Retrospective analysis and decentralized distribution to improve the lifecycle of Ah receptor research assets.

Retrospective Analysis and Decentralized Distribution to Improve the Lifecycle of Ah Receptor Research Assets - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Biochem Pharmacol . Author manuscript; available in PMC: 2026 Jun 1. Published in final edited form as: Biochem Pharmacol. 2025 Mar 19;236:116874. doi: 10.1016/j.bcp.2025.116874 Retrospective Analysis and Decentralized Distribution to Improve the Lifecycle of Ah Receptor Research Assets Alex C Veith 1 , Patrick R Carney 1 , Aijing Wu 2 , Brenda L Rojas 1 , Emmanuel Vazquez-Rivera 1 , Mark E Berres 2 , Christopher Harrison 2 , C Dustin Rubinstein 2 , Christopher A Bradfield 1, 2, * Author information Article notes Copyright and License information 1 McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health; Madison, WI, 53706, USA 2 University of Wisconsin Biotechnology Center, University of Wisconsin-Madison; Madison, WI, 53706, USA * Corresponding author: Christopher A. Bradfield, 1400 University Ave, McArdle Building Room 318, Madison WI 53706, USA, cabradfi@wisc.edu Issue date 2025 Jun. PMC Copyright notice PMCID: PMC12155054  NIHMSID: NIHMS2069466  PMID: 40118284 The publisher's version of this article is available at Biochem Pharmacol Abstract Scientific progress benefits from the sharing of “research assets” such as data, reagents, models, and experimental samples. To improve asset shareability, we evaluated the availability, quality, and characterization of recombinant DNA molecules, recombinant mouse models, and extant tissue samples described by our laboratory in ten publications spanning over thirty years. Employing state-of-the-art molecular technologies, we identified extant samples, updated their localization, generated modern sequences and maps of recombinant models, and ported the associated metadata to an internal blockchain-dependent resource using a standardized description for each asset class. We also created non-fungible tokens representing research assets on the public blockchain network Solana. In addition to providing an audit of previously reported shareable assets and improving the value of recombinant models, this re-analysis also provides evidence for the utility of extant tissue samples that may be difficult and expensive to regenerate. The results demonstrate how retrospective analysis can improve and expand upon the spectrum of shareable research assets through updates on molecular characterization and physical location, as well as improving the availability of biological samples of potential high experimental value. Moreover, the development of a decentralized ledger harboring this revised metadata provides a path to the description and tokenization of scientific assets and provides a strategy to extend the life of scientific assets even after laboratories or sources close. Keywords: Aryl hydrocarbon receptor, retrospective analysis, research tools, blockchain technology, resource sharing, rigor and reproducibility Graphical Abstract 1. INTRODUCTION Sharing of research assets (e.g., data, reagents, models, or biological samples) increases research productivity, enhances interdisciplinary research, fosters collaboration, decreases costs, shortens experimental time, and promotes creativity within the scientific community ( 1 – 9 ). The importance of asset sharing is exemplified by one of the most important discoveries of our time: the assembly of the human genome ( 10 – 12 ). Despite these benefits, the sharing of research assets may not be uniformly practiced and is subject to evolving policies and limited financial resources to support the effort ( 13 , 14 ). Additional difficulties arise from the coexistence of over 3,300 databases (per re3data.org as of 2/3/25) for the sharing of digital scientific information and repositories of genetic or cell culture models that may not accept all “real world assets” (RWAs) due to high costs of storage and curation ( 15 ). While some research sponsors have proposed new data-sharing policies and “best practices,” the guidelines regarding the sharing of RWAs such as genetically engineered animal models or biospecimens have not been updated in years ( 16 – 19 ). Further, sharing guidelines can be ambiguous, difficult to enforce, and do not address asset sharing after a laboratory closes or a research asset is generated outside a sponsor’s purview ( 20 , 21 ). Over the past few decades, our laboratory has generated hundreds of recombinant DNA molecules and model organisms and perhaps an equal number of related biological samples, such as protein and nucleic acid extracts. This observation led to the proposition that many research laboratories, like ours, harbor an untapped wealth of digital and RWAs that if shared, could be used to reduce barriers to obtaining preliminary data and reduce costs of complicated or high-risk experiments. Such benefits could be especially significant for junior investigators or scientific communities with limited resources. In thinking of how to improve the sharing of existing research assets described in past publications, we noted a few major obstacles. First, many of the assets we have described over the pasth thirty years were generated prior to the development of sharing mandates as well as many data/asset databases and repositories. Second, many repositories cannot accept all biological assets available and the costs and time required to deposit many assets can be prohibitive. For example, while we have attempted to follow the sharing guidance to support asset sharing over the years, many of our murine strains were not accepted by The Jackson Laboratory mouse repository due to a lack of resources necessary to store all murine lines generated by the scientific community and a conclusion by that repository that such assets did not rise to the value level required for their acceptance. In an effort to improve the shareability of previously described assets from our laboratory, and to serve as a model for the sharing of previously described assets by other laboratories, we retrospectively reevaluated research assets described in ten reports published by our laboratory over the past thirty years. We predicted that this re-analysis and modernized description would produce multiple benefits. First, the exercise will allow an audit of the fidelity of our work over the past two decades through an objective recharacterization of research assets described in peer-reviewed manuscripts. Second, we predicted that recent advances in DNA sequencing will provide improved characterization of recombinant molecules or alleles that were previously described only by cloning, gene editing, and homologous recombination strategies ( 22 – 24 ). Third, while biological samples described in a prior report may be presumed to be commonly discarded or unavailable due to the passage of time and their potential for degradation, the modern recharacterization of extant assets documents their continued shareability and scientific utility. Finally, as a method to simplify the sharing and provenance of research assets we engaged two decentralized blockchain platforms, one for internal use and one as a seed for a global sharing strategy, that can be widely adopted and that can allow access to any laboratory’s assets, long after it closes. 2. MATERIALS AND METHODS 2.1. Manuscript selection We audited ten manuscripts published from our laboratory that met the following criteria: 1) inclusion in PubMed database, 2) listed Bradfield CA as the corresponding author, 3) publication dates before 2015, 4) cited by more than ten other manuscripts (as determined by PubMed), and 5) described research assets. Assets were identified by interrogation of the “ Methods ” and “ Results ” sections of each audited manuscript ( Table 1 ). Table 1. List of audited manuscripts, the asset classes interrogated in each, the number of each asset class identified and the number of assets successfully recovered and characterized. Manuscript Asset Class Assets Identified (#) Assets Recovered (#) Carver LA, Jackiw V, Bradfield CA. The 90-kDa heat shock protein is essential for Ah receptor signaling in a yeast expression system. J Biol Chem. 1994;269(48):30109-12. PubMed PMID: 7982913. Plasmid 5 5 Jain S, Dolwick KM, Schmidt JV, Bradfield CA. Potent transactivation domains of the Ah receptor and the Ah receptor nuclear translocator map to their carboxyl termini. J Biol Chem. 1994;269(50):31518–24. PubMed PMID: 7989319. Plasmid 19 17 Schmidt JV, Su GH, Reddy JK, Simon MC, Bradfield CA. Characterization of a murine Ahr null allele: involvement of the Ah receptor in hepatic growth and development. Proc Natl Acad Sci U S A. 1996;93(13):6731–6. Epub 1996/06/25. doi: 10.1073/pnas.93.13.6731 . PubMed PMID: 8692887; PMCID: PMC39095 . Plasmid 1 1 Mouse 1 1 Carver LA, Bradfield CA. Ligand-dependent interaction of the aryl hydrocarbon receptor with a novel immunophilin homolog in vivo. J Biol Chem. 1997;272(17):11452–6. doi: 10.1074/jbc.272.17.11452 . PubMed PMID: 9111057. Plasmid 6 6 Gu YZ, Moran SM, Hogenesch JB, Wartman L, Bradfield CA. Molecular characterization and chromosomal localization of a third alpha-class hypoxia inducible factor subunit, HIF3alpha. Gene Expr. 1998;7(3):205–13. Epub 1998/12/05. PubMed PMID: 9840812; PMCID: PMC6151950. Plasmid 11 8 Yao G, Craven M, Drinkwater N, Bradfield CA. Interaction networks in yeast define and enumerate the signaling steps of the vertebrate aryl hydrocarbon receptor. PLoS Biol. 2004;2(3):E65. Epub 20040316. doi: 10.1371/journal.pbio.0020065 . PubMed PMID: 15024417; PMCID: PMC368161. Plasmid 6 6 Walisser JA, Glover E, Pande K, Liss AL, Bradfield CA. Aryl hydrocarbon receptor-dependent liver development and hepatotoxicity are mediated by different cell types. Proc Natl Acad Sci U S A. 2005;102(49):17858–63. Epub 20051121. doi: 10.1073/pnas.0504757102 . PubMed PMID: 16301529; PMCID: PMC1308889. Plasmid 1 1 Mouse 1 1 Dunham EE, Stevens EA, Glover E, Bradfield CA. The aryl hydrocarbon receptor signaling pathway is modified through interactions with a Kelch protein. Mol Pharmacol. 