Behavioural immune landscapes of inflammation.

Transcriptional and proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues1,2. These approaches, however, do not describe dynamic scenarios in which cells continuously change their biochemical properties and downstream 'behavioural' outputs3-5. Here we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamics of individual leukocytes at sites of active inflammation. By analysing more than 100,000 reconstructions of cell shapes and tracks over time, we obtained behavioural descriptors of individual cells and used these high-dimensional datasets to build behavioural landscapes. These landscapes recognized leukocyte identities in the inflamed skin and trachea, and uncovered a continuum of neutrophil states inside blood vessels, including a large, sessile state that was embraced by the underlying endothelium and associated with pathogenic inflammation. Behavioural screening in 24 mouse mutants identified the kinase Fgr as a driver of this pathogenic state, and interference with Fgr protected mice from inflammatory injury. Thus, behavioural landscapes report distinct properties of dynamic environments at high cellular resolution.

Physiologically-based pharmacokinetic modelling of long-acting injectable cabotegravir and rilpivirine in pregnancy.

AIMS: Long-acting cabotegravir and rilpivirine have been approved to manage HIV in adults, but data regarding safe use in pregnancy are limited. Physiologically-based pharmacokinetic (PBPK) modelling was used to simulate the approved dosing regimens in pregnancy and explore if Ctrough was maintained above cabotegravir and rilpivirine target concentrations (664 and 50 ng/mL, respectively). METHODS: An adult PBPK model was validated using clinical data of cabotegravir and rilpivirine in nonpregnant adults. This was modified by incorporating pregnancy-induced metabolic and physiological changes. The pregnancy PBPK model was validated with data on oral rilpivirine and raltegravir (UGT1A1 probe substrate) in pregnancy. Twelve weeks' disposition of monthly and bimonthly dosing of long-acting cabotegravir and rilpivirine was simulated at different trimesters and foetal exposure was also estimated. RESULTS: Predicted Ctrough at week 12 for monthly long-acting cabotegravir was above 664 ng/mL throughout pregnancy, but below the target in 0.5% of the pregnant population in the third trimester with bimonthly long-acting cabotegravir. Predicted Ctrough at week 12 for monthly and bimonthly long-acting rilpivirine was below 50 ng/mL in at least 40% and over 90% of the pregnant population, respectively, throughout pregnancy. Predicted medians (range) of cord-to-maternal blood ratios were 1.71 (range, 1.55-1.79) for cabotegravir and 0.88 (0.78-0.93) for rilpivirine between weeks 38 and 40. CONCLUSIONS: Model predictions suggest that monthly long-acting cabotegravir could maintain antiviral efficacy throughout pregnancy, but that bimonthly administration may require careful clinical evaluation. Both monthly and bimonthly long-acting rilpivirine may not adequately maintain antiviral efficacy in pregnancy.

Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts.

BACKGROUND: Use of alternative non-Saccharomyces yeasts in wine and beer brewing has gained more attention the recent years. This is both due to the desire to obtain a wider variety of flavours in the product and to reduce the final alcohol content. Given the metabolic differences between the yeast species, we wanted to account for some of the differences by using in silico models. RESULTS: We created and studied genome-scale metabolic models of five different non-Saccharomyces species using an automated processes. These were: Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora osmophila, Torulaspora delbrueckii and Kluyveromyces lactis. Using the models, we predicted that M. pulcherrima, when compared to the other species, conducts more respiration and thus produces less fermentation products, a finding which agrees with experimental data. Complex I of the electron transport chain was to be present in M. pulcherrima, but absent in the others. The predicted importance of Complex I was diminished when we incorporated constraints on the amount of enzymatic protein, as this shifts the metabolism towards fermentation. CONCLUSIONS: Our results suggest that Complex I in the electron transport chain is a key differentiator between Metschnikowia pulcherrima and the other yeasts considered. Yet, more annotations and experimental data have the potential to improve model quality in order to increase fidelity and confidence in these results. Further experiments should be conducted to confirm the in vivo effect of Complex I in M. pulcherrima and its respiratory metabolism.

Predictive evolution of metabolic phenotypes using model-designed environments.

Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.

Inclusion Complexes of Concentrated Orange Oils and ß-Cyclodextrin: Physicochemical and Biological Characterizations.

