Genes2GO: A web application for querying gene sets for specific GO terms.

Bioinformation. 2016;12(3):231-232

Authors: Chawla K, Kuiper M

Abstract
Gene ontology annotations have become an essential resource for biological interpretations of experimental findings. The process of gathering basic annotation information in tables that link gene sets with specific gene ontology terms can be cumbersome, in particular if it requires above average computer skills or bioinformatics expertise. We have therefore developed Genes2GO, an intuitive R-based web application. Genes2GO uses the biomaRt package of Bioconductor in order to retrieve custom sets of gene ontology annotations for any list of genes from organisms covered by the Ensembl database. Genes2GO produces a binary matrix file, indicating for each gene the presence or absence of specific annotations for a gene. It should be noted that other GO tools do not offer this user-friendly access to annotations.
AVAILABILITY: Genes2GO is freely available and listed under http://www.semantic-systems-biology.org/tools/externaltools/.

PMID: 28149059 [PubMed]

Mixing omics: combining genetics and metabolomics to study rheumatic diseases.

Nat Rev Rheumatol. 2017 Feb 02;:

Authors: Menni C, Zierer J, Valdes AM, Spector TD

Abstract
Metabolomics is an exciting field in systems biology that provides a direct readout of the biochemical activities taking place within an individual at a particular point in time. Metabolite levels are influenced by many factors, including disease status, environment, medications, diet and, importantly, genetics. Thanks to their dynamic nature, metabolites are useful for diagnosis and prognosis, as well as for predicting and monitoring the efficacy of treatments. At the same time, the strong links between an individual's metabolic and genetic profiles enable the investigation of pathways that underlie changes in metabolite levels. Thus, for the field of metabolomics to yield its full potential, researchers need to take into account the genetic factors underlying the production of metabolites, and the potential role of these metabolites in disease processes. In this Review, the methodological aspects related to metabolomic profiling and any potential links between metabolomics and the genetics of some of the most common rheumatic diseases are described. Links between metabolomics, genetics and emerging fields such as the gut microbiome and proteomics are also discussed.

PMID: 28148918 [PubMed - as supplied by publisher]

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

BMC Bioinformatics. 2017 Feb 01;18(1):78

Authors: Nair G, Jungreuthmayer C, Zanghellini J

Abstract
BACKGROUND: Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives.
RESULTS: To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock.
CONCLUSIONS: PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

PMID: 28143607 [PubMed - in process]

From molecules to cellular networks: past and future outlook.

Phys Biol. 2017 Jan 23;:

Authors: Fang X, Wang J

Abstract
Cellular networks have been the focuses of the studies in the modern systems biology. They are crucial in understanding the cell functions and the related diseases. We review some past progresses in both theory and experiments. We also provide several future perspectives of the field.

PMID: 28140350 [PubMed - as supplied by publisher]

Operon mRNAs are organized into ORF-centric structures that predict translation efficiency.

Elife. 2017 Jan 31;6:

Authors: Burkhardt DH, Rouskin S, Zhang Y, Li GW, Weissman JS, Gross CA

Abstract
Bacterial mRNAs are organized into operons consisting of discrete open reading frames (ORFs) in a single polycistronic mRNA. Individual ORFs on the mRNA are differentially translated, with rates varying as much as 100-fold. The signals controlling differential translation are poorly understood. Our genome-wide mRNA secondary structure analysis indicated that operonic mRNAs are comprised of ORF-wide units of secondary structure that vary across ORF boundaries such that adjacent ORFs on the same mRNA molecule are structurally distinct. ORF translation rate is strongly correlated with its mRNA structure in vivo, and correlation persists, albeit in a reduced form, with its structure when translation is inhibited and with that of in vitro refolded mRNA. These data suggests that intrinsic ORF mRNA structure encodes a rough blueprint for translation efficiency. This structure is then amplified by translation, in a self-reinforcing loop, to provide the structure that ultimately specifies the translation of each ORF.

PMID: 28139975 [PubMed - as supplied by publisher]

Propelling the paradigm shift from reductionism to systems nutrition.

