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Background: Sarcoptic mange is an emerging and neglected contagious skin disease caused by the mite Sarcoptes scabiei, affecting humans, domestic animals, and wildlife. Mange is the main disease and a major concern for the management and conservation of populations of Iberian ibex (Capra pyrenaica), a medium-sized mountain ungulate endemic to the Iberian Peninsula and Northern Pyrenees. Differences in host-parasite interaction and host immune response determine mange clinical outcome, but little is known about the related differences in gene expression. This study determined blood and skin gene expressions in S. scabiei-experimentally infested Iberian ibexes. Results: Infestation with S. scabiei promoted immune and inflammatory genomic responses both in skin and blood, with two different clinical outcomes: either severe infestation or recovery. Sarcoptes scabiei induced local skin immunosuppression to favour its multiplication and establishment of the infestation in the host. Skin gene expression was mostly inflammatory and inefficient to control mange in the severely infected ibexes. Conversely, the immune skin response of the recovered ibexes effectively recognised S. scabiei and activated T-cells, limiting the infestation. Consequently, inflammation-related genes were more expressed in the blood of the severely infested ibexes than in those that recovered. Conclusions: The results demonstrate that skin local cellular immune response is key to control sarcoptic mange and prevent the systemic spread of the disease and the associated inflammatory response. These results will be useful to understand the pathogenesis and drivers of the differential outcome of mange at individual scale, and the population and ecological consequences of such variability in Iberian ibex, as well as in other wildlife species, domestic animals, and humans. [ABSTRACT FROM AUTHOR]

Abstract Background Sarcoptic mange is an emerging and neglected contagious skin disease caused by the mite Sarcoptes scabiei, affecting humans, domestic animals, and wildlife. Mange is the main disease and a major concern for the management and conservation of populations of Iberian ibex (Capra pyrenaica), a medium-sized mountain ungulate endemic to the Iberian Peninsula and Northern Pyrenees. Differences in host-parasite interaction and host immune response determine mange clinical outcome, but little is known about the related differences in gene expression. This study determined blood and skin gene expressions in S. scabiei-experimentally infested Iberian ibexes. Results Infestation with S. scabiei promoted immune and inflammatory genomic responses both in skin and blood, with two different clinical outcomes: either severe infestation or recovery. Sarcoptes scabiei induced local skin immunosuppression to favour its multiplication and establishment of the infestation in the host. Skin gene expression was mostly inflammatory and inefficient to control mange in the severely infected ibexes. Conversely, the immune skin response of the recovered ibexes effectively recognised S. scabiei and activated T-cells, limiting the infestation. Consequently, inflammation-related genes were more expressed in the blood of the severely infested ibexes than in those that recovered. Conclusions The results demonstrate that skin local cellular immune response is key to control sarcoptic mange and prevent the systemic spread of the disease and the associated inflammatory response. These results will be useful to understand the pathogenesis and drivers of the differential outcome of mange at individual scale, and the population and ecological consequences of such variability in Iberian ibex, as well as in other wildlife species, domestic animals, and humans.

Abstract Background Dendrobium huoshanense, a traditional medicinal and food plant, has a rich history of use. Recently, its genome was decoded, offering valuable insights into gene function. However, there is no comprehensive gene functional analysis platform for D. huoshanense. Result To address this, we created a platform for gene function analysis and comparison in D. huoshanense (DhuFAP). Using 69 RNA-seq samples, we constructed a gene co-expression network and annotated D. huoshanense genes by aligning sequences with public protein databases. Our platform contained tools like Blast, gene set enrichment analysis, heatmap analysis, sequence extraction, and JBrowse. Analysis revealed co-expression of transcription factors (C2H2, GRAS, NAC) with genes encoding key enzymes in alkaloid biosynthesis. We also showcased the reliability and applicability of our platform using Chalcone synthases (CHS). Conclusion DhuFAP ( www.gzybioinformatics.cn/DhuFAP ) and its suite of tools represent an accessible and invaluable resource for researchers, enabling the exploration of functional information pertaining to D. huoshanense genes. This platform stands poised to facilitate significant biological discoveries in this domain.

