Methods of MicroRNA Promoter Prediction and Transcription Factor Mediated Regulatory Network

Replenishing co-downregulated miR-100-5p and miR-125b-5p in malignant germ cell tumors causes growth inhibition through cell cycle disruption

MicroRNAs (miRNAs) are short, nonprotein-coding RNAs, and their expression is dysregulated in malignant germ cell tumors (GCTs). Here, we investigated the causes and consequences of downregulated miR-99a-5p/miR-100-5p (functionally identical) and miR-125b-5p levels in malignant GCTs regardless of age, site, or subtype. Quantitative RT-PCR was used to assess miR-99a-5p/miR-100-5p, miR-125b-5p, and associated gene expression in malignant GCT tissues/cell lines [seminoma (Sem), yolk sac tumor (YST), embryonal carcinoma (EC)]. Cells were treated with demethylating 5-azacytidine and pyrosequencing was performed. Combination miR-100-5p/miR-125b-5p mimic replenishment was used to treat malignant GCT cells. Global messenger RNA (mRNA) targets of the replenished miRNAs were identified and Metascape used to study pathway effects. We found that expression levels of miR-99a-5p/miR-100-5p and miR-125b-5p, their respective pri-miRNAs, and associated genes from chromosomes 11 and 21 (chr11/chr21) were downregulated and highly correlated in malignant GCT cells. Treatment with 5-azacytidine caused upregulation of these miRNAs, with pyrosequencing revealing hypermethylation of their chr11/chr21 loci, likely contributing to miR-100-5p/miR-125b-5p downregulation. Combination miR-100-5p/miR-125b-5p mimic replenishment resulted in growth inhibition in Sem/YST cells, with miR-100-5p/miR-125b-5p mRNA targets enriched in downregulated genes, which were involved in cell cycle (confirmed by flow cytometry) and signaling pathways. Knockdown of the miR-100-5p/miR-125b-5p target tripartite motif containing 71 (TRIM71kd) recapitulated miR-100-5p/miR-125b-5p replenishment, with growth inhibition and cell cycle disruption of Sem/YST/EC cells. Further, replenishment led to reduced lin-28 homolog A (LIN28A) levels and concomitant increases in let-7 (MIRLET7B) tumor suppressor miRNAs, creating a sustained reversion of cell phenotype. In summary, combination miR-100-5p/miR-125b-5p mimic replenishment or TRIM71kd caused growth inhibition in malignant GCT cells via cell cycle disruption. Further studies are now warranted, including mimic treatment alongside conventional platinum-based chemotherapy.

Identification of Key Genes Related to Immune-Lipid Metabolism in Skin Barrier Damage and Analysis of Immune Infiltration

Several physical and chemical factors regulate skin barrier function. Skin barrier dysfunction causes many inflammatory skin diseases, such as atopic dermatitis and psoriasis. Activation of the immune response may lead to damage to the epidermal barrier. Abnormal lipid metabolism is defined as abnormally high or low values of plasma lipid components such as plasma cholesterol and triglycerides. The mouse skin barrier damage model was used for RNA sequencing. Bioinformatics analysis and validation were performed. Differently expressed genes (DEGs) related to immune and lipid metabolism were screened by differentially expressed gene analysis, and the enriched biological processes and pathways of these genes were identified by GO-KEGG. The interactions between DEGs were confirmed by constructing a PPI network. GSEA, transcription factor regulatory network, and immune infiltration analyses were performed for the 10 genes. Expression validation was performed by public datasets. The expression of key genes in mouse skin tissue was detected by qPCR. The expression of differentially expressed immune cell markers in the skin was detected by immunofluorescence. Based on the trans epidermal water loss (TEWL) score, the expression of key genes was detected by qPCR before skin barrier injury, at 4h and 7d, and at recovery from injury. Il17a, Il6, Tnf, Itgam, and Cxcl1 were immune-related key genes. Pla2g2f, Ptgs2, Plb1, Pla2g3, and Pla2g2d were key genes for lipid metabolism. Database validation and experimental results revealed that the expression trends of these genes were consistent with our analyses. The research value of these genes has been demonstrated through mouse datasets and experimental validation, and future therapeutic approaches may be able to mitigate the disease by targeting these genes to modulate the function of the skin barrier.

MicroRNA-138: an emerging regulator of skeletal development, homeostasis, and disease

Noncoding microRNAs are powerful epigenetic regulators of cellular processes by their ability to target and suppress expression of numerous protein-coding mRNAs. This multitargeting function is a unique and complex feature of microRNAs. It is now well-described that microRNAs play important roles in regulating the development and homeostasis of many cell/tissue types, including those that make up the skeletal system. In this review, we focus on microRNA-138 (miR-138) and its effects on regulating bone and cartilage cell differentiation and function. In addition to its reported role as a tumor suppressor, miR-138 appears to function as an inhibitor of osteoblast differentiation. This review provides additional information on studies that have attempted to alter miR-138 expression in vivo as a means to dampen ectopic calcification or alter bone mass. However, a review of the published literature on miR-138 in cartilage reveals a number of contradictory and inconclusive findings with respect to regulating chondrogenesis and chondrocyte catabolism. This highlights the need for more research in understanding the role of miR-138 in cartilage biology and disease. Interestingly, a number of studies in other systems have reported miR-138-mediated effects in dampening inflammation and pain responses. Future studies will reveal if a multifunctional role of miR-138 involving suppression of ectopic bone, inflammation, and pain will be beneficial in skeletal conditions such as osteoarthritis and heterotopic ossification.

