Discovery and characterization of potent And-1 inhibitors for cancer treatment

Acidic nucleoplasmic DNA-binding protein 1 (And-1), an important factor for deoxyribonucleic acid (DNA) replication and repair, is overexpressed in many types of cancer but not in normal tissues. Although multiple independent studies have elucidated And-1 as a promising target gene for cancer therapy, an And-1 inhibitor has yet to be identified. Using an And-1 luciferase reporter assay to screen the Library of Pharmacologically Active Compounds (LOPAC) in a high throughput screening (HTS) platform, and then further screen the compound analog collection, we identified two potent And-1 inhibitors, bazedoxifene acetate (BZA) and an uncharacterized compound [(E)-5-(3,4-dichlorostyryl)benzo[c][1,2]oxaborol-1(3H)-ol] (CH3), which specifically inhibit And-1 by promoting its degradation. Specifically, through direct interaction with And-1 WD40 domain, CH3 interrupts the polymerization of And-1. Depolymerization of And-1 promotes its interaction with E3 ligase Cullin 4B (CUL4B), resulting in its ubiquitination and subsequent degradation. Furthermore, CH3 suppresses the growth of a broad range of cancers. Moreover, And-1 inhibitors re-sensitize platinum-resistant ovarian cancer cells to platinum drugs in vitro and in vivo. Since BZA is an FDA approved drug, we expect a clinical trial of BZA-mediated cancer therapy in the near future. Taken together, our findings suggest that targeting And-1 by its inhibitors is a potential broad-spectrum anti-cancer chemotherapy regimen.

Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations-deformable and non-deformable sRBCs-utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring.

Cardiotoxicity Hazard and Risk Characterization of ToxCast Chemicals Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes from Multiple Donors

Heart disease remains a significant human health burden worldwide with a significant fraction of morbidity attributable to environmental exposures. However, the extent to which the thousands of chemicals in commerce and the environment may contribute to heart disease morbidity is largely unknown, because in contrast to pharmaceuticals, environmental chemicals are seldom tested for potential cardiotoxicity. Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes have become an informative in vitro model for cardiotoxicity testing of drugs with the availability of cells from multiple individuals allowing in vitro testing of population variability. In this study, we hypothesized that a panel of iPSC-derived cardiomyocytes from healthy human donors can be used to screen for the potential cardiotoxicity hazard and risk of environmental chemicals. We conducted concentration-response testing of 1029 chemicals (drugs, pesticides, flame retardants, polycyclic aromatic hydrocarbons (PAHs), plasticizers, industrial chemicals, food/flavor/fragrance agents, etc.) in iPSC-derived cardiomyocytes from 5 donors. We used kinetic calcium flux and high-content imaging to derive quantitative measures as inputs into Bayesian population concentration-response modeling of the effects of each chemical. We found that many environmental chemicals pose a hazard to human cardiomyocytes in vitro with more than half of all chemicals eliciting positive or negative chronotropic or arrhythmogenic effects. However, most of the tested environmental chemicals for which human exposure and high-throughput toxicokinetics data were available had wide margins of exposure and, thus, do not appear to pose a significant human health risk in a general population. Still, relatively narrow margins of exposure (<100) were estimated for some perfuoroalkyl substances and phthalates, raising concerns that cumulative exposures may pose a cardiotoxicity risk. Collectively, this study demonstrated the value of using a population-based human in vitro model for rapid, high-throughput hazard and risk characterization of chemicals for which little to no cardiotoxicity data are available from guideline studies in animals.