Genes Nutr. 2017;12:3

Authors: Kaput J, Perozzi G, Radonjic M, Virgili F

Abstract
The complex physiology of living organisms represents a challenge for mechanistic understanding of the action of dietary bioactives in the human body and of their possible role in health and disease. Animal, cell, and microbial models have been extensively used to address questions that could not be pursued experimentally in humans, posing an additional level of complexity in translation of the results to healthy and diseased metabolism. The past few decades have witnessed a surge in development of increasingly sensitive molecular techniques and bioinformatic tools for storing, managing, and analyzing increasingly large datasets. Application of such powerful means to molecular nutrition research led to a major leap in study designs and experimental approaches yielding experimental data connecting dietary components to human health. Scientific journals bear major responsibilities in the advancement of science. As primary actors of dissemination to the scientific community, journals can impose rigid criteria for publishing only sound, reliable, and reproducible data. Journal policies are meant to guide potential authors to adopt the most updated standardization guidelines and shared best practices. Such policies evolve in parallel with the evolution of novel approaches and emerging challenges and therefore require constant updating. We highlight in this manuscript the major scientific issues that led to formulating new, updated journal policies for Genes & Nutrition, a journal which targets the growing field of nutritional systems biology interfacing personalized nutrition and preventive medicine, with the ultimate goal of promoting health and preventing or treating disease. We focus here on relevant issues requiring standardization in nutrition research. We also introduce new sections on human genetic variation and nutritional bioinformatics which follow the evolution of nutritional science into the twenty-first century.

PMID: 28138347 [PubMed - in process]

An Interactive Macrophage Signal Transduction Map Facilitates Comparative Analyses of High-Throughput Data.

J Immunol. 2017 Jan 30;:

Authors: Wentker P, Eberhardt M, Dreyer FS, Bertrams W, Cantone M, Griss K, Schmeck B, Vera J

Abstract
Macrophages (Mϕs) are key players in the coordination of the lifesaving or detrimental immune response against infections. The mechanistic understanding of the functional modulation of Mϕs by pathogens and pharmaceutical interventions at the signal transduction level is still far from complete. The complexity of pathways and their cross-talk benefits from holistic computational approaches. In the present study, we reconstructed a comprehensive, validated, and annotated map of signal transduction pathways in inflammatory Mϕs based on the current literature. In a second step, we selectively expanded this curated map with database knowledge. We provide both versions to the scientific community via a Web platform that is designed to facilitate exploration and analysis of high-throughput data. The platform comes preloaded with logarithmic fold changes from 44 data sets on Mϕ stimulation. We exploited three of these data sets-human primary Mϕs infected with the common lung pathogens Streptococcus pneumoniae, Legionella pneumophila, or Mycobacterium tuberculosis-in a case study to show how our map can be customized with expression data to pinpoint regulated subnetworks and druggable molecules. From the three infection scenarios, we extracted a regulatory core of 41 factors, including TNF, CCL5, CXCL10, IL-18, and IL-12 p40, and identified 140 drugs targeting 16 of them. Our approach promotes a comprehensive systems biology strategy for the exploitation of high-throughput data in the context of Mϕ signal transduction. In conclusion, we provide a set of tools to help scientists unravel details of Mϕ signaling. The interactive version of our Mϕ signal transduction map is accessible online at https://vcells.net/macrophage.

PMID: 28137890 [PubMed - as supplied by publisher]

Systematic dissection of genomic features determining transcription factor binding and enhancer function.

Proc Natl Acad Sci U S A. 2017 Jan 30;:

Authors: Grossman SR, Zhang X, Wang L, Engreitz J, Melnikov A, Rogov P, Tewhey R, Isakova A, Deplancke B, Bernstein BE, Mikkelsen TS, Lander ES