2006;70(1):8–15. Epub 20060331. doi: 10.1124/mol.106.024380 . PubMed PMID: 16582008. Plasmid 33 32 Hayes KR, Zastrow GM, Nukaya M, Pande K, Glover E, Maufort JP, Liss AL, Liu Y, Moran SM, Vollrath AL, Bradfield CA. Hepatic transcriptional networks induced by exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin. Chem Res Toxicol. 2007;20(11):1573–81. Epub 20071020. doi: 10.1021/tx7003294 . PubMed PMID: 17949056; PMCID: PMC2515491. Biospecimens 174 * 80 * Johnson BP, Walisser JA, Liu Y, Shen AL, McDearmon EL, Moran SM, McIntosh BE, Vollrath AL, Schook AC, Takahashi JS, Bradfield CA. Hepatocyte circadian clock controls acetaminophen bioactivation through NADPH-cytochrome P450 oxidoreductase. Proc Natl Acad Sci U S A. 2014;111(52):18757–62. Epub 20141215. doi: 10.1073/pnas.1421708111 . PubMed PMID: 25512522; PMCID: PMC4284582. Plasmid 2 1 Mouse 1 1 Open in a new tab Table 1 denotes assets identified in the manuscript, not unique assets. * See results and discussion for RNA samples. 2.2. Biospecimen Audit Freezers were manually searched for all RNA samples from a select prior manuscript that described exposure and time course hepatic transcriptional responses to 2,3,7,8-tetrachlorodibenzo- p -dioxin (TCDD) ( 25 ). Eighty historical samples from a “long-term” experiment were recovered, including control and three treatment groups (corn oil, 1 μg/kg TCDD, 10 μg/kg TCDD, and 64 μg/kg TCDD) at five different time points (1 day, 4-days, 8-days, 16-days, 32-days). Each group was comprised of four eight-week-old male C57BL/6J mice. Samples have been stored at −80°C since their original description. The quality of each sample was assessed by electrophoresis (Tapestation, Agilent Technologies, Santa Clara, CA) to generate an RNA integrity number (RIN). Samples with potential quality issues were re-run using a second technology (Bioanalyzer, Agilent Technologies, Santa Clara, CA). The reverse transcriptase quantitative PCR (RT-qPCR) analysis was completed following preparation with Taqman Reverse Transcriptase reagents and TaqMan Gene Expression Master Mix with the following TaqMan probes: Mm03024075_m1 ( Hprt ), Mm00487218_m1 ( Cyp1a1 ), Mm00477443_m1 ( Ahrr ), Mm00724822_m1 ( Tiparp ) (Applied Biosystems, Waltham, MA). Expression of the target genes was compared using Hprt as a housekeeping gene. Fold change was calculated relative to the corn oil control for each time point. Figures were prepared, and two-way ANOVA and post-hoc multiple comparisons (Dunnett correction) were performed using GraphPad Prism version 10.2.1 (Dotmatics, Boston, MA). 2.3. Mouse model sequencing and annotation Three mouse models were identified among the manuscripts selected in the audit. Since their original description, each mouse model has been maintained in our laboratory for multiple generations with backcrossing to the C57BL/6J background. All procedures were approved by the School of Medicine and Public Health IACUC at the University of Wisconsin-Madison (Protocol #M005987). Blood was collected from one homozygous mouse from each genotype by the University of Wisconsin Biotechnology Center (UWBC) Animal Models Core (RRID:SCR_024797). The UWBC DNA Sequencing Core Facility (RRID: SCR_017759) prepared high molecular weight DNA from whole blood using the Nanobind Blood DNA kit (Pacific BioSciences [PacBio], Menlo Park, CA). PacBio HiFi libraries were prepared (PN 101–853-100 Version 03) and sequenced on the Sequel II (Sequel Polymerase Binding Kit 2.2; Pacific BioSciences, Menlo Park, CA). The UWBC Bioinformatics Resource Core Facility (RRID: SCR_017799) aligned the CCS reads using minimap2 , and variants were called by clair3 v1.0.4 ( 26 , 27 ). Data was visualized with Integrative Genomics Viewer (IGV; v2.16.2) ( 28 ). Variant density plots were generated using karyoploteR ( 29 ). The following reference genomes were used: C57BL/6J: GRCm39 (GCA_000001635.9) and 129: 129S1/SvImJ (GCA_921998555.2). The unaligned sequencing data are available on NCBI at the Sequence Read Archive (BioSamples: SAMN41408886, SAMN41408887, SAMN41408888). These sequences are a part of our laboratory’s ongoing allele auditing project (BioProject: PRJNA1111952). Figures were prepared using R, IGV, and Adobe Illustrator v 29.2.1 (Adobe Inc, San Jose, CA). 2.4. Plasmid identification and annotation 2.4.1. Plasmid recovery process Plasmids were manually identified from each manuscript and matched to an entry in our written plasmid notebooks. The formal criteria for including a plasmid were that the plasmid was described within the manuscript and used experimentally (i.e., corresponding data was included in a figure or results ). Exclusion criteria included plasmids that were intermediates in a cloning process or those plasmids that were from commercial vendors. The plasmid recovery process involved locating all available plasmids as diluted DNA stocks stored at 4°C or their corresponding E. coli glycerol stocks stored at −80°C. The E. coli glycerol stocks were propagated and miniprepped (Qiaprep Spin Miniprep Kit, Qiagen, Hilden, DE) for sequencing (see below). Plasmids were designated as unrecovered if (1) we could not match a described plasmid to our internal ledger, (2) the plasmid was unavailable as a diluted stock, and the E. coli glycerol stock could not be revived. Plasmids were considered incorrect if major sequencing differences from predicted were identified. 2.4.2. Plasmid DNA sequencing Plasmids were sequenced by either Illumina or Oxford Nanopore Technology (ONT). Plasmid libraries in the Illumina (short-read) pipeline were generated by the QIAGEN FX DNA Library preparation kit (Qiagen, Hilden, DE). The 150bp paired-end sequencing was performed on the Illumina MiSeq (Illumina, San Diego, CA). Plasmids were barcoded, and libraries prepared (RBK110.96 or RBK114.96 kit) for the Oxford Nanopore pipeline (long-read) and sequenced on a “GridION” (Oxford Nanopore Technologies, Oxford, UK). Sequencing was performed by the UWBC DNA Sequencing Core Facility (RRID: SCR_017759). Assembly pipelines were employed by the UWBC Bioinformatics Core Facility (RRID: SCR_017799) ( 30 ). Plasmid assemblies from both sequencing approaches were annotated by pLannotate v1.2 ( 24 ). Plasmid maps and GenBank files were generated using SnapGene Software (GSL Biotech LLC, San Diego, CA). 2.5. Blockchain Implementation and Wallet Creation BigchainDB (Interplanetary Database Association e.V., Berlin, DE) was used as the blockchain protocol for our internal sharing platform ( 31 ). Three nodes were initialized on elastic compute cloud (EC2) instances Amazon Web Services (AWS) (Amazon, Seattle, WA). A custom-built wallet application was deployed at scishare.org and functions as the interface with our instances of BigchainDB. Access has been restricted to registered users at our institution. The wallet was written in the Ionic Framework and provides users with public and private keys and unique identification and security. Our public sharing strategy relies on the Solana blockchain network (Solana Foundation, Zug, CH) ( 32 ). Compressed non-fungible tokens (cNFTs) were minted to the Solana network from Crossmint (Crossmint, Inc, DE, USA). 