Characterization and identification of microRNA from Andrographis paniculata and its role in Cancer: A Cross Kingdom Approach

doi:10.21203/rs.3.rs-1871927/v1

Download PDF

Research Article

Characterization and identification of microRNA from Andrographis paniculata and its role in Cancer: A Cross Kingdom Approach

https://doi.org/10.21203/rs.3.rs-1871927/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background and Objective

Andrographis paniculata belonging to Acanthaceae family is well-known potential hepatoprotective herb. The role of diet on human health and diseases including Hepatocellular Carcinoma has been demonstrated by innumerable studies which have also testified that plant-derived micro-RNAs, transferred to animals via oral consumption wherein it alters host gene expression and this provides the evidence for inter-species gene regulation. Herein, we aim to explore the role of predicted miRNAs of A. paniculata on human health.

Methods and Results

In the present study, A. paniculata miRNAs have been predicted and their impact on human transcriptome is uncovered by employing computational approach. By implementing miRNA prediction on A. paniculata genomic data and plant miRNA sequences present in miRBase repository, we have identified 20 miRNAs which are confined to 16 miR families. 197 human target genes were predicted for the identified miRNAs, which were further subjected to Gene Ontology analysis and network analysis revealing their significant role in various biological processes and signaling pathways. The identified hub proteins HGF, NRP2 and CCND1 show their significant involvement in cancerous pathways including STAT3, MAPK, TGF-β, mTOR and VEGFR pathways. Deregulation of these pathways has been reported as oncogenic response involved in Hepatocellular Carcinoma, cell proliferation and metastasis.

Conclusion

The findings of our study suggest potentially beneficiary role of A. paniculata miRNAs in regulating cancer via cross-species genetic regulation. Conclusively, our study sustains the promising theory of inter-species gene regulation.

MicroRNAs

Andrographis paniculate

Hepato cellular carcinoma

Target prediction

Pathway analysis

A class of small non-coding epigenetic modulators, microRNAs (miRNAs) has recently gained much interest in terms of genetic expression alteration in diverse disorders(Malan-Müller, Joanna Hemmings, and Seedat 2013). In 1993, for the first time ever small RNA lin-4 was identified in nematode Caenorhabditis elegans and it was also reported to regulate lin-14 expression, which is regarded as the first evidence to demonstrate regulatory function of small RNAs and also coupled the term ‘regulators’ with miRNAs. These single stranded, approximately 22 nucleotide length species of RNAs with regulatory properties are categorized as miRNAs(O’Brien et al. 2018). Since then, intensive research has been conducted in the broad spectra of miRNAs as a new layer of epigenetic modulators leading to identification of thousands of miRNAs favoring RNA interference (RNAi) theory. Thereafter enormous miRNAs have been identified from plant species ranging from Arabidopsis to Oryza, as these miRNAs elicit their role to regulate functional mRNAs by translation hindrance or degradation(Li et al. 2021). However, the extensive research in this ground gained huge concern only after the researchers from Monsanto Company claimed the complementarity of plant miRNAs to mammalian genes(W. Wang et al. 2018). These findings were further supported by another study by Zhang’s team, which in 2011 confirmed and published the intact absorption and stability of plant derived miR168a in human system. It also revealed the regulation of low-density lipoprotein receptor adapter protein 1 (LDLRAP1) mRNA by the same miR(L. Zhang et al. 2012). A number of similar publications reveal and confirm that plant miRs target exogenous genes as well along with endogenous regulation. This inspired a rise in a study into microRNA biogenesis, transport, and function. The miRNA family consists of small single-stranded non-coding RNAs that range in length from 18 to 25 nucleotides. Plants and animals have different biogenesis processes for microRNAs(Wahid et al. 2010). The Ribonuclease III enzyme cleaves primary transcripts (pri-miRNAs), and the enzyme Dicer Like (DCL1) (Drosha enzyme in animal system) processes these Pri-miRNAs into pre-miRNAs (precursors) with a particular fold structure in the nucleus. The mature miRNA is led to a complementary target mRNA after the development of the miR-miR* duplex, which is included in the miRNA-induced silencing complex (miRISC). When there is complete complementarity between miRNA and mRNA, the target transcript is destroyed; however, when there isn't perfect complementarity, the target is subjected to translational suppression(Wahid et al. 2010; J. Wang, Mei, and Ren 2019; Wen-wen, Hong-bo, and Jing 2014). Advancements in high throughput sequencing technologies have paved a way for computational identification of plant miRNAs with bioinformatics analysis by utilizing the available genomic or EST data. This has accelerated the process of identifying targets and annotating their functions(Eminaga et al. 2013).

Aboriginal sources state the use of a number of medicinal plants for treatment disorders since ancient times. Such medicinal plants have become an integrated part of human life to combat life threatening disorders. Andrographis paniculata, member of Acanthaceae (family), is widely distributed in India, China, Thailand and Malaysia. It is also known as ‘Kalmegh’ and ‘King of bitters’ in India. Extensive research has been conducted on this plant to uncover its pharmacological properties and its medicinal health benefits report its use for the treatment of various diseases including fever, diarrhea, Hepato Cellular Carcinoma, Liver tumor, hepatitis, neurodegenerative disorders and liver diseases(Hossain et al. 2014). A cascade of publications has stated that dietary and medicinal plants execute their pharmacological activities by transferring regulatory miRNAs from plants to animal systems(Sala-cirtog, Marian, and Anghel 2015).

