Misspellings or "miscellings"-Non-verifiable and unknown cell lines in cancer research publications.

Reproducible laboratory research relies on correctly identified reagents. We have previously described gene research papers with wrongly identified nucleotide sequence(s), including papers studying miR-145. Manually verifying reagent identities in 36 recent miR-145 papers found that 56% and 17% of papers described misidentified nucleotide sequences and cell lines, respectively. We also found 5 cell line identifiers in miR-145 papers with misidentified nucleotide sequences and cell lines, and 18 cell line identifiers published elsewhere, that did not represent indexed human cell lines. These 23 identifiers were described as non-verifiable (NV), as their identities were unclear. Studying 420 papers that mentioned 8 NV identifier(s) found 235 papers (56%) that referred to 7 identifiers (BGC-803, BSG-803, BSG-823, GSE-1, HGC-7901, HGC-803, and MGC-823) as independent cell lines. We could not find any publications describing how these cell lines were established. Six cell lines were sourced from cell line repositories with externally accessible online catalogs, but these cell lines were not indexed as claimed. Some papers also stated that short tandem repeat (STR) profiles had been generated for three cell lines, yet no STR profiles could be identified. In summary, as NV cell lines represent new challenges to research integrity and reproducibility, further investigations are required to clarify their status and identities.

MS-CleanR: A Feature-Filtering Workflow for 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. However, this approach still has many limitations; notably, the difficulty of accurately estimating the number of unique metabolites 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 according to the database chosen by the user, which enhance identification accuracy. Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions fostered class separation resulting from multivariate data analysis and led 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).

Changes in evidence for studies assessing interventions for COVID-19 reported in preprints: meta-research study.

BACKGROUND: The increasing use of preprints to disseminate evidence on the effect of interventions for the coronavirus disease 2019 (COVID-19) can lead to multiple evidence sources for a single study, which may differ in the reported evidence. We aim to describe the proportion of evidence on the effect of interventions for COVID-19 from preprints and journal articles and map changes in evidence between and within different sources reporting on the same study. METHODS: Meta-research study. We screened the Cochrane living systematic review and network meta-analysis (COVID-NMA) database to identify all preprints and journal articles on all studies assessing interventions for COVID-19 published up to 15 August 2020. We compared all evidence sources (i.e., preprint and associated journal article) and the first and latest versions of preprints for each study to identify changes in two evidence components: study results (e.g., numeric change in hazard ratio, odds ratio, event rate, or change in p value > or < 0.05 in any outcome) and abstract conclusions (classified as positive, negative or neutral regarding the intervention effect, and as reporting uncertainty in the findings or not). Changes in study results were further classified as important changes if they (1) represented a change in any effect estimate by ≥ 10% and/or (2) led to a change in the p value crossing the threshold of 0.05. RESULTS: We identified 556 studies. In total, 338 (61%) had been reported in a preprint: 66 (20%) of these had an associated journal article (median time to publication 76 days [interquartile range (IQR) 55-106]) and 91 (27%) had > 1 preprint version. A total of 139 studies (25% of the overall sample) were reported in multiple evidence sources or versions of the same source: for 63 (45%), there was a change in at least one evidence component between or within sources (42 [30%] had a change in study results, and in 29 [21%] the change was classified as important; 33 [24%] had a change in the abstract conclusion). For studies with both a preprint and an article, a median of 29% (IQR 14-50) of total citations were attributed to the preprint instead of the article. CONCLUSIONS: Results on the effect of interventions for COVID-19 are often reported in multiple evidence sources or source versions for a single study. Evidence is not stable between and within evidence sources. Real-time linkage of all sources per study could help to keep systematic reviews up-to-date.

Metabotyping of Andean pseudocereals and characterization of emerging mycotoxins.

Pseudocereals are best known for three crops derived from the Andes: quinoa (Chenopodium quinoa), canihua (C. pallidicaule), and kiwicha (Amaranthus caudatus). Their grains are recognized for their nutritional benefits; however, there is a higher level of polyphenism. Meanwhile, the chemical food safety of pseudocereals remains poorly documented. Here, we applied untargeted and targeted metabolomics approaches by LC-MS to achieve both: i) a comprehensive chemical mapping of pseudocereal samples collected in the Andes; and ii) a quantification of their contents in emerging mycotoxins. An inventory of the fungal community was also realized to better know the fungi present in these grains. Metabotyping permitted to add new insights into the chemotaxonomy of pseudocereals, confirming the previously established phylotranscriptomic clades. Sixteen samples from Peru (out of 27) and one from France (out of one) were contaminated with Beauvericin, an emerging mycotoxin. Several mycotoxigenic fungi were detected, including Aspergillus sp., Penicillium sp., and Alternaria sp.

Packaged foods with pulse ingredients in Europe: A dataset of text-mined product formulations.

