Search

No description available

Environmental factors can promote phenotypic variation through alterations in the epigenome and facilitate adaptation of an organism to the environment. Although hydrogen sulfide is toxic to most organisms, the fish Poecilia mexicana has adapted to survive in environments with high levels that exceed toxicity thresholds by orders of magnitude. Epigenetic changes in response to this environmental stressor were examined by assessing DNA methylation alterations in red blood cells, which are nucleated in fish. Males and females were sampled from sulfidic and nonsulfidic natural environments; individuals were also propagated for two generations in a nonsulfidic laboratory environment. We compared epimutations between the sexes as well as field and laboratory populations. For both the wild-caught (F0) and the laboratory-reared (F2) fish, comparing the sulfidic and nonsulfidic populations revealed evidence for significant differential DNA methylation regions (DMRs). More importantly, there was over 80% overlap in DMRs across generations, suggesting that the DMRs have stable generational inheritance in the absence of the sulfidic environment. This is an example of epigenetic generational stability after the removal of an environmental stressor. The DMR-associated genes were related to sulfur toxicity and metabolic processes. These findings suggest that adaptation of P. mexicana to sulfidic environments in southern Mexico may, in part, be promoted through epigenetic DNA methylation alterations that become stable and are inherited by subsequent generations independent of the environment.

Additional file 2. Fig S2: The accuracy of different models (Hybrid, DL, DeepCpG, DeepSEA, and DanQ) for DMR prediction. Each line shows the accuracy of each approach when trained and tested on the DMRs and non-DMRs for an individual chromosome. The points at the far right for ���All��� are the accuracies of these ���all chr��� models on all the DMRs and non-DMRs across the entire genome.

Additional file 4: Table S1. DMR lists at p

Additional file 5: Table S2. Gene and protein regulators of autism. The DMR-associated autism-related genes with symbol, description, and relevant references PubMed ID (PMID) numbers.

Additional file 1: Figure S1. Vinclozolin lineage F3 generation conserved DMR in common with DHR and F1 generation ncRNA. Figure S2. DDT lineage F3 generation conserved DMR in common with DHR and F1 generation ncRNA. Table S1. Vinclozolin lineage F1 generation ncRNA & F1, F2 and F3 generation DMR overlap list. Table S2. DDT lineage F1 generation ncRNA & F1, F2 and F3 generation DMR overlap list. Table S3. F3 generation vinclozolin DMR & F3 generation DHR overlap list. Table S4. F3 generation DDT DMR & F3 generation DHR overlap list. Table S5. Vinclozolin lineage F1, F2, F3 generation DMR overlap list. Table S6. DDT lineage F1, F2, F3 generation DMR overlap list.

Additional file 1: Supplemental methods.

Additional file 3. Table S1: Description of the hyper-parameters used to optimize the DL model. The underlined values resulted in the best-performing network

Additional file 4. Table S2: Summary of the datasets, including a brief description and the number of DMRs in the dataset due to the exposure. The number of non-DMRs is also shown, but they are determined from the genome, not individual exposure data

Additional file 1. Fig S1: Deep learning DNA sequence features for non-DMRs. Sequence motif visualizations for the 11 non-DMR detector features out of the 32 features extracted from the DL model. Non-DMR detectors are those features whose average activation for non-DMR examples is greater than for DMR examples. The feature ID, motif visualization, and the difference between the average non-DMR activation and the average DMR activation are presented. A larger difference indicates a feature motif more biased toward non-DMRs

Additional file 3: Figure S2. Permutation analysis. The number of DMR for autism case versus control patient comparison for all permutation analyses. The vertical red line shows the number of DMR found in the original analysis. All DMRs are defined using an edgeR p value threshold of p

Additional file 2: Figure S1. Clinical group statistic comparison. The various sperm/semen characteristics in the Study case and control group were compared with the Blind group. The n-value, mean, standard deviation, and standard error mean are presented. The blind groups are within the mean ± SD of the case and control study.

