Expression-based prediction of human essential genes and candidate lncRNAs in cancer cells.

MOTIVATION: Essential genes are required for the reproductive success at either cellular or organismal level. The identification of essential genes is important for understanding the core biological processes and identifying effective therapeutic drug targets. However, experimental identification of essential genes is costly, time consuming and labor intensive. Although several machine learning models have been developed to predict essential genes, these models are not readily applicable to lncRNAs. Moreover, the currently available models cannot be used to predict essential genes in a specific cancer type. RESULTS: In this study, we have developed a new machine learning approach, XGEP (eXpression-based Gene Essentiality Prediction), to predict essential genes and candidate lncRNAs in cancer cells. The novelty of XGEP lies in the utilization of relevant features derived from the TCGA transcriptome dataset through collaborative embedding. When evaluated on the pan-cancer dataset, XGEP was able to accurately predict human essential genes and achieve significantly higher performance than previous models. Notably, several candidate lncRNAs selected by XGEP are reported to promote cell proliferation and inhibit cell apoptosis. Moreover, XGEP also demonstrated superior performance on cancer-type-specific datasets to identify essential genes. The comprehensive lists of candidate essential genes in specific cancer types may be used to guide experimental characterization and facilitate the discovery of drug targets for cancer therapy. AVAILABILITY AND IMPLEMENTATION: The source code and datasets used in this study are freely available at https://github.com/BioDataLearning/XGEP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RNA markers for ultra-rapid molecular antimicrobial susceptibility testing in fluoroquinolone-treated Klebsiella pneumoniae.

OBJECTIVES: Traditional antimicrobial susceptibility testing (AST) is growth dependent and time-consuming. With rising rates of drug-resistant infections, a novel diagnostic method is critically needed that can rapidly reveal a pathogen's antimicrobial susceptibility to guide appropriate treatment. Recently, RNA sequencing has been identified as a powerful diagnostic tool to explore transcriptional gene expression and improve AST. METHODS: RNA sequencing was used to investigate the potential of RNA markers for rapid molecular AST using Klebsiella pneumoniae and ciprofloxacin as a model. Downstream bioinformatic analysis was applied for optimal marker selection. Further validation on 11 more isolates of K. pneumoniae was performed using quantitative real-time PCR. RESULTS: From RNA sequencing, we identified RNA signatures that were induced or suppressed following exposure to ciprofloxacin. Significant shifts at the transcript level were observed as early as 10 min after antibiotic exposure. Lastly, we confirmed marker expression profiles with concordant MIC results from traditional culture-based AST and validated across 11 K. pneumoniae isolates. recA, coaA and metN transcripts harbour the most sensitive susceptibility information and were selected as our top markers. CONCLUSIONS: Our results suggest that RNA signature is a promising approach to AST development, resulting in faster clinical diagnosis and treatment of infectious disease. This approach is potentially applicable in other models including other pathogens exposed to different classes of antibiotics.

Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases.

Transcription factors (TFs) and microRNAs (miRNAs) can jointly regulate target gene expression in the forms of feed-forward loops (FFLs) or feedback loops (FBLs). These regulatory loops serve as important motifs in gene regulatory networks and play critical roles in multiple biological processes and different diseases. Major progress has been made in bioinformatics and experimental study for the TF and miRNA co-regulation in recent years. To further speed up its identification and functional study, it is indispensable to make a comprehensive review. In this article, we summarize the types of FFLs and FBLs and their identified methods. Then, we review the behaviors and functions for the experimentally identified loops according to biological processes and diseases. Future improvements and challenges are also discussed, which includes more powerful bioinformatics approaches and high-throughput technologies in TF and miRNA target prediction, and the integration of networks of multiple levels.

MicroRNA and transcription factor co-regulatory network analysis reveals miR-19 inhibits CYLD in T-cell acute lymphoblastic leukemia.

