scholarly journals Comprehensive mass spectrometry-guided plant specialized metabolite phenotyping reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae

2018 ◽  
Author(s):  
Kyo Bin Kang ◽  
Madeleine Ernst ◽  
Justin J. J. van der Hooft ◽  
Ricardo R. da Silva ◽  
Junha Park ◽  
...  
Keyword(s):  
Large Scale ◽  
Biosynthetic Pathway ◽  
Flavonoid Glycosides ◽  
Plant Family ◽  
Specialized Metabolites ◽  
Chemical Structures ◽  
Automated Annotation ◽  
Molecular Features ◽  
Evolutionary Context ◽  
Metabolite Diversity

SUMMARYPlants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped specialized metabolite diversity, which drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating specialized metabolite diversity in an evolutionary context have been limited by the impossibility to identify chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here, we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites, and apply it for over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5 % based on spectral library matches alone to 42.6 % of total MS/MS molecular features extending annotations from well-known plant compound classes into the dark plant metabolomics matter. To gain insights in substructural diversity within the plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations towards the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.

scholarly journals Cancer metabolic subtypes and their association with molecular and clinical features

2021 ◽  
Author(s):  
Enrico Moiso ◽  
Paolo Provero
Keyword(s):  
Clinical Data ◽  
Large Scale ◽  
Tumor Grade ◽  
Point Of View ◽  
General Point ◽  
Protein Abundance ◽  
Phenotypic Data ◽  
Cancer Subtypes ◽  
Molecular Features

Alteration of metabolic pathways in cancer has been investigated for many years, beginning way before the discovery of the role of oncogenes and tumor suppressors, and the last few years have witnessed a renewed interest in this topic. Large-scale molecular and clinical data on tens of thousands of samples allow us today to tackle the problem from a general point of view. Here we show that trancriptomic profiles of tumors can be exploited to define metabolic cancer subtypes, that can be systematically investigated for association with other molecular and clinical data. We find thousands of significant associations between metabolic subtypes and molecular features such as somatic mutations, structural variants, epigenetic modifications, protein abundance and activation; and with clinical/phenotypic data including survival probability, tumor grade, and histological types. Our work provides a methodological framework and a rich database of statistical associations, accessible from https://metaminer.unito.it, that will contribute to the understanding of the role of metabolic alterations in cancer and to the development of precision therapeutic strategies.

scholarly journals Inductive Transfer Learning for Molecular Activity Prediction: Next-Gen QSAR Models with MolPMoFiT

2020 ◽  
Author(s):  
Xinhao Li ◽  
Denis Fourches
Keyword(s):  
Transfer Learning ◽  
High Throughput Screening ◽  
Structure Prediction ◽  
Large Scale ◽  
High Reliability ◽  
Structural Features ◽  
Fine Tuning ◽  
Qsar Modeling ◽  
Chemical Structures ◽  
Effective Transfer

<p>Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific structural features and ensure reasonable prediction accuracy. Even though large datasets are becoming the new normal in drug discovery, especially when it comes to high-throughput screening or metabolomics datasets, one should also consider smaller datasets with challenging endpoints to model and forecast. Thus, it would be highly relevant to better utilize the tremendous compendium of unlabeled compounds from publicly-available datasets for improving the model performances for the user’s particular series of compounds. In this study, we propose the <b>Mol</b>ecular <b>P</b>rediction <b>Mo</b>del <b>Fi</b>ne-<b>T</b>uning (<b>MolPMoFiT</b>) approach, an effective transfer learning method based on self-supervised pre-training + task-specific fine-tuning for QSPR/QSAR modeling. A large-scale molecular structure prediction model is pre-trained using one million unlabeled molecules from ChEMBL in a self-supervised learning manner, and can then be fine-tuned on various QSPR/QSAR tasks for smaller chemical datasets with specific endpoints. Herein, the method is evaluated on four benchmark datasets (lipophilicity, FreeSolv, HIV, and blood-brain barrier penetration). The results showed the method can achieve strong performances for all four datasets compared to other state-of-the-art machine learning modeling techniques reported in the literature so far. <br></p>

scholarly journals The cardiac nanoenvironment: form and function at the nanoscale

