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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.

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 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.

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.

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.

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.

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 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.

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.

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.

Prediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs’ predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations.

Recent advances in single-cell RNA sequencing technologies have made detection of transcripts in single cells possible. The level of resolution provided by these technologies can be used to study changes in transcript usage across cell populations and help investigate new biology. Here, we introduce RNA-Scoop, an interactive cell cluster and transcriptome visualization tool to analyze transcript usage across cell categories and clusters. The tool allows users to examine differential transcript expression across clusters and investigate how usage of specific transcript expression mechanisms varies across cell groups.

Tumor tissues are heterogeneous with different cell types in tumor microenvironment, which play an important role in tumorigenesis and tumor progression. Several computational algorithms and tools have been developed to infer the cell composition from bulk transcriptome profiles. However, they ignore the tissue specificity and thus a new resource for tissue-specific cell transcriptomic reference is needed for inferring cell composition in tumor microenvironment and exploring their association with clinical outcomes and tumor omics. In this study, we developed SCISSOR™ (https://thecailab.com/scissor/), an online open resource to fulfill that demand by integrating five orthogonal omics data of >6031 large-scale bulk samples, patient clinical outcomes and 451 917 high-granularity tissue-specific single-cell transcriptomic profiles of 16 cancer types. SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches. SCISSOR™ is valuable as a new resource for promoting tumor heterogeneity and tumor–tumor microenvironment cell interaction research, by delineating cells in the tissue-specific tumor microenvironment and characterizing their associations with tumor omics and clinical outcomes., Graphical Abstract Graphical AbstractSCISSOR™ preprocessed and hosted The Cancer Genome Atlas (TCGA) bulk transcriptomic data and tissue-specific single-cell transcriptomic data. Using these data, SCISSOR™ infers the proportion of tissue-specific cell types in tumor microenvironment. With the omics data and survival data from TCGA, the relationship and interaction can be tested and visualized in five modules, including Overview, Survival, Gene–TME cell correlation with two submodules for gene expression and genetic aberration, Genome-wide TME–omics association and Deconvolution. The Deconvolution module will allow users to upload their bulk transcriptome data and perform deconvolution with tissue-specific single-cell transcriptome profile references and deconvolution methods specified by users. The result will be automatically sent by email.

5-Fluorouracil (5-FU) is a chemotherapeutic drug widely used to treat patients with solid tumours, such as colorectal and pancreatic cancers. Colorectal cancer (CRC) is the second leading cause of cancer-related death and half of patients experience tumour recurrence. Used for over 60 years, 5-FU was long thought to exert its cytotoxic effects by altering DNA metabolism. However, 5-FU mode of action is more complex than previously anticipated since 5-FU is an extrinsic source of RNA modifications through its ability to be incorporated into most classes of RNA. In particular, a recent report highlighted that, by its integration into the most abundant RNA, namely ribosomal RNA (rRNA), 5-FU creates fluorinated active ribosomes and induces translational reprogramming. Here, we review the historical knowledge of 5-FU mode of action and discuss progress in the field of 5-FU-induced RNA modifications. The case of rRNA, the essential component of ribosome and translational activity, and the plasticity of which was recently associated with cancer, is highlighted. We propose that translational reprogramming, induced by 5-FU integration in ribosomes, contributes to 5-FU-driven cell plasticity and ultimately to relapse., Graphical Abstract Graphical Abstract5-FU integration in ribosomes contributes to cell plasticity through translational reprogramming.

Small nucleolar RNAs (snoRNAs) are an omnipresent class of non-coding RNAs involved in the modification and processing of ribosomal RNA (rRNA). As snoRNAs are required for ribosome production, the increase of which is a hallmark of cancer development, their expression would be expected to increase in proliferating cancer cells. However, assessing the nature and extent of snoRNAs’ contribution to cancer biology has been largely limited by difficulties in detecting highly structured RNA. In this study, we used a dedicated midsize non-coding RNA (mncRNA) sensitive sequencing technique to accurately survey the snoRNA abundance in independently verified high-grade serous ovarian carcinoma (HGSC) and serous borderline tumour (SBT) tissues. The results identified SNORA81, SNORA19 and SNORA56 as an H/ACA snoRNA signature capable of discriminating between independent sets of HGSC, SBT and normal tissues. The expression of the signature SNORA81 correlates with the level of ribosomal RNA (rRNA) modification and its knockdown inhibits 28S rRNA pseudouridylation and accumulation leading to reduced cell proliferation and migration. Together our data indicate that specific subsets of H/ACA snoRNAs may promote tumour aggressiveness by inducing rRNA modification and synthesis., GRAPHICAL ABSTRACT Graphical AbstractIdentification H/ACA snoRNA signature of high-grade serous ovarian cancer.

