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Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal. Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body. We find that few genes are truly ‘housekeeping’, whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles. TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved. Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs. The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses. The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research. A study from the FANTOM consortium using single-molecule cDNA sequencing of transcription start sites and their usage in human and mouse primary cells, cell lines and tissues reveals insights into the specificity and diversity of transcription patterns across different mammalian cell types. Mapping the human transcription FANTOM5 (standing for functional annotation of the mammalian genome 5) is the fifth major stage of a major international collaboration that aims to dissect the transcriptional regulatory networks that define every human cell type. Two Articles in this issue of Nature present some of the project's latest results. The first paper uses the FANTOM5 panel of tissue and primary cell samples to define an atlas of active, in vivo bidirectionally transcribed enhancers across the human body. These authors show that bidirectional capped RNAs are a signature feature of active enhancers and identify more than 40,000 enhancer candidates from over 800 human cell and tissue samples. The enhancer atlas is used to compare regulatory programs between different cell types and identify disease-associated regulatory SNPs, and will be a resource for studies on cell-type-specific enhancers. In the second paper, single-molecule sequencing is used to map human and mouse transcription start sites and their usage in a panel of distinct human and mouse primary cells, cell lines and tissues to produce the most comprehensive mammalian gene expression atlas to date. The data provide a plethora of insights into open reading frames and promoters across different cell types in addition to valuable annotation of mammalian cell-type-specific transcriptomes.

Root hairs play important roles for the acquisition of nutrients, microbe interaction and plant anchorage. In addition, root hairs provide an excellent model system to study cell patterning, differentiation and growth. Arabidopsis root hairs have been thoroughly studied to understand how plants regulate cell fate and growth in response to environmental signals. Accumulating evidence suggests that a multi-layered gene regulatory network is the molecular secret to enable the flexible and adequate response to multiple signals. In this review, we describe the key transcriptional regulators controlling cell fate and/or cell growth of root hairs. We also discuss how plants integrate phytohormonal and environmental signals, such as auxin, ethylene and phosphate availability, and modulate the level of these transcriptional regulators to tune root hair development.

Plants respond to shade-modulated light signals via phytochrome (phy)-induced adaptive changes, termed shade avoidance. To examine the roles of Phytochrome-lnteracting basic helix-loop-helix Factors, PIF1, 3, 4, and 5, in relaying such signals to the transcriptional network, we compared the shade-responsive transcriptome profiles of wild-type and quadruple pif (pifq) mutants. We identify a subset of genes, enriched in transcription factor-encoding loci, that respond rapidly to shade, in a PIF-dependent manner, and contain promoter G-box motifs, known to bind PIFs. These genes are potential direct targets of phy-PIF signaling that regulate the primary downstream transcriptional circuitry. A second subset of PIF-dependent, early response genes, lacking G-box motifs, are enriched for auxin-responsive loci, and are thus potentially indirect targets of phy-PIF signaling, mediating the rapid cell expansion induced by shade. Comparing deetiolation-and shade-responsive transcriptomes identifies another subset of G-box-containing genes that reciprocally display rapid repression and induction in response to light and shade signals. These data define a core set of transcriptional and hormonal processes that appear to be dynamically poised to react rapidly to light-environment changes via perturbations in the mutually antagonistic actions of the phys and PIFs. Comparing the responsiveness of the pifq and triple pif mutants to light and shade confirms that the PIFs act with overlapping redundancy on seedling morphogenesis and transcriptional regulation but that each PIF contributes differentially to these responses.

