Showing papers in "Molecular BioSystems in 2016"
TL;DR: Reactome is a manually curated pathway annotation database for unveiling high-order biological pathways from high-throughput data and ReactomePA is an R/Bioconductor package providing enrichment analyses, including hypergeometric test and gene set enrichment analyses.
Abstract: Reactome is a manually curated pathway annotation database for unveiling high-order biological pathways from high-throughput data. ReactomePA is an R/Bioconductor package providing enrichment analyses, including hypergeometric test and gene set enrichment analyses. A functional analysis can be applied to the genomic coordination obtained from a sequencing experiment to analyze the functional significance of genomic loci including cis-regulatory elements and non-coding regions. Comparison among different experiments is also supported. Moreover, ReactomePA provides several visualization functions to produce highly customizable, publication-quality figures. The source code and documents of ReactomePA are freely available through Bioconductor (http://www.bioconductor.org/packages/ReactomePA).
TL;DR: The role of the milk microbiota in the physiology and health of both the mother and the offspring is discussed, and how it can be changed by farming practices and during infection is reported.
Abstract: Recent significant progress in culture-independent techniques, together with the parallel development of -omics technologies and data analysis capabilities, have led to a new perception of the milk microbiota as a complex microbial community with great diversity and multifaceted biological roles, living in an environment that was until recently believed to be sterile. In this review, we summarize and discuss the latest findings on the milk microbiota in dairy cows, with a focus on the role it plays in bovine physiology and health. Following an introduction on microbial communities and the importance of their study, we present an overview of the -omics methods currently available for their characterization, and outline the potential offered by a systems biology approach encompassing metatranscriptomics, metaproteomics, and metametabolomics. Then, we review the recent discoveries on the dairy cow milk microbiome enabled by the application of -omics approaches. Learning from studies in humans and in the mouse model, and after a description of the endogenous route hypothesis, we discuss the role of the milk microbiota in the physiology and health of both the mother and the offspring, and report how it can be changed by farming practices and during infection. In conclusion, we shortly outline the impact of the milk microbiota on the quality of milk and of dairy products.
TL;DR: Certain methods, exemplified by ultracentrifugation combined with iodixanol density gradient centrifugation or gel filtration, although labor-intensive, provide superior quality exosome preparations suitable for reliable analysis by mass spectrometry.
Abstract: The re-discovery of exosomes as intercellular messengers with high potential for diagnostic and therapeutic utility has led to them becoming a popular topic of research in recent years. One of the essential research areas in this field is the characterization of exosomal cargo, which includes numerous non-randomly packed proteins and nucleic acids. Unexpectedly, a very challenging aspect of exploration of extracellular vesicles has turned out to be their effective and selective isolation. The plurality of developed protocols leads to qualitative and quantitative variability in terms of the obtained exosomes, which significantly affects the results of downstream analyses and makes them difficult to compare, reproduce and interpret between research groups. Currently, there is a general consensus among the exosome-oriented community concerning the urgent need for the optimization and standardization of methods employed for the purification of these vesicles. Hence, we review here several strategies for exosome preparation including ultracentrifugation, chemical precipitation, affinity capturing and filtration techniques. The advantages and disadvantages of different approaches are discussed with special emphasis being placed on their adequacy for proteomics applications, which are particularly sensitive to sample quality. We conclude that certain methods, exemplified by ultracentrifugation combined with iodixanol density gradient centrifugation or gel filtration, although labor-intensive, provide superior quality exosome preparations suitable for reliable analysis by mass spectrometry.
TL;DR: Peptide-tag based labelling can be achieved by recognition of metal ions or small molecules and peptide-peptide interactions and enables site-specific protein visualization to investigate protein localization and trafficking.
Abstract: Peptide-tag based labelling can be achieved by (i) enzymes (ii) recognition of metal ions or small molecules and (iii) peptide-peptide interactions and enables site-specific protein visualization to investigate protein localization and trafficking.
TL;DR: A predictor called "IGPred" was designed by formulating protein sequences with the pseudo amino acid composition into which nine physiochemical properties of amino acids were incorporated, indicating that IGPred holds very high potential to become a useful tool for antibody analysis.
Abstract: Immunoglobulins, also called antibodies, are a group of cell surface proteins which are produced by the immune system in response to the presence of a foreign substance (called antigen). They play key roles in many medical, diagnostic and biotechnological applications. Correct identification of immunoglobulins is crucial to the comprehension of humoral immune function. With the avalanche of protein sequences identified in postgenomic age, it is highly desirable to develop computational methods to timely identify immunoglobulins. In view of this, we designed a predictor called "IGPred" by formulating protein sequences with the pseudo amino acid composition into which nine physiochemical properties of amino acids were incorporated. Jackknife cross-validated results showed that 96.3% of immunoglobulins and 97.5% of non-immunoglobulins can be correctly predicted, indicating that IGPred holds very high potential to become a useful tool for antibody analysis. For the convenience of most experimental scientists, a web-server for IGPred was established at http://lin.uestc.edu.cn/server/IGPred. We believe that the web-server will become a powerful tool to study immunoglobulins and to guide related experimental validations.
