Reverse Solute Diffusion Enhances Sludge Dewatering in Dead-End Forward Osmosis.

Wastewater treatment plants produce high quantities of excess sludge. However, traditional sludge dewatering technology has high energy consumption and occupies a large area. Dead-end forward osmosis (DEFO) is an efficient and energy-saving deep dewatering technology for sludge. In this study, the reverse osmosis of salt ions in the draw solution was used to change the sludge cake structure and further reduce its moisture content in cake by releasing the bound water in cell. Three salts, NaCl, KCl, and CaCl2, were added to the excess sludge feed solution to explore the roles of the reverse osmosis of draw solutes in DEFO. When the added quantities of NaCl and CaCl2 were 15 and 10 mM, respectively, the moisture content of the sludge after dewatering decreased from 98.1% to 79.7% and 67.3%, respectively. However, KCl did not improve the sludge dewatering performance because of the "high K and low Na" phenomenon in biological cells. The water flux increased significantly for the binary draw solute involving NaCl and CaCl2 compared to the single draw solute. The extracellular polymer substances in the sludge changed the structure of the filter cake to improve the formation of water channels and decrease osmosis resistance, resulting in an increase in sludge dewatering efficiency. These findings provide support for improving the sludge dewatering performance of DEFO.

Association of SNP-SNP interactions of surfactant protein genes with severity of respiratory syncytial virus infection in children.

The severity of respiratory syncytial virus (RSV) may be linked to host genetic susceptibility. Surfactant protein (SP) genetic variants have been associated with RSV severity, but the impact of single-nucleotide polymorphism (SNP)-SNP interactions remains unexplored. Therefore, we used a novel statistical model to investigate the association of SNP-SNP interactions of SFTP genes with RSV severity in two- and three-interaction models. We analyzed available genotype and clinical data from prospectively enrolled 405 children diagnosed with RSV, categorizing them into moderate or severe RSV groups. Using Wang's statistical model, we studied significant associations of SNP-SNP interactions with RSV severity in a case-control design. We observed, first, association of three interactions with increased risk of severe RSV in a two-SNP model. One intragenic interaction was between SNPs of SFTPA2, and the other two were intergenic, involving SNPs of hydrophilic and hydrophobic SPs alone. We also observed, second, association of 22 interactions with RSV severity in a three-SNP model. Among these, 20 were unique, with 12 and 10 interactions associated with increased or decreased risk of RSV severity, respectively, and included at least one SNP of either SFTPA1 or SFTPA2. All interactions were intergenic except one, among SNPs of SFTPA1. The remaining interactions were either among SNPs of hydrophilic SPs alone (n = 8) or among SNPs of both hydrophilic or hydrophobic SPs (n = 11). Our findings indicate that SNPs of all SFTPs may contribute to genetic susceptibility to RSV severity. However, the predominant involvement of SFTPA1 and/or SFTPA2 SNPs in these interactions underscores their significance in RSV severity.NEW & NOTEWORTHY Although surfactant protein (SP) genetic variants are associated with respiratory syncytial virus (RSV) severity, the impact of single-nucleotide polymorphism (SNP)-SNP interactions of SP genes remained unexplored. Using advanced statistical models, we uncovered 22 SNP-SNP interactions associated with RSV severity, with notable involvement of SFTPA1 and SFTPA2 SNPs. This highlights the comprehensive role of all SPs in genetic susceptibility to RSV severity, shedding light on potential avenues for targeted interventions.

Hypernetwork modeling and topology of high-order interactions for complex systems.

Interactions among the underlying agents of a complex system are not only limited to dyads but can also occur in larger groups. Currently, no generic model has been developed to capture high-order interactions (HOI), which, along with pairwise interactions, portray a detailed landscape of complex systems. Here, we integrate evolutionary game theory and behavioral ecology into a unified statistical mechanics framework, allowing all agents (modeled as nodes) and their bidirectional, signed, and weighted interactions at various orders (modeled as links or hyperlinks) to be coded into hypernetworks. Such hypernetworks can distinguish between how pairwise interactions modulate a third agent (active HOI) and how the altered state of each agent in turn governs interactions between other agents (passive HOI). The simultaneous occurrence of active and passive HOI can drive complex systems to evolve at multiple time and space scales. We apply the model to reconstruct a hypernetwork of hexa-species microbial communities, and by dissecting the topological architecture of the hypernetwork using GLMY homology theory, we find distinct roles of pairwise interactions and HOI in shaping community behavior and dynamics. The statistical relevance of the hypernetwork model is validated using a series of in vitro mono-, co-, and tricultural experiments based on three bacterial species.

