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A correction to the article titled "Analysis of gene expression in two large schizophrenia cohorts identifies multiple changes associated with nerve terminal function," published in the previous issue of the journal is presented.

Single nucelotide polymorphisms (SNPs) at the FAM13A locus are among the most commonly reported risk alleles associated with chronic obstructive pulmonary disease (COPD) and other respiratory diseases; however, the physiological role of FAM13A is unclear. In humans, two major protein isoforms are expressed at the FAM13A locus: "long" and "short," but their functions remain unknown, partly because of a lack of isoform conservation in mice. We performed in-depth characterization of organotypic primary human airway epithelial cell subsets and show that multiciliated cells predominantly express the FAM13A long isoform containing a putative N-terminal Rho GTPase-activating protein (RhoGAP) domain. Using purified proteins, we directly demonstrate the RhoGAP activity of this domain. In Xenopus laevis , which conserve the long-isoform, Fam13a deficiency impaired cilia-dependent embryo motility. In human primary epithelial cells, long-isoform deficiency did not affect multiciliogenesis but reduced cilia coordination in mucociliary transport assays. This is the first demonstration that FAM13A isoforms are differentially expressed within the airway epithelium, with implications for the assessment and interpretation of SNP effects on FAM13A expression levels. We also show that the long FAM13A isoform coordinates cilia-driven movement, suggesting that FAM13A risk alleles may affect susceptibility to respiratory diseases through deficiencies in mucociliary clearance.

This Perspective discusses the published data and recent developments in the research area of bromodomains in parasitic protozoa. Further work is needed to evaluate the tractability of this target class in the context of infectious diseases and launch drug discovery campaigns to identify and develop antiparasite drugs that can offer differentiated mechanisms of action.

In this study, we sought to characterize synovial tissue obtained from individuals with arthralgia and disease-specific auto-antibodies and patients with established rheumatoid arthritis (RA), by applying an integrative multi-omics approach where we investigated differences at the level of DNA methylation and gene expression in relation to disease pathogenesis. We performed concurrent whole-genome bisulphite sequencing and RNA-Sequencing on synovial tissue obtained from the knee and ankle from 4 auto-antibody positive arthralgia patients and thirteen RA patients. Through multi-omics factor analysis we observed that the latent factor explaining the variance in gene expression and DNA methylation was associated with Swollen Joint Count 66 (SJC66), with patients with SJC66 of 9 or more displaying separation from the rest. Interrogating these observed differences revealed activation of the immune response as well as dysregulation of cell adhesion pathways at the level of both DNA methylation and gene expression. We observed differences for 59 genes in particular at the level of both transcript expression and DNA methylation. Our results highlight the utility of genome-wide multi-omics profiling of synovial samples for improved understanding of changes associated with disease spread in arthralgia and RA patients, and point to novel candidate targets for the treatment of the disease., Competing Interests: AL, KM, GR, HL, DT, CL, DG, and RP were employed by GSK when this study was conducted. EF and PT were employed by Novartis and Candel Therapeutics when this study was conducted, respectively. WJ was financially supported by GSK and Mead Johnson. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Li Yim, Ferrero, Maratou, Lewis, Royal, Tough, Larminie, Mannens, Henneman, de Jonge, van de Sande, Gerlag, Prinjha and Tak.)

Light-induced pulsed EPR dipolar spectroscopic methods allow the determination of nanometer distances between paramagnetic sites. Here we employ orthogonal spin labels, a chromophore triplet state and a stable radical, to carry out distance measurements in singly nitroxide-labeled human neuroglobin. We demonstrate that Zn-substitution of neuroglobin, to populate the Zn(II) protoporphyrin IX triplet state, makes it possible to perform light-induced pulsed dipolar experiments on hemeproteins, extending the use of light-induced dipolar spectroscopy to this large class of metalloproteins. The versatility of the method is ensured by the employment of different techniques: relaxation-induced dipolar modulation enhancement (RIDME) is applied for the first time to the photoexcited triplet state. In addition, an alternative pulse scheme for laser-induced magnetic dipole (LaserIMD) spectroscopy, based on the refocused-echo detection sequence, is proposed for accurate zero-time determination and reliable distance analysis., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states., Methods: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification., Results: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes., Conclusions: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Objectives: To use literature mining to catalogue Behçet's associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet's patients. To extend this technique to identify potential treatment targets for further experimental validation., Methods: Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms., Results: This approach identified 247 human genes associated with Behçet's disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet's associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation., Conclusions: Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet's disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

