Epigenetic Modulations for Prevention of Infectious Diseases in Shrimp Aquaculture.

Aquaculture assumes a pivotal role in meeting the escalating global food demand, and shrimp farming, in particular, holds a significant role in the global economy and food security, providing a rich source of nutrients for human consumption. Nonetheless, the industry faces formidable challenges, primarily attributed to disease outbreaks and the diminishing efficacy of conventional disease management approaches, such as antibiotic usage. Consequently, there is an urgent imperative to explore alternative strategies to ensure the sustainability of the industry. In this context, the field of epigenetics emerges as a promising avenue for combating infectious diseases in shrimp aquaculture. Epigenetic modulations entail chemical alterations in DNA and proteins, orchestrating gene expression patterns without modifying the underlying DNA sequence through DNA methylation, histone modifications, and non-coding RNA molecules. Utilizing epigenetic mechanisms presents an opportunity to enhance immune gene expression and bolster disease resistance in shrimp, thereby contributing to disease management strategies and optimizing shrimp health and productivity. Additionally, the concept of epigenetic inheritability in marine animals holds immense potential for the future of the shrimp farming industry. To this end, this comprehensive review thoroughly explores the dynamics of epigenetic modulations in shrimp aquaculture, with a particular emphasis on its pivotal role in disease management. It conveys the significance of harnessing advantageous epigenetic changes to ensure the long-term viability of shrimp farming while deliberating on the potential consequences of these interventions. Overall, this appraisal highlights the promising trajectory of epigenetic applications, propelling the field toward strengthening sustainability in shrimp aquaculture.

Systemic interindividual epigenetic variation in humans is associated with transposable elements and under strong genetic control.

BACKGROUND: Genetic variants can modulate phenotypic outcomes via epigenetic intermediates, for example at methylation quantitative trait loci (mQTL). We present the first large-scale assessment of mQTL at human genomic regions selected for interindividual variation in CpG methylation, which we call correlated regions of systemic interindividual variation (CoRSIVs). These can be assayed in blood DNA and do not reflect interindividual variation in cellular composition. RESULTS: We use target-capture bisulfite sequencing to assess DNA methylation at 4086 CoRSIVs in multiple tissues from each of 188 donors in the NIH Gene-Tissue Expression (GTEx) program. At CoRSIVs, DNA methylation in peripheral blood correlates with methylation and gene expression in internal organs. We also discover unprecedented mQTL at these regions. Genetic influences on CoRSIV methylation are extremely strong (median R2=0.76), cumulatively comprising over 70-fold more human mQTL than detected in the most powerful previous study. Moreover, mQTL beta coefficients at CoRSIVs are highly skewed (i.e., the major allele predicts higher methylation). Both surprising findings are independently validated in a cohort of 47 non-GTEx individuals. Genomic regions flanking CoRSIVs show long-range enrichments for LINE-1 and LTR transposable elements; the skewed beta coefficients may therefore reflect evolutionary selection of genetic variants that promote their methylation and silencing. Analyses of GWAS summary statistics show that mQTL polymorphisms at CoRSIVs are associated with metabolic and other classes of disease. CONCLUSIONS: A focus on systemic interindividual epigenetic variants, clearly enhanced in mQTL content, should likewise benefit studies attempting to link human epigenetic variation to the risk of disease.

Sex-specific epigenetic development in the mouse hypothalamic arcuate nucleus pinpoints human genomic regions associated with body mass index.

Recent genome-wide association studies corroborate classical research on developmental programming indicating that obesity is primarily a neurodevelopmental disease strongly influenced by nutrition during critical ontogenic windows. Epigenetic mechanisms regulate neurodevelopment; however, little is known about their role in establishing and maintaining the brain's energy balance circuitry. We generated neuron and glia methylomes and transcriptomes from male and female mouse hypothalamic arcuate nucleus, a key site for energy balance regulation, at time points spanning the closure of an established critical window for developmental programming of obesity risk. We find that postnatal epigenetic maturation is markedly cell type and sex specific and occurs in genomic regions enriched for heritability of body mass index in humans. Our results offer a potential explanation for both the limited ontogenic windows for and sex differences in sensitivity to developmental programming of obesity and provide a rich resource for epigenetic analyses of developmental programming of energy balance.