BERT2OME: Prediction of 2'-O-Methylation Modifications From RNA Sequence by Transformer Architecture Based on BERT

Recent work on language models has resulted in state-of-the-art performance on various language tasks. Among these, Bidirectional Encoder Representations from Transformers (BERT) has focused on contextualizing word embeddings to extract context and semantics of the words. On the other hand, post-transcriptional 2'-O-methylation (Nm) RNA modification is important in various cellular tasks and related to a number of diseases. The existing high-throughput experimental techniques take longer time to detect these modifications, and costly in exploring these functional processes. Here, to deeply understand the associated biological processes faster, we come up with an efficient method Bert2Ome to infer 2'-O-methylation RNA modification sites from RNA sequences. Bert2Ome combines BERT-based model with convolutional neural networks (CNN) to infer the relationship between the modification sites and RNA sequence content. Unlike the methods proposed so far, Bert2Ome assumes each given RNA sequence as a text and focuses on improving the modification prediction performance by integrating the pretrained deep learning-based language model BERT. Additionally, our transformer-based approach could infer modification sites across multiple species. According to 5-fold cross-validation, human and mouse accuracies were 99.15% and 94.35% respectively. Similarly, ROC AUC scores were 0.99, 0.94 for the same species. Detailed results show that Bert2Ome reduces the time consumed in biological experiments and outperforms the existing approaches across different datasets and species over multiple metrics. Additionally, deep learning approaches such as 2D CNNs are more promising in learning BERT attributes than more conventional machine learning methods.

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.

iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraints and global topological constraints

Growing evidence proves that small nucleolar RNAs (snoRNAs) have important functions in various biological processes, the malfunction of which leads to the emergence and development of complex diseases. However, identifying snoRNA-disease associations is an ongoing challenging task due to the considerable time- and money-consuming biological experiments. Therefore, it is urgent to design efficient and economical methods for the identification of snoRNA-disease associations. In this regard, we propose a computational method named iSnoDi-LSGT, which utilizes snoRNA sequence similarity and disease similarity as local similarity constraints. The iSnoDi-LSGT predictor further employs network embedding technology to extract topological features of snoRNAs and diseases, based on which snoRNA topological similarity and disease topological similarity are calculated as global topological constraints. To the best of our knowledge, the iSnoDi-LSGT is the first computational method for snoRNA-disease association identification. The experimental results indicate that the iSnoDi-LSGT predictor can effectively predict unknown snoRNA-disease associations. The web server of the iSnoDi-LSGT predictor is freely available at http://bliulab.net/iSnoDi-LSGT.

HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA-Disease Association Prediction

Identifying disease-related miRNAs can improve the understanding of complex diseases. However, experimentally finding the association between miRNAs and diseases is expensive in terms of time and resources. The computational screening of reliable miRNA-disease associations has thus become a necessary tool to guide biological experiments. "Similar miRNAs will be associated with the same disease" is the assumption on which most current miRNA-disease association prediction methods rely; however, biased prior knowledge, and incomplete and inaccurate miRNA similarity data and disease similarity data limit the performance of the model. Here, we propose heuristic learning based on graph neural networks to predict microRNA-disease associations (HLGNN-MDA). We learn the local graph topology features of the predicted miRNA-disease node pairs using graph neural networks. In particular, our improvements to the graph convolution layer of the graph neural network enable it to learn information among homogeneous nodes and among heterogeneous nodes. We illustrate the performance of HLGNN-MDA by performing tenfold cross-validation against excellent baseline models. The results show that we have promising performance in multiple metrics. We also focus on the role of the improvements to the graph convolution layer in the model. The case studies are supported by evidence on breast cancer, hepatocellular carcinoma and renal cell carcinoma. Given the above, the experiments demonstrate that HLGNN-MDA can serve as a reliable method to identify novel miRNA-disease associations.

CRISPRi for specific inhibition of miRNA clusters and miRNAs with high sequence homology

