Correlation-based Network Analysis Research Articles

Large airway T cells in adults with former bronchopulmonary dysplasia

BackgroundBronchopulmonary Dysplasia (BPD) in infants born prematurely is a risk factor for chronic airway obstruction later in life. The distribution of T cell subtypes in the large airways is largely unknown.ObjectiveTo characterize cellular and T cell profiles in the large airways of young adults with a history of BPD.MethodsForty-three young adults born prematurely (preterm (n = 20), BPD (n = 23)) and 45 full-term-born (asthma (n = 23), healthy (n = 22)) underwent lung function measurements, and bronchoscopy with large airway bronchial wash (BW). T-cells subsets in BW were analyzed by immunocytochemistry.ResultsThe proportions of both lymphocytes and CD8 + T cells in BW were significantly higher in BPD (median, 6.6%, and 78.0%) when compared with asthma (3.4% and 67.8%, p = 0.002 and p = 0.040) and healthy (3.8% and 40%, p < 0.001 and p < 0.001). In all adults born prematurely (preterm and BPD), lymphocyte proportion correlated negatively with forced vital capacity (r= -0.324, p = 0.036) and CD8 + T cells correlated with forced expiratory volume in one second, FEV1 (r=-0.448, p = 0.048). Correlation-based network analysis revealed that lung function cluster and BPD-birth cluster were associated with lymphocytes and/or CD4 + and CD8 + T cells. Multivariate regression analysis showed that lymphocyte proportions and BPD severity qualified as independent factors associated with FEV1.ConclusionsThe increased cytotoxic T cells in the large airways in young adults with former BPD, suggest a similar T-cell subset pattern as in the small airways, resembling features of COPD. Our findings strengthen the hypothesis that mechanisms involving adaptive and innate immune responses are involved in the development of airway disease due to preterm birth.

Glycans modulate lipid binding in Lili-Mip lipocalin protein: insights from molecular simulations and protein network analyses.

The unique viviparous Pacific Beetle cockroaches provide nutrition to their embryo by secreting milk proteins Lili-Mip, a lipid-binding glycoprotein that crystallises in-vivo. The resolved in-vivo crystal structure of variably glycosylated Lili-Mip shows a classical Lipocalin fold with an eight-stranded antiparallel beta-barrel enclosing a fatty acid. The availability of physiologically unaltered glycoprotein structure makes Lili-Mip a very attractive model system to investigate the role of glycans on protein structure, dynamics, and function. Towards that end, we have employed all-atom molecular dynamics simulations on various glycosylated stages of a bound and free Lili-Mip protein and characterised the impact of glycans and the bound lipid on the dynamics of this glycoconjugate. Our work provides important molecular-level mechanistic insights into the role of glycans in the nutrient storage function of the Lili-Mip protein. Our analyses show that the glycans stabilise spatially proximal residues and regulate the low amplitude opening motions of the residues at the entrance of the binding pocket. Glycans also preserve the native orientation and conformational flexibility of the ligand. However, we find that either deglycosylation or glycosylation with high-mannose and paucimannose on the core glycans, which better mimic the natural insect glycosylation state, significantly affects the conformation and dynamics. A simple but effective distance- and correlation-based network analysis of the protein also reveals the key residues regulating the barrel's architecture and ligand binding characteristics in response to glycosylation.

Co-occurrence patterns in biofloc microbial communities revealed by network analysis and their impact on the host

The complex aggregation of bacteria flocs (bioflocs) plays a vital role in the inorganic nitrogen transformation process within biofloc systems. However, few reports have focused on the bacterial co-occurrence of flocs supplemented with different carbohydrate sources. In this study, a correlation-based microecological network analysis method was applied to reveal the bacterial interactions of flocs under three different aquaculture systems: a clear water system (CON group), a biofloc system using glucose (GLU) as a carbon source, and a biofloc system using starch (STA) as a carbon source. The results showed that (i) the GLU group exhibited higher nitrogen removal efficiency and a higher DOC/total inorganic nitrogen ratio (68.80 ± 9.18% and 19.19 ± 6.78, respectively) than the STA group (55.59 ± 16.26% and 15.30 ± 11.08, respectively). The bacterial co-occurrence patterns in the three systems were not random, with each carbohydrate corresponding to a unique bacterial community niche. (ii) Firmicutes was the core phylum in the biofloc technology (BFT) system, while Bacillus, Carnobacterium, and Staphylococcus were the core genera that ensured the stability of nitrogen removal in the system based on network analysis results. (iii) In the GLU group, the abundance of Firmicutes was increased and corresponded to a higher Firmicutes/Bacteroidetes (F/B) ratio in the fish gut, which promoted host in situ growth performance. Application of the ecological concepts in this study could lead to the identification of core/satellite genera in bioflocs and promote scientific hypotheses toward more in-depth metabolic and functional research on biofloc systems.

