Fuentes, M. | bioRxiv Science

bioRxiv2022

E4F1 COORDINATES PYRUVATE METABOLISM AND THE ACTIVITY OF THE ELONGATOR COMPLEX TO ENSURE PROTEIN TRANSLATION FIDELITY DURING NEURONAL DEVELOPMENT

Pyruvate metabolism defects lead to severe neuropathies such as the Leigh syndrome (LS) but the molecular mechanisms underlying neuronal cell death remain poorly understood. Here, we unravel a connection between pyruvate metabolism and the regulation of the epitranscriptome that is relevant to LS pathogenesis. We identified the transcription factor E4F1 as a key coordinator of AcetylCoenzyme A (AcCoA) production by the pyruvate dehydrogenase complex (PDC) and its utilization as an essential co-factor by the Elongator complex to acetylate tRNAs at the wobble position uridine 34 (U34). E4F1-mediated direct transcriptional regulation of Dlat and Elp3, two genes encoding key subunits of the PDC and of the Elongator complex, respectively, ensured proper translation fidelity and cell survival in the central nervous system (CNS) during mouse embryonic development. Furthermore, analysis of PDH-deficient cells highlighted a crosstalk linking the PDC to ELP3 expression that is perturbed in LS patients.

developmental biology↗

bioRxiv2022

BirdFlow: Learning Seasonal Bird Movements from Citizen Science Data

Large-scale monitoring of seasonal animal movement is integral to science, conservation, and outreach. However, gathering representative movement data across entire species ranges is frequently intractable. Citizen science databases collect millions of animal observations throughout the year, but it is challenging to infer individual movement behavior solely from observational data. We present BO_SCPLOWIRDC_SCPLOWFO_SCPLOWLOWC_SCPLOW, a probabilistic modeling framework that draws on citizen science data from the eBird database to model the population flows of migratory birds. We apply the model to 11 species of North American birds, using GPS and satellite tracking data to tune and evaluate model performance. We show that BO_SCPLOWIRDC_SCPLOWFO_SCPLOWLOWC_SCPLOW models can accurately infer individual seasonal movement behavior directly from eBird relative abundance estimates. Supplementing the model with a sample of tracking data from wild birds improves performance. Researchers can extract a number of behavioral inferences from model results, including migration routes, timing, connectivity, and forecasts. The BO_SCPLOWIRDC_SCPLOWFO_SCPLOWLOWC_SCPLOW framework has the potential to advance migration ecology research, boost insights gained from direct tracking studies, and serve a number of applied functions in conservation, disease surveillance, aviation, and public outreach.

ecology↗