Multi-omics reveals the switch role of abnormal methylation in the regulation of decidual macrophages function in recurrent spontaneous abortion.

RSA, recurrent spontaneous abortion, often causes serious physical damage and psychological pressure in reproductive women with unclarified pathogenesis. Abnormal function of decidual cells and aberrant DNA methylation have been reported to cause RSA, but their association remains unclear. Here, we integrated transcriptome, DNA methylome, and scRNA-seq to clarify the regulatory relationship between DNA methylation and decidual cells in RSA. We found that DNA methylation mainly influenced the function of decidual macrophages (DMs), of which four hub genes, HLA-A, HLA-F, SQSTM1/P62, and Interferon regulatory factor 7 (IRF7), related to 22 hypomethylated CpG sites, regulated 16 hub pathways to participate in RSA pathogenesis. In particular, using transcription factor analysis, it is suggested that the upregulation of IRF7 transcription was associated with enhanced recruitment of the transcription factor STAT1 by the hypomethylated promoter region of IRF7. As the current research on DNA methylation of macrophages in the uterine microenvironment of RSA is still blank, our systematic picture of abnormal DNA methylation in regulating DM function provides new insights into the role of DNA methylation in RSA occurrence, which may aid in further prevention and treatment of RSA.

Combinations of plasma cfDNA concentration, integrity and tumor markers are promising biomarkers for early diagnosis of non-small cell lung cancer.

Background: Circulating cell-free DNA (cfDNA) concentration and integrity as noninvasive biomarkers play an important role in cancer diagnosis, prognosis and therapy monitoring. However, few studies have been conducted on the combination of plasma cfDNA concentration, integrity and tumor markers (CEA, CA125, NSE and CYFRA21-1) for cancer detection. Thus, the purpose of this study was to investigate the diagnostic value of combining plasma cfDNA concentration, integrity and tumor markers in early detection of non-small cell lung cancer (NSCLC). Methods: Plasma cfDNA concentration from 50 healthy controls and 84 NSCLC patients were assessed by quantitative real-time PCR of ALU repeated sequence. Plasma cfDNA integrity was calculated as the ratio of long to short fragments (ALU115/60). Results: Plasma cfDNA concentration (ALU60 and ALU115) and integrity ALU115/60 were significantly higher in NSCLC patients with stage III/IV than in healthy controls (p = 0.0002, p < 0.0001, and p = 0.0093, respectively). The receiver operating characteristic (ROC) curve for discriminating NSCLC patients from healthy controls had an area under the curve (AUC) of 0.936 (95 % CI, 0.939-0.996). Moreover, the combination of plasma cfDNA concentration, integrity and tumor markers (CEA, CA125, NSE and CYFRA21-1) had higher diagnostic performance than either plasma cfDNA concentration alone, integrity alone or tumor markers alone, with sensitivity, specificity and AUC value of 94.05%, 90.00% and 0.968, respectively. These results demonstrated that the combination of plasma cfDNA concentration, integrity and tumor markers could significantly improve the diagnostic accuracy of NSCLC. Conclusion: Combination of plasma cfDNA concentration, integrity and tumor markers is a promising biomarker for early diagnosis of NSCLC.

Perturbed gut microbiota is gender-segregated in unipolar and bipolar depression.

OBJECTIVE: This study aimed to explore the gender specificity of gut microbiome in patients with unipolar and bipolar depression disorder by analyzing the data of gut microbiome in this two mental disorders and healthy people. METHODS: A case-control study using 16S ribosomal RNA gene sequencing from fecal samples of MDD (male set, n = 43; female set, n = 77) and BD (male set, n = 82; female set, n = 83) compared with HCs (male set, n = 71; female set, n = 100) was conducted. Linear discriminant analysis was used to identify microbial characteristics. Through cooccurrence analysis, the potential correlations of the differential gut microbiota in different genders was explored. Finally, the gender-specific distinguishing microorganisms were identified as biomaker, and the diagnostic performance was verified by five-fold cross validation. RESULTS: A specific cluster was found enriched only in female MDD set, including 4 Bacteroideae OTUs. Similarly, 3 Lachnospiraceae OTUs was found significantly increased in female BD compared with other groups. In addition, the consistent enrichment of Pseudomonadacea in male and female may be the characteristic disease-related gut microbiota of BD. Besides, the diagnostic potential of gender specific biomarker panel in male (male validation AUC: 0.758-0.874, accurancy: 0.693-0.792; female validation AUC: 0.727-0.883, accurancy: 0.678-0.781) and female (male validation AUC: 0.787-0.883, accurancy: 0.719-0.784; female validation AUC: 0.795-0.898, accurancy: 0.689-0.838) has also been identified and confirmed. CONCLUSIONS: The microbiological changes in both MDD and BD are sex specific, and gender specific biomarker panel has better diagnostic performance, which provide a certain reference in sex difference for future clinical differentiation and microbial intervention.