TL;DR: HOTAIR, overexpressed in HCC and associated with tumor size, could activate autophagy by increasing ATG3 and ATG7 expression, promoting HCC cell proliferation.
Abstract: The long noncoding RNA HOX transcript antisense RNA (HOTAIR) has been reported to be an oncogene that influences tumor cell development and that correlates with prognosis in hepatocellular carcinoma (HCC). Accumulating evidence indicates that autophagy plays a significant role in tumorigenesis and cancer cell survival, but whether HOTAIR modulates autophagy in HCC cells remains unknown. In this study, HOTAIR expression was measured in 54 matched paired HCC tissues and the adjacent non-tumor tissues. HOTAIR was overexpressed in the HCC tissues as compared with the adjacent non-tumor tissues and was associated with tumor size. In vitro assays revealed that the overexpression of HOTAIR promoted the activation of autophagy in HCC cell lines, whereas HOTAIR knockdown suppressed it. Further investigation revealed that overexpressed HOTAIR upregulated autophagy-related 3 (ATG3) and ATG7 expression in HCC cells. In conclusion, HOTAIR, overexpressed in HCC and associated with tumor size, could activate autophagy by increasing ATG3 and ATG7 expression, promoting HCC cell proliferation.
TL;DR: Intrinsically disordered proteins and protein regions offer numerous advantages in the context of protein-protein interactions when compared to the structured proteins and domains but have similar abundance and amino acid composition across the three domains of life.
Abstract: Intrinsically disordered proteins and protein regions offer numerous advantages in the context of protein–protein interactions when compared to the structured proteins and domains. These advantages include ability to interact with multiple partners, to fold into different conformations when bound to different partners, and to undergo disorder-to-order transitions concomitant with their functional activity. Molecular recognition features (MoRFs) are widespread elements located in disordered regions that undergo disorder-to-order transition upon binding to their protein partners. We characterize abundance, composition, and functions of MoRFs and their association with the disordered regions across 868 species spread across Eukaryota, Bacteria and Archaea. We found that although disorder is substantially elevated in Eukaryota, MoRFs have similar abundance and amino acid composition across the three domains of life. The abundance of MoRFs is highly correlated with the amount of intrinsic disorder in Bacteria and Archaea but only modestly correlated in Eukaryota. Proteins with MoRFs have significantly more disorder and MoRFs are present in many disordered regions, with Eukaryota having more MoRF-free disordered regions. MoRF-containing proteins are enriched in the ribosome, nucleus, nucleolus and microtubule and are involved in translation, protein transport, protein folding, and interactions with DNAs. Our insights into the nature and function of MoRFs enhance our understanding of the mechanisms underlying the disorder-to-order transition and protein–protein recognition and interactions. The fMoRFpred method that we used to annotate MoRFs is available at http://biomine.ece.ualberta.ca/fMoRFpred/.
TL;DR: This review will give an overview of immune relevant molecules in fish skin mucus of studied species, andplement complement molecules, heat shock molecules and superoxide dismutase present in mucus show differential expression during pathogen challenge in some species, but their functions in mucu need to be shown.
Abstract: This review will give an overview of immune relevant molecules in fish skin mucus. The skin of fish is continuously exposed to a water environment, and unlike that of terrestrial vertebrates, it is a mucosal surface with a thin epidermis of live cells covered by a mucus layer. The mucosa plays an important role in maintaining the homeostasis of the fish and preventing the entry of invading pathogens. This review provides an overview of proteins, RNA, DNA, lipids and carbohydrates found in the skin mucus of studied species. Proteins such as actin, histones, lectins, lysozyme, mucin, and transferrin have extracellular immune relevant functions. Complement complement molecules, heat shock molecules and superoxide dismutase present in mucus show differential expression during pathogen challenge in some species, but their functions in mucus, if any, need to be shown. RNA, DNA, lipids, carbohydrates and metabolites in mucus have been studied to a limited extent in fish, the current knowledge is summarized and knowledge gaps are pointed out.
TL;DR: This work was able to quantitatively reconstruct the complete structural dynamics picture of binding of free SBPs to their target domains for five representative SBP systems by carrying out the state-of-the-art molecular dynamics simulations.
