TRANSFAC: an integrated system for gene expression regulation

Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph

Motivation

The interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in various diseases. However, current methods for identifying DNA motif pairs rely on the recognition of single DNA motifs or probabilities, which may result in local optimal solutions and can be sensitive to the choice of initial values. A method for precisely identifying DNA motif pairs is still lacking.

Results

Here, we propose a novel computational method for predicting DNA Motif Pairs based on Composite Heterogeneous Graph (MPCHG). This approach leverages a composite heterogeneous graph model to identify DNA motif pairs on paired sequences. Compared with the existing methods, MPCHG has greatly improved the accuracy of motifs prediction. Furthermore, the predicted DNA motifs demonstrate heightened DNase accessibility than the background sequences. Notably, the two DNA motifs forming a pair exhibit functional consistency. Importantly, the interacting TF pairs obtained by predicted DNA motif pairs were significantly enriched with known interacting TF pairs, suggesting their potential contribution to chromatin interactions. Collectively, we believe that these identified DNA motif pairs held substantial implications for revealing gene transcriptional regulation under long-range chromatin interactions.

Unveiling a Microexon Switch: Novel Regulation of the Activities of Sugar Assimilation and Plant-Cell-Wall-Degrading Xylanases and Cellulases by Xlr2 in Trichoderma virens

Functional microexons have not previously been described in filamentous fungi. Here, we describe a novel mechanism of transcriptional regulation in Trichoderma requiring the inclusion of a microexon from the Xlr2 gene. In low-glucose environments, a long mRNA including the microexon encodes a protein with a GAL4-like DNA-binding domain (Xlr2-α), whereas in high-glucose environments, a short mRNA that is produced encodes a protein lacking this DNA-binding domain (Xlr2-β). Interestingly, the protein isoforms differ in their impact on cellulase and xylanase activity. Deleting the Xlr2 gene reduced both xylanase and cellulase activity and growth on different carbon sources, such as carboxymethylcellulose, xylan, glucose, and arabinose. The overexpression of either Xlr2-α or Xlr2-β in T. virens showed that the short isoform (Xlr2-β) caused higher xylanase activity than the wild types or the long isoform (Xlr2-α). Conversely, cellulase activity did not increase when overexpressing Xlr2-β but was increased with the overexpression of Xlr2-α. This is the first report of a novel transcriptional regulation mechanism of plant-cell-wall-degrading enzyme activity in T. virens. This involves the differential expression of a microexon from a gene encoding a transcriptional regulator.

Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates

Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient-derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Expression and function of NF-Y subunits in cancer

NF-Y is a Transcription Factor (TF) targeting the CCAAT box regulatory element. It consists of the NF-YB/NF-YC heterodimer, each containing an Histone Fold Domain (HFD), and the sequence-specific subunit NF-YA. NF-YA expression is associated with cell proliferation and absent in some post-mitotic cells. The review summarizes recent findings impacting on cancer development. The logic of the NF-Y regulome points to pro-growth, oncogenic genes in the cell-cycle, metabolism and transcriptional regulation routes. NF-YA is involved in growth/differentiation decisions upon cell-cycle re-entry after mitosis and it is widely overexpressed in tumors, the HFD subunits in some tumor types or subtypes. Overexpression of NF-Y -mostly NF-YA- is oncogenic and decreases sensitivity to anti-neoplastic drugs. The specific roles of NF-YA and NF-YC isoforms generated by alternative splicing -AS- are discussed, including the prognostic value of their levels, although the specific molecular mechanisms of activity are still to be deciphered.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

BACKGROUND

Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters.

METHODS

We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships.

RESULTS

We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community.

CONCLUSIONS

Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

Drug Target Identification and Drug Repurposing in Psoriasis through Systems Biology Approach, DNN-Based DTI Model and Genome-Wide Microarray Data

Psoriasis is a chronic skin disease that affects millions of people worldwide. In 2014, psoriasis was recognized by the World Health Organization (WHO) as a serious non-communicable disease. In this study, a systems biology approach was used to investigate the underlying pathogenic mechanism of psoriasis and identify the potential drug targets for therapeutic treatment. The study involved the construction of a candidate genome-wide genetic and epigenetic network (GWGEN) through big data mining, followed by the identification of real GWGENs of psoriatic and non-psoriatic using system identification and system order detection methods. Core GWGENs were extracted from real GWGENs using the Principal Network Projection (PNP) method, and the corresponding core signaling pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Comparing core signaling pathways of psoriasis and non-psoriasis and their downstream cellular dysfunctions, STAT3, CEBPB, NF-κB, and FOXO1 are identified as significant biomarkers of pathogenic mechanism and considered as drug targets for the therapeutic treatment of psoriasis. Then, a deep neural network (DNN)-based drug-target interaction (DTI) model was trained by the DTI dataset to predict candidate molecular drugs. By considering adequate regulatory ability, toxicity, and sensitivity as drug design specifications, Naringin, Butein, and Betulinic acid were selected from the candidate molecular drugs and combined into potential multi-molecule drugs for the treatment of psoriasis.

Genetic and Epigenetic Host-Virus Network to Investigate Pathogenesis and Identify Biomarkers for Drug Repurposing of Human Respiratory Syncytial Virus via Real-World Two-Side RNA-Seq Data: Systems Biology and Deep-Learning Approach

Human respiratory syncytial virus (hRSV) affects more than 33 million people each year, but there are currently no effective drugs or vaccines approved. In this study, we first constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via big-data mining. Then, we employed reversed dynamic methods via two-side host-pathogen RNA-seq time-profile data to prune false positives in candidate HPI-GWGEN to obtain the real HPI-GWGEN. With the aid of principal-network projection and the annotation of KEGG pathways, we can extract core signaling pathways during hRSV infection to investigate the pathogenic mechanism of hRSV infection and select the corresponding significant biomarkers as drug targets, i.e., TRAF6, STAT3, IRF3, TYK2, and MAVS. Finally, in order to discover potential molecular drugs, we trained a DNN-based DTI model by drug-target interaction databases to predict candidate molecular drugs for these drug targets. After screening these candidate molecular drugs by three drug design specifications simultaneously, i.e., regulation ability, sensitivity, and toxicity. We finally selected acitretin, RS-67333, and phenformin to combine as a potential multimolecule drug for the therapeutic treatment of hRSV infection.