Topological change of soil microbiota networks for forest resilience under global warming.

Forest management by thinning can mitigate the detrimental impact of increasing drought caused by global warming. Growing evidence shows that the soil microbiota can coordinate the dynamic relationship between forest functions and drought intensity, but how they function as a cohesive whole remains elusive. We outline a statistical topology model to chart the roadmap of how each microbe acts and interacts with every other microbe to shape the dynamic changes of microbial communities under forest management. To demonstrate its utility, we analyze a soil microbiota data collected from a two-way longitudinal factorial experiment involving three stand densities and three levels of rainfall over a growing season in artificial plantations of a forest tree - larix (Larix kaempferi). We reconstruct the most sophisticated soil microbiota networks that code maximally informative microbial interactions and trace their dynamic trajectories across time, space, and environmental signals. By integrating GLMY homology theory, we dissect the topological architecture of these so-called omnidirectional networks and identify key microbial interaction pathways that play a pivotal role in mediating the structure and function of soil microbial communities. The statistical topological model described provides a systems tool for studying how microbial community assembly alters its structure, function and evolution under climate change.

Mapping the Influence of Light Intensity on the Transgenerational Genetic Architecture of Arabidopsis thaliana.

Light is a crucial environmental factor that influences the phenotypic development of plants. Despite extensive studies on the physiological, biochemical, and molecular mechanisms of the impact of light on phenotypes, genetic investigations regarding light-induced transgenerational plasticity in Arabidopsis thaliana remain incomplete. In this study, we used thaliana as the material, then gathered phenotypic data regarding leaf number and plant height under high- and low-light conditions from two generations. In addition to the developed genotype data, a functional mapping model was used to locate a series of significant single-nucleotide polymorphisms (SNPs). Under low-light conditions, a noticeable adaptive change in the phenotype of leaf number in the second generation suggests the presence of transgenerational genetic effects in thaliana under environmental stress. Under different lighting treatments, 33 and 13 significant genes associated with transgenerational inheritance were identified, respectively. These genes are largely involved in signal transduction, technical hormone pathways, light responses, and the regulation of organ development. Notably, genes identified under high-light conditions more significantly influence plant development, whereas those identified under low-light conditions focus more on responding to external environmental stimuli.

A nested reciprocal experimental design to map the genetic architecture of transgenerational phenotypic plasticity.

Extensive studies have revealed the ecological and evolutionary significance of phenotypic plasticity, but little is known about how it is inherited between generations and the genetic architecture of its transgenerational inheritance. To address these issues, we design a mapping study by growing Arabidopsis thaliana RILs in high- and low-light environments and further growing their offspring RILs from each maternal light environment in the same contrasting environments. This tree-like design of the controlled ecological experiment provides a framework for analysing the genetic regulation of phenotypic plasticity and its non-genetic inheritance. We implement the computational approach of functional mapping to identify specific QTLs for transgenerational phenotypic plasticity. By estimating and comparing the plastic response of leaf-number growth trajectories to light environment between generations, we find that the maternal environment affects phenotypic plasticity, whereas transgenerational plasticity is shaped by the offspring environment. The genetic architecture underlying the light-induced change of leaf number not only changes from parental to offspring generations, but also depends on the maternal environment the parental generation experienced and the offspring environment the offspring generation is experiencing. Most plasticity QTLs are annotated to the genomic regions of candidate genes for specific biological functions. Our computational-experimental design provides a unique insight into dissecting the non-genetic and genetic mechanisms of phenotypic plasticity shaping plant adaptation and evolution in various forms.

Prolonged lymphopenia and prognoses among inpatients with different respiratory virus infections: A retrospective cohort study.

