A Clinical Breathomics Dataset

This study entailed a comprehensive GC‒MS analysis conducted on 121 patient samples to generate a clinical breathomics dataset. Breath molecules, indicative of diverse conditions such as psychological and pathological states and the microbiome, were of particular interest due to their non-invasive nature. The highlighted noninvasive approach for detecting these breath molecules significantly enhances diagnostic and monitoring capacities. This dataset cataloged volatile organic compounds (VOCs) from the breath of individuals with asthma, bronchiectasis, and chronic obstructive pulmonary disease. Uniform and consistent sample collection protocols were strictly adhered to during the accumulation of this extensive dataset, ensuring its reliability. It encapsulates extensive human clinical breath molecule data pertinent to three specific diseases. This consequential clinical breathomics dataset is a crucial resource for researchers and clinicians in identifying and exploring important compounds within the patient's breath, thereby augmenting future diagnostic and therapeutic initiatives.

FastEval Parkinsonism: an instant deep learning-assisted video-based online system for Parkinsonian motor symptom evaluation

The Motor Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is designed to assess bradykinesia, the cardinal symptoms of Parkinson's disease (PD). However, it cannot capture the all-day variability of bradykinesia outside the clinical environment. Here, we introduce FastEval Parkinsonism ( https://fastevalp.cmdm.tw/ ), a deep learning-driven video-based system, providing users to capture keypoints, estimate the severity, and summarize in a report. Leveraging 840 finger-tapping videos from 186 individuals (103 patients with Parkinson's disease (PD), 24 participants with atypical parkinsonism (APD), 12 elderly with mild parkinsonism signs (MPS), and 47 healthy controls (HCs)), we employ a dilated convolution neural network with two data augmentation techniques. Our model achieves acceptable accuracies (AAC) of 88.0% and 81.5%. The frequency-intensity (FI) value of thumb-index finger distance was indicated as a pivotal hand parameter to quantify the performance. Our model also shows the usability for multi-angle videos, tested in an external database enrolling over 300 PD patients.

Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait

We present PathoOpenGait, a cloud-based platform for comprehensive gait analysis. Gait assessment is crucial in neurodegenerative diseases such as Parkinson's and multiple system atrophy, yet current techniques are neither affordable nor efficient. PathoOpenGait utilizes 2D and 3D data from a binocular 3D camera for monitoring and analyzing gait parameters. Our algorithms, including a semi-supervised learning-boosted neural network model for turn time estimation and deterministic algorithms to estimate gait parameters, were rigorously validated on annotated gait records, demonstrating high precision and consistency. We further demonstrate PathoOpenGait's applicability in clinical settings by analyzing gait trials from Parkinson's patients and healthy controls. PathoOpenGait is the first open-source, cloud-based system for gait analysis, providing a user-friendly tool for continuous patient care and monitoring. It offers a cost-effective and accessible solution for both clinicians and patients, revolutionizing the field of gait assessment. PathoOpenGait is available at https://pathoopengait.cmdm.tw.

Characterization of markers, functional properties, and microbiome composition in human gut-derived bacterial extracellular vesicles

Past studies have confirmed the etiologies of bacterial extracellular vesicles (BEVs) in various diseases, including inflammatory bowel disease (IBD) and colorectal cancer (CRC). This study aimed to investigate the characteristics of stool-derived bacterial extracellular vesicles (stBEVs) and discuss their association with stool bacteria. First, three culture models - gram-positive (G+)BcBEVs (from B.coagulans), gram-negative (G-)EcBEVs (from E.coli), and eukaryotic cell-derived EVs (EEV, from Colo205 cell line) - were used to benchmark various fractions of stEVs separated from optimized density gradient approach (DG). As such, WB, TEM, NTA, and functional assays, were utilized to analyze properties and distribution of EVs in cultured and stool samples. Stool samples from healthy individuals were interrogated using the approaches developed. Results demonstrated successful separation of most stBEVs (within DG fractions 8&9) from stEEVs (within DG fractions 5&6). Data also suggest the presence of stBEV DNA within vesicles after extraction of BEV DNA and DNase treatment. Metagenomic analysis from full-length (FL) region sequencing results confirmed significant differences between stool bacteria and stBEVs. Significantly, F8&9 and the pooled sample (F5-F9) exhibited a similar microbial composition, indicating that F8&9 were enriched in most stBEV species, primarily dominated by Firmicutes (89.6%). However, F5&6 and F7 still held low-density BEVs with a significantly higher proportion of Proteobacteria (20.5% and 40.7%, respectively) and Bacteroidetes (24% and 13.7%, respectively), considerably exceeding the proportions in stool and F8&9. Importantly, among five healthy individuals, significant variations were observed in the gut microbiota composition of their respective stBEVs, indicating the potential of stBEVs as a target for personalized medicine and research.

Biopharma innovation trends during COVID-19 and beyond: an evidence from global partnerships and fundraising activities, 2011-2022

BACKGROUND

Co-development alliances and capital-raising activities are essential supports for biopharmaceutical innovation. During the initial outbreak of the COVID-19, the level of these business activities has increased greatly. Yet the magnitude, direction, and duration of the trend remain ambiguous. Real-time real-world data are needed to inform strategic redirections and industrial policies.

