Abstract
Background
Trichophyton mentagrophytes is an important zoonotic dermatophytic
(ringworm) pathogen; causing severe skin infection in humans and other
animals worldwide. Fortunately, commonly used fungal skin disease
prevention and treatment measures are relatively simple. However, T.
mentagrophytes is primarily studied at the epidemiology and drug
efficacy research levels, yet current study has been unable to meet the
needs of clinical medicine.
Zinc is a crucial trace element for the growth and reproduction of
fungi and other microorganisms. The metal ions coordinate within a
variety of proteins to form zinc finger proteins, which perform many
vital biological functions. Zinc transport regulatory networks have not
been resolved in T. mentagrophytes. The T. mentagrophytes transcriptome
will allow us to discover new genes, particularly those genes involved
in zinc uptake.
Result
We found T. mentagrophytes growth to be restricted by zinc deficiency;
natural T. mentagrophytes growth requires zinc ions. T. Mentagrophytes
must acquire zinc ions for growth and development.
The transcriptome of T. mentagrophytes was sequenced by using Illumina
HiSeq™ 2000 technology and the de novo assembly of the transcriptome
was performed by using the Trinity method, and functional annotation
was analyzed. We got 10,751 unigenes. The growth of T. mentagrophytes
is severely inhibited and there were many genes showing significant up
regulation and down regulation respectively in T. mentagrophytes when
zinc deficiency. Zinc deficiency can affect the expression of multiple
genes of T. mentagrophytes. The effect of the zinc deficiency could be
recovered in the normal medium. And we finally found the
zinc-responsive activating factor (ZafA) and speculated that 4 unigenes
are zinc transporters. We knocked ZafA gene by ATMT transformation in
T. mentagrophytes, the result showed that ZafA gene is very important
for the growth and the generation of conidia in T. mentagrophytes. The
expression of 4 zinc transporter genes is potentially regulated by the
zinc-responsive activating factor. The data of this study is also
sufficient to be used as a support to study T. mentagrophytes.
Conclusion
We reported the first large transcriptome study carried out in T.
mentagrophytes where we have compared physiological and transcriptional
responses to zinc deficiency, and analyzed the expression of genes
involved in zinc uptake. The study also produced high-resolution
digital profiles of global genes expression relating to T.
mentagrophytes growth.
Electronic supplementary material
The online version of this article (10.1186/s12864-017-4284-3) contains
supplementary material, which is available to authorized users.
Keywords: Trichophyton mentagrophytes, Transcriptome sequencing,
Functional annotation, Zinc uptake, Zinc-responsive activating factor
Background
Trichophyton mentagrophytes is an important zoonotic dermatophytic
(ringworm) pathogen that can cause severe skin infections in humans and
other animals, seriously threatening to human health and animal
husbandry [[37]1, [38]2].
Particular nutrients play an extremely important role during T.
mentagrophytes invasion; these elements include zinc, iron, nitrogen,
and selenium [[39]3]. Among these, zinc is a crucial trace element,
which often coordinates within a variety of proteins to form zinc
finger proteins, which perform many vital biological functions.
Although zinc is essential for fungi, it can also be toxic. When the
intracellular zinc level rises to some critical level, the zinc ions
can affect other important physiological processes [[40]4]. Therefore,
fungi have successfully evolved zinc transporter systems to maintain a
homeostatic balance of zinc ions for survival and virulence.
Zinc transporter system expression in the model fungus Saccharomyces
cerevisiae is primarily regulated by the C[2]H[2]-type zinc finger
transcription factor Zap1 at the transcriptional level [[41]5, [42]6].
Studies have shown that various fungi can secrete functionally similar
zinc finger transcriptional factors. For example in the fungi,
Aspergillus fumigatus, Candida albicans, and Cryptococcus gattii,
mutations in similar zinc transport mechanism genes stop growth and
development, and can even cause loss of virulence [[43]7–[44]11]. T.
mentagrophytes expresses various zinc finger proteins; of prime
importance for growth and virulence is the exocrine zinc finger
protein. Perhaps most representative are the zinc metalloproteinases,
which can digest and absorb nutrients, and invade the body cuticle
[[45]12]. Previous research has shown that metalloproteinase gene
mutations can affect T. mentagrophytes virulence at different levels
[[46]13]. At the same time, only if zinc metalloproteinase combines
zinc element will it be able to exhibit biological activity. We
speculated that T. mentagrophytes has a C[2]H[2]-type zinc finger
transcription factor that can serve as an upstream regulator in the
absorption of zinc.
The T. mentagrophytes zinc transport regulation network has not been
determined. Our study sequenced the T. mentagrophytes transcriptome
using Illumina HiSeq™ 2000 technology, a de novo assembly of the
transcriptome was performed using the Trinity method, and we performed
functional annotation analysis. This allowed us to produce
high-resolution digital profiles of global gene expression relating to
T. mentagrophytes growth. The T. mentagrophytes transcriptome will be
characterized further, and zinc-uptake-related gene families will be
systematically explored as discovered, in future work.
