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Ajila V, Colley L, Ste-Croix DT, Nissan N, Cober ER, Mimee B, Samanfar B, Green JR. Species-specific microRNA discovery and target prediction in the soybean cyst nematode. Sci Rep 2023; 13:17657. [PMID: 37848601 PMCID: PMC10582106 DOI: 10.1038/s41598-023-44469-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 10/09/2023] [Indexed: 10/19/2023] Open
Abstract
The soybean cyst nematode (SCN) is a devastating pathogen for economic and food security considerations. Although the SCN genome has recently been sequenced, the presence of any miRNA has not been systematically explored and reported. This paper describes the development of a species-specific SCN miRNA discovery pipeline and its application to the SCN genome. Experiments on well-documented model nematodes (Caenorhabditis elegans and Pristionchus pacificus) are used to tune the pipeline's hyperparameters and confirm its recall and precision. Application to the SCN genome identifies 3342 high-confidence putative SCN miRNA. Prediction specificity within SCN is confirmed by applying the pipeline to RNA hairpins from known exonic regions of the SCN genome (i.e., sequences known to not be miRNA). Prediction recall is confirmed by building a positive control set of SCN miRNA, based on a limited deep sequencing experiment. Interestingly, a number of novel miRNA are predicted to be encoded within the intronic regions of effector genes, known to be involved in SCN parasitism, suggesting that these miRNA may also be involved in the infection process or virulence. Beyond miRNA discovery, gene targets within SCN are predicted for all high-confidence novel miRNA using a miRNA:mRNA target prediction system. Lastly, cross-kingdom miRNA targeting is investigated, where putative soybean mRNA targets are identified for novel SCN miRNA. All predicted miRNA and gene targets are made available in appendix and through a Borealis DataVerse open repository ( https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/30DEXA ).
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Affiliation(s)
- Victoria Ajila
- Department of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6, Canada
| | - Laura Colley
- Department of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6, Canada
| | - Dave T Ste-Croix
- Saint-Jean-sur-Richelieu Research and Development Centre, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, J3B 7B5, Canada
| | - Nour Nissan
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, K1A 0C6, Canada
- Department of Biology and Ottawa Institute of Systems Biology, Carleton University, Ottawa, K1S 5B6, Canada
| | - Elroy R Cober
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, K1A 0C6, Canada
| | - Benjamin Mimee
- Saint-Jean-sur-Richelieu Research and Development Centre, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, J3B 7B5, Canada
| | - Bahram Samanfar
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, K1A 0C6, Canada
- Department of Biology and Ottawa Institute of Systems Biology, Carleton University, Ottawa, K1S 5B6, Canada
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6, Canada.
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Ajila V, Colley L, Ste-Croix DT, Nissan N, Golshani A, Cober ER, Mimee B, Samanfar B, Green JR. P-TarPmiR accurately predicts plant-specific miRNA targets. Sci Rep 2023; 13:332. [PMID: 36609461 PMCID: PMC9822942 DOI: 10.1038/s41598-022-27283-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding. Here, the recently published TarDB database of plant miRNA-mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca . The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.
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Affiliation(s)
- Victoria Ajila
- grid.34428.390000 0004 1936 893XDepartment of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6 Canada
| | - Laura Colley
- grid.34428.390000 0004 1936 893XDepartment of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6 Canada
| | - Dave T. Ste-Croix
- grid.55614.330000 0001 1302 4958Saint-Jean-sur-Richelieu Research and Development Center, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, J3B 7B5 Canada
| | - Nour Nissan
- grid.55614.330000 0001 1302 4958Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa, K1A 0C6 Canada ,grid.34428.390000 0004 1936 893XDepartment of Biology, Carleton University, Ottawa, K1S 5B6 Canada
| | - Ashkan Golshani
- grid.34428.390000 0004 1936 893XDepartment of Biology, Carleton University, Ottawa, K1S 5B6 Canada
| | - Elroy R. Cober
- grid.55614.330000 0001 1302 4958Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa, K1A 0C6 Canada
| | - Benjamin Mimee
- grid.55614.330000 0001 1302 4958Saint-Jean-sur-Richelieu Research and Development Center, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, J3B 7B5 Canada
| | - Bahram Samanfar
- grid.55614.330000 0001 1302 4958Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa, K1A 0C6 Canada ,grid.34428.390000 0004 1936 893XDepartment of Biology, Carleton University, Ottawa, K1S 5B6 Canada
| | - James R. Green
- grid.34428.390000 0004 1936 893XDepartment of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6 Canada
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Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling. BIOLOGY 2022; 11:biology11121798. [PMID: 36552307 PMCID: PMC9775672 DOI: 10.3390/biology11121798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 11/27/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022]
Abstract
MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.