High-Throughput Screening and Quantum Mechanics for Identifying Potent Inhibitors Against Mac1 Domain of SARS-CoV-2 Nsp3

SARS-CoV-2 encodes the Mac1 domain within the large nonstructural protein 3 (Nsp3), which has an ADP-ribosylhydrolase activity conserved in other coronaviruses. The enzymatic activity of Mac1 makes it an essential virulence factor for the pathogenicity of coronavirus (CoV). They have a regulatory role in counteracting host-mediated antiviral ADP-ribosylation, which is unique part of host response towards viral infections. Mac1 shows highly conserved residues in the binding pocket for the mono and poly ADP-ribose. Therefore, SARS-CoV-2 Mac1 enzyme is considered as an ideal drug target and inhibitors developed against them can possess a broad antiviral activity against CoV. ADP-ribose-1 phosphate bound closed form of Mac1 domain is considered for screening with large database of ZINC. XP docking and QPLD provides strong potential lead compounds, that perfectly fits inside the binding pocket. Quantum mechanical studies expose that, substrate and leads have similar electron donor ability in the head regions, that allocates tight binding inside the substrate-binding pocket. Molecular dynamics study confirms the substrate and new lead molecules presence of electron donor and acceptor makes the interactions tight inside the binding pocket. Overall binding phenomenon shows both substrate and lead molecules are well-adopt to bind with similar binding mode inside the closed form of Mac1.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

BACKGROUND

Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model.

METHODS

This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA).

RESULTS

Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model.

CONCLUSIONS

The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

Development and validation of a multiplex immunoassay for the simultaneous quantification of type-specific IgG antibodies to E6/E7 oncoproteins of HPV16 and HPV18

More than 170 types of human papilloma viruses (HPV) exist with many causing proliferative diseases linked to malignancy in indications such as cervical cancer and head and neck squamous cell carcinoma. Characterization of antibody levels toward HPV serology is challenging due to complex biology of oncoproteins, pre-existing titers to multiple HPV types, cross-reactivity, and low affinity, polyclonal responses. Using multiplex technology from MSD, we have developed an assay that simultaneously characterizes antibodies against E6 and E7 oncoproteins of HPV16 and 18, the primary drivers of HPV-associated oncogenesis. We fusion tagged our E6 and E7 proteins with MBP via two-step purification, spot-printed an optimized concentration of protein into wells of MSD 96-well plates, and assayed various cynomolgus monkey, human and HPV+ cervical cancer patient serum to validate the assay. The dynamic range of the assay covered 4-orders of magnitude and antibodies were detected in serum at a dilution up to 100,000-fold. The assay was very precise (n = 5 assay runs) with median CV of human serum samples ~ 5.3% and inter-run variability of 11.4%. The multiplex serology method has strong cross-reactivity between E6 oncoproteins from human serum samples as HPV18 E6 antigens neutralized 5 of 6 serum samples as strongly as HPV16 E6. Moderate concordance (Spearman’s Rank = 0.775) was found between antibody responses against HPV16 E7 in the multiplex assay compared to standard ELISA serology methods. These results demonstrate the development of a high-throughput, multi-plex assay that requires lower sample quantity input with greater dynamic range to detect type-specific anti-HPV concentrations to E6 and E7 oncoproteins of HPV16 and 18.

Quality control of cervical cytology using a 3-type HPV mRNA test increases screening program sensitivity of cervical intraepithelial neoplasia grade 2+ in young Norwegian women-A cohort study

Within 2021, Norway intends to complete implementation of HPV DNA-based primary screening for cervical cancer for women 34–69 years, while continue cytology-based screening for women 25–33 years. Over the recent years, the incidence of cervical cancer has increased by 30% among women younger than 40 years. In this subset of women, nearly 30% were diagnosed with a normal smear, as most recent smear, prior the cancer diagnosis. This observation demands quality control of normal smears. The aim of this study was to assess increase in program sensitivity of CIN2+ after follow-up of women with false negative Pap-smears testing positive for a 3-type (-16, -18, -45) HPV mRNA test in a cohort design over one screening interval. 521 women, aged 23–39 years, and no prior history of CIN1+ or HSIL, with an ASC-US or worse smear (ASC-US+) and 1444 women with normal screening cytology comprised the study cohorts. The positivity rate for the 3-type HPV mRNA was 1.9% (28/1444). Rescreening revealed 23 women with ASC-US, two women with LSIL, two women with ASC-H, and one woman with AGUS. If the HPV mRNA-positivity rate and histology findings from samples rescreened were applied to all women with normal cytology, an estimated increase in screening sensitivity of 16.4% (95% CI:15.3–17.5) for CIN2+ and 17.3% (95% CI:16.2–18.4) for CIN3+ were achieved. By rescreening less than 2% of women with normal cytology positive for a 3-type HPV mRNA test, we achieved a significant increase in screening program sensitivity.

Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions

Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.

Estimating the local false discovery rate via a bootstrap solution to the reference class problem

Methods of estimating the local false discovery rate (LFDR) have been applied to different types of datasets such as high-throughput biological data, diffusion tensor imaging (DTI), and genome-wide association (GWA) studies. We present a model for LFDR estimation that incorporates a covariate into each test. Incorporating the covariates may improve the performance of testing procedures, because it contains additional information based on the biological context of the corresponding test. This method provides different estimates depending on a tuning parameter. We estimate the optimal value of that parameter by choosing the one that minimizes the estimated LFDR resulting from the bias and variance in a bootstrap approach. This estimation method is called an adaptive reference class (ARC) method. In this study, we consider the performance of ARC method under certain assumptions on the prior probability of each hypothesis test as a function of the covariate. We prove that, under these assumptions, the ARC method has a mean squared error asymptotically no greater than that of the other method where the entire set of hypotheses is used and assuming a large covariate effect. In addition, we conduct a simulation study to evaluate the performance of estimator associated with the ARC method for a finite number of hypotheses. Here, we apply the proposed method to coronary artery disease (CAD) data taken from a GWA study and diffusion tensor imaging (DTI) data.

High-Throughput H295R Steroidogenesis Assay: Utility as an Alternative and a Statistical Approach to Characterize Effects on Steroidogenesis

The U.S. Environmental Protection Agency Endocrine Disruptor Screening Program and the Organization for Economic Co-operation and Development (OECD) have used the human adrenocarcinoma (H295R) cell-based assay to predict chemical perturbation of androgen and estrogen production. Recently, a high-throughput H295R (HT-H295R) assay was developed as part of the ToxCast program that includes measurement of 11 hormones, including progestagens, corticosteroids, androgens, and estrogens. To date, 2012 chemicals have been screened at 1 concentration; of these, 656 chemicals have been screened in concentration-response. The objectives of this work were to: (1) develop an integrated analysis of chemical-mediated effects on steroidogenesis in the HT-H295R assay and (2) evaluate whether the HT-H295R assay predicts estrogen and androgen production specifically via comparison with the OECD-validated H295R assay. To support application of HT-H295R assay data to weight-of-evidence and prioritization tasks, a single numeric value based on Mahalanobis distances was computed for 654 chemicals to indicate the magnitude of effects on the synthesis of 11 hormones. The maximum mean Mahalanobis distance (maxmMd) values were high for strong modulators (prochloraz, mifepristone) and lower for moderate modulators (atrazine, molinate). Twenty-five of 28 reference chemicals used for OECD validation were screened in the HT-H295R assay, and produced qualitatively similar results, with accuracies of 0.90/0.75 and 0.81/0.91 for increased/decreased testosterone and estradiol production, respectively. The HT-H295R assay provides robust information regarding estrogen and androgen production, as well as additional hormones. The maxmMd from this integrated analysis may provide a data-driven approach to prioritizing lists of chemicals for putative effects on steroidogenesis.

Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome

Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

A deep learning and novelty detection framework for rapid phenotyping in high-content screening

Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening.

Census and evaluation of p53 target genes

The tumor suppressor p53 functions primarily as a transcription factor. Mutation of the TP53 gene alters its response pathway, and is central to the development of many cancers. The discovery of a large number of p53 target genes, which confer p53's tumor suppressor function, has led to increasingly complex models of p53 function. Recent meta-analysis approaches, however, are simplifying our understanding of how p53 functions as a transcription factor. In the survey presented here, a total set of 3661 direct p53 target genes is identified that comprise 3509 potential targets from 13 high-throughput studies, and 346 target genes from individual gene analyses. Comparison of the p53 target genes reported in individual studies with those identified in 13 high-throughput studies reveals limited consistency. Here, p53 target genes have been evaluated based on the meta-analysis data, and the results show that high-confidence p53 target genes are involved in multiple cellular responses, including cell cycle arrest, DNA repair, apoptosis, metabolism, autophagy, mRNA translation and feedback mechanisms. However, many p53 target genes are identified only in a small number of studies and have a higher likelihood of being false positives. While numerous mechanisms have been proposed for mediating gene regulation in response to p53, recent advances in our understanding of p53 function show that p53 itself is solely an activator of transcription, and gene downregulation by p53 is indirect and requires p21. Taking into account the function of p53 as an activator of transcription, recent results point to an unsophisticated means of regulation.