Henceforth in the present study Andrographis paniculata genome from publically available resources was exploited for in-silico identification of miRNAs that regulate human transcriptome. Additionally, the predicted targets were further subjected fur functional annotation, pathway and network analysis by implementing computational approach, in order to uncover their disease association and identify the most deregulated pathways(Sharma et al. 2017).

2.1 Data Retrieval

As the implemented methodology is represented in schematic workflow (Supplementary 1), the prediction and identification of probable A. Paniculata miRNAs and their target genes was performed by employing amalgamation of computational approach. The chromosome level whole genome shotgun sequence assembly of Andrographis paniculata leaf tissue was retrieved from public repository (NCBI accession number GCA_004354405.1)(Sun et al. 2020)(Sayers et al. 2021). The genome assembly with size 269.41 Mb, 257scaffolds and1,278 contigs was retrieved in Fasta Format for further analysis.To extract known miRNA sequences, publically available miRNA repository miRBase Release 22.1 containing 38,589 entries corresponding to mature and precursor sequences was used(Kozomara, Birgaoanu, and Griffiths-jones 2019). A total of 10,414 miRNA sequences were downloaded in Fasta format from Viridiplantae of miRBase and were considered as reference set of miRNAs.

2.2 Identification of Potential A. Paniculata miRNAs and Secondary Structure Prediction

For sequence homology search the extracted reference set of plant miRNAs were screened against A. paniculata whole genome sequence(NCBI accession number GCA_004354405.1) by subjecting it to alignment process performed by using BLASTn program of NCBI BLAST + 2.12.0. To operate BLASTn program, Expected value (E-value) parameter was fixed as 1000, maximum target sequence and word size constraints were customized as 1000 and 18 respectively(Altschup et al. 1990). The generated results with appropriate alignment of plant miRNAs were further sieved with checks such as 18–24 nucleotide miRNA length, 0–2 nucleotides as allowed mismatch in alignment. Nearly 100 nucleotides flanking regions (upstream and downstream) along with the aligned region were fetched and regarded as probable precursor miRNA candidates, which were then subjected to secondary structure prediction. The probable pre-miRNA candidate sequences were exercised for secondary structure prediction and obtaining structures with minimum free folding energy using MFold web server(Zuker 2003). The RNA folding web server was operated with default parameters and the resulted secondary structures were further filtered based on the following gauges(D. Kumar et al. 2017):

The resultant secondary structure of precursor miRNA candidate must be folded in an appropriate stem-loop hairpin construct.

The mature miRNA sequence must be positioned at either of the arms region of the hairpin structure.

There is no room for any loops or breaks in the mature miRNA sequence and its complementary strand (miR*).

The length of candidate precursor miRNA sequence must not be less than 60 nucleotides.

Mature miR and its complementary strand should be having minimum possible mismatches, precisely not more than 6 matches are allowed.

For an appropriately considerable structure bulge size must not exceed the range of 3–7 nucleotides.

Considering a stable structure higher G + C content is desirable and permitted than that of A + C content, otherwise condition leads to discarding unstable structure.

The predicted structure must have a higher minimum folding energy index (MFEI) ranging from 0.53 Kcal/mol to 1.01 Kcal/mol and the permissible negative minimum folding energy must be lower than − 18 kcal /mol.

For folding energy based screening of predicted stem-loop structures of miRNAs, the Minimal Folding Energy Index (MFEI) and Adjusted Minimal Folding Energy (AMFE)(Trotta 2014) were calculated according to widely accepted equation reported by Zhang et al:

AMFE = (MFE)/ (length of potential pre-miRNA) * 100

MFEI = AFME / (G + C) %

2.3 Nomenclature of Predicted miRNAs

The nomenclature for predicted structures was accomplished in accordance to miRBase customs. Each distinguished miRNA was designated a name with prefix consist of initials of genus and species name i.e. for Andrgraphis paniculata ‘Apa’ becomes the assigned prefix followed by label ‘miR’ and family, separated by ‘-’, as enlisted in Table-1(Griffiths-jones et al. 2006).

2.4 Phylogenetic Analysis

miRNAs have a highly conserved nature, thus they share evolutionary relations with other homolog miRNAs. Predicted miRNA sequences along with their homolog miRNAs sequences from miRBase were fetched and subjected for multiple sequence alignment using MEGAX software. Further Neighbor-joining tree was constructed to derive phylogenetic relations(S. Kumar et al. 2018).

2.5 Target Prediction and Functional Annotation

The probable target transcripts for computationally predicted A. Paniculata miRNAs were predicted by using psRNATarget webserver. The putative target transcripts were identified against Homo sapiens with perfect or near-perfect complementarity between miRNAs and Homo sapiens transcripts(Dai and Zhao 2011). The webserver allowed customised parameters as complementarity scoring length (hsp size): 18, Max UPE(maximum energy to unpair target site): 25, translation inhibition range were set ranging from 9 to 11 nucleotides and maximum expected threshold: 3, to identify the plausible human genes being targeted by A. paniculata miRs. For functional annotation of predicted transcripts Gene Ontology analysis was performed by using Blast2GO program of OmicsBox platform(Terol et al. 2008). The target genes were analyzed for their GO IDs, terms and descriptions and their functionalities were further distinguished into Biological process (BP), molecular functions (MF) and cellular compartments (CC).

2.6 Protein - Protein Interaction Network and Pathway Analysis

In order to identify key genes from all the predicted targets, STRING database was used to decipher the protein – protein Interaction network(Szklarczyk et al. 2019). Further, Cytoscape software was used with Cytohubba plugin to construct regulatory network and identify the most significant genes with BottleNeck algorithm(C. Chin et al. 2014). KEGG Mapper, MalaCards and GeneAlaCart were investigated and explored to infer the role of noteworthy genes in different diseases and various regulatory pathways(Kanehisa and Goto 2000; Rappaport et al. 2013).

