Cannabis microbiome sequencing reveals several mycotoxic fungi native to dispensary grade Cannabis flowers

Introduction

Many states in the U.S. are crafting regulations for microbial detection on Cannabis in absence of any comprehensive survey of Cannabis microbiomes. A few of these regulations are inducing growers to “heat kill” or pasteurize Cannabis flowers to lower microbial content. While this is a harmless suggestion, we must remain aware of how these drying techniques often create false negatives in culture-based safety tests used to monitor colony-forming units (CFU). Even though pasteurization may be effective at sterilizing some of the microbial content, it does not eliminate various pathogenic toxins or spores. Aspergillus spores and mycotoxins are known to resist pasteurization^1,2. Similar thermal resistance has been reported for E. coli produced Shiga toxin³. While pasteurization may reduce CFU’s used in petri-dish or plating based safety tests, it does not reduce the microbial toxins, spores or DNA encoding these toxins.

Mycotoxin monitoring in Cannabis preparations is important since aflatoxin produced by Aspergillus species is a carcinogen. The clearance of aflatoxin requires the human liver enzyme CYP3A4 and this liver enzyme is potently inhibited by cannabinoids^4,5. Modern day Cannabis flowers can produce up to 25% (w/v) cannabinoids presenting potent inhibition of CYP3A4 and CYP2C19. Health compromised patients exposed to aflatoxin and clearance-inhibiting cannabinoids raise new questions in regards to the current safety tolerances to aflatoxin. Similarly, Fusarium species are known to produce fungal toxins and has proven to be difficult to selectively culture with tailored media^6–8. This is a common fault of culture-based systems as carbon sources are not exclusive to certain microbes and only 1% of microbial species are believed to be culturable⁹.

While these risks have been well studied in the food markets, the presence of the microbial populations present on Cannabis flowers has never been surveyed with next generation sequencing techniques^10–15. With the publication of the Cannabis genome^16,17 and many other pathogenic microbial genomes, quantitative PCR assays have been developed that can accurately quantify fungal DNA present in Cannabis samples¹⁸. Here, we analyze the yeast and mold species present in 10 real world, dispensary-derived Cannabis samples by quantitative PCR and sequencing, and demonstrate the presence of several mycotoxin producing fungal strains that are not detected by widely used culture-based assays.

Methods

Results

A commercially available total yeast and mold qPCR assay (TYM-PathogINDICAtor, Medicinal Genomics, Woburn MA) was used to screen for fungal DNA in a background of host Cannabis DNA. The TYM qPCR assay targets the ribosomal DNA Internal Transcribed Spacer region 2 (ITS2) using modified primers described previously^21,22. Fungal DNA amplified using these primers may also be subjected to next generation sequencing to identify the contributing yeast and mold species. ITS sequencing has been widely used to identify and enumerate fungal species present in a given sample²³.

We purified DNA from Cannabis samples obtained from two different geographic regions (Amsterdam and Massachusetts) several years apart (2011 and 2015). The majority of samples purified and screened with ITS qPCR were negative for amplification signal implying reagents clean of fungal contamination. Six of the 17 dispensary-derived Cannabis samples tested positive for yeast and mold in the TYM qPCR assay. These results were compared with the results derived from three commercially available culture-based detection systems for each of the 17 samples (3M Petrifilm^TM 3M Microbiology, St. Paul, MN, USA; SimPlates^TM Biocontrol Systems, Bellevue, WA, USA; BioLumix^TM Neogen, Lansing MI, USA; Figure 1). Of the 6 qPCR positive samples, two tested negative in all 3 culture-based assays and four tested negative in 1 or 2 of the culture-based assays (Table 1). None of the qPCR negative samples tested positive in any of the culture-based assays. Each of the 6 discordant samples was subjected to ITS sequencing to precisely identify the collection of microbes present. Four additional samples from a different geographic origin (Amsterdam) were also subjected to ITS sequencing, for a total of 10 Cannabis samples.

Figure 1. Comparison of 4 different microbial detection technologies.

