Cancer is a disease characterized by high mitosis rates with a loss of regulation. Many antineoplastics, those drugs used to treat cancer, act by slowing or halting mitosis. We are developing a whole-organism screening protocol to identify novel antineoplastic or their off-target effects. After exposing Drosophila melanogaster eggs and larva to an antineoplastic compound, their growth rate and population decrease. We screened several compounds from the National Cancer Institute’s (NCI) Developmental Therapeutics Program (DTP). Our screen successfully identified two compounds, toyocamycin and stictic acid, previously identified as possible antineoplastics. Toyocamycin killed a fraction of the population proportional to the dose concentration resulting in full mortality at 100 and 200 µM. At low doses, toyocamycin also slowed larval development by a mean of one day. RNAseq seemed to show that no genes were differentially expressed in mature flies after toyocamycin exposure was halted. Stictic acid delayed larval growth by an equal or greater margin compared to toyocamycin. Decreases in Drosophila growth or population may predict a compound’s antineoplastic activity and toxicity.

NCI, National Cancer Institute; 

DTP, Developmental Therapeutics Program; 

RNAseq, RNA sequencing; 

µM, micromolar; 

DMSO, dimethyl sulfoxide;

ELISA, enzyme-linked immunosorbent assay;

PBS, phosphate-buffered saline;

A plethora of screens for drug discovery, design, and toxicity exist, especially for novel antineoplastics. In silico and most in vitro screens require prior knowledge of a potential drug target, typically a protein or genetic sequence. Many diseases, including some cancers, are poorly characterized at the molecular level, rendering in silico methods and most in vitro methods less tractable [1]. In vivo screens look for phenotype production or rescue making them ideal for drug discovery for less-

understood diseases. Additionally, in vitro assays cannot recreate the cellular microenvironments present in an organism while whole-organism screens more accurately portray a compound’s biological activity and toxicity [1]. For example, Marstein at al [2] discovered that class II chemotherapeutics may fuel tumor reoccurrence, a potential side effect only uncovered because both cancerous and wild-type intestinal stem cells were present in the Drosophila model .While Drosophila cancer models have been used [2-5] to discover successful antineoplastics in the past, we have developed a screen that does not require generation of a cancer-related genotype. This screen is an inexpensive, widely accessible technique enabling compound characterization in physiological context.

The purpose of antineoplastics is to stop the rampant cell division characterizing cancer tumors. By testing chemical libraries in whole organisms for an ability to produce slow growth or lethality these inhibitors of cell division can be identified [6], although morphology can be an important indicator [7]. Cell division inhibition will be most noticeable where swift growth is ordinarily expected. Thus, screens using organisms in rapid stages of growth will detect such compounds with greater sensitivity than if organisms in slower stages of growth (i.e., adult Drosophila) were used. Our assay exposes Drosophila larva to potential drug candidates directly after emergence from the egg stage.

Many antineoplastics work by altering gene expression, either indirectly (e.g., through transcription factors phosphorylation) or by directly interrupting nucleic acid synthesis. For example, COX inhibitors are known to alter expression of many proteins in addition to their anti-inflammatory effects [8]. 5-fluoroacil, a thymine antimetabolite, has been shown to suppress miR-200b expression in tumors [9]. Such changes in gene expression should be quantifiable by differential [removed]DE) analysis of RNA sequencing data. Here, we investigate possible gene expression effects of the top hit found by our screen. 

We were especially interested in gene expression effects long after drug exposure was terminated. The altered gene expression inherent to many antineoplastics could be detrimental to normal physiology if it persists long after treatment has ended. For example, Kurikara et al [10] discovered that toyocamycin strongly increases p16/INK4a expression, thereby inhibiting cell cycle progression. While this contributes to toyocamycin’s antitumor capability, cell senescence can also delay growth. Here, we demonstrate that low-concentration toyocamycin does delay growth, but the effect is short-lived once exposure terminates. 

Our protocol correctly identified toyocamycin, a nucleoside isolated from Streptomyces [11], and stictic acid, a β-orcinol depsidone isolated from the lichen Usnea articulata [12], as positive hits out of a panel provided by the NCI Developmental Therapeutics Program (DTP). Toyocamycin is a pyrrolo[2,3‐d]pyrimidine (7‐deazapurine), which are known to have antitumor and antiviral properties [13] in addition to its antibiotic properties [11]. Nishioka et al [14] recorded phosphatidyl kinase inhibition by toyocamycin. Through these mechanisms, toyocamycin perturbs nucleic acid synthesis and translation.

