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The auxin response factor (ARF) and auxin/indole-3-acetic acid (Aux/IAA) family of genes are central components of the auxin signaling pathway and play essential roles in plant growth and development. Their large-scale analysis and evolutionary trajectory of origin are currently not known. Here, we identified the corresponding ARF and Aux/IAA family members and performed a large-scale analysis by scanning 406 plant genomes. The results showed that the ARF and Aux/IAA gene families originated from charophytes. The ARF family sequences were more conserved than the Aux/IAA family sequences. Dispersed duplications were the common expansion mode of ARF and Aux/IAA families in bryophytes, ferns, and gymnosperms; however, whole-genome duplication was the common expansion mode of the ARF and Aux/IAA families in basal angiosperms, magnoliids, monocots, and dicots. Expression and regulatory network analyses revealed that the Arabidopsis thaliana ARF and Aux/IAA families responded to multiple hormone, biotic, and abiotic stresses. The APETALA2 and serum response factor-transcription factor gene families were commonly enriched in the upstream and downstream genes of the ARF and Aux/IAA gene families. Our study provides a comprehensive overview of the evolutionary trajectories, structural functions, expansion mechanisms, expression patterns, and regulatory networks of these two gene families. [ABSTRACT FROM AUTHOR]

Background: Endothelial cells (ECs) make up the innermost layer throughout the entire vasculature. Their phenotypes and physiological functions are initially regulated by developmental signals and extracellular stimuli. The underlying molecular mechanisms responsible for the diverse phenotypes of ECs from different organs are not well understood. Results: To characterize the transcriptomic and epigenomic landscape in the vascular system, we cataloged gene expression and active histone marks in nine types of human ECs (generating 148 genome-wide datasets) and carried out a comprehensive analysis with chromatin interaction data. We developed a robust procedure for comparative epigenome analysis that circumvents variations at the level of the individual and technical noise derived from sample preparation under various conditions. Through this approach, we identified 3765 EC-specific enhancers, some of which were associated with disease-associated genetic variations. We also identified various candidate marker genes for each EC type. We found that the nine EC types can be divided into two subgroups, corresponding to those with upper-body origins and lower-body origins, based on their epigenomic landscape. Epigenomic variations were highly correlated with gene expression patterns, but also provided unique information. Most of the deferentially expressed genes and enhancers were cooperatively enriched in more than one EC type, suggesting that the distinct combinations of multiple genes play key roles in the diverse phenotypes across EC types. Notably, many homeobox genes were differentially expressed across EC types, and their expression was correlated with the relative position of each organ in the body. This reflects the developmental origins of ECs and their roles in angiogenesis, vasculogenesis and wound healing. Conclusions: This comprehensive analysis of epigenome characterization of EC types reveals diverse transcriptional regulation across human vascular systems. These datasets provide a valuable resource for understanding the vascular system and associated diseases. [ABSTRACT FROM AUTHOR]

Background: Promiscuity in molecular interactions between small-molecules, including drugs, and proteins is widespread. Such unintended interactions can be exploited to suggest drug repurposing possibilities as well as to identify potential molecular mechanisms responsible for observed side-effects. Methods: We perform a large-scale analysis to detect binding-site molecular interaction field similarities between the binding-sites of the primary target of 400 drugs against a dataset of 14082 cavities within 7895 different proteins representing a non-redundant dataset of all proteins with known structure. Statistically-significant cases with high levels of similarities represent potential cases where the drugs that bind the original target may in principle bind the suggested off-target. Such cases are further analysed with docking simulations to verify if indeed the drug could, in principle, bind the off-target. Diverse sources of data are integrated to associated potential cross-reactivity targets with side-effects. Results: We observe that promiscuous binding-sites tend to display higher levels of hydrophobic and aromatic similarities. Focusing on the most statistically significant similarities (Z-score ≥ 3.0) and corroborating docking results (RMSD < 2.0 Å), we find 2923 cases involving 140 unique drugs and 1216 unique potential cross-reactivity protein targets. We highlight a few cases with a potential for drug repurposing (acetazolamide as a chorismate pyruvate lyase inhibitor, raloxifene as a bacterial quorum sensing inhibitor) as well as to explain the side-effects of zanamivir and captopril. A web-interface permits to explore the detected similarities for each of the 400 binding-sites of the primary drug targets and visualise them for the most statistically significant cases. Conclusions: The detection of molecular interaction field similarities provide the opportunity to suggest drug repurposing opportunities as well as to identify potential molecular mechanisms responsible for side-effects. All methods utilized are freely available and can be readily applied to new query binding-sites. All data is freely available and represents an invaluable source to identify further candidates for repurposing and suggest potential mechanisms responsible for side-effects. [ABSTRACT FROM AUTHOR]

Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the analysis and comparison of compounds and activity data, several studies have reported AC predictions over the past decade. Different from typical compound classification tasks, AC predictions must be carried out at the level of compound pairs representing ACs or nonACs. Most AC predictions reported so far have focused on individual methods or comparisons of two or three approaches and only investigated a few compound activity classes (from 2 to 10). Although promising prediction accuracy has been reported in most cases, different system set-ups, AC definitions, methods, and calculation conditions were used, precluding direct comparisons of these studies. Therefore, we have carried out a large-scale AC prediction campaign across 100 activity classes comparing machine learning methods of greatly varying complexity, ranging from pair-based nearest neighbor classifiers and decision tree or kernel methods to deep neural networks. The results of our systematic predictions revealed the level of accuracy that can be expected for AC predictions across many different compound classes. In addition, prediction accuracy did not scale with methodological complexity but was significantly influenced by memorization of compounds shared by different ACs or nonACs. In many instances, limited training data were sufficient for building accurate models using different methods and there was no detectable advantage of deep learning over simpler approaches for AC prediction. On a global scale, support vector machine models performed best, by only small margins compared to others including simple nearest neighbor classifiers., (© 2023. The Author(s).)

Background: Over the years, a number of reports have revealed that Ty1 integration occurs in a 1-kb window upstream of Pol III-transcribed genes with an approximate 80-bp periodicity between each integration hotspot and that this targeting requires active Pol III transcription at the site of integration. However, the molecular bases of Ty1 targeting are still not understood. Findings: The publications by Baller et al. and Mularoni et al. in the April issue of Genome Res. report the first high-throughput sequencing analysis of Ty1 de novo insertion events. Their observations converge to the same conclusion, that Ty1 targets a specific surface of the nucleosome at he H2A/H2B interface. Conclusion: This discovery is important, and should help identifying factor(s) involved in Ty1 targeting. Recent data on transposable elements and retroviruses integration site choice obtained by large-scale analyses indicate that transcription and chromatin structure play an important role in this process. The studies reported in this commentary add a new evidence of the importance of chromatin in integration selectivity that should be of interest for everyone interested in transposable elements integration. [ABSTRACT FROM AUTHOR]