Identification of regionalmarkers based on the flavor molecular matrix analysis of sauce-aroma style baijiu

BACKGROUND

Baijiu is a very complex system and its flavor substances are endogenous, influenced by raw materials, starter, production process, production region and other factors. The production region directly affects the composition of flavor substances and quality of baijiu. However, identification of baijiu region is challenging because the corresponding relationship between the production region and baijiu quality is not clear, and the identification of regionalmarkers is indeterminate. In this study, the differences in volatile components of sauce-aroma style baijiu from four representative regions were investigated.

RESULTS

A total of 94 volatile compounds were identified in samples tested. Additionally, it was verified that 35 potential flavor substances had important contributions to the aroma of sauce-aroma style baijiu. Meanwhile, nine potential regionalmarkers were screened through multivariate analysis. Further, based on distribution of volatile compounds and the results of sensory evaluation combined with multivariate analysis, a molecular matrix and correlation network were established according to the results of addition experiments, which showed that six substances had a significant impact on the flavor of the tested samples.

CONCLUSION

Six key flavor substances (ethyl octanoate, ethyl 2-methylpropanoate, propyl acetate, ethyl heptanoate, 2-nonanone and butyl hexanoate) were considered as important regionalmarkers to effectively identify the production region of sauce-aroma style baijiu. © 2023 Society of Chemical Industry.

Terroir Influence on Polyphenol Metabolism from Grape Canes: A Spatial Metabolomic Study at Parcel Scale

The composition of bioactive polyphenols from grape canes, an important viticultural byproduct, was shown to be varietal-dependent; however, the influence of soil-related terroir factors remains unexplored. Using spatial metabolomics and correlation-based networks, we investigated how continuous changes in soil features and topography may impact the polyphenol composition in grape canes. Soil properties, topography, and grape cane extracts were analyzed at georeferenced points over 3 consecutive years, followed by UPLC-DAD-MS-based metabolomic analysis targeting 42 metabolites. Principal component analyses on intra-vintage metabolomic data presented a good reproducibility in relation to geographic coordinates. A correlation-driven approach was used to explore the combined influence of soil and topographic variables on metabolomic responses. As a result, a metabolic cluster including flavonoids was correlated with elevation and curvature. Spatial metabolomics driven by correlation-based networks represents a powerful approach to spatialize field-omics data and may serve as new field-phenotyping tool in precision agriculture.

Altered Faecal Microbiota Composition and Structure of Ghanaian Children with Acute Gastroenteritis

Acute gastroenteritis (AGE) is a disease of global public health importance. Recent studies show that children with AGE have an altered gut microbiota relative to non-AGE controls. Yet, how the gut microbiota differs in Ghanaian children with and without AGE remains unclear. Here, we explore the 16S rRNA gene-based faecal microbiota profiles of Ghanaian children five years of age and younger, comprising 57 AGE cases and 50 healthy controls. We found that AGE cases were associated with lower microbial diversity and altered microbial sequence profiles relative to the controls. The faecal microbiota of AGE cases was enriched for disease-associated bacterial genera, including Enterococcus, Streptococcus, and Staphylococcus. In contrast, the faecal microbiota of controls was enriched for potentially beneficial genera, including Faecalibacterium, Prevotella, Ruminococcus, and Bacteroides. Lastly, distinct microbial correlation network characteristics were observed between AGE cases and controls, thereby supporting broad differences in faecal microbiota structure. Altogether, we show that the faecal microbiota of Ghanaian children with AGE differ from controls and are enriched for bacterial genera increasingly associated with diseases.

Frailty, Quality of Life, and Loneliness of Aging in Native and Diasporic Chinese Adults

BACKGROUND

Global migration has increased in the past century, and aging in a foreign country is relevant to the Chinese diaspora.

OBJECTIVE

With regard to migration, this study focuses on the places of aging as the context of older Chinese adults. This study aimed to describe the general health and wellbeing of this population with respect to their location.

DESIGN

This study has a cross sectional design.

SETTING AND PARTICIPANTS

Participants were recruited who were "aging in place" from Tianjin, China (199 participants), and "aging out of place" from the Netherlands (134 participants). Data from April to May 2019 in China and November 2018 to March 2019 in the Netherlands were aggregated.

MEASUREMENTS

frailty, QoL and loneliness were used in both samples.

RESULTS

T-tests and regression analyses demonstrated that social domains of frailty and QoL, as well as loneliness and frailty prevalence characterized the major differences between both places of aging. A correlation analysis and visual correlation network revealed that frailty, quality of life (QoL), and loneliness were more closely related in the aging out of place sample. Social domains of frailty and QoL, as well as the prevalence of loneliness and frailty, characterized the major differences between both places of aging.

