metagene Profiles Analyses Reveal Regulatory Element's Factor-Specific Recruitment Patterns

A data-driven genome annotation approach for cassava

Genome annotation files play a critical role in dictating the quality of downstream analyses by providing essential predictions for gene positions and structures. These files are pivotal in decoding the complex information encoded within DNA sequences. Here, we generated experimental data resolving RNA 5'- and 3'-ends as well as full-length RNAs for cassava TME12 sticklings in ambient temperature and cold. We used these data to generate genome annotation files using the TranscriptomeReconstructoR (TR) tool. A careful comparison to high-quality genome annotations suggests that our new TR genome annotations identified additional genes, resolved the transcript boundaries more accurately and identified additional RNA isoforms. We enhanced existing cassava genome annotation files with the information from TR that maintained the different transcript models as RNA isoforms. The resultant merged annotation was subsequently utilized for comprehensive analysis. To examine the effects of genome annotation files on gene expression studies, we compared the detection of differentially expressed genes during cold using the same RNA-seq data but alternative genome annotation files. We found that our merged genome annotation that included cold-specific TR gene models identified about twice as many cold-induced genes. These data indicate that environmentally induced genes may be missing in off-the-shelf genome annotation files. In conclusion, TR offers the opportunity to enhance crop genome annotations with implications for the discovery of differentially expressed candidate genes during plant-environment interactions.

MetageneCluster: a Python package for filtering conflicting signal trends in metagene plots

BACKGROUND

Metagene plots provide a visualization of biological signal trends over subsections of the genome and are used to perform high-level analysis of experimental data by aggregating genome-level data to create an average profile. The generation of metagene plots is useful for summarizing the results of many sequencing-based applications. Despite their prevalence and utility, the standard metagene plot is blind to conflicting signals within data. If multiple distinct trends occur, they can interact destructively, creating a plot that does not accurately represent any of the underlying trends.

RESULTS

We present MetageneCluster, a Python tool to generate a collection of representative metagene plots based on k-means clustering of genomic regions of interest. Clustering the data by similarity allows us to identify patterns within the features of interest. We are then able to summarize each pattern present in the data, rather than averaging across the entire feature space. We show that our method performs well when used to identify conflicting signals in real-world genome-level data.

CONCLUSIONS

Overall, MetageneCluster is a user-friendly tool for the creation of metagene plots that capture distinct patterns in underlying sequence data.

Bayesian inference of polymerase dynamics over the exclusion process

Transcription is a complex phenomenon that permits the conversion of genetic information into phenotype by means of an enzyme called RNA polymerase, which erratically moves along and scans the DNA template. We perform Bayesian inference over a paradigmatic mechanistic model of non-equilibrium statistical physics, i.e. the asymmetric exclusion processes in the hydrodynamic limit, assuming a Gaussian process prior for the polymerase progression rate as a latent variable. Our framework allows us to infer the speed of polymerases during transcription given their spatial distribution, while avoiding the explicit inversion of the system's dynamics. The results, which show processing rates strongly varying with genomic position and minor role of traffic-like congestion, may have strong implications for the understanding of gene expression.

Connectome and regulatory hubs of CAGE highly active enhancers

Evidence indicates that enhancers are transcriptionally active. Herein, we investigated transcriptionally active enhancers by using cap analysis of gene expression (CAGE) combined with epigenetic marks and chromatin interactions. We identified CAGE-tag highly active (CHA) enhancers as distant regulatory elements with CAGE-tag ≥ 90th percentile and overlapping with H3K27ac peaks (4.5% of enhancers). CHA enhancers were conserved between mouse and man and were independent from super-enhancers in predicting cell identity with lower P-values. CHA enhancers had increased open chromatin and a higher recruitment of cell-specific transcription factors as well as molecules involved in 3D genome interactions. HiChIP analysis of enhancer-promoter looping indicated that CHA enhancers had a higher density of anchor loops when compared to regular enhancers. A subset of CHA enhancers and promoters characterized by a high density of chromatin loops and forming hub regulatory units were connected to the promoter of immediate early response genes, genes involved in cancer and encoding for transcription factors. Promoter of genes within hub CHA regulatory units were less likely to be paused. CHA enhancers were enriched in gene variants associated with autoimmune disorders and had looping with causal candidate genes as revealed by Mendelian randomization. Hence, CHA enhancers form a dense hierarchical network of chromatin interactions between regulatory elements and genes involved in cell identity and disorders.