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data.

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman's rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

Uncovering the diverse hosts of tigecycline resistance gene tet(X4) in anaerobic digestion systems treating swine manure by epicPCR

The tet(X4) gene is a clinically important tigecycline resistance gene and has shown high persistence in livestock-related environments. However, the bacterial hosts of tet(X4) remain unknown due to the lack of appropriate approaches. Herein, a culture-independent and high-throughput epicPCR (emulsion, paired isolation, and concatenation polymerase chain reaction) method was developed, optimized, and demonstrated for the identification of bacterial hosts carrying tet(X4) from environmental samples. Considering the high sequence similarity between tet(X4) and other tet(X)-variant genes, specific primers and amplification conditions were screened and optimized to identify tet(X4) accurately and link tet(X4) with the 16S rRNA gene, which were further validated using artificially constructed bacterial communities. The epicPCR targeting tet(X4) was applied for the identification of bacterial hosts carrying this resistance gene in anaerobic digestion systems treating swine manure. A total of 19 genera were identified as tet(X4) hosts, which were distributed in the phyla Proteobacteria, Bacteroidota, Firmicutes, and Caldatribacteriota. Sixteen genera and two phyla that were identified have not been previously reported as tet(X4) bacterial hosts. The results indicated that a far more diverse range of bacteria was involved in harboring tet(X4) than previously realized. Compared with the tet(X4) hosts determined by correlation-based network analysis and metagenomic binning, epicPCR revealed a high diversity of tet(X4) hosts even at the phylum level. The epicPCR method developed in this study could be effectively employed to reveal the presence of tet(X4) bacterial hosts from a holistic viewpoint.

Integrating Genetics, Transcriptomics, and Proteomics in Lung Tissue to Investigate Chronic Obstructive Pulmonary Disease.

The integration of transcriptomic and proteomic data from lung tissue with chronic obstructive pulmonary disease (COPD)-associated genetic variants could provide insight into the biological mechanisms of COPD. Here, we assessed associations between lung transcriptomics and proteomics with COPD in 98 subjects from the Lung Tissue Research Consortium. Low correlations between transcriptomics and proteomics were generally observed, but higher correlations were found for COPD-associated proteins. We integrated COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins to identify regulatory cis-quantitative trait loci (QTLs). Significant expression QTLs (eQTLs) and protein QTLs (pQTLs) were found regulating multiple COPD-associated biomarkers. We investigated mediated associations from significant pQTLs through transcripts to protein levels of COPD-associated proteins. We also attempted to identify colocalized effects between COPD genome-wide association studies and eQTL and pQTL signals. Evidence was found for colocalization between COPD genome-wide association study signals and a pQTL for RHOB and an eQTL for DSP. We applied weighted gene co-expression network analysis to find consensus COPD-associated network modules. Two network modules generated by consensus weighted gene co-expression network analysis were associated with COPD with a false discovery rate lower than 0.05. One network module is related to the catenin complex, and the other module is related to plasma membrane components. In summary, multiple cis-acting determinants of transcripts and proteins associated with COPD were identified. Colocalization analysis, mediation analysis, and correlation-based network analysis of multiple omics data may identify key genes and proteins that work together to influence COPD pathogenesis.

Monkeypox outbreaks in the context of the COVID-19 pandemic: Network and clustering analyses of global risks and modified SEIR prediction of epidemic trends.