CONCLUSIONS

The findings indicate that frailty, QoL, and loneliness have a complex relationship, confirming that loneliness is a major detriment to the general wellbeing of older Chinese adults aging out of place. This study examined the places of aging of the larger Chinese population and allows a comprehensive understanding of health and wellbeing. The social components, especially loneliness, among the aging out of place Chinese community should receive more attention practice and clinical wise. On the other hand, frailty as well as its prevention is of more importance for the Chinese community aging in place.

The interplay between the gut microbiota and metabolism during the third trimester of pregnancy

The gut microbiota undergoes dynamic changes during pregnancy. The gut microbial and metabolic networks observed in pregnant women have not been systematically analyzed. The primary purpose of this study was to explore the alterations in the gut microbiota and metabolism during late pregnancy and investigate the associations between the gut microbiota and metabolism. A total of thirty healthy pregnant women were followed from 30 to 32 weeks of gestation to full term. Fecal samples were collected for microbiome analysis and untargeted metabolomic analysis. The characteristics of the gut microbiota were evaluated by 16S ribosomal RNA gene sequencing of the V3-V4 regions. The plasma samples were used for untargeted metabolomic analysis with liquid chromatography-tandem mass spectrometry. The interplay between the gut microbiota and metabolism was analyzed further by bioinformatics approaches. We found that the relative abundances of Sellimonas and Megamonas were higher at full term, whereas that of Proteobacteria was lower. The correlation network of the gut microbiota tended to exhibit weaker connections from 32 weeks of gestation to the antepartum timepoint. Changes in the gut microbiota during late pregnancy were correlated with the absorbance and metabolism of microbiota-associated metabolites, such as fatty acids and free amino acids, thereby generating a unique metabolic system for the growth of the fetus. Decreasing the concentration of specific metabolites in plasma and increasing the levels of palmitic acid and 20-hydroxyarachidonic acid may enhance the transformation of a proinflammatory immune state as pregnancy progresses.

Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions

Invasive fungal infections are a significant reason for morbidity and mortality among organ transplant recipients. Therefore, it is critical to investigate the host and candida niches to understand the epidemiology of fungal infections in transplantation. Candida albicans is an opportunistic fungal pathogen that causes fatal invasive mucosal infections, particularly in solid organ transplant patients. Therefore, identifying and characterizing these genes would play a vital role in understanding the complex regulation of host-pathogen interactions. Using 32 RNA-sequencing samples of human cells infected with C. albicans, we developed WGCNA coexpression networks and performed DESeq2 differential gene expression analysis to identify the genes that positively correlate with human candida infection. Using hierarchical clustering, we identified 5 distinct modules. We studied the inter- and intramodular gene network properties in the context of sample status traits and identified the highly enriched genes in the correlated modules. We identified 52 genes that were common in the most significant WGCNA turquoise module and differentially expressed genes in human endothelial cells (HUVEC) infection vs. control samples. As a validation step, we identified the differentially expressed genes from the independent Candida-infected human oral keratinocytes (OKF6) samples and validated 30 of the 52 common genes. We then performed the functional enrichment analysis using KEGG and GO. Finally, we performed protein-protein interaction (PPI) analysis using STRING and CytoHubba from 30 validated genes. We identified 8 hub genes (JUN, ATF3, VEGFA, SLC2A1, HK2, PTGS2, PFKFB3, and KLF6) that were enriched in response to hypoxia, angiogenesis, vasculogenesis, hypoxia-induced signaling, cancer, diabetes, and transplant-related disease pathways. The discovery of genes and functional pathways related to the immune system and gene coexpression and differential gene expression analyses may serve as novel diagnostic markers and potential therapeutic targets.

Multiplex Analysis of Serum Cytokine Profiles in Systemic Lupus Erythematosus and Multiple Sclerosis

