Ultra-high throughput screen of two-million-member combinatorial compound collection in a miniaturized, 1536-well assay format

Results of a complete survey of the more than 2-million-member Pharmacopeia compound collection in a 1536-well microvolume screening assay format are reported. A complete technology platform, enabling the performance of ultra-high throughput screening in a miniaturized 1536-well assay format, has been assembled and utilized. The platform consists of tools for performing microvolume assays, including assay plates, liquid handlers, optical imagers, and data management software. A fluorogenic screening assay for inhibition of a protease enzyme target was designed and developed using this platform. The assay was used to perform a survey screen of the Pharmacopeia compound collection for active inhibitors of the target enzyme. The results from the survey demonstrate the successful implementation of the ultra-high throughout platform for routine screening purposes. Performance of the assay in the miniaturized format is equivalent to that of a standard 96-well assay, showing the same dependence on kinetic parameters and ability to measure enzyme inhibition. The survey screen identified an active class of compounds within the Pharmacopeia compound collection. These results were confirmed using a standard 96-well assay.     

A computational pipeline for high- throughput discovery of cis-regulatory noncoding RNA in prokaryotes

Noncoding RNAs (ncRNAs) are important functional RNAs that do not code for proteins. We present a highly efficient computational pipeline for discovering cis-regulatory ncRNA motifs de novo. The pipeline differs from previous methods in that it is structure-oriented, does not require a multiple-sequence alignment as input, and is capable of detecting RNA motifs with low sequence conservation. We also integrate RNA motif prediction with RNA homolog search, which improves the quality of the RNA motifs significantly. Here, we report the results of applying this pipeline to Firmicute bacteria. Our top-ranking motifs include most known Firmicute elements found in the RNA family database (Rfam). Comparing our motif models with Rfam's hand-curated motif models, we achieve high accuracy in both membership prediction and base-pair–level secondary structure prediction (at least 75% average sensitivity and specificity on both tasks). Of the ncRNA candidates not in Rfam, we find compelling evidence that some of them are functional, and analyze several potential ribosomal protein leaders in depth.     

High throughput analysis of grape genetic diversity as a tool for germplasm collection management

Using 20 SSR markers well scattered across the 19 grape chromosomes, we analyzed 4,370 accessions of the INRA grape repository at Vassal, mostly cultivars of Vitis vinifera subsp. sativa (3,727), but also accessions of V. vinifera subsp. sylvestris (80), interspecific hybrids (364), and rootstocks (199). The analysis revealed 2,836 SSR single profiles: 2,323 sativa cultivars, 72 wild individuals (sylvestris), 306 interspecific hybrids, and 135 rootstocks, corresponding to 2,739 different cultivars in all. A total of 524 alleles were detected, with a mean of 26.20 alleles per locus. For the 2,323 cultivars of V. vinifera, 338 alleles were detected with a mean of 16.9 alleles per locus. The mean genetic diversity (GDI) was 0.797 and the level of heterozygosity was 0.76, with broad variation from 0.20 to 1. Interspecific hybrids and rootstocks were more heterozygous and more diverse (GDI?=?0.839 and 0.865, respectively) than V. vinifera cultivars (GDI?=?0.769), Vitis vinifera subsp. sylvestris being the least divergent with GDI?=?0.708. Principal coordinates analysis distinguished the four groups. Slight clonal polymorphism was detected. The limit between clonal variation and cultivar polymorphism was set at four allelic differences out of 40. SSR markers were useful as a complementary tool to traditional ampelography for cultivar identification. Finally, a set of nine SSR markers was defined that was sufficient to distinguish 99.8% of the analyzed accessions. This set is suitable for routine characterization and will be valuable for germplasm management.     

A small RNA controls a protein regulator involved in antibiotic resistance in Staphylococcus aureus

The emergence of Staphylococcus aureus strains that are resistant to glycopeptides has led to alarming scenarios where serious staphylococcal infections cannot be treated. The bacterium expresses many small regulatory RNAs (sRNAs) that have unknown biological functions for the most part. Here we show that an S. aureus sRNA, SprX (alias RsaOR), shapes bacterial resistance to glycopeptides, the invaluable treatments for Methicillin-resistant staphylococcal infections. Modifying SprX expression levels influences Vancomycin and Teicoplanin glycopeptide resistance. Comparative proteomic studies have identified that SprX specifically downregulates stage V sporulation protein G, SpoVG. SpoVG is produced from the yabJ-spoVG operon and contributes to S. aureus glycopeptide resistance. SprX negatively regulates SpoVG expression by direct antisense pairings at the internal translation initiation signals of the second operon gene, without modifying bicistronic mRNA expression levels or affecting YabJ translation. The SprX and yabJ-spoVG mRNA domains involved in the interaction have been identified, highlighting the importance of a CU-rich loop of SprX in the control of SpoVG expression. We have shown that SpoVG might not be the unique SprX target involved in the glycopeptide resistance and demonstrated that the regulation of glycopeptide sensitivity involves the CU-rich domain of SprX. Here we report the case of a sRNA influencing antibiotic resistance of a major human pathogen.     

