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R-loops are three-stranded structures that harbour an RNA–DNA hybrid and frequently form during transcription. R-loop misregulation is associated with DNA damage, transcription elongation defects, hyper-recombination and genome instability. In contrast to such ‘unscheduled’ R-loops, evidence is mounting that cells harness the presence of RNA–DNA hybrids in scheduled, ‘regulatory’ R-loops to promote DNA transactions, including transcription termination and other steps of gene regulation, telomere stability and DNA repair. R-loops formed by cellular RNAs can regulate histone post-translational modification and may be recognized by dedicated reader proteins. The two-faced nature of R-loops implies that their formation, location and timely removal must be tightly regulated. In this Perspective, we discuss the cellular processes that regulatory R-loops modulate, the regulation of R-loops and the potential differences that may exist between regulatory R-loops and unscheduled R-loops.R-loops (three-stranded RNA–DNA structures) are often associated with transcription defects, DNA damage and genome instability, but ‘regulatory’ R-loops can promote gene regulation, telomere stability and DNA repair. This dual functionality of R-loops requires tight control of their formation, location and timely removal.

R-loops are three-stranded nucleic acid structures that accumulate on chromatin in neurological diseases and cancers and contribute to genome instability. Using a proximity-dependent labeling system, we identified distinct classes of proteins that regulate R-loops in vivo through different mechanisms. We show that ATRX suppresses R-loops by interacting with RNAs and preventing R-loop formation. Our proteomics screen also discovered an unexpected enrichment for proteins containing zinc fingers and homeodomains. One of the most consistently enriched proteins was activity-dependent neuroprotective protein (ADNP), which is frequently mutated in ASD and causal in ADNP syndrome. We find that ADNP resolves R-loops in vitro and that it is necessary to suppress R-loops in vivo at its genomic targets. Furthermore, deletion of the ADNP homeodomain severely diminishes R-loop resolution activity in vitro, results in R-loop accumulation at ADNP targets, and compromises neuronal differentiation. Notably, patient-derived human induced pluripotent stem cells that contain an ADNP syndrome-causing mutation exhibit R-loop and CTCF accumulation at ADNP targets. Our findings point to a specific role for ADNP-mediated R-loop resolution in physiological and pathological neuronal function and, more broadly, to a role for zinc finger and homeodomain proteins in R-loop regulation, with important implications for developmental disorders and cancers. R-loops are three-stranded nucleic acid structures that contribute to genome instability and accumulate in neurological diseases. Here the authors identify R-loop proximal factors, which are enriched for zinc finger and homeodomain proteins, including activity-dependent neuroprotective protein (ADNP). ADNP plays a role in R-loop resolution and loss-of-function leads to R-loop accumulation.

Identifying how R-loops are generated is crucial to know how transcription compromises genome integrity. We show by genome-wide analysis of conditional yeast mutants that the THO transcription complex, prevents R-loop formation in G1 and S-phase, whereas the Sen1 DNA-RNA helicase prevents them only in S-phase. Interestingly, damage accumulates asymmetrically downstream of the replication fork in sen1 cells but symmetrically in the hpr1 THO mutant. Our results indicate that: R-loops form co-transcriptionally independently of DNA replication; that THO is a general and cell-cycle independent safeguard against R-loops, and that Sen1, in contrast to previously believed, is an S-phase-specific R-loop resolvase. These conclusions have important implications for the mechanism of R-loop formation and the role of other factors reported to affect on R-loop homeostasis. Different factors protect cells from harmful R-loops, but the way these are formed is still unclear. Authors show here that R-loops form co-transcriptionally by different manners and cells possess specialized mechanisms to prevent them in each case, a major mechanism being independent of replication and another one being linked to replication.

