Search

Background The Thermo Scientific™ SureTect™ Salmonella species PCR Assay utilizes Solaris™ reagents for performing PCR for the rapid and specific detection of Salmonella species in a broad range of foods and select environmental surfaces. Objective The aims were to demonstrate the reproducibility of the Thermo Scientific SureTect Salmonella species PCR Assay in a collaborative study using a challenging matrix, cocoa powder, and to extend the scope of the method. Method In the collaborative study, the candidate method was compared to the US Food and Drug Administration (FDA) Bacteriological Analytical Manual (BAM) Chapter 5 Salmonella reference method. The candidate method used two PCR thermocyclers, the Applied Biosystems™ QuantStudio™ 5 Real-Time PCR instrument (QS5) and the Applied Biosystems 7500 Fast Real-Time PCR instrument (7500 Fast). Fourteen participants from nine laboratories located within the United States and Europe were solicited for the collaborative study, with 12 participants submitting valid data. Three levels of contamination were evaluated for each matrix. Statistical analysis was conducted according to the probability of detection statistical model. In addition, 11 matrix studies were performed comparing the candidate method to the FDA/BAM Chapter 5 or US Department of Agriculture, Food Safety and Inspection Service, Microbiology Laboratory Guidebook 4.10 Isolation and Identification of Salmonella from Meat, Poultry, Pasteurized Egg, and Siluriformes (Fish) Products and Carcass and Environmental Sponges reference method. Nine of these matrices were also compared to the EN ISO 6579–1:2017/Amd.1:2020(E) Microbiology of the food chain—Horizontal method for the detection, enumeration and serotyping of Salmonella—Part 1: Detection of Salmonella spp.—AMENDMENT 1: Broader range of incubation temperatures, amendment to the status of Annex D, and correction of the composition of MSRV and SC reference method. Results In the collaborative study, the difference in laboratory results indicates equivalence between the candidate method and reference method for the matrix evaluated, and the method demonstrated acceptable interlaboratory reproducibility as determined in the collaborative evaluation. False-positive and false-negative rates were determined for the matrix and produced values of Conclusions Based on the data generated, the method demonstrated acceptable interlaboratory reproducibility data and statistical analysis. Highlights Due to the COVID-19 pandemic, some participants had to be trained remotely. Additionally, 375 g cocoa powder is known to be a challenging matrix for PCR methods. No unusual cross-contamination or false-positive/negative was reported, highlighting the ease of use, reproducibility, and robustness of the method.

Background Stable carbon isotope analysis of sugars in honey by LC–isotope ratio mass spectrometry (IRMS) is a useful tool for detecting adulteration of honey with extraneous sugar. Purity criteria based on 13C/12C ratios of saccharides in honey, determined by LC–IRMS of a large number of authentic honey samples, have been elaborated. However, no interlaboratory comparison (ILC) has yet been performed to estimate the precision of the method under reproducibility conditions. Objective To address this knowledge gap an ILC involving 14 laboratories and using six honey samples was conducted. Methods The participants were allowed to use their LC–IRMS-based method of choice for sample preparation and compound separation. Results The precision figures were estimated according to ISO 5725:1994. The repeatability relative standard deviation (RSDr) for the determination of δ13C values of fructose and glucose varied between 0.3 and 0.5%, with 0.3 and 1.0% for disaccharides, and 0.7 and 2.8% for trisaccharides. The RSDR varied between 0.8 and 1.8% for the monosaccharides, 1.0 and 1.5% for disaccharides, and 1.4 and 2.8% for trisaccharides. Conclusion Based on the obtained precision data the LC–IRMS method for the determination of 13C/12C ratios of saccharides in honey was considered fit for the conformity assessment of honey with established purity criteria. Highlights Precision estimates for a LC–IRMS method to determine 13C/12C ratios of saccharides in honey were obtained through an ILC. The data created can form the basis for the standardization of the method by interested standards-developing organizations for use in official control.

Background Pyrrolizidine alkaloids (PAs) are naturally occurring plant toxins associated with potential hepatic and carcinogenic diseases in humans and animals. The concern over PAs has increased as the consumption of herbal medicines has increased. Objective This study aimed to develop and validate a sensitive analytical method to determine 28 PAs in five herbal medicines using liquid chromatography (LC)-electrospray ionization (ESI)-tandem mass spectrometry (MS/MS). Additionally, this study identified and quantified the amount of PAs in 10 samples of each herbal medicine. Methods The pretreatment in the proposed LC-MS/MS analysis comprised solvent extraction using 0.05M H2SO4 in 50% methanol and clean-up step using an mixed-mode cationic exchange (MCX)-solid-phase extraction (SPE) cartridge. The PA contents in herbal medicines were measured by using the developed method. Results The proposed method had recoveries ranging from 72.5–123.7% for the Atractylodis Rhizoma Alba, 70.6–151.7% for Alba Chrysanthmi Flos, 80.6–130.9% for Leonuri Herba, 70.3–122.9% for Gastrodiae Rhizoma, and 67.1–106.9% for Glycyrrhizae Radix. Even though a few samples showed recoveries in unsatisfactory values, the proposed method indicated entirely sufficient recoveries and precision in most samples. In monitoring results, only Leonuri Herba contained two PAs, which indicated Retrorsine (4/10) of 84.7–120.9 μg/kg and Senkirkine (10/10) of 60.9–170.7 μg/kg. Conclusion The results obtained from this study demonstrate that the proposed method is fit for purpose to determine 28 PAs in herbal medicines. Therefore it could serve as a regulatory method capable of being used for controlling the risks of PAs in certain medicinal plants and dietary supplements. Highlights An LC-MS/MS method for the determination of 28 pyrrolizidine alkaloids in herbal medicines was developed and validated through this study. The proposed method is considered as an useful method for monitoring pyroolizidine alkaloids in herbal medicines.

This report describes updates to the National Safety Council’s Alcohol, Drugs and Impairment Division’s recommendations for drug testing in driving under the influence of drug (DUID) cases and motor vehicle fatalities. The updates are based on a survey of drug testing practices in laboratories in the USA and Canada, a comprehensive review of the prior recommendations and data and research on drugs most frequently detected in DUID cases. A consensus meeting was held with representative forensic science practitioners and the authors of this report to update recommendations. No changes were made to the Tier I scope; however, there were changes to cutoffs of some analytes for blood, urine and oral fluid. Due to increased prevalence in DUID cases, trazodone and difluoroethane were added to the Tier II scope. For clarification, Tier I cutoffs reflect free concentrations, and hydrolysis is recommended but not required. The consensus panel concluded that urine is an inferior matrix to blood and oral fluid as it may represent historical use or exposure unrelated to observed impairment; therefore, future iterations of these recommendations will not include urine as a recommended matrix. Laboratories currently testing urine should work with traffic safety partners to encourage the use of blood and oral fluid as more appropriate specimens and adjust their capabilities to provide that testing.