Abstract: Self-binding peptides (SBPs) represent a novel biomolecular phenomenon spanning between folding and binding, where a short peptide segment within a monomeric protein fulfills biological functions by dynamically binding to/unbinding from its target domain in the same monomer. Here, we were able to quantitatively reconstruct the complete structural dynamics picture of binding of free SBPs to their target domains for five representative SBP systems by carrying out the state-of-the-art molecular dynamics (MD) simulations. In the picture, a two-step binding mechanism for SBP–domain recognition and association was proposed, which includes a fast, nonspecific diffusive phase and a slow, specific organizational phase. The electrostatic interactions and desolvation effects play a predominant role in the first phase that leads to the formation of a metastable encounter complex, while conformational rearrangement is observed in the second phase to optimize the exquisite network of nonbonded chemical forces such as hydrogen bonds and salt bridges across the complex interface. From an energetic point of view, a funnel-shape enthalpy landscape steers these SBPs towards their native bound state and thus facilitates the binding process. However, the binding exhibits typical enthalpy–entropy compensation due to the high flexibility of peptides that results in a relatively low affinity for SBP–domain binding and forces the SBP systems to rapidly switch between the bound and unbound states. In addition, slight conformational changes in the target domain and/or in the polypeptide linker between the domain and peptide can significantly affect the energetic properties and dynamic behavior of the fine-tuned binding process of SBP–domain recognition.
TL;DR: These studies, in conjunction with earlier studies, suggest alterations in energy utilization pathways and have identified and further validated perturbed metabolites to be used in panels of biomarkers for the diagnosis of ALS and PD.
Abstract: Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) are protein-aggregation diseases that lack clear molecular etiologies. Biomarkers could aid in diagnosis, prognosis, planning of care, drug target identification and stratification of patients into clinical trials. We sought to characterize shared and unique metabolite perturbations between ALS and PD and matched controls selected from patients with other diagnoses, including differential diagnoses to ALS or PD that visited our clinic for a lumbar puncture. Cerebrospinal fluid (CSF) and plasma from rigorously age-, sex- and sampling-date matched patients were analyzed on multiple platforms using gas chromatography (GC) and liquid chromatography (LC)-mass spectrometry (MS). We applied constrained randomization of run orders and orthogonal partial least squares projection to latent structure-effect projections (OPLS-EP) to capitalize upon the study design. The combined platforms identified 144 CSF and 196 plasma metabolites with diverse molecular properties. Creatine was found to be increased and creatinine decreased in CSF of ALS patients compared to matched controls. Glucose was increased in CSF of ALS patients and α-hydroxybutyrate was increased in CSF and plasma of ALS patients compared to matched controls. Leucine, isoleucine and ketoleucine were increased in CSF of both ALS and PD. Together, these studies, in conjunction with earlier studies, suggest alterations in energy utilization pathways and have identified and further validated perturbed metabolites to be used in panels of biomarkers for the diagnosis of ALS and PD.
TL;DR: Fundamental technological features are assessed and some recent advances and applications of display technologies are pointed out to address some of the disadvantages of peptides and nucleotides such as their low affinity, low stability, high immunogenicity and difficulty to cross membranes.
Abstract: Over the past two decades, library-based display technologies have been staggeringly optimized since their appearance in order to mimic the process of natural molecular evolution. Display technologies are essential for the isolation of specific high-affinity binding molecules (proteins, polypeptides, nucleic acids and others) for diagnostic and therapeutic applications in cancer, infectious diseases, autoimmune, neurodegenerative, inflammatory pathologies etc. Applications extend to other fields such as antibody and enzyme engineering, cell-free protein synthesis and the discovery of protein-protein interactions. Phage display technology is the most established of these methods but more recent fully in vitro alternatives, such as ribosome display, mRNA display, cis-activity based (CIS) display and covalent antibody display (CAD), as well as aptamer display and in vitro compartmentalization, offer advantages over phage in library size, speed and the display of unnatural amino acids and nucleotides. Altogether, they have produced several molecules currently approved or in diverse stages of clinical or preclinical testing and have provided researchers with tools to address some of the disadvantages of peptides and nucleotides such as their low affinity, low stability, high immunogenicity and difficulty to cross membranes. In this review we assess the fundamental technological features and point out some recent advances and applications of display technologies.
TL;DR: A novel computational tool termed SuccinSite has been developed to predict protein succinylation sites by incorporating three sequence encodings, i.e., k-spaced amino acid pairs, binary and amino acid index properties, and performs significantly better than existing predictors on a comprehensive independent test set.
