Cyber relationship motives scale development and validation of bioaccumulation


cyber relationship motives scale development and validation of bioaccumulation

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cyber relationship motives scale development and validation of bioaccumulation

Predicted PCBs concentrations in bloater were also in agreement with available data, further validating the bioaccumulation predictions.

The results of model validation using PCBs were generally better thcin expected considering the preliminary nature of this model. The steady-state model solution was used to generate load-response predictions and mass fate and transport fluxes, to examine model sensitivity, and to estimate predictive uncertainty.

The mass balance for toxic chemicals was dominated by internal fluxes associated with particles settling and resuspension and particle burial, volatilization, and photolysis. The relative magnitude of these fluxes, and hence, the model sensitivity to parameterization, were found to vary between chemicals and model segments.

Transport of the critical pollutants between lake basins or from Green Bay to the main lake was not significant. Volatilization and redeposition is likely to be a much more significant transport mechanism.

Monte Carlo analysis was used to estimate the uncertainty in model predictions. The confidence intervals "bracket" the load-concentration predictions and define the expected bounds of model error due to parameterization. The magnitude of predictive uncertainty suggests that model results may be qualitatively useful, for instance, in comparing alternative simulations, but quantitative use of the results would be inappropriate.

Uncertainty analysis was also used to identify "critically" uncertain model parameters to be prioritized for process research. Several factors complicate the relationship between loads and concentrations.

First, reducing the load from only one source category tributaries, for instance will have a less-than-proportional effect upon concentrations. PCBs concentrations, for example, were demonstrated to be largely insensitive to reduced tributary loads. The effectiveness of loading reduction even reductions to zero load at a given time is ultimately constrained by the system lag time. The model was used to predict the effect of eliminating tributary and total PCBs loadings, compared to a "No-Action" scenario of constant PCBs loading after The results suggest that significant reductions in trout PCBs concentrations will occur in the next 10 years, even if no additional loading reductions are made.

Additional reductions of PCBs concentrations in Lake Michigan will be achieved only if significant reductions in atmospheric sources are made. These results are, however, uncertain because the p The model was also used to predict the potential impact of a severe storm "event" on the remobilization of toxic chemical from the lake sediments.

A two-day storm resuspending the entire surficial sediment layer in southern Lake Michigan resulted in elevated total water column PCBs for almost a year. However, because most of the resuspended PCBs remain sorbed to: Jarticles, the effect of the storm upon biota conce' ,':: Finally, uncertainty in the dynamic model predictions was evaluated. The thickness of the surficial sediment layer, which determines the residence time of particles and particle-associated contaminants in the lake, was demonstrated to be a critical parameter.

Uncertainty in initial concentrations, loading history, and dynamics of the Lake Michigan trophic structure were considered as additional factors leading to uncertainty in model predictions. Further verification to contemporary data is necessary to quantitatively demonstrate the predictive ability of the model.

Such a demonstration is the fundamental test of a model's adequacy as a predictive tool. Data should be collected for all chemicals that are identified as the highest priority toxics.

For critical pollutant "mixtures," chemical-specific representatives of the mixture should be quantified. These data would include representative measurements of the following: If based upon shore station measurements, then methods for over-lake extrapolation must be devised.

This should be accompanied by more limited sediment core sampling in focusing zones to measure vertical distributions of contaminants and particle tracers.

Near-surface distribution should be resolved on 1 cm or finer intervals. Chemical concentrations and lipid should be measured in age seven male and female trout.

It would be preferable to analyze individual fish instead of composites. Analysis of whole fish would be preferred. Monitoring should be conducted at a location on each tributary where lake water inflow does not persist except during low flow. Point sources below the monitoring location should be sampled separately. Total chemical concentration, particulate and dissolved organic carbon, total suspended solids, chlorophyll, and chloride should be measured based upon flow- proportioned sampling.

Flow, conductivity, transmissivity, and temperature should be measured daily or hourly, for event responsive tributaries. Chemical monitoring should be proceeded or at least accompanied by evaluation of in-place sediment contamination. In-place pollutants may be mobilized only under extremely high flows; their contribution to tributary loading is, therefore, unlikely to be detected by conventional monitoring. Particulate and dissolved organic carbon, total suspended solids, chlorophyll, and other standard limnological parameters should also be measured.

