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Protein normalization of western blots has relied upon housekeeping proteins which exhibit signal saturation and varied cellular expression level variations. These issues can produce spurious results leading to erroneous conclusions. A superior method to protein normalization using housekeeping proteins is Total Protein Normalization, a method now recognized as the gold standard for quantitative westerns. Total Protein Normalization requires that all proteins on a membrane be stained or labeled uniformly, imaged, and then analyzed for total protein. It is important that such a normalization process not interfere with typical immunodetection methods, fits within existing western workflows, and exhibits a linear relationship of signal intensity to protein load under all experimental conditions. Here we report that we developed a new reagent enabling Total Protein Normalization, and we demonstrate its superior protein normalization capabilities through analysis of target proteins in different cell backgrounds. These data illustrate how housekeeping proteins exhibit signal saturation, yield erroneous normalization data, and display sample-to-sample variations averaging 48.2 % overall. Signal intensities obtained using our new method show a linear relationship to protein sample load, thus providing accurate protein normalization with an overall average variation of 7.7 %.

Background The Gouqi-wentang formula (GQWTF) is a herbal formula used by Academician Xiao-lin Tong for the clinical treatment of T2DM. GQWTF is beneficial to qi, nourishes Yin, clears heat, and promotes fluid production, but the effective components and their mechanism of action remain unclear. Methods The main components of GQWTF were detected by LC–MS, and the multi-target mechanisms of GQWTF in T2DM were elucidated using network pharmacology analysis, including target prediction, protein–protein interaction network construction and analysis, Gene Ontology (GO) terms, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway annotation, and other network construction. Finally, the efficacy of the GQWTF was verified using biological experiments. Results First, the “herb-channel tropism” network suggested that GQWTF focuses more on treating diseases by recuperating the liver, which is considered as an important insulin-sensitive organ. Subsequently, a total of 16 active ingredients in GQWTF were detected and screened, and their biological targets were predicted. Then, “compound-target” network was constructed, where enrichment analysis of GQWTF targets reflected its potential pharmacological activities. After T2DM-related target identification, 39 cross targets of GQWTF and T2DM were obtained, and 30 key targets highly responsible for the beneficial effect of GQWTF on T2DM were identified by PPI analysis. GO analysis of these key targets showed that many biological processes of GQWTF in treating T2DM are key in the occurrence and development of T2DM, including components related to inflammatory/immune response, insulin, and metabolism. KEGG analysis revealed the regulation of multiple signalling pathways, such as insulin resistance, PPAR signalling pathway, FoxO signalling pathway, Fc epsilon RI signalling pathway, and pathways that influence diabetes primarily by regulating metabolism as well as other T2DM directly related pathways. Furthermore, a “formula-compound-pathway-symptom” network was constructed to represent a global view of GQWTF in the treatment of T2DM. Conclusions This study explored the mechanism of action of GQWTF in T2DM by multi-component and multi-target multi pathways, which could provide a theoretical basis for the development and clinical application of GQWTF.

The current work was focused on predicting the potential of several anticancer drugs as potential inhibitors of COVID-19 disease. The work was accoplished using molecular docking performed using SwissDock tool. Information about COVID-19 proteins and anticancer drugs from databases like PDB, PubChem and Drugbank has been incorporated appropriately in the manuscript. Data analysis has revealed some highly promising anticancer drugs which can further be critically analyzed through both computational and biological validation methods.This work was intended to support the urgent need of finding drugs/remedies against COVID-19. The results can be a foundation for other researchers around the world to further validate/test these anticancer agents against the pathogen.

24 pags., 7 figs., 3 tabs., New treatments for diseases caused by antimicrobial-resistant microorganisms can be developed by identifying unexplored therapeutic targets and by designing efficient drug screening protocols. In this study, we have screened a library of compounds to find ligands for the flavin-adenine dinucleotide synthase (FADS) -a potential target for drug design against tuberculosis and pneumonia- by implementing a new and efficient virtual screening protocol. The protocol has been developed for the in silico search of ligands of unexplored therapeutic targets, for which limited information about ligands or ligand-receptor structures is available. It implements an integrative funnel-like strategy with filtering layers that increase in computational accuracy. The protocol starts with a pharmacophore-based virtual screening strategy that uses ligand-free receptor conformations from molecular dynamics (MD) simulations. Then, it performs a molecular docking stage using several docking programs and an exponential consensus ranking strategy. The last filter, samples the conformations of compounds bound to the target using MD simulations. The MD conformations are scored using several traditional scoring functions in combination with a newly-proposed score that takes into account the fluctuations of the molecule with a Morse-based potential. The protocol was optimized and validated using a compound library with known ligands of the Corynebacterium ammoniagenes FADS. Then, it was used to find new FADS ligands from a compound library of 14,000 molecules. A small set of 17 in silico filtered molecules were tested experimentally. We identified five inhibitors of the activity of the flavin adenylyl transferase module of the FADS, and some of them were able to inhibit growth of three bacterial species: C. ammoniagenes, Mycobacterium tuberculosis, and Streptococcus pneumoniae, where the last two are human pathogens. Overall, the results show that the integrative VS protocol is a cost-effective solution for the discovery of ligands of unexplored therapeutic targets., The authors would like to acknowledge the use of Servicios Generales de Apoyo a la Investigacion-SAI, Universidad de Zaragoza. Some computations were performed in a local server with an NVIDIA Titan X GPU. P.C. gratefully acknowledges the support of NVIDIA Corporation for the donation of this GPU.