3. RESULTS 3.1. Laboratory Self Audit We selected ten manuscripts from our laboratory that were published between 1994 and 2014 ( Table 1 ) ( 25 , 33 – 41 ). These manuscripts were selected without prior knowledge of our ability to recover the associated reagents. They were chosen to represent a spectrum of research assets, including biospecimens (RNA extracts), recombinant mouse models, and recombinant plasmids. The manuscripts in the collection had an average of 204 citations each, with a low of 12 and a high of 716 (Web of Science - retrieved 10/23/23). These publications spanned several major research areas (aryl hydrocarbon receptor [AHR], hypoxia, and circadian rhythm) and included reports describing 174 RNA samples, three recombinant mouse strains, and 84 recombinant plasmids ( Table 1 ). Our attempts to identify and recharacterize each asset class were largely successful, as described in the following sections. 3.2. Biospecimens We asked whether biospecimens, such as RNA extracts, could be improved or further utilized through a recharacterization using modern technology. Many laboratories keep RNA extracts following their initial publication in the event experiments must be repeated or validated. To demonstrate that RNA assets may still be of value to the scientific community, even after two decades, we identified and recovered 80 samples from the experiment designed to characterize the “long-term” dose- and time-dependent effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on target liver gene expression ( 25 ). In the original manuscript, these samples were used to generate gene expression data using the EDGE cDNA microarray, which interrogates approximately 1,000 liver-specific mRNAs ( 42 ). Given that these samples have been stored at − 80°C for over 10 years, we assessed their integrity by modern microfluidic electrophoresis analysis ( Figure 1A )( 43 ). Of the 79 samples tested, 77 samples had a RIN ≥5.5, which has been reported to be of sufficient quality for high-quality RT-qPCR analysis ( Figure 1A ) ( 44 ). One sample was withheld from this analysis due to low volume. Figure 1. Quality and value assessment of archived RNA samples. Open in a new tab (A) QC analysis of historical RNA samples. RIN values are plotted on the x-axis for each sample tested (N=79). One sample was withheld from QC analysis due to low volume. Red circles are the RIN values obtained from the Agilent Tapestation, and blue circles are the RIN values obtained from the Agilent Bioanalyzer. RT-qPCR of AHR-responsive genes Cyp1a1 (B) , Ahrr (C) , and Tiparp (D) . Individual data points are shown with the bars indicating the mean +/− standard deviation (y-axis cut off at 0). Normalized to Hprt . Significance (p<0.05) from time-matched control shown by *. The original dataset was comprised of EDGE microarray results (an in-house printed cDNA microarray) supported by northern and western blot analysis. In an effort to repeat our earlier results, we employed modern RT-qPCR to demonstrate the current utility of these samples. All 80 recovered samples were subjected to this analysis. The analysis of the Cyp1a1 gene, a TCDD-inducible target included in the original report, was used as a model ( Figure 1B ). Importantly, although these samples were over 10 years in storage, we identified a greater change in Cyp1a1 expression using current RT-qPCR protocols than initially reported by the EDGE microarray in the original manuscript (~8-fold) ( 25 ). As an example of applying extant research assets to generate novel data, we chose to quantify the response of two additional TCDD target genes ( Ahrr and Tiparp ) not considered in our original report ( Figure 1C - D ). While not described in our original report, these targets were found to be significantly upregulated adding novel data to an existing published report ( 45 , 46 ). 3.3. Recombinant mouse models Recombinant mouse models are another research asset that benefits from re-analysis and re-description using modern technologies. Recombinant animal models generated by homologous recombination or gene editing are costly to generate, share, cryopreserve, or re-derive. It is uncommon for recombinant mouse models to undergo repeated genetic characterization, which may be important as these models are subjected to genomic change due to genetic drift and intentional backcrossing strategies ( 47 ). Here, three unique animal models were described from the audited manuscripts: Ahr -null ( Ahr tm1Bra /J; MGI:1857427), Ahr fx/fx ( Ahr tm3.1Bra /J; MGI:3612477), and Bmal1 fx/fx ( Bmal1 tm2Bra ; MGI:4819968). Each of these models was generated using homologous recombination in embryonic stem (ES) cells derived from a 129/Sv strain. The characterization of these strains depended on Sanger-based DNA sequencing approaches and Southern blot analysis to determine fidelity. Each has been distributed globally and extensively backcrossed to the C57BL/6J background in our lab. To determine the integrity of the initial genetic modifications and evaluate genetic background changes since the original report, we developed an “allele-auditing” method using long-read technology to determine the sequences of the recombination site but also to define the genetics of the currently available murine line at the genome level. Each mouse model was subjected to this analysis pipeline, with the locus-specific results for the Bmal1 fx/fx (also originally characterized as Mop3 ) mouse model depicted in Figure 2 ( 48 ). Figure 2. Allele auditing of the Bmal1 fx/fx mouse model. Open in a new tab (A) Schematic of the recombination site at exon 8 of the Bmal1 locus, including the homology arms (HA), loxP sites, and the FRT site that remains after excision of the Neo cassette by Flp1 recombinase ( 41 ). The sequence for the targeting vector (pL2029) is deposited on Figshare (see data sharing). (B) Simplified view of the sequencing reads near the recombination site on chromosome 7. Sequencing reveals the loxP sites and FRT site at their expected locations. We also identified a strain-specific deletion in between exons 10–11. Additional tracks below the reference sequence denote the homology arms, FRT site, and loxP sites. (C) Analysis of the variant density across chromosomes 5, 6, and 7 of the Bmal1 fx/fx mouse model against the C57BL/6J and 129S1/SvImJ, with the Bmal1 locus noted. An increase in relative variant density (max=1) indicates mismatches from the strain genomes noted. The Bmal1 fx/fx ( Bmal1 tm2Bra ) mouse model was designed such that loxP sites flank exon 8 of the endogenous Bmal1 gene, with flippase recognition target (FRT) sites flanking the neomycin selection cassette (chromosome 7:112,806,672–112,913,333; Figure 2A ) ( 41 ). In the original report, this recombinant allele was characterized by Southern blot analysis and PCR. Using our allele auditing approach, we identified the loxP and FRT sites proximal to exon 8 as expected ( Figure 2B ). After aligning the genomic sequencing reads to the C57BL/6J genome, we also identified an intronic deletion between exons 10–11 that reflects the sequence of the targeted 129/Sv genome. This was also reflected in the targeting plasmid (pL2029), prompting us to examine genetic background more thoroughly. The application of modern sequencing technology allowed us to examine variation across the entire genome of all three mouse models. As a representation of the allele audit, we present the relative variant density of the Bmal1 fx/fx mouse model at chromosomes 5, 6, and 7 ( Figure 2C ). We observed a peak in variation density near the Bmal1 locus in the C57BL/6J genome and absent in the 129S1/SvImJ genome. Our analysis across the entire genome is illustrated in Figure 3 . These results were expected as this line has been backcrossed to the C57BL/6J background for over ten generations. Similar results were observed for the Ahr -null and Ahr fx/fx mouse models, as the Ahr locus is located on chromosome 12:35,547,973–35,585,037 ( Figures 4 & 5 ). The span of the 129-like region surrounding the Ahr locus was different between the Ahr -null and Ahr fx/fx mouse. This was expected, as the Ahr- null has been backcrossed for 21 generations, and the Ahr fx/fx was backcrossed for 11 generations. This analysis demonstrates another advantage of auditing research assets from prior manuscripts since the backcrossing and genetic drift that occurs in any allele leads to an animal model distinct from that described in the original manuscript. Figure 3. Variant analysis of Bmal1 fx/fx mouse model across the genome. Open in a new tab (A) Relative quantification of variation in the Bmal1 fx/fx mouse model compared to the C57BL/6J genome (GRCm39 GCA_000001635.9). (B) Relative quantification of variation in the Bmal1 fx/fx mouse model compared to the 129/Sv genome (129S1/SvImJ GCA_921998555.2). The Bmal1 locus is located at chr7:112,806,672–112,913,333 per GRCm39/mm39 reference. Figure 4. Variant analysis of Ahr -null mouse model across the genome. Open in a new tab (A) Relative quantification of variation in the Ahr- null mouse model compared to the C57BL/6J genome (GRCm39 GCA_000001635.9). (B) Relative quantification of variation in the Ahr- null mouse model compared to the 129/Sv genome (129S1/SvImJ GCA_921998555.2). The Ahr locus is located at chr12:35,547,973–35,585,037 per GRCm39/mm39 reference. Figure 5. Variant analysis of Ahr fx/fx mouse model across the genome. Open in a new tab (A) Relative quantification of variation in the Ahr fx/fx mouse model compared to the C57BL/6J genome (GRCm39 GCA_000001635.9). (B) Relative quantification of variation in the Ahr fx/fx mouse model compared to the 129/Sv genome (129S1/SvImJ GCA_921998555.2). The Ahr locus is located at chr12:35,547,973–35,585,037 per GRCm39/mm39 reference. 