Background: Lymphopenia is common in respiratory viral infection. However, no studies elucidated the impact of prolonged lymphopenia on worse outcome in the way of quantitative risk. Methods: Adult patients with laboratory-confirmed respiratory virus infection (influenza, SARS-CoV-2, and other viruses) between January 1st, 2016, and February 1st, 2023 were enrolled in this retrospective cohort study. Serial data of laboratory examination during hospitalization were acquired. The primary outcome was in-hospital all-cause death, and all information was obtained from the electronic medical records system. Legendre orthogonal polynomials (LOP), restricted cubic splines, and multivariable logistic regression were performed. Results: Finally, 2388 inpatients were involved in this study, including 436 patients with influenza, 1397 with SARS-CoV-2, and 319 with other respiratory virus infections. After being adjusted for age, corticosteroids, chronic kidney disease, chronic respiratory disease, cardiovascular disease, lymphopenia on admission and length of hospital stay, prolonged lymphopenia was significantly associated with death in influenza (OR 7.20, 95 % CI 2.27-22.77, p = 0. 0008 for lasting for 3-7 days; OR 17.80, 95 % CI 5.21-60.82, p < 0.0001 for lasting for more than 7 days) and SARS-CoV-2 (OR 3.07, 95 % CI 1.89-5.01, p < 0.0001 for lasting for 3-7 days; OR 6.28, 95 % CI 3.53-11.18, p < 0.0001 for lasting for more than 7 days), compared with a transient lymphopenia of 1-2 days, while no significant association was found in other respiratory viruses. Prolonged lymphopenia was also associated with multi-organ damage in influenza and SARS-CoV-2 infections. Conclusions: Prolonged lymphopenia was significantly associated with worse clinical prognoses in influenza and SARS-CoV-2 infections, but not in other respiratory virus infections.

Concentration properties of biopolymers via dead-end forward osmosis.

Extracellular polymeric substances (EPSs) in excess sludge of wastewater treatment plants are valuable biopolymers that can act as recovery materials. However, effectively concentrating EPSs consumes a significant amount of energy. This study employed novel energy-saving pressure-free dead-end forward osmosis (DEFO) technology to concentrate various biopolymers, including EPSs and model biopolymers [sodium alginate (SA), bovine serum albumin (BSA), and a mixture of both (denoted as BSA-SA)]. The feasibility of the DEFO technology was proven and the largest concentration ratios for these biopolymers were 94.8 % for EPSs, 97.1 % for SA, 97.8 % for BSA, and 98.4 % for BSA-SA solutions. An evaluation model was proposed, incorporating the FO membrane's water permeability coefficient and the concentrated substances' osmotic resistance, to describe biopolymers' concentration properties. Irrespective of biopolymer type, the water permeability coefficient decreased with increasing osmotic pressure, remained constant with increasing feed solution (FS) concentration, increased with increasing crossing velocity in the draw side, and showed little dependence on draw salt type. In the EPS DEFO concentration process, osmotic resistance was minimally impacted by osmotic pressure, FS concentration, and crossing velocity, and monovalent metal salts were proposed as draw solutes. The interaction between reverse diffusion metal cations and EPSs affected the structure of the concentrated substances on the FO membrane, thus changing the osmotic resistance in the DEFO process. These findings offer insights into the efficient concentration of biopolymers using DEFO.

Unlocking allelic variation in circadian clock genes to develop environmentally robust and productive crops.

MAIN CONCLUSION: Molecular mechanisms of biological rhythms provide opportunities to harness functional allelic diversity in core (and trait- or stress-responsive) oscillator networks to develop more climate-resilient and productive germplasm. The circadian clock senses light and temperature in day-night cycles to drive biological rhythms. The clock integrates endogenous signals and exogenous stimuli to coordinate diverse physiological processes. Advances in high-throughput non-invasive assays, use of forward- and inverse-genetic approaches, and powerful algorithms are allowing quantitation of variation and detection of genes associated with circadian dynamics. Circadian rhythms and phytohormone pathways in response to endogenous and exogenous cues have been well documented the model plant Arabidopsis. Novel allelic variation associated with circadian rhythms facilitates adaptation and range expansion, and may provide additional opportunity to tailor climate-resilient crops. The circadian phase and period can determine adaptation to environments, while the robustness in the circadian amplitude can enhance resilience to environmental changes. Circadian rhythms in plants are tightly controlled by multiple and interlocked transcriptional-translational feedback loops involving morning (CCA1, LHY), mid-day (PRR9, PRR7, PRR5), and evening (TOC1, ELF3, ELF4, LUX) genes that maintain the plant circadian clock ticking. Significant progress has been made to unravel the functions of circadian rhythms and clock genes that regulate traits, via interaction with phytohormones and trait-responsive genes, in diverse crops. Altered circadian rhythms and clock genes may contribute to hybrid vigor as shown in Arabidopsis, maize, and rice. Modifying circadian rhythms via transgenesis or genome-editing may provide additional opportunities to develop crops with better buffering capacity to environmental stresses. Models that involve clock gene‒phytohormone‒trait interactions can provide novel insights to orchestrate circadian rhythms and modulate clock genes to facilitate breeding of all season crops.

Genome-Wide Network Analysis of Above- and Below-Ground Co-growth in Populus euphratica.

