Efficient and specific DNA oligonucleotide rRNA probe-based rRNA removal in Talaromyces marneffei

ABSTRACT

Emerging evidence showed that lncRNAs play important roles in a wide range of biological processes of fungi such as Saccharomyces cerevisiae. However, systemic identification of lncRNAs in non-model fungi is a challenging task as the efficiency of rRNA removal has been proved to be affected by mismatches of universal rRNA-targeting probes of commercial kits, which forces deeper sequencing depth and increases costs. Here, we developed a low-cost and simple rRNA depletion method (rProbe) that could efficiently remove more than 99% rRNA in both yeast and mycelium samples of Talaromyces marneffei. The efficiency and robustness of rProbe were demonstrated to outperform the Illumina Ribo-Zero kit. Using rProbe RNA-seq, we identified 115 differentially expressed lncRNAs and constructed lncRNA-mRNA co-expression network related to dimorphic switch of T. marneffei. Our rRNA removal method has the potential to be a useful tool to explore non-coding transcriptomes of non-model fungi by adjusting rRNA probe sequences species specifically.

KEYWORDS:

• Lncrna
• rRNA depletion
• RNase H
• Ribo-Zero Gold rRNA Removal Kit
• Talaromyces marneffei

Introduction

Long non-coding RNAs (lncRNAs) have been demonstrated to widely participate in development, metabolism and other key biological processes in organism systems (Fatica and Bozzoni 2014; Beermann et al. 2016). Compared with hundreds of thousands of well-studied lncRNAs in human (Volders et al. 2019), only a small set of fungal lncRNAs are confirmed to be functional (Cemel et al. 2017; Kim et al. 2018; Till et al. 2018; Wang et al. 2019a, 2019b; Ye et al. 2021). Genome-wide screening of fungal transcriptomes is warranted to improve the understanding of fungal lncRNAs as most non-model fungi without comprehensive lncRNA annotations. To increase sequencing depth and reduce cost, rRNA removal preparation is recommended for non-coding transcriptome analysis (Stark et al. 2019). However, one of the commonly used commercial kits for fungal rRNA depletion, the Ribo-Zero Gold rRNA Removal Kit from Illumina, has been discontinued in 2018.

As so far, most rRNA-depleted libraries are constructed by enzyme-based hybridisation subtraction or custom-designed biotinylated oligonucleotide pull-down (Stark et al. 2019). Probes that are complementary to rRNA allow selective depletion in both approaches and thus become a key step in determining removal efficiency. The mismatches in target rRNA sequences have been proved to be an important factor affecting the rRNA residual ratio due to the sequence-specific hybridisation between the targeted rRNA and the oligonucleotide probes (Huang et al. 2020; Zeng et al. 2020). However, fungal rRNA sequences vary largely among main clades (Tedersoo et al. 2018), which makes it difficult for universal rRNA probes, such as Saccharomyces cerevisiae-based kits, to provide effective and stable rRNA removal effects for fungi with diverse phylogenetic affinity (Telzrow et al. 2021).

Here, we developed a highly efficient species-specific rRNA removal protocol (rProbe) based on Talaromyces marneffei, a dimorphic fungus that exists in the form of both mould and yeast. Our results showed that compared with Illumina Ribo-Zero Kit, the rProbe had a more efficient and stable rRNA removal performance without disrupting transcriptome. Using rProbe RNA-seq, we constructed a dimorphic switch-related network consisting of DE lncRNAs as well as co-expressed mRNAs. Our rProbe protocol could be a valuable tool for mining fungal non-coding transcriptomes by adjusting the sequence of the rRNA probes.