Concentrated orange oils (5x, 10x, 20x) are ingredients used in different industries as components of flavors and aromas due to their great organoleptic qualities. This research focuses on the search for alternative uses for their application through encapsulation in inclusion complexes with ß-cyclodextrin (ß-CD). Inclusion complexes of concentrated orange oils (COEO) and ß-CD were developed by the co-precipitated method in ratios of 4:96, 12:88, and 16:84 (w/w, COEO: ß-CD). The best powder recovery was in the ratio 16:84 for the three oils, with values between 82% and 84.8%. The 20x oil in relation 12:88 showed the highest entrapment efficiency (89.5%) with 102.3 mg/g of ß-CD. The FT-IR analysis may suggest an interaction between the oil and the ß-CD. The best antioxidant activity was observed in the ratio 12:88 for the three oils. The antifungal activity was determined for all the inclusion complexes, and the 10x fraction showed the highest inhibition at a concentration of 10 mg/mL in ratios 12:88 and 16:84. Antibacterial activity was determined by the minimum inhibitory concentration (MIC) and was found at a concentration of 1.25 mg/mL in ratios 12:88 and 16:84 for 5x and 20x oils.

PIK3CA mutations in vascular malformations.

PURPOSE OF REVIEW: Recently, it has been discovered that a subset of vascular malformations, of the lymphatic and venous type, are caused by oncogenic mutations in the PIK3CA gene. Now, efforts have been focused in the understanding of the molecular and cellular consequences of these mutations and the opportunities for novel-targeted therapies for these diseases. RECENT FINDINGS: Here, we review the latest findings in the biology of oncogenic PIK3CA mutations in the pathogenesis of vascular malformations. We focus on the recent development of in-vitro and in-vivo tools for the study of PIK3CA-mutant vascular malformations with special interest in preclinical models for drug testing. Also, we review the latest advances in phosphoinositide 3-kinase (PI3K) inhibitors in the clinic and their repurposing for the treatment of lymphatic malformations and venous malformations. SUMMARY: Oncogenic mutations on PIK3CA causing lymphatic malformations and venous malformations are also frequently found in epithelial cancer. Thus, fundamental research done in the cancer field during the past decades might be applied to the understanding of lymphatic malformations and venous malformations. Likewise, repurposing PI3K pathway inhibitors that are currently in cancer clinical trials can be used as a novel strategy for the treatment of these diseases. Here, we also open a debate for the consideration of lymphatic malformations and venous malformations as developmental tumours.

Yeast Genome-Scale Metabolic Models for Simulating Genotype-Phenotype Relations.

Understanding genotype-phenotype dependency is a universal aim for all life sciences. While the complete genotype-phenotype relations remain challenging to resolve, metabolic phenotypes are moving within the reach through genome-scale metabolic model simulations. Genome-scale metabolic models are available for commonly investigated yeasts, such as model eukaryote and domesticated fermentation species Saccharomyces cerevisiae, and automatic reconstruction methods facilitate obtaining models for any sequenced species. The models allow for investigating genotype-phenotype relations through simulations simultaneously considering the effects of nutrient availability, and redox and energy homeostasis in cells. Genome-scale models also offer frameworks for omics data integration to help to uncover how the translation of genotypes to the apparent phenotypes is regulated at different levels. In this chapter, we provide an overview of the yeast genome-scale metabolic models and the simulation approaches for using these models to interrogate genotype-phenotype relations. We review the methodological approaches according to the underlying biological reasoning in order to inspire formulating novel questions and applications that the genome-scale metabolic models could contribute to. Finally, we discuss current challenges and opportunities in the genome-scale metabolic model simulations.

Principal metabolic flux mode analysis.

Motivation: In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and not best suited for exploratory analysis on a large sets of samples. Results: We propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches. Availability and implementation: Matlab software for PMFA and SPMFA and dataset used for experiments are available in https://github.com/aalto-ics-kepaco/PMFA. Supplementary information: Supplementary data are available at Bioinformatics online.

Common Variants in Lipid Metabolism-Related Genes Associate with Fat Mass Changes in Response to Dietary Monounsaturated Fatty Acids in Adults with Abdominal Obesity.

