Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

Increasing Levels of Positive End-expiratory Pressure Cause Stepwise Biventricular Stroke Work Reduction in a Porcine Model.

BACKGROUND: Positive end-expiratory pressure (PEEP) is commonly applied to avoid atelectasis and improve oxygenation in patients during general anesthesia but affects cardiac pressures, volumes, and loading conditions through cardiorespiratory interactions. PEEP may therefore alter stroke work, which is the area enclosed by the pressure-volume loop and corresponds to the external work performed by the ventricles to eject blood. The low-pressure right ventricle may be even more susceptible to PEEP than the left ventricle. The authors hypothesized that increasing levels of PEEP would reduce stroke work in both ventricles. METHODS: This was a prospective, observational, experimental study. Six healthy female pigs of approximately 60 kg were used. PEEP was stepwise increased from 0 to 5, 7, 9, 11, 13, 15, 17, and 20 cm H2O to cover the clinical spectrum of PEEP. Simultaneous, biventricular invasive pressure-volume loops, invasive blood pressures, and ventilator data were recorded. RESULTS: Increasing PEEP resulted in stepwise reductions in left (5,740 ± 973 vs. 2,303 ± 1,154 mmHg · ml; P < 0.001) and right (2,064 ± 769 vs. 468 ± 133 mmHg · ml; P < 0.001) ventricular stroke work. The relative stroke work reduction was similar between the two ventricles. Left ventricular ejection fraction, afterload, and coupling were preserved. On the contrary, PEEP increased right ventricular afterload and caused right ventriculo-arterial uncoupling (0.74 ± 0.30 vs. 0.19 ± 0.13; P = 0.01) with right ventricular ejection fraction reduction (64 ± 8% vs. 37 ± 7%, P < 0.001). CONCLUSIONS: A stepwise increase in PEEP caused stepwise reduction in biventricular stroke work. However, there are important interventricular differences in response to increased PEEP levels. PEEP increased right ventricular afterload leading to uncoupling and right ventricular ejection fraction decline. These findings may support clinical decision-making to further optimize PEEP as a means to balance between improving lung ventilation and preserving right ventricular function.

ncdDetect2: improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation.

Motivation: Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources. Results: Here, we analyse patterns of observed-to-expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation-rate model likely operate on a megabase length scale. We extend our site-specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P-values. Availability and implementation: ncdDetect v.2 is implemented as an R-package and is freely available at http://github.com/TobiasMadsen/ncdDetect2. Supplementary information: Supplementary data are available at Bioinformatics online.

EBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples.

DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types. Here, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets. EBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets.

Significance evaluation in factor graphs.

BACKGROUND: Factor graphs provide a flexible and general framework for specifying probability distributions. They can capture a range of popular and recent models for analysis of both genomics data as well as data from other scientific fields. Owing to the ever larger data sets encountered in genomics and the multiple-testing issues accompanying them, accurate significance evaluation is of great importance. We here address the problem of evaluating statistical significance of observations from factor graph models. RESULTS: Two novel numerical approximations for evaluation of statistical significance are presented. First a method using importance sampling. Second a saddlepoint approximation based method. We develop algorithms to efficiently compute the approximations and compare them to naive sampling and the normal approximation. The individual merits of the methods are analysed both from a theoretical viewpoint and with simulations. A guideline for choosing between the normal approximation, saddle-point approximation and importance sampling is also provided. Finally, the applicability of the methods is demonstrated with examples from cancer genomics, motif-analysis and phylogenetics. CONCLUSIONS: The applicability of saddlepoint approximation and importance sampling is demonstrated on known models in the factor graph framework. Using the two methods we can substantially improve computational cost without compromising accuracy. This contribution allows analyses of large datasets in the general factor graph framework.

Fast, Accurate and Automatic Ancient Nucleosome and Methylation Maps with epiPALEOMIX.