Stictic acid has been shown to inhibit growth in MCF-7 breast cancer and HT29 colon cancer cell lines compared to MRC-5 normal cells [15]. Wassmen et al [16] discovered stictic acid reactivates mutant p53. Stictic acid derivatives also have antioxidant activity against ROS [12]. We report similar growth inhibition in Drosophila in both stictic acid and toyocamycin treatments. In general, we believe this work will be useful for studying antineoplastic strength and toxicity in vivo.

Drosophila Genetics and Maintenance; Chemical Library

Wild-type strain 25210 DGRP 859 flies were used from the Bloomington Drosophila Stock Center (Illinois, USA). Stocks were maintained on commercial media (Formula 424, Carolina Biological, North Carolina, USA) in clear plastic vials 1 1/4" diam × 4" height. Flies were moved to new vials every 30 days.

All compounds were sourced through the Drug Synthesis and Chemistry Branch, Developmental Therapeutics Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute (Maryland, USA). Compounds were stored in crystalline form at -70°C until just prior to use.

In Vivo Drug Screening

15 adult male and 15 adult females were added to each vial and treatment groups consisted of three vials each. All compounds were dissolved in DMSO (HPLC grade; ThermoFisher, Massachusetts, USA) and diluted to 200 µM, unless otherwise specified. In each vial, 15mL of dry media was reconstituted with 13 mL of 200 µM drug solution. Control vials were prepared with 15 mL of dry media and 13 mL of vehicle control. Adult flies were introduced to media for 24 hrs and then removed. Vials were maintained at 21°C with 12 hours lighting per day. 200 µM drug solutions were stored at -20°C.

An additional 2 mL of drug solution or DI water was added to each vial at 24, 48, and 72 hrs from addition of adults. Each vial was observed and the number of pupae and adult offspring recorded daily. Flies were examined for abnormal phenotypes. After emergence of all pupae, the adult vial populations were sexed and counted.

To quantify when flies pupated on average, cumulative pupation was plotted as a function of time in days and fitted with a 4-parameter logistic regression. The inflection point, tmid, was used as a measure of pupation time; the mean and standard deviations are reported in Table 1. See Supplemental Information for further explanation of the logistic regression. 

RNAseq and Differential Expression Analysis

Specimens were randomly selected from each vial population and frozen in 1 mL RNAlater (ThermoFisher). Preliminary RNAseq specimens contained one fly per 1 ml RNAlater; triplicates were combined due to low RNA yield. Followup RNAseq specimens were allocated three flies with 1 mL RNAlater per replicate to ensure adequate yield. Specimens were stored at -70°C and shipped overnight on dry ice.

 RNAseq was performed by Omega Bioservices (Georgia, USA) using Illumina sequencers at a 10M read sequencing depth. DESeq2 was utilized for differential expression analyses. DESeq2 analysis was performed using the Illumina BaseSpace platform by Omega Bioservices staff.

Apc11 protein ELISA

To verify upregulation of lmgA past the transcription stage, ELISA of the protein Apc11 was undertaken. ELISA was performed according to the kit manufacturer’s instructions (MyBioSource, California, USA). Briefly, samples were prepared by grinding single whole flies (n=6 with equal numbers of each sex flies per treatment.) into 500 µL PBS each using glass tissue grinders in a wet ice bath. Each sample was subjected to three freeze-thaw cycles in liquid N2 at room temperature. All samples were centrifugated at 5000 rpm for 15 minutes. Samples were assayed in triplicate technical replications on a single competitive human ANAPC11 ELISA plate (MyBioSource, California, USA) to avoid interassay variability. Six standards ranging from 100 ng/mL to 0 ng/mL were run in triplicate. 5-parameter logistic regression calibration curves were generated using the nplr package [17] in R. 

Statistical Analysis

All statistical analysis and data visualization was done using R v4.0.3 [18] using packages ggplot2 [19], dplyr [20], and nplr, except for DESeq2 analyses. Unless otherwise noted, unpaired two sample t-tests were used to calculate p-values. Confidence levels were set at 0.95.

Toyocamycin results in death or delayed growth of Drosophila from the larval to adult stages. 200 and 100 µM toyocamycin resulted in 100% larval mortality; 50 µM resulted in a nonsignificant decrease (P = 0.1233, Fig. 1). Toyocamycin at 25 µM showed a slight reduction in time to pupation and time to emergence (Fig. 2-3). Additionally, a non-significant decrease in mean population (P>.05) was observed (Fig. 1). 

Time to half-pupation is summarized in Table 1. Toyocamycin and stictic groups showed a mean delay of 1 day in pupation (Fig. 2-5); some stictic acid trials showed a delay of 3 days before pupation (individual data not shown). There was no significant difference in the mean population gender proportion between the treatment and control groups (P = 1) in either stictic acid or toyocamycin groups.