Molecular characterization and antimicrobial susceptibility of Corynebacterium pseudotuberculosis isolated from skin abscesses of native Korean goats (Capra hircus coreanae).

AIMS: This study aimed to investigate the molecular characterization and antimicrobial susceptibility of Corynebacterium pseudotuberculosis from skin abscesses of Korean native black goats (KNBG, Capra hircus coreanae) in South Korea. METHODS AND RESULTS: A total of 83 isolates were recovered from skin abscesses of KNBG. Of these isolates, 74 isolates were identified as C. pseudotuberculosis by phospholipase D (PLD) gene-based PCR assay. Each of the isolates possessed all 18 virulence genes (FagA, FagB, FagC, FagD, SigE, SpaC, SodC, PknG, NanH, OppA, OppB, OppC, OppD, OppF, CopC, NrdH and CpaE). The genetic diversity of C. pseudotuberculosis isolates was assessed by the phylogenetic analysis using the concatenated sequences (3073 bp) of five housekeeping genes (fusA, dnaK, infB, groeL1 and leuA) for investigating their genetic diversity. In the results, the isolates belonged to three groups: group 1 (67 isolates), group 2 (one isolate) and group 3 (six isolates) within biovar ovis. However, the groups exhibited low genetic diversity (0.20%-0.41%). In the antimicrobial susceptibility test, most isolates were susceptible to tetracycline, vancomycin, chloramphenicol, ciprofloxacin, erythromycin, enrofloxacin, cefoxitin, ampicillin, gentamycin, cephalothin and doxycycline, whereas they were not susceptible to cefotaxime, trimethoprim and streptomycin. CONCLUSION: This results suggest the involvement of relatively few clones of C. pseudotuberculosis in Korea. Further, present isolates can threaten public health due to potentially virulent strains with all 18 virulence genes and non-susceptible strains to clinically important antibiotics (CIA) and highly important antibiotics. SIGNIFICANCE AND IMPACT OF THE STUDY: This study is the first to investigate the genetic diversity and potential pathogenicity of C. pseudotuberculosis biovar ovis isolates from skin abscesses of KBNG in South Korea, and could provide useful information in controlling its infections.

DNA methylation at a nutritionally sensitive region of the PAX8 gene is associated with thyroid volume and function in Gambian children.

PAX8 is a key thyroid transcription factor implicated in thyroid gland differentiation and function, and PAX8 gene methylation is reported to be sensitive to the periconceptional environment. Using a novel recall-by-epigenotype study in Gambian children, we found that PAX8 hypomethylation at age 2 years is associated with a 21% increase in thyroid volume and an increase in free thyroxine (T4) at 5 to 8 years, the latter equivalent to 8.4% of the normal range. Free T4 was associated with a decrease in DXA-derived body fat and bone mineral density. Furthermore, offspring PAX8 methylation was associated with periconceptional maternal nutrition, and methylation variability was influenced by genotype, suggesting that sensitivity to environmental exposures may be under partial genetic control. Together, our results demonstrate a possible link between early environment, PAX8 gene methylation and thyroid gland development and function, with potential implications for early embryonic programming of thyroid-related health and disease.

A machine learning case-control classifier for schizophrenia based on DNA methylation in blood.