The first epigenomes from archaic hominins (AH) and ancient anatomically modern humans (AMH) have recently been characterized, based, however, on a limited number of samples. The extent to which ancient genome-wide epigenetic landscapes can be reconstructed thus remains contentious. Here, we present epiPALEOMIX, an open-source and user-friendly pipeline that exploits post-mortem DNA degradation patterns to reconstruct ancient methylomes and nucleosome maps from shotgun and/or capture-enrichment data. Applying epiPALEOMIX to the sequence data underlying 35 ancient genomes including AMH, AH, equids and aurochs, we investigate the temporal, geographical and preservation range of ancient epigenetic signatures. We first assess the quality of inferred ancient epigenetic signatures within well-characterized genomic regions. We find that tissue-specific methylation signatures can be obtained across a wider range of DNA preparation types than previously thought, including when no particular experimental procedures have been used to remove deaminated cytosines prior to sequencing. We identify a large subset of samples for which DNA associated with nucleosomes is protected from post-mortem degradation, and nucleosome positioning patterns can be reconstructed. Finally, we describe parameters and conditions such as DNA damage levels and sequencing depth that limit the preservation of epigenetic signatures in ancient samples. When such conditions are met, we propose that epigenetic profiles of CTCF binding regions can be used to help data authentication. Our work, including epiPALEOMIX, opens for further investigations of ancient epigenomes through time especially aimed at tracking possible epigenetic changes during major evolutionary, environmental, socioeconomic, and cultural shifts.

PINCAGE: probabilistic integration of cancer genomics data for perturbed gene identification and sample classification.

MOTIVATION: Cancer development and progression is driven by a complex pattern of genomic and epigenomic perturbations. Both types of perturbations can affect gene expression levels and disease outcome. Integrative analysis of cancer genomics data may therefore improve detection of perturbed genes and prediction of disease state. As different data types are usually dependent, analysis based on independence assumptions will make inefficient use of the data and potentially lead to false conclusions. MODEL: Here, we present PINCAGE (Probabilistic INtegration of CAncer GEnomics data), a method that uses probabilistic integration of cancer genomics data for combined evaluation of RNA-seq gene expression and 450k array DNA methylation measurements of promoters as well as gene bodies. It models the dependence between expression and methylation using modular graphical models, which also allows future inclusion of additional data types. RESULTS: We apply our approach to a Breast Invasive Carcinoma dataset from The Cancer Genome Atlas consortium, which includes 82 adjacent normal and 730 cancer samples. We identify new biomarker candidates of breast cancer development (PTF1A, RABIF, RAG1AP1, TIMM17A, LOC148145) and progression (SERPINE3, ZNF706). PINCAGE discriminates better between normal and tumour tissue and between progressing and non-progressing tumours in comparison with established methods that assume independence between tested data types, especially when using evidence from multiple genes. Our method can be applied to any type of cancer or, more generally, to any genomic disease for which sufficient amount of molecular data is available. AVAILABILITY AND IMPLEMENTATION: R scripts available at http://moma.ki.au.dk/prj/pincage/ CONTACT: : michal.switnicki@clin.au.dk or jakob.skou@clin.au.dk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparison of admittance and cardiac magnetic resonance generated pressure-volume loops in a porcine model.

Objective. Pressure-volume loop analysis, traditionally performed by invasive pressure and volume measurements, is the optimal method for assessing ventricular function, while cardiac magnetic resonance (CMR) imaging is the gold standard for ventricular volume estimation. The aim of this study was to investigate the agreement between the assessment of end-systolic elastance (Ees) assessed with combined CMR and simultaneous pressure catheter measurements compared with admittance catheters in a porcine model.Approach. Seven healthy pigs underwent admittance-based pressure-volume loop evaluation followed by a second assessment with CMR during simultaneous pressure measurements.Main results. Admittance overestimated end-diastolic volume for both the left ventricle (LV) and the right ventricle (RV) compared with CMR. Further, there was an underestimation of RV end-systolic volume with admittance. For the RV, however, Ees was systematically higher when assessed with CMR plus simultaneous pressure measurements compared with admittance whereas there was no systematic difference in Ees but large differences between admittance and CMR-based methods for the LV.Significance. LV and RV Ees can be obtained from both admittance and CMR based techniques. There were discrepancies in volume estimates between admittance and CMR based methods, especially for the RV. RV Ees was higher when estimated by CMR with simultaneous pressure measurements compared with admittance.

Multiple dimensions of semantic and perceptual similarity contribute to mnemonic discrimination for pictures.

