Cancer drug sensitivity estimation using modular deep Graph Neural Networks

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drugs components that are tailored to the transcriptomic profile of a given primary tumor. The SMILES representation of molecules that is used by state-of-the-art drug-sensitivity models is not conducive for neural networks to generalize to new drugs, in part because the distance between atoms does not generally correspond to the distance between their representation in the SMILES strings. Graph-attention networks, on the other hand, are high-capacity models that require large training-data volumes which are not available for drug-sensitivity estimation. We develop a modular drug-sensitivity graph-attentional neural network. The modular architecture allows us to separately pre-train the graph encoder and graph-attentional pooling layer on related tasks for which more data are available. We observe that this model outperforms reference models for the use cases of precision oncology and drug discovery; in particular, it is better able to predict the specific interaction between drug and cell line that is not explained by the general cytotoxicity of the drug and the overall survivability of the cell line. The complete source code is available at https://zenodo.org/doi/10.5281/zenodo.8020945. All experiments are based on the publicly available GDSC data.

Development, Evaluation, and Molecular Dynamics Study of Ampicillin-Loaded Chitosan-Hyaluronic Acid Films as a Drug Delivery System

Periodontitis is an inflammatory periodontal disease defined by the progressive loss of tissues surrounding the tooth. Ampicillin is an antibiotic for managing and treating specific bacterial infections, including periodontitis. Periodontal pockets occur due to periodontal disease progression and act as a natural reservoir that is easily reachable for the insertion of a delivery system, and the amount of drug to be released has a major role in the efficiency of treatment of the disease. Polyelectrolyte complexes (PECs), particularly those based on chitosan and hyaluronic acid combinations, offer a promising avenue to overcome the challenges associated with drug delivery. These complexes are both biodegradable and biocompatible, making them an optimal choice for enabling targeted drug delivery. This study centers on developing and assessing the structure and dynamic attributes of a drug-PEC system encompassing ampicillin and chitosan-hyaluronic acid components, which represents a targeted drug delivery system to better alleviate the periodontitis. To achieve this goal, we conducted experiments including weight and drug content uniformity, swelling ındex, drug release %, FT-IR and SEM analyses, and atomistic molecular dynamics simulations on the drug PECs loaded with ampicillin with varying amounts of hyaluronic acid. All simulations and the experimental analysis suggested that increased HA amount resulted in an increase in drug release % and swelling index. The simulation outcomes provide insights into the nature of the drug and PEC interactions alongside transport properties such as drug diffusion coefficients. These coefficients offer valuable insights into the molecular behavior of ampicillin-PEC drug delivery systems, particularly in the context of their application in periodontitis treatment.

Depletion of TBC1D4 improves the metabolic exercise response by overcoming genetically induced peripheral insulin resistance

The RabGTPase-activating protein (RabGAP) TBC1D4 (=AS160) represents a key component in the regulation of glucose transport into skeletal muscle and white adipose tissue (WAT) and is therefore crucial during the development of insulin resistance and type-2 diabetes. Increased daily activity has been shown to be associated with improved postprandial hyperglycemia in allele carriers of a loss-of-function variant in the human TBC1D4 gene. Using conventional Tbc1d4-deficient mice (D4KO) fed a high-fat diet (HFD), we show that already a moderate endurance exercise training leads to substantially improved glucose and insulin tolerance and enhanced expression levels of markers for mitochondrial activity and browning in WAT from D4KO animals. Importantly, in vivo and ex vivo analyses of glucose uptake revealed increased glucose clearance in interscapular brown adipose tissue (iBAT) and WAT from trained D4KO mice. Thus, chronic exercise is able to overcome the genetically induced insulin resistance caused by the Tbc1d4-depletion. Gene variants in TBC1D4 may be relevant in future precision medicine as determinants of exercise response.

Structural dynamics and anti-biofilm screening of novel imidazole derivative to explore their anti-biofilm inhibition mechanism against Pseudomonas Aeruginosa

The biofilm formation is still prevalent mechanism of developing the drug resistance in the Pseudomonas aeruginosa, gram-negative bacteria, known for its major role in nosocomial, ventilator-associated pneumonia (VAP), lung infections and catheter-associated urinary tract infections. As best of our knowledge, current study first time reports the most potent inhibitors of LasR, a transcriptional activator of biofilm and virulence regulating genes in, Pseudomonas aeruginosa LasR, utilizing newly functionalized imidazoles (5a-d), synthesized via 1,3-dipolar cycloaddition using click approach. The synthesized ligands were characterized through Mass Spectrometry and 1H NMR. The binding potency and mode of biding of ligands. Quantum Mechanical(QM) methods were utilized to investigate the electronic basis, HOMO/LUMO and dipole moment of the geometry of the ligands for their binding potency. Dynamics cross correlation matrix (DCCMs) and protein surface analysis were further utilized to explore the structural dynamics of the protein. Free energy of binding of ligands and protein were further estimated using Molecular Mechanical Energies with the Poisson-Boltzmann surface area (MMPBSA) method. Molecular Docking studies revealed significant negative binding energies (5a - 10.33, 5b -10.09, 5c - 10.11, and 5d -8.33 KJ/mol). HOMO/LUMO and potential energy surface map estimation showed the ligands(5a) with lower energy gaps and larger dipole moments had relatively larger binding potency. The significant change in the structural dynamics of LasR protein due to complex formation with newlyfunctionalized imidazoles ligands. Hydrogen bond surface analysis followed by MMPBSA calculations of free energy of binding further complemented the Molecular docking revelations showing the specifically ligand (5a) having the relatively higher energy of binding(-65.22kj/mol).Communicated by Ramaswamy H. Sarma.

Different RONS Generation in MTC-SK and NSCL Cells Lead to Varying Antitumoral Effects of Alpha-Ketoglutarate + 5-HMF

BACKGROUND

Carbonylated proteins (CPs) serve as specific indicators of increased reactive oxygen and nitrogen species (RONS) production in cancer cells, attributed to the dysregulated mitochondrial energy metabolism known as the Warburg effect. The aim of this study was to investigate the potential of alpha-ketoglutarate (aKG), 5-hydroxymethylfurfural (5-HMF), and their combination as mitochondrial-targeting antioxidants in MTC-SK or NCI-H23 cancer cells.

METHODS

MTC-SK and NCI-H23 cells were cultured in the absence or presence of varying concentrations (0-500 µg/mL) of aKG, 5-HMF, and the combined aKG + 5-HMF solutions. After 0, 24, 48, and 72 h, mitochondrial activity, cancer cell membrane CP levels, cell growth, and caspase-3 activity were assessed in aliquots of MTC-SK and NCI-H23 cells.

