An Automated High Throughput Screening-Compatible Assay to Identify Regulators of Stem Cell Neural Differentiation

The use of Embryonic Stem Cells (ESCs) holds considerable promise both for drug discovery programs and the treatment of degenerative disorders in regenerative medicine approaches. Nevertheless, the successful use of ESCs is still limited by the lack of efficient control of ESC self-renewal and differentiation capabilities. In this context, the possibility to modulate ESC biological properties and to obtain homogenous populations of correctly specified cells will help developing physiologically relevant screens, designed for the identification of stem cell modulators. Here, we developed a high throughput screening-suitable ESC neural differentiation assay by exploiting the Cell ^maker robotic platform and demonstrated that neural progenies can be generated from ESCs in complete automation, with high standards of accuracy and reliability. Moreover, we performed a pilot screening providing proof of concept that this assay allows the identification of regulators of ESC neural differentiation in full automation.     

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

Here, a streamlined, scalable, laboratory approach is discussed that enables medium‐to‐large dataset analysis. The presented approach combines data management, artificial intelligence, containerization, cluster orchestration, and quality control in a unified analytic pipeline. The unique combination of these individual building blocks creates a new and powerful analysis approach that can readily be applied to medium‐to‐large datasets by researchers to accelerate the pace of research. The proposed framework is applied to a project that counts the number of plasmonic nanoparticles bound to peripheral blood mononuclear cells in dark‐field microscopy images. By using the techniques presented in this article, the images are automatically processed overnight, without user interaction, streamlining the path from experiment to conclusions.     

An Automated High-Throughput Cell-Based Multiplexed Flow Cytometry Assay to Identify Novel Compounds to Target Candida albicans Virulence-Related Proteins

Although three major classes of systemic antifungal agents are clinically available, each is characterized by important limitations. Thus, there has been considerable ongoing effort to develop novel and repurposed agents for the therapy of invasive fungal infections. In an effort to address these needs, we developed a novel high-throughput, multiplexed screening method that utilizes small molecules to probe candidate drug targets in the opportunistic fungal pathogen Candida albicans. This method is amenable to high-throughput automated screening and is based upon detection of changes in GFP levels of individually tagged target proteins. We first selected four GFP-tagged membrane-bound proteins associated with virulence or antifungal drug resistance in C. albicans. We demonstrated proof-of-principle that modulation of fluorescence intensity can be used to assay the expression of specific GFP-tagged target proteins to inhibitors (and inducers), and this change is measurable within the HyperCyt automated flow cytometry sampling system. Next, we generated a multiplex of differentially color-coded C. albicans strains bearing C-terminal GFP-tags of each gene encoding candidate drug targets incubated in the presence of small molecules from the Prestwick Chemical Library in 384-well microtiter plate format. Following incubation, cells were sampled through the HyperCyt system and modulation of protein levels, as indicated by changes in GFP-levels of each strain, was used to identify compounds of interest. The hit rate for both inducers and inhibitors identified in the primary screen did not exceed 1% of the total number of compounds in the small-molecule library that was probed, as would be expected from a robust target-specific, high-throughput screening campaign. Secondary assays for virulence characteristics based on null mutant strains were then used to further validate specificity. In all, this study presents a method for the identification and verification of new antifungal drugs targeted to fungal virulence proteins using C. albicans as a model fungal pathogen.     

Using the Electronic Medical Record to Identify Community-Acquired Pneumonia: Toward a Replicable Automated Strategy

Background

Timely information about disease severity can be central to the detection and management of outbreaks of acute respiratory infections (ARI), including influenza. We asked if two resources: 1) free text, and 2) structured data from an electronic medical record (EMR) could complement each other to identify patients with pneumonia, an ARI severity landmark.

Methods

A manual EMR review of 2747 outpatient ARI visits with associated chest imaging identified x-ray reports that could support the diagnosis of pneumonia (kappa score  = 0.88 (95% CI 0.82∶0.93)), along with attendant cases with Possible Pneumonia (adds either cough, sputum, fever/chills/night sweats, dyspnea or pleuritic chest pain) or with Pneumonia-in-Plan (adds pneumonia stated as a likely diagnosis by the provider). The x-ray reports served as a reference to develop a text classifier using machine-learning software that did not require custom coding. To identify pneumonia cases, the classifier was combined with EMR-based structured data and with text analyses aimed at ARI symptoms in clinical notes.

Results

370 reference cases with Possible Pneumonia and 250 with Pneumonia-in-Plan were identified. The x-ray report text classifier increased the positive predictive value of otherwise identical EMR-based case-detection algorithms by 20–70%, while retaining sensitivities of 58–75%. These performance gains were independent of the case definitions and of whether patients were admitted to the hospital or sent home. Text analyses seeking ARI symptoms in clinical notes did not add further value.

