Revealing Gene Function and Transcription Relationship by Reconstructing Gene-Level Chromatin Interaction

Transcriptional Hubs Within Cliques in Ensemble Hi-C Chromatin Interaction Networks

Chromatin conformation capture technologies permit the study of chromatin spatial organization on a genome-wide scale at a variety of resolutions. Despite the increasing precision and resolution of high-throughput chromatin conformation capture (Hi-C) methods, it remains challenging to conclusively link transcriptional activity to spatial organizational phenomena. We have developed a clique-based approach for analyzing Hi-C data that helps identify chromosomal hotspots that feature considerable enrichment of chromatin annotations for transcriptional start sites and, building on previously published work, show that these chromosomal hotspots are not only significantly enriched in RNA polymerase II binding sites as identified by the ENCODE project, but also identify a noticeable increase in FANTOM5 and GTEx transcription within our identified cliques across a variety of tissue types. From the obtained data, we surmise that our cliques are a suitable method for identifying transcription factories in Hi-C data, and outline further extensions to the method that may make it useful for locating regions of increased transcriptional activity in datasets where in-depth expression or polymerase data may not be available.

Prediction of CTCF loop anchor based on machine learning

Introduction: Various activities in biological cells are affected by three-dimensional genome structure. The insulators play an important role in the organization of higher-order structure. CTCF is a representative of mammalian insulators, which can produce barriers to prevent the continuous extrusion of chromatin loop. As a multifunctional protein, CTCF has tens of thousands of binding sites in the genome, but only a portion of them can be used as anchors of chromatin loops. It is still unclear how cells select the anchor in the process of chromatin looping.

Methods: In this paper, a comparative analysis is performed to investigate the sequence preference and binding strength of anchor and non-anchor CTCF binding sites. Furthermore, a machine learning model based on the CTCF binding intensity and DNA sequence is proposed to predict which CTCF sites can form chromatin loop anchors.

Results: The accuracy of the machine learning model that we constructed for predicting the anchor of the chromatin loop mediated by CTCF reached 0.8646. And we find that the formation of loop anchor is mainly influenced by the CTCF binding strength and binding pattern (which can be interpreted as the binding of different zinc fingers).

Discussion: In conclusion, our results suggest that The CTCF core motif and it’s flanking sequence may be responsible for the binding specificity. This work contributes to understanding the mechanism of loop anchor selection and provides a reference for the prediction of CTCF-mediated chromatin loops.

A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships

BACKGROUND

Chromatin contacts are essential for gene-expression regulation; however, obtaining a high-resolution genome-wide chromatin contact map is still prohibitively expensive owing to large genome sizes and the quadratic scale of pairwise data. Chromosome conformation capture (3C)-based methods such as Hi-C have been extensively used to obtain chromatin contacts. However, since the sparsity of these maps increases with an increase in genomic distance between contacts, long-range or trans-chromatin contacts are especially challenging to sample.

RESULTS

Here, we create a high-density reference genome-wide chromatin contact map using a meta-analytic approach. We integrate 3600 human, 6700 mouse, and 500 fly Hi-C experiments to create species-specific meta-Hi-C chromatin contact maps with 304 billion, 193 billion, and 19 billion contacts in respective species. We validate that meta-Hi-C contact maps are uniquely powered to capture functional chromatin contacts in both cis and trans. We find that while individual dataset Hi-C networks are largely unable to predict any long-range coexpression (median 0.54 AUC), meta-Hi-C networks perform comparably in both cis and trans (0.65 AUC vs 0.64 AUC). Similarly, for long-range expression quantitative trait loci (eQTL), meta-Hi-C contacts outperform all individual Hi-C experiments, providing an improvement over the conventionally used linear genomic distance-based association. Assessing between species, we find patterns of chromatin contact conservation in both cis and trans and strong associations with coexpression even in species for which Hi-C data is lacking.

CONCLUSIONS

We have generated an integrated chromatin interaction network which complements a large number of methodological and analytic approaches focused on improved specificity or interpretation. This high-depth "super-experiment" is surprisingly powerful in capturing long-range functional relationships of chromatin interactions, which are now able to predict coexpression, eQTLs, and cross-species relationships. The meta-Hi-C networks are available at https://labshare.cshl.edu/shares/gillislab/resource/HiC/ .

