SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand

The identification of conserved syntenic regions enables discovery of predicted locations for orthologous and homeologous genes, evenwhennosuchgeneispresent.Thiscapabilitymeansthatsynteny-basedmethodsarefarmoreeffectivethansequencesimilaritybased methods in identifying true-negatives, a necessity forstudying gene loss and gene transposition. However, the identification of syntenicregionsrequirescomplexanalyseswhichmustberepeatedforpairwisecomparisonsbetweenanytwospecies.Therefore,as the number of published genomes increases, there is a growing demand for scalable, simple-to-use applications to perform comparative genomic analyses that cater to both gene family studies and genome-scale studies. We implemented SynFind, a web-based tool that addresses this need. Given one query genome, SynFind is capable of identifying conserved syntenic regions in any set of targetgenomes.SynFindiscapableofreportingper-geneinformation,usefulforresearchersstudyingspecificgenefamilies,aswellas genome-wide data sets of syntenic gene and predicted gene locations, critical for researchers focused on large-scale genomic analyses. Inference of syntenic homologs provides the basis for correlation of functional changes around genes of interests between related organisms. Deployed on the CoGe online platform, SynFind is connected to the genomic data from over 15,000 organisms from all domains of life as well as supporting multiple releases of the same organism. SynFind makes use of a powerful job execution framework that promises scalability and reproducibility. SynFind can be accessed at http://genomevolution.org/CoGe/SynFind.pl. A video tutorial of SynFind using Phytophthrora as an example is available at http://www.youtube.com/watch?v=2Agczny9Nyc

SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand

The identification of conserved syntenic regions enables discovery of predicted locations for orthologous and homeologous genes, evenwhennosuchgeneispresent.Thiscapabilitymeansthatsynteny-basedmethodsarefarmoreeffectivethansequencesimilaritybased methods in identifying true-negatives, a necessity forstudying gene loss and gene transposition. However, the identification of syntenicregionsrequirescomplexanalyseswhichmustberepeatedforpairwisecomparisonsbetweenanytwospecies.Therefore,as the number of published genomes increases, there is a growing demand for scalable, simple-to-use applications to perform comparative genomic analyses that cater to both gene family studies and genome-scale studies. We implemented SynFind, a web-based tool that addresses this need. Given one query genome, SynFind is capable of identifying conserved syntenic regions in any set of targetgenomes.SynFindiscapableofreportingper-geneinformation,usefulforresearchersstudyingspecificgenefamilies,aswellas genome-wide data sets of syntenic gene and predicted gene locations, critical for researchers focused on large-scale genomic analyses. Inference of syntenic homologs provides the basis for correlation of functional changes around genes of interests between related organisms. Deployed on the CoGe online platform, SynFind is connected to the genomic data from over 15,000 organisms from all domains of life as well as supporting multiple releases of the same organism. SynFind makes use of a powerful job execution framework that promises scalability and reproducibility. SynFind can be accessed at http://genomevolution.org/CoGe/SynFind.pl. A video tutorial of SynFind using Phytophthrora as an example is available at http://www.youtube.com/watch?v=2Agczny9Nyc

Extracellular Tuning of Mitochondrial Respiration Leads to Aortic Aneurysm

Marfan syndrome (MFS) is an autosomal dominant disorder of the connective tissue caused by mutations in the FBN1 (fibrillin-1) gene encoding a large glycoprotein in the extracellular matrix called fibrillin-1. The major complication of this connective disorder is the risk to develop thoracic aortic aneurysm. To date, no effective pharmacologic therapies have been identified for the management of thoracic aortic disease and the only options capable of preventing aneurysm rupture are endovascular repair or open surgery. Here, we have studied the role of mitochondrial dysfunction in the progression of thoracic aortic aneurysm and mitochondrial boosting strategies as a potential treatment to managing aortic aneurysms.Fondo de Investigacion Sanitaria del Instituto de Salud Carlos III (PI16/188, PI19/855), the European Regional D evelopment Fund, and the European Commission through H2020-EU.1.1, European Research Council grant ERC-2016-StG 715322-EndoMitTalk, and Gobierno de Espana SAF2016-80305P. This work was partially supported by Comunidad de Madrid (S2017/BMD 3867 RENIM-CM) and cofinanced by the European Structural and Investment Fund. M.M. is supported by the Miguel Servet Program (CP 19/014, Fundacion de Investigacion del Hospital 12 de Octubr

Extracellular Tuning of Mitochondrial Respiration Leads to Aortic Aneurysm

BACKGROUND: Marfan syndrome (MFS) is an autosomal dominant disorder of the connective tissue caused by mutations in the FBN1 (fibrillin-1) gene encoding a large glycoprotein in the extracellular matrix called fibrillin-1. The major complication of this connective disorder is the risk to develop thoracic aortic aneurysm. To date, no effective pharmacologic therapies have been identified for the management of thoracic aortic disease and the only options capable of preventing aneurysm rupture are endovascular repair or open surgery. Here, we have studied the role of mitochondrial dysfunction in the progression of thoracic aortic aneurysm and mitochondrial boosting strategies as a potential treatment to managing aortic aneurysms. METHODS: Combining transcriptomics and metabolic analysis of aortas from an MFS mouse model (Fbn1(c1039g/+)) and MFS patients, we have identified mitochondrial dysfunction alongside with mtDNA depletion as a new hallmark of aortic aneurysm disease in MFS. To demonstrate the importance of mitochondrial decline in the development of aneurysms, we generated a conditional mouse model with mitochondrial dysfunction specifically in vascular smooth muscle cells (VSMC) by conditional depleting Tfam (mitochondrial transcription factor A; Myh11-Cre(ERT2)Tfam(flox/flox) mice). We used a mouse model of MFS to test for drugs that can revert aortic disease by enhancing Tfam levels and mitochondrial respiration. RESULTS: The main canonical pathways highlighted in the transcriptomic analysis in aortas from Fbn1(c1039g/+) mice were those related to metabolic function, such as mitochondrial dysfunction. Mitochondrial complexes, whose transcription depends on Tfam and mitochondrial DNA content, were reduced in aortas from young Fbn1(c1039g/+) mice. In vitro experiments in Fbn1-silenced VSMCs presented increased lactate production and decreased oxygen consumption. Similar results were found in MFS patients. VSMCs seeded in matrices produced by Fbn1-deficient VSMCs undergo mitochondrial dysfunction. Conditional Tfam-deficient VSMC mice lose their contractile capacity, showed aortic aneurysms, and died prematurely. Restoring mitochondrial metabolism with the NAD precursor nicotinamide riboside rapidly reverses aortic aneurysm in Fbn1(c1039g/+) mice. CONCLUSIONS: Mitochondrial function of VSMCs is controlled by the extracellular matrix and drives the development of aortic aneurysm in Marfan syndrome. Targeting vascular metabolism is a new available therapeutic strategy for managing aortic aneurysms associated with genetic disorders

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.