Methoxyacetylfentanyl is one of many fentanyl analogs available as new psychoactive substances. It have been encountered in both the European Union and the United States, and existing literature suggest that methoxyacetylfentanyl is around 3- to 5-fold less potent than fentanyl. The aim of the present work was to combine case information with blood concentrations and abundance of urinary metabolites to investigate the importance of these parameters for toxicological interpretation. Quantification of methoxyacetylfentanyl in femoral blood was performed by LCMS-MS and urinary metabolites were analyzed by LC-QTOF-MS with and without hydrolysis with beta-glucuronidase/arylsulfatase. For confirmation of identified metabolites, methoxyacetylfentanyl was incubated with hepatocytes for up to 5 hours and analyzed with the same method as the urine samples. In eleven postmortem cases (27 to 41 years old and including one female) methoxyacetylfentanyl was reported in femoral blood. The cause of death was intoxication by methoxyacetylfentanyl alone or in combination with other drugs in all but one case, where death was attributed to acute complications of an underlying heart disease but with possible contribution from methoxyacetylfentanyl. In total, 27 urinary metabolites were found, including eight glucuronides. Major biotransformations were O-demethylation, dealkylation to form the nor-metabolite, monoand dihydroxylations of the phenethyl moiety, as well as combinations thereof. The most abundant metabolites in hydrolyzed urine included O-desmethyl-, O-desmethyl-phenethyl-hydroxy-, Odesmethyl-phenethyl-hydroxymethoxy- and nor-methoxyacetylfentanyl. Differences in the abundance of methoxyacetylfentanyl and its major metabolites could be interpreted to indicate fatal intoxications in abstinent or chronic users. We postulate that urinary concentrations of methoxyacetylfentanyl and two metabolites, in combination with the methoxyacetylfentanyl concentration in femoral blood, might be good indicators of the time between administration and death as well as prior use. Funding Agencies|Strategiomradet Forensiska Vetenskaper (Strategic Research Area Forensic Sciences) at Linkoping University, Sweden [2017-10]

Background In Japan, the residue definition for fosetyl-Al is “sum of fosetyl-Al and phosphonic acid expressed as fosetyl-Al” and its current provisional maximum residue limit (MRL) in cereals is under review. Objective For establishment and enforcement of fosetyl-Al MRL in cereals, a new analytical method for fosetyl-Al and phosphonic acid in cereals should be developed and validated. Method The new method involves water extraction, cleanup using tandem cation- and anion-exchange mini columns, and determination by LC-MS/MS. It was validated in a single laboratory and multiple laboratories. Using the method, 41 samples of rice, wheat, and barley were analyzed. Results In the multiple-laboratory validation: repeatability and reproducibility for three concentrations of fosetyl-Al and phosphonic acid were in ranges of 4.8–20 and 5.9–34%; calculated sum of fosetyl-Al and phosphonic acid, expressed as fosetyl-Al, showed good recoveries; linearity was observed for fosetyl-Al and phosphonic acid in ranges of 0.005–0.4 and 0.025–2.0 mg/kg; and specificity was sufficient. The method was verified for rice matrixes. In 41 samples, phosphonic acid was detected up to 0.2 mg/kg while fosetyl was not. Conclusions The method was successfully validated with high precision, accuracy, linearity, and specificity and capable of analyzing fosetyl-Al and phosphonic acid with a practical LOQ of 0.01 and 0.05 mg/kg. The LOQs and concentrations of phosphonic acid in samples indicate that a potential MRL would be 0.5 mg/kg for fosetyl-Al in cereals. Highlights The validated method was simpler than many methods and did not require derivatization, matrix-matched orisotopically-labeled internal standards.

Background The Eurofins GeneScan Technologies’ VIRSeek SARS-CoV-2 Mplex kit is a RT (reverse transcription) real-time polymerase chain reaction (RT-qPCR) assay for the detection of two targets on the N-gene (nucleocapsid) of SARS-CoV-2. An extraction control, that allows monitoring of the extraction procedure and PCR inhibition, is included. Objective In silico analysis and wet testing showed inclusivity and exclusivity of the assay. The complete workflow starting from surface swabbing (VIRSeek PATHOSwab kit), RNA extraction (VIRSeek RNAExtractor), RT-PCR (VIRSeek SARS-CoV-2 Mplex), and evaluation with FastFinder was validated in comparison to the CDC method for detection of SARS-CoV-2 on stainless steel. Method In silico analysis was performed by using the MFOLD online program. The matrix study was performed for stainless steel inoculated with SARS-CoV-2 isolated from the first documented US case of a traveler from Wuhan, China. Results For inclusivity, 15 764 sequences were analyzed and all mismatches (0.37% of the sequences had single mismatches) were considered non-critical. Cross reactivity for closely related viruses and background organisms was performed, resulting in correct exclusion of all. No significant differences were observed for the probability of detection (POD) study when comparing to the CDC method. Conclusions Results of the inclusivity and exclusivity study show that the assay is specific for detection of SARS-CoV-2. The POD study showed no statistically significant difference compared to the CDC reference method, results were identical for the uninoculated and the high level. For the fractional recovery level, the candidate method detected 9/17 samples leading to a POD of 0.47, the reference method detected 11/20 samples leading to a POD of 0.55. Highlight The complete workflow starting from swabbing of the surface (VIRSeek PATHOSwab kit), RNA extraction (VIRSeek RNAExtractor), RT-PCR (VIRSeek SARS CoV-2 Mplex) and evaluation with FastFinder was validated in comparison to the US Centers for Disease Control and Prevention method for detection of SARS-CoV-2 on Stainless Steel.

Background The PhageDx™Cronobacter Assay is based on the infection of Cronobacter spp. by specific bacteriophages and expression of a luciferase reporter gene. Results are generated in as little as 18.5 h for powdered infant formula (PIF). Objective An AOAC Performance Tested MethodsSM (PTM) study was conducted to validate the PhageDx Cronobacter Assay for the detection of Cronobacter in 10, 100, and 300 g milk- and soy-based PIF test portions. Method The performance of the PhageDx method was compared to the ISO 22964:2006/2017 Microbiology of the Food Chain—Horizontal Method for the Detection of Cronobacter spp. and the U.S. Food and Drug Administration (FDA) Bacteriological Analytical Manual (BAM) Chapter 29 Cronobacter: 2012. Inclusivity/exclusivity, product consistency and stability, and robustness testing also were conducted. Results There was no significant difference between the 10, 100, or 300 g test portions for the milk and soy PIF matrixes between the PhageDx Cronobacter Assay, the ISO 22964:2006/2017, and the FDA BAM Chapter 29 Cronobacter: 2012 methods. The reporter bacteriophages were specific for Cronobacter and infected 75 strains in inclusivity testing. They did not infect 35 non-Cronobacter bacteria in exclusivity testing. Robustness testing showed that the method performed well with specific deviations from the standard protocol. Consistency and stability testing demonstrated that the recombinant phage gave consistent results across three production lots and was stable when stored under appropriate conditions for at least 3 months. Conclusions Work in the submitting and independent laboratories demonstrated that the PhageDx Cronobacter Assay meets the qualifications for PTM status. Highlights The PhageDx Cronobacter Assay is a rapid, simple, and specific test that has shown equivalence to both the FDA BAM and ISO reference methods for detecting Cronobacter spp. in PIF.

