Antidepressants influence somatostatin levels and receptor pharmacology in brain

This study investigated how the administration (acute and chronic) of the antidepressants citalopram and desmethylimipramine (DMI) influences somatostatin (somatotropin release inhibitory factor, SRIF) levels and SRIF receptor density (sst1-5) in rat brain. Animals received either of the following treatments: (1) saline for 21 days (control group), (2) saline for 20 days and citalopram or DMI for 1 day (citalopram or DMI acute groups), (3) citalopram or DMI for 21 days (citalopram or DMI chronic groups). Somatostatin levels were determined by radioimmunoassay. [125I]LTT SRIF-28 binding in the absence (labeling of sst1-5) or presence of 3 nM MK678 (labeling of sst1/4) and [125I]Tyr3 octreotide (labeling of sst2/5) binding with subsequent autoradiography was performed in brains of rats treated with both antidepressants. Somatostatin levels were increased after citalopram, but not DMI administration, in the caudate-putamen, hippocampus, nucleus accumbens, and prefrontal cortex. Autoradiography studies illustrated a significant decrease in receptor density in the superficial and deep layers of frontal cortex (sst2), as well as a significant increase in the CA1 (sst1/4) hippocampal field in brains of chronically citalopram-treated animals. DMI administration increased sst 1/4 receptors levels in the CA1 hippocampal region. These results suggest that citalopram and to a lesser extent DMI influence the function of the somatostatin system in brain regions involved in the emotional, motivational, and cognitive aspects of behavior. © 2009 Nature Publishing Group All rights reserved

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.