Sampling should be conducted during both stratified and unstratified periods, with mid-epilimnion, metalimnion, mid-hypolimnion, and benthic nephloid sampling during stratification.

A true field blank for dissolved and particulate chemicals must also be obtained to validate these measurements. Although the effort and cost associated with such an undertaking would be substantial, it may be argued that most of these data would be necessary to justify additional priority toxics load reductions regardless of modeling effort.

Mercury is one such chemical that is not addressed by the present model. A substantial process research effort will be necessary before management-level simulation of mercury mass balance, transformation, and bioaccumulation can be made. Planning for such efforts by organizations such as the Mid-Continent Ecology Division MED -Duluth are underway; rapid progress is not expected, given the many fundamental unknowns regarding mercury's behavior in the ecosystem.

Extensive monitoring of loads and ambient concentrations in Lake Michigan will be necessary as well, as virtually no mercury data exist for this system. This will require the development of analytical capabilities that presently do not exist. Thus, the process of developing models for other critical pollutants may be lengthy.

It may also be necessary to extend the modeling effort to other target organisms. These could include a variety of birds and wildlife which consume fish from the lake: Although toxicokinetics of hydrophobic organic chemicals HOCs in herring gulls have been studied Clark et al. This would, again, necessitate a developmental effort. Because the development of the model was based largely upon existing information, numerous aspects of the structure and parameterization of MICHTOX lack adequate justification to establish scientific credibility.

Furthermore, a variety of assumptions which are critical to model performance have not been validated. However, specific process research to improve process descriptions and parameterization will also be necessary. Particle transport at the water-sediment interface. Chemical partitioning to organic carbon and plankton. Chemical assimilation efficiency, particularly in benthos The second aspect of further model development is improving the predictive ability of the model. Predictive ability, in terms of both accuracy and resolution of predictions, may be improved by incorporating more fundamental and realistic process descriptions and linkages in the simulation.

This, in turn, will allow for finer spatial and temporal resolution of predictions. Specific areas for further development include the following: The alternative approach is to predict circulation using hydrodynamic models, which are based upon momentum and continuity balances in two or preferably, three dimensions.

  • Online gaming motivations scale: development and validation

Hydrodynamic predictions are driven by meteorologic forcing functions and provide fine-scale spatial and temporal resolution. Basing circulation simulations on hydrodynamics will be necessary for accurate mass balance modeling in nearshore zones.

This approach is descriptive rather than predictive. It does not relate particle transport to actual forcing 4 functions, and its accuracy and resolution are limited by measurement availability.

As was the case for hydrodynamics, this coupling would be particularly important in shallow, nearshore zones where sediment resuspension is highly episodic.

Sediment transport coupling would also be very useful in predicting particle redistribution and focusing, which produce complex contaminant distributions in Lake Michigan sediments. Yet most of the particulate matter in the Great Lakes water column is phytoplankton, at least during the growing season.

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Organic carbon, the principal sorbent for HOCs, is therefore, cycled largely by biotic processes - production, grazing, respiration, and decay. Because phytoplankton and toxic chemicals are states related through organic carbon, their behavior is expected to be coupled. Not only would this coupling improve model realism and accuracy, but it would also allow the model to simulate how nutrient control may impact toxic chemicals in the ecosystem.

Yet it is unclear whether this model is capable of describing the variability in bioaccumulation observed for organisms in many data sets. Individual-based bioaccumulation models have been proposed Hallam et al. Bioaccumulation predicted for many individuals then defines the probability distribution for the population or species, which in some instances Le. This approach to modeling may be particularly advantageous at the point of modeling effects of bioaccumulating chemicals, where a given body burden may affect only a portion of the population.

Another weakness of the present bioaccumulation model is the static structure of the food chain. This limitation is discussed in Section 1. Just as toxic chemical dynamics are coupled to those of phytoplankton, there may be linkages to higher trophic levels as well.

In particular, as the structure of the food chain changes, so may bioaccumulation in higher trophic levels. Ecosystem models capable of simulating the dynamics of trophic structure have been developed and proposed DePinto, Their data requirements are extensive, however, and their predictive ability in systems as large as the Great Lakes is unknown. The linkage of bioaccumulation and ecosystem models is probably the most ambitious modeling recommendation, but it would provide the ability to predict the consequences of stresses such as fisheries management and exotic species in terms of population diversity and bioaccumulation.