Background The PhageDx™ Listeria Assay is a simple, specific, and sensitive assay based on the infection of Listeria spp. by selected bacteriophages and the resultant expression of a luciferase reporter gene. Results are generated in as little as 24.5 h for stainless steel and ceramic environmental surfaces. Objective An AOAC Performance Tested MethodsSM (PTM) study was conducted to validate the PhageDx Listeria Assay for the detection of Listeria on stainless steel and ceramic surfaces. Method The performance of the PhageDx method was compared to that of the U.S. Food and Drug Administration (FDA) Bacterial Analytical Manual (BAM) Ch. 10. Inclusivity/exclusivity, product consistency and stability, and robustness testing also were conducted. Results Inclusivity testing demonstrated that the reporter bacteriophages were specific for Listeria ssp. and detected 58/61 Listeria strains tested, including all 34 L. monocytogenes strains. The reporter bacteriophage also was shown to not detect 46/47 non-Listeria bacteria in exclusivity testing. Robustness testing showed that the method performed well with specific deviations from the standard protocol. Consistency and stability testing demonstrated that the recombinant phage gave consistent results across three production lots and was stable when stored under appropriate conditions for at least 6 months. Matrix studies on stainless steel and ceramic surfaces showed that there was no significant difference between the PhageDx Listeria Assay and the FDA/BAM Chapter 10 reference method. Conclusions and Highlights The validation study demonstrates that the PhageDx Listeria Assay is an effective method for the detection of Listeria spp. on stainless steel and ceramic environmental surfaces and meets the qualifications for AOAC PTM status.

Background Paracetamol (PC) is one of the most widely used analgesic and antipyretic drugs and has recently been integrated into the supportive therapy of COVID-19. Pharmaceuticals containing methionine (MT) with PC may contribute to avoid hepatotoxicity and eventual PC overdose-dependent death. Objective The current work purposes to develop and validate two chromatographic methods for the simultaneous determination of MT and PC in the presence of two PC impurities (4-nitrophenol [NP] and 4-aminophenol [AP]). Method Two chromatographic methods were established and validated according to the International Conference on Harmonization guidelines. The first one was an RP-HPLC/UV method based on applying a “dual-mode” gradient elution. The separation was realized via varying both the composition of the ternary mobile phase (acetonitrile–methanol–water) and its flow rate. This strategy enabled a relatively rapid analysis with a satisfactory resolution, although the investigated compounds exhibit a significant difference in lipophilicity. The second one relied on TLC-densitometry, where the optimum separation was realized using a quaternary mobile phase system composed of butanol–dioxane–toluene–methanol (8:2.5:3.5:0.3, by volume). Both methods were monitored at 220 nm. Results The developed methods were proven to be robust, accurate, specific, and appropriate for the routine analysis of PC in its pure form or in pharmaceutical formulations with MT in quality control laboratories. Conclusions The corresponding methods are suitable to determine MT and PC in the presence of PC impurities. Highlights The study achieves the analysis of MT and PC in the presence of PC impurities via the application of HPLC and TLC-densitometry methods.

Background The level of available carbohydrates in our diet is directly linked to two major diseases: obesity and Type II diabetes. Despite this, to date there is no method available to allow direct and accurate measurement of available carbohydrates in human and animal foods. Objective The aim of this research was to develop a method that would allow simple and accurate measurement of available carbohydrates, defined as non-resistant starch, maltodextrins, maltose, isomaltose, sucrose, lactose, glucose, fructose, and galactose. Method Non-resistant (digestible) starch is hydrolyzed to glucose and maltose by pancreatic α-amylase (PAA) and amyloglucosidase at pH 6.0 with shaking or stirring at 37°C for 4 h. Sucrose, lactose, maltose, and isomaltose are completely hydrolyzed by specific enzymes to their constituent monosaccharides, which are then measured using pure enzymes in a single reaction cuvette. Results A method has been developed that allows the accurate measurement of available carbohydrates in all cereal, vegetable, fruit, food, and feed products, including dairy products. Conclusions A single-laboratory validation was performed on a wide range of food and feed products. The inter-day repeatability (RSDr, %) was Highlights A unique method has been developed for the direct measurement of available carbohydrates, entailing separate measurement of glucose, fructose, and galactose, information of value in determining the glycemic index of foods.

Legionnaires’ disease has been recognized since 1976 and Legionella pneumophila still accounts for more than 95% of cases. Approaches in countries, including France, suggest that focusing risk reduction specifically on L. pneumophila is an effective strategy, as detecting L. pneumophila has advantages over targeting multiple species of Legionella. In terms of assays, the historically accepted plate culture method takes 10 days for confirmed Legionella spp. results, has variabilities which affect trending and comparisons, requires highly trained personnel to identify colonies on a plate in specialist laboratories, and does not recover viable-but-non-culturable bacteria. PCR is sensitive, specific, provides results in less than 24 h, and determines the presence/absence of Legionella spp. and/or L. pneumophila DNA. Whilst specialist personnel and laboratories are generally required, there are now on-site PCR options, but there is no agreement on comparing genome units to colony forming units and action limits. Immunomagnetic separation assays are culture-independent, detect multiple Legionella species, and results are available in 24 h, with automated processing options. Field-use lateral flow devices provide presence/absence determination of L. pneumophila serogroup 1 where sufficient cells are present, but testing potable waters is problematic. Liquid culture most probable number (MPN) assays provide confirmed L. pneumophila results in 7 days that are equivalent to or exceed plate culture, are robust and reproducible, and can be performed in a variety of laboratory settings. MPN isolates can be obtained for epidemiological investigations. This accessible, non-technical review will be of particular interest to building owners, operators, risk managers, and water safety groups and will enable them to make informed decisions to reduce the risk of L. pneumophila.

High throughput and high-resolution lipid analyses are important for many biological model systems and research questions. This comprises both monitoring at the individual lipid species level and broad lipid classes. Here, we present a nontarget semiquantitative lipidomics workflow based on ultrahigh performance supercritical fluid chromatography (UHPSFC)-mass spectrometry (MS). The optimized chromatographic conditions enable the base-line separation of both nonpolar and polar classes in a single 7-minute run. Ionization efficiencies of lipid classes vary 10folds in magnitude and great care must be taken in a direct interpretation of raw data. Therefore, the inclusion of internal standards or experimentally determined Response factors (RF) are highly recommended for the conversion of raw abundances into (semi) quantitative data. We have deliberately developed an algorithm for automatic semiquantification of lipid classes by RF. The workflow was tested and validated using a bovine liver extract with satisfactory results. The RF corrected data provide a more representative relative lipid class determination, but also the interpretation of individual lipid species should be performed on RF corrected data. In addition, semiquantification can be improved by using internal or also external standards when more accurate quantitative data are of interest but this requires validation for all new sample types. The workflow established greatly extends the potential of nontarget UHPSFC–MS/MS based analysis.

