Quantifying the determinants of evolutionary dynamics leading to drug resistance

The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution

High-order epistasis in catalytic power of dihydrofolate reductase gives rise to a rugged fitness landscape in the presence of trimethoprim selection

Evolutionary fitness landscapes of several antibiotic target proteins have been comprehensively mapped showing strong high-order epistasis between mutations, but understanding these effects at the biochemical and structural levels remained open. Here, we carried out an extensive experimental and computational study to quantitatively understand the evolutionary dynamics of Escherichia coli dihydrofolate reductase (DHFR) enzyme in the presence of trimethopriminduced selection. To facilitate this, we developed a new in vitro assay for rapidly characterizing DHFR steady-state kinetics. Biochemical and structural characterization of resistance-conferring mutations targeting a total of ten residues spanning the substrate binding pocket of DHFR revealed distinct changes in the catalytic efficiencies of mutated DHFR enzymes. Next, we measured biochemical parameters (Km, Ki, and kcat) for a mutant library carrying all possible combinations of six resistance-conferring DHFR mutations and quantified epistatic interactions between them. We found that the high-order epistasis in catalytic power of DHFR (kcat and Km) creates a rugged fitness landscape under trimethoprim selection. Taken together, our data provide a concrete illustration of how epistatic coupling at the level of biochemical parameters can give rise to complex fitness landscapes, and suggest new strategies for developing mutant specific inhibitors

AXL Knock-Out in SNU475 Hepatocellular Carcinoma Cells Provides Evidence for Lethal Effect Associated with G2 Arrest and Polyploidization

AXL, a member of the TAM family, is a promising therapeutic target due to its elevated expression in advanced hepatocellular carcinoma (HCC), particularly in association with acquired drug resistance. Previously, RNA interference was used to study its role in cancer, and several phenotypic changes, including attenuated cell proliferation and decreased migration and invasion, have been reported. The mechanism of action of AXL in HCC is elusive. We first studied the AXL expression in HCC cell lines by real-time PCR and western blot and showed its stringent association with a mesenchymal phenotype. We then explored the role of AXL in mesenchymal SNU475 cells by CRISPR-Cas9 mediated gene knock-out. AXL-depleted HCC cells displayed drastic phenotypic changes, including increased DNA damage response, prolongation of doubling time, G2 arrest, and polyploidization in vitro and loss of tumorigenicity in vivo. Pharmacological inhibition of AXL by R428 recapitulated G2 arrest and polyploidy phenotype. These observations strongly suggest that acute loss of AXL in some mesenchymal HCC cells is lethal and points out that its inhibition may represent a druggable vulnerability in AXL-high HCC patients

3D Organoid modelling of hepatoblast-like and mesenchymal-like hepatocellular carcinoma cell lines

Aim: We wished to establish 3D organoid-like hepatocellular carcinoma (HCC) models from HCC cell lines.Methods: Hep3B, Huh7, HepG2, SNU398, SNU449, and SNU475 cell lines were inoculated into Matrigel and grown up to 9 days in hepatocyte specific or standard RPMI media. Spheroid formation was followed by light microscopy. Matrigel scaffolds were immobilized and embedded in paraffin, and sections were subjected to H&E and immunohistochemical staining for different hepatobiliary biomarkers. Stained material was examined under light microscopy and micro photo were taken.Results: Organoid-like structures were obtained successfully from all selected cell lines except mesenchymal-like SNU475 cells. Hep3B, Huh7, and HepG2 cell lines from hepatoblast-like sub-group formed compact 3D colonies and showed hepatocyte-like morphology and staining with different hepatocyte lineage markers as well as hepatobiliary progenitor markers. SNU398 and SNU449 cell lines from mesenchymal-like group formed irregular and loose 3D colonies that expressed vimentin homogeneously, but also several epithelial and hepatocyte lineage markers. The pattern of biomarker expression was unique for each cell line tested. Such features, not observed in tested monolayer cultures were confirmed with single-cell derived Hep3B cells.Conclusion: We described experimental conditions to obtain organoid-like structures from five different HCC cell lines representing hepatoblast-like and mesenchymal-like subgroups. These models are useful as an alternative to monolayer cultures to study phenotypic features of HCC cells. Our detailed analysis of biomarker expression in five different organoid-like structures provide convincing evidence for highly specific phenotypic features of these cell lines although they share some common or subtype-restricted features also

AXL Knock-Out in SNU475 Hepatocellular Carcinoma Cells Provides Evidence for Lethal Effect Associated with G2 Arrest and Polyploidization

AXL, a member of the TAM family, is a promising therapeutic target due to its elevated expression in advanced hepatocellular carcinoma (HCC), particularly in association with acquired drug resistance. Previously, RNA interference was used to study its role in cancer, and several phenotypic changes, including attenuated cell proliferation and decreased migration and invasion, have been reported. The mechanism of action of AXL in HCC is elusive. We first studied the AXL expression in HCC cell lines by real-time PCR and western blot and showed its stringent association with a mesenchymal phenotype. We then explored the role of AXL in mesenchymal SNU475 cells by CRISPR-Cas9 mediated gene knock-out. AXL-depleted HCC cells displayed drastic phenotypic changes, including increased DNA damage response, prolongation of doubling time, G2 arrest, and polyploidization in vitro and loss of tumorigenicity in vivo. Pharmacological inhibition of AXL by R428 recapitulated G2 arrest and polyploidy phenotype. These observations strongly suggest that acute loss of AXL in some mesenchymal HCC cells is lethal and points out that its inhibition may represent a druggable vulnerability in AXL-high HCC patients

Considerations for the selection of tests for SARS-CoV-2 molecular diagnostics

During the course of 2020, the outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) spread rapidly across the world. Clinical diagnostic testing for SARS-Cov-2 infection has relied on the real-time Reverse Transcriptase Polymerase Chain Reaction and is considered the gold standard assay. Commercial vendors and laboratories quickly mobilised to develop diagnostic tests to detect the novel coronavirus, which was fundamentally important in the pandemic response. These SARS-Cov-2 assays were developed in line with the Food Drug Administration-Emergency Use Authorization guidance. Although new tests are continuously being developed, information about SARS-CoV-2 diagnostic molecular test accuracy has been limited and at times controversial. Therefore, the analytical and clinical performance of SARS-CoV-2 test kits should be carefully considered by the appropriate regulatory authorities and evaluated by independent laboratory validation. This would provide improved end-user confidence in selecting the most reliable and accurate diagnostic test. Moreover, it is unclear whether some of these rapidly developed tests have been subjected to rigorous quality control and assurance required under good manufacturing practice. Variable target gene regions selected for currently available tests, potential mutation in target gene regions, non-standardized pre-analytic phase, a lack of manufacturer independent validation data all create difficulties in selecting tests appropriate for different countries and laboratories. Here we provide information on test criteria which are important in the assessment and selection of SARS-CoV-2 molecular diagnostic tests and outline the potential issues associated with a proportion of the tests on the market

Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance

<div><p>The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide <i>Escherichia coli</i> gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution.</p></div