3.1 Identification and Characterization of A. paniculata miRNAs

For identification of putative miRNAs from whole genome sequence of A.paniculata, Blastn was implemented with miRNA sequences from miRBase as query sequences against A.paniculata whole genome sequence which resulted in 3078 hits. These hits were manually processed by using Mfold webserver in order to validate the predicted miRNAs based on secondary structures and folding energy criteria as listed in materials and methodology subdivision. Analyzing the stem-loop structures with folding energy resulted in identification of 20 potential miRNAs (Apa-miR156f-3p, Apa-miR156f-5p, Apa-miR159b-5p, Apa-miR164-3p, Apa-miR164-5p, Apa-miR171a-3p, Apa-miR171n-5p, Apa-miR172b-5p, Apa-miR319c-3p, Apa-miR390a-3p, Apa-miR390a-5p, Apa-miR393-5p, Apa-miR395k-3p, Apa-miR396-5p, Apa-miR399e-3p, Apa-miR530-5p, Apa-miR1533-5p, Apa-miR2111-5p, Apa-miR2118b-3p, Apa-miR8672-3p) belonging to 16 families. The length of predicted mature sequences prevailed between 18–21 nucleotides with the precursor length ranging from 86–185 nucleotides. Thermodynamic stability of these miRNAs was observed in terms of folding energy i.e. MFE ranging from − 95.20 to − 18 kcal/mol. The AMFE was observed to prevail within the scale of -66.22 to -20.57 to kcal/Mol, thus ensuring stable hairpin structures of predicted miRNAs. (Table 1, Fig. 1). Characteristics of all the predicted potential miRNAs were found to be in compliance with secondary structure prediction norms(Gadhavi et al. 2019).

Table 1

Details and characterization of identified miRNAs from *A. paniculata*
miRNA ID	mature sequence	mature seq length	precursor length	miRNA start	miRNA end	(A + U) %	(G + C) %	MFE	AMFE	MFEI
Apa-miR156f-3p	CTCACTTCTCTTTCTGTCA	19	107	79	97	57.89	42.11	-52.3	-48.88	-1.09
Apa-miR156f-5p	TGACAGAAGAGAGTGAGCA	19	107	15	33	52.63	47.37	-52.3	-48.88	-1.09
Apa-miR159b-5p	CTGCCGACTCATTCATTCA	19	86	6	24	52.63	47.37	-31.7	-36.86	-0.93
Apa-miR164-3p	CATGTGCTCTAGCTCTCCAGC	21	118	93	113	42.86	57.14	-50.6	-42.88	-0.99
Apa-miR164-5p	GCTGGAGAAGCAGGGCAC	18	118	4	21	33.33	66.67	-50.6	-42.88	-0.99
Apa-miR171a-3p	GATTGAGCCGCGCCAATATC	20	86	61	80	45	55	-37	-43.02	-1.06
Apa-miR171n-5p	TATTGGTGAGGTTCAATCCGA	21	86	8	28	57.14	42.86	-37	-43.02	-1.06
Apa-miR172b-5p	GCAGCATCATCAAGATTC	18	107	5	22	55.56	44.44	-45.4	42.43	-1.19
Apa-miR319c-3p	TTGGACTGAAGGGAGCTCCC	20	185	164	183	40	60	-95.2	-51.46	-1.09
Apa-miR390a-3p	CGCTATCCATCCTGAGTTTCA	21	131	108	128	52.38	47.62	-55	-41.98	-0.96
Apa-miR390a-5p	AAGCTCAGGAGGGATAGC	18	131	5	22	44.44	55.56	-55	-41.98	-0.96
Apa-miR393-5p	TTCCAAAGGGATCGCATTGAT	21	108	11	31	57.14	42.86	-55.7	-51.57	-1.05
Apa-miR395k-3p	TGAAGCGTTTGGGGGAACT	19	90	61	79	47.37	52.63	-59.6	-66.22	-1.32
Apa-miR396-5p	CCACAGCTTTCTTGAACT	18	94	4	21	55.56	44.44	-37.6	-40	-1.18
Apa-miR399e-3p	TGCCAAAGGAGATTTGCC	18	100	76	93	50	50	-46.2	-46.2	-1.05
Apa-miR530-5p	TGCATTTGCACCTGCACCT	19	86	7	25	47.37	52.63	-36.2	-42.09	-0.86
Apa-miR1533-5p	ATAATAAAAATAATAATGA	19	69	11	29	94.74	5.26	-30.5	-44.2	-15.25
Apa-miR2111-5p	TAATCTGCATCCTGAGGT	18	99	11	28	55.56	44.44	-40.9	-41.31	-0.93
Apa-miR2118b-3p	TTCCCGATTCCACCCATTCCT	21	96	71	91	47.62	52.38	-44.9	-46.77	-0.94
Apa-miR8672-3p	AGAAATGATTGTATGAAA	18	88	67	84	77.78	22.22	-18.1	-20.57	-0.72