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy. The understanding of its gene expression regulation and molecular mechanisms still remains elusive. Started from experimentally verified T-ALL-related miRNAs and genes, we obtained 120 feed-forward loops (FFLs) among T-ALL-related genes, miRNAs and TFs through combining target prediction. Afterwards, a T-ALL miRNA and TF co-regulatory network was constructed, and its significance was tested by statistical methods. Four miRNAs in the miR-17-92 cluster and four important genes (CYLD, HOXA9, BCL2L11 and RUNX1) were found as hubs in the network. Particularly, we found that miR-19 was highly expressed in T-ALL patients and cell lines. Ectopic expression of miR-19 represses CYLD expression, while miR-19 inhibitor treatment induces CYLD protein expression and decreases NF-κB expression in the downstream signaling pathway. Thus, miR-19, CYLD and NF-κB form a regulatory FFL, which provides new clues for sustained activation of NF-κB in T-ALL. Taken together, we provided the first miRNA-TF co-regulatory network in T-ALL and proposed a model to demonstrate the roles of miR-19 and CYLD in the T-cell leukemogenesis. This study may provide potential therapeutic targets for T-ALL and shed light on combining bioinformatics with experiments in the research of complex diseases.

Phylogenetic analysis reveals the evolution and diversification of cyclins in eukaryotes.

Cyclins are a family of diverse proteins that play fundamental roles in regulating cell cycle progression in Eukaryotes. Cyclins have been identified from protists to higher Eukaryotes, while its evolution remains vague and the findings turn out controversial. Current classification of cyclins is mainly based on their functions, which may not be appropriate for the systematic evolutionary analysis. In this work, we performed comparative and phylogenetic analysis of cyclins to investigate their classification, origin and evolution. Cyclins originated in early Eukaryotes and evolved from protists to plants, fungi and animals. Based on the phylogenetic tree, cyclins can be divided into three major groups designated as the group I, II and III with different functions and features. Group I plays key roles in cell cycle, group II varied in actions are kingdom (plant, fungi and animal) specific, and group III functions in transcription regulation. Our results showed that the dominating cyclins (group I) diverged from protists to plants, fungi and animals, while divergence of the other cyclins (groups II and III) has occurred in protists. We also discussed the evolutionary relationships between cyclins and cyclin-dependent kinases (CDKs) and found that the cyclins have undergone divergence in protists before the divergence of animal CDKs. This reclassification and evolutionary analysis of cyclins might facilitate understanding eukaryotic cell cycle control.

Exploring the Roles of RNAs in Chromatin Architecture Using Deep Learning.

Recent studies have highlighted the impact of both transcription and transcripts on 3D genome organization, particularly its dynamics. Here, we propose a deep learning framework, called AkitaR, that leverages both genome sequences and genome-wide RNA-DNA interactions to investigate the roles of chromatin-associated RNAs (caRNAs) on genome folding in HFFc6 cells. In order to disentangle the cis- and trans-regulatory roles of caRNAs, we compared models with nascent transcripts, trans-located caRNAs, open chromatin data, or DNA sequence alone. Both nascent transcripts and trans-located caRNAs improved the models' predictions, especially at cell-type-specific genomic regions. Analyses of feature importance scores revealed the contribution of caRNAs at TAD boundaries, chromatin loops and nuclear sub-structures such as nuclear speckles and nucleoli to the models' predictions. Furthermore, we identified non-coding RNAs (ncRNAs) known to regulate chromatin structures, such as MALAT1 and NEAT1, as well as several novel RNAs, RNY5, RPPH1, POLG-DT and THBS1-IT, that might modulate chromatin architecture through trans-interactions in HFFc6. Our modeling also suggests that transcripts from Alus and other repetitive elements may facilitate chromatin interactions through trans R-loop formation. Our findings provide new insights and generate testable hypotheses about the roles of caRNAs in shaping chromatin organization.

Comparing chromatin contact maps at scale: methods and insights.

