An in vivo, neuron-specific approach for pairing translational and epigenetic signatures of early-life exercise

Aerobic exercise promotes physiological and molecular adaptations in neurons to influence brain function and behavior. The most well studied neurobiological consequences of exercise are those which underlie exercise-induced improvements in hippocampal memory, including the expression and regulation of the neurotrophic factor Bdnf. Whether aerobic exercise taking place during early-life periods of postnatal brain maturation has similar impacts on gene expression and its regulation remains to be investigated. Using unbiased next-generation sequencing we characterize gene expression programs and their regulation by specific, memory-associated histone modifications during juvenile-adolescent voluntary exercise (ELE). Traditional transcriptomic and epigenomic sequencing approaches have either used heterogeneous cell populations from whole tissue homogenates or flow cytometry for single cell isolation to distinguish cell types / subtypes. These methods fall short in providing cell-type specificity without compromising sequencing depth or procedure-induced changes to cellular phenotype. In this study, we use simultaneous isolation of translating mRNA and nuclear chromatin from a neuron-enriched cell population to more accurately pair ELE-induced changes in gene expression with epigenetic modifications. We employ a line of transgenic mice expressing the NuTRAP (Nuclear Tagging and Translating Ribosome Affinity Purification) cassette under the Emx1 promoter allowing for brain cell-type specificity. We then developed a technique that combines nuclear isolation using Isolation of Nuclei TAgged in Specific Cell Types (INTACT) with Translating Ribosomal Affinity Purification (TRAP) methods to determine cell type-specific epigenetic modifications influencing gene expression programs from a population of Emx1 expressing hippocampal neurons. Data from RNA-seq and CUT&RUN-seq were coupled to evaluate histone modifications influencing the expression of translating mRNA in neurons after early-life exercise (ELE). We also performed separate INTACT and TRAP isolations for validation of our protocol and demonstrate similar molecular functions and biological processes implicated by gene ontology (GO) analysis. Finally, as prior studies use tissue from opposite brain hemispheres to pair transcriptomic and epigenomic data from the same rodent, we take a bioinformatics approach to compare hemispheric differences in gene expression programs and histone modifications altered by by ELE. Our data reveal transcriptional and epigenetic signatures of ELE exposure and identify novel candidate gene-histone modification interactions for further investigation. Importantly, our novel approach of combined INTACT/TRAP methods from the same cell suspension allows for simultaneous transcriptomic and epigenomic sequencing in a cell-type specific manner.

Artificial Intelligence uncovers carcinogenic human metabolites

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats due to its constant exposure to a myriad of heterogeneous chemical compounds. Despite the availability of innate DNA damage repair pathways, some genomic lesions trigger cells for malignant transformation. Accurate prediction of carcinogens is an ever-challenging task due to the limited information about bonafide (non)carcinogens. This, in turn, constrains the generalisability of such models. We developed a novel ensemble classifier (Metabokiller) that accurately recognizes carcinogens by quantitatively assessing their chemical composition as well as potential to induce proliferation, oxidative stress, genotoxicity, alterations in epigenetic signatures, and activation of anti-apoptotic pathways, therefore obviates the need for bonafide (non)carcinogens for training model. Concomitant with the carcinogenicity prediction, it also reveals the contribution of the aforementioned biochemical processes in carcinogenicity, thereby making the proposed approach highly interpretable. Metabokiller outwits existing best practice methods for the carcinogenicity prediction task. We used Metabokiller to decode the cellular endogenous metabolic threats by screening a large pool of human metabolites and identified putative metabolites that could potentially trigger malignancy in normal cells. To cross-validate our predictions, we performed an array of functional assays and genome-wide transcriptome analysis on two Metabokiller-flagged, and previously uncharacterized human metabolites by using Saccharomyces cerevisiae as a model organism and observed larger synergy with the prediction probabilities. Finally, the carcinogenicity potential of these metabolites was evaluated using a malignancy transformation assay on human cells.