RESULTS

The mitochondrial activity of MTC-SK cells exhibited a concentration- and time-dependent reduction upon treatment with aKG, 5-HMF, or the combined aKG + 5-HMF. The half-maximal inhibitory concentration (IC50%) for mitochondrial activity was achieved at 500 µg/mL aKG, 200 µg/mL 5-HMF, and 200 µg/mL aKG + 66.7 µg/mL 5-HMF after 72 h. In contrast, NCI-H23 cells showed a minimal reduction (10%) in mitochondrial activity even at the highest combined concentration of aKG + 5-HMF. The CP levels in MTC-SK cells were measured at 8.7 nmol/mg protein, while NCI-H23 cells exhibited CP levels of 1.4 nmol/mg protein. The combination of aKG + 5-HMF led to a decrease in CP levels specifically in MTC-SK cells. The correlation between mitochondrial activity and CP levels in the presence of different concentrations of combined aKG + 5-HMF in MTC-SK cells demonstrated a linear and concentration-dependent decline in CP levels and mitochondrial activity. Conversely, the effect was less pronounced in NCI-H23 cells. Cell growth of MTC-CK cells was reduced to 60% after 48 h and maintained at 50% after 72 h incubation when treated with 500 µg/mL aKG (IC50%). Addition of 500 µg/mL 5-HMF inhibited cell growth completely regardless of the incubation time. The IC50% for 5-HMF on MTC-CK cell growth was calculated at 375 µg/mL after 24 h incubation and 200 µg/mL 5-HMF after 72 h. MTC-SK cells treated with 500 µg/mL aKG + 167 µg/mL 5-HMF showed no cell growth. The calculated IC50% for the combined substances was 250 µg/mL aKG + 83.3 µg/mL 5-HMF (48 h incubation) and 200 µg/mL aKG + 66.7 µg/mL 5-HMF (72 h incubation). None of the tested concentrations of aKG, 5-HMF, or the combined solution had any effect on NCI-H23 cell growth at any incubation time. Caspase-3 activity increased to 21% in MTC-CK cells in the presence of 500 µg/mL aKG, while an increase to 59.6% was observed using 500 µg/mL 5-HMF. The combination of 500 µg/mL aKG + 167.7 µg/mL 5-HMF resulted in a caspase-3 activity of 55.2%. No caspase-3 activation was observed in NCI-H23 cells when treated with aKG, 5-HMF, or the combined solutions.

CONCLUSION

CPs may serve as potential markers for distinguishing between cancer cells regulated by RONS. The combination of aKG + 5-HMF showed induced cell death in high-RONS-generating cancer cells compared to low-RONS-generating cancer cells.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

IsoTools: a flexible workflow for long-read transcriptome sequencing analysis

MOTIVATION

Long-read transcriptome sequencing (LRTS) has the potential to enhance our understanding of alternative splicing and the complexity of this process requires the use of versatile computational tools, with the ability to accommodate various stages of the workflow with maximum flexibility.

RESULTS

We introduce IsoTools, a Python-based LRTS analysis framework that offers a wide range of functionality for transcriptome reconstruction and quantification of transcripts. Furthermore, we integrate a graph-based method for identifying alternative splicing events and a statistical approach based on the beta-binomial distribution for detecting differential events. To demonstrate the effectiveness of our methods, we applied IsoTools to PacBio LRTS data of human hepatocytes treated with the histone deacetylase inhibitor valproic acid. Our results indicate that LRTS can provide valuable insights into alternative splicing, particularly in terms of complex and differential splicing patterns, in comparison to short-read RNA-seq.

AVAILABILITY AND IMPLEMENTATION

IsoTools is available on GitHub and PyPI, and its documentation, including tutorials, CLI, and API references, can be found at https://isotools.readthedocs.io/.

Deletion of Tbc1d4/As160 abrogates cardiac glucose uptake and increases myocardial damage after ischemia/reperfusion

BACKGROUND

Type 2 Diabetes mellitus (T2DM) is a major risk factor for cardiovascular disease and associated with poor outcome after myocardial infarction (MI). In T2DM, cardiac metabolic flexibility, i.e. the switch between carbohydrates and lipids as energy source, is disturbed. The RabGTPase-activating protein TBC1D4 represents a crucial regulator of insulin-stimulated glucose uptake in skeletal muscle by controlling glucose transporter GLUT4 translocation. A human loss-of-function mutation in TBC1D4 is associated with impaired glycemic control and elevated T2DM risk. The study's aim was to investigate TBC1D4 function in cardiac substrate metabolism and adaptation to MI.

METHODS

Cardiac glucose metabolism of male Tbc1d4-deficient (D4KO) and wild type (WT) mice was characterized using in vivo [¹⁸F]-FDG PET imaging after glucose injection and ex vivo basal/insulin-stimulated [³H]-2-deoxyglucose uptake in left ventricular (LV) papillary muscle. Mice were subjected to cardiac ischemia/reperfusion (I/R). Heart structure and function were analyzed until 3 weeks post-MI using echocardiography, morphometric and ultrastructural analysis of heart sections, complemented by whole heart transcriptome and protein measurements.

RESULTS

Tbc1d4-knockout abolished insulin-stimulated glucose uptake in ex vivo LV papillary muscle and in vivo cardiac glucose uptake after glucose injection, accompanied by a marked reduction of GLUT4. Basal cardiac glucose uptake and GLUT1 abundance were not changed compared to WT controls. D4KO mice showed mild impairments in glycemia but normal cardiac function. However, after I/R D4KO mice showed progressively increased LV endsystolic volume and substantially increased infarction area compared to WT controls. Cardiac transcriptome analysis revealed upregulation of the unfolded protein response via ATF4/eIF2α in D4KO mice at baseline. Transmission electron microscopy revealed largely increased extracellular matrix (ECM) area, in line with decreased cardiac expression of matrix metalloproteinases of D4KO mice.

CONCLUSIONS

TBC1D4 is essential for insulin-stimulated cardiac glucose uptake and metabolic flexibility. Tbc1d4-deficiency results in elevated cardiac endoplasmic reticulum (ER)-stress response, increased deposition of ECM and aggravated cardiac damage following MI. Hence, impaired TBC1D4 signaling contributes to poor outcome after MI.

Host M-CSF induced gene expression drives changes in susceptible and resistant mice-derived BMdMs upon Leishmania major infection

Leishmaniases are a group of diseases with different clinical manifestations. Macrophage-Leishmania interactions are central to the course of the infection. The outcome of the disease depends not only on the pathogenicity and virulence of the parasite, but also on the activation state, the genetic background, and the underlying complex interaction networks operative in the host macrophages. Mouse models, with mice strains having contrasting behavior in response to parasite infection, have been very helpful in exploring the mechanisms underlying differences in disease progression. We here analyzed previously generated dynamic transcriptome data obtained from Leishmania major (L. major) infected bone marrow derived macrophages (BMdMs) from resistant and susceptible mouse. We first identified differentially expressed genes (DEGs) between the M-CSF differentiated macrophages derived from the two hosts, and found a differential basal transcriptome profile independent of Leishmania infection. These host signatures, in which 75% of the genes are directly or indirectly related to the immune system, may account for the differences in the immune response to infection between the two strains. To gain further insights into the underlying biological processes induced by L. major infection driven by the M-CSF DEGs, we mapped the time-resolved expression profiles onto a large protein-protein interaction (PPI) network and performed network propagation to identify modules of interacting proteins that agglomerate infection response signals for each strain. This analysis revealed profound differences in the resulting responses networks related to immune signaling and metabolism that were validated by qRT-PCR time series experiments leading to plausible and provable hypotheses for the differences in disease pathophysiology. In summary, we demonstrate that the host's gene expression background determines to a large degree its response to L. major infection, and that the gene expression analysis combined with network propagation is an effective approach to help identifying dynamically altered mouse strain-specific networks that hold mechanistic information about these contrasting responses to infection.