A highly discussed step in hair sample preparation for forensic analytics is the applied decontamination. The here presented investigations aim to gain insight and give recommendations on how to conduct this decontamination for the analysis of cocaine consumption in hair. Key insights were gained from the investigation of cocaine consumer hair, which was artificially contaminated in a humid atmosphere with 13C6 labelled cocaine and from cocaine powder contaminated hair. Several decontamination protocols were investigated, whereby the usage of a decontamination protocol consisting of multiple short repetitive washes allowed to visualize the wash out of (13C6-) cocaine. Multiple methanol washes proved to be an efficient and simple decontamination approach. Our findings showed that decontamination protocols can successfully wash out recent cocaine contaminations. They were observed to be rather quickly washed out, whereas cocaine from consumption or “older” cocaine contaminations were shown to eliminate both at a constant rate (from inner hair compartments). Thus, the usage of decontamination protocols to differentiate between consumption and contamination was shown to be limited. As contamination can happen any time at any level, only the application of elaborated decision trees, based on cocaine metabolite ratios and thresholds, can provide the distinction between consumption and contamination. Thus, the authors highly recommend the usage of such tools on all hair samples analyzed for cocaine consumption.

Ewing sarcoma breakpoint region 1 (EWSR1) encodes a multifunctional protein that can cooperate with the transcription factor ERG to promote prostate cancer. The EWSR1 gene is also commonly involved in oncogenic gene rearrangements in Ewing sarcoma. Despite the cancer relevance of EWSR1, its regulation is poorly understood. Here we find that in prostate cancer, androgen signaling upregulates a 5′ EWSR1 isoform by promoting usage of an intronic polyadenylation site. This isoform encodes a cytoplasmic protein that can strongly promote cell migration and clonogenic growth. Deletion of an Androgen Receptor (AR) binding site near the 5′ EWSR1 polyadenylation site abolished androgen-dependent upregulation. This polyadenylation site is also near the Ewing sarcoma breakpoint hotspot, and androgen signaling promoted R-loop and breakpoint formation. RNase H overexpression reduced breakage and 5′ EWSR1 isoform expression suggesting an R-loop dependent mechanism. These data suggest that androgen signaling can promote R-loops internal to the EWSR1 gene leading to either early transcription termination, or breakpoint formation.

A young man with an unremarkable medical history suffered a seizure with subsequent cardiorespiratory arrest and severe neurological sequelae after ingesting a blotter. Analysis of a similar blotter and a serum sample obtained 3 h after the event detected lysergic acid diethylamide (LSD) at an amount of 300 µg in the blotter and at a concentration of 4.0 ng/mL (12.4 nmol/L) in the serum. No other drugs were present in concentrations which may confer significant effects. In addition, no individual traits which would make the patient particularly susceptible to adverse LSD effects have subsequently been identified. This suggests that LSD may confer toxic effects in previously healthy individuals.

A positive non-linear relation between the dose of ethanol ingested and the area under the curve (AUC) for ethyl glucuronide (EtG) in urine is previously observed. The relation between both doses and AUC of ethanol and the AUC for EtG in blood is not previously published, and this study aimed to investigate this relationship. After an overnight fast, 10 healthy volunteers ingested 0.5-g ethanol per kilo body weight (low dose) in one occasion and 1.0-g ethanol per kilo body weight (high dose) in the next occasion. Results showed that there was a significant higher median ratio between blood AUC for EtG and dose of ethanol in the high-dose (8.99; range 7.37–10.94) group compared to the low-dose (5.02; range 4.25–6.15) group (P = 0.005). The median ratio between the AUC for EtG and AUC for ethanol was actually significantly higher in the low-dose (1.77; range 1.51–2.24) group compared to the high-dose (1.67; range 1.30–2.02) group (P = 0.005), although values are quite similar. This study therefore showed that the ratio between the AUC for EtG in blood and dose of ethanol is higher after intake of 1.0 g/kg than 0.5 g/kg. This pattern is however not seen when AUC for EtG is compared to AUC for ethanol. Results therefore support that the percentage of ethanol converted to EtG is not increasing when the doses increase. An explanation for the positive non-linear relation previously observed between the dose of ethanol ingested and amount of EtG formed may be a relative higher first-pass metabolism of ethanol at lower doses.

