DNA-binding properties of the MADS-domain transcription factor SEPALLATA3 and mutant variants characterized by SELEX-seq

The emerging roles of circRNAs in traits associated with livestock breeding

Many indicators can be used to evaluate the productivity and quality of livestock, such as meat and milk production as well as fat deposition. Meat and milk production are measures of livestock performance, while fat deposition affects the taste and flavor of the meat. The circRNAs, are non-coding RNAs, that are involved in the regulation of all these three traits. We review the functions and mechanisms of circRNAs in muscle and fat development as well as lactation to provide a theoretical basis for circRNA research in animal husbandry. Various phenotypic changes presented in livestock may be produced by different circRNAs. Our current concern is how to use the roles played by circRNAs to our advantage to produce the best possible livestock. Hence, we describe the advantages and disadvantages of knockout techniques for circRNAs. In addition, we also put forward our thoughts regarding the mechanism and network of circRNAs to provide researchers with novel ideas of how molecular biology can help us advance our goals in animal farming. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Cracking the Floral Quartet Code: How Do Multimers of MIKCC-Type MADS-Domain Transcription Factors Recognize Their Target Genes?

MADS-domain transcription factors (MTFs) are involved in the control of many important processes in eukaryotes. They are defined by the presence of a unique and highly conserved DNA-binding domain, the MADS domain. MTFs bind to double-stranded DNA as dimers and recognize specific sequences termed CArG boxes (such as 5'-CC(A/T)6GG-3') and similar sequences that occur hundreds of thousands of times in a typical flowering plant genome. The number of MTF-encoding genes increased by around two orders of magnitude during land plant evolution, resulting in roughly 100 genes in flowering plant genomes. This raises the question as to how dozens of different but highly similar MTFs accurately recognize the cis-regulatory elements of diverse target genes when the core binding sequence (CArG box) occurs at such a high frequency. Besides the usual processes, such as the base and shape readout of individual DNA sequences by dimers of MTFs, an important sublineage of MTFs in plants, termed MIKCC-type MTFs (MC-MTFs), has evolved an additional mechanism to increase the accurate recognition of target genes: the formation of heterotetramers of closely related proteins that bind to two CArG boxes on the same DNA strand involving DNA looping. MC-MTFs control important developmental processes in flowering plants, ranging from root and shoot to flower, fruit and seed development. The way in which MC-MTFs bind to DNA and select their target genes is hence not only of high biological interest, but also of great agronomic and economic importance. In this article, we review the interplay of the different mechanisms of target gene recognition, from the ordinary (base readout) via the extravagant (shape readout) to the idiosyncratic (recognition of the distance and orientation of two CArG boxes by heterotetramers of MC-MTFs). A special focus of our review is on the structural prerequisites of MC-MTFs that enable the specific recognition of target genes.

Floral Homeotic Factors: A Question of Specificity

MADS-domain transcription factors are involved in the control of a multitude of processes in eukaryotes, and in plants, they play particularly important roles during reproductive development. Among the members of this large family of regulatory proteins are the floral organ identity factors, which specify the identities of the different types of floral organs in a combinatorial manner. Much has been learned over the past three decades about the function of these master regulators. For example, it has been shown that they have similar DNA-binding activities and that their genome-wide binding patterns exhibit large overlaps. At the same time, it appears that only a minority of binding events lead to changes in gene expression and that the different floral organ identity factors have distinct sets of target genes. Thus, binding of these transcription factors to the promoters of target genes alone may not be sufficient for their regulation. How these master regulators achieve specificity in a developmental context is currently not well understood. Here, we review what is known about their activities and highlight open questions that need to be addressed to gain more detailed insights into the molecular mechanisms underlying their functions. We discuss evidence for the involvement of cofactors as well as the results from studies on transcription factors in animals that may be instructive for a better understanding of how the floral organ identity factors achieve regulatory specificity.

Elucidating the biology of transcription factor-DNA interaction for accurate identification of cis-regulatory elements

Transcription factors (TFs) play a critical role in determining cell fate decisions by integrating developmental and environmental signals through binding to specific cis-regulatory modules and regulating spatio-temporal specificity of gene expression patterns. Precise identification of functional TF binding sites in time and space not only will revolutionize our understanding of regulatory networks governing cell fate decisions but is also instrumental to uncover how genetic variations cause morphological diversity or disease. In this review, we discuss recent advances in mapping TF binding sites and characterizing the various parameters underlying the complexity of binding site recognition by TFs.

Motif models proposing independent and interdependent impacts of nucleotides are related to high and low affinity transcription factor binding sites in Arabidopsis

Position weight matrix (PWM) is the traditional motif model representing the transcription factor (TF) binding sites. It proposes that the positions contribute independently to TFs binding affinity, although this hypothesis does not fit the data perfectly. This explains why PWM hits are missing in a substantial fraction of ChIP-seq peaks. To study various modes of the direct binding of plant TFs, we compiled the benchmark collection of 111 ChIP-seq datasets for Arabidopsis thaliana, and applied the traditional PWM, and two alternative motif models BaMM and SiteGA, proposing the dependencies of the positions. The variation in the stringency of the recognition thresholds for the models proposed that the hits of PWM, BaMM, and SiteGA models are associated with the sites of high/medium, any, and low affinity, respectively. At the medium recognition threshold, about 60% of ChIP-seq peaks contain PWM hits consisting of conserved core consensuses, while BaMM and SiteGA provide hits for an additional 15% of peaks in which a weaker core consensus is compensated through intra-motif dependencies. The presence/absence of these dependencies in the motifs of alternative/traditional models was confirmed by the dependency logo DepLogo visualizing the position-wise partitioning of the alignments of predicted sites. We exemplify the detailed analysis of ChIP-seq profiles for plant TFs CCA1, MYC2, and SEP3. Gene ontology (GO) enrichment analysis revealed that among the three motif models, the SiteGA had the highest portions of genes with the significantly enriched GO terms among all predicted genes. We showed that both alternative motif models provide for traditional PWM greater extensions in predicted sites for TFs MYC2/SEP3 with condition/tissue specific functions, compared to those for TF CCA1 with housekeeping functions. Overall, the combined application of standard and alternative motif models is beneficial to detect various modes of the direct TF-DNA interactions in the maximal portion of ChIP-seq loci.