A Novel Computer-Assisted Approach to evaluate Multicellular Tumor Spheroid Invasion Assay

Multicellular tumor spheroids (MCTSs) embedded in a matrix are re-emerging as a powerful alternative to monolayer-based cultures. The primary information gained from a three-dimensional model is the invasiveness of treatment-exposed MCTSs through the acquisition of light microscopy images. The amount and complexity of the acquired data and the bias arisen by their manual analysis are disadvantages calling for an automated, high-throughput analysis. We present a universal algorithm we developed with the scope of being robust enough to handle images of various qualities and various invasion profiles. The novelty and strength of our algorithm lie in: the introduction of a multi-step segmentation flow, where each step is optimized for each specific MCTS area (core, halo, and periphery); the quantification through the density of the two-dimensional representation of a three-dimensional object. This latter offers a fine-granular differentiation of invasive profiles, facilitating a quantification independent of cell lines and experimental setups. Progression of density from the core towards the edges influences the resulting density map thus providing a measure no longer dependent on the sole area size of MCTS, but also on its invasiveness. In sum, we propose a new method in which the concept of quantification of MCTS invasion is completely re-thought.

Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics

In the quest for deciphering disease-associated biomarkers, high-performing tools for multiplexed protein expression profiling of crude clinical samples will be crucial. Affinity proteomics, mainly represented by antibody-based microarrays, have during recent years been established as a proteomic tool providing unique opportunities for parallelized protein expression profiling. But despite the progress, several main technical features and assay procedures remains to be (fully) resolved. Among these issues, the handling of protein microarray data, i.e. the biostatistics parts, is one of the key features to solve. In this study, we have therefore further optimized, validated, and standardized our in-house designed recombinant antibody microarray technology platform. To this end, we addressed the main remaining technical issues (e.g. antibody quality, array production, sample labelling, and selected assay conditions) and most importantly key biostatistics subjects (e.g. array data pre-processing and biomarker panel condensation). This represents one of the first antibody array studies in which these key biostatistics subjects have been studied in detail. Here, we thus present the next generation of the recombinant antibody microarray technology platform designed for clinical immunoproteomics.

Metadata management for high content screening in OMERO

High content screening (HCS) experiments create a classic data management challenge-multiple, large sets of heterogeneous structured and unstructured data, that must be integrated and linked to produce a set of "final" results. These different data include images, reagents, protocols, analytic output, and phenotypes, all of which must be stored, linked and made accessible for users, scientists, collaborators and where appropriate the wider community. The OME Consortium has built several open source tools for managing, linking and sharing these different types of data. The OME Data Model is a metadata specification that supports the image data and metadata recorded in HCS experiments. Bio-Formats is a Java library that reads recorded image data and metadata and includes support for several HCS screening systems. OMERO is an enterprise data management application that integrates image data, experimental and analytic metadata and makes them accessible for visualization, mining, sharing and downstream analysis. We discuss how Bio-Formats and OMERO handle these different data types, and how they can be used to integrate, link and share HCS experiments in facilities and public data repositories. OME specifications and software are open source and are available at https://www.openmicroscopy.org.

[OMICS AND BIG DATA, MAJOR ADVANCES TOWARDS PERSONALIZED MEDICINE OF THE FUTURE?]

