Complex biological network analysis revealed the regulation of genes under multiple fungal infected silk of Zea mays

doi:10.21203/rs.3.rs-3763122/v1

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Complex biological network analysis revealed the regulation of genes under multiple fungal infected silk of Zea mays

https://doi.org/10.21203/rs.3.rs-3763122/v1

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Background

Maize (Zea mays L.) is a more versatile and multi-purpose crop, primarily grown as food and feed, but now extensively applied in beauty, drug, and biofuel energy. However, the decline in production is reported to be promoted by the fungal pathogens, which invade developing grain using silk channels and hampering the quantity and quality of maize silk. Since understanding the molecular mechanisms will increase the chance of reducing the fungal infection, an integrated omics approach was undertaken on the transcriptome datasets with the multi-fungus at the maize silks.

Results

The available data sets were classed to A and B for simplicity, but enhancement in the analysis based on the intra and inter-transcriptomic level, keeping control data sets for comparing under the infected conditions. Hence, based on the analysis, the differentially expressed transcript isoforms were identified, which revealed approximately 39651 and 34027 transcript isoforms, 3819 and 3320 alternatively spliced regions along classes A and B. On examination, about 3212 lncRNAs were identified, among which 530 lncRNAs expressed were recognized to play a key role in multiple infectious conditions. Investigation on the complex biological network mediated by the combinations of miRNAs with lncRNAs, lncRNAs with transcript isoforms, lncRNAs with alternatively spliced genes. Interestingly, about 82 lncRNAs exhibited intersectional interactions with miRNAs, AS, and Tiso, which decoded for 103 genes.

Conclusions

This study might have contributed to key functions of response to stimulus, regulating the metabolic and cellular process during the development and of the plants, which eventually enhanced the plants immune or defence mechanisms.

RNASeq

transcript isoform

Alternative splicing

lncRNAs

fungal

maize silks

Maize (Zea mays L.), a stable crop in the world after wheat and rice, also called the "queen of cereals,” was domesticated more than 9000 years ago in south Mexico/ Meso America [1], over around 5.5% out of the total energy content of food is the consumption of human food across 166 countries [2]. Maize/corn is a more adaptable crop with a wider range of uses. It is mainly farmed for nutrition food and feed for human and animal consumption but is now extensively applied in beauty, drug, and biofuel energy [3]. While all the parts of the plant are used to generate revenue, maize silk, one the fastest growing tissues in nature (~ 1–3 mm/h)[4], referred to as the stigmas or female flowers, is a yellowish thread-like strand [5] has demonstrated to contribute significant health benefits. The presence of bioactive substances, including alkaloids, carbohydrates, flavonoids, mineral salts, polysaccharides, proteins, steroids, and vitamins [6, 7]. Silk has evidenced the positive role of defense against age-related illnesses like cancer, high blood pressure, diabetes, hepatitis, and cardiovascular disease[8]. Interestingly, corn silk has been investigated to reduce blood sugar levels and body weight, other than to boost insulin serum secretion, manage hyperglycemia and enhance glucose intolerance in type 2 diabetic mice[9].

Owing to its importance and contribution in wider perspectives, we understand that the reduction in its growth and yield need good management practices, especially from the biological factors, mainly from fungal diseased occupied silk of Zea mays. Silk have important properties, including soft, nutrient-rich, and moisture-laden tissue between the husks leaf and corn seed which provides a perfect environment to growth and propagules [10]. The particular genus of fungal species such as Ustilago, Penicillum, Fusarium and Aspergillus, can grow their hyphae in the maize silk parenchymatous cells and cause diseases like ear rot, maize smut, and brown spot [11], apart from economic and production, there is a 5 to 42% yield loss each year[12]. Trichoderma spp. is a common soil inhabitant that infects the root tissue to help plant defense mechanisms and prevent disease progression by suppressing the pathogen growth with growth-related plant hormones[13] and is therefore considered a beneficial organism. The plant contains the 1,3-beta-glucan synthase genes and MYB transcription factors necessary to produce the papillae's callose, inhibiting fungus growth[14–16]. The Nectriaceae family F. graminearum (Fg) and F. verticillioides (Fv) fungi makes it clear that the normally infect the surface of the maize silk in order to suck nutrients for hyphal development, while that of U. maydis infect along its full length and produce mycotoxins, which are cancerous to liver in humans[17]. Interestingly, our earlier study on comprehensive gene-level studies revealed the impact of fungus on the silk of Zea mays [18]. Before examining differential gene expression, transcript readings were mapped to exons and added to a gene-level count table. The genuine change in gene expression is recovered by raw count methods when all of a gene's isoforms are up- or downregulated between two conditions. However, when some isoforms are elevated while others are downregulated, raw count approaches are unreliable. In order to accurately and statistically detect changes in gene expression at the resolution level, transcript-level calculations are necessary[19, 20]. Nowadays develop many bioinformatics tools and algorithms, which made it easy to unravel the hidden molecular mechanism and explored the transcript isoforms of genes, alternative spliced genes (AS genes), and long noncoding RNAs (lncRNAs) from transcriptome data [21–23]. Specific regulations influencing controlling the disease, have been applied in the present study.