BACKGROUND: Different fatty acids (FAs) can vary in their obesogenic effect, and genetic makeup can contribute to fat deposition in response to dietary FA composition. However, the antiobesogenic effects of the interactions between dietary MUFAs and genetics have scarcely been tested in intervention studies. OBJECTIVE: We evaluated the overall (primary outcome) and genetically modulated (secondary outcome) response in body weight and fat mass to different levels of MUFA consumption. METHODS: In the Canola Oil Multicenter Intervention Trial II, a randomized, crossover, isocaloric, controlled-feeding multicenter trial, 44 men and 71 women with a mean age of 44 y and an increased waist circumference (men ∼108 cm and women ∼102 cm) consumed each of 3 oils for 6 wk, separated by four 12-wk washout periods. Oils included 2 high-MUFA oils-conventional canola and high-oleic canola (<7% SFAs, >65% MUFAs)-and 1 low-MUFA/high-SFA oil blend (40.2% SFAs, 22.0% MUFAs). Body fat was measured using DXA. Five candidate single-nucleotide polymorphisms (SNPs) were genotyped using qualitative PCR. Data were analyzed using a repeated measures mixed model. RESULTS: No significant differences were observed in adiposity measures following the consumption of either high-MUFA diet compared with the low-MUFA/high-SFA treatment. However, when stratified by genotype, 3 SNPs within lipoprotein lipase (LPL), adiponectin, and apoE genes influenced, separately, fat mass changes in response to treatment (n = 101). Mainly, the LPL rs13702-CC genotype was associated with lower visceral fat (high-MUFA: -216.2 ± 58.6 g; low-MUFA: 17.2 ± 81.1 g; P = 0.017) and android fat mass (high-MUFA: -267.3 ± 76.4 g; low-MUFA: -21.7 ± 102.2 g; P = 0.037) following average consumption of the 2 high-MUFA diets. CONCLUSIONS: Common variants in LPL, adiponectin, and apoE genes modulated body fat mass response to dietary MUFAs in an isocaloric diet in adults with abdominal obesity. These findings might eventually help in developing personalized dietary recommendations for weight control. The trial was registered at clinicaltrials.gov as NCT02029833 (https://www.clinicaltrials.gov/ct2/show/NCT02029833?cond=NCT02029833&rank=1).

Cortisol and IgA are Involved in the Progression of Alzheimer's Disease. A Pilot Study.

It is known that stress and immune systems are related with Alzheimer's disease (AD). However, the relationship of both systems in the progression of disease is not clearly demonstrated. Hair cortisol and salivary immunoglobulin A (IgA) were quantified in 49 patients with mild, moderate, and severe AD. A significant change was seen in both molecules as AD progressed from mild to moderate and severe. Low levels of cortisol were observed in mild AD patients compared with moderate and severe. However, IgA showed a contrary pattern. High levels were observed in mild AD patientes but low in moderate and severe AD subjects. The secretion of cortisol and IgA seems to be very different at the start compared with posterior development of AD suggesting that neuroinflammation can be involved. Both molecules could be used as possible therapeutical tools.

Chemical Composition and Biological Activities of Oregano Essential Oil and Its Fractions Obtained by Vacuum Distillation.

Oregano (Poliomintha longiflora) essential oil (Ooil) is a product of high commercial value and many applications, including chemotherapy. Aiming to achieve the best use of this resource, the present study focuses on the characterization of separated fractions of Ooil by fractional vacuum distillation at low pressure. Four fractions (F1-F4) and undistilled oil (Unoil) were separated from Ooil and analyzed for their chemical composition and biological activities, such as antioxidant and antimicrobial activities. Gas chromatography-mass spectrometry shows differences in the composition among the fractions and Ooil. The amount of monoterpenes oxygenated (MO), sesquiterpenes hydrocarbon (SeH) and monoterpenes hydrocarbon (MH) varied between the fractions in ranges of 1.51-68.08, 3.31-25.12 and 1.91-97.75%, respectively. The major concentrations of MO and SeH were observed in F4 and Unoil. On the other hand, the highest concentrations of MH were found in F1 and F2, while the lowest were in F4 and Unoil. These results were correlated with the biological activity. Free-radical scavenging activity varied among fractions, with F4 and Unoil showing the highest activity. The antimicrobial test showed that F4 and Unoil had the highest activity in almost all cases. The correlation between the variables studied in the different fractions allows the definition of the particular properties for each one of them.

Exploring the potential of Saccharomyces eubayanus as a parent for new interspecies hybrid strains in winemaking.