Methods
Fungal culture and RNA extraction
The T. mentagrophytes wild-type strain ATCC 28185 (a gift from Ruoyu
Li, Peking University First Hospital, China) was maintained at 28 °C on
solid Sabouraud dextrose medium (SDA) for 14 days. Five mL of sterile
saline was used to wash off spores so as to collect fungus liquid. The
fungus liquid concentration [47]was [48]adjusted [49]to 10^8 CFU/mL by
cell count plate. SDA with 1 mM EDTA was supplemented to generate zinc
deficient SDA, named SDA-Zn (zinc ions have been chelated). The 150-μL
fungus liquid was inoculated to SDA (sufficient zinc ions, grouped into
Norm) and SDA-Zn with 200, 400, 600, and 1000 μM of zinc sulfate
(grouped into Zn200, Zn400, Zn600, and Zn1000) respectively. Culture
conditions were 28 °C for 14 days.
Total RNA was extracted using TRIzol® reagent (Invitrogen, USA)
following the manufacturer’s protocol, and DNase Ι (Takara, Japan) was
used to remove genomic DNA. Integrity and size distributions were
checked using an Agilent 2100 (Agilent, USA) with an RNA integrity
number (RIN: 8.0) and GE Image Quant 350 ([50]GE Healthcare, USA).
cDNA library construction and Illumina sequencing
The extracted RNA samples (Norm, Zn400, and Zn1000) were used for cDNA
synthesis. Poly(A) mRNA was enriched by Oligo (dT) beads (Qiagen,
German). Next, the enriched mRNA was fragmented and reverse transcribed
into first-strand cDNAs with random hexamers. Use DNA polymerase I
(Thermo Fisher Scientific, USA), RNase H, dNTP, and buffer to
synthesize second-strand cDNA. Then using QiaQuick PCR extraction kit
(Qiagen, German) to purify the cDNA fragments, and the cDNA fragments
were ligated to Illumina sequencing adapters. The ligation products
were size selected by agarose gel electrophoresis, PCR amplified, and
sequenced using Illumina HiSeq™ 2000 by Gene De novo Biotechnology Co
(Guangzhou, China). Sequence data were deposited at the NCBI Short Read
Archive database
([51]https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=search_obj&m
=&s=&term=SRR5097230&go=Search) under the accession numbers SRR5097135,
SRR5097227, SRR5097226, SRR5097228 (Norm), SRR5097229, SRR5097230,
SRR5097231, SRR5097232 (Zn10 00), and SRR5895930 (Zn400).
De novo assembly and functional annotation
High quality, clean reads for the assembly library were generated by
filtering according to the following rules: reads containing adapters,
more than 10% of unknown nucleotides (N) and more than 50% low quality
(Q-value ≤10) bases were removed. The quality-filtered reads obtained
were then de novo assembled into contigs by the Trinity Program
[[52]14]. Trinity is a modular method and software package, which
combines three components: Inchworm, Chrysalis, and Butterfly.
Initially, Inchworm assembles reads, resulting in a collection of
linear contigs. Next, Chrysalis clusters related contigs, and then
builds de Bruijn graphs for each cluster of related contigs. Finally,
Butterfly analyzes the paths and outputs one linear sequence and
ultimately generates unigenes. We used the BLASTx program
([53]https://blast.ncbi.nlm.nih.gov/Blast.cgi) with an E-value
threshold of 1 × 10^−5 to obtain protein functional annotations, by
aligning our unigenes to protein sequences from NCBI Nr (non-redundant
protein database, [54]https://blast.ncbi.nlm.nih.gov/Blast.cgi),
Swiss-Prot (annotated protein sequence database,
[55]http://www.expasy.org/), KEGG (Kyoto encyclopedia of genes and
genomes, [56]http://www.genome.jp/kegg/), and COG (clusters of
orthologous groups of protein, [57]https://www.ncbi.nlm.nih.gov/COG/).
The Blast2GO program [[58]15] was used to obtain gene ontology (GO)
annotation of our unigenes from Nr annotation, and then WEGO software
[[59]16] was used to perform GO functional classifications. KEGG is a
major public pathway-related database [[60]17] with which one is able
to analyze gene products within the context of metabolic and cellular
processes.
Identification of Differentially Expressed Genes (DEGs)
The RPKM (reads per kb per million reads) was used to calculate and
normalize the number of unique-match reads. The formula follows:
RPKM = (1000,000 × C)/(N × L/1000), with RPKM set as the expression of
unigene A, C as the number of reads that are uniquely mapped to unigene
A, N as the total number of reads that are uniquely mapped to all
unigenes, and L as the length (base number) of unigene A. The RPKM
measure can provide normalized values of gene expression to enable
transcript comparisons between Norm, Zn400, and Zn1000. We used the
edgeR package
([61]https://bioconductor.org/packages/release/bioc/html/edgeR.html) to
identify differentially expressed genes across samples. We specified
|log2FC| > 1 with the false discovery rate (FDR) < 0.05, as the
thresholds necessary to determine significant differences in gene
expression between Norm, Zn400, and Zn1000. Differentially expressed
genes (DEGs) were then subjected to GO functional and KEGG pathway
enrichment analyses. GO enrichment analysis provides all GO terms that
are significantly enriched in DEGs compared with the genome background,
and filter DEGs corresponding to biological function. Initially all
DEGs are mapped to GO terms in the Gene Ontology database
([62]http://geneontology.org/), gene numbers are calculated for every
term, and significantly enriched GO terms in DEGs compared with the
genome background are defined by a hypergeometric test. The P-value
formula is:
[MATH: P=1−∑i=0
m−1iM
n−iN−MnN
:MATH]
Here N is the number of all genes with GO annotation; M is the number
of all genes that are annotated to the certain GO terms; n is the
number of DEGs in N; m is the number of DEGs in M. The calculated
p-value then goes through FDR correction, taking FDR ≤ 0.05 as a
threshold. GO terms meeting this condition are defined as significantly
enriched GO terms in DEGs. Our analysis successfully recognized the
putative biological functions of our DEGs.