Abstract Background Rosaceae species are economically highly relevant crops. Their cultivation systems are constrained by phytopathogens causing severe losses. Plants respond to invading pathogens through signaling mechanisms, a component of which are of them being plant elicitor peptides (Peps). Exogenous application of Peps activates defense mechanisms and reduces the symptoms of pathogen infection in various pathosystems. We have previously identified the Rosaceae Peps and showed, in an ex vivo system, that their topical application efficiently enhanced resistance to the bacterial pathogen Xanthomonas arboricola pv. pruni (Xap). Results Here we demonstrate the effectiveness of Prunus persica peptides PpPep1 and PpPep2 in protecting peach plants in vivo at nanomolar doses, with 40% reduction of the symptoms following Xap massive infection. We used deep sequencing to characterize the transcriptomic response of peach plants to preventive treatment with PpPep1 and PpPep2. The two peptides induced highly similar massive transcriptomic reprogramming in the plant. One hour, 1 day and 2 days after peptide application there were changes in expression in up to 8% of peach genes. We visualized the transcriptomics dynamics in a background knowledge network and detected the minor variations between plant responses to PpPep1 and PpPep2, which might explain their slightly different protective effects. By designing a P. persica Pep background knowledge network, comparison of our data and previously published immune response datasets was possible. Conclusions Topical application of P. persica Peps mimics the PTI natural response and protects plants against massive Xap infection. This makes them good candidates for deployment of natural, targeted and environmental-friendly strategies to enhance resistance in Prunus species and prevent important biotic diseases.

Background: Rosaceae species are economically highly relevant crops. Their cultivation systems are constrained by phytopathogens causing severe losses. Plants respond to invading pathogens through signaling mechanisms, a component of which are of them being plant elicitor peptides (Peps). Exogenous application of Peps activates defense mechanisms and reduces the symptoms of pathogen infection in various pathosystems. We have previously identified the Rosaceae Peps and showed, in an ex vivo system, that their topical application efficiently enhanced resistance to the bacterial pathogen Xanthomonas arboricola pv. pruni (Xap). Results: Here we demonstrate the effectiveness of Prunus persica peptides PpPep1 and PpPep2 in protecting peach plants in vivo at nanomolar doses, with 40% reduction of the symptoms following Xap massive infection. We used deep sequencing to characterize the transcriptomic response of peach plants to preventive treatment with PpPep1 and PpPep2. The two peptides induced highly similar massive transcriptomic reprogramming in the plant. One hour, 1 day and 2 days after peptide application there were changes in expression in up to 8% of peach genes. We visualized the transcriptomics dynamics in a background knowledge network and detected the minor variations between plant responses to PpPep1 and PpPep2, which might explain their slightly different protective effects. By designing a P. persica Pep background knowledge network, comparison of our data and previously published immune response datasets was possible. Conclusions: Topical application of P. persica Peps mimics the PTI natural response and protects plants against massive Xap infection. This makes them good candidates for deployment of natural, targeted and environmental-friendly strategies to enhance resistance in Prunus species and prevent important biotic diseases. [ABSTRACT FROM AUTHOR]

Background: Inference of biological pathway activity via gene set enrichment analysis is frequently used in the interpretation of clinical and other omics data. With the proliferation of new omics profiling approaches and ever-growing size of data sets generated, there is a lack of tools available to perform and visualise gene set enrichments in analyses involving multiple contrasts. Results: To address this, we developed mitch, an R package for multi-contrast gene set enrichment analysis. It uses a rank-MANOVA statistical approach to identify sets of genes that exhibit joint enrichment across multiple contrasts. Its unique visualisation features enable the exploration of enrichments in up to 20 contrasts. We demonstrate the utility of mitch with case studies spanning multi-contrast RNA expression profiling, integrative multi-omics, tool benchmarking and single-cell RNA sequencing. Using simulated data we show that mitch has similar accuracy to state of the art tools for single-contrast enrichment analysis, and superior accuracy in identifying multi-contrast enrichments. Conclusion: mitch is a versatile tool for rapidly and accurately identifying and visualising gene set enrichments in multi-contrast omics data. Mitch is available from Bioconductor (https://bioconductor.org/packages/mitch). [ABSTRACT FROM AUTHOR]

Abstract Background Nearly 6 million deaths and over a half trillion dollars in healthcare costs worldwide are attributed to tobacco smoking each year. Extensive research efforts have been pursued to elucidate the molecular underpinnings of smoking addiction and facilitate cessation. In this study, we genotyped and obtained both resting state and task-based functional magnetic resonance imaging from 64 non-smokers and 42 smokers. Smokers were imaged after having smoked normally (“sated”) and after having not smoked for at least 12 h (“abstinent”). Results While abstinent smokers did not differ from non-smokers with respect to pairwise resting state functional connectivities (RSFCs) between 12 brain regions of interest, RSFCs involving the caudate and putamen of sated smokers significantly differed from those of non-smokers (P