Results

rProbe protocol enables to remove approximately 99% of rRNA in the yeast phase of Talaromyces marneffei

rRNA subtraction hybridisation strategies are widely used for mammalian rRNA depletion (Morlan et al. 2012; Adiconis et al. 2013). We thus aimed to implement and modify a similar method for fungi rRNA depletion. Among the ribosomes of eukaryotes, 5S, 5.8S, 18S and 28S are the most abundant rRNA transcripts, accounting for a rough 80% of the total RNA (Kraus et al. 2019; Phelps et al. 2021). In order to deplete most of the rRNAs from total RNA, we first identified the 5S, 5.8S, 18S and 28S, as well as ITS1 and ITS2 rRNA sequences based on draft genome from Illumina assemblies of Talaromyces marneffei PM1 strain (Woo et al. 2011) using software RNAmmer (Lagesen et al. 2007) and blasted against NCBI to confirm the rRNA sequences. The rDNA tandem repeats consisting of 5S rRNA of 116 bp, 5.8S rRNA of 158 bp, 18S rRNA of 1800 bp as well as 28S rRNA of 3459 bp were identified in the PM1 strain.

To ensure efficient removal, the length of rRNA-targeting oligonucleotide DNA probes should satisfy specific rRNA fragments binding, as well as be accessible to the rRNA structural hidden sites (Culviner et al. 2020; Huang et al. 2020). On that account, continuous complementary DNA probes with the length of 80-nt were synthesised in a head-to-tail manner and evaluated for possible cross-hybridisation using BLAST (Altschul et al. 1990). Then the probes were mixed with the input total RNA at the ratio of probes/rRNA equal to 2:1. At a final volume of 15 μL, the probe and target rRNA were hybridised through a gradient cooling programmeat a controlled rate of −0.1°C/s from 95°C to 22°C. Subsequently, the rRNA fragments in the hybrid double-stranded structure formed during this process were specifically hydrolysed by RNase H. The remaining DNA probes were then removed from the mixture by DNase I, and the RNA was purified for downstream analysis (Figure 1a).

Figure 1. Efficiency of the rProbe protocol in yeast samples of T. marneffei.

(a) The schematic representation of the workflow for the rRNA probe subtraction hybridisation (rProbe) protocol.(b) The removal efficiency of rRNA probes in yeast RNA samples. Two biological replicates in each treatment. The representative gel image is shown.(c) The removal efficiency of rRNA probes in yeast RNA samples. The results of the Qseq₁ Bio-Fragment Analyser are shown. The upper is untreated and the bottom is treated.(d) The rRNA residual ratio in rProbe processed yeast samples. The rRNA residual ratio is defined as rRNA read counts divided by total read counts in each library.

In order to assess the efficiency of rRNA removal, total RNA samples and processed RNA samples were analysed by RNA agarose gel electrophoresis and Qseq₁ Bio-Fragment Analyser (BiOptic Inc., China). Compared with untreated RNA samples, no significant residual 18S and 28S rRNA was detected after rRNA depletion (Figure 1(b,c)). To further accurately evaluate the efficiency of our rRNA removal protocol, we prepared the rRNA depletion strand-specific libraries for Illumina sequencing. Using paired-end sequencing, paired reads >65 million were generated in each library. By mapping reads of RNA-seq to the 5S, 5.8S 18S, 28S, ITS1 and ITS2 rRNA sequences, all the rRNA depletion libraries showed a low residual of rRNA less than 1% (Figure 1d). We then calculated the fraction of reads aligning to different rRNAs in each sample separately. Among the remaining rRNA fragments, 18S and 28S rRNA accounted for the main parts, which may be due to the complex secondary structure restricting the binding of the probe (Supplementary Table S1). These results showed that the rRNA probes were effective to remove the vast majority of ribosomal RNAs.

rProbe protocol is comparable to commercial Ribo-Zero Kit in rRNA removal efficiency and transcriptome disruption

In order to compare the rRNA depletion efficiency of rProbe protocol and commercially available kits in yeast RNA samples of T. marneffei, we downloaded RNA-seq of T. marneffei PM1 strain processed by Ribo-Zero Gold rRNA Removal Kit from NCBI (Lau et al. 2018) and mapping reads to rRNA sequences as above. To check whether the potential batch effect was caused by experimental artefacts that would confound our results, we performed principal component analysis (PCA) between our RNA-seq and downloaded data as suggested (Reese et al. 2013). No significant batch effects were observed (Supplementary Fig. S1 A and B). Through mapping RNA-seq to the target rRNA sequences, we calculated the rRNA residual ratio of samples separately. The rRNA residual ratio of rProbe protocol was lower than that of Ribo-Zero Kit (p value = 0.02875, Wilcoxon rank-sum exact test), with better robustness among samples (Figure 2a). To confirm whether the sequencing depth and library size have an impact on the efficiency quantitation, we randomly sampled 1 million reads from the original fastq files of each sample and to achieve equivalent library size. The rRNA residual ratio proved to be independent of the sequencing depth (Supplementary Fig. S1 C).