The increased use of electronic health records has made possible the automated extraction of medical policies from patient records to aid in the development of clinical decision support systems. We adapted a boosted Statistical Relational Learning (SRL) framework to learn probabilistic rules from clinical hospital records for the management of physiologic parameters of children with severe cardiac or respiratory failure who were managed with extracorporeal membrane oxygenation. In this preliminary study, the results were promising. In particular, the algorithm returned logic rules for medical actions that are consistent with medical reasoning., Comment: 6 pages, 1 figure, presented at the 2020 AAAI StarAI workshop

Additional file 1: Fig. S1. Principal component analysis (PCA) of control versus DDT genomic data sets for (A) DMRs and (B) DHRs in the F3 generation sperm. Separate clustering of the control and DDT data includes negligible overlap.

Additional file 3: Table S1. F1 DMR pÂ

Additional file 13: Fig. S5. Epimutation-associated gene pathways for endocytosis containing 5 DMRs (blue box), 5 ncRNA (red box) and 6 DHRs (pink box).

Additional file 5: Table S3. F3 DMR pÂ

Additional file 14: Fig. S6. Evaluation of the quality of the RNA-Seq data generated in this study. (A) Variations among biological replicates of the six RNA samples (pachytene RNP, pachytene polysome, round spermatid RNP, round spermatid polysome, elongating RNP and elongating polysome). The biological variation is reflected by the coefficient of variation to the power of two (CV2) of FPKM values for each gene. The CV2 represents a normalized measure of cross-replicate variability, which has been widely used for evaluating quality of RNA-Seq data. The data presented here show that the abundance of the genes varied between replicate RNA samples, especially for the ones with lower FPKM values, which is expected. (B) Scatterplot matrix showing the pairwise scatterplots of the log10-normalized FPKMÂ scores across biological replicates of all six RNA samples. (C) Density plots showing the distribution of log10-normalized FPKM scores across biological replicates of all six RNA samples. (D) Overdispersion plots demonstrating the estimated overdispersion for each sample as a quality control measure.

Additional file 11: Fig. S3. Genome Browser representative map of site with two DMRs, one ncRNA and one histone retention site on chromosome 22q. One exposed sequence tag (EST) is present.

Additional file 2: Fig. S2. DMR permutation analysis. (A) F1 generation control versus DDT lineage, (B) F2 generation control versus DDT lineage, (C) F3 generation control versus DDT lineage analysis. The number of DMRs for all comparisons in the permutation analyses. The vertical red line shows the number of DMRs found in the original full analysis. All DMRs are defined using an edgeR p value threshold of 1e−06. The number of DMRs in the control versus DDT lineage analysis is higher than would be expected due to random chance (p ≤ 0.1).

Sertoli DMR clusters. DMR clusters with start-end, statistical significance for each DMR with start and stop information for the Sertoli Cell dataset. (PDF 35 kb)

Additional file 1. Table of curated ChISGs identified by individual or multiple technologies. (1) An asterisk indicates a Gene ID not annotated in ENSEMBL. (2) Technologies identifying significant IRGs are listed as ‘1’ RNA-seq (using Kal’s Z test); ‘2’ Affymetrix 32K GeneChip Chicken Genome Array and ‘3’ Chicken Gene 1.0 ST Array’. ChISGs significant by one or both microarrays and RNA-seq using Kal’s Z test under relaxed criteria (FC > 2.5 or FDR

Female gene clusters. Gene cluster sites with start-end location of each cluster site, genes in each cluster and classification, and gene start and stop, and types of the tissue for the Female Tissue Array dataset. (PDF 59 kb)

Primordial germ cell DMR clusters. DMR clusters with start-end, statistical significance for each DMR with start and stop information regarding the Primordial Germ cell dataset. (PDF 36 kb)

Granulosa DMR clusters. DMR clusters with start-end, statistical significance for each DMR with start and stop information for the Granulosa Cell dataset. (PDF 35 kb)

