Mitophagy Regulates the Circadian Rhythms by Degrading NR1D1 in Simulated Microgravity and Isolation Environments.

Long-term spaceflight is known to induce disruptions in circadian rhythms, which are driven by a central pacemaker located in the suprachiasmatic nucleus (SCN) of the hypothalamus, but the underlying molecular mechanisms remain unclear. Here, we developed a rat model that simulated microgravity and isolation environments through tail suspension and isolation (TSI). We found that the TSI environment imposed circadian disruptions to the core body temperature, heart rate, and locomotor-activity rhythms of rats, especially in the amplitude of these rhythms. In TSI model rats' SCNs, the core circadian gene NR1D1 showed higher protein but not mRNA levels along with decreased BMAL1 levels, which indicated that NR1D1 could be regulated through post-translational regulation. The autophagosome marker LC3 could directly bind to NR1D1 via the LC3-interacting region (LIR) motifs and induce the degradation of NR1D1 in a mitophagy-dependent manner. Defects in mitophagy led to the reversal of NR1D1 degradation, thereby suppressing the expression of BMAL1. Mitophagy deficiency and subsequent mitochondrial dysfunction were observed in the SCN of TSI models. Urolithin A (UA), a mitophagy activator, demonstrated an ability to enhance the amplitude of core body temperature, heart rate, and locomotor-activity rhythms by prompting mitophagy induction to degrade NR1D1. Cumulatively, our results demonstrate that mitophagy exerts circadian control by regulating NR1D1 degradation, revealing mitophagy as a potential target for long-term spaceflight as well as diseases with SCN circadian disruption.

Biological characteristics of Cordyceps militaris single mating-type strains.

Cordyceps militaris has been extensively cultivated as a model cordyceps species for commercial purposes. Nevertheless, the problems related to strain degeneration and breeding technologies remain unresolved. This study assessed the physiology and fertility traits of six C. militaris strains with distinct origins and characteristics, focusing on single mating-type strains. The results demonstrated that the three identified strains (CMDB01, CMSY01, and CMJB02) were single mating-type possessing only one mating-type gene (MAT1-1). In contrast, the other three strains (CMXF07, CMXF09, and CMMS05) were the dual mating type. The MAT1-1 strains sourced from CMDB01, CMSY01, and CMJB02 consistently produced sporocarps but failed to generate ascospores. However, when paired with MAT1-2 strains, the MAT1-1 strains with slender fruiting bodies and normal morphology were fertile. The hyphal growth rate of single mating-type strains (CMDB01, CMSY01, and CMJB02) typically surpassed that of dual mating-type strains (CMXF07, CMXF09, and CMMS05). The growth rates of MAT1-2 and MAT1-1 strains were proportional to their ratios, such that a single mating-type strain with a higher ratio exhibited an increased growth rate. As C. militaris matured, the adenosine content decreased. In summary, the C. militaris strains that consistently produce sporocarps and have a single mating type are highly promising for production and breeding.

Integrating bioinformatic strategies in spatial life science research.

As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.

DNA Methylation Markers and Prediction Model for Depression and Their Contribution for Breast Cancer Risk.

Background: Major depressive disorder (MDD) has become a leading cause of disability worldwide. However, the diagnosis of the disorder is dependent on clinical experience and inventory. At present, there are no reliable biomarkers to help with diagnosis and treatment. DNA methylation patterns may be a promising approach for elucidating the etiology of MDD and predicting patient susceptibility. Our overarching aim was to identify biomarkers based on DNA methylation, and then use it to propose a methylation prediction score for MDD, which we hope will help us evaluate the risk of breast cancer. Methods: Methylation data from 533 samples were extracted from the Gene Expression Omnibus (GEO) database, of which, 324 individuals were diagnosed with MDD. Statistical difference of DNA Methylation between Promoter and Other body region (SIMPO) score for each gene was calculated based on the DNA methylation data. Based on SIMPO scores, we selected the top genes that showed a correlation with MDD in random resampling, then proposed a methylation-derived Depression Index (mDI) by combining the SIMPO of the selected genes to predict MDD. A validation analysis was then performed using additional DNA methylation data from 194 samples extracted from the GEO database. Furthermore, we applied the mDI to construct a prediction model for the risk of breast cancer using stepwise regression and random forest methods. Results: The optimal mDI was derived from 426 genes, which included 245 positive and 181 negative correlations. It was constructed to predict MDD with high predictive power (AUC of 0.88) in the discovery dataset. In addition, we observed moderate power for mDI in the validation dataset with an OR of 1.79. Biological function assessment of the 426 genes showed that they were functionally enriched in Eph Ephrin signaling and beta-catenin Wnt signaling pathways. The mDI was then used to construct a predictive model for breast cancer that had an AUC ranging from 0.70 to 0.67. Conclusion: Our results indicated that DNA methylation could help to explain the pathogenesis of MDD and assist with its diagnosis.