miRNAs form a class of noncoding RNAs, involved in post-transcriptional regulation of gene expression, broadly studied for their involvement in physiological and pathological context. Inhibition of mature miRNA transcripts, commonly used in miRNA loss-of-function experiments, may not be specific in case of miRNAs with high sequence homology, e.g. miRNAs from the same seed family. Phenotypic effects of miRNA repression might be biased by the repression of highly similar miRNAs. Another challenge is simultaneous inhibition of multiple miRNAs encoded within policistronic clusters, potentially co-regulating common biological processes. To elucidate roles of miRNA clusters and miRNAs with high sequence homology, it is of key importance to selectively repress only the miRNAs of interest. Targeting miRNAs on genomic level with CRISPR/dCas9-based methods is an attractive alternative to blocking mature miRNAs. Yet, so far no clear guidelines on the design of CRISPR inhibition (CRISPRi) experiments, specifically for miRNA repression, have been proposed. To address this need, here we propose a strategy for effective inhibition of miRNAs and miRNA clusters using CRISPRi. We provide clues on how to approach the challenges in using CRISPR/dCas in miRNA studies, which include prediction of miRNA transcription start sites (TSSs) and the design of single guide RNAs (sgRNAs). The strategy implements three TSS prediction online tools, dedicated specifically for miRNAs: miRStart, FANTOM 5 miRNA atlas, DIANA-miRGen, and CRISPOR tool for sgRNAs design; it includes testing and selection of optimal sgRNAs. We demonstrate that compared to siRNA/shRNA-based miRNA silencing, CRISPRi improves the repression specificity for miRNAs with highly similar sequence and contribute to higher uniformity of the effects of silencing the whole miRNA clusters. This strategy may be adapted for CRISPR-mediated activation (CRISPRa) of miRNA expression.

Covid-19: a novel challenge to human immune genetic machinery

COVID-19 also called corona virus emerged in China in December 2019. This turned into a global pandemic in a short period of time. Covid-19 is a novel strain of corona virus that was not seen earlier in human beings. It is important to study the molecular structure of Covid-19 so as to aid in the development of therapeutic measures. Existing Covid-19 pandemic poses an extraordinary risk to health and healthcare systems worldwide. Corona viruses are made of single stranded RNA present within the coat proteins. The virus has a diameter of nearly 80–120 nm. Usually, Covid-19 presents with the signs and symptoms of respiratory illness. Cough commonly dry cough, fever, associated with myalgias and sometimes breathing difficulties due to decrease in oxygen saturation rates are also present in these patients. Some people show fever with body aches, while some are relatively asymptomatic. Corona virus is primarily transmitted in humans through respiratory route and is highly contagious. Mostly old people and those having comorbid illnesses suffer most. After invading into the human body, the virus may lead to a sequence of processes such as viral invasion, replication, and programmed cell death, that is, apoptosis. To control and prevent this viral infection, we need to study the molecular aspects of Covid-19 in detail so as to design therapeutic agents as well as for vaccine formation. The micro-RNA is defined as the single-stranded noncoding RNA molecule. They have a length of about 22 nucleotides approximately and help in the post transcriptional regulation of gene expression. Micro RNAs regulate many types of cancers in addition to Covid-19 and other infections. Viral micro RNA is a newer type of mi-RNA and controls the host cell expression and viral target genes. This was completed by inducing micro-RNA cleavage, breakdown, translation, inhibition, or other mechanisms. The micro-RNAs of Covid-19 are explained to give an authoritative means to study this novel coronavirus. These control the host cell expression and also viral target genes by inducing micro-RNA cleavage, breakdown, translation, inhibition, and also other mechanisms.

iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences

2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.

The Development of Machine Learning Methods in discriminating Secretory Proteins of Malaria Parasite

Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learning-based identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.

Differentially expressed miRNAs in bone after methotrexate treatment

Previous studies have shown that administration of antimetabolite methotrexate (MTX) caused a reduced trabecular bone volume and increased marrow adiposity (bone/fat switch), for which the underlying molecular mechanisms and recovery potential are unclear. Altered expression of microRNAs (miRNAs) has been shown to be associated with dysregulation of osteogenic and/or adipogenic differentiation by disrupting target gene expression. First, the current study confirmed the bone/fat switch following MTX treatment in precursor cell culture models in vitro. Then, using a rat intensive 5-once daily MTX treatment model, this study aimed to identify miRNAs associated with bone damage and recovery (in a time course over Days 3, 6, 9, and 14 after the first MTX treatment). RNA isolated from bone samples of treated and control rats were subjected to miRNA array and reverse transcription-polymerase chain reaction validation, which identified five upregulated miRNA candidates, namely, miR-155-5p, miR-154-5p, miR-344g, miR-6215, and miR-6315. Target genes of these miRNAs were predicted using TargetScan and miRDB. Then, the protein-protein network was established via STRING database, after which the miRNA-key messenger RNA (mRNA) network was constructed by Cytoscape. Functional annotation and pathway enrichment analyses for miR-6315 were performed by DAVID database. We found that TGF-β signaling was the most significantly enriched pathway and subsequent dual-luciferase assays suggested that Smad2 was the direct target of miR-6315. Our current study showed that miR-6315 might be a vital regulator involved in bone and marrow fat formation. Also, this study constructed a comprehensive miRNA-mRNA regulatory network, which may contribute to the pathogenesis/prognosis of MTX-associated bone loss and bone marrow adiposity.