Cellular reprogramming is a promising technology to develop disease models and cell-based therapies. Identification of the key regulators defining the cell type specificity is pivotal to devising reprogramming cocktails for successful cell conversion but remains a great challenge. Here, we present a systems biology approach called Taiji-reprogram to efficiently uncover transcription factor (TF) combinations for conversion between 154 diverse cell types or tissues. This method integrates the transcriptomic and epigenomic data to construct cell-type specific genetic networks and assess the global importance of TFs in the network. Comparative analysis across cell types revealed TFs that are specifically important in a particular cell type and often tightly associated with cell-type specific functions. A systematic search of TFs with differential importance in the source and target cell types uncovered TF combinations for desired cell conversion. We have shown that Taiji-reprogram outperformed the existing methods to better recover the TFs in the experimentally validated reprogramming cocktails. This work not only provides a comprehensive catalog of TFs defining cell specialization but also suggests TF combinations for direct cell conversion.

Collectively referred to as the epitranscriptome, RNA modifications play important roles in gene expression control regulating relevant cellular processes. In the last few decades, growing numbers of RNA modifications have been identified not only in abundant ribosomal (rRNA) and transfer RNA (tRNA) but also in messenger RNA (mRNA). In addition, many writers, erasers and readers that dynamically regulate the chemical marks have also been characterized. Correct deposition of RNA modifications is prerequisite for cellular homeostasis, and its alteration results in aberrant transcriptional programs that dictate human disease, including breast cancer, the most frequent female malignancy, and the leading cause of cancer-related death in women. In this review, we emphasize the major RNA modifications that are present in tRNA, rRNA and mRNA. We have categorized breast cancer-associated chemical marks and summarize their contribution to breast tumorigenesis. In addition, we describe less abundant tRNA modifications with related pathways implicated in breast cancer. Finally, we discuss current limitations and perspectives on epitranscriptomics for use in therapeutic strategies against breast and other cancers., Graphical Abstract Graphical AbstractModifications of RNA known in breast cancer.

RNA interference (RNAi)-based therapeutics (miRNAs, siRNAs) have great potential for treating various human diseases through their ability to downregulate proteins associated with disease progression. However, the development of RNAi-based therapeutics is limited by lack of safe and specific delivery strategies. A great effort has been made to overcome some of these challenges resulting in development of N-acetylgalactosamine (GalNAc) ligands that are being used for delivery of siRNAs for the treatment of diseases that affect the liver. The successes achieved using GalNAc-siRNAs have paved the way for developing RNAi-based delivery strategies that can target extrahepatic diseases including cancer. This includes targeting survival signals directly in the cancer cells and indirectly through targeting cancer-associated immunosuppressive cells. To achieve targeting specificity, RNAi molecules are being directly conjugated to a targeting ligand or being packaged into a delivery vehicle engineered to overexpress a targeting ligand on its surface. In both cases, the ligand binds to a cell surface receptor that is highly upregulated by the target cells, while not expressed, or expressed at low levels on normal cells. In this review, we summarize the most recent RNAi delivery strategies, including extracellular vesicles, that use a ligand-mediated approach for targeting various oncological diseases., Graphical Abstract Graphical AbstractLigands developed and tested for delivery of RNAi therapeutics.

Chromosome 11q13-14 amplification is a defining feature of high-risk hormone receptor-positive (HR+) breast cancer; however, the mechanism(s) by which this amplicon contributes to breast tumorigenesis remains unclear. In the current study, proteogenomic analyses of >3000 breast tumors from the TCGA, METABRIC and CPTAC studies demonstrated that carnitine palmitoyltransferase 1A (CPT1A), which is localized to this amplicon, is overexpressed at the mRNA and protein level in aggressive luminal tumors, strongly associated with indicators of tumor proliferation and a predictor of poor prognosis. In vitro genetic studies demonstrated that CPT1A is required for and can promote luminal breast cancer proliferation, survival, as well as colony and mammosphere formation. Since CPT1A is the rate-limiting enzyme during fatty acid oxidation (FAO), our data indicate that FAO may be essential for these tumors. Pharmacologic inhibition of FAO prevented in vitro and in vivo tumor growth and cell proliferation as well as promoted apoptosis in luminal breast cancer cells and orthotopic xenograft tumor models. Collectively, our data establish an oncogenic role for CPT1A and FAO in HR+ luminal tumors and provide preclinical evidence and rationale supporting further investigation of FAO as a potential therapeutic opportunity for the treatment of HR+ breast cancer.