People often misrecognize objects that are similar to those they have previously encountered. These mnemonic discrimination errors are attributed to shared memory representations (gist) typically characterized in terms of meaning. In two experiments, we investigated multiple semantic and perceptual relations that may contribute: at the concept level, a feature-based measure of concept confusability quantified each concept's tendency to activate other similar concepts via shared features; at the item level, rated item exemplarity indexed the degree to which the specific depicted objects activated their particular concepts. We also measured visual confusability over items using a computational model of vision, and an index of color confusability. Participants studied single (Experiment 1, N = 60) or multiple (Experiment 2, N = 60) objects for each basic-level concept, followed by a recognition memory test including studied items, similar lures, and novel items. People were less likely to recognize studied items with high concept confusability, and less likely to falsely recognize their lures. This points to weaker basic-level semantic gist representations for objects with more confusable concepts because of greater emphasis on coarse processing of shared features relative to fine-grained processing of individual concepts. In contrast, people were more likely to misrecognize lures that were better exemplars of their concept, suggesting that enhanced basic-level semantic gist processing increased errors due to gist across items. False recognition was also more frequent for more visually confusable lures. The results implicate semantic similarity at multiple levels and highlight the importance of perceptual as well as semantic relations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Veno-occlusive unloading of the heart reduces infarct size in experimental ischemia-reperfusion.

Mechanical unloading of the left ventricle reduces infarct size after acute myocardial infarction by reducing cardiac work. Left ventricular veno-occlusive unloading reduces cardiac work and may reduce ischemia and reperfusion injury. In a porcine model of myocardial ischemia-reperfusion injury we randomized 18 pigs to either control or veno-occlusive unloading using a balloon engaged from the femoral vein into the inferior caval vein and inflated at onset of ischemia. Evans blue and 2,3,5-triphenyltetrazolium chloride were used to determine the myocardial area at risk and infarct size, respectively. Pressure-volume loops were recorded to calculate cardiac work, left ventricular (LV) volumes and ejection fraction. Veno-occlusive unloading reduced infarct size compared with controls (Unloading 13.9 ± 8.2% versus Control 22.4 ± 6.6%; p = 0.04). Unloading increased myocardial salvage (54.8 ± 23.4% vs 28.5 ± 14.0%; p = 0.02), while the area at risk was similar (28.4 ± 6.7% vs 27.4 ± 5.8%; p = 0.74). LV ejection fraction was preserved in the unloaded group, while the control group showed a reduced LV ejection fraction. Veno-occlusive unloading reduced myocardial infarct size and preserved LV ejection fraction in an experimental acute ischemia-reperfusion model. This proof-of-concept study demonstrated the potential of veno-occlusive unloading as an adjunctive cardioprotective therapy in patients undergoing revascularization for acute myocardial infarction.

Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments.

BACKGROUND: Motif analysis methods have long been central for studying biological function of nucleotide sequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked by an experimentally acquired functional property such as gene expression or protein binding affinity. Current motif discovery tools suffer from limitations in searching large motif spaces, and thus more complex motifs may not be included. There is thus a need for motif analysis methods that are tailored for analyzing specific complex motifs motivated by biological questions and hypotheses rather than acting as a screen based motif finding tool. METHODS: We present Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented motifs in ranked lists of sequences. Regmex uses regular expressions to define motifs or families of motifs and embedded Markov models to calculate exact p-values for motif observations in sequences. Biases in motif distributions across ranked sequence lists are evaluated using random walks, Brownian bridges, or modified rank based statistics. A modular setup and fast analytic p value evaluations make Regmex applicable to diverse and potentially large-scale motif analysis problems. RESULTS: We demonstrate use cases of combined motifs on simulated data and on expression data from micro RNA transfection experiments. We confirm previously obtained results and demonstrate the usability of Regmex to test a specific hypothesis about the relative location of microRNA seed sites and U-rich motifs. We further compare the tool with an existing motif discovery tool and show increased sensitivity. CONCLUSIONS: Regmex is a useful and flexible tool to analyze motif hypotheses that relates to large data sets in functional genomics. The method is available as an R package (https://github.com/muhligs/regmex).

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival.