Figure 1A. qPCR signal from TYM (red line) test run concurrently (multiplexed) with a plant internal control marker (green line). This marker targets a conserved region in the Cannabis genome and should show up in every assay (upper left). SimPlates count the number of discolored wells (purple to pink) as a proxy for CFU/gram. Only total aerobic show growth (upper right). Petrifilm only demonstrate colonies on total aerobic platings (lower left). Biolumix demonstrate no signal across all 4 tests (lower right). Figure 1B. Sample KD8 fails to culture any total yeast and mold yet demonstrates significant TYM qPCR signal. Sample was graduated to ITS based next generation sequencing. Figure 1C. Sample Liberty Haze was tested with 3 culture based methods and compared to qPCR. Sample was graduated to ITS based next generation sequencing.

Each discordant sample presented with an array of microbial species, as shown in Figure 2. No sample presented with a single dominant species, and each sample displayed multiple species of interest. Of particular concern were the identified DNA sequences from toxin producing species: Aspergillus versicolor^24–28, Aspergillus terreus²⁹, Penicillium citrinum^30–32, Penicillium paxilli^33,34.

Figure 2. DNA sequencing of ITS2 amplicons from culture negative samples that are qPCR positive for total yeast and mold tests.

Penicillium and Aspergillus are commonly found (Y axis) but at different read counts in each sample (X axis). Read counts are more a reflection of sample normalization for sequencing than inter sample quantitation provided by qPCR.

We further analyzed the ITS sequence alignments using the whole genome shotgun based microbiome classification software known as One Codex³⁵. Nine of the ten samples sequenced showed the presence of P. paxilli (Figure 3). To verify the accuracy of this ITS phylotyping, a gene involved in the paxilline toxin biosynthesis pathway of P. paxilli was amplified with PaxPss1 and PaxPss2 primers described by Saikia et al.³⁶ The resulting 725bp amplicon (expected size) was sequenced to confirm the presence of the P. paxilli biosynthesis gene in the Cannabis sample KD8 (Figure 4). This was successfully repeated with primers designed to target genes in the citrinin pathway of P. citrinum. There were some discrepancies between the results derived from the two software platforms (One Codex and MG-RAST). The MG-RAST analysis, using merged, paired reads correlated better with the PCR results. While One Codex predicted and confirmed KD8 as having the highest P. paxilli content, the One Codex platform is optimized for whole genome shotgun data and may not be able to differentiate the 18S sequence differences (391/412 aligned bases) between these two species with a K-mer based approach.

Figure 3. One Codex classification of ITS reads.

P. paxilli is the most frequently found contaminant in Cannabis flowers. P. citrinum is not in the One Codex database at this time. One Codex utilizes a fast k-mer based approach for whole genome shotgun classification and can be influenced by read trimming and database content. The reads provided to MG-RAST were trimmed and FLASH’d (paired end reads merged when overlapping) prior to classification. K-mer based approaches can significantly differ from longer word size methods and this underscores the importance of confirmatory PCR in microbiome analysis.

Figure 4. PCR of genes encoding Paxilline and Citrinin demonstrates amplification of the expected size.

Citrinum primers we designed from Genbank accession number LKUP01000753. Paxilline primers were used as described in Saikia et al. PCR products were made into shotgun libraries with Nextera and sequenced on an Illumina MiSeq with 2×75bp reads to over 10,000X coverage. Reads were mapped with CLCbio 4 to NCBI accession number HM171111.1 (A) and LKUP01000000 respectively (B). Paired reads are displayed as blue lines, green and red lines are unpaired reads. Read coverage over the amplicons are depicted in a blue histogram over the cluster while paired end read distance is measured in a red histogram over the region. Off target read mapping is limited. P. paxilli mappings are displayed on top (A) and P. citrinum mappings are displayed on bottom (B). Alignment of PCR primers to P. paxilli reference shows a 5 prime mismatch that is a result of the primers being designed to target spliced RNA according to Saikia et al.