METHODS

This observational study aims to characterize trends in global biopharma innovation activities throughout the global pandemic outbreak. Our extensive deal dataset is retrieved from the commercial database GlobalData (12,866 partnership deals and 32,250 fundraising deals announced between 2011 and 2022). We perform Chi-squared tests to examine the changes in qualitative deal attributes during and beyond the outbreak. Our deal-level sample is further aggregated into category-level panel data according to deal characteristics such as therapy area, molecule type, and development phase. We run a series of regressions to examine how the monthly investment amount raised in each category changed with the onset of the pandemic, controlling for the US Federal funds rate.

RESULTS

The temporary surge of partnership and capital-raising activities was associated with the increase in infectious disease-related deals. Academic and government institutions played an increased role in supporting COVID-related co-development partnerships in 2020, and biopharma ventures had been securing more investments in the capital market throughout 2020 and 2021. The partnership and investment boom did not last till the later pandemic in 2022. The most significant and enduring trend was the shifting focus toward discovery-phase investments. Our regression model reveals that the discovery-phase fundraising deals did not suffer from a bounce back in the late pandemic, consistent with a persistent focus on early innovation.

CONCLUSIONS

Despite the reduced level of partnership and fundraising activities during 2022, we observe a lasting change in focus toward biopharmaceutical innovation after the pandemic outbreak. Our evidence suggests how entrepreneurs and investors should allocate resources in response to the post-pandemic tight monetary environment. We also suggest the need for policy interventions in financing private/public co-development partnerships and non-COVID-related technologies, to maintain their research capacity and generate breakthroughs when faced with unforeseen diseases.

MetaMOPE: a web service for mobile phase determination and fast chromatography peaks evaluation for metabolomics

Motivation

Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used in metabolomics studies, while HILIC LC-MS is particularly suited for polar metabolites. Determining an optimized mobile phase and developing a proper liquid chromatography method tend to be laborious, time-consuming and empirical.

Results

We developed a containerized web tool providing a workflow to quickly determine the optimized mobile phase by batch-evaluating chromatography peaks for metabolomics LC-MS studies. A mass chromatographic quality value, an asymmetric factor, and the local maximum intensity of the extracted ion chromatogram were calculated to determine the number of peaks and peak retention time. The optimal mobile phase can be quickly determined by selecting the mobile phase that produces the largest number of resolved peaks. Moreover, the workflow enables one to automatically process the repeats by evaluating chromatography peaks and determining the retention time of large standards. This workflow was validated with 20 chemical standards and successfully constructed a reference library of 571 metabolites for the HILIC LC-MS platform.

Availability and implementation

MetaMOPE is freely available at https://metamope.cmdm.tw. Source code and installation instructions are available on GitHub: https://github.com/CMDM-Lab/MetaMOPE.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry

Since the first food database was released over one hundred years ago, food databases have become more diversified, including food composition databases, food flavor databases, and food chemical compound databases. These databases provide detailed information about the nutritional compositions, flavor molecules, and chemical properties of various food compounds. As artificial intelligence (AI) is becoming popular in every field, AI methods can also be applied to food industry research and molecular chemistry. Machine learning and deep learning are valuable tools for analyzing big data sources such as food databases. Studies investigating food compositions, flavors, and chemical compounds with AI concepts and learning methods have emerged in the past few years. This review illustrates several well-known food databases, focusing on their primary contents, interfaces, and other essential features. We also introduce some of the most common machine learning and deep learning methods. Furthermore, a few studies related to food databases are given as examples, demonstrating their applications in food pairing, food-drug interactions, and molecular modeling. Based on the results of these applications, it is expected that the combination of food databases and AI will play an essential role in food science and food chemistry.

MSIr: Automatic Registration Service for Mass Spectrometry Imaging and Histology

Mass spectrometry imaging (MSI) is a powerful tool that can be used to simultaneously investigate the spatial distribution of different molecules in samples. However, it is difficult to comprehensively analyze complex biological systems with only a single analytical technique due to different analytical properties and application limitations. Therefore, many analytical methods have been combined to extend data interpretation, evaluate data credibility, and facilitate data mining to explore important temporal and spatial relationships in biological systems. Image registration is an initial and critical step for multimodal imaging data fusion. However, the image registration of multimodal images is not a simple task. The property difference between each data modality may include spatial resolution, image characteristics, or both. The image registrations between MSI and different imaging techniques are often achieved indirectly through histology. Many methods exist for image registration between MSI data and histological images. However, most of them are manual or semiautomatic and have their prerequisites. Here, we built MSI Registrar (MSIr), a web service for automatic registration between MSI and histology. It can help to reduce subjectivity and processing time efficiently. MSIr provides an interface for manually selecting region of interests from histological images; the user selects regions of interest to extract the corresponding spectrum indices in MSI data. In the performance evaluation, MSIr can quickly map MSI data to histological images and help pinpoint molecular components at specific locations in tissues. Most registrations were adequate and were without excessive shifts. MSIr is freely available at https://msir.cmdm.tw and https://github.com/CMDM-Lab/MSIr.