3.4. Plasmids Our laboratory has recorded plasmids used in our research in a written notebook for over 30 years and contains our internal plasmid nomenclature (e.g., pL703). From the audited manuscripts, we identified 84 plasmids and matched these plasmids to our written notebook ( Table 1 ). Plasmids were then recovered, sequenced, mapped, and annotated to verify identity ( Figure 6A ). We then matched these plasmid descriptions to entries in our written notebooks. Complete sequencing information allows us to generate maps that serve as digital representations of those assets and provides the necessary information to generate that asset de novo using DNA synthesis if the physical asset is unavailable. Successful identification was determined if the plasmid map conformed to the description in the manuscript, which could include correct open reading frames, amino acid deletions, selection markers, or promoters. We successfully recovered 92% ( 77 ) of the plasmids identified in our audit, with some plasmids being included in multiple manuscripts ( Figure 6B ). The criteria for success and failure of each unique plasmid are recorded as a csv file available at our Figshare repository (see Data Availability ). Figure 6. Plasmid sequencing, annotation, and visualization workflow. Open in a new tab (A.) Plasmids were revived from E. coli stocks or as diluted DNA stocks. Plasmids were submitted for short-read or long-read sequencing for characterization. FASTA sequences were annotated with pLannotate , and GenBank files (.gbk) were downloaded. Maps were generated from SnapGene software. The map shown here was simplified with Adobe Illustrator. (B.) The success rate in identifying plasmids. Plasmids are often reported by a description by the cloning process, restriction mapping, or limited Sanger sequencing. While the majority of recovered plasmids matched the description in the originating manuscript, we also also revealed plasmids from our stocks with errors. One plasmid (pL238) has a concatamer of the cloned AHR fragment in a Gal4 fusion. As expected, the first copy of the AHR fragment was in frame with the Gal4 DNA binding domain, while the second fragment was not. Therefore, the plasmid is expected to function as described in the original manuscript, but the genetic characterization reveals that the plasmid sequence is not as anticipated ( 34 ). Another plasmid, pL287, has the anticipated ARNT fragment, but the anticipated C-terminal VP16 activation domain is inverted and repeated. In this case, the inversion leads to a 28 amino acid tag on the C-terminus. In both cases, restriction mapping would not have readily shown duplications or inversions due to the cloning process employed, highlighting the value of our retrospective analysis with modern sequencing approaches. 3.5. Asset sharing platform development During the laboratory audit, it became clear that we needed a platform to disseminate the results of our retrospective analysis and offer the opportunity to share these assets with other investigators. The current paradigm involves disseminating reagent information across disparate databases, repositories, or static publications. To make these assets available, we employed two unique decentralized ledgers that could harbor complex and detailed descriptions of these research assets with defined metadata that could be maintained and shared at low cost. Blockchains are a type of decenstralize ledger that consists of linked data blocks detailing transactions that are cryptographically linked ( Figure 7A ). Blockchain solutions have been proposed to address several emergent issues in clinical trials, healthcare, medical imaging, and open science ( 49 – 54 ). We encourage readers seeking additional background on blockchain technology to examine the following reviews that cover advantages and disadvantages beyond the scope of this work ( 49 , 55 – 57 ). A key feature of blockchain technology that we sought to exploit was the use of non-fungible tokens (NFTs) representing both real world assets (RWAs) and digital information ( 57 , 58 ). Figure 7. Overview of BigchainDB blockchain solution and custom wallet application features. Open in a new tab (A) The blockchain is comprised of a series of linked data blocks consisting of transactions. These transactions can involve the creation of an asset and its addition to the blockchain or transferring an asset between investigators. (B) The wallet application is the user interface with the blockchain. The wallet identifies users with public and private keys and allows users to view the assets they own and those they have transferred. The wallet is where users initiate Create and Transfer functions to handle assets on the blockchain. Finally, the wallet application has built-in search functionality. (C) The assets available on the blockchain are created as non-fungible tokens (NFTs). These NFTs contain metadata associated with that asset and any files the user has associated with that asset, such as spreadsheets, sequencing information, or images – depending on the asset class. We initially pursued an internal solution that would allow us to use blockchain technology to facilitate sharing of research assets within our university. As a proof-of-concept, we chose BigchainDB (now Planetmint), an open-source protocol that allowed us to combine database features with blockchain principles (i.e., immutability and decentralization) ( 31 ). The primary benefit of this protocol is that it does not rely on existing currencies (i.e., Bitcoin or Ether) and is therefore independent on transaction-associated fees, which could inhibit sharing at this stage. To run our platform, we initialized three nodes of our BigchainDB servers on AWS across three EC2 instances with access restricted to users at our home institution. Digital wallets identify individual users or accounts and stores users NFTs in our blockchain solution. We designed a custom wallet application that would also allow users to interact with the blockchain, including search functionality. This application allows users to create (i.e. “mint”) research asset NFTs and share them with other users ( Figure 7B ). The wallet application has several pre-defined asset categories (e.g., tissue extract, animal model, or plasmid) with suggested metadata descriptors associated with the different asset classes ( Figure 7C ). Additional files, such as plasmid maps or GenBank sequence files, can also be included to provide a more comprehensive description of an asset. A complete list of metadata fields for each asset class is in Table 2 . An essential benefit of a blockchain platform is that the fields associated with an asset class can be changed and updated as needed without harming or losing the data from previously created assets. Table 2. Custom Metadata Fields for Internal Blockchain Research Asset Classes Plasmid Mouse Tissue Name Name Name Alias Alias Alias PI PI PI Reference Reference Reference AvailableFormat AvailableFormat AvailableFormat DepositedInDatabase DepositedInDatabase DepositedInDatabase VectorType Mutation ExperimentalUse SourceOrCloningMethod Development AliquotInformation Size Description ExperimentalConditions VectorBackbone GeneticBackground Species Insert Phenotype ValidationInformation Antibiotic HusbandryNotes CSVFile SelectionCriteria GenotypingInformation SupplementalFiles PlasmidSequencingModality Validation SupplementalNotes GenBankFile AvailableSequenceInformation HowToCite MapFile SupplementalFiles TermsAndLicenses SupplementalFiles SupplementalNotes SupplementalNotes HowToCite HowToCite TermsAndLicenses TermsAndLicenses Open in a new tab We also examined existing public blockchains for their ability to host our research asset NFTs. Innovations in compressing NFTs on the Solana network allowed us to cost-effectively mint NFTs with robust metadata ( 59 ). We created a laboratory wallet with the Crossmint platform ( crossmint.com ) through their console no coding solution to mint compressed NFTs for a selection of our research assets. The research assets are associated with the collection “Bradfield Lab” (E3CGGo1oRVMQnzbbLbxENzdUNq7hmwgaMMrnFwMy8k8g) and can be viewed through Solana explorers. We have included simplified depictions of example NFTs and metadata that one might be expected to observe through explorer web portals ( Figure 8 ). Further, we have included example metadata for our Ahr −/− mouse