Predicting persisting disability in musculoskeletal pain patients with the STarT MSK screening tool: Results from a prospective cohort study.

BACKGROUND: The STarT MSK screening tool aims to categorise musculoskeletal patients into three risk groups for treatment stratification. The tool has been translated and validated into Hebrew. However, its ability to predict persistent disability in patients has yet to be evaluated. OBJECTIVE: The primary aim of this study was to assess the ability of the Hebrew version of the STarT MSK tool to predict persistent disability in patients experiencing musculoskeletal pain. METHODS: A prospective observational cohort study was conducted, recruiting 135 patients with musculoskeletal pain in five common areas: back, neck, shoulder, knee, or multisite pain over the age of 21. At the first consultation, all patients completed demographic information, the Focus On Therapeutic Outcomes (FOTO) questionnaire (function, pain, and fear avoidance score), and the STarT MSK questionnaire. The patients completed the FOTO questionnaire again at the end of the physiotherapy treatments. RESULTS: 25 patients (18.5%) were classified into the low-risk group, 68 patients (50.3%) into the medium-risk group, and 42 (31.1%) into the high-risk group. The baseline STarT MSK tool score demonstrated an excellent ability to identify patients at high risk of developing persistent disability (AUC = 0.795, 95% CI 0.716-0.873). CONCLUSIONS: The Hebrew version of the STarT MSK tool can differentiate between three chronic risk groups and has high predictive validity for chronicity. This may provide a tool to assist clinicians in identifying patients who require more intensive care, and thus, potentially prevent the transition to chronic disabling pain.

Metabolic Investigation of Phelipanche aegyptiaca Reveals Significant Changes during Developmental Stages and in Its Different Organs.

Phelipanche aegyptiaca Pers. is a root holoparasitic plant considered to be among the most destructive agricultural weeds worldwide. In order to gain more knowledge about the metabolic profile of the parasite during its developmental stages, we carried out primary metabolic and lipid profiling using GC-MS analysis. In addition, the levels of amino acids that incorporate into proteins, total protein in the albumin fraction, nitrogen, reduced sugars, and phenols were determined. For the assays, the whole plants from the four developmental stages-tubercle, pre-emergent shoot, post-emergent shoot, and mature flowering plants-were taken. Thirty-five metabolites out of 66 differed significantly between the various developmental stages. The results have shown that the first three developmental stages were distinguished in their profiles, but the latter two did not differ from the mature stage. Yet, 46% of the metabolites detected did not change significantly during the developmental stages. This is unlike other studies of non-parasitic plants showing that their metabolic levels tend to alter significantly during development. This implies that the parasite can control the levels of these metabolites. We further studied the metabolic nature of five organs (adventitious roots, lower and upper shoot, floral buds, and flowers) in mature plants. Similar to non-parasitic plants, the parasite exhibited significant differences between the vegetative and reproductive organs. Compared to other organs, floral buds had higher levels of free amino acids and total nitrogen, whereas flowers accumulated higher levels of simple sugars such as sucrose, and the putative precursors for nectar synthesis, color, and volatiles. This suggests that the reproductive organs have the ability to accumulate metabolites that are required for the production of seeds and as a source of energy for the reproductive processes. The data contribute to our knowledge about the metabolic behavior of parasites that rely on their host for its basic nutrients.

PathCards: multi-source consolidation of human biological pathways.

The study of biological pathways is key to a large number of systems analyses. However, many relevant tools consider a limited number of pathway sources, missing out on many genes and gene-to-gene connections. Simply pooling several pathways sources would result in redundancy and the lack of systematic pathway interrelations. To address this, we exercised a combination of hierarchical clustering and nearest neighbor graph representation, with judiciously selected cutoff values, thereby consolidating 3215 human pathways from 12 sources into a set of 1073 SuperPaths. Our unification algorithm finds a balance between reducing redundancy and optimizing the level of pathway-related informativeness for individual genes. We show a substantial enhancement of the SuperPaths' capacity to infer gene-to-gene relationships when compared with individual pathway sources, separately or taken together. Further, we demonstrate that the chosen 12 sources entail nearly exhaustive gene coverage. The computed SuperPaths are presented in a new online database, PathCards, showing each SuperPath, its constituent network of pathways, and its contained genes. This provides researchers with a rich, searchable systems analysis resource. Database URL: http://pathcards.genecards.org/

MalaCards: A Comprehensive Automatically-Mined Database of Human Diseases.

Systems medicine provides insights into mechanisms of human diseases, and expedites the development of better diagnostics and drugs. To facilitate such strategies, we initiated MalaCards, a compendium of human diseases and their annotations, integrating and often remodeling information from 64 data sources. MalaCards employs, among others, the proven automatic data-mining strategies established in the construction of GeneCards, our widely used compendium of human genes. The development of MalaCards poses many algorithmic challenges, such as disease name unification, integrated classification, gene-disease association, and disease-targeted expression analysis. MalaCards displays a Web card for each of >19,000 human diseases, with 17 sections, including textual summaries, related diseases, related genes, genetic variations and tests, and relevant publications. Also included are a powerful search engine and a variety of categorized disease lists. This unit describes two basic protocols to search and browse MalaCards effectively.