Comparing chromatin contact maps is an essential step in quantifying how three-dimensional (3D) genome organization shapes development, evolution, and disease. However, no gold standard exists for comparing contact maps, and even simple methods often disagree. In this study, we propose novel comparison methods and evaluate them alongside existing approaches using genome-wide Hi-C data and 22,500 in silico predicted contact maps. We also quantify the robustness of methods to common sources of biological and technical variation, such as boundary size and noise. We find that simple difference-based methods such as mean squared error are suitable for initial screening, but biologically informed methods are necessary to identify why maps diverge and propose specific functional hypotheses. We provide a reference guide, codebase, and benchmark for rapidly comparing chromatin contact maps at scale to enable biological insights into the 3D organization of the genome.

Sequence-based machine learning reveals 3D genome differences between bonobos and chimpanzees.

Phenotypic divergence between closely related species, including bonobos and chimpanzees (genus Pan), is largely driven by variation in gene regulation. The 3D structure of the genome mediates gene expression; however, genome folding differences in Pan are not well understood. Here, we apply machine learning to predict genome-wide 3D genome contact maps from DNA sequence for 56 bonobos and chimpanzees, encompassing all five extant lineages. We use a pairwise approach to estimate 3D divergence between individuals from the resulting contact maps in 4,420 1 Mb genomic windows. While most pairs were similar, ∼17% were predicted to be substantially divergent in genome folding. The most dissimilar maps were largely driven by single individuals with rare variants that produce unique 3D genome folding in a region. We also identified 89 genomic windows where bonobo and chimpanzee contact maps substantially diverged, including several windows harboring genes associated with traits implicated in Pan phenotypic divergence. We used in silico mutagenesis to identify 51 3D-modifying variants in these bonobo-chimpanzee divergent windows, finding that 34 or 66.67% induce genome folding changes via CTCF binding motif disruption. Our results reveal 3D genome variation at the population-level and identify genomic regions where changes in 3D folding may contribute to phenotypic differences in our closest living relatives.

Comparing chromatin contact maps at scale: methods and insights.

Comparing chromatin contact maps is an essential step in quantifying how three-dimensional (3D) genome organization shapes development, evolution, and disease. However, no gold standard exists for comparing contact maps, and even simple methods often disagree. In this study, we propose novel comparison methods and evaluate them alongside existing approaches using genome-wide Hi-C data and 22,500 in silico predicted contact maps. We also quantify the robustness of methods to common sources of biological and technical variation, such as boundary size and noise. We find that simple difference-based methods such as mean squared error are suitable for initial screening, but biologically informed methods are necessary to identify why maps diverge and propose specific functional hypotheses. We provide a reference guide, codebase, and benchmark for rapidly comparing chromatin contact maps at scale to enable biological insights into the 3D organization of the genome.

Machine learning reveals the diversity of human 3D chromatin contact patterns.

Understanding variation in chromatin contact patterns across human populations is critical for interpreting non-coding variants and their ultimate effects on gene expression and phenotypes. However, experimental determination of chromatin contacts at a population-scale is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the diversity 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We then apply this approach to thousands of diverse modern humans and the inferred human-archaic hominin ancestral genome. While patterns of 3D contact divergence genome-wide are qualitatively similar to patterns of sequence divergence, we find that 3D divergence in local 1-megabase genomic windows does not follow sequence divergence. In particular, we identify 392 windows with significantly greater 3D divergence than expected from sequence. Moreover, 26% of genomic windows have rare 3D contact variation observed in a small number of individuals. Using in silico mutagenesis we find that most sequence changes to do not result in changes to 3D chromatin contacts. However in windows with substantial 3D divergence, just one or a few variants can lead to divergent 3D chromatin contacts without the individuals carrying those variants having high sequence divergence. In summary, inferring 3D chromatin contact maps across human populations reveals diverse contact patterns. We anticipate that these genetically diverse maps of 3D chromatin contact will provide a reference for future work on the function and evolution of 3D chromatin contact variation across human populations.