Changes in gut viral and bacterial species correlate with altered 1,2-diacylglyceride levels and structure in the prefrontal cortex in a depression-like non-human primate model.

Major depressive disorder (MDD) is a debilitating mental disease, but its underlying molecular mechanisms remain obscure. Our previously established model of naturally occurring depression-like (DL) behaviors in Macaca fascicularis, which is characterized by microbiota-gut-brain (MGB) axis disturbances, can be used to interrogate how a disturbed gut ecosystem may impact the molecular pathology of MDD. Here, gut metagenomics were used to characterize how gut virus and bacterial species, and associated metabolites, change in depression-like monkey model. We identified a panel of 33 gut virus and 14 bacterial species that could discriminate the depression-like from control macaques. In addition, using lipidomic analyses of central and peripheral samples obtained from these animals, we found that the DL macaque were characterized by alterations in the relative abundance, carbon-chain length, and unsaturation degree of 1,2-diacylglyceride (DG) in the prefrontal cortex (PFC), in a brain region-specific manner. In addition, lipid-reaction analysis identified more active and inactive lipid pathways in PFC than in amygdala or hippocampus, with DG being a key nodal player in these lipid pathways. Significantly, co-occurrence network analysis showed that the DG levels may be relevant to the onset of negative emotions behaviors in PFC. Together our findings suggest that altered DG levels and structure in the PFC are hallmarks of the DL macaque, thus providing a new framework for understanding the gut microbiome's role in depression.

Dynamic changes occur in the DNA gut virome of female cynomolgus macaques during aging.

Aging is a critical factor affecting physical health and disease in mammals. Emerging evidence indicates that aging may affect the gut bacteriome in cynomolgus macaques, but little is known about whether or how the gut virome changes with age. Here, we compared the DNA gut viral composition of 16 female cynomolgus monkeys (Macaca fascicularis) at three life stages (young, adult, and old) using the shotgun metagenome sequencing method. We found that the DNA gut virome from these monkeys differed substantially among the three groups. The gut viruses were dominated by bacteriophages, the most abundant of which was the Caudovirales order (i.e., Siphoviridae, Myoviridae, and Podoviridae families). Additionally, the co-occurrence analysis revealed that the age-related bacteriophages were correlated in an extensive and complex manner with the main intestinal bacteria (i.e., Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria phyla). Furthermore, the age-related DNA gut viral functions were enriched for genetic information processing, nucleotide, and folate metabolism. Our gut virome analysis provides new insight into how aging influences the gut virome of non-human primates.

Characterization of gut microbiome in mice model of depression with divergent response to escitalopram treatment.

Depression is a common and heterogeneous mental disorder. Although several antidepressants are available to treat the patients with depression, the factors which could affect and predict the treatment response remain unclear. Here, we characterize the longitudinal changes of microbial composition and function during escitalopram treatment in chronic unpredictable mild stress (CUMS) mice model of depression based on 16 S rRNA sequencing and metabolomics. Consequently, we found that escitalopram (ESC) administration serves to increase the alpha-diversity of the gut microbiome in ESC treatment group. The microbial signatures between responder (R) and non-responder (NR) groups were significantly different. The R group was mainly characterized by increased relative abundances of genus Prevotellaceae_UCG-003, and depleted families Ruminococcaceae and Lactobacillaceae relative to NR group. Moreover, we identified 15 serum metabolites responsible for discriminating R and NR group. Those differential metabolites were mainly involved in phospholipid metabolism. Significantly, the bacterial OTUs belonging to family Lachnospiraceae, Helicobacteraceae, and Muribaculaceae formed strong co-occurring relationships with serum metabolites, indicating alternations of gut microbiome and metabolites as potential mediators in efficiency of ESC treatment. Together, our study demonstrated that the alterations of microbial compositions and metabolic functions might be relevant to the different response to ESC, which shed new light in uncovering the mechanisms of differences in efficacy of antidepressants.

Proteomic Profiling of Lysine Acetylation Indicates Mitochondrial Dysfunction in the Hippocampus of Gut Microbiota-Absent Mice.

Major depressive disorder (MDD) is a leading cause of disability around the world and contributes greatly to the global burden of disease. Mounting evidence suggests that gut microbiota dysbiosis may be involved in the pathophysiology of MDD through the microbiota-gut-brain axis. Recent research suggests that epigenetic modifications might relate to depression. However, our knowledge of the role of epigenetics in host-microbe interactions remains limited. In the present study, we used a combination of affinity enrichment and high-resolution liquid chromatography tandem mass spectrometry analysis to identify hippocampal acetylated proteins in germ-free and specific pathogen-free mice. In total, 986 lysine acetylation sites in 543 proteins were identified, of which 747 sites in 427 proteins were quantified. Motif analysis identified several conserved sequences surrounding the acetylation sites, including D∗Kac, DKac, KacY, KacD, and D∗∗Kac. Gene ontology annotations revealed that these differentially expressed acetylated proteins were involved in multiple biological functions and were mainly located in mitochondria. In addition, pathway enrichment analysis demonstrated that oxidative phosphorylation and the tricarboxylic acid cycle II (eukaryotic), both of which are exclusively localized to the mitochondria, were the primarily disturbed functions. Taken together, this study indicates that lysine acetylation alterations may play a pivotal role in mitochondrial dysfunction and may be a mechanism by which gut microbiota regulate brain function and behavioral phenotypes.

