Identification of a Disulfide Bridge in Sodium-Coupled Neutral Amino Acid Transporter 2(SNAT2) by Chemical Modification

Sodium-coupled neutral amino acid transporter 2 (SNAT2) belongs to solute carrier 38 (SLC38) family of transporters, which is ubiquitously expressed in mammalian tissues and mediates transport of small, neutral amino acids, exemplified by alanine(Ala, A). Yet structural data on SNAT2, including the relevance of intrinsic cysteine residues on structure and function, is scarce, in spite of its essential roles in many tissues. To better define the potential of intrinsic cysteines to form disulfide bonds in SNAT2, mutagenesis experiments and thiol-specific chemical modifications by N-ethylmaleimide (NEM) and methoxy-polyethylene glycol maleimide (mPEG-Mal, MW 5000) were performed, with or without the reducing regent dithiothreitol (DTT) treatment. Seven single mutant transporters with various cysteine (Cys, C) to alanine (Ala, A) substitutions, and a C245,279A double mutant were introduced to SNAT2 with a hemagglutinin (HA) tag at the C-terminus. The results showed that the cells expressing C245A or C279A were labeled by one equivalent of mPEG-Mal in the presence of DTT, while wild-type or all the other single Cys to Ala mutants were modified by two equivalents of mPEG-Mal. Furthermore, the molecular weight of C245,279A was not changed in the presence or absence of DTT treatment. The results suggest a disulfide bond between Cys245 and Cys279 in SNAT2 which has no effect on cell surface trafficking, as well as transporter function. The proposed disulfide bond may be important to delineate proximity in the extracellular domain of SNAT2 and related proteins

A stable transcription factor complex nucleated by oligomeric AML1-ETO controls leukaemogenesis.

Transcription factors are frequently altered in leukaemia through chromosomal translocation, mutation or aberrant expression(1). AML1-ETO, a fusion protein generated by the t(8;21) translocation in acute myeloid leukaemia, is a transcription factor implicated in both gene repression and activation(2). AML1-ETO oligomerization, mediated by the NHR2 domain, is critical for leukaemogenesis(3-6), making it important to identify co-regulatory factors that 'read' the NHR2 oligomerization and contribute to leukaemogenesis(4). Here we show that, in human leukaemic cells, AML1-ETO resides in and functions through a stable AML1-ETO-containing transcription factor complex (AETFC) that contains several haematopoietic transcription (co)factors. These AETFC components stabilize the complex through multivalent interactions, provide multiple DNA-binding domains for diverse target genes, co-localize genome wide, cooperatively regulate gene expression, and contribute to leukaemogenesis. Within the AETFC complex, AML1-ETO oligomerization is required for a specific interaction between the oligomerized NHR2 domain and a novel NHR2-binding (N2B) motif in E proteins. Crystallographic analysis of the NHR2-N2B complex reveals a unique interaction pattern in which an N2B peptide makes direct contact with side chains of two NHR2 domains as a dimer, providing a novel model of how dimeric/oligomeric transcription factors create a new protein-binding interface through dimerization/oligomerization. Intriguingly, disruption of this interaction by point mutations abrogates AML1-ETO-induced haematopoietic stem/progenitor cell self-renewal and leukaemogenesis. These results reveal new mechanisms of action of AML1-ETO, and provide a potential therapeutic target in t(8;21)-positive acute myeloid leukaemia

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background: The NORMAN Association (https://www.norman-.network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-.network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.Results: The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https:// zenodo.org/communities/norman-.sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox. epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).Conclusions: The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-.network.com/nds/SLE/)

The NORMAN Suspect List Exchange (NORMAN-SLE): Facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background: The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for “suspect screening” lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. Results: The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA’s CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101). Conclusions: The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the “one substance, one assessment” approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/)