Abstract: Lysine succinylation is an emerging protein post-translational modification, which plays an important role in regulating the cellular processes in both eukaryotic and prokaryotic cells. However, the succinylation modification site is particularly difficult to detect because the experimental technologies used are often time-consuming and costly. Thus, an accurate computational method for predicting succinylation sites may help researchers towards designing their experiments and to understand the molecular mechanism of succinylation. In this study, a novel computational tool termed SuccinSite has been developed to predict protein succinylation sites by incorporating three sequence encodings, i.e., k-spaced amino acid pairs, binary and amino acid index properties. Then, the random forest classifier was trained with these encodings to build the predictor. The SuccinSite predictor achieves an AUC score of 0.802 in the 5-fold cross-validation set and performs significantly better than existing predictors on a comprehensive independent test set. Furthermore, informative features and predominant rules (i.e. feature combinations) were extracted from the trained random forest model for an improved interpretation of the predictor. Finally, we also compiled a database covering 4411 experimentally verified succinylation proteins with 12 456 lysine succinylation sites. Taken together, these results suggest that SuccinSite would be a helpful computational resource for succinylation sites prediction. The web-server, datasets, source code and database are freely available at http://systbio.cau.edu.cn/SuccinSite/.
TL;DR: The corresponding mechanism and biological function of HOTTIP during tumor development is illustrated and affected by various malignancies including hepatocellular carcinoma, pancreatic cancer, gastric cancer and colorectal cancer.
Abstract: Long non-coding RNAs (lncRNAs), which represent a novel group of non-protein-coding RNAs and are commonly defined as RNA molecules larger than 200 nucleotides in length, have been shown to get involved in diverse biological processes, such as cell growth, apoptosis, migration and invasion. In addition, aberrant expression of lncRNAs has been discovered in human tumors, where they function as either oncogenes or tumor suppressor genes. Recently tumorigenic effects of one specific lncRNA, termed as ‘HOXA transcript at the distal tip’ (HOTTIP), on the initiation and progression of human cancer has been widely reported. An increasing amount of data has shown that dysregulation of HOTTIP is associated with various malignancies including hepatocellular carcinoma, pancreatic cancer, gastric cancer and colorectal cancer, and affects the survival and prognosis of cancer patients. Here, we focus on the current knowledge of HOTTIP in various cancers and illustrate the corresponding mechanism and biological function of HOTTIP during tumor development.
TL;DR: It is ascertained that the "iNuc-STNC" model will provide a rudimentary framework for the pharmaceutical industry in the development of drug design and is higher than current state of the art methods in the literature so far.
Abstract: The nucleosome is the fundamental unit of eukaryotic chromatin, which participates in regulating different cellular processes. Owing to the huge exploration of new DNA primary sequences, it is indispensable to develop an automated model. However, identification of novel protein sequences using conventional methods is difficult or sometimes impossible because of vague motifs and the intricate structure of DNA. In this regard, an effective and high throughput automated model “iNuc-STNC” has been proposed in order to identify accurately and reliably nucleosome positioning in genomes. In this proposed model, DNA sequences are expressed into three distinct feature extraction strategies containing dinucleotide composition, trinucleotide composition and split trinucleotide composition (STNC). Various statistical models were utilized as learner hypotheses. Jackknife test was employed to evaluate the success rates of the proposed model. The experiential results expressed that SVM, in combination with STNC, has obtained an outstanding performance on all benchmark datasets. The predicted outcomes of the proposed model “iNuc-STNC” is higher than current state of the art methods in the literature so far. It is ascertained that the “iNuc-STNC” model will provide a rudimentary framework for the pharmaceutical industry in the development of drug design.
TL;DR: The basics of NGS technologies and their application in human diseases to foster human healthcare and personalized medicine are reviewed.
Abstract: A breakthrough in next generation sequencing (NGS) in the last decade provided an unprecedented opportunity to investigate genetic variations in humans and their roles in health and disease. NGS offers regional genomic sequencing such as whole exome sequencing of coding regions of all genes, as well as whole genome sequencing. RNA-seq offers sequencing of the entire transcriptome and ChIP-seq allows for sequencing the epigenetic architecture of the genome. Identifying genetic variations in individuals can be used to predict disease risk, with the potential to halt or retard disease progression. NGS can also be used to predict the response to or adverse effects of drugs or to calculate appropriate drug dosage. Such a personalized medicine also provides the possibility to treat diseases based on the genetic makeup of the patient. Here, we review the basics of NGS technologies and their application in human diseases to foster human healthcare and personalized medicine.
TL;DR: A comprehensive atomic behaviour of the gain-of-function mutation (R132H) in the IDH1 enzyme is reported which in turn provides a direction towards new therapeutics.