Detection of circRNA Biomarker for Acute Myocardial Infarction Based on System Biological Analysis of RNA Expression

Acute myocardial infarction (AMI) is myocardial necrosis caused by the persistent interruption of myocardial blood supply, which has high incidence rate and high mortality in middle-aged and elderly people in the worldwide. Biomarkers play an important role in the early diagnosis and treatment of AMI. Recently, more and more researches confirmed that circRNA may be a potential diagnostic biomarker and therapeutic target for cardiovascular diseases. In this paper, a series of biological analyses were performed to find new effective circRNA biomarkers for AMI. Firstly, the expression levels of circRNAs in blood samples of patients with AMI and those with mild coronary stenosis were compared to reveal circRNAs which were involved in AMI. Then, circRNAs which were significant expressed abnormally in the blood samples of patients with AMI were selected from those circRNAs. Next, a ceRNA network was constructed based on interactions of circRNA, miRNA and mRNA through biological analyses to detect crucial circRNA associated with AMI. Finally, one circRNA was selected as candidate biomarker for AMI. To validate effectivity and efficiency of the candidate biomarker, fluorescence in situ hybridization, hypoxia model of human cardiomyocytes, and knockdown and overexpression analyses were performed on candidate circRNA biomarker. In conclusion, experimental results demonstrated that the candidate circRNA was an effective biomarker for diagnosis and therapy of AMI.

PMDFI: Predicting miRNA-Disease Associations Based on High-Order Feature Interaction

MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA–disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA–disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA–disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.

Application of Multilayer Network Models in Bioinformatics

Multilayer networks provide an efficient tool for studying complex systems, and with current, dramatic development of bioinformatics tools and accumulation of data, researchers have applied network concepts to all aspects of research problems in the field of biology. Addressing the combination of multilayer networks and bioinformatics, through summarizing the applications of multilayer network models in bioinformatics, this review classifies applications and presents a summary of the latest results. Among them, we classify the applications of multilayer networks according to the object of study. Furthermore, because of the systemic nature of biology, we classify the subjects into several hierarchical categories, such as cells, tissues, organs, and groups, according to the hierarchical nature of biological composition. On the basis of the complexity of biological systems, we selected brain research for a detailed explanation. We describe the application of multilayer networks and chronological networks in brain research to demonstrate the primary ideas associated with the application of multilayer networks in biological studies. Finally, we mention a quality assessment method focusing on multilayer and single-layer networks as an evaluation method emphasizing network studies.

MicroRNA expression profile in bovine mammary gland parenchyma infected by coagulase-positive or coagulase-negative staphylococci

MicroRNAs (miRNAs) are short, non-coding RNAs, 21-23 nucleotides in length which are known to regulate biological processes that greatly impact immune system activity. The aim of the study was to compare the miRNA expression in non-infected (H) mammary gland parenchyma samples with that of glands infected with coagulase-positive staphylococci (CoPS) or coagulase-negative staphylococci (CoNS) using next-generation sequencing. The miRNA profile of the parenchyma was found to change during mastitis, with its profile depending on the type of pathogen. Comparing the CoPS and H groups, 256 known and 260 potentially new miRNAs were identified, including 32 that were differentially expressed (p ≤ 0.05), of which 27 were upregulated and 5 downregulated. Comparing the CoNS and H groups, 242 known and 171 new unique miRNAs were identified: 10 were upregulated (p ≤ 0.05), and 2 downregulated (p ≤ 0.05). In addition, comparing CoPS with H and CoNS with H, 5 Kyoto Encyclopedia of Genes and Genomes pathways were identified; in both comparisons, differentially-expressed miRNAs were associated with the bacterial invasion of epithelial cells and focal adhesion pathways. Four gene ontology terms were identified in each comparison, with 2 being common to both immune system processes and signal transduction. Our results indicate that miRNAs, especially miR-99 and miR-182, play an essential role in the epigenetic regulation of a range of cellular processes, including immunological systems bacterial growth in dendritic cells and disease pathogenesis (miR-99), DNA repair and tumor progression (miR-182).

Annotation of miRNAs in COVID-19 coronavirus

The COVID-19 coronavirus is a new strain of coronavirus that had not been previously detected in humans. As its severe pathogenicity is concerned, it is important to study it thoroughly to aid in the discovery of a cure. In this study, the microRNAs (miRNAs) of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus. We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database (miRbase), and then used the miRNA mature sequences of the virus to perform the Basic Local Alignment Search Tool (BLAST) analysis in the coronavirus genome, extending the matched regions of approximately 20 bp to two segments by 200 bp. Six sequences were obtained after deleting redundant sequences. Then, the hairpin structures of the mature miRNAs were determined using RNAfold. The mature sequence on one hairpin arm was selected into a total of 4 sequences, and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus. The miRNAs of the novel coronavirus were annotated by our newly developed method, which will lay the foundation for further study of this virus.

Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples

Preeclampsia (PE) is a maternal disease that causes maternal and child death. Treatment and preventive measures are not sound enough. The problem of PE screening has attracted much attention. The purpose of this study is to screen placental mRNA to obtain the best PE biomarkers for identifying patients with PE. We use Limma in the R language to screen out the 48 differentially expressed genes with the largest differences and used correlation-based feature selection algorithms to reduce the dimensionality and avoid attribute redundancy arising from too many mRNA samples participating in the classification. After reducing the mRNA attributes, the mRNA samples are sorted from large to small according to information gain. In this study, a classifier model is designed to identify whether samples had PE through mRNA in the placenta. To improve the accuracy of classification and avoid overfitting, three classifiers, including C4.5, AdaBoost, and multilayer perceptron, are used. We use the majority voting strategy integrated with the differentially expressed genes and the genes filtered by the best subset method as comparison methods to train the classifier. The results show that the classification accuracy rate has increased from 79% to 82.2%, and the number of mRNA features has decreased from 48 to 13. This study provides clues for the main PE biomarkers of mRNA in the placenta and provides ideas for the treatment and screening of PE.