Background Current developments in portable photonic devices for fast authentication of extra virgin olive oil (EVOO) or EVOO with non-EVOO additions steer towards hyphenation of different optic technologies. The multiple spectra or so-called “fingerprints” of samples are then analyzed with multivariate statistics. For EVOO authentication, one-class classification (OCC) to identify “out-of-class” EVOO samples in combination with data-fusion is applicable. Objective Prospecting the application of a prototype photonic device (“PhasmaFood”) which hyphenates visible, fluorescence, and near-infrared spectroscopy in combination with OCC modelling to classify EVOOs and discriminate them from other edible oils and adulterated EVOOs. Method EVOOs were adulterated by mixing in 10–50% (v/v) of refined and virgin olive oils, olive-pomace olive oils, and other common edible oils. Samples were analyzed by the hyphenated sensor. OCC, data-fusion, and decision thresholds were applied and optimized for two different scenarios. Results By high-level data-fusion of the classification results from the three spectral databases and several multivariate model vectors, a 100% correct classification of all pure edible oils using OCC in the first scenario was found. Reducing samples being falsely classified as EVOOs in a second scenario, 97% of EVOOs adulterated with non-EVOO olive oils were correctly identified and ones with other edible oils correctly classified at score of 91%. Conclusions Photonic sensor hyphenation in combination with high-level data fusion, OCC, and tuned decision thresholds delivers significantly better screening results for EVOO compared to individual sensor results. Highlights Hyphenated photonics and its data handling solutions applied to extra virgin olive oil authenticity testing was found to be promising.

Background The use of hypoallergenic infant formulas and the need for reliable tests to determine the presence of residual antigens have increased in parallel. Objective An LC-MS method for quantitation of casein was validated using incurred samples and a matrix-matched external standard curve. Method Powdered infant formula samples were extracted in a buffer of sodium deoxycholate and ammonium bicarbonate at 60°C and filtered through 7 kDa desalting columns. Samples were digested overnight with trypsin and precipitated with acid prior to analysis of marker peptides by tandem mass spectrometry. Results Based on three marker peptides, the linear range for casein was 1.8–42 μg/g of powdered infant formula with an LOQ of 1.8 μg/g. The determination coefficients (R2) for each curve were ≥0.99 for casein peptides. Method repeatability was ≤22% RSD and intermediate precision was ≤23% RSD; recovery of casein from incurred material (2–20 µg/g) ranged from 78% to 118%. Conclusions An LC-MS/MS method was developed and validated for confirmation of casein allergens in hypoallergenic infant formula. Highlights A method was developed to accurately and reliably quantify casein allergens in extensively hydrolyzed casein infant formula by LC-MS without the need for custom peptide standards.

Hypertension is one of the most important modifiable risk factor causing cardiovascular disease. Unfortunately, non-adherence to antihypertensive medications is frequently observed in hypertensive patients and can lead to an increase in morbidity and mortality. Until recently, there was no robust clinical method to objectively diagnose non-adherence. Recently, the detection of medications in urine or blood by mass spectrometry techniques such as liquid chromatography-tandem mass spectrometry (LC–MS-MS) has been accepted as the diagnostic method of choice for the detection of non-adherence. Despite this, it is unclear whether the concentration of urine can affect the detection of medications in urine. Therefore, this study aimed to assess the effect of urine concentration on detection of antihypertensive medications by LC–MS-MS in which urine creatinine is used as an independent marker of urine concentration. Biochemical adherence results for 22 different medications (1,709 prescriptions) in 463 different subjects were converted to an adherence score. The adherence score was defined as the ratio of the total number of subjects in which the drug was detected to the total number of subjects to whom the drug was prescribed. The adherence scores for each medication were correlated with urine creatinine concentration for each medication. Non-adherence was observed in 47.1% of samples with a mean urine creatinine concentration of these samples of 9.4 ± 7.1 mmol/L. There was no significant difference between the urine creatinine concentrations in the detected vs non-detected groups for each of the 22 medications. Furthermore, there are no differences in adherence scores across the urine creatinine concentration. This is the first study to demonstrate that urine creatinine concentration does not affect the results of the adherence screening by LC–MS-MS.

Amplifex Diene reagent was employed to derivatize estradiol (E2) to enhance the analyte signal at low picogram concentrations. This derivatization enabled measurement of E2 (and other estrogens) in ESI+ mode, earlier retention times for analytes than other methods, avoidance of MS harmful ammonium fluoride in mobile phases, and an LLOQ below 1 pg/mL. The sample preparation workflow involved liquid–liquid extraction followed by Amplifex Diene derivatization for 10 min at ambient temperature. Samples were chromatographed using a standard C18 column and analyzed using a SCIEX 6500+ mass spectrometer. The assay calibrators were prepared in-house, traceable to certified reference materials, and ranged from 1.29 to 624 pg/mL. A method comparison to samples from the CDC HoSt program yielded a correlation coefficient of 0.9858 and bias of −1.37%. The LLOQ using certified reference material was 0.66 pg/mL. The intra-run precision was

Two chromatographic methods were validated for the determination of the widely prescribed analgesic and antipyretic drug combination of paracetamol (PC) (recently integrated into the supportive treatment of COVID-19), propyphenazone (PZ) and caffeine (CF) in the presence of two PC impurities, namely 4-aminophenol and 4-nitrophenol. A “dual-mode” gradient high-performance liquid chromatography method was developed, where the separation was achieved via “dual-mode” gradient by changing both the ternary mobile phase composition (acetonitrile: methanol: water) and the flow rate. This enables a good resolution within a relatively shorter analysis time. The analysis was realized using Zorbax Eclipse XDB column C18, 5 μm (250 × 4.6 mm) and the UV detector was set at 220 nm. The other method is a thin-layer chromatography densitometry method, where the separation was achieved using a mobile phase composed of chloroform: toluene: ethyl acetate: methanol: acetic acid (6: 6: 1: 2: 0.1, by volume). Densitometric detection was performed at 220 nm on silica gel 60 F254 plates. The developed methods were fully validated as per the ICH guidelines and proved to be accurate, robust, specific and suitable for application as purity indicating methods for routine analysis of PC in pure form or in pharmaceuticals with PZ and CF in quality control laboratories.

This paper is a continuation of lipophilicity research on 14 compounds (tryptophan, kynurenine pathway products, auxin pathway products, serotonin pathway products, tryptamine, as well as two synthetic auxin analogs): indole-2-acetic acid sodium salt (IAA), serotonin, 5-hydroxy-L-tryptophan, tryptamine, L-tryptophan, L-kynurenine (KYN), kynurenic acid (KYA), 3-hydroxy-DL-kynurenine, naphtyl-1-acetamide, indole-3-propionic acid (IPA), naphthalene-1-acetic acid (NAA), indole-3-butyric acid (IBA), indole-3-pyruvic acid (IPV), as well as melatonin. They were chromatographed in high performance liquid chromatography gradient conditions on tree stationary phases (C18, CN, DIOL) using three modifiers on each phase (methanol, acetonitrile and acetone). The resulting retention data was correlated with computational lipophilicity indices. Six compounds were proven to be ionized in neutral pH physiological conditions (IAA, KYA, IPA, NAA, IBA and IPV) and they were rechromatographed with acidic mobile phase to enhance the resulting dataset. It can be concluded that the retention times are highly correlated with lipophilicity regardless of used modifier and column and the main differentiating trend can be only connected to presence of naphthalene or indole ring. The principal component analysis, additive linear modeling, as well as multiplicative trilinear parallel factor analysis (PARAFAC) modeling helped to understand the internal structure of the obtained results.