With the confirmed presence of P. paxilli, we are curious to find out whether the toxin, paxilline, is present in the samples. Development of monoclonal antibodies to paxilline has recently been described³⁷, but commercial ELISA assays with sensitivity under 50ppb do not appear to be available at this time. A >50ppb multiplexed ELISA assay is available from Randox Food Diagnostics (Crumlin, UK). Detection with LC-MS/MS has also been described^38,39, however, and experiments are underway to determine whether paxilline can be identified in the background of cannabinoids and terpenes present in Cannabis samples.

Discussion

Several potentially harmful fungal species were detected in dispensary-derived Cannabis samples by qPCR and subsequent sequencing in this study. Three different culture-based assays failed to detect all of the positive samples and one, BioLumix^TM, detected only one out of 7 positive samples. A review of the literature suggests that Penicillium microbes can be cultured on CYA media, but some may require colder temperatures (21-24C) and 7 day growth times⁴⁰. Of the Penicillium, only P. citrinum has been previously reported to culture with 3M Petri-Film⁴¹. In addition, several studies have demonstrated plant phytochemicals and terpenoids like eugenol can inhibit the growth of fungi⁴². It is possible the different water activity of the culture assay compared to the natural terpene rich flower environment is contributing to the false negative test results.

Quantitative PCR is agnostic to water activity and can be performed in hours instead of days. The specificity and sensitivity provides important information on samples that present risks invisible to culture based systems. The draw back to qPCR is the method’s indifference to living or non-living DNA. While techniques exist to perform live-dead qPCR, the live status of the microbes is unrelated to toxin potentially produced while the microbes were alive. ELISA assays exist to screen for some toxins⁴³. Current state-recommended ELISA’s do not detect citrinin or paxilline, the toxins produced by P. citrinum and P. paxilli, respectively. The predominance of these Penicillium species in a majority of the samples tested is interesting. Several Penicillium species are known to be endophytes on various plant species, including P. citrinum¹⁰, and this raises the question of whether they are also Cannabis endophytes.

Paxilline is a tremorgenic and ataxic potassium channel blocker and has been shown to attenuate the anti-seizure properties of cannabidiol in certain mouse models^44–46. Paxilline is reported to have tremorgenic effects at nanomolar concentrations and is responsible for Ryegrass-staggers disease⁴⁷. Cannabidiol is often used at micromolar concentrations for seizure reduction implying sub-percentage contamination of paxilline could still be a concern. Citrinin is a mycotoxin that disrupts Ca2+ efflux in the mitochondrial permeability transition pore (mPTP)^48–55. Ryan et al. demonstrated that cannabidiol affects this pathway suggesting a potential concern for CBD-mycotoxin interaction⁵⁶. Considering the hydrophobicity of paxilline and the recent interest in the use of cannabidiol derived from Cannabis flower oils for drug resistant epilepsy, more precise molecular screening of fungal toxins may be warranted^57–62.

Our survey of Cannabis flowers in this study was limited. Further screening will be required to define a set of tests that can adequately capture all risks. While ELISA assays are easy point of use tests that can be used to detect fungal toxins, they can suffer from lack of sensitivity and cross reactivity. ITS amplification and sequencing offers hypothesis-free testing that can complement the lack of specificity in ELISA assays. Appropriate primer design can survey a broad spectrum of microbial genomes while affording rapid iteration of design. Quantitative PCR has also demonstrated single molecule sensitivity and linear dynamic range over 5 orders of magnitude offering a very robust approach for detection of microbial risks. This may be important for the detection of nanomolar potency mycotoxins. Further studies are required to validate better detection methods for these toxins and verify whether paxilline or citrinin are present on Cannabis at concentrations that present a clinical risk.

Conclusions

These results demonstrate that culture based techniques superimposed from the food industry should be re-evaluated based on the known microbiome of actual Cannabis flowers in circulation at dispensaries. Several mycotoxin producing molds were detected that can potentially interfere with the medical use of cannabidiol. These microbes failed to grow on traditional culture-based platforms but were rapidly detected with molecular based techniques. Further studies are required to quantitate the presence and concentration of mycotoxin production.