G-quadruplexes are transcription factor binding hubs in human chromatin

BackgroundThe binding of transcription factors (TF) to genomic targets is critical in the regulation of gene expression. Short, double-stranded DNA sequence motifs are routinely implicated in TF recruitment, but many questions remain on how binding site specificity is governed.ResultsHerein, we reveal a previously unappreciated role for DNA secondary structures as key features for TF recruitment. In a systematic, genome-wide study, we discover that endogenous G-quadruplex secondary structures (G4s) are prevalent TF binding sites in human chromatin. Certain TFs bind G4s with affinities comparable to double-stranded DNA targets. We demonstrate that, in a chromatin context, this binding interaction is competed out with a small molecule. Notably, endogenous G4s are prominent binding sites for a large number of TFs, particularly at promoters of highly expressed genes.ConclusionsOur results reveal a novel non-canonical mechanism for TF binding whereby G4s operate as common binding hubs for many different TFs to promote increased transcription.

PRDM9 directs recombination hotspots to prevent competition with meiotic transcription

PRDM9 drives recombination hotspots in some mammals, including mice and apes. Non-functional orthologs of PRDM9 are present in a wide variety of vertebrates, but why it is functionally maintained in some lineages is not clear. One possible explanation is that PRDM9 plays a role in ensuring that meiosis is successful. During meiosis, available DNA may be a limiting resource given the tight packaging of chromosomes and could lead to competition between two key processes: meiotic transcription and recombination. Here we explore this potential competition and the role that PRDM9 might play in their interaction. Leveraging existing mouse genomic data, we use resampling schemes that simulate shuffled features along the genome and models that account for the rarity of features in the genome, to test if PRDM9 influences interactions between recombination hotspots and meiotic transcription in a whole genome framework. We also explored possible DNA sequence motifs associated to clusters of hotspots not tied to transcription or PRDM9. We find evidence of competition between meiotic transcription and recombination, with PRDM9 appearing to relocate recombination to avoid said conflict. We also find that retrotransposons may be playing a role in directing hotspots in the absence of other factors.

S100A8/S100A9 cytokine acts as a transcriptional coactivator during breast cellular transformation

Cytokines are extracellular proteins that convey messages between cells by interacting with cognate receptors at the cell surface and triggering signaling pathways that alter gene expression and other phenotypes in an autocrine or paracrine manner. Here, we show that the calcium-dependent cytokines S100A8 and S100A9 are recruited to numerous promoters and enhancers in a model of breast cellular transformation. This recruitment is associated with multiple DNA sequence motifs recognized by DNA binding transcription factors that are linked to transcriptional activation and are important for transformation. The cytokines interact with these transcription factors in nuclear extracts, and they activate transcription when artificially recruited to a target promoter. Nuclear-specific expression of S100A8/A9 promotes oncogenic transcription and leads to enhanced breast transformation phenotype. These results suggest that, in addition to its classical cytokine function, S100A8/A9 can act as a transcriptional coactivator.

Genome Wide Analysis of the Transcriptional Profiles in Different Regions of the Developing Rice Grains

Background Rice is an important food source for humans worldwide. Because of its nutritional and agricultural significance, a number of studies addressed various aspects of rice grain development and grain filling. Nevertheless, the molecular processes underlying grain filling and development, and in particular the contributions of different grain tissues to these processes, are not understood. Main Text Using RNA-sequencing, we profiled gene expression activity in grain tissues comprised of cross cells (CC), the nucellar epidermis (NE), ovular vascular trace (OVT), endosperm (EN) and the aleurone layer (AL). These tissues were dissected using laser capture microdissection (LCM) at three distinct grain development stages. The mRNA expression datasets offer comprehensive and new insights into the gene expression patterns in different rice grain tissues and their contributions to grain development. Comparative analysis of the different tissues revealed their similar and/or unique functions, as well as the spatio-temporal regulation of common and tissue-specific genes. The expression patterns of genes encoding hormones and transporters indicate an important role of the OVT tissue in metabolite transport during grain development. Gene co-expression network prediction on OVT-specific genes identified several distinct and common development-specific transcription factors. Further analysis of enriched DNA sequence motifs proximal to OVT-specific genes revealed known and novel DNA sequence motifs relevant to rice grain development. Conclusion Together, the dataset of gene expression in rice grain tissues is a novel and useful resource for further work to dissect the molecular and metabolic processes during rice grain development.

