Biaryl carboxamide-based peptidomimetics analogs as potential pancreatic lipase inhibitors for treating obesity.

A series of 1,1'-biphenyl-3-carboxamide and furan-phenyl-carboxamide analogs were synthesized using an optimized scheme and confirmed by 1H and 13C nuclear magnetic resonance and high-resolution mass spectrometry techniques. The synthesized peptidomimetics analogs were screened in vitro to understand the inhibitory potential of pancreatic lipase (PL). Analogs were assessed for the PL inhibitory activity based on interactions, geometric complementarity, and docking score. Among the synthesized analogs, 9, 29, and 24 were found to have the most potent PL inhibitory activity with IC50 values of 3.87, 4.95, and 5.34 µM, respectively, compared to that of the standard drug, that is, orlistat, which inhibits PL with an IC50 value of 0.99 µM. The most potent analog, 9, exhibited a competitive-type inhibition with an inhibition constant (Ki) of 2.72 µM. In silico molecular docking of analog 9 with the PL (PDB ID:1LPB) showed a docking score of -11.00 kcal/mol. Analog 9 formed crucial hydrogen bond interaction with Ser152, His263, π-cation interaction with Asp79, Arg256, and π-π stacking with Phe77, Tyr114 at the protein's active site. The molecular dynamic simulation confirmed that analog 9 forms stable interactions with PL at the end of 200 ns with root mean square deviation values of 2.5 and 6 Å. No toxicity was observed for analog 9 (concentration range of 1-20 µM) when tested by MTT assay in RAW 264.7 cells.

Active buckling of pressurized spherical shells: Monte Carlo simulation.

We study the buckling of pressurized spherical shells by Monte Carlo simulations in which the detailed balance is explicitly broken-thereby driving the shell to be active, out of thermal equilibrium. Such a shell typically has either higher (active) or lower (sedate) fluctuations compared to one in thermal equilibrium depending on how the detailed balance is broken. We show that, for the same set of elastic parameters, a shell that is not buckled in thermal equilibrium can be buckled if turned active. Similarly a shell that is buckled in thermal equilibrium can unbuckle if sedated. Based on this result, we suggest that it is possible to experimentally design microscopic elastic shells whose buckling can be optically controlled.

Kolmogorov Turbulence Coexists with Pseudo-Turbulence in Buoyancy-Driven Bubbly Flows.

We investigate the spectral properties of buoyancy-driven bubbly flows. Using high-resolution numerical simulations and phenomenology of homogeneous turbulence, we identify the relevant energy transfer mechanisms. We find (a) at a high enough Galilei number (ratio of the buoyancy to viscous forces) the velocity power spectrum shows the Kolmogorov scaling with a power-law exponent -5/3 for the range of scales between the bubble diameter and the dissipation scale (η). (b) For scales smaller than η, the physics of pseudo-turbulence is recovered.

Hidden jerk in universal creep and aftershocks.

Most materials exhibit creep memory under the action of a constant load. The memory behavior is governed by Andrade's creep law, which also has an inherent connection with the Omori-Utsu law of earthquake aftershocks. Both empirical laws lack a deterministic interpretation. Coincidentally, the Andrade law is similar to the time-varying part of the creep compliance of the fractional dashpot in anomalous viscoelastic modeling. Consequently, fractional derivatives are invoked, but since they lack a physical interpretation, the physical parameters of the two laws extracted from curve fit lack confidence. In this Letter, we establish an analogous linear physical mechanism that underlies both laws and relates its parameters with the material's macroscopic properties. Surprisingly, the explanation does not require the property of viscosity. Instead, it necessitates the existence of a rheological property that relates strain with the first order time derivative of stress, which involves jerk. Further, we justify the constant quality factor model of acoustic attenuation in complex media. The obtained results are validated in light of the established observations.

Regulators of male and female sexual development are critical for the transmission of a malaria parasite.