Inverse sampling of controls in a matched case control study

A method of inverse sampling of controls in a matched case-control study is described in which, for each case, controls are sampled until a discordant set is achieved. For a binary exposure, inverse sampling is used to determine the number of controls for each case. When most individuals in a population have the same exposure, standard case-control sampling may result in many case-control sets being concordant with respect to exposure and thus uninformative in the conditional logistic analysis. The method using inverse control sampling is proposed as a solution to this problem in situations when it is practically feasible. In many circumstances, inverse control sampling is found to offer improved statistical efficiency relative to a comparable study with a fixed number of controls per case.     

Solvent interaction analysis as a proteomic approach to structure-based biomarker discovery and clinical diagnostics

Proteins have several measurable features in biological fluids that may change under pathological conditions. The current disease biomarker discovery is mostly based on protein concentration in the sample as the measurable feature. Changes in protein structures, such as post-translational modifications and in protein–partner interactions are known to accompany pathological processes. Changes in glycosylation profiles are well-established for many plasma proteins in various types of cancer and other diseases. The solvent interaction analysis method is based on protein partitioning in aqueous two-phase systems and is highly sensitive to changes in protein structure and protein–protein- and protein–partner interactions while independent of the protein concentration in the biological sample. It provides quantitative index: partition coefficient representing changes in protein structure and interactions with partners. The fundamentals of the method are presented with multiple examples of applications of the method to discover and monitor structural protein biomarkers as disease-specific diagnostic indicators.     

Mass spectrometry-based analysis of glycoproteins and its clinical applications in cancer biomarker discovery

Glycosylation is one of the most important posttranslational modifications of proteins and plays essential roles in various biological processes. Aberration in the glycan moieties of glycoproteins is associated with many diseases. It is especially critical to develop the rapid and sensitive methods for analysis of aberrant glycoproteins associated with diseases. Mass spectrometry (MS) has become a powerful tool for glycoprotein analysis. Especially, tandem mass spectrometry can provide highly informative fragments for structural identification of glycoproteins. This review provides an overview of the development of MS technologies and their applications in identification of abnormal glycoproteins and glycans in human serum to screen cancer biomarkers in recent years.     

Proteomic analysis of renal diseases: unraveling the pathophysiology and biomarker discovery

Current biomedical applications of proteomics have been conducted with four main objectives: to better understand the normal biology and physiology of cells, microorganisms, tissues and organs; to explore the pathogenic mechanisms and better understand the pathophysiology of medical diseases; to identify novel biomarkers for early disease detection, prediction and prognosis; and to define new therapeutic targets, drugs and vaccines. This review focuses predominantly on proteomic applications to unravel the pathophysiology and to define novel biomarkers for various renal diseases (i.e., glomerular diseases, tubulointerstitial diseases, renal vascular disorders and renal cancers). In addition, proteomic evaluations of renal transplantation and renal replacement therapy (for acute renal failure and end-stage renal disease) are summarized. Personal opinion, future perspectives and information resources for the field of renal and urinary proteomics are provided.     

Assessing computational tools for the discovery of small RNA genes in bacteria

Over the past decade, a number of biocomputational tools have been developed to predict small RNA (sRNA) genes in bacterial genomes. In this study, several of the leading biocomputational tools, which use different methodologies, were investigated. The performance of the tools, both individually and in combination, was evaluated on ten sets of benchmark data, including data from a novel RNA-seq experiment conducted in this study. The results of this study offer insight into the utility as well as the limitations of the leading biocomputational tools for sRNA identification and provide practical guidance for users of the tools.     

Proteomic analysis of renal diseases: unraveling the pathophysiology and biomarker discovery

Current biomedical applications of proteomics have been conducted with four main objectives: to better understand the normal biology and physiology of cells, microorganisms, tissues and organs; to explore the pathogenic mechanisms and better understand the pathophysiology of medical diseases; to identify novel biomarkers for early disease detection, prediction and prognosis; and to define new therapeutic targets, drugs and vaccines. This review focuses predominantly on proteomic applications to unravel the pathophysiology and to define novel biomarkers for various renal diseases (i.e., glomerular diseases, tubulointerstitial diseases, renal vascular disorders and renal cancers). In addition, proteomic evaluations of renal transplantation and renal replacement therapy (for acute renal failure and end-stage renal disease) are summarized. Personal opinion, future perspectives and information resources for the field of renal and urinary proteomics are provided.     