Resistance to anti-estrogen therapy is an unsolved clinical challenge in successfully treating ER+ breast cancer patients. Acquisition of mutations can confer heritable resistance to cancer cells, enabling their clonal selection to establish a drug-resistant population. Recent studies have demonstrated that cells can tolerate drug treatment without any genetic alterations too; however, the mechanisms and dynamics of such non-genetic adaptation remain elusive. Here, we investigate coupled dynamics of epithelial-mesenchymal transition (EMT) in breast cancer cells and emergence of reversible drug resistance. Our mechanism-based model for the underlying regulatory network reveals that these two axes can drive one another, thus conferring bidirectional plasticity. This network can also enable non-genetic heterogeneity in a population of cells by allowing for six co-existing phenotypes: epithelial-sensitive, mesenchymal-resistant, hybrid E/M-sensitive, hybrid E/M-resistant, mesenchymal-sensitive and epithelial-resistant, with the first two ones being most dominant. Next, in a population dynamics framework, we exemplify the implications of phenotypic plasticity (both drug-induced and intrinsic stochastic switching) and/or non-genetic heterogeneity in promoting population survival in a mixture of sensitive and resistant cells, even in the absence of any cell-cell cooperation. Finally, we propose the potential therapeutic use of MET (mesenchymal-epithelial transition) inducers besides canonical anti-estrogen therapy to limit the emergence of reversible drug resistance. Our results offer mechanistic insights into empirical observations on EMT and drug resistance and illustrate how such dynamical insights can be exploited for better therapeutic designs.

Viruses are abundant, diverse and ancestral biological entities. Their diversity is high, both in terms of the number of different protein families encountered and in the sequence heterogeneity of each protein family. The recent increase in sequenced viral genomes constitutes a great opportunity to gain new insights into this diversity and consequently urges the development of annotation resources to help functional and comparative analysis. Here, we introduce PHROG (Prokaryotic Virus Remote Homologous Groups), a library of viral protein families generated using a new clustering approach based on remote homology detection by HMM profile-profile comparisons. Considering 17 473 reference (pro)viruses of prokaryotes, 868 340 of the total 938 864 proteins were grouped into 38 880 clusters that proved to be a 2-fold deeper clustering than using a classical strategy based on BLAST-like similarity searches, and yet to remain homogeneous. Manual inspection of similarities to various reference sequence databases led to the annotation of 5108 clusters (containing 50.6 % of the total protein dataset) with 705 different annotation terms, included in 9 functional categories, specifically designed for viruses. Hopefully, PHROG will be a useful tool to better annotate future prokaryotic viral sequences thus helping the scientific community to better understand the evolution and ecology of these entities.

Gene essentiality estimation is a popular empirical approach to link genotypes to phenotypes. In humans, essentiality is estimated based on loss-of-function (LoF) mutation intolerance, either from population exome sequencing (in vivo) data or CRISPR-based in vitro perturbation experiments. Both approaches identify genes presumed to have detrimental consequences on the organism upon mutation. Are these genes constrained by having key cellular/organismal roles? Do in vivo and in vitro estimations equally recover these constraints? Insights into these questions have important implications in generalizing observations from cell models and interpreting disease risk genes. To empirically address these questions, we integrate genome-scale datasets and compare structural, functional and evolutionary features of essential genes versus genes with extremely high mutational tolerance. We found that essentiality estimates do recover functional constraints. However, the organismal or cellular context of estimation leads to functionally contrasting properties underlying the constraint. Our results suggest that depletion of LoF mutations in human populations effectively captures organismal-level functional constraints not experimentally accessible through CRISPR-based screens. Finally, we identify a set of genes (OrgEssential), which are mutationally intolerant in vivo but highly tolerant in vitro. These genes drive observed functional constraint differences and have an unexpected preference for nervous system expression.

Long non-coding RNAs (lncRNAs) play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here, we present the Cancer LncRNA Census 2 (CLC2). With 492 cancer lncRNAs, CLC2 is 4-fold greater in size than its predecessor, without compromising on strict criteria of confident functional/genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis screening data, yielding 92 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, contains numerous unique genes (not found in other databases) and carries functional labels (oncogene/tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical/genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs’ evolutionary and clinical significance., Graphical Abstract Graphical AbstractCancer LncRNA Census 3 integrates manual and automated curation to identify high confidence functional cancer lncRNAs, whose disease roles are supported by a range of clinical features.