Multiple fungal infected silk [24] were used to identified transcription factor and resistance genes expression[18]. In this study have focus on complex regulatory network, which can interact in a coordinating manner miRNAs mimics target to long non-coding RNA (lncRNAs), lncRNA-Isoform, and lncRNA-AS to fine tune gene expression. These regulatory RNAs and proteins control how precisely genes are expressed in response to diverse physiological signals and circumstances. They are crucial for maintaining mechanisms like homeostasis, reaction to environmental changes, and processes like development and growth. For cells and organisms to operate properly, it is essential to be able to regulate gene expression at different levels. The entire study based on statistical estimates was reframed using DE-Tiso, DE-AS, and DE-lncRNAs on the fungal infected silk of Zea mays transcriptome data, where in a total of 3212 lncRNAs were found, including 149 DE-lncRNAs with multiple fungal infected samples. In addition, we identified 389 DE-lncRNA-miRNA, 9147 DE-lncRNA-DEAS, and 460916 DE-lncRNA-DETiso interactions and constructed reciprocal regulatory networks. Hence, to curb this, an attempt was undertaken to investigate the effect of fungi on the silk of maize through the available datasets by deep analysis on the isoformic nature of the transcript encoded by specific genes and their physiological changes accounted through the mechanism of alternate splicing (AS) and role of miRNAs and long noncoding RNAs (lncRNAs). By reciprocal analysis and different combinations performed, interesting information was derived, as presented below, and these analysis helps scientists use molecular breeding and biotechnology to achieve fungal stress resistance and increase maize silk yield.

Pre-processing and Alignment of RNA-Seq Data

Many transcriptomes, genomics, proteomics, and etc. data are available in sequence read archive of National Centre for Biotechnology Information (NCBI) database, in which, we were used two fungal infected silk of Zea mays transcriptome datasets A (PRJEB13048) and dataset B (PRJNA362306). 3 X 6 = 18 samples in the present study, which samples of dataset A infected with F. graminearum and U. maydis, while dataset B infected by F. verticillioides and T. atroviride, as well as a control sample of each data set [24] (Table S1A). These public dataset samples have their quality checked using the FASTQC programme (https://www.bioinformatics.babraham.a‌c.uk/proje cts/fastqc/), and HISAT2 [25] tool were used for the read sequences aligned with the most recent Zea mays B73 (V5.0) (http://ftp.ebi.ac.uk/ensemblgenomes/pub/release-51/plants/fasta/zea_mays/) reference sequences.

Differential expression analysis of transcript isoform

Reads per gene were counted with the RSEM (RNA-seq by Expectation Maximization) software, including using the program HISAT2 human Cell Atals SMART-Seq2 pipeline after then generating data matrix calculation (case/ control) (Table S1B). The statistical analysis and identification of differentially expressed (DE) transcripts at whole gene (or whole noncoding RNA) level or at transcript isoform level performed using EBSeq package invoked by RSEM script [26]. EBSeq offers posterior probabilities of differential expression (PPDE) to quantify the evidence supporting differential expression; PPDE values above 0.95 were deemed statistically significant. Compared to the DESeq [27], which delivers P-values adjusted for multiple testing using the Benjamini-Hochberg technique[28], EBSeq is a more cautious method for finding differentially expressed genes.

Differential alternative splicing analysis

The multiple types of differential alternative splicing (DAS) events were identified by used rMATS [29]tool, namely, alternative 5’ or 3’ splicing site (A5SS or A3SS), exon skipping (ES), intron retention(IR), and mutually exclusive exons(MXE) within samples with and without fungal infections from an aligned output BAM file and an recently updated annotation GTF file. The significant threshold cut off values were used |IncLevelDifference| ≥ 0.1 and FDR ≤ 0.05. Further, intra and intersection alternative splicing genes were identified between control A and B datasets. The intersection of genes common to the sepsis types was explored and visualized using an upset plot by the UpSetR package[30]. IGV (Integrated Genomic Viewer) was used to quantitatively visualise the splice junctions of the genes exhibiting a substantial psi score difference in conjunction with an uninfected sample as a sashimi plot[31].

lncRNA identifications in fungal infected samples

The reference annotation file and newly assembled transcripts of read alignment sequences were used to produce a non-redundant transcriptome with the help of StringTie-merge[32] tool. After comparing the gene structure information of samples with known annotations information with the help of gffcompare, then filtered out the unqualified transcript with class code of "i", "o", "u," and "x" and made gtf file. The lncRNAs were separated for using the following parameters: (1) Transcripts sequences less than 200 bp were removed. (2) lncRNA screen with CPC2[33], CNCI[34], FEElnc[35], PLEK[36] and the intersection of the four methods were retained(3) to check similarity with known databases, such as Pfam[37], Rfam[38], and UniProt[39] databases. Further, a comparison of our lncRNA set with Zea Mays lncRNA reported in databases, such as PlncDB[40], GreeNC[41], CANTATA2.0[42], NONCODE[43] through standalone blastn with default parameters[44].