Ninety-eight percent of documented cases of the zoonotic disease human monkeypox (MPX) were reported after 2001, with especially dramatic global spread in 2022. This longitudinal study aimed to assess spatiotemporal risk factors of MPX infection and predict global epidemiological trends. Twenty-one potential risk factors were evaluated by correlation-based network analysis and multivariate regression. Country-level risk was assessed using a modified Susceptible-Exposed-Infectious-Removed (SEIR) model and a risk-factor-driven k-means clustering analysis. Between historical cases and the 2022 outbreak, MPX infection risk factors changed from relatively simple [human immunodeficiency virus (HIV) infection and population density] to multiple [human mobility, population of men who have sex with men, coronavirus disease 2019 (COVID-19) infection, and socioeconomic factors], with human mobility in the context of COVID-19 being especially key. The 141 included countries classified into three risk clusters: 24 high-risk countries mainly in West Europe and Northern America, 70 medium-risk countries mainly in Latin America and Asia, and 47 low-risk countries mainly in Africa and South Asia. The modified SEIR model predicted declining transmission rates, with basic reproduction numbers ranging 1.61-7.84 in the early stage and 0.70-4.13 in the current stage. The estimated cumulative cases in Northern and Latin America may overtake the number in Europe in autumn 2022. In the current outbreak, risk factors for MPX infection have changed and expanded. Forecasts of epidemiological trends from our modified SEIR models suggest that Northern America and Latin America are at greater risk of MPX infection in the future.

Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.

Drug-induced nephrotoxicity is a relatively common preventable cause of acute kidney injury (AKI), providing early recognition and management. The pharmacokinetics or pharmacodynamics of drug-drug interactions may lead to additive or synergistic toxicity. The influx of new medications or off-label use of medications in the critical care setting can lead to additional nephrotoxicities, often challenging to predict or detect. This study evaluates the patterns of medication utilization, their combinations, and the related associations with AKI. We utilized correlation-based network analysis (CNA) to investigate the relationship between medications or their combinations with AKI in a large cohort of critically ill patients in a tertiary medical center between 2007 and 2018. Pairwise medication-AKI correlation analysis was performed to evaluate drug synergistic or additive effects. To investigate the inherent nephrotoxicity of medications, we further analyzed medications that were not paired with any other medications within 24 hours before or after their administration time (isolated medication analysis). Among 147,289 ICU admissions, we identified 244 associations among 1,555 unique medication types. In pairwise analysis, 233 significant correlations were found among 13,150,198 medication pair instances. In isolated medication analysis, ten significant AKI associations were noted. When stratified by eGFR level, substantial differences between eGFR<90 vs. eGFR≥90 patients were observed. This highlights a need to determine eGFR as a risk factor for nephrotoxicity assessment when drug interactions are considered. This large-scale cohort study identified an artificial intelligence model to identify patient-agnostic relationships between medication or their pairs with AKI incidence among critically ill patients. It could be used as a continuous quality assurance tool to monitor drug-associated risk nephrotoxicity.

Relationship between offensive tactical efficiency and technical actions performed by school futsal players

Purpose.The aim was to analyse the significance and magnitude of the correlation between the offensive tactical efficiency and technical actions performed by schoolchildren practicing futsal during a reduced game in the GR3-3GR test format.Methods.The sample was composed of 33 child and adolescent futsal players aged between 9 and 14 years – from elementary and middle schools – and divided into Under-10 (21 players), Under-12 (6 players), and Under-14 (6 players) categories. The players were filmed during the execution of a small-sided game (GR3-3GR). To assess tactical efficiency, core tactical principles of penetration, offensive coverage, depth mobility, width and length (with and without ball), and offensive unit were observed through the FUT-SAT test, while technical actions were analysed using an observational protocol, which recorded the occurrence of the actions ‘passing’, ‘shooting’, ‘receiving’, and ‘1:1 duel’. Spearman’s correlation coefficient was used to verify the degree of relationship of the analysed variables, followed by correlation-based network analysis. Significance was set at 5%.Results.The results showed associations between efficiency in the execution of offensive tactical principles performed by the attacker with the ball and the execution of technical actions. The principle ‘penetration’ was positively correlated with ‘receiving’ (&lt;i&gt;r&lt;/i&gt; = 0.41), and the principle ‘width and length with ball’ was negatively correlated with the execution of ‘1×1 duel’ (&lt;i&gt;r&lt;/i&gt; = –0.34).Conclusions.It is concluded that the technical quality when receiving and controlling the ball favours movements related to progression on the playing court. Players who are less skilled in direct confrontations tend to prefer to carry the ball to spaces with less adversary pressure.