Malaria transmission to mosquitoes requires a developmental switch in asexually dividing blood-stage parasites to sexual reproduction. In Plasmodium berghei, the transcription factor AP2-G is required and sufficient for this switch, but how a particular sex is determined in a haploid parasite remains unknown. Using a global screen of barcoded mutants, we here identify genes essential for the formation of either male or female sexual forms and validate their importance for transmission. High-resolution single-cell transcriptomics of ten mutant parasites portrays the developmental bifurcation and reveals a regulatory cascade of putative gene functions in the determination and subsequent differentiation of each sex. A male-determining gene with a LOTUS/OST-HTH domain as well as the protein interactors of a female-determining zinc-finger protein indicate that germ-granule-like ribonucleoprotein complexes complement transcriptional processes in the regulation of both male and female development of a malaria parasite.

Rate of formation of caustics in heavy particles advected by turbulence.

The rate of collision and the relative velocities of the colliding particles in turbulent flows are a crucial part of several natural phenomena, e.g. rain formation in warm clouds and planetesimal formation in protoplanetary discs. The particles are often modelled as passive, but heavy and inertial. Within this model, large relative velocities emerge due to formation of singularities (caustics) of the gradient matrix of the velocities of the particles. Using extensive direct numerical simulations of heavy particles in both two (direct and inverse cascade) and three-dimensional turbulent flows, we calculate the rate of formation of caustics, [Formula: see text] as a function of the Stokes number ([Formula: see text]). The best approximation to our data is [Formula: see text], in the limit [Formula: see text] where [Formula: see text] is a non-universal constant. This article is part of the theme issue 'Scaling the turbulence edifice (part 2)'.

Genome reconstructions of metabolism of Plasmodium RBC and liver stages.

Genome scale metabolic models (GEMs) offer a powerful means of integrating genome and biochemical information on an organism to make testable predictions of metabolic functions at different conditions and to systematically predict essential genes that may be targeted by drugs. This review describes how Plasmodium GEMs have become increasingly more accurate through the integration of omics and experimental genetic data. We also discuss how GEMs contribute to our increasing understanding of how Plasmodium metabolism is reprogrammed between life cycle stages.

Pseudo-turbulence in two-dimensional buoyancy-driven bubbly flows: A DNS study.

We present a direct numerical simulation (DNS) study of buoyancy-driven bubbly flows in two dimensions. We employ the volume of fluid (VOF) method to track the bubble interface. To investigate the spectral properties of the flow, we derive the scale-by-scale energy budget equation. We show that the Galilei number (Ga) controls different scaling regimes in the energy spectrum. For high Galilei numbers, we find the presence of an inverse energy cascade. Our study indicates that the density ratio of the bubble with the ambient fluid or the presence of coalescence between the bubbles does not alter the scaling behaviour.

Author Correction: Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

Genome-Scale Identification of Essential Metabolic Processes for Targeting the Plasmodium Liver Stage.

Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development.

Clustering and energy spectra in two-dimensional dusty gas turbulence.

We present direct numerical simulation of heavy inertial particles (dust) immersed in two-dimensional turbulent flow (gas). The dust particles are modeled as monodispersed heavy particles capable of modifying the flow through two-way coupling. By varying the Stokes number (St) and the mass-loading parameter (ϕ_{m}), we study the clustering phenomenon and the gas phase kinetic energy spectra. We find that the dust-dust correlation dimension (d_{2}) also depends on ϕ_{m}. In particular, clustering decreases as mass loading (ϕ_{m}) is increased. In the kinetic energy spectra of gas we show (i) the emergence of a different scaling regime and that (ii) the scaling exponent in this regime is not universal but a function of both St and ϕ_{m}. Using a scale-by-scale enstrophy budget analysis we show that in this emerged scaling regime, which we call the dust-dissipative range, viscous dissipation due to the gas balances the back-reaction from the dust.

Asymmetricity and sign reversal of secondary Bjerknes force from strong nonlinear coupling in cavitation bubble pairs.