Analysis of 1276 Haplotype-Resolved Genomes Allows Characterization of Cis- and Trans-Abundant Genes

Many methods for haplotyping have materialized, but their application on a significant scale has been rare to date. Here we summarize analyses that were carried out in 1092 genomes from the 1000 Genomes Consortium and validated in an unprecedented number of 184 PGP genomes that have been experimentally haplotype-resolved by application of the Long-Fragment Read (LFR) technology. These analyses provided first insights into the diplotypic nature of human genomes and its potential functional implications. Thus, protein-changing variants were not randomly distributed between the two homologues of 18,121 autosomal protein-coding genes but occurred significantly more frequently in cis than in trans configurations in virtually each of the 1276 phased genomes. This resulted in global cis/trans ratios of ~60:40, establishing "cis abundance" as a universal characteristic of diploid human genomes. This phenomenon was based on two different classes of genes, a larger one exhibiting cis configurations of protein-changing variants in excess, so-called "cis-abundant" genes, and a smaller one of "trans-abundant" genes. These two gene classes, which together constitute a common diplotypic exome, were further functionally distinguished by means of gene ontology (GO) and pathway enrichment analysis. Moreover, they were distinguishable in terms of their effects on the human interactome, where they constitute distinct cis and trans modules, as shown with network propagation on a large integrated protein-protein interaction network. These analyses, recently performed with updated database and analysis tools, further consolidated the characterization of cis- and trans-abundant genes while expanding previous results. In this chapter, we present the key results along with the materials and methods to motivate readers to investigate these findings independently and gain further insights into the diplotypic nature of genes and genomes.

Deregulation and epigenetic modification of BCL2-family genes cause resistance to venetoclax in hematologic malignancies

The BCL2 inhibitor venetoclax has been approved to treat different hematological malignancies. Because there is no common genetic alteration causing resistance to venetoclax in chronic lymphocytic leukemia (CLL) and B-cell lymphoma, we asked if epigenetic events might be involved in venetoclax resistance. Therefore, we employed whole-exome sequencing, methylated DNA immunoprecipitation sequencing, and genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 screening to investigate venetoclax resistance in aggressive lymphoma and high-risk CLL patients. We identified a regulatory CpG island within the PUMA promoter that is methylated upon venetoclax treatment, mediating PUMA downregulation on transcript and protein level. PUMA expression and sensitivity toward venetoclax can be restored by inhibition of methyltransferases. We can demonstrate that loss of PUMA results in metabolic reprogramming with higher oxidative phosphorylation and adenosine triphosphate production, resembling the metabolic phenotype that is seen upon venetoclax resistance. Although PUMA loss is specific for acquired venetoclax resistance but not for acquired MCL1 resistance and is not seen in CLL patients after chemotherapy-resistance, BAX is essential for sensitivity toward both venetoclax and MCL1 inhibition. As we found loss of BAX in Richter's syndrome patients after venetoclax failure, we defined BAX-mediated apoptosis to be critical for drug resistance but not for disease progression of CLL into aggressive diffuse large B-cell lymphoma in vivo. A compound screen revealed TRAIL-mediated apoptosis as a target to overcome BAX deficiency. Furthermore, antibody or CAR T cells eliminated venetoclax resistant lymphoma cells, paving a clinically applicable way to overcome venetoclax resistance.

Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction

Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases.

Vitamin D-Dimer: A Possible Biomolecule Modulator in Cytotoxic and Phagocytosis Processes?

Background: Vitamin D3 complexed to deglycosylated vitamin D binding protein (VitD-dgVDBP) is a water-soluble vitamin D dimeric compound (VitD-dgVDBP). It is not clear how VitD-dgVDBP affects circulating monocytes, macrophages, other immune cell systems, including phagocytosis and apoptosis, and the generation of reactive oxygen species (ROS) compared to dgVDBP. Methods: Flow cytometry was used to measure superoxide anion radical (O2*−) levels and macrophage activity in the presence of VitD-dgVDBP or dgVDBP. VitD-dgVDBP was incubated with normal human lymphocytes (nPBMCs), and several clusters of determination (CDs) were estimated. dgVDBP and VitD-dgVDBP apoptosis was estimated on malignant prostatic cells. Results: The macrophage activity was 2.8-fold higher using VitD-dgVDBP (19.8·106 counts) compared to dgVDBP (7.0·106 counts), but O2*− production was 1.8-fold lower in favor of VitD-dgVDBP (355·103 counts) compared to dgVDBP (630·106 counts). The calculated ratio of the radical/macrophage activity was 5-fold lower compared to that of dgVDBP. Only VitD-dgVDBP activated caspase-3 (8%), caspase-9 (13%), and cytochrome-C (11%) on prostatic cancer cells. PE-Cy7-labeled VitD-dgVDBP was found to bind to cytotoxic suppressor cells, monocytes/macrophages, dendritic and natural killer cells (CD8+), and helper cells (CD4+). After 12 h of co-incubation of nPBMCs with VitD-dgVDBP, significant activation and expression were measured for CD16++/CD16 (0.6 ± 0.1% vs. 0.4 ± 0.1%, p < 0.05), CD45k+ (96.0 ± 6.0% vs. 84.7 ± 9.5%, p < 0.05), CD85k+ (24.3 ± 13.2% vs. 3.8 ± 3.2%, p < 0.05), and CD85k+/CD123+ (46.8 ± 8.1% vs. 3.5 ± 3.7%, p < 0.001) compared to the control experiment. No significant difference was found using CD3+, CD4+, CD8+, CD4/CD8, CD4/CD8, CD16+, CD16++, CD14+, or CD123+. A significant decline in CD14+/CD16+ was obtained in the presence of VitD-dgVDBP (0.7 ± 0.2% vs. 3.1 ± 1.7%; p < 0.01). Conclusion: The newly developed water-soluble VitD3 form VitD-dgVDBP affected cytotoxic suppressor cells by activating the low radical-dependent CD16 pathway and seemed to induce apoptosis in malignant prostatic cells.

