Biology subjects

Cabanac, G.

Publications and source records attributed to Cabanac, G..

4 recordsLinked to original sources

bioRxiv2024

Misspellings or miscellings- non-verifiable cell lines in cancer research publications

Reproducible laboratory research relies on correctly identified reagents. We have previously described human gene research papers with wrongly identified nucleotide sequence reagent(s), including papers studying miR-145. Manually verifying reagent identities in more recent miR-145 papers found 20/36 (56%) and 6/36 (17%) miR-145 papers with misidentified nucleotide sequence reagent(s) and human cell line(s), respectively. We also found 5 cell line identifiers in two miR-145 papers with wrongly identified nucleotide sequences and cell lines, and 18 identifiers published elsewhere that did not correspond to indexed cell lines. These cell line identifiers were described as non-verifiable, as their identities appeared uncertain. Studying 420 papers that mentioned 8 different non-verifiable cell line identifier(s) found 235 papers (56%) that appeared to refer to BGC-803, BSG-803, BSG-823, GSE-1, HGC-7901, HGC-803 and/or MGC-823 as independent cell lines. We could not find publications describing how these cell lines were established, and they were not indexed in claimed externally accessible cell line repositories. While some papers stated that STR profiles had been generated for BGC-803, GSE-1 and/or MGC-823 cells, no STR profiles were identified. In summary, non-verifiable human cell lines represent new challenges to research reproducibility and require further investigation to clarify their identities. Novelty and Impact StatementThrough verifying reagent identities in research publications, our team found 23 non-verifiable human cell line identifiers, most of which could represent misspellings of contaminated cancer cell lines. Of 8 identifiers studied in detail, 7 non-verifiable identifiers were unexpectedly referred to as independent cell lines across 235 publications. We therefore describe a process "miscelling", where published cell lines lack descriptions of how they were established, cannot be found in claimed external repositories and lack STR profiles.

cancer biology↗

bioRxiv2022

Metabotyping of Andean pseudocereals and characterization of emerging mycotoxins

Pseudocereals are best known for three crops derived from the Andes: quinoa (Chenopodium quinoa, Chenopods I), canihua (C. pallidicaule, Chenopods I), and kiwicha (Amaranthus caudatus). Their grains are recognized for their nutritional benefits; however, there is a higher level of polyphenism and the chemical foundation that would rely with such polyphenism has not been thoroughly investigated. Meanwhile, the chemical food safety of pseudocereals remains poorly documented. Here we applied untargeted and targeted metabolomics approach by LC-MS to achieve both: i) a comprehensive chemical mapping of pseudocereal samples collected in the Andes to classify them according to their chemotype; ii) a quantification of their contents in emerging mycotoxins. An inventory of the fungal community was also realized with the aims to better know the filamentous fungi present in these grains and try to parallel this information with the presence of the molecules produced, especially mycotoxins. Metabotyping permitted to add new insights into the chemotaxonomy of pseudocereals, confirming the previously established phylotranscriptomic clades: Chenopods I (clusters quinoa and canihua), and Amaranthaceae s.s. (cluster kiwicha). Moreover, we report for the first time the presence of mycotoxins in pseudocereals. Sixteen samples of Peru (out of 27) and one sample from France (out of one) were contaminated with Beauvericin, an emerging mycotoxin. There were several mycotoxigenic fungi detected, including Aspergillus sp., Penicillium sp., and Alternaria sp., but not Fusaria. Highlights{blacksquare} Twenty-seven grain samples of Andean pseudocereals were profiled by LC-HRMS. {blacksquare}Untargeted metabolomics was used to differentiate varieties from the whole metabolome dataset. {blacksquare}Five mycotoxins were quantify using targeted metabolomics. {blacksquare}Sixteen samples of Peru and one sample from France were contaminated with Beauvericin. {blacksquare}An inventory of the fungal community infesting the Andean pseudocereal samples was documented.

biochemistry↗

bioRxiv2021

Human gene function publications that describe wrongly identified nucleotide sequence reagents are unacceptably frequent within the genetics literature

Nucleotide sequence reagents underpin a range of molecular genetics techniques that have been applied across hundreds of thousands of research publications. We have previously reported wrongly identified nucleotide sequence reagents in human gene function publications and described a semi-automated screening tool Seek & Blastn to fact-check the targeting or non-targeting status of nucleotide sequence reagents. We applied Seek & Blastn to screen 11,799 publications across 5 literature corpora, which included all original publications in Gene from 2007-2018 and all original open-access publications in Oncology Reports from 2014-2018. After manually checking the Seek & Blastn screening outputs for over 3,400 human research papers, we identified 712 papers across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of over 13,700 nucleotide sequences highlighted 1,535 wrongly identified sequences, most of which were claimed targeting reagents for the analysis of 365 human protein-coding genes and 120 non-coding RNAs, respectively. The 712 problematic papers have received over 17,000 citations, which include citations by human clinical trials. Given our estimate that approximately one quarter of problematic papers are likely to misinform or distract the future development of therapies against human disease, urgent measures are required to address the problem of unreliable gene function papers within the literature. Author summaryThis is the first study to have screened the gene function literature for nucleotide sequence errors at the scale that we describe. The unacceptably high rates of human gene function papers with incorrect nucleotide sequences that we have discovered represent a major challenge to the research fields that aim to translate genomics investments to patients, and that commonly rely upon reliable descriptions of gene function. Indeed, wrongly identified nucleotide sequence reagents represent a double concern, as both the incorrect reagents themselves and their associated results can mislead future research, both in terms of the research directions that are chosen and the experiments that are undertaken. We hope that our research will inspire researchers and journals to seek out other problematic human gene function papers, as we are unfortunately concerned that our results represent the tip of a much larger problem within the literature. We hope that our research will encourage more rigorous reporting and peer review of gene function results, and we propose a series of responses for the research and publishing communities.

genetics↗

bioRxiv2020

MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the gold-standard technique to determine the full chemical diversity in biological samples. This approach still has many limitations, however; notably, the difficulty of estimating accurately the number of unique metabolites being profiled among the thousands of MS ion signals arising from chromatograms. Here, we describe a new workflow, MS-CleanR, based on the MS-DIAL/MS-FINDER suite, which tackles feature degeneracy and improves annotation rates. We show that implementation of MS-CleanR reduces the number of signals by nearly 80% while retaining 95% of unique metabolite features. Moreover, the annotation results from MS-FINDER can be ranked with respect to database chosen by the user, which improves identification accuracy. Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions improved class separation resulting from multivariate data analysis and lead to annotation of 75% of the final features. The full workflow was applied to metabolomic profiles from three strains of the leguminous plant Medicago truncatula that have different susceptibilities to the oomycete pathogen Aphanomyces euteiches; a group of glycosylated triterpenoids overrepresented in resistant lines were identified as candidate compounds conferring pathogen resistance. MS-CleanR is implemented through a Shiny interface for intuitive use by end-users (available at: https://github.com/eMetaboHUB/MS-CleanR).

bioinformatics↗