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.The NORMAN-SLE project has received funding from the NORMAN Association via its joint proposal of activities. HMT and ELS are supported by the Luxembourg National Research Fund (FNR) for project A18/BM/12341006. ELS, PC, SEH, HPHA, ZW acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036756, project ZeroPM: Zero pollution of persistent, mobile substances. The work of EEB, TC, QL, BAS, PAT, and JZ was supported by the National Center for Biotechnology Information of the National Library of Medicine (NLM), National Institutes of Health (NIH). JOB is the recipient of an NHMRC Emerging Leadership Fellowship (EL1 2009209). KVT and JOB acknowledge the support of the Australian Research Council (DP190102476). The Queensland Alliance for Environmental Health Sciences, The University of Queensland, gratefully acknowledges the financial support of the Queensland Department of Health. NR is supported by a Miguel Servet contract (CP19/00060) from the Instituto de Salud Carlos III, co-financed by the European Union through Fondo Europeo de Desarrollo Regional (FEDER). MM and TR gratefully acknowledge financial support by the German Ministry for Education and Research (BMBF, Bonn) through the project “Persistente mobile organische Chemikalien in der aquatischen Umwelt (PROTECT)” (FKz: 02WRS1495 A/B/E). LiB acknowledges funding through a Research Foundation Flanders (FWO) fellowship (11G1821N). JAP and JMcL acknowledge financial support from the NIH for CCSCompendium (S50 CCSCOMPEND) via grants NIH NIGMS R01GM092218 and NIH NCI 1R03CA222452-01, as well as the Vanderbilt Chemical Biology Interface training program (5T32GM065086-16), plus use of resources of the Center for Innovative Technology (CIT) at Vanderbilt University. TJ was (partly) supported by the Dutch Research Council (NWO), project number 15747. UFZ (TS, MaK, WB) received funding from SOLUTIONS project (European Union’s Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No. 603437). TS, MaK, WB, JPA, RCHV, JJV, JeM and MHL acknowledge HBM4EU (European Union’s Horizon 2020 research and innovation programme under the grant agreement no. 733032). TS acknowledges funding from NFDI4Chem—Chemistry Consortium in the NFDI (supported by the DFG under project number 441958208). TS, MaK, WB and EMLJ acknowledge NaToxAq (European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 722493). S36 and S63 (HPHA, SEH, MN, IS) were funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) Project No. (FKZ) 3716 67 416 0, updates to S36 (HPHA, SEH, MN, IS) by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) Project No. (FKZ) 3719 65 408 0. MiK acknowledges financial support from the EU Cohesion Funds within the project Monitoring and assessment of water body status (No. 310011A366 Phase III). The work related to S60 and S82 was funded by the Swiss Federal Office for the Environment (FOEN), KK and JH acknowledge the input of Kathrin Fenner’s group (Eawag) in compiling transformation products from European pesticides registration dossiers. DSW and YDF were supported by the Canadian Institutes of Health Research and Genome Canada. The work related to S49, S48 and S77 was funded by the MAVA foundation; for S77 also the Valery Foundation (KG, JaM, BG). DML acknowledges National Science Foundation Grant RUI-1306074. YL acknowledges the National Natural Science Foundation of China (Grant No. 22193051 and 21906177), and the Chinese Postdoctoral Science Foundation (Grant No. 2019M650863). WLC acknowledges research project 108C002871 supported by the Environmental Protection Administration, Executive Yuan, R.O.C. Taiwan (Taiwan EPA). JG acknowledges funding from the Swiss Federal Office for the Environment. AJW was funded by the U.S. Environmental Protection Agency. LuB, AC and FH acknowledge the financial support of the Generalitat Valenciana (Research Group of Excellence, Prometeo 2019/040). KN (S89) acknowledges the PhD fellowship through Marie Skłodowska-Curie grant agreement No. 859891 (MSCA-ETN). Exposome-Explorer (S34) was funded by the European Commission projects EXPOsOMICS FP7-KBBE-2012 [308610]; NutriTech FP7-KBBE-2011-5 [289511]; Joint Programming Initiative FOODBALL 2014–17. CP acknowledges grant RYC2020-028901-I funded by MCIN/AEI/1.0.13039/501100011033 and “ESF investing in your future”, and August T Larsson Guest Researcher Programme from the Swedish University of Agricultural Sciences. The work of ML, MaSe, SG, TL and WS creating and filling the STOFF-IDENT database (S2) mostly sponsored by the German Federal Ministry of Education and Research within the RiSKWa program (funding codes 02WRS1273 and 02WRS1354). XT acknowledges The National Food Institute, Technical University of Denmark. MaSch acknowledges funding by the RECETOX research infrastructure (the Czech Ministry of Education, Youth and Sports, LM2018121), the CETOCOEN PLUS project (CZ.02.1.01/0.0/0.0/15_003/0000469), and the CETOCOEN EXCELLENCE Teaming 2 project supported by the Czech ministry of Education, Youth and Sports (No CZ.02.1.01/0.0/0.0/17_043/0009632).Peer reviewe

