Legal constraints and opportunities for biochar: a case analysis of EU law

This article addresses biochar from a legal point of view. It analyses different policies and regulations from a European (Flemish) point of view and provides a first and general insight in what potential legal constraints the development of a biochar industry might face and what opportunities lie ahead. This is due to the fact that biochar is a recent product and a lot of scientific uncertainty still exists regarding the consequences of its application. From the analysis it appears a multitude of policies and legislative measures influence the development of the biochar industry. Hence, it is important that all these policies and legislative measures are analyzed in an appropriate manner. Moreover, considerable lobbying, negotiating and cooperation between different disciplines (legal, scientific, economical, etc.) will be required so as to develop a feasible and safe biochar framework.     

The Complete Chloroplast and Mitochondrial Genomes of the Green Macroalga Ulva sp. UNA00071828 (Ulvophyceae,Chlorophyta)

Sequencing mitochondrial and chloroplast genomes has become an integral part in understanding the genomic machinery and the phylogenetic histories of green algae. Previously, only three chloroplast genomes (Oltmannsiellopsis viridis, Pseudendoclonium akinetum, and Bryopsis hypnoides) and two mitochondrial genomes (O. viridis and P. akinetum) from the class Ulvophyceae have been published. Here, we present the first chloroplast and mitochondrial genomes from the ecologically and economically important marine, green algal genus Ulva. The chloroplast genome of Ulva sp. was 99,983 bp in a circular-mapping molecule that lacked inverted repeats, and thus far, was the smallest ulvophycean plastid genome. This cpDNA was a highly compact, AT-rich genome that contained a total of 102 identified genes (71 protein-coding genes, 28 tRNA genes, and three ribosomal RNA genes). Additionally, five introns were annotated in four genes: atpA (1), petB (1), psbB (2), and rrl (1). The circular-mapping mitochondrial genome of Ulva sp. was 73,493 bp and follows the expanded pattern also seen in other ulvophyceans and trebouxiophyceans. The Ulva sp. mtDNA contained 29 protein-coding genes, 25 tRNA genes, and two rRNA genes for a total of 56 identifiable genes. Ten introns were annotated in this mtDNA: cox1 (4), atp1 (1), nad3 (1), nad5 (1), and rrs (3). Double-cut-and-join (DCJ) values showed that organellar genomes across Chlorophyta are highly rearranged, in contrast to the highly conserved organellar genomes of the red algae (Rhodophyta). A phylogenomic investigation of 51 plastid protein-coding genes showed that Ulvophyceae is not monophyletic, and also placed Oltmannsiellopsis (Oltmannsiellopsidales) and Tetraselmis (Chlorodendrophyceae) closely to Ulva (Ulvales) and Pseudendoclonium (Ulothrichales).     

