Full-length ITS Amplicon Sequencing of Feline Gut Mycobiome of Malaysian Local Breeds Using Nanopore Flongle

doi:10.21203/rs.3.rs-1456898/v3

PDF

Research Article

Full-length ITS Amplicon Sequencing of Feline Gut Mycobiome of Malaysian Local Breeds Using Nanopore Flongle

https://doi.org/10.21203/rs.3.rs-1456898/v3

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

The gut mycobiome exhibits major influence on the gastrointestinal health and disease but received less attention due to low abundance. This study characterises the fungal community and compares the microbial diversity between indoor and outdoor cats. Genomic DNA was extracted and sequenced by targeting the full-length internal transcribed spacer (ITS) region using Flongle flow cell on MinION™ sequencing platform. Results show the Ascomycota and Peniophorella were numerous in indoor cats, whereas the Basidiomycota and Pichia were abundant in outdoor cats. Peniophorella formed the core mycobiome in both feline populations. Furthermore, alpha (p-value = 0.0207) and beta diversities results (p-value = 0.009) showed significant differences between the two groups. Overall, indoor cats have greater amounts of Peniophorella, whereas outdoor cats have higher Trichosporon and unclassified Sordariaceae. The study also suggests that keeping a cat indoors or left as a stray will affect their respective gut mycobiome.

mycobiome

fungi

Flongle

feline gut

Malaysia

Microbiome refers to the collective genomes of microorganisms in a particular environment. The gastrointestinal tract (GIT) is an environment that hosts complex microbial ecosystems dominated by bacteria and other organisms such as fungus, Archaea, viruses and protozoa, collectively known as gut microbiomes [1]. The fungal community found in the GI tract is known as gut mycobiota, and like their bacteria counterpart, they have a profound impact on health and disease [2] by playing an important role in maintaining gut functioning and immune development [3], [4]. Gut microbiota is involved in energy harvesting, storage, and regulation of host metabolic functioning and is essential for the production and absorption of metabolites like short-chain fatty acids [5] and other metabolites necessary for health and survival [6]. Colonisation of the gut microbiome in homeostasis is also vital in protecting the host against pathogens as it interacts with the immune system by sending signals that support normal immune function development [7]. An imbalance in gut microbiome composition or dysbiosis can lead to various diseases such as inflammatory bowel, colon cancer [8] and obesity, which has been linked to low gut bacterial diversity [9].

Cats have been a companion to mankind since time immemorial. The close correlation between pets' health and their lifestyle makes it increasingly important for researchers to look into the impact of animal feed on gut microbiome alteration [10]. Previous study [11] focusing on bacterial diversity of feline gut in Malaysia suggested that indoor and outdoor cats differ slightly in their gut microbiome composition based on alpha and beta diversity results, where indoor cats had a higher incidence of the bacteria phylum Firmicutes while outdoor cats were more abundant with Actinobacteria. Another feline study found that indoor cats had a more diversified microbial community and exhibited a more closely related bacterial population than outdoor cats [12]. However, compared to microbiome research, fungal communities in the GI tract received less attention due to their low abundance (< 0.3%) [13], [14], and the makeup of the fungus community in the feline gut remains unclear [15]. Similarly, the composition and interaction of fungal population in the feline intestine is poorly understood due to inadequate or limited reference sequences that are only annotated at high taxonomic levels [16]. Research [16] reported that the most abundant fungal class observed in the cats were the Saccharomycetes, which were exclusively composed of the Saccharomyces genus, followed by the Eurotiomycetes class, represented by the Aspergillus and Penicillium genera. Further studies are necessary to understand the extent of diversity and richness of fungal communities present in both indoor and outdoor cats.

Conventional bacterial classification research utilises a culture-dependent technique to identify potential microorganisms in faecal samples. This technique is relatively inexpensive and frequently used to characterise the microbiome of cats and dogs [17]. However, the culture-dependent approach is extremely time-consuming and ineffective for investigating microbial ecology, as less than 5% of the microbes are truly capable of growing in culture medium, thereby underestimating the extent of microbial diversity in the GI tract [15]. Currently, powerful Next Generation Sequencing (NGS) technologies based on molecular markers are becoming the primary method for studying microbial ecology as it is capable of sequencing thousands to millions of base pairs in a short period and has aided in the comprehensive characterisation of complex intestinal mycobiota [18]. The 18S rRNA gene sequences are the most prominent phylogenetic markers utilised for characterisation of eukaryotic genomes for taxonomic categorisation of fungi. The fungus-specific primers target the Internal Transcribed Spacer 2 (ITS2) region of the 18S rRNA gene to accurately categorise the detected fungi into various taxonomic levels [16]. Using improved sequencing methods, it is feasible to describe the entire fungal population, both culturable and non-culturable, and gain new knowledge into the microbial community found in both indoor and outdoor cats.

This study aims to characterise the gut fungal community in indoor and outdoor cats using high throughput technique and to compare the microbial diversity of indoor and outdoor cats fed with different diets intake. We hypothesised that outdoor cats may show higher abundance of gut microbial composition because they are exposed to more diversified living conditions and food sources.

Sample collection

The felines in this study were selected randomly by volunteers (pet owners) who joined this project with verbal owner consent. A total of ten stool samples were collected from five indoor and five outdoor cats ranging in age from six months to three years in Kuantan, Pahang. Prior to stool collection, only the indoor cats were fed commercial diets for a week upon request. The stool of street cats were collected from the streets surrounding the residential area where street cats were spotted. The stool samples were collected using a disposable spoon and placed in screw-top containers with proper labels. Necessary information on the indoor cats was gathered from their owners and recorded.

DNA extraction, library preparation and sequencing

DNA extraction was carried out using DNeasy Powersoil Pro Kit with minor modification. Briefly, 250mg of stool sample were placed in a PowerBead ProTube and mixed with solution CD 1 followed by addition of 5µL of Proteinase K [19]. Subsequent extraction process was performed following the manufacturer's protocol [11].