This linkage is necessary to relate actual sources of toxics to their delivery to and removal from the lake, instead of measuring this delivery as loads. The linkage to atmospheric mass balance is particularly critical, because it is not at all apparent that present measurements of atmospheric concentrations and deposition fluxes are free of the "boundary effects" due to volatilizing chemicals. Furthermore, the atmospheric transport and subsequent redeposition of volatilizing chemicals can only be simulated by coupling air and water mass balances.

The primary objective of modeling was to provide guidance to Region V and the Lake Michigan Lake-wide Management Plan LaMP as to expected water quality improvements in response to critical pollutant loading reductions. A secondary objective was to demonstrate the potential utility of the mass balance modeling approach. The model addressed two primary questions related to the LaMP. If the loads of these chemicals to Lake Michigan were reduced, how rapidly would concentrations change?

In response, a management-level model has been developed which, within expected confidence limits addresses these management questions. The model is based upon available theory and data characterizing the sources, transport, fate, and bioaccumulation of toxic chemicals in Lake Michigan and the other Great Lakes.

The results of MICHTOX should be considered preliminary because the model is based upon a variety of assumptions which have not been validated for either Lake Michigan or individual toxic chemicals. In addition, significant compromise was made regarding model calibration. The customary application of mass balance models, such as MICHTOX, includes extensive calibration of model parameters to site-specific load, chemical concentration, and process variable data.

Calibration is a step of model development necessary for accurate parameterization and simulation. Because only some of the necessary data for calibration were available for toxic chemicals in Lake Michigan, this step of r: To this end, extensive analysis of model uncertainty was applied to the model.

Such 6 identification of research and data needs seNes as a useful planning exercise prior to large-scale project s designed to improve our understanding of toxic chemical behavior in the ecosystem.

Concentrations peak somewhere between andthen decline almost as abruptly. Similar trends throughout the Great Lakes have been correlated to chemical production and usage rates Oliver et al. However, concentrations during the last 10 years appear to be nearly constant at values elevated above pre-contamination conditions.

Dieldrin 90 80 o LPCB 1: In general, monitoring of toxic chemicals in Lake Michigan in the past decade suggests little trend in concentrations. Prior to this, concentrations of several toxic chemicals, including chlordane, as well as PCBs and DOT, declined dramatically as bans on chemical production were initiated. What has changed is unclear: Resolving this issue is important because further reductions in toxic chemical concentrations appear necessary to protect human health and the ecosystem.

Yet, further reductions in loading mayor may not be necessary to achieve those concentration reductions. The approach to modeling toxics is strongly related to the desired scale and resolution of the analysis. The Lake Michigan LaMP addresses water quality in the open lake waters, defined to include all waters within the Figure 1. Long-term concentration trends for toxic chemicals in small Lake Michigan fish. This specifically excludes the simulation of toxic chemicals in other nearshore regions, because MICHTOX is not predictive at the scale of variability exhibited in the nearshore.

Modeling water quality in nearshore regions, such as bays, harbors, and inlets, would require site-specific data collection efforts and potentially different modeling techniques. These AOCs should be treated as source components to the mass balance; this remains to be accomplished. As sources, the AOCs may be highly significant. Marti and Armstrong estimate that half of the tributary loading of PCBs in the early s was discharged from the Fox River.

The mass balance model was adapted from the general water quality model WA8P4 Ambrose et al. The model implements mass balance equations describing the input, transport, and fate of hydrophobic toxic chemicals in the Great Lakes. A schematic of the mass balance model for a vertically-segmented lake basin is presented in Figure 1. Chemical concentrations in the epilimnion cEhypolimnion cH and surficial sediment cs are calculated by solving the coupled mass balance equations. The mass balance in the epilimnion is: The mass balance in the surficial sediment is: A single surficial sediment layer has been used in MICHTOX; this is assumed to be adequate to simulate sediment-water chemical exchange processes.

Vertical resolution of chemical concentrations in deeper sediment layers may be obtained from the model by transforming the time scale to sediment depth using the burial sedimentation rate. This segmentation represents an intermediate level of spatial resolution, which balances the desire to predict concentration response to spatially non. The main lake is divided into southern, central, and northern basins, according to the large-scale circulation and sedimentation patterns observed in Lake Michigan.