Cancer develops by accumulation of somatic driver mutations, which impact cellular function. Mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC whole-genomes from 10 cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including the TERT promoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g., PAX5, TOX3, PCF11, MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g., TERT and CDH10) and survival (e.g., CDH9 and CDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., near GATA3, CDC6, ZNF217, and CTCF transcription factor binding sites). Survival analysis further pointed to MIR122, a known marker of poor prognosis in liver cancer. In conclusion, the screen for significant mutation patterns coupled with correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

Soluble 1:1 complexes and insoluble 3:2 complexes - Understanding the phase-solubility diagram of hydrocortisone and γ-cyclodextrin.

The molecular mechanisms underlying the drug-solubilizing properties of γ-cyclodextrin were explored using hydrocortisone as a model drug. The BS-type phase-solubility diagram of hydrocortisone with γ-cyclodextrin was thoroughly characterized by measuring the concentrations of hydrocortisone and γ-cyclodextrin in solution and the solid phase. The drug-solubilizer interaction was also studied by isothermal titration calorimetry from which a precise value of the 1:1 binding constant (K11=4.01mM-1 at 20°C) was obtained. The formation of water-soluble 1:1 complexes is responsible for the initial increase in hydrocortisone solubility while the precipitation of entities with a 3:2 ratio of γ-cyclodextrin:hydrocortisone is responsible for the plateau and the ensuing strong decrease in solubility once all solid hydrocortisone is used up. The complete phase-solubility diagram is well accounted for by a model employing the 1:1 binding constant and the solubility product of the precipitating 3:2 entity (K32S=5.51 mM5). For such systems, a small surplus of γ-cyclodextrin above the optimum concentration may result in a significant decrease in drug solubility, and the implications for drug formulations are briefly discussed.

Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate.

Non-coding mutations may drive cancer development. Statistical detection of non-coding driver regions is challenged by a varying mutation rate and uncertainty of functional impact. Here, we develop a statistically founded non-coding driver-detection method, ncdDetect, which includes sample-specific mutational signatures, long-range mutation rate variation, and position-specific impact measures. Using ncdDetect, we screened non-coding regulatory regions of protein-coding genes across a pan-cancer set of whole-genomes (n = 505), which top-ranked known drivers and identified new candidates. For individual candidates, presence of non-coding mutations associates with altered expression or decreased patient survival across an independent pan-cancer sample set (n = 5454). This includes an antigen-presenting gene (CD1A), where 5'UTR mutations correlate significantly with decreased survival in melanoma. Additionally, mutations in a base-excision-repair gene (SMUG1) correlate with a C-to-T mutational-signature. Overall, we find that a rich model of mutational heterogeneity facilitates non-coding driver identification and integrative analysis points to candidates of potential clinical relevance.

A safflower oil based high-fat/high-sucrose diet modulates the gut microbiota and liver phospholipid profiles associated with early glucose intolerance in the absence of tissue inflammation.

SCOPE: Omega-6 (n-6) PUFA-rich diets are generally considered obesogenic in rodents. Here, we examined how long-term intake of a high-fat/high-sucrose (HF/HS) diet based on safflower oil affected metabolism, inflammation, and gut microbiota composition. METHODS AND RESULTS: We fed male C57BL/6J mice a HF/HS diet based on safflower oil-rich in n-6 PUFAs-or a low-fat/low-sucrose diet for 40 wk. Compared to the low-fat/low-sucrose diet, intake of the safflower-based HF/HS diet only led to moderate weight gain, while glucose intolerance developed at week 5 prior to signs of inflammation, but concurrent with increased levels of linoleic acid and arachidonic acid in hepatic phospholipids. Intake of the HF/HS diet resulted in early changes in the gut microbiota, including an increased abundance of Blautia, while late changes coincided with altered inflammatory profiles and increased fasting plasma insulin. Analysis of immune cells in visceral fat and liver revealed no differences between diets before week 40, where the number of immune cells decreased in the liver of HF/HS-fed mice. CONCLUSION: We suggest that a diet-dependent increase in the n-6 to omega-3 (n-3) PUFA ratio in hepatic phospholipids together with gut microbiota changes contributed to early development of glucose intolerance without signs of inflammation.