3.2 Target Identification in Human Transcriptome and Functional Annotation of Target Genes

It has been reported that a plant derived miRNAs regulate host gene expression by binding to complementary mRNAs. Thus by employing a fundamental target prediction approach and subjecting the predicted miRNAs against human transcripts by using psRNAtarget webserver, we have identified 197 human transcripts against 20 predicted A. Paniculata miRNAs (Provided in Supplementary 2). The results also indicated translation inhibition of 10 genes and cleavage of 187 genes. Another important finding from our result suggests that Apa-miR-1533 targets maximum number of human transcripts. Keeping in mind the significance and appropriate visualization of these results, a network representing miRNA – Gene interaction was generated (Fig. 2(A)). The identified target genes were further considered for functional annotation, in order to summarize their purpose in human body. To serve this purpose, Gene ontology (GO) analysis was conducted(Patel et al. 2019) using OmicsBox. As a result GO terms were broadly distinguished as Biological Process (BP), Molecular Functions (MF) and Cellular Components (CC)(Ye et al. 2018) (Fig. 2(B)). Analyzing these results, we have observed that in Molecular Function, majority of genes were involved in Binding (GO: 0005488) constituting about 95.9%. Whereas genes involved in protein binding (GO: 0005515) constitute about 86.89%. Biological Process category includes genes involved in cellular process (GO: 0009987), biological regulation (GO: 0050789) and metabolic process (GO: 0008152) with 98.29%, 70.94% and 68.38% proportions respectively. Another finding from these interpretations indicate that major portion of genes are involved in cellular anatomical entity (GO: 0110165) including cell, cell parts and organelle approximately constituting more than 90%.

Figure2(A). The network depicts regulation of different human genes, each targeted by A. paniculatamiRNAs (Teal colour nodes represent predicted A. paniculata miRNAs and light pink nodes represent human target genes). A connection between miRNA and a gene implies that the target gene is being regulated by predicted A. paniculata miRNAs.

Figure2(B). Distribution of genes assigned to broad Gene Ontology categories: Cellular component, Biological process, Molecular function of human target genes. WEGO plot showing the number and percentage of genes involved with different functions

3.3 Protein – Protein Interaction Network and Pathway Analysis

To deduce the functions of these genes in host system it isnecessary to conduct protein – protein interaction network and pathway analysis, so as to recognize the chief regulatory genes identified in this study. Network analysis was performed by using Cytoscape software and by applying Bottleneck algorithm we identified the topmost hub nodes(Jha et al. 2021). Interpretation of these results was based on node score, HGF (Hepatocyte Growth Factor) being the topmost node with a significant score of 51. However, we regarded 28 and more node score as considerable for this study, which helps to regard NRP2 (Neuropilin 2) and CCND1 (Cyclin D1) as significant nodes for further pathway analysis having 45 and 28 as the node scores respectively (Fig. 3). Other Nodes identified in the network include TAOK1, ITGA4, KCNN3, KCNJ3, ARCN1, NXF1, and PPARGC1A. The most significant nodes with highest scores (HGF, NRP2 and CCND1) were further subjected to pathway and disease association analysis in order to identify their downstream cascades by mining and exploring KEGG Mapper, GeneAlaCart and MalaCards, which helped us to assemble a comprehensive pathway (Fig. 4). We have identified the involvement and association of the most significant genes in Hepato cellular carcinoma. The results also suggest their involvement in oncogenic signaling pathways including MAPK, ERK, WNT, PI3K-AKT, JAK-STAT and P38 signaling pathways as illustrated (Fig. 4).

3.4 Phylogeny

A number of studies have reported highly conserved nature of miRNAs, to understand the evolutionary relationship of identified miRNAs of A. paniculata with other reported miRNA families, we conducted the phylogenetic analyses. As far as author’s knowledge miRBase release 22.1 doesn’t contain any miRNAs reported for A. paniculata, thus the 16 families identified in this study were reported for the first time for Andrographis paniculata. Our results indicate higher degree of conservation and sequence similarity among miRNAs, as a number of homolog miRs were observed for freshly identified families (Fig. 5). These interpretations indicate that all the identified A. paniculata miRNAs maintain the conserved nature along with the consistency of evolution(Bhadresha et al. 2021).

A number of ethno-medicinal studies have reported A. paniculata as a highly potential medicinal herb with antioxidant pharmacological activities, conventionally used for treatment of fever, jaundice and as a tonic for liver and cardiovascular health. Since paniculata has been narrated to have broad range of pharmacological impacts scaling from anti-HIV, anticancer, anti-inflammatory to hepatoprotective, its projected influences need to be evaluated significantly(Sivananthan and Elamaran 2013; Mishra, Sangwan, and Sangwan 2007). miRNAs have been portrayed as modulatory components appearing in eukaryotic genome(A. R. Chin et al. 2016). Nonetheless, to uncover the mechanism by which A. paniculata regulates human genome expression, we have performed whole genome analysis by computational approach, which facilitated the identification of paniculata derived miRNAs regulating host gene expressions. By sourcing miRBase and NCBI data, we have computationally identified and characterized 20 A. paniculata miRNAs belonging to 16 known families having interactions and regulatory impacts on human transcripts. The predicted potential miRNAs were further subjected for inter species target identification for human host by implementing psRNATarget webserver, to evaluate the biological role of these miRNAs in human system and regulation of human genome expression by these miRs. Remarkably, the predicted miRNAs retain plausible capabilities to modulate host target gene expression with immense biological importance. The results demonstrated complementarities of multiple genes against a single miRNA as well as multiple miRNAs were observed to target a common transcript. Notably, the predicted targets were annotated for biological processes (cellular process), molecular functions (Binding) and cellular components(Jha et al. 2021). The identified hub nodes (TAOK1, ITGA4, KCNN3, KCNJ3, ARCN1, NXF1, and PPARGC1A) were further analyzed for pathway and disease association and conveniently the results demonstrated their striking involvement in various cancers including hepato cellular carcinoma, breast, gastric, lung, and colorectal, prostate and pancreatic cancers. The outcomes also discovered crucial roles of target transcripts in oncogenic pathways comprising MAPK, ERK, WNT, PI3K-AKT, JAK-STAT and P38 signaling pathways.