The circulation of the southern and central basins of the lake are characterized by distinct counter-rotating gyres Schwab, LJ water column rriI completely-mixed L Each main lake basin is segmented vertically into epilimnion, hypolimnion, and surficial sediment. Green Bay is divided into three completely-mixed water column and sediment segments.

Additional model resolution is provided in Green Bay because 1 significant past and present PCBs loading from the Fox River has resulted in persistent water and sediment concentration gradients, and 2 results here may be compared to those of the calibrated models under development for the GBMBP.

Segment geometry volumes, interfacial and surface areas, and depths were based upon the 2 km digitized bathymetry data of Schwab and Sellers Flows were based upon the whole-lake water balance of Quinnwhich provided monthly average changes in storage, tributary flow, outflow and diversion, precipitation, and evaporation. Tributary flow was apportioned to the various surface water segments according to the river mouth location and mean flow of the 14 largest Lake Michigan tributaries.

Changes in storage, precipitation, and evaporation were apportioned based upon segment surface area. Based upon this water balance, flows between all surface water segments were calculated for each month. In comparison, the hydraulic residence time for the lake as a whole is years Winchester, Hydraulic residence times in Green Bay segments are much shorter: There are numerous biological phenomena that have been explored for their bio-HMR potential, but two have received notable attention: Both are natural processes that all living cells undergo, so it is theoretically possible to screen all microorganisms and plants dead or alive for their bio-HMR potential.

Moreover, the number of simulated and real wastewater effluent conditions that could be tested in these screens is countless Gadd, Rather than exhaustively characterize combinations of species and conditions, other researchers have chosen to focus on a more direct and rational approach that leverages major developments in molecular biotechnology.

2017 Maps of Meaning 1: Context and Background

This review first presents rationale for choosing bioaccumulative genetically engineered microorganisms GEMs as a bio-HMR technology section Biosorption or Bioaccumulation? Using a biochemical framework, this review then evaluates progress in developing GEMs that recombinantly express heterologous import-storage machinery section Strategies and Limitations. To advance bioaccumulation, exploring its use for bioextraction is proposed by highlighting past efforts in developing bioprocesses and recent advances in molecular biotechnology section Re-thinking Bioaccumulation.

Biosorption and bioaccumulation are not the same and should not be used interchangeably. Biosorption is the adsorption of particles to a biological matrix using physical interactions electrostatic forceschemical interactions ion or proton displacementcomplexation, or chelation. At neutral pH, the extracellular surface of microorganisms contains anionic moieties that provide binding sites for cationic HMs Michalak et al. Numerous microbial species have been tested for their adsorption properties with HMs and are reviewed elsewhere Volesky, ; Srivastava and Majumder, ; Vijayaraghavan and Yun, ; Wang and Chen, ; Ayangbenro and Babalola, ; Ilyas et al.

Researchers have also engineered microorganisms to have recombinant metal-binding proteins and peptides on the extracellular surface, thus improving the capacity and specificity of these microbial biosorbents Kuroda and Ueda,; Ueda, This area has seen remarkable progress and has leveraged molecular biotechnology.

However, since it is based on adsorption, it encounters challenges like those faced by some adsorption-based conventional methods, particularly the susceptibility to variations in pH and ionic strength that exists in heterogeneous wastewater effluents.

Biosorbents also have limited lifespans because they often use dead biomass that degrades over time, and because fouling renders the binding sites unavailable Gadd, ; Fomina and Gadd, In contrast, bioaccumulation is a metabolically-active process where microorganisms uptake HMs into their intracellular space using importer complexes that create a translocation pathway through the lipid bilayer i. Once inside the intracellular space, the HMs can be sequestered by proteins and peptide ligands i.

This is visualized in Figure 1. Bio-HMR technology overview using a Gram-negative bacterium. HMs are depicted as black circles. Biosorption is indicated by an orange arrow.

Bioaccumulation can use an import-storage system where HMs are translocated across lipid membranes through transporters purple arrows into the cytoplasm for sequestration by metal-binding entities green arrows. The light purple arrows specify the source of energy required for HM uptake: Summary of bioaccumulation studies for various heavy metal ions. It also implies the process involves proteins in the cytosol and those embedded in the lipid membrane.

This imposes more unique challenges: Wildtype or Engineered Microorganisms? Solutions for biosorption are often inspired from solutions for conventional sorption-based methods: Conversely, limitations of bioaccumulation concern the inner architecture of the cell: While the former necessitates synthetic biology to optimize trade-offs between recombinantly expressing protein machinery i.