Genome wide analysis of the transcriptional profiles in different regions of the developing rice grains

Background Rice is an important food source for humans worldwide. Because of its nutritional and agricultural significance, a number of studies addressed various aspects of rice grain development and grain filling. Nevertheless, the molecular processes underlying grain filling and development, and in particular in different contributions of grain tissues to these process, are not understood. Main text Using RNA-sequencing, we profiled gene expression activity in grain tissues comprised of cross cells (CC), the nucellar epidermis (NE), ovular vascular trace (OVT), endosperm (EN) and the aleurone layer (AL). These tissues were dissected using laser capture microdissection (LCM) at three distinct grain development stages. The mRNA expression datasets offer comprehensive and new insights into the gene expression patterns in different rice grain tissues and their contributions to grain development. Comparative analysis of the different tissues revealed their similar and/or unique functions, as well as the spatio-temporal regulation of common and tissue-specific genes. The expression patterns of genes encoding hormones and transporters indicate an important role of the OVT tissue in metabolite transport during grain development. Gene co-expression network prediction on OVT-specific genes identified several distinct and common development-specific transcription factors. Further analysis of enriched DNA sequence motifs proximal to OVT-specific genes revealed known and novel DNA sequence motifs relevant to rice grain development. Conclusion Together, the dataset of gene expression in rice grain tissues is a novel and useful resource for further work to dissect the molecular and metabolic processes during rice grain development.

Genome Wide Analysis of the Transcriptional Profiles in Different Regions of the Developing Rice Grains

Background Rice is an important food source for humans worldwide. Because of its nutritional and agricultural significance, a number of studies addressed various aspects of rice grain development and grain filling. Nevertheless, the molecular processes underlying grain filling and development, and in particular in different contributions of grain tissues to these process, are not understood.Main text Using RNA-sequencing, we profiled gene expression activity in grain tissues comprised of cross cells (CC), the nucellar epidermis (NE), ovular vascular trace (OVT), endosperm (EN) and the aleurone layer (AL). These tissues were dissected using laser capture microdissection (LCM) at three distinct grain development stages. The mRNA expression datasets offer comprehensive and new insights into the gene expression patterns in different rice grain tissues and their contributions to grain development. Comparative analysis of the different tissues revealed their similar and/or unique functions, as well as the spatio-temporal regulation of common and tissue-specific genes. The expression patterns of genes encoding hormones and transporters indicate an important role of the OVT tissue in metabolite transport during grain development. Gene co-expression network prediction on OVT-specific genes identified several distinct and common development-specific transcription factors. Further analysis of enriched DNA sequence motifs proximal to OVT-specific genes revealed known and novel DNA sequence motifs relevant to rice grain development.Conclusion Together, the dataset of gene expression in rice grain tissues is a novel and useful resource for further work to dissect the molecular and metabolic processes during rice grain development.

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function

Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which TFs are prone to be affected by a given variant. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their functional effects. Here, we present MAGGIE, a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutation of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared to the state-of-the-art motif analysis approaches. We use MAGGIE to reveal insights into the divergent functions of distinct NF-κB factors in the pro-inflammatory macrophages, showing its promise in discovering novel functions of TFs. The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie.

Unified single-cell analysis of testis gene regulation and pathology in five mouse strains

To fully exploit the potential of single-cell functional genomics in the study of development and disease, robust methods are needed to simplify the analysis of data across samples, time-points and individuals. Here we introduce a model-based factor analysis method, SDA, to analyze a novel 57,600 cell dataset from the testes of wild-type mice and mice with gonadal defects due to disruption of the genes Mlh3, Hormad1, Cul4a or Cnp. By jointly analyzing mutant and wild-type cells we decomposed our data into 46 components that identify novel meiotic gene-regulatory programs, mutant-specific pathological processes, and technical effects, and provide a framework for imputation. We identify, de novo, DNA sequence motifs associated with individual components that define temporally varying modes of gene expression control. Analysis of SDA components also led us to identify a rare population of macrophages within the seminiferous tubules of Mlh3-/- and Hormad1-/- mice, an area typically associated with immune privilege.

Identifying DNA Sequence Motifs of Pdx-1 and NeuroD1 Transcription Factors

Diabetes is a disease reported to be the 8th leading cause of death across the world. Nearly 38 million people worldwide have Type I diabetes caused by a dysfunction of beta cells that impairs insulin production. A better understanding of mechanisms related to gene expression in beta cells might help in the development of novel strategies for the effective treatment of diabetes. Two known transcription factors, Pdx-1 and NeuroD1, are shown to regulate gene expression in beta cells. Recently gene targets that are regulated by both Pdx-1 and NeuroD1 have been identified experimentally [7]. However, the motifs for this set of genes have not been found yet. Here we undertake the task of finding statistically overrepresented motifs in genes regulated by Pdx-1 and NeuroD1. The challenge of this project is to identify statistically significant pairs of motifs: one motif of each pair is for Pdx-1 and the other for NeuroD1. Commonly known motif-finding methods are usually restricted to finding a set of potential candidates, each of which is a single motif.