dataesr/scientific-paragraphs-categorization huggingface dataset:
Harmonisation and FAIRification of HBM studies, needs, challenges and opportunities Systematic harmonisation and FAIRification of HBM (meta)data production through the data value chain can improve all aspects of HBM. First, results from several HBM studies could be compared easier to delineate e. g. , exposure patterns. This refers to challenges for instance regarding differences in the definition of age groups (teenagers e. g. , 10-18 years old or 13-19 years old), lifestyle parameters measured, biological matrices used, and of sampling methods, storage conditions as well as analytical procedures  (Ågerstrand et al. , 2018; Bocato et al. , 2019; Joas et al. , 2012). Moreover, the metadata including information about the studies are not always readily available. Even when such data are accessible, data are often scattered and/or incomplete thereby undermining the proper understanding, interpretation, and final use of the HBM data collected to support chemical risk assessment.
Second, it could facilitate performing combined (meta)analyses of data from different HBM studies, aiming for increased statistical power, to find e. g. , correlations between exposure biomarkers and effect biomarkers to support exposure to outcome assessments  (Boyden and Walnicki, 2021). The use of systematic reviews and (meta)analyses is gaining acceptance in exposure science to transparently synthesize and evaluate a body of scientific evidence to answer research or policy questions  (Hubal, 2019; Wikoff et al. , 2020; Wolffe et al. , 2019). Nevertheless, handling and comparing heterogeneous data generated across multiple scientific disciplines is challenging. This is especially true when a high degree of variability across studies is expected in terms of study aims and designs, population characteristics, exposure assessment procedures, data analysis and reporting  (Burns et al.
, 2019; Goodman et al. , 2019; Hubal, 2019). Third, it would also enable data sharing and data integration which are of particular relevance for decision making in environmental and occupational health policies  (Kromerová and Bencko, 2019; Louro et al. , 2019). It is clear that there is an increasing use purpose for HBM data in compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Currently, the abovementioned opportunities are unfortunately hindered by problems with data comparability due to lack of harmonisation. Based on a survey regarding national practices in risk assessment and risk assessors' views on HBM use in Europe  (Louro et al. , 2019) , the European Human Biomonitoring Initiative HBM4EU 1  recognized that regulatory practices related to HBM still vary across different countries.
HBM data should be collected using harmonised study protocols to a significant extent to facilitate data interpretation  (Fiddicke et al. , 2021). In addition, the need for harmonised coding of substances and metabolites measured and the statistical analysis such as aggregation of individual data to percentiles of a distribution of HBM levels has received particular attention under recent HBM research projects. Harmonised study designs and structured study registries could counter these problems. The HBM community would profit from global sharing of information of existing, new, and planned HBM studies. The authors envisage that any registry system should be globally useable and legislative framework independent, be it national legislation such as in the USA, Canada, Japan, Korea, Germany, France, or regional legislations in the EU or a framework like the OECD.
Given that, we hereby present the concept for a web-based system based on modern IT-technology and independent of any website host requirements as part of an overarching HBM GRF. The HBM GRF would facilitate and promote prospective (a priori) harmonisation of HBM study designs and thus the resulting HBM data would follow the FAIR data principals. As a consequence, data wrangling activities to make data ready for analysis will be minimised. Therefore, prospective (a priori) harmonisation of foreseen or already planned HBM studies is recommended rather than focusing on retrospective (post-hoc) harmonisation. The prospective (a priori) harmonisation leads to a higher degree of data homogeneity in an effective way. It should be noted that aligning in terms of harmonisation is essential but is different from standardization.
) in a harmonised structure and format. Statistical analysis protocols scripted in R were used to extract comparable and machine-readable summary statistics or "aggregated data" that can be compared across data collections of the HBM4EU aligned studies and also across existing studies that share similarities in design. This has allowed the development of an interactive dashboard to display the data of the HBM4EU project (https://www. hbm4eu. eu/eu-h bm-dashboard/). Work on occupational exposure under the HBM4EU project is also directed to harmonise methodologies and data collection by developing standard operating procedures (SOPs), that can be used in multiple countries and analysed in an integrative approach  (Santonen et al. , 2019). Registries containing some (meta)data on HBM studies or studies containing an HBM part exist such as the WHO International Clinical Trials Registry Platform 2  , the US Clinical Trials database 3.