GFI1 tethers the NuRD complex to open and transcriptionally active chromatin in myeloid progenitors

Growth factor indepdendent 1 (GFI1) is a SNAG-domain, DNA binding transcriptional repressor which controls myeloid differentiation through molecular mechanisms and co-factors that still remain to be clearly identified. Here we show that GFI1 associates with the chromodomain helicase DNA binding protein 4 (CHD4) and other components of the Nucleosome remodeling and deacetylase (NuRD) complex. In granulo-monocytic precursors, GFI1, CHD4 or GFI1/CHD4 complexes occupy sites enriched for histone marks associated with active transcription suggesting that GFI1 recruits the NuRD complex to target genes regulated by active or bivalent promoters and enhancers. GFI1 and GFI1/CHD4 complexes occupy promoters that are either enriched for IRF1 or SPI1 consensus binding sites, respectively. During neutrophil differentiation, chromatin closure and depletion of H3K4me2 occurs at different degrees depending on whether GFI1, CHD4 or both are present, indicating that GFI1 is more efficient in depleting of H3K4me2 and -me1 marks when associated with CHD4. Our data suggest that GFI1/CHD4 complexes regulate histone modifications differentially to enable regulation of target genes affecting immune response, nucleosome organization or cellular metabolic processes and that both the target gene specificity and the activity of GFI1 during myeloid differentiation depends on the presence of chromatin remodeling complexes.

Helness et al. show that GFI1/CHD4 complexes critically regulate chromatin accessibility and histone modifications to regulate target genes affecting diverse cellular processes in neutrophils. Their results provide further insight into the molecular network operated by GFI1 for neutrophil differentiation programs.

MetaTX: deciphering the distribution of mRNA-related features in the presence of isoform ambiguity, with applications in epitranscriptome analysis

MOTIVATION

The distribution of biological features strongly indicates their functional relevance. Compared to DNA-related features, deciphering the distribution of mRNA-related features is non-trivial due to the existence of isoform ambiguity and compositional diversity of mRNAs.

RESULTS

We propose here a rigorous statistical framework, MetaTX, for deciphering the distribution of mRNA-related features. Through a standardized mRNA model, MetaTX firstly unifies various mRNA transcripts of diverse compositions, and then corrects the isoform ambiguity by incorporating the overall distribution pattern of the features through an EM algorithm. MetaTX was tested on both simulated and real data. Results suggested that MetaTX substantially outperformed existing direct methods on simulated datasets, and that a more informative distribution pattern was produced for all the three datasets tested, which contain N6-Methyladenosine sites generated by different technologies. MetaTX should make a useful tool for studying the distribution and functions of mRNA-related biological features, especially for mRNA modifications such as N6-Methyladenosine.

AVAILABILITY AND IMPLEMENTATION

The MetaTX R package is freely available at GitHub: https://github.com/yue-wang-biomath/MetaTX.1.0.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SOX10-regulated promoter use defines isoform-specific gene expression in Schwann cells

Background

Multicellular organisms adopt various strategies to tailor gene expression to cellular contexts including the employment of multiple promoters (and the associated transcription start sites (TSSs)) at a single locus that encodes distinct gene isoforms. Schwann cells—the myelinating cells of the peripheral nervous system (PNS)—exhibit a specialized gene expression profile directed by the transcription factor SOX10, which is essential for PNS myelination. SOX10 regulates promoter elements associated with unique TSSs and gene isoforms at several target loci, implicating SOX10-mediated, isoform-specific gene expression in Schwann cell function. Here, we report on genome-wide efforts to identify SOX10-regulated promoters and TSSs in Schwann cells to prioritize genes and isoforms for further study.