The increasing interest for personalized medicine evolves together with two major technological advances. First, the new-generation, rapid and less expensive, DNA sequencing method, combined with remarkable progresses in molecular biology leading to the post-genomic era (transcriptomics, proteomics, metabolomics). Second, the refinement of computing tools (IT), which allows the immediate analysis of a huge amount of data (especially, those resulting from the omics approaches) and, thus, creates a new universe for medical research, that of <<big data>> analyzed by computerized modelling. This article for scientific communication and popularization briefly describes the main advances in these two fields of interest. These technological progresses are combined with those occurring in communication, which makes possible the development of artificial intelligence. These major advances will most probably represent the grounds of the future personalized medicine.

Protocols and programs for high-throughput growth and aging phenotyping in yeast

In microorganisms, and more particularly in yeasts, a standard phenotyping approach consists in the analysis of fitness by growth rate determination in different conditions. One growth assay that combines high throughput with high resolution involves the generation of growth curves from 96-well plate microcultivations in thermostated and shaking plate readers. To push the throughput of this method to the next level, we have adapted it in this study to the use of 384-well plates. The values of the extracted growth parameters (lag time, doubling time and yield of biomass) correlated well between experiments carried out in 384-well plates as compared to 96-well plates or batch cultures, validating the higher-throughput approach for phenotypic screens. The method is not restricted to the use of the budding yeast Saccharomyces cerevisiae, as shown by consistent results for other species selected from the Hemiascomycete class. Furthermore, we used the 384-well plate microcultivations to develop and validate a higher-throughput assay for yeast Chronological Life Span (CLS), a parameter that is still commonly determined by a cumbersome method based on counting "Colony Forming Units". To accelerate analysis of the large datasets generated by the described growth and aging assays, we developed the freely available software tools GATHODE and CATHODE. These tools allow for semi-automatic determination of growth parameters and CLS behavior from typical plate reader output files. The described protocols and programs will increase the time- and cost-efficiency of a number of yeast-based systems genetics experiments as well as various types of screens.

Quantitative high-throughput screening data analysis: challenges and recent advances

In vitro HTS holds much potential to advance drug discovery and provide cell-based alternatives for toxicity testing. In quantitative HTS, concentration-response data can be generated simultaneously for thousands of different compounds and mixtures. However, nonlinear modeling in these multiple-concentration assays presents important statistical challenges that are not problematic for linear models. The uncertainty of parameter estimates obtained from the widely used Hill equation model can be extremely large when using standard designs. Failure to properly consider standard errors of these parameter estimates would greatly hinder chemical genomics and toxicity testing efforts. In this light, optimal study designs should be developed to improve nonlinear parameter estimation; or alternative approaches with reliable performance characteristics should be used to describe concentration-response profiles.

NEMATIC: a simple and versatile tool for the in silico analysis of plant-nematode interactions

Novel approaches for the control of agriculturally damaging nematodes are sorely needed. Endoparasitic nematodes complete their life cycle within the root vascular cylinder, inducing specialized feeding cells: giant cells for root-knot nematodes and syncytia for cyst nematodes. Both nematodes hijack parts of the transduction cascades involved in developmental processes, or partially mimic the plant responses to other interactions with microorganisms, but molecular evidence of their differences and commonalities is still under investigation. Transcriptomics has been used to describe global expression profiles of their interaction with Arabidopsis, generating vast lists of differentially expressed genes. Although these results are available in public databases and publications, the information is scattered and difficult to handle. Here, we present a rapid, visual, user-friendly and easy to handle spreadsheet tool, called NEMATIC (NEMatode-Arabidopsis Transcriptomic Interaction Compendium; http://www.uclm.es/grupo/gbbmp/english/nematic.asp). It combines existing transcriptomic data for the interaction between Arabidopsis and plant-endoparasitic nematodes with data from different transcriptomic analyses regarding hormone and cell cycle regulation, development, different plant tissues, cell types and various biotic stresses. NEMATIC facilitates efficient in silico studies on plant-nematode biology, allowing rapid cross-comparisons with complex datasets and obtaining customized gene selections through sequential comparative and filtering steps. It includes gene functional classification and links to utilities from several databases. This data-mining spreadsheet will be valuable for the understanding of the molecular bases subjacent to feeding site formation by comparison with other plant systems, and for the selection of genes as potential tools for biotechnological control of nematodes, as demonstrated in the experimentally confirmed examples provided.