Abstract Background Gene co-expression, the similarity of gene expression profiles under various experimental conditions, has been used as an indicator of functional relationships between genes, and many co-expression databases have been developed for predicting gene functions. These databases usually provide users with a co-expression network and a list of strongly co-expressed genes for a query gene. Several of these databases also provide functional information on a set of strongly co-expressed genes (i.e., provide biological processes and pathways that are enriched in these strongly co-expressed genes), which is generally analyzed via over-representation analysis (ORA). A limitation of this approach may be that users can predict gene functions only based on the strongly co-expressed genes. Results In this study, we developed a new co-expression database that enables users to predict the function of tomato genes from the results of functional enrichment analyses of co-expressed genes while considering the genes that are not strongly co-expressed. To achieve this, we used the ORA approach with several thresholds to select co-expressed genes, and performed gene set enrichment analysis (GSEA) applied to a ranked list of genes ordered by the co-expression degree. We found that internal correlation in pathways affected the significance levels of the enrichment analyses. Therefore, we introduced a new measure for evaluating the relationship between the gene and pathway, termed the percentile (p)-score, which enables users to predict functionally relevant pathways without being affected by the internal correlation in pathways. In addition, we evaluated our approaches using receiver operating characteristic curves, which concluded that the p-score could improve the performance of the ORA. Conclusions We developed a new database, named Co-expressed Pathways DataBase for Tomato, which is available at http://cox-path-db.kazusa.or.jp/tomato . The database allows users to predict pathways that are relevant to a query gene, which would help to infer gene functions.

Background: Nearly 6 million deaths and over a half trillion dollars in healthcare costs worldwide are attributed to tobacco smoking each year. Extensive research efforts have been pursued to elucidate the molecular underpinnings of smoking addiction and facilitate cessation. In this study, we genotyped and obtained both resting state and taskbased functional magnetic resonance imaging from 64 non-smokers and 42 smokers. Smokers were imaged after having smoked normally ("sated") and after having not smoked for at least 12 h ("abstinent"). Results: While abstinent smokers did not differ from non-smokers with respect to pairwise resting state functional connectivities (RSFCs) between 12 brain regions of interest, RSFCs involving the caudate and putamen of sated smokers significantly differed from those of non-smokers (P < 0.01). Further analyses of caudate and putamen activity during elicited experiences of reward and disappointment show that caudate activity during reward (CR) correlated with smoking status (P = 0.015). Moreover, abstinent smokers with lower CR experienced greater withdrawal symptoms (P = 0.024), which suggests CR may be related to smoking urges. Associations between genetic variants and CR, adjusted for smoking status, were identified by genome-wide association study (GWAS). Genes containing or exhibiting caudate-specific expression regulation by these variants were enriched within Gene Ontology terms that describe cytoskeleton functions, synaptic organization, and injury response (P < 0.001, FDR < 0.05). Conclusions: By integrating genomic and imaging data, novel insights into potential mechanisms of caudate activation and homeostasis are revealed that may guide new directions of research toward improving our understanding of addiction pathology. [ABSTRACT FROM AUTHOR]

Background: The study of local adaptation processes is a very important research topic in the field of population genomics. There is a particular interest in the study of human populations because they underwent a process of rapid spatial expansion and faced important environmental changes that translated into changes in selective pressures. New mutations may have been selected for in the new environment and previously existing genetic variants may have become detrimental. Immune related genes may have been released from the selective pressure exerted by pathogens in the ancestral environment and new variants may have been positively selected due to pathogens present in the newly colonized habitat. Also, variants that had a selective advantage in past environments may have become deleterious in the modern world due to external stimuli including climatic, dietary and behavioral changes, which could explain the high prevalence of some polygenic diseases such as diabetes and obesity. Results: We performed an enrichment analysis to identify gene sets enriched for signals of positive selection in humans. We used two genome scan methods, XPCLR and iHS to detect selection using a dense coverage of SNP markers combined with two gene set enrichment approaches. We identified immune related gene sets that could be involved in the protection against pathogens especially in the African population. We also identified the glycolysis & gluconeogenesis gene set, related to metabolism, which supports the thrifty genotype hypothesis invoked to explain the current high prevalence of diseases such as diabetes and obesity. Extending our analysis to the gene level, we found signals for 23 candidate genes linked to metabolic syndrome, 13 of which are new candidates for positive selection. Conclusions: Our study provides a list of genes and gene sets associated with immunity and metabolic syndrome that are enriched for signals of positive selection in three human populations (Europeans, Africans and Asians). Our results highlight differences in the relative importance of pathogens as drivers of local adaptation in different continents and provide new insights into the evolution and high incidence of metabolic syndrome in modern human populations. [ABSTRACT FROM AUTHOR]