Abstract
Enhancers regulate gene expression through the binding of sequence-specific transcription factors (TFs) to cognate motifs. Various features influence TF binding and enhancer function-including the chromatin state of the genomic locus, the affinities of the binding site, the activity of the bound TFs, and interactions among TFs. However, the precise nature and relative contributions of these features remain unclear. Here, we used massively parallel reporter assays (MPRAs) involving 32,115 natural and synthetic enhancers, together with high-throughput in vivo binding assays, to systematically dissect the contribution of each of these features to the binding and activity of genomic regulatory elements that contain motifs for PPARγ, a TF that serves as a key regulator of adipogenesis. We show that distinct sets of features govern PPARγ binding vs. enhancer activity. PPARγ binding is largely governed by the affinity of the specific motif site and higher-order features of the larger genomic locus, such as chromatin accessibility. In contrast, the enhancer activity of PPARγ binding sites depends on varying contributions from dozens of TFs in the immediate vicinity, including interactions between combinations of these TFs. Different pairs of motifs follow different interaction rules, including subadditive, additive, and superadditive interactions among specific classes of TFs, with both spatially constrained and flexible grammars. Our results provide a paradigm for the systematic characterization of the genomic features underlying regulatory elements, applicable to the design of synthetic regulatory elements or the interpretation of human genetic variation.

PMID: 28137873 [PubMed - as supplied by publisher]

Posttranslational Modifications and Plant-Environment Interaction.

Methods Enzymol. 2017;586:97-113

Authors: Hashiguchi A, Komatsu S

Abstract
Posttranslational modifications (PTMs) of proteins such as phosphorylation and ubiquitination are crucial for controlling protein stability, localization, and conformation. Genetic information encoded in DNA is transcribed, translated, and increases its complexity by multiple PTMs. Conformational change introduced by PTMs affects interacting partners of each proteins and their downstream signaling; therefore, PTMs are the major level of modulations of total outcome of living cells. Plants are living in harsh environment that requires unremitting physiological modulation to survive, and the plant response to various environment stresses is regulated by PTMs of proteins. This review deals with the novel knowledge of PTM-focused proteomic studies on various life conditions. PTMs are focused that mediate plant-environment interaction such as stress perception, protein homeostasis, control of energy shift, and defense by immune system. Integration of diverse signals on a protein via multiple PTMs is discussed as well, considering current situation where signal integration became an emerging area approached by systems biology into account.

PMID: 28137579 [PubMed - in process]

Recent Achievements in Characterizing the Histone Code and Approaches to Integrating Epigenomics and Systems Biology.

Methods Enzymol. 2017;586:359-378

Authors: Janssen KA, Sidoli S, Garcia BA

Abstract
Functional epigenetic regulation occurs by dynamic modification of chromatin, including genetic material (i.e., DNA methylation), histone proteins, and other nuclear proteins. Due to the highly complex nature of the histone code, mass spectrometry (MS) has become the leading technique in identification of single and combinatorial histone modifications. MS has now overcome antibody-based strategies due to its automation, high resolution, and accurate quantitation. Moreover, multiple approaches to analysis have been developed for global quantitation of posttranslational modifications (PTMs), including large-scale characterization of modification coexistence (middle-down and top-down proteomics), which is not currently possible with any other biochemical strategy. Recently, our group and others have simplified and increased the effectiveness of analyzing histone PTMs by improving multiple MS methods and data analysis tools. This review provides an overview of the major achievements in the analysis of histone PTMs using MS with a focus on the most recent improvements. We speculate that the workflow for histone analysis at its state of the art is highly reliable in terms of identification and quantitation accuracy, and it has the potential to become a routine method for systems biology thanks to the possibility of integrating histone MS results with genomics and proteomics datasets.

PMID: 28137571 [PubMed - in process]

Circadian Gating of the Mammalian Cell Cycle Restriction Point: A Mathematical Analysis.

IEEE Life Sci Lett. 2015 Jun;1(1):11-14

Authors: Su J, Henson MA

Abstract
A critical decision in the mammalian cell cycle is whether to pass through the restriction point (R-point) or enter the cell cycle. In this letter, we modeled the decision-making system of the mammalian cell cycle entry and the simulated circadian regulation of the R-point driven by external epithelial growth factor (EGF) patterns. Our conceptual model replicated key signaling behaviors observed experimentally, suggesting that the proposed network captured the essential system features. The model revealed the dramatic importance of the EGF dynamics on promoting cell proliferation, showed that the EGF signal duration was more important than the signal strength for driving cells past the R-point, and suggested that the loss of circadian control of the cell cycle entry could be associated with cancer development.