Changes in cytokine profiles and cytokine networks are known to be a hallmark of autoimmune diseases, including systemic lupus erythematosus (SLE) and multiple sclerosis (MS). However, cytokine profiles research studies are usually based on the analysis of a small number of cytokines and give conflicting results. In this work, we analyzed cytokine profiles of 41 analytes in patients with SLE and MS compared with healthy donors using multiplex immunoassay. The SLE group included treated patients, while the MS patients were drug-free. Levels of 11 cytokines, IL-1b, IL-1RA, IL-6, IL-9, IL-10, IL-15, MCP-1/CCL2, Fractalkine/CX3CL1, MIP-1a/CCL3, MIP-1b/CCL4, and TNFa, were increased, but sCD40L, PDGF-AA, and MDC/CCL22 levels were decreased in SLE patients. Thus, changes in the cytokine profile in SLE have been associated with the dysregulation of interleukins, TNF superfamily members, and chemokines. In the case of MS, levels of 10 cytokines, sCD40L, CCL2, CCL3, CCL22, PDGF-AA, PDGF-AB/BB, EGF, IL-8, TGF-a, and VEGF, decreased significantly compared to the control group. Therefore, cytokine network dysregulation in MS is characterized by abnormal levels of growth factors and chemokines. Cross-disorder analysis of cytokine levels in MS and SLE showed significant differences between 22 cytokines. Protein interaction network analysis showed that all significantly altered cytokines in both SLE and MS are functionally interconnected. Thus, MS and SLE may be associated with impaired functional relationships in the cytokine network. A cytokine correlation networks analysis revealed changes in correlation clusters in SLE and MS. These data expand the understanding of abnormal regulatory interactions in cytokine profiles associated with autoimmune diseases.

Whole genome sequencing-based copy number variations reveal novel pathways and targets in Alzheimer's disease

INTRODUCTION

A few copy number variations (CNVs) have been reported for Alzheimer's disease (AD). However, there is a lack of a systematic investigation of CNVs in AD based on whole genome sequencing (WGS) data.

METHODS

We used four methods to identify consensus CNVs from the WGS data of 1,411 individuals and further investigated their functional roles in AD using the matched transcriptomic and clinicopathological data.

RESULTS

We identified 3,012 rare AD-specific CNVs whose residing genes are enriched for cellular glucuronidation and neuron projection pathways. Genes whose mRNA expressions are significantly correlated with common CNVs are involved in major histocompatibility complex class II receptor activity. Integration of CNVs, gene expression, and clinical and pathological traits further pinpoints a key CNV that potentially regulates immune response in AD.

DISCUSSION

We identify CNVs as potential genetic regulators of immune response in AD. The identified CNVs and their downstream gene networks reveal novel pathways and targets for AD.

The Histone Acetyltransferase HpGCN5 Involved in the Regulation of Abiotic Stress Responses and Astaxanthin Accumulation in Haematococcus pluvialis

The histone acetyltransferases (HATs), together with histone deacetylases, regulate the gene transcription related to various biological processes, including stress responses in eukaryotes. This study found a member of HATs (HpGCN5) from a transcriptome of the economically important microalgae Haematococcus pluvialis. Its expression pattern responding to multiple abiotic stresses and its correlation with transcription factors and genes involved in triacylglycerols and astaxanthin biosynthesis under stress conditions were evaluated, aiming to discover its potential biological function. The isolated HpGCN5 was 1,712 bp in length encoding 415 amino acids. The signature domains of Acetyltransf_1 and BROMO were presented, as the GCN5 gene from Arabidopsis and Saccharomyces cerevisiae, confirming that HpGCN5 belongs to the GCN5 subfamily of the GNAT superfamily. The phylogenetic analysis revealed that HpGCN5 is grouped with GNAT genes from algae and is closer to that from higher plants, compared with yeast, animal, fungus, and bacteria. It was predicted that HpGCN5 is composed of 10 exons and contains multiple stress-related cis-elements in the promoter region, revealing its potential role in stress regulation. Real-time quantitative PCR revealed that HpGCN5 responds to high light and high salt stresses in similar behavior, evidenced by their down-regulation exposing to stresses. Differently, HpGCN5 expression was significantly induced by SA and Nitrogen-depletion stresses at the early stage but was dropped back after then. The correlation network analysis suggested that HpGCN5 has a strong correlation with major genes and a transcription factor involved in astaxanthin biosynthesis. Besides, the correlation was only found between HpGCN5 and a few genes involved in triacylglycerols biosynthesis. Therefore, this study proposed that HpGCN5 might play a role in the regulation of astaxanthin biosynthesis. This study firstly examined the role of HATs in stress regulation and results will enrich our understanding of the role of HATs in microalgae.

Network-based approaches for the investigation of microbial community structure and function using metagenomics-based data

Network-based approaches offer a powerful framework to evaluate microbial community organization and function as it relates to a variety of environmental processes. Emerging studies are exploring network theory as a method for data integration that is likely to be critical for the integration of 'omics' data using systems biology approaches. Intricacies of network theory and methodological and computational complexities in network construction, however, impede the use of these tools for translational science. We provide a perspective on the methods of network construction, interpretation and emerging uses for these techniques in understanding host-microbiota interactions.