Purpose Initiatives such as The Cancer Genome Atlas and International Cancer Genome Consortium have generated high-quality, multiplatform molecular data from thousands of frozen tumor samples. Although these initiatives have provided invaluable insight into cancer biology, a tremendous potential resource remains largely untapped in formalin-fixed, paraffin-embedded (FFPE) samples that are more readily available but which can present technical challenges because of crosslinking of fragile molecules such as RNA. Materials and Methods We extracted RNA from FFPE primary melanomas and assessed two gene expression platforms—genome-wide RNA sequencing and targeted NanoString—for their ability to generate coherent biologic signals. To do so, we generated an improved approach to quantifying gene expression pathways. We refined pathway scores through correlation-guided gene subsetting. We also make comparisons to The Cancer Genome Atlas and other publicly available melanoma datasets. Results The comparison of the gene expression patterns to each other, to established biologic modules, and to clinical and immunohistochemical data confirmed the fidelity of biologic signals from both platforms using FFPE samples to known biology. Moreover, correlations with patient outcome data were consistent with previous frozen-tissue–based studies. Conclusion FFPE samples from previously difficult-to-access cancer types, such as small primary melanomas, represent a valuable and previously unexploited source of analyte for RNA sequencing and NanoString platforms. This work provides an important step toward the use of such platforms to unlock novel molecular underpinnings and inform future biologically driven clinical decisions.

This work benefitted from the French GDR "Aquatic Ecotoxicology" framework which is aimed at fostering stimulating scientific discussions and collaborations for more integrative approaches.; International audience; A highly multiplexed liquid chromatography mass spectrometry-multiple reaction monitoring (MRM)-based assay has been developed for evaluating 107 candidate immune biomarkers in both hemocytes and plasma of the zebra musselDreissena polymorpha. The Scout-MRM strategy was employed for the first time, shortening the implementation of a targeted MRM bottom-up proteomics assay using selected immune protein-related peptides identified by shotgun discovery proteogenomics. This strategy relies on spiking scout peptides during the discovery phase and using them to build and deploy the MRM targeted proteomics method. It proved to be highly relevant, since about 90% of the targeted peptides and proteins were monitored and rapidly measured in both hemocyte and plasma samples. The sample preparation protocol was optimized by evaluating the digestion efficiency of tryptic peptides over time. The accuracy and precision of 50 stable isotope-labeled peptides were evaluated for use as internal standards. Finally, the specificity of the transitions was thoroughly assessed to ensure the reliable measurement of protein biomarkers. Several analytical and biological validation criteria were evaluated across hemocytes and plasma samples exposed ex vivo to biological contaminants, resulting in the validation of two Scout-MRM assays for the relative quantitation of 85 and 89 proteins in hemocytes and plasma, respectively.

Identifying significant mutations in cancer is a challenging problem in Cancer Genomics. Computational methods for identifying significant mutations have been developed in recent years. In this work, we present a flexible computational method named GeNWeMME (Gene Network + Weighted Mutations + Mutual Exclusivity). Our method uses an extensive biological base for prioritizing groups of significant and related genes in cancer. Our method considers data about mutations, type of mutations, gene interaction networks and mutual exclusivity pattern. All these aspects can be used according to the objective of the analysis by cancer genomics professionals, that can choose weights for each aspect. We test our method in four types of cancer where it was possible to identify known cancer genes and suggest others for further biological validation.