model ( Table 3 ). We sought to include enough metadata information so that recipients could quickly identify utilize these reagents. For example, with regard to the Ahr −/− mouse, we have included a 2,000 base pair sequence of the wild type Ahr locus as well as the corresponding knockout sequence, allowing recipients to easily generate genotyping protocols or replicate given mutations. Figure 8. Example NFTs. Open in a new tab We have created example NFTs to illustrate what users could potentially interact with on a public blockchain through an explorer website. As outlined in this report, we have focused on (A) genetically engineered animal models, (B) plasmids, and (C) biospecimens, such as RNA extracts. In these NFTs, we have included basic pictorial representations that can be used to easily differentiate between NFTs. Each also has metadata attributes that can provide others with sample specific information. Table 3 indicates an example of the types of metadata that we have included in our NFT descriptions. Table 3. Example NFT Metadata Attribute Response Name Ahr tm1Bra Description This is a digital research asset representing the Ahr-null mouse model generated by the Christopher Bradfield Lab. For a detailed description please see PMID:8692887 and MGI:1857427. The genomic data can be found at NCBI under BioProject PRJNA1111952 and reflects animals backcrossed over 20 generations. We have included ~500bp 5’ and 3’ of the mutation in wild type (B6) and mutant (129). The figure was made with Snapgene software ( www.snapgene.com ). Alias Ahr-null ; Ahr−/− ; AhrKO Reference PMID:8692887 ; MGI:1857427 Available Format Digital and Physical Genomic Data NCBI BioProject: PRJNA1111952 Deposited in Database Yes, The Jackson Laboratory strains #002727 and #002831 (fewer backcrosses) Mutation Deletion of Exon 2 Genotyping Information Oligos: CAGTGGGAATAAGGCAAGAGTGA ; AGGGAGATGAAGTATGTGTATGTA ; Wild type (B6) ~300bp ; Null (D) ~260bp ; Wild Type Sequence AACTAGGGTCGAGCACACTC… ~1880bp …AAACACCCACTTTCTTTGAC Mutant Sequence AACTAGGGTCGAGCACACTC… ~960bp …TCTCCCCATTTCTATCTCTC Validation Loss of CYP1 induction Open in a new tab 4. DISCUSSION We set out to perform a retrospective analysis of our published reagents to 1) assess the fidelity of our prior research, 2) improve the characterization of extant assets and 3) improve the shareability lifecycle of these scientific research assets (both digital and RWA). Many, if not all, of these research assets were created with support from governmental sources. Therefore, it is our responsibility to maximize the utility and lifecycle of every research asset. To accomplish this, we performed a retrospective analysis of ten manuscripts to validate the utility of published research assets still in our possession. Our analysis of critical biological resources shows how modern technical and informatics approaches can authenticate biological resources, enhancing scientific rigor and reproducibility. Moreover novel analysis of archived samples can spur innovation in new scientific areas by lowering costs and reducing lead time for many experiments or grant applications, even if only used as preliminary data ( 6 ). This report also details a novel proof-of-concept for employing blockchain technology to facilitate the sharing of research assets between laboratories. Plasmids were identified as one example of a research asset that is easily shared, as they are stable, easily propogated, and can be shared in multiple forms (i.e., agar stabs or purified nucleic acid). In summary, we recovered over 90% of the plasmids we identified in the audited manuscripts, which are still freely available to be shared or resynthesized. Our modern re-analysis provided molecular confirmation of the identity of 70 unique plasmids, with only a few that were not recovered. The methods employed here allowed us to identify plasmid errors that would not have been easily detected by methods at the time of publication. Perhaps more importantly, the modern genomic approaches allowed us to identify two plasmids with unanticipated concatemeric inserts. While it is possible that the plasmids used in the original publications did not contain these inserts, these plasmid aliquots of plasmid are those available to be shared. This information has led us to draft an errata for the original publication but is not expected to influence the results of that paper ( 34 ). The relative ease of storing and sharing plasmids has supported centralized respositories to alleviate the distribution burden of individual investigators. One of our primary motivations for pursuing this work is the complexity and cost of creating, characterizing, maintaining and sharing of the genetically engineered mouse models we have developed over the years ( 60 , 61 ). Therefore, many groups rely on existing repositories or vendors, such as the Jackson Laboratories, to archive, distribute, and receive mouse models. However, there are significant challenges associated with maintaining and sharing mouse lines, requiring repositories to be more discerning about what reagents they can distribute. Examples of rejected alleles from our laboratory include Ahr Ter383 and Ahr NG367R ( 62 ). This limitation is but one reason why not every recombinant allele is available in repositories as an RWA, and it leads to the necessity that many mouse models are shared directly between laboratories. While necessary, this direct sharing can obfuscate the provenance or history of that model. This was a particularly important issue in the early days of AHR research, with multiple knockout alleles were generated with seeminly different phenotypes ( 63 ). Further, mouse models, even inbred lines, can have nucleotide variations and detailed provenance records could be used to identify allele dependent or genetic background effects ( 64 ). Manuscript methods, databases and repositories also often capture static descriptions of a model or allele. As such, when the Bmal1 fx/fx mouse model was originally described, the loxP sites were described as flanking exon 4, which is reflected in the MGI entry and numerous manuscripts ( 41 , 48 ). However, based on the current annotation of the C57BL/6J mouse genome, this is now identified as exon 8. This discrepancy is likely due the historical exon numbering in the Bmal1 locus, which may not have reflected the first four noncoding exons. In these cases, retrospective characterization of an allele, such as that provided here, can clarify the complex and often confusing descriptions of the genetics of these models. In the process of auditing this allele and sequencing the targeting plasmid, we identified an intronic deletion in the Bmal1 locus ( Figure 2 ). Our analysis suggests that this deletion is a reflection of genomic variation between the C57BL/6J and Sv/129 genomes that has been retained through backcrossing. While variants associated with the targeted loci, excluding the targeted mutation, are presumed to be strain-specific polymorphisms or indels, these variants may exert phenotypic differences unassociated with the targeted allele ( 65 ). Our group, and others in the AHR field, are sensitive to strain-specific polymorphisms as a single variant in the Ahr gene conveys significant functional differences ( 66 ). Importantly, additional backcrosses would be expected to change the polymorphism profile around the targeted locus with additional recombination events, highlighting the dynamic nature of these models, as observed between the Ahr -null and Ahr fx/fx mouse models. While endonuclease-mediated editing approaches will mitigate these haploblock differences, allele auditing will still be needed to verify allele fidelity and potential off-target modifications ( 67 , 68 ). Retrospective characterization can be critical when different alleles have resulted in different phenotypes, in cases where recombination sites are unknown, or when concern arises about accumulated mutations due to prolonged breeding in distinct colonies ( 47 , 63 ). Therefore the scientific community would benefit from dynamic methods to continuously update our descriptions of historical reagents in addition to databases and repositories. Biospecimen samples, including tissues and and tissue extracts (i.e., RNA , DNA, and protein) present unique issues in our retrospective analysis. This reagent class consists of limited and consumable reagents that investigators are unlikely to deposit into an off-site repository (i.e., biobanks) unless required for fear of losing immediate access to those assets for their own experiments. In searching for the RNA assets associated with this manuscript, we found nearly all (53/62) of the RNA samples associated with the “long-term” experiment described in our results. An unexpected benefit of our retrospective analysis was the identification of excess replicates not described in