In addition, registries exist in which planned work is registered before the execution of the study. PROSPERO 4  is an example of such an international database of prospectively registered systematic reviews with a health-related outcome. Others such as the EU Clinical Trials Register 5  , already contains human intervention studies. Moreover, there are several initiatives as listed in Table  1  regarding data collections containing mainly environmental monitoring and external exposure monitoring data. The Information Platform for Chemical Monitoring (IPCHEM 6  ), the NORMAN network 7  , the Elixir community (ELIXIR, 2017), the BBMRI-ERIC 8  and the Hazchem@work for occupational exposure data have been created to tackle the lack of harmonised information  (EU, 2016). Most of these portals have their own challenges, some of which are highlighted in Table  1.
In addition, there are many health surveys and HBM studies at national, regional and/or international levels which are not typically integrated into these platforms despite their potential for enhancing informed decision making in environment, public and occupational health. IPCHEM was established by the European Commission (EC) to support a coordinated approach for collecting, storing, sharing, and assessing data on the occurrence of chemicals and chemical mixtures in humans and the environment. It is structured in four thematic modules: 'environmental monitoring', 'human biomonitoring', 'food and feed' and 'products and indoor air. ' IPCHEM is designed to specifically provide access to chemical monitoring data and underlying (meta)data currently not readily accessible, making them findable and accessible and to a certain extent interoperable and reusable  (Comero et al.
, 2020; Knetsch and Ruether, 2016; Wilkinson et al. , 2016). IPCHEM is intended to assist scientists and risk managers/policy makers to discover and access chemical monitoring data on existing, new, emerging and less-investigated chemicals, covering a wide range of matrices and media  (Comero et al. , 2020). It is however not intended to register study designs ex ante. IPCHEM is the European Commission's reference access point for searching, accessing and retrieving chemical occurrence data collected and managed in Europe. IPCHEM is a distributed infrastructure. Data owners/data providers can decide on the level of detail to which the different IPCHEM user groups can access their data. As shown in Fig. 2 , the volume of HBM datasets uploaded to the IPCHEM platform has increased by over 400% in the last two years.
It includes now more than 100 (meta)data for HBM data collections gathered under the HBM4EU project (EC, 2020b). The platform already includes particularly useful (meta)data, containing crucial information concerning study design, population size and age, sampling, analytics, and substances/biomarkers monitored, etc. , amongst others. However, it is not able (yet) to quickly upload new data from data collections as the data collections themselves and the preceding study designs are lacking sufficient harmonisation. This limits easy and wider reuse and meta-analyses. There is for example currently not yet an optimal use of ontologies to facilitate linking different biomarker metabolites measured in different data collections but originating from the same parent substance to that parent substance.
Remaining challenges to be addressed by development of the HBM registry system under the HBM GRF The current absence of harmonised study design templates makes searching HBM studies and their (meta)data cumbersome, and retrospective information validation processes time-consuming and often subject to errors. Data and metadata data harmonisation, at least from a retrospective point of view, requires access to a lot of information on each study including objectives, measured biomarkers and biological matrices, sampling methods, protocols, questionnaires, etc. , which needs to be validated. Moreover, retrospective harmonisation processes are, to some extent, subjective processes and can thus present an inherent risk of biased (meta)data in a 'post-hoc harmonised' repository. In addition, researchers typically provide the data, including the (meta)data, quite some time after study completion.
This can be problematic as other researchers and regulatory risk assessors, providing scientific advice to risk managers/policy makers, are often looking for fast and user-friendly access to information as well as protocols regarding planned and ongoing studies. Early-on registration of the plan for an HBM study, even in draft form, would enable other researchers to prepare for (re)using the new data once available. Another main issue is the lack of harmonisation in reporting of the chemical compounds investigated in HBM studies. Hundreds of nonendogenous compounds are found in HBM samples, some of them being known chemicals with a Chemical Abstract Services Registry Number (CAS RN), but many are unknown. Even known metabolites do not have CAS RNs as usually only the parent substance has been registered.