A survey on algorithms to characterize transcription factor binding sites

Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor binding sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding the regulatory mechanisms governing the transcriptional state of cells. During the last decades, several experimental methods have been developed to recover DNA sequences containing TFBS. In parallel, computational methods have been proposed to discover and identify TFBS motifs based on these DNA sequences. This is one of the most widely investigated problems in bioinformatics and is referred to as the motif discovery problem. In this manuscript, we review classical and novel experimental and computational methods developed to discover and characterize TFBS motifs in DNA sequences, highlighting their advantages and drawbacks. We also discuss open challenges and future perspectives that could fill the remaining gaps in the field.

Strategies for activity analysis of single nucleotide polymorphisms associated with human diseases

Genome-wide association studies (GWAS) have identified a large number of single nucleotide polymorphism (SNP) sites associated with human diseases. In the annotation of human diseases, especially cancers, SNPs, as an important component of genetic factors, have gained increasing attention. Given that most of the SNPs are located in non-coding regions, the functional verification of these SNPs is a great challenge. The key to functional annotation for risk SNPs is to screen SNPs with regulatory activity from thousands of disease associated-SNPs. In this review, we systematically recapitulate the characteristics and functional roles of SNP sites, discuss three parallel reporter screening strategies in detail based on barcode tag classification, and recommend the common in silico strategies to help supplement the annotation of SNP sites with epigenetic activity analysis, prediction of target genes and trans-acting factors. We hope that this review will contribute to this exuberant research field by providing robust activity analysis strategies that can facilitate the translation of GWAS results into personalized diagnosis and prevention measures for human diseases.

Computational Methods for Single-cell DNA Methylome Analysis

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

Promoter–motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model

Gene expression varies due to the intrinsic stochasticity of transcription or as a reaction to external perturbations that generate cellular mutations. Co-regulation, co-expression and functional similarity of substances have been employed for indoctrinating the process of the transcriptional paradigm. The difficult process of analysing complicated proteomes and biological switches has been made easier by technical improvements, and microarray technology has flourished as a viable platform. Therefore, this research enables Microarray to cluster genes that are co-expressed and co-regulated into specific segments. Copious search algorithms have been employed to ascertain diacritic motifs or a combination of motifs that are performing regular expression, and their relevant information corresponding to the gene patterns is also documented. The associated genes co-expression and relevant cis-elements are further explored by engaging Escherichia coli as a model organism. Various clustering algorithms have also been used to generate classes of genes with similar expression profiles. A promoter database ‘EcoPromDB’ has been developed by referring RegulonDB database; this promoter database is freely available at www.ecopromdb.eminentbio.com and is divided into two sub-groups, depending upon the results of co-expression and co-regulation analyses.

Decoding transcriptional regulation via a human gene expression predictor

Transcription factors (TFs) regulate cellular activities by controlling gene expression, but a predictive model describing how TFs quantitatively modulate human transcriptomes is lacking. We construct a universal human gene expression predictor and utilize it to decode transcriptional regulation. Using the expression of 1613 TFs, the predictor reconstitutes highly accurate transcriptomes for samples derived from a wide range of tissues and conditions. The broad applicability of the predictor indicates that it recapitulates the quantitative relationships between TFs and target genes ubiquitous across tissues. Significant interacting TF-target gene pairs are extracted from the predictor and enable downstream inference of TF regulators for diverse pathways involved in development, immunity, metabolism, and stress response. A detailed analysis of the hematopoiesis process reveals an atlas of key TFs regulating the development of different hematopoietic cell lineages, and a portion of these TFs are conserved between humans and mice. The results demonstrate that our method is capable of delineating the TFs responsible for fate determination. Compared to other existing tools, our approach shows better performance in recovering the correct TF regulators. Thus, we present a novel approach that can be used to study human transcriptional regulation in general.

Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications

Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel therapeutic approaches are urgently needed. In this study, we utilized the systems biology method via genome-wide microarray data to explore the oncogenic mechanisms of MIBC and ABC to identify their respective drug targets for systems drug discovery. First, we constructed the candidate genome-wide genetic and epigenetic networks (GWGEN) through big data mining. Second, we applied the system identification and system order detection method to delete false positives in candidate GWGENs to obtain the real GWGENs of MIBC and ABC from their genome-wide microarray data. Third, we extracted the core GWGENs from the real GWGENs by selecting the significant proteins, genes and epigenetics via the principal network projection (PNP) method. Finally, we obtained the core signaling pathways from the corresponding core GWGEN through the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to investigate the carcinogenic mechanisms of MIBC and ABC. Based on the carcinogenic mechanisms, we selected the significant drug targets NFKB1, LEF1 and MYC for MIBC, and LEF1, MYC, NOTCH1 and FOXO1 for ABC. To design molecular drug combinations for MIBC and ABC, we employed a deep neural network (DNN)-based drug-target interaction (DTI) model with drug specifications. The DNN-based DTI model was trained by drug-target interaction databases to predict the candidate drugs for MIBC and ABC, respectively. Subsequently, the drug design specifications based on regulation ability, sensitivity and toxicity were employed as filter criteria for screening the potential drug combinations of Embelin and Obatoclax for MIBC, and Obatoclax, Entinostat and Imiquimod for ABC from their candidate drugs. In conclusion, we not only investigated the oncogenic mechanisms of MIBC and ABC, but also provided promising therapeutic options for MIBC and ABC, respectively.

TFLink: an integrated gateway to access transcription factor-target gene interactions for multiple species

Analysis of transcriptional regulatory interactions and their comparisons across multiple species are crucial for progress in various fields in biology, from functional genomics to the evolution of signal transduction pathways. However, despite the rapidly growing body of data on regulatory interactions in several eukaryotes, no databases exist to provide curated high-quality information on transcription factor-target gene interactions for multiple species. Here, we address this gap by introducing the TFLink gateway, which uniquely provides experimentally explored and highly accurate information on transcription factor-target gene interactions (∼12 million), nucleotide sequences and genomic locations of transcription factor binding sites (∼9 million) for human and six model organisms: mouse, rat, zebrafish, fruit fly, worm and yeast by integrating 10 resources. TFLink provides user-friendly access to data on transcription factor-target gene interactions, interactive network visualizations and transcription factor binding sites, with cross-links to several other databases. Besides containing accurate information on transcription factors, with a clear labelling of the type/volume of the experiments (small-scale or high-throughput), the source database and the original publications, TFLink also provides a wealth of standardized regulatory data available for download in multiple formats. The database offers easy access to high-quality data for wet-lab researchers, supplies data for gene set enrichment analyses and facilitates systems biology and comparative gene regulation studies. Database URL https://tflink.net/.

Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications

In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identify some important biomarkers as drug targets. Further, a systematic drug discovery method with a deep neural network (DNN)-based drug–target interaction (DTI) model and drug design specifications is proposed to design a potential multiple-molecule drug for the medical treatment of OSCC before clinical trials. First, we use big database mining to construct the candidate genome-wide genetic and epigenetic network (GWGEN) including a protein–protein interaction network (PPIN) and a gene regulatory network (GRN) for OSCC and non-OSCC. In the next step, real GWGENs are identified for OSCC and non-OSCC by system identification and system order detection methods based on the OSCC and non-OSCC microarray data, respectively. Then, the principal network projection (PNP) method was used to extract core GWGENs of OSCC and non-OSCC from real GWGENs of OSCC and non-OSCC, respectively. Afterward, core signaling pathways were constructed through the annotation of KEGG pathways, and then the carcinogenic mechanism of OSCC was investigated by comparing the core signal pathways and their downstream abnormal cellular functions of OSCC and non-OSCC. Consequently, HES1, TCF, NF-κB and SP1 are identified as significant biomarkers of OSCC. In order to discover multiple molecular drugs for these significant biomarkers (drug targets) of the carcinogenic mechanism of OSCC, we trained a DNN-based drug–target interaction (DTI) model by DTI databases to predict candidate drugs for these significant biomarkers. Finally, drug design specifications such as adequate drug regulation ability, low toxicity and high sensitivity are employed to filter out the appropriate molecular drugs metformin, gefitinib and gallic-acid to combine as a potential multiple-molecule drug for the therapeutic treatment of OSCC.

High-Resolution Secretome Analysis of Chemical Hypoxia Treated Cells Identifies Putative Biomarkers of Chondrosarcoma

Chondrosarcoma is the second most common bone tumor, accounting for 20% of all cases. Little is known about the pathology and molecular mechanisms involved in the development and in the metastatic process of chondrosarcoma. As a consequence, there are no approved therapies for this tumor and surgical resection is the only treatment currently available. Moreover, there are no available biomarkers for this type of tumor, and chondrosarcoma classification relies on operator-dependent histopathological assessment. Reliable biomarkers of chondrosarcoma are urgently needed, as well as greater understanding of the molecular mechanisms of its development for translational purposes. Hypoxia is a central feature of chondrosarcoma progression. The hypoxic tumor microenvironment of chondrosarcoma triggers a number of cellular events, culminating in increased invasiveness and migratory capability. Herein, we analyzed the effects of chemically-induced hypoxia on the secretome of SW 1353, a human chondrosarcoma cell line, using high-resolution quantitative proteomics. We found that hypoxia induced unconventional protein secretion and the release of proteins associated to exosomes. Among these proteins, which may be used to monitor chondrosarcoma development, we validated the increased secretion in response to hypoxia of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), a glycolytic enzyme well-known for its different functional roles in a wide range of tumors. In conclusion, by analyzing the changes induced by hypoxia in the secretome of chondrosarcoma cells, we identified molecular mechanisms that can play a role in chondrosarcoma progression and pinpointed proteins, including GAPDH, that may be developed as potential biomarkers for the diagnosis and therapeutic management of chondrosarcoma.

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications

The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among COVID-19 patients, SARS-CoV-2-associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug–target interaction (DNN-DTI) model could be trained by drug–target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates to repurpose potential multiple-molecule drugs by the filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity, and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.

Refactoring transcription factors for metabolic engineering

Due to the ability to regulate target metabolic pathways globally and dynamically, metabolic regulation systems composed of transcription factors have been widely used in metabolic engineering and synthetic biology. This review introduced the categories, action principles, prediction strategies, and related databases of transcription factors. Then, the application of global transcription machinery engineering technology and the transcription factor-based biosensors and quorum sensing systems are overviewed. In addition, strategies for optimizing the transcriptional regulatory tools' performance by refactoring transcription factors are summarized. Finally, the current limitations and prospects of constructing various regulatory tools based on transcription factors are discussed. This review will provide theoretical guidance for the rational design and construction of transcription factor-based metabolic regulation systems.

Regulatory patterns analysis of transcription factor binding site clustered regions and identification of key genes in endometrial cancer

Endometrial cancer (EC) is one of the three fatal tumors of the female reproductive system. Epigenetic alterations have been reported to be important in tumorigenesis, especially the chromatin accessibility changes and transcription factor binding differences. However, the regulatory mechanism underlying epigenetic alterations in EC development remains unclear. Here, we identified and characterized transcription factor binding site clustered regions (TFCRs) by integrating chromatin accessibility and transcription factor binding information. We totally identified 78,820 TFCRs and explored the relationship between TFCRs and regulatory elements, gene expression and mutation. Finally, we constructed a bioinformatic framework to identify candidate oncogenes and screened 13 candidate key genes, which may serve as potential diagnostic markers or therapeutic targets of EC.

Systematic Evaluation of DNA Sequence Variations on in vivo Transcription Factor Binding Affinity

The majority of the single nucleotide variants (SNVs) identified by genome-wide association studies (GWAS) fall outside of the protein-coding regions. Elucidating the functional implications of these variants has been a major challenge. A possible mechanism for functional non-coding variants is that they disrupted the canonical transcription factor (TF) binding sites that affect the in vivo binding of the TF. However, their impact varies since many positions within a TF binding motif are not well conserved. Therefore, simply annotating all variants located in putative TF binding sites may overestimate the functional impact of these SNVs. We conducted a comprehensive survey to study the effect of SNVs on the TF binding affinity. A sequence-based machine learning method was used to estimate the change in binding affinity for each SNV located inside a putative motif site. From the results obtained on 18 TF binding motifs, we found that there is a substantial variation in terms of a SNV’s impact on TF binding affinity. We found that only about 20% of SNVs located inside putative TF binding sites would likely to have significant impact on the TF-DNA binding.

