Transcription factor co-localization patterns affect human cell type-specific gene expression

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.

Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning

The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility between well characterized reference cell types and a query cellular context, and copies over signal of transcription factor binding and modification of histones from reference cell types when chromatin profiles are similar to the query. Epitome achieves state-of-the-art accuracy when predicting transcription factor binding sites on novel cellular contexts and can further improve predictions as more epigenetic signals are collected from both reference cell types and the query cellular context of interest.

Prediction of condition-specific regulatory genes using machine learning

Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5-25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

Revealing transcription factor and histone modification co-localization and dynamics across cell lines by integrating ChIP-seq and RNA-seq data

Background

Interactions among transcription factors (TFs) and histone modifications (HMs) play an important role in the precise regulation of gene expression. The context specificity of those interactions and further its dynamics in normal and disease remains largely unknown. Recent development in genomics technology enables transcription profiling by RNA-seq and protein’s binding profiling by ChIP-seq. Integrative analysis of the two types of data allows us to investigate TFs and HMs interactions both from the genome co-localization and downstream target gene expression.

Results

We propose a integrative pipeline to explore the co-localization of 55 TFs and 11 HMs and its dynamics in human GM12878 and K562 by matched ChIP-seq and RNA-seq data from ENCODE. We classify TFs and HMs into three types based on their binding enrichment around transcription start site (TSS). Then a set of statistical indexes are proposed to characterize the TF-TF and TF-HM co-localizations. We found that Rad21, SMC3, and CTCF co-localized across five cell lines. High resolution Hi-C data in GM12878 shows that they associate most of the Hi-C peak loci with a specific CTCF-motif “anchor” and supports that CTCF, SMC3, and RAD2 co-localization serves important role in 3D chromatin structure. Meanwhile, 17 TF-TF pairs are highly dynamic between GM12878 and K562. We then build SVM models to correlate high and low expression level of target genes with TF binding and HM strength. We found that H3k9ac, H3k27ac, and three TFs (ELF1, TAF1, and POL2) are predictive with the accuracy about 85~92%.

Conclusion

We propose a pipeline to analyze the co-localization of TF and HM and their dynamics across cell lines from ChIP-seq, and investigate their regulatory potency by RNA-seq. The integrative analysis of two level data reveals new insight for the cooperation of TFs and HMs and is helpful in understanding cell line specificity of TF/HM interactions.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5278-5) contains supplementary material, which is available to authorized users.

ASH1L (a histone methyltransferase protein) is a novel candidate globin gene regulator revealed by genetic study of an English family with beta-thalassaemia unlinked to the beta-globin locus

In 1993, we described an English family with beta-thalassaemia that was not linked to the beta-globin locus. Whole genome sequence analyses revealed potential causative mutations in 15 different genes, of which 4 were consistently and uniquely associated with the phenotype in all 7 affected family members, also confirmed by genetic linkage analysis. Of the 4 genes, which are present in a centromeric region of chromosome 1, ASH1L was proposed as causative through functional mRNA knock-down and chromatin-immunoprecipitation studies in human erythroid progenitor cells. Our data suggest a putative role for ASH1L (Trithorax protein) in the regulation of globin genes.

Shared transcription factors contribute to distinct cell fates

Genome-wide transcription factor (TF) binding profiles differ dramatically between cell types. However, not much is known about the relationship between cell-type-specific binding patterns and gene expression. A recent study demonstrated how the same TFs can have functional roles when binding to largely non-overlapping genomic regions in hematopoietic progenitor and mast cells. Cell-type specific binding profiles of shared TFs are therefore not merely the consequence of opportunistic and functionally irrelevant binding to accessible chromatin, but instead have the potential to make meaningful contributions to cell-type specific transcriptional programs.

LRIG and cancer prognosis

BACKGROUND

Optimal treatment decisions for cancer patients require reliable prognostic and predictive information. However, this information is inadequate in many cases. Several recent studies suggest that the leucine-rich repeats and immunoglobulin-like domains (LRIG) genes, transcripts, and proteins have prognostic implications in various cancer types.

MATERIAL AND METHODS

Relevant literature was identified on PubMed using the key words lrig1, lrig2, and lrig3. LRIG mRNA expression in cancer versus normal tissues was investigated using the Oncomine database.