2021 ◽  
Author(s):  
Jashan P. Singh ◽  
Jennifer L. Young
Keyword(s):  
Large Scale ◽  
New Materials ◽  
Length Scales ◽  
Form And Function ◽  
Molecular Features ◽  
Wide Range ◽  
Physiological Homeostasis ◽  
Organ Level ◽  
And Function

AbstractMechanical forces in the cardiovascular system occur over a wide range of length scales. At the whole organ level, large scale forces drive the beating heart as a synergistic unit. On the microscale, individual cells and their surrounding extracellular matrix (ECM) exhibit dynamic reciprocity, with mechanical feedback moving bidirectionally. Finally, in the nanometer regime, molecular features of cells and the ECM show remarkable sensitivity to mechanical cues. While small, these nanoscale properties are in many cases directly responsible for the mechanosensitive signaling processes that elicit cellular outcomes. Given the inherent challenges in observing, quantifying, and reconstituting this nanoscale environment, it is not surprising that this landscape has been understudied compared to larger length scales. Here, we aim to shine light upon the cardiac nanoenvironment, which plays a crucial role in maintaining physiological homeostasis while also underlying pathological processes. Thus, we will highlight strategies aimed at (1) elucidating the nanoscale components of the cardiac matrix, and (2) designing new materials and biosystems capable of mimicking these features in vitro.

scholarly journals OncoThreads: Visualization of Large Scale Longitudinal Cancer Molecular Data

2021 ◽  
Author(s):  
Theresa A Harbig ◽  
Sabrina Nusrat ◽  
Tali Mazor ◽  
Qianwen Wang ◽  
Alexander Thomson ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Large Scale ◽  
Cancer Genomics ◽  
Temporal Patterns ◽  
Molecular Data ◽  
Liquid Biopsies ◽  
Sequencing Technologies ◽  
Molecular Features ◽  
Wide Range ◽  
The Impact

Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, e.g. emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2-4 timepoints. OncoThreads is freely available at http://oncothreads.gehlenborglab.org and implemented in Javascript using the cBioPortal web API as a backend.

scholarly journals Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i516-i524
Author(s):  
Midori Iida ◽  
Michio Iwata ◽  
Yoshihiro Yamanishi
Keyword(s):  
Large Scale ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Molecular Networks ◽  
Supplementary Information ◽  
New Associations ◽  
Disease States ◽  
Molecular Features ◽  
Novel Approach

Abstract Motivation Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease–disease relationships remain uncharacterized. Results In this study, we proposed a novel approach for network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease–disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Bioactive Flavonoid Glycosides and HPLC and UPLC Quantification of Commercial Astragali Complanati Semen

Molecules ◽  
2020 ◽  
Vol 25 (20) ◽  
pp. 4762
Author(s):  
Jenny Chun-Ling Kuo ◽  
Li-Jie Zhang ◽  
Hung-Tse Huang ◽  
Chia-Ching Liaw ◽  
Zhi-Hu Lin ◽  
...  
Keyword(s):  
Ethanol Extract ◽  
2D Nmr ◽  
Biological Evaluation ◽  
Uv Spectra ◽  
Flavonoid Glycosides ◽  
No Production ◽  
Chemical Structures ◽  
Isoflavone Glycoside ◽  
Two Samples ◽  
Hplc Profiles