Preliminary study to classify mechanisms of mitochondrial toxicity by in vitro metabolomics and bioinformatics

AimMitochondrial toxicity is one of the causes for drug-induced liver injury, and the classification of phenotypes or mitochondrial toxicity are highly required though there are no molecular-profiling approaches for classifying mitochondrial toxicity. Therefore, the aim of this study was to classify the mechanisms of mitochondrial toxicity by metabolic profiling in vitro and bioinformatics. Main methodsWe applied an established gas chromatography tandem mass spectrometry-based metabolomics to human hepatoma grade 2 (HepG2) cells that were exposed to mitochondrial toxicants, whose mechanisms are different, such as rotenone (0.1 μM), carbonyl cyanide-3-chlorophenylhydrazone (CCCP, 0.5 μM), nefazodone (20 μM), perhexiline (6.25 μM), or digitonin (positive cytotoxic substance, 4 μM). These concentrations were determined by the Mitochondrial ToxGlo Assay. Galactose medium was used for suppressing the Warburg effect in HepG2 cells, and the metabolome analysis successfully identified 125 metabolites in HepG2 cells. Multivariate, metabolic pathway and network analyses were performed by the R software. Key findingsMetabolic profiling enabled the classifying the mitochondrial toxicity mechanisms of RCC inhibition and uncoupling. The metabolic profiles of respiratory chain complex (RCC) inhibitors (rotenone and nefazodone) and an uncoupler (CCCP) were fully differentiated from those of other compounds. The metabolic pathway analysis revealed that the RCC inhibitors and the uncoupler mainly disrupted TCA-cycle and related metabolic pathways. In addition, the correlation-based network analysis revealed that succinic acid, β-alanine, and glutamic acid were potential metabolic indicators for RCC inhibition and uncoupling. SignificanceOur results provided new insights into classifying mechanisms of mitochondrial toxicity by in vitro metabolomics.

Metabolic, physiological and anatomical responses of soybean plants under water deficit and high temperature condition

Water deficit (WD) combined with high temperature (HT) is the major factor limiting agriculture worldwide, and it is predicted to become worse according to the current climate change scenario. It is thus important to understand how current cultivated crops respond to these stress conditions. Here we investigated how four soybean cultivars respond to WD and HT isolated or in combination at metabolic, physiological, and anatomical levels. The WD + HT increased the level of stress in soybean plants when compared to plants under well-watered (WW), WD, or HT conditions. WD + HT exacerbates the increases in ascorbate peroxidase activity, which was associated with the greater photosynthetic rate in two cultivars under WD + HT. The metabolic responses to WD + HT diverge substantially from plants under WW, WD, or HT conditions. Myo-inositol and maltose were identified as WD + HT biomarkers and were connected to subnetworks composed of catalase, amino acids, and both root and leaf osmotic potentials. Correlation-based network analyses highlight that the network heterogeneity increased and a higher integration among metabolic, physiological, and morphological nodes is observed under stress conditions. Beyond unveiling biochemical and metabolic WD + HT biomarkers, our results collectively highlight that the mechanisms behind the acclimation to WD + HT cannot be understood by investigating WD or HT stress separately.

Network autonomic analysis of post-acute sequelae of COVID-19 and postural tachycardia syndrome.

BackgroundThe autonomic nervous system (ANS) is a complex network where sympathetic and parasympathetic domains interact inside and outside of the network. Correlation-based network analysis (NA) is a novel approach enabling the quantification of these interactions. The aim of this study is to assess the applicability of NA to assess relationships between autonomic, sensory, respiratory, cerebrovascular, and inflammatory markers on post-acute sequela of COVID-19 (PASC) and postural tachycardia syndrome (POTS).MethodsIn this retrospective study, datasets from PASC (n = 15), POTS (n = 15), and matched controls (n = 11) were analyzed. Networks were constructed from surveys (autonomic and sensory), autonomic tests (deep breathing, Valsalva maneuver, tilt, and sudomotor test) results using heart rate, blood pressure, cerebral blood flow velocity (CBFv), capnography, skin biopsies for assessment of small fiber neuropathy (SFN), and various inflammatory markers. Networks were characterized by clusters and centrality metrics.ResultsStandard analysis showed widespread abnormalities including reduced orthostatic CBFv in 100%/88% (PASC/POTS), SFN 77%/88%, mild-to-moderate dysautonomia 100%/100%, hypocapnia 87%/100%, and elevated inflammatory markers. NA showed different signatures for both disorders with centrality metrics of vascular and inflammatory variables playing prominent roles in differentiating PASC from POTS.ConclusionsNA is suitable for a relationship analysis between autonomic and nonautonomic components. Our preliminary analyses indicate that NA can expand the value of autonomic testing and provide new insight into the functioning of the ANS and related systems in complex disease processes such as PASC and POTS.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10072-022-06423-y.

