The role of miRNAs in complex formation and control

miR-1306 induces cell apoptosis by targeting BMPR1B gene in the ovine granulosa cells

Bone morphogenetic protein receptor type-1B (BMPR1B) is one of the major gene for sheep prolificacy. However, few studies investigated its regulatory region. In this study, we reported that miR-1306 is a direct inhibitor of BMPR1B gene in the ovine granulosa cells (ovine GCs). We detected a miRNA response element of miR-1306 in the 3’ untranslated region of the ovine BMPR1B gene. Luciferase assay showed that the ovine BMPR1B gene is a direct target of miR-1306. qPCR and western blotting revealed that miR-1306 reduces the expression of BMPR1B mRNA and protein in the ovine granulosa cells. Furthermore, miR-1306 promoted cell apoptosis by suppressing BMPR1B expression in the ovine granulosa cells. Overall, our results suggest that miR-1306 is an epigenetic regulator of BMPR1B, and may serve as a potential target to improve the fecundity of sheep.

Resolving missing protein problems using functional class scoring

Despite technological advances in proteomics, incomplete coverage and inconsistency issues persist, resulting in “data holes”. These data holes cause the missing protein problem (MPP), where relevant proteins are persistently unobserved, or sporadically observed across samples, hindering biomarker discovery and proper functional characterization. Network-based approaches can provide powerful solutions for resolving these issues. Functional Class Scoring (FCS) is one such method that uses protein complex information to recover missing proteins with weak support. However, FCS has not been evaluated on more recent proteomic technologies with higher coverage, and there is no clear way to evaluate its performance. To address these issues, we devised a more rigorous evaluation schema based on cross-verification between technical replicates and evaluated its performance on data acquired under recent Data-Independent Acquisition (DIA) technologies (viz. SWATH). Although cross-replicate examination reveals some inconsistencies amongst same-class samples, tissue-differentiating signal is nonetheless strongly conserved, confirming that FCS selects for biologically meaningful networks. We also report that predicted missing proteins are statistically significant based on FCS p values. Despite limited cross-replicate verification rates, the predicted missing proteins as a whole have higher peptide support than non-predicted proteins. FCS also predicts missing proteins that are often lost due to weak specific peptide support.

PROTREC: A probability-based approach for recovering missing proteins based on biological networks

A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods - such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) - across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p-values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened. SIGNIFICANCE: Mass spectrometry (MS) has developed rapidly in recent years; however, an obvious proportion of proteins is still undetected, leading to missing protein problems. A few existing protein recovery methods are based on biological networks, but the performance is not satisfactory. We propose a new protein recovery method, PROTREC, a Bayesian-inspired approach based on biological networks, which shows exceptional performance across multiple validation strategies. It does not rely on peptide information, so it avoids the ambiguity issue that most protein assembly methods face.

Analyzing the regulation of miRNAs on protein-protein interaction network in Hodgkin lymphoma

Background

Hodgkin Lymphoma (HL) is a type of aggressive malignancy in lymphoma that has high incidence in young adults and elderly patients. Identification of reliable diagnostic markers and efficient therapeutic targets are especially important for the diagnosis and treatment of HL. Although many HL-related molecules have been identified, our understanding on the molecular mechanisms underlying the disease is still far from complete due to its complex and heterogeneous characteristics. In such situation, exploring the molecular mechanisms underlying HL via systems biology approaches provides a promising option. In this study, we try to elucidate the molecular mechanisms related to the disease and identify potential pharmaceutical targets from a network-based perspective.

Results

We constructed a series of network models. Based on the analysis of these networks, we attempted to identify the biomarkers and elucidate the molecular mechanisms underlying HL. Initially, we built three different but related protein networks, i.e., background network, HL-basic network and HL-specific network. By analyzing these three networks, we investigated the connection characteristic of the HL-related proteins. Subsequently, we explored the miRNA regulation on HL-specific network and analyzed three kinds of simple regulation patterns, i.e., co-regulation of protein pairs, as well as the direct and indirect regulation of triple proteins. Finally, we constructed a simplified protein network combined with the regulation of miRNAs on proteins to better understand the relation between HL-related proteins and miRNAs.

Conclusions

We find that the HL-related proteins are more likely to connect with each other compared to other proteins. Moreover, the HL-specific network can be further divided into five sub-networks and 49 proteins as the backbone of HL-specific network make up and connect these 5 sub-networks. Thus, they may be closely associated with HL. In addition, we find that the co-regulation of protein pairs is the main regulatory pattern of miRNAs on the protein network in the HL-specific network. According to the regulation of miRNA on protein network, we have identified 5 core miRNAs as the potential biomarkers for diagnostic of HL. Finally, several protein pathways have been identified to closely associated with HL, which provides deep insights into underlying mechanism of HL.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3041-9) contains supplementary material, which is available to authorized users.

