Lithocholic Acid Amides as Potent Vitamin D Receptor Agonists

1α,25-Dihydroxyvitamin D3 [1α,25(OH)2D3, 1] is an active form of vitamin D3 and regulates various biological phenomena, including calcium and phosphate homeostasis, bone metabolism, and immune response via binding to and activation of vitamin D receptor (VDR). Lithocholic acid (LCA, 2) was identified as a second endogenous agonist of VDR, though its potency is very low. However, the lithocholic acid derivative 3 (Dcha-20) is a more potent agonist than 1α,25(OH)2D3, (1), and its carboxyl group has similar interactions to the 1,3-dihydroxyl groups of 1 with amino acid residues in the VDR ligand-binding pocket. Here, we designed and synthesized amide derivatives of 3 in order to clarify the role of the carboxyl group. The synthesized amide derivatives showed HL-60 cell differentiation-inducing activity with potency that depended upon the substituent on the amide nitrogen atom. Among them, the N-cyanoamide 6 is more active than either 1 or 3.

Supramolecular assembly of protein building blocks: from folding to function

Several phenomena occurring throughout the life of living things start and end with proteins. Various proteins form one complex structure to control detailed reactions. In contrast, one protein forms various structures and implements other biological phenomena depending on the situation. The basic principle that forms these hierarchical structures is protein self-assembly. A single building block is sufficient to create homogeneous structures with complex shapes, such as rings, filaments, or containers. These assemblies are widely used in biology as they enable multivalent binding, ultra-sensitive regulation, and compartmentalization. Moreover, with advances in the computational design of protein folding and protein–protein interfaces, considerable progress has recently been made in the de novo design of protein assemblies. Our review presents a description of the components of supramolecular protein assembly and their application in understanding biological phenomena to therapeutics.

A Graph-Based Framework for Multiscale Modeling of Physiological Transport

Trillions of chemical reactions occur in the human body every second, where the generated products are not only consumed locally but also transported to various locations in a systematic manner to sustain homeostasis. Current solutions to model these biological phenomena are restricted in computability and scalability due to the use of continuum approaches in which it is practically impossible to encapsulate the complexity of the physiological processes occurring at diverse scales. Here, we present a discrete modeling framework defined on an interacting graph that offers the flexibility to model multiscale systems by translating the physical space into a metamodel. We discretize the graph-based metamodel into functional units composed of well-mixed volumes with vascular and cellular subdomains; the operators defined over these volumes define the transport dynamics. We predict glucose drift governed by advective–dispersive transport in the vascular subdomains of an islet vasculature and cross-validate the flow and concentration fields with finite-element–based COMSOL simulations. Vascular and cellular subdomains are coupled to model the nutrient exchange occurring in response to the gradient arising out of reaction and perfusion dynamics. The application of our framework for modeling biologically relevant test systems shows how our approach can assimilate both multi-omics data from in vitro–in vivo studies and vascular topology from imaging studies for examining the structure–function relationship of complex vasculatures. The framework can advance simulation of whole-body networks at user-defined levels and is expected to find major use in personalized medicine and drug discovery.

The Establishment of Quantitatively Regulating Expression Cassette with sgRNA Targeting BIRC5 to Elucidate the Synergistic Pathway of Survivin with P-Glycoprotein in Cancer Multi-Drug Resistance

Quantitative analysis and regulating gene expression in cancer cells is an innovative method to study key genes in tumors, which conduces to analyze the biological function of the specific gene. In this study, we found the expression levels of Survivin protein (BIRC5) and P-glycoprotein (MDR1) in MCF-7/doxorubicin (DOX) cells (drug-resistant cells) were significantly higher than MCF-7 cells (wild-type cells). In order to explore the specific functions of BIRC5 gene in multi-drug resistance (MDR), a CRISPR/Cas9-mediated knocking-in tetracycline (Tet)-off regulatory system cell line was established, which enabled us to regulate the expression levels of Survivin quantitatively (clone 8 named MCF-7/Survivin was selected for further studies). Subsequently, the determination results of doxycycline-induced DOX efflux in MCF-7/Survivin cells implied that Survivin expression level was opposite to DOX accumulation in the cells. For example, when Survivin expression was down-regulated, DOX accumulation inside the MCF-7/Survivin cells was up-regulated, inducing strong apoptosis of cells (reversal index 118.07) by weakening the release of intracellular drug from MCF-7/Survivin cells. Also, down-regulation of Survivin resulted in reduced phosphorylation of PI3K, Akt, and mTOR in MCF-7/Survivin cells and significantly decreased P-gp expression. Previous studies had shown that PI3K/Akt/mTOR could regulate P-gp expression. Therefore, we speculated that Survivin might affect the expression of P-gp through PI3K/Akt/mTOR pathway. In summary, this quantitative method is not only valuable for studying the gene itself, but also can better analyze the biological phenomena related to it.