For characterisation of fungi, the nanopore libraries were prepared using PCR methods that targeted the partial 18S and full length ITS1 regions with the 515FNGS (TTTCTGTTGGCTGATATTGCGCCAGCAACCGCGGTAA) and ITS2 (ACTTGCCTGTCGCTCTATCTTCGCTGCGTTCTTCATCGATGC) primers with a Nanopore partial adapter (underlined) on the primer 5' end [20]. The PCR was carried out with WizBio HotStart 2x Mastermix (WizBio, Korea) at 95°C for 3 minutes, followed by 35 cycles of 95°C for 20s, 55°C for 20s, and 72°C for 120s. PCR products were visualised on gel and purified using SPRI Bead [21], then index PCR was performed with EXP-PBC001 kit (Oxford Nanopore, UK). The pooled barcoded libraries were gel-purified using the WizPrepTM Gel/PCR Purification Mini Kit (WizBio, Korea) according to manufacturer's instructions. Denovix high sensitivity was utilised to quantify the pooled barcoded amplicons, and an adequate amount (200 fmol) of the amplicons were used as the input for LSK109 library preparation (Oxford Nanopore, UK). Sequencing was carried out using a Nanopore Flongle Flowcell for 24 hours with high accuracy basecalling using Guppy V4.4.1.

Amplicon Data Analysis

For fungal analysis, the Nanopore raw reads were filtered by read length (> 500bp and < 1000 bp) and quality (Qscore > 8) and clustered using NanoClust [22]. The read clusters were error-corrected with one round of Racon and Medaka, respectively [23]. Extraction of the full-length ITS region used ITSx [24] followed by BLAST-based taxonomic classification of the ITS1 region using qiime2 classify-consensus-blast tool [25]. The abundance table was derived from the NanoClust intermediate file. Then, the abundance table (OTU Table), taxonomic assignment as well as sample metadata were manually formatted to make it compatible for secondary analysis in the MicrobiomeAnalyst webserver.

Statistical analysis

Statistical analysis was performed using MicrobiomeAnalyst [26], [27] for comparison and profiling of the fungal community between two groups with adjusted parameters [28] . Briefly, the data was filtered at a minimum count of four and with 20% prevalence filter mean. Based on the interquartile range, features with variance less than 10% were eliminated. The data was then adjusted using cumulative sum scaling (CSS). Alpha diversity was analysed using observed features index, while beta diversity was evaluated based on the Jaccard Index, Bray-Curtis, Weighted, and Unweighted UniFrac Distances matrices, with p<0.05 considered as significant. LEfSe was used to determine the difference in taxonomic abundance between the two groups at p<0.01.

The microbial and fungal communities inhabiting each human individual are varied and distinct, shaped by a range of internal and external variables, such as food, environments and host genotype [29], [30]. This study focuses on feline mycobiota in the gastrointestinal tract (GIT) and highlights the differences in fungal composition in response to dietary changes and different environmental variables. In this study, the high throughput amplicon sequencing was employed to characterise the composition of gut mycobiome in 10 cat samples amplified at ITS1 and ITS2 region via 18S rRNA gene marker. The sequence data of the cats' samples (indoor n=5, outdoor n=5) were filtered and processed resulting in 17 operational taxonomic units (OTUs) with an average of 1600 read counts per sample.

Feline mycobiome taxonomic classification

The taxonomic classification of the samples was classified at genus and phylum as presented in stacked bar plots (Figure 1). The results showed the composition of fungi in each sample at different taxonomy levels. Ascomycota and Basidiomycota are the two major phyla of fungus and poses important roles as decomposers which break down organic materials and pathogens for plants and animals. The Ascomycota are also the most prevalent phylum in soil fungal communities [31]. Figure 1(C) illustrates the fungal features at the phylum level which showed a clear distinction in composition between indoor cats (IC) and outdoor cats (OC). Ascomycota (66%) can be seen to be more pronounced within OC, while Basidiomycota is more commonly found among IC which accounts for 74% of the OTUs. From Figure 1(D), IC are observed to be more present with Peniophorella (33%) while OC seem to contain more Trichosporon (35%) and Pichia (36%) at the genus level. Such observations can be further verified using core microbiome results as displayed in Figure 2.

Figure 1 Top fungal phyla and genera found within the Indoor Cats (IC) and Outdoor Cats (OC) cat samples. Gut mycobiome composition for cats were conducted with Indoor Cats (IC) samples (n=5) and Outdoor Cats (OC) samples (n=5) through ITS1 gene amplicon sequencing. Results were extracted from MicrobiomeAnalyst after postprocessing in QIIME2: A. Taxa summary - Bar plot illustrating the relative abundant fungal features present within the two groups for each sample at phylum level; B. Taxa summary - Bar plot illustrating the top 10 relative abundant fungal features present within the two groups for each sample at genus level; C. Taxa summary - Bar plot illustrating the relative abundant fungal features present within the two groups which combines all appropriate samples at phylum level where Ascomycota (abundance=0.6551) is more prevalent in Indoor Cats (IC) and Basidiomycota (abundance=0.7400) in Outdoor Cats (OC); D. Taxa summary - Bar plot illustrating the top 10 relative abundant fungal features present within the two groups which combines all appropriate samples at genus level where Peniophorella (abundance=0.3274) is most abundant in Indoor Cats (IC) samples while Trichosporon (abundance= 0.3484) and Pichia (abundance= 0.3641) are most abundant in Outdoor Cats (OC) samples.

Figure 2 Core microbiome results found within the Indoor Cats (IC) and Outdoor Cats (OC) cat gut mycobiome. Gut mycobiome composition for cats were conducted with Indoor Cats (IC) samples (n=5) and Outdoor Cats (OC) samples (n=5) through ITS1 gene amplicon sequencing. Results were extracted from MicrobiomeAnalyst after postprocessing in QIIME2 : A. Heatmap illustrating genus Peniophorella that was detected the most (prevalence=0.7) across both Indoor Cats (IC) and Outdoor Cats (OC) samples B. Heatmap illustrating genus Peniophorella (prevalence=1.0) that was detected the most across all Indoor Cats (IC) samples followed by presence of Coprinopsis (prevalence=0.6) and Montagnula (prevalence=0.6); C. Heatmap illustrating genus Trichosporon (prevalence=0.6), Candida (prevalence=0.6) and unclassified Sordariaceae (prevalence=0.6) that was detected the most across all Outdoor Cats (OC) samples.