Hypoxia influences the vascular expansion and differentiation of embryonic stem cell cultures through the temporal expression of vascular endothelial growth factor receptors in an ARNT-dependent manner.

Adaptive responses to low oxygen (O(2)) tension (hypoxia) are mediated by the heterodimeric transcription factor hypoxia inducible factor (HIF). When stabilized by hypoxia, bHLH-PAS alpha- and beta- (HIF-1beta or ARNT) HIF complex regulate the expression of multiple genes, including vascular endothelial growth factor (VEGF). To investigate the mechanism(s) through which hypoxia contributes to blood vessel development, we used embryonic stem cell (ESC) differentiation cultures that develop into embryoid bodies (EBs) mimicking early embryonic development. Significantly, low O(2) levels promote vascular development and maturation in wild-type (WT) ESC cultures measured by an increase in the numbers of CD31(+) endothelial cells (ECs) and sprouting angiogenic EBs, but refractory in Arnt(-/-) and Vegf(-/-) ESC cultures. Thus, we propose that hypoxia promotes the production of ECs and contributes to the development and maturation of vessels. Our findings further demonstrate that hypoxia alters the temporal expression of VEGF receptors Flk-1 (VEGFR-2) and the membrane and soluble forms of the antagonistic receptor Flt-1 (VEGFR-1). Moreover, these receptors are distinctly expressed in differentiating Arnt(-/-) and Vegf(-/-) EBs. These results support existing models in which VEGF signaling is tightly regulated during specific biologic events, but also provide important novel evidence that, in response to physiologic hypoxia, HIF mediates a distinct stoichiometric pattern of VEGF receptors throughout EB differentiation analogous to the formation of vascular networks during embryogenesis.

Deep Learning of Sequence Patterns for CCCTC-Binding Factor-Mediated Chromatin Loop Formation.

The three-dimensional (3D) organization of the human genome is of crucial importance for gene regulation, and the CCCTC-binding factor (CTCF) plays an important role in chromatin interactions. However, it is still unclear what sequence patterns in addition to CTCF motif pairs determine chromatin loop formation. To discover the underlying sequence patterns, we have developed a deep learning model, called DeepCTCFLoop, to predict whether a chromatin loop can be formed between a pair of convergent or tandem CTCF motifs using only the DNA sequences of the motifs and their flanking regions. Our results suggest that DeepCTCFLoop can accurately distinguish the CTCF motif pairs forming chromatin loops from the ones not forming loops. It significantly outperforms CTCF-MP, a machine learning model based on word2vec and boosted trees, when using DNA sequences only. Furthermore, we show that DNA motifs binding to several transcription factors, including ZNF384, ZNF263, ASCL1, SP1, and ZEB1, may constitute the complex sequence patterns for CTCF-mediated chromatin loop formation. DeepCTCFLoop has also been applied to disease-associated sequence variants to identify candidates that may disrupt chromatin loop formation. Therefore, our results provide useful information for understanding the mechanism of 3D genome organization and may also help annotate and prioritize the noncoding sequence variants associated with human diseases.

Identification and analysis of consensus RNA motifs binding to the genome regulator CTCF.

CCCTC-binding factor (CTCF) is a key regulator of 3D genome organization and gene expression. Recent studies suggest that RNA transcripts, mostly long non-coding RNAs (lncRNAs), can serve as locus-specific factors to bind and recruit CTCF to the chromatin. However, it remains unclear whether specific sequence patterns are shared by the CTCF-binding RNA sites, and no RNA motif has been reported so far for CTCF binding. In this study, we have developed DeepLncCTCF, a new deep learning model based on a convolutional neural network and a bidirectional long short-term memory network, to discover the RNA recognition patterns of CTCF and identify candidate lncRNAs binding to CTCF. When evaluated on two different datasets, human U2OS dataset and mouse ESC dataset, DeepLncCTCF was shown to be able to accurately predict CTCF-binding RNA sites from nucleotide sequence. By examining the sequence features learned by DeepLncCTCF, we discovered a novel RNA motif with the consensus sequence, AGAUNGGA, for potential CTCF binding in humans. Furthermore, the applicability of DeepLncCTCF was demonstrated by identifying nearly 5000 candidate lncRNAs that might bind to CTCF in the nucleus. Our results provide useful information for understanding the molecular mechanisms of CTCF function in 3D genome organization.