Tree growth is the consequence of developmental interactions between above- and below-ground compartments. However, a comprehensive view of the genetic architecture of growth as a cohesive whole is poorly understood. We propose a systems biology approach for mapping growth trajectories in genome-wide association studies viewing growth as a complex (phenotypic) system in which above- and below-ground components (or traits) interact with each other to mediate systems behavior. We further assume that trait-trait interactions are controlled by a genetic system composed of many different interactive genes and integrate the Lotka-Volterra predator-prey model to dissect phenotypic and genetic systems into pleiotropic and epistatic interaction components by which the detailed genetic mechanism of above- and below-ground co-growth can be charted. We apply the approach to analyze linkage mapping data of Populus euphratica, which is the only tree species that can grow in the desert, and characterize several loci that govern how above- and below-ground growth is cooperated or competed over development. We reconstruct multilayer and multiplex genetic interactome networks for the developmental trajectories of each trait and their developmental covariation. Many significant loci and epistatic effects detected can be annotated to candidate genes for growth and developmental processes. The results from our model may potentially be useful for marker-assisted selection and genetic editing in applied tree breeding programs. The model provides a general tool to characterize a complete picture of pleiotropic and epistatic genetic architecture in growth traits in forest trees and any other organisms.

LD block disorder-specific pleiotropic roles of novel CRHR1 in type 2 diabetes and depression disorder comorbidity.

Major depressive disorder (MDD) and type 2 diabetes (T2D) are complex disorders whose comorbidity can be due to hypercortisolism and may be explained by dysfunction of the corticotropin-releasing hormone receptor 1 (CRHR1) and cortisol feedback within the hypothalamic-pituitary-adrenal axis (HPA axis). To investigate the role of the CRHR1 gene in familial T2D, MDD, and MDD-T2D comorbidity, we tested 152 CRHR1 single-nucleotide-polymorphisms (SNPs), via 2-point parametric linkage and linkage disequilibrium (LD; i.e., association) analyses using 4 models, in 212 peninsular families with T2D and MDD. We detected linkage/LD/association to/with MDD and T2D with 122 (116 novel) SNPs. MDD and T2D had 4 and 3 disorder-specific novel risk LD blocks, respectively, whose risk variants reciprocally confirm one another. Comorbidity was conferred by 3 novel independent SNPs. In silico analyses reported novel functional changes, including the binding site of glucocorticoid receptor-alpha [GR-α] on CRHR1 for transcription regulation. This is the first report of CRHR1 pleiotropic linkage/LD/association with peninsular familial MDD and T2D. CRHR1 contribution to MDD is stronger than to T2D and may antecede T2D onset. Our findings suggest a new molecular-based clinical entity of MDD-T2D and should be replicated in other ethnic groups.

Competition-cooperation mechanism between Escherichia coli and Staphylococcus aureus based on systems mapping.

Introduction: Interspecies interactions are a crucial driving force of species evolution. The genes of each coexisting species play a pivotal role in shaping the structure and function within the community, but how to identify them at the genome-wide level has always been challenging. Methods: In this study, we embed the Lotka-Volterra ordinary differential equations in the theory of community ecology into the systems mapping model, so that this model can not only describe how the quantitative trait loci (QTL) of a species directly affects its own phenotype, but also describe the QTL of the species how to indirectly affect the phenotype of its interacting species, and how QTL from different species affects community behavior through epistatic interactions. Results: By designing and implementing a co-culture experiment for 100 pairs of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), we mapped 244 significant QTL combinations in the interaction process of the two bacteria using this model, including 69 QTLs from E. coli and 59 QTLs from S. aureus, respectively. Through gene annotation, we obtained 57 genes in E. coli, among which the genes with higher frequency were ypdC, nrfC, yphH, acrE, dcuS, rpnE, and ptsA, while we obtained 43 genes in S. aureus, among which the genes with higher frequency were ebh, SAOUHSC_00172, capF, gdpP, orfX, bsaA, and phnE1. Discussion: By dividing the overall growth into independent growth and interactive growth, we could estimate how QTLs modulate interspecific competition and cooperation. Based on the quantitative genetic model, we can obtain the direct genetic effect, indirect genetic effect, and genome-genome epistatic effect related to interspecific interaction genes, and then further mine the hub genes in the QTL networks, which will be particularly useful for inferring and predicting the genetic mechanisms of community dynamics and evolution. Systems mapping can provide a tool for studying the mechanism of competition and cooperation among bacteria in co-culture, and this framework can lay the foundation for a more comprehensive and systematic study of species interactions.

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.

A pleiotropic-epistatic entangelement model of drug response.