Cluster R-Code. (PDF 52 kb)

Sperm and DMR clusters. DMR clusters with start-end, statistical significance for each DMR with start and stop information regarding clusters for the sperm DMR dataset. (PDF 41 kb)

Male gene clusters. Gene cluster sites with start-end location, and the gene names and classification, and gene start-stop, and types of the tissues for the Male Tissue Array dataset. (PDF 140 kb)

In medicine and biology, image analysis is often used to quantify outcomes for a wide range of applications, including diagnostics, surgery, and general research. Image analysis tasks can include counting the number of cells in a given area of a fluorescently stained image or measuring the geometry of tissues during surgery. Currently, in these situations, one of the more common and powerful programs used for image analysis is ImageJ. However, ImageJ is a computer application, so before an image can be analyzed, it has to be loaded and saved to the computer. On the other hand, while there are mobile image processing applications, most of them have limited functionality, performing manipulations such as adding filters and masks to pictures to make them look more appealing, qualitatively. My project focuses on creating a mobile application with various useful analytic functions, so that image analysis can be done quickly and without need for an external computer. Some of the functions that will be included are: cropping, zooming, measuring, counting, brightness, drawing/labeling, transforming, and masking

Additional file 12: Fig. S4. Epimutation-associated gene pathway for the pathways in cancer containing 5 DMRs (blue box), 6 ncRNA (red box) and 7 DHRs (pink box).

Additional file 6: Table S4. (A) Site Table F1 lncRNA p

Despite the significant disease burden caused by human norovirus infection, an efficient tissue-culture system for these viruses remains elusive. Murine norovirus (MNV) is an ideal surrogate for the study of norovirus biology, as the virus replicates efficiently in tissue culture and a low-cost animal model is readily available. In this report, a reverse-genetics system for MNV is described, using a fowlpox virus (FWPV) recombinant expressing T7 RNA polymerase to recover genetically defined MNV in tissue culture for the first time. These studies demonstrated that approaches that have proved successful for other members of the family Caliciviridae failed to lead to recovery of MNV. This was due to our observation that vaccinia virus infection had a negative effect on MNV replication. In contrast, FWPV infection had no deleterious effect and allowed the recovery of infectious MNV from cells previously transfected with MNV cDNA constructs. These studies also indicated that the nature of the 3'-terminal nucleotide is critical for efficient virus recovery and that inclusion of a hepatitis delta virus ribozyme at the 3' end can increase the efficiency with which virus is recovered. This system now allows the recovery of genetically defined noroviruses and will facilitate the analysis of the effects of genetic variation on norovirus pathogenesis.

Additional file 7: Table S5. (A) F2 lncRNA p

Additional file 10: Table S8. F3 H3K27me3 DHR pÂ

Additional file 1: Fig. S1. Principal component analysis (PCA) of control versus DDT genomic data sets for (A) DMRs and (B) DHRs in the F3 generation sperm. Separate clustering of the control and DDT data includes negligible overlap.

Additional file 6: Table S4. (A) Site Table F1 lncRNA p

Additional file 2: Fig. S2. DMR permutation analysis. (A) F1 generation control versus DDT lineage, (B) F2 generation control versus DDT lineage, (C) F3 generation control versus DDT lineage analysis. The number of DMRs for all comparisons in the permutation analyses. The vertical red line shows the number of DMRs found in the original full analysis. All DMRs are defined using an edgeR p value threshold of 1e−06. The number of DMRs in the control versus DDT lineage analysis is higher than would be expected due to random chance (p ≤ 0.1).