Influence of physical and chemical properties of HTSXT-FTIR samples on the quality of prediction models developed to determine absolute concentrations of total proteins, carbohydrates and triglycerides: a preliminary study on the determination of their absolute concentrations in fresh microalgal biomass

Absolute concentrations of total macromolecules (triglycerides, proteins and carbohydrates) in microorganisms can be rapidly measured by FTIR spectroscopy, but caution is needed to avoid non-specific experimental bias. Here, we assess the limits within which this approach can be used on model solutions of macromolecules of interest. We used the Bruker HTSXT-FTIR system. Our results show that the solid deposits obtained after the sampling procedure present physical and chemical properties that influence the quality of the absolute concentration prediction models (univariate and multivariate). The accuracy of the models was degraded by a factor of 2 or 3 outside the recommended concentration interval of 0.5-35 µg spot(-1). Change occurred notably in the sample hydrogen bond network, which could, however, be controlled using an internal probe (pseudohalide anion). We also demonstrate that for aqueous solutions, accurate prediction of total carbohydrate quantities (in glucose equivalent) could not be made unless a constant amount of protein was added to the model solution (BSA). The results of the prediction model for more complex solutions, here with two components: glucose and BSA, were very encouraging, suggesting that this FTIR approach could be used as a rapid quantification method for mixtures of molecules of interest, provided the limits of use of the HTSXT-FTIR method are precisely known and respected. This last finding opens the way to direct quantification of total molecules of interest in more complex matrices.

Parallel clustering algorithm for large-scale biological data sets

BACKGROUNDS

Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs.

METHODS

Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes.

RESULT

A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.

Challenges in secondary analysis of high throughput screening data

Repurposing an existing drug for an alternative use is not only a cost effective method of development, but also a faster process due to the drug's previous clinical testing and established pharmokinetic profiles. A potentially rich resource for computational drug repositioning approaches is publically available high throughput screening data, available in databases such as PubChem Bioassay and ChemBank. We examine statistical and computational considerations for secondary analysis of publicly available high throughput screening (HTS) data with respect to metadata, data quality, and completeness. We discuss developing methods and best practices that can help to ameliorate these issues.

Systems analysis of high-throughput data

Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g.,  sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.

Identification of antioxidants from sequence information using naïve Bayes

Antioxidant proteins are substances that protect cells from the damage caused by free radicals. Accurate identification of new antioxidant proteins is important in understanding their roles in delaying aging. Therefore, it is highly desirable to develop computational methods to identify antioxidant proteins. In this study, a Naïve Bayes-based method was proposed to predict antioxidant proteins using amino acid compositions and dipeptide compositions. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife test, the proposed method achieved an accuracy of 66.88% for the discrimination between antioxidant and nonantioxidant proteins, which is superior to that of other state-of-the-art classifiers. These results suggest that the proposed method could be an effective and promising high-throughput method for antioxidant protein identification.

Identification of bicluster regions in a binary matrix and its applications

Biclustering has emerged as an important approach to the analysis of large-scale datasets. A biclustering technique identifies a subset of rows that exhibit similar patterns on a subset of columns in a data matrix. Many biclustering methods have been proposed, and most, if not all, algorithms are developed to detect regions of "coherence" patterns. These methods perform unsatisfactorily if the purpose is to identify biclusters of a constant level. This paper presents a two-step biclustering method to identify constant level biclusters for binary or quantitative data. This algorithm identifies the maximal dimensional submatrix such that the proportion of non-signals is less than a pre-specified tolerance δ. The proposed method has much higher sensitivity and slightly lower specificity than several prominent biclustering methods from the analysis of two synthetic datasets. It was further compared with the Bimax method for two real datasets. The proposed method was shown to perform the most robust in terms of sensitivity, number of biclusters and number of serotype-specific biclusters identified. However, dichotomization using different signal level thresholds usually leads to different sets of biclusters; this also occurs in the present analysis.