Downregulation of microRNA‑155 by preoperative administration of valproic acid prevents postoperative seizures by upregulating SCN1A

The risk of seizure is increased following brain surgery such as cranioplasty. Patients with seizures that are treated with valproic acid (VPA) may have a decreased risk of further seizures. To verify microRNA (miR)‑155 as a potential biomarker for the occurrence of seizures, reverse transcription quantitative polymerase chain reaction (RT‑qPCR) was used. Computational analysis and luciferase reporter assay was performed to identify the putative target of miR‑155. RT‑qPCR and western blot analyses were used to determine the expression level of miR‑155, sodium voltage‑gated channel α subunit 1 (SCN1A) mRNA and protein. RT‑qPCR analysis indicated that miR‑155 levels in patients who experienced seizures increased 2.45‑fold compared with patient who did not experience seizures, indicating miR‑155 may be a potential biomarker for the occurrence of seizures. SCN1A was identified as a target gene of miR‑155; the luciferase reporter assay revealed a negative regulatory relationship between miR‑155 and SCN1A. The expression of SCN1A mRNA of patients receiving VPA was higher compared with the control group patients. Furthermore, the expression levels of SCN1A mRNA and protein were reduced or elevated following transfection with miR‑155 mimics or inhibitors, respectively, compared with the scramble control. Furthermore, a concentration‑dependent effect of miR‑155 on the expression of SCN1A was observed. In conclusion, miR‑155 may be associated with the risk of seizure and SCN1A may be a target gene of miR‑155. Downregulation of microRNA‑155 by preoperative administration of VPA may prevent postoperative seizure by upregulating the expression of SCN1A.

Protein complex-based analysis is resistant to the obfuscating consequences of batch effects --- a case study in clinical proteomics

Background

In proteomics, batch effects are technical sources of variation that confounds proper analysis, preventing effective deployment in clinical and translational research.

Results

Using simulated and real data, we demonstrate existing batch effect-correction methods do not always eradicate all batch effects. Worse still, they may alter data integrity, and introduce false positives. Moreover, although Principal component analysis (PCA) is commonly used for detecting batch effects. The principal components (PCs) themselves may be used as differential features, from which relevant differential proteins may be effectively traced. Batch effect are removable by identifying PCs highly correlated with batch but not class effect.

However, neither PC-based nor existing batch effect-correction methods address well subtle batch effects, which are difficult to eradicate, and involve data transformation and/or projection which is error-prone. To address this, we introduce the concept of batch-effect resistant methods and demonstrate how such methods incorporating protein complexes are particularly resistant to batch effect without compromising data integrity.

Conclusions

Protein complex-based analyses are powerful, offering unparalleled differential protein-selection reproducibility and high prediction accuracy. We demonstrate for the first time their innate resistance against batch effects, even subtle ones. As complex-based analyses require no prior data transformation (e.g. batch-effect correction), data integrity is protected. Individual checks on top-ranked protein complexes confirm strong association with phenotype classes and not batch. Therefore, the constituent proteins of these complexes are more likely to be clinically relevant.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3490-3) contains supplementary material, which is available to authorized users.

Valproic acid mediates miR-124 to down-regulate a novel protein target, GNAI1

Valproic acid (VPA) is an anti-convulsant drug that is recently shown to have neuroregenerative therapeutic actions. In this study, we investigate the underlying molecular mechanism of VPA and its effects on Bdnf transcription through microRNAs (miRNAs) and their corresponding target proteins. Using in silico algorithms, we predicted from our miRNA microarray and iTRAQ data that miR-124 is likely to target at guanine nucleotide binding protein alpha inhibitor 1 (GNAI1), an adenylate cyclase inhibitor. With the reduction of GNAI1 mediated by VPA, the cAMP is enhanced to increase Bdnf expression. The levels of GNAI1 protein and Bdnf mRNA can be manipulated with either miR-124 mimic or inhibitor. In summary, we have identified a novel molecular mechanism of VPA that induces miR-124 to repress GNAI1. The implication of miR-124→GNAI1→BDNF pathway with valproic acid treatment suggests that we could repurpose an old drug, valproic acid, as a clinical application to elevate neurotrophin levels in treating neurodegenerative diseases.

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organisation of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight their limitations and challenges, in particular at detecting sparse and small or sub-complexes and discerning overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.