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Shakyawar S, Southekal S, Guda C. mintRULS: Prediction of miRNA–mRNA Target Site Interactions Using Regularized Least Square Method. Genes (Basel) 2022; 13:genes13091528. [PMID: 36140696 PMCID: PMC9498445 DOI: 10.3390/genes13091528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
Identification of miRNA–mRNA interactions is critical to understand the new paradigms in gene regulation. Existing methods show suboptimal performance owing to inappropriate feature selection and limited integration of intuitive biological features of both miRNAs and mRNAs. The present regularized least square-based method, mintRULS, employs features of miRNAs and their target sites using pairwise similarity metrics based on free energy, sequence and repeat identities, and target site accessibility to predict miRNA-target site interactions. We hypothesized that miRNAs sharing similar structural and functional features are more likely to target the same mRNA, and conversely, mRNAs with similar features can be targeted by the same miRNA. Our prediction model achieved an impressive AUC of 0.93 and 0.92 in LOOCV and LmiTOCV settings, respectively. In comparison, other popular tools such as miRDB, TargetScan, MBSTAR, RPmirDIP, and STarMir scored AUCs at 0.73, 0.77, 0.55, 0.84, and 0.67, respectively, in LOOCV setting. Similarly, mintRULS outperformed other methods using metrics such as accuracy, sensitivity, specificity, and MCC. Our method also demonstrated high accuracy when validated against experimentally derived data from condition- and cell-specific studies and expression studies of miRNAs and target genes, both in human and mouse.
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Affiliation(s)
- Sushil Shakyawar
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Siddesh Southekal
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Chittibabu Guda
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Center for Biomedical Informatics Research and Innovation (CBIRI), University of Nebraska Medical Center, Omaha, NE 68198, USA
- Correspondence:
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Reciprocal perspective as a super learner improves drug-target interaction prediction (MUSDTI). Sci Rep 2022; 12:13237. [PMID: 35918366 PMCID: PMC9344797 DOI: 10.1038/s41598-022-16493-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/11/2022] [Indexed: 11/08/2022] Open
Abstract
The identification of novel drug-target interactions (DTI) is critical to drug discovery and drug repurposing to address contemporary medical and public health challenges presented by emergent diseases. Historically, computational methods have framed DTI prediction as a binary classification problem (indicating whether or not a drug physically interacts with a given protein target); however, framing the problem instead as a regression-based prediction of the physiochemical binding affinity is more meaningful. With growing databases of experimentally derived drug-target interactions (e.g. Davis, Binding-DB, and Kiba), deep learning-based DTI predictors can be effectively leveraged to achieve state-of-the-art (SOTA) performance. In this work, we formulated a DTI competition as part of the coursework for a senior undergraduate machine learning course and challenged students to generate component DTI models that might surpass SOTA models and effectively combine these component models as part of a meta-model using the Reciprocal Perspective (RP) multi-view learning framework. Following 6 weeks of concerted effort, 28 student-produced component deep-learning DTI models were leveraged in this work to produce a new SOTA RP-DTI model, denoted the Meta Undergraduate Student DTI (MUSDTI) model. Through a series of experiments we demonstrate that (1) RP can considerably improve SOTA DTI prediction, (2) our new double-cold experimental design is more appropriate for emergent DTI challenges, (3) that our novel MUSDTI meta-model outperforms SOTA models, (4) that RP can improve upon individual models as an ensembling method, and finally, (5) RP can be utilized for low computation transfer learning. This work introduces a number of important revelations for the field of DTI prediction and sequence-based, pairwise prediction in general.
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Assessing sequence-based protein-protein interaction predictors for use in therapeutic peptide engineering. Sci Rep 2022; 12:9610. [PMID: 35688894 PMCID: PMC9187631 DOI: 10.1038/s41598-022-13227-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 04/25/2022] [Indexed: 12/01/2022] Open
Abstract
Engineering peptides to achieve a desired therapeutic effect through the inhibition of a specific target activity or protein interaction is a non-trivial task. Few of the existing in silico peptide design algorithms generate target-specific peptides. Instead, many methods produce peptides that achieve a desired effect through an unknown mechanism. In contrast with resource-intensive high-throughput experiments, in silico screening is a cost-effective alternative that can prune the space of candidates when engineering target-specific peptides. Using a set of FDA-approved peptides we curated specifically for this task, we assess the applicability of several sequence-based protein–protein interaction predictors as a screening tool within the context of peptide therapeutic engineering. We show that similarity-based protein–protein interaction predictors are more suitable for this purpose than the state-of-the-art deep learning methods publicly available at the time of writing. We also show that this approach is mostly useful when designing new peptides against targets for which naturally-occurring interactors are already known, and that deploying it for de novo peptide engineering tasks may require gathering additional target-specific training data. Taken together, this work offers evidence that supports the use of similarity-based protein–protein interaction predictors for peptide therapeutic engineering, especially peptide analogs.