Background BioSystems has developed a histamine kit for automated procedure, through the use of a Y15 analyzer, for quantification of histamine in fish and fishery products. Objective Validate the method under the specific guidelines of the AOAC Research Institute Performance Tested MethodSM (PTM) program. Method Samples are extracted with boiling water. The enzymatic method is based on histamine dehydrogenase that catalyzes the oxidation of histamine in the presence of an electron mediator that reduces a dye that is measured at 420 nm. The increase of absorbance is proportional to the histamine concentration. Dispensing of reagents and sample, absorbance readings, calibration, and calculation of results are performed automatically in the analyzer BioSystems Y15. Results The linearity ranges from 0 to 200 mg/kg (r2 > 0.99). The LOQ is 10 mg/kg in all the matrixes. Recoveries range from 75 to 107% at concentrations from 5 to 200 mg/kg, with repeatability precision values between 0.8 and 5.5%. Comparisons with the HPLC reference method shows a good correlation. Cross-reactivity of the assay is negligible for all biogenic amines tested except for agmatine (6.3%). Product consistency was verified by validating lot-to-lot variations and variations within the same lot. Shelf life was verified by real-time stability testing during 40 months at 2–8°C. No differences in histamine detection were observed in robustness testing, in which minor changes are introduced to the assay protocol. Conclusions The automated, simple, and rapid BioSystems Y15 Histamine Dehydrogenase Kit has been successfully validated. Highlights The method is qualified for PTM certification No. 072001.

Background Flavanols and procyanidins are complex bioactives found in many foods such as cocoa. As their consumption is associated with health benefits, cocoa flavanols and procyanidins are receiving increasing attention from consumers, industry, researchers, and regulators. Objective The objective of this study is to validate a method using hydrophilic interaction chromatography (HILIC) with fluorescence detection (FLD) and a commercially available reference material for the determination of flavanols and procyanidins (CF) in cocoa-based products. Methods Method performances were evaluated for cocoa matrices with CF content that ranged from 0.8 to 500 mg/g, which included low CF matrices (milk and dark chocolate, cocoa powder, and liquor) and high CF matrices (cocoa extract and dietary supplement products). The method was validated in a single-laboratory by determining sensitivity, selectivity, linearity, stability, robustness, accuracy, and precision for each of the matrices. Results The method was validated for cocoa matrices with CF content that ranged from 0.8 to 500 mg/g. Accuracy ranged from 86 to 99% and repeatability (RSDr) from 1.5 to 8.6% for CF. Conclusions Analytical performances acquired through this single-laboratory validation study for a wide range of cocoa-based matrices demonstrate that this method is fit-for-purpose for the determination of flavanols and procyanidins in cocoa-based products. Highlights Hydrophilic interaction chromatography (HILIC) with fluorescence detection was successfully used to determine total CF content in multiple product types. Single-laboratory method validation results demonstrate that the method is fit for purpose for cocoa-based matrices containing

Postmortem ethanol formation is a well-known problem in forensic toxicology. Ethyl glucuronide (EtG) and ethyl sulfate (EtS) are ethanol metabolites that can be used to distinguish antemortem alcohol intake from postmortem formation of ethanol and in addition can be a helpful tool in assessment of the hip-flask defense. To an aliquot of 100 µL whole blood, internal standard (IS) and water was added before protein precipitation treatment (PPT) with ice-cold acetonitrile (ACN). The supernatants were filtered through a 96-well phospholipid removal plate, evaporated to dryness and reconstituted in 150 µL water/ACN/formic acid (FA). Identification of compounds was performed using multiple reaction monitoring (MRM) in negative mode. Gradient elution was performed on a C18 column with methanol (MeOH) and 0.1% FA. The run time was 4.5 min, and 0.5 µL was injected on an ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC–MS-MS) instrument. Linearity was achieved (coefficient of determination (R2) ≥ 0.999) for EtG in the range of 0.089 to 22 mg/L (0.40–100 µM) and EtS 0.025 to 6.3 mg/L (0.20–50 µM). The limit of quantification (LOQ) was 0.067 mg/L (0.30 µM) for EtG and 0.019 mg/L (0.15 µM) for EtS. Between assay accuracy was –15% to 8% and precision reported as relative standard deviation (RSD) was ≤ 4.5%. Precision, estimated as the RSD of the concentration difference between results from two independent analyses of authentic whole blood samples, was ≤ 6.7%. Recovery was ≥ 61% for EtG and ≥ 77% for EtS and matrix effects (ME) were 99% to 103%. Method comparison was carried out with a previously used UHPLC–MS-MS method, and satisfactory agreement was achieved, and external proficiency testing control samples had z-score

Background Kombucha is a fermented beverage made with tea, sugar, and a symbiotic colony of bacteria and yeast that is usually marketed as a non-alcoholic beverage. Products must contain Objective Given the public safety concerns and legal requirements associated with the level of alcohol within Kombucha beverages, there is a need for accurate and reliable methods. Herein we describe the validation of a sensitive, rapid, and simple Headspace Gas Chromatographic method with mass spectrometric detection for determining ethanol in Kombucha. Methods Method performance characteristics measured included linearity, accuracy, precision, limit of detection (LOD), and limit of quantification (LOQ) as per AOAC International guideline Appendix K Part 1. Performance was evaluated against the AOAC Standard Method Performance Requirements 2016.001 for determination of ethanol in Kombucha. Results The linear dynamic range for this method was confirmed over the range of 0.025 to 2.47% ABV. The LOD and LOQ were determined to be 0.0002% and 0.002% ABV, respectively. With a spike recovery of 102% for accuracy and precision of RSDr ≤ 4% the method met the SMPR requirements within the analytical range. Conclusions The results of this validation study demonstrated the method is fit for the purpose of quantifying ethanol in Kombucha and is suitable for rapid and easy integration by laboratories to ensure that regulatory requirements are met.

