Background. The bay cat Catopuma badia is endemic to Borneo, whereas its sister species the Asian golden cat Catopuma temminckii is distributed from the Himalayas and southern China through Indochina, Peninsular Malaysia and Sumatra. Based on morphological data, up to five subspecies of the Asian golden cat have been recognized, but a taxonomic assessment, including molecular data and morphological characters, is still lacking. Results. We combined molecular data (whole mitochondrial genomes), morphological data (pelage) and species distribution projections (up to the Late Pleistocene) to infer how environmental changes may have influenced the distribution of these sister species over the past 120 000 years. The molecular analysis was based on sequenced mitogenomes of 3 bay cats and 40 Asian golden cats derived mainly from archival samples. Our molecular data suggested a time of split between the two species approximately 3.16 Ma and revealed very low nucleotide diversity within the Asian golden cat population, which supports recent expansion of the population. Discussion. The low nucleotide diversity suggested a population bottleneck in the Asian golden cat, possibly caused by the eruption of the Toba volcano in Northern Sumatra (approx. 74 kya), followed by a continuous population expansion in the Late Pleistocene/Early Holocene. Species distribution projections, the reconstruction of the demographic history, a genetic isolation-by-distance pattern and a gradual variation of pelage pattern support the hypothesis of a post-Toba population expansion of the Asian golden cat from south China/Indochina to Peninsular Malaysia and Sumatra. Our findings reject the current classification of five subspecies for the Asian golden cat, but instead support either a monotypic species or one comprising two subspecies: (i) the Sunda golden cat, distributed south of the Isthmus of Kra: C. t. temminckii and (ii) Indochinese, Indian, Himalayan and Chinese golden cats, occurring north of the Isthmus: C. t. moormensis.
Galloway-Mowat syndrome (GAMOS) is an autosomal-recessive disease characterized by the combination of early-onset nephrotic syndrome (SRNS) and microcephaly with brain anomalies. Here we identified recessive mutations in OSGEP, TP53RK, TPRKB, and LAGE3, genes encoding the four subunits of the KEOPS complex, in 37 individuals from 32 families with GAMOS. CRISPR-Cas9 knockout in zebrafish and mice recapitulated the human phenotype of primary microcephaly and resulted in early lethality. Knockdown of OSGEP, TP53RK, or TPRKB inhibited cell proliferation, which human mutations did not rescue. Furthermore, knockdown of these genes impaired protein translation, caused endoplasmic reticulum stress, activated DNA-damage-response signaling, and ultimately induced apoptosis. Knockdown of OSGEP or TP53RK induced defects in the actin cytoskeleton and decreased the migration rate of human podocytes, an established intermediate phenotype of SRNS. We thus identified four new monogenic causes of GAMOS, describe a link between KEOPS function and human disease, and delineate potential pathogenic mechanisms.
The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.