Results

We performed global TSS analyses and mined previously reported ChIP-seq datasets to assess the activity of SOX10-bound promoters in three models: (i) an adult mammalian nerve; (ii) differentiating primary Schwann cells, and (iii) cultured Schwann cells with ablated SOX10 function. We explored specific characteristics of SOX10-dependent TSSs, which provides confidence in defining them as SOX10 targets. Finally, we performed functional studies to validate our findings at four previously unreported SOX10 target loci: ARPC1A, CHN2, DDR1, and GAS7. These findings suggest roles for the associated SOX10-regulated gene products in PNS myelination.

Conclusions

In sum, we provide comprehensive computational and functional assessments of SOX10-regulated TSS use in Schwann cells. The data presented in this study will stimulate functional studies on the specific mRNA and protein isoforms that SOX10 regulates, which will improve our understanding of myelination in the peripheral nerve.

Gut Microbiota Has a Widespread and Modifiable Effect on Host Gene Regulation

The composition of the gut microbiome has been associated with various aspects of human health, but the mechanism of this interaction is still unclear. We utilized a cellular system to characterize the effect of the microbiome on human gene expression. We showed that some of these changes in expression may be mediated by changes in chromatin accessibility. Furthermore, we validate the role of a specific microbe and show that changes in its abundance can modify the host gene expression response. These results show an important role of gut microbiota in regulating host gene expression and suggest that manipulation of microbiome composition could be useful in future therapies.

Variation in gut microbiome is associated with wellness and disease in humans, and yet the molecular mechanisms by which this variation affects the host are not well understood. A likely mechanism is that of changing gene regulation in interfacing host epithelial cells. Here, we treated colonic epithelial cells with live microbiota from five healthy individuals and quantified induced changes in transcriptional regulation and chromatin accessibility in host cells. We identified over 5,000 host genes that change expression, including 588 distinct associations between specific taxa and host genes. The taxa with the strongest influence on gene expression alter the response of genes associated with complex traits. Using ATAC-seq, we showed that a subset of these changes in gene expression are associated with changes in host chromatin accessibility and transcription factor binding induced by exposure to gut microbiota. We then created a manipulated microbial community with titrated doses of Collinsella, demonstrating that manipulation of the composition of the microbiome under both natural and controlled conditions leads to distinct and predictable gene expression profiles in host cells. Taken together, our results suggest that specific microbes play an important role in regulating expression of individual host genes involved in human complex traits. The ability to fine-tune the expression of host genes by manipulating the microbiome suggests future therapeutic routes.

IMPORTANCE The composition of the gut microbiome has been associated with various aspects of human health, but the mechanism of this interaction is still unclear. We utilized a cellular system to characterize the effect of the microbiome on human gene expression. We showed that some of these changes in expression may be mediated by changes in chromatin accessibility. Furthermore, we validate the role of a specific microbe and show that changes in its abundance can modify the host gene expression response. These results show an important role of gut microbiota in regulating host gene expression and suggest that manipulation of microbiome composition could be useful in future therapies.

Association analysis identifies 65 new breast cancer risk loci

Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

Association of breast cancer risk in BRCA1 and BRCA2 mutation carriers with genetic variants showing differential allelic expression: identification of a modifier of breast cancer risk at locus 11q22.3

PURPOSE

Cis-acting regulatory SNPs resulting in differential allelic expression (DAE) may, in part, explain the underlying phenotypic variation associated with many complex diseases. To investigate whether common variants associated with DAE were involved in breast cancer susceptibility among BRCA1 and BRCA2 mutation carriers, a list of 175 genes was developed based of their involvement in cancer-related pathways.