PMID: 28133623 [PubMed - in process]

Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy.

Mol Cell. 2017 Jan 23;:

Authors: Kramer MH, Farré JC, Mitra K, Yu MK, Ono K, Demchak B, Licon K, Flagg M, Balakrishnan R, Cherry JM, Subramani S, Ideker T

Abstract
We have developed a general progressive procedure, Active Interaction Mapping, to guide assembly of the hierarchy of functions encoding any biological system. Using this process, we assemble an ontology of functions comprising autophagy, a central recycling process implicated in numerous diseases. A first-generation model, built from existing gene networks in Saccharomyces, captures most known autophagy components in broad relation to vesicle transport, cell cycle, and stress response. Systematic analysis identifies synthetic-lethal interactions as most informative for further experiments; consequently, we saturate the model with 156,364 such measurements across autophagy-activating conditions. These targeted interactions provide more information about autophagy than all previous datasets, producing a second-generation ontology of 220 functions. Approximately half are previously unknown; we confirm roles for Gyp1 at the phagophore-assembly site, Atg24 in cargo engulfment, Atg26 in cytoplasm-to-vacuole targeting, and Ssd1, Did4, and others in selective and non-selective autophagy. The procedure and autophagy hierarchy are at http://atgo.ucsd.edu/.

PMID: 28132844 [PubMed - as supplied by publisher]

Metabolomic Strategies Involving Mass Spectrometry Combined with Liquid and Gas Chromatography.

Adv Exp Med Biol. 2017;965:77-98

Authors: Lopes AS, Cruz EC, Sussulini A, Klassen A

Abstract
Amongst all omics sciences, there is no doubt that metabolomics is undergoing the most important growth in the last decade. The advances in analytical techniques and data analysis tools are the main factors that make possible the development and establishment of metabolomics as a significant research field in systems biology. As metabolomic analysis demands high sensitivity for detecting metabolites present in low concentrations in biological samples, high-resolution power for identifying the metabolites and wide dynamic range to detect metabolites with variable concentrations in complex matrices, mass spectrometry is being the most extensively used analytical technique for fulfilling these requirements. Mass spectrometry alone can be used in a metabolomic analysis; however, some issues such as ion suppression may difficultate the quantification/identification of metabolites with lower concentrations or some metabolite classes that do not ionise as well as others. The best choice is coupling separation techniques, such as gas or liquid chromatography, to mass spectrometry, in order to improve the sensitivity and resolution power of the analysis, besides obtaining extra information (retention time) that facilitates the identification of the metabolites, especially when considering untargeted metabolomic strategies. In this chapter, the main aspects of mass spectrometry (MS), liquid chromatography (LC) and gas chromatography (GC) are discussed, and recent clinical applications of LC-MS and GC-MS are also presented.

PMID: 28132177 [PubMed - in process]

Metabolomics: Definitions and Significance in Systems Biology.

Adv Exp Med Biol. 2017;965:3-17

Authors: Klassen A, Faccio AT, Canuto GA, da Cruz PL, Ribeiro HC, Tavares MF, Sussulini A

Abstract
Nowadays, there is a growing interest in deeply understanding biological mechanisms not only at the molecular level (biological components) but also the effects of an ongoing biological process in the organism as a whole (biological functionality), as established by the concept of systems biology. Within this context, metabolomics is one of the most powerful bioanalytical strategies that allow obtaining a picture of the metabolites of an organism in the course of a biological process, being considered as a phenotyping tool. Briefly, metabolomics approach consists in identifying and determining the set of metabolites (or specific metabolites) in biological samples (tissues, cells, fluids, or organisms) under normal conditions in comparison with altered states promoted by disease, drug treatment, dietary intervention, or environmental modulation. The aim of this chapter is to review the fundamentals and definitions used in the metabolomics field, as well as to emphasize its importance in systems biology and clinical studies.

PMID: 28132174 [PubMed - in process]

Systems biology of robustness and flexibility: Lactobacillus buchneri-A show case.