HGF (Hepatocyte Growth Factor), the identified topmost hub node has been reported to promote cancer cell proliferation, migration and invasion, which also causes poor survival outcomes by granting therapy resistance(H. Wang et al. 2020). HGF interacting with its receptor MET, causes dimerization of the receptor, this initiates a series of downstream cascade. HGF activated MET instigates Ras-dependent ERK and MAPK pathways, which contribute to proliferation and migration of cancer cells. It also triggers PI3K/AKT and STAT3 pathways which are associated with tumor aggression and cancer cell survival and metastatic migration of cancer cells(Owusu et al. 2017). Elevated expression levels of HGF have been reported in Colon cancer patients, with liver metastasis, these high levels of HGF correlate with lymph node metastasis and relapse in breast cancer patients. Due to its triggering and stimulating role in cancer progression, HGF has emerged as a potential and valid target for targeted therapy having fewer side effects(Huang et al. 2020). Another identified significant hub node, NRP2 (Neuropilin 2) is also reported to be up-regulated in multiple cancers including colon cancer, bladder cancer, pancreatic cancer and breast cancer tissues causing poor prognosis and regulates tumor progression. NRP2 overexpression triggers VEGFR phosphorylation which further causes tumor cell growth, migration and invasion. VEGFR is also testified to stimulate carcinogenesis ERK-MAPK-P38 cascades(Grandclement and Borg 2011). NRP2 is also known to interact with TGFβ and instigates SMAD2/3 activation resulting in tumorogenesis. Studies reveal decreased expression of miRNA by targeting it with shRNAs reduces cancer cell migration and tumor growth. Thus NRP2 justifies the parameters to be an appropriate target for targeted therapies(Fung et al. 2016; H. Zhang, Wang, and Wang 2019). CCND1(Cyclin D1), one of the identified top most significant hub nodes has been elucidated as a proto-oncogene as it regulates tumorigenesis and metastasis in cancers including Hepato cellular carcinoma and found to be associated with poor prognosis and tumor recurrence. CCND1 has been found to be frequently up regulated in HCC and breast cancer. Studies confirm overexpression of CCND1 correlated with proliferation, migration and invasion of tumors. Studies also reveal that silencing of CCND1 inhibits HCC tumor growth by reducing cell proliferation. Such findings strongly suggest and recommend CCND1 as one of the potential therapeutic target for management of liver cancer(Chen et al. 2016; Ding, Wang, and Zhang 2020).

Taken together, these findings intensely indicate that miRNAs identified from Andrographis paniculata play a vital role in targeting crucial genes such as HGF, NRP2 and CCND1 which have been identified as top most significant nodes. This study evidently demonstrates the role of A. paniculata miRNAs and their corresponding targets have a potential role for negative regulation of MAPK, STAT3, ERK, PI3K/AKT and P38 cascades for Hepato Cellular Carcinoma management. Throughout the study, our results demonstrate the role of plant derived miRNAs and its cross species target genes to be involved in crucial downstream signaling pathways having disease involvements which also includes oncogenic alleyways. These results convincingly establish the potential role of miRNAs as anti-neoplastic components with curative significance along with its therapeutic promise. Thus this study recommends across the species genetic regulation providing preliminary indications for the interaction among plant and animal species and molecular communication across the species barriers(Samad, Kamaroddin, and Sajad 2021; Yang et al. 2015).

In the present study,Andrographis paniculata genome was analyzed to identify conserved miRNAs and their human target transcripts. 20 miRNAs and their interaction with 197 human targets were identified using in-silico approach, which were further processed for annotation, network and pathway regulation in human system by employing computational approach. The identified significant target genes HGF, NRP2 and CCND1 were found to be involved in various downstream signaling pathways including STAT3, MAPK, mTOR, TGF-β, VEGFR oncogenic pathways involved in carcinomas predominantly hepatocellular carcinoma. In the present study identified A. paniculatamiRNAs are found to regulate HCC via cleavage and translation inhibition mechanism for these genes. Thus these results present a plausible beneficiary role of A. paniculata derived miRNAs on human transcriptome. Since plant derived miRNAs could regulate host genes involved in various pathways, conclusively, our study sustains the promising theory of inter species gene regulation, which further suggests the designing and development of miRNA-based drugs as potential alternate therapeutic approach for treatment of cancer, even though it is still regarded as a challenging approach. Nevertheless the competence of plant miRNAs to regulate complex disease networks in human system necessitates their experimental validation. Authentication of the mechanism of miRNA mediated host gene regulation is still warranted with implementation of impartial systems biology alongside computational biology. Which needs to be further comprehended by confirmation based molecular validations for treatment of cancer.

miRNAs / miRs

micro RNAs

shRNA

Small Hairpin RNA

MFE

Minimum Free Energy

MFEI

Minimal Folding Energy Index

AMFE

Adjusted Minimal Folding Energy

HGF

Hepatocyte Growth Factor

NRP2

Neuropilin 2

CCND1

Cyclin D1

STAT3

Signal Transducer and Activator of Transcription 3

MAPK

Mitogen-Activated Protein Kinase

TGF-β

Transforming Growth Factor

mTOR

Mammalian Target Of Rapamycin

VEGFR

Vascular Endothelial Growth Factor Receptor

ERK

Extracellular signal-Regulated Kinase

PI3K

Phosphoinositide 3-kinases

Acknowledgement

Author Harsha Motwani acknowledges the SHODH-ScHeme Of Developing High Quality Research, Education Department, Gujarat State for the financial assistance in form of fellowship. Naman Mangukia acknowledges the Prime Minister’s Fellowship award from Science and Engineering Research Board (SERB), Department of Science and technology (DST), Government of India and the Confederation of Indian Industry (CII) and industry support aided by BioInnovations.

