The metabolomic physics of complex diseases.

Human diseases involve metabolic alterations. Metabolomic profiles have served as a vital biomarker for the early identification of high-risk individuals and disease prevention. However, current approaches can only characterize individual key metabolites, without taking into account the reality that complex diseases are multifactorial, dynamic, heterogeneous, and interdependent. Here, we leverage a statistical physics model to combine all metabolites into bidirectional, signed, and weighted interaction networks and trace how the flow of information from one metabolite to the next causes changes in health state. Viewing a disease outcome as the consequence of complex interactions among its interconnected components (metabolites), we integrate concepts from ecosystem theory and evolutionary game theory to model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and through extrinsic influences from its conspecifics. We code intrinsic contributions as nodes and extrinsic contributions as edges into quantitative networks and implement GLMY homology theory to analyze and interpret the topological change of health state from symbiosis to dysbiosis and vice versa. The application of this model to real data allows us to identify several hub metabolites and their interaction webs, which play a part in the formation of inflammatory bowel diseases. The findings by our model could provide important information on drug design to treat these diseases and beyond.

Hydrogel electrodes with conductive and substrate-adhesive layers for noninvasive long-term EEG acquisition.

Noninvasive brain-computer interfaces (BCIs) show great potential in applications including sleep monitoring, fatigue alerts, neurofeedback training, etc. While noninvasive BCIs do not impose any procedural risk to users (as opposed to invasive BCIs), the acquisition of high-quality electroencephalograms (EEGs) in the long term has been challenging due to the limitations of current electrodes. Herein, we developed a semidry double-layer hydrogel electrode that not only records EEG signals at a resolution comparable to that of wet electrodes but is also able to withstand up to 12 h of continuous EEG acquisition. The electrode comprises dual hydrogel layers: a conductive layer that features high conductivity, low skin-contact impedance, and high robustness; and an adhesive layer that can bond to glass or plastic substrates to reduce motion artifacts in wearing conditions. Water retention in the hydrogel is stable, and the measured skin-contact impedance of the hydrogel electrode is comparable to that of wet electrodes (conductive paste) and drastically lower than that of dry electrodes (metal pin). Cytotoxicity and skin irritation tests show that the hydrogel electrode has excellent biocompatibility. Finally, the developed hydrogel electrode was evaluated in both N170 and P300 event-related potential (ERP) tests on human volunteers. The hydrogel electrode captured the expected ERP waveforms in both the N170 and P300 tests, showing similarities in the waveforms generated by wet electrodes. In contrast, dry electrodes fail to detect the triggered potential due to low signal quality. In addition, our hydrogel electrode can acquire EEG for up to 12 h and is ready for recycled use (7-day tests). Altogether, the results suggest that our semidry double-layer hydrogel electrodes are able to detect ERPs in the long term in an easy-to-use fashion, potentially opening up numerous applications in real-life scenarios for noninvasive BCI.

Seasonal Variation Characteristics and the Factors Affecting Plankton Community Structure in the Yitong River, China.

To explore how environmental factors affected the plankton structure in the Yitong River, we surveyed the water environmental factors and plankton population in different seasons. The results showed high total nitrogen concentrations in Yitong River throughout the year, while the total phosphorus, water temperature (WT), and chemical oxygen demand in summer were significantly higher than those in other seasons (p < 0.05), and the dissolved oxygen (DO) concentrations and TN/TP ratio were significantly lower (p < 0.01) than those in other seasons. There was no significant seasonal change in other environmental factors. Cyanophyta, Chlorophyta, and Bacillariophyta were the main phytoplankton phylum, while Protozoa and Rotifera were the main zooplankton phylum. The abundance and biomass of zooplankton and phytoplankton in the summer were higher than those in other seasons. Non-Metric Multidimensional scaling methods demonstrated obvious seasonal variation of phytoplankton in summer compared to spring and winter, while the seasonal variation of the zooplankton community was not obvious. The results of the redundancy analysis showed that WT, DO and nitrate nitrogen were the main environmental factors affecting phytoplankton abundance. In contrast to environmental factors, phytoplankton was the main factor driving the seasonal variation of the zooplankton community structure. Cyanophyta were positively correlated with the changes in the plankton community.

