Cell type-specific genome scans of DNA methylation divergence indicate an important role for transposable elements

Whole-Genome Bisulfite Sequencing with a Small Amount of DNA

Whole-genome bisulfite sequencing (WGBS) is the most widely used method to study DNA methylation profiles across the genome. Since the bisulfite reaction causes DNA degradation, a new approach called post-bisulfite adapter tagging (PBAT) was developed to overcome this problem by adding adapters after bisulfite treatment. In mammals, the PBAT method is used for single-cell bisulfite sequencing (scBS-seq), which enables DNA methylation analysis using a very small amount of DNA from only a few cells, including single-cell input. This protocol involves bisulfite conversion, followed by preamplification and tagging with random hexamer primers prior to Illumina library preparation. Since many procedures are completed in one single test tube, the loss of DNA can be minimized, enabling highly sensitive experiments to study DNA methylation profiles from a very small amount of input material.

Macrophage migration inhibitory factor facilitates astrocytic production of the CCL2 chemokine following spinal cord injury

Spinal cord injury causes accumulation of a large number of leukocytes at the lesion site where they contribute to excessive inflammation. Overproduced chemokines are responsible for the migratory process of the leukocytes, but the regulatory mechanism underlying the production of chemokines from resident cells of the spinal cord has not been fully elucidated. We examined the protein levels of macrophage migration inhibitory factor and chemokine C-C motif chemokine ligand 2 in a spinal cord contusion model at different time points following spinal cord injury. The elevation of macrophage migration inhibitory factor at the lesion site coincided with the increase of chemokine C-C motif chemokine ligand 2 abundance in astrocytes. Stimulation of primary cultured astrocytes with different concentrations of macrophage migration inhibitory factor recombinant protein induced chemokine C-C motif chemokine ligand 2 production from the cells, and the macrophage migration inhibitory factor inhibitor 4-iodo-6-phenylpyrimidine attenuated the stimulatory effect. Further investigation into the underlying mechanism on macrophage migration inhibitory factor-mediated astrocytic production of chemokine C-C motif chemokine ligand 2 revealed that macrophage migration inhibitory factor activated intracellular JNK signaling through binding with CD74 receptor. Administration of the macrophage migration inhibitory factor inhibitor 4-iodo-6-phenylpyrimidine following spinal cord injury resulted in the reduction of chemokine C-C motif chemokine ligand 2-recruited microglia/macrophages at the lesion site and remarkably improved the hindlimb locomotor function of rats. Our results have provided insights into the functions of astrocyte-activated chemokines in the recruitment of leukocytes and may be beneficial to develop interventions targeting chemokine C-C motif chemokine ligand 2 for neuroinflammation after spinal cord injury.

Re-analysis of publicly available methylomes using signal detection yields new information

Cytosine methylation is an epigenetic mark that participates in regulation of gene expression and chromatin stability in plants. Advancements in whole genome sequencing technologies have enabled investigation of methylome dynamics under different conditions. However, the computational methods for analyzing bisulfite sequence data have not been unified. Contention remains in the correlation of differentially methylated positions with the investigated treatment and exclusion of noise, inherent to these stochastic datasets. The prevalent approaches apply Fisher's exact test, logistic, or beta regression, followed by an arbitrary cut-off for differences in methylation levels. A different strategy, the MethylIT pipeline, utilizes signal detection to determine cut-off based on a fitted generalized gamma probability distribution of methylation divergence. Re-analysis of publicly available BS-seq data from two epigenetic studies in Arabidopsis and applying MethylIT revealed additional, previously unreported results. Methylome repatterning in response to phosphate starvation was confirmed to be tissue-specific and included phosphate assimilation genes in addition to sulfate metabolism genes not implicated in the original study. During seed germination plants undergo major methylome reprogramming and use of MethylIT allowed us to identify stage-specific gene networks. We surmise from these comparative studies that robust methylome experiments must account for data stochasticity to achieve meaningful functional analyses.

MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data

DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.

Evaluation of various distance computation methods for construction of haplotype-based phylogenies from large MLST dataset

Multi-locus sequence typing (MLST) is widely used to investigate genetic relationships among eukaryotic taxa, including parasitic pathogens. MLST analysis workflows typically involve construction of alignment-based phylogenetic trees - i.e., where tree structures are computed from nucleotide differences observed in a multiple sequence alignment (MSA). Notably, alignment-based phylogenetic methods require that all isolates/taxa are represented by a single sequence. When multiple loci are sequenced these sequences may be concatenated to produce one tree that includes information from all loci. Alignment-based phylogenetic techniques are robust and widely used yet possess some shortcomings, including how heterozygous sites are handled, intolerance for missing data (i.e., partial genotypes), and differences in the way insertions-deletions (indels) are scored/treated during tree construction. In certain contexts, 'haplotype-based' methods may represent a viable alternative to alignment-based techniques, as they do not possess the aforementioned limitations. This is namely because haplotype-based methods assess genetic similarity based on numbers of shared (i.e., intersecting) haplotypes as opposed to similarities in nucleotide composition observed in an MSA. For haplotype-based comparisons, choosing an appropriate distance statistic is fundamental, and several statistics are available to choose from. However, a comprehensive assessment of various available statistics for their ability to produce a robust haplotype-based phylogenetic reconstruction has not yet been performed. We evaluated seven distance statistics by applying them to extant MLST datasets from the gastrointestinal parasite Cyclospora cayetanensis and two species of pathogenic nematode of the genus Strongyloides. We compare the genetic relationships identified using each statistic to epidemiologic, geographic, and host metadata. We show that Barratt's heuristic definition of genetic distance was the most robust among the statistics evaluated. Consequently, it is proposed that Barratt's heuristic represents a useful approach for use in the context of challenging MLST datasets possessing features (i.e., high heterozygosity, partial genotypes, and indel or repeat-based polymorphisms) that confound or preclude the use of alignment-based methods.