Generally, all cats within Malaysia seem to contain at least some amounts of the genus Peniophorella at prevalence 0.7 regardless of their habitual status as evident in Figure 2(A). Additionally, Figure 2(B) suggests that the most prevalent fungal genera found in IC after Peniophorella is Coprinopsis and Montagnula. Conversely, Figure 2(C) shows that the genera of Trichosopron, Candida, and unclassified Sordariaceae seem to be the most prevalent features found within OC. The Candida albican and Candida tropicalis are common isolates in cats.

Additional information regarding the taxonomic abundance in class, order, family and species levels can be found in the supplementary data section as shown in supplementary figures S1 and S2. The alpha rarefaction plot describing the number of gene clusters observed in each sample is also attached in the Supplementary Figure S4, while alpha diversity plot results at sample level can be found in Figure S3.

Alpha diversity and beta diversity

The community profiling approach was performed to describe the diversity of gut mycobiome in both IC and OC groups. Observed features index was used to count the number of OTUs in a sample, which was shown to be positively associated with the species richness of the samples, and the Kruskal-Wallis method was used to generate the P-value for the index (p = 0.0208). Aside from the alpha diversity analysis, the total species diversity may be referred to by the similarity or distance between two samples.

Beta diversity is a measure that can be used to compare the composition or feature similarity found between samples. The beta diversity metrics, Jaccard Index (p = 0.009), Bray-Curtis (p = 0.009), Weighted (p = 0.009) and Unweighted UniFrac Distance (p = 0.029) are used to assess the overall fungal diversity between IC and OC. These results are displayed in Principal coordinate analysis (PCoA) plots as shown in Figure 4. PCoA plots clustered indoor and outdoor samples using green and red colour respectively, representing different sample groups.

Figure 3 Alpha diversity results for Indoor Cats (IC) and Outdoor Cats (OC) cat gut mycobiome. Gut mycobiome diversity analysis for cats were conducted with Indoor Cats (IC) samples (n=5) and Outdoor Cats (OC) samples (n=5) through ITS1 gene amplicon sequencing using Kruskal-Wallis statistical method with Cumulative sum scaling (CSS) and no data rarefying process. Results were extracted from MicrobiomeAnalyst after postprocessing in QIIME2: Boxplot of alpha diversity result using index observed feature at group level which shows higher diversity in Outdoor Cats (OC) samples compared to Indoor Cats (IC) (p-value=0.0207).

Figure 4 Beta diversity results for Indoor Cats (IC) and Outdoor Cats (OC) cat gut mycobiome.

Gut mycobiome diversity analysis for cats were conducted with Indoor Cats (IC) samples (n=5) and Outdoor Cats (OC) samples (n=5) through ITS1 gene amplicon sequencing using Permutational MANOVA (PERMANOVA) statistical method with Cumulative sum scaling (CSS) and no data rarefying process. Results were extracted from MicrobiomeAnalyst after postprocessing in QIIME2: A. PCoA plot using Bray-Curtis index distance method (p-value=0.009) which shows clear separation between Indoor Cats (IC) and Outdoor Cats (OC) group; B. PCoA plot using Jaccard index distance method (p-value=0.009) which shows clear separation between Indoor Cats (IC) and Outdoor Cats (OC) group; C. PCoA plot using unweighted UniFrac distance method (p-value=0.029) which shows clear separation between Indoor Cats (IC) and Outdoor Cats (OC) group; D. PCoA plot using weighted UniFrac distance method (p-value=0.009) which shows clear separation between Indoor Cats (IC) and Outdoor Cats (OC) group.

Comparison between indoor and outdoor groups

Heatmap comparison among taxa at genus level illustrated in Figure 5(A) revealed patterns where IC and OC samples harbour different gut microbial communities. IC show overall less genera compared to OC. IC reported a high abundance of Montagnula, Peniophorella, Coprinopsis, Letendraea, Sarocladium and unclassified Agaricales. Interestingly, the presence of both Peniophorella and Coprinopsis are seen to be absent within the OC group, suggesting the idea that these features are exclusive only within IC. In contrast, OC reported more different genera than IC, indicating the makeup of gut flora associated with their living environment. OC are observed to have a high abundance of the genera Trichosporon, Pichia, Candida, Collariella, unclassified Ascomycota and unclassified Sordariaceae. Similarly, these six features are observed to be only present within the OC samples and non-existent within the opposing group: establishing a distinct pattern among both. These results suggest that the composition of gut mycobiome in cats influenced by the wide range of environmental exposure leads to a more diverse gut mycobiome. Additionally, such results indicate that certain genera features can only be found within certain sample groups depending on the feature in question such as the presence of both Peniophorella and Coprinopsis exclusive to IC while the genera Trichosporon, Pichia, Candida, Collariella, unclassified Ascomycota, and unclassified Sordariaceae are exclusively found within OC. The result also described the association of infection risk in outdoor cats with an increase in the abundance of the fungal pathogen, including Trichosporon and Candida genera, suggesting potential hazards from outdoor can threaten the cats' well-being.

Next, Linear Discriminant Analysis (LDA) Effect Size (LEfSe) method and pattern search were used to show substantial differences in the fungal distribution among the feline groups at the genus level. The LEfSe analysis is a biomarker discovery method used to find significantly different features across groups. The LEfSe effect sizes were evaluated based on the LDA scores and it was discovered that the relative abundances of Peniophorella (FDR=0.0541) are significant to the IC group, whereas Trichosporon (FDR=0.0601) and unclassified Sordariaceae (FDR=0.0541) are significant to the OC group, as shown in Figure 5(B). These results share similar stances to the previous heatmap results where certain features are deemed significantly different due to their exclusivity to a certain group; Peniophorella being only present in the guts of IC while Trichosporon and unclassified Sordariaceae are absent within the same group discussed. In addition, the distribution pattern of the gut microbiome reflects either healthy and illness status, which is linked to the physiology and function of the mycobiota. Figure 5(C) shows a pattern search map that describes the top genera connected with indoor and outdoor sample groups using Spearman rank correlation. The genera Peniophorella (correlation=0.8704, p-value=0.0011) was found to possess the highest positive correlation to the IC group while, the opposite was observed for the genera Trichosporon (correlation=0.8106, p-value=0.0044) and unclassified Sordariaceae (correlation=0.9285, p-value=0.0001), which were found to be positively correlated to the outdoor group. Once more, these findings echo and seem to support the previous notion regarding feature exclusivity.

