Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers.

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type-specific features. Using organ-matched healthy tissues, we identified the "nearest healthy" cell types in diverse cancers, demonstrating that the chromatin signature of basal-like-subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.

Effects of Vibrio vulnificus and Microcystis aeruginosa co-exposures on microplastic accumulation and depuration in the Eastern Oyster (Crassostrea virginica).

Microplastics are ubiquitous in the aquatic environment, and bivalves such as the Eastern oyster (Crassostrea virginica) can accumulate these particles directly from the water column. Bivalves are concurrently exposed to pathogenic and toxin-producing bacteria, including Vibrio spp. and Microcystis spp., which have been shown to adversely impact filtration rates. Exposure to these bacteria could thus affect oysters' ability to accumulate and depurate microplastics. As climate change creates conditions that favor Vibrio spp. and Microcystis spp. growth in estuaries, it is increasingly important to understand how these co-occurring biotic stressors influence microplastic contamination in bivalves. The objective of this study was to examine how co-exposures to Vibrio vulnificus and Microcystis aeruginosa influence microplastic accumulation and depuration in Eastern oysters. Oysters were exposed to nylon microplastics (5000 particles L-1) and either V. vulnificus, M. aeruginosa, or both species (104 colony-forming units or cells mL-1, respectively) and sampled over time up to 96 h. Following exposure, remaining oysters were allowed to depurate in clean seawater and sampled over time for up to 96 h. Microplastic concentrations in oysters were quantified and compared among treatments, and rate constants for uptake (ku) and depuration (kd) were calculated using nonlinear regression and two-compartment kinetic models. Overall, microplastic concentrations in oysters exposed to V. vulnificus (X‾ = 2.885 ± 0.350 (SE) particles g-1 w.w.) and V. vulnificus with M. aeruginosa (X‾ = 3.089 ± 0.481 particles g-1 w.w.) were higher than oysters exposed to M. aeruginosa (X‾ = 1.540 ± 0.235 particles g-1 w.w.) and to microplastics alone (X‾ = 1.599 ± 0.208 particles g-1 w.w.). Characterizing microplastic accumulation and depuration in oysters co-exposed to these biotic stressors is an important first step in understanding how contaminant loads in bivalves can change. With this research, the efficacy of depuration for commonly-consumed seafood species can be estimated.

An IROA Workflow for correction and normalization of ion suppression in mass spectrometry-based metabolomic profiling data.

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and signal-to-noise sensitivity. Here we report a new method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) plus novel companion algorithms to 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We have evaluated the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reverse phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibited ion suppression ranging from 1% to 90+% and coefficient of variations ranging from 1% to 20%, but the Workflow and companion algorithms were highly effective at nulling out that suppression and error. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044.

The journal and authors wish to retract the article entitled 'Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images' cited above [...].

PCA-Plus: Enhanced principal component analysis with illustrative applications to batch effects and their quantitation.

Background: Principal component analysis (PCA), a standard approach to analysis and visualization of large datasets, is commonly used in biomedical research for detecting similarities and differences among groups of samples. We initially used conventional PCA as a tool for critical quality control of batch and trend effects in multi-omic profiling data produced by The Cancer Genome Atlas (TCGA) project of the NCI. We found, however, that conventional PCA visualizations were often hard to interpret when inter-batch differences were moderate in comparison with intra-batch differences; it was also difficult to quantify batch effects objectively. We, therefore, sought enhancements to make the method more informative in those and analogous settings. Results: We have developed algorithms and a toolbox of enhancements to conventional PCA that improve the detection, diagnosis, and quantitation of differences between or among groups, e.g., groups of molecularly profiled biological samples. The enhancements include (i) computed group centroids; (ii) sample-dispersion rays; (iii) differential coloring of centroids, rays, and sample data points; (iii) trend trajectories; and (iv) a novel separation index (DSC) for quantitation of differences among groups. Conclusions: PCA-Plus has been our most useful single tool for analyzing, visualizing, and quantitating batch effects, trend effects, and class differences in molecular profiling data of many types: mRNA expression, microRNA expression, DNA methylation, and DNA copy number. An early version of PCA-Plus has been used as the central graphical visualization in our MBatch package for near-real-time surveillance of data for analysis working groups in more than 70 TCGA, PanCancer Atlas, PanCancer Analysis of Whole Genomes, and Genome Data Analysis Network projects of the NCI. The algorithms and software are generic, hence applicable more generally to other types of multivariate data as well. PCA-Plus is freely available in a down-loadable R package at our MBatch website.

DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples.

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we introduce an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using the better-matched, i.e., benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using a benchmark dataset of healthy retinas suggest much-improved deconvolution accuracy. Further analysis of a cohort of 453 patients with age-related macular degeneration supports the broad applicability of DeMixSC. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched dataset to resolve this challenge. The developed DeMixSC framework is generally applicable for deconvolving large cohorts of disease tissues, and potentially cancer.

Rising seas and roadway debris: Microplastic and low-density tire wear particles in street-associated tidal floodwater.

Tidal flooding is increasingly common in low-lying coastal regions as sea levels rise. This type of flooding can occur on sunny days with no rainfall and may transport street-associated debris, such as microplastics (MPs) including tire wear particles (TWPs), to coastal systems. This research aimed to quantify MP abundance in tidal floodwater and investigate their fate. Three locations around Charleston, SC (USA) were sampled during 12 tidal floods, and their adjacent tidal creeks were sampled before and after 5 floods. Floodwater contained an average of 342 ± 60 MP/L. Most MPs in floodwater were low-density TWP (86.5 %). MP abundance in tidal creek surface water following flooding did not change, suggesting that MPs were not immediately transferred to coastal waterways but deposited in adjacent marsh sediment. Elucidating transport routes of MPs in coastal environments is critical to understanding and preventing this type of contamination in the face of a changing climate.

Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images.

BACKGROUND: Ovarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images. METHODS: We have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan-Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response. RESULTS: The study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results. CONCLUSIONS: This type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities.

Ether phospholipids are required for mitochondrial reactive oxygen species homeostasis.

Mitochondria are hubs where bioenergetics, redox homeostasis, and anabolic metabolism pathways integrate through a tightly coordinated flux of metabolites. The contributions of mitochondrial metabolism to tumor growth and therapy resistance are evident, but drugs targeting mitochondrial metabolism have repeatedly failed in the clinic. Our study in pancreatic ductal adenocarcinoma (PDAC) finds that cellular and mitochondrial lipid composition influence cancer cell sensitivity to pharmacological inhibition of electron transport chain complex I. Profiling of patient-derived PDAC models revealed that monounsaturated fatty acids (MUFAs) and MUFA-linked ether phospholipids play a critical role in maintaining ROS homeostasis. We show that ether phospholipids support mitochondrial supercomplex assembly and ROS production; accordingly, blocking de novo ether phospholipid biosynthesis sensitized PDAC cells to complex I inhibition by inducing mitochondrial ROS and lipid peroxidation. These data identify ether phospholipids as a regulator of mitochondrial redox control that contributes to the sensitivity of PDAC cells to complex I inhibition.

Spatial and temporal variability of microplastic abundance in estuarine intertidal sediments: Implications for sampling frequency.

Microplastics (<5 mm) are well documented across shorelines worldwide; however, high variability in microplastic abundance is often observed within and among field studies. The majority of microplastic surveys to date consist of single sampling events that do not consider spatiotemporal variability as a potential confounding factor in the interpretation of their results. Therefore, these surveys may not accurately capture or reflect levels of microplastic contamination in the environment. Here, we provide the first investigation of small-scale spatial and temporal variability of microplastic abundance, distribution, and composition in the intertidal zone of an urbanized US estuary to better understand the short-term, daily spatiotemporal variability of microplastics in dynamic coastal environments. Intertidal sediment was collected from both the low and high intertidal zones of a sandy estuarine beach located in South Carolina, southeastern US every 1 to 2 days at low tide over 17 days (12 sampling events; total n = 72). Study-wide, microplastic abundance ranged from 44 to 912 microplastics/m2 and consisted primarily of polyethylene, nylon, polyester, and tire (or tyre) wear particles. High temporal variability was observed, with microplastic abundance differing significantly among sampling events (p = 0.00025), as well as among some consecutive tidal cycles occurring within 12 h of each other (p = 0.007). By contrast, low spatial variability was observed throughout the study with no significant differences in microplastic abundance detected between the low and high intertidal zones (p = 0.76). Of the environmental factors investigated, wind direction on the day of sampling had the greatest effect on temporal microplastic variability. Our results demonstrate that there can be significant temporal variability of microplastic abundance in estuarine intertidal sediments and are important for informing the methods and interpretation of future microplastic surveys in dynamic coastal environments worldwide.