Mishra and Malik provide the most recent summary of wildtype yeast, fungi, bacteria, and algae with bioaccumulative capacities larger than those of GEMs reported in Table 1 of this review.

However, these wildtype microorganisms were mostly incubated in simulated and real wastewater effluents for longer periods, which may explain the differences. While it is valuable to compare which wildtype microorganism or GEM can treat an effluent sample better and faster, none outstandingly outperform others as shown by the paucity of industrial adoption of bio-HMR.

It may be more valuable to compare practical implications than performance. Using wildtype microorganisms adapted to a wastewater effluent's nutrient composition confers a survival advantage over non-native GEMs that carry the burden of expressing an import-storage system that it is not mandatory for growth.

Although true, wildtype microorganisms are less robust than GEMs because the protein machinery they use to uptake and sequester HMs are transcriptionally and translationally controlled by genetic regulatory systems that the species has evolved. It is unlikely that wildtype microorganisms will bioaccumulate HMs beyond their minimum inhibitory concentrations, thus imposing a limit to their bioaccumulative performance.

GEMs use characterized genetic regulatory elements e. Therefore, GEMs may offer superior control over when, how long, and how strongly an import-storage system is expressed to allow for predictable uptake and sequestration of HMs. Transferring the gene sequence encoding this import-storage system to microorganisms that robustly grow in single or multiple wastewater effluents may allow for targeted and generalized bio-HMR through bioaccumulation section Chassis Selection.

This review does not suggest there cannot be synergies between biosorption and bioaccumulation by wild-type microorganisms with bioaccumulation by GEMs.

However, it does argue that the specificity and selectivity conferred by import systems, and capacity afforded through storage systems can be engineered to create GEMs that are potentially more robust and versatile. By first understanding the state of research in bioaccumulative GEMs, gaps in knowledge can be identified and unexplored potential avenues can be delineated to direct future research to accomplish these goals.

Strategies and Limitations To genetically engineer microorganisms for enhanced bioaccumulation, researchers have recombinantly expressed import-storage systems, which is summarized in Figure 1 and discussed further in the following sections. Engineering Heavy Metal Ion Import Systems Efforts to improve biological HM uptake have focused on improving uptake from the periplasm into the cytoplasm of Gram-negative bacteria using recombinantly expressed inner membrane importers from three major transporter classes Transporter Classification Database, TCDB: These are summarized in Table 2.

Transporters used as the import system in bioaccumulation studies. Additionally, MerC, MerE, and MerF are other importers that can uptake Hg and although they differ in topology, it is suggested they share the same uptake mechanism Wilson et al. The zero-energy requirement of small ion channels for uptake makes them appear to be the best choice for bioaccumulating HMs since there is less energetic burden on the cell.

However, the rate of this passive uptake is a function of the concentration gradient of the target HM. Once this gradient reaches equilibrium, the GEM can no longer perform bioaccumulation.

Although this is not detrimental to removing HMs overall, it must be considered when designing the bacteria to treat wastewater effluents containing relatively high concentrations of HMs. If there are regulatory limits that require the GEM to bioaccumulate against the equilibrium concentration viz.

Nearly all studies using channels have used storage systems. A similar major transporter class are the porins TCDB 1. In one gene knock-out approach, Schauer et al. Hence, it may be possible to increase periplasmic HM concentrations by overexpressing divalent cation-selective porins to improve the overall uptake rate from the periplasm into the cytoplasm for storage. It may also be possible to engineer porins with altered HM selectivity using the PoreDesigner workflow Chowdhury et al.

A are single component proteins that can be further classified as uniporters, symporters, and antiporters Forrest and Rudnick, ; Saier, Uniporters depend on the presence of the PMF by using the charge difference across the inner membrane to drive the translocation of positively charged substrates like cationic HMs.

Symporters depend on the PMF because they use the protons that generate the charge difference as a co-substrate during uptake of their target substrate. Both secondary carriers thus deplete a portion of the PMF which may impose an energetic burden that negatively impacts growth due to the reduction of protons used by the ATP synthetase to generate ATP.