Figure 2. rRNA removal efficiency of rProbe method and Illumina Ribo-Zero Kit.

(a) The rRNA residual ratio of rProbe and Illumina Ribo-Zero Kit. Experiments were repeated using three biological replicates in Probe and four biological replicates in Ribo-Zero Kit.(b) The separate rRNA residual ratio of Illumina Ribo-Zero kit.(c) 18S rRNA base depth distribution of rProbe and Illumina Ribo-Zero Kit. The base depth distribution is normalised by the library size. The bottom bar shows the mismatches of 18S rRNA between T. marneffei and S. cerevisiae.(d) 28S rRNA base depth distribution of rProbe and Illumina Ribo-Zero Kit. The base depth distribution is normalised by the library size. The bottom bar shows the mismatches of 28S rRNA between T. marneffei and S. cerevisiae.

Noted that the 28S rRNA removal efficiency of the Ribo-Zero Kit was unstable (Figure 2b), we wonder whether the instability performance of the Ribo-Zero Kit was related to the differences of rRNA sequences between T. marneffei and S. cerevisiae. We then calculated the base depth distribution of 18S and 28S rRNA in RNA-seq of rProbe and Ribo-Zero Kit. In contrast with rProbe libraries, reads mapping to 18S and 28S rRNA in Ribo-Zero Kit libraries formed distinct peaks in mismatch enriched regions (Figure 2c,d). Notably, most of the mismatch-dependent rRNA residue was enriched in the 5ʹ end, while the fewer rRNA residual in the 3ʹ end may be related to the slight degradation of rRNA in the process of RNA extraction.

In order to determine whether our rProbe protocol could be applied to construct high-quality RNA-seq libraries, we first compared the evenness of transcript coverage between the two methods. The mean coefficient of variation (CV) for the 1,000 most highly expressed genes in each library was calculated, and the lower CV value indicated the lower variation biological replicates (Figure 3a). We next performed gene expression correlation analysis between these libraries. Overall, the rProbe samples showed high consistency with Ribo-Zero Kit samples (R = 0.92, p-value < 2.2e-16, Figure 3b), and the consistency remained even in low sequencing depth when all libraries were adjusted to one million paired reads (R = 0.93, p-value < 2.2e-16, Supplementary Fig. S1 D). Quantile–quantile plot showed points lie on a straight line, indicating there was no global shift in expression profile between the rProbe method and Ribo-Zero Kit (Figure 3b). Further analysis showed that the libraries generated from the rProbe protocol were highly correlated with the Ribo-Zero Kit libraries separately, with correlation coefficients exceeding 0.9 (Figure 3c). The consistency among rProbe samples was 0.99, suggesting the good repeatability of our methods. Totally, RNA-seq of rProbe rRNA depletion method exhibited similar transcriptomic landscapes with Ribo-Zero Kit.

Figure 3. RNA-seq quality control metrics.