The p38 mitogen-activated protein kinase (MAPK) intracellular signaling pathway responds to a variety of extracellular stimuli, including cytokines, Toll-like receptor agonists, and components of cigarette smoke to influence the expression of proinflammatory mediators. Activation of p38 MAPK is increased within the lungs of chronic obstructive pulmonary disease (COPD) patients. In clinical trials, treatment of COPD patients with p38 MAPK inhibitors has been shown to reduce systemic inflammation plasma biomarkers C-reactive protein (CRP) and fibrinogen. As CRP and fibrinogen have been associated with poor clinical outcomes in COPD patients, such as mortality, exacerbation, and hospitalization, we analyzed gene expression data from COPD subjects treated with dilmapimod with the aim of understanding the effects of p38 MAPK inhibition on the inflammatory genome of immune cells within the systemic circulation. Whole blood and induced sputum samples were used to measure mRNA levels by gene array and PCR. Pathway and network analysis showed STAT1, MMP-9, CAV1, and IL-1β as genes regulated by dilmapimod that could also influence fibrinogen levels, while only IL-1β was identified as a gene regulated by dilmapimod that could influence CRP levels. This suggests that p38 MAPK inhibits specific inflammatory pathways, leading to to differential effects on CRP and fibrinogen levels in COPD patients.

The growing body of transcriptomic, proteomic, metabolomic and genomic data generated from disease states provides a great opportunity to improve our current understanding of the molecular mechanisms driving diseases and shared between diseases. The use of both clinical and molecular phenotypes will lead to better disease understanding and classification. In this study, we set out to gain novel insights into diseases and their relationships by utilising knowledge gained from system-level molecular data. We integrated different types of biological data including genome-wide association studies data, disease-chemical associations, biological pathways and Gene Ontology annotations into an Integrated Disease Network (IDN), a heterogeneous network where nodes are bio-entities and edges between nodes represent their associations. We also introduced a novel disease similarity measure to infer disease-disease associations from the IDN. Our predicted associations were systemically evaluated against the Medical Subject Heading classification and a statistical measure of disease co-occurrence in PubMed. The strong correlation between our predictions and co-occurrence associations indicated the ability of our approach to recover known disease associations. Furthermore, we presented a case study of Crohn's disease. We demonstrated that our approach not only identified well-established connections between Crohn's disease and other diseases, but also revealed new, interesting connections consistent with emerging literature. Our approach also enabled ready access to the knowledge supporting these new connections, making this a powerful approach for exploring connections between diseases.

Background: Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment., Results: We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships., Conclusions: We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships.

Background: An important step toward understanding the biological mechanisms underlying a complex disease is a refined understanding of its clinical heterogeneity. Relating clinical and molecular differences may allow us to define more specific subtypes of patients that respond differently to therapeutic interventions., Results: We developed a novel unbiased method called diVIsive Shuffling Approach (VIStA) that identifies subgroups of patients by maximizing the difference in their gene expression patterns. We tested our algorithm on 140 subjects with Chronic Obstructive Pulmonary Disease (COPD) and found four distinct, biologically and clinically meaningful combinations of clinical characteristics that are associated with large gene expression differences. The dominant characteristic in these combinations was the severity of airflow limitation. Other frequently identified measures included emphysema, fibrinogen levels, phlegm, BMI and age. A pathway analysis of the differentially expressed genes in the identified subtypes suggests that VIStA is capable of capturing specific molecular signatures within in each group., Conclusions: The introduced methodology allowed us to identify combinations of clinical characteristics that correspond to clear gene expression differences. The resulting subtypes for COPD contribute to a better understanding of its heterogeneity.

The advent of genome-scale genetic and genomic studies allows new insight into disease classification. Recently, a shift was made from linking diseases simply based on their shared genes towards systems-level integration of molecular data. Here, we aim to find relationships between diseases based on evidence from fusing all available molecular interaction and ontology data. We propose a multi-level hierarchy of disease classes that significantly overlaps with existing disease classification. In it, we find 14 disease-disease associations currently not present in Disease Ontology and provide evidence for their relationships through comorbidity data and literature curation. Interestingly, even though the number of known human genetic interactions is currently very small, we find they are the most important predictor of a link between diseases. Finally, we show that omission of any one of the included data sources reduces prediction quality, further highlighting the importance in the paradigm shift towards systems-level data fusion.