Insulin-resistance and depression cohort data mining to identify nutraceutical related DNA methylation biomarker for type 2 diabetes.

Insulin-resistance (IR) is one of the most important precursors of type 2 diabetes (T2D). Recent evidence suggests an association of depression with the onset of T2D. Accumulating evidence shows that depression and T2D share common biological origins, and DNA methylation examination might reveal the link between lifestyle, disease risk, and potential therapeutic targets for T2D. Here we hypothesize that integrative mining of IR and depression cohort data will facilitate predictive biomarkers identification for T2D. We utilized a newly proposed method to extract gene-level information from probe level data on genome-wide DNA methylation array. We identified a set of genes associated with IR and depression in clinical cohorts. By overlapping the IR-related nutraceutical-gene network with depression networks, we identified a common subnetwork centered with Vitamin D Receptor (VDR) gene. Preliminary clinical validation of gene methylation set in a small cohort of T2D patients and controls was established using the Sequenome matrix-assisted laser desorption ionization-time flight mass spectrometry. A set of sites in the promoter regions of VDR showed a significant difference between T2D patients and controls. Using a logistic regression model, the optimal prediction performance of these sites was AUC = 0.902，and an odds ratio = 19.76. Thus, monitoring the methylation status of specific VDR promoter region might help stratify the high-risk individuals who could potentially benefit from vitamin D dietary supplementation. Our results highlight the link between IR and depression, and the DNA methylation analysis might facilitate the search for their shared mechanisms in the etiology of T2D.

Mining the Selective Remodeling of DNA Methylation in Promoter Regions to Identify Robust Gene-Level Associations With Phenotype.

Epigenetics is an essential biological frontier linking genetics to the environment, where DNA methylation is one of the most studied epigenetic events. In recent years, through the epigenome-wide association study (EWAS), researchers have identified thousands of phenotype-related methylation sites. However, the overlaps of identified phenotype-related DNA methylation sites between various studies are often quite small, and it might be due to the fact that methylation remodeling has a certain degree of randomness within the genome. Thus, the identification of robust gene-phenotype associations is crucial to interpreting pathogenesis. How to integrate the methylation values of different sites on the same gene and to mine the DNA methylation at the gene level remains a challenge. A recent study found that the DNA methylation difference of the gene body and promoter region has a strong correlation with gene expression. In this study, we proposed a Statistical difference of DNA Methylation between Promoter and Other Body Region (SIMPO) algorithm to extract DNA methylation values at the gene level. First, by choosing to smoke as an environmental exposure factor, our method led to significant improvements in gene overlaps (from 5 to 17%) between different datasets. In addition, the biological significance of phenotype-related genes identified by SIMPO algorithm is comparable to that of the traditional probe-based methods. Then, we selected two disease contents (e.g., insulin resistance and Parkinson's disease) to show that the biological efficiency of disease-related gene identification increased from 15.43 to 44.44% (p-value = 1.20e-28). In summary, our results declare that mining the selective remodeling of DNA methylation in promoter regions can identify robust gene-level associations with phenotype, and the characteristic remodeling of a given gene's promoter region can reflect the essence of disease.

Blood Cell DNA Methylation of Aging-Related Ubiquitination Gene DZIP3 Can Predict the Onset of Early Stage Colorectal Cancer.

There is a body of evidence that the aging immune system is linked to cancer. In this study, with aging- and immune-related DNA methylation data, we investigated the DNA methylation regulation changes in promoters with other regions of genes during aging and their association with the immune-cell proportion in the circulating whole blood of individuals. The analyses for aging- and CD4+ T cell proportion-derived differential genes showed that ubiquitination plays an important role in the aging immune system and tumorigenesis. Therefore, starting from a set of pre-annotated ubiquitination genes, we found that among the differentially ubiquitinated genes, DZIP3, an E3 ubiquitin ligase with no reports on its function in immune cells and tumorigenesis, was significantly associated with both aging (P-value = 3.86e-06) and CD4+ T cell proportion (P-value = 1.97e-05) in circulating blood. By collecting a cohort of 100 colon cancer patients and 50 healthy individuals, we validated that the 1st exon DNA methylation of DZIP3 could predict the onset of early stage (AUC = 0.833, OR = 8.82) and all pTNM stages of colorectal cancer (AUC = 0.782, OR = 5.70). Thus, the epigenetically regulated ubiquitination machine plays an important role in immune aging and tumorigenesis.