Differential expression of lncRNAs

Differential expression of long noncoding RNA (DElncRNAS) identified from same transcriptome datasets of multiple fungal infected samples, were used StringTie with parameter -B and -e was executed for each sample against self-made annotation (gtf) files and obtained read counts using prepDE.py3 (http://ccb.jhu.edu/software/stringtie/dl/prepDE.py3) script of StringTie [32]. DESeq2 R package [45] were used to identification differential expression of the lncRNAs with an absolute threshold value log2 fold change ≥ |1.5| and adjusted p-values ≤ 0.05.

Prediction of miRNAs mimics targets and network analysis

All Zea maize mature miRNA sequences were downloaded from the public domain miRBase (https://www.mirbase.org/) database. Further, miRNAs targeted to lncRNAs identified were used plant-targeted gene prediction software psRNATarget webtool (http://plntgrn.noble.org/psRNATarget/) with default parameters, other than identified interaction between lncRNA and found significant isoform and alternative splicing genes were used as the cDNA library for the target search by the RIblast tools with parameter interaction energy < -16 kcal/mol, and interaction length ≥ 20 nt[46]. Further, constructed reciprocal interactions network miRNA ,lncRNA, isoform, and AS gene and visualized the complex network through Cytoscape software[47].

Functional analysis of lncRNA, DASs, and differential gene or Isoform

The GO enrichment analyses of significant DETiso, DEAS and target genes were performed using the multiple web tools, such as Biomart[48], ShinyGo[49], and UniProt web tools[50]. With a cutoff FDR = 0.05. Gene Ontology (GO) annotations were found in the categories of biological process, molecular function, and cellular component using ShinyGO v0.77.

Comparative transcriptome analysis of publicly available transcriptome data was carried out to understand better the critical transcript isoform, alternative splicing, long noncoding RNA, and miRNA involved in the molecular and biological process of Zea mays silk against different fungiform infections. The current study focuses on differential expression (DE) of transcript isoform (Tiso) of genes, alternative splicing, and long noncoding RNAs to identify the commonly modulated set-in infected maize datasets to contribute the target that reciprocal interaction network construction of lncRNA -miRNA-Tiso-AS.

Differential expression of transcript isoform of genes under multiple fungal infected samples

The RSEM-EBSeq tool was used to analyze the two datasets for differences in the transcript isoforms of the gene expression. The DETiso was filtered out |log2FC| value was > 1.5, change posterior fold change (PostFC) and considered as significant. 51% of the transcript isoforms genes (39651 and 34027 of the 77,341 transcript isoforms genes) in the datasets PRJEB13048 and PRJNA362306 had distinct expression patterns in fungus-infected versus control samples (Table 1). DETiso genes were varied in fungal-infected samples to compare up- and down-regulated genes in controls. In a comparative analysis, 12568 (16.3%) transcript isoforms were expressed uniquely, among which roughly 4633 (5%) transcript isoforms genes were recognized as duplicated (Table S2A). Since, (Fig. 1A), displays that 64.3% (11045) of maize silk transcript isoform genes were significantly expressed in Fg, 4% (687) were expressed in Fv, 30.6% (5,251) were described in Um, and 1.2% (218) were expressed in Ta expressed infection. Therefore, it's crucial to find out which transcript isoform gene is playing responsible role for regulating genes expression under different fungal perturbations. It seems that a fungus infection was the source of these variations in the DETiso gene values[51], and this might be explained by the disparities in sizes factor of datasets sample, where dataset A control (Ca) has 7.5 GB, while dataset B control (Cb) has 2.5 GB. The idea, which is well supported by this investigation, is that varying size of samples and the spatiotemporal specificity of samples may impact the striking differences between the various data sets ( Wang et al., 2021)[52].

Table 1

Differentially expressed transcript isoform of genes.
Dataset A			Dataset B			Dataset A ∩ Dataset B
Samples	Down	Up	Samples	Down	Up	Down	Up
Ca Vs Fg	8925	8075	Cb Vs Fv	8999	8957	5302	5743
Ca Vs Fv	3086	3195	Cb Vs Ta	1073	1259	254	433
Ca Vs Ta	3505	3122	Cb Vs Um	349	383	115	103
Ca Vs Um	5558	4185	Cb Vs Fg	6956	6051	3039	2212
Ca, control of dataset A; Cb, control of dataset B; Fg, F. graminearum; Fv, F. verticillioides; Ta, T. atroviride; Um, U. maydis; Down, Down regulated; Up, Up regulated genes.