Tumour hypoxia is associated with poor patient prognosis and therapy resistance. A unique transcriptional response is initiated by hypoxia which includes the rapid activation of numerous transcription factors in a background of reduced global transcription. Here, we show that the biological response to hypoxia includes the accumulation of R-loops and the induction of the RNA/DNA helicase SETX. In the absence of hypoxia-induced SETX, R-loop levels increase, DNA damage accumulates, and DNA replication rates decrease. Therefore, suggesting that, SETX plays a role in protecting cells from DNA damage induced during transcription in hypoxia. Importantly, we propose that the mechanism of SETX induction in hypoxia is reliant on the PERK/ATF4 arm of the unfolded protein response. These data not only highlight the unique cellular response to hypoxia, which includes both a replication stress-dependent DNA damage response and an unfolded protein response but uncover a novel link between these two distinct pathways. Hypoxia induces a change in transcriptional response in mammalian cells. Here the authors reveal a role for the RNA/DNA helicase Senataxin in protecting cells from DNA damage induced during transcription in hypoxia.

As space exploration activities are developing rapidly, spacecraft with large antennas have gained wide acceptance in providing reliable telecommunications and astrophysical observations. In this paper, the dynamic responses and control strategy for a spacecraft with a large hoop-truss antenna under solar flux shock are studied. According to the momentum and angular momentum principle, the rigid-flexible coupled rotational dynamic equation and the translational dynamic equation of the system are established, which include the attitude motion of the rigid main body and the vibration of the antenna. Then, a finite element model of the antenna is established to analytically obtain the corresponding vibration modal shape matrix and natural frequencies. Last, the coupled responses for the attitude motion and vibration are investigated. The corresponding control strategy is designed based on a double-loop structure sliding mode control method. The Lyapunov method is used to demonstrate the global asymptotic stability of the system. Simulations verify the effectiveness of the proposed rigid-flexible coupled model and control strategy.

Selective RNA processing and stabilization (SRPS) facilitates the differential expression of multiple genes in polycistronic operons. However, how the coordinated actions of SRPS‐related enzymes affect stoichiometric regulation remains unclear. In the present study, the first genome‐wide targetome analysis is reported of these enzymes in Escherichia coli, at a single‐nucleotide resolution. A strictly linear relationship is observed between the RNA pyrophosphohydrolase processing ratio and scores assigned to the first three nucleotides of the primary transcript. Stem‐loops associated with PNPase targetomes exhibit a folding free energy that is negatively correlated with the termination ratio of PNPase at the 3′ end. More than one‐tenth of the RNase E processing sites in the 5′‐untranslated regions（UTR） form different stem‐loops that affect ribosome‐binding and translation efficiency. The effectiveness of the SRPS elements is validated using a dual‐fluorescence reporter system. The findings highlight a multi‐layer and quantitative regulatory method for optimizing the stoichiometric expression of genes in bacteria and promoting the application of SRPS in synthetic biology. The selective RNA processing and stabilization (SRPS) facilitate differential expression of multiple genes in polycistronic operons. The discovery presents a multilayer, quantitative, and ubiquitous posttranscriptional regulatory mechanism as well as elements to precisely optimize gene expression stoichiometry. Moreover, this finding suggests a novel strategy of ‘polycistronic operon plus SRPS elements’ to address the challenge of ‘balanced expression of multiple genes in chassis cells’ in synthetic biology.

R loops are three‐stranded nucleic acid structures that form naturally in cells under various conditions, mainly as intermediates during replication or as by‐products during transcription. R loops are involved in the regulation of many important cellular processes, including replication, transcription, centromere stabilization, protection of chromosome ends, or control of telomere length. Unscheduled R loops are linked to many diseases, including cancer, neurodegenerative, or inflammatory disorders. The list of cancer diseases linked to excessive R loop accumulation is growing rapidly. There is currently much debate about the understanding of abnormal R loop formation and its impact on genome instability and cancer development. In this review, we briefly describe the nature of R loops, their formation under physiological and pathological conditions, and the proteins involved in the regulation of R loops. In addition, we emphasize the possible role of the human ribonuclease Dicer, a multi‐tasking protein mostly known for its important role in microRNA biogenesis, in the regulation of R loops. We also discuss the involvement of R loops in cancer development and their potential use as diagnostic biomarkers. Knowledge of the molecular mechanisms underlying R loop dysregulation may significantly improve our understanding of cancer biology and provide new directions for research. R loops play an important role in regulating key cellular processes such as replication, transcription, centromere stabilization, or control of telomere length. However, the unscheduled accumulation of R loops can cause many diseases, including cancer, and neurodegenerative or inflammatory disorders. Interestingly, accumulating data indicate a possible role of human ribonuclease Dicer in the regulation of R loops.