(a) The mean coefficient of variation (CV) for the top 1,000 most highly expressed genes of libraries generated from the rProbe and the Illumina Ribo-Zero Kit. The CV of each gene is calculated by dividing the standard deviation of its expression measure in the sample population by its average expression.(b) Comparison of gene expression levels of two methods. Pairwise scatter plot comparing average TPM values of genes (upper). The correlation coefficient is shown in the top-left corner. The x- and y-axes are normalised by log₁₀(TPM + 1). Plots along the diagonal represent similar expression levels. Quantile–quantile plot of RNA-seq libraries (bottom). A straight line indicates no systemic drift between the x-axis and y-axis.(c) Pearson correlation of RNA-seq libraries of two methods. Correlation coefficients are calculated based on the TPM value of genes, excluding rRNA transcripts.

rProbe method has concordant gene expression with poly(A) selection method in both yeast and mycelium samples

We further applied the rProbe protocol to mycelium RNA samples of T. marneffei. As expected, the rProbe method was able to efficiently deplete 99% rRNA as that of yeast RNA samples (Figure 4(a,b)). RNA-seq analysis revealed that there was no significant removal difference between different phases (p value = 0.1, Wilcoxon rank-sum exact test). We then examined the consistency of samples, correlation coefficients among mycelium groups were greater than 0.9, and the correlation coefficients between mycelium samples and yeast samples were above 0.8, indicating libraries processed by the rProbe method had strong robustness.

Figure 4. Efficiency of the rProbe protocol in mycelium samples of T. marneffei.

(a) The removal efficiency of rRNA probes in mycelium RNA samples. Two biological replicates in each treatment. The representative gel image is shown.(b) The rRNA residual ratio in rProbe processed mycelium samples. The rRNA residual ratio is defined as rRNA read counts divided by total read counts in each library.(c) The consistency of RNA-seq data of yeast samples between the poly(A) selection method and two rRNA depletion methods. The correlation coefficients are shown in the top-left corner. The x- and y-axes indicate log₁₀(TPM+1). Genes with exceptionally high expressions and large differences (Δ|log₁₀TPM|>1) between the rRNA depletion method and poly(A) selection method are labelled in red and blue. Plots along the diagonal red line represent that the gene has similar expression levels in the poly(A) selection and the rRNA depletion method.

Since a subset of lncRNA transcripts lack poly(A) tails, poly(A) selection isolated only the 3ʹ-most portion of transcripts, leading to bias in enriching low abundance poly(A) tail RNAs and non-poly(A) tail lncRNAs (Yang et al. 2011). We reasoned that the rProbe RNA-seq was able to enrich non-poly(A) tail lncRNA that were neglected in the poly(A) selection method. Therefore, we prepared poly(A) libraries and sequenced mycelium and yeast samples of T. marneffei. In order to understand the differences between the two methods as a whole, we compared the expression of the global transcript between the rRNA depletion method and poly(A) selection libraries (Figure 4c). In general, libraries of the rProbe method and Ribo-Zero Kit were highly correlated to the poly(A) selection method (R = 0.93, 0.91 separately). Although the overall concordance was high, less than 3% of genes showed exceptionally high expressions and large differences between the two protocols, without cross-hybridisation with the rRNA probes.

Construction of lncRNA-mRNA co-expression dimorphic switch network

We then identified lncRNA of rProbe libraries and poly(A) libraries. In the rProbe libraries, over 10% of the reads were identified as lncRNA reads, while in the poly(A) selection library only 6% of reads were detected as lncRNA, suggesting the rRNA depletion method enable to enrich lncRNAs (Figure 5a). Final lncRNA candidates were determined by the intersection of CPC2, CNCI, PfamScan and FEElnc. A total 229 of lncRNAs with the threshold of TPM >1 in all three samples of yeast or mycelium phase were kept for downstream analysis. Differentially expressed lncRNAs (DE-lncRNAs) between yeast and mycelium were determined using DEseq2. Overall, 115 lncRNA transcripts were differentially expressed between yeast and mycelium (p < 0.05 and fold change > 2), in which 89 lncRNAs were up-regulated in the yeast phase and 26 lncRNAs were up-regulated in mycelium (Figure 5b). Intriguingly, we found among all DE-lncRNAs, five lncRNAs could only be detected in rProbe libraries but not in poly(A) selection libraries, which indicates that they might not have poly(A) tails.

Figure 5. Identification of DE lncRNA between mycelium and yeast.