J Biotechnol. 2017 Jan 25;:

Authors: Heinl S, Grabherr R

Abstract
Lactobacillus buchneri is a lactic acid bacterium that naturally inhabits very different ecological niches and plays an ambivalent role in many food and feed fermentation processes, where it can act as useful starter or as spoilage organism. Due to its vicinity to important biotechnological processes like silage making, ethanol production, baking, fermenting vegetables or brewing, L. buchneri was subject of extensive research and is now a quite well studied microorganism. Recently, next generation 'OMICS'-methods were applied to investigate L. buchneri in more detail on a systems biology level. These studies give insights into genetic equipment of L. buchneri, its metabolism. interaction with microbial consortia, and gene regulation under different growth conditions. The present review article is a compilation of the available results and is an attempt that aims to understand how L. buchneri, equipped with a relatively small set of genes, can adapt to so many highly distinct ecological niches, resist the associated, sometimes tough environmental conditions and prevail against other members of the microbial consortia present in the same niche.

PMID: 28131859 [PubMed - as supplied by publisher]

The core regulatory network in human cells.

Biochem Biophys Res Commun. 2017 Jan 25;:

Authors: Kim MS, Kim D, Kang NS, Kim JR

Abstract
In order to discover the common characteristics of various cell types in the human body, many researches have been conducted to find the set of genes commonly expressed in various cell types and tissues. However, the functional characteristics of a cell is determined by the complex regulatory relationships among the genes rather than by expressed genes themselves. Therefore, it is more important to identify and analyze a core regulatory network where all regulatory relationship between genes are active across all cell types to uncover the common features of various cell types. Here, based on hundreds of tissue-specific gene regulatory networks constructed by recent genome-wide experimental data, we constructed the core regulatory network. Interestingly, we found that the core regulatory network is organized by simple cascade and has few complex regulations such as feedback or feed-forward loops. Moreover, we discovered that the regulatory links from genes in the core regulatory network to genes in the peripheral regulatory network are much more abundant than the reverse direction links. These results suggest that the core regulatory network locates at the top of regulatory network and plays a role as a 'hub' in terms of information flow, and the information that is common to all cells can be modified to achieve the tissue-specific characteristics through various types of feedback and feed-forward loops in the peripheral regulatory networks. We also found that the genes in the core regulatory network are evolutionary conserved, essential and non-disease, non-druggable genes compared to the peripheral genes. Overall, our study provides an insight into how all human cells share a common function and generate tissue-specific functional traits by transmitting and processing information through regulatory network.

PMID: 28131826 [PubMed - as supplied by publisher]

The Systems Biology of Auxin in Developing Embryos.

Trends Plant Sci. 2017 Jan 25;:

Authors: Mironova V, Teale W, Shahriari M, Dawson J, Palme K

Abstract
Systems biology orientates signaling pathways in their biological context. This aim invariably requires models that ignore extraneous factors and focus on the most crucial pathways of any given process. The developing embryo encapsulates many important processes in plant development; understanding their interaction will be key to designing crops able to maximize yield in an ever-more challenging world. Here, we briefly summarize the role of auxin during embryo development. We highlight recent advances in our understanding of auxin signaling and discuss implications for a systems understanding of development.

PMID: 28131745 [PubMed - as supplied by publisher]

Intracellular growth of Mycobacterium tuberculosis after macrophage cell death leads to serial killing of host cells.

Elife. 2017 Jan 28;6:

Authors: Mahamed D, Boulle M, Ganga Y, Mc Arthur C, Skroch S, Oom L, Catinas O, Pillay K, Naicker M, Rampersad S, Mathonsi C, Hunter J, Sreejit G, Pym AS, Lustig G, Sigal A

Abstract
A hallmark of pulmonary tuberculosis is formation of macrophage-rich granulomas. These may restrict Mycobacterium tuberculosis (Mtb) growth, or progress to central necrosis and cavitation, facilitating pathogen growth. To determine factors leading to Mtb proliferation and host cell death, we used live cell imaging to track Mtb infection outcomes in individual primary human macrophages. Internalization of Mtb aggregates caused macrophage death, and phagocytosis of large aggregates was more cytotoxic than multiple small aggregates containing similar numbers of bacilli. Macrophage death did not result in clearance of Mtb. Rather, it led to accelerated intracellular Mtb growth regardless of prior activation or macrophage type. In contrast, bacillary replication was controlled in live phagocytes. Mtb grew as a clump in dead cells, and macrophages which internalized dead infected cells were very likely to die themselves, leading to a cell death cascade. This demonstrates how pathogen virulence can be achieved through numbers and aggregation states.