Most of the current applications of acoustic cavitation use bubble clusters that exhibit multibubble dynamics. This necessitates a complete understanding of the mutual nonlinear coupling between individual bubbles. In this study, strong nonlinear coupling is investigated in bubble pairs which is the simplest case of a bubble-cluster. This leads to the derivation of a more comprehensive set of coupled Keller-Miksis equations (KMEs) that contain nonlinear coupling terms of higher order. The governing KMEs take into account the convective contribution that stems from the Navier-Stokes equation. The system of KMEs is numerically solved for acoustically excited bubble pairs. It is shown that the higher-order corrections are important in the estimation of secondary Bjerknes force for closely spaced bubbles. Further, asymmetricity is witnessed in both magnitude and sign reversal of the secondary Bjerknes force in weak, regular, and strong acoustic fields. The obtained results are examined in the light of published scientific literature. It is expected that the findings reported in this paper may have implications in industries where there is a requirement to have a control on cavitation and its effects.

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 allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. 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 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.

Investigating the deregulation of metabolic tasks via Minimum Network Enrichment Analysis (MiNEA) as applied to nonalcoholic fatty liver disease using mouse and human omics data.

Nonalcoholic fatty liver disease (NAFLD) is associated with metabolic syndromes spanning a wide spectrum of diseases, from simple steatosis to the more complex nonalcoholic steatohepatitis. To identify the deregulation that occurs in metabolic processes at the molecular level that give rise to these various NAFLD phenotypes, algorithms such as pathway enrichment analysis (PEA) can be used. These analyses require the use of predefined pathway maps, which are composed of reactions describing metabolic processes/subsystems. Unfortunately, the annotation of the metabolic subsystems can differ depending on the pathway database used, making these approaches subject to biases associated with different pathway annotations, and these methods cannot capture the balancing of cofactors and byproducts through the complex nature and interactions of genome-scale metabolic networks (GEMs). Here, we introduce a framework entitled Minimum Network Enrichment Analysis (MiNEA) that is applied to GEMs to generate all possible alternative minimal networks (MiNs), which are possible and feasible networks composed of all the reactions pertaining to various metabolic subsystems that can synthesize a target metabolite. We applied MiNEA to investigate deregulated MiNs and to identify key regulators in different NAFLD phenotypes, such as a fatty liver and liver inflammation, in both humans and mice by integrating condition-specific transcriptomics data from liver samples. We identified key deregulations in the synthesis of cholesteryl esters, cholesterol, and hexadecanoate in both humans and mice, and we found that key regulators of the hydrogen peroxide synthesis network were regulated differently in humans and mice. We further identified which MiNs demonstrate the general and specific characteristics of the different NAFLD phenotypes. MiNEA is applicable to any GEM and to any desired target metabolite, making MiNEA flexible enough to study condition-specific metabolism for any given disease or organism.

Publisher's Note: Linking the fractional derivative and the Lomnitz creep law to non-Newtonian time-varying viscosity [Phys. Rev. E 94, 032606 (2016)].

This corrects the article DOI: 10.1103/PhysRevE.94.032606.

Linking the fractional derivative and the Lomnitz creep law to non-Newtonian time-varying viscosity.

Many of the most interesting complex media are non-Newtonian and exhibit time-dependent behavior of thixotropy and rheopecty. They may also have temporal responses described by power laws. The material behavior is represented by the relaxation modulus and the creep compliance. On the one hand, it is shown that in the special case of a Maxwell model characterized by a linearly time-varying viscosity, the medium's relaxation modulus is a power law which is similar to that of a fractional derivative element often called a springpot. On the other hand, the creep compliance of the time-varying Maxwell model is identified as Lomnitz's logarithmic creep law, making this possibly its first direct derivation. In this way both fractional derivatives and Lomnitz's creep law are linked to time-varying viscosity. A mechanism which yields fractional viscoelasticity and logarithmic creep behavior has therefore been found. Further, as a result of this linking, the curve-fitting parameters involved in the fractional viscoelastic modeling, and the Lomnitz law gain physical interpretation.

Connecting the grain-shearing mechanism of wave propagation in marine sediments to fractional order wave equations.

