METTL3 drives telomere targeting of TERRA lncRNA through m6A-dependent R-loop formation: a therapeutic target for ALT-positive neuroblastoma.

Telomerase-negative tumors maintain telomere length by alternative lengthening of telomeres (ALT), but the underlying mechanism behind ALT remains poorly understood. A proportion of aggressive neuroblastoma (NB), particularly relapsed tumors, are positive for ALT (ALT+), suggesting that a better dissection of the ALT mechanism could lead to novel therapeutic opportunities. TERRA, a long non-coding RNA (lncRNA) derived from telomere ends, localizes to telomeres in a R-loop-dependent manner and plays a crucial role in telomere maintenance. Here we present evidence that RNA modification at the N6 position of internal adenosine (m6A) in TERRA by the methyltransferase METTL3 is essential for telomere maintenance in ALT+ cells, and the loss of TERRA m6A/METTL3 results in telomere damage. We observed that m6A modification is abundant in R-loop enriched TERRA, and the m6A-mediated recruitment of hnRNPA2B1 to TERRA is critical for R-loop formation. Our findings suggest that m6A drives telomere targeting of TERRA via R-loops, and this m6A-mediated R-loop formation could be a widespread mechanism employed by other chromatin-interacting lncRNAs. Furthermore, treatment of ALT+ NB cells with a METTL3 inhibitor resulted in compromised telomere targeting of TERRA and accumulation of DNA damage at telomeres, indicating that METTL3 inhibition may represent a therapeutic approach for ALT+ NB.

Global loss of cellular m6A RNA methylation following infection with different SARS-CoV-2 variants.

Insights into host-virus interactions during SARS-CoV-2 infection are needed to understand COVID-19 pathogenesis and may help to guide the design of novel antiviral therapeutics. N 6-Methyladenosine modification (m6A), one of the most abundant cellular RNA modifications, regulates key processes in RNA metabolism during stress response. Gene expression profiles observed postinfection with different SARS-CoV-2 variants show changes in the expression of genes related to RNA catabolism, including m6A readers and erasers. We found that infection with SARS-CoV-2 variants causes a loss of m6A in cellular RNAs, whereas m6A is detected abundantly in viral RNA. METTL3, the m6A methyltransferase, shows an unusual cytoplasmic localization postinfection. The B.1.351 variant has a less-pronounced effect on METTL3 localization and loss of m6A than did the B.1 and B.1.1.7 variants. We also observed a loss of m6A upon SARS-CoV-2 infection in air/liquid interface cultures of human airway epithelia, confirming that m6A loss is characteristic of SARS-CoV-2-infected cells. Further, transcripts with m6A modification are preferentially down-regulated postinfection. Inhibition of the export protein XPO1 results in the restoration of METTL3 localization, recovery of m6A on cellular RNA, and increased mRNA expression. Stress granule formation, which is compromised by SARS-CoV-2 infection, is restored by XPO1 inhibition and accompanied by a reduced viral infection in vitro. Together, our study elucidates how SARS-CoV-2 inhibits the stress response and perturbs cellular gene expression in an m6A-dependent manner.

Tunicamycin Sensitivity-Suppression by High Gene Dosage Reveals New Functions of the Yeast Hog1 MAP Kinase.

In the yeast Saccharomyces cerevisiae, components of the High Osmolarity Glycerol (HOG) pathway are important for the response to diverse stresses including response to endoplasmic reticulum stress (ER stress), which is produced by the accumulation of unfolded proteins in the lumen of this organelle. Accumulation of unfolded proteins may be due to the inhibition of protein N-glycosylation, which can be achieved by treatment with the antibiotic tunicamycin (Tn). In this work we were interested in finding proteins involved in the ER stress response regulated by Hog1, the mitogen activated protein kinase (MAPK) of the HOG pathway. A high gene dosage suppression screening allowed us to identify genes that suppressed the sensitivity to Tn shown by a hog1Δ mutant. The suppressors participate in a limited number of cellular processes, including lipid/carbohydrate biosynthesis and protein glycosylation, vesicle-mediated transport and exocytosis, cell wall organization and biogenesis, and cell detoxification processes. The finding of suppressors Rer2 and Srt1, which participate in the dolichol biosynthesis pathway revealed that the hog1Δ strain has a defective polyprenol metabolism. This work uncovers new genetic and functional interactors of Hog1 and contributes to a better understanding of the participation of this MAPK in the ER stress response.

The SQUAD Method for the Qualitative Modeling of Regulatory Networks.

The wealth of molecular information provided by high-throughput technologies has enhanced the efforts dedicated to the reconstruction of regulatory networks in diverse biological systems. This information, however, has proven to be insufficient for the construction of quantitative models due to the absence of sufficiently accurate measurements of kinetic constants. As a result, there have been efforts to develop methodologies that permit the use of qualitative information about patterns of expression to infer the regulatory networks that generate such patterns. One of these approaches is the SQUAD method, which approximates a Boolean network with the use of a set of ordinary differential equations. The main benefit of the SQUAD method over purely Boolean approaches is the possibility of evaluating the effect of continuous external signals, which are pervasive in biological phenomena. A brief description and code on how to implement this method can be found at the following link: https://github.com/caramirezal/SQUADBookChapter .

A Network Model to Describe the Terminal Differentiation of B Cells.

Terminal differentiation of B cells is an essential process for the humoral immune response in vertebrates and is achieved by the concerted action of several transcription factors in response to antigen recognition and extracellular signals provided by T-helper cells. While there is a wealth of experimental data regarding the molecular and cellular signals involved in this process, there is no general consensus regarding the structure and dynamical properties of the underlying regulatory network controlling this process. We developed a dynamical model of the regulatory network controlling terminal differentiation of B cells. The structure of the network was inferred from experimental data available in the literature, and its dynamical behavior was analyzed by modeling the network both as a discrete and a continuous dynamical systems. The steady states of these models are consistent with the patterns of activation reported for the Naive, GC, Mem, and PC cell types. Moreover, the models are able to describe the patterns of differentiation from the precursor Naive to any of the GC, Mem, or PC cell types in response to a specific set of extracellular signals. We simulated all possible single loss- and gain-of-function mutants, corroborating the importance of Pax5, Bcl6, Bach2, Irf4, and Blimp1 as key regulators of B cell differentiation process. The model is able to represent the directional nature of terminal B cell differentiation and qualitatively describes key differentiation events from a precursor cell to terminally differentiated B cells.

A dynamical model of the regulatory network controlling lymphopoiesis.

Due to the large number of diseases associated to a malfunction of the hematopoietic system, there is an interest in knowing the molecular mechanisms controlling the differentiation of blood cell lineages. However, the structure and dynamical properties of the underlying regulatory network controlling this process is not well understood. This manuscript presents a regulatory network of 81 nodes, representing several types of molecules that regulate each other during the process of lymphopoiesis. The regulatory interactions were inferred mostly from published experimental data. However, 15 out of 159 regulatory interactions are predictions arising from the present study. The network is modelled as a continuous dynamical system, in the form of a set of differential equations. The dynamical behaviour of the model describes the differentiation process from the common lymphocyte precursor (CLP) to several mature B and T cell types; namely, plasma cell (PC), cytotoxic T lymphocyte (CTL), T helper 1 (Th1), Th2, Th17, and T regulatory (Treg) cells. The model qualitatively recapitulates key cellular differentiation events, being able to represent the directional and branched nature of lymphopoiesis, going from a multipotent progenitor to fully differentiated cell types.