Comparative genomic analyses of multiple backcross mouse populations suggest SGCG as a novel potential obesity-modifier gene

To nominate novel disease genes for obesity and type 2 diabetes (T2D), we recently generated two mouse backcross populations of the T2D-susceptible New Zealand Obese (NZO/HI) mouse strain and two genetically different, lean and T2D-resistant strains, 129P2/OlaHsd and C3HeB/FeJ. Comparative linkage analysis of our two female backcross populations identified seven novel body fat-associated quantitative trait loci (QTL). Only the locus Nbw14 (NZO body weight on chromosome 14) showed linkage to obesity-related traits in both backcross populations, indicating that the causal gene variant is likely specific for the NZO strain as NZO allele carriers in both crosses displayed elevated body weight and fat mass. To identify candidate genes for Nbw14, we used a combined approach of gene expression and haplotype analysis to filter for NZO-specific gene variants in gonadal white adipose tissue (gWAT), defined as the main QTL-target tissue. Only two genes, Arl11 and Sgcg, fulfilled our candidate criteria. In addition, expression QTL analysis revealed cis-signals for both genes within the Nbw14 locus. Moreover, retroviral overexpression of Sgcg in 3 T3-L1 adipocytes resulted in increased insulin-stimulated glucose uptake. In humans, mRNA levels of SGCG correlated with BMI and body fat mass exclusively in diabetic subjects, suggesting that SGCG may present a novel marker for metabolically unhealthy obesity. In conclusion, our comparative-cross analysis could substantially improve the mapping resolution of the obesity locus Nbw14. Future studies will shine light on the mechanism by which Sgcg may protect from the development of obesity.

Gradient tree boosting and network propagation for the identification of pan-cancer survival networks

Cancer survival prediction is typically done with uninterpretable machine learning techniques, e.g., gradient tree boosting. Therefore, additional steps are needed to infer biological plausibility of the predictions. Here, we describe a protocol that combines pan-cancer survival prediction with XGBoost tree-ensemble learning and subsequent propagation of the learned feature weights on protein interaction networks. This protocol is based on TCGA transcriptome data of 8,024 patients from 25 cancer types but can easily be adapted to cancer patient data from other sources.

For complete details on the use and execution of this protocol, please refer to Thedinga and Herwig (2022).

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Efficient pan-cancer survival prediction with XGBoost

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Network propagation with NetCore improves biological plausibility of features

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Combined approach identifies pan-cancer survival networks

Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

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

ConsensusPathDB 2022: molecular interactions update as a resource for network biology

Molecular interactions are key drivers of biological function. Providing interaction resources to the research community is important since they allow functional interpretation and network-based analysis of molecular data. ConsensusPathDB (http://consensuspathdb.org) is a meta-database combining interactions of diverse types from 31 public resources for humans, 16 for mice and 14 for yeasts. Using ConsensusPathDB, researchers commonly evaluate lists of genes, proteins and metabolites against sets of molecular interactions defined by pathways, Gene Ontology and network neighborhoods and retrieve complex molecular neighborhoods formed by heterogeneous interaction types. Furthermore, the integrated protein–protein interaction network is used as a basis for propagation methods. Here, we present the 2022 update of ConsensusPathDB, highlighting content growth, additional functionality and improved database stability. For example, the number of human molecular interactions increased to 859 848 connecting 200 499 unique physical entities such as genes/proteins, metabolites and drugs. Furthermore, we integrated regulatory datasets in the form of transcription factor–, microRNA– and enhancer–gene target interactions, thus providing novel functionality in the context of overrepresentation and enrichment analyses. We specifically emphasize the use of the integrated protein–protein interaction network as a scaffold for network inferences, present topological characteristics of the network and discuss strengths and shortcomings of such approaches.

Alpha-Ketoglutarate: A Potential Inner Mitochondrial and Cytosolic Protector against Peroxynitrite and Peroxynitrite-Induced Nitration?

The generation of peroxynitrite (ONOO^-) is associated with several diseases, including atherosclerosis, hypertension, neurodegeneration, cancer, inflammation, and sepsis. Alpha-ketoglutarate (αKG) is a known potential highly antioxidative agent for radical oxidative species such as peroxides. The question arises as to whether αKG is also a potential scavenger of ONOO^- and a potential protector against ONOO^--mediated nitration of proteins. NMR studies of 1 mM αKG in 100 mM phosphate-buffered saline at pH 7.4 and pH 6.0 were carried out in the presence or absence of a final concentration of 2 mM ONOO^-. An ONOO^--luminol-induced chemiluminescence reaction was used to measure the scavenging function of several concentrations of αKG; quantification of αKG was performed via spectrophotometric enzymatic assay of αKG in the absence or presence of 0, 1, or 2 mM ONOO^-. The nitration of tyrosine residues on proteins was measured on ONOO^--treated bovine serum albumin (BSA) in the presence or absence of 0-24 mM αKG by an ELISA technique using a specific anti-IgG against nitro-tyrosine. The addition of ONOO^- to αKG led to the formation of succinic acid and nitrite at pH 7.0, but not at pH 6.0, as αKG was stable against ONOO^-. The absorbance of enzymatically estimated αKG at the time point of 30 min was significantly lower in favour of ONOO^- (1 mM: 0.21 ± 0.03, 2 mM: 0.12 ± 0.05 vs. 0 mM: 0.32 ± 0.02; p < 0.001). The luminol technique showed an inverse logarithmic correlation of the ONOO^- and αKG concentrations (y = -2 × 10⁵ ln(x) + 1 × 10⁶; r² = 0.99). The usage of 4 mM αKG showed a significant reduction by nearly half in the chemiluminescence signal (284,456 ± 29,293 cps, p < 0.001) compared to the control (474,401 ± 18,259); for 20 and 200 mM αKG, there were further reductions to 163,546 ± 26,196 cps (p < 0.001) and 12,658 ± 1928 cps (p < 0.001). Nitrated tyrosine residues were estimated using the ELISA technique. A negative linear correlation was obtained by estimating nitrated tyrosine residues in the presence of αKG (r² = 0.94): a reduction by half of nitrated tyrosine was estimated using 12 mM αKG compared to the control (326.1 ± 39.6 nmol vs. 844.5 ± 128.4 nmol; p < 0.001).

Altered DNA Methylation Profiles in SF3B1 Mutated CLL Patients

Mutations in splicing factor genes have a severe impact on the survival of cancer patients. Splicing factor 3b subunit 1 (SF3B1) is one of the most frequently mutated genes in chronic lymphocytic leukemia (CLL); patients carrying these mutations have a poor prognosis. Since the splicing machinery and the epigenome are closely interconnected, we investigated whether these alterations may affect the epigenomes of CLL patients. While an overall hypomethylation during CLL carcinogenesis has been observed, the interplay between the epigenetic stage of the originating B cells and SF3B1 mutations, and the subsequent effect of the mutations on methylation alterations in CLL, have not been investigated. We profiled the genome-wide DNA methylation patterns of 27 CLL patients with and without SF3B1 mutations and identified local decreases in methylation levels in SF3B1^mut CLL patients at 67 genomic regions, mostly in proximity to telomeric regions. These differentially methylated regions (DMRs) were enriched in gene bodies of cancer-related signaling genes, e.g., NOTCH1, HTRA3, and BCL9L. In our study, SF3B1 mutations exclusively emerged in two out of three epigenetic stages of the originating B cells. However, not all the DMRs could be associated with the methylation programming of B cells during development, suggesting that mutations in SF3B1 cause additional epigenetic aberrations during carcinogenesis.

Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types

Background

There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually.

Methods

In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data.

Results

We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: https://knowledgebase.microdiscovery.de/heatmap.

Conclusions

Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02941-z.