Enabling high-throughput genotype-phenotype associations in the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project as part of the Population Architecture using Genomics and Epidemiology (PAGE) study

Genetic association studies have rapidly become a major tool for identifying the genetic basis of common human diseases. The advent of cost-effective genotyping coupled with large collections of samples linked to clinical outcomes and quantitative traits now make it possible to systematically characterize genotype-phenotype relationships in diverse populations and extensive datasets. To capitalize on these advancements, the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project, as part of the collaborative Population Architecture using Genomics and Epidemiology (PAGE) study, accesses two collections: the National Health and Nutrition Examination Surveys (NHANES) and BioVU, Vanderbilt University's biorepository linked to de-identified electronic medical records. We describe herein the workflows for accessing and using the epidemiologic (NHANES) and clinical (BioVU) collections, where each workflow has been customized to reflect the content and data access limitations of each respective source. We also describe the process by which these data are generated, standardized, and shared for meta-analysis among the PAGE study sites. As a specific example of the use of BioVU, we describe the data mining efforts to define cases and controls for genetic association studies of common cancers in PAGE. Collectively, the efforts described here are a generalized outline for many of the successful approaches that can be used in the era of high-throughput genotype-phenotype associations for moving biomedical discovery forward to new frontiers of data generation and analysis.

ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levels

Technology is driving the field of human genetics research with advances in techniques to generate high-throughput data that interrogate various levels of biological regulation. With this massive amount of data comes the important task of using powerful bioinformatics techniques to sift through the noise to find true signals that predict various human traits. A popular analytical method thus far has been the genome-wide association study (GWAS), which assesses the association of single nucleotide polymorphisms (SNPs) with the trait of interest. Unfortunately, GWAS has not been able to explain a substantial proportion of the estimated heritability for most complex traits. Due to the inherently complex nature of biology, this phenomenon could be a factor of the simplistic study design. A more powerful analysis may be a systems biology approach that integrates different types of data, or a meta-dimensional analysis. For this study we used the Analysis Tool for Heritable and Environmental Network Associations (ATHENA) to integrate high-throughput SNPs and gene expression variables (EVs) to predict high-density lipoprotein cholesterol (HDL-C) levels. We generated multivariable models that consisted of SNPs only, EVs only, and SNPs + EVs with testing r-squared values of 0.16, 0.11, and 0.18, respectively. Additionally, using just the SNPs and EVs from the best models, we generated a model with a testing r-squared of 0.32. A linear regression model with the same variables resulted in an adjusted r-squared of 0.23. With this systems biology approach, we were able to integrate different types of high-throughput data to generate meta-dimensional models that are predictive for the HDL-C in our data set. Additionally, our modeling method was able to capture more of the HDL-C variation than a linear regression model that included the same variables.

A high-throughput time-resolved mini-silicon photomultiplier with embedded fluorescence lifetime estimation in 0.13 μm CMOS

We describe a miniaturized, high-throughput, time-resolved fluorescence lifetime sensor implemented in a 0.13 m CMOS process, combining single photon detection, multiple channel timing and embedded pre-processing of fluorescence lifetime estimations on a single device. Detection is achieved using an array of single photon avalanche diodes (SPADs) arranged in a digital silicon photomultiplier (SiPM) architecture with 400 ps output pulses and a 10% fill-factor. An array of time-to-digital converters (TDCs) with ≈50 ps resolution records up to 8 photon events during each excitation period. Data from the TDC array is then processed using a centre-of-mass method (CMM) pre-calculation to produce fluorescence lifetime estimations in real-time. The sensor is believed to be the first reported implementation of embedded fluorescence lifetime estimation. The system is demonstrated in a practical laboratory environment with measurements of a variety of fluorescent dyes with different single exponential lifetimes, successfully showing the sensor's ability to overcome the classic pile-up limitation of time-correlated single photon counting (TCSPC) by over an order of magnitude.