Genomic data mining for functional annotation of human long noncoding RNAs.

Life may have begun in an RNA world, which is supported by increasing evidence of the vital role that RNAs perform in biological systems. In the human genome, most genes actually do not encode proteins; they are noncoding RNA genes. The largest class of noncoding genes is known as long noncoding RNAs (lncRNAs), which are transcripts greater in length than 200 nucleotides, but with no protein-coding capacity. While some lncRNAs have been demonstrated to be key regulators of gene expression and 3D genome organization, most lncRNAs are still uncharacterized. We thus propose several data mining and machine learning approaches for the functional annotation of human lncRNAs by leveraging the vast amount of data from genetic and genomic studies. Recent results from our studies and those of other groups indicate that genomic data mining can give insights into lncRNA functions and provide valuable information for experimental studies of candidate lncRNAs associated with human disease.

MHC class I chain-related A: Polymorphism, regulation and therapeutic value in cancer.

MICA and MICB are stress-induced molecules recognized by NKG2D, one of major activation receptors of natural killer (NK) cells. Upon binding to NKG2D, NKG2D-mediated cytolytic immune response of immune effector cells will be activated against virally infected and tumor cells expressing MICA. In the early oncogenic development, membrane-bound MICA serves as a key signal to recruit anti-tumor immune effectors. Nevertheless, both MICA polymorphic features and its dysregulated expression in evolving tumors have resulted in tumor evasion in various cancer types. Therefore, in order to reconstitute tumor immunosurveilance, it is of great significance that we understand MICA genetics, polymorphisms, mechanisms of MICA-associated tumor escape and molecular/cellular modulation of MICA. In this review, the MICA-associated co-expression networks involving microRNAs (miRNAs) and novel candidate long non-coding RNAs (lncRNAs) were also discussed. Given the current importance in the study of MICA gene, this review paper focuses on the role of MICA in different cancer types, and strategies that we manipulate MICA regulation against tumor proliferation.

The regulation of microRNA expression by DNA methylation in hepatocellular carcinoma.

UNLABELLED: Emerging evidence indicates that microRNAs (miRNAs) are often dysregulated and play a fundamental role in hepatocellular carcinoma (HCC). However, the mechanism underlying miRNA dysregulation is still elusive. In the present study, we adopted an integrated analysis strategy combining data from genome-wide methylated DNA immunoprecipitation chip and miRNA expression microarray to study the regulation of DNA methylation on miRNA expression in HCC. We first characterized 864 differentially methylated regions (DMRs) located in 236 miRNA regions between cancerous and normal hepatocytes in HCC. We observed that the occurrence of miRNA DNA hypomethylation was more common than its hypermethylation while miRNA DNA hypermethylation was usually found in CpG islands. Then through correlation analysis between miRNA methylation and expression data, we identified 10 dysregulated miRNAs under the potential regulation of DNA methylation in HCC. Five of them (miR-148a, miR-375, miR-195, miR-497 and miR-378) were in hypermethylation and down-regulation status, while another five (miR-106b, miR-25, miR-93, miR-23a and miR-27a) were in hypomethylation and up-regulation status in HCC. Bioinformatics analysis showed that miR-148a may form a negative feedback loop with its targets DNMT1 and DNMT3B and the expression of the miR-195/497 cluster may be affected not only by their hypermethylated promoter region but also by their hypermethylated transcription factors NEUROG2 and DDIT3. CONCLUSION: our preliminary data and bioinformatics analysis suggest that DNA methylation plays an important and complex role in the regulation of miRNA expression in HCC, which may provide insights into the pathogenesis of HCC and thus may be used for diagnosis and intervention.