Background The extensive adaptability of polyploidy wheat is attributed to its complex genome, and accurately controlling heading stage is a prime target in wheat breeding process. Wheat heading stage is an essential growth and development processes since it starts at a crucial point in the transition from vegetative phase to reproductive phase. Main body Heading stage is mainly decided by vernalization, photoperiod, hormone (like gibberellic acid, GA), and earliness per se ( Eps ). As a polyploidy species, common wheat possesses the abundant genetic variation, such as allelic variation, copy number variation etc., which have a strong effect on regulation of wheat growth and development. Therefore, understanding genetic manipulation of heading stage is pivotal for controlling the heading stage in wheat. In this review, we summarized the recent advances in the genetic regulatory mechanisms and abundant variation in genetic diversity controlling heading stage in wheat, as well as the interaction mechanism of different signals and the contribution of different genetic variation. We first summarized the genes involved in vernalization, photoperoid and other signals cross-talk with each other to control wheat heading stage, then the abundant genetic variation related to signal components associated with wheat heading stage was also elaborated in detail. Conclusion Our knowledge of the regulatory network of wheat heading can be used to adjust the duration of the growth phase for the purpose of acclimatizing to different geographical environments.

Calvarial bone is one of the most complex sequences of developmental events in embryology, featuring a uniquely transient, pluripotent stem cell-like population known as the cranial neural crest (CNC). The skull is formed through intramembranous ossification with distinct tissue lineages (e.g. neural crest derived frontal bone and mesoderm derived parietal bone). Due to CNC’s vast cell fate potential, in response to a series of inductive secreted cues including BMP/TGF-β, Wnt, FGF, Notch, Hedgehog, Hippo and PDGF signaling, CNC enables generations of a diverse spectrum of differentiated cell types in vivo such as osteoblasts and chondrocytes at the craniofacial level. In recent years, since the studies from a genetic mouse model and single-cell sequencing, new discoveries are uncovered upon CNC patterning, differentiation, and the contribution to the development of cranial bones. In this review, we summarized the differences upon the potential gene regulatory network to regulate CNC derived osteogenic potential in mouse and human, and highlighted specific functions of genetic molecules from multiple signaling pathways and the crosstalk, transcription factors and epigenetic factors in orchestrating CNC commitment and differentiation into osteogenic mesenchyme and bone formation. Disorders in gene regulatory network in CNC patterning indicate highly close relevance to clinical birth defects and diseases, providing valuable transgenic mouse models for subsequent discoveries in delineating the underlying molecular mechanisms. We also emphasized the potential regenerative alternative through scientific discoveries from CNC patterning and genetic molecules in interfering with or alleviating clinical disorders or diseases, which will be beneficial for the molecular targets to be integrated for novel therapeutic strategies in the clinic.

Summary Reliably generating rice varieties with low glycaemic index (GI) is an important nutritional intervention given the high rates of Type II diabetes incidences in Asia where rice is staple diet. We integrated a genome‐wide association study (GWAS) with a transcriptome‐wide association study (TWAS) to determine the genetic basis of the GI in rice. GWAS utilized 305 re‐sequenced diverse indica panel comprising ~2.4 million single nucleotide polymorphisms (SNPs) enriched in genic regions. A novel association signal was detected at a synonymous SNP in exon 2 of LOC_Os05g03600 for intermediate‐to‐high GI phenotypic variation. Another major hotspot region was predicted for contributing intermediate‐to‐high GI variation, involves 26 genes on chromosome 6 (GI6.1). These set of genes included GBSSI, two hydrolase genes, genes involved in signalling and chromatin modification. The TWAS and methylome sequencing data revealed cis‐acting functionally relevant genetic variants with differential methylation patterns in the hot spot GI6.1 region, narrowing the target to 13 genes. Conversely, the promoter region of GBSSI and its alternative splicing allele (G allele of Wxa) explained the intermediate‐to‐high GI variation. A SNP (C˃T) at exon‐10 was also highlighted in the preceding analyses to influence final viscosity (FV), which is independent of amylose content/GI. The low GI line with GC haplotype confirmed soft texture, while other two low GI lines with GT haplotype were characterized as hard and cohesive. The low GI lines were further confirmed through clinical in vivo studies. Gene regulatory network analysis highlighted the role of the non‐starch polysaccharide pathway in lowering GI.