Small molecule cholinesterase (ChE) inhibitors represent one of the most effective therapeutic strategies for the treatment of Alzheimer's disease (AD). However, only three of these drugs have entered the market. Understanding of the structures of successful ChE inhibitors is still very limited; therefore, it is an urgent task to identify new scaffolds for the design of ChE inhibitors. In the present study, we report an effective computer-aided workflow using a hierarchical structure-based virtual screening to identify new ChE inhibitors. 3D shape-based similarity screening combined with structure-based pharmacophore models was applied to obtain efficiently narrow potential hits from large compound collections, such as Chemdiv. Molecular docking was then used to further restrict the screening results. Five new ChE inhibitors, namely C629-0196, G070-1566, G115-0283, G801-0274, and F048-0694, were identified from the biological validation of 24 potential hits from the virtual screening. These compounds provide interesting templates for the design of new ChE inhibitors. Among the compounds, G801-0274 exhibited the lowest nanomolar range IC50 (0.031 ± 0.006 μM) and the highest selectivity (ratio = 66.13) against BuChE. Another compound, C629-0196, showed a low micromolar range IC50 (1.28 ± 0.83 μM) against AChE; however, it had no activity against BuChE. Most importantly, these two compounds provide good starting points for the discovery of highly selective ChE inhibitors.

Traditional medicine (TM) is a valuable source for drug discovery. The knowledge in healing traditions has led to the success of some of the best-known drugs. However, the concept of ancient medical knowledge, such as herbal remedies and their therapeutic experience is rarely used in the current methodologies for developing therapeutics from TM. As a result, the screening procedure of TM compounds remains tough and labor-intensive. This study aimed to develop a new strategy that is capable of efficiently identifying antiviral leads from complex traditional Chinese medicine (TCM) by integrating knowledge from ancient healing practices with luciferase-based high-throughput screening (HTS). 'Shanghan Zabing Lun', an ancient TCM treatise which contains over 200 formulae was selected for knowledge mining based on its antiviral activity. Thirty formulae were rationally selected and utilized for the preparation of a 1306-fraction herbal formulae extract library by standardized chromatographic fractionation. The antiviral activity of the library was screened on a HEK293T cell model carrying a luciferase-driven interferon stimulated response element (ISRE). Hit compounds were further identified using liquid chromatography mass spectrometry analysis, and the mechanism of action of which were preliminarily explored through western blotting and immunofluorescence. A total of 18 fractions and 3 compounds were found activating ISRE. The three compounds, namely ononin, sec-O-β-d-glucosylhamaudol and astragaloside I could activate p65 phosphorylation and nuclear translocation. By doing so, a new strategy termed Knowledge-Based High Throughput Screening (KB-HTS) has been established, which provides an alternative approach to unearth antiviral lead compounds from TM. This new discovery pipeline can expedite the discovery process by improving dereplication and lead prioritization strategies, which is valuable for novel lead discovery from traditional medicine.

The widespread use of Cry proteins in insecticide formulations and transgenic crops for insect control has led to an increased interest in the environmental fate of these proteins. Several detection methods are available to monitor the fate of Cry proteins in the environment, but enzyme-linked immunosorbent assays (ELISAs) have emerged as the preferred detection method, because they are cost-effective, easy to use, and provide rapid results. Validation methodology, which is essential to ensure accurate measurement of Cry proteins in environmental matrices, has been researched extensively, but most of this research has overlooked biological validation of ELISA results. This oversight has led to concerns that environmental studies utilizing ELISAs may be overestimating the concentrations of Cry proteins in the environment, which may affect the risk assessments for these proteins. A literature review discusses the history and usage of Cry proteins for insect pest control and discusses the use and validation of ELISAs for detection of Cry proteins in environmental samples. The concept of biologically validating ELISA results is introduced, and a critical review of published literature examines the state of ELISA usage and validation, including identifying areas for improvement. Eight different types of model systems were screened for their ability to produce fragments of Cry1Ab protein, and five of these model systems prove capable of generating Cry1Ab fragments. The fragments from these five model systems are then analyzed with ELISAs and bioassays to determine if the fragments are detectable or retain bioactivity. Fragments from four of the model systems are not detectable by ELISA and do not retain bioactivity. Fragments from the fifth model system are detectable by ELISA and do retain bioactivity. These results indicate that the use of ELISAs in environmental fate studies are providing an

