Several types of cancers can divide indefinitely because of their ability to circumvent telomere shortening by mechanisms such as the telomerase-independent, hence Alternative Lengthening of Telomeres (ALT) pathway. The ALT pathway is greatly enhanced in the absence of the ATRX chromatin remodeler. The current study provides evidence that increased amounts of trapped proteins on DNA in the absence of ATRX cause replication forks to collapse, which on telomeres induces ALT. These results may help to better understand tumors with active ALT pathway and may eventually be instrumental in developing new therapeutic strategies.
NAR’s Breakthrough Articles present high-impact studies answering long-standing questions in the field of nucleic acids research and/or opening up new areas and mechanistic hypotheses for investigation. These articles are chosen by the Editors on the recommendation of Editorial Board Members and Referees. Articles are accompanied by a brief synopsis explaining the findings of the paper and where they fit in the broader context of nucleic acids research. They represent the very best papers published at NAR.
Ribosomes are macromolecular machines responsible for protein synthesis in all living beings. Recent studies have shown that ribosomes can be heterogeneous in their structure, possibly leading to a specialized function. Here, we focus on RPL3L, a ribosomal protein expressed exclusively in striated muscles. We find that the deletion of the Rpl3l gene in a mouse model triggers a compensation mechanism, in which the missing RPL3L protein is replaced by its paralog, RPL3. Furthermore, we find that RPL3-containing ribosomes establish closer interactions with mitochondria, cellular organelles responsible for energy production, leading to higher energy production when compared to RPL3L-containing ribosomes. Finally, we show that the RPL3-RPL3L compensation mechanism is also triggered in heart disease conditions, such as hypertrophy and myocardial infarction.
Both human diseases and agricultural traits can be predicted by incorporating phenotypic observations and relationship matrix among individuals in a linear mixed model. Due to the great demand of processing massive data of genotyped individuals, the existing algorithms that require several times of inverse computing on increasingly big dense matrices (e.g., the relationship matrix and the coefficient matrix of mixed model equation) have encountered a bottleneck. Here, we presented a software named ‘HIBLUP’ to address the challenges. Powered by our advanced algorithms (e.g., HE+PCG), elaborate design, and efficient programming, HIBLUP can successfully avoid the inverse computing for any big matrix and compute fastest under the lowest memory, which makes it very promising for genetic evaluation using big genomic data.
