Fig. 1. The workflow of the synergy scoring system of drug combination.
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•Drug combination can significant boot the efficacy of the therapy.•Ontology fingerprint could further improve ...drug synergy prediction.•Deep belief network is a good approach for drug synergy prediction.•Integration of biologic information from multiple resources may improve the accuracy.
The synergistic effect of drug combination is one of the most desirable properties for treating cancer. However, systematically predicting effective drug combination is a significant challenge. We report here a novel method based on deep belief network to predict drug synergy from gene expression, pathway and the Ontology Fingerprints—a literature derived ontological profile of genes. Using data sets provided by 2015 DREAM competition, our analysis shows that this integrative method outperforms published results from the DREAM website for 4999 drug pairs, demonstrating the feasibility of predicting drug synergy from literature and the –omics data using advanced artificial intelligence approach.
Imiquimod (IMQ) produces a cutaneous phenotype in mice frequently studied as an acute model of human psoriasis. Whether this phenotype depends on strain or sex has never been systematically ...investigated on a large scale. Such effects, however, could lead to conflicts among studies, while further impacting study outcomes and efforts to translate research findings.
RNA-seq was used to evaluate the psoriasiform phenotype elicited by 6 days of Aldara (5% IMQ) treatment in both sexes of seven mouse strains (C57BL/6 J (B6), BALB/cJ, CD1, DBA/1 J, FVB/NJ, 129X1/SvJ, and MOLF/EiJ).
In most strains, IMQ altered gene expression in a manner consistent with human psoriasis, partly due to innate immune activation and decreased homeostatic gene expression. The response of MOLF males was aberrant, however, with decreased expression of differentiation-associated genes (elevated in other strains). Key aspects of the IMQ response differed between the two most commonly studied strains (BALB/c and B6). Compared with BALB/c, the B6 phenotype showed increased expression of genes associated with DNA replication, IL-17A stimulation, and activated CD8+ T cells, but decreased expression of genes associated with interferon signaling and CD4+ T cells. Although IMQ-induced expression shifts mirrored psoriasis, responses in BALB/c, 129/SvJ, DBA, and MOLF mice were more consistent with other human skin conditions (e.g., wounds or infections). IMQ responses in B6 mice were most consistent with human psoriasis and best replicated expression patterns specific to psoriasis lesions.
These findings demonstrate strain-dependent aspects of IMQ dermatitis in mice. We have shown that IMQ does not uniquely model psoriasis but in fact triggers a core set of pathways active in diverse skin diseases. Nonetheless, our findings suggest that B6 mice provide a better background than other strains for modeling psoriasis disease mechanisms.
Objective
To identify risk alleles relevant to the causal and biologic mechanisms of antineutrophil cytoplasmic antibody (ANCA)–associated vasculitis (AAV).
Methods
A genome‐wide association study ...and subsequent replication study were conducted in a total cohort of 1,986 cases of AAV (patients with granulomatosis with polyangiitis Wegener's GPA or microscopic polyangiitis MPA) and 4,723 healthy controls. Meta‐analysis of these data sets and functional annotation of identified risk loci were performed, and candidate disease variants with unknown functional effects were investigated for their impact on gene expression and/or protein function.
Results
Among the genome‐wide significant associations identified, the largest effect on risk of AAV came from the single‐nucleotide polymorphism variants rs141530233 and rs1042169 at the HLA–DPB1 locus (odds ratio OR 2.99 and OR 2.82, respectively) which, together with a third variant, rs386699872, constitute a triallelic risk haplotype associated with reduced expression of the HLA–DPB1 gene and HLA–DP protein in B cells and monocytes and with increased frequency of complementary proteinase 3 (PR3)–reactive T cells relative to that in carriers of the protective haplotype. Significant associations were also observed at the SERPINA1 and PTPN22 loci, the peak signals arising from functionally relevant missense variants, and at PRTN3, in which the top‐scoring variant correlated with increased PRTN3 expression in neutrophils. Effects of individual loci on AAV risk differed between patients with GPA and those with MPA or between patients with PR3‐ANCAs and those with myeloperoxidase‐ANCAs, but the collective population attributable fraction for these variants was substantive, at 77%.
Conclusion
This study reveals the association of susceptibility to GPA and MPA with functional gene variants that explain much of the genetic etiology of AAV, could influence and possibly be predictors of the clinical presentation, and appear to alter immune cell proteins and responses likely to be key factors in the pathogenesis of AAV.