RESULTS

The three human LRIG genes, LRIG1, LRIG2, and LRIG3, encode single-pass transmembrane proteins. LRIG1 is a negative regulator of growth factor signaling that has been shown to function as a tumor suppressor in vitro and in vivo in mice. The functions of LRIG2 and LRIG3 are less well defined. LRIG gene and protein expression are commonly dysregulated in human cancer. In early stage breast cancer, LRIG1 copy number was recently shown to predict early and late relapse in addition to overall survival; in nasopharyngeal carcinoma, loss of LRIG1 is also associated with poor survival. LRIG gene and protein expression have prognostic value in breast cancer, uterine cervical cancer, head-and-neck cancer, glioma, non-small cell lung cancer, prostate cancer, and cutaneous squamous cell carcinoma. In general, expression of LRIG1 and LRIG3 is associated with good survival, whereas expression of LRIG2 is associated with poor survival. Additionally, LRIG1 regulates cellular sensitivity to anti-cancer drugs, which indicates a possible role as a predictive marker.

CONCLUSIONS

LRIG gene statuses and mRNA and protein expression are clinically relevant prognostic indicators in several types of human cancer. We propose that LRIG analyses could become important when making informed and individualized clinical decisions regarding the management of cancer patients.

Robust shifts in S100a9 expression with aging: a novel mechanism for chronic inflammation

The S100a8 and S100a9 genes encode a pro-inflammatory protein (calgranulin) that has been implicated in multiple diseases. However, involvement of S100a8/a9 in the basic mechanisms of intrinsic aging has not been established. In this study, we show that shifts in the abundance of S100a8 and S100a9 mRNA are a robust feature of aging in mammalian tissues, involving a range of cell types including the central nervous system. To identify transcription factors that control S100a9 expression, we performed a large-scale transcriptome analysis of 62 mouse and human cell types. We identified cell type-specific trends, as well as robust associations linking S100a9 coexpression to elevated frequency of ETS family motifs, and in particular, to motifs recognized by the transcription factor SPI/PU.1. Sparse occurrence of SATB1 motifs was also a strong predictor of S100a9 coexpression. These findings offer support for a novel mechanism by which a SPI1/PU.1-S100a9 axis sustains chronic inflammation during aging.

Transcription factor and chromatin features predict genes associated with eQTLs

Cell type-specific gene expression in humans involves complex interactions between regulatory factors and DNA at enhancers and promoters. Mapping studies for expression quantitative trait loci (eQTLs), transcription factors (TFs) and chromatin markers have become widely used tools for identifying gene regulatory elements, but prediction of target genes remains a major challenge. Here, we integrate genome-wide data on TF-binding sites, chromatin markers and functional annotations to predict genes associated with human eQTLs. Using the random forest classifier, we found that genomic proximity plus five TF and chromatin features are able to predict >90% of target genes within 1 megabase of eQTLs. Despite being regularly used to map target genes, proximity is not a good indicator of eQTL targets for genes 150 kilobases away, but insulators, TF co-occurrence, open chromatin and functional similarities between TFs and genes are better indicators. Using all six features in the classifier achieved an area under the specificity and sensitivity curve of 0.91, much better compared with at most 0.75 for using any single feature. We hope this study will not only provide validation of eQTL-mapping studies, but also provide insight into the molecular mechanisms explaining how genetic variation can influence gene expression.

Genome-scale technology driven advances to research into normal and malignant haematopoiesis

Haematopoiesis or blood development has long served as a model system for adult stem cell biology. Moreover, when combined, the various cancers of the blood represent one of the commonest human malignancies. Large numbers of researchers have therefore dedicated their scientific careers to studying haematopoiesis for more than a century. Throughout this period, many new technologies have first been applied towards the study of blood cells, and the research fields of normal and malignant haematopoiesis have also been some of the earliest adopters of genome-scale technologies. This has resulted in significant new insights with implications ranging from basic biological mechanisms to patient diagnosis and prognosis and also produced lessons likely to be relevant for many other areas of biomedical research. This paper discusses the current state of play for a range of genome-scale applications within haemopoiesis research, including gene expression profiling, ChIP-sequencing, genomewide association analysis, and cancer genome sequencing. A concluding outlook section explores likely future areas of progress as well as potential technological and educational bottlenecks.