Energetic Neutral Atom Distribution on the Lunar Surface and Its Relationship with Solar Wind Conditions

The Advanced Small Analyzer for Neutrals (ASAN) on board the Chang’E-4 Yutu-2 rover first detected energetic neutral atoms (ENAs) originating from the lunar surface at various lunar local times on the lunar farside. In this work, we examine the ENA energy spectra, obtained in the first 23 lunar days from 2019 January 11 to 2020 October 12, and find a higher ENA differential flux on the lunar dawnside than on the duskside. Combined with Acceleration, Reconnection, Turbulence and Electrodynamics of the Moon’s Interaction with the Sun (ARTEMIS) data, we analyze the correlation between the ENA differential flux and solar wind parameters, such as flux, density, dynamic pressure, and velocity, for each ASAN energy channel on the dawnside and duskside. The results show that ENA differential flux is positively correlated with solar wind flux, density, and dynamic pressure and relatively lower on the duskside than on the dawnside. To determine the relationship between solar wind energy and ENA energy, we analyze the correlation between solar wind energy and ENA cutoff energy and temperature on the dawnside and duskside. The results show that the ENA cutoff energy and temperature are lower on the duskside than on the dawnside at the same solar wind energy. The difference between the ENA–solar wind observation on the dawnside and duskside is possibly caused by solar wind deflection and deceleration on the duskside, which can be attributed to the interaction between solar wind and the lunar magnetic anomalies located nearby in the northwestern direction of the Chang’E-4 landing site.

Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS-CoV-2 infection

The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS-CoV-2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS-CoV-2 infection, lipid metabolism particularly fatty acid oxidation, cholesterol biosynthesis and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux.

Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS Cov2 infection

The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS Cov2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS Cov2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS Cov2 infection, lipid metabolism particularly fatty acid oxidation and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux.

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI is the first method that integrates thermodynamics together with relative gene-expression and metabolomic data as constraints for FBA. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was also able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.Author SummaryThe recent advances in omics technologies have provided us with an unprecedented abundance of data spanning genomes, global gene expression, and metabolomes. Though these advancements in high-throughput data collection offer an excellent opportunity for a more thorough understanding of metabolic capacities of a wide range of species, they have caused a considerable gap between “data generation” and “data integration.” reconstructed model to predict the observed physiology, e.g., growth phase through omics data integration. In this study, we present a new method named REMI (Relative Expression and Metabolomic Integrations) that enables the co-integration of gene expression, metabolomics and thermodynamics data as constraints in genome-scale models. This not only allows the better understanding of how different phenotypes originate from a given genotype but also aid to understanding the interactions between different types of omics data.

EB 2017 Article: Interpretation of the lactulose:mannitol test in rural Malawian children at risk for perturbations in intestinal permeability

The dual sugar absorption test, specifically the lactulose:mannitol test, is used to assess gut health. Lactulose absorption is said to represent gut damage and mannitol absorption is used as a measure of normal small bowel function and serves as normalizing factor for lactulose. A underappreciated limitation of this common understanding of the lactulose:mannitol test is that mannitol is not absorbed to any substantial extent by a transcellular process. Additionally, this interpretation of lactulose:mannitol is not consistent with current understanding of paracellular pathways, where three pathway types exist: pore, leak, and unrestricted. Pore and leak pathways are regulated biological constructions of the small bowel barrier, and unrestricted pathways represent micropathological damage. We analyzed 2334 lactulose:mannitol measurements rigorously collected from 622 young rural Malawian children at high risk for poor gut health in light of the pathway model. An alternative method of normalizing for gut length utilizing autopsy data is described. In our population, absorbed lactulose and mannitol are strongly correlated, r = 0.68 P <0.0001, suggesting lactulose and mannitol are traversing the gut barrier via the same pathways. Considering measurements where pore pathways predominate, mannitol flux is about 14 times that of lactulose. As more leak pathways are present, this differential flux mannitol:lactulose falls to 8:1 and when increased numbers of unrestricted pathways are present, the differential flux of mannitol:lactulose is 6:1. There was no substantial correlation between the lactulose:mannitol and linear growth. Given that mannitol will always pass through a given pathway at a rate at least equal to that of lactulose, and lactulose absorption is a composite measure of flux through both physiologic and pathologic pathways, we question the utility of the lactulose:mannitol test. We suggest using lactulose alone is as informative as lactulose:mannitol in a sugar absorption testing in subclinical gut inflammation. Impact statement Our work integrates the standard interpretation of the lactulose:mannitol test (L:M), with mechanistic insight of intestinal permeability. There are three paracellular pathways in the gut epithelium; pore, leak, and unrestricted. Using thousands of L:M measurements from rural Malawian children at risk for increased intestinal permeability, we predict the differential flux of L and M through the pathways. Our findings challenge the traditional notions that little L is absorbed through a normal epithelial barrier and that M is a normalizing factor for L. Our observations are consistent with pore pathways allowing only M to pass. And that substantial amounts of L and M pass through leak pathways which are normal, regulated, cell-junctional adaptations. So M is a composite measure of all pathways, and L is not a measure solely of pathologic gut damage. Using L alone as a probe will yield more information about gut health than L:M.

On asymptotic power corrections to differential fluxes and generalization of optical theorem for potential scattering

In a wide class of potentials, the exact asymptotic dependence on finite distance R from scattering center is established for outgoing differential flux. It is shown how this dependence is eliminated by integration over solid angle for total flux, unitarity relation, and optical theorem. Thus, their applicability domain extends naturally to the finite R.