Background Transcription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Maize has been a model organism for over one hundred years, but little is known about its tissue-specific gene regulation through TFs. In this study, we used a network approach to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, SAM and seed) in maize. We utilized GENIE3, a machine-learning algorithm combined with large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Unlike some other techniques, this approach is not limited by high-quality Position Weighed Matrix (PWM), and can therefore predict GRNs for over 2000 TFs in maize. Results Although many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. Our GRNs were also validated by ChIP-Seq datasets (KN1, FEA4 and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. We also found functional modules in each network by clustering analysis with the MCL algorithm. Conclusions By combining publicly available genome-wide expression data and network analysis, we can uncover GRNs at tissue-level resolution in maize. Since ChIP-Seq and PWMs are still limited in several model organisms, our study provides a uniform platform that can be adapted to any species with genome-wide expression data to construct GRNs. We also present a publicly available database, maize tissue-specific GRN (mGRN, https://www.bio.fsu.edu/mcginnislab/mgrn/ ), for easy querying. All source code and data are available at Github ( https://github.com/timedreamer/maize_tissue-specific_GRN ).

More than three transcription factors often work together to enable cells to respond to various signals. The detection of combinatorial regulation by multiple transcription factors, however, is not only computationally nontrivial but also extremely unlikely because of multiple testing correction. The exponential growth in the number of tests forces us to set a strict limit on the maximum arity. Here, we propose an efficient branch-and-bound algorithm called the “limitless arity multiple-testing procedure” (LAMP) to count the exact number of testable combinations and calibrate the Bonferroni factor to the smallest possible value. LAMP lists significant combinations without any limit, whereas the family-wise error rate is rigorously controlled under the threshold. In the human breast cancer transcriptome, LAMP discovered statistically significant combinations of as many as eight binding motifs. This method may contribute to uncover pathways regulated in a coordinated fashion and find hidden associations in heterogeneous data.

A recent initiative named ‘Crops in silico’ proposes that multi-scale models ‘have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts’ in plant science, particularly directed to crop species. To that end, the group called for ‘a paradigm shift in plant modelling, from largely isolated efforts to a connected community’. ‘Wet’ (experimental) research has been especially productive in plant science, since the adoption of Arabidopsis thaliana as a laboratory model species allowed the emergence of an Arabidopsis research community. Parts of this community invested in ‘dry’ (theoretical) research, under the rubric of Systems Biology. Our past research combined concepts from Systems Biology and crop modelling. Here we outline the approaches that seem most relevant to connected, ‘digital organism’ initiatives. We illustrate the scale of experimental research required, by collecting the kinetic parameter values that are required for a quantitative, dynamic model of a gene regulatory network. By comparison with the Systems Biology Markup Language (SBML) community, we note computational resources and community structures that will help to realize the potential for plant Systems Biology to connect with a broader crop science community.

Abstract Macro-autophagy is a highly conserved catabolic process among eukaryotes affecting macrophages. This work studies the genetic regulatory network involving the interplay between autophagy and macrophage polarization (activation). Autophagy-related genes (Atgs) and differentially expressed genes (DEGs) of macrophage polarization (M1–M2) were predicted, and their regulatory networks constructed. Naïve (M0) mouse bone marrow–derived monocytes were differentiated into M1 and M2a. Validation of the targets of Smad1, LC3A and LC3B, Atg16L1, Atg7, IL-6, CD68, Arg-1, and Vamp7 was performed in vitro. Immunophenotyping by flow cytometry revealed three macrophage phenotypes: M0 (IL-6 + /CD68 +), M1 (IL-6 + /CD68 + /Arg-1 +), and M2a (CD68 + /Arg-1). Confocal microscopy revealed increased autophagy in both M1 and M2a and a significant increase in the pre-autophagosomes size and number. Bafilomycin A increased the expression of CD68 and Arg-1 in all cell lineages. In conclusion, our approach predicted the protein targets mediating the interplay between autophagy and macrophage polarization. We suggest that autophagy reprograms macrophage polarization via CD68, arginase 1, Atg16L1-1, and Atg16L1-3. The current findings provide a foundation for the future use of macrophages in immunotherapy of different autoimmune disorders.