Physiological Acclimatization of the Liver to 180-Day Isolation and the Mars Solar Day.

Physiological changes in humans are evident under environmental conditions similar to those on a Mars mission involving both a space factor (long-term isolation) and a time factor (the Mars solar day). However, very few studies have investigated the response of the liver to those conditions. Serum protein levels, bilirubin levels, aminotransferase activities, blood alkaline phosphatase, gamma-glutamyltransferase, lipid levels, and serum cytokines interleukin-6 and interferon-γ levels were analyzed 30 days before the mock mission; on days 2, 30, 60, 75, 90, 105, 120, 150, and 175 of the mission; and 30 days after the mission, in four subjects in 4-person 180-day Controlled Ecological Life Support System Experiment. Serum protein levels (total protein and globulin) decreased and bilirubin increased under the isolation environment from day 2 and exhibited chronic acclimatization from days 30 to 175. Effects of the Mars solar day were evident on day 75. Blood lipid levels were somewhat affected. No obvious peak in any enzyme level was detected during the mission. The change tendency of these results indicated that future studies should explore whether protein parameters especially globulin could serve as indicators of immunological function exposure to the stress of a Mars mission.

DNA Methylation Biomarkers Predict Objective Responses to PD-1/PD-L1 Inhibition Blockade.

Immune checkpoint inhibitor (ICI) treatment could bring long-lasting clinical benefits to patients with metastatic cancer. However, only a small proportion of patients respond to PD-1/PD-L1 blockade, so predictive biomarkers are needed. Here, based on DNA methylation profiles and the objective response rates (ORRs) of PD-1/PD-L1 inhibition therapy, we identified 269 CpG sites and developed an initial CpG-based model by Lasso to predict ORRs. Notably, as measured by the area under the receiver operating characteristic curve (AUC), our model can produce better performance (AUC = 0.92) than both a model based on tumor mutational burden (TMB) (AUC = 0.77) and a previously reported TMB model (AUC = 0.71). In addition, most CpGs also have additional synergies with TMB, which can achieve a higher prediction accuracy when joined with TMB. Furthermore, we identified CpGs that are associated with TMB at the individual level. DNA methylation modules defined by protein networks, Kyoto Encylopedia of Genes and Genomes (KEGG) pathways, and ligand-receptor gene pairs are also associated with ORRs. This method suggested novel immuno-oncology targets that might be beneficial when combined with PD-1/PD-L1 blockade. Thus, DNA methylation studies might hold great potential for individualized PD1/PD-L1 blockade or combinatory therapy.

Personalized Epigenome Remodeling Under Biochemical and Psychological Changes During Long-Term Isolation Environment.

It has been reported that several aspects of human health could be disturbed during a long-term isolated environment (for instance, the Mars-500 mission), including psychiatric disorders, circadian disruption, temporal dynamics of gut microbiota, immune responses, and physical-activity-related neuromuscular performance. Nevertheless, the mechanisms underlying these disturbances and the interactions among different aspects of human adaptation to extreme environments remain to be elucidated. Epigenetic features, like DNA methylation, might be a linking mechanism that explains the involvement of environmental factors between the human genome and the outcome of health. We conducted an exploration of personalized longitudinal DNA methylation patterns of the peripheral whole blood cells, profiling six subjects across six sampling points in the Mars-500 mission. Specifically, we developed a Personalized Epigenetic-Phenotype Synchronization Analysis (PeSa) algorithm to explore glucose- and mood-state-synchronized DNA methylation sites, focusing on finding the dynamic associations between epigenetic patterns and phenotypes in each individual, and exploring the underling epigenetic connections between glucose and mood-state disturbance. Results showed that DMPs (differentially methylated-probes) were significantly enriched in pathways related to glucose metabolism (Type II diabetes mellitus pathway), mood state (Long-term depression) and circadian rhythm (Circadian entrainment pathway) during the mission. Furthermore, our data revealed individualized glucose-synchronized and mood-state-synchronized DNA methylation sites, and PTPRN2 was found to be associated with both glucose and mood state disturbances across all six subjects. Our findings suggest that personalized phenotype-synchronized epigenetic features could reflect the effects on the human body, including the disturbances of glucose and mood-states. The association analysis of DNA methylation and phenotypes, like the PeSa analysis, could provide new possibilities in understanding the intrinsic relationship between phenotypic changes of the human body adapting to long-term isolation environmental factors.