Critical Evaluation of the Volumetric “Bottle Effect” on Microbial Batch Growth

We have analyzed the impact of surface-to-volume ratio on final bacterial concentrations after batch growth. We examined six bottle sizes (20 to 1,000 ml) using three independent enumeration methods to quantify growth. We found no evidence of a so-called volumetric bottle effect, thus contradicting numerous previous reports.Microbial batch growth during confined incubation in bottles of various sizes is used daily in a broad variety of microbiological studies and methods, including bioassays such as the assimilable organic carbon (AOC) assay (6, 10, 18) and the analysis of pure culture or microbial community growth in freshwater (3, 11, 19, 20). In this context, “bottle effect” or “volume effect” is a term that has cropped up frequently in aquatic microbiology papers (e.g., references 12, 13, and 21) during the last 100 years to explain inexplicable phenomena and variations in results obtained from such batch growth studies. The uncertainty surrounding this apparent effect was clearly summarized in a recent paper by Pernthaler and Amann (16): “Such investigations are often plagued by the mysterious ‘bottle effect’, a hard-to-define concept that reflects the worry of whether phenomena observed in confined assemblages are nonspecific consequences of the confinement rather than a result of the planned manipulation.” The “bottle effect” alludes to an apparent reaction of bacteria to batchwise incubation in a confined environment, and this concept has intermittently been linked to influences on final cell concentrations (3) and grazing/bacterivory (13), a change in viability/activity parameters (9), a change in cultivability (5), and a change in population composition (1).The fact that microbiological processes during confined incubation differ from those in the environment is indisputable. However, a particular section of “bottle effect” literature focuses specifically on a volumetric “bottle effect”, where the above-mentioned effects are linked specifically to the size (or surface-to-volume ratio) of the incubation vessel (3, 8, 11-13, 15, 21). One of the oldest and best-known studies summarized clearly: “It will be observed that the densest bacterial populations appear in the bottles of water which offer the largest area of glass surface per unit volume of water” (21). This idea has established itself as dogma during the last century, with only a few differing opinions (4). However, precious little empirical data that actually quantify and explain the volumetric “bottle effect” are ever presented. In one example, Bischofberger et al. (3) observed that incubation of groundwater led to significantly more growth (about 2 log units) in small bottles (100 ml) than in big ones (10 liters). More often, however, the “bottle effect” is merely mentioned, as if it is self-explanatory and indisputable (2, 11, 12). In the present study, we took a simple but detailed look at the effect of bottle size on the outcome of short-term (<5-day) batch growth assays and compared the data critically to information in the literature and current opinion on this topic.Three batch growth experiments were conducted to assess the volumetric bottle effect on final cell concentrations after growth into stationary phase. Six different bottle sizes were used, covering the ranges most often reported in “bottle effect” literature. All glassware and Teflon-coated caps were cleaned comprehensively as described elsewhere (6) to remove any traces of organic carbon that might have been present on surfaces. The bottle sizes were as follows (water volumes and surface area-to-volume ratios [square centimeters to milliliters] are respectively included in parentheses): 1,000 ml (900 ml, 0.3:1), 500 ml (400 ml, 0.4:1), 250 ml (200 ml, 0.6:1), 100 ml (90 ml, 0.8:1), 40 ml (35 ml, 1.5:1), and 20 ml (15 ml, 2.4:1). In the first experiment, a sample of natural river water (dissolved organic carbon [DOC], 3.8 mg/liter; AOC, 0.3 mg/liter) from a small oligotrophic stream was obtained, filter sterilized with a 50-kDa dialysis filter (Fresenius Medical Care), and inoculated (at 10³ cells/ml) with a microbial community used for AOC assays (19). In the second experiment, a sample of the effluent (DOC, 1.2 mg/liter; AOC, 0.03 mg/liter; total cell concentration [TCC], 3 × 10⁵ cells/ml) from a granulated active carbon filter situated in a drinking water pilot plant (7) was collected and used directly for the experiment without additional treatment or inoculation. For the third experiment, sterile Luria-Bertani (LB) medium (diluted 1:10,000; DOC, 0.7 mg/liter; AOC, 0.46 mg/liter) was inoculated with Vibrio cholerae O1 (10³ cells/ml) as described previously (19). The water from each experiment was distributed into triplicate flasks of each size and incubated (at 30°C) until stationary phase was reached. Stationary phase was indicated by no significant increase in the TCC (measured after 3, 4, and 5 days) on consecutive days. Samples from all experiments were analyzed (i) for TCCs after being stained with SYBR green I and subjected to flow cytometry (7, 19), (ii) for ATP by using a commercial luciferin-luciferase assay (Promega Corporation) (7), and (iii) for heterotrophic plate counts (HPC) on R2A agar by a pour plate method with incubation at 30°C for 10 days. Possible biofilm growth was checked by applying sonication to selected samples. However, no wall growth in bottles of any size was observed.Growth was observed in all three experiments. The results show the net growth after subtraction of the initial cell/ATP/HPC concentrations