Epigenetic dysregulation is thought to contribute to the etiology of schizophrenia (SZ), but the cell type-specificity of DNA methylation makes population-based epigenetic studies of SZ challenging. To train an SZ case-control classifier based on DNA methylation in blood, therefore, we focused on human genomic regions of systemic interindividual epigenetic variation (CoRSIVs), a subset of which are represented on the Illumina Human Methylation 450K (HM450) array. HM450 DNA methylation data on whole blood of 414 SZ cases and 433 non-psychiatric controls were used as training data for a classification algorithm with built-in feature selection, sparse partial least squares discriminate analysis (SPLS-DA); application of SPLS-DA to HM450 data has not been previously reported. Using the first two SPLS-DA dimensions we calculated a "risk distance" to identify individuals with the highest probability of SZ. The model was then evaluated on an independent HM450 data set on 353 SZ cases and 322 non-psychiatric controls. Our CoRSIV-based model classified 303 individuals as cases with a positive predictive value (PPV) of 80%, far surpassing the performance of a model based on polygenic risk score (PRS). Importantly, risk distance (based on CoRSIV methylation) was not associated with medication use, arguing against reverse causality. Risk distance and PRS were positively correlated (Pearson r = 0.28, P = 1.28 × 10-12), and mediational analysis suggested that genetic effects on SZ are partially mediated by altered methylation at CoRSIVs. Our results indicate two innate dimensions of SZ risk: one based on genetic, and the other on systemic epigenetic variants.

Identification of biological pathway and process regulators using sparse partial least squares and triple-gene mutual interaction.

Identification of biological process- and pathway-specific regulators is essential for advancing our understanding of regulation and formation of various phenotypic and complex traits. In this study, we applied two methods, triple-gene mutual interaction (TGMI) and Sparse Partial Least Squares (SPLS), to identify the regulators of multiple metabolic pathways in Arabidopsis thaliana and Populus trichocarpa using high-throughput gene expression data. We analyzed four pathways: (1) lignin biosynthesis pathway in A. thaliana and P. trichocarpa; (2) flavanones, flavonol and anthocyannin biosynthesis in A. thaliana; (3) light reaction pathway and Calvin cycle in A. thaliana. (4) light reaction pathway alone in A. thaliana. The efficiencies of two methods were evaluated by examining the positive known regulators captured, the receiver operating characteristic (ROC) curves and the area under ROC curves (AUROC). Our results showed that TGMI is in general more efficient than SPLS in identifying true pathway regulators and ranks them to the top of candidate regulatory gene lists, but the two methods are to some degree complementary because they could identify some different pathway regulators. This study identified many regulators that potentially regulate the above pathways in plants and are valuable for genetic engineering of these pathways.

Perfluorooctanoic acid (PFOA) or perfluorooctane sulfonate (PFOS) and DNA methylation in newborn dried blood spots in the Upstate KIDS cohort.

Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) are persistent organic pollutants which may alter prenatal development, potentially through epigenetic modifications. Prior studies examining PFOS/PFOA and DNA methylation have relatively few subjects (n < 200) and inconsistent results. We examined relations of PFOA/PFOS with DNA methylation among 597 neonates in the Upstate KIDS cohort study. PFOA/PFOS were quantified in newborn dried blood spots (DBS) using high-performance liquid chromatography/tandem mass spectrometry. DNA methylation was measured using the Infinium MethylationEPIC BeadChip with DNA extracted from DBS. Robust linear regression was used to examine the associations of PFOA/PFOS with DNA methylation at individual CpG sites. Covariates included sample plate, estimated cell type, epigenetically derived ancestry, infant sex and plurality, indicators of maternal socioeconomic status, and prior pregnancy loss. In supplemental analysis, we restricted the analysis to 2242 CpG sites previously identified as Correlated Regions of Systemic Interindividual Variation (CoRSIVs) which include metastable epialleles. At FDR<0.05, PFOA concentration >90th percentile was related to DNA methylation at cg15557840, near SCRT2, SRXN1; PFOS>90th percentile was related to 2 CpG sites in a sex-specific manner (cg19039925 in GVIN1 in boys and cg05754408 in ZNF26 in girls). When analysis was restricted to CoRSIVs, log-scaled, continuous PFOS concentration was related to DNA methylation at cg03278866 within PTBP1. In conclusion, there was limited evidence of an association between high concentrations of PFOA/PFOS and DNA methylation in newborn DBS in the Upstate KIDS cohort. These findings merit replication in populations with a higher median concentration of PFOA/PFOS.

Identification of cell type-specific methylation signals in bulk whole genome bisulfite sequencing data.

