Saladin, B. | bioRxiv Science

bioRxiv2026

Structural basis for repeat-associated non-AUG translation initiation on C9orf72 mRNA

A GGGGCC (G4C2) hexanucleotide repeat expansion in the first intron of C9orf72 is the most common monogenic cause of Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD). This element serves as a template for repeat-associated non-AUG (RAN) translation, which generates neurotoxic dipeptide repeat (DPR) proteins that aggregate in patient brains and contribute to neurodegeneration in model systems. To understand the mechanisms underlying RAN translation initiation, we used cryo-electron microscopy to determine the structure of an in vitro assembled human late-stage C9orf72 translation initiation complex at 3.2 A resolution and a near-native complex purified from human cell lysate at 3.5 A resolution. RAN translation on C9orf72 mRNA initiates predominantly at a near-cognate CUG codon that is located 24 nucleotides upstream of the G4C2 repeat and is flanked by a strong endogenous Kozak sequence. The structures further reveal a direct interaction between the G4C2 repeat and 18S ribosomal RNA expansion segment 9 (ES9S) on the 40S small ribosomal subunit through a kissing-loop interaction. Preventing this RNA-RNA interaction using complementary antisense oligonucleotides significantly reduces RAN translation in vitro and in patient-derived iNeurons, indicating that this structural interaction is functionally important. These findings provide mechanistic insights into RAN translation initiation and highlight a novel potential therapeutic approach to mitigate toxic DPR production in C9orf72-associated neurodegenerative disease.

molecular biology↗

bioRxiv2021

Interannual climate variability data improves niche estimates in species distribution models

AimClimate is an essential element of species niche estimates in many current ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values. Yet, climate can also be described as spatial or temporal variability for variables like temperature or precipitation. Such variability, spatial or temporal, offers additional insights into niche properties. Here, we test to what degree spatial variability and long-term temporal variability in temperature and precipitation improve SDM predictions globally. LocationGlobal. Time period1979-2013 Major taxa studiesMammal, Amphibians, Reptiles MethodsWe use three different SDM algorithms, and a set of 833 amphibian, 779 reptile, and 2211 mammal species to quantify the effect of spatial and temporal climate variability in SDMs. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). ResultsMean performance of SDMs with climatic means as predictors was TSS=0.71 and AUC=0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS=0.74, mean AUC=0.92), as does the inclusion of temporal variability (mean TSS=0.80, mean AUC=0.94). Including both spatial and temporal variability in SDMs shows similarly high TSS and AUC scores. Main conclusionsAccounting for temporal rather than spatial variability in climate improved the SDM prediction especially in exotherm groups such as amphibians and reptiles, while for endotermic mammals no such improvement was observed. These results indicate that more detailed information about temporal climate variability offers a highly promising avenue for improving niche estimates and calls for a new set of standard bioclimatic predictors in SDM research.

ecology↗