(a) Percentage of lncRNA reads in rProbe libraries and poly(A) selection libraries. The left panel shows the percentage of all lncRNAs identified by CPC2, CNCI, PfamScan and FEElnc, and the right panel shows the percentage of intersected lncRNAs of four software. LncRNA percentage is calculated as reads of lncRNAs divided by total reads.(b) Heatmap of differently expressed lncRNAs between mycelium and yeast. In the heatmap, the red cell represents upregulated lncRNAs, while the blue cell represents downregulated lncRNAs. In the sample panel, darkred represents yeast samples and darkblue represents mycelium samples.(c) Validation of differentially expressed lncRNAs in mycelium and yeast condition by RT-qPCR. The experiment is technically repeated three times for each lncRNA. RT-qPCR data were calculated by the 2^−ΔΔCt method with β-actin as an internal control. The expression value computed from the RT-qPCR data is present as the mean ± standard deviation of the mean. * p value < 0.05.(d) Gene expression correlation between RT-qPCR and RNA-seq data. The y-axis represents the CT values and the x-axis represents the log2(TPM) of lncRNAs. The Pearson correlation coefficients and linear regression line are indicated with 95% CI.

In order to understand the potential function of lncRNAs in dimorphic switch, we first selected the top 10 DE lncRNAs for RT-qPCR validation, excluding the lncRNA MSTRG.6914 which did not have available primers (Figure 5c). Due to the inevitable influence of mRNA isoforms, the expression levels of selected lncRNAs did not show an ideal linear relationship with RNA-seq data (Figure 5d). We next constructed the lncRNA-mRNA co-expression network of the top 10 DE lncRNA using WGCNA. Remarkably, nine of the top 10 DE-lncRNA were enriched in the black module which was high correlative with the phenotype of yeast/mycelium (correlation coefficient = 0.98 and p value < 1e-05, Figure 6a). According to the value of intra-module connectivity, genes co-expressed with DE lncRNA ranked in the top 30 were selected for visualisation using Cytoscape (Figure 6b). LncRNA-1, lncRNA-4, lncRNA-7 and lncRNA-8 formed a highly connected centre with the hub gene MSTRG.6454 which highly co-expressed with seven of the top 10 DE lncRNA. Gene MSTRG.6454 encoded a homolog of D-aminopeptidase, which is reported to be one of the important components of fungal secretome that associated with the fungal environmental adaptation (Muszewska et al. 2017). Functional enrichment analysis of co-expressed genes showed Gene Ontology terms related to “metabolism of nucleic acid”, “protein kinase activity”, “oxidative stress”, “carbohydrate metabolism” as well as “cellulose binding” were significantly enriched in the network (Figure 6c, Supplementary Table S2).

Figure 6. Co-expression network of top 10 DE-lncRNA.

(a) Co-expression analysis of transcriptome (left) and the most relevant module with dimorphic switch (right).(b) Co-expression network of top 10 DE lncRNA. DE lncRNAs are labelled orange and co-expressed mRNAs are labelled grey. mRNAs co-expressed with more than two DE lncRNAs are labelled red. The shade of red is related to the number of co-expressed lncRNAs.(c) Enrichment of GO terms of lncRNA-mRNA co-expression network of B. GO terms with p value > 0.05 and counts > 3 are shown.

Discussion

High-throughput RNA sequencing and analysis revolutionise microbial research and provide new insights into coding and/or non-coding transcriptome profiling. As rRNA constitutes a very large fraction of the total RNA, RNA enrichment is an essential step to reduce cost and increase the sequencing depth of desired transcripts. Thus, numerous rRNA-depletion methods have been developed including commercial kits such as Ribo-Zero Gold rRNA Removal Kit (Yeast), which has not yet launched new alternatives for fungi after discontinued. Several studies have reported the species-specific RNA-seq library construction methods that use hybrid-subtraction strategies for rRNA removal in higher eukaryotes and bacteria (Giannoukos et al. 2012; Thompson et al. 2020; Zeng et al. 2020). Recently, a similar method has also been reported in Cryptococcus neoformans (Telzrow et al. 2021).