Metagenomic insights into resistome coalescence in an urban sewage treatment plant-river system

The effluents of sewage treatment plants (eSTP) are one of the critical contributors of antibiotic resistiome in rivers. Recently, community coalescence has been focused as the entire microbiome interchanges with one another. While works have reported the prevalence of antibiotic resistance genes (ARGs) in eSTP and their effects on river resistome, little research has investigated the extent of resistome coalescence in the environment. In the study, we have addressed the issue and focused on the resistome coalescence of eSTP in an urban river with a typical effluent/river coalescence model, by utilizing high-throughput sequencing (HTS)-based metagenomic assembly analysis. In all, a total of 609 ARGs were found in the eSTP-river system, conferring resistance to 30 antibiotic classes and including some emerging ARGs such as mcr-type, tetX and carbapenemase genes. Statistical analyses including linear discriminant analysis effect size (LEfSe) showed the coalescence of STP effluents increased the diversity and abundance of river resistome, indicating its low resistance to disturb the invasion of resistome community in eSTP. After coalescence in the river, the imprints of STP-derived ARGs presented a temporary increase and gradually decreased trend along the flow path. Further, an innovative fast expectation-maximization microbial source tracking (FEAST) method was used to quantitatively apportion the coalescence event, and demonstrated the contribution of eSTP on river resistome and its attenuation dynamics in the downstream. Notably, correlation-based network analysis and contig-based co-occurrence analysis showed the coalesced resistome in the downstream river co-occurred with human bacterial pathogens, mobile genetic elements and virulence factor genes, indicating potential resistome dissemination risk in the environment. This study provides more profound understanding of resistome coalescence between engineered and natural contexts, which is helpful for optimizing strategies to prevent and control resistome risk in aquatic environment.

Metabolomics analysis reveals dysregulation in one carbon metabolism in Friedreich Ataxia.

Friedreich Ataxia (FA) is a rare and often fatal autosomal recessive disease in which a mitochondrial protein, frataxin (FXN), is severely reduced in all tissues. With loss of FXN, mitochondrial metabolism is severely disrupted. Multiple therapeutic approaches are in development, but a key limitation is the lack of biomarkers reflecting the activity of FXN in a timely fashion. We predicted this dysregulated metabolism would present a unique metabolite profile in blood of FA patients versus Controls (Con). Plasma from 10 FA and 11 age and sex matched Con subjects was analyzed by targeted mass spectrometry and untargeted NMR. This combined approach yielded quantitative measurements for 540 metabolites and found 59 unique metabolites (55 from MS and 4 from NMR) that were significantly different between cohorts. Correlation-based network analysis revealed several clusters of pathway related metabolites including a cluster associated with one‑carbon (1C) metabolism composed of formate, sarcosine, hypoxanthine, and homocysteine. Receiver operator characteristics analyses demonstrated an excellent ability to discriminate between Con and FA with AUC values >0.95. These results are the first reported metabolomic analyses of human patients with FA. The metabolic perturbations, especially those related to 1C metabolism, may serve as a valuable biomarker panel of disease progression and response to therapy. The identification of dysregulated 1C metabolism may also inform the search for new therapeutic targets related to this pathway.