Vaginal microbiota networks as a mechanistic predictor of aerobic vaginitis.

Aerobic vaginitis (AV) is a complex vaginal dysbiosis that is thought to be caused by the micro-ecological change of the vaginal microbiota. While most studies have focused on how changes in the abundance of individual microbes are associated with the emergence of AV, we still do not have a complete mechanistic atlas of the microbe-AV link. Network modeling is central to understanding the structure and function of any microbial community assembly. By encapsulating the abundance of microbes as nodes and ecological interactions among microbes as edges, microbial networks can reveal how each microbe functions and how one microbe cooperate or compete with other microbes to mediate the dynamics of microbial communities. However, existing approaches can only estimate either the strength of microbe-microbe link or the direction of this link, failing to capture full topological characteristics of a network, especially from high-dimensional microbial data. We combine allometry scaling law and evolutionary game theory to derive a functional graph theory that can characterize bidirectional, signed, and weighted interaction networks from any data domain. We apply our theory to characterize the causal interdependence between microbial interactions and AV. From functional networks arising from different functional modules, we find that, as the only favorable genus from Firmicutes among all identified genera, the role of Lactobacillus in maintaining vaginal microbial symbiosis is enabled by upregulation from other microbes, rather than through any intrinsic capacity. Among Lactobacillus species, the proportion of L. crispatus to L. iners is positively associated with more healthy acid vaginal ecosystems. In a less healthy alkaline ecosystem, L. crispatus establishes a contradictory relationship with other microbes, leading to population decrease relative to L. iners. We identify topological changes of vaginal microbiota networks when the menstrual cycle of women changes from the follicular to luteal phases. Our network tool provides a mechanistic approach to disentangle the internal workings of the microbiota assembly and predict its causal relationships with human diseases including AV.

An eco-evo-devo genetic network model of stress response.

The capacity of plants to resist abiotic stresses is of great importance to agricultural, ecological and environmental sustainability, but little is known about its genetic underpinnings. Existing genetic tools can identify individual genetic variants mediating biochemical, physiological, and cellular defenses, but fail to chart an overall genetic atlas behind stress resistance. We view stress response as an eco-evo-devo process by which plants adaptively respond to stress through complex interactions of developmental canalization, phenotypic plasticity, and phenotypic integration. As such, we define and quantify stress response as the developmental change of adaptive traits from stress-free to stress-exposed environments. We integrate composite functional mapping and evolutionary game theory to reconstruct omnigenic, information-flow interaction networks for stress response. Using desert-adapted Euphrates poplar as an example, we infer salt resistance-related genome-wide interactome networks and trace the roadmap of how each SNP acts and interacts with any other possible SNPs to mediate salt resistance. We characterize the previously unknown regulatory mechanisms driving trait variation; i.e. the significance of a SNP may be due to the promotion of positive regulators, whereas the insignificance of a SNP may result from the inhibition of negative regulators. The regulator-regulatee interactions detected are not only experimentally validated by two complementary experiments, but also biologically interpreted by their encoded protein-protein interactions. Our eco-evo-devo model of genetic interactome networks provides an approach to interrogate the genetic architecture of stress response and informs precise gene editing for improving plants' capacity to live in stress environments.

Modeling spatial interaction networks of the gut microbiota.

How the gut microbiota is organized across space is postulated to influence microbial succession and its mutualistic relationships with the host. The lack of dynamic or perturbed abundance data poses considerable challenges for characterizing the spatial pattern of microbial interactions. We integrate allometric scaling theory, evolutionary game theory, and prey-predator theory into a unified framework under which quasi-dynamic microbial networks can be inferred from static abundance data. We illustrate that such networks can capture the full properties of microbial interactions, including causality, the sign of the causality, strength, and feedback loop, and are dynamically adaptive along spatial gradients, and context-specific, characterizing variability between individuals and within the same individual across time and space. We design and conduct a gut microbiota study to validate the model, characterizing key spatial determinants of the microbial differences between ulcerative colitis and healthy controls. Our model provides a sophisticated means of unraveling a complete atlas of how microbial interactions vary across space and quantifying causal relationships between such spatial variability and change in health state.