The evolution of the spike protein and hACE2 interface of SARS-CoV-2 omicron variants determined by hydrogen bond formation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in December 2019. As of mid-2021, the delta variant was the primary type; however, in January 2022, the omicron (BA.1) variant rapidly spread and became the dominant type in the United States. In June 2022, its subvariants surpassed previous variants in different temporal and spatial situations. To investigate the high transmissibility of omicron variants, we assessed the complex of spike protein 1 receptor-binding domain (S1RBD) and human angiotensin-converting enzyme 2 (hACE2) from the Protein Data Bank (6m0j, 7a91, 7mjn, 7v80, 7v84, 7v8b, 7wbl and 7xo9) and directly mutated specific amino acids to simulate several variants, including variants of concern (alpha, beta, gamma, delta), variants of interest (delta plus, epsilon, lambda, mu, mu without R346K) and omicron variants (BA.1, BA.2, BA.2.12.1, BA.4, BA.5). Molecular dynamics (MD) simulations for 100 ns under physiological conditions were then performed. We found that the omicron S1RBD-hACE2 complexes become more compact with increases in hydrogen-bond interactions at the interface, which is related to the transmissibility of SARS-CoV-2. Moreover, the relaxation time of hydrogen bonds is relatively short among the omicron variants, which implies that the interface conformation alterations are fast. From the molecular perspective, PHE486 and TYR501 in omicron S1RBDs need to involve hydrogen bonds and hydrophobic interactions on the interface. Our study provides structural features of the dominant variants that explain the evolution trend and their increased contagiousness and could thus also shed light on future variant changes.

Coexisting gastrointestinal and hepatobiliary tract anomalies in omphalocele and gastroschisis: A twenty-year experience in a single tertiary medical center

BACKGROUND

Omphalocele and gastroschisis are the two most common congenital abdominal wall defects; however, no previous study has focused on gastrointestinal and hepatobiliary tract malformations in these two conditions. This study aimed to investigate the demographic characteristics, coexisting congenital gastrointestinal and hepatobiliary tract anomalies, hospital course, and outcomes of patients with gastroschisis and omphalocele.

METHODS

This is retrospective chart review of all patients admitted to one tertiary medical center in Taiwan between January 1, 2000 and June 30, 2020 with a diagnosis of gastroschisis or omphalocele. The medical records were reviewed to obtain demographic data regarding coexisting gastrointestinal and hepatobiliary tract anomalies and outcomes.

RESULTS

Of the 51 patients included, 21 had gastroschisis and 30 had omphalocele. Gastroschisis was associated with a significantly younger maternal age and a higher incidence of small for gestational age. Of the 30 patients with omphalocele, twelve had associated gastrointestinal and hepatobiliary anomalies. Seven of the 21 patients with gastroschisis had gastrointestinal anomalies, and none had hepatobiliary anomalies. Among the omphalocele patients, three (10%) had documented malrotation, and one developed midgut volvulus. Among gastroschisis patients, four patients (19%) had malrotation, and two developed midgut volvulus. There were no statistically significant differences in postoperative complications or mortality rates between those with and without gastrointestinal/hepatobiliary tract anomalies.

CONCLUSION

The diversity of coexisting gastrointestinal and hepatobiliary tract anomalies is higher in the omphalocele than in gastroschisis. In addition, we demonstrate that patients with gastroschisis or omphalocele have a higher rate of intestinal malrotation and midgut volvulus.

Taiwan Controlled Substances Database

New psychoactive substances (NPS) have increasingly been illegally synthesized and used around the world in recent years. Due to the large volume and the variety of NPS, most do not have sufficient information about their addictive potential and harmful effects to human subjects. This makes it difficult to evaluate these potential substances of abuse. This study aims to build a database based on Taiwan's controlled substances, to provide quick structural and pharmacological feedback. Taiwan Controlled Substances Database (TCSD) includes the collection of controlled substances, relevant experimental and structural information, as well as computational features such as molecular fingerprints and descriptors. Two types of structural search were added: substructure search and topological fingerprint similarity search. A web framework was used to enhance accessibility and usability (https://cs2search.cmdm.tw).

Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors

Neural network (NN)-based protein modeling methods have improved significantly in recent years. Although the overall accuracy of the two non-homology-based modeling methods, AlphaFold and RoseTTAFold, is outstanding, their performance for specific protein families has remained unexamined. G-protein-coupled receptor (GPCR) proteins are particularly interesting since they are involved in numerous pathways. This work directly compares the performance of these novel deep learning-based protein modeling methods for GPCRs with the most widely used template-based software—Modeller. We collected the experimentally determined structures of 73 GPCRs from the Protein Data Bank. The official AlphaFold repository and RoseTTAFold web service were used with default settings to predict five structures of each protein sequence. The predicted models were then aligned with the experimentally solved structures and evaluated by the root-mean-square deviation (RMSD) metric. If only looking at each program’s top-scored structure, Modeller had the smallest average modeling RMSD of 2.17 Å, which is better than AlphaFold’s 5.53 Å and RoseTTAFold’s 6.28 Å, probably since Modeller already included many known structures as templates. However, the NN-based methods (AlphaFold and RoseTTAFold) outperformed Modeller in 21 and 15 out of the 73 cases with the top-scored model, respectively, where no good templates were available for Modeller. The larger RMSD values generated by the NN-based methods were primarily due to the differences in loop prediction compared to the crystal structures.

