Profiling the effects of process changes on residual host cell proteins in biotherapeutics by mass spectrometry

An advanced liquid chromatography/mass spectrometry (MS) platform was used to identify and quantify residual Escherichia coli host cell proteins (HCPs) in the drug substance (DS) of several peptibodies (Pbs). Significantly different HCP impurity profiles were observed among different biotherapeutic Pbs as well as one Pb purified via multiple processes. The results can be rationally interpreted in terms of differences among the purification processes, and demonstrate the power of this technique to sensitively monitor both the quantity and composition of residual HCPs in DS, where these may represent a safety risk to patients. The breadth of information obtained using MS is compared to traditional multiproduct enzyme‐linked immunosorbent assay (ELISA) values for total HCP in the same samples and shows that, in this case, the ELISA failed to detect multiple HCPs. The HCP composition of two upstream samples was also analyzed and used to demonstrate that HCPs that carry through purification processes to be detectable in DS are not always among those that are the most abundant upstream. Compared to ELISA, we demonstrate that MS can provide a more comprehensive, and accurate, characterization of DS HCPs, thereby facilitating process development as well as more rationally assessing potential safety risks posed by individual, identified HCPs. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:951–957, 2013     

Visualization of molecular and cellular events with green fluorescent proteins in developing embryos: a review

An Erratum has been published for this article in Luminescence (2003) 18(4) 243 During the past 5 years, green fluorescent protein (GFP) has become one of the most widely used in vivo protein markers for studying a number of different molecular processes during development, such as promoter activation, gene expression, protein trafficking and cell lineage determination. GFP fluorescence allows observation of dynamic developmental processes in real time, in both transiently and stably transformed cells, as well as in live embryos. In this review, we include the most up‐to‐date use of GFP during embryonic development and point out the unique contribution of GFP visualization, which resulted in novel discoveries. Copyright © 2002 John Wiley & Sons, Ltd.     

A sensitive switch for visualizing natural gene silencing in single cells

RNA interference is a natural gene expression silencing system that appears throughout the tree of life. As the list of cellular processes linked to RNAi grows, so does the demand for tools to accurately measure RNAi dynamics in living cells. We engineered a synthetic RNAi sensor that converts this negative regulatory signal into a positive output in living mammalian cells thereby allowing increased sensitivity and activation. Furthermore, the circuit's modular design allows potentially any microRNA of interest to be detected. We demonstrated that the circuit responds to an artificial microRNA and becomes activated when the RNAi target is replaced by a natural microRNA target (miR-34) in U2OS osteosarcoma cells. Our studies extend the application of rationally designed synthetic switches to RNAi, providing a sensitive way to visualize the dynamics of RNAi activity rather than just the presence of miRNA molecules.     

Special issue: Genome editing and gene therapy

Gene therapy is one of the most rapidly developing fields of molecular medicine. Gene therapy allows simple transfer of genetic methods aimed at correcting pathological processes into clinical practice. However, a number of technical problems still exists limiting broad use of gene therapy approaches. This special issue discusses modern methods and approaches used for the development of novel, effective, and safe agents for gene therapy.     

Advances in encapsulin nanocompartment biology and engineering

Compartmentalization is an essential feature of all cells. It allows cells to segregate and coordinate physiological functions in a controlled and ordered manner. Different mechanisms of compartmentalization exist, with the most relevant to prokaryotes being encapsulation via self‐assembling protein‐based compartments. One widespread example of such is that of encapsulins—cage‐like protein nanocompartments able to compartmentalize specific reactions, pathways, and processes in bacteria and archaea. While still relatively nascent bioengineering tools, encapsulins exhibit many promising characteristics, including a number of defined compartment sizes ranging from 24 to 42 nm, straightforward expression, the ability to self‐assemble via the Hong Kong 97‐like fold, marked physical robustness, and internal and external handles primed for rational genetic and molecular manipulation. Moreover, encapsulins allow for facile and specific encapsulation of native or heterologous cargo proteins via naturally or rationally fused targeting peptide sequences. Taken together, the attributes of encapsulins promise substantial customizability and broad usability. This review discusses recent advances in employing engineered encapsulins across various fields, from their use as bionanoreactors to targeted delivery systems and beyond. A special focus will be provided on the rational engineering of encapsulin systems and their potential promise as biomolecular research tools.     

Photoactivated structural dynamics of fluorescent proteins

Proteins of the GFP (green fluorescent protein) family have revolutionized life sciences because they allow the tagging of biological samples in a non-invasive genetically encoded way. 'Phototransformable' fluorescent proteins, in particular, have recently attracted widespread interest, as their fluorescence state can be finely tuned by actinic light, a property central to the development of super-resolution microscopy. Beyond microscopy applications, phototransformable fluorescent proteins are also exquisite tools to investigate fundamental protein dynamics. Using light to trigger processes such as photoactivation, photoconversion, photoswitching, blinking and photobleaching allows the exploration of the conformational landscape in multiple directions. In the present paper, we review how structural dynamics of phototransformable fluorescent proteins can be monitored by combining X-ray crystallography, in crystallo optical spectroscopy and simulation tools such as quantum chemistry/molecular mechanics hybrid approaches. Besides their usefulness to rationally engineer better performing fluorescent proteins for nanoscopy and other biotechnological applications, these investigations provide fundamental insights into protein dynamics.     

