Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient.

Radiotherapy response of rectal cancer patients is dependent on a myriad of molecular mechanisms including response to stress, cell death, and cell metabolism. Modulation of lipid metabolism emerges as a unique strategy to improve radiotherapy outcomes due to its accessibility by bioactive molecules within foods. Even though a few radioresponse modulators have been identified using experimental techniques, trying to experimentally identify all potential modulators is intractable. Here we introduce a machine learning (ML) approach to interrogate the space of bioactive molecules within food for potential modulators of radiotherapy response and provide phytochemically-enriched recipes that encapsulate the benefits of discovered radiotherapy modulators. Potential radioresponse modulators were identified using a genomic-driven network ML approach, metric learning and domain knowledge. Then, recipes from the Recipe1M database were optimized to provide ingredient substitutions maximizing the number of predicted modulators whilst preserving the recipe's culinary attributes. This work provides a pipeline for the design of genomic-driven nutritional interventions to improve outcomes of rectal cancer patients undergoing radiotherapy.

Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil.

Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.

Virus-induced Volatile Organic Compounds Are Detectable in Exhaled Breath during Pulmonary Infection.

Rationale: Chronic obstructive pulmonary disease (COPD) is a condition punctuated by acute exacerbations commonly triggered by viral and/or bacterial infection. Early identification of exacerbation triggers is important to guide appropriate therapy, but currently available tests are slow and imprecise. Volatile organic compounds (VOCs) can be detected in exhaled breath and have the potential to be rapid tissue-specific biomarkers of infection etiology. Objectives: To determine whether volatile organic compound measurement could distinguish viral from bacterial infection in COPD. Methods: We used serial sampling within in vitro and in vivo studies to elucidate the dynamic changes that occur in VOC production during acute respiratory viral infection. Highly sensitive gas chromatography-mass spectrometry techniques were used to measure VOC production from infected airway epithelial-cell cultures and in exhaled breath samples from healthy subjects experimentally challenged with rhinovirus (RV)-A16 and from subjects with COPD with naturally occurring exacerbations. Measurements and Main Results: We identified a novel VOC signature comprising decane and other long-chain alkane compounds that is induced during RV infection of cultured airway epithelial cells and is also increased in the exhaled breath from healthy subjects experimentally challenged with RV and from patients with COPD during naturally occurring viral exacerbations. These compounds correlated with the magnitude of antiviral immune responses, viral burden, and exacerbation severity but were not induced by bacterial infection, suggesting that they represent a specific virus-inducible signature. Conclusions: Our study highlights the potential for measurement of exhaled breath VOCs as rapid, noninvasive biomarkers of viral infection. Further studies are needed to determine whether measurement of these signatures could be used to guide more targeted therapy with antibiotic/antiviral agents for COPD exacerbations.

Predicting anticancer hyperfoods with graph convolutional networks.

BACKGROUND: Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field used an unsupervised network propagation algorithm to learn the systemic-wide effect on the human interactome of 1962 FDA-approved drugs and a supervised algorithm to predict anticancer therapeutics using the learned representations. Then, a set of bioactive molecules within foods was fed into the model, which predicted molecules with cancer-beating potential.The employed methodology consisted of disjoint unsupervised feature generation and classification tasks, which can result in sub-optimal learned drug representations with respect to the classification task. Additionally, due to the disjoint nature of the tasks, the employed approach proved cumbersome to optimize, requiring testing of thousands of hyperparameter combinations and significant computational resources.To overcome the technical limitations highlighted above, we represent each drug as a graph (human interactome) with its targets as binary node features on the graph and formulate the problem as a graph classification task. To solve this task, inspired by the success of graph neural networks in graph classification problems, we use an end-to-end graph neural network model operating directly on the graphs, which learns drug representations to optimize model performance in the prediction of anticancer therapeutics. RESULTS: The proposed model outperforms the baseline approach in the anticancer therapeutic prediction task, achieving an F1 score of 67.99%±2.52% and an AUPR of 73.91%±3.49%. It is also shown that the model is able to capture knowledge of biological pathways to predict anticancer molecules based on the molecules' effects on cancer-related pathways. CONCLUSIONS: We introduce an end-to-end graph convolutional model to predict cancer-beating molecules within food. The introduced model outperforms the existing baseline approach, and shows interpretability, paving the way to the future of a personalized nutritional science approach allowing the development of nutrition strategies for cancer prevention and/or therapeutics.

Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods.

