Laia Llovera

Cell death plays an important role in cancer growth and progression, as well as in the efficiency of chemotherapy. Although apoptosis is commonly regarded as the principal mechanism of programmed cell death, it has been increasingly reported that several anticancer agents do not only induce apoptosis but other forms of cell death such as necrosis, autophagy and mitotic catastrophe, as well as the state of permanent loss of proliferative capacity known as senescence. A deeper understanding of what we know about chemotherapy-induced death is rather relevant considering the emerging knowledge of non-apoptotic cell death signaling pathways, and the fact that many tumors have the apoptosis pathway seriously compromised. In this review we examine the effects that various anti-cancer agents have on pathways involved in the different cell death outcomes. Novel and specific anti-cancer agents directed toward members of the cell death signaling pathways are being developed and currently being tested in clinical trials. If we precisely activate or inhibit molecules that mediate the diversity of cell death outcomes, we might succeed in more effective and less toxic chemotherapy.

Abstract RNA modifications influence RNA function and fate, but detecting them in individual molecules remains challenging for most modifications. Here we present a novel methodology to generate training sets and build modification-aware basecalling models. Using this approach, we develop the m 6 ABasecaller , a basecalling model that predicts m 6 A modifications from raw nanopore signals. We validate its accuracy in vitro and in vivo, revealing stable m 6 A modification stoichiometry across isoforms, m 6 A co-occurrence within RNA molecules, and m 6 A-dependent effects on poly(A) tails. Finally, we demonstrate that our method generalizes to other RNA and DNA modifications, paving the path towards future efforts detecting other modifications.

Evolution of vertebrate endemics in oceanic islands follows a predictable pattern, known as the island rule, according to which gigantism arises in originally small-sized species and dwarfism in large ones. Species of extinct insular giant rodents are known from all over the world. In the Canary Islands, two examples of giant rats, † Canariomys bravoi and † Canariomys tamarani , endemic to Tenerife and Gran Canaria, respectively, disappeared soon after human settlement. The highly derived morphological features of these insular endemic rodents hamper the reconstruction of their evolutionary histories. We have retrieved partial nuclear and mitochondrial data from † C. bravoi and used this information to explore its evolutionary affinities. The resulting dated phylogeny confidently places † C. bravoi within the African grass rat clade ( Arvicanthis niloticus ). The estimated divergence time, 650 000 years ago (95% higher posterior densities: 373 000–944 000), points toward an island colonization during the Günz–Mindel interglacial stage. † Canariomys bravoi ancestors would have reached the island via passive rafting and then underwent a yearly increase of mean body mass calculated between 0.0015 g and 0.0023 g; this corresponds to fast evolutionary rates (in darwins (d), ranging from 7.09 d to 2.78 d) that are well above those observed for non-insular mammals.

In recent years, nanopore direct RNA sequencing (DRS) became a valuable tool for studying the epitranscriptome, owing to its ability to detect multiple modifications within the same full-length native RNA molecules. Although RNA modifications can be identified in the form of systematic basecalling “errors” in DRS data sets, N6 -methyladenosine (m 6 A) modifications produce relatively low “errors” compared with other RNA modifications, limiting the applicability of this approach to m 6 A sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully unmodified sequences, increases the “error” signal of m 6 A, leading to enhanced detection and improved sensitivity even at low stoichiometries. Moreover, we find that high-accuracy alternative RNA basecalling models can show up to 97% median basecalling accuracy, outperforming currently available RNA basecalling models, which show 91% median basecalling accuracy. Notably, the use of high-accuracy basecalling models is accompanied by a significant increase in the number of mapped reads—especially in shorter RNA fractions—and increased basecalling error signatures at pseudouridine (Ψ)- and N1 -methylpseudouridine (m 1 Ψ)-modified sites. Overall, our work demonstrates that alternative RNA basecalling models can be used to improve the detection of RNA modifications, read mappability, and basecalling accuracy in nanopore DRS data sets.