Conclusion

Specialized software development is not required for automated text analyses to help identify pneumonia patients. These results begin to map an efficient, replicable strategy through which EMR data can be used to stratify ARI severity.     

RNA结合蛋白与RNA互作鉴定技术

RNA结合蛋白（RNA binding proteins,RBPs）通过与RNA相互作用,广泛参与到RNA的剪切、转运、编辑、胞内定位及翻译调控等过程中。RNA领域尤其是非编码RNA（non-coding RNA,ncRNA）研究的快速发展,催生了多种RBPs RNAs相互作用鉴定技术。这些技术反之又推动了 RNA领域的研究进程。本文对紫外交联免疫沉淀（ultraviolet crosslinking and immunoprecipitation,CLIP）,CLIP cDNA文库高通量测序 (high-throughput sequencing of CLIP cDNA library,HITS-CLIP),光活化核苷增强的CLIP(photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation,PAR-CLIP),单核苷酸分离CLIP (individual nucleotide resolution CLIP,iCLIP),TRIBE (targets of RNA-binding protein identified by editing),RNA 标记,相互作用组捕获(interactome capture,IC) 和SerIC (serial RNA interactome capture)等RBPs-RNAs相互作用鉴定技术的基本原理和优缺点以及应用进行综述。     

ICan: An Integrated Co-Alteration Network to Identify Ovarian Cancer-Related Genes

Background

Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer.

Results

We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183).

Conclusion

In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.     

An RNA Interference Lethality Screen of the Human Druggable Genome to Identify Molecular Vulnerabilities in Epithelial Ovarian Cancer

Targeted therapies have been used to combat many tumor types; however, few have effectively improved the overall survival in women with epithelial ovarian cancer, begging for a better understanding of this deadly disease and identification of essential drivers of tumorigenesis that can be targeted effectively. Therefore, we used a loss-of-function screening approach to help identify molecular vulnerabilities that may represent key points of therapeutic intervention. We employed an unbiased high-throughput lethality screen using a 24,088 siRNA library targeting over 6,000 druggable genes and studied their effects on growth and/or survival of epithelial ovarian cancer (EOC) cell lines. The top 300 “hits” affecting the viability of A1847 cells were rescreened across additional EOC cell lines and non-tumorigenic, human immortalized ovarian epithelial cell lines. Fifty-three gene candidates were found to exhibit effects in all tumorigenic cell lines tested. Extensive validation of these hits refined the list to four high quality candidates (HSPA5, NDC80, NUF2, and PTN). Mechanistic studies show that silencing of three genes leads to increased apoptosis, while HSPA5 silencing appears to alter cell growth through G1 cell cycle arrest. Furthermore, two independent gene expression studies show that NDC80, NUF2 and PTN were significantly aberrantly overexpressed in serous adenocarcinomas. Overall, our functional genomics results integrated with the genomics data provide an important unbiased avenue towards the identification of prospective therapeutic targets for drug discovery, which is an urgent and unmet clinical need for ovarian cancer.     

Automated Preparation of DNA-RNA Hybrids and their Joining with RNA Ligase; An Approach to Single-Strand Gene Synthesis

Abstract

3′-riboterminated oligodeoxynucleotide hybrid strands were successively joined to a 3′-terminal deoxynucleotide using T4 RNA ligase to produce a 121 b DNA-RNA hybrid single-strand corresponding to a gene for g-endorphin (Fig. 1).     

Computational approaches to RNA structure prediction, analysis, and design

RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires RNA tertiary structure knowledge. Although modeling approaches for the study of RNA structures and dynamics lag behind efforts in protein folding, much progress has been achieved in the past two years. Here, we review recent advances in RNA folding algorithms, RNA tertiary motif discovery, applications of graph theory approaches to RNA structure and function, and in silico generation of RNA sequence pools for aptamer design. Advances within each area can be combined to impact many problems in RNA structure and function.     

Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.     