Recent data indicate that up-to 30-40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have been identified through their molecular biological network commonality with clinically approved anti-cancer therapies. A machine-learning algorithm of random walks on graphs (operating within the supercomputing DreamLab platform) was used to simulate drug actions on human interactome networks to obtain genome-wide activity profiles of 1962 approved drugs (199 of which were classified as "anti-cancer" with their primary indications). A supervised approach was employed to predict cancer-beating molecules using these 'learned' interactome activity profiles. The validated model performance predicted anti-cancer therapeutics with classification accuracy of 84-90%. A comprehensive database of 7962 bioactive molecules within foods was fed into the model, which predicted 110 cancer-beating molecules (defined by anti-cancer drug likeness threshold of >70%) with expected capacity comparable to clinically approved anti-cancer drugs from a variety of chemical classes including flavonoids, terpenoids, and polyphenols. This in turn was used to construct a 'food map' with anti-cancer potential of each ingredient defined by the number of cancer-beating molecules found therein. Our analysis underpins the design of next-generation cancer preventative and therapeutic nutrition strategies.

Trapping of the transport-segment DNA by the ATPase domains of a type II topoisomerase.

Type II topoisomerases alter DNA topology to control DNA supercoiling and chromosome segregation and are targets of clinically important anti-infective and anticancer therapeutics. They act as ATP-operated clamps to trap a DNA helix and transport it through a transient break in a second DNA. Here, we present the first X-ray crystal structure solved at 2.83 Å of a closed clamp complete with trapped T-segment DNA obtained by co-crystallizing the ATPase domain of S. pneumoniae topoisomerase IV with a nonhydrolyzable ATP analogue and 14-mer duplex DNA. The ATPase dimer forms a 22 Å protein hole occupied by the kinked DNA bound asymmetrically through positively charged residues lining the hole, and whose mutagenesis impacts the DNA decatenation, DNA relaxation and DNA-dependent ATPase activities of topo IV. These results and a side-bound DNA-ParE structure help explain how the T-segment DNA is captured and transported by a type II topoisomerase, and reveal a new enzyme-DNA interface for drug discovery.

Exploiting and assessing multi-source data for supervised biomedical named entity recognition.

Motivation: Recognition of biomedical entities from scientific text is a critical component of natural language processing and automated information extraction platforms. Modern named entity recognition approaches rely heavily on supervised machine learning techniques, which are critically dependent on annotated training corpora. These approaches have been shown to perform well when trained and tested on the same source. However, in such scenario, the performance and evaluation of these models may be optimistic, as such models may not necessarily generalize to independent corpora, resulting in potential non-optimal entity recognition for large-scale tagging of widely diverse articles in databases such as PubMed. Results: Here we aggregated published corpora for the recognition of biomolecular entities (such as genes, RNA, proteins, variants, drugs and metabolites), identified entity class overlap and performed leave-corpus-out cross validation strategy to test the efficiency of existing models. We demonstrate that accuracies of models trained on individual corpora decrease substantially for recognition of the same biomolecular entity classes in independent corpora. This behavior is possibly due to limited generalizability of entity-class-related features captured by individual corpora (model 'overtraining') which we investigated further at the orthographic level, as well as potential annotation standard differences. We show that the combined use of multi-source training corpora results in overall more generalizable models for named entity recognition, while achieving comparable individual performance. By performing learning-curve-based power analysis we further identified that performance is often not limited by the quantity of the annotated data. Availability and implementation: Compiled primary and secondary sources of the aggregated corpora are available on: https://github.com/dterg/biomedical_corpora/wiki and https://bitbucket.org/iAnalytica/bioner. Supplementary information: Supplementary data are available at Bioinformatics online.

BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology.

Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. Here, we present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly integrated system for translational MSI.

ChemDistiller: an engine for metabolite annotation in mass spectrometry.

Motivation: High-resolution mass spectrometry permits simultaneous detection of thousands of different metabolites in biological samples; however, their automated annotation still presents a challenge due to the limited number of tailored computational solutions freely available to the scientific community. Results: Here, we introduce ChemDistiller, a customizable engine that combines automated large-scale annotation of metabolites using tandem MS data with a compiled database containing tens of millions of compounds with pre-calculated 'fingerprints' and fragmentation patterns. Our tests using publicly and commercially available tandem MS spectra for reference compounds show retrievals rates comparable to or exceeding the ones obtainable by the current state-of-the-art solutions in the field while offering higher throughput, scalability and processing speed. Availability and implementation: Source code freely available for download at https://bitbucket.org/iAnalytica/chemdistillerpython. Supplementary information: Supplementary data are available at Bioinformatics online.

Translational utility of a hierarchical classification strategy in biomolecular data analytics.