The life science industries (including pharmaceuticals, agrochemicals and consumer goods) are exploring new business models for research and development that focus on external partnerships. In parallel, there is a desire to make better use of data obtained from sources such as human clinical samples to inform and support early research programmes. Success in both areas depends upon the successful integration of heterogeneous data from multiple providers and scientific domains, something that is already a major challenge within the industry. This issue is exacerbated by the absence of agreed standards that unambiguously identify the entities, processes and observations within experimental results. In this article we highlight the risks to future productivity that are associated with incomplete biological and chemical vocabularies and suggest a new model to address this long-standing issue., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Bioactive molecules such as drugs, pesticides and food additives are produced in large numbers by many commercial and academic groups around the world. Enormous quantities of data are generated on the biological properties and quality of these molecules. Access to such data - both on licensed and commercially available compounds, and also on those that fail during development - is crucial for understanding how improved molecules could be developed. For example, computational analysis of aggregated data on molecules that are investigated in drug discovery programmes has led to a greater understanding of the properties of successful drugs. However, the information required to perform these analyses is rarely published, and when it is made available it is often missing crucial data or is in a format that is inappropriate for efficient data-mining. Here, we propose a solution: the definition of reporting guidelines for bioactive entities - the Minimum Information About a Bioactive Entity (MIABE) - which has been developed by representatives of pharmaceutical companies, data resource providers and academic groups.

The human tissue distribution of the nineteen known human regulators of G-protein signaling (RGS) is described. Measurement of RGS mRNA levels in human brain and in nine peripheral tissues revealed striking tissue preferences in gene expression. Five RGS members were identified with enriched expression in brain. RGS4, RGS7, RGS8, RGS11 and RGS17 were all significantly expressed in striatal regions including the nucleus accumbens and putamen. RGS4 had the highest measured levels of mRNA expression and was highly enriched in the gyrus of the cortex and in the parahippocampus. RGS7 and RGS17 had overlapping distribution profiles and were both noticeably enriched in the cerebellum. Several RGS family members showed high expression in peripheral tissues. RGS5 was preferentially expressed in heart, and RGS1, RGS13, RGS18 and GAIP were predominately expressed in lymphocytes. RGS1 was also highly enriched in the lung, as was RGS2 and RGS16. Five family members, RGS3, RGS9, RGS10, RGS 12 and RGS14 had a broad and overlapping mRNA distribution. These results suggest roles of the individual RGS members in a diversity of functions in humans and support a role of several RGS members in the regulation of central nervous system function via modulation of signaling by G-protein coupled receptors.

RNA polymerase (Pol) III transcription is abnormally active in fibroblasts that have been transformed by simian virus 40 (SV40). This report presents evidence that two separate components of the general Pol III transcription apparatus, TFIIIB and TFIIIC2, are deregulated following SV40 transformation. TFIIIC2 subunits are expressed at abnormally high levels in SV40-transformed cells, an effect which is observed at both protein and mRNA levels. In untransformed fibroblasts, TFIIIB is subject to repression through association with the retinoblastoma protein RB. The interaction between RB and TFIIIB is compromised following SV40 transformation. Furthermore, the large T antigen of SV40 is shown to relieve repression by RB. The E7 oncoprotein of human papillomavirus can also activate Pol III transcription, an effect that is dependent on its ability to bind to RB. The data provide evidence that both TFIIIB and TFIIIC2 are targets for activation by DNA tumor viruses.

We have identified a putative gene within the Caenorhabditis elegans genome which has the potential to encode a protein homologous to BRF, an RNA polymerase III general transcription factor. The predicted protein shares very similar overall structure with human and yeast BRF. In particular, its N-terminal half comprises a zinc-ribbon motif and a TR domain which is also present in the cyclin box. The C. elegans protein is more similar to human BRF than to the yeast BRF proteins, as would be expected from an evolutionary standpoint. Alignment of the C. elegans protein with the four known BRF proteins reveals two blocks conserved between all five sequences within the diverged C-terminal region. Profile searches using these regions suggest that they may contain evolutionarily conserved motifs. These comparisons provide insight into the structure and function of an important transcription factor.

The rate of protein synthesis is a critical determinant of cellular growth. Abnormal activation of this process is a frequent feature of transformed and tumour cells. Several distinct components of the translation apparatus have been shown to be deregulated in response to malignant transformation. Indeed, overexpression of certain translation factors has been found to predispose cells to transformation or even initiate it. The latest twist to this story comes from the discovery that the retinoblastoma protein RB plays a major role in restricting the production of tRNA and rRNA. RB is an important tumour suppressor. Its ability to limit the synthesis of these principle determinants of biosynthetic capacity could provide a mechanism for restraining cell growth. The loss of this control may constitute a significant step towards tumour progression.

Cathepsin B cysteine protease enzymes have been shown to be involved in a variety of different biological processes in eukaryotes. We have isolated and characterized four distinct cathepsin B-like genes from the genetically tractable nematode, Caenorhabditis elegans. This is the first reported finding of a cathepsin B-like multigene family within a nonparasitic metazoan. The four genes possess distinct genomic architectures, with variations in the position, number, and size of introns. The predicted amino acid sequences of the four genes are highly diverged. Phylogenetic analysis indicates the divergence of this multigene family within C. elegans is as great as the interspecies divergence between the vertebrates and nematode cathepsin B-like genes. In addition, each of the four genes described here shows a distinct temporal pattern of expression during C. elegans development.