The use of designer benzodiazepines appears to be increasing in many countries, but data concerning blood concentrations are scarce, making interpretation of concentrations difficult. The aim of this study was to report blood concentrations of clonazolam, diclazepam, etizolam, flualprazolam, flubromazepam, flubromazolam and phenazepam and to investigate the relationship between blood concentrations and impairment. The concentration data are from blood samples collected from living cases (apprehended drivers and other drug offences) and medico-legal autopsies. The blood samples were analysed for the seven designer benzodiazepines mentioned above by ultra high performance liquid chromatography–tandem mass spectrometry. Positive cases from between 1 June 2016 and 30 September 2019 were included. Blood concentrations and the conclusion from a clinical test of impairment (when available) are reported. The presented seven benzodiazepines were detected in a total of 575 cases, where 554 of these cases concerned apprehended drivers or other criminal offenders. The number of findings and the median (range) concentrations were as follows: clonazolam, n = 22, 0.0041 mg/L (0.0017–0.053 mg/L); diclazepam, n = 334, 0.0096 mg/L (0.0016–0.25 mg/L); etizolam, n = 40, 0.054 mg/L (0.015–0.30 mg/L); flualprazolam, n = 10, 0.0080 mg/L (0.0033–0.056 mg/L); flubromazepam, n = 5, 0.037 mg/L (0.0070–0.70 mg/L); flubromazolam, n = 20, 0.0056 mg/L (0.0004–0.036 mg/L); and phenazepam, n = 138, 0.022 mg/L (0.0018–0.85 mg/L). A designer benzodiazepine was the only drug detected with relevance for impairment in 25 of the 554 living cases. The physician concluded with impairment in 19 of the 25 cases. Most of the concentrations in these cases were relatively similar to or higher than the median reported concentrations. The most frequent other drugs detected were amphetamine, tetrahydrocannabinol, clonazepam and methamphetamine. The presented blood concentrations can be helpful with the interpretation of cases involving one or more of these seven benzodiazepines. The results indicate that concentrations commonly observed in forensic cases are associated with impairment.

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install - all dependencies are provided within containers -, portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All code is hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

The epigenetic state of chromatin is altered by regulators which influence gene expression in response to environmental stimuli. While several post-translational modifications contribute to chromatin accessibility and transcriptional programs, our understanding of the role that specific phosphorylation sites play is limited. In cancer, kinases and phosphatases are commonly deregulated resulting in increased oncogenic signaling and loss of epigenetic regulation. Aberrant epigenetic states are known to promote cellular plasticity and the development of therapeutic resistance in many cancer types, highlighting the importance of these mechanisms to cancer cell phenotypes. Protein Phosphatase 2A (PP2A) is a heterotrimeric holoenzyme that targets a diverse array of cellular proteins. The composition of the PP2A complex influences its cellular targets and activity. For this reason, PP2A can be tumor suppressive or oncogenic depending on cellular context. Understanding the nuances of PP2A regulation and its effect on epigenetic alterations can lead to new therapeutic avenues that afford more specificity and contribute to the growth of personalized medicine in the oncology field. In this review, we summarize the known PP2A-regulated substrates and potential phosphorylation sites that contribute to cancer cell epigenetics and possible strategies to therapeutically leverage this phosphatase to suppress tumor growth., Graphical Abstract Graphical AbstractThe diverse family of PP2A holoenzymes dynamically regulates the cancer epigenome.

Changes in DNA methylation have been found to be strongly correlated with age, enabling the creation of ‘epigenetic clocks’. Previously, studies on the relationship between ageing and DNA methylation have assumed a linear relationship. Here, we show that several markers show a non-linear behaviour. In particular, we observe a tendency for saturation with age, especially in the cerebellum. Further, we show that the relationships between significant methylation changes and ageing are different in different tissues. We suggest a straightforward method of assessing all methylation-age relationships and cluster them according to their relative fold change. Our fold change selection outperforms the most common epigenetic clocks in predicting age for the cerebellum, but not for Blood or the Frontal Cortex. Further, we find that the saturation of methylation observed at older ages for the cerebellum explains why epigenetic clocks consistently underestimate the age there. The findings imply that assuming linear correlations might cause biologically important markers to be missed.

The identity of cancer cells is defined by the interplay between genetic, epigenetic transcriptional and post-transcriptional variation. A lot of this variation is present in RNA-seq data and can be captured at once using reference-free, k-mer analysis. An important issue with k-mer analysis, however, is the difficulty of distinguishing signal from noise. Here, we use two independent lung adenocarcinoma datasets to identify all reproducible events at the k-mer level, in a tumor versus normal setting. We find reproducible events in many different locations (introns, intergenic, repeats) and forms (spliced, polyadenylated, chimeric etc.). We systematically analyze events that are ignored in conventional transcriptomics and assess their value as biomarkers and for tumor classification, survival prediction, neoantigen prediction and correlation with the immune microenvironment. We find that unannotated lincRNAs, novel splice variants, endogenous HERV, Line1 and Alu repeats and bacterial RNAs each contribute to different, important aspects of tumor identity. We argue that differential RNA-seq analysis of tumor/normal sample collections would benefit from this type k-mer analysis to cast a wider net on important cancer-related events. The code is available at https://github.com/Transipedia/dekupl-lung-cancer-inter-cohort., Graphical Abstract Graphical AbstractDiscovery and analysis of shared differential contigs from two cancer RNA-seq datasets.

Although microRNAs (miRNAs) contribute to all hallmarks of cancer, miRNA dysregulation in metastasis remains poorly understood. The aim of this work was to reliably identify miRNAs associated with metastatic progression of colorectal cancer (CRC) using novel and previously published next-generation sequencing (NGS) datasets generated from 268 samples of primary (pCRC) and metastatic CRC (mCRC; liver, lung and peritoneal metastases) and tumor adjacent tissues. Differential expression analysis was performed using a meticulous bioinformatics pipeline, including only bona fide miRNAs, and utilizing miRNA-tailored quality control and processing. Five miRNAs were identified as up-regulated at multiple metastatic sites Mir-210_3p, Mir-191_5p, Mir-8-P1b_3p [mir-141–3p], Mir-1307_5p and Mir-155_5p. Several have previously been implicated in metastasis through involvement in epithelial-to-mesenchymal transition and hypoxia, while other identified miRNAs represent novel findings. The use of a publicly available pipeline facilitates reproducibility and allows new datasets to be added as they become available. The set of miRNAs identified here provides a reliable starting-point for further research into the role of miRNAs in metastatic progression.

There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour–normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.

The integration of multi-omics data can greatly facilitate the advancement of research in Life Sciences by highlighting new interactions. However, there is currently no widespread procedure for meaningful multi-omics data integration. Here, we present a robust framework, called InterTADs, for integrating multi-omics data derived from the same sample, and considering the chromatin configuration of the genome, i.e. the topologically associating domains (TADs). Following the integration process, statistical analysis highlights the differences between the groups of interest (normal versus cancer cells) relating to (i) independent and (ii) integrated events through TADs. Finally, enrichment analysis using KEGG database, Gene Ontology and transcription factor binding sites and visualization approaches are available. We applied InterTADs to multi-omics datasets from 135 patients with chronic lymphocytic leukemia (CLL) and found that the integration through TADs resulted in a dramatic reduction of heterogeneity compared to individual events. Significant differences for individual events and on TADs level were identified between patients differing in the somatic hypermutation status of the clonotypic immunoglobulin genes, the core biological stratifier in CLL, attesting to the biomedical relevance of InterTADs. In conclusion, our approach suggests a new perspective towards analyzing multi-omics data, by offering reasonable execution time, biological benchmarking and potentially contributing to pattern discovery through TADs. ‘Hellenic Network for Precision Medicine’ in the framework of the Hellenic Republic – Siemens Settlement Agreement; European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE [GenOptics, project code: T2E1DK-00407]

