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Comparative Analysis of Fungal Proteomes for
Identifying Resistance Genes: A preliminary Step
towards Experimental Validation
Abstract— Antimicrobial resistance (AMR) has been declared as
a global threat to public and environmental health by the World
Health Organization (WHO). AMR investigation in fungal
organisms has been limited to mainly clinically relevant species,
leaving atypical and environmental isolates neglected. Due to this,
the aim of this study was to employ computational methods for the
identification of potential proteins associated with antifungal
resistance genes. This served as the initial phase to determine a
shorter list of genes to look for functioning as a molecular catalog of
specific sequences to experimentally validate in environmental
fungal isolates currently available at the Laboratorio de
Bioinformática Aplicada at the UNA. Our approach involved the
analysis of ten proteomes, where we aimed to establish orthologous
relationships with a well-validated database of antifungal genes;
additionally we characterized this orthologous by identifying the
most frequent functional features. To achieve this we used
Orthofinder version 2.5.2 and observed that 96 284 genes (87.6% of
the total) were assigned into 12 005 orthogroups. Four orthogroups
(OG0000000, OG0000004, OG0000378 and OG0000630) were the
only ones that contained sequences from all the analyzed species.
Uncharacterized and hypothetical proteins (UP and HP respectively)
were the most prevailing attributes, followed by ergosterol fungal cell
wall and MFS and ABC transporters, showing sequences related to
different mechanisms of antifungal resistance in the analyzed
species. Nonetheless, we determined that many AMR proteins are
present in non-pathogenic fungal genome sequences, which is of
particular interest as these are usually not studied. AMR in fungi is
complex as it is related to molecular and metabolic mechanisms
rather than only specific molecules, this is integrated to eukaryotic
intrinsic genomic complexity and fungal lack of knowledge This
study represents the initial phase in a series of ongoing investigations
at the LABAP aimed at advancing our understanding of AMR in
environmental fungi. Furthermore, our findings lay the groundwork
for the development of a future surveillance pipeline for
environmental fungi resistance genes to validate in Costa Rica.
Keywords—Antimicrobial resistance (AMR), antifungal
resistance genes, environmental fungi, drug resistance proteins,
orthogroups
I. INTRODUCTION
Antimicrobial resistance (AMR) has been declared as a
global threat to public and environmental health by the World
Health Organization (WHO) [1]. AMR is the ability of a
microorganism to grow and develop in presence of an
antimicrobial agent that used to be effective against that specific
microorganism [2]. This biological capability is conferred by
antimicrobial resistance genes which encodes molecular
mechanisms that counteract the antimicrobial effects [3].
Evolutionary and genomic factors are crucial for the rise and
propagation of AMR. Mutations and changes within the genome
promote resistance mechanisms, hence a genetic and molecular
understanding of these factors provides detailed information to
embrace and discover new strategies to tackle AMR when
analyzing non-model or atypical microorganisms [4]. Bacteria
and Fungi have become organisms of interest due to their rapidly
increasing resistant capabilities. For example, infections caused
by the methicillin-resistant bacteria Staphylococcus aureus
cause high mortality and its treatment is complicated [5]. On the
other hand, new emerging fungal species like Candida auris, is
already considered multiresistant to various families of
antifungal compounds [6]. Antibiotic resistance in bacteria has
been well studied and described, however, antifungal resistance
in fungi has been limited to mainly clinically relevant species
such as Candida albicans, Aspergillus fumigatus and
Crytococcus neoformans [7]. Leaving atypical and
environmental isolates neglected.