Conflict of Interest

All authors declare no conflict of interest.

Author contributions

Solanki H, Rawal R and Patel SK conceived and designed the experiments; Motwani H, Gadhavi H, Mangukia N; performed the experiments; Motwani H,Gadhavi H, Mangukia N,Dixit N analyzed and interpreted the data; Gadhavi H ,Motwani H and Solanki H drafted the article, revised it critically for important intellectual content and final approval of the paper.

Funding

The computational analysis in this study was supported by Bioinformatics Node facility

provided by Gujarat State Biotechnology Mission (GSBTM), Department of Science &

Technology (DST) and Government of Gujarat [grant number GSBTM/MD/JDR/1409/2017–

18].

Availability of data and material

The additional data is provided in Supplementary material.

Authors Approval

All authors have read and approved the final manuscript.

Ethics approval

The manuscript does not contain experiments using animals and human.

Altschup, Stephen F, Warren Gish, The Pennsylvania, and Universily Park. 1990. “Basic Local Alignment Search Tool 2Department of Computer Science,” 403–10.
Bhadresha, Kinjal, Maulikkumar Patel, Jpan Brahmbhatt, Nayan Jain, and Rakesh Rawal. 2021. “Targeting Bone Metastases Signaling Pathway Using Moringa Oleifera Seed Nutri-MiRs: A Cross Kingdom Approach.” Nutrition and Cancer 0 (0): 1–18. https://doi.org/10.1080/01635581.2021.2001547.
Chen, Yiyu, Jingwen Jiang, Mengyang Zhao, Xiaojun Luo, Zixi Liang, Yan Zhen, Qiaofen Fu, et al. 2016. “MicroRNA-374a Suppresses Colon Cancer Progression by Directly Reducing CCND1 to Inactivate the PI3K/AKT Pathway.” Oncotarget 7 (27): 41306–19. https://doi.org/10.18632/oncotarget.9320.
Chin, Andrew R., Miranda Y. Fong, George Somlo, Jun Wu, Piotr Swiderski, Xiwei Wu, and Shizhen Emily Wang. 2016. “Cross-Kingdom Inhibition of Breast Cancer Growth by Plant MiR159.” Cell Research 26 (2): 217–28. https://doi.org/10.1038/cr.2016.13.
Chin, Chia-hao, Shu-hwa Chen, Hsin-hung Wu, Chin-wen Ho, Ming-tat Ko, and Chung-yen Lin. 2014. “CytoHubba: Identifying Hub Objects and Sub- Networks from Complex Interactome” 8 (Suppl 4): 1–7.
Dai, Xinbin, and Patrick Xuechun Zhao. 2011. “PsRNATarget: A Plant Small RNA Target Analysis Server” 39 (May): 155–59. https://doi.org/10.1093/nar/gkr319.
Ding, Hao, Yijun Wang, and Hongying Zhang. 2020. “CCND1 Silencing Suppresses Liver Cancer Stem Cell Differentiation and Overcomes 5-Fluorouracil Resistance in Hepatocellular Carcinoma.” Journal of Pharmacological Sciences 143 (3): 219–25. https://doi.org/10.1016/j.jphs.2020.04.006.
Eminaga, Seda, Danos C Christodoulou, Francois Vigneault, George M Church, and J G Seidman. 2013. “Quantification of MicroRNA Expression with Next-Generation Sequencing,” no. July: 1–14. https://doi.org/10.1002/0471142727.mb0417s103.
Fung, Tsun Ming, Kai Yu Ng, Man Tong, Jin Na Chen, Stella Chai, Kin Tak Chan, Simon Law, et al. 2016. “Neuropilin-2 Promotes Tumourigenicity and Metastasis in Oesophageal Squamous Cell Carcinoma through ERK–MAPK–ETV4–MMP–E-Cadherin Deregulation.” Journal of Pathology 239 (3): 309–19. https://doi.org/10.1002/path.4728.
Gadhavi, Harshida, Maulikkumar Patel, Naman Mangukia, and Kanisha Shah. 2019. “Transcriptome-Wide MiRNA Identification of Bacopa Monnieri: A Cross-Kingdom Approach” 2324.
Grandclement, Camille, and Christophe Borg. 2011. “Neuropilins: A New Target for Cancer Therapy.” Cancers 3 (2): 1899–1928. https://doi.org/10.3390/cancers3021899.
Griffiths-jones, Sam, Russell J Grocock, Stijn Van Dongen, Alex Bateman, and Anton J Enright. 2006. “MiRBase: MicroRNA Sequences, Targets and Gene Nomenclature” 34: 140–44. https://doi.org/10.1093/nar/gkj112.
Hossain, Sanower, Zannat Urbi, Abubakar Sule, and K M Hafizur Rahman. 2014. “Andrographis Paniculata (Burm. f .) Wall. Ex Nees : A Review of Ethnobotany, Phytochemistry, and Pharmacology” 2014.
Huang, Xing, Enliang Li, Hang Shen, Xun Wang, Tianyu Tang, Xiaozhen Zhang, Jian Xu, et al. 2020. “Targeting the HGF/MET Axis in Cancer Therapy: Challenges in Resistance and Opportunities for Improvement.” Frontiers in Cell and Developmental Biology 8 (May): 1–15. https://doi.org/10.3389/fcell.2020.00152.