A c-di-GMP Signaling Cascade Controls Motility, Biofilm Formation, and Virulence in Burkholderia thailandensis

As a key bacterial second messenger, cyclic di-GMP (c-di-GMP) regulates various physiological processes, such as motility, biofilm formation, and virulence. Cellular c-di-GMP levels are regulated by the opposing activities of diguanylate cyclases (DGCs) and phosphodiesterases (PDEs). Beyond that, the enzymatic activities of c-di-GMP metabolizing proteins are controlled by a variety of extracellular signals and intracellular physiological conditions. Here, we report that pdcA (BTH_II2363), pdcB (BTH_II2364), and pdcC (BTH_II2365) are cotranscribed in the same operon and are involved in a regulatory cascade controlling the cellular level of c-di-GMP in Burkholderia thailandensis. The GGDEF domain-containing protein PdcA was found to be a DGC that modulates biofilm formation, motility, and virulence in B. thailandensis. Moreover, the DGC activity of PdcA was inhibited by phosphorylated PdcC, a single-domain response regulator composed of only the phosphoryl-accepting REC domain. The phosphatase PdcB affects the function of PdcA by dephosphorylating PdcC. The observation that homologous operons of pdcABC are widespread among betaproteobacteria and gammaproteobacteria suggests a general mechanism by which the intracellular concentration of c-di-GMP is modulated to coordinate bacterial behavior and virulence. IMPORTANCE The transition from planktonic cells to biofilm cells is a successful strategy adopted by bacteria to survive in diverse environments, while the second messenger c-di-GMP plays an important role in this process. Cellular c-di-GMP levels are mainly controlled by modulating the activity of c-di-GMP-metabolizing proteins via the sensory domains adjacent to their enzymatic domains. However, in most cases how c-di-GMP-metabolizing enzymes are modulated by their sensory domains remains unclear. Here, we reveal a new c-di-GMP signaling cascade that regulates motility, biofilm formation, and virulence in B. thailandensis. While pdcA, pdcB, and pdcC constitute an operon, the phosphorylated PdcC binds the PAS sensory domain of PdcA to inhibit its DGC activity, with PdcB dephosphorylating PdcC to derepress the activity of PdcA. We also show this c-di-GMP regulatory model is widespread in the phylum Proteobacteria. Our study expands the current knowledge of how bacteria regulate intracellular c-di-GMP levels.

Chromatin Interaction Responds to Breast Muscle Development and Intramuscular Fat Deposition Between Chinese Indigenous Chicken and Fast-Growing Broiler

Skeletal muscle development and intramuscular fat (IMF) content, which positively contribute to meat production and quality, are regulated by precisely orchestrated processes. However, changes in three-dimensional chromatin structure and interaction, a newly emerged mediator of gene expression, during the skeletal muscle development and IMF deposition have remained unclear. In the present study, we analyzed the differences in muscle development and IMF content between one-day-old commercial Arbor Acres broiler (AA) and Chinese indigenous Lushi blue-shelled-egg chicken (LS) and performed Hi-C analysis on their breast muscles. Our results indicated that significantly higher IMF content, however remarkably lower muscle fiber diameter was detected in breast muscle of LS chicken compared to that of AA broiler. The chromatin intra-interaction was prior to inter-interaction in both AA and LS chicken, and chromatin inter-interaction was heavily focused on the small and gene-rich chromosomes. For genomic compartmentalization, no significant difference in the number of B type compartments was found, but AA had more A type compartments versus LS. The A/B compartment switching of AA versus LS showed more A to B switching than B to A switching. There were no significant differences in the average sizes and distributions of topologically associating domains (TAD). Additionally, approximately 50% of TAD boundaries were overlapping. The reforming and disappearing events of TAD boundaries were identified between AA and LS chicken breast muscles. Among these, the HMGCR gene was located in the TAD-boundary regions in AA broilers, but in TAD-interior regions in LS chickens, and the IGF2BP3 gene was located in the AA-unique TAD boundaries. Both HMGCR and IGF2BP3 genes exhibited increased mRNA expression in one-day-old AA broiler breast muscles. It was demonstrated that the IGF2BP3 and HMGCR genes regulated by TAD boundary sliding were potential biomarkers for chicken breast muscle development and IMF deposition. Our data not only provide a valuable understanding of higher-order chromatin dynamics during muscle development and lipid accumulation but also reveal new insights into the regulatory mechanisms of muscle development and IMF deposition in chicken.

Application of Sparse Representation in Bioinformatics

Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority. Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices. Moreover, sparse representation has been applied to pattern recognition with good results. Because of its multiple advantages, such as insensitivity to noise, strong robustness, less sensitivity to selected features, and no “overfitting” phenomenon, the application of sparse representation in bioinformatics should be studied further. This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules, low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.

A deep learning model to identify gene expression level using cobinding transcription factor signals

Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.

Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.