The anti-viral immune response is dependent on the ability of infected cells to sense foreign nucleic acids. In multiple species, the pattern recognition receptor (PRR) cyclic GMP-AMP synthase (cGAS) senses viral DNA as an essential component of the innate response. cGAS initiates a range of signaling outputs that are dependent on generation of the second messenger cGAMP that binds to the adaptor protein stimulator of interferon genes (STING). Here we show that in chicken macrophages, the cGAS/STING pathway is essential not only for the production of type-I interferons in response to intracellular DNA stimulation, but also for regulation of macrophage effector functions including the expression of MHC-II and co-stimulatory molecules. In the context of fowlpox, an avian DNA virus infection, the cGAS/STING pathway was found to be responsible for type-I interferon production and MHC-II transcription. The sensing of fowlpox virus DNA is therefore essential for mounting an anti-viral response in chicken cells and for regulation of a specific set of macrophage effector functions.

Additional file 9: Table S7. F3 DHR pÂ

Additional file 8: Table S6. (A) F3 lncRNA p

Bats are notorious reservoirs of diverse, potentially zoonotic viruses, exemplified by the evolutionarily distinct, influenza A-like viruses H17N10 and H18N11 (BatIVs). The surface glycoproteins [haemagglutinin (H) and neuraminidase (N)] of BatIVs neither bind nor cleave sialic acid receptors, which suggests that these viruses employ cell attachment and entry mechanisms that differ from those of classical influenza A viruses (IAVs). Identifying the cellular factors that mediate entry and determine susceptibility to infection will help assess the host range of BatIVs. Here, we investigated a range of cell lines from different species for their susceptibility to infection by pseudotyped viruses bearing bat H17 and/or N10 envelope proteins. We show that a number of human haematopoietic cancer cell lines and the canine kidney MDCK II (but not MDCK I) cells are susceptible to H17-pseudotypes (H17-PV). We observed with microarrays and qRT-PCR that the dog leukocyte antigen DLA-DRA mRNA is overexpressed in late passaged parental MDCK and commercial MDCK II cells, compared to early passaged parental MDCK and MDCK I cells, respectively. The human orthologue HLA-DRA encodes the alpha subunit of the MHC class II HLA-DR antigen-binding heterodimer. Small interfering RNA- or neutralizing antibody-targeting HLA-DRA, drastically reduced the susceptibility of Raji B cells to H17-PV. Conversely, overexpression of HLA-DRA and its paralogue HLA-DRB1 on the surface of the unsusceptible HEK293T/17 cells conferred susceptibility to H17-PV. The identification of HLA-DR as an H17N10 entry mediator will contribute to a better understanding of the tropism of the virus and will elucidate its zoonotic transmission.

Additional file 9: Table S7. F3 DHR pÂ

Additional file 12: Fig. S4. Epimutation-associated gene pathway for the pathways in cancer containing 5 DMRs (blue box), 6 ncRNA (red box) and 7 DHRs (pink box).

Additional file 8: Table S6. (A) F3 lncRNA p

Bats are notorious reservoirs of diverse, potentially zoonotic viruses, exemplified by the evolutionarily distinct, influenza A-like viruses H17N10 and H18N11 (BatIVs). The surface glycoproteins [haemagglutinin (H) and neuraminidase (N)] of BatIVs neither bind nor cleave sialic acid receptors, which suggests that these viruses employ cell attachment and entry mechanisms that differ from those of classical influenza A viruses (IAVs). Identifying the cellular factors that mediate entry and determine susceptibility to infection will help assess the host range of BatIVs. Here, we investigated a range of cell lines from different species for their susceptibility to infection by pseudotyped viruses bearing bat H17 and/or N10 envelope proteins. We show that a number of human haematopoietic cancer cell lines and the canine kidney MDCK II (but not MDCK I) cells are susceptible to H17-pseudotypes (H17-PV). We observed with microarrays and qRT-PCR that the dog leukocyte antigen DLA-DRA mRNA is overexpressed in late passaged parental MDCK and commercial MDCK II cells, compared to early passaged parental MDCK and MDCK I cells, respectively. The human orthologue HLA-DRA encodes the alpha subunit of the MHC class II HLA-DR antigen-binding heterodimer. Small interfering RNA- or neutralizing antibody-targeting HLA-DRA, drastically reduced the susceptibility of Raji B cells to H17-PV. Conversely, overexpression of HLA-DRA and its paralogue HLA-DRB1 on the surface of the unsusceptible HEK293T/17 cells conferred susceptibility to H17-PV. The identification of HLA-DR as an H17N10 entry mediator will contribute to a better understanding of the tropism of the virus and will elucidate its zoonotic transmission.