Jha, Neha, Naman Mangukia, Maulik P Patel, Mansi Bhavsar, Harshida Gadhavi, Rakesh M Rawal, and Saumya K Patel. 2021. “Gene Reports Exploring the MiRnome of Carica Papaya: A Cross Kingdom Approach.” Gene Reports 23 (September 2020): 101089. https://doi.org/10.1016/j.genrep.2021.101089.
Kanehisa, Minoru, and Susumu Goto. 2000. “KEGG: Kyoto Encyclopedia of Genes and Genomes” 28 (1): 27–30.
Kozomara, Ana, Maria Birgaoanu, and Sam Griffiths-jones. 2019. “MiRBase: From MicroRNA Sequences to Function” 47 (November 2018): 155–62. https://doi.org/10.1093/nar/gky1141.
Kumar, Dinesh, Swapnil Kumar, Garima Ayachit, Shivarudrappa B. Bhairappanavar, Afzal Ansari, Priyanka Sharma, Subhash Soni, and Jayashankar Das. 2017. “Cross-Kingdom Regulation of Putative MiRNAs Derived from Happy Tree in Cancer Pathway: A Systems Biology Approach.” International Journal of Molecular Sciences 18 (6): 1–21. https://doi.org/10.3390/ijms18061191.
Kumar, Sudhir, Glen Stecher, Michael Li, Christina Knyaz, and Koichiro Tamura. 2018. “MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms” 35 (May): 1547–49. https://doi.org/10.1093/molbev/msy096.
Li, Dan, Jianhui Yang, Yong Yang, Jianxin Liu, Hui Li, Rongfei Li, Chunya Cao, Liping Shi, Weihua Wu, and Kai He. 2021. “A Timely Review of Cross-Kingdom Regulation of Plant-Derived MicroRNAs.” Frontiers in Genetics 12 (May): 1–10. https://doi.org/10.3389/fgene.2021.613197.
Malan-Müller, Stefanie, Sîan Megan Joanna Hemmings, and Soraya Seedat. 2013. “Big Effects of Small RNAs: A Review of MicroRNAs in Anxiety.” Molecular Neurobiology 47 (2): 726–39. https://doi.org/10.1007/s12035-012-8374-6.
Mishra, Siddhartha K, Neelam S Sangwan, and Rajender S Sangwan. 2007. “Phcog Rev.: Plant Review Andrographis Paniculata (Kalmegh): A Review” 1 (2): 283–98.
O’Brien, Jacob, Heyam Hayder, Yara Zayed, and Chun Peng. 2018. “Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation.” Frontiers in Endocrinology 9 (AUG): 1–12. https://doi.org/10.3389/fendo.2018.00402.
Owusu, Benjamin Yaw, Robert Galemmo, James Janetka, and Lidija Klampfer. 2017. “Hepatocyte Growth Factor, a Key Tumor-Promoting Factor in the Tumor Microenvironment.” Cancers 9 (4): 1–16. https://doi.org/10.3390/cancers9040035.
Patel, Maulikkumar, Naman Mangukia, Neha Jha, Harshida Gadhavi, Kanisha Shah, Saumya Patel, Archana Mankad, Himanshu Pandya, and Rakesh Rawal. 2019. “Computational Identification of MiRNA and Their Cross Kingdom Targets from Expressed Sequence Tags of Ocimum Basilicum.” Molecular Biology Reports 46 (3): 2979–95. https://doi.org/10.1007/s11033-019-04759-x.
Rappaport, Noa, Noam Nativ, Gil Stelzer, Michal Twik, Yaron Guan-golan, Tsippi Iny Stein, Iris Bahir, et al. 2013. “Original Article MalaCards: An Integrated Compendium for Diseases and Their Annotation” 2013: 1–14. https://doi.org/10.1093/database/bat018.
Sala-cirtog, Maria, Catalin Marian, and Andrei Anghel. 2015. “ScienceDirect New Insights of Medicinal Plant Therapeutic Activity — The MiRNA Transfer” 74: 228–32.
Samad, Abdul Fatah A., Mohd Farizal Kamaroddin, and Muhammad Sajad. 2021. “Cross-Kingdom Regulation by Plant MicroRNAs Provides Novel Insight into Gene Regulation.” Advances in Nutrition 12 (1): 197–211. https://doi.org/10.1093/advances/nmaa095.
Sayers, Eric W, Evan E Bolton, J Rodney Brister, Kathi Canese, Jessica Chan, Donald C Comeau, Ryan Connor, et al. 2021. “Database Resources of the National Center for Biotechnology Information,” 1–7.
Sharma, Ankita, Sarika Sahu, Pooja Kumari, Soundhara Rajan Gopi, Rajesh Malhotra, and Sagarika Biswas. 2017. “Genome-Wide Identification and Functional Annotation of MiRNAs in Anti-Inflammatory Plant and Their Cross-Kingdom Regulation in Homo Sapiens.” Journal of Biomolecular Structure and Dynamics 35 (7): 1389–1400. https://doi.org/10.1080/07391102.2016.1185381.
Sivananthan, Manoharan, and Manoharan Elamaran. 2013. “Medicinal and Pharmacological Properties of Andrographis Paniculata Department of Biomedical Science, Faculty of Biomedicine and Health, ASIA Metropolitan University, Sivananthan and Elamaran Sivananthan and Elamaran.” International Journal of Biomolecules and Biomedicine 3 (2): 1–12. https://www.researchgate.net/publication/331767904_Medicinal_and_pharmacological_properties_of_Andrographis_paniculata.