from the final concentrations (Fig. ​(Fig.1).1). The proposed concept of the volumetric bottle effect implies that more growth should occur in smaller bottles. All data sets were subjected to regression analysis, and we observed no significant correlation (P < 0.01) between bottle size and final growth in any of the experiments by any of the three independent methods used for quantification. Figure ​Figure1A1A shows the batch growth results for a natural microbial community in prefiltered river water. This experimental setup is reflective of a typical AOC assay (6) or batch cultivation of natural microbial communities (20). Figure ​Figure1B1B shows the results for direct incubation of a treated drinking water sample. This sample and experimental setup were chosen specifically to assess any potential volumetric “bottle effect” on an indigenous microbial community in a biologically stable water sample, where only limited growth is expected. Indeed, the final cell concentration in the sample was only about 25% higher than the original cell concentration. The cultivability (HPC/TCC × 100) at day 0 was 0.4%, and at the end of the experimental period it had increased to 2.5%. This points to increased cultivability as a result of growth during confinement (5), yet it does not relate at all to the size of the incubation vessel. Figure ​Figure1C1C shows the data for V. cholerae grown in sterile LB medium (diluted 1:10,000) to stationary phase. Again, this particular setup is of specific relevance since a recently published paper on the growth of V. cholerae referred directly to the volumetric “bottle effect” to explain rather large differences between growth results from two separate studies (11, 19). The data from Fig. ​Fig.1C1C suggest at least that a “bottle effect” should be ruled out as an interfering factor in this case.Open in a separate windowFIG. 1.Effects of bottle size on bacterial batch growth of a natural microbial community in filter-sterilized surface water (A), growth of bacteria during direct incubation of water from a drinking water treatment plant (B), and batch growth of a V. cholerae pure culture in diluted LB medium (C). Growth (expressed as the net growth) was quantified by flow cytometric total cell counting (circles), total ATP analysis (diamonds), and conventional plating (squares). All data points represent averages of triplicate measurements.The results presented in this study clearly dispute the concept of a volumetric “bottle effect” on the outcome of short-term batch growth assays, be it for pure cultures or natural microbial communities. These findings contradict evidence reported by many other researchers (3, 8, 11-13, 15, 21). Although the volumetric “bottle effect” is often cited as a somewhat mysterious occurrence, it is imperative that clear experimental data are required for the critical appraisal thereof. The main experimental theory behind the phenomenon is that organic carbon adsorbs to clean glass surfaces, thus locally concentrating the carbon and creating more favorable growth conditions (2, 14). This adsorption and the fact that bacteria can utilize such adsorbed carbon have been demonstrated experimentally (14). What has, in our opinion, not been shown conclusively is that these effects can be so dramatic that they would alter the growth of samples to the extent that different sizes of bottles would render different final cell numbers after growth. Since we have not observed any volumetric “bottle effect” in our work, we can only speculate on the possible reasons why this has been observed previously. One explanation may be that glassware contaminated with organic carbon can contribute to the perception of a volumetric “bottle effect,” as large surface-to-volume ratios (found in small bottles) would account for increased contamination compared to that in bottles with smaller ratios. Hence, more additional available carbon would be introduced into smaller bottles, giving rise to higher final cell numbers after growth. In this context, it is essential that a comprehensive glassware-cleaning protocol be followed, including heating to a high temperature (>500°C) and storage away from volatile organics (6). In addition, it is important that such experiments at low carbon concentrations are complemented with the inclusion of correct and sensitive controls to assess potential organic carbon contamination. For example, the use of deionized water as a negative control should be avoided, since the absence of inorganic nutrients is bound to lead to no growth and thus false-negative results (10). A good negative control would be water that is only carbon limited, e.g., bottled drinking water (17). Moreover, the use of multiple tools for analyzing growth, including cultivation-independent methods, is encouraged.In conclusion, we did not observe evidence of a volumetric bottle effect on short-term (<5-day) batch incubations. The findings of this study suggest that reference to the so-called volumetric bottle effect should be considered carefully unless supported by clear experimental data. This study does not dispute the fact that many authors have observed results implying apparent bottle effects during growth studies, but it questions the interpretation and understanding of this concept and the random use of the term “bottle effect” to explain uncertainty in results, specifically in relation to bottle size. Hopefully, these data will assist with experimental setups and comparison of data among different groups and stimulate discussion of and future research on this interesting, but slightly controversial, topic.     