Asymptotic tests for Hardy-Weinberg equilibrium in hexaploids.

Hexaploids, a group of organisms containing three complete sets of chromosomes in a single nucleus, are of utmost importance to evolutionary studies and breeding programs. Many studies have focused on hexaploid linkage analysis and QTL mapping in controlled crosses, but little methodology has been developed to reveal how hexaploids diversify and evolve in natural populations. We formulate a general framework for studying the pattern of genetic variation in autohexaploid populations through testing deviation from Hardy-Weinberg equilibrium (HWE) at individual molecular markers. We confirm that hexaploids cannot reach exact HWE but can approach asymptotic HWE at 8-9 generations of random mating. We derive a statistical algorithm for testing HWE and the occurrence of double reduction for autopolyploids, a phenomenon that affects population variation during long evolutionary processes. We perform computer simulation to validate the statistical behavior of our test procedure and demonstrate its usefulness by analyzing a real data set for autohexaploid chrysanthemum. When extended to allohexaploids, our test procedure will provide a generic tool for illustrating the genome structure of hexaploids in the quest to infer their evolutionary status and design association studies of complex traits.

The Genetic Architecture of Juvenile Growth Traits in the Conifer Torreya grandis as Revealed by Joint Linkage and Linkage Disequilibrium Mapping.

Despite its high economical and ornamental values, Torreya grandis, a dioecious non-timber coniferous species, has long been an underrepresented species. However, the advent and application of advanced genotyping technologies have stimulated its genetic research, making it possible to gain new insight into the genetic architecture of complex traits that may not be detected for model species. We apply an open-pollination (OP) mapping strategy to conduct a QTL mapping experiment of T. grandis, in which nearly 100 unrelated trees randomly chosen from the species' natural distribution and their half-sib progeny are simultaneously genotyped. This strategy allows us to simultaneously estimate the recombination fractions and linkage disequilibrium (LD) coefficients between each pair of markers. We reconstruct a high-density linkage map of 4,203 SNPs covering a total distance of 8,393.95 cM and plot pairwise normalized LD values against genetic distances to build up a linkage-LD map. We identify 13 QTLs for stem basal diameter growth and 4 QTLs for stem height growth in juvenile seedlings. From the linkage-LD map, we infer the evolutionary history of T. grandis and each of its QTLs. The slow decay of QTL-related LDs indicates that these QTLs and their harboring genomic regions are evolutionarily relatively young, suggesting that they can better utilized by clonal propagation rather than through seed propagation. Genetic results from the OP sampling strategy could provide useful guidance for genetic studies of other dioecious species.

Genetic Architecture of Heterophylly: Single and Multi-Leaf Genome-Wide Association Mapping in Populus euphratica.

Heterophylly is an adaptive strategy used by some plants in response to environmental changes. Due to the lack of representative plants with typical heteromorphic leaves, little is known about the genetic architecture of heterophylly in plants and the genes underlying its control. Here, we investigated the genetic characteristics underlying changes in leaf shape based on the model species, Populus euphratica, which exhibits typical heterophylly. A set of 401,571 single-nucleotide polymorphisms (SNPs) derived from whole-genome sequencing of 860 genotypes were associated with nine leaf traits, which were related to descriptive and shape data using single- and multi-leaf genome-wide association studies (GWAS). Multi-leaf GWAS allows for a more comprehensive understanding of the genetic architecture of heterophylly by considering multiple leaves simultaneously. The single-leaf GWAS detected 140 significant SNPs, whereas the multi-leaf GWAS detected 200 SNP-trait associations. Markers were found across 19 chromosomes, and 21 unique genes were implicated in traits and serve as potential targets for selection. Our results provide novel insights into the genomic architecture of heterophylly, and provide candidate genes for breeding or engineering P. euphratica. Our observations also improve understanding of the intrinsic mechanisms of plant growth, evolution, and adaptation in response to climate change.