Recent Advances in Predicting Protein S-Nitrosylation Sites

Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.

Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China

To distinguish Methicillin-Resistant Staphylococcus aureus (MRSA) from Methicillin-Sensitive Staphylococcus aureus (MSSA) in the protein sequences level, test the susceptibility to antibiotic of all Staphylococcus aureus isolates from Quanzhou hospitals, define the virulence factor and molecular characteristics of the MRSA isolates. MRSA and MSSA Pfam protein sequences were used to extract feature vectors of 188D, n-gram and 400D. Weka software was applied to classify the two Staphylococcus aureus and performance effect was evaluated. Antibiotic susceptibility testing of the 81 Staphylococcus aureus was performed by the Mérieux Microbial Analysis Instrument. The 65 MRSA isolates were characterized by Panton-Valentine leukocidin (PVL), X polymorphic region of Protein A (spa), multilocus sequence typing test (MLST), staphylococcus chromosomal cassette mec (SCCmec) typing. After comparing the results of Weka six classifiers, the highest correctly classified rates were 91.94, 70.16, and 62.90% from 188D, n-gram and 400D, respectively. Antimicrobial susceptibility test of the 81 Staphylococcus aureus: Penicillin-resistant rate was 100%. No resistance to teicoplanin, linezolid, and vancomycin. The resistance rate of the MRSA isolates to clindamycin, erythromycin and tetracycline was higher than that of the MSSAs. Among the 65 MRSA isolates, the positive rate of PVL gene was 47.7% (31/65). Seventeen sequence types (STs) were identified among the 65 isolates, and ST59 was the most prevalent. SCCmec type III and IV were observed at 24.6 and 72.3%, respectively. Two isolates did not be typed. Twenty-one spa types were identified, spa t437 (34/65, 52.3%) was the most predominant type. MRSA major clone type of molecular typing was CC59-ST59-spa t437-IV (28/65, 43.1%). Overall, 188D feature vectors can be applied to successfully distinguish MRSA from MSSA. In Quanzhou, the detection rate of PVL virulence factor was high, suggesting a high pathogenic risk of MRSA infection. The cross-infection of CA-MRSA and HA-MRSA was presented, the molecular characteristics were increasingly blurred, HA-MRSA with typical CA-MRSA molecular characteristics has become an important cause of healthcare-related infections. CC59-ST59-spa t437-IV was the main clone type in Quanzhou, which was rare in other parts of mainland China.

A prognosis-related based method for miRNA selection on liver hepatocellular carcinoma prediction

Hepatocellular carcinoma (HCC) is considered as the sixth most common cancer in the world, and it is also considered as one of the causes of death. Moreover, the poor prognosis of recurrence of HCC after surgery and metastasis is also a big problem for human health. If the disease can be diagnosed earlier, the survival rate of the patients will be improved significantly. In the early stage of hepatocellular carcinoma, the expression of miRNAs is likely to become abnormal. In our work, the expression profile of miRNAs of human HCC in cancer tissue is compared with their adjacent tissue samples collected from tumor cancer genomic Atlas (TCGA) platform, then the genes with significant difference are selected by Limma test. Selected genes are referred to predict miRNAs related to the prognosis of HCC patients. Finally, miRNAs regulated by target genes are selected by our method, and the experimental results demonstrated that our method is more efficient than biology wet experimental method with lower cost.

MicroRNA-302a is involved in folate deficiency-induced apoptosis through the AKT-FOXO1-BIM pathway in mouse embryonic stem cells

Background

Our previous study had shown that microRNA (miR)-302a played a key role in folate deficiency-induced apoptosis in mouse embryonic stem cells. However, details regarding the mechanism remain unclear. Transcription factors (TFs) and miRNAs are two key elements in gene regulation. The aim of this study is to construct the TF-miRNA gene regulation network and demonstrate its possible mechanism.

Methods

The TF-miRNA gene regulation network was constructed via bioinformatics methods. Chromatin immuno-coprecipitation PCR was selected to confirm the binding between miR-302a and TF. mRNA and protein levels were detected by Real-time quantitative PCR and western blotting. TargetScan prediction and Dual-Luciferase Reporter Assay system were used to confirm whether the miRNA binded directly to the predicted target gene.

Results

FOXO1 and miR-302a were selected as the key TF and miRNA, respectively. FOXO1 was confirmed to bind directly to the upstream promoter region of miR-302a. Real-time quantitative PCR and immunoblotting showed that in folate-free conditions, miR-302a and AKT were down regulated, while FOXO1 and Bim were up-regulated significantly. Additionally, treatment with LY294002 inhibitor revealed the involvement of the Akt/FOXO1/Bim signaling pathway in folate deficiency-induced apoptosis, rather than the ERK pathway. Finally, TargetScan prediction and double luciferase reporting experiments illustrated the ability of miR-302a to target the Bim 3′UTR region.

