Evaluation of SHOX defects in the era of next‐generation sequencing

Short stature homeobox (SHOX) haploinsufficiency is a frequent cause of short stature. Despite advances in sequencing technologies, the identification of SHOX mutations continues to be performed using standard methods, including multiplex ligation‐dependent probe amplification (MLPA) followed by Sanger sequencing. We designed a targeted panel of genes associated with growth impairment, including SHOX genomic and enhancer regions, to improve the resolution of next‐generation sequencing for SHOX analysis. We used two software packages, CONTRA and Nexus Copy Number, in addition to visual analysis to investigate the presence of copy number variants (CNVs). We evaluated 15 patients with previously known SHOX defects, including point mutations, deletions and a duplication, and 77 patients with idiopathic short stature (ISS). The panel was able to confirm all known defects in the validation analysis. During the prospective evaluation, we identified two new partial SHOX deletions (one detected only by visual analysis), including an intragenic deletion not detected by MLPA. Additionally, we were able to determine the breakpoints in four cases. Our results show that the designed panel can be used for the molecular investigation of patients with ISS, and it may even detect CNVs in SHOX and its enhancers, which may be present in a significant fraction of patients.Copy number variants analyses and Sanger sequencing of breakpoint regions in Case 11, which has a heterozygous deletions involving exons 4, 5, and 6a of short stature homeobox (SHOX).Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151254/1/cge13587.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151254/2/CGE_13587-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151254/3/cge13587_am.pd

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.