Beyond this, even the use of a CAS RN for known compounds can be ambiguous  (Williams and Yerin, 2013). Therefore, it is essential to harmonise how chemical compounds (parents and metabolites) are identified and findable (fulfil the FAIR criteria) in HBM studies. The InChI/InChIKey framework of IUPAC (IUPAC, 2018) is, to our knowledge, the only system for unique identification of chemical substances with a known structure (including stereochemistry). Ontologies based on fragmentation in molecular structure might help in linking metabolites to parent compounds. Although several environmental exposure studies have been registered in existing platforms (e. g. , Study number NCT03440307 in the US ClinicalTrials database 9 ), these platforms currently lack the design to enhance and optimize the dialogue between the scientific disciplines of epidemiology, HBM, toxicology and risk assessment.
, 2021). A rigorous structure for reporting of screening-level monitoring data in environmental matrices is already incorporated in the NORMAN Database System in the EMPODAT (Environmental Monitoring of POllutants DATabase) module  (Dulio et al. , 2020)  and the first prototype for structured collection of non-target and suspect screening data has been developed in its Digital Sample Freezing Platform module (DSFP (Alygizakis et al. , 2019)). Another critical issue is how to deal with observations below the LOD (limit of determination) or LOQ (limit of quantification) in the statistical analyses and aggregation of the individual data. Obviously, these are issues that could be included in the harmonisation efforts as part of the HBM GRF. In addition, policy support is not a strong component of existing platforms, which tend to function primarily as a repository of scientific information for researchers and not as an easily useable source of information for regulatory risk assessors, providing advice to risk managers/policy makers.
Summary and conclusion Overall, we propose to develop an HBM Global Registry Framework (HBM GRF) aiming at harmonising the design and structuring the registration of HBM studies and by such, making the ultimate HBM (meta)data more FAIR (Findable, Accessible, Interoperable and Reusable). Consequently, this will improve the assessment, comparability and combination, reuse, and interpretation of HBM-study results. Preparing HBM study protocols would be facilitated with such a web-based system as they would be based on digitised and harmonised templates. This would result in harmonised data entries into registries. The ultimate objective could be to make HBM (meta)data accessible in a virtual, federated, infrastructure, so that data with the right credentials become instantaneously accessible for human and machine interactions.
Implementing the FAIR principles in the HBM domain could act as fundamental enabler for digital transformation in the environment and health domain. HBM GRF would facilitate the process of data creation and use to its final use and reuse (the data value chain). This is expected to better channel knowledge on the internal exposure of chemicals (exposure biomarkers) and early warnings of effects (effects biomarkers) in human samples into regulatory risk assessment and risk management  (Burns et al. , 2019). The HBM GRF would help to close the science to policy interface gap between scientific ambitions and regulatory and policy requirements resulting in an added value in protecting human health  (Burns et al. , 2019). This knowledge transfer is of particular relevance for the European Chemicals Sustainability Strategy, which needs more and better exposure data, as well as in the scope of international conventions aiming to protect human health from chemical exposures.
Human Biomonitoring Data for • Tabulated CSV-downloadable results Environmental Chemicals Health Canada, Ottawa, ON (2020) http://open. canada. ca/data/en/dataset /8cc88229-8132-4ccd-a3dd-b4565 79158c6 NORMAN Association Europe, Not for profit network; • Enhancing the exchange of information and • Focus only on environment https://www. norman-network. com/ North self-funded by its collection of data on emerging • Absence of information regarding America, and members environmental substances human data Asia • Encouraging the validation and harmonisation of common measurement methods and monitoring tools • Ensuring that knowledge on emerging pollutants is maintained and developed by stimulating coordinated, interdisciplinary projects on problem-oriented research and knowledge transfer to address identified needs BBMRI-ERIC (Biobanking and Bio- Europe European Research • Collecting and making available information • Only puts in contact people who molecular Resources Research Infrastructure about biobanks throughout Europe that are search information on biobanks Infrastructure) Consortium funded by willing to share their data and/or samples, • Sample query processing missing https://www.