Genes usually interact with each other to play roles in particular
biological functions. Pathway-based analysis helps to further determine
genes’ biological functions. KEGG is the major public pathway-related
database [[63]17]. Pathway enrichment analysis identifies significantly
enriched metabolic pathways or signal transduction pathways in DEGs.
The significance formula is the same as that in GO analysis. Here N is
the number of all genes with KEGG annotation, M is the number of all
genes annotated to specific pathways, n is the number of DEGs in N, and
m is the number of DEGs in M. The calculated p-value then goes through
FDR Correction, taking FDR ≤ 0.05 as a threshold. Pathways meeting this
condition are defined as significantly enriched pathways in those DEGs.
Validation of differential expression using qRT-PCR
Total RNA was extracted as described above, and cDNA was generated from
total RNA. Primers for quantitative real time PCR (qRT-PCR) were
designed using Primer Premier 6.0 software (Premier, Canada), and
synthesized by Gene De novo Biotechnology Co (Guangzhou, China). All
primers are shown in Additional file [64]1. The 18S gene was used as an
internal control. qRT-PCR was performed on a Step One Plus™ Real-Time
PCR System (Thermo Fisher Scientific, USA). Each 20-μL reaction mixture
contained 10 μL of Maxima SYBR Green/ROX qPCR Master Mix (2X) (Thermo
Scientific, USA), 0.3 μL of each primer (10 μM), 0.8 μL of cDNA, and
8.6 μL of nuclease-free water. The qRT-PCR run protocol was as follows:
95 °C, 10 min; followed by 40 cycles of 95 °C, 15 s; 60 °C, 30 s; and
72 °C, 15 s in 96-well optical reaction plates. Three biological
replicates with three technical replicates for each value determined
the Ct values. Expression levels of the tested reference genes were
determined by Ct values and calculated by2^-△△Ct.
Construction of transformation vectors and ATMT transformation
The binary vector pDHt/ZafA::hph used for site-directed mutagenesis was
constructed by reorganizing the hygromycin B resistance gene (hph) of
plasmid pAN7–1, the left and right flanking sequences of the ZafA gene
of T.mentagrophytes simultaneously into XhoI/HindIII digested plasmid
pDHt/SK (a gift from Dr. K. J. Kwon-Chung). The ZafA gene was knocked
by Agrobactirium tumfacience mediated-transformant (ATMT) in T.
mentagrophytes. The two pairs of primers (ZafA-F: CCAGACTGAAGGTGCTAAG,
ZafA-R: CCTGTTAGTATCGTCGTGTT; hph-F: TACATCCATACTCCATCCTTC, hph-R:
CGGCATCTACTCTATTCCTT) designed by ZafA gene fragment disrupted and hph
gene fragment were used to verified ZafA gene mutant strain, and its
amplification length is 400 and 1200 bp, respectively.
Results
Effect of zinc deficiency on the growth of T. mentagrophytes
The T. mentagrophytes cells of five groups were maintained at 28 °C for
14 days. We found T. mentagrophytes can grow well on SDA in Norm,
presenting a white colony with fluffy, fine mycelium on its surface.
The colony morphology of the Zn1000 group was not significantly
different from that of the Norm group, but the growth rate was slower.
In contrast, with the decreasing zinc ion concentration in its medium,
the growth of Zn400 and Zn600 T. mentagrophytes was severely inhibited,
with pale-yellow mucus-covered colonies in which mycelium could not be
seen. The growth of Zn200 T. mentagrophytes was most seriously
inhibited, with the colony appearing to be folded over (Fig. [65]1a).
Fig. 1.
Fig. 1
[66]Open in a new tab
a The growth situation of T. mentagrophytes in 5 groups. SDA with 1 mM
EDTA was supplemented to generate zinc deficient SDA, named SDA-Zn
(zinc ions have been chelated). The 150-μL fungus liquid was inoculated
to SDA (sufficient zinc ions, grouped into Norm) and SDA-Zn with 200,
400, 600, and 1000 μM of zinc sulfate (grouped into Zn200, Zn400,
Zn600, and Zn1000) respectively. T. mentagrophytes can grow well on SDA
in Norm and Zn1000, the growth of Zn200、Zn400 and Zn600 T.
mentagrophyte was inhibited, especially in Zn200. b The each group was
stained by lacto phenol cotton blue. Norm and Zn1000 mycelium grow well
and with numerous round microconidia. A small number of microconidia
and mycelium can be observed in Zn600, and even fewer in Zn400. We
could not detect favorable microconidia and the mycelium was
particularly weak in Zn200. c T. mentagrophytes from Zn200, Zn400 and
Zn600 were inoculated into normal medium and Zn1000 medium, the T.
mentagrophytes growth traits returned to normal, T. mentagrophytes can
grow well on SDA in Norm and Zn1000
Upon microscopy Norm and Zn1000 mycelium can be seen to grow well, with
numerous round microconidia in grape-like clusters. A small number of
microconidia and mycelium can be observed in Zn600, and even fewer in
Zn400. However, we could not detect microconidia in Zn200 at all, and
its mycelium were particularly weak (Fig. [67]1b).
T. mentagrophytes from Zn200, Zn400 and Zn600 were then inoculated into
normal medium and Zn1000 medium, and the T. mentagrophytes growth
traits returned to normal (Fig. [68]1c).