Regulation of Gut Microbiota Disrupts the Glucocorticoid Receptor Pathway and Inflammation-related Pathways in the Mouse Hippocampus.

An increasing number of studies have recently indicated the important effects of gut microbes on various functions of the central nervous system. However, the underlying mechanisms by which gut microbiota regulate brain functions and behavioral phenotypes remain largely unknown. We therefore used isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomic analysis to obtain proteomic profiles of the hippocampus in germ-free (GF), colonized GF, and specific pathogen-free (SPF) mice. We then integrated the resulting proteomic data with previously reported mRNA microarray data, to further explore the effects of gut microbes on host brain functions. We identified that 61 proteins were upregulated and 242 proteins were downregulated in GF mice compared with SPF mice. Of these, 124 proteins were significantly restored following gut microbiota colonization. Bioinformatic analysis of these significant proteins indicated that the glucocorticoid receptor signaling pathway and inflammation-related pathways were the most enriched disrupted pathways. This study provides new insights into the pathological mechanisms of gut microbiota-regulated diseases.

The gut microbiome modulates gut-brain axis glycerophospholipid metabolism in a region-specific manner in a nonhuman primate model of depression.

Emerging research demonstrates that microbiota-gut-brain (MGB) axis changes are associated with depression onset, but the mechanisms underlying this observation remain largely unknown. The gut microbiome of nonhuman primates is highly similar to that of humans, and some subordinate monkeys naturally display depressive-like behaviors, making them an ideal model for studying these phenomena. Here, we characterized microbial composition and function, and gut-brain metabolic signatures, in female cynomolgus macaque (Macaca fascicularis) displaying naturally occurring depressive-like behaviors. We found that both microbial and metabolic signatures of depressive-like macaques were significantly different from those of controls. The depressive-like monkeys had characteristic disturbances of the phylum Firmicutes. In addition, the depressive-like macaques were characterized by changes in three microbial and four metabolic weighted gene correlation network analysis (WGCNA) clusters of the MGB axis, which were consistently enriched in fatty acyl, sphingolipid, and glycerophospholipid metabolism. These microbial and metabolic modules were significantly correlated with various depressive-like behaviors, thus reinforcing MGB axis perturbations as potential mediators of depression onset. These differential brain metabolites were mainly mapped into the hippocampal glycerophospholipid metabolism in a region-specific manner. Together, these findings provide new microbial and metabolic frameworks for understanding the MGB axis' role in depression, and suggesting that the gut microbiome may participate in the onset of depressive-like behaviors by modulating peripheral and central glycerophospholipid metabolism.

Landscapes of bacterial and metabolic signatures and their interaction in major depressive disorders.

Gut microbiome disturbances have been implicated in major depressive disorder (MDD). However, little is known about how the gut virome, microbiome, and fecal metabolome change, and how they interact in MDD. Here, using whole-genome shotgun metagenomic and untargeted metabolomic methods, we identified 3 bacteriophages, 47 bacterial species, and 50 fecal metabolites showing notable differences in abundance between MDD patients and healthy controls (HCs). Patients with MDD were mainly characterized by increased abundance of the genus Bacteroides and decreased abundance of the genera Blautia and Eubacterium These multilevel omics alterations generated a characteristic MDD coexpression network. Disturbed microbial genes and fecal metabolites were consistently mapped to amino acid (γ-aminobutyrate, phenylalanine, and tryptophan) metabolism. Furthermore, we identified a combinatorial marker panel that robustly discriminated MDD from HC individuals in both the discovery and validation sets. Our findings provide a deep insight into understanding of the roles of disturbed gut ecosystem in MDD.

Differential Gut Microbiota and Fecal Metabolites Related With the Clinical Subtypes of Myasthenia Gravis.