Topoisomerase inhibitors are potent DNA damaging agents which are widely used in oncology, and they demonstrate robust synergistic tumor cell killing in combination with DNA repair inhibitors, including poly(ADP)-ribose polymerase (PARP) inhibitors. However, their use has been severely limited by the inability to achieve a favorable therapeutic index due to severe systemic toxicities. Antibody-drug conjugates address this issue via antigen-dependent targeting and delivery of their payloads, but this approach requires specific antigens and yet still suffers from off-target toxicities. There is a high unmet need for a more universal tumor targeting technology to broaden the application of cytotoxic payloads. Acidification of the extracellular milieu arises from metabolic adaptions associated with the Warburg effect in cancer. Here we report the development of a pH-sensitive peptide-drug conjugate to deliver the topoisomerase inhibitor, exatecan, selectively to tumors in an antigen-independent manner. Using this approach, we demonstrate potent in vivo cytotoxicity, complete suppression of tumor growth across multiple human tumor models, and synergistic interactions with a PARP inhibitor. These data highlight the identification of a peptide-topoisomerase inhibitor conjugate for cancer therapy that provides a high therapeutic index, and is applicable to all types of human solid tumors in an antigen-independent manner., Graphical Abstract Graphical AbstractCBX-12 is a pH-sensitive peptide-drug conjugate that selectively delivers the TOP1 inhibitor exatecan into tumor cells in an antigen agnostic manner. The peptide forms an alpha helix only in the low pH tumor microenvironment, allowing directional insertion of the peptide across the tumor membrane, cleavage of the linker within the cytosol, and release of free exatecan within the tumor cell. This pH-based delivery approach results in a high therapeutic index universally across solid tumors.

The development of new synthetic biology circuits for biotechnology and medicine requires deeper mechanistic insight into allosteric transcription factors (aTFs). Here we studied the aTF UxuR, a homodimer of two domains connected by a highly flexible linker region. To explore how ligand binding to UxuR affects protein dynamics we performed molecular dynamics simulations in the free protein, the aTF bound to the inducer D-fructuronate or the structural isomer D-glucuronate. We then validated our results by constructing a sensor plasmid for D-fructuronate in Escherichia coli and performed site-directed mutagenesis. Our results show that zinc coordination is necessary for UxuR function since mutation to alanines prevents expression de-repression by D-fructuronate. Analyzing the different complexes, we found that the disordered linker regions allow the N-terminal domains to display fast and large movements. When the inducer is bound, UxuR can sample an open conformation with a more pronounced negative charge at the surface of the N-terminal DNA binding domains. In opposition, in the free and D-glucuronate bond forms the protein samples closed conformations, with a more positive character at the surface of the DNA binding regions. These molecular insights provide a new basis to harness these systems for biological systems engineering.

Cancer cells utilize epigenetic alterations to acquire autonomous capabilities for tumor maintenance. Here, we show that pancreatic ductal adenocarcinoma (PDA) cells utilize super-enhancers (SEs) to activate the transcription factor EVI1 (ecotropic viral integration site 1) gene, resulting in activation of an EVI1-dependent transcription program conferring PDA tumorigenesis. Our data indicate that SE is the vital cis-acting element to maintain aberrant EVI1 transcription in PDA cells. Consistent with disease progression and inferior survival outcomes of PDA patients, we further show that EVI1 upregulation is a major cause of aggressive tumor phenotypes. Specifically, EVI1 promotes anchorage-independent growth and motility in vitro and enhances tumor propagation in vivo. Mechanistically, EVI1-dependent activation of tumor-promoting gene expression programs through the stepwise configuration of the active enhancer chromatin attributes to these phenotypes. In sum, our findings support the premise that EVI1 is a crucial driver of oncogenic transcription programs in PDA cells. Further, we emphasize the instructive role of epigenetic aberrancy in establishing PDA tumorigenesis.

Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. Yet, applications of this technique have been underwhelming, as three independent studies failed to show shared differentially expressed genes in T2D β-cells. We performed an integrative analysis of the available datasets from these studies to overcome confounding sources of variability and better highlight common T2D β-cell transcriptomic signatures. After removing low-quality transcriptomes, we retained 3046 single cells expressing 27 931 genes. Cells were integrated to attenuate dataset-specific biases, and clustered into cell type groups. In T2D β-cells (

Genome instability is a characteristic enabling factor for carcinogenesis. HelQ helicase is a component of human DNA maintenance systems that prevent or reverse genome instability arising during DNA replication. Here, we provide details of the molecular mechanisms that underpin HelQ function—its recruitment onto ssDNA through interaction with replication protein A (RPA), and subsequent translocation of HelQ along ssDNA. We describe for the first time a functional role for the non-catalytic N-terminal region of HelQ, by identifying and characterizing its PWI-like domain. We present evidence that this domain of HelQ mediates interaction with RPA that orchestrates loading of the helicase domains onto ssDNA. Once HelQ is loaded onto the ssDNA, ATP-Mg2+ binding in the catalytic site activates the helicase core and triggers translocation along ssDNA as a dimer. Furthermore, we identify HelQ-ssDNA interactions that are critical for the translocation mechanism. Our data are novel and detailed insights into the mechanisms of HelQ function relevant for understanding how human cells avoid genome instability provoking cancers, and also how cells can gain resistance to treatments that rely on DNA crosslinking agents.