Figure 5 Comparison feature results between the Indoor Cats (IC) and Outdoor Cats (OC) cat gut mycobiome. Gut mycobiome composition for cats were conducted with Indoor Cats (IC) samples (n=5) and Outdoor Cats (OC) samples (n=5) through ITS1 gene amplicon sequencing. Results were extracted from MicrobiomeAnalyst after postprocessing in QIIME2: A. Heatmap illustrating the hierarchical clustering and genus distribution for all samples using Euclidean distance; B. Bar plot illustrating significant genus found in both sample groups using Linear Discriminant Analysis (LDA) Effect Size (LEfSe) algorithm where Peniophorella (FDR=0.0541) is significant to Indoor Cats (IC) group while both Trichosporon (FDR= 0.0601) and unclassified Sordariaceae (FDR=0.0541) are significant to Outdoor Cats (OC) group; C. Pattern search plot showing the top 12 genus that show correlation between both sample groups using Spearman rank correlation where Peniophorella (correlation=0.8704, p-value=0.0011) is positively correlated to Indoor Cats (IC) while Trichosporon (correlation=0.8106, p-value=0.0044) and unclassified Sordariaceae (correlation=0.9285, p-value=0.0001) are positively correlated to Outdoor Cats (OC).

Significance of features observed within mycobiome classification

Strays tend to dig in soft soil or roll in dirt, and soil-based microorganisms coating their fur will be ingested during subsequent grooming. Hence, OC are likely to possess a higher abundance of Ascomycota than IC living in sheltered homes. On the other hand, higher abundance of Basidiomycota in IC may be due to fungi exposure in indoor environments. Majority of the Basidiomycota are distributed on both natural and synthetic wood materials causing decay of litter or wood [32]. However, as there is limited information on the variation of fungal population in feline raw and dry foods, comparisons cannot be made on the type of diet consumed.

The Peniophorella genera is a soil Basidiomycota, reported as wood-inhabiting fungal species found in temperate areas [33], [34]. A study [32] on lung mycobiome reported that Peniophorella was found among the shared fungi communities in healthy and asthmatic human populations but has no known clinical pathogenicity towards animals. The Pichia genus is found in decaying plants in nature and present in humans and animals. Pichia was found to have promising antifungal properties with the production of recombinant AhPDF1.1b, the plant defensins and exerts toxin into fungal cells and inhibit root growth [35], [36]. Pichia is also proven to inhibit the growth of candida through nutrient limitation and promote animal health [37].

Organisms under the genus Coprinopsis are commonly found to possess the ability to produce the toxin coprine (1-cyclopropanol-1-N5-glutamine) which when digested, may cause marked ethanol sensitivity [38]. The phenomena resemble the effects of the drug disulfiram, where in mechanism it inhibits the low Km form of liver acetaldehyde dehydrogenase [38], [39]. Essentially, the compound causes the immediate side-effects of alcohol consumption despite only a small intake of alcohol being ingested. This unique quirk could suggest a way to avoid harmful amounts of alcohol to the subject. Unique interactions between felines and humans allow potential close contact with alcohol and require deterrence for feline safety. Likewise, the genus Montagnula are often identified as saprobes that grow on dead plants, noticeably on dead bark and wood or even on perished leaves [40], though the taxa specificity towards host is yet to be clarified. Past research points out the cosmopolitan nature of the fungus group; its distribution being quite varied as it was found in both tropical and temperate countries (i.e. Algeria, Australia, Italy, South Africa, Thailand) [41]. It could be postulated that its widespread nature warrants common occurrence within the guts of felines.

The genus Trichosporon on the other hand is found as a soil fungus and also inhabits the GIT, respiratory tract and oral cavity in humans and other animals [42]. Trichosporon significance lies as a yeast pathogen and reports as causative agents of fungal infections. They can cause superficial, deep-seated and life-threatening infections in immunocompromised patients, but rarely within animals [42], [43]. Multiple species of Trichosporon are associated with clinical diseases, such as nasal granuloma formation and systemic fungal infection which has been reported in cats and other animals [44], [45]. Similarly, while the Candida genus is another natural microflora found in the gut, mouth and skin of animals and humans, it can lead to various health problems in individuals with compromised immune systems [46]. Candidiasis is a localised fungal disease caused by overgrowth of fungal pathogens. The Candida albican being the predominant Candida species causes infection in cats by invading the gut epithelial barrier and entering bloodstreams, affecting the mucous membranes and skin to develop infection involving various organs and systems, including ear, oral cavity and urinary bladder [47]. Meanwhile, Sordariaceae is a fungus family belonging to the Sordariomycetes class that grows as decomposers in soil and decaying plants [48]. Only a few species of the fungal class Sordariomycetes have been identified as fungal pathogens in plants, with no pathogenic illness reported in animals [49], [50]. It is known that Trichosporon and Candida genera constituents present in animals' bodies are generally non-pathogenic. Still, the hosts may be vulnerable to various diseases caused by these fungi in dysbiosis. The relative abundance of these fungi is greater in OC, indicating that they are more likely from outdoor origin. In addition, the OC are more prone to disease due to environmental hazards such as transmission of fungal pathogens from their surroundings and an unbalanced diet increases the risk of illness due to poor nutrition. A high sugar and high starch diet from leftover foods promote candida overgrowth and causes outdoor cats to be more susceptible to fungal infections. We suggested that the make up in the fungal microbiota in the outdoor cats varied due to adaptation of the intestinal microbiota to the environment as well as their diet strategy. Thus, the genera discussed found within the cats reflect the living conditions of their host; a pathogen-rich environment which requires careful adaptation for survival for outdoor felines in comparison to its indoor counterpart which is a relatively safe environment that aids with slow immunity growth

Insights towards observed fungal species diversity and gut mycobiome composition within study groups

The results from the index observed features within Figure 3 shows that OC had a greater alpha diversity value in comparison to IC samples; suggesting that the fungal composition found within the gut of cats is more diverse if the subject was exposed to or originated from the open surroundings compared to if they were to be kept indoors. As such, OC have better fungal species diversity in comparison to IC. Additionally, as shown in Figures 4(A), 4(B) and 4(D), there exists a clear separation between indoor and outdoor group samples. This clear separation suggests that the fungal compositions found within these samples are very distinct from the opposing sample group. These findings can be supported by observing the P-values that each metric returned from the analysis process. Due to the significance of the results, this would indicate that the fungal composition found within outdoor cats is indeed very different in comparison to the features that would be found within indoor cats.