Long noncoding RNA#45 exerts broad inhibitory effect on influenza a virus replication via its stem ring arms

A growing body of evidence suggests the pivotal role of long non-coding RNA (lncRNA) in influenza virus infection. Based on next-generation sequencing, we previously demonstrated that Lnc45 was distinctively stimulated by H5N1 influenza virus in mice. In this study, we systematically investigated the specific role of Lnc45 during influenza A virus (IAV) infection. Through qRT-PCR, we first demonstrated that Lnc45 is highly up-regulated by different subtypes of IAV strains, including H5N1, H7N9, and H1N1 viruses. Using RNA-FISH and qRT-PCR, we then found that Lnc45 can translocate from nuclear to cytoplasm during H5N1 virus infection. In addition, forced Lnc45 expression dramatically impeded viral replication of H1N1, H5N1, and H7N9 virus, while abolish of Lnc45 expression by RNA interference favored replication of these viruses, highlighting the potential broad antiviral activity of Lnc45 to IAV. Correspondingly, overexpression of Lnc45 inhibited viral polymerase activity and suppressed IAV-induced cell apoptosis. Moreover, Lnc45 significantly restrained nuclear aggregation of viral NP and PA proteins during H5N1 virus infection. Further functional study revealed that the stem ring arms of Lnc45 mainly mediated the antiviral effect. Therefore, we here demonstrated that Lnc45 functions as a broad-spectrum antiviral factor to inhibit influenza virus replication probably through inhibiting polymerase activity and NP and PA nuclear accumulation via its stem ring arms. Our study not only advances our understanding of the complexity of the IAV pathogenesis but also lays the foundation for developing novel anti-IAV therapeutics targeting the host lncRNA.

Evidence for a functional interaction of WNT10A and EBF1 in male-pattern baldness

More than 300 genetic risk loci have been identified for male pattern baldness (MPB) but little is known about the exact molecular mechanisms through which the associated variants exert their effects on MPB pathophysiology. Here, we aimed at further elucidating the regulatory architecture of the MPB risk locus on chromosome (chr.) 2q35, where we have previously reported a regulatory effect of the MPB lead variant on the expression of WNT10A. A HaploReg database research for regulatory annotations revealed that the association signal at 2q35 maps to a binding site for the transcription factor EBF1, whose gene is located at a second MPB risk locus on chr. 5q33.3. To investigate a potential interaction between EBF1 and WNT10A during MPB development, we performed in vitro luciferase reporter assays as well as expression analyses and immunofluorescence co-stainings in microdissected human hair follicles. Our experiments confirm that EBF1 activates the WNT10A promoter and that the WNT10A/EBF1 interaction is impacted by the allelic expression of the MPB risk allele at 2q35. Expression analyses across different hair cycle phases and immunhistochemical (co)stainings against WNT10A and EBF1 suggest a predominant relevance of EBF1/WNT10A interaction for hair shaft formation during anagen. Based on these findings we suggest a functional mechanism at the 2q35 risk locus for MPB, where an MPB-risk allele associated reduction in WNT10A promoter activation via EBF1 results in a decrease in WNT10A expression that eventually results in anagen shortening, that is frequently observed in MPB affected hair follicles. To our knowledge, this study is the first follow-up study on MPB that proves functional interaction between two MPB risk loci and sheds light on the underlying pathophysiological mechanism at these loci.

In Silico Prediction for ncRNAs in Prokaryotes

The identification and characterization of non-coding RNAs (ncRNAs) in prokaryotes is an important step in the study of the interaction of these molecules with mRNAs-or target proteins, in the post-transcriptional regulation process. Here, we describe one of the main in silico prediction methods in prokaryotes, using the TargetRNA2 tool to predict target mRNAs.

FOXA2 inhibits doxorubicin-induced apoptosis via transcriptionally activating HBP rate-limiting enzyme GFPT1 in HCC cells

Apoptosis plays an important role in both carcinogenesis and cancer treatment. Understanding the mechanisms through which resistance to apoptosis occurs in cancer cells has huge implications for cancer treatment. Although pieces of evidence have shown that elevated levels of global O-GlcNAcylation play an anti-apoptotic role in myriad cancers, the underlying mechanism is still ambiguous. In this study, we demonstrated that FOXA2, an essential transcription factor for liver homeostasis and hepatocellular carcinoma (HCC) development, inhibits doxorubicin (DOX)-induced apoptosis through elevating cellular O-GlcNAcylation in HCC cells. In response to DOX treatment, elevated FOXA2 and global O-GlcNAcylation level was observed in HCC cells, and higher FOXA2 levels indicated lower levels of DOX-induced apoptosis. Subsequently, we demonstrated that FOXA2 is a direct transcriptional activator of the hexosamine biosynthetic pathway (HBP) rate-limiting enzyme GFPT1. The upregulation of FOXA2 expression induced the synthesis of intracellular UDP-GlcNAc, which is the sugar substrate of O-GlcNAcylation produced by the HBP. The flux through the HBP elevated the global O-GlcNAcylation level and led to the activation of survival signaling pathways in HCC cells. Furthermore, GFPT1 was proved to be an important downstream regulator of FOXA2-mediated apoptotic suppression. These results provide insights into the molecular mechanism by which FOXA2 inhibits DOX-induced HCC cell apoptosis and suggest that targeting FOXA2 might offer a new strategy for HCC treatment.

Identification of transcription factors controlling cell wall invertase gene expression for reproductive development via bioinformatic and transgenic analyses

Cell wall invertase (CWIN) hydrolyses sucrose into glucose and fructose in the extracellular matrix and plays crucial roles in assimilate partitioning and sugar signalling. However, the molecular regulators controlling CWIN gene transcription remain unknown. As the first step to address this issue, we performed bioinformatic and transgenic studies, which identified a cohort of transcription factors (TFs) modulating CWIN gene expression in Arabidopsis thaliana. Comprehensive bioinformatic analyses identified 18 TFs as putative regulators of the expression of AtCWIN2 and AtCWIN4 that are predominantly expressed in Arabidopsis reproductive organs. Among them, MYB21, ARF6, ARF8, AP3 and CRC were subsequently shown to be the most likely regulators of CWIN gene expression based on molecular characterization of the respective mutant of each candidate TF. More specifically, the obtained data indicate that ARF6, ARF8 and MYB21 regulate CWIN2 expression in the anthers and CWIN4 in nectaries, anthers and petals, whereas AP3 and CRC were determined primarily to regulate the transcriptional activity of CWIN4. TF-promoter interaction assays demonstrated that ARF6 and ARF8 directly control CWIN2 and CWIN4 transcription with AP3 activating CWIN4. The involvement of ARF8 in regulating CWIN4 expression was further supported by the finding that enhanced CWIN4 expression partially recovered the short silique phenotype displayed by the arf8-3 mutant. The identification of the five TFs regulating CWIN expression serves as a launching pad for future studies to dissect the upstream molecular network underpinning the transcription of CWINs and provides a new avenue, potentially, to engineer assimilate allocation and reproductive development for improving seed yield.