Parent-of-origin effects on nuclear chromatin organization and behavior in a Drosophila model for Williams-Beuren Syndrome

Prognosis of neuropsychiatric disorders in progeny requires consideration of individual (1) parent-of-origin effects (POEs) relying on (2) the nerve cell nuclear 3D chromatin architecture and (3) impact of parent-specific miRNAs. Additionally, the shaping of cognitive phenotypes in parents depends on both learning acquisition and forgetting, or memory erasure. These processes are independent and controlled by different signal cascades: the first is cAMPdependent, the second relies on actin remodeling by small GTPase Rac1 - LIMK1 (LIM-kinase 1). Simple experimental model systems such as Drosophila help probe the causes and consequences leading to human neurocognitive pathologies. Recently, we have developed a Drosophila model for Williams-Beuren Syndrome (WBS): a mutant agn^ts3 of the agnostic locus (X:11AB) harboring the dlimk1 gene. The agn^ts3 mutation drastically increases the frequency of ectopic contacts (FEC) in specific regions of intercalary heterochromatin, suppresses learning/memory and affects locomotion. As is shown in this study, the polytene X chromosome bands in reciprocal hybrids between agn^ts3 and the wild type strain Berlin are heterogeneous in modes of FEC regulation depending either on maternal or paternal gene origin. Bioinformatic analysis reveals that FEC between X:11AB and the other X chromosome bands correlates with the occurrence of short (~30 bp) identical DNA fragments partly homologous to Drosophila 372-bp satellite DNA repeat. Although learning acquisition in a conditioned courtship suppression paradigm is similar in hybrids, the middle-term memory formation shows patroclinic inheritance. Seemingly, this depends on changes in miR-974 expression. Several parameters of locomotion demonstrate heterosis. Our data indicate that the agn^ts3 locus is capable of trans-regulating gene activity via POEs on the chromatin nuclear organization, thereby affecting behavior.

Application of Multilayer Network Models in Bioinformatics

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

A computational framework for identifying the transcription factors involved in enhancer-promoter loop formation

The pairwise interaction between transcription factors (TFs) plays an important role in enhancer-promoter loop formation. Although thousands of TFs in the human genome have been found, only a few TF pairs have been demonstrated to be related to loop formation. It is still a challenge to determine which TF pairs could be involved in the enhancer-promoter regulation network. This work describes a computational framework to identify TF pairs in enhancer-promoter regulation. By integrating different levels of data derived from Promoter Capture Hi-C, chromatin immunoprecipitation sequencing (ChIP-seq) of histone marks, RNA-seq, protein-protein interaction (PPI), and TF motif, we identified 361 significant TF pairs and constructed a TF interaction network. From the network, we found several hub-TFs, which may have important roles in the regulation of long-range interactions. Our studies extended TF pairs identified in other experimental and computational approaches. These findings will help the further study of long-range interactions between enhancers and promoters.

Neurochemical regulation of the expression and function of glial fibrillary acidic protein in astrocytes

Glial fibrillary acidic protein (GFAP), a type III intermediate filament, is a marker of mature astrocytes. The expression of GFAP gene is regulated by many transcription factors (TFs), mainly Janus kinase-2/signal transducer and activator of transcription 3 cascade and nuclear factor κ-light-chain-enhancer of activated B cell signaling. GFAP expression is also modulated by protein kinase and other signaling molecules that are elicited by neuronal activity and hormones. Abnormal expression of GFAP proteins occurs in neuroinflammation, neurodegeneration, brain edema-eliciting diseases, traumatic brain injury, psychiatric disorders and others. GFAP, mainly in α-isoform, is the major component of cytoskeleton and the scaffold of astrocytes, which is essential for the maintenance of astrocytic structure and shape. GFAP also has highly morphological plasticity because of its quick changes in assembling and polymerizing states in response to environmental challenges. This plasticity and its corresponding cellular morphological changes endow astrocytes the functions of physical barrier between adjacent neurons and stabilizer of extracellular environment. Moreover, GFAP colocalizes and even molecularly associates with many functional molecules. This feature allows GFAP to function as a platform for direct interactions between different molecules. Last, GFAP involves transportation and localization of other functional proteins and thus serves as a protein transport guide in astrocytes. This guiding role of GFAP involves an elastic retraction and extension cytoskeletal network that couples with GFAP reassembling, transporting, and membrane protein recycling machinery. This paper reviews our current understanding of the expression and functions of GFAP as well as their regulation.

Genetic Contexts Characterize Dynamic Histone Modification Patterns Among Cell Types

Histone modifications play critical roles in mammalian development, regulating chromatin structure and gene expression. Dynamic histone modifications among cell types have been shown to associate with changes in mammalian development. However, how to quantitatively measure the histone modification alterations and how histone modifications vary across cell types under different genetic contexts remain largely unexplored and whether these changes are related to the primary DNA sequence remains limited. Here, we employed an entropy-based method to measure histone modification alterations in six definite genomic regions across five cell types and identified lineage-specific histone modification genes. We observed that histone modification alterations prefer to enrich in 5'-UTR exons, and also in 3'-UTR exons and its downstream. Then we built a model to predict the histone modification patterns from the primary DNA sequence. We found that the frequencies of k-mer sequence compositions are predictive of histone modification patterns, suggesting that the primary DNA sequence correlated with the histone modification alterations among cell types. Additionally, the lineage-specific histone modification genes display a higher conservation and lower GC-content. Together, we performed a systematic analysis for histone modification alterations and demonstrated how to identify genomic region-specific elements of epigenetic and genetic regulation and histone modification patterns across different cell types.