Because drug response is multifactorial, graph models are uniquely powerful for comprehending its genetic architecture. We deconstruct drug response into many different and interdependent sub-traits, with each sub-trait controlled by multiple genes that act and interact in a complicated manner. The outcome of drug response is the consequence of multileveled intertwined interactions between pleiotropic effects and epistatic effects. Here, we propose a general statistical physics framework to chart the 3D geometric network that codes how epistasis pleiotropically influences a complete set of sub-traits to shape body-drug interactions. This model can dissect the topological architecture of epistatically induced pleiotropic networks (EiPN) and pleiotropically influenced epistatic networks (PiEN). We analyze and interpret the practical implications of the pleiotropic-epistatic entanglement model for pharmacogenomic studies.

Novel corticotropin-releasing hormone receptor genes (CRHR1 and CRHR2) linkage to and association with polycystic ovary syndrome.

BACKGROUND: Women with polycystic ovarian syndrome (PCOS) have increased hypothalamic-pituitary-adrenal (HPA) axis activation, pro-inflammatory mediators, and psychological distress in response to stressors. In women with PCOS, the corticotropin-releasing hormone (CRH) induces an exaggerated HPA response, possibly mediated by one of the CRH receptors (CRHR1 or CRHR2). Both CRHR1 and CRHR2 are implicated in insulin secretion, and variants in CRHR1 and CRHR2 genes may predispose to the mental-metabolic risk for PCOS. METHODS: We phenotyped 212 Italian families with type 2 diabetes (T2D) for PCOS following the Rotterdam diagnostic criteria. We analyzed within CRHR1 and CRHR2 genes, respectively, 36 and 18 microarray-variants for parametric linkage to and/or linkage disequilibrium (LD) with PCOS under the recessive with complete penetrance (R1) and dominant with complete penetrance (D1) models. Subsequentially, we ran a secondary analysis under the models dominant with incomplete penetrance (D2) and recessive with incomplete penetrance (R2). RESULTS: We detected 22 variants in CRHR1 and 1 variant in CRHR2 significantly (p < 0.05) linked to or in LD with PCOS across different inheritance models. CONCLUSIONS: This is the first study to report CRHR1 and CRHR2 as novel risk genes in PCOS. In silico analysis predicted that the detected CRHR1 and CRHR2 risk variants promote negative chromatin activation of their related genes in the ovaries, potentially affecting the female cycle and ovulation. However, CRHR1- and CRHR2-risk variants might also lead to hypercortisolism and confer mental-metabolic pleiotropic effects. Functional studies are needed to confirm the pathogenicity of genes and related variants.

A personalized pharmaco-epistatic network model of precision medicine.

Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine.

The genetic architecture of trait covariation in Populus euphratica, a desert tree.

Introduction: The cooperative strategy of phenotypic traits during the growth of plants reflects how plants allocate photosynthesis products, which is the most favorable decision for them to optimize growth, survival, and reproduction response to changing environment. Up to now, we still know little about why plants make such decision from the perspective of biological genetic mechanisms. Methods: In this study, we construct an analytical mapping framework to explore the genetic mechanism regulating the interaction of two complex traits. The framework describes the dynamic growth of two traits and their interaction as Differential Interaction Regulatory Equations (DIRE), then DIRE is embedded into QTL mapping model to identify the key quantitative trait loci (QTLs) that regulate this interaction and clarify the genetic effect, genetic contribution and genetic network structure of these key QTLs. Computer simulation experiment proves the reliability and practicability of our framework. Results: In order to verify that our framework is universal and flexible, we applied it to two sets of data from Populus euphratica, namely, aboveground stem length - underground taproot length, underground root number - underground root length, which represent relationships of phenotypic traits in two spatial dimensions of plant architecture. The analytical result shows that our model is well applicable to datasets of two dimensions. Discussion: Our model helps to better illustrate the cooperation-competition patterns between phenotypic traits, and understand the decisions that plants make in a specific environment that are most conducive to their growth from the genetic perspective.

Novel Risk Variants in the Oxytocin Receptor Gene (OXTR) Possibly Linked to and Associated with Familial Type 2 Diabetes.

The oxytocin system is well-known for its role in social bonding and reproduction. Recently, the oxytocin system was found to play other metabolic roles such as regulation of food intake, peripheral glucose uptake, and insulin sensitivity. Variants in OXTR gene have been associated with overeating, increased cardiovascular risk, and type 2 diabetes (T2D). We tested 20 microarray-derived single nucleotide polymorphisms in the OXTR gene in 212 Italian families with rich family history for T2D and found four novel and one previously reported variant suggestively significant for linkage and association with the risk of T2D. Our study has shed some light into the genetics of susceptibility to T2D at least in Italian families.