Feller processes: the next generation in modeling. Brownian motion, Lévy processes and beyond

We present a simple construction method for Feller processes and a framework for the generation of sample paths of Feller processes. The construction is based on state space dependent mixing of Lévy processes. Brownian Motion is one of the most frequently used continuous time Markov processes in applications. In recent years also Lévy processes, of which Brownian Motion is a special case, have become increasingly popular. Lévy processes are spatially homogeneous, but empirical data often suggest the use of spatially inhomogeneous processes. Thus it seems necessary to go to the next level of generalization: Feller processes. These include Lévy processes and in particular brownian motion as special cases but allow spatial inhomogeneities. Many properties of Feller processes are known, but proving the very existence is, in general, very technical. Moreover, an applicable framework for the generation of sample paths of a Feller process was missing. We explain, with practitioners in mind, how to overcome both of these obstacles. In particular our simulation technique allows to apply Monte Carlo methods to Feller processes.     

Think ratio! A stoichiometric view on biodiversity–ecosystem functioning research

Ecological stoichiometry (ES) has become one of the most pervasive theoretical frameworks in environmental sciences and biology in the last two decades. ES allows predicting processes on all organizational levels from subcellular structures to ecosystems by relating the elemental composition and demand of organisms to the relative availability of resources. However, ES has been rarely used to understand and predict the relationship between biodiversity and ecosystem functioning (BEF), although ES would be ideally suited as it makes predictions on both population processes underlying biodiversity as well as on matter transformations underlying ecosystem processes. Here, we propose to link the two fields of research on ES and BEF relationships and highlight a number of potential avenues for further research. First, we cast a stoichiometric view on drivers of biodiversity change. Second, we address the stoichiometric underpinning of biodiversity–productivity relationships. Third, we discuss potential interactions between stoichiometry and diversity in a food web context.     

Genome-wide Mapping of Cellular Protein–RNA Interactions Enabled by Chemical Crosslinking

RNA–protein interactions influence many biological processes. Identifying the binding sites of RNA-binding proteins（RBPs） remains one of the most fundamental and important challenges to the studies of such interactions. Capturing RNA and RBPs via chemical crosslinking allows stringent purification procedures that significantly remove the non-specific RNA and protein interactions. Two major types of chemical crosslinking strategies have been developed to date, i.e., UV-enabled crosslinking and enzymatic mechanism-based covalent capture. In this review, we compare such strategies and their current applications, with an emphasis on the technologies themselves rather than the biology that has been revealed. We hope such methods could benefit broader audience and also urge for the development of new methods to study RNA RBP interactions.     

Bistable secondary structures of small RNAs and their structural probing by comparative imino proton NMR spectroscopy

We investigate 25-34 nucleotide RNA sequences, that have been rationally designed to adopt two different secondary structures that are in thermodynamic equilibrium. Experimental evidence for the co-existence of the two conformers results from the NH...N 1H NMR spectra. When compared to the NH...N 1H NMR spectra of appropriate reference sequences the equilibrium position is easily quantifiable even without the assignment of the individual NH resonances. The reference sequences represent several Watson-Crick base-paired double helical segments, each encountered in either of the two conformers of the bistable target sequence. In addition, we rationalize the influence of nucleotide mutations on the equilibrium position of one of the bistable RNA sequences. The approach further allows a detailed thermodynamic analysis and the evaluation of secondary structure predictions for multistable RNAs obtained by computational methods.     

海北藏系绵羊种群结构及其出栏方案最优化的探讨

配置畜群结构是管理畜牧生产最重要的工作之一。目前我国普遍存在着畜群结构不合理的现象。藏羊是我国第二大绵羊品种,其生产管理落后,种群结构普遍不合理。为组织合理生产,本文用系统分析的方法对藏羊种群结构进行了研究。首先,根据实地调查研究,作者构成了一个矩阵模型,以描述藏羊种群的性别年龄结构状态: N_(t+1)=AN_t-BU_t 其中AN_t反映羊群的自然变动情况,U_t是人为控制量。 然后,以最大羊产品收获为目标,以牧草资源和种群平衡态为限制条件,本文构造了一个线性规划模型,用以计算最优藏羊种群结构及其出栏方案; 除了给出模型这个研究种群结构问题的方法之外,本文使用线性规划模型,利用作者在青海省门源县风闸口地区调查测定的数据,通过计算机,算出了该地最优藏羊种群结构及其出栏方案。在最大能量收获的目标下。最优结构应为,67.80％的繁殖母羊,28.36％的后备母羊,3.84％的种公羊和后备种公 羊。相应出栏方案是每年秋季出栏全部羯羊羔和老弱羊,并且出栏33.17％的成年母羊。在这种方案下,按现有羊只生产能力,出栏率可提高到52.79％,平均从每百公斤牧草中收获合11.72千千卡能量或3.65公斤活重的羊产品。     