Unlike bacteria, fungal organisms are complex due to their
genomes containing metabolic profiles with unique
characteristics and capabilities [8]. These qualities are related to
the production of secondary metabolites, non-essential
molecules that are associated with functional advantages for the
development of the organism [9]. Due to these metabolic
1st
Alonso Segura-Valverde
Escuela de Ciencias Biológicas
Laboratorio de Bioinformática Aplicada
Laboratorio de Análisis Genómico
Universidad Nacional
Heredia, Costa Rica
alonso.segura.valverde@est.una.ac.cr
2nd
Stefany Solano-González
Escuela de Ciencias Biológicas
Laboratorio de Bioinformática Aplicada
Universidad Nacional
Heredia, Costa Rica
stefany.solano.gonzalez@una.ac.cr
3rd
Carolina Sancho-Blanco
Escuela de Ciencias Biológicas
Laboratorio de Análisis Genómico
Universidad Nacional
Heredia, Costa Rica
carolina.sancho.blanco@una.ac.cr

2. profiles, fungal secondary metabolites have been extensively
studied resulting in various interesting reports of fascinating
molecules with antioxidant, antimicrobial and antitumoral
activities [10] – [12]. Despite these many metabolic fungal
studies, a serious gap of information regarding antifungal
resistance and its metabolic profile in environmental isolates is
evident consequently, great uncertainty on this issue associated
with non-model environments.
Thus, the investigation and identification of antifungal
resistance genes in environmental fungal isolates would increase
our current knowledge in a new perspective for the AMR
problematic. In order to do so, a first approach to optimize
resources is to analyze available genome sequences and through
the implementation of different bioinformatics approaches
correlate fungal metabolic capabilities with the molecular
mechanisms of antifungal resistance. For this, a complete
genome sequence of the target organisms is an ideal strategy,
nonetheless the access of funding to do so is usually a limitation.
Therefore, the aim of this study was to compare available
fungal genome sequences to preliminary identify antifungal
resistance genes in environmental relevant fungi species using
computational bioinformatics tools. This information aids to
narrow the plethora of genes to look for and serves as a catalog
of specific sequences to experimentally validate in
environmental fungal isolates currently available at the
Laboratorio de Bioinformática Aplicada at the UNA.
Subsequently, isolates carrying the targeted genes can be
sequenced and more information inferred from it, nonetheless a
first step needs to be taken to construct the foundations. Thus,
this work corresponds to the first step to eventually design a
pipeline of future environmental fungi resistance gene
surveillance in Costa Rica.
II. MATERIALS AND METHODS
A. Omics data integration and database creation
Aspergillus alliaceus, Aspergillus niger, Aspergillus terreus,
Fusarium fujikuroi, Trichoderma atroviride, Trichoderma
harzianum, Trichoderma reesei, Candida albicans,
Cryptococcus neoformans and Aspergillus fumigatus proteomes
were retrieved from the NCBI Database
(https://www.ncbi.nlm.nih.gov) in FASTA format (TABLE 1).
The first seven fungi are representative organisms from the
fungal collection of the Laboratorio de Bioinformática Aplicada
of the Universidad Nacional in Costa Rica.
Additionally, C. albicans, C. neoformans, and A. fumigatus
were included in the analysis as validated positive controls of
antifungal resistance because they represent three of the four
species classified as critical priority fungal pathogens by the
World Health Organization [13].
TABLE 1. Species, GenBank ID, assembly level and number of assembly units
of the proteomes used
Species GenBank ID Assembly level Total # of assembly units
A. alliaceus GCF_009176365.1 Scaffold 331
A. fumigatus GCF_000002655.1 Chromosome 8
A. niger GCF_000002855.4 Scaffold 19
A. terreus GCF_000149615.1 Scaffold 26
C. albicans GCF_000182965.3 Chromosome 8
C. neoformans GCF_000091045.1 Chromosome 14
F. fujikuroi GCF_900079805.1 Chromosome 12
T. atroviride GCF_000171015.1 Contig 29
T. harzianum GCF_003025095.1 Scaffold 532
T. reesei GCF_000167675.1 Scaffold 77
The database of antifungal resistance proteins was
constructed through a bibliographic compilation including the
most common molecular elements associated with antifungal
resistance present in fungal pathogens such as C. albicans, C.
neoformans and A. fumigatus Candida parapsilosis, Candida
tropicalis, Nakaseomyces glabrata and other species [14] – [15].
A total of 40 sequences were used for the database creation in
which we include, pharmacological targets of most common
antifungals drugs (14-α-sterol demethylase and 1,3-beta-D-
glucan synthase), transcription factors (TAC1, MRR1 and
PDR1), multidrug resistance proteins (MDR1, FLU1, CDR1
and CDR2) and other sequences present in mechanisms of
resistance [16] – [18].