Hierarchical classification (HC) stratifies and classifies data from broad classes into more specific classes. Unlike commonly used data classification strategies, this enables the probabilistic prediction of unknown classes at different levels, minimizing the burden of incomplete databases. Despite these advantages, its translational application in biomedical sciences has been limited. We describe and demonstrate the implementation of a HC approach for "omics-driven" classification of 15 bacterial species at various taxonomic levels achieving 90-100% accuracy, and 9 cancer types into morphological types and 35 subtypes with 99% and 76% accuracy, respectively. Unknown bacterial species were probabilistically assigned with 100% accuracy to their respective genus or family using mass spectra (n = 284). Cancer types were predicted by mRNA data (n = 1960) for most subtypes with 95-100% accuracy. This has high relevance in clinical practice where complete datasets are difficult to compile with the continuous evolution of diseases and emergence of new strains, yet prediction of unknown classes, such as bacterial species, at upper hierarchy levels may be sufficient to initiate antimicrobial therapy. The algorithms presented here can be directly translated into clinical-use with any quantitative data, and have broad application potential, from unlabeled sample identification, to hierarchical feature selection, and discovery of new taxonomic variants.

Exploring the active site of the Streptococcus pneumoniae topoisomerase IV-DNA cleavage complex with novel 7,8-bridged fluoroquinolones.

As part of a programme of synthesizing and investigating the biological properties of new fluoroquinolone antibacterials and their targeting of topoisomerase IV from Streptococcus pneumoniae, we have solved the X-ray structure of the complexes of two new 7,8-bridged fluoroquinolones (with restricted C7 group rotation favouring tight binding) in complex with the topoisomerase IV from S. pneumoniae and an 18-base-pair DNA binding site-the E-site-found by our DNA mapping studies to bind drug strongly in the presence of topoisomerase IV (Leo et al. 2005 J. Biol. Chem. 280, 14 252-14 263, doi:10.1074/jbc.M500156200). Although the degree of antibiotic resistance towards fluoroquinolones is much lower than that of ß-lactams and a range of ribosome-bound antibiotics, there is a pressing need to increase the diversity of members of this successful clinically used class of drugs. The quinolone moiety of the new 7,8-bridged agents ACHN-245 and ACHN-454 binds similarly to that of clinafloxocin, levofloxacin, moxifloxacin and trovofloxacin but the cyclic scaffold offers the possibility of chemical modification to produce interactions with other topoisomerase residues at the active site.

Structure of a quinolone-stabilized cleavage complex of topoisomerase IV from Klebsiella pneumoniae and comparison with a related Streptococcus pneumoniae complex.

Klebsiella pneumoniae is a Gram-negative bacterium that is responsible for a range of common infections, including pulmonary pneumonia, bloodstream infections and meningitis. Certain strains of Klebsiella have become highly resistant to antibiotics. Despite the vast amount of research carried out on this class of bacteria, the molecular structure of its topoisomerase IV, a type II topoisomerase essential for catalysing chromosomal segregation, had remained unknown. In this paper, the structure of its DNA-cleavage complex is reported at 3.35 Å resolution. The complex is comprised of ParC breakage-reunion and ParE TOPRIM domains of K. pneumoniae topoisomerase IV with DNA stabilized by levofloxacin, a broad-spectrum fluoroquinolone antimicrobial agent. This complex is compared with a similar complex from Streptococcus pneumoniae, which has recently been solved.

Interceptor effect of C60 fullerene on the in vitro action of aromatic drug molecules.

C60 fullerenes are spherical molecules composed purely of carbon atoms. They inspire a particularly strong scientific interest because of their specific physico-chemical properties and potential medical and nanotechnological applications. In this work we are focusing on studying the influence of the pristine C60 fullerene on biological activity of some aromatic drug molecules in human buccal epithelial cells. Assessment of the heterochromatin structure in the cell nucleus as well as the barrier function of the cell membrane was performed. The methods of cell microelectrophoresis and atomic force microscopy were also applied. A concentration-dependent restoration of the functional activity of the cellular nucleus after exposure to DNA-binding drugs (doxorubicin, proflavine and ethidium bromide) has been observed in human buccal epithelial cells upon addition of C60 fullerene at a concentration of ~10(-5 )M. The results were shown to follow the framework of interceptor/protector action theory, assuming that non-covalent complexation between C60 fullerene and the drugs (i.e., hetero-association) is the major process responsible for the observed biological effects. An independent confirmation of this hypothesis was obtained via investigation of the cellular response of buccal epithelium to the coadministration of the aromatic drugs and caffeine, and it is based on the well-established role of hetero-association in drug-caffeine systems. The results indicate that C60 fullerene may reverse the effects caused by the aromatic drugs, thereby pointing out the potential possibility of the use of aromatic drugs in combination with C60 fullerene for regulation of their medico-biological action.

Structure of an 'open' clamp type II topoisomerase-DNA complex provides a mechanism for DNA capture and transport.