These results collectively indicate that when T. mentagrophytes cells
grow in zinc-deficient conditions growth status is adversely affected,
showing that zinc is very important for the growth of T.
mentagrophytes.
De novo assembly and sequence annotation
A total of 36,793,459 raw reads and 36,227,708 quality filtered (clean)
reads were obtained from the Norm library. We obtained 30,028,768 raw
reads and 29,576,580 quality filtered (clean) reads from Zn1000. And in
Zn400, we obtained 47,306,862 raw reads and 46,394,758 quality filtered
(clean) reads. The saturation curves shown depict the detected number
of genes that tend to be saturated (Additional file [69]2). The Q20
percentages (percentage of sequences with sequencing error rates) of
the three libraries, Norm, Zn1000, and Zn400, were 97.69, 97.72, and
94.58% respectively, and the GC content ranged from 51.52 to 52.83%.
All clean reads were pooled together and then de novo assembled by
Trinity. The assembly produced a substantial number of contigs and
10,751 unigenes.
These unigenes were annotated using the Nr, Swiss-Prot, KEGG, and KOG
databases. A final number of 9593, 6113, 3765, and 5172 unigenes had
matches in the Nr, Swiss-Prot, KEGG, and KOG databases, respectively
(Additional file [70]3). Up to 89.23% of all machine annotated unigenes
showed similarity to known proteins in the Nr database. Additionally,
the unigenes were searched against the Nr database using BLASTx, and
homologous sequences and species identification were ascertained. The
five highest number of homologous sequences corresponding to particular
species follows: 24.86% Trichophyton equinum CBS 127.97, 23.98%
Trichophyton tonsurans CBS 112818, 10.40% Trichophyton interdigitale
H6, 6.60% Trichophyton rubrum CBS 118892, and 4.35% Microsporum gypseum
CBS 118893.
As shown in Fig. [71]2a, 5172 unigenes (48.11% of total) were
classified into 25 functional KOG classifications, based on sequence
similarity. The predominant term was “general function prediction
only,” for which 1648 unigenes (31.86%) were qualified.
“Posttranslational modification, protein turnover, chaperone” (1193
unigenes), “signal transduction mechanism” (1048 unigenes), and “RNA
processing and modification” (721 unigenes) were other major categories
selected, and only eight, 22, 53, and 54 unigenes matched the terms
“cell motility,” “extracellular structure,” “defense mechanism,” and
“nuclear structure,” respectively.
Fig. 2.
Fig. 2
[72]Open in a new tab
a The KOG classification of unigenes, 5172 unigenes (48.11% of total)
were classified into 25 functional KOG classifications. b The GO
functional classification of unigenes. A total of 6053 machine
annotated unigenes were grouped into 40 functional group categories
using GO assignment. c, d and e The GO functional classification of
DEGs. These DEGs were classified into three main categories including
cellular component, biological process and molecular function
A total of 6053 machine annotated unigenes were grouped into 40
functional group categories using GO assignment (Fig. [73]2b). Among
these categories, 15 are involved in “biological process,” 11 in
“molecular function,” and 14 in “cellular component.” “Metabolic
process” (3422 unigenes) and “cellular process” (3121 unigenes) are
dominant among these; “detoxification (four unigenes) and growth” (21
unigenes) are the scarcest in the “biological process” category. Within
the “molecular function” category, a high percentage of genes are
associated with “catalytic activity” (3538 unigenes) and “binding”
(3272 unigenes). Minimal “molecular function” GO assignments included
“signal transducer activity” (21 unigenes) and “electron carrier
activity” (22 unigenes). Within the “cellular components category,”
“cell” (2374 unigenes) and “cell part” (2374 unigenes) are predominant;
“nucleoid” (six unigenes) and “supramolecular fiber” (six unigenes) had
the fewest matches.
A total of 2292 unigenes were annotated to 114 pathways in this study;
the pathways of all our unigenes are shown in Additional file [74]4.
“Metabolic pathway” represented the largest group (1916 unigenes), with
most being involved in the “biosynthesis of amino acids” (138
unigenes), “carbon metabolism” (118 unigenes), “purine metabolism” (117
unigenes), and “oxidative phosphorylation” (113 unigenes). Secondary
pathways included “genetic information processing” (1103 unigenes),
which included “ribosome” (151 unigenes), “RNA transport” (99
unigenes), “spliceosome” (94 unigenes), “protein processing in
endoplasmic reticulum” (93 unigenes), and “endocytosis” (92 unigenes).
These probable pathways provide a valuable resource for investigating
specific metabolic processes and gene functions in T. mentagrophytes.
Overview of differentially expressed genes
FDR and log2FC were both used to filter our DEGs, with the filter
conditions of FDR < 0.05 and |log2FC| > 1. We compared Norm (as a
control) to Zn400 and Zn1000. Results showed 2314 and 2127 genes are
significantly up- and down-regulated, respectively, in Zn400. In
Zn1000, 1395 and 1446 genes displayed significant up-regulation and
down-regulation, respectively. A comparison was also performed using
Zn1000 as the control, against Zn400. This result showed significant
up- and down-regulation: 2268 and 2040 genes were regulated in Zn400 as
compared with Zn1000, respectively. All DEGs are shown in
Additional file [75]5, and a group diffuse analysis ‘volcano plot’ is
shown in Additional file [76]6.