Detection of H3K4me3 Identifies NeuroHIV Signatures, Genomic Effects of Methamphetamine and Addiction Pathways in Postmortem HIV+ Brain Specimens that Are Not Amenable to Transcriptome Analysis

Human postmortem specimens are extremely valuable resources for investigating translational hypotheses. Tissue repositories collect clinically assessed specimens from people with and without HIV, including age, viral load, treatments, substance use patterns and cognitive functions. One challenge is the limited number of specimens suitable for transcriptional studies, mainly due to poor RNA quality resulting from long postmortem intervals. We hypothesized that epigenomic signatures would be more stable than RNA for assessing global changes associated with outcomes of interest. We found that H3K27Ac or RNA Polymerase (Pol) were not consistently detected by Chromatin Immunoprecipitation (ChIP), while the enhancer H3K4me3 histone modification was abundant and stable up to the 72 h postmortem. We tested our ability to use HeK4me3 in human prefrontal cortex from HIV+ individuals meeting criteria for methamphetamine use disorder or not (Meth +/-) which exhibited poor RNA quality and were not suitable for transcriptional profiling. Systems strategies that are typically used in transcriptional metadata were applied to H3K4me3 peaks revealing consistent genomic activity differences in regions where addiction and neuronal synapses pathway genes are represented, including genes of the dopaminergic system, as well as inflammatory pathways. The resulting comparisons mirrored previously observed effects of Meth on suppressing gene expression and provided insights on neurological processes affected by Meth. The results suggested that H3K4me3 detection in chromatin may reflect transcriptional patterns, thus providing opportunities for analysis of larger numbers of specimens from cases with substance use and neurological deficits. In conclusion, the detection of H3K4me3 in isolated chromatin can be an alternative to transcriptome strategies to increase the power of association using specimens with long postmortem intervals and low RNA quality.

Endocan, a novel inflammatory marker, is upregulated in human chondrocytes stimulated with IL-1 beta

Endocan is a circulating proteoglycan, involved in immunity, inflammation, and endothelial function. It has been recently suggested as a biomarker of inflammation, increased angiogenesis, and cancer. In vitro studies have shown that endocan expression could be upregulated by inflammatory cytokines and proangiogenic molecules. High endocan levels were also shown in arthritic joint tissues and particularly in sites characterized by severe inflammation. This study was performed to evaluate endocan expression in chondrocytes stimulated with IL-ß. mRNA and related protein production were measured for endocan, TNF-α, and IL-6. NF-kB activity was also evaluated. IL-1ß treatment induced a significant upregulation of both endocan and the inflammatory parameters as well as NF-kB activity. The treatment of chondrocytes with the specific NF-kB inhibitor before IL-1ß stimulation was able to reduce endocan and the inflammatory markers over-expression. The results of our study indicated that endocan is also expressed in human chondrocytes; furthermore, consistent with previous results in other cell types and tissues, IL-1ß-induced inflammatory response involves the expression of endocan through NF-kB activation. In this context, endocan seems to be an important inflammatory marker associated with the activation of NF-kB pathway.

Comprehensive characterization of a drug-resistance-related ceRNA network across 15 anti-cancer drug categories

Cancer is still a major health problem around the world. The treatment failure of cancer has largely been attributed to drug resistance. Competitive endogenous RNAs (ceRNAs) are involved in various biological processes and thus influence the drug sensitivity of cancers. However, a comprehensive characterization of drug-sensitivity-related ceRNAs has not yet been performed. In the present study, we constructed 15 ceRNA networks across 15 anti-cancer drug categories, involving 217 long noncoding RNAs (lncRNAs), 158 microRNAs (miRNAs), and 1,389 protein coding genes (PCGs). We found that these ceRNAs were involved in hallmark processes such as “self-sufficiency in growth signals,” “insensitivity to antigrowth signals,” and so on. We then identified an intersection ceRNA network (ICN) across the 15 anti-cancer drug categories. We further identified interactions between genes in the ICN and clinically actionable genes (CAGs) by analyzing the co-expressions, protein-protein interactions, and transcription factor-target gene interactions. We found that certain genes in the ICN are correlated with CAGs. Finally, we found that genes in the ICN were aberrantly expressed in tumors, and some were associated with patient survival time and cancer stage.

Biochemical and Computational Approaches for the Large-Scale Analysis of Protein Arginine Methylation by Mass Spectrometry

The absence of efficient mass spectrometry-based approaches for the large-scale analysis of protein arginine methylation has hindered the understanding of its biological role, beyond the transcriptional regulation occurring through histone modification. In the last decade, however, several technological advances of both the biochemical methods for methylated polypeptide enrichment and the computational pipelines for MS data analysis have considerably boosted this research field, generating novel insights about the extent and role of this post-translational modification. Here, we offer an overview of state-of-the-art approaches for the high-confidence identification and accurate quantification of protein arginine methylation by high-resolution mass spectrometry methods, which comprise the development of both biochemical and bioinformatics methods. The further optimization and systematic application of these analytical solutions will lead to ground-breaking discoveries on the role of protein methylation in biological processes.

Abiraterone Acetate Induces CREB1 Phosphorylation and Enhances the Function of the CBP-p300 Complex, Leading to Resistance in Prostate Cancer Cells

PURPOSE

Abiraterone acetate (AA), an inhibitor of cytochrome P450 17alpha-hydroxylase/17, 20 lyase, is an FDA-approved drug for advanced prostate cancer. However, not all patients respond to AA, and AA resistance ultimately develops in patients who initially respond. We aimed to identify AA resistance mechanisms in prostate cancer cells.

EXPERIMENTAL DESIGN

We established several AA-resistant cell lines and performed a comprehensive study on mechanisms involved in AA resistance development. RNA sequencing and phospho-kinase array screenings were performed to discover that the cAMP-response element CRE binding protein 1 (CREB1) was a critical molecule in AA resistance development.

RESULTS

The drug-resistant cell lines are phenotypically stable without drug selection, and exhibit permanent global gene expression changes. The phosphorylated CREB1 (pCREB1) is increased in AA-resistant cell lines and is critical in controlling global gene expression. Upregulation of pCREB1 desensitized prostate cancer cells to AA, while blocking CREB1 phosphorylation resensitized AA-resistant cells to AA. AA treatment increases intracellular cyclic AMP (cAMP) levels, induces kinases activity, and leads to the phosphorylation of CREB1, which may subsequently augment the essential role of the CBP/p300 complex in AA-resistant cells because AA-resistant cells exhibit a relatively higher sensitivity to CBP/p300 inhibitors. Further pharmacokinetics studies demonstrated that AA significantly synergizes with CBP/p300 inhibitors in limiting the growth of prostate cancer cells.

CONCLUSIONS

Our studies suggest that AA treatment upregulates pCREB1, which enhances CBP/p300 activity, leading to global gene expression alterations, subsequently resulting in drug resistance development. Combining AA with therapies targeting resistance mechanisms may provide a more effective treatment strategy.