Plastic, It's What's for Dinner: A Preliminary Comparison of Ingested Particles in Bottlenose Dolphins and Their Prey.

Microplastic ingestion was reported for common bottlenose dolphins (Tursiops truncatus) inhabiting Sarasota Bay, FL, USA, a community that also has prevalent exposure to plasticizers (i.e., phthalates) at concentrations higher than human reference populations. Exposure sources are currently unknown, but plastic-contaminated prey could be a vector. To explore the potential for trophic exposure, prey fish muscle and gastrointestinal tract (GIT) tissues and contents were screened for suspected microplastics, and particle properties (e.g., color, shape, surface texture) were compared with those observed in gastric samples from free-ranging dolphins. Twenty-nine fish across four species (hardhead catfish, Ariopsis felis; pigfish, Orthopristis chrysoptera; pinfish, Lagodon rhomboides; and Gulf toadfish, Opsanus beta) were collected from Sarasota Bay during September 2022. Overall, 97% of fish (n = 28) had suspected microplastics, and GIT abundance was higher than muscle. Fish and dolphin samples contained fibers and films; however, foams were common in dolphin samples and not observed in fish. Suspected tire wear particles (TWPs) were not in dolphin samples, but 23.1% and 32.0% of fish muscle and GIT samples, respectively, contained at least one suspected TWP. While some similarities in particles were shared between dolphins and fish, small sample sizes and incongruent findings for foams and TWPs suggest further investigation is warranted to understand trophic transfer potential.

RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data.

SUMMARY: Reverse-Phase Protein Array (RPPA) is a robust high-throughput, cost-effective platform for quantitatively measuring proteins in biological specimens. However, converting raw RPPA data into normalized, analysis-ready data remains a challenging task. Here, we present the RPPA SPACE (RPPA Superposition Analysis and Concentration Evaluation) R package, a substantially improved successor to SuperCurve, to meet that challenge. SuperCurve has been used to normalize over 170 000 samples to date. RPPA SPACE allows exclusion of poor-quality samples from the normalization process to improve the quality of the remaining samples. It also features a novel quality-control metric, 'noise', that estimates the level of random errors present in each RPPA slide. The noise metric can help to determine the quality and reliability of the data. In addition, RPPA SPACE has simpler input requirements and is more flexible than SuperCurve, it is much faster with greatly improved error reporting. AVAILABILITY AND IMPLEMENTATION: The standalone RPPA SPACE R package, tutorials and sample data are available via https://rppa.space/, CRAN (https://cran.r-project.org/web/packages/RPPASPACE/index.html) and GitHub (https://github.com/MD-Anderson-Bioinformatics/RPPASPACE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Are Green Household Consumer Products Less Toxic than Conventional Products? An Assessment Involving Grass Shrimp (Palaemon pugio) and Daphnia magna.

Although it is generally assumed that green household consumer products (HCPs) contain individual compounds that are less toxic and/or more degradable than conventional HCPs, little research on this topic has been conducted. In our assessments, larval grass shrimp (Palaemon pugio) were used in a biodegradation study and juvenile freshwater cladocerans, Daphnia magna, were used in a photodegradation study. In each study, organisms were exposed to nondegraded and degraded treatments consisting of one green HCP and two conventional HCPs in six different categories (laundry detergent, dish detergent, mouthwash, insecticide, dishwasher gel, and all-purpose cleaner). Sensitivity to these products were assessed using 48-h static acute toxicity tests, and the median lethal concentrations (LC50s) then compared using an LC50 ratio test. For grass shrimp, only one green HCP (insecticide) was less toxic than both conventional HCPs. In one category (laundry detergent), the green HCP was the more toxic than either conventional HCP. Following a biodegradation treatment, none of the green product formulations became less toxic, whereas 44.4% of the conventional HCPs demonstrated decreased toxicity. For daphnids, green HCPs in three categories (dish detergent, insecticide, and all-purpose cleaner) were less toxic than both conventional products tested. Following a photodegradation treatment, two green product formulations (dish detergent and dishwasher gel) became less toxic (33.3%), whereas 87.5% of the conventional HCPs demonstrated decreased toxicity. The present study demonstrates that green HCPs are not necessarily less toxic and/or more degradable than their conventional counterparts. These results also suggest that the toxicity and degradability of end-product formulations need to be considered in the overall framework for green product evaluation. Environ Toxicol Chem 2022;41:2444-2453. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Erratum: The origin of bladder cancer from mucosal field effects.

