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W2984287174 abstract "Full text Figures and data Side by side Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Candida albicans is both a member of the healthy human microbiome and a major pathogen in immunocompromised individuals. Infections are typically treated with azole inhibitors of ergosterol biosynthesis often leading to drug resistance. Studies in clinical isolates have implicated multiple mechanisms in resistance, but have focused on large-scale aberrations or candidate genes, and do not comprehensively chart the genetic basis of adaptation. Here, we leveraged next-generation sequencing to analyze 43 isolates from 11 oral candidiasis patients. We detected newly selected mutations, including single-nucleotide polymorphisms (SNPs), copy-number variations and loss-of-heterozygosity (LOH) events. LOH events were commonly associated with acquired resistance, and SNPs in 240 genes may be related to host adaptation. Conversely, most aneuploidies were transient and did not correlate with drug resistance. Our analysis also shows that isolates also varied in adherence, filamentation, and virulence. Our work reveals new molecular mechanisms underlying the evolution of drug resistance and host adaptation. https://doi.org/10.7554/eLife.00662.001 eLife digest Nearly all humans are infected with the fungus Candida albicans. In most people, the infection does not produce any symptoms because their immune system is able to counteract the fungus' attempts to spread around the body. However, if the balance between fungal attack and body defence fails, the fungus is able to spread, which can lead to serious disease that is fatal in 42% of cases. How does C. albicans outcompete the body's defences to cause disease? This is a pertinent question because the most effective antifungal medicines—including the drug fluconazole—do not kill the fungus; they only stop it from growing. This gives the fungus time to develop resistance to the drug by becoming able to quickly replace the fungal proteins the drug destroys, or to efficiently remove the drug from its cells. In this study, Ford et al. studied the changes that occur in the DNA of C. albicans over time in patients who are being treated with fluconazole. Ford et al. took 43 samples of C. albicans from 11 patients with weakened immune systems. The experiments show that the fungus samples collected early on were more sensitive to the drug than the samples collected later. In most cases, the genetic data suggest that the infections begin with a single fungal cell; the cells in the later samples are its offspring. Despite this, there is a lot of genetic variation between samples from the same patient, which indicates that the fungus is under pressure to become more resistant to the drug. There were 240 genes—including those that can alter the surface on the fungus cells to make it better at evading the host immune system—in which small changes occurred over time in three or more patients. Laboratory tests revealed that many of these genes are likely important for the fungus to survive in an animal host in the presence of the drug. C. albicans cells usually have two genetically distinct copies of every gene. Ford et al. found that for some genes—including some that make surface components or are involved in expelling drugs from cells—the loss of genetic information from one copy, so that both copies become identical, is linked to resistance to fluconazole. However, the gain of whole or partial chromosomes—which contain large numbers of genes—is not linked to resistance, but may provide additional genetic material for generating diversity in the yeast population that may help the cells to evolve resistance in the future. These experiments have identified many new candidate genes that are important for drug resistance and evading the host immune system, and which could be used to guide the development of new therapeutics to treat these life-threatening infections. https://doi.org/10.7554/eLife.00662.002 Introduction Virtually all humans are colonized with Candida albicans, but in some individuals this benign commensal organism becomes a serious, life-threatening pathogen. C. albicans possesses an arsenal of traits that promote its pathogenicity, including phenotypic switching (Alby and Bennett, 2009), yeast–hyphae transition (Kumamoto and Vinces, 2005) and the secretion of molecules that promote adhesion to abiotic surfaces (Chandra et al., 2001). As a commensal, an intricate balance is maintained between the ability of C. albicans to invade host tissues and the host's defense mechanisms (Kim and Sudbery, 2011; Kumamoto and Pierce, 2011). Alteration of this delicate host–fungus balance can result in high levels of patient mortality (Pittet et al., 1994; Charles et al., 2003): systemic C. albicans infections are fatal in 42% of cases (Wisplinghoff et al., 2003), despite the use