The Zn400 DEGs were subjected to GO-term analysis (Fig. [77]2c); these
DEGs partitioned into three major categories: “cellular component”
(1165), “biological process” (1880), and “molecular function” (2024).
These major categories sorted into several subcategories (based on
Pvalue <0.05 and Qvalue <0.05): “electron carrier activity” within the
“molecular function” category, and “generation of precursor metabolites
and energy,” “oxidation-reduction process,” “electron transport chain,”
“purine nucleoside biosynthetic process,” “purine ribonucleoside
biosynthetic process,” “multi-organism process,” “monovalent inorganic
cation transport,” and “proton transport” within the “biological
process” category.
The Zn1000 DEGs were also subjected to GO-term analysis (Fig. [78]2d),
These DEGs mainly tagged “preribosome,” “oxidation-reduction process,”
“oxidoreductase activity,” and “acting on other nitrogenous compounds
as donor” subcategories (based on Pvalue <0.05 and Qvalue <0.05). DEGs
in the comparison of Zn1000 with Zn400 mainly tagged “ion binding” and
“cation binding” subcategories (based on Pvalue <0.05 and Qvalue <0.05)
(Fig. [79]2e).
All Zn400 DEGs were subjected to pathway enrichment analysis. Up to
19.34% of the DEGs could be annotated, and 112 pathways were obtained
(Additional file [80]5). Many pathways were significantly enriched
(Pvalue <0.05, Qvalue <0.05) including oxidative phosphorylation;
valine, leucine, and isoleucine biosynthesis; biosynthesis of amino
acids; carbon metabolism; pantothenate and CoA biosynthesis; and the
citrate cycle (TCA cycle). In Zn1000, 19.39% of the DEGs could be
annotated, and 109 pathways were obtained. Oxidative phosphorylation is
significantly enriched (Pvalue <0.05, Qvalue <0.05)
(Additional file [81]7).
Identifying T. mentagrophytes zinc-uptake-related genes
Zinc uptake system expression in the model fungus S. cerevisiae is
primarily regulated at the transcriptional level by the C[2]H[2]-type
zinc finger transcription factor Zap1 [[82]5]. Subsequent studies have
shown other fungi, for example A. fumigatus, can secrete functionally
similar zinc finger transcriptional factor ZafA proteins [[83]18]. A
BLASTx [[84]19] sequencing similarity search was performed using the A.
fumigatus ZafA and S. cerevisiae Zap1 protein sequences against our
unigenes. We found a total of 100 unigenes similar to A. fumigatus
ZafA, and 53 unigenes to S. cerevisiae Zap 1, respectively. A total of
36 of these unigenes have similarities with both ZafA and Zap1, but
only 28 have a zinc finger structure (Additional file [85]8), according
to functional annotation matches. Of these 28 unigenes with annotated
zinc fingers, the sequence with the highest similarity to both ZafA and
Zap1 was Unigene0008014, which was a DEG in the transcriptome
sequencing comparison of Norm versus Zn400, but was not a DEG in Norm
versus Zn1000. Regardless, the predicted protein encoded by this gene
is the most similar to the ZafA protein of A. fumigatus [[86]18],
unambiguously fits the zinc finger consensus [[87]20], and has putative
zinc-binding domains [[88]21] (Fig. [89]3). Furthermore, Unigene0008014
qualified for “zinc-responsiveness transcriptional activator”
(Trichophyton equinum CBS 127.97) and “zinc-responsive transcriptional
regulator Zap1” (S. cerevisiae strain ATCC 204508/S288c) Nr and
Swiss-Prot annotations in our analyses, respectively. We hypothesize
that Unigene0008014 plays an important role in regulating zinc ion
uptake in T. mentagrophytes, and be named a zinc-responsive activating
factor. The zinc-responsive activating factor genomic sequence can be
amplified using three different pairs of primers in T. mentagrophytes
(primers shown in Additional file [90]9). The nucleotide sequence of
our putative zinc-responsive activating factor has been submitted to
NCBI GenBank under the accession [91]KY420911.
Fig. 3.
Fig. 3
[92]Open in a new tab
The predicted amino acid sequence encoded by the unigene0008014.
Putative zinc-binding domains (ZBD) and activating domains (AD) are
shaded and underlined respectively. Zinc fingers are squared including
typical fingers (solid line square) and non-typical fingers (dotted
line square). Cysteine and histidine residues are in bold
Four of our unigenes, Unigene0002709, Unigene0002593, Unigene0004712,
and Unigene0005637, are likely zinc transporters. Nr annotations for
these transcripts, “zinc/iron transporter” (Trichophyton tonsurans CBS
112818), “plasma membrane zinc ion transporter” (Trichophyton equinum
CBS 127.97), “membrane zinc transporter” (Trichophyton tonsurans CBS
112818), and “ZIP family zinc transporter” (Trichophyton tonsurans CBS
112818), respectively, support this hypothesis. Moreover, Swiss-Prot
annotations, “zinc-regulated transporter” (S. cerevisiae strain ATCC
204508/S288c), “RNA polymerase II transcription factor B subunit”
(Candida glabrata strain ATCC 2001/CBS 138/JCM 3761/NBRC 0622/NRRL
Y-65), “zinc-regulated transporter” (S. cerevisiae strain ATCC
204508/S288c), “zinc-regulated transporter” (S. cerevisiae strain ATCC
204508/S288c), respectively, corroborate the assertion. Additionally,
these four unigenes have high BLASTx [[93]19] similarity with ZrfA,
ZrfB, ZrfC, and Aspf2 of A. fumigatus based on E-value.