MS‐Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments

LC–MS/MS has become the standard platform for the characterization of immunopeptidomes, the collection of peptides naturally presented by major histocompatibility complex molecules to the cell surface. The protocols and algorithms used for immunopeptidomics data analysis are based on tools developed for traditional bottom‐up proteomics that address the identification of peptides generated by tryptic digestion. Such algorithms are generally not tailored to the specific requirements of MHC ligand identification and, as a consequence, immunopeptidomics datasets suffer from dismissal of informative spectral information and high false discovery rates. Here, a new pipeline for the refinement of peptide‐spectrum matches (PSM) is proposed, based on the assumption that immunopeptidomes contain a limited number of recurring peptide motifs, corresponding to MHC specificities. Sequence motifs are learned directly from the individual peptidome by training a prediction model on high‐confidence PSMs. The model is then applied to PSM candidates with lower confidence, and sequences that score significantly higher than random peptides are rescued as likely true ligands. The pipeline is applied to MHC class I immunopeptidomes from three different species, and it is shown that it can increase the number of identified ligands by up to 20–30%, while effectively removing false positives and products of co‐precipitation. Spectral validation using synthetic peptides confirms the identity of a large proportion of rescued ligands in the experimental peptidome.     

OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets

The availability of user‐friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://www.ebi.ac.uk/ols ) is a popular centralized service to query, browse and navigate biomedical ontologies and controlled vocabularies. Recently, the OLS framework has been completely redeveloped (version 3.0), including enhancements in the data model, like the added support for Web Ontology Language based ontologies, among many other improvements. However, the new OLS is not backwards compatible and new software tools are needed to enable access to this widely used framework now that the previous version is no longer available. We here present the OLS Client as a free, open‐source Java library to retrieve information from the new version of the OLS. It enables rapid tool creation by providing a robust, pluggable programming interface and common data model to programmatically access the OLS. The library has already been integrated and is routinely used by several bioinformatics resources and related data annotation tools. Secondly, we also introduce an updated version of the OLS Dialog (version 2.0), a Java graphical user interface that can be easily plugged into Java desktop applications to access the OLS. The software and related documentation are freely available at https://github.com/PRIDE-Utilities/ols-client and https://github.com/PRIDE-Toolsuite/ols-dialog .     

Computational RNA secondary structure design: empirical complexity and improved methods

Background  

We investigate the empirical complexity of the RNA secondary structure design problem, that is, the scaling of the typical difficulty of the design task for various classes of RNA structures as the size of the target structure is increased. The purpose of this work is to understand better the factors that make RNA structures hard to design for existing, high-performance algorithms. Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.     

Combinatorial and Computational Approaches to Identify Interactions of Macrophage Colony-stimulating Factor (M-CSF) and Its Receptor c-FMS

The molecular interactions between macrophage colony-stimulating factor (M-CSF) and the tyrosine kinase receptor c-FMS play a key role in the immune response, bone metabolism, and the development of some cancers. Because no x-ray structure is available for the human M-CSF·c-FMS complex, the binding epitope for this complex is largely unknown. Our goal was to identify the residues that are essential for binding of the human M-CSF to c-FMS. For this purpose, we used a yeast surface display (YSD) approach. We expressed a combinatorial library of monomeric M-CSF (M-CSF_M) single mutants and screened this library to isolate variants with reduced affinity for c-FMS using FACS. Sequencing yielded a number of single M-CSF_M variants with mutations both in the direct binding interface and distant from the binding site. In addition, we used computational modeling to map the identified mutations onto the M-CSF_M structure and to classify the mutations into three groups as follows: those that significantly decrease protein stability; those that destroy favorable intermolecular interactions; and those that decrease affinity through allosteric effects. To validate the YSD and computational data, M-CSF_M and three variants were produced as soluble proteins; their affinity and structure were analyzed; and very good correlations with both YSD data and computational predictions were obtained. By identifying the M-CSF_M residues critical for M-CSF·c-FMS interactions, we have laid down the basis for a deeper understanding of the M-CSF·c-FMS signaling mechanism and for the development of target-specific therapeutic agents with the ability to sterically occlude the M-CSF·c-FMS binding interface.     

Automated identification of RNA conformational motifs: theory and application to the HM LSU 23S rRNA

We develop novel methods for recognizing and cataloging conformational states of RNA, and for discovering statistical rules governing those states. We focus on the conformation of the large ribosomal subunit from Haloarcula marismortui. The two approaches described here involve torsion matching and binning. Torsion matching is a pattern-recognition code which finds structural repetitions. Binning is a classification technique based on distributional models of the data. In comparing the results of the two methods we have tested the hypothesis that the conformation of a very large complex RNA molecule can be described accurately by a limited number of discrete conformational states. We identify and eliminate extraneous and redundant information without losing accuracy. We conclude, as expected, that four of the torsion angles contain the overwhelming bulk of the structural information. That information is not significantly compromised by binning the continuous torsional information into a limited number of discrete values. The correspondence between torsion matching and binning is 99% (per residue). Binning, however, does have several advantages. In particular, we demonstrate that the conformation of a large complex RNA molecule can be represented by a small alphabet. In addition, the binning method lends itself to a natural graphical representation using trees.