The characteristic time-dependent viscosity of the intergranular pore-fluid in Buckingham's grain-shearing (GS) model [Buckingham, J. Acoust. Soc. Am. 108, 2796-2815 (2000)] is identified as the property of rheopecty. The property corresponds to a rare type of a non-Newtonian fluid in rheology which has largely remained unexplored. The material impulse response function from the GS model is found to be similar to the power-law memory kernel which is inherent in the framework of fractional calculus. The compressional wave equation and the shear wave equation derived from the GS model are shown to take the form of the Kelvin-Voigt fractional-derivative wave equation and the fractional diffusion-wave equation, respectively. Therefore, an analogy is drawn between the dispersion relations obtained from the fractional framework and those from the GS model to establish the equivalence of the respective wave equations. Further, a physical interpretation of the characteristic fractional order present in the wave equations is inferred from the GS model. The overall goal is to show that fractional calculus is not just a mathematical framework which can be used to curve-fit the complex behavior of materials. Rather, it can also be derived from real physical processes as illustrated in this work by the example of GS.

Multi-omic profiles of human non-alcoholic fatty liver disease tissue highlight heterogenic phenotypes.

Non-alcoholic fatty liver disease (NAFLD) is a consequence of sedentary life style and high fat diets with an estimated prevalence of about 30% in western countries. It is associated with insulin resistance, obesity, glucose intolerance and drug toxicity. Additionally, polymorphisms within, e.g., APOC3, PNPLA3, NCAN, TM6SF2 and PPP1R3B, correlate with NAFLD. Several studies have already investigated later stages of the disease. This study explores the early steatosis stage of NAFLD with the aim of identifying molecular mechanisms underlying the etiology of NAFLD. We analyzed liver biopsies and serum samples from patients with high- and low-grade steatosis (also pre-disease states) employing transcriptomics, ELISA-based serum protein analyses and metabolomics. Here, we provide a detailed description of the various related datasets produced in the course of this study. These datasets may help other researchers find new clues for the etiology of NAFLD and the mechanisms underlying its progression to more severe disease states.

Comparative analysis and modeling of the severity of steatohepatitis in DDC-treated mouse strains.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) has a broad spectrum of disease states ranging from mild steatosis characterized by an abnormal retention of lipids within liver cells to steatohepatitis (NASH) showing fat accumulation, inflammation, ballooning and degradation of hepatocytes, and fibrosis. Ultimately, steatohepatitis can result in liver cirrhosis and hepatocellular carcinoma. METHODOLOGY AND RESULTS: In this study we have analyzed three different mouse strains, A/J, C57BL/6J, and PWD/PhJ, that show different degrees of steatohepatitis when administered a 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) containing diet. RNA-Seq gene expression analysis, protein analysis and metabolic profiling were applied to identify differentially expressed genes/proteins and perturbed metabolite levels of mouse liver samples upon DDC-treatment. Pathway analysis revealed alteration of arachidonic acid (AA) and S-adenosylmethionine (SAMe) metabolism upon other pathways. To understand metabolic changes of arachidonic acid metabolism in the light of disease expression profiles a kinetic model of this pathway was developed and optimized according to metabolite levels. Subsequently, the model was used to study in silico effects of potential drug targets for steatohepatitis. CONCLUSIONS: We identified AA/eicosanoid metabolism as highly perturbed in DDC-induced mice using a combination of an experimental and in silico approach. Our analysis of the AA/eicosanoid metabolic pathway suggests that 5-hydroxyeicosatetraenoic acid (5-HETE), 15-hydroxyeicosatetraenoic acid (15-HETE) and prostaglandin D2 (PGD2) are perturbed in DDC mice. We further demonstrate that a dynamic model can be used for qualitative prediction of metabolic changes based on transcriptomics data in a disease-related context. Furthermore, SAMe metabolism was identified as being perturbed due to DDC treatment. Several genes as well as some metabolites of this module show differences between A/J and C57BL/6J on the one hand and PWD/PhJ on the other.