Alternative exon splicing and differential expression in pancreatic islets reveals candidate genes and pathways implicated in early diabetes development

Type 2 diabetes (T2D) has a strong genetic component. Most of the gene variants driving the pathogenesis of T2D seem to target pancreatic β-cell function. To identify novel gene variants acting at early stage of the disease, we analyzed whole transcriptome data to identify differential expression (DE) and alternative exon splicing (AS) transcripts in pancreatic islets collected from two metabolically diverse mouse strains at 6 weeks of age after three weeks of high-fat-diet intervention. Our analysis revealed 1218 DE and 436 AS genes in islets from NZO/Hl vs C3HeB/FeJ. Whereas some of the revealed genes present well-established markers for β-cell failure, such as Cd36 or Aldh1a3, we identified numerous DE/AS genes that have not been described in context with β-cell function before. The gene Lgals2, previously associated with human T2D development, was DE as well as AS and localizes in a quantitative trait locus (QTL) for blood glucose on Chr.15 that we reported recently in our N₂(NZOxC3H) population. In addition, pathway enrichment analysis of DE and AS genes showed an overlap of only half of the revealed pathways, indicating that DE and AS in large parts influence different pathways in T2D development. PPARG and adipogenesis pathways, two well-established metabolic pathways, were overrepresented for both DE and AS genes, probably as an adaptive mechanism to cope for increased cellular stress. Our results provide guidance for the identification of novel T2D candidate genes and demonstrate the presence of numerous AS transcripts possibly involved in islet function and maintenance of glucose homeostasis.

Histone deacetylase 5 regulates interleukin 6 secretion and insulin action in skeletal muscle

OBJECTIVE

Physical exercise training is associated with increased glucose uptake in skeletal muscle and improved glycemic control. HDAC5, a class IIa histone deacetylase, has been shown to regulate transcription of the insulin-responsive glucose transporter GLUT4 in cultured muscle cells. In this study, we analyzed the contribution of HDAC5 to the transcriptional network in muscle and the beneficial effect of muscle contraction and regular exercise on glucose metabolism.

METHODS

HDAC5 knockout mice (KO) and wild-type (WT) littermates were trained for 8 weeks on treadmills, metabolically phenotyped, and compared to sedentary controls. Hdac5-deficient skeletal muscle and cultured Hdac5-knockdown (KD) C2C12 myotubes were utilized for studies of gene expression and glucose metabolism. Chromatin immunoprecipitation (ChIP) studies were conducted to analyze Il6 promoter activity using H3K9ac and HDAC5 antibodies.

RESULTS

Global transcriptome analysis of Hdac5 KO gastrocnemius muscle demonstrated activation of the IL-6 signaling pathway. Accordingly, knockdown of Hdac5 in C2C12 myotubes led to higher expression and secretion of IL-6 with enhanced insulin-stimulated activation of AKT that was reversed by Il6 knockdown. Moreover, Hdac5-deficient myotubes exhibited enhanced glucose uptake, glycogen synthesis, and elevated expression levels of the glucose transporter GLUT4. Transcription of Il6 was further enhanced by electrical pulse stimulation in Hdac5-deficient C2C12 myotubes. ChIP identified a ∼1 kb fragment of the Il6 promoter that interacts with HDAC5 and demonstrated increased activation-associated histone marker AcH3K9 in Hdac5-deficient muscle cells. Exercise intervention of HDAC5 KO mice resulted in improved systemic glucose tolerance as compared to WT controls.

CONCLUSIONS

We identified HDAC5 as a negative epigenetic regulator of IL-6 synthesis and release in skeletal muscle. HDAC5 may exert beneficial effects through two different mechanisms, transcriptional control of genes required for glucose disposal and utilization, and HDAC5-dependent IL-6 signaling cross-talk to improve glucose uptake in muscle in response to exercise.

NetCore: a network propagation approach using node coreness

We present NetCore, a novel network propagation approach based on node coreness, for phenotype–genotype associations and module identification. NetCore addresses the node degree bias in PPI networks by using node coreness in the random walk with restart procedure, and achieves improved re-ranking of genes after propagation. Furthermore, NetCore implements a semi-supervised approach to identify phenotype-associated network modules, which anchors the identification of novel candidate genes at known genes associated with the phenotype. We evaluated NetCore on gene sets from 11 different GWAS traits and showed improved performance compared to the standard degree-based network propagation using cross-validation. Furthermore, we applied NetCore to identify disease genes and modules for Schizophrenia GWAS data and pan-cancer mutation data. We compared the novel approach to existing network propagation approaches and showed the benefits of using NetCore in comparison to those. We provide an easy-to-use implementation, together with a high confidence PPI network extracted from ConsensusPathDB, which can be applied to various types of genomics data in order to obtain a re-ranking of genes and functionally relevant network modules.

PWD/Ph-Encoded Genetic Variants Modulate the Cellular Wnt/β-Catenin Response to Suppress Apc Min-Triggered Intestinal Tumor Formation

Genetic predisposition affects the penetrance of tumor-initiating mutations, such as APC mutations that stabilize β-catenin and cause intestinal tumors in mice and humans. However, the mechanisms involved in genetically predisposed penetrance are not well understood. Here, we analyzed tumor multiplicity and gene expression in tumor-prone Apc ^Min/+ mice on highly variant C57BL/6J (B6) and PWD/Ph (PWD) genetic backgrounds. (B6 × PWD) F1 APC ^Min offspring mice were largely free of intestinal adenoma, and several chromosome substitution (consomic) strains carrying single PWD chromosomes on the B6 genetic background displayed reduced adenoma numbers. Multiple dosage-dependent modifier loci on PWD chromosome 5 each contributed to tumor suppression. Activation of β-catenin-driven and stem cell-specific gene expression in the presence of Apc ^Min or following APC loss remained moderate in intestines carrying PWD chromosome 5, suggesting that PWD variants restrict adenoma initiation by controlling stem cell homeostasis. Gene expression of modifier candidates and DNA methylation on chromosome 5 were predominantly cis controlled and largely reflected parental patterns, providing a genetic basis for inheritance of tumor susceptibility. Human SNP variants of several modifier candidates were depleted in colorectal cancer genomes, suggesting that similar mechanisms may also affect the penetrance of cancer driver mutations in humans. Overall, our analysis highlights the strong impact that multiple genetic variants acting in networks can exert on tumor development. SIGNIFICANCE: These findings in mice show that, in addition to accidental mutations, cancer risk is determined by networks of individual gene variants.

Vitamin D - Deglycosylated Vitamin D Binding Protein Dimer: Positive Synergistic Effects on Recognition, Activation, Phagocytosis and Oxidative Stress on Macrophages

BACKGROUND

We have recently shown positive effects in the quality of life in autism and amyloid lateral sclerosis patients using a newly developed 25-OH vitamin D deglycosylated vitamin D binding protein complex (VitD~dgVDBP) by reducing oxidative stress. The question arises whether this reduction of oxidative stress was due to a synergistic effect of the dimer in the recognition and activation of phagocytosis on macrophages combined with a lower oxidative burst compared to the VitD free proteins, namely vitamin D binding protein (VDBP: Gc Protein) and deglycosylated dgVDBP (GcMAF).