Phenothiazines alter plasma membrane properties and sensitize cancer cells to injury by inhibiting annexin-mediated repair

Repair of damaged plasma membrane in eukaryotic cells is largely dependent on the binding of annexin repair proteins to phospholipids. Changing the biophysical properties of the plasma membrane may provide means to compromise annexin-mediated repair and sensitize cells to injury. Since, cancer cells experience heightened membrane stress and are more dependent on efficient plasma membrane repair, inhibiting repair may provide approaches to sensitize cancer cells to plasma membrane damage and cell death. Here, we show that derivatives of phenothiazines, which have widespread use in the fields of psychiatry and allergy treatment, strongly sensitize cancer cells to mechanical-, chemical-, and heat-induced injury by inhibiting annexin-mediated plasma membrane repair. Using a combination of cell biology, biophysics, and computer simulations, we show that trifluoperazine acts by thinning the membrane bilayer, making it more fragile and prone to ruptures. Secondly, it decreases annexin binding by compromising the lateral diffusion of phosphatidylserine, inhibiting the ability of annexins to curve and shape membranes, which is essential for their function in plasma membrane repair. Our results reveal a novel avenue to target cancer cells by compromising plasma membrane repair in combination with noninvasive approaches that induce membrane injuries.     

Modeling Lignin Polymerization. I. Simulation Model of Dehydrogenation Polymers

Lignin is a heteropolymer that is thought to form in the cell wall by combinatorial radical coupling of monolignols. Here, we present a simulation model of in vitro lignin polymerization, based on the combinatorial coupling theory, which allows us to predict the reaction conditions controlling the primary structure of lignin polymers. Our model predicts two controlling factors for the β-O-4 content of syringyl-guaiacyl lignins: the supply rate of monolignols and the relative amount of supplied sinapyl alcohol monomers. We have analyzed the in silico degradability of the resulting lignin polymers by cutting the resulting lignin polymers at β-O-4 bonds. These are cleaved in analytical methods used to study lignin composition, namely thioacidolysis and derivatization followed by reductive cleavage, under pulping conditions, and in some lignocellulosic biomass pretreatments.Lignins are aromatic polymers that are predominantly present in secondarily thickened cell walls. These polymers make the cell wall rigid and impervious, allowing transport of water and nutrients through the vascular system and protecting plants against microbial invasion. Lignins are heterogeneous polymers derived from phenylpropanoid monomers, mainly the hydroxycinnamyl alcohols coniferyl alcohol (G-monomer) and sinapyl alcohol (S-monomer) and minor amounts of p-coumaryl alcohol (H-monomer). These monolignols differ in their degree of aromatic methoxylation (-OCH₃ group; Fig. 1). The resulting units in the lignin polymer are the guaiacyl (G), syringyl (S), and p-hydroxyphenyl (H) units. They are linked by a variety of chemical bonds (Fig. 2) that have different chemical properties (Boerjan et al., 2003; Ralph et al., 2004; Vanholme et al., 2008).Open in a separate windowFigure 1.Chemical structures of three monolignols. A, H-monomer (p-coumaryl alcohol). B, G-monomer (coniferyl alcohol). C, S-monomer (sinapyl alcohol). G- and S-monomers are considered in our simulations. The G-monomer is methoxylated (-OCH₃ group) on position 3, and the S-monomer is methoxylated on positions 3 and 5.Open in a separate windowFigure 2.Chemical structures resulting from the possible bonding between two monomers (A) or a monomer and the bindable end of an oligomer (B). X and Y in the monomers denote the absence (for a G-unit) or presence (for an S-unit) of a methoxyl group at position 5 (see Fig. 1). The red line indicates the bonds generated by couplings of the B position and B, 4, or 5 position.Lignification is the process by which monomers and/or oligomers are polymerized via radical coupling reactions and typically occurs after the polysaccharides have been laid down in the cell wall. Lignin composition varies among cell types and can even be different in individual cell wall layers (Ruel et al., 2009). Lignin composition is also influenced by environmental conditions; for example, lignin in compression wood is enriched in H-units (Timell, 1986). Hence, both developmental and environmental parameters influence the composition and thus the structure of the lignin polymer (Boerjan et al., 2003; Ralph et al., 2004).Lignin is one of the main negative factors in the conversion of lignocellulosic plant biomass into pulp and bioethanol (Lynd et al., 1991; Hill et al., 2006). In these processes, lignin needs to be degraded by chemical or mechanical processes that are expensive and often environmentally polluting. Hence, major research efforts are devoted toward understanding lignin biosynthesis and structure. It has already been shown that reducing lignin content and modifying its composition in transgenic plants can result in dramatic improvements