Mutations in the isocitrate dehydrogenase-1 and -2 (IDH1/2) genes were first identified in glioma and acute myeloid leukemia (AML), and subsequently found in multiple other tumor types. These neomorphic mutations convert the normal product of enzyme, α-ketoglutarate (αKG), to the oncometabolite 2-hydroxyglutarate (2HG). Our group recently demonstrated that 2HG suppresses the high-fidelity homologous recombination (HR) DNA repair pathway, resulting in a state referred to as ‘BRCAness’, which confers exquisite sensitivity to poly(ADP-ribose) polymerase (PARP) inhibitors. In this study, we sought to elucidate sensitivity of IDH1/2-mutant cells to DNA damage response (DDR) inhibitors and, whether combination therapies could enhance described synthetic lethal interactions. Here, we report that ATR (ataxia telangiectasia and Rad3-related protein kinase) inhibitors are active against IDH1/2-mutant cells, and that this activity is further potentiated in combination with PARP inhibitors. We demonstrate this interaction across multiple cell line models with engineered and endogenous IDH1/2 mutations, with robust anti-tumor activity in vitro and in vivo. Mechanistically, we found ATR and PARP inhibitor treatment induces premature mitotic entry, which is significantly elevated in the setting of IDH1/2-mutations. These data highlight the potential efficacy of targeting HR defects in IDH1/2-mutant cancers and support the development of this combination in future clinical trials., Graphical Abstract Graphical AbstractIn the presence of elevated genomic instability in HR-deficient IDH1/2 mutant cells and accumulation of PARP-inhibitor mediated DNA breaks, ATR inhibition abrogates S-phase arrest and causes apoptosis by premature mitotic entry (Created using Biorender.com).

Patterns of somatic single nucleotide variants observed in human cancers vary widely between different tumor types. They depend not only on the activity of diverse mutational processes, such as exposure to ultraviolet light and the deamination of methylated cytosines, but largely also on the sequence content of different genomic regions on which these processes act. With MutViz (http://gmql.eu/mutviz/), we have presented a user-friendly web tool for the identification of mutation enrichments that offers preloaded mutations from public datasets for a variety of cancer types, well organized within an effective database architecture. Somatic mutation patterns can be visually and statistically analyzed within arbitrary sets of small, user-provided genomic regions, such as promoters or collections of transcription factor binding sites. Here, we present MutViz 2.0, a largely extended and consolidated version of the tool: we took into account the immediate (trinucleotide) sequence context of mutations, improved the representation of clinical annotation of tumor samples and devised a method for signature refitting on limited genomic regions to infer the contribution of individual mutational processes to the mutation patterns observed in these regions. We described both the features of MutViz 2.0, concentrating on the novelties, and the substantial re-engineering of the cloud-based architecture., Graphical Abstract Graphical AbstractSimplified workflow of data analysis and visualization with MutViz: Analysis of somatic mutations within user-defined genomic regions for identification and visualization of region-specific mutation characteristics.

The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, the vast majority of gene expression studies are conducted on bulk tissues, necessitating computational approaches to infer biological insights on cell type-specific contribution to diseases. Several computational methods are available for cell type deconvolution (that is, inference of cellular composition) from bulk RNA-Seq data, but cannot impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq (scRNA-seq) and population-wide expression profiles, it can be a computationally tractable and identifiable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations by employing genome-wide tissue-wise expression signatures from GTEx to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations, and uses a multi-variate stochastic search algorithm to estimate the expression level of each gene in each cell type. Extensive analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease, and type 2 diabetes validated efficiency of CellR, while revealing how specific cell types contribute to different diseases. We conducted numerical simulations on human cerebellum to generate pseudo-bulk RNA-seq data and demonstrated its efficiency in inferring cell-specific expression profiles. Moreover, we inferred cell-specific expression levels from bulk RNA-seq data on schizophrenia and computed differentially expressed genes within certain cell types. Using predicted gene expression profile on excitatory neurons, we were able to reproduce our recently published findings on TCF4 being a master regulator in schizophrenia and showed how this gene and its targets are enriched in excitatory neurons. In summary, CellR compares favorably (both accuracy and stability of inference) against competing approaches on inferring cellular composition from bulk RNA-seq data, but also allows direct imputation of cell type-specific gene expression, opening new doors to re-analyze gene expression data on bulk tissues in complex diseases.