In the interaction study of DETiso expressed genes in various fungal infections, a venn diagram of all DETiso genes was compared between up- and down-regulated genes within controls. 5 Tiso genes were present across all four fungal infection maize silk samples. Five isoform genes were expressed in the intersection of three fungal infections, exactly in Ta, Fv, and Fg infections. Fg, Ta, and Um infected conditions were identified 8 Tiso genes. Fg, Fv, and Um fungalform infected silk of Zea mays, 221 Tiso genes intersectional expressed. 3 Tiso was expressed in Um, Ta, and Fv. The cross construction between two fungal infection conditions, such as, Ta and Um, expressed 4 Tiso genes. In Fv and Ta, 35 Tiso genes were expressed. 27 Tiso genes expression were found under Um and Fv infection conditions. 3959 Tiso genes were found in Fg and Um fungal contaminations maize silk samples. 91 Tiso genes were present in the same Nectriaceae family belonging Fg and Fv samples. In Fg and Ta infection conditions, 29 Tiso were expressed. A single sample of the Fg fungus induced the expression of 6728 Tiso, 1024 Tiso during the Um infection. 130 Tiso, and 300 Tiso during the Ta and Fv infections, respectively (Fig. 1A). In the silk of Z. mays, these transcript isoforms contain genes involved in a change of physiological processes, such as the organization of the cell wall, hormone response, secondary metabolism, phosphorylation, photosynthesis, replication, and reaction to stimuli. As a result, the fundamental premise of our study is that these transcript isoforms may exhibit various expression patterns and counts of transcript isofom based on their inherent genetic makeup. Although several physical and/or chemical processes in samples of other fungal infections were overlapping, F. graminearum infection showed greater transcript isoform genes expression levels in the silk of Z. mays.

Identification of significant alternative splicing events in samples

Differential alternative splicing (DAS) genes (3819 and 3320 of 44303 genes) of dataset A and dataset B were identified with fungal vs. without fungal infection. Further, this significant differential expression AS genes comparison between up-and down-regulated genes within controls and 712 AS event genes found (Table 2; Table S2B), in which 511 genes uniquely, 90 and 7 genes recognized intersectional in two and three samples, respectively (Fig. 1B). A total of 712 alternative splicing (AS) events genes were identified in fungal infected samples, majorly five types of AS events including Alternative 3’ splicing site (A3SS) with 151 genes, Alternative 5’ splice site(A5SS) 112 genes involved, Exon skipping (ES) presence in 252 genes, mutually exclusive exons (MXE) found in 15 genes and intron retention (IR) occurred in 182 genes. A total of 570 differentially alternative splicing (DAS) genes were identified, and 142 AS events occurred multiple splicing in the same genes. The splicing factor U2af small subunit A proteins (Zm00001eb316870) genes have been identified eight AS event in Fg, Fv and Ta, including A3SS X 3, IR X 2, SE X 3, respectively (Fig. 2).

Table 2

Differentially expressed alternative splicing genes.
Dataset A				Dataset B				Dataset A ∩ Dataset B
Samples	Down	Up	Total	Samples	Down	Up	Total	Down	Up	Total
Ca Vs Fg	517	520	1037	Cb Vs Fg	570	624	1194	124	154	278
Ca Vs Fv	550	422	972	Cb Vs Fv	231	206	437	74	45	119
Ca Vs Ta	537	445	982	Cb Vs Ta	81	98	179	28	10	38
Ca vs Um	443	386	829	Cb vs Um	762	748	1510	140	137	277
Ca, control of dataset A; Cb, control of dataset B; Fg, F. graminearum; Fv, F. verticillioides; Ta, T. atroviride; Um, U. maydis; Down, Down regulated; Up, Up regulated genes.

Screening of lncRNA in multiple fungal-infected samples

Long noncoding RNA (lncRNAs) sequences were identified from the these datasets of multiple fungal-infected samples. After comparing gene structure information of samples with known annotations information, filtered out the unqualified transcript and found 8805 transcript lncRNA based on the filtration cut-off (length < 200, coverage < 3, and exon numbers < 2) with class_code of "i", "o", "u", and "x" [53]. Further, simplified by the selective bioinformatics lncRNA predictive tools, including CNCI, FEELnc, PLEK, and CPC2. All 3212 lncRNA intersections were found (Fig. 3A), with 598 lncRNAs removed from 3810 lncRNAs due to matches with Pfam, Rfam, and UniProt database of coding sequences.