Conclusion

The involvement of miR-302a in folate deficiency-induced apoptosis through the AKT-FOXO1-BIM pathway in mESCs is a unique demonstration of the regulation mechanism of nutrient expression in embryonic development.

Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions

Excessive oxidative stress responses can threaten our health, and thus it is essential to produce antioxidant proteins to regulate the body’s oxidative responses. The low number of antioxidant proteins makes it difficult to extract their representative features. Our experimental method did not use structural information but instead studied antioxidant proteins from a sequenced perspective while focusing on the impact of data imbalance on sensitivity, thus greatly improving the model’s sensitivity for antioxidant protein recognition. We developed a method based on the Composition of k-spaced Amino Acid Pairs (CKSAAP) and the Conjoint Triad (CT) features derived from the amino acid composition and protein-protein interactions. SMOTE and the Max-Relevance-Max-Distance algorithm (MRMD) were utilized to unbalance the training data and select the optimal feature subset, respectively. The test set used 10-fold crossing validation and a random forest algorithm for classification according to the selected feature subset. The sensitivity was 0.792, the specificity was 0.808, and the average accuracy was 0.8.

Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction

Thermophilicity is a very important property of proteins, as it sometimes determines denaturation and cell death. Thus, methods for predicting thermophilic proteins and non-thermophilic proteins are of interest and can contribute to the design and engineering of proteins. In this article, we describe the use of feature dimension reduction technology and LIBSVM to identify thermophilic proteins. The highest accuracy obtained by cross-validation was 96.02% with 119 parameters. When using only 16 features, we obtained an accuracy of 93.33%. We discuss the importance of the different characteristics in identification and report a comparison of the performance of support vector machine to that of other methods.

iGlu_AdaBoost: Identification of Lysine Glutarylation Using the AdaBoost Classifier

Lysine glutarylation is a newly reported post-translational modification (PTM) that plays significant roles in regulating metabolic and mitochondrial processes. Accurate identification of protein glutarylation is the primary task to better investigate molecular functions and various applications. Due to the common disadvantages of the time-consuming and expensive nature of traditional biological sequencing techniques as well as the explosive growth of protein data, building precise computational models to rapidly diagnose glutarylation is a popular and feasible solution. In this work, we proposed a novel AdaBoost-based predictor called iGlu_AdaBoost to distinguish glutarylation and non-glutarylation sequences. Here, the top 37 features were chosen from a total of 1768 combined features using Chi2 following incremental feature selection (IFS) to build the model, including 188D, the composition of k-spaced amino acid pairs (CKSAAP), and enhanced amino acid composition (EAAC). With the help of the hybrid-sampling method SMOTE-Tomek, the AdaBoost algorithm was performed with satisfactory recall, specificity, and AUC values of 87.48%, 72.49%, and 0.89 over 10-fold cross validation as well as 72.73%, 71.92%, and 0.63 over independent test, respectively. Further feature analysis inferred that positively charged amino acids RK play critical roles in glutarylation recognition. Our model presented the well generalization ability and consistency of the prediction results of positive and negative samples, which is comparable to four published tools. The proposed predictor is an efficient tool to find potential glutarylation sites and provides helpful suggestions for further research on glutarylation mechanisms and concerned disease treatments.

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics. Protein function prediction is a classic research topic in this subject area. Though many scholars have made achievements in identifying protein by different algorithms, they often extract a large number of feature types and use very complex classification methods to obtain little improvement in the classification effect, and this process is very time-consuming. In this research, we attempt to utilize as few features as possible to classify vesicular transportation proteins and to simultaneously obtain a comparative satisfactory classification result. We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method. We use the SMOTE method to deal with the problem of dataset imbalance. There are 11619 protein sequences in our dataset. We selected 4428 sequences to train our classification model and selected other 1832 sequences from our dataset to test the classification effect and finally achieved an accuracy of 71.77%. After dimension reduction by MRMD, the accuracy is 72.16%.

Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information

Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/.

Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features

N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https://github.com/MapFM/m7g_model.git.

Diagnostic and therapeutic applications of genomic medicine in progressive, late-onset, nonsyndromic sensorineural hearing loss