Metabolomics integrated with transcriptomics reveals the changes during developmental stages in Shiraia bambusicola

Shiraia bambusicola is a fungus with high economic value widely used in medicine, agriculture, and food. We wished to understand the genes and metabolites changes involved in the different developmental stages of S. bambusicola. So, to reveal key genes and metabolites in the main active metabolite, the  were analyzed in different developmental stages of S. bambusicola fruiting body. A total of 29,137 Unigenes were annotated. In the whole growth process, differentially expressed genes were involved in the pathways of cytochrome P450, transcription factors, transporters, and so on, while in the early stage of growth, genes enriching to synthesis pathways of basic substances. In the middle stage of growth, genes with more prominent changes were involved in the pathways of the cell cycle, cancer mechanisms, and aminobenzoate degradation; in the later stage of growth, differentially expressed genes that enriched synthesis pathways of secondary metabolites. A total of 612 metabolites were detected from different growth stages of S. bambusicola. Among them, coumarins, alkaloids, rutin, liquiritigenin, quercetin, and other medically relevant metabolites were detected for the first time. We have identified 31 secondary metabolites,  relevantly only accumulated in the early and middle stage, but not detected in the later stage, such as flavonols, coumarins, nucleotides and its derivates and hydroxycinnamoyl derivatives. The differential genes and metabolites of the same group were enriched in 127 pathways, and more significantly in ubiquinone and other terpenoid quinone biosynthesis, phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and phenylpropanoid biosynthesis. The correlation networks of several significantly enriched pathways were analyzed, and the relationships within and between these pathways, genes, and metabolites, were analyzed. The synthetic pathway of hypocrellin has been speculated upon. We believe that hypocrellin is synthesized in S. bambusicola via the shikimic acid pathway followed by phenylalanine, tyrosine, and tryptophan biosynthesis pathway, then the ubiquinone and other terpenoid quinone biosynthesis pathway, and finally a series of polymerization and modification reactions. Several genes and metabolites involved in the biosynthesis of hypocrellin have been identified. This study provides a reference for further research on S. bambusicola, by providing a basis for its use and development.

Beyond the Biosynthetic Gene Cluster Paradigm: Genome-Wide Coexpression Networks Connect Clustered and Unclustered Transcription Factors to Secondary Metabolic Pathways

Fungal secondary metabolites are widely used as therapeutics and are vital components of drug discovery programs. A major challenge hindering discovery of novel secondary metabolites is that the underlying pathways involved in their biosynthesis are transcriptionally silent under typical laboratory growth conditions, making it difficult to identify the transcriptional networks that they are embedded in. Furthermore, while the genes participating in secondary metabolic pathways are typically found in contiguous clusters on the genome, known as biosynthetic gene clusters (BGCs), this is not always the case, especially for global and pathway-specific regulators of pathways’ activities. To address these challenges, we used 283 genome-wide gene expression data sets of the ascomycete cell factory Aspergillus niger generated during growth under 155 different conditions to construct two gene coexpression networks based on Spearman’s correlation coefficients (SCCs) and on mutual rank-transformed Pearson’s correlation coefficients (MR-PCCs). By mining these networks, we predicted six transcription factors, named MjkA to MjkF, to regulate secondary metabolism in A. niger. Overexpression of each transcription factor using the Tet-On cassette modulated the production of multiple secondary metabolites. We found that the SCC and MR-PCC approaches complemented each other, enabling the delineation of putative global (SCC) and pathway-specific (MR-PCC) transcription factors. These results highlight the potential of coexpression network approaches to identify and activate fungal secondary metabolic pathways and their products. More broadly, we argue that drug discovery programs in fungi should move beyond the BGC paradigm and focus on understanding the global regulatory networks in which secondary metabolic pathways are embedded.

IMPORTANCE There is an urgent need for novel bioactive molecules in both agriculture and medicine. The genomes of fungi are thought to contain vast numbers of metabolic pathways involved in the biosynthesis of secondary metabolites with diverse bioactivities. Because these metabolites are biosynthesized only under specific conditions, the vast majority of the fungal pharmacopeia awaits discovery. To discover the genetic networks that regulate the activity of secondary metabolites, we examined the genome-wide profiles of gene activity of the cell factory Aspergillus niger across hundreds of conditions. By constructing global networks that link genes with similar activities across conditions, we identified six putative global and pathway-specific regulators of secondary metabolite biosynthesis. Our study shows that elucidating the behavior of the genetic networks of fungi under diverse conditions harbors enormous promise for understanding fungal secondary metabolism, which ultimately may lead to novel drug candidates.