Simple and sensitive microRNA labeling by terminal deoxynucleotidyl transferase

MicroRNAs (miRNAs) constitute a critically important class of non-translated, small RNAs, which post-transcriptionally regulate gene expression via one of the multiple mechanisms. To profile miRNA expression, microarrays have been extensively applied to the high-throughput detection of miRNAs. Here, we described a novel 3'-end miRNA-labeling method for microarray detection by terminal-deoxynucleotidyl transferase (TdT). TdT can catalyze the formation of polynucleotides at RNA receptor molecule with deoxycytidine triphosphate (dCTP). Using this activity, miRNA was successfully labeled by adding fluorescence dCTPs to its 3'-end. This labeling method was very simple and sensitive. The TdT-labeling method can detect as little as 0.04 fmol of synthetic small RNA, and produce precise and accurate measurements that span a linear dynamic range from 0.04 to 5 fmol of synthetic small RNA. The high consistency of miRNA expression data between our TdT method and real-time polymerase chain reaction analysis indicated the reliability and accuracy of the TdT method. Taken together, these results emphasize the immense potential application of the TdT-labeling method for sensitive and high-throughput microarray analysis of miRNA expression.

Identification of aberrant pathway and network activity from high-throughput data

The workshop focused on approaches to deduce changes in biological activity in cellular pathways and networks that drive phenotype from high-throughput data. Work in cancer has demonstrated conclusively that cancer etiology is driven not by single gene mutation or expression change, but by coordinated changes in multiple signaling pathways. These pathway changes involve different genes in different individuals, leading to the failure of gene-focused analysis to identify the full range of mutations or expression changes driving cancer development. There is also evidence that metabolic pathways rather than individual genes play the critical role in a number of metabolic diseases. Tools to look at pathways and networks are needed to improve our understanding of disease and to improve our ability to target therapeutics at appropriate points in these pathways.

Computational overview of GPCR gene universe to support reverse chemical genomics study

In order to support high-throughput screening for ligands of G-protein coupled receptors (GPCRs) by using bioinformatics technology, we introduce a database (SEVENS) with genome-scale annotation and software (GRIFFIN) that can simulate GPCR function. SEVENS ( http://sevens.cbrc.jp/ ) is an integrated database that includes GPCR genes that are identified with high accuracy (99.4% sensitivity and 96.6% specificity) from various types of genomes, by a pipeline that integrates such software as a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix (TMH) predictor. SEVENS provides the user a genome-scale overview of the "GPCR universe" with detailed information of chromosomal mapping, phylogenetic tree, protein sequence and structure, and experimental evidence, all of which are accessible via a user-friendly interface. GRIFFIN ( http://griffin.cbrc.jp/ ) can predict GPCR and G-protein coupling selectivity induced by ligand binding with high sensitivity and specificity (more than 87% on average), based on the support vector machine (SVM) and hidden Markov Model (HMM). SEVENS and GRIFFIN are expected to contribute to revealing the function of orphan and unknown GPCRs.

Challenges and approaches to statistical design and inference in high-dimensional investigations

Advances in modern technologies have facilitated high-dimensional experiments (HDEs) that generate tremendous amounts of genomic, proteomic, and other "omic" data. HDEs involving whole-genome sequences and polymorphisms, expression levels of genes, protein abundance measurements, and combinations thereof have become a vanguard for new analytic approaches to the analysis of HDE data. Such situations demand creative approaches to the processes of statistical inference, estimation, prediction, classification, and study design. The novel and challenging biological questions asked from HDE data have resulted in many specialized analytic techniques being developed. This chapter discusses some of the unique statistical challenges facing investigators studying high-dimensional biology and describes some approaches being developed by statistical scientists. We have included some focus on the increasing interest in questions involving testing multiple propositions simultaneously, appropriate inferential indicators for the types of questions biologists are interested in, and the need for replication of results across independent studies, investigators, and settings. A key consideration inherent throughout is the challenge in providing methods that a statistician judges to be sound and a biologist finds informative.

Fully automated two-dimensional electrophoresis system for high-throughput protein analysis

A fully automated two-dimensional electrophoresis (2DE) system for rapid and reproducible protein analysis is described. 2DE that is a combination of isoelectric focusing (IEF) and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) is widely used for protein expression analysis. Here, all the operations are achieved in a shorter time and all the transferring procedures are performed automatically. The system completed the entire process within 1.5 h. A device configuration, operational procedure, and data analysis are described using this system.