B. Sequence comparision, orthology and gene clustering
analysis
Orthofinder version 2.5.2 [19] was used to determine
orthologous comparisons and relationships between fungal
proteomes and the antifungal database through standard mode
parameters. Orthologous genes are grouped into orthogroups,
and as a result, genes within the same orthogroup are expected
to retain their functions. This provides a useful method for
predicting the functions of uncharacterized genes or proteins.
Subsequently, CAGECAT version 1.0 [20] web platform
(https://cagecat.bioinformatics.nl/) was used to analyze proteins
marked as hypothetical and uncharacterized of the most
important orthogroups according to the orthologues results, this
to determine homologous gene clusters and have more
information about the relationship between the
putative/uncharacterized proteins and resistance mechanisms.
For the CAGECAT analysis, Refseq database were used with a
maximum of 100 hits, e-value of 0.01, identity percentage of
30% and query coverage of 50%. These parameters were
selected to remove redundancy and be permissive to find
homologous gene clusters similar to the input sequences.

3. III. RESULTS
Our orthofinder results, comparing ten fungal proteomes to
an antifungal database, showed that 96 284 genes (87.6% of the
total) were assigned into 12 005 orthogroups. Additionally, there
were 13 638 unassigned genes (12.4% of the total). Four
orthogroups (OG0000000, OG0000004, OG0000378 and
OG0000630) were the only ones that contained sequences from
all the analyzed species , including genes from the antifungal
database, therefore we focused our attention on these
orthogroups.
In order to describe these four orthogroups and understand
its constituent proteins, we identified the most recurring
features. OG0000000 showed a total of 347 proteins grouped
with four sequences of the antifungal database (fluconazole
resistance protein from C. albicans GenBank ID: AAF99573.1
and three multidrug resistance proteins from C. albicans and C.
parapsilosis GenBank ID´s: BAG07033.2, KAF6067925.1 and
KAF6070790.1). The predominant traits (Figure 1a) in this
orthogroup were related with major facilitator superfamily
(MFS) transporters (29.1%) and resistance related proteins
(9.3%), although the vast majority of features present in the
orthogroup were hypothetical proteins (35.3%).
OG0000004 was conformed by 119 proteins including eight
sequences of our database, mostly drug resistance proteins
CDR1/CDR2 and ATP-binding cassette (ABC) transporters of
C. albicans (GenBank ID´s ABE98657.1, ABE98658.1,
ABE98662.1, AOW28537.1, CAA54692.1, P43071.1,
P78595.2 and KHC63800.1). The features of this orthogroup
(Figure 1b) were mostly ABC transporters (48.7%), followed
by hypothetical proteins (25.2%) and uncharacterized proteins
(20.7%).
The features of OG0000378 (21 proteins total) and
OG0000630 (17 proteins total) (Figure 1c and 1d,
respectively) were principally related to the biosynthesis of
ergosterol (64.8%) and the fungal cell wall (91.7%),
respectively. The proteins of the database present in these
orthogroups were also associated with these main features.
From our gene resistance database, the orthogroup
OG0000378 included 4 sequences (GenBank ID´s:
AEX20236.1, AGH55424.1, AIX03623.1 and UNE56009.1)
and the orthogroup OG0000630 contained 5 sequences
(GenBank ID´s: ACF22801.2 , ACL35766.1, ADB43260.1,
ADB43261.1 and AYN77786.1) from various organisms like
C. neoformans, A. fumigatus, C. albicans and C. tropicalis.
Main features among the species (Table 2) showed that A.
alliaceus (83), A. niger (77) and A. terreus (64) had the
highest number of total protein orthologs considering the four
analyzed orthogroups. 100% of the counts of A. niger were
classified as uncharacterized proteins and 85.8% of the
proteins of A. terreus were hypothetical. F. fujikuroi
sequences were the most related with resistance proteins
(RRP). On the other hand, Tricoderma species presented a
high percentage of protein counts associated with hypothetical
and uncharacterized proteins. C. albicans and C. neoformans
had distributed traits, unlike the sequences of A.fumigatus that
were mostly associated with MFS and ABC transporters.