Flies exposed to > 100 µM toyocamycin at the egg and larval stage saw complete mortality. Flies exposed to 25 µM toyocamycin saw a nonsignificant level of mortality.

Figure 1. Number of viable flies decreases as toyocamycin concentration increases.

95% CI given for treatments where all 3 replicates could be fit with the model. See Supplementary Information for regression details.

Table 1. Mean days to half-maximum pupation, tmid.

The cumulative number of pupae visible in each vial, averaged over treatments. Toyocamycin slows the appearance of pupae.

Figure 2. Toyocamycin, 25 µM, delays pupation of larvae.

The cumulative number of adults visible in each vial, averaged over treatments. Toyocamycin slows the emergence of adults.

Figure 3. Toyocamycin, 25 µM, delays adult emergence. 

The cumulative number of pupae visible in each vial, averaged over treatments. Stictic acid slows the appearance of pupae.

Figure 4. Stictic acid, 200 µM, delays pupation of larvae.

The cumulative number of adults visible in each vial, averaged over treatments. Stictic acid slows the emergence of adults.

Figure 5. Stictic acid, 200 µM, delays adult emergence. 

Preliminary RNAseq data showed 5 genes were differentially expressed significantly; CG14042, CG14933, Fbp2, and lmgA were upregulated (p = 0.0474, p = 0.0181, p = 0.0012, p = 0.0017 respectively) and snRNA:U1:95Ca was downregulated (p = 0.023) (Fig. 6). lmgA had the highest log2-fold change as calculated by DESeq2, 8.065 (Fig. 6). 

Competitive ELISA demonstrated that lmgA was not significantly DE in the toyocamycin group compared to control (Fig. 7, n = 6 flies per group, p = 0.1075). Specimens used in ELISA assay were from trials independent of those used for preliminary RNAseq. Thus, another set of toyocamycin and control trials was run with a greater number of specimens sent for RNAseq.

RNAseq performed on male and female from both control and toyocamycin groups, (n = 3 flies per sex, with RNA combined before sequencing).

Figure 6. DE genes from preliminary RNAseq of toyocamycin-treated flies.

Competitive ELISA of Apc11 encoded by lmgA. Apc11 was not upregulated as preliminary RNAseq suggests.

Figure 7. ELISA of Apc11 (lmgA) shows that lmgA was not upregulated in toyocamycin-treated flies.

Follow-up RNAseq with greater replicates (n = 6 per group) indicated no significant DE in toyocamycin treated flies at maturity. However, when flies in the toyocamycin group were compared to control flies of the same sex, 4 genes were significantly DE in males and none in females (Fig. 8). However, there are only three replicates per group when sex is considered, which does not meet Schurch et al’s [21] suggested number of biological replicates for RNAseq studies. More extensive study with greater read depth is needed to determine if sex-related differences in gene expression occur after toyocamycin exposure. It is likely that the effect we observed is due to small sampling size and interindividual variability.

Further RNAseq, showing DE between males in control and toyocamycin treatments (n = 3 flies per biological replicates, n = 3 biological replicates). When sex is ignored, no DE genes were found.

Fig 8. DE genes from more extensive RNAseq of male-only toyocamycin treated flies.

Due to the distinct response in Drosophila, our results show toyocamycin and stictic acid are potent inhibitors of actively growing tissues. Toyocamycin is clearly toxic to larva in Drosophila melanogaster, as demonstrated by mortality and the increased time by larva to reach milestones like pupation and emergence. The inhibitory effect quickly becomes toxic as concentration increases beyond 25 µM. Moreover, toyocamycin appears to affect male and female organisms equally, though further gene expression profiling could be warranted. Stictic acid delayed pupation by the same margin as toyocamycin (Fig. 4-5). This agrees with Pejin et al’s [15] observations in cell lines, where clear growth inhibition was noted. These results show that our screening protocol can identify growth inhibitors by observable effects on a developing Drosophila population.

While a preliminary RNAseq experiment suggested some differences in expression in mature flies after toyocamycin exposure, further analysis showed no difference. In turn, this suggest that toyocamycin ceases to affect gene expression relatively quickly when exposure stops. The RNAseq dataset, when specimens were separated by sex and compared, suggested differential expression of four genes; this analysis is inconclusive due to only three biological replicates of each sex per treatment. Furthermore, pupation did not lag far behind controls in the toyocamycin exposed flies. These flies typically pupated only one day after those in the control, while as flies in some stictic acid trials saw a delay of 3 days. Our results suggest toyocamycin’s affect fades rapidly after discontinuation. For patients undergoing chemotherapy, new drugs without long-lasting side effects once treatment is stopped are desirable. Further study of toyocamycin analogues at various time points after exposure is needed. Additionally, RNAseq with greater read depth may reveal more DE genes.