High stability of autochthonous dissolved organic matter in karst aquatic ecosystems: Evidence from fluorescence

Biological carbon pump (BCP) in karst areas has received intensive attention for years due to their significant contribution to the global missing carbon sink. The stability of autochthonous dissolved organic matter (Auto-DOM) produced by BCP in karst aquatic ecosystems may play a critical role in the missing carbon sink. However, the source of dissolved organic matter (DOM) in inland waters and its consumption by planktonic bacteria have not been thoroughly examined. Recalcitrant dissolved organic matter (RDOM) may exist in karst aquatic ecosystem as in the ocean. Through the study of the chromophoric dissolved organic matter (CDOM) and the interaction between CDOM and the planktonic bacterial community under different land uses at the Shawan Karst Water-carbon Cycle Test Site, SW China, we found that C2, as the fluorescence component of Auto-DOM mineralised by planktonic bacteria, may have some of the characteristics of RDOM and is an important DOM source in karst aquatic ecosystems. The stability ratio (Fmax(C2/(C1+C2))) of Auto-DOM reached 89.6 ± 6.71% in winter and 64.1 ± 7.19% in spring. Moreover, correlation-based network analysis determined that the planktonic bacterial communities were controlled by different fluorescence types of CDOM, of which C1 (fresh Auto-DOM), C3 (conventional allochthonous DOM (Allo-DOM)) and C4 (the Allo-DOM mineralised by bacteria) were clustered in one module together with prevalent organic-degrading planktonic bacteria; C2 was clustered in another tightly combined module, suggesting specific microbial utilization strategies for the C2 component. In addition, some important planktonic bacterium and functional genes (including chemotrophic heterotrophs and photosynthetic bacteria) were found to be affected by high Ca2+ and dissolved inorganic carbon (DIC) concentrations in karst aquatic ecosystems. Our research showed that Auto-DOM may be as an important carbon sink as the Allo-DOM in karst ecosystems, the former generally being neglected based on a posit that it is easily and first mineralized by planktonic bacteria.

In-Depth Proteomic Analysis of the Secondary Dormancy Induction by Hypoxia or High Temperature in Barley Grains.

In barley, incubation of primary dormant (D1) grains on water under conditions that do not allow germination, i.e. 30°C in air and 15°C or 30°C in 5% O2, induces a secondary dormancy (D2) expressed as a loss of the ability to germinate at 15°C in air. The aim of this study was to compare the proteome of barley embryos isolated from D1 grains and D2 ones after induction of D2 at 30°C or in hypoxia at 15°C or 30°C. Total soluble proteins were analyzed by 2DE gel-based proteomics, allowing the selection of 130 differentially accumulated proteins (DAPs) among 1,575 detected spots. According to the protein abundance profiles, the DAPs were grouped into six abundance-based similarity clusters. Induction of D2 is mainly characterized by a down-accumulation of proteins belonging to cluster 3 (storage proteins, proteases, alpha-amylase inhibitors and histone deacetylase HD2) and an up-accumulation of proteins belonging to cluster 4 (1-Cys peroxiredoxin, lipoxygenase2 and caleosin). The correlation-based network analysis for each cluster highlighted central protein hub. In addition, most of genes encoding DAPs display high co-expression degree with 19 transcription factors. Finally, this work points out that similar molecular events accompany the modulation of dormancy cycling by both temperature and oxygen, including post-translational, transcriptional and epigenetic regulation.

Correlation-Based Network Analysis of the Influence of Bemisia tabaci Feeding on Photosynthesis and Foliar Sugar and Starch Composition in Soybean

