Porphyrinoid-Based Photosensitizers for Diagnostic and Therapeutic Applications: An Update

Porphyrin-based molecules are actively studied as dual function theranostics: fluorescence-based imaging for diagnostics and fluorescence-guided therapeutic treatment of cancers. The intrinsic fluorescent and photodynamic properties of the bimodal molecules allows for these theranostic approaches. Several porphyrinoids bearing both hydrophilic and/or hydrophobic units at their periphery have been developed for the aforementioned applications, but better tumor selectivity and high efficacy to destroy tumor cells is always a key setback for their use. Another issue related to their effective clinical use is that, most of these chromophores form aggregates under physiological conditions. Nanomaterials that are known to possess incredible properties that cannot be achieved from their bulk systems can serve as carriers for these chromophores. Porphyrinoids, when conjugated with nanomaterials, can be enabled to perform as multifunctional nanomedicine devices. The integrated properties of these porphyrinoid-nanomaterial conjugated systems make them useful for selective drug delivery, theranostic capabilities, and multimodal bioimaging. This review highlights the use of porphyrins, chlorins, bacteriochlorins, phthalocyanines and naphthalocyanines as well as their multifunctional nanodevices in various biomedical theranostic platforms

Expression Levels of SMAD Specific E3 Ubiquitin Protein Ligase 2 (Smurf2) and its Interacting Partners Show Region-specific Alterations During Brain Aging

Aging occurs due to a combination of several factors, such as telomere attrition, cellular senescence, and stem cell exhaustion. The telomere attrition-dependent cellular senescence is regulated by increased levels of SMAD specific E3 ubiquitin protein ligase 2 (smurf2). With age smurf2 expression increases and Smurf2 protein interacts with several regulatory proteins including, Smad7, Ep300, Yy1, Sirt1, Mdm2, and Tp53, likely affecting its function related to cellular aging. The current study aimed at analyzing smurf2 expression in the aged brain because of its potential regulatory roles in the cellular aging process. Zebrafish were used because like humans they age gradually and their genome has 70% similarity. In the current study, we demonstrated that smurf2 gene and protein expression levels altered in a region-specific manner during the aging process. Also, in both young and old brains, Smurf2 protein was enriched in the cytosol. These results imply that during aging Smurf2 is regulated by several mechanisms including post-translational modifications (PTMs) and complex formation. Also, the expression levels of its interacting partners defined by the STRING database, tp53, mdm2, ep300a, yy1a, smad7, and sirt1, were analyzed. Multivariate analysis indicated that smurf2, ep300a, and sirt1, whose proteins regulate ubiquitination, acetylation, and deacetylation of target proteins including Smad7 and Tp53, showed age- and brain region-dependent patterns. Our data suggest a likely balance between Smurf2- and Mdm2-mediated ubiquitination, and Ep300a-mediated acetylation/Sirt1-mediated deacetylation, which most possibly affects the functionality of other interacting partners in regulating cellular and synaptic aging and ultimately cognitive dysfunction. (C) 2020 IBRO. Published by Elsevier Ltd. 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.