Background: The acquisition of multidrug resistance by Plasmodium falciparum underscores the need to understand the underlying molecular mechanisms so as to counter their impact on malaria control. For the many antimalarials whose mode of action relates to inhibition of heme detoxification inside infected erythrocytes, the digestive vacuole transporters PfCRT and PfMDR1 constitute primary resistance determinants. Results: Using gene expression microarrays over the course of the parasite intra-erythrocytic developmental cycle, we compared the transcriptomic profiles between P. falciparum strains displaying mutant or wild-type pfcrt or varying in pfcrt or pfmdr1 expression levels. To account for differences in the time of sampling, we developed a computational method termed Hypergeometric Analysis of Time Series, which combines Fast Fourier Transform with a modified Gene Set Enrichment Analysis. Our analysis revealed coordinated changes in genes involved in protein catabolism, translation initiation and DNA/RNA metabolism. We also observed differential expression of genes with a role in transport or coding for components of the digestive vacuole. Interestingly, a global comparison of all profiled transcriptomes uncovered a tight correlation between the transcript levels of pfcrt and pfmdr1, extending to dozens of other genes, suggesting an intricate regulatory balance in order to maintain optimal physiological processes. Conclusions: This study provides insight into the mechanisms by which P. falciparum adjusts to the acquisition of mutations or gene amplification in key transporter loci that mediate drug resistance. Our results implicate several biological pathways that may be differentially regulated to compensate for impaired transporter function and alterations in parasite vacuole physiology. [ABSTRACT FROM AUTHOR]

Background: Recent advances in the analysis of high-throughput expression data have led to the development of tools that scaled-up their focus from single-gene to gene set level. For example, the popular Gene Set Enrichment Analysis (GSEA) algorithm can detect moderate but coordinated expression changes of groups of presumably related genes between pairs of experimental conditions. This considerably improves extraction of information from high-throughput gene expression data. However, although many gene sets covering a large panel of biological fields are available in public databases, the ability to generate home-made gene sets relevant to one's biological question is crucial but remains a substantial challenge to most biologists lacking statistic or bioinformatic expertise. This is all the more the case when attempting to define a gene set specific of one condition compared to many other ones. Thus, there is a crucial need for an easy-to-use software for generation of relevant home-made gene sets from complex datasets, their use in GSEA, and the correction of the results when applied to multiple comparisons of many experimental conditions. Result: We developed BubbleGUM (GSEA Unlimited Map), a tool that allows to automatically extract molecular signatures from transcriptomic data and perform exhaustive GSEA with multiple testing correction. One original feature of BubbleGUM notably resides in its capacity to integrate and compare numerous GSEA results into an easy-to-grasp graphical representation. We applied our method to generate transcriptomic fingerprints for murine cell types and to assess their enrichments in human cell types. This analysis allowed us to confirm homologies between mouse and human immunocytes. Conclusions: BubbleGUM is an open-source software that allows to automatically generate molecular signatures out of complex expression datasets and to assess directly their enrichment by GSEA on independent datasets. Enrichments are displayed in a graphical output that helps interpreting the results. This innovative methodology has recently been used to answer important questions in functional genomics, such as the degree of similarities between microarray datasets from different laboratories or with different experimental models or clinical cohorts. BubbleGUM is executable through an intuitive interface so that both bioinformaticians and biologists can use it. It is available at http://www.ciml.univ-mrs.fr/ applications/BubbleGUM/index.html. [ABSTRACT FROM AUTHOR]

Background: Gene co-expression, the similarity of gene expression profiles under various experimental conditions, has been used as an indicator of functional relationships between genes, and many co-expression databases have been developed for predicting gene functions. These databases usually provide users with a co-expression network and a list of strongly co-expressed genes for a query gene. Several of these databases also provide functional information on a set of strongly co-expressed genes (i.e., provide biological processes and pathways that are enriched in these strongly co-expressed genes), which is generally analyzed via over-representation analysis (ORA). A limitation of this approach may be that users can predict gene functions only based on the strongly co-expressed genes., Results: In this study, we developed a new co-expression database that enables users to predict the function of tomato genes from the results of functional enrichment analyses of co-expressed genes while considering the genes that are not strongly co-expressed. To achieve this, we used the ORA approach with several thresholds to select co-expressed genes, and performed gene set enrichment analysis (GSEA) applied to a ranked list of genes ordered by the co-expression degree. We found that internal correlation in pathways affected the significance levels of the enrichment analyses. Therefore, we introduced a new measure for evaluating the relationship between the gene and pathway, termed the percentile (p)-score, which enables users to predict functionally relevant pathways without being affected by the internal correlation in pathways. In addition, we evaluated our approaches using receiver operating characteristic curves, which concluded that the p-score could improve the performance of the ORA., Conclusions: We developed a new database, named Co-expressed Pathways DataBase for Tomato, which is available at http://cox-path-db.kazusa.or.jp/tomato . The database allows users to predict pathways that are relevant to a query gene, which would help to infer gene functions.