In this study, we described the establishment of an efficient rRNA depletion method (rProbe) to understand the lncRNAome of T. marneffei during the dimorphic switch. For both forms of mycelium and yeast, the low rRNA residual and slight target-off effect were sufficient for most RNA-seq study conditions. Systematic RNA-seq data comparison indicated that RNA-seq from rProbe libraries yielded a highly correlative transcriptomic landscape with that of the commonly used Illumina Ribo-Zero Kit and poly(A) selection method. However, about 3% of genes showed significant expression changes between rProbe and poly(A) selection method, as same as the disturbance have been reported (Morlan et al. 2012; Zhao et al. 2018; Thompson et al. 2020). Culture condition and library preparation that makes the RNA pool differ in concentration and composition might be one of the explanations.

Approximately half of the lncRNAs showed different expression levels between mycelium and yeast forms, as previously mentioned lncRNA plays fine-tuning roles in environment response (Beermann et al. 2016; Yu et al. 2019). In the co-expression network, we found mRNAs co-expressed with lncRNA were mainly enriched in fungal metabolism, protease activity and development processes, which supported that metabolically pre-adapted is a key step of the dimorphic switch in order to adapt host environment (Pasricha et al. 2017).

In conclusion, through species-specific rRNA probes, we offered an efficient and scalable tool for rRNA removal that is available to both yeast and mycelium RNA samples of T. marneffei. The rProbe has the potential to be applied to other fungi by adjusting the sequence of oligonucleotide probes.

Materials and methods

Strains, media and culture conditions

Wild type T. marneffei strain PM1 used in this study was separated from an HIV-negative patient from Hong Kong (Woo et al. 2011). Yeast was cultivated at 37°C in Sabouraud Dextrose Agar (SDA) medium for 7 days and mycelium was cultivated at 25°C in the SDA medium for 7 days. All cultures were harvested and purified as described (Yang et al. 2014), and immediately frozen in liquid nitrogen for total RNA extraction.

RNA extraction

Total RNA of T. marneffei was extracted by E.Z.N.A. Fungal RNA Mini Kit. RNA quality was determined using Qsep₁ Bio-Fragment Analyser (BiOptic, Inc., New Taipei City, Taiwan). RNA with Q-score > 8 were selected for downstream processing.

rRNA depletion

Ribosomal RNA sequences, including 5S, 5.8S, 18S, 28S, ITS1 and ITS2 rRNA, were searched in the whole genome using RNAmmer (Lagesen et al. 2007). Similar to the previous method (Phelps et al. 2021), non-overlapping complementary DNA probes of 80-nt were synthesised (TSINGKE, Beijing, China) and mixed, in which 18S and 28S rRNA with a final concentration of 1 μM, and ITS1, ITS2, 5S as well as 5.8S rRNA with a final concentration of 0.05 μM. The primer sequences are listed in Supplementary Table S2. First, a total of 2 μg RNA was mixed with 1 μL rRNA probes mixture and 2 μL of hybridisation buffer (750 mM Tris-HCl, 750 mM NaCl) at a final volume of 15 μL, and incubated at 95°C for 2 minutes, slowly cooled to 22°C with the speed of −0.1°C/s, then kept at 22°C for 5 minutes. Next, the rRNA in DNA-RNA hybrid molecules were hydrolysed by thermostable RNase H mixture (NEB, USA) at 50°C for 30 minutes according to protocol. Residual DNA probes were removed by DNase I mixture (NEB, USA) at 37°C for 30 minutes. Finally, RNA was isolated and purified by 2.5 × RNA Clean Beads (Vazyme, Nanjing, China) and stored at −80°C. In order to ensure a valid comparison of the rRNA depletion efficiency, all samples were performed on the same input RNA, with the comparisons repeated using biological triplicate for yeast and mycelium respectively.

rProbe RNA-seq library construction and sequencing

Libraries of rProbe protocol were prepared with VAHTS Universal V6 RNA-seq Library Prep Kit for Illumina (Vazyme, Nanjing, China) according to the manufacturer’s protocol. Briefly, the purified RNA was fragmented into short fragments of 250–300 nt using fragmentation buffer and reverse transcribed into cDNA with random hexamer primers. The second strands were synthesised with dUTP for the strand-specific RNA-seq library. Samples were mixed with adaptors, respectively, for ligation and amplification. Final PCR products were purified with 0.9 × DNA Clean Beads (Vazyme, Nanjing, China) and sequenced on Illumina HiSeq Xten platform (Annoroad, Beijing, China) using paired-end sequencing (2 × 150 bases). About 60 to 90 million pairs of reads were generated from each library (n = 6).