MalaCards: an integrated compendium for diseases and their annotation.

Comprehensive disease classification, integration and annotation are crucial for biomedical discovery. At present, disease compilation is incomplete, heterogeneous and often lacking systematic inquiry mechanisms. We introduce MalaCards, an integrated database of human maladies and their annotations, modeled on the architecture and strategy of the GeneCards database of human genes. MalaCards mines and merges 44 data sources to generate a computerized card for each of 16 919 human diseases. Each MalaCard contains disease-specific prioritized annotations, as well as inter-disease connections, empowered by the GeneCards relational database, its searches and GeneDecks set analyses. First, we generate a disease list from 15 ranked sources, using disease-name unification heuristics. Next, we use four schemes to populate MalaCards sections: (i) directly interrogating disease resources, to establish integrated disease names, synonyms, summaries, drugs/therapeutics, clinical features, genetic tests and anatomical context; (ii) searching GeneCards for related publications, and for associated genes with corresponding relevance scores; (iii) analyzing disease-associated gene sets in GeneDecks to yield affiliated pathways, phenotypes, compounds and GO terms, sorted by a composite relevance score and presented with GeneCards links; and (iv) searching within MalaCards itself, e.g. for additional related diseases and anatomical context. The latter forms the basis for the construction of a disease network, based on shared MalaCards annotations, embodying associations based on etiology, clinical features and clinical conditions. This broadly disposed network has a power-law degree distribution, suggesting that this might be an inherent property of such networks. Work in progress includes hierarchical malady classification, ontological mapping and disease set analyses, striving to make MalaCards an even more effective tool for biomedical research. Database URL: http://www.malacards.org/

HORDE: comprehensive resource for olfactory receptor genomics.

Olfactory receptors (ORs) constitute the largest gene family in the mammalian genome. The existence of these proteins underlies the nature of, and variability in, odorant perception. The Human Olfactory Receptor Data Explorer (HORDE, http://genome.weizmann.ac.il/horde/ ) is a free online resource, which presents a complete compendium of all OR genes and pseudogenes in the genome of human and four other vertebrates. HORDE includes three parts: (1) an automated pipeline, which mines OR gene and pseudogene sequences out of complete genomes, and generates gene symbols based on sequence similarity; (2) a card generator that obtains and displays annotative information on individual ORs retrieved from external databases and relevant studies; and (3) a search engine that allows user retrieval of OR information. For human ORs, HORDE specifically addresses the universe of interindividual variation, as obtained from several sources, including whole genome sequences made possible by next-generation sequencing. This encompasses single nucleotide polymorphisms (SNP) and copy number variation (CNV), including deleterious mutational events. HORDE also hosts a number of tools designed specifically to assist in the study of OR evolution and function. In this chapter, we describe the status of HORDE (build #43). We also discuss plans for future enhancements and a road map for HORDE to become a better community-based bioinformatics tool. We highlight HORDE's role as a major research tool in the study of an expanding cohort of OR repertoires.

Non-redundant compendium of human ncRNA genes in GeneCards.

MOTIVATION: Non-coding RNA (ncRNA) genes are increasingly acknowledged for their importance in the human genome. However, there is no comprehensive non-redundant database for all such human genes. RESULTS: We leveraged the effective platform of GeneCards, the human gene compendium, together with the power of fRNAdb and additional primary sources, to judiciously unify all ncRNA gene entries obtainable from 15 different primary sources. Overlapping entries were clustered to unified locations based on an algorithm employing genomic coordinates. This allowed GeneCards' gamut of relevant entries to rise ∼5-fold, resulting in ∼80,000 human non-redundant ncRNAs, belonging to 14 classes. Such 'grand unification' within a regularly updated data structure will assist future ncRNA research. AVAILABILITY AND IMPLEMENTATION: All of these non-coding RNAs are included among the ∼122,500 entries in GeneCards V3.09, along with pertinent annotation, automatically mined by its built-in pipeline from 100 data sources. This information is available at www.genecards.org. CONTACT: Frida.Belinky@weizmann.ac.il SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

General olfactory sensitivity database (GOSdb): candidate genes and their genomic variations.