bbmri-eric. eu/ its members and to collaborate with other research groups Elixir community Europe National funding in Managing and safeguarding the increasing https://elixir-europe. org/ each country (hubs) volume of data being generated by publicly funded life sciences research • Currently mostly focussed on human endogenous compounds (DNA, proteins, small molecules) HazChem@work https://www. certifico. com/sicurezza-l avoro/documenti-sicurezza/68-docume nti-ue/3859-hazchem-work-projectto-estimate-occupational-exposure-che micals Europe European Commission (DG Employment) Create a database and developing a model to estimate the occupational exposure for a list of hazardous chemicals in EU countries and in the EFTA/EEA countries. • Discontinued • Dedicated only to specific chemicals EC-European Commission; EFTA -European Free Trade Association; EEA -European Economic Area.
Christensenella minuta are human gut dwelling bacteria that have been proposed as key members of the gut microbiome, regulating energy balance and adiposity of their host. We formerly identified that a novel strain of C. minuta (strain DSM33407) boosted microbiota diversity and stimulated deconjugation of the primary bile acid taurocholic acid in human samples. However, there is no description of a bile salt hydrolase (BSH) protein carried in the genome of C. minuta. Here, we identified and cloned a protein from C. minuta's genome that carries a potent BSH activity, which preferentially deconjugates glycine-conjugated bile acids. We then retrieved 14,319 putative BSH sequences from the NCBI database and filtered them using the UHGP database to collect a total of 6701 sequences that were used to build the most comprehensive phylogenetic tree of BSH-related enzymes identified in the human microbiome so far.
Protein Sequence and In Silico Structural Analysis The full-length protein Cm33407BSH was screened for the presence of a putative signal peptide sequence using SignalP (version 5. 0), LipoP 1. 0 and PSORTb  [28] [29] [30]. Alignments with representative BSH sequences were achieved using Clustal Omega  [31]  and ESPript 3 software  [32]. In ESPript 3. 0, strict β-turns are indicated as TT letters, strict α-turns as TTT and the η symbol refers to a 310-helix. The 3D model of Cm33407BSH was achieved using advanced remote homology detection methods via the Phyre2 web portal  [33]. The structure representations were made using the PyMOL Molecular Graphics System (v2. 4. 0, by Schrödinger).
BSH Alignment, Phylogenetic Analysis and Clustering The BSH protein sequence from C. minuta DSM 22607 was retrieved from the NCBI protein database (GenBank: AYH40638. 1). The amino acid sequences of the BSH from C. minuta DSM 22607 and DSM 33407 were aligned using Muscle (v. 3. 8. 31) with default parameters and the alignment was viewed using MView  [34, 35]. A dataset of 6701 putative BSH sequences from the human gut was constructed using the flowchart shown in Figure  S1. Briefly, a total of 14,319 sequences were extracted from the NCBI refseq protein database based on their annotation, length and sources. The sequences were blasted (Blastp v2. 9. 0+) against the Unified Human Gastrointestinal Protein (UHGP-100 v1. 0, downloaded on 2 December 2020) catalog and the gastrointestinal tract protein database from the Human Microbiome Project (HMP)  [36] [37] [38] [39]  Out of the 14,319 putative BSH sequences from the NCBI refseq protein database, the sequences sharing 85% identity or more with at least one sequence of the UHGP database were selected to form a dataset of 6701 putative BSH sequences from the human gut.
A clustering analysis was performed using CD-hit (v. 4. 6. 1) on this dataset using a 70% identity threshold and a word size of 4. For each tree, the selected sequences were aligned using Muscle (v. 3. 8. 31) with default parameters  [34]. Gblocks (v0. 91b) with default parameters was used to eliminate divergent and poorly aligned sequences in the alignment  [40]. MEGA-X for macOS (v10. 1. 8) was used to create the phylogenetic tree  [41, 42]. The initial tree was obtained using neighbor joining and checked using the maximum likelihood method, using 1000 iterations to validate the bootstrap values.