Myasthenia gravis (MG) is a devastating acquired autoimmune disease. Previous studies have observed that disturbances of gut microbiome may attribute to the development of MG through fecal metabolomic signatures in humans. However, whether there were differential gut microbial and fecal metabolomic phenotypes in different subtypes of MG remains unclear. Here, our objective was to explore whether the microbial and metabolic signatures of ocular (OMG) and generalized myasthenia gravis (GMG) were different, and further identify the shared and distinct markers for patients with OMG and GMG. In this study, 16S ribosomal RNA (rRNA) gene sequencing and gas chromatography-mass spectrometry (GC/MS) were performed to capture the microbial and metabolic signatures of OMG and GMG, respectively. Random forest (RF) classifiers was used to identify the discriminative markers for OMG and GMG. Compared with healthy control (HC) group, GMG group, but not OMG group, showed a significant decrease in α-phylogenetic diversity. Both OMG and GMG groups, however, displayed significant gut microbial and metabolic disorders. Totally, we identified 20 OTUs and 9 metabolites specific to OMG group, and 23 OTUs and 7 metabolites specific to GMG group. Moreover, combinatorial biomarkers containing 15 discriminative OTUs and 2 differential metabolites were capable of discriminating OMG and GMG from each other, as well as from HCs, with AUC values ranging from 0.934 to 0.990. In conclusion, different subtypes of MG harbored differential gut microbiota, which generated discriminative fecal metabolism.

Gut Microbial Signatures Can Discriminate Unipolar from Bipolar Depression.

Discriminating depressive episodes of bipolar disorder (BD) from major depressive disorder (MDD) is a major clinical challenge. Recently, gut microbiome alterations are implicated in these two mood disorders; however, little is known about the shared and distinct microbial characteristics in MDD versus BD. Here, using 16S ribosomal RNA (rRNA) gene sequencing, the microbial compositions of 165 subjects with MDD are compared with 217 BD, and 217 healthy controls (HCs). It is found that the microbial compositions are different between the three groups. Compared to HCs, MDD is characterized by altered covarying operational taxonomic units (OTUs) assigned to the Bacteroidaceae family, and BD shows disturbed covarying OTUs belonging to Lachnospiraceae, Prevotellaceae, and Ruminococcaceae families. Furthermore, a signature of 26 OTUs is identified that can distinguish patients with MDD from those with BD or HCs, with area under the curve (AUC) values ranging from 0.961 to 0.986 in discovery sets, and 0.702 to 0.741 in validation sets. Moreover, 4 of 26 microbial markers correlate with disease severity in MDD or BD. Together, distinct gut microbial compositions are identified in MDD compared to BD and HCs, and a novel marker panel is provided for distinguishing MDD from BD based on gut microbiome signatures.

Commensal Microbiota Regulation of Metabolic Networks During Olfactory Dysfunction in Mice.

INTRODUCTION: Recently, an increasing number of studies have focused on commensal microbiota. These microorganisms have been suggested to impact human health and disease. However, only a small amount of data exists to support the assessment of the influences that commensal microbiota exert on olfactory function. METHODS: We used a buried food pellet test (BFPT) to investigate and compare olfactory functions in adult, male, germ-free (GF) and specific-pathogen-free (SPF) mice, then examined and compared the metabolomic profiles for olfactory bulbs (OBs) isolated from GF and SPF mice to uncover the mechanisms associated with olfactory dysfunction. RESULTS: We found that the absence of commensal microbiota was able to influence olfactory function and the metabolic signatures of OBs, with 38 metabolites presenting significant differences between the two groups. These metabolites were primarily associated with disturbances in glycolysis, the tricarboxylic acid (TCA) cycle, amino acid metabolism, and purine catabolism. Finally, the commensal microbiota regulation of metabolic networks during olfactory dysfunction was identified, based on an integrated analysis of metabolite, protein, and mRNA levels. CONCLUSION: This study demonstrated that the absence of commensal microbiota may impair olfactory function and disrupt metabolic networks. These findings provide a new entry-point for understanding olfactory-associated disorders and their potential underlying mechanisms.

Age-related changes in microbial composition and function in cynomolgus macaques.

Age can significantly affect human physiology and disease risk. Recent studies have shown that age may affect the composition and function of the gut microbiota, but the underlying mechanisms remain largely unknown. Non-human primates are an ideal model for uncovering how age shapes the gut microbiota, as their microbial composition is highly similar to that of humans and is not easily affected by confounding factors. Here, using the 16S rRNA and metagenomic sequencing methods, we characterized the microbial phenotypes of 16 female cynomolgus macaques from three age groups (young, adult and old). Our findings revealed significant differences in microbial composition among the three groups. With increased age, the relative abundances of Veillonellaceae, Coriobacteriaceae and Succinivibrionaceae were significantly increased, Ruminococcaceae and Rikenellaceae were significantly decreased at the family level. Functional enrichment showed that genes that differed among the three groups were mainly involved in arginine biosynthesis, purine metabolism and microbial polysaccharides metabolism. Moreover, CAZymes corresponding to polysaccharide degrading activities were also observed among the three groups. In conclusion, we characterized the composition and function of the gut microbiota at different ages, and our findings provide a new entry point for understanding the effects of age on the human body.