A Graphical User Interface for a Method to Infer Kinetics and Network Architecture (MIKANA)

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis–Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1)

Neutrosophic bipolar vague binary topological space

In this paper, we make an interesting connection between the mathematical approaches to vagueness and the bipolar binary set. The concept of bipolar binary sets over two universal sets. To eliminate ambiguity in a data set, bipolarity is primarily introduced in imprecise binary sets. This article's main objective is to familiarize the reader with the novel concept of a neutrosophic bipolar vague binary set. We also discuss operations like ‘union’, ‘intersection’ and ‘complement’. The concepts of neutrosophic bipolar vague binary topology, zero, unit, open and closed set, neutrosophic bipolar vague binary ‘????-open’ and ‘????-closed’ sets are introduced and their properties are also discussed. Furthermore, the neutrosophic bipolar vague binary ‘b-interior’ and ‘b-closure’ set are defined. Theorems related to these concepts are stated and proved

PHYTOCHEMICAL STUDIES AND QUALITATIVE ANALYSIS BY TLC OF MURRAYA KOENIGII BARK EXTRACT

Murraya koenigii is a medium size, ever green plant which has been utilized as a source of food, medicine, and other agricultural purposes in different communities. Thus, the preliminary phytochemical analysis and TLC separation was done using methanol, n-hexane, and ethyl acetate(1:3:1), as solvent system while iodine vapour as spotting agent. The phytochemical screening of diethyl ether extracts of bark revealed the presence of carbohydrates , anthraquinones glycosides ,saponins ,flavanoids, and alkaloids, while chloroform extracts of bark revealed carbohydrates, tannins, saponins, and alkaloids, while acetone extracts of bark revealed the presence of carbohydrates, anthraquinones glycosides, flavanoids and alkaloids,while ethanol extracts of bark revealed the presence of carbohydrates, tannins, anthraquinones glycosides,s aponins, flavanoids and alkaloids.TLC separation showed (3) spots each of Diethyl Ether, Chloroform, Acetone, Ethanol from bark extracts. From our findings, it can be concluded that Murraya Koenigii contains some significant phytochemicals that can exhibit desired therapeutic activities such as Antioxidant, Anti-Microbial, Anti-Fungal, Anti-Diabetic, Anti-Ulcer and Cosmetic use. However, there is a need to conduct further Pharmaceutical Analysis on test extracts in order to establish these biomedical applications. Keywords: Thin Layer Chromatography, Murraya koenigii Bark, Phytochemical screening

Virtual-tissue computer simulations define the roles of cell adhesion and proliferation in the onset of kidney cystic disease

In autosomal dominant polycystic kidney disease (ADPKD), cysts accumulate and progressively impair renal function. Mutations in PKD1 and PKD2 genes are causally linked to ADPKD, but how these mutations drive cell behaviors that underlie ADPKD pathogenesis is unknown. Human ADPKD cysts frequently express cadherin-8 (cad8), and expression of cad8 ectopically in vitro suffices to initiate cystogenesis. To explore cell behavioral mechanisms of cad8-driven cyst initiation, we developed a virtual-tissue computer model. Our simulations predicted that either reduced cell-cell adhesion or reduced contact inhibition of proliferation triggers cyst induction. To reproduce the full range of cyst morphologies observed in vivo, changes in both cell adhesion and proliferation are required. However, only loss-of-adhesion simulations produced morphologies matching in vitro cad8-induced cysts. Conversely, the saccular cysts described by others arise predominantly by decreased contact inhibition, that is, increased proliferation. In vitro experiments confirmed that cell-cell adhesion was reduced and proliferation was increased by ectopic cad8 expression. We conclude that adhesion loss due to cadherin type switching in ADPKD suffices to drive cystogenesis. Thus, control of cadherin type switching provides a new target for therapeutic intervention

Trapping of a polyketide synthase module after C−C bond formation reveals transient acyl carrier domain interactions