Hence, to rapidly identify putative BSH genes, the genome sequences of CmDSM33407 and CmDSM22607 were screened for linear amide C-N hydrolase proteins that encompass choloylglycine hydrolase (conjugated bile acid hydrolase, CBAH, EC:3. 5. 1. 24), penicillin acylase (EC:3. 5. 1. 11) and acid ceramidase (EC:3. 5. 1. 23). Our bioinformatic analysis determined that both Christensenella minuta strains included in this study encoded one putative BSH, Cm33407BSH (GenBank: MZ028606) and Cm22607BSH (GenBank: AYH40638. 1). Both protein sequences are strictly identical and the Cm33407BSH sequence contained 325 amino acid residues (Figure  S2 ). Sequence alignment with structurally defined BSH and PVA (Penicillin V acylase) sequences exhibited 67. 9%, 45. 37%, 38. 14%, 54. 63%, 20. 47% and 31. 48% identity with Enterococcus faecalis T2 (BSH), Clostridium perfringens (BSH), Bifidobacterium longum (BSH), Lactobacillus salivarius (BSH), Bacteroides thetaiotaomicron VPI-5482 (BSH) and Lysinibacillus sphaericus (PVA), respectively (Figure  3 ).
Hence, to rapidly identify putative BSH genes, the genome sequences of CmDSM33407 and CmDSM22607 were screened for linear amide C-N hydrolase proteins that encompass choloylglycine hydrolase (conjugated bile acid hydrolase, CBAH, EC:3. 5. 1. 24), penicillin acylase (EC:3. 5. 1. 11) and acid ceramidase (EC:3. 5. 1. 23). Our bioinformatic analysis determined that both Christensenella minuta strains included in this study encoded one putative BSH, Cm33407BSH (GenBank: MZ028606) and Cm22607BSH (Gen-Bank: AYH40638. 1). Both protein sequences are strictly identical and the Cm33407BSH sequence contained 325 amino acid residues (Figure  S2 ). Sequence alignment with structurally defined BSH and PVA (Penicillin V acylase) sequences exhibited 67. 9%, 45. 37%, 38. 14%, 54. 63%, 20. 47% and 31. 48% identity with Enterococcus faecalis T2 (BSH), Clostridium perfringens (BSH), Bifidobacterium longum (BSH), Lactobacillus salivarius (BSH), Bacteroides thetaiotaomicron VPI-5482 (BSH) and Lysinibacillus sphaericus (PVA), respectively (Figure  3 ).
These four loops constitute the substrate binding pocket archetype of the CGH family. Notable amino acid differences are Leu18, Leu20, Y65 and E269 residues located within loops 1, 2 and 4, which were only observed in Cm33407BSH. These residues were shown to be important in the substrate binding step  [44]. Cm33407BSH shared 68% sequence identity with an Enterococcus faecalis BSH (EfBSH; PDB code 4WL3  [19] ) and, as such, was amenable to tertiary structure homology modeling. Phyre2  [33] , a protein structure prediction software, utilized PDB code 4WL3 as the The amino acid sequence alignment revealed conservation of the secondary structure organization and of the key active-site residues Cys2, Arg16, Asp19, Asn79, Asn170 and Arg223 (numbering is done according to Cm33407BSH sequence; highlighted as black stars in the multiple sequence alignment) as previously described within the large choloylglycine hydrolase family (CGH)  [43]  (Figure  3A ).
The Cys2 acts as the catalytic nucleophilic residue. An interesting difference is the Asn79, which is usually replaced by an aromatic amino acid (Phe, Tyr, Trp) within the PVA members as exemplified by the sequence from Lysinibacillus sphaericus included in our analysis. Moreover, residues within four loops were conservatively altered (Figure  3A ; dashed rectangle): 15-25 (loop 1), 55-65 (loop 2), 127-142 (loop 3), 258-270 (loop 4). These four loops constitute the substrate binding pocket archetype of the CGH family. Notable amino acid differences are Leu18, Leu20, Y65 and E269 residues located within loops 1, 2 and 4, which were only observed in Cm33407BSH. These residues were shown to be important in the substrate binding step  [44]. Cm33407BSH shared 68% sequence identity with an Enterococcus faecalis BSH (EfBSH; PDB code 4WL3  [19] ) and, as such, was amenable to tertiary structure homology modeling.