Similarity checks with known lncRNA database of Zea Mays

To the check similarity between identified 3212 lncRNA and publicly available lncRNA transcript databases NONCODE, PlncDB, CANTATAdb, and GreeNC of Zea mays. This analysis revealed that 27.5% (882) lncRNA similar with all databases, 16.4% (526) lncRNA intersections similar to three databases, 16.1% (515) lncRNA similar connected with two databases, 15.7% (502) lncRNA match with single databases (Fig. 3B), while 24.6% (787) of lncRNAs were not found to be similar to any databases. Finally, 787 lncRNA out of reputed 3212 lncRNA can be designed as novel lncRNA from our multiple fungal infections samples of Zea mays.

Expression pattern of lncRNAs

The 8805 lncRNAs filter out based on class code and length, while 3810 lncRNAs were found by with CPC2, CNCI, FEElnc, PLEK (Fig. 3A). in which 3212 lncRNAs verified from Pfam, Rfam, UniProt databases. Further, cross-check with known lncRNAs databases. In addition, out of 8805 lncRNAs, 2813 lncRNAs were found to read the expression in samples by self-made general feature format (GFF) file. A log2 fold change ≥ |1.5| and with p-values ≤ 0.05, 530 lncRNAs were significantly identified, that is 54% (286) lncRNA expression found in Fg infections samples, while 8.7% (46) lncRNA were expressed during Fv infection, 5.5% (29) lncRNA were found expression when was infected with Ta, and 31.9% (169) significant lncRNA involved in Um fungal (Table S2C). Further, these lncRNA cross-checks with 3212 validated lncRNAs databases of Zea mays, and we found 108, 24, 13, and 67 lncRNAs in Fg, Fv, Ta, and Um samples, respectively, whereas 149 lncRNAs differentially expressed (Table 3), in which, some lncRNAs were intersectional expressed in different fungal infection samples.

Table 3

Similarity check of identified long noncoding RNA fungal infected silk of *Zea mays*.
Names of lncRNAs Databases	Total number of lncRNAs	Number of DEGs lncRNAs	Fg	Fv	Ta	Um
CANTATAdb GreeNC NONCODE PlncDB	882	50	37	5	3	24
CANTATAdb NONCODE PlncDB	407	19	14	6	1	7
CANTATAdb GreeNC NONCODE	98	5	5			4
CANTATAdb GreeNC PlncDB	21	3	3
CANTATAdb NONCODE	345	9	7	1		5
CANTATAdb PlncDB	103	1	1			1
CANTATAdb GreeNC	57	3	2	1	1	2
GreeNC PlncDB	8	1	1
NONCODE PlncDB	2
CANTATAdb	334	15	10	2	4	4
GreeNC	91	6	4	1		2
PlncDB	67	3	2	1		2
NONCODE	10
Not Found	787	34	22	7	4	16
Total	3212	149	108	24	13	67
Fg, F. graminearum; Fv, F. verticillioides; Ta, T. atroviride; Um, U. maydis.

Construction reciprocal intersection RNA-RNA network

The miRbase data have been reported 174 precursors and 325 mature miRNAs of Zea mays (B73_RefGen_v4). We extracted all mature miRNAs and found the target with identified 149 DElncRNAs, in which 190 miRNAs targeted to 88 lncRNA were identified with 389 interactions to 331 cleavage and 58 translate sites (Table S3A). In contrast, 9147 interaction sites have been found: 556 out of 570 significant AS genes and 131 out of 149 lncRNA (Table S3B). In addition, out of 12568 significant transcript isoform’s genes, 4144 transcript isoforms targeted 149 lncRNA and made interaction 460916 sites (Table S3C). Interestingly, about 82 lncRNAs exhibited intersectional interactions with miRNAs, AS, and Tiso, which decoded for 103 genes that might have contributed to key functions of response to stimulus, regulating the metabolic and cellular process during the development and of the plants, which eventually enhanced the plants immune or defence mechanisms (Fig. 4).

Functional Annotations of transcript Isoform and Differential Alternative Splicing of Genes

A total out of 17201, 13987 differential expressed transcript isoforms (DETiso) genes were functionally recognized in four fungal-affected samples. In all, 2362 high functional categories with 28.6%, 16.4%, 30.4%, and 6.1% isoforms genes conforming to biological processes (BP), cellular components (CC), molecular functions (MF), and pathways were found, respectively. In Fg samples, up regulated transcript isoform genes encoding for protein phosphorylation, defense response, pollen-pistil interaction, recognition of pollen, and cell recognition are highly signified, while down regulated Tiso genes involved in carbohydrate metabolic process, photosynthesis, polysaccharide metabolic process, cellular polysaccharide metabolic process, and cellular glucan metabolic process. However, Fv belongs same Nectriaceae family, up regulated Tiso genes participated in defense response, regulation of protein serine/threonine phosphatase activity, negative regulation of phosphoprotein phosphatase activity, negative regulation of dephosphorylation, negative regulation of phosphatase activity. On the other side, down regulated genes of Fv contributors in photosynthesis, light harvesting in photosystem I, protein phosphorylation, photosynthesis light harvesting/reaction, protein-chromophore linkage, etc biological process. In Um infected samples up regulated genes have an important role in protein phosphorylation, cell surface receptor signaling pathway, transmembrane transport, defense response, enzyme-linked receptor protein signaling pathway, while down regulated Tiso genes response in photosynthesis, generation of precursor metabolites and energy, photosynthesis, light reaction, photosynthesis, light harvesting, photosynthesis, light harvesting in photosystem I (Table S4). In conclusion, these fungi significantly negatively impact photosynthesis while positively in the protein phosphorylation process.