Persistent R-loops (RNA–DNA hybrids with a displaced single-stranded DNA) create DNA damage and lead to genomic instability. The 5′-3′-exoribonuclease 2 (XRN2) degrades RNA to resolve R-loops and promotes transcription termination. Previously, XRN2 was implicated in DNA double strand break (DSB) repair and in resolving replication stress. Here, using tandem affinity purification-mass spectrometry, bioinformatics, and biochemical approaches, we found that XRN2 associates with proteins involved in DNA repair/replication (Ku70-Ku80, DNA-PKcs, PARP1, MCM2-7, PCNA, RPA1) and RNA metabolism (RNA helicases, PRP19, p54(nrb), splicing factors). Novel major pathways linked to XRN2 include cell cycle control of chromosomal replication and DSB repair by non-homologous end joining. Investigating the biological implications of these interactions led us to discover that XRN2 depletion compromised cell survival after additional knockdown of specific DNA repair proteins, including PARP1. XRN2-deficient cells also showed enhanced PARP1 activity. Consistent with concurrent depletion of XRN2 and PARP1 promoting cell death, XRN2-deficient fibroblast and lung cancer cells also demonstrated sensitivity to PARP1 inhibition. XRN2 alterations (mutations, copy number/expression changes) are frequent in cancers. Thus, PARP1 inhibition could target cancers exhibiting XRN2 functional loss. Collectively, our data suggest XRN2’s association with novel protein partners and unravel synthetic lethality between XRN2 depletion and PARP1 inhibition.

Loops are irregular structures which connect two secondary structure elements in proteins. They often play important roles in function, including enzyme reactions and ligand binding. Despite their importance, their structure remains difficult to predict. Most protein loop structure prediction methods sample local loop segments and score them. In particular protein loop classifications and database search methods depend heavily on local properties of loops. Here we examine the distance between a loop's end points (span). We find that the distribution of loop span appears to be independent of the number of residues in the loop, in other words the separation between the anchors of a loop does not increase with an increase in the number of loop residues. Loop span is also unaffected by the secondary structures at the end points, unless the two anchors are part of an anti-parallel beta sheet. As loop span appears to be independent of global properties of the protein we suggest that its distribution can be described by a random fluctuation model based on the Maxwell-Boltzmann distribution. It is believed that the primary difficulty in protein loop structure prediction comes from the number of residues in the loop. Following the idea that loop span is an independent local property, we investigate its effect on protein loop structure prediction and show how normalised span (loop stretch) is related to the structural complexity of loops. Highly contracted loops are more difficult to predict than stretched loops.

A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the dynamic model are very well known and have been validated in previous papers. The control solution is a closed-loop structure whose controller generates predicted control sequences. An efficient way to make optimal predictions is to use a metaheuristic algorithm, the particle swarm optimization algorithm. Even if this metaheuristic is efficient in treating predictions with a very large prediction horizon, the main objective of this paper is to find a tool to reduce the controller’s computational complexity. We propose a soft sensor that gives information used to reduce the interval where the control input’s values are placed in each sampling period. The sensor is based on measurement of the biomass concentration and numerical integration of the process model. The returned information concerns the specific growth rate of microalgae and the biomass yield on light energy. Algorithms, which can be used in real-time implementation, are proposed for all modules involved in the simulation series. Details concerning the implementation of the closed loop, controller, and soft sensor are presented. The simulation results prove that the soft sensor leads to a significant decrease in computational complexity.