The type VI secretion system of Xanthomonas phaseoli pv. manihotis is involved in virulence and in vitro motility

Background

The type VI protein secretion system (T6SS) is important in diverse cellular processes in Gram-negative bacteria, including interactions with other bacteria and with eukaryotic hosts. In this study we analyze the evolution of the T6SS in the genus Xanthomonas and evaluate its importance of the T6SS for virulence and in vitro motility in Xanthomonas phaseoli pv. manihotis (Xpm), the causal agent of bacterial blight in cassava (Manihot esculenta). We delineate the organization of the T6SS gene clusters in Xanthomonas and then characterize proteins of this secretion system in Xpm strain CIO151.

Results

We describe the presence of three different clusters in the genus Xanthomonas that vary in their organization and degree of synteny between species. Using a gene knockout strategy, we also found that vgrG and hcp are required for maximal aggressiveness of Xpm on cassava plants while clpV is important for both motility and maximal aggressiveness.

Conclusion

We characterized the T6SS in 15 different strains in Xanthomonas and our phylogenetic analyses suggest that the T6SS might have been acquired by a very ancient event of horizontal gene transfer and maintained through evolution, hinting at their importance for the adaptation of Xanthomonas to their hosts. Finally, we demonstrated that the T6SS of Xpm is functional, and significantly contributes to virulence and motility. This is the first experimental study that demonstrates the role of the T6SS in the Xpm-cassava interaction and the T6SS organization in the genus Xanthomonas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-020-02066-1.

Functional variants fine-mapping and gene function characterization provide insights into the role of ZNF323 in schizophrenia pathogenesis

Schizophrenia is a severe mental disease characterized with positive symptoms, negative symptoms, and cognitive impairments. Although recent genome-wide association studies (GWASs) have identified over 145 risk loci for schizophrenia, pinpointing the causal variants and genes at the reported loci and elucidating their roles in schizophrenia remain major challenges. Here we identify a functional single-nucleotide polymorphism (SNP; rs213237) in ZNF323 promoter by using functional fine-mapping. We found that allelic differences at rs213237 affected the ZNF323 promoter activity significantly. Consistently, expression quantitative trait loci (eQTL) analysis showed that rs213237 was significantly associated with ZNF323 expression in diverse human brain tissues, suggesting that rs213237 may contribute to schizophrenia risk through regulating ZNF323 expression. Interestingly, we found that ZNF323 protein was localized in the nucleus and knockdown of ZNF323 in macaque neural stem cells (mNSCs) significantly impaired proliferation and survival of mNSCs. We further showed that stable knockdown of ZNF323 in SH-SY5Y cells resulted in significant decrease of the tyrosine hydroxylase (TH) protein expression. Finally, transcriptome analysis revealed that ZNF323 may regulate pivotal schizophrenia risk genes (including VIPR2 and NPY) and schizophrenia-associated pathways (including PI3K-AKT and NOTCH signaling pathways), suggesting that ZNF323 may be a major regulator of schizophrenia risk genes. Our study reveals how a genetic variant in ZNF323 promoter contributes to schizophrenia risk through regulating ZNF323 expression. More importantly, our findings demonstrate that ZNF323 may have a pivotal role in schizophrenia pathogenesis through regulating schizophrenia risk genes and schizophrenia-associated biological processes (including neurodevelopment, PI3K-AKT, and NOTCH signaling pathways).

Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications

In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets and a systematic drug discovery strategy to design a potential multiple-molecule targeting drug for type 2 diabetes (T2D) treatment. We first integrated databases to construct the genome-wide genetic and epigenetic networks (GWGENs), which consist of protein–protein interaction networks (PPINs) and gene regulatory networks (GRNs) for T2D and non-T2D (health), respectively. Second, the relevant “real GWGENs” are identified by system identification and system order detection methods performed on the T2D and non-T2D RNA-seq data. To simplify network analysis, principal network projection (PNP) was thereby exploited to extract core GWGENs from real GWGENs. Then, with the help of KEGG pathway annotation, core signaling pathways were constructed to identify significant biomarkers. Furthermore, in order to discover potential drugs for the selected pathogenic biomarkers (i.e., drug targets) from the core signaling pathways, not only did we train a deep neural network (DNN)-based drug–target interaction (DTI) model to predict candidate drug’s binding with the identified biomarkers but also considered a set of design specifications, including drug regulation ability, toxicity, sensitivity, and side effects to sieve out promising drugs suitable for T2D.

RFCM3: Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer

Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Functional genomics data provides opportunities to study the aberrant microRNA-messenger RNA (miRNA-mRNA) interaction. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM³), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects set of miRNA-mRNA modules by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later, using the knowledge of the miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The prognostic value of the genes in a module with respect to cervical cancer is also demonstrated. The approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.

Investigating Core Signaling Pathways of Hepatitis B Virus Pathogenesis for Biomarkers Identification and Drug Discovery via Systems Biology and Deep Learning Method

Hepatitis B Virus (HBV) infection is a major cause of morbidity and mortality worldwide. However, poor understanding of its pathogenesis often gives rise to intractable immune escape and prognosis recurrence. Thus, a valid systematic approach based on big data mining and genome-wide RNA-seq data is imperative to further investigate the pathogenetic mechanism and identify biomarkers for drug design. In this study, systems biology method was applied to trim false positives from the host/pathogen genetic and epigenetic interaction network (HPI-GEN) under HBV infection by two-side RNA-seq data. Then, via the principal network projection (PNP) approach and the annotation of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, significant biomarkers related to cellular dysfunctions were identified from the core cross-talk signaling pathways as drug targets. Further, based on the pre-trained deep learning-based drug-target interaction (DTI) model and the validated pharmacological properties from databases, i.e., drug regulation ability, toxicity, and sensitivity, a combination of promising multi-target drugs was designed as a multiple-molecule drug to create more possibility for the treatment of HBV infection. Therefore, with the proposed systems medicine discovery and repositioning procedure, we not only shed light on the etiologic mechanism during HBV infection but also efficiently provided a potential drug combination for therapeutic treatment of Hepatitis B.