The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anti-cancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here we used single-cell RNA-seq to examine transcriptomic profiles of cancer cells treated with paclitaxel, celecoxib, and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that celluar mRNA abundance shows dynamic regulation after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification, and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription across single cells. Interestingly, the drug combination resulted in transcriptional amplification and hyperactivation of mitochondrial oxidative phosphorylation pathway linking to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammation-related genes activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response and emphasizes the importance of adding spike-in molecules to study gene expression regulation using single-cell RNA-seq.

Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to perform these analyses. Here, we present Reboot, an integrative approach to find and validate genes and transcripts (splicing isoforms) associated with cancer patient prognosis from high dimensional expression datasets. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests and plots to support the findings. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients’ prognosis. Similarly, Reboot was able to find a seven-splicing isoforms signature related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive and straightforward way of finding genes or splicing isoforms signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated cancer-related genes and splicing isoforms.

Glioblastoma (GBM) is the most common and aggressive intrinsic brain tumour in adults. Epigenetic mechanisms controlling normal brain development are often dysregulated in GBM. Among these, BMI1, a structural component of the Polycomb Repressive Complex 1 (PRC1), which promotes the H2AK119ub catalytic activity of Ring1B, is upregulated in GBM and its tumorigenic role has been shown

Background The VERIPRO® SARS-CoV-2 Env assay uses reverse transcriptase (RT) PCR to detect SARS-CoV-2, the causative agent of COVID-19, from stainless-steel environmental sample swabs. Objective To validate the VERIPRO SARS-CoV-2 Env assay as part of the AOAC Research Institute’s Emergency Response Validation Performance Tested Method(s)SM program. Method The VERIPRO SARS-CoV-2 Env assay was evaluated for specificity using in silico analysis of 15 764 SARS-CoV-2 sequences and 65 exclusivity organisms (both near neighbors and background organisms). The candidate method was evaluated in an unpaired study design for one environmental surface (stainless steel) and compared to the U.S. Centers for Disease Control and Prevention 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel, Instructions for Use (Revision 4, Effective 6/12/2020). Results Results of the in silico analysis demonstrate the specificity of the method in being able to detect target sequences and discriminate them from near neighbors. In the matrix study, the candidate method demonstrated statistically significant better recovery of the target analyte than the reference method. Conclusions The VERIPRO SARS-CoV-2 Env assay is a rapid and accurate method that can be utilized by food producers to detect the causative agent of COVID-19 on food contact surfaces. Highlights The VERIPRO SARS-CoV-2 Env assay can be performed without the need for an optional RNA purification step to detect SARS-CoV-2 from environmental surfaces.

Background The SureFast® SARS-CoV-2 PLUS Test is a reverse transcription qPCR (RT-qPCR) assay for the direct, qualitative detection of novel coronavirus (SARS-CoV-2) RNA from stainless-steel environmental sample swabs. Objective To validate the SureFast SARS-CoV-2 PLUS Kit as part of the AOAC Research Institute’s Emergency Response Validation Performance Tested Method(s)SM program. Method The SureFast SARS-CoV-2 PLUS Kit was evaluated for specificity using in silico analysis of 15 764 SARS-CoV-2 sequences and 65 exclusivity organisms (both near neighbors and background organisms) using the ThermoBLAST program. The candidate method was evaluated in an unpaired study design for one environmental surface (stainless steel) and compared to the US Centers for Disease Control and Prevention 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel, Instructions for Use (Revision 4, Effective 6/12/2020). Results Results of the in silico analysis demonstrated 99.99% selectivity of the method in being able to detect target sequences of the known CoV-2 genomes and discriminate them from near neighbors. In the matrix study, the candidate method demonstrated statistically significant better recovery of the target analyte than the PCR detection reference method. Conclusions The SureFast SARS-CoV-2 PLUS Kit is a rapid and accurate method that can be utilized by food producers to detect the causative agent of COVID-19 on stainless-steel contact surfaces. Highlights SureFast SARS-CoV-2 PLUS test method is highly specific for primer/probe binding to the E target genome region for the SARS-CoV-2 virus, 99.99% binding specificity using in silico analysis.

The RNA methyltransferase TRDMT1 has recently emerged as a key regulator of homologous recombination (HR) in the transcribed regions of the genome, but how it is regulated and its relevance in cancer remain unknown. Here, we identified that TRDMT1 is poly-ubiquitinated at K251 by the E3 ligase TRIM28, removing TRDMT1 from DNA damage sites and allowing completion of HR. Interestingly, K251 is adjacent to G155 in the 3D structure, and the G155V mutation leads to hyper ubiquitination of TRDMT1, reduced TRDMT1 levels and impaired HR. Accordingly, a TRDMT1 G155V mutation in an ovarian cancer super responder to platinum treatment. Cells expressing TRDMT1-G155V are sensitive to cisplatin in vitro and in vivo. In contrast, high expression of TRDMT1 in patients with ovarian cancer correlates with platinum resistance. A potent TRDMT1 inhibitor resensitizes TRDMT1-high tumor cells to cisplatin. These results suggest that TRDMT1 is a promising therapeutic target to sensitize ovarian tumors to platinum therapy.