Functional annotation of significant 712 AS genes analysis did by ShinyGO online tools identified 1079 with duplicate AS genes involved for 113 biological processes GO term, 473 alternative splicing genes were detected in 48 cellular components GO term, 599 genes were responses in 51 GO term of molecular functions, and 154 AS genes were performed in 45 pathways, highly AS genes involved in the regulation of cellular process establishment of localization, localization, response to stimulus, and etc biological process (Table S5).

Functional annotation of non-coding RNA target to AS and Tiso of common genes

556 AS genes and 4144 isoforms have the influence of lncRNA and miRNA. Interestingly, about 82 lncRNAs exhibited intersectional interactions with miRNAs, AS, and Tiso, which decoded for 103 genes that might have contributed to key functions of response to stimulus, regulating the metabolic and cellular process during the development and of the plants, which eventually enhanced the plants immune or defence mechanisms (Fig. 5).

Maize silk is the long thread-like strand of Zea mays plant material, which has been recognized in multiple benefits, including medicine and cosmetics, and is also known as a traditional Chinese medicine [54]. Perhaps, biotic factors, especially fungi, reduce crop yield production on the second level after insects. Some corn smut, Fusarium ear rot, Gibberella ear rot, brown spot etc., have been identified as major fungal diseases as well as mycotoxins production in maize plants [12, 24]. The previous study identified gene level expression that in Fusarium graminearum (Fg) infection conditions, more genes were influced compared to other Fusarium verticillioides (Fv), Trichoderma atroviride (Ta), and Ustilago maydis (Um) fungal infections silk of Zea mays[18]. In this study, we used these transcriptome data with different bioinformatics analysis tools (details in materials and methods) to analyze the expression profiles of the transcript isoform genes, alternative splicing, and lncRNAs and construct reciprocal interaction network with the target to significant transcript isoform, AS, of lncRNA and miRNA. Globally been reported about lncRNA work as precursors of miRNA to influence miRNA biosynthesis as well as interfere with the functions [55, 56], and alternatively, spliced and transcript isoform gene expression profiles have impact on the targets of miRNA during development, biotic and abiotic stress [57–59]. We identified 39651 and 34027 transcript isoform of genes, 3819 and 3320 alternative splicing significantly differentially expressed of control datasets A and B. In addition, 3212 lncRNAs have been identified, and 530 lncRNAs identified are significantly differentially expressed in multiple fungal infection conditions.

Transcript isoforms are generated from the same genes by alternative splicing events that produce phenotypes complexity and diversity of eukaryotes[60, 61]. In this study, we found 12568 transcript isoform genes(9502 genes), but 2409 genes of transcript isoform have never been identified in previous gene-level analyses (Fig. 6)[18]. For that analysis, transcript fragment was aligned to exons after than summed to a gene-level count table formerly differential gene expression analysis. Summarizing diverse transcript isoforms to the gene level ignores the dynamics of alternative splicing and may unclear transcript isoform-specific associations with phenotypes[19]. The enrichment analysis of this transcript isoform of genes, 17 of Um and 14 of Fg conditions downregulated transcript isoforms involved in the pyruvate metabolism pathway, in which genes ZM00001EB289570 have three transcript variants (isoform), that two variant involved Aldo-keto reductase family 4 member C9 proteins (EC:1.1.1.2), while transcript variant (ZM00001EB289570_T001) synthesis NADP-dependent oxidoreductase domain-containing protein (Fig. 7; Table S4). These pathways plays a key role in energy production through glycolysis to maintain cell metabolic homeostasis[62]. These transcript isoform expressions value influences under the different fungal infections in silk of Zea mays, that pyruvate dehydrogenase E2 components and pyruvate kinase down regulated in Ustilago maydis fungal infection infections, but in others infection not significant expression,[63] et al. have been identified upregulated in the glycolysis pathway Poa pratensis plants under cold treatments and also found in root tissue of wheat and rice abiotic stress conditions[64, 65]. The alcohol dehydrogenase proteins transcript isoform expression level was upregulated and downregulated in F. graminearum (Fg) other than did not have significant expression. Dihydrolipoamide dehydrogenase is an essential enzyme involved in various metabolic pathways [65] but in our study have down regulated in Fg infection conditions.