Abstract: Arginine to histidine mutation at position 132 (R132H) in isocitrate dehydrogenase 1 (IDH1) led to reduced affinity of the respective enzymes for isocitrate and increased affinity for α-ketoglutarate (AKG) and NADPH. This phenomenon retarded oxidative decarboxylation of isocitrate to AKG and conferred a novel enzymatic activity that facilitated the reduction of AKG to D-2-hydroxyglutarate (D-2HG). The loss of isocitrate utilization and gain of 2HG production from IDH1 R132H had been taken up as a fundamental problem and to solve this, structural biology approaches were adopted. Interaction analysis was carried out to investigate the IDH1 substrate binding environment. The altered behaviour of mutant and native IDH1 in interaction analysis was explored by performing long-term molecular dynamics simulations (∼300 ns). This study reports a comprehensive atomic behaviour of the gain-of-function mutation (R132H) in the IDH1 enzyme which in turn provides a direction towards new therapeutics.
TL;DR: The results suggest that MALAT1 functions to promote cervical cancer invasion and metastasis via induction of EMT, and it may be a target for the prevention and therapy of cervical cancers.
Abstract: The metastasis-associated lung adenocarcinoma transcript 1(MALAT1), a member of the long non-coding RNA (lncRNA) family, has been reported to be highly enriched in many kinds of cancers and to be a metastasis marker and a prognostic factor. In this study, we found that MALAT1 expression levels were significantly increased in cervical cancer (CC) cells and tissues. The down-regulation of MALAT1 by shRNA in CC cells inhibited the invasion and metastasis in vitro and in vivo. Microarray analysis showed that the knockdown of MALAT1 up-regulated the epithelial markers E-cadherin and ZO-1, and down-regulated the mesenchymal markers β-catenin and Vimentin. This regulation was further confirmed by subsequent observation from RT-PCR, western blot, and immunofluorescence results. Meanwhile, the transcription factor snail, which functions to modulate epithelial–mesenchymal transition (EMT), was also down-regulated at both transcript and protein levels by MALAT1 down-regulation. In addition, we found that MALAT1 expression levels were positively related to HPV infection in cervical epithelial tissues by microarray analysis. Taken together, these results suggest that MALAT1 functions to promote cervical cancer invasion and metastasis via induction of EMT, and it may be a target for the prevention and therapy of cervical cancers.
TL;DR: The presence of intracellular proteins on the cell surface is more common than previously expected and suggests that many additional proteins might be candidates for being intrace cellular/surface moonlighting proteins.
Abstract: Proteins expressed on the bacterial cell surface play important roles in infection and virulence and can be targets for vaccine development or used as biomarkers. Surprisingly, an increasing number of surface proteins are being found to be identical to intracellular enzymes and chaperones, and a few dozen intracellular/surface moonlighting proteins have been found that have different functions inside the cell and on the cell surface. The results of twenty-two published bacterial surface proteomics studies were analyzed using bioinformatics tools to consider how many additional intracellular proteins are also found on the cell surface. More than 1000 out of the 3619 proteins observed on the cell surface lack the transmembrane alpha-helices or transmembrane beta-barrels found in integral membrane proteins and also lack the signal peptides found in proteins secreted through the Sec pathway. Many of the proteins found on the cell surface are intracellular chaperones or enzymes involved in central metabolic pathways, including some that have previously been shown to have a moonlighting function on the cell surface in at least one species, such as Hsp60/GroEL, DnaK, glyceraldehyde 3-phosphate dehydrogenase, enolase, and fructose 1,6-bisphosphate aldolase. The results of the proteomics studies suggest they could also be moonlighting on the surface of many other species. Hundreds of other intracellular proteins are also found on the cell surface, although a second function on the surface has not yet been demonstrated, for example, glutamine synthetase, gamma-glutamyl phosphate reductase, and cysteine desulfurase. The presence of intracellular proteins on the cell surface is more common than previously expected and suggests that many additional proteins might be candidates for being intracellular/surface moonlighting proteins.
TL;DR: Low urinary or high serum 3-IS levels may be more useful for early detection of AKI than conventional biomarkers, and the area under the curve receiver operating characteristics (AUC-ROC) for3-IS was higher than for SCr, BUN, lactate dehydrogenase, total protein, and glucose.