Papillomaviruses infecting cetaceans exhibit signs of genome adaptation following a recombination event

Papillomaviruses (PVs) have evolved through a complex evolutionary scenario where virus-host co-evolution alone is not enough to explain the phenotypic and genotypic PV diversity observed today. Other evolutionary processes, such as host switch and recombination, also appear to play an important role in PV evolution. In this study, we have examined the genomic impact of a recombination event between distantly related PVs infecting Cetartiodactyla (even-toed ungulates and cetaceans). Our phylogenetic analyses suggest that one single recombination was responsible for the generation of extant 'chimeric' PV genomes infecting cetaceans. By correlating the phylogenetic relationships to the genomic content, we observed important differences between the recombinant and non-recombinant cetartiodactyle PV genomes. Notably, recombinant PVs contain a unique set of conserved motifs in the upstream regulatory region (URR). We interpret these regulatory changes as an adaptive response to drastic changes in the PV genome. In terms of codon usage preferences (CUPrefs), we did not detect any particular differences between orthologous open reading frames in recombinant and non-recombinant PVs. Instead, our results are in line with previous observations suggesting that CUPrefs in PVs are rather linked to gene expression patterns as well as to gene function. We show that the non-coding URR of PVs infecting cetaceans, the central regulatory element in these viruses, exhibits signs of adaptation following a recombination event. Our results suggest that also in PVs, the evolution of gene regulation can play an important role in speciation and adaptation to novel environments.

Construction of an integrated human osteosarcoma database, HOsDb, based on literature mining, microarray analysis, and database retrieval

Background

Osteosarcoma (OS) is the most frequent primary malignancy of bone with a high incidence in adolescence. This study aimed to construct a publicly available, integrated database of human OS, named HOsDb.

Methods

Microarray data, current databases, and a literature search of PubMed were used to extract information relevant to human OS-related genes and their transcription factors (TFs) and single nucleotide polymorphisms (SNPs), as well as methylation sites and microRNAs (miRNAs). This information was collated for constructing the HOsDb.

Results

In total, we identified 7191 OS tumor-related genes, 763 OS metastasis-related genes, and 1589 OS drug-related genes, corresponding to 190,362, 21,131, and 41,135 gene-TF pairs, respectively, 3,749,490, 358,361, and 767,674 gene-miRNA pairs, respectively; and 28,386, 2532, and 3943 SNPs, respectively. Additionally, 240 OS-related miRNAs, 1695 genes with copy number variations in OS, and 18 genes with methylation sites in OS were identified. These data were collated to construct the HOsDb, which is available at www.hosdatabase.com. Users can search OS-related molecules using this database.

Conclusion

The HOsDb provides a platform that is comprehensive, quick, and easily accessible, and it will enrich our current knowledge of OS.

Expanding Role of Ubiquitin in Translational Control

The eukaryotic proteome has to be precisely regulated at multiple levels of gene expression, from transcription, translation, and degradation of RNA and protein to adjust to several cellular conditions. Particularly at the translational level, regulation is controlled by a variety of RNA binding proteins, translation and associated factors, numerous enzymes, and by post-translational modifications (PTM). Ubiquitination, a prominent PTM discovered as the signal for protein degradation, has newly emerged as a modulator of protein synthesis by controlling several processes in translation. Advances in proteomics and cryo-electron microscopy have identified ubiquitin modifications of several ribosomal proteins and provided numerous insights on how this modification affects ribosome structure and function. The variety of pathways and functions of translation controlled by ubiquitin are determined by the various enzymes involved in ubiquitin conjugation and removal, by the ubiquitin chain type used, by the target sites of ubiquitination, and by the physiologic signals triggering its accumulation. Current research is now elucidating multiple ubiquitin-mediated mechanisms of translational control, including ribosome biogenesis, ribosome degradation, ribosome-associated protein quality control (RQC), and redox control of translation by ubiquitin (RTU). This review discusses the central role of ubiquitin in modulating the dynamism of the cellular proteome and explores the molecular aspects responsible for the expanding puzzle of ubiquitin signals and functions in translation.

RAD9A promotes metastatic phenotypes through transcriptional regulation of anterior gradient 2 (AGR2)

RAD9A plays an important role in prostate tumorigenesis and metastasis-related phenotypes. The protein classically functions as part of the RAD9A-HUS1-RAD1 complex but can also act independently. RAD9A can selectively transactivate multiple genes, including CDKN1A and NEIL1 by binding p53-consensus sequences in or near promoters. RAD9A is overexpressed in human prostate cancer specimens and cell lines; its expression correlates with tumor progression. Silencing RAD9A in prostate cancer cells impairs their ability to form tumors in vivo and migrate as well as grow anchorage independently in vitro. We demonstrate herein that RAD9A transcriptionally controls AGR2, a gene aberrantly overexpressed in patients with metastatic prostate cancer. Transient or stable knockdown of RAD9A in PC-3 cells caused downregulation of AGR2 protein abundance. Reduced AGR2 protein levels were due to lower abundance of AGR2 mRNA. The AGR2 genomic region upstream of the coding initiation site contains several p53 consensus sequences. RAD9A bound specifically to the 5'-untranslated region of AGR2 in PC-3 cells at a partial p53 consensus sequence at position +3136 downstream from the transcription start site, determined by chromatin immunoprecipitation, followed by PCR amplification. Binding of RAD9A to the p53 consensus sequence was sufficient to drive AGR2 gene transcription, shown by a luciferase reporter assay. In contrast, when the RAD9A-binding sequence on the AGR2 was mutated, no luciferase activity was detected. Knockdown of RAD9A in PC-3 cells impaired cell migration and anchorage-independent growth. However, ectopically expressed AGR2 in RAD9A-depleted PC-3 cells restored these phenotypes. Our results suggest RAD9A drives metastasis by controlling AGR2 abundance.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Inference of plant gene regulatory networks using data-driven methods: A practical overview

Transcriptional regulation is a complex and dynamic process that plays a vital role in plant growth and development. A key component in the regulation of genes is transcription factors (TFs), which coordinate the transcriptional control of gene activity. A gene regulatory network (GRN) is a collection of regulatory interactions between TFs and their target genes. The accurate delineation of GRNs offers a significant contribution to our understanding about how plant cells are organized and function, and how individual genes are regulated in various conditions, organs or cell types. During the past decade, important progress has been made in the identification of GRNs using experimental and computational approaches. However, a detailed overview of available platforms supporting the analysis of GRNs in plants is missing. Here, we review current databases, platforms and tools that perform data-driven analyses of gene regulation in Arabidopsis. The platforms are categorized into two sections, 1) promoter motif analysis tools that use motif mapping approaches to find TF motifs in the regulatory sequences of genes of interest and 2) network analysis tools that identify potential regulators for a set of input genes using a range of data types in order to generate GRNs. We discuss the diverse datasets integrated and highlight the strengths and caveats of different platforms. Finally, we shed light on the limitations of the above approaches and discuss future perspectives, including the need for integrative approaches to unravel complex GRNs in plants.