Integrated Analysis of DNA Methylation and Biochemical/Metabolic Parameter During the Long-Term Isolation Environment.

Numerous studies have shown that changes in the epigenome are an important cause of human biochemical or metabolic parameter changes. Biochemical/metabolic parameter disorders of the human body are usually closely related to the occurrence of disease. Therefore, constructing credible DNA methylation site-biochemical/metabolic parameter associations are key in interpreting the pathogenesis of diseases. However, there is a lack of research on systematic integration analysis of DNA methylation with biochemical/metabolic parameter and diseases. In this study, we attempted to use the four-people, multiple time point detected data from the long-term isolation experiment to conduct a correlation analysis. We used the biclustering algorithm FABIA to cluster the DNA methylation site-parameter correlation matrixes into 28 biclusters. The results of the biological function analysis for these biclusters were consistent with the biochemical/metabolic parameter change characteristics of the human body during long-term isolation, demonstrating the reliability of the biclusters identified by our method. In addition, from these biclusters, we obtained highly credible biochemical/metabolic parameter-disease associations, which is supported by several studies. Our results indicate that there is an overlap of biochemical/metabolic parameter-disease associations derived from a small sample, multiple time point data in healthy populations and the associations obtained from a large sample data in patients during disease development. These findings provide insights into understanding the role of the epigenome in biochemical/metabolic parameter change and disease development and has potential applications in biology and medicine research.

Expression profiles and functional annotation analysis of mRNAs in suprachiasmatic nucleus of Clock mutant mice.

The core circadian clock gene, Clock, is a positive component of the transcription/translation feedback loop in the master pacemaker suprachiasmatic nucleus (SCN) in mammals. The robust daytime peak of some clock genes in the wild-type SCN is absent in Clock mutant mice. However, very little is known about the impact of Clock mutation on the expression of other functional genes in SCN. Here, we performed cDNA microarray and found 799 differentially expressed genes (DEGs) at zeitgeber time 2 (ZT2) and 1289 DEGs at ZT14 in SCN of Clock△19/△19 mutant mice. KEGG pathway analysis showed that the changed mRNAs were highly associated with hedgehog signaling pathway, retinol metabolism, allograft rejection, drug metabolism, hematopoietic cell lineage and neuroactive ligand-receptor interaction. The top 14 and 71 hub genes were identified from the protein-protein interaction (PPI) network at ZT2 and ZT14, respectively. The sub-networks revealed hub genes were involved in olfactory transduction and neuroactive ligand-receptor interaction pathways. These results demonstrate the Clock△19/△19 mutation alters the expression of various genes involved in a wide spectrum of biological function in mouse SCN, which are helpful for better understanding the function of Clock and potential regulatory mechanisms.

Circulating microRNAs Correlated with Bone Loss Induced by 45 Days of Bed Rest.

The purpose of this study was to find the circulating microRNAs (miRNAs) co-related with bone loss induced by bed rest, and testify whether the selected miRNAs could reflect the bone mineral status of human after bed-rest. We analyzed plasma miRNA levels of 16 subjects after 45 days of -6° head-down tilt bed rest, which is a reliable model for the simulation of microgravity. We characterize the circulating miRNA profile in individuals after bed rest and identify circulating miRNAs which can best reflect the level of bone loss induced by bed rest. Expression profiling of circulating miRNA revealed significant downregulation of 37 miRNAs and upregulation of 2 miRNAs, while only 11 of the downregulated miRNAs were further validated in a larger volunteer cohort using qPCR. We found that 10 of these 11 miRNAs (miR-103, 130a, 1234, 1290, 151-5p, 151-3p, 199a-3p, 20a, 363, and 451a) had ROC curve that distinguished the status after bed rest. Importantly, significant positive correlations were identified between bone loss parameters and several miRNAs, eventually miR-1234 showed clinical significance in detecting the bone loss of individuals after 45 days of bed rest.

Functional constraints on adaptive evolution of protein ubiquitination sites.