Abstract: The discovery of new biomarkers for early detection of drug-induced acute kidney injury (AKI) is clinically important. In this study, sensitive metabolomic biomarkers identified in the urine of rats were used to detect cisplatin-induced AKI. Cisplatin (10 mg kg−1, i.p.) was administered to Sprague-Dawley rats, which were subsequently euthanized after 1, 3 or 5 days. In cisplatin-treated rats, mild histopathological alterations were noted at day 1, and these changes were severe at days 3 and 5. Blood urea nitrogen (BUN) and serum creatinine (SCr) levels were significantly increased at days 3 and 5. The levels of new urinary protein-based biomarkers, including kidney injury molecule-1 (KIM-1), glutathione S-transferase-α (GST-α), tissue inhibitor of metalloproteinase-1 (TIMP-1), vascular endothelial growth factor (VEGF), calbindin, clusterin, neutrophil, neutrophil gelatinase-associated lipocalin (NGAL), and osteopontin, were significantly elevated at days 3 and 5. Among urinary metabolites, trigonelline and 3-indoxylsulfate (3-IS) levels were significantly decreased in urine collected from cisplatin-treated rats prior to histological kidney damage. However, carbon tetrachloride (CCl4), a hepatotoxicant, did not affect these urinary biomarkers. Trigonelline is closely associated with GSH depletion and results in insufficient antioxidant capacity against cisplatin-induced AKI. The predominant cisplatin-induced AKI marker appeared to be reduced in urinary 3-IS levels. Because 3-IS is predominantly excreted via active secretion in proximal tubules, a decrease is indicative of tubular damage. Further, urinary excretion of 3-IS levels was markedly reduced in patients with AKI compared to normal subjects. The area under the curve receiver operating characteristics (AUC-ROC) for 3-IS was higher than for SCr, BUN, lactate dehydrogenase (LDH), total protein, and glucose. Therefore, low urinary or high serum 3-IS levels may be more useful for early detection of AKI than conventional biomarkers.
TL;DR: This review will highlight the progress that has been made in the field of metabolomics including technological advancements, the identification of potential biomarkers, and metabolic pathways relevant to macro- and microvascular diabetic complications.
Abstract: With a global prevalence of 9%, diabetes is the direct cause of millions of deaths each year and is quickly becoming a health crisis. Major long-term complications of diabetes arise from persistent oxidative stress and dysfunction in multiple metabolic pathways. The most serious complications involve vascular damage and include cardiovascular disease as well as microvascular disorders such as nephropathy, neuropathy, and retinopathy. Current clinical analyses like glycated hemoglobin and plasma glucose measurements hold some value as prognostic indicators of the severity of complications, but investigations into the underlying pathophysiology are still lacking. Advancements in biotechnology hold the key to uncovering new pathways and establishing therapeutic targets. Metabolomics, the study of small endogenous molecules, is a powerful toolset for studying pathophysiological processes and has been used to elucidate metabolic signatures of diabetes in various biological systems. Current challenges in the field involve correlating these biomarkers to specific complications to provide a better prediction of future risk and disease progression. This review will highlight the progress that has been made in the field of metabolomics including technological advancements, the identification of potential biomarkers, and metabolic pathways relevant to macro- and microvascular diabetic complications.
TL;DR: Quercetin and rutin were the highly desirable flavonoids for the inhibition of P-gp transport function and they significantly reduced resistance in cytotoxicity assays to paclitaxel in P- gp overexpressing MDR cell lines, and may be considered as potential chemosensitizing agents to overcome multidrug resistance in cancer.
Abstract: P-Glycoprotein (P-gp) serves as a therapeutic target for the development of inhibitors to overcome multidrug resistance in cancer cells. Although various screening procedures have been practiced so far to develop first three generations of P-gp inhibitors, their toxicity and drug interaction profiles are still a matter of concern. To address the above important problem of developing safe and effective P-gp inhibitors, we have made systematic computational and experimental studies on the interaction of natural phytochemicals with human P-gp. Molecular docking and QSAR studies were carried out for 40 dietary phytochemicals in the drug-binding site of the transmembrane domains (TMDs) of P-gp. Dietary flavonoids exhibit better interactions with homology modeled human P-gp. Based on the computational analysis, selected flavonoids were tested for their inhibitory potential against P-gp transport function in drug resistant cell lines using calcein-AM and rhodamine 123 efflux assays. It has been found that quercetin and rutin were the highly desirable flavonoids for the inhibition of P-gp transport function and they significantly reduced resistance in cytotoxicity assays to paclitaxel in P-gp overexpressing MDR cell lines. Hence, quercetin and rutin may be considered as potential chemosensitizing agents to overcome multidrug resistance in cancer.
TL;DR: A robust and general NMR analysis approach for studying the saliva metabolome that has potential use for screening and early detection of dementia.
Abstract: Saliva is a biofluid that is sensitive to metabolic changes and is straightforward to collect in a non-invasive manner, but it is seldom used for metabolite analysis when studying neurodegenerative ...
TL;DR: A number of primary studies carried out over the past decade have turned up specific and valuable clues regarding the composition and roles of glycan structures and also glycan binding proteins involved EV biogenesis and transfer.
