fig. 1. As can be seen, compounds may exhibit preferential partitioning (Log Klipid/octanol ≠ 0) into one lipid class versus another depending on the inherent properties of the chemical, and this frequently differs from octanol as well. The compound-specific parameters needed to use these LSERs are determined empirically or can be estimated from structure. When combined with better information on the physiological compositions of organisms, these relationships may be useful in validating measures of bioaccumulation and for rationalizing differences in bioaccumulation behaviors of chemicals.

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Figure 1. Comparison of theoretical log Klipid/octanol values for different chemical classes, calculated from equations found in Endo et al. (2013). Solid and hatched bars represent membrane and storage lipids, respectively. Deviations from zero (Log Klipid/octanol ≠ 0 or ZlipidZoctanol) arise from differences in the solvation properties of octanol and membrane or storage lipid, combined with differences in capacity for different types of molecular interactions among chemical classes.

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Improving Assessments of Bioaccumulative Potential

  1. Top of page
  2. INTRODUCTION
  3. Shortcomings of Current Approach to Lipid Normalization
  4. Improving Assessments of Bioaccumulative Potential
  5. REFERENCES

Currently, the differences in partitioning of chemicals between lipid classes are not considered, and the accumulation capacity (i.e., fugacity capacity) of “lipid” is considered to be equivalent to that of n-octanol when evaluating bioaccumulative potential. To more accurately estimate and measure the bioaccumulative potential of chemicals, the relative proportions of membrane and storage lipids should be taken into consideration. By applying lipid-specific partition coefficients and summing the contribution from each lipid class, the overall accumulation capacity of an organism's lipid pool could be estimated. It may seem most appropriate to select a lipid extraction method that most effectively extracts total lipids; however, representing the lipid pool as a single value does not account for the presence of different lipid classes and could artificially underestimate lipid normalized contaminant concentrations. Thus, understanding what lipid class the compound of interest will preferentially partition to and what class of lipid is being extracted and quantified will lead to more accurate normalizations of residue concentrations in environmental matrices. To alleviate the need to characterize lipid composition for every organism included in preliminary bioaccumulation assessments, it would be beneficial to have a generalized database available of the lipid composition for representatives from various classes of organisms (e.g., benthic invertebrates, salmonid fish).

REFERENCES

  1. Top of page
  2. INTRODUCTION
  3. Shortcomings of Current Approach to Lipid Normalization
  4. Improving Assessments of Bioaccumulative Potential
  5. REFERENCES
  • Bremle G, Ewald G. 1995. Bioconcentration of polychlorinated biphenyls (PCBs) in chironomid larvae, oligochaete worms and fish from contaminated lake sediment. Mar Freshw Res 46:267273.
  • Di Toro DM, Zarba CS, Hansen DJ, Berry WJ, Swartz RC, Cowan CE, Pavlou SP, Allen HE, Thomas NA, Paquin PR. 1991. Technical basis for establishing sediment quality criteria for nonionic organic chemicals using equilibrium partitioning. Environ Toxicol Chem 10:15411583.
  • Endo S, Brown TN, Goss KU. 2013. General model for estimating partition coefficients to organisms and their tissues using the biological compositions and polyparameter linear free energy relationships. Environ Sci Technol 47:66306639.
  • Ewald G, Bremle G, Karlsson A. 1998. Differences between Bligh and Dyer and Soxhlet extractions of PCBs and lipids from fat and lean fish muscle: implications for data evaluation. Mar Pollut Bull 36:222230.
  • Schlechtriem C, Fliedner A, Schäfers C. 2012. Determination of lipid content in fish samples from bioaccumulation studies: contributions to the revision of guideline OECD 305. Environ Sci Europe 24:13. Available from: http://www.enveurope.com/content/24/1/13
  • van der Heijden SA, Jonker MTO. 2011. Intra- and interspecies variation in bioconcentration potential of polychlorinated biphenyls: Are all lipids equal? Environ Sci Technol 45:1040810414.