A 7514-Da chymotrypsin inhibitor was isolated from the seed extract of Momordica cochinchinensis (Family Cucurbitaceae) by chromatography on chymotrypsin Sepharose 4B and subsequently by C18 ...reversed-phase HPLC. This inhibitor, named MCoCI, possessed remarkable thermostability and was stable from pH 2 to 12. MCoCI also inhibited subtilisin, but had at least 50-fold lower inhibitory activity towards trypsin and elastase. Amino acid sequencing of a peptide fragment of MCoCI revealed a sequence of 23 amino acids. Comparison of this sequence and the molecular mass with those of other protease inhibitors suggests that MCoCI belongs to the potato I inhibitor family.
High-throughput experiments are employed more and more in the investigation of the pathology, biological mechanism, or genetic epidemiology of different diseases and traits (Ozaki and Tanaka 2005; ...Smyth et al. 2006; Willer et al. 2008; Cookson et al. 2009; Rhodes et al. 2004). Analyses of the results from these experiments yield candidate genes that can provide biological insights into disease mechanisms and potentially act as biomarkers with significant clinical impact. However, making inferences from these studies proves difficult due to the large number of statistical tests that must be performed and the complex nature of most common diseases and disease-related traits (Hunter and Kraft 2007; Kane et al. 2000; Thomas, Haile, and Duggan 2005; Yauk et al. 2004). Many current approaches use "pathway analysis'' to address the challenges. But relying on functional annotations has shortcomings because of the limited annotations for most of the human genes and genomes. To ameliorate this difficulty, we introduced Ontology Fingerprinting (OntoFing), a bioinformatics approach that characterizes genes and other biological concepts (phenotypes, pathways, diseases, etc.) through the identification of a set of Gene Ontology (GO) terms, standardised controlled vocabularies to represent biological attributes for genes/gene products, being overrepresented among PubMed abstracts annotated with the gene or biological concept in question. We used OntoFing as a tool to evaluate the results of high throughput experiments and provided literature-supported evidence to assist researchers in making biological inferences. The objectives of this study were the following: (1) to employ ontology and biomedical literature to develop the ontology fingerprinting (OntoFing) approach for quantifying biological relevance between different biological concepts; (2) to develop an OntoFing-derived gene-gene network to identify functional gene nodules: (3) to apply the OntoFing approach to GWA results to inter potential polygenic effects; and (4) to apply the OntoFing approach to the results of a proposed meta-analysis method of microarray data to make biological inference. The OntoFing approach is a novel method that combines gene-phenotype and gene-gene relationships to provide researchers additional biological knowledge derived from ontological information and literature data. By using candidate genes from the results of high-throughput experiments, we also illustrate that OntoFing can use the network approach to identify functional gene modules relevant to the phenotype being studied.
High-throughput single-cell RNA-sequencing (scRNA-seq) methodologies enable characterization of complex biological samples by increasing the number of cells that can be profiled contemporaneously. ...Nevertheless, these approaches recover less information per cell than low-throughput strategies. To accurately report the expression of key phenotypic features of cells, scRNA-seq platforms are needed that are both high fidelity and high throughput. To address this need, we created Seq-Well S3 (“Second-Strand Synthesis”), a massively parallel scRNA-seq protocol that uses a randomly primed second-strand synthesis to recover complementary DNA (cDNA) molecules that were successfully reverse transcribed but to which a second oligonucleotide handle, necessary for subsequent whole transcriptome amplification, was not appended due to inefficient template switching. Seq-Well S3 increased the efficiency of transcript capture and gene detection compared with that of previous iterations by up to 10- and 5-fold, respectively. We used Seq-Well S3 to chart the transcriptional landscape of five human inflammatory skin diseases, thus providing a resource for the further study of human skin inflammation.
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•Seq-Well S3 uses second-strand synthesis to improve transcript capture•Seq-Well S3 was benchmarked against a best-in-class commercial platform•Seq-Well S3 was applied to profile inflammatory cell states in skin diseases•Analysis of skin inflammation uncovered unique and conserved cellular phenotypes
Hughes et al. report the development of a technique for high-throughput single-cell RNA-sequencing, “Seq-Well S3,” that enables increased sensitivity and improved detection of genes including transcription factors, cytokines, and cytokine receptors. Using Seq-Well S3, the authors define inflammatory cell states across multiple skin diseases.
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