In different biotic and abiotic stresses, such as salinity [66], heat[67], cold[68], viruses[69], fungi[70], viruses, drought [71–74] and chemical deficiencies [75] have been identified that alternative splicing has a major impact on plant responses via genes and proteins enables produce protein isoforms with various functions, localizations, and regulatory features to react and adapt to particular biotic and abiotic stress conditions[76–78]. In this study, we identified the influence of AS events genes in the multiple fungal infected silks of Zea mays. Out of 712 genes, 570 differentially alternative splicing (DAS) genes were identified, and 142 AS events occurred with multiple splicing in the same genes (Fig. 2). The 305 AS splicing genes found only in AS, did not find previous gene levels[18] and transcript isoform genes (Fig. 6). Interestingly, out of them, the five genes ZM00001EB115220; U2AF, ZM00001EB356610: P68, ZM00001EB174130: SPF30, ZM00001EB283000: Prp38, ZM00001EB358100: hnRNPs involved in the spliceosome pathway in which three genes down regulated in both Fg and Um fungal infections conditions, and two genes up regulated in Fg fungal infection condition silk of Zea mays (Table S2B) with different AS events. [71] have found one gene involved in the spliceosome, the U2AF (U2 small nuclear ribonucleoprotein auxiliary factor), plays an important role in recognizing 3' splicing sites and binding to 3' AG intron border[79, 80] have proposed that U2AF35 gene expression level increased in flower but the root of Arabidopsis thaliana decreasing. [81] et al. found splicing factor 30(SPF30) in multiple plant species, mostly in rice and Arabidopsis thaliana, have variations of expression value. In this analysis, U2AF and SPF30 come under the ZM00001EB115220 and ZM00001EB174130 genes, which down regulated in Um fungal infections conditions, but others have no significant expressions. ZM00001EB358100: hnRNPs are RNA-binding proteins and play an important role in multiple aspects, including alternative splicing, nucleic acid, and translation regulations [82, 83]. These genes positively express in Fg infection of maize silk, but other conditions are not significant. 134 genes found in all segment means AS, alternative isoforms, and genes level (Fig. 6), the 21 genes were involved in the metabolic pathway, in which 14 genes present in the biosynthesis of secondary metabolites. The gene (Zm00001eb397190) have been identified five transcript isoforms, in which transcript isoform three (Zm00001eb397190_T003) involved in non-specific serine/threonine protein kinase, while others four transcript isoform participate in protein activated protein kinase, these proteins pay important role in mRNA cis splicing, via spliceosome biological process[84], in this study have been identified transcript isoform three upregulated in Fg and Um fungal infected samples out of five. In addition, have been saw that alternative slicing 5’ stop side (A5SS) as well as Exon skipping (ES) in Fg and Um fungal infected (Fig. 8).