Methylation-based study is currently a popular ongoing research topic. The researchers generally use 5-methylcytosine (5-mC) samples for their study since this category of samples is the highest stable methylation cytosine variant, and the impact of 5-mC methylation on different diseases is known to the common people. But, through recent studies, it has been observed that other cytosine variants (e.g., 5-hmC) have also high impact on those diseases. Therefore, in this chapter, we firstly demonstrate the abovementioned different cytosine variants. In the second part of the chapter, we describe a framework of identifying co-methylated gene modules on a methylation profile having multiple cytosine variants (viz., 5-hmC and 5-mC samples). For this, at first we determine significant genes using statistical method. Thereafter, weighted topological overlap matrix (weighted TOM) measure and average linkage method are applied, consecutively on the resultant significant genes. Then dynamic tree cut method with color thresholding is utilized, and co-methylated gene modules are identified from it. The resultant gene modules are then validated biologically by KEGG pathway and gene ontology analyses. Moreover, regulatory transcription factors (TFs) and targeter miRNAs connected with the genes belonging to the different modules are found, and further biological validation has been carried out on them. Finally, other related module-based and correlation-based popular computation methodologies and applications are also shortly demonstrated.

The identification of biomarkers is very important to know the presence or severity of a particular disease state in the patient body. According to the latest studies on miRNAs and their behaviors, it is known to us that miRNAs involve in the regulation mechanism of several biological processes. Sometimes the abnormal change in miRNA expressions in different conditions may lead to malignant growth in tissues. In this article, our proposed approach not only helps to detect differentially coexpressed modules but also helps to identify biomarker candidates from those modules. The proposed algorithm uses the WCGNA software package to explore coexpression profiles of the miRNAs. The algorithm has been applied to existing miRNA datasets to point out the miRNA markers. Then, biological validation analysis has been performed for the obtained miRNA markers.

Gene set analysis is a quantitative approach for generating biological insight from gene expression datasets. The abundance of gene set analysis methods speaks to their popularity, but raises the question of the extent to which results are affected by the choice of method. Our systematic analysis of 13 popular methods using 6 different datasets, from both DNA microarray and RNA-Seq origin, shows that this choice matters a great deal. We observed that the overall number of gene sets reported by each method differed by up to 2 orders of magnitude, and there was a bias toward reporting large gene sets with some methods. Furthermore, there was substantial disagreement between the 20 most statistically significant gene sets reported by the methods. This was also observed when expanding to the 100 most statistically significant reported gene sets. For different datasets of the same phenotype/condition, the top 20 and top 100 most significant results also showed little to no agreement even when using the same method. GAGE, PAGE, and ORA were the only methods able to achieve relatively high reproducibility when comparing the 20 and 100 most statistically significant gene sets. Biological validation on a juvenile idiopathic arthritis (JIA) dataset showed wide variation in terms of the relevance of the top 20 and top 100 most significant gene sets to known biology of the disease, where GAGE predicted the most relevant gene sets, followed by GSEA, ORA, and PAGE.

This study presents a novel computational approach to find relevant pathways from dose-dependent time series gene expression data which are significantly associated with a phenotype pattern pathological patterns in the comprehensive evaluation of a database of pathways. Our system uses four steps: 1) identify a set of genes which change significantly in dose or time, 2) find phenotype patterns and gene coefficients for the genes found in step 1; 3) expand to genome-wide coefficients, and 4) identify pathways which are significantly relevant to a phenotype pattern. Our technique finds biologically relevant pathways with and without phenotype constraints. Our system has been used on genome-wide expression profiles of mouse lungs (n=160) following aspiration of well dispersed multi-walled carbon nanotubes (MWCNT), in order to detect MWCNT-induced lung inflammation and related pathways. The identified significant pathways are supported by evidence in the literature and biological validation.

High-throughput mRNA expression profiling is a widely used first step in the identification of new targets. Proper consideration of this step is essential before initiating the time-consuming and more elaborate biological validation of a target. This review discusses the strengths and limitations of this approach, with a special emphasis on studies in cardiac tissue. Specifically, it focuses on the basis of any such study, the tissue samples and the implications for statistical analysis of data retrieved from heterogeneous biological material. It also discusses studies performed in cardiac tissue and considers strategies to define target candidates based on expression profiles.

Summarization: Statistical evaluation of temporal gene expression profiles plays an important role in particular biological processes and conditions. We introduce a clustering method for this purpose, which is based on the expression patterns but is also influenced by temporal changes. We compare the results of our platform with methods based on expression or the rank of temporal changes. The proposed platform is illustrated with a temporal gene expression dataset comprised of primary human chondrocytes and mesenchymal stem cells (MSCs). We derived three clusters in each cell type and compared the content of these classes in terms of temporal changes, which can support biological performance. For statistical evaluation we introduce a validity measure that takes under consideration these temporal changes and we also perform an enrichment analysis of three central genes in each cluster. Even though we can detect certain statistical similarities, these might be due to different biological processes. Our proposed platform contributes to both the statistical and biological validation of temporal profiles. Παρουσιάστηκε στο: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

The great amount of biological information provides scientists with an incomparable framework for testing the results of new algorithms. Several tools have been developed for analysing gene-enrichment and most of them are Gene Ontology-based tools. We developed a Kyoto Encyclopedia of Genes and Genomes (Kegg)-based tool that provides a friendly graphical environment for analysing gene-enrichment. The tool integrates two statistical corrections and simultaneously analysing the information about many groups of genes in both visual and textual manner. We tested the usefulness of our approach on a previous analysis (Huttenshower et al.). Furthermore, our tool is freely available (http://www.upo.es/eps/bigs/cargene.html).