BACKGROUND: The traditional approach to studying the epigenetic mechanism CpG methylation in tissue samples is to identify regions of concordant differential methylation spanning multiple CpG sites (differentially methylated regions). Variation limited to single or small numbers of CpGs has been assumed to reflect stochastic processes. To test this, we developed software, Cluster-Based analysis of CpG methylation (CluBCpG), and explored variation in read-level CpG methylation patterns in whole genome bisulfite sequencing data. RESULTS: Analysis of both human and mouse whole genome bisulfite sequencing datasets reveals read-level signatures associated with cell type and cell type-specific biological processes. These signatures, which are mostly orthogonal to classical differentially methylated regions, are enriched at cell type-specific enhancers and allow estimation of proportional cell composition in synthetic mixtures and improved prediction of gene expression. In tandem, we developed a machine learning algorithm, Precise Read-Level Imputation of Methylation (PReLIM), to increase coverage of existing whole genome bisulfite sequencing datasets by imputing CpG methylation states on individual sequencing reads. PReLIM both improves CluBCpG coverage and performance and enables identification of novel differentially methylated regions, which we independently validate. CONCLUSIONS: Our data indicate that, rather than stochastic variation, read-level CpG methylation patterns in tissue whole genome bisulfite sequencing libraries reflect cell type. Accordingly, these new computational tools should lead to an improved understanding of epigenetic regulation by DNA methylation.

A genomic atlas of systemic interindividual epigenetic variation in humans.

BACKGROUND: DNA methylation is thought to be an important determinant of human phenotypic variation, but its inherent cell type specificity has impeded progress on this question. At exceptional genomic regions, interindividual variation in DNA methylation occurs systemically. Like genetic variants, systemic interindividual epigenetic variants are stable, can influence phenotype, and can be assessed in any easily biopsiable DNA sample. We describe an unbiased screen for human genomic regions at which interindividual variation in DNA methylation is not tissue-specific. RESULTS: For each of 10 donors from the NIH Genotype-Tissue Expression (GTEx) program, CpG methylation is measured by deep whole-genome bisulfite sequencing of genomic DNA from tissues representing the three germ layer lineages: thyroid (endoderm), heart (mesoderm), and brain (ectoderm). We develop a computational algorithm to identify genomic regions at which interindividual variation in DNA methylation is consistent across all three lineages. This approach identifies 9926 correlated regions of systemic interindividual variation (CoRSIVs). These regions, comprising just 0.1% of the human genome, are inter-correlated over long genomic distances, associated with transposable elements and subtelomeric regions, conserved across diverse human ethnic groups, sensitive to periconceptional environment, and associated with genes implicated in a broad range of human disorders and phenotypes. CoRSIV methylation in one tissue can predict expression of associated genes in other tissues. CONCLUSIONS: In addition to charting a previously unexplored molecular level of human individuality, this atlas of human CoRSIVs provides a resource for future population-based investigations into how interindividual epigenetic variation modulates risk of disease.

Co-expression analysis aids in the identification of genes in the cuticular wax pathway in maize.

Epicuticular waxes provide a hydrophobic barrier that protects land plants from environmental stresses. To elucidate the molecular functions of maize glossy mutants that reduce the accumulation of epicuticular waxes, eight non-allelic glossy mutants were subjected to transcriptomic comparisons with their respective wild-type siblings. Transcriptomic comparisons identified 2279 differentially expressed (DE) genes. Other glossy genes tended to be down-regulated in glossy mutants; by contrast stress-responsive pathways were induced in mutants. Gene co-expression network (GCN) analysis found that glossy genes were clustered, suggestive of co-regulation. Genes that potentially regulate the accumulation of glossy gene transcripts were identified via a pathway level co-expression analysis. Expression data from diverse organs showed that maize glossy genes are generally active in young leaves, silks, and tassels, while largely inactive in seeds and roots. Through reverse genetics, a DE gene homologous to Arabidopsis CER8 and co-expressed with known glossy genes was confirmed to participate in epicuticular wax accumulation. GCN data-informed forward genetics approach enabled cloning of the gl14 gene, which encodes a putative membrane-associated protein. Our results deepen understanding of the transcriptional regulation of the genes involved in the accumulation of epicuticular wax, and provide two maize glossy genes and a number of candidate genes for further characterization.