Eleven compounds, including nine known flavonoid glycosides (1–4, 6–8, and 10–11), one isoflavone glycoside (5), and a glansreginic acid (9), were isolated from the 80% ethanol extract of commercial Astragali Complanati Semen (ACS). All chemical structures were determined by spectroscopic analyses, including 1D and 2D NMR. Compounds 2, 4, 5, 6, 9, and 10 were isolated and identified from the title plant for the first time. Biological evaluation revealed that all the isolates showed promising anti-NO production, and 1, 2, 3, and 8 were more potent in antioxidant activity than vitamin E. The major peaks in the UPLC and HPLC profiles identified their chemical structures by comparing their retention time and UV spectra with those of the reference substances. Furthermore, nine of the eleven samples collected from North, Middle, and South regions of Taiwan possessed similar HPLC fingerprints and were identified as Astragali Complanati Semen, whereas the other two samples from southern Taiwan would be the adulterants due to the different fingerprinting patterns. In addition, an HPLC-UV method was employed to determine the content of target compound complanatuside (11) with good linear regression (R2 = 0.9998) for ACS in the Taiwanese market. Of the isolates, flavonol glycosides 1 and 3 were the major peaks in HPLC/UPLC, and showed more potent antioxidant and anti-NO production activities than that of 11, revealing that these compounds can be the available agents for the quality control of ACS.

scholarly journals Chemical Impurities: An Epistemological Riddle with Serious Side Effects

2020 ◽  
Vol 17 (3) ◽  
pp. 1030
Author(s):  
Ahmad Yaman Abdin ◽  
Prince Yeboah ◽  
Claus Jacob
Keyword(s):  
Side Effects ◽  
Large Scale ◽  
Biological Activities ◽  
Pharmaceutical Research ◽  
Pharmaceutical Products ◽  
Regulatory Agencies ◽  
Chemical Structures ◽  
Chemical Impurities ◽  
Large Scale Manufacture ◽  
Modern Analytical

Chemical synthesis is a science and an art. Rooted in laboratory or large-scale manufacture, it results in certain side products, eventually compromising the integrity of the final products. Such “impurities” occur in small amounts and, within chemistry itself, are of little concern. In pharmacy, in contrast, impurities increase the potential for toxicity, side effects, and serious implications for human health and the environment. The pharmaceutical regulatory agencies have therefore developed regulatory and strategic systems to minimize the chemical presence or biological impact of such substances. Here, pharmaceuticals are turned from impure into more defined materials as part of a complex socio-technological system revolving around and constantly evolving its specific rules and regulations. Whilst modern analytical methods indicate the presence of impurities, the interpretations of corresponding results are gated by risk management and agreed thresholds. Ironically, this allows for entities with no identified chemical structures, and hence epistemologically outside chemistry, to be regulated in pharmaceutical products. We will refer to such substances which are not, epistemologically speaking, “chemicals” as Xpurities, in order to distinguish them from recognized and identified impurities. The presence of such Xpurities is surprisingly common and constitutes a major issue in pharmaceutical research and practice. We propose a Space of Information to deal with such impurities based on values regarding the presence, chemical identities, and biological activities. It is anticipated that this may enable pharmacists to handle such Xpurities more efficiently.

scholarly journals Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 1535 ◽  
Author(s):  
Francisco Azuaje ◽  
Sang-Yoon Kim ◽  
Daniel Perez Hernandez ◽  
Gunnar Dittmar
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Diagnostic Value ◽  
Classification Model ◽  
Clinical Approach ◽  
Proteomics Data ◽  
Cell Renal Cell Carcinoma ◽  
Molecular Features ◽  
Genes Encoding ◽  
New Research

Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene–protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types.

scholarly journals Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE)

BMC Genomics ◽  
2019 ◽  
Vol 20 (S11) ◽  
Author(s):  
Tianle Ma ◽  
Aidong Zhang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Domain Knowledge ◽  
Large Scale ◽  
Learning Model ◽  
Interaction Networks ◽  
Omics Data ◽  
Biological Interaction ◽  
Molecular Features ◽  
Training Objective