Semi-Targeted Metabolomics to Validate Biomarkers of Grape Downy Mildew Infection Under Field Conditions

Grape downy mildew is a devastating disease worldwide and new molecular phenotyping tools are required to detect metabolic changes associated to plant disease symptoms. In this purpose, we used UPLC-DAD-MS-based semi-targeted metabolomics to screen downy mildew symptomatic leaves that expressed oil spots (6 dpi, days post-infection) and necrotic lesions (15 dpi) under natural infections in the field. Leaf extract analyses enabled the identification of 47 metabolites belonging to the primary metabolism including 6 amino acids and 1 organic acid, as well as an important diversity of specialized metabolites including 9 flavonols, 11 flavan-3-ols, 3 phenolic acids, and stilbenoids with various degree of polymerization (DP) including 4 stilbenoids DP1, 8 stilbenoids DP2, and 4 stilbenoids DP3. Principal component analysis (PCA) was applied as unsupervised multivariate statistical analysis method to reveal metabolic variables that were affected by the infection status. Univariate and multivariate statistics revealed 33 and 27 metabolites as relevant infection biomarkers at 6 and 15 dpi, respectively. Correlation-based networks highlighted a general decrease of flavonoid-related metabolites, whereas stilbenoid DP1 and DP2 concentrations increased upon downy mildew infection. Stilbenoids DP3 were identified only in necrotic lesions representing late biomarkers of downy mildew infection.

Salivary Microbiota Shifts under Sustained Consumption of Oolong Tea in Healthy Adults

Tea is the most widely consumed beverages next to water, however little is known about the influence of sustained tea consumption on the oral bacteria of healthy adults. In this study, three oral healthy adults were recruited and instructed to consume 1.0 L of oolong tea infusions (total polyphenol content, 2.83 g/L) daily, for eight weeks. Salivary microbiota pre-, peri-, and post-treatment were fully compared by high-throughput 16S rRNA sequencing and multivariate statistical analysis. It was revealed that oolong tea consumption reduced salivary bacterial diversity and the population of some oral disease related bacteria, such as Streptococcus sp., Prevotella nanceiensis, Fusobacterium periodonticum, Alloprevotella rava, and Prevotella elaninogenica. Moreover, via correlation network and Venn diagram analyses, seven bacterial taxa, including Streptococcus sp. (OTU_1), Ruminococcaceae sp. (OTU_33), Haemophilus sp. (OTU_696), Veillonella spp. (OTU_133 and OTU_23), Actinomyces odontolyticus (OTU_42), and Gemella haemolysans (OTU_6), were significantly altered after oolong tea consumption, and presented robust strong connections (|r| > 0.9 and p < 0.05) with other oral microbiota. These results suggest sustained oolong tea consumption would modulate salivary microbiota and generate potential oral pathogen preventative benefits. Additionally, diverse responses to oolong tea consumption among subjects were also noticed.

BioNetStat: A Tool for Biological Networks Differential Analysis

The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks (e.g., control vs. disease). However, biological systems often present more than two biological states (e.g., tumor grades). To compare two or more networks simultaneously, we developed BioNetStat, a Bioconductor package with a user-friendly graphical interface. BioNetStat compares correlation networks based on the probability distribution of a feature of the graph (e.g., centrality measures). The analysis of the structural alterations on the network reveals significant modifications in the system. For example, the analysis of centrality measures provides information about how the relevance of the nodes changes among the biological states. We evaluated the performance of BioNetStat in both, toy models and two case studies. The latter related to gene expression of tumor cells and plant metabolism. Results based on simulated scenarios suggest that the statistical power of BioNetStat is less sensitive to the increase of the number of networks than Gene Set Coexpression Analysis (GSCA). Also, besides being able to identify nodes with modified centralities, BioNetStat identified altered networks associated with signaling pathways that were not identified by other methods.

Arbuscular mycorrhizal fungi serve as keystone taxa for revegetation on the Tibetan Plateau

Revegetation is widely used to enhance degraded topsoil recovery with the enhancements of soil nutrient accumulation and soil structure stabilization. Arbuscular mycorrhizal fungi (AMF) are important for the allocation of carbon into the soil and the formation of soil aggregates. Thus, we hypothesized that AMF could construct more niches for other microbes during revegetation, making AMF keystone taxa of soil. Soil fungal and bacterial communities were investigated under a revegetation experiment and correlation networks between soil fungi and bacteria were constructed. Simultaneously, the plant growth level, soil properties and structure, and soil microbial carbon decomposition abilities were measured. The results revealed that AMF were the most central fungi at the phylum (degree = 3), class (degree = 11), and family (degree = 15) levels. The reads number of AMF were positively correlated with both fungal (R²  = 0.431, P < 0.001) and bacterial (R²  = 0.106, P = 0.044) richness. Higher colonization of AMF in roots and/or more AMF extraradical mycelium and spores in soil indicated a better plant growth, more stable soil aggregates, and a higher carbon decomposition ratio. Our results highlight that AMF are keystone taxa in revegetation, as they play significant roles in enhancing the recovery of the belowground microbiome diversity, soil structure stability, and nutrients cycling. The positive roles of AMF in revegetation support the application of AMF in ecosystem recovery.