Bader G, Bismar TA, Alhajj R. Coordinate microRNA-mediated regulation of protein complexes in prostate cancer

MicroRNAs are a class of small non-coding regulatory RNA molecules that regulate mRNAs post-transcriptionally. Recent evidence has shown that miRNAs target entire functionally related proteins such as protein complexes and biological pathways. However, characterizing the influence of miRNAs on genes whose encoded proteins are part of protein complexes has not been studied in the context of disease. We propose an entropy-based framework to identify miRNA-mediated dysregulation of functionally related proteins during prostate cancer progression. The proposed framework uses experimentally verified miRNA-target interactions, functionally related proteins and expression data to identify miRNA-influenced protein complexes in prostate cancer, and identify genes that are dysregulated as a result. The framework constructs correlation matrixes between functionally related proteins and miRNAs that have targets in the complex, and assesses the changes in the Shannon entropy of the modules across different stages of prostate cancer. Results reveal that SMAD4 and HDAC containing protein complexes are highly affected and disrupted by miRNAs, particularly miRNA-1 and miRNA-16. Using biological pathways to define functionally related proteins reveals that NF-kB-, RAS-, and Syndecan-mediated pathways are dysregulated due to miRNA-1- and miRNA-16-mediated regulation. These results suggest that miRNA-1 and miRNA-16 are important master regulators of miRNA-mediated regulation in prostate cancer. Moreover, results reveal that miRNAs with high-influence on the disrupted protein complexes are diagnostic and prognostic biomarker candidates for prostate cancer progression. The observation of miRNA-mediated protein complex regulation and miRNA-mediated pathway regulation, with partial experimental verification from previous studies, demonstrates that our framework is a promising approach for the identification of novel miRNAs and protein complexes related to disease progression.

miRNA regulation in the context of functional protein networks: principles and applications

MicroRNAs (miRNAs) are small non-coding endogenous regulatory RNAs that fine-tune gene expression in a wide range of biological processes and diseases. miRNAs exert their function by targeting mRNAs to trigger their degradation or inhibit protein translation. The proteins encoded by the genes targeted by miRNAs may act as key components of cellular networks, thus the use of biological molecular network information for the purposes of elucidating the role of miRNAs in molecular disease mechanism is a key objective in systems biomedicine. The crosstalk layer between miRNA-target networks and functional protein is rich sources of information to explore the function of miRNAs at the system level. Characterizing the influence of miRNAs in the context of the target (protein interactors of the target) is in the early stages with potential to help better understand how miRNAs function within the cellular networks. In this article, the latest research on the cross-talk between miRNAs and protein networks, particularly physical protein interactions and gene regulatory networks is summarized. This article also covers recent research on understanding the biology of miRNAs at the system level and defines principles of miRNA regulation of protein and gene regulatory networks. The second part of the article highlights the promise of considering the protein context of the miRNA target when searching for functional miRNA-target interactions. Some of the applications of integrating protein networks with miRNA-targets that have clinical and functional utility are described.

Complex-forming proteins escape the robust regulations of miRNA in human

Most proteins carry out their functions by participating in protein complexes. Recently, miRNAs were identified as promising post-transcriptional regulators that influence a large proportion of genes in higher eukaryotes. We aim to understand the role of miRNAs in the regulation of human proteins that are present in protein complexes. Here, we show that robust regulation by miRNA is absent in human complex-forming proteins. Moreover, the numbers of miRNA hits cannot direct the evolutionary fate of complex-forming proteins independently. However, the duplicated complex-forming proteins having a severe effect on organismal fitness are profoundly targeted by miRNA, probably to reduce the chances of dosage imbalance.

Comparative network-based recovery analysis and proteomic profiling of neurological changes in valproic acid-treated mice

Despite its prominence for characterization of complex mixtures, LC–MS/MS frequently fails to identify many proteins. Network-based analysis methods, based on protein–protein interaction networks (PPINs), biological pathways, and protein complexes, are useful for recovering non-detected proteins, thereby enhancing analytical resolution. However, network-based analysis methods do come in varied flavors for which the respective efficacies are largely unknown. We compare the recovery performance and functional insights from three distinct instances of PPIN-based approaches, viz., Proteomics Expansion Pipeline (PEP), Functional Class Scoring (FCS), and Maxlink, in a test scenario of valproic acid (VPA)-treated mice. We find that the most comprehensive functional insights, as well as best non-detected protein recovery performance, are derived from FCS utilizing real biological complexes. This outstrips other network-based methods such as Maxlink or Proteomics Expansion Pipeline (PEP). From FCS, we identified known biological complexes involved in epigenetic modifications, neuronal system development, and cytoskeletal rearrangements. This is congruent with the observed phenotype where adult mice showed an increase in dendritic branching to allow the rewiring of visual cortical circuitry and an improvement in their visual acuity when tested behaviorally. In addition, PEP also identified a novel complex, comprising YWHAB, NR1, NR2B, ACTB, and TJP1, which is functionally related to the observed phenotype. Although our results suggest different network analysis methods can produce different results, on the whole, the findings are mutually supportive. More critically, the non-overlapping information each provides can provide greater holistic understanding of complex phenotypes.