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      Mapping the Inorganic and Proteomic Differences among Different Types of Human Teeth: A Preliminary Compositional Insight

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          Abstract

          In recent years, studies on mineralized tissues are becoming increasingly popular not only due to the diverse mechanophysical properties of such materials but also because of the growing need to understand the intricate mechanism involved in their assembly and formation. The biochemical mechanism that results in the formation of such hierarchical structures through a well-coordinated accumulation of inorganic and organic components is termed biomineralization. Some prime examples of such tissues in the human body are teeth and bones. Our current study is an attempt to dissect the compositional details of the inorganic and organic components in four major types of human teeth using mass spectrometry-based approaches. We quantified inorganic materials using inductively coupled plasma resonance mass spectrometry (ICP-MS). Differential level of ten different elements, Iron (Fe), Cadmium (Cd), Potassium (K), Sulphur (S), Cobalt (Co), Magnesium (Mg), Manganese (Mn), Zinc (Zn), Aluminum (Al), and Copper (Cu) were quantified across different teeth types. The qualitative and quantitative details of their respective proteomic milieu revealed compositional differences. We found 152 proteins in total tooth protein extract. Differential abundance of proteins in different teeth types were also noted. Further, we were able to find out some significant protein-protein interaction (PPI) backbone through the STRING database. Since this is the first study analyzing the differential details of inorganic and organic counterparts within teeth, this report will pave new directions to the compositional understanding and development of novel in-vitro repair strategies for such biological materials.

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          Most cited references61

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          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            Human health effects of air pollution.

            Hazardous chemicals escape to the environment by a number of natural and/or anthropogenic activities and may cause adverse effects on human health and the environment. Increased combustion of fossil fuels in the last century is responsible for the progressive change in the atmospheric composition. Air pollutants, such as carbon monoxide (CO), sulfur dioxide (SO(2)), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O(3)), heavy metals, and respirable particulate matter (PM2.5 and PM10), differ in their chemical composition, reaction properties, emission, time of disintegration and ability to diffuse in long or short distances. Air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs. It ranges from minor upper respiratory irritation to chronic respiratory and heart disease, lung cancer, acute respiratory infections in children and chronic bronchitis in adults, aggravating pre-existing heart and lung disease, or asthmatic attacks. In addition, short- and long-term exposures have also been linked with premature mortality and reduced life expectancy. These effects of air pollutants on human health and their mechanism of action are briefly discussed.
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              Finding one's way in proteomics: a protein species nomenclature

              Our knowledge of proteins has greatly improved in recent years, driven by new technologies in the fields of molecular biology and proteome research. It has become clear that from a single gene not only one single gene product but many different ones - termed protein species - are generated, all of which may be associated with different functions. Nonetheless, an unambiguous nomenclature for describing individual protein species is still lacking. With the present paper we therefore propose a systematic nomenclature for the comprehensive description of protein species. The protein species nomenclature is flexible and adaptable to every level of knowledge and of experimental data in accordance with the exact chemical composition of individual protein species. As a minimum description the entry name (gene name + species according to the UniProt knowledgebase) can be used, if no analytical data about the target protein species are available.
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                Author and article information

                Journal
                Biomolecules
                Biomolecules
                biomolecules
                Biomolecules
                MDPI
                2218-273X
                11 November 2020
                November 2020
                : 10
                : 11
                : 1540
                Affiliations
                [1 ]Departement of Biophysics, All India Institute of Medical Sciences, New Delhi 110029, India; sr_1997du@ 123456yahoo.com
                [2 ]Louvain Institute of Biomolecular Science, Université Catholique de Louvain, 1348 Ottignies-Louvain-la-Neuve, Belgium; kaushalkbhati@ 123456gmail.com
                [3 ]Department of Biochemistry, Jamia Hamdard University, New Delhi 110062, India; srinivasanalagiri@ 123456gmail.com
                [4 ]Centre for Dental Education and Research (CDER), All India Institute of Medical Sciences, New Delhi 110029, India; ajoyroy@ 123456hotmail.com
                [5 ]Department of Health Sciences, Lulea University of Technology, 97187 Lulea, Sweden; Fredrik.Nikolajeff@ 123456angstrom.uu.se
                Author notes
                [* ]Correspondence: vaibhavsharma.aiims@ 123456gmail.com (V.S.); sarojgupta.k@ 123456gmail.com (S.K.); Tel.: +91-11-6594240 (S.K.)
                Author information
                https://orcid.org/0000-0003-4471-9546
                https://orcid.org/0000-0002-2322-0431
                https://orcid.org/0000-0003-3586-2818
                https://orcid.org/0000-0001-7029-1163
                Article
                biomolecules-10-01540
                10.3390/biom10111540
                7697572
                33187273
                907636d4-5e8d-4e6e-bd39-68babfce36f7
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 22 September 2020
                : 09 November 2020
                Categories
                Article

                tooth proteome,mass spectrometry,teeth composition,inductively coupled plasma resonance mass spectrometry,protein-protein interactions

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