Additional file 10: Table S8. F3 H3K27me3 DHR pÂ

Additional file 7: Table S5. (A) F2 lncRNA p

Viruses are known to interact with the innate immune pathways and, in some cases, strains that differ in virulence are known to interact with these pathways in different ways. This thesis aimed to directly compare IBDV strains of differing virulence to determine key interactions with the innate immune response that may contribute to disease outcome. Infection of chicken B cells with the very virulent s UK661 strain, suppressed type I IFN responses compared to both infection with the cell-adapted IBDV strain, D78 in primary bursal cells, and infection with the classical strain, F52/70 in DT40 cells. Birds infected with UK661 also had down-regulated type I IFN and pro-inflammatory responses in the bursa of Fabricius (BF), compared to infection with F52/70. No difference in the peak virus titres was detected in the BF or spleen, although UK661 reached higher titres in the caecal tonsils than F52/70. Increased type I IFN production following F52/70 infection coincided with a reduced mortality in these birds, indicating a protective role of this immune response. The UK661 VP4 protein was found to suppress IFNβ production in vitro compared to the F52/70 VP4, which instead suppressed Mx1 production, indicating that the IBDV VP4 from different strains impairs either IFN production or signalling pathways. Upon knocking out the protease function of UK661 VP4, IFNβ production remained suppressed, and multiple amino acids are likely responsible for the different phenotype between strains. This work demonstrates that UK661 and F52/70 have strain-specific differences in their interactions with the innate immune response, mediated by the VP4 protein, therefore differences in this protein between strains may contribute to virulence. This information could be useful in the development of recombinant rationally designed live attenuated IBDV vaccines, by generating a vvIBDV backbone containing a VP4 from a classical or cell-adapted strain, as a vaccine candidate. Open Access

Epigenetic mechanisms operate at the interface between the environment and genome, by converting the environmental stimuli to phenotypic responses through changes in the chromatin landscape, which ultimately affects gene expression in the absence of alterations in DNA sequence. In this scenario, transgenerational inheritance occurs when epigenetic variations induced by environmental stimuli are transmitted through the germ line to succeeding generations that had never experienced those stimuli. There is an ever-growing list of reports indicating that histones are fundamental players in these processes in a variety of organisms. In this chapter, we provide a perspective on histone-driven epigenetic mechanisms, such as histone posttranslational modifications, incorporation of histone variants, and nucleosome positioning, illustrating their interwoven hierarchies of activity and highlighting their roles in the transgenerational inheritance of genomic functions.

Estrogen agonists are natural, synthetic, or metabolic products that selectively stimulate estrogen-like actions in target tissues. Wildlife and domestic animals and humans are exposed to estrogen agonists present in the environment (environmental estrogens). The developmental origins of health and disease paradigm assumes that, during development, there are sensitive windows in which tissue differentiation/function can be modified by estrogen agonists, leading to increased susceptibility to adverse health outcomes. Studies with a variety of estrogenic agonists have demonstrated effects on female reproductive organs and fertility. A better understanding of this research area will prevent exposures related to adverse female reproductive outcomes. Fil: Luque, Enrique Hugo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Salud y Ambiente del Litoral. Universidad Nacional del Litoral. Instituto de Salud y Ambiente del Litoral; Argentina Fil: Muñoz de Toro, Monica Milagros. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Salud y Ambiente del Litoral. Universidad Nacional del Litoral. Instituto de Salud y Ambiente del Litoral; Argentina Fil: Ramos, Jorge Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Salud y Ambiente del Litoral. Universidad Nacional del Litoral. Instituto de Salud y Ambiente del Litoral; Argentina