To validate changes in gene expression patterns we used qRT-PCR against
six unigenes: Unigene0008014, Unigene0002709, Unigene0002593,
Unigene0002886, Unigene0005062, and Unigene0005193. Unigene0002709,
Unigene0008014, Unigene0002886, and Unigene0005062 exhibited
differential expression levels, identical to those obtained by
sequencing in Zn400. Unigene0002593 and Unigene0005193 exhibited
differential expression levels, identical to those obtained by
sequencing in Zn1000 (Fig. [94]4). A statistical analysis was performed
on the qRT-PCR differential expression analyses and is shown in
Additional file [95]10.
Fig. 4.
Fig. 4
[96]Open in a new tab
The result of qRT-PCR in 6 unigenes. Unigene0002709, Unigene0008014,
Unigene0002886, and Unigene0005062 exhibited differential expression
levels, in Zn400. Unigene0002593 and Unigene0005193 exhibited
differential expression levels in Zn1000
Data from this study sufficiently support our investigation into genes
related to zinc ion uptake regulation in T. mentagrophytes.
The change of phenotype in ZafA gene mutant strain
To observe the changes of phenotype and the growth ability after ZafA
gene deleted in T. mentagrophytes. The ZafA gene was knocked by ATMT
transformation in T. mentagrophytes. The fragment of hph gene could be
amplified, and the fragment of ZafA gene could not be amplified in ZafA
gene mutant strain (Fig. [97]5), this means that the ZafA gene is
completely removed. The T. mentagrophytes wild-type strain and ZafA
gene mutant strain were maintained at 28 °C on SDA-Zn medium with 800,
1000, 1200, 1400 and 1600 μM zinc sulfate for 16 days. The changes of
phenotypic and growth ability are shown in Fig. [98]6. The wild-type
strain can begin growing normally in third day, and with increasing of
zinc ion concentration, the growth rate is accelerated. But the ZafA
gene mutant strain can begin growing in eighth day, and the growth rate
and state are much lower than the wild-type strain in same situation.
Under the microscope, there was no significant difference in the
quality and quantity of mycelium between wild-type strain and ZafA gene
mutant strain, but the number of conidia of ZafA gene mutant strain was
obviously less than wild-type strain in same culture situation. The
result showed that the deletion of Zafa gene can negatively affect the
growth and the number of conidia of T. mentagrophytes.
Fig. 5.
Fig. 5
[99]Open in a new tab
PCR analysis of transformants. a Amplification of hph (1200 bp) using
the primers hph-F and hph-R. Lane 1, DNA sample from the T.
mentagrophytes ZafA gene mutant strain; lane 2, DNA sample from the
wild-type T. mentagrophytes strain 28,185, lane 3, the transformation
vector pDHt/ZafA:: hph. b Amplification of the ZafA gene fragment
disrupted (400 bp) using the primers ZafA-F and ZafA-R. Lane 1, DNA
sample from the T. mentagrophytes ZafA gene mutant strain; lane 2, the
transformation vector pDHt/ZafA:: hph; lane 3, DNA sample from the
wild-type T. mentagrophytes strain 28,185
Fig. 6.
Fig. 6
[100]Open in a new tab
The growth situation of T. mentagrophytes wild-type strain and ZafA
gene mutant strain on SDA-Zn medium with 800, 1000, 1200, 1400 and
1600 μM zinc sulfate. a The growth situation of ZafA gene mutant
strain, the growth of T. mentagrophyte is inhibited, especially in
Zn800 and Zn1000. b Under the microscope, there is good quantity of
mycelium in ZafA gene mutant strain, but the number of conidia is
reduced. c The wild-type strain grows normally, and with increasing of
zinc ion concentration, the growth rate is accelerated. d Under the
microscope, there are good quantity of mycelium and a mass of conidia
in wild-type strain
Discussion
Illumina sequencing and sequence annotation in T. mentagrophytes
The infection of skin, nail, hair, and fur caused by dermatophytes is
called dermatophytosis. As early as 1839, scientists confirmed that
dermatophytes can cause human disease. Furthermore, at least 10–20% of
the world’s population may be infected with dermatophytes [[101]22].
Dermatophytes comprise three genera: Microsporum, Trichophyton, and
Epidermophyton. An important member of Trichophyton, T. mentagrophytes
can cause severe skin infections in humans and other animals, and has a
wide distribution around the world [[102]23]. Therefore, T.
mentagrophytes warrants investigation. Our study aimed to generate a
large amount of cDNA sequence data to facilitate more detailed
transcriptomics studies in T. mentagrophytes, and, in particular, to
identify genes related to the regulation of zinc ion uptake in that
organism. RNA-seq is a powerful tool that can provide a global overview
of genes expression at the transcriptome level [[103]24]; however, it
has not been extensively applied to fungi. We believe RNA-seq will
prove to be a powerful method for fungi study. The availability of our
T. mentagrophytes transcriptome data will meet the initial information
needs for functional studies of this species and its relatives.
We chose to sequence the transcriptomes of Norm, Zn400, and Zn1000,
based on our growth experiment results. Zn1000 and Norm samples grew
similarly in our trials; there was a significant difference between
Zn400 and Norm, microconidia and mycelium morphology are also
significantly different between the two. The RNA-seq method was then
performed on these samples using Illumina sequencing, which generated a
total of 10,751 unigenes, of which more than 89% were annotated by our
analyses. These data will provide a valuable resource for the study of
T. mentagrophytes.
Zn-deficiency induced changes in T. mentagrophytes growth and gene expression
Fungi rely on zinc for growth; the zinc ions serve as a cofactor in
numerous proteins, including important transcription factors [[104]25].