Type II topoisomerases regulate DNA supercoiling and chromosome segregation. They act as ATP-operated clamps that capture a DNA duplex and pass it through a transient DNA break in a second DNA segment via the sequential opening and closure of ATPase-, G-DNA- and C-gates. Here, we present the first 'open clamp' structures of a 3-gate topoisomerase II-DNA complex, the seminal complex engaged in DNA recognition and capture. A high-resolution structure was solved for a (full-length ParE-ParC55)2 dimer of Streptococcus pneumoniae topoisomerase IV bound to two DNA molecules: a closed DNA gate in a B-A-B form double-helical conformation and a second B-form duplex associated with closed C-gate helices at a novel site neighbouring the catalytically important ß-pinwheel DNA-binding domain. The protein N gate is present in an 'arms-wide-open' state with the undimerized N-terminal ParE ATPase domains connected to TOPRIM domains via a flexible joint and folded back allowing ready access both for gate and transported DNA segments and cleavage-stabilizing antibacterial drugs. The structure shows the molecular conformations of all three gates at 3.7 Å, the highest resolution achieved for the full complex to date, and illuminates the mechanism of DNA capture and transport by a type II topoisomerase.

Structural basis of gate-DNA breakage and resealing by type II topoisomerases.

Type II DNA topoisomerases are ubiquitous enzymes with essential functions in DNA replication, recombination and transcription. They change DNA topology by forming a transient covalent cleavage complex with a gate-DNA duplex that allows transport of a second duplex though the gate. Despite its biological importance and targeting by anticancer and antibacterial drugs, cleavage complex formation and reversal is not understood for any type II enzyme. To address the mechanism, we have used X-ray crystallography to study sequential states in the formation and reversal of a DNA cleavage complex by topoisomerase IV from Streptococcus pneumoniae, the bacterial type II enzyme involved in chromosome segregation. A high resolution structure of the complex captured by a novel antibacterial dione reveals two drug molecules intercalated at a cleaved B-form DNA gate and anchored by drug-specific protein contacts. Dione release generated drug-free cleaved and resealed DNA complexes in which the DNA gate instead adopts an unusual A/B-form helical conformation with a Mg(2+) ion repositioned to coordinate each scissile phosphodiester group and promote reversible cleavage by active-site tyrosines. These structures, the first for putative reaction intermediates of a type II topoisomerase, suggest how a type II enzyme reseals DNA during its normal reaction cycle and illuminate aspects of drug arrest important for the development of new topoisomerase-targeting therapeutics.

Structural insight into the quinolone-DNA cleavage complex of type IIA topoisomerases.

Type II topoisomerases alter DNA topology by forming a covalent DNA-cleavage complex that allows DNA transport through a double-stranded DNA break. We present the structures of cleavage complexes formed by the Streptococcus pneumoniae ParC breakage-reunion and ParE TOPRIM domains of topoisomerase IV stabilized by moxifloxacin and clinafloxacin, two antipneumococcal fluoroquinolones. These structures reveal two drug molecules intercalated at the highly bent DNA gate and help explain antibacterial quinolone action and resistance.

The difficult case of crystallization and structure solution for the ParC55 breakage-reunion domain of topoisomerase IV from Streptococcus pneumoniae.

BACKGROUND: Streptococcus pneumoniae is the major cause of community-acquired pneumonia and is also associated with bronchitis, meningitis, otitis and sinusitis. The emergence and increasing prevalence of resistance to penicillin and other antibiotics has led to interest in other anti-pneumonococcal drugs such as quinolones that target the enzymes DNA gyrase and topoisomerase IV. During crystallization and in the avenues to finding a method to determine phases for the structure of the ParC55 breakage-reunion domain of topoisomerase IV from Streptococcus pneumoniae, obstacles were faced at each stage of the process. These problems included: majority of the crystals being twinned, either non-diffracting or exhibiting a high mosaic spread. The crystals, which were grown under conditions that favoured diffraction, were difficult to flash-freeze without loosing diffraction. The initial structure solution by molecular replacement failed and the approach proved to be unviable due to the complexity of the problem. In the end the successful structure solution required an in-depth data analysis and a very detailed molecular replacement search. METHODOLOGY/PRINCIPAL FINDINGS: Crystal anti-twinning agents have been tested and two different methods of flash freezing have been compared. The fragility of the crystals did not allow the usual method of transferring the crystals into the heavy atom solution. Consequently, it was necessary to co-crystallize in the presence of the heavy atom compound. The multiple isomorphous replacement approach was unsuccessful because the 7 cysteine mutants which were engineered could not be successfully derivatized. Ultimately, molecular replacement was used to solve the structure by sorting through a large number of solutions in space group P1 using CNS. CONCLUSIONS/SIGNIFICANCE: The main objective of this paper is to describe the obstacles which were faced and overcome in order to acquire data sets on such difficult crystals and determine phases for successful structure solution.