Whole-genome sequence and methylome profiling of the almond (Prunus dulcis [Mill.] D.A.Webb) cultivar Nonpareil

Almond (Prunus dulcis [Mill.] D.A. Webb) is an economically important, specialty nut crop grown almost exclusively in the United States. Breeding and improvement efforts worldwide have led to the development of key, productive cultivars, including Nonpareil, which is the most widely grown almond cultivar. Thus far, genomic resources for this species have been limited, and a whole-genome assembly for Nonpareil is not currently available despite its economic importance and use in almond breeding worldwide. We generated a 615.89X coverage genome sequence using Illumina, PacBio, and optical mapping technologies. Gene prediction revealed 27,487 genes using MinION Oxford nanopore and Illumina RNA sequencing, and genome annotation found that 68% of predicted models are associated with at least one biological function. Further, epigenetic signatures of almond, namely DNA cytosine methylation, have been implicated in a variety of phenotypes including self-compatibility, bud dormancy, and development of non-infectious bud failure. In addition to the genome sequence and annotation, this report also provides the complete methylome of several key almond tissues, including leaf, flower, endocarp, mesocarp, fruit skin, and seed coat. Comparisons between methylation profiles in these tissues revealed differences in genome-wide weighted percent methylation and chromosome-level methylation enrichment. The raw sequencing data are available on NCBI Sequence Read Archive, and the complete genome sequence and annotation files are available on NCBI Genbank. All data can be used without restriction.

Alveolar Macrophages: Adaptation to Their Anatomic Niche during and after Inflammation

At the early stages of life development, alveoli are colonized by embryonic macrophages, which become resident alveolar macrophages (ResAM) and self-sustain by local division. Genetic and epigenetic signatures and, to some extent, the functions of ResAM are dictated by the lung microenvironment, which uses cytokines, ligand-receptor interactions, and stroma cells to orchestrate lung homeostasis. In resting conditions, the lung microenvironment induces in ResAM a tolerogenic programming that prevents unnecessary and potentially harmful inflammation responses to the foreign bodies, which continuously challenge the airways. Throughout life, any episode of acute inflammation, pneumonia being likely the most frequent cause, depletes the pool of ResAM, leaving space for the recruitment of inflammatory monocytes that locally develop in monocyte-derived alveolar macrophages (InfAM). During lung infection, the local microenvironment induces a temporary inflammatory signature to the recruited InfAM to handle the tissue injury and eliminate the pathogens. After a few days, the recruited InfAM, which locally self-sustain and develop as new ResAM, gain profibrotic functions required for tissue healing. After the complete resolution of the infectious episode, the functional programming of both embryonic and monocyte-derived ResAM remains altered for months and possibly for the entire life. Adult lungs thus contain a wide diversity of ResAM since every infection brings new waves of InfAM which fill the room left open by the inflammatory process. The memory of these innate cells called trained immunity constitutes an immunologic scar left by inflammation, notably pneumonia. This memory of ResAM has advantages and drawbacks. In some cases, lung-trained immunity offers better defense capacities against autoimmune disorders and the long-term risk of infection. At the opposite, it can perpetuate a harmful process and lead to a pathological state, as is the case among critically ill patients who have immune paralysis and are highly susceptible to hospital-acquired pneumonia and acute respiratory distress syndrome. The progress in understanding the kinetics of response of alveolar macrophages (AM) to lung inflammation is paving the way to new treatments of pneumonia and lung inflammatory process.

Dissecting the Epigenomic Differences between Smoking and Nicotine Dependence in a Veteran Cohort

ABSTRACTBackgroundSmoking is a serious public health issue linked to more than 8 million deaths per year worldwide. It also may lead to nicotine dependence (ND). Smoking can induce long-lasting epigenetic changes. Although epigenetic alterations related to tobacco smoke have been largely studied, few works have investigated ND and its interaction with smoking status (SS).ObjectiveWe investigated the peripheral epigenomic profile of SS and ND in a U.S. male veteran cohort.MethodsDNA from saliva was collected from 1,135 European American (EA) male U.S. military veterans. DNAm was assessed using the Illumina Infinium Human MethylationEPIC BeadChip array. SS was evaluated as: current smokers (n=137; 12.1%) and non-current smokers (never and former smokers; n=998; 87.9%). ND was assessed using the Fagerström Test for Nicotine Dependence (FTND). EWAS and co-methylation analyses were conducted for SS and ND.ResultsA total of 450 and 22 genome-wide significant differentially methylated sites (DMS) were associated with SS and ND, respectively (fifteen overlapped sites). We identified 97 DMS (43 genes) in SS-EWAS previously reported in the literature, including AHRR, and F2RL3 genes (p-value range: 1.95×10−83 to 4.5×10−33). ND novel DMS mapped to NEUROG1, ANPEP, and SLC29A1. Co-methylation analysis identified 386 modules (11 SS-related and 19 ND-related). SS-related modules showed enrichment for alcoholism, chemokine signaling pathway, and neurogenesis; while ND-related modules were enriched for cellular adhesion, and nicotine addiction.ConclusionsThis study confirms previous findings and identifies novel and -potentially specific - epigenetic signatures for SS and ND in a sample of EA male veterans.