of antifungal therapies, and C. albicans is the fourth most common infection in hospitals (Gudlaugsson et al., 2003; Pappas et al., 2003). While compromised immune function contributes to pathogenesis (Gow and Hube, 2012), it is less clear how C. albicans evolves to better exploit the host environment during the course of infection. Two classes of antifungals in clinical use target ergosterol, a major component of the fungal cell membrane: polyenes and azoles. Polyenes (e.g., Amphotericin B) are used sparingly due to toxicity (Rex et al., 1994), whereas azoles (e.g., fluconazole) are used widely because they can be administered orally and have few side effects (Rex et al., 2003). However, resistance to the azoles arises within the commensal population of the treated individual, primarily because azoles are fungistatic (inhibit growth but do not kill) (Cowen et al., 2002). Epidemiological data suggest that the intensity of fluconazole use is driving the appearance of resistant isolates (Pfaller et al., 1998). Studies of clinical isolates of C. albicans suggest that drug resistance can increase during an infection through the acquisition of aneuploidies (Selmecki et al., 2009) due to genomic plasticity and rapid evolutionary selection during infection. Previous studies have identified two molecular mechanisms of azole resistance in C. albicans. First, increased activity or level of the enzymes of the ergosterol pathway (e.g., ERG11) reduces direct impact of the drug on its target (Asai et al., 1999; Oliver et al., 2007). Second, increased efflux of the drug from cells by ABC transporters (encoded by CDR1 and CDR2) (Coste et al., 2006) or by the major facilitator superfamily efflux pump (encoded by MDR1) (Dunkel et al., 2008) reduces the effective intracellular drug concentration. In both cases, such alterations can result from point mutations in genes encoding these proteins (Marichal et al., 1999), in transcription factors that regulate mRNA expression levels (MacPherson et al., 2005; Coste et al., 2006; Dunkel et al., 2008), or from increased copy number of the relevant genes, via genome rearrangements such as whole chromosome and segmental aneuploidies (Selmecki et al., 2006; 2008; 2009). Indeed, the genomes of drug-resistant strains isolated following clinical treatment often exhibit large-scale changes, such as loss of heterozygosity (LOH) (Coste et al., 2006; Dunkel and Morschhauser, 2011), copy-number variation (CNV), including short segmental CNV, and whole chromosome aneuploidy (Selmecki et al., 2010) accompanied by point mutations. While we understand some aspects of the molecular basis of resistance, we understand less about the mechanisms that drive the evolution of drug resistance and overall pathogenicity in C. albicans. It is challenging to use forward genetic approaches in C. albicans due to its diploid genome and lack of a complete sexual cycle. Although C. albicans has conserved the genomic elements needed for mating, mating occurs instead through rare mating-competent haploids (Hickman et al., 2013) or via a parasexual cycle consisting of mating of diploid strains to form tetraploids followed by chromosome loss to regenerate diploids (Bennett and Johnson, 2005). An alternative approach is to use isolates sampled consecutively from the same patient to study the changes in the frequency of variants in natural populations under selection for drug resistance. Studies in evolved isolates have implicated multiple mechanisms in drug resistance, but have focused on large-scale aberrations such as aneuploidies and LOH (Selmecki et al., 2008; 2009) or candidate genes (Perea et al., 2001; White et al., 2002), and do not comprehensively chart the genetic basis of adaptation. Here, we used genome sequencing of isolates sampled consecutively from patients that were clinically treated with fluconazole to systematically analyze the genetic dynamics that accompany the appearance of drug resistance during oral candidiasis in human HIV patients. Most isolates from each individual patient were highly related, suggesting a clonal population structure and facilitating the identification of variation. Because each clinical sample was purified from a single colony, we cannot assess the population structure at any single time point. Instead, we have measured the occurrence of single-nucleotide polymorphisms (SNPs), CNV, and LOH events in each isolate and then compared them between isolates from the same patient and across patients' series. Consistent with previous studies, we found that LOH events were recurrent across patients' series and were associated with increased drug resistance. To identify SNPs with likely functional impact in the context of substantial genetic diversity, we focused on those events that were both persistent across isolates within a patient and were recurrent in the same gene across multiple patient series. We found 240 genes that recurrently