Yeast cryotolerance brings some advantages for wine fermentations, including the improved aromatic complexity of white wines. Naturally cold-tolerant strains are generally less adept at wine fermentation but fermentative fitness can potentially be improved through hybridization. Here we studied the potential of using hybrids involving Saccharomyces eubayanus and a S. cerevisiae wine strain for low-temperature winemaking. Through screening the performance in response to variable concentrations of sugar, nitrogen and temperature, we isolated one hybrid strain that exhibited the superior performance. This hybrid strain was propagated and dried in pilot scale and tested for the fermentation of Macabeu and Sauvignon blanc grape musts. We obtained highly viable active dry yeast, which was able to efficiently ferment the grape musts with superior production of aroma active volatiles, in particular, 2-phenylethanol. The genome sequences of the hybrid strains revealed variable chromosome inheritance among hybrids, particularly within the S. cerevisiae subgenome. With the present paper, we expand the knowledge on the potentialities of using S. eubayanus hybrids in industrial fermentation at beverages other than lager beer.

Inheritance of brewing-relevant phenotypes in constructed Saccharomyces cerevisiae × Saccharomyces eubayanus hybrids.

BACKGROUND: Interspecific hybridization has proven to be a potentially valuable technique for generating de novo lager yeast strains that possess diverse and improved traits compared to their parent strains. To further enhance the value of hybridization for strain development, it would be desirable to combine phenotypic traits from more than two parent strains, as well as remove unwanted traits from hybrids. One such trait, that has limited the industrial use of de novo lager yeast hybrids, is their inherent tendency to produce phenolic off-flavours; an undesirable trait inherited from the Saccharomyces eubayanus parent. Trait removal and the addition of traits from a third strain could be achieved through sporulation and meiotic recombination or further mating. However, interspecies hybrids tend to be sterile, which impedes this opportunity. RESULTS: Here we generated a set of five hybrids from three different parent strains, two of which contained DNA from all three parent strains. These hybrids were constructed with fertile allotetraploid intermediates, which were capable of efficient sporulation. We used these eight brewing strains to examine two brewing-relevant phenotypes: stress tolerance and phenolic off-flavour formation. Lipidomics and multivariate analysis revealed links between several lipid species and the ability to ferment in low temperatures and high ethanol concentrations. Unsaturated fatty acids, such as oleic acid, and ergosterol were shown to positively influence growth at high ethanol concentrations. The ability to produce phenolic off-flavours was also successfully removed from one of the hybrids, Hybrid T2, through meiotic segregation. The potential application of these strains in industrial fermentations was demonstrated in wort fermentations, which revealed that the meiotic segregant Hybrid T2 not only didn't produce any phenolic off-flavours, but also reached the highest ethanol concentration and consumed the most maltotriose. CONCLUSIONS: Our study demonstrates the possibility of constructing complex yeast hybrids that possess traits that are relevant to industrial lager beer fermentation and that are derived from several parent strains. Yeast lipid composition was also shown to have a central role in determining ethanol and cold tolerance in brewing strains.

Phosphoinositide 3-kinase: a new kid on the block in vascular anomalies.

Vascular anomalies are broadly divided into vascular tumours and malformations. These lesions are composed of abnormal vascular elements of various types, and mainly affect infants, children, and young adults. Vascular anomalies may be painful, may be complicated by bleeding, infection, or organ dysfunction, and can have secondary effects on other tissues. Current treatment strategies include surgical excision, pulsed laser, and sclerotherapy, which are invasive, with risks of recurrence. There are growing pharmacological options for these vascular anomalies, but, to date, no specific targeted therapies have been developed. Phosphoinositide 3-kinases (PI3Ks) constitute a family of lipid kinases that are involved in signal transduction and vesicular traffic, and that modulate important cellular processes such as proliferation, growth, and migration. Recent findings have indicated that the PI3K signalling pathway is important in the pathogenesis of vascular anomalies. This provides an opportunity to use PI3K inhibitors, which are in clinical trials for cancer treatment, for such lesions. Here, we provide an update on the classification of vascular anomalies, with their major features, and discuss the role of the PI3K signalling pathway in the pathogenesis of vascular anomalies, and their clinical implications and therapeutic opportunities. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

An obesity-associated risk allele within the FTO gene affects human brain activity for areas important for emotion, impulse control and reward in response to food images.