The progressive, late-onset, nonsyndromic, sensorineural hearing loss (PNSHL) is the most common cause of sensory impairment globally, with presbycusis affecting greater than a third of individuals over the age of 65. The etiology underlying PNSHL include presbycusis, noise-induced hearing loss, drug ototoxicity, and delayed-onset autosomal dominant hearing loss (AD PNSHL). The objective of this article is to discuss the potential diagnostic and therapeutic applications of genomic medicine in PNSHL. Genomic factors contribute greatly to PNSHL. The heritability of presbycusis ranges from 25 to 75%. Current therapies for PNSHL range from sound amplification to cochlear implantation (CI). PNSHL is an excellent candidate for genomic medicine approaches as it is common, has well-described pathophysiology, has a wide time window for treatment, and is amenable to local gene therapy by currently utilized procedural approaches. AD PNSHL is especially suited to genomic medicine approaches that can disrupt the expression of an aberrant protein product. Gene therapy is emerging as a potential therapeutic strategy for the treatment of PNSHL. Viral gene delivery approaches have demonstrated promising results in human clinical trials for two inherited causes of blindness and are being used for PNSHL in animal models and a human trial. Non-viral gene therapy approaches are useful in situations where a transient biologic effect is needed or for delivery of genome editing reagents (such as CRISPR/Cas9) into the inner ear. Many gene therapy modalities that have proven efficacious in animal trials have potential to delay or prevent PNSHL in humans. The development of new treatment modalities for PNSHL will lead to improved quality of life of many affected individuals and their families.

Identification of Human Enzymes Using Amino Acid Composition and the Composition of k-Spaced Amino Acid Pairs

Enzymes are proteins that can efficiently catalyze specific biochemical reactions, and they are widely present in the human body. Developing an efficient method to identify human enzymes is vital to select enzymes from the vast number of human proteins and to investigate their functions. Nevertheless, only a limited amount of research has been conducted on the classification of human enzymes and nonenzymes. In this work, we developed a support vector machine- (SVM-) based predictor to classify human enzymes using the amino acid composition (AAC), the composition of k-spaced amino acid pairs (CKSAAP), and selected informative amino acid pairs through the use of a feature selection technique. A training dataset including 1117 human enzymes and 2099 nonenzymes and a test dataset including 684 human enzymes and 1270 nonenzymes were constructed to train and test the proposed model. The results of jackknife cross-validation showed that the overall accuracy was 76.46% for the training set and 76.21% for the test set, which are higher than the 72.6% achieved in previous research. Furthermore, various feature extraction methods and mainstream classifiers were compared in this task, and informative feature parameters of k-spaced amino acid pairs were selected and compared. The results suggest that our classifier can be used in human enzyme identification effectively and efficiently and can help to understand their functions and develop new drugs.

Comprehensive Analysis of Differentially Expressed lncRNAs in Gastric Cancer

Gastric cancer (GC) is the fourth most common malignant tumor. The mechanism underlying GC occurrence and development remains unclear. Previous studies have indicated that long non-coding RNAs (lncRNAs) are significantly associated with gastric cancer, but a systematic understanding of the role of lncRNAs in gastric cancer is lacking. In recent years, with the development of next-generation sequencing technology, tens of thousands of lncRNAs have been discovered. However, a large number of unannotated lncRNAs remain unidentified in different tissues, including potential gastric cancer-related lncRNAs. In this study, RNA sequencing (RNA-seq) data from 16 samples of eight gastric cancer patients were obtained and analyzed. A total of 1,854 previously unannotated lncRNAs were identified by ab initio assembly, and 520 differentially expressed lncRNAs were validated in the TCGA expression dataset. Methylation and copy number variation (CNV) array data from the same sample were integrated in the analysis. Changes in DNA methylation levels and CNVs may be responsible for the differential expression of 91 lncRNAs. Differentially expressed lncRNAs were enriched in coexpressed clusters of genes related to functions such as cell signaling, cell cycle, immune response, metabolic processes, angiogenesis, and regulation of retinoic acid (RA) receptors. Finally, a differentially expressed lncRNA, AC004510.3, was identified as a potential biomarker for the prediction of the overall survival of gastric cancer patients.

Computational identification of N6-methyladenosine sites in multiple tissues of mammals

N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques. In the proposed predictor, RNA sequences were encoded by physical-chemical property matrix, mono-nucleotide binary encoding and nucleotide chemical property. Subsequently, these features were optimized by using minimum Redundancy Maximum Relevance (mRMR) feature selection method. Based on the optimal feature subset, the best m6A classification models were trained by Support Vector Machine (SVM) with 5-fold cross-validation test. Prediction results on independent dataset showed that our proposed method could produce the excellent generalization ability. We also established a user-friendly webserver called iRNA-m6A which can be freely accessible at http://lin-group.cn/server/iRNA-m6A. This tool will provide more convenience to users for studying m6A modification in different tissues.

PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides

Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at http://server.malab.cn/PSBP-SVM/index.jsp.

Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features

Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells. Correctly identifying the hydrolases coded by phage is not only beneficial to their function study, but also conducive to antibacteria drug discovery. Thus, this work aims to recognize the enzymes and hydrolases in phage. A combination of different features was used to represent samples of phage and hydrolase. A feature selection technique called analysis of variance was developed to optimize features. The classification was performed by using support vector machine (SVM). The prediction process includes two steps. The first step is to identify phage enzymes. The second step is to determine whether a phage enzyme is hydrolase or not. The jackknife cross-validated results showed that our method could produce overall accuracies of 85.1 and 94.3%, respectively, for the two predictions, demonstrating that the proposed method is promising.