Abstract Background Comprehensive molecular profiling of various cancers and other diseases has generated vast amounts of multi-omics data. Each type of -omics data corresponds to one feature space, such as gene expression, miRNA expression, DNA methylation, etc. Integrating multi-omics data can link different layers of molecular feature spaces and is crucial to elucidate molecular pathways underlying various diseases. Machine learning approaches to mining multi-omics data hold great promises in uncovering intricate relationships among molecular features. However, due to the “big p, small n” problem (i.e., small sample sizes with high-dimensional features), training a large-scale generalizable deep learning model with multi-omics data alone is very challenging. Results We developed a method called Multi-view Factorization AutoEncoder (MAE) with network constraints that can seamlessly integrate multi-omics data and domain knowledge such as molecular interaction networks. Our method learns feature and patient embeddings simultaneously with deep representation learning. Both feature representations and patient representations are subject to certain constraints specified as regularization terms in the training objective. By incorporating domain knowledge into the training objective, we implicitly introduced a good inductive bias into the machine learning model, which helps improve model generalizability. We performed extensive experiments on the TCGA datasets and demonstrated the power of integrating multi-omics data and biological interaction networks using our proposed method for predicting target clinical variables. Conclusions To alleviate the overfitting problem in deep learning on multi-omics data with the “big p, small n” problem, it is helpful to incorporate biological domain knowledge into the model as inductive biases. It is very promising to design machine learning models that facilitate the seamless integration of large-scale multi-omics data and biomedical domain knowledge for uncovering intricate relationships among molecular features and clinical features.

Non-Hodgkin's Lymphoma: Molecular Features of B Cell Lymphoma

Hematology ◽  
2000 ◽  
Vol 2000 (1) ◽  
pp. 180-204
Author(s):  
Elizabeth Macintyre ◽  
Dennis Willerford ◽  
Stephan W. Morris
Keyword(s):  
B Cell ◽  
Large Scale ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Fusion Transcript ◽  
Molecular Techniques ◽  
Molecular Abnormalities ◽  
Lymphoid Neoplasia ◽  
Molecular Features ◽  
Hodgkin's Lymphomas

The rapid increase in the incidence of the B cell non-Hodgkin's lymphomas (NHL) and improved understanding of the mechanisms involved in their development renders timely a review of the theoretical and practical aspects of molecular abnormalities in B cell NHL. In Section I, Dr. Macintyre addresses the practical aspects of the use of molecular techniques for the diagnosis and therapeutic management of patients with B cell NHL. While detection of clonal Ig rearrangements is widely used to distinguish reactive from malignant lymphoproliferative disorders, molecular informativity is variable. The relative roles of cytogenetic, molecular and immunological techniques in the detection of genetic abnormalities and their protein products varies with the clinical situation. Consequently, the role of molecular analysis relative to morphological classification is evolving. Integrated diagnostic services are best equipped to cope with these changes. Recent evidence that large scale gene expression profiling allows improved prognostic stratification of diffuse large cell lymphoma suggests that the choice of diagnostic techniques will continue to change significantly and rapidly. In Section II, Dr. Willerford reviews current understanding of the mechanisms involved in immunoglobulin (Ig) gene rearrangement during B lymphoid development and the way in which these processes may contribute to Ig-locus chromosome translocations in lymphoma. Recent insights into the regulation of Ig gene diversification indicate that genetic plasticity in B lymphocytes is much greater than previously suspected. Physiological genomic instability, which may include isotype switching, recombination revision and somatic mutation, occurs in germinal centers in the context of immune responses and may explain longstanding clinical observations that link immunity and lymphoid neoplasia. Data from murine models and human disorders predisposing to NHL have been used to illustrate these issues. In Section III, Dr. Morris reviews the characteristics and consequences of deregulation of novel “proto-oncogenes” involved in B cell NHL, including PAX5 (chromosome 9p 13), BCL8 (15q11-q13), BCL9, MUC1, FcγRIIB and other 1q21-q22 genes and BCL10 (1p22). The AP12-MLT/MALT1 [t(11;18)(q21;q21)] fusion transcript is also described.

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