Abstract: Extracellular vesicles (EVs) are a diverse population of complex biological particles with diameters ranging from approximately 20 to 1000 nm. Tremendous interest in EVs has been generated following a number of recent, high-profile reports describing their potential utility in diagnostic, prognostic, drug delivery, and therapeutic roles. Subpopulations, such as exosomes, are now known to directly participate in cell–cell communication and direct material transfer. Glycomics, the ‘omic’ portion of the glycobiology field, has only begun to catalog the surface oligosaccharide and polysaccharide structures and also the carbohydrate-binding proteins found on and inside EVs. The EV glycome undoubtedly contains vital clues essential to better understanding the function, biogenesis, release and transfer of vesicles, however getting at this information is technically challenging and made even more so because of the small physical size of the vesicles and the typically minute yield from physiological-scale biological samples. Vesicle micro-heterogeneity which may be related to specific vesicle origins and functions presents a further challenge. A number of primary studies carried out over the past decade have turned up specific and valuable clues regarding the composition and roles of glycan structures and also glycan binding proteins involved EV biogenesis and transfer. This review explores some of the major EV glycobiological research carried out to date and discusses the potential implications of these findings across the life sciences.
TL;DR: In this article, structure-based virtual screening targeting nucleocapsid protein (NP) was performed to identify good chemical starting points for medicinal chemistry, which is performed through docking with varying precisions and computational intensities to identify eight potential compounds.
Abstract: Nucleocapsid protein (NP), an essential RNA-binding viral protein in human coronavirus (CoV)-infected cells, is required for the replication and transcription of viral RNA. Recent studies suggested that human CoV NP is a valid target for antiviral drug development. Based on this aspect, structure-based virtual screening targeting nucleocapsid protein (NP) was performed to identify good chemical starting points for medicinal chemistry. The present study utilized structure-based virtual screening against human CoV-OC43 using the Zinc database, which is performed through docking with varying precisions and computational intensities to identify eight potential compounds. The chosen potential leads were further validated experimentally using biophysical means. Surface plasmon resonance (SPR) analysis indicated that one among the potential leads, 6-chloro-7-(2-morpholin-4-yl-ethylamino) quinoxaline-5,8-dione (small-compound H3), exhibited a significant decrease of RNA-binding capacity of NP by more than 20%. The loss of binding activity was manifested as a 20% decrease in the minimum on-rate accompanied with a 70% increase in the maximum off-rate. Fluorescence titration and X-ray crystallography studies indicated that H3 antagonizes the binding between HCoV-OC43 NP and RNA by interacting with the N-terminal domain of the NP. Our findings provide insight into the development of new therapeutics that disrupt the interaction between RNA and viral NP in the HCoV. The discovery of the new compound would be an impetus to design novel NP inhibitors against human CoV.
TL;DR: An improved computational method named NTSMDA that is based on known miRNA-disease network topological similarity to exploit more potential disease-related miRNAs and achieves an AUC of 89.4% by using the leave-one-out cross-validation experiment, demonstrating the excellent predictive performance of NTSMda.
Abstract: Recently, accumulating studies have indicated that microRNAs (miRNAs) play an important role in exploring the pathogenesis of various human diseases at the molecular level and may result in the design of specific tools for diagnosis, treatment evaluation and prevention. Experimental identification of disease-related miRNAs is time-consuming and labour-intensive. Hence, there is a stressing need to propose efficient computational methods to detect more potential miRNA–disease associations. Currently, several computational approaches for identifying disease-related miRNAs on the miRNA–disease network have gained much attention by means of integrating miRNA functional similarities and disease semantic similarities. However, these methods rarely consider the network topological similarity of the miRNA–disease association network. Here, in this paper we develop an improved computational method named NTSMDA that is based on known miRNA–disease network topological similarity to exploit more potential disease-related miRNAs. We achieve an AUC of 89.4% by using the leave-one-out cross-validation experiment, demonstrating the excellent predictive performance of NTSMDA. Furthermore, predicted highly ranked miRNA–disease associations of breast neoplasms, lung neoplasms and prostatic neoplasms are manually confirmed by different related databases and literature, providing evidence for the good performance and potential value of the NTSMDA method in inferring miRNA–disease associations. The R code and readme file of NTSMDA can be downloaded from https://github.com/USTC-HIlab/NTSMDA.
TL;DR: This review addresses this topic by showing clear examples where proteomics has been used to study stress-induced changes at various levels and clearly demonstrating how proteomics and systems biology are key elements to the study of stress and welfare in farm animals and powerful tools for animal welfare, health and productivity.
Abstract: Stress and welfare are important factors in animal production in the context of growing production optimization and scrutiny by the general public. In a context in which animal and human health are intertwined aspects of the one-health concept it is of utmost importance to define the markers of stress and welfare. These are important tools for producers, retailers, regulatory agents and ultimately consumers to effectively monitor and assess the welfare state of production animals. Proteomics is the science that studies the proteins existing in a given tissue or fluid. In this review we address this topic by showing clear examples where proteomics has been used to study stress-induced changes at various levels. We adopt a multi-species (cattle, swine, small ruminants, poultry, fish and shellfish) approach under the effect of various stress inducers (handling, transport, management, nutritional, thermal and exposure to pollutants) clearly demonstrating how proteomics and systems biology are key elements to the study of stress and welfare in farm animals and powerful tools for animal welfare, health and productivity.