Considering this factor during testing of import system expression levels may minimize growth inhibition, which is often an obstacle during scale-up studies that use propagating cells. For bioaccumulation, primary active transporters have been shown to import Cd and Cu. Bioaccumulation studies that used cdtB did not specify its accession number for a database query or any record of the DNA sequence. Based on a differential proteomics study of L.

By using cellular ATP reserves, these primary active transporters directly consume chemical energy, which likely imposes a heavier energetic burden on the bacteria. Similar to secondary carriers, researchers ought to be aware of this relationship between bioaccumulative capacity and cellular growth due to the burden of HM import if they are considering scale-up efforts.

cyber relationship motives scale development and validation of bioaccumulation

Interestingly, another large class of primary active transporters has seen no attention in bioaccumulation studies: There are three classifications of ABC importers: Some pathogens encode a gene for solute-binding components that use biosynthesized metallophores capable of scavenging metals by binding them with picomolar affinities. This prevents the metal from being captured by the host cells' transporters which is a defense mechanism to minimize the pathogens' virulence.

This high degree of specificity may allow for the bioaccumulation of valuable metals present at very low concentrations in wastewater. The majority of research in ABC transporters is directed at understanding the role of ABC transporters in infections and diseases Remy et al. This provides a strong molecular understanding of ABC transporters that positions them for testing in bioaccumulation studies.

Engineering Heavy Metal Ion Storage Systems Efforts to improve the HM storage in bacteria have focused on the production of cytoplasmic metal-binding entities for sequestration of HMs to minimize poisoning from oxidative stress. These entities are mostly metal-binding proteins MBPsbut also include enzymes that produce peptides and other polymers that can also bind to HMs. Table 3 is not an exhaustive list of all proteins used to sequester HMs due to the vast body of literature on metallothioneins MTs.

MICHTOX: a Mass Balance and Bioaccumulation Model for Toxic Chemicals in Lake Michigan

Metal-binding proteins and soluble fusion partners used as the storage system in bioaccumulation studies. Phytochelatin synthesis enzymes used in storage systems for bioaccumulation studies. Genetically Encoded Metal-Binding Proteins Ligands that provide binding sites for storage in bioaccumulation studies have mostly come from genetically encoded MBPs. MTs are ubiquitous because they are found in prokaryotes, archaea, and eukaryotes.

However, their evolutionary relationships are obscure, so it has been suggested that they are products of convergent evolution Capdevila and Atrian, Since MTs do not have a single common ancestor, the features used to help identify them include low molecular mass, characteristic amino acid composition i. These cysteine residues are necessary for sequestration because they can strongly coordinate the HMs. Researchers realized early on that aggregation of overexpressed MTs can reduce their effective storage capacity Irons and Crispin Smith, MTs have since been fused to a variety of soluble fusion partners, including the popular maltose-binding protein and glutathione-S-transferase Table 3.

Although successful, many bioaccumulation studies that use MTs as the storage system fail to report a control where only the fusion partner is expressed. This is important as it reveals the extent to which these fusion partners participate in HM sequestration. It might be unclear whether the MT makes a difference given the relative size comparisons between a 5 kDa MT and a 43 kDa maltose-binding protein that may adsorb HMs to its surface.

Expressing the fusion partner alone, especially larger ones like maltose-binding protein, may deplete material resources i.

Additionally, bacteria maintain a redox environment in the cytoplasm that inhibits disulfide bridge formation in cytoplasmic proteins Raina and Missiakas, ; Bessette et al. By overexpressing these cysteine-rich MTs, an increase in demand for cysteine and methionine for biosynthesis of other endogenous proteins may alter this homeostasis and negatively impact growth.

These proteins have recently been discovered and found to function as natural HM storage systems that play crucial physiological roles in metal homeostasis.

These discoveries suggest there are numerous undiscovered HM storage systems that have been evolved to retain HMs in intracellular spaces, perhaps better than MTs which are normally used as a stress response. This is a potential avenue in bioaccumulation research. Enzymatically Produced Metal-Binding Peptides and Polymers HM storage can also be mediated through small polymers that are enzymatically produced from materials readily available in the cytoplasm summarized in Table 4.

The first step requires ligase GshI EC 6. Earlier studies identified GSH production as the bottleneck in the pathway for phytochelatin production, so a mutant GshI insensitive to feedback inhibition was discovered and later used in bioaccumulation studies Murata et al.

Another storage system uses the production of polyphosphate polyP using polyphosphate kinase EC 2.