A method for identifying allele-specific hydroxymethylation

We previously identified sequence-dependent allele-specific methylation (sd-ASM) in adult human peripheral blood leukocytes, in which ASM occurs in cis depending on adjacent polymorphic sequences. A number of groups have identified sd-ASM sites in the human and mouse genomes, illustrating the prevalence of sd-ASM in mammalian genomes. In addition, sd-ASM can lead to sequence-dependent allele-specific expression of neighbouring genes. Imprinted genes also often exhibit parent-of-origin-dependent allele-specific methylation (pd-ASM), which causes parent-of-origin-dependent allele-specific expression. However, whether most of the already known sd-ASM and pd-ASM sites are methylated or hydroxymethylated remains unclear due to technical restrictions. Accordingly, a novel method that enables examination of allelic methylation and hydroxymethylation status and also overcomes the drawbacks of conventional methods is needed. Such a method could also be used to elucidate the mechanisms underlying polymorphism-associated inter-individual differences in disease susceptibility and the mechanism of genomic imprinting. Here, we developed a simple method to determine allelic hydroxymethylation status and identified novel sequence- and parent-of-origin-dependent allele-specific hydroxymethylation sites. Correlation analyses of TF binding sequences and methylation or hydroxymethylation between three mouse strains revealed the involvement of Pax5 in strain-specific methylation and hydroxymethylation in exon 7 of Pdgfrb.

A Muscle Hybrid Promoter as a Novel Tool for Gene Therapy

Gene therapy is a promising strategy to cure rare diseases. The lack of regulatory sequences ensuring specific and robust expression in skeletal and cardiac muscle is a substantial limitation of gene therapy efficiency targeting the muscle tissue. Here we describe a novel muscle hybrid (MH) promoter that is highly active in both skeletal and cardiac muscle cells. It has an easily exchangeable modular structure, including an intronic module that highly enhances the expression of the gene driven by it. In cultured myoblasts, myotubes, and cardiomyocytes, the MH promoter gives relatively stable expression as well as higher activity and protein levels than the standard CMV and desmin gene promoters or the previously developed synthetic or CKM-based promoters. Combined with AAV2/9, the MH promoter also provides a high in vivo expression level in skeletal muscle and the heart after both intramuscular and systemic delivery. It is much more efficient than the desmin-encoding gene promoter, and it maintains the same specificity. This novel promoter has potential for gene therapy in muscle cells. It can provide stable transgene expression, ensuring high levels of therapeutic protein, and limited side effects because of its specificity. This constitutes an improvement in the efficiency of genetic disease therapy.

A network-based analysis for mining the risk pathways in glioblastoma

The most malignant type of brain tumour is glioblastoma multiforme (GBM). Patients with GBM often have a poor prognosis, as a result of incomplete or inaccurate diagnoses. Regulatory pathways have been demonstrated to serve important roles in complex human diseases. Therefore, deciphering these risk pathways may shed light on the molecular mechanisms underlying GBM progression. In the present study, differentially expressed genes and microRNAs (miRNAs) in a publicly available database were identified between normal and tumour samples. To determine the pathophysiology and molecular mechanisms underlying GBM, integrated network analysis was performed to mine GBM-specific risk pathways. Specifically, a GBM-specific regulatory network was constructed that integrated manually curated GBM-associated transcription and post-transcriptional data resources, including transcription factors and miRNAs. A total of 1,827 differentially expressed genes and 30 miRNAs were identified. The differentially expressed genes were significantly enriched in a number of immune response-associated functions. Based on the GBM-specific regulatory network, 15 risk regulatory pathways containing not only known regulators, but also potential novel targets that might be involved in tumourigenesis were identified. Network analysis provides a strategy for leveraging genomic data to identify potential oncogenic pathways and molecular targets for GBM.

MeDReaders: a database for transcription factors that bind to methylated DNA

Understanding the molecular principles governing interactions between transcription factors (TFs) and DNA targets is one of the main subjects for transcriptional regulation. Recently, emerging evidence demonstrated that some TFs could bind to DNA motifs containing highly methylated CpGs both in vitro and in vivo. Identification of such TFs and elucidation of their physiological roles now become an important stepping-stone toward understanding the mechanisms underlying the methylation-mediated biological processes, which have crucial implications for human disease and disease development. Hence, we constructed a database, named as MeDReaders, to collect information about methylated DNA binding activities. A total of 731 TFs, which could bind to methylated DNA sequences, were manually curated in human and mouse studies reported in the literature. In silico approaches were applied to predict methylated and unmethylated motifs of 292 TFs by integrating whole genome bisulfite sequencing (WGBS) and ChIP-Seq datasets in six human cell lines and one mouse cell line extracted from ENCODE and GEO database. MeDReaders database will provide a comprehensive resource for further studies and aid related experiment designs. The database implemented unified access for users to most TFs involved in such methylation-associated binding actives. The website is available at http://medreader.org/.

YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in Saccharomyces cerevisiae

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT—www.yeastract.com) information system has been, for 11 years, a key tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in Saccharomyces cerevisiae. Since its last update in June 2017, YEASTRACT includes approximately 163000 regulatory associations between transcription factors (TF) and target genes in S. cerevisiae, based on more than 1600 bibliographic references; it also includes 247 specific DNA binding consensus recognized by 113 TFs. This release of the YEASTRACT database provides new visualization tools to visualize each regulatory network in an interactive fashion, enabling the user to select and observe subsets of the network such as: (i) considering only DNA binding evidence or both DNA binding and expression evidence; (ii) considering only either positive or negative regulatory associations; or (iii) considering only one set of related environmental conditions. A further tool to observe TF regulons is also offered, enabling a clear-cut understanding of the exact meaning of the available data. We believe that with this new version, YEASTRACT will improve its role as an open web resource instrumental for Yeast Biologists and Systems Biology researchers.

TFutils: Data structures for transcription factor bioinformatics

DNA transcription is intrinsically complex. Bioinformatic work with transcription factors (TFs) is complicated by a multiplicity of data resources and annotations. The Bioconductor package TFutils includes data structures and functions to enhance the precision and utility of integrative analyses that have components involving TFs. TFutils provides catalogs of human TFs from three reference sources (CISBP, HOCOMOCO, and GO), a catalog of TF targets derived from MSigDb, and multiple approaches to enumerating TF binding sites, including an interface to results of 690 ENCODE experiments. Aspects of integration of TF binding patterns and genome-wide association study results are explored in examples.