Study on Improving the Air Quality with Emission Enhanced Control Measures in Beijing during a National Parade Event

Research on the enhanced control and emission-reduction measures to improve air quality during major events could provide data theory and scientific support for air-quality improvement during non-activities. Based on the air-quality data published by the China Environmental Monitoring Station and the meteorological elements and weather conditions released by the China Meteorological Administration, this paper explored the characteristics of air-quality evolution in Beijing from 5 August to 18 September 2015 and the weather situation during the Military Parade. The results showed that: (1) Emission-reduction measures implemented for air quality by Beijing and its surrounding area were induced, and we explored the contribution of these measures to pollutants or AQI in the locality. (2) During the 2015 Military Parade, Beijing was in the front or lower part of the high-pressure system. Due to the strong effect of North or Northeast winds, the weather situation was conducive to the diffusion of pollutants. When before or after the implementation, once the atmospheric diffusion was poor, the pollutants would accumulate gradually. Thus, it can be seen that the weather situation had a great impact on air quality. (3) During the implementation, PM2.5, PM10, NO2 and other pollutants decreased significantly, of which the concentration of PM10 decreased the most, from 109 μg·m−3 down to 34 μg·m−3, and the concentration of PM2.5 decreased by 72.73%. According to the changes between before and during the implementation or during and after the implementation, the concentration of PM10 and PM2.5 increased when the implementation of the emission-reduction measures had been finished, indicating that the enhanced control measures made a great contribution to the emission reduction in particles. (4) In addition, the annual average of AQI in the three years is 87.49, and the average value of a normal year was the average value of 2013 and 2014. The average value of the normal year during the military parade is 64.63, which was 70.40% lower than the average value of AQI during the military parade. The goal of reaching the secondary standard of GB-3095-2012 was achieved, and there was still a long way to go from the primary standard. In a few words, in order to achieve the goal of better air quality throughout the year, all parties still needed to coordinate control and make joint efforts

Study on Improving the Air Quality with Emission Enhanced Control Measures in Beijing during a National Parade Event