METHODS

VDBP sandwich ELISA of equal protein concentration of VDBP, dgVDBP, and VitD~dgVDBP (1 µg/ mL by BCA protein technique) was used to identify immune affinity to polyclonal antibodies raised against human VDBP. The 25(OH) vitamin D levels of VDBP, dgVDBP and VitD~dgVDBP were estimated by a competitive immune assay using a monoclonal antibody. Macrophage phagocytosis and oxidative burst in absence or presence of 400 pg/mL VDBP, 400 pg/mL dgVDBP, and 400 pg/mL VitD~dgVDBP was measured.

RESULTS

The recognition of the antibody against VDBP protein was significantly more than 4-fold higher for VitD~dgVDBP (769.2 +/- 35.1%) compared to dgVDBP (186.5 +/- 16.8 %; p < 0.01) and 7-fold higher to VDBP (100 +/- 11.4 %; p < 0.001). 25(OH) vitamin D levels of VDBP (20.7 nmol/mg; p < 0.001) and dgVDBP (28.8 +/- 3.9 nmol/mL; p < 0.001) was significantly lower than of VitD~dgVDBP (324.0 +/- 12.8 nmol/mL). The calculated VitD/ protein ratio showed significantly higher results in favor of VitD~dgVDBP (1.01 +/- 0.12) compared to dgVDBP (0.06 +/- 0.03; p < 0.001) and VDBP (0.05 +/- 0.01; p < 0.001). The estimation of macrophage phagocytosis rate of VitD~dgVDBP (5,864.3 +/- 742.2 cps) was significantly higher compared to dgVDBP (2,789.6 +/- 102.7 cps; p < 0.01) and VDBP (1,134.3 +/- 135.9 cps) whereas the production of macrophage superoxide anion radicals showed significantly higher levels of dgVDBP (255.3 +/- 14.5 cps) in comparison to VDBP (148.6 +/- 24.7 cps, p < 0.01) and VitD~dgVDBP (142.3 +/- 20.0 cps; p < 0.001). Linear regression between VDBP antibody affinity and macrophage phagocytosis of VDBP, dgVDBP and VitD~dgVDBP resulted in a correlation coefficient of r = 0.95 in favor of VitD~dgVDBP.

CONCLUSIONS

VitD~dgVDBP (Il-42) showed higher macrophage activation and lower oxidative burst than VitD free dgVDBP (GcMaf) and VDBP (Gc) which may result from a synergistic effect by presenting protein bound Vitamin D better to macrophages.

Significant abundance of cis configurations of coding variants in diploid human genomes

To fully understand human genetic variation and its functional consequences, the specific distribution of variants between the two chromosomal homologues of genes must be known. The 'phase' of variants can significantly impact gene function and phenotype. To assess patterns of phase at large scale, we have analyzed 18 121 autosomal genes in 1092 statistically phased genomes from the 1000 Genomes Project and 184 experimentally phased genomes from the Personal Genome Project. Here we show that genes with cis-configurations of coding variants are more frequent than genes with trans-configurations in a genome, with global cis/trans ratios of ∼60:40. Significant cis-abundance was observed in virtually all genomes in all populations. Moreover, we identified a large group of genes exhibiting cis-configurations of protein-changing variants in excess, so-called 'cis-abundant genes', and a smaller group of 'trans-abundant genes'. These two gene categories were functionally distinguishable, and exhibited strikingly different distributional patterns of protein-changing variants. Underlying these phenomena was a shared set of phase-sensitive genes of importance for adaptation and evolution. This work establishes common patterns of phase as key characteristics of diploid human exomes and provides evidence for their functional significance, highlighting the importance of phase for the interpretation of protein-coding genetic variation and gene function.

Superficial penile cancer treated with complete excision of the glans epithelium and coverage with a tissue sealant matrix (TachoSil®)

Introduction

The aim of our work was to demonstrate the feasibility and clinical outcomes after partial excision of the epithelial and subepithelial layer of the glans with subsequent tissue sealant matrix coverage (TachoSil®).

Material and methods

We enrolled 11 consecutive patients with superficial penile cancer. Under the microscopic guidance, the tumor in the glans area was excised continuously with a minimal lateral margin of 5 mm. The cosmetic result was accessed using a 5-graded scale ranging from very dissatisfied to very satisfied.

Results

The median patient's age at the presentation was 46 years (range 38-53). Histopathological examination of the specimen confirmed squamous cell carcinoma and tumor-free surgical margins were obtained in all cases. Overall, the tumors were TaG1 in 3 patients, TaG2 in 1 patient, TisG1 in 2 patients, TisG2 in 2 patient, T1aG1 in 2 patients, and T1aG2 in 1 patient. All patients had clinically negative lymph-node status - cN0 (confirmed by aabdominopelvic computed tomography (CT) scan with contrast). During the follow-up of 6 to 36 months (median 18), local recurrence occurred in 1 patient with carcinoma in situ six months after surgery, which was managed by a second glans-preserving surgery without recurrence. The others showed no signs of local recurrence or metastasis during the period of observation.

Conclusions

These preliminary data suggests that glans-preserving surgical technique using TachoSil® as a defect coverage is technically feasible, functionally safe and cosmetically satisfying. However, well-designed prospective-randomized trial is warranted, to further confirm the clinical utility of our approach.

Genetics of intellectual disability in consanguineous families

Autosomal recessive (AR) gene defects are the leading genetic cause of intellectual disability (ID) in countries with frequent parental consanguinity, which account for about 1/7th of the world population. Yet, compared to autosomal dominant de novo mutations, which are the predominant cause of ID in Western countries, the identification of AR-ID genes has lagged behind. Here, we report on whole exome and whole genome sequencing in 404 consanguineous predominantly Iranian families with two or more affected offspring. In 219 of these, we found likely causative variants, involving 77 known and 77 novel AR-ID (candidate) genes, 21 X-linked genes, as well as 9 genes previously implicated in diseases other than ID. This study, the largest of its kind published to date, illustrates that high-throughput DNA sequencing in consanguineous families is a superior strategy for elucidating the thousands of hitherto unknown gene defects underlying AR-ID, and it sheds light on their prevalence.

Effective Increase of Serum Vitamin D3 by IV Application of a Cholecalciferol-N-Acetyl-Galactosamine-Stabilized Dimer: a Clinical Murine Trial Study

BACKGROUND

Pre-clinical toxicology studies of human Gc-protein (vitamin D binding protein) are of special interest as to the transport of vitamin D and its biological activities. We have demonstrated that the oral application of a special dimeric vitamin D complex reduces oxidative stress and increases the quality of life in autistic children. Therefore, safety and toxic effects of two dimeric cholecalciferol-N-acetyl-galactosamine-albumin complexes were evaluated in increasing intravenous (iv.) vitamin D levels administered in a pre-clinical trial in mice over a 5-week period.

METHODS

Over a period of 5 weeks, two times a week, mice received iv. administration of one of the following: (a) 1.2 IE of vitamin D-N-acetyl-galactosamine-albumin (Vitamin D3 NAGA, ImmunoD® group), (b) 1.2 IE of vitamin-D-poly-N-acetyl-galactosamine-albumin (Poly-Nac group), or (c) isotonic saline solution (sham group). Before and after the trial, red and white blood cell panels (RBS, WBC and platelets) were determined. Furthermore, vitamin D levels, electrolytes, and C-reactive protein levels were measured directly before sacrificing.