Exosomal Proteins and Lipids as Potential Biomarkers for Lung Cancer Diagnosis, Prognosis, and Treatment

Simple Summary

Exosomes (or extracellular vesicles) are known to mediate intercellular communication and to transmit molecular signals between cells. Molecules carried by exosomes have their own molecular roles in affecting surrounding and distant environment, as well as recipient cells. Molecular components of exosomes can be used as cancer biomarkers for diagnosis and prognosis, being promising therapeutic targets for the interruption of cellular signals. Therefore, the understanding of the molecular compositions and their functional indications of exosomes has the potential to help doctors to diagnose and monitor diseases and to allow researchers to design and develop potential targeted therapies. This review aims to provide a comprehensive protein and lipid characterization of lung cancer exosomes and to explore their molecular functions and mechanisms regulating physiological and pathological processes. This organization offers informative insight for lung cancer diagnosis and treatment.

Abstract

Exosomes participate in cell–cell communication by transferring molecular components between cells. Previous studies have shown that exosomal molecules derived from cancer cells and liquid biopsies can serve as biomarkers for cancer diagnosis and prognosis. The exploration of the molecules transferred by lung cancer-derived exosomes can advance the understanding of exosome-mediated signaling pathways and mechanisms. However, the molecular characterization and functional indications of exosomal proteins and lipids have not been comprehensively organized. This review thoroughly collected data concerning exosomal proteins and lipids from various lung cancer samples, including cancer cell lines and cancer patients. As potential diagnostic and prognostic biomarkers, exosomal proteins and lipids are available for clinical use in lung cancer. Potential therapeutic targets are mentioned for the future development of lung cancer therapy. Molecular functions implying their possible roles in exosome-mediated signaling are also discussed. Finally, we emphasized the importance and value of lung cancer stem cell-derived exosomes in lung cancer therapy. In summary, this review presents a comprehensive description of the protein and lipid composition and function of lung cancer-derived exosomes for lung cancer diagnosis, prognosis, and treatment.

Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power

The trade-off between a machine learning (ML) and deep learning (DL) model's predictability and its interpretability has been a rising concern in central nervous system-related quantitative structure-activity relationship (CNS-QSAR) analysis. Many state-of-the-art predictive modeling failed to provide structural insights due to their black box-like nature. Lack of interpretability and further to provide easy simple rules would be challenging for CNS-QSAR models. To address these issues, we develop a protocol to combine the power of ML and DL to generate a set of simple rules that are easy to interpret with high prediction power. A data set of 940 market drugs (315 CNS-active, 625 CNS-inactive) with support vector machine and graph convolutional network algorithms were used. Individual ML/DL modeling methods were also constructed for comparison. The performance of these models was evaluated using an additional external dataset of 117 market drugs (42 CNS-active, 75 CNS-inactive). Fingerprint-split validation was adopted to ensure model stringency and generalizability. The resulting novel hybrid ensemble model outperformed other constituent traditional QSAR models with an accuracy of 0.96 and an F1 score of 0.95. With the power of the interpretability provided with this protocol, our model laid down a set of simple physicochemical rules to determine whether a compound can be a CNS drug using six sub-structural features. These rules displayed higher classification ability than classical guidelines, with higher specificity and more mechanistic insights than just for blood-brain barrier permeability. This hybrid protocol can potentially be used for other drug property predictions.

Intelligent pharmaceutical patent search on a near-term gate-based quantum computer

Pharmaceutical patent analysis is the key to product protection for pharmaceutical companies. In patent claims, a Markush structure is a standard chemical structure drawing with variable substituents. Overlaps between apparently dissimilar Markush structures are nearly unrecognizable when the structures span a broad chemical space. We propose a quantum search-based method which performs an exact comparison between two non-enumerated Markush structures with a constraint satisfaction oracle. The quantum circuit is verified with a quantum simulator and the real effect of noise is estimated using a five-qubit superconductivity-based IBM quantum computer. The possibilities of measuring the correct states can be increased by improving the connectivity of the most computation intensive qubits. Depolarizing error is the most influential error. The quantum method to exactly compares two patents is hard to simulate classically and thus creates a quantum advantage in patent analysis.

A general optimization protocol for molecular property prediction using a deep learning network

The key to generating the best deep learning model for predicting molecular property is to test and apply various optimization methods. While individual optimization methods from different past works outside the pharmaceutical domain each succeeded in improving the model performance, better improvement may be achieved when specific combinations of these methods and practices are applied. In this work, three high-performance optimization methods in the literature that have been shown to dramatically improve model performance from other fields are used and discussed, eventually resulting in a general procedure for generating optimized CNN models on different properties of molecules. The three techniques are the dynamic batch size strategy for different enumeration ratios of the SMILES representation of compounds, Bayesian optimization for selecting the hyperparameters of a model and feature learning using chemical features obtained by a feedforward neural network, which are concatenated with the learned molecular feature vector. A total of seven different molecular properties (water solubility, lipophilicity, hydration energy, electronic properties, blood-brain barrier permeability and inhibition) are used. We demonstrate how each of the three techniques can affect the model and how the best model can generally benefit from using Bayesian optimization combined with dynamic batch size tuning.