Exact results for fixation probability of bithermal evolutionary graphs

One of the most fundamental concepts of evolutionary dynamics is the “fixation” probability, i.e. the probability that a mutant spreads through the whole population. Most natural communities are geographically structured into habitats exchanging individuals among each other and can be modeled by an evolutionary graph (EG), where directed links weight the probability for the offspring of one individual to replace another individual in the community. EGs have recently spurred huge interest, as it has been shown that some topology can amplify or suppress the effect of beneficial mutations. Very few exact analytical results however are known for EGs. In this article we show that the use of a new technique, the fixed point of probability generating function, allows us to compute the exact fixation probability for a large subset of bithermal graphs. We also show by numerical simulations that the computed solution holds for all bithermal graphs. Moreover, the analytical solution allows us to clarify the opposing consequences of birth–death versus death–birth processes as amplifier or suppressor of beneficial mutations for the same bithermal topology.     

Rapid fluorescent visualization of multiple NAD(P)H oxidoreductases in homogenate,permeabilized cells,and tissue slices

Intracellular NAD(P)H oxidoreductases are a class of diverse enzymes that are the key players in a number of vital processes. The method we present and validate here is based on the ability of many NAD(P)H oxidoreductases to reduce the superoxide probe lucigenin, which is structurally similar to flavins, to its highly fluorescent water-insoluble derivative dimethylbiacridene. Two modifications of the method are proposed: (i) an express method for tissue homogenate and permeabilized cells in suspensions and (ii) a standard procedure for cells in culture and acute thin tissue slices. The method allows one to assess, visualize, and localize, using fluorescent markers of cellular compartments, multiple NADH and NADPH oxidoreductase activities. The application of selective inhibitors (e.g., VAS2870, a NOX2 inhibitor; plumbagin, a NOX4 inhibitor) allows one to distinguish and compare specific NAD(P)H oxidoreductase activities in cells and tissues and to attribute them to known enzymes. The method is simple, rapid, and flexible. It can be easily adapted to a variety of tasks. It will be useful for investigations of the role of various NAD(P)H oxidoreductases in a number of physiological and pathophysiological processes.     

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes.     

Stabilization of chloroperoxidase by polyethylene glycols in aqueous media: kinetic studies and synthetic applications

Chloroperoxidase (CPO) is one of the most versatile of the heme peroxidase enzymes for synthetic applications. Despite the potential use of CPO, commercial processes have not been developed because of the low water solubility of many organic substrates of synthetic interest and the limited stability due to inactivation by H(2)O(2). CPO catalytic properties have been studied in aqueous solutions in the presence of short-chain poly(ethylene glycol)s (PEGs), and the sulfoxidation of thioanisole, as model substrate, has been investigated. The addition of PEGs allows a better substrate solubilization in the reaction mixture and the enzyme to retain more of its initial activity, with respect to pure buffer. Kinetic studies were performed to optimize the experimental conditions, and complete enantioselective conversion to the (R)-sulfoxide (ee = 99%) was observed in the presence of PEG 200 and tri(ethylene glycol). The relevant stabilization of chloroperoxidase due to the presence of PEGs allows the enzyme to convert the substrate with significant product yields even after 10 days, with a consequent increase in enzyme productivity. This is a promising result in view of industrial application of the enzyme.     

Development and application of physiologically based pharmacokinetic-modeling tools to support drug discovery

Physiologically based pharmacokinetic (PBPK) modeling integrates physicochemical (PC) and in vitro pharmacokinetic (PK) data using a mechanistic framework of principal ADME (absorption, distribution, metabolism, and excretion) processes into a physiologically based whole-body model. Absorption, distribution, and clearance are modeled by combining compound-specific PC and PK properties with physiological processes. Thereby, isolated in vitro data can be upgraded by means of predicting full concentration-time profiles prior to animal experiments. The integrative process of PBPK modeling leads to a better understanding of the specific ADME processes driving the PK behavior in vivo, and has the power to rationally select experiments for a more focussed PK project support. This article presents a generic disposition model based on tissue-composition-based distribution and directly scaled hepatic clearance. This model can be used in drug discovery to identify the critical PK issues of compound classes and to rationally guide the optimization path of the compounds toward a viable development candidate. Starting with a generic PBPK model, which is empirically based on the most common PK processes, the model will be gradually tailored to the specifics of drug candidates as more and more experimental data become available. This will lead to a growing understanding of the 'drug in the making', allowing a range of predictions to be made for various purposes and conditions. The stage is set for a wide penetration of PK modeling and simulations to form an intrinsic part of a project starting from lead discovery, to lead optimization and candidate selection, to preclinical profiling and clinical trials.