Zinc chelation can reduce fungal growth in both rich and defined media
[[105]26]. Zinc chelation occurs during infection, and is an important
strategy evolved in immune cells to hamper pathogen growth [[106]27].
Zn1000 cells grew with no obvious morphological differences compared
with Norm in our study. However, with sequential zinc ion concentration
decreases in Zn200, Zn400, and Zn600, microconidia and mycelium
morphology showed T. mentagrophytes growth to be increasingly
restricted by zinc deficiency. Furthermore, when T. mentagrophytes
growth was inhibited in Zn200, Zn400, and Zn600 zinc deficient samples,
which were then inoculated into normal medium or Zn1000 zinc deficiency
medium, T. mentagrophytes cells restore to normal growth patterns due
to the increase in zinc concentration. This suggests that the growth
inhibition we observed in T. mentagrophytes is primarily caused by a
lack of zinc ions, rather than other metal ions, and it reinforces the
hypothesis that T. mentagrophytes natural growth requires zinc ions.
Zinc ion acquisition is, therefore, crucial for the growth and
development of T. Mentagrophytes.
The S. cerevisiae yeast cell employs several different strategies to
cope with stress caused by zinc deprivation [[107]28]. Zinc deprivation
induced by TPEN also induces a variety of changes in the gene
expression in C. gattii cells [[108]9]. Studies have shown that the
growth and gene expression, in particular, high-levels of zinc
transporter system expression, within a variety of fungi, including A.
fumigatus [[109]18], C. albicans [[110]10], and C. gattii [[111]9], are
affected by zinc deficiency. Similarly, our RNA-seq data differential
expression analyses revealed 4441 DEGs (2314 up-regulated and 2127
down-regulated) in Zn400, versus 2841 DEGs (1395 up-regulated and 1446
down-regulated) in Zn1000. Because of the higher zinc ion
concentration, Zn1000 has fewer DEGs than Zn400. This further indicates
that the change of gene expression in T. mentagrophytes is
predominantly caused by a lack of zinc ions, versus other metal ions.
Zinc deficiency definitely affects gene expression in T.
mentagrophytes. We speculate that T. mentagrophytes regulates the
expression of many genes under conditions of zinc deficiency.
A genome-wide, functional analysis revealed that almost 400 different
gene products are necessary for proper growth in zinc-limiting
conditions, using a S. cerevisiae mutant library [[112]29]. Of these
proteins, most are associated with oxidative stress response,
endoplasmic reticulum function, peroxisome biogenesis, or zinc uptake.
Furthermore, as revealed by transcriptomic and functional analyzes,
also in S. cerevisiae, low zinc conditions lead to alterations in lipid
synthesis, sulfate metabolism, and oxidative stress tolerance [[113]30,
[114]31]. Our KEGG and GO DEG analyses showed that T. mentagrophytes
has the same response as S. cerevisiae under zinc deficient
environments. Most of our DEGs partitioned into either
oxidation-reduction process, electron carrier activity, purine
ribonucleoside biosynthetic process, monovalent inorganic cation
transport, or proton transport, in the GO analysis. Furthermore,
oxidative phosphorylation; valine, leucine and isoleucine biosynthesis;
biosynthesis of amino acids; carbon metabolism; and the citrate cycle
(TCA cycle) were significantly enriched in our pathway enrichment
analysis. These results suggest that zinc deprivation can affect T.
mentagrophytes development, growth, and gene expression. We speculate
T. mentagrophytes can change particular metabolic pathways to resist
zinc deficiency. However, this stressed cellular state cannot last
forever, thus T. Mentagrophytes growth and development are eventually
negatively impacted, to the point of cellular death under extended zinc
deficiency.
Zinc-uptake-related genes in T. mentagrophytes
Fungi cells must acquire zinc ions for proper life cycle development,
even as saprophytes, or during infective processes [[115]32]. Zinc
transport mechanisms were initially characterized in fungi with S.
cerevisiae Zap1, Zrt1, and Zrt2 genes [[116]33]. Zap1 can activate the
transcription of Zrt1 and Zrt2 by binding to the zinc-response element
in its promoter region; binding affinity is controlled by zinc level
[[117]5]. Zinc uptake and homeostasis is also important in the
physiology and virulence of A. fumigatus, C. albicans, and C. gattii.
The main function of the A. fumigatus ZafA protein is to regulate zinc
uptake; it is a requisite for the growth of A. fumigatus in
zinc-limited conditions [[118]18]. Furthermore, ZafA mutants do not
survive and have no pathogenicity in the mice lung, supporting the
essential role of A. fumigatus ZafA in growth and virulence [[119]18].
In addition, ZafA can induce Zrf and Aspf2 gene expression under zinc
deficient conditions, and its expression is also influenced by zinc
concentration [[120]8, [121]34]. Csr1/Zap1 is considered a homolog of
S. cerevisiae Zap1 in C. albicans. Mutants lacking Csr1/Zap1 alleles
show growth deficiencies under zinc deficient conditions, and cannot
form germ tubes or hyphae, demonstrating that Csr1/Zap1 contributes to
zinc uptake and homeostasis, as well as morphological transitions, in
C. albicans [[122]10]. Another pathogenic Candida species, C.
dubliniensis also possesses the S. cerevisiae Zap1 homolog Csr1.