Simple SummaryBemisia tabaci affects plant performance by feeding directly from its energy sources, making it difficult to quantify crop damage, which is indirectly assessed through losses in productivity. The goal of this study was to characterize the influence of B. tabaci feeding on soybean, an economically valuable crop, and attempt to identify the optimal parameter for direct quantification of crop damage. A correlation network was created to extract biological interactions between the plant and B. tabaci nymphs at different densities and plant stages. Nymphs were more abundant during the vegetative stage, and a strong correlation between the density of nymphs and starch and fructose content was observed. The photosynthetic parameter turn-over number N was positively correlated with nymph density at a low-infestation level and negatively correlated with nymphs when they occur at a high infestation level. This association between nymph density and N may allow for development of a ranking scale to predict pest density, representing a useful tool for evaluating the potential impact of B. tabaci on soybean, especially in large areas, where nymph monitoring can be time-consuming.Bemisia tabaci (MEAM1) represents a species of economic importance in soybean. One of the obstacles to the management of B. tabaci is the quantification of damage by the pest because damage is indirectly inferred through losses in productivity. The objective of this study was to characterize the influence of B. tabaci feeding on soybean by assessing effects on photosynthetic parameters and the sugar and starch content of soybean leaves. The goal was to identify the optimal parameter to directly quantify pest damage on crop yield. Correlation networks were created among data on sugar content (fructose, glucose, and sucrose), starch and photosynthetic parameters (initial fluorescence, performance index on absorption basis, and turn-over number), and the number of nymphs at each of three infestations level (low, medium, and high) during both the vegetative and reproductive stage of the crop. In general, nymphs were more abundant during the vegetative stage. Starch content was strongly correlated with nymph density. A strong positive correlation was observed between fructose and nymph density during the vegetative stage. Among the photosynthetic parameters, the turn-over number N was positively correlated with nymph density at a low-infestation level and negatively correlated with nymphs when they occurred at a high-infestation level. B. tabaci feeding affected the plant’s physiology and its interaction is reflected in part by the relationships among photosynthetic parameters as well as the levels of sugars and starch. This understanding might be useful in developing better monitoring tools for pest management.

Intestinal microbiomics and liver metabolomics insights into the preventive effects of chromium (III)-enriched yeast on hyperlipidemia and hyperglycemia induced by high-fat and high-fructose diet

In recent years, organic chromium (III) supplements have received increasing attentions for their low toxicity, high bioavailability and wide range of health-promoting benefits. This study aimed to investigate the preventive effects of chromium (III)-enriched yeast (YCr) on high-fat and high-fructose diet (HFHFD)-induced hyperlipidemia and hyperglycemia in mice, and further clarify its mechanism of action from the perspective of intestinal microbiomics and liver metabolomics. The results indicated that oral administration of YCr remarkably inhibited the aberrant elevations of body weight, blood glucose and lipid levels, hepatic cholesterol (TC) and triglyceride (TG) levels caused by HFHFD. Liver histological examination showed that oral YCr intervention inhibited HFHFD induced liver lipid accumulation. Besides, 16S rDNA amplicon sequencing showed that YCr intervention was beneficial to ameliorating intestinal microbiota dysbiosis by altering the proportion of some intestinal microbial phylotypes. Correlation-based network analysis indicated that the key intestinal microbial phylotypes intervened by YCr were closely related to some biochemical parameters associated with glucose and lipid metabolism. Liver metabolomics analysis revealed that dietary YCr intervention significantly regulated the levels of some biomarkers involved in purine metabolism, glycerophospholipid metabolism, citrate cycle, pyrimidine metabolism, glycerophospholipid metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and so on. Moreover, dietary YCr intervention regulated the mRNA levels of key genes associated with glucose, cholesterol, fatty acids and bile acids metabolism in liver. These findings suggest that dietary YCr intervention has beneficial effects on glucose and lipid metabolism by regulating intestinal microbiota and liver metabolic pathway, and thus can be served as a functional component to prevent hyperlipidemia and hyperglycemia.

Utilizing a Biology-Driven Approach to Map the Exposome in Health and Disease: An Essential Investment to Drive the Next Generation of Environmental Discovery.

Background:Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown.Objectives:We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics.Discussion:The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome—the totality of the biologically active exposures relevant to disease development—through coupling biochemical receptor-binding assays with affinity purification–mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conventional targeted and untargeted approaches for understanding exposure-disease relationships.Conclusions:Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327

Risk contagion of COVID-19 in Japanese firms: A network approach

The novel coronavirus disease (COVID-19) is one of the worst pandemics in human history. Our research objective is to assess the contagion effect on Japanese firms and to evaluate the Japanese government's COVID-19 measures during the period from April 7, 2020, to May 25, 2020. We propose a susceptible-infected-recovered-dead model for COVID-19 and derive COVID-19 parameters for Japan. Subsequently, we analyze the effect of COVID-19 on Japanese firms through correlation-based network and credit risk analyses. The main findings are that the Tokyo Stock Price Index moved in the opposite direction of COVID-19 parameters and COVID-19 parameters are almost the only risk factors that impact a firm's credit risk during the period. Finally, we find that the interconnection analysis between the COVID-19 infection network and the financial networks contribute to the existing pandemic risk management knowledge.