Research on the enhanced control and emission-reduction measures to improve air quality during major events could provide data theory and scientific support for air-quality improvement during non-activities. Based on the air-quality data published by the China Environmental Monitoring Station and the meteorological elements and weather conditions released by the China Meteorological Administration, this paper explored the characteristics of air-quality evolution in Beijing from 5 August to 18 September 2015 and the weather situation during the Military Parade. The results showed that: (1) Emission-reduction measures implemented for air quality by Beijing and its surrounding area were induced, and we explored the contribution of these measures to pollutants or AQI in the locality. (2) During the 2015 Military Parade, Beijing was in the front or lower part of the high-pressure system. Due to the strong effect of North or Northeast winds, the weather situation was conducive to the diffusion of pollutants. When before or after the implementation, once the atmospheric diffusion was poor, the pollutants would accumulate gradually. Thus, it can be seen that the weather situation had a great impact on air quality. (3) During the implementation, PM2.5, PM10, NO2 and other pollutants decreased significantly, of which the concentration of PM10 decreased the most, from 109 μg·m−3 down to 34 μg·m−3, and the concentration of PM2.5 decreased by 72.73%. According to the changes between before and during the implementation or during and after the implementation, the concentration of PM10 and PM2.5 increased when the implementation of the emission-reduction measures had been finished, indicating that the enhanced control measures made a great contribution to the emission reduction in particles. (4) In addition, the annual average of AQI in the three years is 87.49, and the average value of a normal year was the average value of 2013 and 2014. The average value of the normal year during the military parade is 64.63, which was 70.40% lower than the average value of AQI during the military parade. The goal of reaching the secondary standard of GB-3095-2012 was achieved, and there was still a long way to go from the primary standard. In a few words, in order to achieve the goal of better air quality throughout the year, all parties still needed to coordinate control and make joint efforts

Histone deacetylase 6 interference protects mice against experimental stroke-induced brain injury via activating Nrf2/HO-1 pathway

Cerebral stroke is a fatal disease with increasing incidence. The study was to investigate the role and mechanism of Histone deacetylase 6 (HDAC6) on experimental stroke-induced brain injury. The recombinant shRNA-HDAC6 or scramble shRNA lentivirus was transfected to ICR mice. Then, the ischemia/reperfusion injury (I/RI) mice were constructed using middle cerebral artery occlusion (MCAO) method. Brain TTC staining was used to determine infarct areas. Serum levels of oxidative stress-related factors were detected by enzyme linked immunosorbnent assay (ELISA). Realtime-qPCR (RT-qPCR) and Western blot were used to respectively detect mRNA and protein levels. HDAC6 was up-regulated in brain I/RI mice (MCAO group), and down-regulated again in MCAO mice transfected with shRNA-HDAC6 (MCAO + shRNA group). The infarct area of the MCAO mice was increased, neurological scores were higher, and serum protein levels of 3-NT, 4-HNE and 8-OHdG were higher. HDAC6 interference attenuated above effects. Though protein levels of Nrf2 and HO-1 in cytoplasm increased slightly in MCAO group, they increased significantly by HDAC6 interference. The protein levels of Nrf2 in cytoblast decreased significantly in MCAO group, and increased markedly by HDAC6 interference. HDAC6 interference protected mice against experimental stroke-induced brain injury via Nrf2/HO-1 pathway

Preliminary findings on the expression of plasma CD63, CD62P, and PAI 1 in patients with acute cerebral infarction

To investigate the expression and clinical significance of lysosomal granule glycoprotein 63 (CD63), P-selectin (CD62P) and endothelial cell plasminogen activator inhibitor (PAI-1) in patients with acute cerebral infarction. A total of 106 patients with acute cerebral infarction (ACI) admitted to our hospital from January to July in 2017 were selected as the patient group; 80 healthy subjects for physical examination in our hospital were selected as the control group. The expression levels of serum CD63, CD62P, and PAI-1 of the subjects were detected. The levels of CD63, CD62P, and PAI-1 in the serum of the patient group were significantly higher than those in the control group. There was a positive correlation between serum CD63 and CD62P (r = 0.672, P  < 0.05) in the patient group. There was a positive correlation between serum CD63 and PAI-1 (r = 0.643, P  < 0.05) in the patient group. There was also a positive correlation between serum CD62P and PAI-1 (r = 0.601, P  < 0.05) in the patient group. Moreover, in other subtypes of cerebral infarction, the expression of CD63, CD62P, and PAI-1 was significantly higher than that of lacunar infarction. CD63, CD62P, and PAI-1 are highly expressed in peripheral blood mononuclear cells (PBMC) and serum of patients with ACI, which may be closely related to the occurrence and development of patients with ACI. These indices may be used as indicators of clinical diagnosis and prognosis in patients with ACI