A New Method to Correct for Habitat Filtering in Microbial Correlation Networks

Amplicon sequencing of 16S, ITS, and 18S regions of microbial genomes is a commonly used first step toward understanding microbial communities of interest for human health, agriculture, and the environment. Correlation network analysis is an emerging tool for investigating the interactions within these microbial communities. However, when data from different habitats (e.g., sampling sites, host genotype, etc.) are combined into one analysis, habitat filtering (co-occurrence of microbes due to habitat sampled rather than biological interactions) can induce apparent correlations, resulting in a network dominated by habitat effects and masking correlations of biological interest. We developed an algorithm to correct for habitat filtering effects in microbial correlation network analysis in order to reveal the true underlying microbial correlations. This algorithm was tested on simulated data that was constructed to exhibit habitat filtering. Our algorithm significantly improved correlation detection accuracy for these data compared to Spearman and Pearson correlations. We then used our algorithm to analyze a two real data sets of 16S variable region amplicon sequences that were expected to exhibit habitat filtering. Our algorithm was found to effectively reduce habitat effects, enabling the construction of consensus correlation networks from data sets combining multiple related sample habitats.

Urinary metabolomics analysis reveals the effect of volatile oil from Angelica sinensis on LPS-induced inflammation rats

Lipopolysaccharide (LPS)-induced inflammation occurs commonly and volatile oil from Angelica sinensis (VOAS) can be used as an anti-inflammatory agent. The molecular mechanisms that allow the anti-inflammatory factors to be expressed are still unknown. In this paper, we applied gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography-time-of-flight mass spectrometry (LC-Q/TOF-MS) based on a metabolomics platform coupled with a network approach to analyze urine samples in three groups of rats: one with LPS-induced inflammation (MI); one with intervention with VOAS; and normal controls (NC). Our study found definite metabolic footprints of inflammation and showed that all three groups of rats, MI, intervention with VOAS and NC have distinct metabolic profiles in urine. The concentrations of 48 metabolites differed significantly among the three groups. The metabolites in urine were screened by the GC-MS and LC-Q/TOF-MS methods. The significantly changed metabolites (p < 0.05, variable importance in projection > 1.5) between MI, NC and VOAS were included in the metabolic networks. Finally, hub metabolites were screened, including glycine, arachidonic acid, l-glutamate, pyruvate and succinate, which have high values of degree (k). the Results suggest that disorders of glycine, arachidonic acid, l-glutamate, pyruvate and succinate metabolism might play an important part in the predisposition and development of LPS-induced inflammation. By applying metabolomics with network methods, the mechanisms of diseases are clearly elucidated.

GWAS with Heterogeneous Data: Estimating the Fraction of Phenotypic Variation Mediated by Gene Expression Data

Intermediate phenotypes such as gene expression values can be used to elucidate the mechanisms by which genetic variation causes phenotypic variation, but jointly analyzing such heterogeneous data are far from trivial. Here we extend a so-called mediation model to handle the confounding effects of genetic background, and use it to analyze flowering time variation in Arabidopsis thaliana, focusing in particular on the central role played by the key regulator FLOWERING TIME LOCUS C (FLC). FLC polymorphism and FLC expression are both strongly correlated with flowering time variation, but the effect of the former is only partly mediated through the latter. Furthermore, the latter also reflects genetic background effects. We demonstrate that it is possible to partition these effects, shedding light on the complex regulatory network that underlies flowering time variation.

Survival associated alternative splicing events in diffuse large B-cell lymphoma

Growing evidence has revealed that the initiation of various malignancies is closely associated with alternative splicing (AS) events in certain key oncogenes. However, in diffuse large B-cell lymphoma (DLBCL), there is still a great deal to learn about AS variants. In this study, 33,724 AS variant profiles were obtained from 16,278 genes in 48 DLBCL cases. A total of 10 AS variants were identified as overall survival (OS)- related events via multivariate Cox regression analysis. Notably, alternative donor (AD) sites in AS events in the low-risk group showed a significantly better outcome in DLBCL patients than in the high-risk group (P=0.0002). The area under the curve (AUC) of the receiver-operator characteristic curve (ROC) for ADs in DLBCL was 0.746. Furthermore, 66 related splicing factors were obtained to investigate their potential correlations with AS events. Factors SF1, HNRNPC, HNRNPD, and HNRNPH3 were significantly involved in different OS-related AS variants. Collectively, we constructed valuable prognostic predictors for DLBCL patients and mapped novel splicing networks for further investigation of the underlying mechanisms related to AS variants in DLBCLs.