Counting fluorescently labeled proteins in tissues in the spinning disk microscope using single-molecule calibrations

Quantification of molecular numbers and concentrations in living cells is critical for testing models of complex biological phenomena. Counting molecules in cells requires estimation of the fluorescence intensity of single molecules, which is generally limited to imaging near cell surfaces, in isolated cells, or where motions are diffusive. To circumvent this difficulty, we have devised a calibration technique for spinning-disk confocal (SDC) microscopy, commonly used for imaging in tissues, that uses single-step bleaching kinetics to estimate the single-fluorophore intensity. To cross-check our calibrations, we compared the brightness of fluorophores in the SDC microscope to those in the total-internal-reflection (TIRF) and epifluorescence microscopes. We applied this calibration method to quantify the number of EB1-eGFP proteins in the comets of growing microtubule ends and to measure the cytoplasmic concentration of EB1-eGFP in sensory neurons in fly larvae. These measurements allowed us to estimate the dissociation constant of EB1-eGFP from the microtubules as wells as the GTP-tubulin cap size. Our results show the unexplored potential of single-molecule imaging using spinning disk confocal microscopy and provide a straight-forward method to count the absolute number of fluorophores in tissues which can be applied to a wide range of biological systems and imaging techniques.

Towards Self-organized Control: Using Neural Cellular Automata to Robustly Control a Cart-pole Agent

Neural cellular automata (Neural CA) are a recent framework used to model biological phenomena emerging from multicellular organisms. In these systems, artificial neural networks are used as update rules for cellular automata. Neural CA are end-to-end differentiable systems where the parameters of the neural network can be learned to achieve a particular task. In this work, we used neural CA to control a cart-pole agent. The observations of the environment are transmitted in input cells while the values of output cells are used as a readout of the system. We trained the model using deep-Q learning where the states of the output cells were used as the Q-value estimates to be optimized. We found that the computing abilities of the cellular automata were maintained over several hundreds of thousands of iterations, producing an emergent stable behavior in the environment it controls for thousands of steps. Moreover, the system demonstrated life-like phenomena such as a developmental phase, regeneration after damage, stability despite a noisy environment, and robustness to unseen disruption such as input deletion.

Improving force field accuracy by training against condensed phase mixture properties

Developing a sufficiently accurate classical force field representation of molecules is key to realizing the full potential of molecular simulation as a route to gaining fundamental insight into a broad spectrum of chemical and biological phenomena. This is only possible, however, if the many complex interactions between molecules of different species in the system are accurately captured by the model. Historically, the intermolecular van der Waals (vdW) interactions have primarily been trained against densities and enthalpies of vaporization of pure (single-component) systems, with occasional usage of hydration free energies. In this study, we demonstrate how including physical property data of binary mixtures can better inform these parameters, encoding more information about the underlying physics of the system in complex chemical mixtures. To demonstrate this, we re-train a select number of the Lennard-Jones parameters describing the vdW interactions of the OpenFF 1.0.0 (Parsley) fixed charge force field against training sets composed of densities and enthalpies of mixing for binary liquid mixtures as well as densities and enthalpies of vaporization of pure liquid systems, and assess the performance of each of these combinations. We show that retraining against the mixture data almost universally improves the force field's ability to reproduce both pure and mixture properties, reducing some systematic errors that exist when training vdW interactions against properties of pure systems only.

Using Piecewise Regression to Identify Biological Phenomena in Biotelemetry Datasets

Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animal's location, movement characteristics, external environmental conditions, and internal physiological status. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval. We provide five example applications highlighting a variety of taxonomic species, data streams, timescales, and biological phenomena. These examples include a short-term behavioral response (flee and return) by a trumpeter swan (Cygnus buccinator) immediately following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose (Alces alces); a physiological response (spike in heart-rate) in a black bear (Ursus americanus) to a stressful stimulus (presence of a drone); a mortality event of a trumpeter swan signaled by changes in collar temperature and Overall Dynamic Body Acceleration; and an unsupervised method for identifying the onset, return, duration, and staging use of sandhill crane (Antigone canadensis) migration. We implement analyses using the mcp package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criterion and cross-validation measures. This approach uses simple modeling approaches that are accessible to a wide audience and is a straightforward means of assessing a variety of biologically relevant changes in animal behavior.

DeepRank-GNN: A Graph Neural Network Framework to Learn Patterns in Protein-Protein Interfaces

Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using Convolutional Neural Network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized, and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance for scoring docking models using a dedicated graph interaction neural network (GINet). We show that this graph-based model performs better than DeepRank, DOVE and HADDOCK scores and competes with iScore on the CAPRI score set. We show a significant gain in speed and storage requirement using DeepRank-GNN as compared to DeepRank. DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN.