Dynamics Of Resistance Evolution Research Articles

Chloroquine resistance evolution in Plasmodium falciparum is mediated by the putative amino acid transporter AAT1

Malaria parasites break down host haemoglobin into peptides and amino acids in the digestive vacuole for export to the parasite cytoplasm for growth: interrupting this process is central to the mode of action of several antimalarial drugs. Mutations in the chloroquine (CQ) resistance transporter, pfcrt, located in the digestive vacuole membrane, confer CQ resistance in Plasmodium falciparum, and typically also affect parasite fitness. However, the role of other parasite loci in the evolution of CQ resistance is unclear. Here we use a combination of population genomics, genetic crosses and gene editing to demonstrate that a second vacuolar transporter plays a key role in both resistance and compensatory evolution. Longitudinal genomic analyses of the Gambian parasites revealed temporal signatures of selection on a putative amino acid transporter (pfaat1) variant S258L, which increased from 0% to 97% in frequency between 1984 and 2014 in parallel with the pfcrt1 K76T variant. Parasite genetic crosses then identified a chromosome 6 quantitative trait locus containing pfaat1 that is selected by CQ treatment. Gene editing demonstrated that pfaat1 S258L potentiates CQ resistance but at a cost of reduced fitness, while pfaat1 F313S, a common southeast Asian polymorphism, reduces CQ resistance while restoring fitness. Our analyses reveal hidden complexity in CQ resistance evolution, suggesting that pfaat1 may underlie regional differences in the dynamics of resistance evolution, and modulate parasite resistance or fitness by manipulating the balance between both amino acid and drug transport.

Abstract PO-106: Genomic heterogeneity and clonal dynamics of resistance evolution and metastatic progression in rectal cancer

Abstract The standard treatment for locally advanced rectal cancer is chemoradiotherapy followed by surgery. However, patient response to pre-operative chemoradiotherapy is highly heterogeneous, ranging from complete tumor regression to no response. Over the course of disease, up to 40% of patients suffer from local relapse or the development of metachronous metastatic disease, compromising survival. It is not yet known how treatment resistance and disease progression in these patients evolve – whether from the selective outgrowth of pre-existing resistant clones during treatment or through the acquisition of additional, resistance conferring genomic alterations. We aim to delineate the genomic evolution and clonal architecture underlying treatment resistance and metastatic progression in rectal cancer based on a cohort of 42 patients, from which 98 tumor samples were collected longitudinally over the course of treatment and metastatic progression. Tumor diagnosis and treatment response assessment are routinely based on the histopathologic evaluation of formalin-fixed paraffin-embedded (FFPE) patient tissues. While formalin fixation and paraffin embedding optimally preserve histomorphology and allow long-term tissue storage at ambient temperature, they, on the other hand, lead to DNA crosslinks posing a challenge for sequencing analyses. To overcome this limitation, we have successfully developed a protocol for dissociation of FFPE tissues into single cells that are suitable for immunolabeling, flow sorting and genomic analysis by whole exome sequencing of pure tumor cell populations, single cell copy number variation (CNV) sequencing and multiplex interphase fluorescence in situ hybridization (miFISH). Our genomic analyses at the population as well as individual cell level revealed divergent aberration patterns and distinct levels of intra-tumor heterogeneity. Major tumor clones were characterized by gains of EGFR (7p11.2), MYC (8q24.21), CDX2 (13q12.2) and ZNF217 (20q13.2) along with losses of SMAD4 (18q21.2) and TP53 (17p13.1). While in some patients the clonal composition remained largely stable throughout treatment, in others major clonal shifts occurred favoring clones with TP53 loss. Our preliminary results indicate different propensities of genetically distinct tumor cell clones in therapy response and metastatic progression. Furthermore, our data show the feasibility of high-resolution clonal reconstruction from whole exome sequencing and single cell CNV sequencing data of FFPE cells. Citation Format: Daniela Hirsch, Judith Lieberich, Kerstin Heselmeyer-Haddad, Yonca Ceribas, Michael Kelly, Keyur Talsania, Yongmei Zhao, Timo Gaiser, Thomas Ried. Genomic heterogeneity and clonal dynamics of resistance evolution and metastatic progression in rectal cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-106.

Suppression of antibiotic resistance evolution by single-gene deletion

Antibiotic treatment generally results in the selection of resistant bacterial strains, and the dynamics of resistance evolution is dependent on complex interactions between cellular components. To better characterize the mechanisms of antibiotic resistance and evaluate its dependence on gene regulatory networks, we performed systematic laboratory evolution of Escherichia coli strains with single-gene deletions of 173 transcription factors under three different antibiotics. This resulted in the identification of several genes whose deletion significantly suppressed resistance evolution, including arcA and gutM. Analysis of double-gene deletion strains suggested that the suppression of resistance evolution caused by arcA and gutM deletion was not caused by epistatic interactions with mutations known to confer drug resistance. These results provide a methodological basis for combinatorial drug treatments that may help to suppress the emergence of resistant pathogens by inhibiting resistance evolution.

The evolution of no-cost resistance at sub-MIC concentrations of streptomycin in Streptomyces coelicolor.

At the high concentrations used in medicine, antibiotics exert strong selection on bacterial populations for the evolution of resistance. However, these lethal concentrations may not be representative of the concentrations bacteria face in soil, a recognition that has led to questions of the role of antibiotics in soil environments as well as the dynamics of resistance evolution during sublethal challenge. Here we examine the evolution of resistance to sub-minimal inhibitory concentrations (sub-MIC) of streptomycin in the filamentous soil bacterium Streptomyces coelicolor. First, we show that spontaneous resistance to streptomycin causes an average fitness deficit of ~21% in the absence of drugs; however, these costs are eliminated at concentrations as low as 1/10 the MIC of susceptible strains. Using experimental evolution, we next show that resistance to >MIC levels of streptomycin readily evolves when bacteria are exposed to sub-MIC doses for 500 generations. Furthermore, the resistant clones that evolved at sub-MIC streptomycin concentrations carry no fitness cost. Whole-genome analyses reveal that evolved resistant clones fixed some of the same mutations as those isolated at high drug concentrations; however, all evolved clones carry additional mutations and some fixed mutations that either compensate for costly resistance or have no associated fitness costs. Our results broaden the conditions under which resistance can evolve in nature and suggest that rather than low-concentration antibiotics acting as signals, resistance evolves in response to antibiotics used as weapons.

Abstract 5229: An integrated in vitro/in silico platform to study the evolutionary dynamics of resistance to cancer therapy .

Abstract Emerging clinical evidence suggests that tumor response to treatment occurs along stochastic, evolutionary trajectories, with the amplification of pre-existing clones of resistant cells being shown to be responsible for treatment failure in a number of cases. In this evolutionary view of cancer, pre-existing tumor heterogeneity forms a pool of candidate mutations for selection to act on. Thus, a clearer picture of the heterogeneity in cancer cell lines will facilitate a mechanistic understanding of the dynamics of resistance evolution. In this report, we describe the development of an in vitro system based on the soft agar clonogenic assay, to characterize the degree of heterogeneity in net growth rate (fitness) and the effects of this variation in fitness on the population-level response to drug treatment. The in vitro system was developed as a 24-well soft agar clonogenic assay, with sequential images of the growing colonies taken every day, over a period of two weeks. An automated system for plate preparation, imaging, and plate transfer from incubator to imager was developed to increase assay throughput. A modified greedy algorithm was developed in MATLAB to reliably track colonies as they grow and shift in the image frame from day to day. The individual growth rates of colonies in the pre-treatment colonies followed an exponential model, indicative of a clonal origin for the individual colonies, which was confirmed by microscopy. Next, we developed an in silico platform (based on a Monte Carlo stochastic simulation) to study this heterogeneity in growth rates, and derive insights that are relevant to the planning of therapeutic interventions. This simulation was also used to optimize the design of the experimental system and can provide guidance on the optimal seeding density to avoid merging colonies. We are now using this simulation platform to explore the impact of heterogeneity on population fitness and on drug resistance. This platform can also be extended to examine the individual colonies derived from drug treatment, and provides a rapid and simple means of isolation of resistant subclones for further study. Taken together, the combined in vitro/in silico platform presented here forms the basis of a Systems Biology approach for assessing the role of cancer cell heterogeneity in the evolution of resistance in response to treatment Citation Format: Keisuke Kuida, Carl Boand, John Bradley, Jean Courtemanche, John Donovan, Doanh Mai, Jerome Mettetal, Santhosh Palani, Derek Blair, Jeffrey Ecsedy, Andy Dorner, Wen Chyi Shyu, Joe Bolen, Doug Bowman, Arijit Chakravarty. An integrated in vitro/in silico platform to study the evolutionary dynamics of resistance to cancer therapy . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5229. doi:10.1158/1538-7445.AM2013-5229