contain persistent SNPs, many of which may be related not only to antifungal exposure but also to the complex process of adaptation to the host and antifungal exposure. In contrast, aneuploidies were prevalent in the isolates, yet they were more likely to be transient, and aneuploidy, per se, did not correlate with changes in drug resistance. Our work uses comparative analysis of a fungal pathogen to reveal new molecular mechanisms underlying drug resistance and host adaptation and provides a general model for such studies in other eukaryotic pathogens. Results Whole genome sequencing of 43 serial clinical isolates from 11 patients To study the in vivo evolution of azole resistance in C. albicans, we analyzed 43 longitudinal isolates from 11 HIV-infected patients with oropharyngeal candidiasis (White, 1997a; Perea et al., 2001) (Table 1). The isolates were previously collected during incidences of infection and form a time series from each patient (2–16 isolates per series; Figure 1, Figure 2A). Each isolate was derived from a single colony, and thus, represents a single diploid genotype sampled from the within-host C. albicans population at the respective time point. In each series, the first isolate (‘progenitor’) was collected prior to any treatment with azole antifungals and the remaining isolates were collected at later, typically consecutive, time points, culminating in the final ‘endpoint’ isolate (Table 1). Table 1 Isolate history and sequencing summary https://doi.org/10.7554/eLife.00662.003 Publication namePTStrainEntry dateDrug treatmentDose (mg/day)E-test MIC (ug/mL)Depth of coverageReadsPercent alignedWhite, T.C.119/10/90Fluconazole1000.25111.969,896,46887.17%212/14/90Fluconazole100169.2012,797,32887.43%312/21/90Fluconazole100492.0416,987,81486.87%412/31/90Fluconazole100380.6914,858,71087.81%52/8/91Fluconazole1004110.8020,484,58486.75%62/22/91Fluconazole1004101.9418,837,95486.63%73/25/91Fluconazole100481.6515,123,02086.66%84/8/91Fluconazole1004112.5320,778,56286.64%96/4/91Fluconazole1004113.1822,223,22883.20%117/15/91Fluconazole100453.289,896,46887.17%1211/26/91Fluconazole200496.1018,282,47285.54%1312/13/91Fluconazole40032123.6722,070,51889.13%141/28/92Fluconazole4002498.6618,114,91687.41%152/21/92Clotriminazole5024120.9022,401,37486.57%164/1/92Fluconazole4009687.4416,061,56087.17%178/25/92Fluconazole8009697.8318,317,11885.91%Perea, S. et al.74122/15/95Fluconazole00.2593.1517,417,58886.69%230711/22/95Fluconazole4000.7595.7918,014,24285.25%Perea, S. et al.910024/20/95Fluconazole1000.125188.4934,834,97086.74%28234/6/96Fluconazole800282.6252,839,28886.30%37952/26/97Fluconazole80012877.6313,901,06288.78%Perea, S. et al.145803/13/95Fluconazole01.577.0814,711,80485.00%24401/3/96Fluconazole8001.582.9315,446,88285.69%2501*1/4/96Fluconazole8009688.5917,480,27481.98%Perea, S. et al.159454/14/95Fluconazole3004108.5920,591,04485.19%16197/11/95Fluconazole5006493.1417,565,08084.69%Perea, S. et al.1631076/5/96Fluconazole800497.0118,361,26684.84%31196/5/96Fluconazole8009687.9216,615,46284.67%31206/5/96Fluconazole80096105.9519,442,01686.79%31847/1/96Fluconazole800101.8918,487,46287.50%32817/16/96Fluconazole80076.4414,327,37685.69%Perea, S. et al.3051061/7/98Fluconazole8000.587.2116,466,52484.67%51081/7/98Fluconazole8000.7582.3217,480,27481.98%Perea, S. et al.4216918/3/95Fluconazole100122.6022,072,56288.38%373112/27/96Fluconazole400256119.9021,436,03488.72%373312/27/96Fluconazole40025695.5117,295,88888.00%Perea, S. et al.4316497/19/95Fluconazole00.125102.1019,545,53084.08%30345/15/96Fluconazole4000.7592.9717,300,04085.64%Perea, S. et al.5939172/19/97Fluconazole8002113.2721,549,70483.86%46178/28/97Fluconazole4006475.3715,242,90481.42%46399/2/97Fluconazole400128115.3225,468,19075.69%Perea, S. et al.6440184/2/97Fluconazole200110.1620,118,73686.78%43807/14/97Fluconazole20018.0320,970,9469.26% Strains and coverage. (Red) Not clonally derived from progenitor. * isolated on same day from same patient as previously published strain, 2500. Figure 1 with 1 supplement see all Download asset Open asset Overview of study design. (A) Background, persistent, transient, recurrent, and driver mutations in patient time courses. Shown is a schematic illustration of the genomes of isolates (gray bars) from two patient time courses (Patient A and B, left and right panels, respectively), ordered from the first isolate (progenitor, top) to the last (evolved, bottom). Background mutations (purple) exist in the all isolates; persistent mutations (yellow) are not in the progenitor, but found in all subsequent isolates after their first occurrence; transient mutations (pink) are not in the progenitor and only in some later isolates; recurrently polymorphic genes contain persistent mutations that occur in the same gene in more than one patient (black box). LOH events were also evaluated for persistence (light teal bar). Driver mutations, where a new persistent homozygous allele appears (e.g., G/T > A/A), are annotated in association with persistent LOH events (dark teal) and independent of these events (not shown). Each of these can be associated with a change in phenotype, such as drug resistance (boxes, right). (B) Sampling in the context of de novo mutation and selection bottlenecks. Each strain is a single clone (circle) isolated from an evolving population (represented by a phylogenetic tree). The population evolves and undergoes selective sweeps (dashed lines), with phenotypic changes occurring during the course of infection and treatment (i.e., drug resistance, black: high, white: low; gray scale at bottom). Persistent mutations (yellow lightning bolt) have likely swept through the population, whereas transient mutations (pink lightning bolt) have not. (C) Sampling in the context of selection on existing variation. Selection acts to vary the frequency of different pre-existing genotypes in the population. Persistent mutations (yellow lightning bolt) have risen in the population to a frequency that they are repeatedly sampled (large circles) whereas transient mutations (pink lightning bolt) have not (small circle). https://doi.org/10.7554/eLife.00662.004 Figure 2 with 1 supplement see all Download asset Open asset Most isolates from the same patient are clonal. (A) Two possible models of infection may underlie serial isolates. In the ‘clonal model’ (top) each subsequent sample (circle) is related to the other isolates. In the non-clonal model (bottom) isolates in a series are un-related. (B) The phylogenetic relationship of the isolates (black) from 11 patients (blue) was inferred based on 201,793 informative SNP positions using maximum parsimony in PAUP*. Isolates from the same patient separated by a branch distance greater than 20,000 were considered non-clonal (3281, 2823, 3184, 1691, red). Most nodes were supported by 100% of 1000 bootstrap replicates (indicated by *), expect as indicated (in gray). Clade identifiers were included as appropriate. https://doi.org/10.7554/eLife.00662.006 Figure 2—source data 1 (A) SNP category summary and all patient-series SNPs SNP category summary. Listed for each series (PT series SNP summary) are the number of filtered (‘Materials and methods’) coding and noncoding SNPs. Coding SNPs are further classified as synonymous or nonsynonymous. Noncoding SNPs are classified as intronic, promoter region (<800 bps from the start of an ORF), or general noncoding. Patient1–Patient 59: Listed is each base that is mutated in at least one isolate in the respective series. For this base, listed are the chromosomal position, the base in the SC5314 reference genome, the base in each isolate in the series (hyphen (‘-’): homozygous, same as reference; upper case: homozygous mutation; lower case: heterozygous mutation), whether the mutation is a background mutation, transient (trans) or persistent (pers), if it is upstream, downstream or within an ORF, and in the latter case, the effect on the amino acid sequence of the encoded protein. (B) Frequency of nonsynonymous SNP occurrence between serial isolates using different filters. All SNP arising aft prev: For each clinical series (PT1-PT59) listed are the number of ORFs in each chromosome (columns) containing for each isolate (rows) all the ‘newly arising’ SNPs, defined as those not present in the immediately preceding isolate (rows). All NS in ORF aft prev: the same as above, but only for NS SNPs. All SNPs are only outside of LOH regions. All instances of Pers NS SNPs: the same as above, but only for those NS SNPs that persist once they arose. All Rec SNP aft Prev: the same as above but restricted to those ORFs that contain persistent mutations in three or more clinical series. https://doi.org/10.7554/eLife.00662.008 Download elife-00662-fig2-data1-v1.xlsx The progenitor isolates were more sensitive to fluconazole than subsequent isolates, as defined by the minimum inhibitory concentration (MIC) (Table 1, ‘Materials and methods’). Previous studies with some of these patient isolates identified several genomic alterations that may contribute to azole resistance, including segmental aneuploidy (Selmecki et al., 2006), and LOH across large chromosomal segments (Coste et al., 2006; Dunkel et al., 2008), as well as targeted alterations including increased expression of drug efflux genes (Coste et al., 2006), mutations in ergosterol biosynthetic genes (Asai et al., 1999; Oliver et al., 2007), and buffering by the chaperone heat shock protein 90 (Hsp90) (Cowen and Lindquist, 2005). We sequenced the genomic DNA of the isolates as well as the C. albicans lab strain, SC5314, using Illumina sequencing (53-283X coverage, 103X on average, ‘Materials and methods’, Table 1) and identified in each series point mutations, LOH events and aneuploidies that were not present in the first strain in that series. By convention, all mutations were defined relative to SC5314, the C. albicans genome