in pulping efficiency (Pilate et al., 2002; Baucher et al., 2003; Huntley et al., 2003; Leplé et al., 2007) and in the conversion of biomass into bioethanol (Stewart et al., 2006; Chen and Dixon, 2007; Custers, 2009). These altered biomass properties are related to the alterations in lignin composition and structure in terms of the frequencies of the lignin units and the bond types connecting them and possibly also their interaction with hemicelluloses (Ralph et al., 2004; Ralph, 2006).To study the parameters that influence lignin structure, lignin polymerization has been mimicked in vitro by experiments with dehydrogenation polymers (DHPs; Terashima et al., 1995). Indeed, lignification can be mimicked by oxidizing monolignols using a peroxidase, such as horseradish peroxidase (HRP), and supplying its cofactor hydrogen peroxide, producing synthetic DHP lignins. Monolignol oxidation can also be achieved without enzymes (e.g. by using transition metal one-electron oxidants, such as copper acetate). Some of these biomimetic DHPs have been suggested to be better models for wood lignins than HRP-generated DHPs (Landucci, 2000).In DHP experiments, the monolignols are either added in bulk (Zulauf experiment) or dropwise (Zutropf experiment) to the reaction mixture, yielding lignin polymers with very different bond frequencies (Freudenberg, 1956). Zutropf experiments approach the in vivo formation of lignin, which depends on the slow introduction of monolignols into the wall matrix via diffusion to the site of incorporation (Hatfield and Vermerris, 2001). Because the exact reaction conditions are known, such in vitro experiments have provided insight into the lignification process in planta. In this way, numerous factors were shown to influence lignin structure, including the relative supply of the monolignols, the pH, the presence of polysaccharides, hydrogen peroxide concentrations, and cell wall matrix elements in general (Grabber et al., 2003; Vanholme et al., 2008).Computer simulations of lignin polymerization can help explain and predict lignin structure from low-level chemical kinetic factors, including subunit-coupling probabilities and monolignol synthesis rates. Such models are helpful in explaining the mechanism behind a range of controlling factors identified in the experimental work, including (1) the ratio of coniferyl versus sinapyl alcohol monolignols, (2) the monolignol supply rate, and (3) the abundance of alternative monomers present during lignin biosynthesis in mutants and transgenics. Thus, computer models will also help in suggesting new targets for controlled lignin biosynthesis.Here, we propose a simulation model of synthetic lignin polymerization that is based upon an emerging consensus from a variety of observations and derives from a series of previous models of lignin polymerization (Glasser and Glasser, 1974; Glasser et al., 1976; Jurasek, 1995; Roussel and Lim, 1995). Our model uses a symbolic grammar to describe a constructive dynamical system (Fontana, 1992) or a rule-based system (Feret et al., 2009) in which it is not necessary to define all possible products in advance. We assume that G- and S-monomers and newly formed oligomers couple in a well-mixed medium, depending on coupling rules and experimentally measured coupling probabilities. To develop the model, we have used information from DHP experiments rather than natural lignins, as they are formed in a well-mixed medium and their reaction conditions are well known (e.g. the influx rate of monomers). Using information from natural lignin would have further complicated our model, as the structures of natural lignin polymers are influenced by many factors, including the possible involvement of dirigent proteins (Davin and Lewis, 2005), steric hindrance by polysaccharides, spatiotemporal regulation, and modifications during isolation procedures (Boerjan et al., 2003; Ralph et al., 2004).Using our simulation models, we study how putative controlling factors of lignin primary structure, including the influx rate of monomers and the relative amount of S-monomers, affect in silico lignin synthesis, and we compare our predictions with in vitro experiments. To predict the degradability of lignins formed in our simulations, we apply an in silico thioacidolysis, which cleaves the polymers at their β-O-4 positions. This simulates the molecular action of two of the most used methods to analyze lignin composition, thioacidolysis (Lapierre, 1993; Baucher et al., 2003) and derivatization followed by reductive cleavage (Lu and Ralph, 1997). The G+S-monomer yield is often taken as a reflection of the fraction of units bound by β-O-4 bonds. Cleavage of β-O-4 bonds is also the most important reaction in kraft pulping of wood (Baucher et al., 2003). The model predicts from first principles (1) that DHP lignins formed under Zutropf conditions have a higher β-O-4 content than those formed under Zulauf conditions, (2) that DHP lignins formed with high S content have a higher β-O-4 content than those formed with high G content, and (3) that a higher β-O-4 content does not necessarily reduce the average length of lignin fragments generated during in silico thioacidolysis.     