the manuscript. In this particular example, four RNA samples were generated per treatment group, but only three were used in an effort to control microarray costs at the time ( 69 ). We did not recover RNA samples from the other microarray experiments described in the original manuscript (112 samples), presumably due to the exhaustion of samples in other developmental experiments over the years, sample loss due to undocumented freezer breakdown, etc. Our data document the value of RNA RWA classes can be of value to a variety of investigators for periods greater than a decade. We demonstrate that long-term stored samples, even those not rigorously monitored, can retain experimental value and thus represent potentially important research assets that can benefit the scientific community. The availability of previously characterized samples allows the repetition of experiments to confirm results, allows additional experimentation as new technologies become available, and overcomes the financial and logistical hurdles required to repeat complex experiments. These characteristics may benefit junior investigators or groups with limited resources from repeating such experiments. This report details a proof-of-concept use for blockchain technology in the dissemination of research assets. Blockchain technology offers many traits (i.e., decentralization, immutability, and flexibility) that lend itself to an open science future beyond existing solutions ( 49 ). As distributed ledgers, blockchains are decentralized and are not dependent on a single entity for maintenance, governance, and use. Even robustly supported databases can experience outages or communication disruptions ( 70 ). While not immune, the decentralized nature of blockchain technology enhances resilience and is being adopted across different sectors ( 71 – 73 ). One goal of a blockchain-based solution would be to enhance FAIR (Findable, Accessible, Interoperable, and Reusable) principles with regards to existing data and resources ( 74 ). We propose using blockchain technology to complement existing databases and repositories by acting as a central conduit to integrate information. As mentioned previously, there are over 3,300 different data respositories (per re3data.org ) and it is difficult to enumerate the number of physical repositories for real world research assets. Even when investigators detail a repository where research data is deposited in a manuscript, that resource could be shut down or lost ( 75 ). We envision that digital research assets are not stored, in their entirety, on-chain, but rather that updateable NFT representations of this digital information directs investigators to the repository or database, such as URLs or accession numbers ( 76 ). Moreover, as the data associated with an NFT is defined by the user, and therefore highly variable, different asset classes across disciplines can be maintained on a single chain. This would improve the finability and accessibility of scientific data and research assets, improving the lifecycle (i.e., reuse) of those assets. Finally, the blockchain offers a distinct advantage over databases and repositories through its ability to record transactions, including between investigators. The sharing of research asssets is an important, and under appreciated, contribution to science and having records of these transactions could be another metric to gauge research productivity ( 77 ). Similarly, the blockchain would enable investigators to track the provenance of research assets, including other groups that asset has been shared with. For example, we frequently receive requests for animal models that we do not actively maintain. In many of these cases, we have shared that model with other investigators and we must interrogate internal records to identify groups that may still have that animal model. In these instances, it would be helpful to have a blockchain resource that identifies other groups that may be able to share that asset, reducing research lag time and inefficiency. Such a resource will prove valuable after labs close down or researchers pass away. We initially created an internal blockchain platform using BigchainDB, as it combined some of the traditional databases features with the blockchain flexibility ( 31 ). Using this approach, we built a blockchain platform that could be decentralized, with nodes operated by different researchers or departments within our institution. This solution is designed to serve blockchians requiring restricted access to users at a single home institution. We encountered significant hurdles in implementing this solution. The BigchainDB solution requires extensive information technology and cloud infrastructure knowledge to maintain the different nodes and work around deprecated features. There were also institutional challenges, including maintaining network security, that have required us to restrict access and experience service outages. As we progressed with the solution, it was clear that we needed employ a blockchain resource with global applicability and significant independent infrastructure support. For this initial attempt, we began minting NFTs for our research assets on the Solana public blockchain. Solana was initially chosen over other blockchains because of its incorporation of compressed NFTs significant reduced costs as well as its consensus mechanism being relatively energy efficient ( 78 ). While this report has primarily focused on digital information regarding research assets, we also envsision a blockchain-based solution as a method to handle sharing of RWAs in the future ( 79 , 80 ). As we’ve discussed, there are numerous repositories and databases for data and digital information. In both of our blockchain solutions, there is little cost and effort needed to share digital information with other investigators, beyond a wallet identification, through free (or very low cost) distributions known as “airdrops”. Alternatively, as NFT information is pubically available on the Solana network, other investigators can read the NFT metadata for digital assets without requiring receipt of those assets using web-acccessible block explorers. With regards to physical assets, we envision an environment in which digital avatars of RWAs are transferred across the blockchain when the physical assets are shared. For example, one could generate ten NFTs representing ten aliquots of a biospecimen that are have available. As those aliquots are shared, those transactions would be recorded on the blockchain. Further, we can tokenize these assets as a mechanism to reward sharing and remunerate donating investigator costs (i.e., shipping, animal line maintenance). Smart contracts can also be used to simplify the sharing process, including automated material transfer agreements or quality assurance reports ( 81 ). However, the economic models, feasibility of implementing tokenization, and cross-institutional adoption is beyond the scope of this report. 4.1. CONCLUSION In summary, we have presented the results of a retrospective analysis of our published reagents and built lockchain resources to make those research assets readily available long after our laboratory closes. This is the first report to our knowledge that describes a self-audit of prior publications and in our case produced a fairly robust accounting of research assets generated by our laboratory over the last 30 years. This also demonstrates the utility of retrospective analysis, including use of extand RWA to generate novel results and provide improved asset descriptions. This report also details a novel proof-of-concept for deploying blockchain technology as a tool to facilitate the sharing of reagents and resources between laboratories. A blockchain-based solution can complement existing avenues for asset sharing while offering several distinct advantages. This approach can allow the scientific community to update dynamic research assets while preserving the provenance and history of that asset and record transactions between investigators. Documenting transactions would allow the scientific community to track research assets such that when a lab closes, there are records of other investigators who may possess those research assets through prior sharing. The blockchain solutions deployed here have limitations that must be addressed by continued development, including privacy, automation, and accessibility. However, the potential for blockchain technology to impact the scientific community is significant, and there are opportunities to foster innovation, build collaboration, maximize utility, and preserve the legacy of investigators. In future publications, we aim to address limitations, continue auditing previously published manuscripts, and proactively include reagents from manuscripts we publish in the future. ACKNOWLEDGMENTS This work was supported by grants from the National Institutes of Health, R35-ES028377, T32-HL007936, T32-ES007015, T32-GM140935, P30-CA014520, and the University of Wisconsin Biotechnology Center. The authors would also like to acknowledge the Teaching & Research Application Development (TRAD) and Public Cloud teams of the University of Wisconsin-Madison Department of Information Technology for their assistance in implementing the BigchainDB platform. ABBREVIATIONS UWBC University of Wisconsin Biotechnology Center TCDD 2,3,7,8-tetrachlorodibenzo- p -dioxin PacBio Pacific Biosciences ONT Oxford Nanopore Technologies AWS Amazon Web Services AHR Aryl hydrocarbon receptor RT-qPCR Reverse transcriptase quantitative polymerase chain reaction FRT Flippase recognition target EC2 Elastic compute cloud NFTs Non-fungible tokens cNFTs Compressed non-fungible tokens RWAs Real world assets Footnotes Competing Interest Statement: The authors are not aware of any competing interests. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. DATA AVAILABILITY The PacBio sequencing data is available through the SRA database under the BioProject PRJNA1111952. The plasmid sequences, maps, and success criteria are available on Figshare 10.6084/m9.figshare.28344287 . REFERENCES 1. Fischer BA, Zigmond MJ. The essential nature of sharing in science. Sci Eng Ethics. 2010;16(4):783–99. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Piwowar HA, Day RS, Fridsma DB. Sharing detailed research data is associated with increased citation rate. PLoS One. 2007;2(3):e308. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Christensen G, Dafoe A, Miguel E, Moore DA, Rose AK. A study of the impact of data sharing on article citations using journal policies as a natural experiment. PLoS One. 2019;14(12):e0225883. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Reales G, Wallace C. Sharing GWAS summary statistics results in more citations. Commun Biol. 2023;6(1):116. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Nagaraj A, Shears E, de Vaan M. Improving data access democratizes and diversifies science. Proc Natl Acad Sci U S A. 2020;117(38):23490–8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Murray F, Aghion P, Dewatripont M, Kolev J, Stern S. Of Mice and Academics: Examining the Effect of Openness on Innovation. American Economic Journal: Economic Policy. 2016;8(1):212–52. [ Google Scholar ] 7. Lavis LD. What if we just give everything away? Elife. 2021;10. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Joung JK, Voytas DF, Kamens J. Accelerating research through reagent repositories: the genome editing example. Genome Biol. 2015;16:255. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Theologis A, Davis RW. To give or not to give? That is the question. Plant Physiol. 2004;135(1):4–9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860–921. [ DOI ] [ PubMed ] [ Google Scholar ] 11. Powell K The broken promise that undermines human genome research. Nature. 2021;590(7845):198–201. [ DOI ] [ PubMed ] [ Google Scholar ] 12. Data sharing and the future of science. Nat Commun. 2018;9(1):2817. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Gabelica M, Bojcic R, Puljak L. Many researchers were not compliant with their published data sharing statement: a mixed-methods study. J Clin Epidemiol. 2022;150:33–41. [ DOI ] [ PubMed ] [ Google Scholar ] 14. Zinner DE, Pham-Kanter G, Campbell EG. The Changing Nature of Scientific Sharing and Withholding in Academic Life Sciences Research: Trends From National Surveys in 2000 and 2013. Acad Med. 2016;91(3):433–40. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Institute of Medicine Committee on Resource Sharing in Biomedical R. In: Berns KI, Bond EC, Manning FJ, editors. Resource Sharing in Biomedical Research. Washington (DC): National Academies Press (US) Copyright 1996 by the National Academy of Sciences. All rights reserved.; 1996. [ PubMed ] [ Google Scholar ] 16. Office of The Director NIoH. Final NIH Policy for Data Management and Sharing [NOT-OD-21–013]. https://grants.nih.gov/grants/guide/notice-files/not-od-21-013.html2020 . 17. Ensuring Free, Immediate, and Equitable Access to Federally Funded Research [Memorandum] [press release]. https://www.whitehouse.gov/wp-content/uploads/2022/08/08-2022-OSTP-Public-Access-Memo.pdf : Office of Science and Technology Policy, 08/25/2022 2022. [ Google Scholar ] 18. Principles Register F. and Guidelines for Recipients of NIH Research Grants and Contracts on Obtaining and Disseminating Biomedical Research Resources: Final Notice. In: SERVICES DOHAH, Health NIo, editors. Federal Register: Office of the Federal Register, National Archives and Records Administration; 1999. p. 72090–6. [ Google Scholar ] 19. Health NIo. NIH Policy on Sharing of Model Organisms for Biomedical Research [NOT-OD-04–042]. In: Health NIo, editor. https://grants.nih.gov/grants/guide/notice-files/NOT-OD-04-042.html : U.S. Department of Health and Human Services, National Institutes of Health; 2004. [ Google Scholar ] 20. Marx V To share is to be a scientist. Nat Methods. 2023;20(7):984–9. [ DOI ] [ PubMed ] [ Google Scholar ] 21. Kaiser J, Brainard J. Ready, set, share! Science. 2023;379(6630):322–5. [ DOI ] [ PubMed ] [ Google Scholar ] 22. Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, et al. Next-Generation Sequencing Technology: Current Trends and Advancements. Biology (Basel). 2023;12(7). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Taylor TL, Volkening JD, DeJesus E, Simmons M, Dimitrov KM, Tillman GE, et al. Rapid, multiplexed, whole genome and plasmid sequencing of foodborne pathogens using long- read nanopore technology. Sci Rep. 2019;9(1):16350. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. McGuffie MJ, Barrick JE. pLannotate: engineered plasmid annotation. Nucleic Acids Res. 2021;49(W1):W516–W22. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Hayes KR, Zastrow GM, Nukaya M, Pande K, Glover E, Maufort JP, et al. Hepatic transcriptional networks induced by exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin. Chem Res Toxicol. 2007;20(11):1573–81. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Li H New strategies to improve minimap2 alignment accuracy. Bioinformatics. 2021;37(23):4572–4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Zheng Z, Li S, Su J, Leung AW- S, Lam T-W, Luo R. Symphonizing pileup and full- alignment for deep learning-based long-read variant calling. Nature Computational Science. 2022;2(12):797–803. [ DOI ] [ PubMed ] [ Google Scholar ] 28. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29(1):24–6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Gel B, Serra E. karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data. Bioinformatics. 2017;33(19):3088–90. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Gallegos JE, Rogers MF, Cialek CA, Peccoud J. Rapid, robust plasmid verification by de novo assembly of short sequencing reads. Nucleic Acids Res. 2020;48(18):e106. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Bigchain D, Gmb H. Bigchaindb 2.0: The blockchain database. White paper. 2018. [ Google Scholar ] 32. Yakovenko A Solana: A new architecture for a high performance blockchain v0. 8.13. Whitepaper. 2018. [ Google Scholar ] 33. Carver LA, Jackiw V, Bradfield CA. The 90-kDa heat shock protein is essential for Ah receptor signaling in a yeast expression system. J Biol Chem. 1994;269(48):30109–12. [ PubMed ] [ Google Scholar ] 34. Jain S, Dolwick KM, Schmidt JV, Bradfield CA. Potent transactivation domains of the Ah receptor and the Ah receptor nuclear translocator map to their carboxyl termini. J Biol Chem. 1994;269(50):31518–24. [ PubMed ] [ Google Scholar ] 35. Schmidt JV, Su GH, Reddy JK, Simon MC, Bradfield CA. Characterization of a murine Ahr null allele: involvement of the Ah receptor in hepatic growth and development. Proc Natl Acad Sci U S A. 1996;93(13):6731–6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Carver LA, Bradfield CA. Ligand-dependent interaction of the aryl hydrocarbon receptor with a novel immunophilin homolog in vivo. J Biol Chem. 1997;272(17):11452–6. [ DOI ] [ PubMed ] [ Google Scholar ] 37. Gu YZ, Moran SM, Hogenesch JB, Wartman L, Bradfield CA. Molecular characterization and chromosomal localization of a third alpha-class hypoxia inducible factor subunit, HIF3alpha. Gene Expr. 1998;7(3):205–13. [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Yao G, Craven M, Drinkwater N, Bradfield CA. Interaction networks in yeast define and enumerate the signaling steps of the vertebrate aryl hydrocarbon receptor. PLoS Biol. 2004;2(3):E65. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Walisser JA, Glover E, Pande K, Liss AL, Bradfield CA. Aryl hydrocarbon receptor-dependent liver development and hepatotoxicity are mediated by different cell types. Proc Natl Acad Sci U S A. 2005;102(49):17858–63. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Dunham EE, Stevens EA, Glover E, Bradfield CA. The aryl hydrocarbon receptor signaling pathway is modified through interactions with a Kelch protein. Mol Pharmacol. 2006;70(1):8–15. [ DOI ] [ PubMed ] [ Google Scholar ] 41. Johnson BP, Walisser JA, Liu Y, Shen AL, McDearmon EL, Moran SM, et al. Hepatocyte circadian clock controls acetaminophen bioactivation through NADPH-cytochrome P450 oxidoreductase. Proc Natl Acad Sci U S A. 2014;111(52):18757–62. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Hayes KR, Vollrath AL, Zastrow GM, McMillan BJ, Craven M, Jovanovich S, et al. EDGE: a centralized resource for the comparison, analysis, and distribution of toxicogenomic information. Mol Pharmacol. 2005;67(4):1360–8. [ DOI ] [ PubMed ] [ Google Scholar ] 43. Fabre AL, Colotte M, Luis A, Tuffet S, Bonnet J. An efficient method for long-term room temperature storage of RNA. Eur J Hum Genet. 2014;22(3):379–85. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Schroeder A, Mueller O, Stocker S, Salowsky R, Leiber M, Gassmann M, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006;7:3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Ma Q, Baldwin KT, Renzelli AJ, McDaniel A, Dong L. TCDD-inducible poly(ADP-ribose) polymerase: a novel response to 2,3,7,8-tetrachlorodibenzo-p-dioxin. Biochem Biophys Res Commun. 2001;289(2):499–506. [ DOI ] [ PubMed ] [ Google Scholar ] 46. Brauze D, Widerak M, Cwykiel J, Szyfter K, Baer-Dubowska W. The effect of aryl hydrocarbon receptor ligands on the expression of AhR, AhRR, ARNT, Hif1alpha, CYP1A1 and NQO1 genes in rat liver. Toxicol Lett. 2006;167(3):212–20. [ DOI ] [ PubMed ] [ Google Scholar ] 47. Zeldovich L Genetic drift: the ghost in the genome. Lab Anim (NY). 2017;46(6):255–7. [ DOI ] [ PubMed ] [ Google Scholar ] 48. Bunger MK, Wilsbacher LD, Moran SM, Clendenin C, Radcliffe LA, Hogenesch JB, et al. Mop3 is an essential component of the master circadian pacemaker in mammals. Cell. 2000;103(7):1009–17. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Leible S, Schlager S, Schubotz M, Gipp B. A Review on Blockchain Technology and Blockchain Projects Fostering Open Science. Frontiers in Blockchain. 2019;2. [ Google Scholar ] 50. Kuo TT, Kim HE, Ohno-Machado L. Blockchain distributed ledger technologies for biomedical and health care applications. J Am Med Inform Assoc. 2017;24(6):1211–20. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Gursoy G, Brannon CM, Gerstein M. Using Ethereum blockchain to store and query pharmacogenomics data via smart contracts. BMC Med Genomics. 2020;13(1):74. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. McBee MP, Wilcox C. Blockchain Technology: Principles and Applications in Medical Imaging. J Digit Imaging. 2020;33(3):726–34. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. Nugent T, Upton D, Cimpoesu M. Improving data transparency in clinical trials using blockchain smart contracts. F1000Res. 2016;5:2541. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 54. Abdullah S, Rothenberg S, Siegel E, Kim W. School of Block-Review of Blockchain for the Radiologists. Acad Radiol. 2020;27(1):47–57. [ DOI ] [ PubMed ] [ Google Scholar ] 55. Tripathi G, Ahad MA, Casalino G. A comprehensive review of blockchain technology: Underlying principles and historical background with future challenges. Decision Analytics Journal. 2023;9. [ Google Scholar ] 56. Lashkari B, Musilek P. A Comprehensive Review of Blockchain Consensus Mechanisms. IEEE Access. 2021;9:43620–52. [ Google Scholar ] 57. Ali O, Momin M, Shrestha A, Das R, Alhajj F, Dwivedi YK. A review of the key challenges of non-fungible tokens. Technological Forecasting and Social Change. 2023;187. [ Google Scholar ] 58. Hammi B, Zeadally S, Perez AJ. Non-Fungible Tokens: A Review. IEEE Internet of Things Magazine. 2023;6(1):46–50. [ Google Scholar ] 59. Xiao J, Gundotra N, Adams A, Yakovenko A. Compressing Digital Assets with Concurrent Merkle Trees. Whitepaper. [ Google Scholar ] 60. Harrison C Mice on the move. Lab Anim (NY). 2021;50(9):233–5. [ DOI ] [ PubMed ] [ Google Scholar ] 61. Lloyd K, Franklin C, Lutz C, Magnuson T. Reproducibility: use mouse biobanks or lose them. Nature. 2015;522(7555):151–3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 62. Wilson RH, Carney PR, Glover E, Parrott JC, Rojas BL, Moran SM, et al. Generation of an Allelic Series at the Ahr Locus Using an Edited Recombinant Approach. Toxicol Sci. 2021;180(2):239–51. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 63. Lahvis GP, Bradfield CA. Ahr null alleles: distinctive or different? Biochem Pharmacol. 1998;56(7):781–7. [ DOI ] [ PubMed ] [ Google Scholar ] 64. Chebib J, Jackson BC, Lopez-Cortegano E, Tautz D, Keightley PD. Inbred lab mice are not isogenic: genetic variation within inbred strains used to infer the mutation rate per nucleotide site. Heredity (Edinb). 2021;126(1):107–16. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 65. Keane TM, Goodstadt L, Danecek P, White MA, Wong K, Yalcin B, et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature. 2011;477(7364):289–94. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 66. Poland A, Palen D, Glover E. Analysis of the four alleles of the murine aryl hydrocarbon receptor. Mol Pharmacol. 1994;46(5):915–21. [ PubMed ] [ Google Scholar ] 67. Hall B, Cho A, Limaye A, Cho K, Khillan J, Kulkarni AB. Genome Editing in Mice Using CRISPR/Cas9 Technology. Curr Protoc Cell Biol. 2018;81(1):e57. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 68. Chen Q, Chuai G, Zhang H, Tang J, Duan L, Guan H, et al. Genome-wide CRISPR off- target prediction and optimization using RNA-DNA interaction fingerprints. Nat Commun. 2023;14(1):7521. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. Wheelan SJ, Martinez Murillo F, Boeke JD. The incredible shrinking world of DNA microarrays. Mol Biosyst. 2008;4(7):726–32. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 70. Kaiser J, Malakoff D, Mervis J, Pennisi E, Servick K. Science funding. U.S. shutdown spares an ‘essential’ few. Science. 2013;342(6154):22. [ DOI ] [ PubMed ] [ Google Scholar ] 71. Jahankhani H, Kendzierskyj S. The Role of Blockchain in Underpinning Mission Critical Infrastructure. Industry 40 and Engineering for a Sustainable Future 2019. p. 191–210. [ Google Scholar ] 72. Kumar A, Liu R, Shan Z. Is Blockchain a Silver Bullet for Supply Chain Management? Technical Challenges and Research Opportunities. Decision Sciences. 2019;51(1):8–37. [ Google Scholar ] 73. Kumar Singh R, Mishra R, Gupta S, Mukherjee AA. Blockchain applications for secured and resilient supply chains: A systematic literature review and future research agenda. Computers & Industrial Engineering. 2023;175. [ Google Scholar ] 74. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 75. Strecker D, Pampel H, Schabinger R, Weisweiler NL. Disappearing repositories: Taking an infrastructure perspective on the long-term availability of research data. Quantitative Science Studies. 2023;4(4):839–56. [ Google Scholar ] 76. Jayabalan J, Jeyanthi N. Scalable blockchain model using off-chain IPFS storage for healthcare data security and privacy. Journal of Parallel and Distributed Computing. 2022;164:152–67. [ Google Scholar ] 77. Cagan R The San Francisco Declaration on Research Assessment. Dis Model Mech. 2013;6(4):869–70. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 78. Alzoubi YI, Mishra A. Green blockchain – A move towards sustainability. Journal of Cleaner Production. 2023;430. [ Google Scholar ] 79. Notheisen B, Cholewa JB, Shanmugam AP. Trading Real-World Assets on Blockchain. Business & Information Systems Engineering. 2017;59(6):425–40. [ Google Scholar ] 80. Hou D, Ma W, Zhang W, Li Y, Du Y, Hao Y. An on‐chain trading model of real world asset backed digital assets. IET Blockchain. 2023;4(4):315–23. [ Google Scholar ] 81. Hewa T, Ylianttila M, Liyanage M. Survey on blockchain based smart contracts: Applications, opportunities and challenges. J Netw Comput Appl. 2021;177. [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The PacBio sequencing data is available through the SRA database under the BioProject PRJNA1111952. The plasmid sequences, maps, and success criteria are available on Figshare 10.6084/m9.figshare.28344287 . ACTIONS View on publisher site PDF (2.8 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Research hotspots and trends of GLP-1 receptor agonists in the treatment of Parkinson's disease: a bibliometric analysis.#452777
• A survey of researchers' attitudes to preregistration in animal research reveals multiple perceived barriers to adoption#642430
• Patient-Reported Continuity of GLP-1 Receptor Agonist Therapy.#636527
• Long-Term Follow Up Study#800781
• Preregistration Works: Increased Reporting Quality, Internal Validity, and Protocol Adherence in Animal Studies#16071