SNP Diversity in CD14 Gene Promoter Suggests Adaptation Footprints in Trypanosome Tolerant N'Dama (Bos taurus) but not in Susceptible White Fulani (Bos indicus) Cattle

Immune response to infections has been shown to be mediated by genetic diversity in pattern recognition receptors, leading to disease tolerance or susceptibility. We elucidated naturally occurring variations within the bovine CD14 gene promoter in trypanosome-tolerant (N'Dama) and susceptible (White Fulani) cattle, with genomic and computational approaches. Blood samples were collected from White Fulani and N'Dama cattle, genomic DNA extracted and the entire promoter region of the CD14 gene amplified by PCR. We sequenced this region and performed in silico computation to identify SNP variants, transcription factor binding sites, as well as micro RNAs in the region. CD14 promoter sequences were compared with the reference bovine genome from the Ensembl database to identify various SNPs. Furthermore, we validated three selected N'Dama specific SNPs using custom Taqman SNP genotyping assay for genetic diversity. In all, we identified a total of 54 and 41 SNPs at the CD14 promoter for N'Dama and White Fulani respectively, including 13 unique SNPs present in N'Dama only. The significantly higher SNP density at the CD14 gene promoter region in N'Dama may be responsible for disease tolerance, possibly an evolutionary adaptation. Our genotype analysis of the three loci selected for validation show that mutant alleles (A/A, C/C, and A/A) were adaptation profiles within disease tolerant N'Dama. A similar observation was made for our haplotype analysis revealing that haplotypes H1 (ACA) and H2 (ACG) were significant combinations within the population. The SNP effect prediction revealed 101 and 89 new transcription factor binding sites in N'Dama and White Fulani, respectively. We conclude that disease tolerant N'Dama possessing higher SNP density at the CD14 gene promoter and the preponderance of mutant alleles potentially confirms the significance of this promoter in immune response, which is lacking in susceptible White Fulani. We, therefore, recommend further in vitro and in vivo study of this observation in infected animals, as the next step for understanding genetic diversity relating to varying disease phenotypes in both breeds.

Panoramic Visualization of Circulating MicroRNAs Across Neurodegenerative Diseases in Humans

Neurodegenerative diseases (NDs) such as Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), and dementia pose one of the greatest health challenges this century. Although these NDs have been looked at as single entities, the underlying molecular mechanisms have never been collectively visualized to date. With the advent of high-throughput genomic and proteomic technologies, we now have the opportunity to visualize these diseases in a whole new perspective, which will provide a clear understanding of the primary and secondary events vital in achieving the final resolution of these diseases guiding us to new treatment strategies to possibly treat these diseases together. We created a knowledge base of all microRNAs known to be differentially expressed in various body fluids of ND patients. We then used several bioinformatic methods to understand the functional intersections and differences between AD, PD, ALS, and MS. These results provide a unique panoramic view of possible functional intersections between AD, PD, MS, and ALS at the level of microRNA and their cognate genes and pathways, along with the entities that unify and separate them. While the microRNA signatures were apparent for each ND, the unique observation in our study was that hsa-miR-30b-5p overlapped between all four NDS, and has significant functional roles described across NDs. Furthermore, our results also show the evidence of functional convergence of miRNAs which was associated with the regulation of their cognate genes represented in pathways that included fatty acid synthesis and metabolism, ECM receptor interactions, prion diseases, and several signaling pathways critical to neuron differentiation and survival, underpinning their relevance in NDs. Envisioning this group of NDs together has allowed us to propose new ways of utilizing circulating miRNAs as biomarkers  and in visualizing diverse NDs more holistically . The critical molecular insights gained through the discovery of ND-associated miRNAs, overlapping miRNAs, and the functional convergence of microRNAs on vital pathways strongly implicated in neurodegenerative processes can prove immensely valuable in the identifying new generation of biomarkers, along with the development of miRNAs into therapeutics.

MiRNA-target interactions in osteogenic signaling pathways involving zinc via the metal regulatory element

Adequate zinc nutriture is necessary for normal bone growth and development, though the precise mechanisms for zinc-mediated bone growth remain poorly defined. A key transcription factor activated by zinc is metal response element-binding transcription factor 1 (MTF-1), which binds to the metal regulatory element (MRE). We hypothesize that MREs will be found upstream of miRNA genes as well as miRNA target genes in the following bone growth and development signaling pathways: TGF-β, MAPK, and Wnt. A Bioconductor-based workflow in R was designed to identify interactions between MREs, miRNAs, and target genes. MRE sequences were found upstream from 64 mature miRNAs that interact with 213 genes which have MRE sequences in their own promoter regions. MAPK1 exhibited the most miRNA-target interactions (MTIs) in the TGF-β and MAPK signaling pathways; CCND2 exhibited the most interactions in the Wnt signaling pathway. Hsa-miR-124-3p exhibited the most MTIs in the TGF-β and MAPK signaling pathways; hsa-miR-20b-5p exhibited the most MTIs in the Wnt signaling pathway. MYC and hsa-miR-34a-5p were shared between all three signaling pathways, also forming an MTI unit. JUN exhibited the most protein-protein interactions, followed by MAPK8. These in silico data support the hypothesis that intracellular zinc status plays a role in osteogenesis through the transcriptional regulation of miRNA genes via the zinc/MTF-1/MRE complex.