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Turning Data to Knowledge: Online Tools, Databases, and Resources in microRNA Research. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:133-160. [DOI: 10.1007/978-3-031-08356-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Dick K, Pattang A, Hooker J, Nissan N, Sadowski M, Barnes B, Tan LH, Burnside D, Phanse S, Aoki H, Babu M, Dehne F, Golshani A, Cober ER, Green JR, Samanfar B. Human-Soybean Allergies: Elucidation of the Seed Proteome and Comprehensive Protein-Protein Interaction Prediction. J Proteome Res 2021; 20:4925-4947. [PMID: 34582199 DOI: 10.1021/acs.jproteome.1c00138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The soybean crop, Glycine max (L.) Merr., is consumed by humans, Homo sapiens, worldwide. While the respective bodies of literature and -omics data for each of these organisms are extensive, comparatively few studies investigate the molecular biological processes occurring between the two. We are interested in elucidating the network of protein-protein interactions (PPIs) involved in human-soybean allergies. To this end, we leverage state-of-the-art sequence-based PPI predictors amenable to predicting the enormous comprehensive interactome between human and soybean. A network-based analytical approach is proposed, leveraging similar interaction profiles to identify candidate allergens and proteins involved in the allergy response. Interestingly, the predicted interactome can be explored from two complementary perspectives: which soybean proteins are predicted to interact with specific human proteins and which human proteins are predicted to interact with specific soybean proteins. A total of eight proteins (six specific to the human proteome and two to the soy proteome) have been identified and supported by the literature to be involved in human health, specifically related to immunological and neurological pathways. This study, beyond generating the most comprehensive human-soybean interactome to date, elucidated a soybean seed interactome and identified several proteins putatively consequential to human health.
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Affiliation(s)
- Kevin Dick
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Arezo Pattang
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Julia Hooker
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Nour Nissan
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Michael Sadowski
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Bradley Barnes
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Le Hoa Tan
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Daniel Burnside
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Sadhna Phanse
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada S4S 0A2
| | - Hiroyuki Aoki
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada S4S 0A2
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada S4S 0A2
| | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Ashkan Golshani
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Elroy R Cober
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Bahram Samanfar
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
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Leal K, Saavedra K, Rebolledo C, Salazar LA. MicroRNAs hsa-miR-618 and hsa-miR-297 Might Modulate the Pleiotropic Effects Exerted by Statins in Endothelial Cells Through the Inhibition of ROCK2 Kinase: in-silico Approach. Front Cardiovasc Med 2021; 8:704175. [PMID: 34485404 PMCID: PMC8415262 DOI: 10.3389/fcvm.2021.704175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/20/2021] [Indexed: 01/11/2023] Open
Abstract
Several studies show that statin therapy improves endothelial function by cholesterol-independent mechanisms called “pleiotropic effects.” These are due to the inhibition of the RhoA/ROCK kinase pathway, its inhibition being an attractive atheroprotective treatment. In addition, recent work has shown that microRNAs, posttranscriptional regulators of gene expression, can affect the response of statins and their efficacy. For this reason, the objective of this study was to identify by bioinformatic analysis possible new microRNAs that could modulate the pleiotropic effects exerted by statins through the inhibition of ROCK kinases. A bioinformatic study was performed in which the differential expression of miRNAs in endothelial cells was compared under two conditions: Control and treated with simvastatin at 10 μM for 24 h, using a microarray. Seven miRNAs were differentially expressed, three up and four down. Within the up group, the miRNAs hsa-miR-618 and hsa-miR-297 present as a predicted target to ROCK2 kinase. Also, functional and enriched pathway analysis showed an association with mechanisms associated with atheroprotective effects. This work shows an in-silico approach of how posttranscriptional regulation mediated by miRNAs could modulate the pleiotropic effects exerted by statins on endothelial cells, through the inhibition of ROCK2 kinase and its effects.