Mitochondrial DNA (mtDNA) methylation in vertebrates has been hotly debated for over 40 years. Most contrasting results have been reported following bisulfite sequencing (BS-seq) analyses. We addressed whether BS-seq experimental and analysis conditions influenced the estimation of the levels of methylation in specific mtDNA sequences. We found false positive non-CpG methylation in the CHH context (fpCHH) using unmethylated Sus scrofa mtDNA BS-seq data. fpCHH methylation was detected on the top/plus strand of mtDNA within low guanine content regions. These top/plus strand sequences of fpCHH regions would become extremely AT-rich sequences after BS-conversion, whilst bottom/minus strand sequences remained almost unchanged. These unique sequences caused BS-seq aligners to falsely assign the origin of each strand in fpCHH regions, resulting in false methylation calls. fpCHH methylation detection was enhanced by short sequence reads, short library inserts, skewed top/bottom read ratios and non-directional read mapping modes. We confirmed no detectable CHH methylation in fpCHH regions by BS-amplicon sequencing. The fpCHH peaks were located in the D-loop, ATP6, ND2, ND4L, ND5 and ND6 regions and identified in our S. scrofa ovary and oocyte data and human BS-seq data sets. We conclude that non-CpG methylation could potentially be overestimated in specific sequence regions by BS-seq analysis., GRAPHICAL ABSTRACT Graphical AbstractLow guanine content sequences associate with false positive CHH methylation calls.

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.

Querying massive functional genomic and annotation data collections, linking and summarizing the query results across data sources/data types are important steps in high-throughput genomic and genetic analytical workflows. However, these steps are made difficult by the heterogeneity and breadth of data sources, experimental assays, biological conditions/tissues/cell types and file formats. FILER (FunctIonaL gEnomics Repository) is a framework for querying large-scale genomics knowledge with a large, curated integrated catalog of harmonized functional genomic and annotation data coupled with a scalable genomic search and querying interface. FILER uniquely provides: (i) streamlined access to >50 000 harmonized, annotated genomic datasets across >20 integrated data sources, >1100 tissues/cell types and >20 experimental assays; (ii) a scalable genomic querying interface; and (iii) ability to analyze and annotate user’s experimental data. This rich resource spans >17 billion GRCh37/hg19 and GRCh38/hg38 genomic records. Our benchmark querying 7 × 109 hg19 FILER records shows FILER is highly scalable, with a sub-linear 32-fold increase in querying time when increasing the number of queries 1000-fold from 1000 to 1 000 000 intervals. Together, these features facilitate reproducible research and streamline integrating/querying large-scale genomic data within analyses/workflows. FILER can be deployed on cloud or local servers (https://bitbucket.org/wanglab-upenn/FILER) for integration with custom pipelines and is freely available (https://lisanwanglab.org/FILER).

Ewing sarcoma (EwS) is a small round blue cell tumor and is the second most frequent pediatric bone cancer. 85% of EwS tumors express the fusion oncoprotein EWS-FLI1, the product of a t(11;22) reciprocal translocation. Prior work has indicated that transcription regulation alone does not fully describe the oncogenic capacity of EWS-FLI1, nor does it provide an effective means to stratify patient tumors. Research using EwS cell lines and patient samples has suggested that EWS-FLI1 also disrupts mRNA biogenesis. In this work we both describe the underlying characteristics of mRNA that are aberrantly spliced in EwS tumor samples as well as catalogue mRNA splicing events across other pediatric tumor types. Here, we also use short- and long-read sequencing to identify cis-factors that contribute to splicing profiles we observe in Ewing sarcoma. Our analysis suggests that GC content upstream of cassette exons is a defining factor of mRNA splicing in EwS. We also describe specific splicing events that discriminate EwS tumor samples from the assumed cell of origin, human mesenchymal stem cells derived from bone marrow (hMSC-BM). Finally, we identify specific splicing factors PCBP2, RBMX, and SRSF9 by motif enrichment and confirm findings from tumor samples in EwS cell lines., GRAPHICAL ABSTRACT Graphical AbstractRNA splicing in Ewing sarcoma was quantified by first constructing a reference transcriptome using long-read sequencing of cell line models, then using tumor samples for per-event quantification. We find that altered RNA splicing in Ewing sarcoma is characterized by, among other things, higher GC content and lower 3' splice site strength.

Single-nucleotide polymorphism (SNPs) may cause the diverse functional impact on RNA or protein changing genotype and phenotype, which may lead to common or complex diseases like cancers. Accurate prediction of the functional impact of SNPs is crucial to discover the ‘influential’ (deleterious, pathogenic, disease-causing, and predisposing) variants from massive background polymorphisms in the human genome. Increasing computational methods have been developed to predict the functional impact of variants. However, predictive performances of these computational methods on massive genomic variants are still unclear. In this regard, we systematically evaluated 14 important computational methods including specific methods for one type of variant and general methods for multiple types of variants from several aspects; none of these methods achieved excellent (AUC ≥ 0.9) performance in both data sets. CADD and REVEL achieved excellent performance on multiple types of variants and missense variants, respectively. This comparison aims to assist researchers and clinicians to select appropriate methods or develop better predictive methods.

High-throughput sequencing technologies have generated massive protein sequences, but the annotations of protein sequences highly rely on the low-throughput and expensive biological experiments. Therefore, accurate and fast computational alternatives are needed to infer functional knowledge from protein sequences. The gene ontology (GO) directed acyclic graph (DAG) contains the hierarchical relationships between GO terms but is hard to be integrated into machine learning algorithms for functional predictions. We developed a deep learning system named PANDA2 to predict protein functions, which used the cutting-edge graph neural network to model the topology of the GO DAG and integrated the features generated by transformer protein language models. Compared with the top 10 methods in CAFA3, PANDA2 ranked first in cellular component ontology (CCO), tied first in biological process ontology (BPO) but had a higher coverage rate, and second in molecular function ontology (MFO). Compared with other recently-developed cutting-edge predictors DeepGOPlus, GOLabeler, and DeepText2GO, and benchmarked on another independent dataset, PANDA2 ranked first in CCO, first in BPO, and second in MFO. PANDA2 can be freely accessed from http://dna.cs.miami.edu/PANDA2/.

International audience; Most epigenetic marks, such as Transcriptional Regulators or histone marks, are biological objects known to work together in n-wise complexes. A suitable way to infer such functional associations between them is to study the overlaps of the corresponding genomic regions. However, the problem of the statistical significance of n-wise overlaps of genomic features is seldom tackled, which prevent rigorous studies of n-wise interactions. We introduce OLOGRAM-MODL, which considers overlaps between n ≥ 2 sets of genomic regions, and computes their statistical mutual enrichment by Monte Carlo fitting of a Negative Binomial distribution, resulting in more resolutive P-values. An optional machine learning method is proposed to find complexes of interest, using a new itemset mining algorithm based on dictionary learning which is resistant to noise inherent to biological assays. The overall approach is implemented through an easy-to-use CLI interface for workflow integration, and a visual treebased representation of the results suited for explicability. The viability of the method is experimentally studied using both artificial and biological data. This approach is accessible through the command line interface of the pygtftk toolkit, available on Bioconda and from https://github.com/dputhier/pygtftk

Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that a binarized representation of single-cell expression data accurately represents biological variation and reveals the relative abundance of transcripts more robustly than counts.

Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond., NAR Genomics and Bioinformatics, 3 (4), ISSN:2631-9268

In mammals, DNA methyltransferases DNMT1 and DNMT3’s (A, B and L) deposit and maintain DNA methylation in dividing and nondividing cells. Although these enzymes have an unremarkable DNA sequence specificity (CpG), their regional specificity is regulated by interactions with various protein factors, chromatin modifiers, and post-translational modifications of histones. Changes in the DNMT expression or interacting partners affect DNA methylation patterns. Consequently, the acquired gene expression may increase the proliferative potential of cells, often concomitant with loss of cell identity as found in cancer. Aberrant DNA methylation, including hypermethylation and hypomethylation at various genomic regions, therefore, is a hallmark of most cancers. Additionally, somatic mutations in DNMTs that affect catalytic activity were mapped in Acute Myeloid Leukemia cancer cells. Despite being very effective in some cancers, the clinically approved DNMT inhibitors lack specificity, which could result in a wide range of deleterious effects. Elucidating distinct molecular mechanisms of DNMTs will facilitate the discovery of alternative cancer therapeutic targets. This review is focused on: (i) the structure and characteristics of DNMTs, (ii) the prevalence of mutations and abnormal expression of DNMTs in cancer, (iii) factors that mediate their abnormal expression and (iv) the effect of anomalous DNMT-complexes in cancer., Graphical Abstract Graphical AbstractModulation of DNMT activity in cancer.

microRNAs (miRNAs) regulate gene expression and thereby influence biological processes in health and disease. As a consequence, miRNAs are intensely studied and literature on miRNAs has been constantly growing. While this growing body of literature reflects the interest in miRNAs, it generates a challenge to maintain an overview, and the comparison of miRNAs that may function across diverse disease fields is complex due to this large number of relevant publications. To address these challenges, we designed miRetrieve, an R package and web application that provides an overview on miRNAs. By text mining, miRetrieve can characterize and compare miRNAs within specific disease fields and across disease areas. This overview provides focus and facilitates the generation of new hypotheses. Here, we explain how miRetrieve works and how it is used. Furthermore, we demonstrate its applicability in an exemplary case study and discuss its advantages and disadvantages.

Physarum polycephalum belongs to Mycetozoans, a phylogenetic clade apart from the animal, plant and fungus kingdoms. Histones are nuclear proteins involved in genome organization and regulation and are among the most evolutionary conserved proteins within eukaryotes. Therefore, this raises the question of their conservation in Physarum and the position of this organism within the eukaryotic phylogenic tree based on histone sequences. We carried out a comprehensive study of histones in Physarum polycephalum using genomic, transcriptomic and molecular data. Our results allowed to identify the different isoforms of the core histones H2A, H2B, H3 and H4 which exhibit strong conservation of amino acid residues previously identified as subject to post-translational modifications. Furthermore, we also identified the linker histone H1, the most divergent histone, and characterized a large number of its PTMs by mass spectrometry. We also performed an in-depth investigation of histone genes and transcript structures. Histone proteins are highly conserved in Physarum and their characterization will contribute to a better understanding of the polyphyletic Mycetozoan group. Our data reinforce that P. polycephalum is evolutionary closer to animals than plants and located at the crown of the eukaryotic tree. Our study provides new insights in the evolutionary history of Physarum and eukaryote lineages.

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies., Graphical Abstract Graphical AbstractCLEARER is a machine learning approach for predicting essential genes across eukaryotes. The classifier is trained on multiple species, allowing identification of essential genes in a new species with high accuracy.

Cancer genomics has been evolving rapidly, fueled by the emergence of numerous studies and public databases through next-generation sequencing technologies. However, the downstream programs used to preprocess and analyze data on somatic mutations are scattered in different tools, most of which require specific input formats. Here, we developed a user-friendly Python toolkit, MutScape, which provides a comprehensive pipeline of filtering, combination, transformation, analysis and visualization for researchers, to easily explore the cohort-based mutational characterization for studying cancer genomics when obtaining somatic mutation data. MutScape not only can preprocess millions of mutation records in a few minutes, but also offers various analyses simultaneously, including driver gene detection, mutational signature, large-scale alteration identification and actionable biomarker annotation. Furthermore, MutScape supports somatic variant data in both variant call format and mutation annotation format, and leverages caller combination strategies to quickly eliminate false positives. With only two simple commands, robust results and publication-quality images are generated automatically. Herein, we demonstrate the ability of MutScape to correctly reproduce known results using breast cancer samples from The Cancer Genome Atlas. More significantly, discovery of novel results in cancer genomic studies is enabled through the advanced features in MutScape. MutScape is freely available on GitHub, at https://github.com/anitalu724/MutScape.

The increasing burden of cancer requires identifying and protecting individuals at highest risk. The epigenome provides an indispensable complement to genetic alterations for a risk stratification approach for the following reasons: gene transcription necessary for cancer onset is directed by epigenetic modifications and many risk factors studied so far have been associated with alterations related to the epigenome. The risk level depends on the plasticity of the epigenome during phases of life particularly sensitive to environmental and dietary impacts. Modifications in the activity of DNA regulatory regions and altered chromatin compaction may accumulate, hence leading to the increase of cancer risk. Moreover, tissue architecture directs the unique organization of the epigenome for each tissue and cell type, which allows the epigenome to control cancer risk in specific organs. Investigations of epigenetic signatures of risk should help identify a continuum of alterations leading to a threshold beyond which the epigenome cannot maintain homeostasis. We propose that this threshold may be similar in the population for a given tissue, but the pace to reach this threshold will depend on the combination of germline inheritance and the risk and protective factors encountered, particularly during windows of epigenetic susceptibility, by individuals., Graphical Abstract Graphical AbstractThe accumulation of alterations in the epigenome and chromatin organization via additive effects of risk factors participates in risk stratification in populations.

MotivationAs chromatin accessibility data from ATAC-seq experiments continues to expand, there is continuing need for standardized analysis pipelines. Here, we present PEPATAC, an ATAC-seq pipeline that is easily applied to ATAC-seq projects of any size, from one-off experiments to large-scale sequencing projects.ResultsPEPATAC leverages unique features of ATAC-seq data to optimize for speed and accuracy, and it provides several unique analytical approaches. Output includes convenient quality control plots, summary statistics, and a variety of generally useful data formats to set the groundwork for subsequent project-specific data analysis. Downstream analysis is simplified by a standard definition format, modularity of components, and metadata APIs in R and Python. It is restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also demonstrate the advantage of aligning to the mitochondrial genome serially, which improves the accuracy of alignment statistics and quality control metrics. PEPATAC is a robust and portable first step for any ATAC-seq project.AvailabilityBSD2-licensed code and documentation at https://pepatac.databio.org.

In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results—in conjunction with probing data and other annotations—in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast ‘omics’ resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first ‘reference-based’ approach termed ChiTaH (Chimeric Transcripts from High–throughput sequencing data). ChiTaH uses 43,466 non–redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.