Genome-Wide Identification and Analysis of the NAC Transcription Factor Gene Family in Garden Asparagus (Asparagus officinalis).

As a large plant-specific gene family, the NAC (NAM, ATAF1/2, and CUC2) transcription factor is related to plant growth, development, and response to abiotic stresses. Although the draft genome of garden asparagus (Asparagus officinalis) has been released, the genome-wide investigation of the NAC gene family is still unavailable. In this study, a total of 85 A. officinalis NAC genes were identified, and a comprehensive analysis of the gene family was performed, including physicochemical properties, phylogenetic relationship, chromosome localization, gene structure, conserved motifs, intron/exon, cis-acting elements, gene duplication, syntenic analysis, and differential gene expression analysis. The phylogenetic analysis demonstrated that there were 14 subgroups in both A. officinalis and Arabidopsis thaliana, and the genes with a similar gene structure and motif distribution were clustered in the same group. The cis-acting regulatory analysis of AoNAC genes indicated four types of cis-acting elements were present in the promoter regions, including light-responsive, hormone-responsive, plant-growth-and-development-related, and stress-responsive elements. The chromosomal localization analysis found that 81 NAC genes in A. officinalis were unevenly distributed on nine chromosomes, and the gene duplication analysis showed three pairs of tandem duplicated genes and five pairs of segmental duplications, suggesting that gene duplication is possibly associated with the amplification of the A. officinalis NAC gene family. The differential gene expression analysis revealed one and three AoNAC genes that were upregulated and downregulated under different types of salinity stress, respectively. This study provides insight into the evolution, diversity, and characterization of NAC genes in garden asparagus and will be helpful for future understanding of their biological roles and molecular mechanisms in plants.

A Single-Cell Omics Network Model of Cell Crosstalk during the Formation of Primordial Follicles.

The fate of fetal germ cells (FGCs) in primordial follicles is largely determined by how they interact with the surrounding granulosa cells. However, the molecular mechanisms underlying this interactive process remain poorly understood. Here, we develop a computational model to characterize how individual genes program and rewire cellular crosstalk across FGCs and somas, how gene regulatory networks mediate signaling pathways that functionally link these two cell types, and how different FGCs diversify and evolve through cooperation and competition during embryo development. We analyze single-cell RNA-seq data of human female embryos using the new model, identifying previously uncharacterized mechanisms behind follicle development. The majority of genes (70%) promote FGC-soma synergism, only with a small portion (4%) that incur antagonism; hub genes function reciprocally between the FGC network and soma network; and germ cells tend to cooperate between different stages of development but compete in the same stage within a developmental embryo. Our network model could serve as a powerful tool to unravel the genomic signatures that mediate folliculogenesis from single-cell omics data.

A graph model of combination therapies.

The simultaneous use of multiple medications causes drug-drug interactions (DDI) that impact therapeutic efficacy. Here, we argue that graph theory, in conjunction with game theory and ecosystem theory, can address this issue. We treat the coexistence of multiple drugs as a system in which DDI is modeled by game theory. We develop an ordinary differential equation model to characterize how the concentration of a drug changes as a result of its independent capacity and the dependent influence of other drugs through the metabolic response of the host. We coalesce all drugs into personalized and context-specific networks, which can reveal key DDI determinants of therapeutical efficacy. Our model can quantify drug synergy and antagonism and test the translational success of combination therapies to the clinic.

FunGraph: A statistical protocol to reconstruct omnigenic multilayer interactome networks for complex traits.