Significance of observed differences between the two groups

All comparison results seemed to point to three features of interest for the study: the genera Peniophorella, Trichosporon and unclassified Sordariaceae. These features mentioned can be used as tools or indicators to identify potential gut samples from Malaysian cats and determine their domestic conditions due to the significance these features have to the dataset. Nevertheless, while certain other features also seemed to be noteworthy candidates to achieve this same task, such as the case of the genus Coprinopsis which portrayed similar patterns; being exclusive to a certain sample group and is abundantly present within said group, such features cannot be used since it is not deemed significant according to the data gathered.

From a previous study [11], it was concluded that the alpha diversity within Malaysian cats' guts depicted that the IC group had slightly better bacterial species diversity composition in comparison to their outdoor counterparts. Furthermore, beta diversity results indicated that the two groups were quite similar regarding bacteria composition. However, both results were deemed statistically not significant. Additionally, the differential abundance analysis supports the previous observations as no significant features could be found within the dataset. The study concluded that overall, the bacteria composition found within the guts of Malaysian cats are no different if they were kept indoors or exposed to the wild and that diets would seem to be the main influence for any inconsistencies towards the gut microbiome found within subjects.

Interestingly in this study, the findings paint a different picture as both the alpha and beta results are considered significant while there also exists significantly different features within the dataset. The data seemed to propose that the two groups are unique to each other at least in terms of fungal composition. It also became apparent that the OC samples were the group that showcased the most variety in species diversity. Moreover, the data showed the two groups could also be differentiated by the significant features found within each sample group. This shows that despite the observed lack of impact that domestication has towards bacteria gut composition within Malaysia cats, the same cannot be said for the fungal composition. Evidence dictates that keeping a cat indoors or allowing them to be free like a stray will indeed affect their gut mycobiome. This would most likely be due to harsher and more varied environments that the subject would be faced with while also having a much more varied source of food compared to if they were to be fostered and taken care of. As such, most of the differences observed between the fungal compositions of OC to their indoor counterparts can be linked to their given circumstances.

In this study, we found an interesting pattern on the showcases of unique community of fungal species present in the GI tract of indoor and outdoor felines in Malaysia. The IC were given a fixed diet to reduce the rate of variability that would be faced within the study. Results indicate that IC are observed to be more present with Peniophorella (33%) while outdoor samples seem to harbour more Trichosporon (33%) and Pichia (34%) at the genus level. Overall, the genus Peniophorella was observed within Malaysian felines but was more likely to be associated with the indoor variety. Additionally, both IC and OC have their distinct genera of organisms that can only be found exclusively within their group. The greater alpha diversity observed within OC suggests that they have better fungal species diversity in comparison to IC. Likewise, beta diversity indicates that the fungal composition between the two groups is unique and distinct. Furthermore, unlike the previous study [11], the current findings point to the understanding that the environment a feline is subjected to will indeed affect their gut composition, at least in terms of fungal abundance.

Acknowledgement

The authors would like to thank the cat owners who provided cat stool samples and data for this research.

Funding

This work was financially supported the Ministry of Higher Education Malaysia, Universiti Malaysia Pahang and Supercat International Sdn. Bhd. for supporting this study through research funding. HFA was awarded for FRGS/1/2019/WAB13/UMP/03/1 and MNR was granted with UMP-MTUN Industry Matching Grant RDU192802/UIC190812, respectively.

Author Information

Affiliations

Faculty of Industrial Sciences & Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang, Malaysia.

Darren Dean Tay, Siew Shing Wei and Hajar Fauzan Ahmad

Faculty of Chemical and Process Engineering Technology, Lebuhraya Tun Razak, Universiti Malaysia Pahang, 26300 Gambang, Pahang, Malaysia.

Mohd Najib Razali

MNR Multitech Sdn. Bhd, UMP Holdings Complex, Lebuhraya Tun Razak, 26300 Gambang, Pahang, Malaysia.

Mohd Najib Razali

Centre for Research in Advanced Tropical Bioscience (Biotropic Centre), Lebuhraya Tun Razak, 26300 Gambang, Pahang, Malaysia.

Shamrulazhar Shamzir Kamal and Hajar Fauzan Ahmad

Contribution

HFA, MNR, and SSK conceived the study and participated in its design. DDT and SSW conducted the experiment and

analysed the data. DDT and SSW drafted the manuscript. All authors read, revised, and approved the manuscript.

Corresponding author

Correspondence to Hajar Fauzan Ahmad

Ethic declaration

This is an observational study. The Institutional Animal Care and Use Committee (IACUC) of Universiti Malaysia Pahang confirmed that no ethical approval is required because only freely passed stool samples were collected from healthy cats, no specific ethical approval was required. All samples used in this analysis were collected with verbal owner consent, who were aware that these samples were taken for research purpose only.

Conflict of Interest

The authors declare that they have no conflict of interest in the publication.

Data Availability

The sequencing data were deposited in the National Center for Biotechnology Information (NCBI) database and registered as BioProject PRJNA813732, Sequence Read Archive (SRA) was deposited as SRR18272551-SRR18272542, and BioSample with accession numbers SAMN26521149 - SAMN26521158. Additional data regarding results can be found in supplementary file.

