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High-throughput decoding of antitrypanosomal drug efficacy and resistance.

Alsford S, Eckert S, Baker N, Glover L, Sanchez-Flores A, Leung KF, Turner DJ, Field MC, Berriman M, Horn D - Nature (2012)

Bottom Line: A bloodstream stage-specific invariant surface glycoprotein (ISG75) family mediates suramin uptake, and the AP1 adaptin complex, lysosomal proteases and major lysosomal transmembrane protein, as well as spermidine and N-acetylglucosamine biosynthesis, all contribute to suramin action.We also demonstrate a major role for aquaglyceroporins in pentamidine and melarsoprol cross-resistance.These advances in our understanding of mechanisms of antitrypanosomal drug efficacy and resistance will aid the rational design of new therapies and help to combat drug resistance, and provide unprecedented molecular insight into the mode of action of antitrypanosomal drugs.

View Article: PubMed Central - PubMed

Affiliation: London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.

ABSTRACT
The concept of disease-specific chemotherapy was developed a century ago. Dyes and arsenical compounds that displayed selectivity against trypanosomes were central to this work, and the drugs that emerged remain in use for treating human African trypanosomiasis (HAT). The importance of understanding the mechanisms underlying selective drug action and resistance for the development of improved HAT therapies has been recognized, but these mechanisms have remained largely unknown. Here we use all five current HAT drugs for genome-scale RNA interference target sequencing (RIT-seq) screens in Trypanosoma brucei, revealing the transporters, organelles, enzymes and metabolic pathways that function to facilitate antitrypanosomal drug action. RIT-seq profiling identifies both known drug importers and the only known pro-drug activator, and links more than fifty additional genes to drug action. A bloodstream stage-specific invariant surface glycoprotein (ISG75) family mediates suramin uptake, and the AP1 adaptin complex, lysosomal proteases and major lysosomal transmembrane protein, as well as spermidine and N-acetylglucosamine biosynthesis, all contribute to suramin action. Further screens link ubiquinone availability to nitro-drug action, plasma membrane P-type H(+)-ATPases to pentamidine action, and trypanothione and several putative kinases to melarsoprol action. We also demonstrate a major role for aquaglyceroporins in pentamidine and melarsoprol cross-resistance. These advances in our understanding of mechanisms of antitrypanosomal drug efficacy and resistance will aid the rational design of new therapies and help to combat drug resistance, and provide unprecedented molecular insight into the mode of action of antitrypanosomal drugs.

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Identification of drug efficacy determinants in T. bruceia, The schematic illustrates the RNAi library screening approach. Expected outcomes are illustrated for RNAi targets that fail to affect drug resistance (black), increase resistance to drug A (blue), drug B (orange) or both (green). b, Each screen yielded a population displaying Tet-inducible (RNAi-dependent) drug-resistance; see Supplementary Fig. 1. The plot indicates the proportion of the resistance phenotype that is Tet-inducible. c, Genome-wide RIT-seq profiles. Each map represents a non-redundant set of 7,435 protein-coding sequences. Red bars represent ‘primary’ read-density signatures. Black bars represent all other signatures of >50 reads (see Supplementary data File 1). All three expected ‘hits’, AAT6, AT1 and NTR, are indicated. d, Selected signatures. Each peak represents a unique RIT-seq tag. ‘+’, numbers of additional genes identified in each category. See Supplementary Figure 2 for details and additional signatures.
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Figure 1: Identification of drug efficacy determinants in T. bruceia, The schematic illustrates the RNAi library screening approach. Expected outcomes are illustrated for RNAi targets that fail to affect drug resistance (black), increase resistance to drug A (blue), drug B (orange) or both (green). b, Each screen yielded a population displaying Tet-inducible (RNAi-dependent) drug-resistance; see Supplementary Fig. 1. The plot indicates the proportion of the resistance phenotype that is Tet-inducible. c, Genome-wide RIT-seq profiles. Each map represents a non-redundant set of 7,435 protein-coding sequences. Red bars represent ‘primary’ read-density signatures. Black bars represent all other signatures of >50 reads (see Supplementary data File 1). All three expected ‘hits’, AAT6, AT1 and NTR, are indicated. d, Selected signatures. Each peak represents a unique RIT-seq tag. ‘+’, numbers of additional genes identified in each category. See Supplementary Figure 2 for details and additional signatures.