Genetic variations in olfactory receptors likely contribute to the diversity of odorant-specific sensitivity phenotypes. Our working hypothesis is that genetic variations in auxiliary olfactory genes, including those mediating transduction and sensory neuronal development, may constitute the genetic basis for general olfactory sensitivity (GOS) and congenital general anosmia (CGA). We thus performed a systematic exploration for auxiliary olfactory genes and their documented variation. This included a literature survey, seeking relevant functional in vitro studies, mouse gene knockouts and human disorders with olfactory phenotypes, as well as data mining in published transcriptome and proteome data for genes expressed in olfactory tissues. In addition, we performed next-generation transcriptome sequencing (RNA-seq) of human olfactory epithelium and mouse olfactory epithelium and bulb, so as to identify sensory-enriched transcripts. Employing a global score system based on attributes of the 11 data sources utilized, we identified a list of 1,680 candidate auxiliary olfactory genes, of which 450 are shortlisted as having higher probability of a functional role. For the top-scoring 136 genes, we identified genomic variants (probably damaging single nucleotide polymorphisms, indels, and copy number deletions) gleaned from public variation repositories. This database of genes and their variants should assist in rationalizing the great interindividual variation in human overall olfactory sensitivity (http://genome.weizmann.ac.il/GOSdb).

Personal receptor repertoires: olfaction as a model.

BACKGROUND: Information on nucleotide diversity along completely sequenced human genomes has increased tremendously over the last few years. This makes it possible to reassess the diversity status of distinct receptor proteins in different human individuals. To this end, we focused on the complete inventory of human olfactory receptor coding regions as a model for personal receptor repertoires. RESULTS: By performing data-mining from public and private sources we scored genetic variations in 413 intact OR loci, for which one or more individuals had an intact open reading frame. Using 1000 Genomes Project haplotypes, we identified a total of 4069 full-length polypeptide variants encoded by these OR loci, average of ~10 per locus, constituting a lower limit for the effective human OR repertoire. Each individual is found to harbor as many as 600 OR allelic variants, ~50% higher than the locus count. Because OR neuronal expression is allelically excluded, this has direct effect on smell perception diversity of the species. We further identified 244 OR segregating pseudogenes (SPGs), loci showing both intact and pseudogene forms in the population, twenty-six of which are annotatively "resurrected" from a pseudogene status in the reference genome. Using a custom SNP microarray we validated 150 SPGs in a cohort of 468 individuals, with every individual genome averaging 36 disrupted sequence variations, 15 in homozygote form. Finally, we generated a multi-source compendium of 63 OR loci harboring deletion Copy Number Variations (CNVs). Our combined data suggest that 271 of the 413 intact OR loci (66%) are affected by nonfunctional SNPs/indels and/or CNVs. CONCLUSIONS: These results portray a case of unusually high genetic diversity, and suggest that individual humans have a highly personalized inventory of functional olfactory receptors, a conclusion that might apply to other receptor multigene families.

In-silico human genomics with GeneCards.

Since 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes (http://www.genecards.org). This human gene compendium was created to help to introduce order into the increasing chaos of information flow. As a consequence of viewing details and deep links related to specific genes, users have often requested enhanced capabilities, such that, over time, GeneCards has blossomed into a suite of tools (including GeneDecks, GeneALaCart, GeneLoc, GeneNote and GeneAnnot) for a variety of analyses of both single human genes and sets thereof. In this paper, we focus on inhouse and external research activities which have been enabled, enhanced, complemented and, in some cases, motivated by GeneCards. In turn, such interactions have often inspired and propelled improvements in GeneCards. We describe here the evolution and architecture of this project, including examples of synergistic applications in diverse areas such as synthetic lethality in cancer, the annotation of genetic variations in disease, omics integration in a systems biology approach to kidney disease, and bioinformatics tools.

GeneCards Version 3: the human gene integrator.

GeneCards (www.genecards.org) is a comprehensive, authoritative compendium of annotative information about human genes, widely used for nearly 15 years. Its gene-centric content is automatically mined and integrated from over 80 digital sources, resulting in a web-based deep-linked card for each of >73,000 human gene entries, encompassing the following categories: protein coding, pseudogene, RNA gene, genetic locus, cluster and uncategorized. We now introduce GeneCards Version 3, featuring a speedy and sophisticated search engine and a revamped, technologically enabling infrastructure, catering to the expanding needs of biomedical researchers. A key focus is on gene-set analyses, which leverage GeneCards' unique wealth of combinatorial annotations. These include the GeneALaCart batch query facility, which tabulates user-selected annotations for multiple genes and GeneDecks, which identifies similar genes with shared annotations, and finds set-shared annotations by descriptor enrichment analysis. Such set-centric features address a host of applications, including microarray data analysis, cross-database annotation mapping and gene-disorder associations for drug targeting. We highlight the new Version 3 database architecture, its multi-faceted search engine, and its semi-automated quality assurance system. Data enhancements include an expanded visualization of gene expression patterns in normal and cancer tissues, an integrated alternative splicing pattern display, and augmented multi-source SNPs and pathways sections. GeneCards now provides direct links to gene-related research reagents such as antibodies, recombinant proteins, DNA clones and inhibitory RNAs and features gene-related drugs and compounds lists. We also portray the GeneCards Inferred Functionality Score annotation landscape tool for scoring a gene's functional information status. Finally, we delineate examples of applications and collaborations that have benefited from the GeneCards suite. Database URL: www.genecards.org.