Whole-genome sequencing combined with transcriptomics can reveal impactful non-coding single nucleotide variants (SNVs) in cancer. Here, we developed an integrative analytical approach that, as a first step, identifies genes altered in expression or DNA methylation in association with nearby somatic SNVs, in contrast to alternative approaches that first identify mutational hotspots. Using genomic datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and the Children's Brain Tumor Tissue Consortium (CBTTC), we identified hundreds of genes and associated CpG islands for which the nearby presence of a non-coding somatic SNV recurrently associated with altered expression or DNA methylation, respectively. Genomic regions upstream or downstream of genes, gene introns and gene untranslated regions were all involved. The PCAWG adult cancer cohort yielded different significant SNV-expression associations from the CBTTC pediatric brain tumor cohort. The SNV-expression associations involved a wide range of cancer types and histologies, as well as potential gain or loss of transcription factor binding sites. Notable genes with SNV-associated increased expression include TERT, COPS3, POLE2 and HDAC2—involving multiple cancer types—MYC, BCL2, PIM1 and IGLL5—involving lymphomas—and CYHR1—involving pediatric low-grade gliomas. Non-coding somatic SNVs show a major role in shaping the cancer transcriptome, not limited to mutational hotspots.

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

Cancer evolves through the accumulation of somatic mutations over time. Although several methods have been developed to characterize mutational processes in cancers, these have not been specifically designed to identify mutational patterns that predict patient prognosis. Here we present CLICnet, a method that utilizes mutational data to cluster patients by survival rate. CLICnet employs Restricted Boltzmann Machines, a type of generative neural network, which allows for the capture of complex mutational patterns associated with patient survival in different cancer types. For some cancer types, clustering produced by CLICnet also predicts benefit from anti-PD1 immune checkpoint blockade therapy, whereas for other cancer types, the mutational processes associated with survival are different from those associated with the improved anti-PD1 survival benefit. Thus, CLICnet has the ability to systematically identify and catalogue combinations of mutations that predict cancer survival, unveiling intricate associations between mutations, survival, and immunotherapy benefit.

Long non-coding RNAs (lncRNAs) have been found to be central players in the epigenetic, transcriptional and post-transcriptional regulation of gene expression. There is an accumulation of evidence on newly discovered lncRNAs, their molecular interactions and their roles in the development and progression of human brain tumors. LncRNAs can have either tumor suppressive or oncogenic functions in different brain cancers, making them attractive therapeutic targets and biomarkers for personalized therapy and precision diagnostics. Here, we summarize the current state of knowledge of the lncRNAs that have been implicated in brain cancer pathogenesis, particularly in gliomas and medulloblastomas. We discuss their epigenetic regulation as well as the prospects of using lncRNAs as diagnostic biomarkers and therapeutic targets in patients with brain tumors.

Although immune checkpoint inhibition (ICI) has shown promising results in metastatic dMMR/MSI-H colorectal cancer (CRC), the majority of pMMR/MSS patients do not respond to such therapies. To systematically evaluate the determinants of immune response in CRC, we explored whether patients with diverse levels of immune cytolytic activity (CYT) have different patterns of chromothripsis and kataegis. Analysis of CRC genomic data from the TCGA, indicated an excess of chromothriptic clusters among CYT-low colon adenocarcinomas, affecting known cancer drivers (APC, KRAS, BRAF, TP53 and FBXW7), immune checkpoints (CD274, PDCD1LG2, IDO1/2 and LAG3) and immune-related genes (ENTPD1, PRF1, NKG7, FAS, GZMA/B/H/K and CD73). CYT-high tumors were characterized by hypermutation, enrichment in APOBEC-associated mutations and kataegis events, as well as APOBEC activation. We also assessed differences in the most prevalent mutational signatures (SBS15, SBS20, SBS54 and DBS2) across cytolytic subgroups. Regarding the composition of immune cells in the tumor milieu, we found enrichment of M1 macrophages, CD8+ T cells and Tregs, as well as higher CD8+ T-cells/Tregs ratio among CYT-high tumors. CYT-high patients had higher immunophenoscores, which is predictive of their responsiveness if they were to be treated with anti-PD-1 alone or in combination with anti-CTLA-4 drugs. These results could have implications for patient responsiveness to immune checkpoint inhibitors.