Sun, Wei, Liang Leng, Qinggang Yin, Meimei Xu, Mingkun Huang, Zhichao Xu, Hui Yao, et al. 2020. “HHS Public Access” 97 (5): 841–57. https://doi.org/10.1111/tpj.14162.The.
Szklarczyk, Damian, Annika L. Gable, David Lyon, Alexander Junge, Stefan Wyder, Jaime Huerta-Cepas, Milan Simonovic, et al. 2019. “STRING V11: Protein-Protein Association Networks with Increased Coverage, Supporting Functional Discovery in Genome-Wide Experimental Datasets.” Nucleic Acids Research 47 (D1): D607–13. https://doi.org/10.1093/nar/gky1131.
Terol, Javier, Tim D Williams, Juan Miguel Garcı, Shivashankar H Nagaraj, De Valencia, and Centro De Geno. 2008. “High-Throughput Functional Annotation and Data Mining with the Blast2GO Suite” 36 (10): 3420–35. https://doi.org/10.1093/nar/gkn176.
Trotta, Edoardo. 2014. “On the Normalization of the Minimum Free Energy of RNAs by Sequence Length” 9 (11). https://doi.org/10.1371/journal.pone.0113380.
Wahid, Fazli, Adeeb Shehzad, Taous Khan, and You Young Kim. 2010. “MicroRNAs: Synthesis, Mechanism, Function, and Recent Clinical Trials.” Biochimica et Biophysica Acta - Molecular Cell Research 1803 (11): 1231–43. https://doi.org/10.1016/j.bbamcr.2010.06.013.
Wang, Haiyu, Benchen Rao, Jiamin Lou, Jianhao Li, Zhenguo Liu, Ang Li, Guangying Cui, Zhigang Ren, and Zujiang Yu. 2020. “The Function of the HGF/c-Met Axis in Hepatocellular Carcinoma.” Frontiers in Cell and Developmental Biology 8 (February): 1–15. https://doi.org/10.3389/fcell.2020.00055.
Wang, Junli, Jun Mei, and Guodong Ren. 2019. “Plant MicroRNAs: Biogenesis, Homeostasis, and Degradation.” Frontiers in Plant Science 10 (March): 1–12. https://doi.org/10.3389/fpls.2019.00360.
Wang, Wei, Dan Liu, Xiaopei Zhang, Dongdong Chen, Yingying Cheng, and Fafu Shen. 2018. “Plant MicroRNAs in Cross-Kingdom Regulation of Gene Expression.” International Journal of Molecular Sciences 19 (7). https://doi.org/10.3390/ijms19072007.
Wen-wen, Kong, Wang Hong-bo, and Li Jing. 2014. “Biogenesis of Plant MicroRNAs.” Journal of Northeast Agricultural University (English Edition) 21 (1): 84–96. https://doi.org/10.1016/s1006-8104(14)60027-1.
Yang, Ningning, Nsikak R. Ekanem, Clement A. Sakyi, and Sidhartha D. Ray. 2015. “Hepatocellular Carcinoma and MicroRNA: New Perspectives on Therapeutics and Diagnostics.” Advanced Drug Delivery Reviews 81: 62–74. https://doi.org/10.1016/j.addr.2014.10.029.
Ye, Jia, Yong Zhang, Huihai Cui, Jiawei Liu, Yuqing Wu, Yun Cheng, Huixing Xu, et al. 2018. “WEGO 2.0: A Web Tool for Analyzing and Plotting GO Annotations, 2018 Update.” Nucleic Acids Research 46 (W1): W71–75. https://doi.org/10.1093/nar/gky400.
Zhang, Hongye, Ruiyu Wang, and Mingxia Wang. 2019. “MiR-331-3p Suppresses Cell Invasion and Migration in Colorectal Carcinoma by Directly Targeting NRP2.” Oncology Letters 18 (6): 6501–8. https://doi.org/10.3892/ol.2019.11029.
Zhang, Lin, Dongxia Hou, Xi Chen, Donghai Li, Lingyun Zhu, Yujing Zhang, Jing Li, et al. 2012. “Exogenous Plant MIR168a Specifically Targets Mammalian LDLRAP1: Evidence of Cross-Kingdom Regulation by MicroRNA.” Cell Research 22 (1): 107–26. https://doi.org/10.1038/cr.2011.158.
Zuker, Michael. 2003. “Mfold Web Server for Nucleic Acid Folding and Hybridization Prediction” 31 (13): 3406–15. https://doi.org/10.1093/nar/gkg595.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Characterization and identification of microRNA from Andrographis paniculata and its role in Cancer: A Cross Kingdom Approach

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methodology

2.1 Data Retrieval

2.2 Identification of Potential A. Paniculata miRNAs and Secondary Structure Prediction

2.4 Phylogenetic Analysis

2.5 Target Prediction and Functional Annotation

2.6 Protein - Protein Interaction Network and Pathway Analysis

3. Results

3.1 Identification and Characterization of A. paniculata miRNAs

3.2 Target Identification in Human Transcriptome and Functional Annotation of Target Genes

3.3 Protein – Protein Interaction Network and Pathway Analysis

3.4 Phylogeny

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1