M. L. Richard and H. Sokol, “The gut mycobiota: insights into analysis, environmental interactions and role in gastrointestinal diseases,” Nat. Rev. Gastroenterol. Hepatol., vol. 16, no. 6, pp. 331–345, 2019, doi: 10.1038/s41575-019-0121-2.
H. F. Ahmad et al., “Gut Mycobiome Dysbiosis Is Linked to Hypertriglyceridemia among Home Dwelling Elderly Danes,” bioRxiv, p. 2020.04.16.044693, 2020, doi: https://doi.org/10.1101/2020.04.16.044693.
N. Gouba, Y. E. Hien, M. L. Guissou, M. D. M. Fonkou, Y. Traoré, and Z. Tarnagda, “Digestive tract mycobiota and microbiota and the effects on the immune system,” Hum. Microbiome J., vol. 12, no. May, p. 100056, 2019, doi: 10.1016/j.humic.2019.100056.
C. J. Yeoman and B. A. White, “Gastrointestinal tract microbiota and probiotics in production animals,” Annu. Rev. Anim. Biosci., vol. 2, pp. 469–486, 2014, doi: 10.1146/annurev-animal-022513-114149.
T. Jensen et al., “Whey protein stories – An experiment in writing a multidisciplinary biography,” Appetite, vol. 107, pp. 285–294, Dec. 2016, doi: 10.1016/j.appet.2016.08.010.
P. Vernocchi, F. Del Chierico, and L. Putignani, “Gut microbiota metabolism and interaction with food components,” Int. J. Mol. Sci., vol. 21, no. 10, pp. 1–18, 2020, doi: 10.3390/ijms21103688.
D. Zheng, T. Liwinski, and E. Elinav, “Interaction between microbiota and immunity in health and disease,” Cell Res., vol. 30, no. 6, pp. 492–506, 2020, doi: 10.1038/s41422-020-0332-7.
S. M. Ahmad Kendong, R. A. Raja Ali, K. N. M. Nawawi, H. F. Ahmad, and N. M. Mokhtar, “Gut Dysbiosis and Intestinal Barrier Dysfunction: Potential Explanation for Early-Onset Colorectal Cancer,” Front. Cell. Infect. Microbiol., vol. 11, no. December, pp. 1–18, Dec. 2021, doi: 10.3389/fcimb.2021.744606.
C. M. Guinane and P. D. Cotter, “Role of the gut microbiota in health and chronic gastrointestinal disease: Understanding a hidden metabolic organ,” Therap. Adv. Gastroenterol., vol. 6, no. 4, pp. 295–308, 2013, doi: 10.1177/1756283X13482996.
P. Celi, A. J. Cowieson, F. Fru-Nji, R. E. Steinert, A. M. Kluenter, and V. Verlhac, “Gastrointestinal functionality in animal nutrition and health: New opportunities for sustainable animal production,” Anim. Feed Sci. Technol., vol. 234, no. September, pp. 88–100, 2017, doi: 10.1016/j.anifeedsci.2017.09.012.
D. D. Tay, S. W. Siew, M. N. Razali, and H. F. Ahmad, “Metadata Analysis For Gut Microbiota between Indoor and Street Cats of Malaysia,” Curr. Sci. Technol., vol. 1, no. 1, pp. 56–65, May 2021, doi: 10.15282/cst.v1i1.6443.
F. B. Ikmal Hisham, “A Comparison of Gastrointestinal Microbial Communities between Indoor Cats and Outdoor Cats Farhana Ikmal Hisham,” Honor. Theses., no. 2629, 2015, [Online]. Available: https://scholarworks.wmich.edu/honors_theses/2629.
G. B. Huffnagle and M. C. Noverr, “The emerging world of the fungal microbiome,” Trends Microbiol., vol. 21, no. 7, pp. 334–341, 2013, doi: 10.1016/j.tim.2013.04.002.
X. Wu, Y. Xia, F. He, C. Zhu, and W. Ren, “Intestinal mycobiota in health and diseases: from a disrupted equilibrium to clinical opportunities,” Microbiome, vol. 9, no. 1, pp. 1–18, 2021, doi: 10.1186/s40168-021-01024-x.
J. S. Suchodolski, “Intestinal Microbiota of Dogs and Cats: A Bigger World than We Thought,” Vet. Clin. North Am. - Small Anim. Pract., vol. 41, no. 2, pp. 261–272, 2011, doi: 10.1016/j.cvsm.2010.12.006.
S. Banos, G. Lentendu, A. Kopf, T. Wubet, F. O. Glöckner, and M. Reich, “A comprehensive fungi-specific 18S rRNA gene sequence primer toolkit suited for diverse research issues and sequencing platforms,” BMC Microbiol., vol. 18, no. 1, pp. 1–15, 2018, doi: 10.1186/s12866-018-1331-4.
J. F. Garcia-Mazcorro and Y. Minamoto, “Gastrointestinal microorganisms in cats and dogs: A brief review,” Arch. Med. Vet., vol. 45, no. 2, pp. 111–124, 2013, doi: 10.4067/S0301-732X2013000200002.
Y. Lyu, C. Su, A. Verbrugghe, T. Van de Wiele, A. Martos Martinez-Caja, and M. Hesta, “Past, Present, and Future of Gastrointestinal Microbiota Research in Cats,” Front. Microbiol., vol. 11, no. July, 2020, doi: 10.3389/fmicb.2020.01661.
H. F. Ahmad, “Improved methods for high yield genomic DNA of cat stools using DNeasy PowerSoil Pro Kit.,” protocols.io, 2020, [Online]. Available: https://dx.doi.org/10.17504/protocols.io.bp56mq9e.
L. Tedersoo et al., “Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi,” MycoKeys, vol. 10, no. May, pp. 1–43, 2015, doi: 10.3897/mycokeys.10.4852.
P. Oberacker et al., “Bio-On-Magnetic-Beads (BOMB): Open platform for high-throughput nucleic acid extraction and manipulation,” PLoS Biol., vol. 17, no. 1, pp. 1–16, 2019, doi: 10.1371/journal.pbio.3000107.
H. Rodríguez-Pérez, L. Ciuffreda, and C. Flores, “NanoCLUST: a species-level analysis of 16S rRNA nanopore sequencing data,” Bioinformatics, vol. 37, no. 11, pp. 1600–1601, Jul. 2020, doi: 10.1093/bioinformatics/btaa900.
R. Vaser, I. Sović, N. Nagarajan, and M. Šikić, “Fast and accurate de novo genome assembly from long uncorrected reads,” Genome Res., vol. 27, no. 5, pp. 737–746, 2017, doi: 10.1101/gr.214270.116.
J. Bengtsson-Palme et al., “Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data,” Methods Ecol. Evol., vol. 4, no. 10, pp. 914–919, 2013, doi: 10.1111/2041-210X.12073.
E. Bolyen et al., “Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2,” Nat. Biotechnol., vol. 37, no. 8, pp. 852–857, 2019, doi: 10.1038/s41587-019-0209-9.
J. Chong, P. Liu, G. Zhou, and J. Xia, “Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data,” Nat. Protoc., vol. 15, no. 3, pp. 799–821, 2020, doi: 10.1038/s41596-019-0264-1.