Mentions: We used genome-scale tetracycline-inducible RNA interference (RNAi) library screens in Trypanosoma brucei to identify the genes that contribute to drug action. In these screens, knockdowns only persist in an otherwise toxic environment if they confer a selective advantage while others are diminished or eliminated (Fig. 1a); note that knockdown is not expected to identify drug targets. The RNAi library consists of ~750,000 clones, each transformed with one RNAi construct, and representing >99% of the approximately 7,500 non-redundant T. brucei gene set. Because each gene is identified by an average of approximately five different RNAi sequences, true leads can be identified with high confidence and potential off-target false leads can be minimised (see Supplementary Methods). Screens were performed using all current HAT drugs and each yielded a population of cells displaying an inducible drug resistance phenotype after eight or fourteen days of selection (Fig. 1b and Supplementary Fig. 1). Genomic DNA from these cells was subjected to RNAi target sequencing (RIT-seq) 10 to create profiles of RNAi targets associated with increased resistance, and to identify the genes that contribute to drug susceptibility. Genome-wide association maps show read-density for 7,435 T. brucei genes (Fig. 1c). We defined genes with ‘primary signatures’ as those associated with two or more independent RIT-seq tags, each with a read-density of >99; the screens yielded 55 of these signatures (red bars in Fig. 1c; see Supplementary Methods and Supplementary data File 1). Previous work linked the P2 adenosine transporter (AT1) to melarsoprol uptake 4,11-13, an amino acid transporter (AAT6) to eflornithine uptake 5,13,14 and a nitroreductase (NTR) to nifurtimox activation 6,14. Each of these genes is identified on the appropriate genome-wide association map (Fig. 1c), providing validation for our screens and indicating excellent genome-scale coverage in the RNAi library. Selected read-density signatures that establish new genetic links to drug susceptibility are shown in Figure 1d.


High-throughput decoding of antitrypanosomal drug efficacy and resistance.

Alsford S, Eckert S, Baker N, Glover L, Sanchez-Flores A, Leung KF, Turner DJ, Field MC, Berriman M, Horn D - Nature (2012)