A Resource for Inactivation of MicroRNAs Using Short Tandem Target Mimic Technology in Model and Crop Plants.

microRNAs (miRNAs) are endogenous small non-coding RNAs that bind to mRNAs and target them for cleavage and/or translational repression, leading to gene silencing. We previously developed short tandem target mimic (STTM) technology to deactivate endogenous miRNAs in Arabidopsis. Here, we created hundreds of STTMs that target both conserved and species-specific miRNAs in Arabidopsis, tomato, rice, and maize, providing a resource for the functional interrogation of miRNAs. We not only revealed the functions of several miRNAs in plant development, but also demonstrated that tissue-specific inactivation of a few miRNAs in rice leads to an increase in grain size without adversely affecting overall plant growth and development. RNA-seq and small RNA-seq analyses of STTM156/157 and STTM165/166 transgenic plants revealed the roles of these miRNAs in plant hormone biosynthesis and activation, secondary metabolism, and ion-channel activity-associated electrophysiology, demonstrating that STTM technology is an effective approach for studying miRNA functions. To facilitate the study and application of STTM transgenic plants and to provide a useful platform for storing and sharing of information about miRNA-regulated gene networks, we have established an online Genome Browser (https://blossom.ffr.mtu.edu/designindex2.php) to display the transcriptomic and miRNAomic changes in STTM-induced miRNA knockdown plants.

TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction.

Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories.

ExactSearch: a web-based plant motif search tool.

BACKGROUND: Plant biologists frequently need to examine if a sequence motif bound by a specific transcription or translation factor is present in the proximal promoters or 3' untranslated regions (3' UTR) of a set of plant genes of interest. To achieve such a task, plant biologists have to not only identify an appropriate algorithm for motif searching, but also manipulate the large volume of sequence data, making it burdensome to carry out or fulfill. RESULT: In this study, we developed a web portal that enables plant molecular biologists to search for DNA motifs especially degenerate ones in custom sequences or the flanking regions of all genes in the 50 plant species whose genomes have been sequenced. A web tool like this is demanded to meet a variety of needs of plant biologists for identifying the potential gene regulatory relationships. We implemented a suffix tree algorithm to accelerate the searching process of a group of motifs in a multitude of target genes. The motifs to be searched can be in the degenerate bases in addition to adenine (A), cytosine (C), guanine (G), and thymine (T). The target sequences to be searched can be custom sequences or the selected proximal gene sequences from any one of the 50 sequenced plant species. The web portal also contains the functionality to facilitate the search of motifs that are represented by position probability matrix in above-mentioned species. Currently, the algorithm can accomplish an exhaust search of 100 motifs in 35,000 target sequences of 2 kb long in 4.2 min. However, the runtime may change in the future depending on the space availability, number of running jobs, network traffic, data loading, and output packing and delivery through electronic mailing. CONCLUSION: A web portal was developed to facilitate searching of motifs presents in custom sequences or the proximal promoters or 3' UTR of 50 plant species with the sequenced genomes. This web tool is accessible by using this URL: http://sys.bio.mtu.edu/motif/index.php.

Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes.

BACKGROUND: Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. RESULTS: A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct a ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory gene significantly interfered two paired pathway genes. The regulatory genes with highest interference frequency were kept as the second layer and the number kept is based on an optimization function. Thereafter, the algorithm was used recursively to build a ML-hGRN in layer-by-layer fashion until the defined number of layers was obtained or terminated automatically. CONCLUSIONS: We validated the algorithm and demonstrated its high efficiency in constructing ML-hGRNs governing biological pathways. The algorithm is instrumental for biologists to learn the hierarchical regulators associated with a given biological pathway from even small-sized microarray or RNA-seq data sets.