A. Dhariwal, J. Chong, S. Habib, I. L. King, L. B. Agellon, and J. Xia, “MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data,” Nucleic Acids Res., vol. 45, no. W1, pp. W180–W188, 2017, doi: 10.1093/nar/gkx295.
N. Ayob, K. Najmi Muhammad Nawawi, R. Affendi Raja Ali, M. Hizami Mohamad Nor, H. F. Ahmad, and N. M. Mokhtar, “IDDF2021-ABS-0187 Exploring gut microbiota composition regulated by probiotics as a potential therapeutic target in non-alcoholic fatty liver disease patients,” in Basic Gastronenterology, Sep. 2021, pp. A55–A56, doi: 10.1136/gutjnl-2021-IDDF.52.
C. E. Older, A. B. Diesel, S. D. Lawhon, C. R. R. Queiroz, L. C. Henker, and A. R. Hoffmann, “The feline cutaneous and oral microbiota are influenced by breed and environment,” PLoS One, vol. 14, no. 7, pp. 1–19, 2019, doi: 10.1371/journal.pone.0220463.
H. F. Ahmad et al., “IDDF2020-ABS-0174 Onset of hypertriglyceridemia in relation to dietary intake, gut microbiome and metabolomics signatures among home dwelling elderly,” in Abstracts, Nov. 2020, vol. 17, no. Cmdi, p. A21.2-A21, doi: 10.1136/gutjnl-2020-IDDF.29.
E. Egidi et al., “A few Ascomycota taxa dominate soil fungal communities worldwide,” Nat. Commun., vol. 10, no. 1, 2019, doi: 10.1038/s41467-019-10373-z.
A. A. Haleem Khan and S. Mohan Karuppayil, “Fungal pollution of indoor environments and its management,” Saudi J. Biol. Sci., vol. 19, no. 4, pp. 405–426, 2012, doi: 10.1016/j.sjbs.2012.06.002.
N. Hallenberg, R. H. Nilsson, A. Antonelli, S. H. Wu, N. Maekawa, and B. Nordén, “The Peniophorella praetermissa species complex (Basidiomycota),” Mycol. Res., vol. 111, no. 12, pp. 1366–1376, 2007, doi: 10.1016/j.mycres.2007.10.001.
Q. X. Guan, T. J. Zhao, and C. L. Zhao, “Morphological characters and phylogenetic analyses reveal two new species of Peniophorella from southern China,” Mycol. Prog., vol. 19, no. 4, pp. 397–404, 2020, doi: 10.1007/s11557-020-01568-6.
O. Mith et al., “The antifungal plant defensin AhPDF1.1b is a beneficial factor involved in adaptive response to zinc overload when it is expressed in yeast cells,” Microbiologyopen, vol. 4, no. 3, pp. 409–422, 2015, doi: 10.1002/mbo3.248.
J. Zhao et al., “Antimicrobial metabolites from the endophytic fungus pichia guilliermondii Isolated from Paris polyphylla var. yunnanensis,” Molecules, vol. 15, no. 11, pp. 7961–7970, 2010, doi: 10.3390/molecules15117961.
P. K. Mukherjee et al., “Oral Mycobiome Analysis of HIV-Infected Patients: Identification of Pichia as an Antagonist of Opportunistic Fungi,” PLoS Pathog., vol. 10, no. 3, 2014, doi: 10.1371/journal.ppat.1003996.
M. J. Edward, “Dictionary of Toxicology second edition, edited by E. Hodgson, R. B. Mailman, and J. E. Chambers, Grove’s Dictionaries, Inc., New York, NY, 600 pages, 1998. $$125.00,” J. Toxicol. Cutan. Ocul. Toxicol., vol. 19, no. 2–3, pp. 169–170, Jan. 2000, doi: 10.3109/15569520009051513 .
D. Benjamin, “Mushrooms: poisons and panaceas: a handbook for naturalists, mycologists, and physicians,” Choice Rev. Online, vol. 33, no. 06, pp. 33-3303-33–3303, 1996, doi: 10.5860/choice.33-3303.
H. A. Ariyawansa et al., “A molecular phylogenetic reappraisal of the Didymosphaeriaceae (= Montagnulaceae),” Fungal Divers., vol. 68, no. 1, pp. 69–104, Oct. 2014, doi: 10.1007/s13225-014-0305-6.
D. N. Wanasinghe et al., “Taxonomy and phylogeny of Laburnicola gen. nov. and Paramassariosphaeria gen. nov. (Didymosphaeriaceae, Massarineae, Pleosporales),” Fungal Biol., vol. 120, no. 11, pp. 1354–1373, Nov. 2016, doi: 10.1016/j.funbio.2016.06.006.
M. J. Biegańska, M. Rzewuska, I. Dąbrowska, B. Malewska-Biel, M. Ostrzeszewicz, and B. Dworecka-Kaszak, “Mixed Infection of Respiratory Tract in a Dog Caused by Rhodotorula mucilaginosa and Trichosporon jirovecii: A Case Report,” Mycopathologia, vol. 183, no. 3, pp. 637–644, 2018, doi: 10.1007/s11046-017-0227-4.
A. L. Colombo, A. C. B. Padovan, and G. M. Chaves, “Current knowledge of trichosporon spp. and trichosporonosis,” Clin. Microbiol. Rev., vol. 24, no. 4, pp. 682–700, 2011, doi: 10.1128/CMR.00003-11.
M. J. Sharman, J. Stayt, S. E. McGill, and C. S. Mansfield, “Clinical resolution of a nasal granuloma caused by Trichosporon loubieri,” J. Feline Med. Surg., vol. 12, no. 4, pp. 345–350, 2010, doi: 10.1016/j.jfms.2009.11.004.
D. R. Rissi, K. D. Kirby, and S. Sanchez, “Systemic Trichosporon loubieri infection in a cat,” J. Vet. Diagnostic Investig., vol. 28, no. 3, pp. 350–353, 2016, doi: 10.1177/1040638716640313.
Y. Tong and J. Tang, “Candida albicans infection and intestinal immunity,” Microbiol. Res., vol. 198, pp. 27–35, 2017, doi: 10.1016/j.micres.2017.02.002.
K. L. Reagan, J. D. Dear, P. H. Kass, and J. E. Sykes, “Risk factors for Candida urinary tract infections in dogs and cats,” J. Vet. Intern. Med., vol. 33, no. 2, pp. 648–653, 2019, doi: 10.1111/jvim.15444.
L. Cai, R. Jeewon, and K. D. Hyde, “Phylogenetic investigations of Sordariaceae based on multiple gene sequences and morphology,” Mycol. Res., vol. 110, no. 2, pp. 137–150, 2006, doi: 10.1016/j.mycres.2005.09.014.
M. Zhang et al., “Effects of Lactobacillus plantarum on the Fermentation Profile and Microbiological Composition of Wheat Fermented Silage Under the Freezing and Thawing Low Temperatures,” Front. Microbiol., vol. 12, no. June, pp. 1–14, 2021, doi: 10.3389/fmicb.2021.671287.
N. Zhang et al., “Genome wide analysis of the transition to pathogenic lifestyles in Magnaporthales fungi,” Sci. Rep., vol. 8, no. 1, pp. 1–13, 2018, doi: 10.1038/s41598-018-24301-6.