Identification of discrete classes of small nucleolar RNA featuring different ends and RNA binding protein dependency

Small nucleolar RNAs (snoRNAs) are among the first discovered and most extensively studied group of small non-coding RNA. However, most studies focused on a small subset of snoRNAs that guide the modification of ribosomal RNA. In this study, we annotated the expression pattern of all box C/D snoRNAs in normal and cancer cell lines independent of their functions. The results indicate that C/D snoRNAs are expressed as two distinct forms differing in their ends with respect to boxes C and D and in their terminal stem length. Both forms are overexpressed in cancer cell lines but display a conserved end distribution. Surprisingly, the long forms are more dependent than the short forms on the expression of the core snoRNP protein NOP58, thought to be essential for C/D snoRNA production. In contrast, a subset of short forms are dependent on the splicing factor RBFOX2. Analysis of the potential secondary structure of both forms indicates that the k-turn motif required for binding of NOP58 is less stable in short forms which are thus less likely to mature into a canonical snoRNP. Taken together the data suggest that C/D snoRNAs are divided into at least two groups with distinct maturation and functional preferences.     

Proteomic analysis of lymphoid and haematopoietic neoplasms: There's more than biomarker discovery

Lymphoid and haematopoietic neoplasms comprise a broad spectrum of different tumours, classified by the World Health Organization (WHO) on the basis of a combination of morphology, immunophenotypic, genetic and clinical features. Up to date for many of these neoplasms no single feature is regarded as a diagnostic gold standard.The application of proteomics to the study of neoplastic haematological diseases could help in the search for new diagnostic and prognostic markers, as well as in the development of new therapeutic strategies. In this review, we focus on the actual role of proteomics technologies in the study of neoplastic haematology. In particular, we analyse the results obtained in the field of body fluid, cell lines, and tissues proteomics, and discuss the improvement allowed by the new developed proteomic strategies, such as nanofluidic systems, analysis of formalin-fixed tissues, and quantitative high throughput techniques (SILAC, ICAT, iTRAQ).     

Evaluating the potential of a novel oral lesion exudate collection method coupled with mass spectrometry-based proteomics for oral cancer biomarker discovery

Introduction  

Early diagnosis of Oral Squamous Cell Carcinoma (OSCC) increases the survival rate of oral cancer. For early diagnosis, molecular biomarkers contained in samples collected non-invasively and directly from at-risk oral premalignant lesions (OPMLs) would be ideal.     

Proteome analysis of isolated perfused organ effluent as a novel model for protein biomarker discovery

The discovery of novel serological biomarkers is critical for improving disease diagnosis and monitoring treatment response. Proteomic analysis of model systems, such as isolated cells in culture and patient plasma and serum, represents the current state-of-the-art. Here, we coupled proteomics with isolated organ perfusion, which allows a disease state to be studied in a physiologic, yet controlled, environment. Potential markers specific to the disease or to changes in the surrounding tissue may be discovered. The effectiveness of this model was evaluated using proteomic analysis of effluent fractions collected from isolated beating rat hearts during reperfusion after brief episodes of ischemia. The detection of clinical markers for myocardial ischemia in this effluent was robust and analytically straightforward, validating the potential of isolated organ perfusion in diagnostic protein discovery.     

Family history interview of a broad phenotype in specific language impairment and matched controls

The aim was to study a broader phenotype of language‐related diagnoses and problems in three generations of relatives of children with specific language impairment (SLI). Our study is based on a family history interview of the parents of 59 children with SLI and of 100 matched control children, exploring the prevalence of problems related to language, reading, attention, school achievement and social communication as well as diagnoses such as attention‐deficit hyperactivity disorder (ADHD), autism, Asperger syndrome, dyslexia, mental retardation, cleft palate and stuttering. The results show a spectrum of language‐related problems in families of SLI children. In all three generations of SLI relatives, we found significantly higher prevalence rates of language, literacy and social communication problems. The risk of one or both parents having language‐related diagnoses or problems was approximately six times higher for the children with SLI (85%) than for the control children (13%) (odds ratio = 37.2). We did not find a significantly higher prevalence of the diagnoses ADHD, autism or Asperger syndrome in the relatives of the children with SLI. However, significantly more parents of the children with SLI had problems with attention/hyperactivity when compared with the parents of controls. Our findings suggest common underlying mechanisms for problems with language, literacy and social communication, and possibly also for attention/hyperactivity symptoms.