The effect of immunomodulators on the immunogenicity of TNF-blocking therapeutic monoclonal antibodies: a review

Therapeutic monoclonal antibodies have revolutionized the treatment of various inflammatory diseases. Immunogenicity against these antibodies has been shown to be clinically important: it is associated with shorter response duration because of diminishing concentrations in the blood and with infusion reactions. Concomitant immunomodulators in the form of methotrexate or azathioprine reduced the immunogenicity of therapeutic antibodies in rheumatoid arthritis, Crohn disease, and juvenile idiopathic arthritis. The occurrence of adverse events does not increase when immunomodulators are added to therapeutic antibodies. The mechanism whereby methotrexate and azathioprine influence immunogenicity remains unclear. Evidence-based consensus on prescribing concomitant immunomodulators is needed.     

Pseudotyped recombinant adeno-associated viral vectors mediate efficient gene transfer into primary human CD34+ peripheral blood progenitor cells

Background and aimsBecause of their pluripotency, human CD34⁺ peripheral blood progenitor cells (PBPC) are targets of interest for the treatment of many acquired and inherited disorders using gene therapeutic approaches. Unfortunately, most current vector systems lack either sufficient transduction efficiency or an appropriate safety profile. Standard single-stranded recombinant adeno-associated virus 2 (AAV2)-based vectors offer an advantageous safety profile, yet lack the required efficiency in human PBPC.MethodsA panel of pseudotyped AAV vectors (designated AAV2/x, containing the vector genome of serotype 2 and capsid of serotype x, AAV2/1–AAV2/6) was screened on primary human granulocyte–colony-stimulating factor (G-CSF)-mobilized CD34⁺ PBPC to determine their gene transfer efficacy. Additionally, double-stranded self-complementary AAV (dsAAV) were used to determine possible second-strand synthesis limitations.ResultsAAV2/6 vectors proved to be the most efficient [12.8% (1.8–25.4%) transgene-expressing PBPC after a single transduction], being significantly more efficient (all P < 0.005) than the other vectors [AAV2/2, 2.0% (0.2–7.3%); AAV2/1, 1.3% (0.1–2.9%); others, <; 1% transgene-expressing PBPC]. In addition, the relevance of the single-to-double-strand conversion block in transduction of human PBPC could be shown using pseudotyped dsAAV vectors: for dsAAV2/2 [9.3% (8.3–20.3%); P < 0.001] and dsAAV2/6 [37.7% (23.6–61.0%); P < 0.001) significantly more PBPC expressed the transgene compared with their single-stranded counterparts; for dsAAV2/1, no significant increase could be observed.ConclusionsWe have shown that clinically relevant transduction efficiency levels using AAV-based vectors in human CD34⁺ PBPC are feasible, thereby offering an efficient alternative vector system for gene transfer into this important target cell population.     

Kinetic model of sucrose accumulation in maturing sugarcane culm tissue

Biochemically, it is not completely understood why or how commercial varieties of sugarcane (Saccharum officinarum) are able to accumulate sucrose in high concentrations. Such concentrations are obtained despite the presence of sucrose synthesis/breakdown cycles (futile cycling) in the culm of the storage parenchyma. Given the complexity of the process, kinetic modelling may help to elucidate the factors governing sucrose accumulation or direct the design of experimental optimisation strategies. This paper describes the extension of an existing model of sucrose accumulation (Rohwer, J.M., Botha, F.C., 2001. Analysis of sucrose accumulation in the sugar cane culm on the basis of in vitro kinetic data. Biochem. J. 358, 437-445) to account for isoforms of sucrose synthase and fructokinase, carbon partitioning towards fibre formation, and the glycolytic enzymes phosphofructokinase (PFK), pyrophosphate-dependent PFK and aldolase. Moreover, by including data on the maximal activity of the enzymes as measured in different internodes, a growth model was constructed that describes the metabolic behaviour as sugarcane parenchymal tissue matures from internodes 3-10. While there was some discrepancy between modelled and experimentally determined steady-state sucrose concentrations in the cytoplasm, steady-state fluxes showed a better fit. The model supports a hypothesis of vacuolar sucrose accumulation against a concentration gradient. A detailed metabolic control analysis of sucrose synthase showed that each isoform has a unique control profile. Fructose uptake by the cell and sucrose uptake by the vacuole had a negative control on the futile cycling of sucrose and a positive control on sucrose accumulation, while the control profile for neutral invertase was reversed. When the activities of these three enzymes were changed from their reference values, the effects on futile cycling and sucrose accumulation were amplified. The model can be run online at the JWS Online database (http://jjj.biochem.sun.ac.za/database/uys).     

Binding-site identification and genotypic profiling of hepatitis C virus polymerase inhibitors

The search for hepatitis C virus polymerase inhibitors has resulted in the identification of several nonnucleoside binding pockets. The shape and nature of these binding sites differ across and even within diverse hepatitis C virus genotypes. These differences confront antiviral drug discovery with the challenge of finding compounds that are capable of inhibition in variable binding pockets. To address this, we have established a hepatitis C virus mutant and genotypic recombinant polymerase panel as a means of guiding medicinal chemistry through the elucidation of the site of action of novel inhibitors and profiling against genotypes. Using a genotype 1b backbone, we demonstrate that the recombinant P495L, M423T, M414T, and S282T mutant enzymes can be used to identify the binding site of an acyl pyrrolidine analog. We assess the inhibitory activity of this analog and other nonnucleoside inhibitors with our panel of enzyme isolates generated from clinical sera representing genotypes 1a, 1b, 2a, 2b, 3a, 4a, 5a, and 6a.