There is a lack of methods and tools to reveal robust information on the ingredients used in packaged foods. To tackle this challenge, we developed an original method to parse ingredient lists of packaged foods. We built a dataset of food product innovations with their parsed ingredient lists. We explain the parser algorithm used to provide this dataset; and a benchmark method assessing the performance of the parsing techniques applied on those food ingredient lists. The primary data we used to test and apply this method were retrieved from MINTEL-GNPD. These data cover new food products containing pulse ingredients launched on European markets over the last decade. This work brings original results informing on the diversity of pulse species used in food products, and on the technological features of these ingredients from whole-grain to ultra-processed uses (such as protein isolates). The parsing techniques we developed can be reused to analyse other ingredient lists. This method also makes it possible to assess marketed crop biodiversity in relation to how species diversity is represented in food products, as well as the level of complexity of food formulations. Hence, this work contributes towards providing more complete information on the characteristics of foodstuffs supplied on markets for both private and public stakeholders.

Metabolomic characterization of 5 native Peruvian chili peppers (Capsicum spp.) as a tool for species discrimination.

Many species of chili peppers have overlapping morphological characters and delimitation by visual descriptors in many cases fails to differentiate one species from another. In Peru, there are 413 accessions of native chili pepper and 296 accessions of rocotos conserved in the Germplasm Collections of the National Institute of Agrarian Innovation (INIA), of which five accessions (three species from three locations) were selected for the present metabolomic study. The Discrimination of the three species of native chili peppers and identification of biomarkers was performed using untargeted metabolomic approach based on profiling by UHPLC-HRMS and multivariate data analysis. The samples of fresh chili peppers (whole fruit) from Chincha area were used to construct an OPLS-DA model. To validate the biomarkers (identified 15 biomarkers, mainly flavonoids), an external validation set of the OPLS-DA model was constructed using Chiclayo and Huaral collection datasets. Consequently, the OPLS-DA based on Chincha samples model has a high predictive capacity demonstrating that the biomarkers have a high probability of continuity in any culture space, being successful in discriminating the species by untargeted metabolomics.

Identification of human gene research articles with wrongly identified nucleotide sequences.

Nucleotide sequence reagents underpin molecular techniques that have been applied across hundreds of thousands of publications. We have previously reported wrongly identified nucleotide sequence reagents in human research publications and described a semi-automated screening tool Seek & Blastn to fact-check their claimed status. We applied Seek & Blastn to screen >11,700 publications across five literature corpora, including all original publications in Gene from 2007 to 2018 and all original open-access publications in Oncology Reports from 2014 to 2018. After manually checking Seek & Blastn outputs for >3,400 human research articles, we identified 712 articles across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of >13,700 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. The 712 problematic articles have received >17,000 citations, including citations by human clinical trials. Given our estimate that approximately one-quarter of problematic articles may misinform the future development of human therapies, urgent measures are required to address unreliable gene research articles.

Day-to-day discovery of preprint-publication links.

Preprints promote the open and fast communication of non-peer reviewed work. Once a preprint is published in a peer-reviewed venue, the preprint server updates its web page: a prominent hyperlink leading to the newly published work is added. Linking preprints to publications is of utmost importance as it provides readers with the latest version of a now certified work. Yet leading preprint servers fail to identify all existing preprint-publication links. This limitation calls for a more thorough approach to this critical information retrieval task: overlooking published evidence translates into partial and even inaccurate systematic reviews on health-related issues, for instance. We designed an algorithm leveraging the Crossref public and free source of bibliographic metadata to comb the literature for preprint-publication links. We tested it on a reference preprint set identified and curated for a living systematic review on interventions for preventing and treating COVID-19 performed by international collaboration: the COVID-NMA initiative (covid-nma.com). The reference set comprised 343 preprints, 121 of which appeared as a publication in a peer-reviewed journal. While the preprint servers identified 39.7% of the preprint-publication links, our linker identified 90.9% of the expected links with no clues taken from the preprint servers. The accuracy of the proposed linker is 91.5% on this reference set, with 90.9% sensitivity and 91.9% specificity. This is a 16.26% increase in accuracy compared to that of preprint servers. We release this software as supplementary material to foster its integration into preprint servers' workflows and enhance a daily preprint-publication chase that is useful to all readers, including systematic reviewers. This preprint-publication linker currently provides day-to-day updates to the biomedical experts of the COVID-NMA initiative.

Deciphering the phylogeny of violets based on multiplexed genetic and metabolomic approaches.

Molecular phylogenetics based on nucleotide sequence comparisons has profoundly influenced plant taxonomy. A comprehensive chemotaxonomical approach based on GC-MS and UHPLC-HRMS profiling was evaluated for its ability to characterize a large collection of plants all in the violet family Violaceae (n = 111) and thus decipher the taxonomy. A thorough identification of violets is challenging due to their natural hybridization and phenotypic variability. Phylogenetic inference performed on ribosomal internal transcribed spacer sequences using maximum likelihood and neighbor-joining distance methods allowed the clear identification of 58% of the collection. Metabolomic approaches with multivariate data analysis were performed on SPME/GC-MS chromatograms of volatile compounds emitted by fresh mature flowers and on UHPLC-HRMS/MS leaf extracts for non-volatile compounds. Interestingly, molecular and biochemical approaches provided separate classifications while highlighting several common clusters. The profiling of secondary metabolites was proved most suitable for the classification of hundreds of extracts. The combination of phylogenetic and chemotaxonomic approaches, allowed the classification of 96% of the entire collection. A correlation network revealed specific chemotaxonomic biomarkers, in particular flavonoids, coumarins and cyclotides. Overall, our pioneering approach could be useful to solve misclassification issues within collections of close plant species.