METHODS

Using data from a genome-wide map of SNPs associated with allelic expression, we assessed the association of ~320 SNPs located in the vicinity of these genes with breast and ovarian cancer risks in 15,252 BRCA1 and 8211 BRCA2 mutation carriers ascertained from 54 studies participating in the Consortium of Investigators of Modifiers of BRCA1/2.

RESULTS

We identified a region on 11q22.3 that is significantly associated with breast cancer risk in BRCA1 mutation carriers (most significant SNP rs228595 p = 7 × 10-6). This association was absent in BRCA2 carriers (p = 0.57). The 11q22.3 region notably encompasses genes such as ACAT1, NPAT, and ATM. Expression quantitative trait loci associations were observed in both normal breast and tumors across this region, namely for ACAT1, ATM, and other genes. In silico analysis revealed some overlap between top risk-associated SNPs and relevant biological features in mammary cell data, which suggests potential functional significance.

CONCLUSION

We identified 11q22.3 as a new modifier locus in BRCA1 carriers. Replication in larger studies using estrogen receptor (ER)-negative or triple-negative (i.e., ER-, progesterone receptor-, and HER2-negative) cases could therefore be helpful to confirm the association of this locus with breast cancer risk.

Association of breast cancer risk with genetic variants showing differential allelic expression: Identification of a novel breast cancer susceptibility locus at 4q21

There are significant inter-individual differences in the levels of gene expression. Through modulation of gene expression, cis-acting variants represent an important source of phenotypic variation. Consequently, cis-regulatory SNPs associated with differential allelic expression are functional candidates for further investigation as disease-causing variants. To investigate whether common variants associated with differential allelic expression were involved in breast cancer susceptibility, a list of genes was established on the basis of their involvement in cancer related pathways and/or mechanisms. Thereafter, using data from a genome-wide map of allelic expression associated SNPs, 313 genetic variants were selected and their association with breast cancer risk was then evaluated in 46,451 breast cancer cases and 42,599 controls of European ancestry ascertained from 41 studies participating in the Breast Cancer Association Consortium. The associations were evaluated with overall breast cancer risk and with estrogen receptor negative and positive disease. One novel breast cancer susceptibility locus on 4q21 (rs11099601) was identified (OR = 1.05, P = 5.6x10-6). rs11099601 lies in a 135 kb linkage disequilibrium block containing several genes, including, HELQ, encoding the protein HEL308 a DNA dependant ATPase and DNA Helicase involved in DNA repair, MRPS18C encoding the Mitochondrial Ribosomal Protein S18C and FAM175A (ABRAXAS), encoding a BRCA1 BRCT domain-interacting protein involved in DNA damage response and double-strand break (DSB) repair. Expression QTL analysis in breast cancer tissue showed rs11099601 to be associated with HELQ (P = 8.28x10-14), MRPS18C (P = 1.94x10-27) and FAM175A (P = 3.83x10-3), explaining about 20%, 14% and 1%, respectively of the variance inexpression of these genes in breast carcinomas.

The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data

BACKGROUND

ChIP-seq and related high-throughput chromatin profilig assays generate ever increasing volumes of highly valuable biological data. To make sense out of it, biologists need versatile, efficient and user-friendly tools for access, visualization and itegrative analysis of such data.

RESULTS

Here we present the ChIP-Seq command line tools and web server, implementing basic algorithms for ChIP-seq data analysis starting with a read alignment file. The tools are optimized for memory-efficiency and speed thus allowing for processing of large data volumes on inexpensive hardware. The web interface provides access to a large database of public data. The ChIP-Seq tools have a modular and interoperable design in that the output from one application can serve as input to another one. Complex and innovative tasks can thus be achieved by running several tools in a cascade.

CONCLUSIONS

The various ChIP-Seq command line tools and web services either complement or compare favorably to related bioinformatics resources in terms of computational efficiency, ease of access to public data and interoperability with other web-based tools. The ChIP-Seq server is accessible at http://ccg.vital-it.ch/chipseq/ .