Mutants show growth defects under zinc-limited conditions. However,
unlike C. albicans, these Csr1 mutants are able to form germ tubes and
undergo morphological transition, although the mutants do exhibit
reduced virulence [[123]35]. Similar to other fungi, the S. cerevisiae
zinc finger transcription factor Zap1 homolog was identified in C.
gattii. The Zap1 homolog mutant showed impaired growth under
zinc-limited conditions compared with wild-type. Furthermore, the Zap1
mutant displayed attenuated virulence in a murine cryptococcosis model.
This suggests that Zap1 plays critical roles in zinc uptake and
virulence in C. gattii [[124]9].
In the present study we found 28 unigenes with similarity to both ZafA
of A. fumigatus and Zap1 of S. cerevisiae according to functional
annotation matches and BLASTx. We hypothesize that Unigene0008014 is
functionally similar to both ZafA of A. fumigatus and Zap1 of S.
cerevisiae, based on sequence similarity, functional annotation, and
the predicted protein product. The significant up-regulation of
Unigene0008014 expression levels, in particular for Zn400, as detected
by qRT-PCR, with such a dramatic difference in our transcriptome
sequencing comparison for Zn400, support this hypothesis. We also
knocked ZafA gene by ATMT transformation in T. mentagrophytes. By
observing the changes of phenotype and the growth ability after ZafA
gene deleted in T. mentagrophytes. We found that ZafA gene is very
important for the growth and the generation of conidia in T.
mentagrophytes. We think that Unigene0008014 can regulate zinc uptake
at the transcriptional level as a C[2]H[2]-type zinc finger
transcription factor in T. mentagrophytes, and name it a
zinc-responsive activating factor.
The other 27 ZafA/Zap1 putative homologs do not appear to be
zinc-responsive activating factors, but do contain zinc finger
structures, and many show significant expression changes under zinc
deficiency, suggesting that these 27 unigenes have important metabolic
functions. Therefore, we consider these 27 unigenes to possibly
function as a regulator of zinc uptake; however, this speculation
requires further research.
We also found four T. mentagrophytes unigenes, Unigene0002709,
Unigene0002593, Unigene0004712, and Unigene0005637, that may be zinc
transporters. These four sequences are similar to the zinc transporters
of A. fumigatus and S. cerevisiae based on E-value, and our gene
function annotation analyses show these four unigenes to be zinc
transporters. Furthermore, Unigene0002709 and Unigene0002593 qRT-PCR
results showed significant up-regulation. Unigene0005637 was not a DEG,
and Unigene0004712 was significantly down-regulated. We think that
these results may be due to the pH value of the medium partly
inhibiting Unigene0005637 and Unigene0004712 expression levels. A
similar effect has been observed in A. fumigatus [[125]7]. Indeed,
different pH values can affect the expression of various zinc
transporters [[126]8]. Thus, we think that the zinc transporter system
in T. mentagrophytes comprises five unigenes: Unigene0008014,
Unigene0002709, Unigene0002593, Unigene0004712, and Unigene0005637.
However, the possibility that these five unigenes are just a part of a
larger zinc transporter system exists, and requires further analysis.
Conclusion
In this study we report the first large transcriptome study carried out
in T. mentagrophytes where we have compared physiological and
transcriptional responses to zinc deficiency. A total of 10,751
unigenes were obtained and more than 89% of them were annotated. This
provided more adequate resources to study T. mentagrophytes. Evidence
from physiological observations, transcriptome and qRT-PCR analysis
indicated that zinc deficiency could induce arrested development, and
numerous genes expression changes in T. mentagrophytes. Importantly, we
found the zinc-responsive activating factor (unigene0008014) and we
speculated that 4 unigenes (unigene0002709, unigene0002593,
unigene0004712, unigene0005637) are zinc transporters. The expression
of these 4 zinc transporter genes is potentially regulated by the
zinc-responsive activating factor. And we knocked ZafA gene in T.
mentagrophytes, the result showed that ZafA gene is very important for
the growth and the generation of conidia in T. mentagrophytes.
Additional files
[127]Additional file 1:^ (15.6KB, docx)
The primers of qRT-PCR. (DOCX 15 kb)
[128]Additional file 2:^ (2MB, tif)
The saturation curves of RNA-seq. (TIFF 2098 kb)
[129]Additional file 3:^ (2.9MB, xlsx)
The Nr, Swiss-Prot, KEGG and KOG annotation of unigenes. (XLSX 3000 kb)
[130]Additional file 4:^ (29.1KB, docx)
The pathways of unigenes. (DOCX 29 kb)
[131]Additional file 5:^ (1.5MB, xlsx)
All DEGs. (XLSX 1503 kb)
[132]Additional file 6:^ (1.8MB, tif)
Group diffuse analysis ‘volcano plot’. (TIFF 1807 kb)
[133]Additional file 7:^ (27.3KB, xlsx)
The pathways of DEGS. (XLSX 27 kb)
[134]Additional file 8:^ (16.6KB, docx)
The unigenes that have zinc finger structure. (DOCX 16 kb)
[135]Additional file 9:^ (15.8KB, docx)
Three pairs of primers that can amplify zinc-responsive. (DOCX 15 kb)
[136]Additional file 10:^ (17.8KB, docx)
The qRT-PCR differential expression analyses. (DOCX 17 kb)
Acknowledgements