No competing interests reported.

SupplementaryFile.xlsx
S1.tif
Supplementary Figure S1. Gut mycobiome composition for cats were conducted with Indoor Cats (IC) samples (n=5) and Outdoor Cats (OC) samples (n=5) through ITS1 gene amplicon sequencing. Results were extracted from MicrobiomeAnalyst after postprocessing in QIIME2: A. Taxa summary - Bar plot illustrating the relative abundant fungal features present within the two groups for each sample at class level; B. Taxa summary - Bar plot illustrating the relative abundant fungal features present within the two groups which combines all appropriate samples at class level where Agaricomycetes (abundance=0.7352) is more prevalent in Indoor Cats (IC) and Saccharomycetes (abundance=0.5733) in Outdoor Cats (OC); C. Taxa summary - Bar plot illustrating the top relative abundant fungal features present within the two groups for each sample at order level; D. Taxa summary - Bar plot illustrating the top relative abundant fungal features present within the two groups which combines all appropriate samples at order level where Agaricales (abundance=0.4077) and Corticiales (abundance=0.3274) are most abundant in Indoor Cats (IC) samples while Saccharomycetales (abundance= 0.5733) and Trichosporonales (abundance=0.3331) are most abundant in Outdoor Cats (OC) samples.
S2.tif
Supplementary Figure S2. Gut mycobiome composition for cats were conducted with Indoor Cats (IC) samples (n=5) and Outdoor Cats (OC) samples (n=5) through ITS1 gene amplicon sequencing. Results were extracted from MicrobiomeAnalyst after postprocessing in QIIME2: A. Taxa summary - Bar plot illustrating the relative abundant fungal features present within the two groups for each sample at family level; B. Taxa summary - Bar plot illustrating the relative abundant fungal features present within the two groups which combines all appropriate samples at family level where Corticiaceae (abundance=0.3274) is more prevalent in Indoor Cats (IC) while Trichosporonaceae (abundance=0.3331) and Pichiaceae (abundance=0.3481) in Outdoor Cats (OC); C. Taxa summary - Bar plot illustrating the top 10 relative abundant fungal features present within the two groups for each sample at species level; D. Taxa summary - Bar plot illustrating the top 10 relative abundant fungal features present within the two groups which combines all appropriate samples at species level where Peniophorella odontiiformis (abundance=0.3274) is the most abundant in Indoor Cats (IC) samples while unclassified Trichosporon (abundance=0.3484) and Pichia kudriavzevii (abundance=0.3641) are most abundant in Outdoor Cats (OC) samples.
S3.tif
Supplementary Figure S3. Results were extracted from MicrobiomeAnalyst after postprocessing in QIIME2. Plot of alpha diversity results using index observed feature at sample level which shows higher diversity in Outdoor Cats (OC) samples compared to Indoor Cats (IC).
S4.tif
Supplementary Figure S4. Analysis was performed on both Indoor Cats (IC) (n=5) and Outdoor Cats (OC) (n=5) sample data obtained from 16S rRNA gene amplicon sequencing. Results were extracted from MicrobiomeAnalyst after postprocessing in QIIME2: Alpha rarefaction plot showcasing that full diversity was observed within all samples.

PDF

Editorial decision: Major revision
05 Apr, 2022
Reviews received at journal
25 Mar, 2022
Reviewers agreed at journal
25 Mar, 2022
Reviewers invited by journal
25 Mar, 2022
Editor assigned by journal
25 Mar, 2022
Submission checks completed at journal
23 Mar, 2022
First submitted to journal
17 Mar, 2022

You are reading this latest preprint version

Full-length ITS Amplicon Sequencing of Feline Gut Mycobiome of Malaysian Local Breeds Using Nanopore Flongle

Status:

Version 3

Abstract

Figures

Introduction

Methods

Results

Discussion

Conclusion

Declarations

References

Competing Interests

Supplementary Files

Status:

Version 3