TL;DR: A novel "pathway-pathway interaction" network-based synergy evaluation method to predict the potential synergistic drug combinations and comparison with previous target-based methods shows that inclusion of systematic pathway- pathway interactions makes this novel method outperform others in predicting drug synergy.
Abstract: Drug combinations have been widely applied to treat complex diseases, like cancer, HIV and cardiovascular diseases. One of the most important characteristics for drug combinations is the synergistic effects among different drugs, that is to say, the combination effects are larger than the sum of individual effects. Although quantitative methods can be utilized to evaluate the synergistic effects based on experimental dose–response data, it is both time and resource consuming to screen all possible combinations by experimental trials. This problem makes it a formidable challenge to recognize synergistic combinations. Various attempts have been made to predict drug synergy by network biology, however, most of them are limited to estimating target associations on the PPI network. Here, we proposed a novel “pathway–pathway interaction” network-based synergy evaluation method to predict the potential synergistic drug combinations. Comparison with previous target-based methods shows that inclusion of systematic pathway–pathway interactions makes this novel method outperform others in predicting drug synergy. Moreover, it can also help to interpret how different drugs in a combination cooperate with each other to implement synergistic therapeutic effects. In general, drugs acting on the same pathway through different targets or drugs regulating a relatively small number of highly-connected pathways are more likely to produce synergistic effects.
TL;DR: Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.
Abstract: It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer’s disease, Parkinson’s disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.
TL;DR: In silico approaches, together with omics data, hold great potential to be utilized for rapid and reliable genome-wide screening for identification of vaccine candidates against devastating infectious diseases.
Abstract: Enterotoxigenic Escherichia coli (ETEC) associated diarrhea remains a global killer with an estimated annual incidence rate of 840 million infections and 3 800 000 deaths worldwide. There are no vaccines available for ETEC and the traditional vaccine development approach is arduous and time consuming. Thus, alternative in silico approaches for epitope prediction have engrossed the interest of researchers to reduce resources and time of vaccine development. Computational approaches are playing a crucial role in fighting against rapidly growing infectious organisms. In this study we employed an integrated comparative genomics and immunoinformatics approach for proteome scale identification of peptide vaccine candidates. The proteins shared between both ETEC E24377A and H10407 strains, but lacking in commensal E. coli SE11, were subjected to immunoinformatics analysis. For a protein pool shared between different pathogenic ETEC strains, we investigated varied physicochemical and immunogenic properties to prioritize potential vaccine candidates. Epitopes were further tested using docking studies to bind in the MHC-I binding cleft. Predicted epitopes provided more than a 95% population coverage in diarrhea endemic regions presented by major MHC-I supertypes, and bind efficiently to a MHC molecule. We conclude by accentuating that the epitopes predicted in this study are believed to accelerate the development of successful vaccines to control or prevent ETEC infections, albeit the results require experimental validation using model organisms. This study underscores that in silico approaches, together with omics data, hold great potential to be utilized for rapid and reliable genome-wide screening for identification of vaccine candidates against devastating infectious diseases.
TL;DR: A metabolic profile reflecting the osteoporosis progression in 364 pre- and postmenopausal Chinese women using GC-MS is described and five free fatty acids have the most potential to be used as osteopore biomarkers due to their better correlations with BMD, and high sensitivity and specificity in distinguishing the low BMD groups from the normal B MD groups calculated by the receiver operating characteristic curve (ROC).
Abstract: The present study describes for the first time, a metabolic profile reflecting the osteoporosis progression in 364 pre- and postmenopausal Chinese women using GC-MS. In order to accurately evaluate the dynamic changes of metabolites along with estrogen deficiency and osteoporosis progression, we divided these subjects into the following four groups: premenopausal women with normal bone mass density (BMD, group I), postmenopausal women with normal BMD (group II), postmenopausal women with osteopenia (group III) and postmenopausal women with osteoporosis (group IV), according to their menopause or low BMD status. Principal component analysis (PCA) and Partial least squares-discriminant analysis (PLS-DA) were used to evaluate the associations of metabolic changes with low BMD or estrogen deficiency. Twelve metabolites identified by the PLS-DA model were found to be able to differentiate low BMD groups from normal BMD groups. Of the 12 metabolites, five free fatty acids (LA, oleic acid, AA and 11,14-eicosadienoic acid) have the most potential to be used as osteoporosis biomarkers due to their better correlations with BMD, and high sensitivity and specificity in distinguishing the low BMD groups from the normal BMD groups calculated by the receiver operating characteristic curve (ROC). The lipid profile may be useful for osteoporosis prediction and diagnosis.