RiceAntherNet: a gene co-expression network for identifying anther and pollen development genes

In plants, normal anther and pollen development involves many important biological events and complex molecular regulatory coordination. Understanding gene regulatory relationships during male reproductive development is essential for fundamental biology and crop breeding. In this work, we developed a rice gene co-expression network for anther development (RiceAntherNet) that allows prediction of gene regulatory relationships during pollen development. RiceAntherNet was generated from 57 rice anther tissue microarrays across all developmental stages. The microarray datasets from nine rice male sterile mutants, including msp1-4, ostdl1a, gamyb-2, tip2, udt1-1, tdr, eat1-1, ptc1 and mads3-4, were used to explore and test the network. Among the changed genes, three clades showing differential expression patterns were constructed to identify genes associated with pollen formation. Many of these have known roles in pollen development, for example, seven genes in Clade 1 (OsABCG15, OsLAP5, OsLAP6, DPW, CYP703A3, OsNP1 and OsCP1) are involved in rice pollen wall formation. Furthermore, Clade 1 contained 12 genes whose predicted orthologs in Arabidopsis have been reported as key during pollen development and may play similar roles in rice. Genes in Clade 2 are expressed earlier than Clade 1 (anther stages 2-9), while genes in Clade 3 are expressed later (stages 10-12). RiceAntherNet serves as a valuable tool for identifying novel genes during plant anther and pollen development. A website is provided (https://www.cpib.ac.uk/anther/riceindex.html) to present the expression profiles for gene characterization. This will assist in determining the key relationships between genes, thus enabling characterization of critical genes associated with anther and pollen regulatory networks.

Optimization of Huang-Lian-Jie-Du-Decoction for Ischemic Stroke Treatment and Mechanistic Study by Metabolomic Profiling and Network Analysis

Optimal drug proportions and mechanism deciphering of multicomponent drugs are critical for developing novel therapies to cope with complex diseases, such as stroke. In the present study, orthogonal experimental design was applied to explore the optimal proportion of the four component herbs in Huang-Lian-Jie-Du-Decoction (HLJDD) on the treatment of ischemic stroke. The treatment efficacies and mechanisms were assessed using global and amino acids (AAs) targeted metabolomics, as well as correlation network analysis. The global NMR metabolomics results revealed that AAs metabolism was significantly perturbed in middle cerebral artery occlusion rats. The levels of 23 endogenous AAs were then subjected to HPLC-QTOF-MS/MS analysis. These results complemented with neurobehavioral evaluations, cerebral infarct assessments, biochemical evaluations, histological inspections and immunohistochemistry observations strongly demonstrated that HLJDD with optimal proportion of 6 (Rhizoma coptidis): 4 (Radix scutellariae): 1 (Cortex phellodendr): 3 (Fructus Gardeniae) had the best efficacy on ischemic stroke, which could be ascribed to its modulation on AA metabolism. This integrated metabolomics approach showed the potential and applicable in deciphering the complex mechanisms of traditional Chinese medicine formulae on the treatment of complicated diseases, which provided new means to assess the treatment effects of herb combinations and to further development of drugs or therapies based on these formulae.

Changes in the Structure of the Microbial Community Associated with Nannochloropsis salina following Treatments with Antibiotics and Bioactive Compounds

Open microalgae cultures host a myriad of bacteria, creating a complex system of interacting species that influence algal growth and health. Many algal microbiota studies have been conducted to determine the relative importance of bacterial taxa to algal culture health and physiological states, but these studies have not characterized the interspecies relationships in the microbial communities. We subjected Nanochroloropsis salina cultures to multiple chemical treatments (antibiotics and quorum sensing compounds) and obtained dense time-series data on changes to the microbial community using 16S gene amplicon metagenomic sequencing (21,029,577 reads for 23 samples) to measure microbial taxa-taxa abundance correlations. Short-term treatment with antibiotics resulted in substantially larger shifts in the microbiota structure compared to changes observed following treatment with signaling compounds and glucose. We also calculated operational taxonomic unit (OTU) associations and generated OTU correlation networks to provide an overview of possible bacterial OTU interactions. This analysis identified five major cohesive modules of microbiota with similar co-abundance profiles across different chemical treatments. The Eigengenes of OTU modules were examined for correlation with different external treatment factors. This correlation-based analysis revealed that culture age (time) and treatment types have primary effects on forming network modules and shaping the community structure. Additional network analysis detected Alteromonadeles and Alphaproteobacteria as having the highest centrality, suggesting these species are “keystone” OTUs in the microbial community. Furthermore, we illustrated that the chemical tropodithietic acid, which is secreted by several species in the Alphaproteobacteria taxon, is able to drastically change the structure of the microbiota within 3 h. Taken together, these results provide valuable insights into the structure of the microbiota associated with N. salina cultures and how these structures change in response to chemical perturbations.