Human Breathomics Database

Breathomics is a special branch of metabolomics that quantifies volatile organic compounds (VOCs) from collected exhaled breath samples. Understanding how breath molecules are related to diseases, mechanisms and pathways identified from experimental analytical measurements is challenging due to the lack of an organized resource describing breath molecules, related references and biomedical information embedded in the literature. To provide breath VOCs, related references and biomedical information, we aim to organize a database composed of manually curated information and automatically extracted biomedical information. First, VOCs-related disease information was manually organized from 207 literature linked to 99 VOCs and known Medical Subject Headings (MeSH) terms. Then an automated text mining algorithm was used to extract biomedical information from this literature. In the end, the manually curated information and auto-extracted biomedical information was combined to form a breath molecule database—the Human Breathomics Database (HBDB). We first manually curated and organized disease information including MeSH term from 207 literatures associated with 99 VOCs. Then, an automatic pipeline of text mining approach was used to collect 2766 literatures and extract biomedical information from breath researches. We combined curated information with automatically extracted biomedical information to assemble a breath molecule database, the HBDB. The HBDB is a database that includes references, VOCs and diseases associated with human breathomics. Most of these VOCs were detected in human breath samples or exhaled breath condensate samples. So far, the database contains a total of 913 VOCs in relation to human exhaled breath researches reported in 2766 publications. The HBDB is the most comprehensive HBDB of VOCs in human exhaled breath to date. It is a useful and organized resource for researchers and clinicians to identify and further investigate potential biomarkers from the breath of patients.

Database URL: https://hbdb.cmdm.tw

Different molecular enumeration influences in deep learning: an example using aqueous solubility

Aqueous solubility is the key property driving many chemical and biological phenomena and impacts experimental and computational attempts to assess those phenomena. Accurate prediction of solubility is essential and challenging, even with modern computational algorithms. Fingerprint-based, feature-based and molecular graph-based representations have all been used with different deep learning methods for aqueous solubility prediction. It has been clearly demonstrated that different molecular representations impact the model prediction and explainability. In this work, we reviewed different representations and also focused on using graph and line notations for modeling. In general, one canonical chemical structure is used to represent one molecule when computing its properties. We carefully examined the commonly used simplified molecular-input line-entry specification (SMILES) notation representing a single molecule and proposed to use the full enumerations in SMILES to achieve better accuracy. A convolutional neural network (CNN) was used. The full enumeration of SMILES can improve the presentation of a molecule and describe the molecule with all possible angles. This CNN model can be very robust when dealing with large datasets since no additional explicit chemistry knowledge is necessary to predict the solubility. Also, traditionally it is hard to use a neural network to explain the contribution of chemical substructures to a single property. We demonstrated the use of attention in the decoding network to detect the part of a molecule that is relevant to solubility, which can be used to explain the contribution from the CNN.

IntelliPatent: a web-based intelligent system for fast chemical patent claim drafting

The first step of automating composition patent drafting is to draft the claims around a Markush structure with substituents. Currently, this process depends heavily on experienced attorneys or patent agents, and few tools are available. IntelliPatent was created to accelerate this process. Users can simply upload a series of analogs of interest, and IntelliPatent will automatically extract the general structural scaffold and generate the patent claim text. The program can also extend the patent claim by adding commonly seen R groups from historical lists of the top 30 selling drugs in the US for all R substituents. The program takes MDL SD file formats as inputs, and the invariable core structure and variable substructures will be identified as the initial scaffold and R groups in the output Markush structure. The results can be downloaded in MS Word format (.docx). The suggested claims can be quickly generated with IntelliPatent. This web-based tool is freely accessible at https://intellipatent.cmdm.tw/.

LipidPedia: a comprehensive lipid knowledgebase

Motivation

Lipids are divided into fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, saccharolipids, sterols, prenol lipids and polyketides. Fatty acyls and glycerolipids are commonly used as energy storage, whereas glycerophospholipids, sphingolipids, sterols and saccharolipids are common used as components of cell membranes. Lipids in fatty acyls, glycerophospholipids, sphingolipids and sterols classes play important roles in signaling. Although more than 36 million lipids can be identified or computationally generated, no single lipid database provides comprehensive information on lipids. Furthermore, the complex systematic or common names of lipids make the discovery of related information challenging.

Results

Here, we present LipidPedia, a comprehensive lipid knowledgebase. The content of this database is derived from integrating annotation data with full-text mining of 3923 lipids and more than 400 000 annotations of associated diseases, pathways, functions and locations that are essential for interpreting lipid functions and mechanisms from over 1 400 000 scientific publications. Each lipid in LipidPedia also has its own entry containing a text summary curated from the most frequently cited diseases, pathways, genes, locations, functions, lipids and experimental models in the biomedical literature. LipidPedia aims to provide an overall synopsis of lipids to summarize lipid annotations and provide a detailed listing of references for understanding complex lipid functions and mechanisms.

Availability and implementation

LipidPedia is available at http://lipidpedia.cmdm.tw.

Supplementary information

Supplementary data are available at Bioinformatics online.

Effects of sarcosine and N, N-dimethylglycine on NMDA receptor-mediated excitatory field potentials

Background

Sarcosine, a glycine transporter type 1 inhibitor and an N-methyl-D-aspartate (NMDA) receptor co-agonist at the glycine binding site, potentiates NMDA receptor function. Structurally similar to sarcosine, N,N-dimethylglycine (DMG) is also N-methyl glycine-derivative amino acid and commonly used as a dietary supplement. The present study compared the effects of sarcosine and DMG on NMDA receptor-mediated excitatory field potentials (EFPs) in mouse medial prefrontal cortex brain slices using a multi-electrode array system.

Results

Glycine, sarcosine and DMG alone did not alter the NMDA receptor-mediated EFPs, but in combination with glutamate, glycine and its N-methyl derivatives significantly increased the frequency and amplitude of EFPs. The enhancing effects of glycine analogs in combination with glutamate on EFPs were remarkably reduced by the glycine binding site antagonist 7-chlorokynurenate (7-CK). However, DMG, but not sarcosine, reduced the frequency and amplitude of EFPs elicited by co-application of glutamate plus glycine. D-cycloserine, a partial agonist at the glycine binding site on NMDA receptors, affected EFPs in a similar manner to DMG. Furthermore, DMG, but not sarcosine, reduced the frequencies and amplitudes of EFPs elicited by glutamate plus D-serine, another endogenous ligand for glycine binding site.

Conclusions

These findings suggest that sarcosine acts as a full agonist, yet DMG is a partial agonist at glycine binding site of NMDA receptors. The molecular docking analysis indicated that the interactions of glycine, sarcosine, and DMG to NMDA receptors are highly similar, supporting that the glycine binding site of NMDA receptors is a critical target site for sarcosine and DMG.

Electronic supplementary material

The online version of this article (doi:10.1186/s12929-016-0314-8) contains supplementary material, which is available to authorized users.

A novel thromboxane receptor antagonist, nstpbp5185, inhibits platelet aggregation and thrombus formation in animal models

A novel benzimidazole derivative, nstpbp5185, was discovered through in vitro and in vivo evaluations for antiplatelet activity. Thromaboxane receptor (TP) is important in vascular physiology, haemostasis and pathophysiological thrombosis. Nstpbp5185 concentration-dependently inhibited human platelet aggregation caused by collagen, arachidonic acid and U46619. Nstpbp5185 caused a right-shift of the concentration-response curve of U46619 and competitively inhibited the binding of 3H-SQ-29548 to TP receptor expressed on HEK-293 cells, with an IC50 of 0.1 µM, indicating that nstpbp5185 is a TP antagonist. In murine thrombosis models, nstpbp5185 significantly prolonged the latent period in triggering platelet plug formation in mesenteric and FeCl3-induced thrombi formation, and increased the survival rate in pulmonary embolism model with less bleeding than aspirin. This study suggests nstpbp5185, an orally selective anti-thrombotic agent, acting through blockade of TXA2 receptor, may be efficacious for prevention or treatment of pathologic thrombosis.

Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models

Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted from the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints.

3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data

Background

Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment.

Results

3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for all transcripts, proteins and metabolites. If only two of three omics datasets are input, then 3Omics supplements the missing transcript, protein or metabolite information related to the input data by text-mining the PubMed database. 3Omics’ coexpression analysis assists in revealing functions shared among different omics datasets. 3Omics’ phenotype analysis integrates Online Mendelian Inheritance in Man with available transcript or protein data. Pathway enrichment analysis on metabolomics data by 3Omics reveals enriched pathways in the KEGG/HumanCyc database. 3Omics performs statistical Gene Ontology-based functional enrichment analyses to display significantly overrepresented GO terms in transcriptomic experiments. Although the principal application of 3Omics is the integration of multiple omics datasets, it is also capable of analyzing individual omics datasets. The information obtained from the analyses of 3Omics in Case Studies 1 and 2 are also in accordance with comprehensive findings in the literature.

Conclusions

3Omics incorporates the advantages and functionality of existing software into a single platform, thereby simplifying data analysis and enabling the user to perform a one-click integrated analysis. Visualization and analysis results are downloadable for further user customization and analysis. The 3Omics software can be freely accessed at http://3omics.cmdm.tw.

Metabolomic characterization of rhubarb species by capillary electrophoresis and ultra-high-pressure liquid chromatography

This study developed CE and ultra-high-pressure LC (UHPLC) methods coupled with UV detectors to characterize the metabolomic profiles of different rhubarb species. The optimal CE conditions used a BGE with 15 mM sodium tetraborate, 15 mM sodium dihydrogen phosphate monohydrate, 30 mM sodium deoxycholate, and 30% ACN v/v at pH 8.3. The optimal UHPLC conditions used a mobile phase composed of 0.05% phosphate buffer and ACN with gradient elution. The gradient profile increased linearly from 10 to 21% ACN within the first 25 min, then increased to 33% ACN for the next 10 min. It took another 5 min to reach the 65% ACN, then for the next 5 min, it stayed unchanged. Sixteen samples of Rheum officinale and Rheum tanguticum collected from various locations were analyzed by CE and UHPLC methods. The metabolite profiles of CE were aligned and baseline corrected before chemometric analysis. Metabolomic signatures of rhubarb species from CE and UHPLC were clustered using principle component analysis and distance-based redundancy analysis; the clusters were not only able to discriminate different species but also different cultivation regions. Similarity measurements were performed by calculating the correlation coefficient of each sample with the authentic samples. Hybrid rhizome was clearly identified through similarity measurement of UHPLC metabolite profile and later confirmed by gene sequencing. The present study demonstrated that CE and UHPLC are efficient and effective tools to identify and authenticate herbs even coupled with simple detectors.

Plasma metabolomic profiles predict near-term death among individuals with lower extremity peripheral arterial disease

BACKGROUND

Individuals with peripheral arterial disease (PAD) have a nearly two-fold increased risk of all-cause and cardiovascular disease mortality compared to those without PAD. This pilot study determined whether metabolomic profiling can accurately identify patients with PAD who are at increased risk of near-term mortality.

METHODS

We completed a case-control study using (1)H NMR metabolomic profiling of plasma from 20 decedents with PAD, without critical limb ischemia, who had blood drawn within 8 months prior to death (index blood draw) and within 10 to 28 months prior to death (preindex blood draw). Twenty-one PAD participants who survived more than 30 months after their index blood draw served as a control population.

RESULTS

Results showed distinct metabolomic patterns between preindex decedent, index decedent, and survivor samples. The major chemical signals contributing to the differential pattern (between survivors and decedents) arose from the fatty acyl chain protons of lipoproteins and the choline head group protons of phospholipids. Using the top 40 chemical signals for which the intensity was most distinct between survivor and preindex decedent samples, classification models predicted near-term all-cause death with overall accuracy of 78% (32/41), a sensitivity of 85% (17/20), and a specificity of 71% (15/21). When comparing survivor with index decedent samples, the overall classification accuracy was optimal at 83% (34/41) with a sensitivity of 80% (16/20) and a specificity of 86% (18/21), using as few as the top 10 to 20 chemical signals.

CONCLUSIONS

Our results suggest that metabolomic profiling of plasma may be useful for identifying PAD patients at increased risk for near-term death. Larger studies using more sensitive metabolomic techniques are needed to identify specific metabolic pathways associated with increased risk of near-term all-cause mortality among PAD patients.

Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data

Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of tumor hypoxia. Here, we investigate the temporal change of the metabolite composition with gene expression data from literature to provide a more comprehensive insight into the system level in response to hypoxia. Nuclear magnetic resonance spectroscopy was used to perform metabolomic profiling on the MDA-MB-231 breast cancer cell line under hypoxic conditions. Multivariate statistical analysis revealed that the metabolic difference between hypoxia and normoxia was similar over 24 h, but became distinct over 48 h. Time dependent microarray data from the same cell line in the literature displayed different gene expressions under hypoxic and normoxic conditions mostly at 12 h or earlier. The direct metabolomic profiles show a large overlap with theoretical metabolic profiles deduced from previous transcriptomic studies. Consistent pathways are glycolysis/gluconeogenesis, pyruvate, purine and arginine and proline metabolism. Ten metabolic pathways revealed by metabolomics were not covered by the downstream of the known transcriptomic profiles, suggesting new metabolic phenotypes. These results confirm previous transcriptomics understanding and expand the knowledge from existing models on correlation and co-regulation between transcriptomic and metabolomics profiles, which demonstrates the power of integrated omics analysis.

Prediction and Mechanistic Interpretation of Human Oral Drug Absorption Using MI-QSAR Analysis

Membrane-interaction [MI]-QSAR analysis, which includes descriptors explicitly derived from simulations of solutes [drugs] interacting with phospholipid membrane models, was used to construct QSAR models for human oral intestinal drug absorption. A data set of 188 compounds, which are mainly drugs, was divided into a parent training set of 164 compounds and a test set of 24 compounds. Stable, but not highly fit [R2 = 0.68] MI-QSAR models could be built for all 188 compounds. However, the relatively large number [47] of drugs having 100% absorption, as well as all zwitterionic compounds [11], had to be eliminated from the training set in order to construct a linear five-term oral absorption diffusion model for 106 compounds which was both stable [R2 = 0.82, Q2 = 0.79] and predictive given the test set compounds were predicted with nearly the same average accuracy as the compounds of the training set. Intermolecular membrane-solute descriptors are essential to building good oral absorption models, and these intermolecular descriptors are displaced in model optimizations and intramolecular solute descriptors found in published oral absorption QSAR models. A general form for all of the oral intestinal absorption MI-QSAR models has three classes of descriptors indicative of three thermodynamic processes: (1) solubility and partitioning, (2) membrane-solute interactions, and (3) flexibility of the solute and/or membrane. The intestinal oral absorption MI-QSAR models were compared to MI-QSAR models previously developed for Caco-2 cell permeation and for blood-brain barrier penetration. The MI-QSAR models for all three of these ADME endpoints share several common descriptors, and suggest a common mechanism of transport across all three barriers. A further analysis of these three types of MI-QSAR models has been done to identify descriptor-term differences across these three models, and the corresponding differences in thermodynamic transport behavior of the three barriers.

4D-fingerprints, universal QSAR and QSPR descriptors

An elusive goal in the field of chemoinformatics and molecular modeling has been the generation of a set of descriptors that, once calculated for a molecule, may be used in a wide variety of applications. Since such universal descriptors are generated free from external constraints, they are inherently independent of the data set in which they are employed. The realization of a set of universal descriptors would significantly streamline such chemoinformatics tasks as virtual high-throughout screening (VHTS) and toxicity profiling. The current study reports the derivation and validation of a potential set of universal descriptors, referred to as the 4D-fingerprints. The 4D-fingerprints are derived from the 4D-molecular similarity analysis. To evaluate the applicability of the 4D-fingerprints as universal descriptors, they are used to generate descriptive QSAR models for 5 independent training sets. Each of the training sets has been analyzed previously by several varying QSAR methods, and the results of the models generated using the 4D-fingerprints are compared to the results of the previous QSAR analyses. It was found that the models generated using the 4D-fingerprints are comparable in quality, based on statistical measures of fit and test set prediction, to the previously reported models for the other QSAR methods. This finding is particularly significant considering the 4D-fingerprints are generated independent of external constraints such as alignment, while the QSAR methods used for comparison all require an alignment analysis.