PMID: 28130921 [PubMed - as supplied by publisher]

Identification of Protein Complexes by Integrating Multiple Alignment of Protein Interaction Networks.

Bioinformatics. 2017 Jan 27;:

Authors: Ma CY, Phoebe Chen YP, Berger B, Liao CS

Abstract
MOTIVATION: Protein complexes are one of the keys to studying the behavior of a cell system. Many biological functions are carried out by protein complexes. During the past decade, the main strategy used to identify protein complexes from high-throughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-protein interaction (PPI) network. Although experimental PPI data has increased significantly over recent years, most PPI networks still have many false positive interactions and false negative edge loss due to the limitations of high-throughput experiments. In particular, the false negative errors restrict the search space of such conventional protein complex identification approaches. Thus, it has become one of the most challenging tasks in systems biology to automatically identify protein complexes.
RESULTS: In this work, we propose a new algorithm, NEOComplex (NECC- and Ortholog-based Complex identification by multiple network alignment), which integrates functional orthology information capable of being obtained by different types of MNA (multiple network alignment) approaches to expand the search space of protein complex detection. As part of our approach, we also define a new edge clustering coefficient to assign weight to interaction edges in PPI networks so that protein complexes can be identified more accurately.The edge clustering coefficient is based on the intuition that there is functional information captured in the common neighbors of the common neighbors as well. Our results show that the algorithm outperforms well-known protein complex identification tools in a balance between precision and recall on three eukaryotic species: human, yeast, and fly. As a result of MNAs of the species, the proposed approach can tolerate the edge loss of PPI networks and even discover sparse protein complexes which have traditionally been a challenge to predict.
AVAILABILITY: http://acolab.ie.nthu.edu.tw/bionetwork/NEOComplex CONTACT: bab@csail.mit.edu, csliao@ie.nthu.edu.tw.

PMID: 28130237 [PubMed - as supplied by publisher]

Serum Metabolomics of Burkitt Lymphoma Mouse Models.

PLoS One. 2017;12(1):e0170896

Authors: Yang F, Du J, Zhang H, Ruan G, Xiang J, Wang L, Sun H, Guan A, Shen G, Liu Y, Guo X, Li Q, Tang Y

Abstract
Burkitt lymphoma (BL) is a rare and highly aggressive type of non-Hodgkin lymphoma. The mortality rate of BL patients is very high due to the rapid growth rate and frequent systemic spread of the disease. A better understanding of the pathogenesis, more sensitive diagnostic tools and effective treatment methods for BL are essential. Metabolomics, an important aspect of systems biology, allows the comprehensive analysis of global, dynamic and endogenous biological metabolites based on their nuclear magnetic resonance (NMR) and mass spectrometry (MS). It has already been used to investigate the pathogenesis and discover new biomarkers for disease diagnosis and prognosis. In this study, we analyzed differences of serum metabolites in BL mice and normal mice by NMR-based metabolomics. We found that metabolites associated with energy metabolism, amino acid metabolism, fatty acid metabolism and choline phospholipid metabolism were altered in BL mice. The diagnostic potential of the metabolite differences was investigated in this study. Glutamate, glycerol and choline had a high diagnostic accuracy; in contrast, isoleucine, leucine, pyruvate, lysine, α-ketoglutarate, betaine, glycine, creatine, serine, lactate, tyrosine, phenylalanine, histidine and formate enabled the accurate differentiation of BL mice from normal mice. The discovery of abnormal metabolism and relevant differential metabolites may provide useful clues for developing novel, noninvasive approaches for the diagnosis and prognosis of BL based on these potential biomarkers.

PMID: 28129369 [PubMed - in process]