Fig. 1. Total feature distribution for predominant traits of orthologous proteins
of the analyzed orthogroups, (a) OGOG0000000, (b) OGOG0000004, (c)
OG0000328, (d) OG0000630.
Species
Features A. a A. f A. n A. t C. a C. n F. f T. a T. h T. r
MFS 71.1% 65.9% 22.2% 15.7%
ABC 21.7% 27.3% 6.3% 10.5% 22.2% 27.5%
RRP 1.6% 15.8% 16.7% 49.1% 4.2%
EBR 1.2% 4.5% 4.7% 5.3% 5.5% 3.9% 3.5%
FCWBR 1.2% 2.3% 1.6% 15.8% 5.5% 1.9% 2.1% 2% 3.5%
UP 2.4% 100% 10.5% 93%
HP 85.8% 22.2% 93.7% 98%
OF 2.4% 42.1% 5.5% 1.9%
Total% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
Total #
of
counts
83 44 77 64 19 18 51 48 50 29
TABLE 2. Total features of orthologous proteins in all analyzed orthogroups
among fungi species

4. Green color indicates presence of a particular feature, the darker the tone the
higher the percentage of the feature, taking into consideration the total protein
orthologs counts. Abbreviations definitions: Major facilitator superfamily
transporters (MFS), ATP-binding cassette transporters (ABC), resistance
related proteins (RRP), ergosterol biosynthesis related (EBR), fungal cell wall
biosynthesis related (FCWBR), uncharacterized proteins (UP), hypothetical
proteins (HP) and other features (OF), Aspergillus alliaceus (A. a), Aspergillus
fumigatus (A. f), Aspergillus niger (A. n), Aspergillus terreus (A. t), Candida
albicans (C. a), Cryptococcus neoformans (C. n), Fusarium fujikuroi (F. f)
Trichoderma atroviride (T. a), Trichoderma harzianum (T. h), Trichoderma
reesei (T. r).
Finally, the analysis of hypothetical and uncharacterized
proteins in the four orthogroups using CAGECAT yielded no
results. This indicates that the software did not identify any
similarity or homology between our query sequences and the
gene clusters available in the Refseq database.
IV. DISCUSSION
Fungi are known for its cosmopolitan distribution ranging
not only in location, from land, deep sea and surface waters but
in substrate; being found in sediments [21], water column [22],
algae [23] and many others [24] – [26]. In addition, plenty of
studies report the use and applicability of fungi and its
derivatives (either from conventional or biotech approaches) in
a wide range of industries [27] – [29]. Nonetheless, taking into
consideration that fungi have great capability to colonize new
habitats is critical to assess whether these microorganisms
could present AMR genes, either by its current exposure or by
any sort of vertical transference. This assessment is particularly
important in environmental samples, as pathogenic strains are
well monitored [30].
Therefore, as a first step towards a future development of a
surveillance protocol we analyzed the proteome of three well-
known pathogenic fungi and seven representative organisms
from the current LABAP collection. We compared these
datasets of proteins to a literature-curated antifungal protein
database. We observed that hypothetical proteins and
uncharacterized proteins were the most frequent features. The
main difference being, the former lack validation as proteins,
while the latter are validated as proteins, but their functions are
unknown. Proteins for ergosterol and fungal cell wall
biosynthesis, followed by MFS and ABC transporters were the
more frequent features after HP and UP.
HP features were more common in A. terreus, T. atroviridae
and T. harzianum. Unlike A. terreus the genome sequences for
the Trichoderma species was of recent public availability 2021
and 2018 [31] – [32] therefore it’s not entirely surprising some
of its functional annotation is lacking. Nevertheless, the
unexpected prevalence of uncharacterized protein (UP) features
in the A. niger and T. reesei reference genomes, constitutes a
noteworthy observation, as each of these genomes comprises
10 plus assemblies [33].