Modular polyketide synthases (PKSs) are giant assembly lines that produce an impressive range of biologically active compounds. However, our understanding of the structural dynamics of these megasynthases, specifically the delivery of acyl carrier protein (ACP)‐bound building blocks to the catalytic site of the ketosynthase (KS) domain, remains severely limited. Using a multipronged structural approach, we report details of the inter‐domain interactions after C−C bond formation in a chain‐branching module of the rhizoxin PKS. Mechanism‐based crosslinking of an engineered module was achieved using a synthetic substrate surrogate that serves as a Michael acceptor. The crosslinked protein allowed us to identify an asymmetric state of the dimeric protein complex upon C−C bond formation by cryo‐electron microscopy (cryo‐EM). The possible existence of two ACP binding sites, one of them a potential “parking position” for substrate loading, was also indicated by AlphaFold2 predictions. NMR spectroscopy showed that a transient complex is formed in solution, independent of the linker domains, and photochemical crosslinking/mass spectrometry of the standalone domains allowed us to pinpoint the interdomain interaction sites. The structural insights into a branching PKS module arrested after C−C bond formation allows a better understanding of domain dynamics and provides valuable information for the rational design of modular assembly lines

Identification of Prophages in Bacterial Genomes by Dinucleotide Relative Abundance Difference

BACKGROUND: Prophages are integrated viral forms in bacterial genomes that have been found to contribute to interstrain genetic variability. Many virulence-associated genes are reported to be prophage encoded. Present computational methods to detect prophages are either by identifying possible essential proteins such as integrases or by an extension of this technique, which involves identifying a region containing proteins similar to those occurring in prophages. These methods suffer due to the problem of low sequence similarity at the protein level, which suggests that a nucleotide based approach could be useful. METHODOLOGY: Earlier dinucleotide relative abundance (DRA) have been used to identify regions, which deviate from the neighborhood areas, in genomes. We have used the difference in the dinucleotide relative abundance (DRAD) between the bacterial and prophage DNA to aid location of DNA stretches that could be of prophage origin in bacterial genomes. Prophage sequences which deviate from bacterial regions in their dinucleotide frequencies are detected by scanning bacterial genome sequences. The method was validated using a subset of genomes with prophage data from literature reports. A web interface for prophage scan based on this method is available at http://bicmku.in:8082/prophagedb/dra.html. Two hundred bacterial genomes which do not have annotated prophages have been scanned for prophage regions using this method. CONCLUSIONS: The relative dinucleotide distribution difference helps detect prophage regions in genome sequences. The usefulness of this method is seen in the identification of 461 highly probable loci pertaining to prophages which have not been annotated so earlier. This work emphasizes the need to extend the efforts to detect and annotate prophage elements in genome sequences

Identifying Biological Network Structure, Predicting Network Behavior, and Classifying Network State With High Dimensional Model Representation (HDMR)

This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state

Protein Translation and Cell Death: The Role of Rare tRNAs in Biofilm Formation and in Activating Dormant Phage Killer Genes

We discovered previously that the small Escherichia coli proteins Hha (hemolysin expression modulating protein) and the adjacent, poorly-characterized YbaJ are important for biofilm formation; however, their roles have been nebulous. Biofilms are intricate communities in which cell signaling often converts single cells into primitive tissues. Here we show that Hha decreases biofilm formation dramatically by repressing the transcription of rare codon tRNAs which serves to inhibit fimbriae production and by repressing to some extent transcription of fimbrial genes fimA and ihfA. In vivo binding studies show Hha binds to the rare codon tRNAs argU, ileX, ileY, and proL and to two prophage clusters D1P12 and CP4-57. Real-time PCR corroborated that Hha represses argU and proL, and Hha type I fimbriae repression is abolished by the addition of extra copies of argU, ileY, and proL. The repression of transcription of rare codon tRNAs by Hha also leads to cell lysis and biofilm dispersal due to activation of prophage lytic genes rzpD, yfjZ, appY, and alpA and due to induction of ClpP/ClpX proteases which activate toxins by degrading antitoxins. YbaJ serves to mediate the toxicity of Hha. Hence, we have identified that a single protein (Hha) can control biofilm formation by limiting fimbriae production as well as by controlling cell death. The mechanism used by Hha is the control of translation via the availability of rare codon tRNAs which reduces fimbriae production and activates prophage lytic genes. Therefore, Hha acts as a toxin in conjunction with co-transcribed YbaJ (TomB) that attenuates Hha toxicity