[This corrects the article DOI: 10.1016/j.isci.2022.104551.].

The origin of bladder cancer from mucosal field effects.

Whole-organ mapping was used to study molecular changes in the evolution of bladder cancer from field effects. We identified more than 100 dysregulated pathways, involving immunity, differentiation, and transformation, as initiators of carcinogenesis. Dysregulation of interleukins signified the involvement of inflammation in the incipient phases of the process. An aberrant methylation/expression of multiple HOX genes signified dysregulation of the differentiation program. We identified three types of mutations based on their geographic distribution. The most common were mutations restricted to individual mucosal samples that targeted uroprogenitor cells. Two types of mutations were associated with clonal expansion and involved large areas of mucosa. The α mutations occurred at low frequencies while the ß mutations increased in frequency with disease progression. Modeling revealed that bladder carcinogenesis spans 10-15 years and can be divided into dormant and progressive phases. The progressive phase lasted 1-2 years and was driven by ß mutations.

Accumulation and depuration of microplastic fibers, fragments, and tire particles in the eastern oyster, Crassostrea virginica: A toxicokinetic approach.

Along the South Carolina coast (U.S.) where the ecologically and economically important eastern oyster (Crassostrea virginica) forms extensive intertidal reefs, recent surface water surveys found that fibers, fragments, and microscopic tire particles represented 43.6%, 30.9%, and 17.7% of the total microplastics, respectively. The aim of this study was to characterize accumulation and depuration of these particles in eastern oysters. Oysters were exposed to purple polyethylene fibers, green nylon fragments, or micronized crumb rubber at a concentration of 5000 microplastics/L, and sacrificed after 0, 24, 48, and 96 h to characterize uptake. Following 96 h, remaining oysters were transferred to microplastic-free brackish water and sacrificed at 24, 48, and 96 h to characterize depuration. For fibers and fragments, levels increased in a nonlinear fashion reaching 1.61 ± 0.6 particles/g w. w. (mean ± SE) and 0.46 ± 0.1 particles/g w. w. after 96 h, respectively. Conditional uptake clearance rate constants (ku) were estimated to be 0.0084 and 0.0025 mL/g*h for fibers and fragments, respectively. For crumb rubber, levels increased in a linear fashion reaching 3.62 ± 0.8 particles/g w. w. after 96 h, and the ku value was estimated to be 0.0077 mL/g*h. Depuration was best described using a two-compartment (double exponential) model suggesting the presence of fast and slow compartments. Conditional depuration rate constants (kd) for the slow compartments were 0.0084, 0.0205, and 0.0048/h for fibers, fragments, and crumb rubber, respectively. These results demonstrate accumulation and depuration of microplastics in eastern oysters is size-and shape-dependent. Depuration, which is a common practice for shellfish safety, is an effective way to reduce microplastic loads in eastern oysters, but the minimum recommended time of 44 h would only reduce loads of these particles by 55.5-67.6%.

Assessment of acute toxicity and developmental transformation impacts of polyethylene microbead exposure on larval daggerblade grass shrimp (Palaemon pugio).

Due to the ubiquity of microplastic contamination in coastal waters, there is potential for adverse impacts to organism development. One organism of interest is the daggerblade grass shrimp, Palaemon pugio, an ecologically important species in estuaries along the east coast of North America. We exposed larval grass shrimp to virgin polyethylene microbeads (35 and 58 µm) at a high (0.375 and 1.95 mg/L), medium (0.0375 and 0.195 mg/L), and a low concentration (0.00375 and 0.0195 mg/L), respectively for 23 days to assess mortality, transformation time from larval to juvenile stage, and weight. Average percent mortality was 3.7 to 4.8 times higher in the experimental treatments compared to controls. The greatest proportion of mortality was observed in the first 11 days. Median time for transformation ranged from 20.2 to 20.8 days. Shrimp exposed to the 35 µm beads in the high treatment (20.2 days) transformed significantly faster than the control shrimp (20.8 days). Although development was not delayed and size of the shrimp did not differ, the acute toxicity of microplastics on grass shrimp is a concern due to their role in energy cycling within tidal-creeks. These findings suggest potential population and community level effects following microplastic exposure.