reference strain. We validated our pipeline for detection of point mutations using Sequenom iPlex genotyping (Storm et al., 2003) (‘Materials and methods’). We interrogated 1973 SNPs in 27 isolates from nine clinical series and found that the iPlex base calls matched 1853 (93.9%, Figure 1—figure supplement 1A, Table 2) of the calls from our computational analysis of the sequencing data. Evaluation of the discordant sites showed somewhat lower quality scores by certain metrics but did not identify any metrics that could be used to systematically revise filtering in our computational pipeline without a radical reduction in sensitivity (Figure 1—figure supplement 1B–G). Table 2 Sequenom iPLEX genotyping assay validation https://doi.org/10.7554/eLife.00662.009 PatientIsolateTotal discordantTotal concordantTotal Assayed% ConcordantPatient_1TWTC12313393.94%Patient_1TWTC21323396.97%Patient_1TWTC31323396.97%Patient_1TWTC121323396.97%Patient_1TWTC131323396.97%Patient_1TWTC151323396.97%Patient_1TWTC161313296.88%Patient_1TWTC171323396.97%Patient_74123606395.24%Patient_723074596393.65%Patient_91002169611285.71%Patient_93795910311291.96%Patient_145803495294.23%Patient_1424402272993.10%Patient_1425013333691.67%Patient_15945812112993.80%Patient_1516191012013092.31%Patient_1631072515396.23%Patient_1631193505394.34%Patient_1631202505296.15%Patient_305106321521898.62%Patient_3051081920422391.48%Patient_4316497899692.71%Patient_4330348889691.67%Patient_5939173626595.38%Patient_5946172636596.92%Patient_5946394596393.65%TOTAL1201853197393.92% Most series are clonal, but there is significant genetic diversity between isolates We designated as background those polymorphisms those that are common to all isolates in a series, including the first (‘progenitor’) isolate (Figure 1A, purple) and use them to determine that isolates within most series were clonally related, suggesting a single (primary) infection source (Figure 1B,C, Figure 2, Figure 2—figure supplement 1, ‘Materials and methods’). To distinguish between a single primary (clonal) infection (Figure 2A, top) and repeated, independent infections (Figure 2A, bottom), we determined the distance between every two isolates based on their SNP profile and used as a heuristic a neighbor-joining algorithm to construct a phylogenetic tree from this distance metric (‘Materials and methods’, Figure 2B). Patient 64 contained one C. albicans isolate (4018) and one C. dubliniensis isolate (4380); therefore, we have excluded this series from further analysis. Additionally, we detected at least one non-clonal C. albicans isolate in three of the remaining ten patient series (PT 9,16, 42; Figure 2B, red), indicating that at least ∼36% of the 11 patients sampled carried more than one unrelated Candida strain. We removed the four non-clonal samples (Figure 2B, red) from further consideration, and all subsequent analyses focused on samples from the 10 patients with at least two clonal isolates. Despite these clonal relationships, the distance between isolates indicated significant genetic diversity within each patient series (Figure 2B), typically with each isolate differing by several thousand SNPs from its ‘progenitor’ isolate (Figure 2—source data 1). These data are consistent with two different evolutionary scenarios: accumulation of de novo mutations followed by selection (Figure 1B), or selection acting on pre-existing variation to vary the frequency of different genotypes in the population (Figure 1C). The large number of SNPs detected suggests that isolates from later time points in a series are not simply direct descendants of the earlier isolate; however, since mutation and mitotic recombination rates can be elevated under stressful conditions (e.g., drug treatment Galhardo et al., 2007; Forche et al., 2011), we cannot rule out the possibility that some of the variation may be due to de novo events occurring between time points. Formally distinguishing between these two models is not possible with the samples and data at hand. However, the role of pre-existing diversity is supported by the observation that different isolates collected on the same day from the same patient (patient 14 [2440 and 2501] and patient 16 [3107 and 3119]) differed by 9668 and 18,291 SNPs, respectively (Figure 2—source data 1) and had very different fluconazole MIC levels (Table 1) and different fitness phenotypes (see below), although in each case the strains were clearly genetically related (Figure 2B). Thus, we conclude that a population of related but divergent genotypes of the same lineage exists within a given patient. We next sought to identify potentially adaptive genetic changes by focusing on large-scale events (LOH and aneuploidies) as well as single-nucleotide polymorphisms. Genetic alterations absent from the progenitor isolate, persistent within a patient, and recurrent across patients are likely adaptive Given the high number of SNPs, LOH events and aneuploidies, we next devised a strategy to identify those changes that are more likely to play an adaptive role in drug resistance and host adaptation. We previously filtered all background polymorphisms, defined as any SNP relative to the reference present in all isolates from a series. Next, we defined alterations as persistent if present within the same patient at all subsequent time points after the ‘non-progenitor’ isolate in which they are first identified. We reasoned that such persistent changes will include those variants that were driven to sufficiently high frequency by selection to ensure repeated sampling (Figure 1B,C, yellow lightning bolt), whereas non-persistent (transient) ones do not (Figure 1B,C, pink lightning bolt). We consider the special case of a genetic change detected only in the endpoint isolate as ‘persistent’ as well, since several of the time courses consist of only two or three isolates. We apply the persistence filter to better identify potentially adaptive aneuploidies, LOH events, and SNPs. Next, we further focused on non-synonymous polymorphisms in coding regions and employed two different strategies to identify potentially adaptive changes. In the first strategy, to identify potential drivers of adaptation, we focused on non-synonymous SNPs that were homozygous for a genotype not found in the progenitor strain that persisted in the subsequent isolates (e.g., G/T > A/A) consistent with positive selection. In the second strategy, we analyzed genes that were recurrently polymorphic across patients, such that persistent, non-synonymous polymorphisms appeared within the same open reading frame (ORF) in different patient series (Figure 1A and Figure 5—source data 1A). For recurrence, we considered only those that were not included in LOH regions, as these regions artificially inflate the estimates of persistence and recurrence. Recurrence allows us to better handle polymorphisms from the endpoint isolate in a series for which ‘persistence’ does not provide a meaningful filter. Thus, we further considered polymorphisms occurring only in the terminal isolate in one patient if polymorphisms also recurred in the same ORF in a series from two other patients. For example, filtering for both persistence and recurrence across at least three series reduced the number of polymorphisms for patient 1 from 13,562 polymorphisms in 5022 genes to 23 recurrent genes (Figure 2—source data 1, Figure 5—source data 1A). LOH events are commonly associated with increased resistance LOH events were detected in all of the series and were often persistent, recurrent, and associated with increased drug resistance (Figures 3 and 4, Figure 3—source data 1). For example, three of four LOH events in Patient 1 were persistent and associated with an increase in MIC and both of these events were recurrent, such that LOH events in these genomic regions coincided with increases in MIC in other patients. Highly recurrent LOH events occurred on the right arm of chromosome 3 (in Patients 1, 9, 14, 16, 42, and 59; Figure 3A, Figure 4A,B,D,F,H, Figure 3—source data 1) and on the left arm of chromosome 5 (in Patients 1, 14, 15, and 43; Figure 3A, Figure 4B,C,G, Figure 3—source data 1). These regions include key genes implicated in drug resistance: on Chromosome 3, genes encoding the Cdr1 and Cdr2 efflux pumps and the Mrr1 transcription factor that regulates the Mdr1 major facilitator superfamily efflux pump (Schubert et al., 2011), and on Chromosome 5, genes encoding the drug target Erg11, and Tac1, a transcription factor that positively regulates expression of CDR1 and CDR2 (Coste et al., 2006). The extent of persistence and recurrence of these two LOH events is statistically significant under a naïve binary model (p < 5 × 10−4 for the Chr3R LOH; p < 0.01 for the Chr5L LOH). The recurrence of LOH events that coincide with changes in MIC suggests that they have been positively selected to rise in frequency relative to the progenitor strain. Notably, some of the recurrent LOH events may have been difficult to detect previously on SNP arrays (Forche et al., 2008; Forche et al., 2004; Forche et al., 2005) due to the relative paucity of SNPs in those regions in the reference strain, SC5413, itself a clinical isolate. Figure 3 Download asset Open asset LOH events were often persistent while aneuploidies were often transient. For each time series shown are the genomes of all isolates (rows) from a patient, ordered from the first isolate (progenitor, top) to the last (evolved, bottom). Boxes on right indicate the MIC of the respective strain (black: high, white: low; gray scale at bottom). Persistent LOHs: blue, transient LOHs: pink; trisom" @default.
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W2984287174 title "Author response: The evolution of drug resistance in clinical isolates of Candida albicans" @default.
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