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Affiliation(s)
- Karla Leal
- Center of Molecular Biology and Pharmacogenetics, Scientific and Technological Bioresource Nucleus, Department of Basic Sciences, Faculty of Medicine, Universidad de La Frontera, Temuco, Chile
| | - Kathleen Saavedra
- Center of Molecular Biology and Pharmacogenetics, Scientific and Technological Bioresource Nucleus, Department of Basic Sciences, Faculty of Medicine, Universidad de La Frontera, Temuco, Chile
| | - Camilo Rebolledo
- Center of Molecular Biology and Pharmacogenetics, Scientific and Technological Bioresource Nucleus, Department of Basic Sciences, Faculty of Medicine, Universidad de La Frontera, Temuco, Chile
| | - Luis A Salazar
- Center of Molecular Biology and Pharmacogenetics, Scientific and Technological Bioresource Nucleus, Department of Basic Sciences, Faculty of Medicine, Universidad de La Frontera, Temuco, Chile
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Dick K, Chopra A, Biggar KK, Green JR. Multi-schema computational prediction of the comprehensive SARS-CoV-2 vs. human interactome. PeerJ 2021; 9:e11117. [PMID: 33868814 PMCID: PMC8029698 DOI: 10.7717/peerj.11117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 02/24/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Understanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 ("coronavirus disease 2019"), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. A holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises to identify putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics. METHODS We leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Three prediction schemas (all, proximal, RP-PPI) are leveraged to obtain our highest-confidence subset of PPIs and human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins considered in this study. Notably, the use of the Reciprocal Perspective (RP) framework demonstrates improved predictive performance in multiple cross-validation experiments. RESULTS The all schema identified 279 high-confidence putative interactions involving 225 human proteins, the proximal schema identified 129 high-confidence putative interactions involving 126 human proteins, and the RP-PPI schema identified 539 high-confidence putative interactions involving 494 human proteins. The intersection of the three sets of predictions comprise the seven highest-confidence PPIs. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors with the all and proximal schemas, corroborating existing evidence for this PPI. Several other predicted PPIs are biologically relevant within the context of the original SARS-CoV virus. Furthermore, the PIPE-Sites algorithm was used to identify the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions. CONCLUSION We publicly released the comprehensive sets of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: https://www.doi.org/10.5683/SP2/JZ77XA. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.
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Affiliation(s)
- Kevin Dick
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada
- Institute for Data Science, Carleton University, Ottawa, Ontario, Canada
| | - Anand Chopra
- Institute of Biochemistry, Carleton University, Ottawa, Ontario, Canada
- Department of Biology, Carleton University, Ottawa, Ontario, Canada
| | - Kyle K. Biggar
- Institute of Biochemistry, Carleton University, Ottawa, Ontario, Canada
- Department of Biology, Carleton University, Ottawa, Ontario, Canada
| | - James R. Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada
- Institute for Data Science, Carleton University, Ottawa, Ontario, Canada
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Yang J, Shi X, Yang M, Luo J, Gao Q, Wang X, Wu Y, Tian Y, Wu F, Zhou H. Glycolysis reprogramming in cancer-associated fibroblasts promotes the growth of oral cancer through the lncRNA H19/miR-675-5p/PFKFB3 signaling pathway. Int J Oral Sci 2021; 13:12. [PMID: 33762576 PMCID: PMC7991655 DOI: 10.1038/s41368-021-00115-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/06/2021] [Accepted: 02/07/2021] [Indexed: 02/07/2023] Open
Abstract
As an important component of the tumor microenvironment, cancer-associated fibroblasts (CAFs) secrete energy metabolites to supply energy for tumor progression. Abnormal regulation of long noncoding RNAs (lncRNAs) is thought to contribute to glucose metabolism, but the role of lncRNAs in glycolysis in oral CAFs has not been systematically examined. In the present study, by using RNA sequencing and bioinformatics analysis, we analyzed the lncRNA/mRNA profiles of normal fibroblasts (NFs) derived from normal tissues and CAFs derived from patients with oral squamous cell carcinoma (OSCC). LncRNA H19 was identified as a key lncRNA in oral CAFs and was synchronously upregulated in both oral cancer cell lines and CAFs. Using small interfering RNA (siRNA) strategies, we determined that lncRNA H19 knockdown affected proliferation, migration, and glycolysis in oral CAFs. We found that knockdown of lncRNA H19 by siRNA suppressed the MAPK signaling pathway, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) and miR-675-5p. Furthermore, the lncRNA H19/miR-675-5p/PFKFB3 axis was involved in promoting the glycolysis pathway in oral CAFs, as demonstrated by a luciferase reporter system assay and treatment with a miRNA-specific inhibitor. Our study presents a new way to understand glucose metabolism in oral CAFs, theoretically providing a novel biomarker for OSCC molecular diagnosis and a new target for antitumor therapy.
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Affiliation(s)
- Jin Yang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & National Center of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Xueke Shi
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & National Center of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Miao Yang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & National Center of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Jingjing Luo
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & National Center of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Qinghong Gao
- Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Xiangjian Wang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & National Center of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Oral Medicine, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yang Wu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & National Center of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of General Dentistry, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yuan Tian
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & National Center of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Fanglong Wu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & National Center of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
| | - Hongmei Zhou
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & National Center of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
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