We describe a statistical protocol of how to reconstruct and dissect functional omnigenic multilayer interactome networks that mediate complex dynamic traits in a genome-wide association study (GWAS). This protocol, named FunGraph, can analyze how each locus affects phenotypic variation through its own direct effect and a complete set of indirect effects due to regulation by other loci co-existing in large-scale networks. FunGraph is applicable to any GWAS aimed to characterize the genetic architecture of dynamic phenotypic traits. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).

Inferring multilayer interactome networks shaping phenotypic plasticity and evolution.

Phenotypic plasticity represents a capacity by which the organism changes its phenotypes in response to environmental stimuli. Despite its pivotal role in adaptive evolution, how phenotypic plasticity is genetically controlled remains elusive. Here, we develop a unified framework for coalescing all single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) into a quantitative graph. This framework integrates functional genetic mapping, evolutionary game theory, and predator-prey theory to decompose the net genetic effect of each SNP into its independent and dependent components. The independent effect arises from the intrinsic capacity of a SNP, only expressed when it is in isolation, whereas the dependent effect results from the extrinsic influence of other SNPs. The dependent effect is conceptually beyond the traditional definition of epistasis by not only characterizing the strength of epistasis but also capturing the bi-causality of epistasis and the sign of the causality. We implement functional clustering and variable selection to infer multilayer, sparse, and multiplex interactome networks from any dimension of genetic data. We design and conduct two GWAS experiments using Staphylococcus aureus, aimed to test the genetic mechanisms underlying the phenotypic plasticity of this species to vancomycin exposure and Escherichia coli coexistence. We reconstruct the two most comprehensive genetic networks for abiotic and biotic phenotypic plasticity. Pathway analysis shows that SNP-SNP epistasis for phenotypic plasticity can be annotated to protein-protein interactions through coding genes. Our model can unveil the regulatory mechanisms of significant loci and excavate missing heritability from some insignificant loci. Our multilayer genetic networks provide a systems tool for dissecting environment-induced evolution.

Modeling genome-wide by environment interactions through omnigenic interactome networks.

How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.

A Tracing Model for the Evolutionary Equilibrium of Octoploids.

Testing Hardy-Weinberg equilibrium (HWE) is a fundamental approach for inferring population diversity and evolution, but its application to octoploids containing eight chromosome sets has not well been justified. We derive a mathematical model to trace how genotype frequencies transmit from parental to offspring generations in the natural populations of autooctoploids. We find that octoploids, including autooctolpoids undergoing double reduction, attach asymptotic HWE (aHWE) after 15 generations of random mating, in a contrast to diploids where one generation can assure exact equilibrium and, also, different from tetraploids that use 5 generations to reach aHWE. We develop a statistical procedure for testing aHWE in octoploids and apply it to analyze a real data set from octoploid switchgrass distributed in two ecologically different regions, demonstrating the usefulness of the test procedure. Our model provides a tool for studying the population genetic diversity of octoploids, inferring their evolutionary history, and identifying the ecological relationship of octoploid-genome structure with environmental adaptation.

The Genomic Physics of COVID-19 Pathogenesis and Spread.

Coronavirus disease (COVID-19) spreads mainly through close contact of infected persons, but the molecular mechanisms underlying its pathogenesis and transmission remain unknown. Here, we propose a statistical physics model to coalesce all molecular entities into a cohesive network in which the roadmap of how each entity mediates the disease can be characterized. We argue that the process of how a transmitter transforms the virus into a recipient constitutes a triad unit that propagates COVID-19 along reticulate paths. Intrinsically, person-to-person transmissibility may be mediated by how genes interact transversely across transmitter, recipient, and viral genomes. We integrate quantitative genetic theory into hypergraph theory to code the main effects of the three genomes as nodes, pairwise cross-genome epistasis as edges, and high-order cross-genome epistasis as hyperedges in a series of mobile hypergraphs. Charting a genome-wide atlas of horizontally epistatic hypergraphs can facilitate the systematic characterization of the community genetic mechanisms underlying COVID-19 spread. This atlas can typically help design effective containment and mitigation strategies and screen and triage those more susceptible persons and those asymptomatic carriers who are incubation virus transmitters.