Our screen successfully identified two known antineoplastics. Additionally, potential gene expression changes related to toyocamycin exposure were recorded. This was achieved without the time-consuming and expensive genetic manipulation needed to produce a cancerous genotype or patient avatar in the flies as in Markstein et al [2] or Levine & Cagan’s [3] work. Our screen represents a low-cost platform for studying antineoplastics in vivo. This technique may be used as an initial screen for suspected antineoplastics, to elucidate the mechanism of known antineoplastics in a whole organism, or to study their off-target affects. Furthermore, our gene expression experiments imply that toyocamycin-based antineoplastics may have little long-term toxicity in that regard. In the future, we hope to correlate pupation delay with inhibition of cell division in cancer lines.

This work was partly supported by the Kansas Academy of Science through the Student Research Grant Program; we thank their Undergraduate Scholarship Committee.

We extend our deep gratitude to Dr. Chunyang Li and the other staff at Omega Bioservices for their expertise in RNAseq, useful discussion, and collaboration. We are indebted to Dr. Mostafa Zamanian of the University of Wisconsin-Madison for timely guidance on RNAseq and analysis. Stocks obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537) were used in this study. Also, we thank Sarah Bottorff of Carolina Biological for suggestions on controlling Drosophila bacterial infections.

We wish to thank the Friends University Division of Science, Technology, Engineering, and Math, especially Dr. Nora Strasser (Chair), Amy Morgan (Admin), and Celia Milam (Drosophila stockkeeper). Additionally, we are grateful to Ramon Emmart, Jessica Boone, Erin E. McCoskey (PA-S), and Abbey L. Fischer (CMA) for their scrutiny of our grant proposal.

P.N.A. conceived the project. P.N.A. and T.A.S. designed the experiments. T.A.S. performed all experiments. T.A.S. analyzed the data. T.A.S wrote the manuscript. P.N.A. and T.A.S. edited and proofread the manuscript. P.N.A. and T.A.S procured funding.

Supplementary Information for “Visualizing Antineoplastic Activity with a Whole Organism Drosophila melanogaster Screen”

Nonlinear Regression of Pupation Data.

The cumulative number of Drosophila pupae in a vial as time increases (if the initial number of eggs is fixed) will follow a sigmoidal pattern (Fig. 2, 4). This is also true for cumulative number of adults emerging from a fixed number of pupae. Given that a vial will first have 0 pupae/adults, and will have T number of eggs present, the number of pupae y as time t increases,

Formula

where tmid is the time at which half of all flies have pupated and b is the Hill slope. Note the similarity to dose-response curves. Conceptually, tmid is equivalent to an EC50. This is an ideal case, since 100 % and 0 % response are strictly defined; that is, 0% response is 0 pupae and 100% is the maximum number of pupae for that vial. The maximum number of pupae is determined by how many eggs were laid in the vial before the parental generation has been removed and thus cannot change over the course of the experiment. Thus, relative and absolute “EC50” are equivalent in this situation (each replicate could be transformed into % pupa by dividing the cumulative number of pupae on each day by the eventually maximum number of pupae,

 to keep y-axes consistent across replicates.

tmid will be determined by environmental factors, the presence of a drug, and the specific properties of the drug. By comparing tmid between trials, the pupation delay caused by a drug can be determined. The Hill slope, b, is related to the timespan over which the eggs were laid. If the eggs were laid within a short time span of each other, the slope will be steeper. Eggs laid over a greater timespan will result in a smaller slope. This slope could also be related to the flies’ rate of development during the larval stage.

The 4-parameter logistic regression model explained above can be fitted to the data with the R package “nplr” (R package nplr: n-parameter logistic regressions, Commo & Bott, 2016). For example:

Suppl. Fig. 1

Suppl. Fig. 2

Suppl. Fig. 3: 

Suppl. Fig. 1-3: 4-parameter nonlinear regression of cumulative pupation vs. time in days. Error bars represent one standard error. Note only one replicate of stictic acid was able to be fit.

nplr calculates “IC50” (actually tmid in this case) from the fitted regression line and the 95% confidence interval. The above regressions were performed on experiments in triplicate. Fitting this model makes it easy to tell that toyocamycin delays the tmid by 1.1 days.

Distributions of ELISA data. 

Suppl. Fig. 4  Distributions of Apc11 ELISA data.

Suppl. Fig.  5: Distributions of Apc11 ELISA data.