It is still unclear whether there exist functional constraints on the evolution of protein ubiquitination sites, because most previous studies regarded all protein ubiquitination sites as a whole or only focused on limited structural properties. We tried to clarify the relation between functional constraints and ubiquitination sites evolution. We investigated the evolutionary conservation of human ubiquitination sites in a broad evolutionary scale from G. gorilla to S. pombe, and we found that in organisms originated after the divergence of vertebrate, ubiquitination sites are more conserved than their flanking regions, while the opposite tendency is observed before this divergence time. By grouping the ubiquitination proteins into different functional categories, we confirm that many functional constraints like certain molecular functions, protein tissue expression specificity and protein connectivity in protein-protein interaction network enhance the evolutionary conservation of ubiquitination sites. Furthermore, by analyzing the gains of ubiquitination sites at different divergence time and their functional characters, we validate that the emergences of ubiquitination sites at different evolutionary time were also affected by the uncovered functional constraints. The above results suggest that functional constraints on the adaptive evolution of ubiquitination sites increase the opportunity for ubiquitination to synthetically regulate various cellular and developmental processes during evolution.

Activation of HIF-1α and its downstream targets in rat hippocampus after long-term simulated microgravity exposure.

Microgravity has many detrimental impact on brain functions, however the underlying mechanism remain unclear. In present study, 28 days of tail-suspension (30°) was used to simulate microgravity in rats. We showed that oxidative stress in hippocampus was increased after 28 days of simulated microgravity in consideration of the decreased expression of NF-E2-related factor 2 (Nrf2) and the declined activities of total superoxide dismutase (T-SOD), CuZn-SOD, glutathione peroxidase (GSH-PX) and total antioxidant capacity (T-AOC). Using RNA-seq, we further investigated the effect of simulated microgravity on the expression of genes in hippocampus, and 849 genes were found to be differentially expressed. According to pathway analysis, the differentially expressed genes involved in cytoskeleton, metabolism, immunity, transcription regulation, etc. It is interesting to note that the differentially expressed genes were involved in hypoxia-associated pathway. In agreement with this, the expression of hypoxia induced factor-1α (HIF-1α), the master regulator of oxygen homeostasis, was significantly increased. Meanwhile, HIF-2α, a HIF-1α paralog, was elevated compared with the control group. The expression of pyruvate dehydrogenase kinase 1 (PDK1), lactate dehydrogenase A (LDHA) and vascular endothelial growth factor (VEGF), three well-defined downstream targets of HIF-1α, were up-regulated in hippocampus after 28 days of simulated microgravity exposure. Additionally, brain oxygen saturation (SO2) and blood flow analyzed by the tissue oxygen analysis system were also significantly reduced. These findings indicate that simulated microgravity might cause an alteration in oxygen homeostasis, providing novel insight into better understanding of how simulated microgravity affects the function of hippocampus and a new direction to the development of countermeasure for brain dysfunction during spaceflight (actual microgravity).

Predicting response to preoperative chemotherapy agents by identifying drug action on modeled microRNA regulation networks.

Identifying patients most responsive to specific chemotherapy agents in neoadjuvant settings can help to maximize the benefits of treatment and minimize unnecessary side effects. Metagene approaches that predict response based on gene expression signatures derived from an associative analysis of clinical data can identify chance associations caused by the heterogeneity of a tumor, leading to reproducibility issues in independent validations. In this study, to incorporate information from drug mechanisms of action, we explore the potential of microRNA regulation networks as a new feature space for identifying predictive markers. We introduce a measure we term the CoMi (Context-specific-miRNA-regulation) pattern to represent a descriptive feature of the miRNA regulation network in the transcriptome. We examine whether the modifications to the CoMi pattern on specific biological processes are a useful representation of drug action by predicting the response to neoadjuvant Paclitaxel treatment in breast cancer and show that the drug counteracts the CoMi network dysregulation induced by tumorigenesis. We then generate a quantitative testbed to investigate the ability of the CoMi pattern to distinguish FDA approved breast cancer drugs from other FDA approved drugs not related to breast cancer. We also compare the ability of the CoMi and metagene methods to predict response to neoadjuvant Paclitaxel treatment in clinical cohorts. We find the CoMi method outperforms the metagene method, achieving area under curve (AUC) values of 0.78 and 0.66 respectively. Furthermore, several of the predicted CoMi features highlight the network-based mechanism of drug resistance. Thus, our study suggests that explicitly modeling the drug action using network biology provides a promising approach for predictive marker discovery.