RESULTS

No toxic effects were observed during iv. injection with dimeric vitamin D complexes, neither in the sham group, nor in the two treatment groups. Vitamin D levels increased significantly within 5 weeks in the Poly-Nac group (26.6 ± 8.8 ng/mL; p = 0.001) compared to the sham group (3.1 ± 0.9 ng/mL), and the Poly-Nac group to the ImmunoD group (7.0 ± 3.6 ng/mL; p = 0.003). A significant increase of vitamin D was also obtained in favor of the ImmunoD group compared to the sham (p = 0.03). Electrolytes (K, Na, Cl, Mg, Ca) and C-reactive protein showed no significant differences after administration in all three mice groups. Also, no significant differences were observed between these three groups in the WBC and RBC blood panels.

CONCLUSIONS

The two dimeric vitamin D complexes used in this pre-clinical study showed no side or toxic effects after iv. administration in mice, but a sole increase in vitamin D levels without any change in electrolytes or blood cells. Therefore, we assume this newly developed composition to be safe in oral or iv.-administration and further pre-clinical studies can be conducted to evaluate the value in treatment of various diseases related to vitamin D deficiencies.

Network and Pathway Analysis of Toxicogenomics Data

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Background

Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide and is primarily treated with radiation, surgery, and platinum-based drugs like cisplatin and carboplatin. The major challenge in the treatment of NSCLC patients is intrinsic or acquired resistance to chemotherapy. Molecular markers predicting the outcome of the patients are urgently needed.

Methods

Here, we employed patient-derived xenografts (PDXs) to detect predictive methylation biomarkers for platin-based therapies. We used MeDIP-Seq to generate genome-wide DNA methylation profiles of 22 PDXs, their parental primary NSCLC, and their corresponding normal tissues and complemented the data with gene expression analyses of the same tissues. Candidate biomarkers were validated with quantitative methylation-specific PCRs (qMSP) in an independent cohort.

Results

Comprehensive analyses revealed that differential methylation patterns are highly similar, enriched in PDXs and lung tumor-specific when comparing differences in methylation between PDXs versus primary NSCLC. We identified a set of 40 candidate regions with methylation correlated to carboplatin response and corresponding inverse gene expression pattern even before therapy. This analysis led to the identification of a promoter CpG island methylation of LDL receptor-related protein 12 (LRP12) associated with increased resistance to carboplatin. Validation in an independent patient cohort (n = 35) confirmed that LRP12 methylation status is predictive for therapeutic response of NSCLC patients to platin therapy with a sensitivity of 80% and a specificity of 84% (p < 0.01). Similarly, we find a shorter survival time for patients with LRP12 hypermethylation in the TCGA data set for NSCLC (lung adenocarcinoma).

Conclusions

Using an epigenome-wide sequencing approach, we find differential methylation patterns from primary lung cancer and PDX-derived cancers to be very similar, albeit with a lower degree of differential methylation in primary tumors. We identify LRP12 DNA methylation as a powerful predictive marker for carboplatin resistance. These findings outline a platform for the identification of epigenetic therapy resistance biomarkers based on PDX NSCLC models.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0562-1) contains supplementary material, which is available to authorized users.

Computational Network Analysis for Drug Toxicity Prediction

The computational prediction of compound effects from molecular data is an important task in hazard and risk assessment and pivotal for judging the safety of any drug, chemical or cosmetic compound. In particular, the identification of such compound effects at the level of molecular interaction networks can be helpful for the construction of adverse outcome pathways (AOPs). AOPs emerged as a guiding concept for toxicity prediction, because of the inherent mechanistic information of such networks. In fact, integrating molecular interactions in transcriptome analysis and observing expression changes in closely interacting genes might allow identifying the key molecular initiating events of compound toxicity.In this work we describe a computational approach that is suitable for the identification of such network modules from transcriptomics data, which is the major molecular readout of toxicogenomics studies. The approach is composed of different tools (1) for primary data analysis, i.e., the biostatistical quantification of the gene expression changes, (2) for functional annotation and prioritization of genes using literature mining, as well as (3) for the construction of an interaction network that consists of interactions with high confidence and the identification of predictive modules from these networks. We describe the different steps of the approach and demonstrate its performance with public data on drugs that induce hepatic and cardiac toxicity.

QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments

Genome-wide enrichment of methylated DNA followed by sequencing (MeDIP-seq) offers a reasonable compromise between experimental costs and genomic coverage. However, the computational analysis of these experiments is complex, and quantification of the enrichment signals in terms of absolute levels of methylation requires specific transformation. In this work, we present QSEA, Quantitative Sequence Enrichment Analysis, a comprehensive workflow for the modelling and subsequent quantification of MeDIP-seq data. As the central part of the workflow we have developed a Bayesian statistical model that transforms the enrichment read counts to absolute levels of methylation and, thus, enhances interpretability and facilitates comparison with other methylation assays. We suggest several calibration strategies for the critical parameters of the model, either using additional data or fairly general assumptions. By comparing the results with bisulfite sequencing (BS) validation data, we show the improvement of QSEA over existing methods. Additionally, we generated a clinically relevant benchmark data set consisting of methylation enrichment experiments (MeDIP-seq), BS-based validation experiments (Methyl-seq) as well as gene expression experiments (RNA-seq) derived from non-small cell lung cancer patients, and show that the workflow retrieves well-known lung tumour methylation markers that are causative for gene expression changes, demonstrating the applicability of QSEA for clinical studies. QSEA is implemented in R and available from the Bioconductor repository 3.4 (www.bioconductor.org/packages/qsea).

ToxDB: pathway-level interpretation of drug-treatment data

Motivation: Extensive drug treatment gene expression data have been generated in order to identify biomarkers that are predictive for toxicity or to classify compounds. However, such patterns are often highly variable across compounds and lack robustness. We and others have previously shown that supervised expression patterns based on pathway concepts rather than unsupervised patterns are more robust and can be used to assess toxicity for entire classes of drugs more reliably.

Results: We have developed a database, ToxDB, for the analysis of the functional consequences of drug treatment at the pathway level. We have collected 2694 pathway concepts and computed numerical response scores of these pathways for 437 drugs and chemicals and 7464 different experimental conditions. ToxDB provides functionalities for exploring these pathway responses by offering tools for visualization and differential analysis allowing for comparisons of different treatment parameters and for linking this data with toxicity annotation and chemical information.