Long noncoding RNAs (lncRNAs) can participate in and regulate numerous gene transcriptions, splicing, and nuclear structure of plants by acting in a trans site away from the transcription site and play significant roles in the biosynthesis and metabolism of secondary metabolites, development process, and stress response[85–87]. Through comparative analysis, out of 8805 lncRNAs, we identified 149 (see results) significantly expressed lncRNAs in multiple fungal-infected silks of Zea mays. Many researchers have identified in plants and animals that lncRNAs have the capacity to attached and modified their miRNAs after than inhibit its downstream functions[53, 56, 88, 89]; so we used 325 known miRNAs from miRbase database and found 389 interaction sites in 88 lncRNAs [90, 91]. miRNA and lncRNA are noncoding RNA, but both play important roles in regulating RNA splicing and gene expression[92]. [58] et al. have proposed that the miRNA and mRNA interactions work as inhibitors or promoters of anti-cancer drug resistance. Thus, miRNA and AS interaction is a potent target for eliciting drug resistance [93]. Some isoforms can avoid being targeted because they lack the miRNA binding site. In some circumstances, the miRNA binding site presence or absence in the isoform, influences the potential of its silence by a complementary miRNA[59]. [94] et al. have found lncRNA act as miRNA targets or decoys, involving gene expression regulations [56, 95–97]. In this study, we investigate the complex regulatory network of lncRNAs, miRNAs, alternative isoforms, and alternative splicing genes by Cytoscape (Fig. 4)[85], in which we found 389 interactions between lncRNAs and miRNAs, 331 cleavages and 58 translate site (Table S3A). The 9147-interaction site within 556 significant AS genes and 131 lncRNA (Table S3B). Out of 12568 significant alternative isoforms, 4144 alternative isoforms targeted 149 lncRNA and made interaction 460916 sites (Table S3C). Further, 103 common target genes between AS and alternative isoforms genes were found. In addition, uncovered functional divergence of lncRNAs targeted alternative isoforms and alternative splicing genes which are present in multiple biological process with the following GO terms: response to stimulus, cellular process, cellular component organization biogenesis, localization, metabolic process, positive regulation, signaling, rhythmic process, carbon utilization common target 103 genes, other than, development process, detoxification, growth process, multiple organismal process, negative of biological process, multi-organism process, and reproduction process, found in both target genes (Fig. 5), but cell proliferation, localization, biological regulation, immune system responses, and regulation of biological process found in only alternative isoform target involvements. Positive expression saw in Fv, Ta, Um infected samples of MSTRG.8680.2 lncRNA, interacted with zma-MIR159g, Zuma-miR166a, and zma-MIR169c miRNA; these ncRNA targeting protein kinase domain-containing protein alternative isoform (Zm00001eb077770_T001) which were expressed down-regulated in Fg, Um but these genes also found alternative 3' stop site in Fg infected samples, these protein phosphorylation play important roles in biotic stress, development of plants[98, 99]. MSTRG.4934.1 down regulated lncRNA in Fg, Ta, and Um infection conditions and targeted exonuclease domain-containing protein, NC domain-containing protein-related, Protein kinase superfamily protein, Integral membrane HPP family protein genes, which have the same influences in Fg and Um infections conditions, but NC domain-containing protein-related genes formed alternative splicing in 5' start side. However, significant express lncRNA and target MADS-box transcription factor 26 (Zm00001eb174000_T006), NAC transcription factor (Zm00001eb183190_T002), Myb family transcription factor PHL6 (Zm00001eb232000_T003), etc. (Table S6). Previous studies have shown that these transcription factors play key role in the flowering, ripening, and development of peanut and tomato [96, 100, 101]. The present study indicated that lncRNAs might play an important role in transcriptional and post-transcriptional levels by regulating non coding and transcription factors in fungal-infected silk of Zea mays.

In this analysis, we identified 3212 long non-coding RNAs (lncRNAs), with 530 exhibiting significant expression across multiple fungal infection conditions. Among these, 149 lncRNAs were consistently found in the samples. Moreover, 88, 131, and 149 lncRNAs were observed to impact 190 microRNAs (miRNAs), 556 alternative splicing (AS) events, and 4144 transcript isoforms (Tiso), respectively. These lncRNAs were found to target genes involved in alternative isoforms and alternative splicing, impacting various biological processes associated with GO terms such as response to stimuli and stress, metabolic processes, cellular and component organization, localization, signaling, developmental processes, negative detoxification, and growth. Through the comprehensive analysis of transcriptome data across all three experiments, our findings indicate that the F. graminearum fungus exhibited a notably higher degree of influence compared to other fungal infections on maize silk.

Alternative splicing

Tiso

Transcript isoform

lncRNAs

long non-coding RNA

miRNA

micro-RNA

DEAS

Differential expression of AS

DETiso

Differential expression of Tiso

F. graminearum

F. verticillioides

T. atroviride

U. maydis.

Acknowledgments

The Department of Bioinformatics at Pondicherry University provided all the necessary resources for this study, and the authors are appreciative of their assistance. Pondicherry University is appreciative for giving AK a Non-National Eligibility Test (NET) grant.

Author contributions

AK and PTVL designed the research. AK performed the analysis and analysed the results. AK wrote the initial manuscript. PTVL and AA majorly reviewed and analysed the draft and finalized the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research has received no financial support from any funding source or sources, including institutional financial assistance.

Ethics approval and consent to participate

This study did not incorporate any human or animal tissue materials, thus ethical approval was not necessary.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests

Availability of data and materials

The datasets utilized in this investigation are available in online repositories https://www.ncbi.nlm.nih.gov/. Specifically, you can find dataset A under accession number PRJEB13048 and dataset B under accession number PRJNA362306.

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No competing interests reported.

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Complex biological network analysis revealed the regulation of genes under multiple fungal infected silk of Zea mays

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Abstract

Background

Results

Conclusions

Figures

Introductions

Materials and methods

Pre-processing and Alignment of RNA-Seq Data

Differential expression analysis of transcript isoform

Differential alternative splicing analysis

lncRNA identifications in fungal infected samples

Differential expression of lncRNAs

Prediction of miRNAs mimics targets and network analysis

Functional analysis of lncRNA, DASs, and differential gene or Isoform

Results

Differential expression of transcript isoform of genes under multiple fungal infected samples

Identification of significant alternative splicing events in samples

Screening of lncRNA in multiple fungal-infected samples

Expression pattern of lncRNAs

Construction reciprocal intersection RNA-RNA network

Functional Annotations of transcript Isoform and Differential Alternative Splicing of Genes

Functional annotation of non-coding RNA target to AS and Tiso of common genes

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1