MicroRNAs (miRNAs) which are on average only 21-25 nucleotides long are key post-transcriptional regulators of gene expression in metazoans and plants. A proper quantitative understanding of miRNAs is required to comprehend their structures, functions, evolutions etc. In this paper, the nucleotide strings of miRNAs of three organisms namely Homo sapiens (hsa), Macaca mulatta (mml) and Pan troglodytes (ptr) have been quantified and classified based on some characterizing features. A network has been built up among the miRNAs for these three organisms through a class of discrete transformations namely Integral Value Transformations (IVTs), proposed by Sk. S. Hassan et al [1, 2]. Through this study we have been able to nullify or justify one given nucleotide string as a miRNA. This study will help us to recognize a given nucleotide string as a probable miRNA, without the requirement of any conventional biological experiment. This method can be amalgamated with the existing analysis pipelines, for small RNA sequencing data (designed for finding novel miRNA). This method would provide more confidence and would make the current analysis pipeline more efficient in predicting the probable candidates of miRNA for biological validation and filter out the improbable candidates.

Conventional 3D culture is typically performed in multi-well plates (e.g. 12 wells). The volumes and dimensions necessitate relatively large numbers of cells and fluid exchange steps are not easily automated limiting throughput. 3D microchannel culture can overcome these challenges simplifying 3D culture processes. However, the adaptation of immunocytochemical endpoint measurements and the validation of microchannel 3D culture with conventional 3D culture are needed before widespread adoption can occur. Here we use a breast carcinoma growth model governed by complex and reciprocal interactions between epithelial carcinoma cells and mesenchymal fibroblasts to validate the 3D microculture system. Specifically, we report the use of a 3D microchannel co-culture assay platform to interrogate paracrine signalling pathways in breast cancer. Using a previously validated 3D co-culture of human mammary fibroblasts and T47D breast carcinoma cells, we demonstrate the use of arrayed microchannels to analyze paracrine signalling pathways and screen for inhibitors. Results in both conventional format (multiwell plate) and microchannels were comparable. This technology represents a significant advancement for high-throughput screening in individual patients and for drug discovery by enabling the use of 3D co-culture models via smaller sample requirements and compatibility with existing HTS infrastructure (e.g. automated liquid handlers, scanners).

Biological validation of a cadre of new obesity genes supports the power of studies that exploit 'expression quantitative trait loci'.

An Institute of Cancer Research team has developed a computational algorithm that hones in on cancer targets with the desired mix of biological validation and druggability. The approach could help prioritize targets.

MicroRNAs are short non-coding RNAs that inhibit translation of target genes by binding to their mRNAs, and have been shown to play a central role in gene regulation in health and disease. Sophisticated computer-based prediction approaches of microRNAs and of their targets, and effective biological validation techniques for validating these predictions, now play a central role in discovery of microRNAs and elucidating their functions.

RNA-interference (RNAi) studies hold great promise for functional investigation of the significance of genetic variations and mutations, as well as potential synthetic lethalities, for understanding and treatment of cancer, yet technical and conceptual issues currently diminish the potential power of this approach. While numerous research groups are usefully employing this kind of functional genomic methodology to identify molecular mediators of disease severity, response, and resistance to treatment, findings are generally confounded by “off-target” effects. These effects arise from a variety of issues beyond non-specific reagent behavior, such as biological cross-talk and feedback processes so thus can occur even with specific perturbation. Interpreting RNAi results in a network framework instead of merely as individual “hits” or “targets” leverages contributions from all hit/target contributions to pathways via their relationships with other network nodes. This interpretation can ameliorate dependence upon individual reagent performance and increase confidence in biological validation. Here we provide background on RNAi studies in cancer applications, review key challenges with functional genomics, and motivate the use of network models grounded in pathway analyses., National Science Foundation (U.S.). Graduate Research Fellowship, National Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant U54-CA112967), National Cancer Institute (U.S.) (Grant U01-CA155758)