Understanding how genetics influences obesity, brain activity and eating behaviour will add important insight for developing strategies for weight-loss treatment, as obesity may stem from different causes and as individual feeding behaviour may depend on genetic differences. To this end, we examined how an obesity risk allele for the FTO gene affects brain activity in response to food images of different caloric content via functional magnetic resonance imaging (fMRI). Thirty participants homozygous for the rs9939609 single nucleotide polymorphism were shown images of low- or high-calorie food while brain activity was measured via fMRI. In a whole-brain analysis, we found that people with the FTO risk allele genotype (AA) had increased activity compared with the non-risk (TT) genotype in the posterior cingulate, cuneus, precuneus and putamen. Moreover, higher body mass index in the AA genotype was associated with reduced activity to food images in areas important for emotion (cingulate cortex), but also in areas important for impulse control (frontal gyri and lentiform nucleus). Lastly, we corroborate our findings with behavioural scales for the behavioural inhibition and activation systems. Our results suggest that the two genotypes are associated with differential neural processing of food images, which may influence weight status through diminished impulse control and reward processing.

Statistical methods for handling unwanted variation in metabolomics data.

Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognized need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available.

Association of lipidome remodeling in the adipocyte membrane with acquired obesity in humans.

Identification of early mechanisms that may lead from obesity towards complications such as metabolic syndrome is of great interest. Here we performed lipidomic analyses of adipose tissue in twin pairs discordant for obesity but still metabolically compensated. In parallel we studied more evolved states of obesity by investigating a separated set of individuals considered to be morbidly obese. Despite lower dietary polyunsaturated fatty acid intake, the obese twin individuals had increased proportions of palmitoleic and arachidonic acids in their adipose tissue, including increased levels of ethanolamine plasmalogens containing arachidonic acid. Information gathered from these experimental groups was used for molecular dynamics simulations of lipid bilayers combined with dependency network analysis of combined clinical, lipidomics, and gene expression data. The simulations suggested that the observed lipid remodeling maintains the biophysical properties of lipid membranes, at the price, however, of increasing their vulnerability to inflammation. Conversely, in morbidly obese subjects, the proportion of plasmalogens containing arachidonic acid in the adipose tissue was markedly decreased. We also show by in vitro Elovl6 knockdown that the lipid network regulating the observed remodeling may be amenable to genetic modulation. Together, our novel approach suggests a physiological mechanism by which adaptation of adipocyte membranes to adipose tissue expansion associates with positive energy balance, potentially leading to higher vulnerability to inflammation in acquired obesity. Further studies will be needed to determine the cause of this effect.

New genetic drivers in hemorrhagic hereditary telangiectasia.

BACKGROUND: Hereditary hemorrhagic telangiectasia (HHT) is a rare vascular disease inherited in an autosomal dominant manner. Disease-causing variants in endoglin (ENG) and activin A receptor type II-like 1 (ACVRL1) genes are detected in around 90% of the patients; also 2% of patients harbor pathogenic variants at SMAD4 and GDF2. Importantly, the genetic cause of 8% of patients with clinical HHT remains unknown. Here, we present new putative genetic drivers of HHT. METHODS: To identify new HHT genetic drivers, we performed exome sequencing of 19 HHT patients and relatives with unknown HHT genetic etiology. We applied a multistep filtration strategy to catalog deleterious variants and prioritize gene candidates based on their known relevance in endothelial cell biology. Additionally, we performed in vitro validation of one of the identified variants. RESULTS: We identified variants in the INHA, HIF1A, JAK2, DNM2, POSTN, ANGPTL4, FOXO1 and SMAD6 genes as putative drivers in HHT. We have identified the SMAD6 p.(Glu407Lys) variant in one of the families; this is a loss-of-function variant leading to the activation of the BMP/TGFß signaling in endothelial cells. CONCLUSIONS: Variants in these genes should be considered for genetic testing in patients with HHT phenotype and negative for ACVRL1/ENG mutations.

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

p53 rapidly restructures 3D chromatin organization to trigger a transcriptional response.

Activation of the p53 tumor suppressor triggers a transcriptional program to control cellular response to stress. However, the molecular mechanisms by which p53 controls gene transcription are not completely understood. Here, we uncover the critical role of spatio-temporal genome architecture in this process. We demonstrate that p53 drives direct and indirect changes in genome compartments, topologically associating domains, and DNA loops prior to one hour of its activation, which escort the p53 transcriptional program. Focusing on p53-bound enhancers, we report 340 genes directly regulated by p53 over a median distance of 116 kb, with 74% of these genes not previously identified. Finally, we showcase that p53 controls transcription of distal genes through newly formed and pre-existing enhancer-promoter loops in a cohesin dependent manner. Collectively, our findings demonstrate a previously unappreciated architectural role of p53 as regulator at distinct topological layers and provide a reliable set of new p53 direct target genes that may help designs of cancer therapies.