Database URL:http://toxdb.molgen.mpg.de

Transcriptional signature induced by a metastasis-promoting c-Src mutant in a human breast cell line

Deletions at the C-terminus of the proto-oncogene protein c-Src kinase are found in the viral oncogene protein v-Src as well as in some advanced human colon cancers. They are associated with increased kinase activity and cellular invasiveness. Here, we analyzed the mRNA expression signature of a constitutively active C-terminal mutant of c-Src, c-Src(mt), in comparison with its wild-type protein, c-Src(wt), in the human non-transformed breast epithelial cell line MCF-10A. We demonstrated previously that the mutant altered migratory and metastatic properties. Genome-wide transcriptome analysis revealed that c-Src(mt) de-regulated the expression levels of approximately 430 mRNAs whose gene products are mainly involved in the cellular processes of migration and adhesion, apoptosis and protein synthesis. 82.9% of these genes have previously been linked to cellular migration, while the others play roles in RNA transport and splicing processes, for instance. Consistent with the transcriptome data, cells expressing c-Src(mt), but not those expressing c-Src(wt), showed the capacity to metastasize into the lungs of mice in vivo. The mRNA expression profile of c-Src(mt)-expressing cells shows significant overlap with that of various primary human tumor samples, possibly reflecting elevated Src activity in some cancerous cells. Expression of c-Src(mt) led to elevated migratory potential. We used this model system to analyze the transcriptional changes associated with an invasive cellular phenotype. These genes and pathways de-regulated by c-Src(mt) may provide suitable biomarkers or targets of therapeutic approaches for metastatic cells.

DATABASE

This project was submitted to the National Center for Biotechnology Information BioProject under ID PRJNA288540. The Illumina RNA-Seq reads are available in the National Center for Biotechnology Information Sequence Read Archive under study ID SRP060008 with accession numbers SRS977414 for MCF-10A cells, SRS977717 for mock cells, SRS978053 for c-Src(wt) cells and SRS978046 for c-Src(mt) cells.

Inter-laboratory study of human in vitro toxicogenomics-based tests as alternative methods for evaluating chemical carcinogenicity: a bioinformatics perspective

The assessment of the carcinogenic potential of chemicals with alternative, human-based in vitro systems has become a major goal of toxicogenomics. The central read-out of these assays is the transcriptome, and while many studies exist that explored the gene expression responses of such systems, reports on robustness and reproducibility, when testing them independently in different laboratories, are still uncommon. Furthermore, there is limited knowledge about variability induced by the data analysis protocols. We have conducted an inter-laboratory study for testing chemical carcinogenicity evaluating two human in vitro assays: hepatoma-derived cells and hTERT-immortalized renal proximal tubule epithelial cells, representing liver and kidney as major target organs. Cellular systems were initially challenged with thirty compounds, genome-wide gene expression was measured with microarrays, and hazard classifiers were built from this training set. Subsequently, each system was independently established in three different laboratories, and gene expression measurements were conducted using anonymized compounds. Data analysis was performed independently by two separate groups applying different protocols for the assessment of inter-laboratory reproducibility and for the prediction of carcinogenic hazard. As a result, both workflows came to very similar conclusions with respect to (1) identification of experimental outliers, (2) overall assessment of robustness and inter-laboratory reproducibility and (3) re-classification of the unknown compounds to the respective toxicity classes. In summary, the developed bioinformatics workflows deliver accurate measures for inter-laboratory comparison studies, and the study can be used as guidance for validation of future carcinogenicity assays in order to implement testing of human in vitro alternatives to animal testing.

diXa: a data infrastructure for chemical safety assessment

MOTIVATION

The field of toxicogenomics (the application of '-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available.

RESULTS

The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal.

AVAILABILITY AND IMPLEMENTATION

http://www.dixa-fp7.eu

CONTACT

d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.eu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Venous leakage treatment revisited: pelvic venoablation using aethoxysclerol under air block technique and Valsalva maneuver

OBJECTIVE

We evaluated the effectiveness of pelvic vein embolization with aethoxysclerol in aero-block technique for the treatment of impotence due to venous leakage in men using sildenafil for intercourse. The aim of the procedure was to reduce the use of sildenafil.

METHODS

A total of 96 patients with veno-occlusive dysfunction, severe enough for the need of PDE5 inhibitors for vaginal penetration, underwent pelvic venoablation with aethoxysclerol. The mean patient age was 53.5 years. Venous leaks were identified by Color Doppler Ultrasound after intracavernous alprostadil injection. Under local anesthesia a 20-gauge needle was inserted into the deep dorsal penile vein. The pelvic venogram was obtained through deep dorsal venography. Aethoxysclerol 3% as sclerosing agent was injected after air-block under Valsalva manoeuver. Success was defined as the ability to achieve vaginal insertion without the aid of any drugs, vasoactive injections, penile prosthesis, or vacuum device. Additionally, a pre- and post- therapy IIEF score and a digital overnight spontaneous erections protocol (OSEP) with the NEVA™-system was performed.

RESULTS

At 3 month follow-up 77 out of 96 patients (80.21%) reported to have erections sufficient for vaginal insertion without the use of any drug or additional device. Four (4.17%) patients did not report any improvement. Follow up with color Doppler ultrasound revealed a new or persistent venous leakage in 8 (8.33%) of the patients. No serious complications occurred.

CONCLUSIONS

Our new pelvic venoablation technique using aethoxysclerol in air-block technique was effective, minimally invasive, and cost-effective. All patients were able to perform sexual intercourse without the previously used dosage of PDE5 inhibitor. This new method may help in patients with contra-indications against PDE5 inhibitors, in patients who cannot afford the frequent usage of expensive oral medication or those who do not fully respond to PDE5-inhibitors.

Computational modeling of drug response with applications to neuroscience

The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.

ARH-seq: identification of differential splicing in RNA-seq data

The computational prediction of alternative splicing from high-throughput sequencing data is inherently difficult and necessitates robust statistical measures because the differential splicing signal is overlaid by influencing factors such as gene expression differences and simultaneous expression of multiple isoforms amongst others. In this work we describe ARH-seq, a discovery tool for differential splicing in case–control studies that is based on the information-theoretic concept of entropy. ARH-seq works on high-throughput sequencing data and is an extension of the ARH method that was originally developed for exon microarrays. We show that the method has inherent features, such as independence of transcript exon number and independence of differential expression, what makes it particularly suited for detecting alternative splicing events from sequencing data. In order to test and validate our workflow we challenged it with publicly available sequencing data derived from human tissues and conducted a comparison with eight alternative computational methods. In order to judge the performance of the different methods we constructed a benchmark data set of true positive splicing events across different tissues agglomerated from public databases and show that ARH-seq is an accurate, computationally fast and high-performing method for detecting differential splicing events.

Evaluation of database-derived pathway development for enabling biomarker discovery for hepatotoxicity

Current testing models for predicting drug-induced liver injury are inadequate, as they basically under-report human health risks. We present here an approach towards developing pathways based on hepatotoxicity-associated gene groups derived from two types of publicly accessible hepatotoxicity databases, in order to develop drug-induced liver injury biomarker profiles. One human liver ‘omics-based and four text-mining-based databases were explored for hepatotoxicity-associated gene lists. Over-representation analysis of these gene lists with a hepatotoxicant-exposed primary human hepatocytes data set showed that human liver ‘omics gene lists performed better than text-mining gene lists and the results of the latter differed strongly between databases. However, both types of databases contained gene lists demonstrating biomarker potential. Visualizing those in pathway format may aid in interpreting the biomolecular background. We conclude that exploiting existing and openly accessible databases in a dedicated manner seems promising in providing venues for translational research in toxicology and biomarker development.