Identification of drug efficacy determinants in T. bruceia, The schematic illustrates the RNAi library screening approach. Expected outcomes are illustrated for RNAi targets that fail to affect drug resistance (black), increase resistance to drug A (blue), drug B (orange) or both (green). b, Each screen yielded a population displaying Tet-inducible (RNAi-dependent) drug-resistance; see Supplementary Fig. 1. The plot indicates the proportion of the resistance phenotype that is Tet-inducible. c, Genome-wide RIT-seq profiles. Each map represents a non-redundant set of 7,435 protein-coding sequences. Red bars represent ‘primary’ read-density signatures. Black bars represent all other signatures of >50 reads (see Supplementary data File 1). All three expected ‘hits’, AAT6, AT1 and NTR, are indicated. d, Selected signatures. Each peak represents a unique RIT-seq tag. ‘+’, numbers of additional genes identified in each category. See Supplementary Figure 2 for details and additional signatures.
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Figure 1: Identification of drug efficacy determinants in T. bruceia, The schematic illustrates the RNAi library screening approach. Expected outcomes are illustrated for RNAi targets that fail to affect drug resistance (black), increase resistance to drug A (blue), drug B (orange) or both (green). b, Each screen yielded a population displaying Tet-inducible (RNAi-dependent) drug-resistance; see Supplementary Fig. 1. The plot indicates the proportion of the resistance phenotype that is Tet-inducible. c, Genome-wide RIT-seq profiles. Each map represents a non-redundant set of 7,435 protein-coding sequences. Red bars represent ‘primary’ read-density signatures. Black bars represent all other signatures of >50 reads (see Supplementary data File 1). All three expected ‘hits’, AAT6, AT1 and NTR, are indicated. d, Selected signatures. Each peak represents a unique RIT-seq tag. ‘+’, numbers of additional genes identified in each category. See Supplementary Figure 2 for details and additional signatures.
Mentions: We used genome-scale tetracycline-inducible RNA interference (RNAi) library screens in Trypanosoma brucei to identify the genes that contribute to drug action. In these screens, knockdowns only persist in an otherwise toxic environment if they confer a selective advantage while others are diminished or eliminated (Fig. 1a); note that knockdown is not expected to identify drug targets. The RNAi library consists of ~750,000 clones, each transformed with one RNAi construct, and representing >99% of the approximately 7,500 non-redundant T. brucei gene set. Because each gene is identified by an average of approximately five different RNAi sequences, true leads can be identified with high confidence and potential off-target false leads can be minimised (see Supplementary Methods). Screens were performed using all current HAT drugs and each yielded a population of cells displaying an inducible drug resistance phenotype after eight or fourteen days of selection (Fig. 1b and Supplementary Fig. 1). Genomic DNA from these cells was subjected to RNAi target sequencing (RIT-seq) 10 to create profiles of RNAi targets associated with increased resistance, and to identify the genes that contribute to drug susceptibility. Genome-wide association maps show read-density for 7,435 T. brucei genes (Fig. 1c). We defined genes with ‘primary signatures’ as those associated with two or more independent RIT-seq tags, each with a read-density of >99; the screens yielded 55 of these signatures (red bars in Fig. 1c; see Supplementary Methods and Supplementary data File 1). Previous work linked the P2 adenosine transporter (AT1) to melarsoprol uptake 4,11-13, an amino acid transporter (AAT6) to eflornithine uptake 5,13,14 and a nitroreductase (NTR) to nifurtimox activation 6,14. Each of these genes is identified on the appropriate genome-wide association map (Fig. 1c), providing validation for our screens and indicating excellent genome-scale coverage in the RNAi library. Selected read-density signatures that establish new genetic links to drug susceptibility are shown in Figure 1d.

Bottom Line: A bloodstream stage-specific invariant surface glycoprotein (ISG75) family mediates suramin uptake, and the AP1 adaptin complex, lysosomal proteases and major lysosomal transmembrane protein, as well as spermidine and N-acetylglucosamine biosynthesis, all contribute to suramin action.We also demonstrate a major role for aquaglyceroporins in pentamidine and melarsoprol cross-resistance.These advances in our understanding of mechanisms of antitrypanosomal drug efficacy and resistance will aid the rational design of new therapies and help to combat drug resistance, and provide unprecedented molecular insight into the mode of action of antitrypanosomal drugs.

View Article: PubMed Central - PubMed

Affiliation: London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.

ABSTRACT
The concept of disease-specific chemotherapy was developed a century ago. Dyes and arsenical compounds that displayed selectivity against trypanosomes were central to this work, and the drugs that emerged remain in use for treating human African trypanosomiasis (HAT). The importance of understanding the mechanisms underlying selective drug action and resistance for the development of improved HAT therapies has been recognized, but these mechanisms have remained largely unknown. Here we use all five current HAT drugs for genome-scale RNA interference target sequencing (RIT-seq) screens in Trypanosoma brucei, revealing the transporters, organelles, enzymes and metabolic pathways that function to facilitate antitrypanosomal drug action. RIT-seq profiling identifies both known drug importers and the only known pro-drug activator, and links more than fifty additional genes to drug action. A bloodstream stage-specific invariant surface glycoprotein (ISG75) family mediates suramin uptake, and the AP1 adaptin complex, lysosomal proteases and major lysosomal transmembrane protein, as well as spermidine and N-acetylglucosamine biosynthesis, all contribute to suramin action. Further screens link ubiquinone availability to nitro-drug action, plasma membrane P-type H(+)-ATPases to pentamidine action, and trypanothione and several putative kinases to melarsoprol action. We also demonstrate a major role for aquaglyceroporins in pentamidine and melarsoprol cross-resistance. These advances in our understanding of mechanisms of antitrypanosomal drug efficacy and resistance will aid the rational design of new therapies and help to combat drug resistance, and provide unprecedented molecular insight into the mode of action of antitrypanosomal drugs.

Show MeSH
Related in: MedlinePlus