Introduction: Medullary cystic kidney disease 2 (MCKD2) and familial juvenile hyperuricaemic nephropathy (FJHN) are both autosomal dominant renal diseases characterised by juvenile onset of hyperuricaemia, gout, and progressive renal failure. Clinical features of both conditions vary in presence and severity. Often definitive diagnosis is possible only after significant pathology has occurred. Genetic linkage studies have localised genes for both conditions to overlapping regions of chromosome 16p11-p13. These clinical and genetic findings suggest that these conditions may be allelic. Aim: To identify the gene and associated mutation(s) responsible for FJHN and MCKD2. Methods: Two large, multigenerational families segregating FJHN were studied by genetic linkage and haplotype analyses to sublocalise the chromosome 16p FJHN gene locus. To permit refinement of the candidate interval and localisation of candidate genes, an integrated physical and genetic map of the candidate region was developed. DNA sequencing of candidate genes was performed to detect mutations in subjects affected with FJHN (three unrelated families) and MCKD2 (one family). Results: We identified four novel uromodulin (UMOD) gene mutations that segregate with the disease phenotype in three families with FJHN and in one family with MCKD2. Conclusion: These data provide the first direct evidence that MCKD2 and FJHN arise from mutation of the UMOD gene and are allelic disorders. UMOD is a GPI anchored glycoprotein and the most abundant protein in normal urine. We postulate that mutation of UMOD disrupts the tertiary structure of UMOD and is responsible for the clinical changes of interstitial renal disease, polyuria, and hyperuricaemia found in MCKD2 and FJHN.

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Ethylene has been regarded as a stress hormone to regulate myriad stress responses. Salinity stress is one of the most serious abiotic stresses limiting plant growth and development. But how ethylene signaling is involved in plant response to salt stress is poorly understood. Here we showed that Arabidopsis plants pretreated with ethylene exhibited enhanced tolerance to salt stress. Gain- and loss-of-function studies demonstrated that EIN3 (ETHYLENE INSENSITIVE 3) and EIL1 (EIN3-LIKE 1), two ethylene-activated transcription factors, are necessary and sufficient for the enhanced salt tolerance. High salinity induced the accumulation of EIN3/EIL1 proteins by promoting the proteasomal degradation of two EIN3/EIL1-targeting F-box proteins, EBF1 and EBF2, in an EIN2-independent manner. Whole-genome transcriptome analysis identified a list of SIED (Salt-Induced and EIN3/EIL1-Dependent) genes that participate in salt stress responses, including several genes encoding reactive oxygen species (ROS) scavengers. We performed a genetic screen for ein3 eil1-like salt-hypersensitive mutants and identified 5 EIN3 direct target genes including a previously unknown gene, SIED1 (At5g22270), which encodes a 93-amino acid polypeptide involved in ROS dismissal. We also found that activation of EIN3 increased peroxidase (POD) activity through the direct transcriptional regulation of PODs expression. Accordingly, ethylene pretreatment or EIN3 activation was able to preclude excess ROS accumulation and increased tolerance to salt stress. Taken together, our study provides new insights into the molecular action of ethylene signaling to enhance plant salt tolerance, and elucidates the transcriptional network of EIN3 in salt stress response.

Background Phages and plasmids are the major components of mobile genetic elements, and fragments from such elements generally co-exist with chromosome-derived fragments in sequenced metagenomic data. However, there is a lack of efficient methods that can simultaneously identify phages and plasmids in metagenomic data, and the existing tools identifying either phages or plasmids have not yet presented satisfactory performance. Findings We present PPR-Meta, a 3-class classifier that allows simultaneous identification of both phage and plasmid fragments from metagenomic assemblies. PPR-Meta consists of several modules for predicting sequences of different lengths. Using deep learning, a novel network architecture, referred to as the Bi-path Convolutional Neural Network, is designed to improve the performance for short fragments. PPR-Meta demonstrates much better performance than currently available similar tools individually for phage or plasmid identification, while testing on both artificial contigs and real metagenomic data. PPR-Meta is freely available via http://cqb.pku.edu.cn/ZhuLab/PPR_Meta or https://github.com/zhenchengfang/PPR-Meta . Conclusions To the best of our knowledge, PPR-Meta is the first tool that can simultaneously identify phage and plasmid fragments efficiently and reliably. The software is optimized and can be easily run on a local PC by non-computer professionals. We developed PPR-Meta to promote the research on mobile genetic elements and horizontal gene transfer.