Selective cytotoxicity of azatyrosinamides against ras-transformed NIH 3T3 cells

This study aims to develop novel azatyrosinamide compounds structurally modified from ras-specific antioncogenic azatyrosine. Analogues 4-15 were prepared and their inhibition on the growth of wild-type and ras-transformed NIH 3T3 cell lines was compared. Compound 12 was found to be the most active with IC50 16.5+/-2.2 microM which is 458-fold more potent than that of azatyrosine. The selective toxicity, defined as IC(50 wild-type)/IC(50 ras-transformed) for this compound was 138.5.

A least-squares error minimization approach in the determination of ferric ion diffusion coefficient of Fricke-infused dosimeter gels

A least-squares error minimization approach was adopted to assess ferric ion diffusion coefficient of Fricke-agarose gels. Ferric ion diffusion process was modeled as a Gaussian-shaped degradation kernel operating on an initial concentration distribution. Diffusion coefficient was iteratively determined by minimizing the error function defined as the difference between the theoretically calculated and the experimentally measured dose distributions. A rapid MR image-based differential gel dosimetry technique that time resolves the evolution of the ferric ion diffusion process minimizes smearing of the dose distribution. Our results showed that for a Fricke-agarose gel contained 1 mM ammonium ferrous sulfate, 1% agarose, 1 mM sodium chloride, and 50 mM sulfuric acid, its ferric ion diffusion coefficient is (1.59 +/- 0.28) x 10(-2) cm2 h(-1) at room temperature. This value falls within the 1.00-2.00 x 10(-2) cm2 h(-1) range previously reported under varying gelling ingredients and concentrations. This method allows a quick, nondestructive evaluation of the ferric ion diffusion coefficient that can be used in conjunction with the in situ gel dosimetry experiment to provide a practical diffusion characterization of the dosimeter gel.

The role of dose distribution gradient in the observed ferric ion diffusion time scale in MRI-Fricke-infused gel dosimetry

Ferric ion diffusion is a detrimental factor in MRI-Fricke-infused gel dosimetry. In this study, a novel approach involving MR image subtraction and a fast image-based dosimetry technique to study ferric ion diffusion effects is presented. The fast image-based approach allows studying dose profile degradation within minutes post-irradiation. The relationship between the rate of dose profile deterioration and dose distribution gradients can be elucidated with the improved imaging temporal resolution also. Our results showed that for a dose distribution with gradient 4 Gy/mm or higher, ferric ion diffusion causes apparent dose profile degradation in 0.5-1 h post-irradiation. For a gradual dose gradient change of 2.1 Gy/mm or smaller, dose profile degradation appears insignificant for a two-hour elapsed diffusion time. These observations agree well with the theoretical analysis of a square dependence between dose profile degradation and dose distribution gradient. Because all stereotactic radiosurgery procedures produce steep dose distributions and because the ideal "snapshot" of MR scanning cannot be achieved, knowledge of the ferric ion diffusion time scale is important in experimental designs in order to avoid potential measurement errors in MRI-Fricke-agarose gel dosimetry.

Seminal plasma zinc levels and sperm motion characteristics in infertile samples

BACKGROUND

Zinc (Zn) in seminal plasma stabilizes the cell membrane and nuclear chromatin of spermatozoa. It may also have an antibacterial function. However, extremely high concentrations of Zn (10 to 100 x the normal range) may inhibit sperm motility and the function of the mannose receptor on the sperm head. In this study, we analyzed the correlation between Zn levels in seminal plasma and the characteristics of semen as measured by conventional and computer aided sperm analysis (CASA).

METHODS

One hundred fifteen infertile couples were recruited for conventional semen analysis and CASA from December 1995 through January 1996, and Zn levels in semen samples were determined by flame atomic absorption spectroscopy (AAS).

RESULTS

A good correlation in a positive direction (r = 0.73, p = 0.0001) was noted between the total amount of Zn per ejaculate and the Zn concentration. The Zn concentration in seminal plasma was negatively correlated with the seminal pH (r = -0.35, p = 0.0081). There was no significant correlation between the total amount of Zn per ejaculate and sperm characteristics, including sperm count, motility (% sperm count), progressive motility (% motility), rapid motility (% motility), average path velocity (VAP, microns/s), straight-line velocity (VSL, microns/s), curvilinear velocity (VCL, microns/s), amplitude of lateral head displacement (ALH, microns), beat/cross frequency (BCF, beats/s), straightness (STR), and linearity (LIN). There was also no significant correlation between the Zn concentration in seminal plasma and the above sperm characteristics.

CONCLUSION

The characteristics of semen as determined by conventional semen analysis or CASA bore no correlation with total seminal Zn amount or Zn concentrations in the ejaculates. Routine determination of the Zn concentration in seminal plasma offers no advantages in infertility work-ups.

Carcinosarcoma of the renal pelvis: a case report with immunohistochemical study

The clinicopathologic feature of one carcinosarcoma of the renal pelvis is reported. The tumor occurred in a 51-year-old woman with a long standing history of renal calculi. The epithelial component was consistent with squamous cell carcinoma, whereas the sarcomatous component was composed of pleomorphic spindle cells. The immunohistochemical studies demonstrated obvious epithelial and mesenchymal reactivity respectively. The tumor progressed rapidly with widespread metastases and the patient died one month after operation.