Analysis of rRNA depletion efficiency

Adapters and low quality reads were removed by cutadapt (Martin 2011) according to library-specific adapter ligation. After quality control, sequencing reads were aligned to rRNA sequences of T. marneffei using Bowtie2 (Langmead and Salzberg 2012) with parameters of – qc-filter and – no-unal for calculating the rRNA residual ratio. To quantify the efficiency of probe-based rRNA depletion, rRNA residual ratio was defined as all reads mapping to 5S, 5.8S, 18S, 28S, ITS1 and ITS2 rRNA loci divided by total reads number in each sample. Then residual rRNA reads were removed by parameter – un-conc-gz and clean reads were obtained and used for downstream processes.

Ploy(A)+ selection RNA-seq library construction and sequencing

A total of 6 libraries of three biological replicates from each condition were constructed using oligo (dT) enrichment independently. Sequencing was carried out on an Illumina HiSeq X platform with paired-end 150 bp reads.

RNA-sequencing read mapping and normalisation

All probe-based rRNA depletion libraries and poly(A)+ libraries were mapped to the Talaromyces marneffei PM1 genome assembly using Hisat2 (Kim et al. 2019) with the following parameters -max-intronlen 2000 and – rna-strandness RF, guided by Talaromyces marneffei PM1 annotation file. The gene-level expression matrix was calculated by Stringtie (Pertea et al. 2016). All reads mapping to specific genome regions were then summed and normalised by transcripts per million (TPM). This normalised quantity was then used in all downstream analyses. The same analysis pipeline was performed on the Illumina Ribo-Zero Gold rRNA Removal Kit processed RNA-seq download from NCBI (PRJNA353903).

Identification and different expression of lncRNA

Transcripts of all samples were deduplicated and merged by Stringtie merge command, and annotated by gffcompare (Pertea and Pertea 2020). Transcripts with annotation of “opxui” were selected as potential lncRNA. Then lncRNAs were filtered by intersecting the results of CPC2 (Kang et al. 2017), PfamScan (Finn et al. 2014), CNCI (Sun et al. 2013) and FEElnc (Wucher et al. 2017), and transcripts with TPM > 1 were selected as the final lncRNA sets. Differentially expressed lncRNAs between the yeast and mycelium samples were identified using R package DEseq2 (Love et al. 2014), with the criterion of |log₂ fold change| > 1 and adjust p value < 0.05.

lncRNA co-expression network construction

The R package WGCNA (Langfelder and Horvath 2008) was used to construct a lncRNA-mRNA co-expression network. In brief, a gene co-expression network was constructed based on RNA-seq of libraries and the estimated best soft threshold power was selected. Average linkage hierarchical clustering was performed and network modules were identified using a dynamic tree cut algorithm with a minimum cluster size of 30. The correlation between modules and phenotype (yeast and mycelium) was calculated. Finally, modules containing DE lncRNA and high correlation were selected for visualisation using Cytoscape.

Co-expressed gene annotation and GO Analysis

Co-expressed genes were annotated using Blastx (https://blast.ncbi.nlm.nih.gov) and UniProt database (https://www.uniprot.org). R package clusterProfiler (Wu et al. 2021) was used to perform enrichment analysis on gene function annotations (GO entries).

Data and code availability

The code used for RNA-seq analysis in this study is available in a GitHub repository (https://github.com/yukkikou/rRNA_depletion_2021).

The raw and processed sequencing data are available on BIG Submission and will be accessible through project number PRJCA006591.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

Thiswork was supported by the National Natural Science Foundation of China [31970008].