It is important to mention some genes are natural to fungal
genomes, which are part of their primary and secondary
metabolite repertoire, others can be obtained by genetic
recombination and horizontal gene transfer [7]. Membrane
transporters have been associated with AMR [34]. MFS and
ABC transporters are involved in the transit of substrates from
membrane to membrane; this action requires a conformational
change, meaning the interaction between the cargo molecule
and the transporter protein is relevant [35] – [36]. As a first
approach towards its understanding was to infer the presence of
MFS and ABC transporters, which were present A. alliaceus,
A. fumigatus C. neoformans, F.fujikuroi and in less proportion
in A. terreus and C. albicans, being these genes to monitor due
to its biological role [37] – [38].
Drug resistance proteins in C.albicans has been well
reported, but the resistance associated with species such as
Tricoderma sp. and F.fujikuroi has not been explored in depth
[18]. Sequences that confer resistance to antifungal compounds
phenamacril, benzimidazole and prochloraz have been found in
F.fujikuroi [39] – [41] therefore probably the sequences in our
results are related to these kind of resistance mechanisms. In the
case of A.niger, A.terreus and A.alliaceus there resistance
mechanisms are not completely known, scientific efforts have
been more focused on studying AMR genes in Aspergillus
fumigatus with interesting findings in azole-resistance
mechanisms [42].
Azoles, in adition with echinocandins, are two of the most
used antifungal drugs for the treatment of fungal infections
[43]. Azoles inhibit the enzyme 14-α-demethylase, a crucial
enzyme for the ergosterol biosynthesis [44]. Echinocandins,
instead affect the 1,3-β-D glucan (important component of the
fungal cell wall) biosynthesis, by targeting the enzyme
glycosyltransferase 1,3-β-D glucan synthase [45]. Orthogroups
378 and 630 were related with these two specific features
respectively. Results indicate the importance of these
orthologous sequences in emerging mechanisms of resistance
in environmental fungi; a change within the genome can cause
the occurrence of a resistance phenotype.
Despite the existence of web tools to study proteomes to
either, identify gene clusters similarities [20] or antimicrobial
resistance [46] these are limited to model species or bacteria.
Therefore, its implementation lacks utility for fungal
environmental sequences. For example, we conducted multiple
analyses using CAGECAT, a web server to identify gene
cluster similarities and visualization, with various combinations
of our orthogroups. However, none of them produced any hits.
Besides new and more sophisticated tools being developed
[47], most of them are currently only applicable to bacteria.
We acknowledge the significance of experimental
validation [15] and the current limitations of our results. We

5. highlight two key findings: firstly, the widespread lack of
information regarding the identification, validation, and
surveillance of fungal resistance genes; secondly, we have
established a methodology for analyzing future genome and
proteome sequences at the national level. This lays the
groundwork for more targeted experimental validation efforts,
by providing specific groups of targeted genes.
In our future work, we plan to experimentally validate the
genes within orthogroups 0 and 04 using conventional PCR and
Sanger sequencing. We will use fungal isolates currently
available at LABAP to identify target organisms for full
genome sequencing. Furthermore, the findings from this
manuscript serve as the foundational basis for developing a
molecular biology surveillance pipeline in Costa Rica.
V. CONCLUSIONS
Overall, we determined a large number of UP and HP across
the analyzed proteomes, which demonstrates the need of studies
to fill this gap of information. We also identified the ergosterol
and fungal cell wall biosynthetic pathway highly present,
demonstrating its important relation to resistance. As well, the
MFS and ABC transporters are relevant molecular elements to
analyze in fungal resistance behavior. We also observed that
RRP proteins are present in non-pathogenic fungal genomes,
which is of particular interest as these are usually not analyzed;
these might be a future human health issue. Further
environmental fungal monitoring is needed to continue
understanding established or emerging mechanisms of
resistance such as metabolic bypass, gain-of-function mutations
and overexpression of efflux pumps, especially to assist the
process of inferring unrevealed resistance-related sequences in
non-model environmental species.
AMR in fungi is more complex than in bacteria as it is
related to molecular and metabolic mechanisms rather than only
specific molecules, this is integrated to eukaryotic intrinsic
genomic complexity and fungal lack of knowledge (in terms of
species distribution, identification, and molecular potential).
This study corresponds to the first approach of a series of
investigations that are being developed in the Laboratorio de
Bioinformática Aplicada of the Universidad Nacional in Costa
Rica to expand the current knowledge about AMR in
environmental fungi.
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