Evidence of perturbations of the cytokine network in preterm labor

OBJECTIVE

Intraamniotic inflammation/infection is the only mechanism of disease with persuasive evidence of causality for spontaneous preterm labor/delivery. Previous studies about the behavior of cytokines in preterm labor have been largely based on the analysis of the behavior of each protein independently. Emerging evidence indicates that the study of biologic networks can provide insight into the pathobiology of disease and improve biomarker discovery. The goal of this study was to characterize the inflammatory-related protein network in the amniotic fluid of patients with preterm labor.

STUDY DESIGN

A retrospective cohort study was conducted that included women with singleton pregnancies who had spontaneous preterm labor and intact membranes (n = 135). These patients were classified according to the results of amniotic fluid culture, broad-range polymerase chain reaction coupled with electrospray ionization mass spectrometry, and amniotic fluid concentration of interleukin (IL)-6 into the following groups: (1) those without intraamniotic inflammation (n = 85), (2) those with microbial-associated intraamniotic inflammation (n = 15), and (3) those with intraamniotic inflammation without detectable bacteria (n = 35). Amniotic fluid concentrations of 33 inflammatory-related proteins were determined with the use of a multiplex bead array assay.

RESULTS

Patients with preterm labor and intact membranes who had microbial-associated intraamniotic inflammation had a higher amniotic fluid inflammatory-related protein concentration correlation than those without intraamniotic inflammation (113 perturbed correlations). IL-1β, IL-6, macrophage inflammatory protein (MIP)-1α, and IL-1α were the most connected nodes (highest degree) in this differential correlation network (degrees of 20, 16, 12, and 12, respectively). Patients with sterile intraamniotic inflammation had correlation patterns of inflammatory-related proteins, both increased and decreased, when compared to those without intraamniotic inflammation (50 perturbed correlations). IL-1α, MIP-1α, and IL-1β were the most connected nodes in this differential correlation network (degrees of 12, 10, and 7, respectively). There were more coordinated inflammatory-related protein concentrations in the amniotic fluid of women with microbial-associated intraamniotic inflammation than in those with sterile intraamniotic inflammation (60 perturbed correlations), with IL-4 and IL-33 having the largest number of perturbed correlations (degrees of 15 and 13, respectively).

CONCLUSIONS

We report for the first time an analysis of the inflammatory-related protein network in spontaneous preterm labor. Patients with preterm labor and microbial-associated intraamniotic inflammation had more coordinated amniotic fluid inflammatory-related proteins than either those with sterile intraamniotic inflammation or those without intraamniotic inflammation. The correlations were also stronger in patients with sterile intraamniotic inflammation than in those without intraamniotic inflammation. The findings herein could be of value in the development of biomarkers of preterm labor.

The role of microbiota in hepatic encephalopathy

Hepatic encephalopathy (HE), which consists of minimal (MHE) and overt (OHE) stages, is a model for impaired gut-liver-brain axis in cirrhosis. Microbiota changes in both stages have been associated with impaired cognition, endotoxemia, and inflammation. There is dysbiosis (reduced autochthonous taxa [Lachnospiraceae, Ruminococcaceae, and Clostridiales XIV] and increased Enterobacteriaceae and Streptococcaceae) with disease progression. In MHE, there is an increased abundance of Streptococcus salivarius linked to cognition and ammonia. In OHE, stool Alcaligenaceae and Porphyromonadaceae are associated with poor cognition. Colonic mucosal microbiome in cirrhosis is significantly different compared with stool and independently related to cognition. HE treatment can affect microbial composition and function; cognitive improvement in MHE after rifaximin, a non-absorbable antibiotic, occurred without significant stool microbiota composition change but improved metabolic linkages. Similarly, there are only modest lactulose and rifaximin-associated changes on microbiota composition in OHE. HE represents an important model to study microbiome-brain interactions.

Making the right connections: biological networks in the light of evolution

Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein-protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre-stage. We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves.