Antidiabetic vanadium compound and membrane interfaces: interface-facilitated metal complex hydrolysis

The interactions of metabolites of the antidiabetic vanadium-containing drug bis(maltolato)oxovanadium(IV) (BMOV) with lipid interface model systems were investigated and the results were used to describe a potentially novel mechanism by which these compounds initiate membrane-receptor-mediated signal transduction. Specifically, spectroscopic studies probed the BMOV oxidation and hydrolysis product interaction with interfaces created from cetyltrimethylammonium bromide (CTAB) which mimics the positively charged head group on phosphatidylcholine. ¹H and ⁵¹V NMR spectroscopies were used to determine the location of the dioxobis(maltolato)oxovanadate(V) and the maltol ligand in micelles and reverse micelles by measuring changes in the chemical shift, signal linewidth, and species distribution. Both micelles and reverse micelles interacted similarly with the complex and the ligand, suggesting that interaction is strong as anticipated by Coulombic attraction between the positively charged lipid head group and the negatively charged complex and deprotonated ligand. The nature of the model system was confirmed using dynamic light scattering studies and conductivity measurements. Interactions of the complex/ligand above and below the critical micelle concentration of micelle formation were different, with much stronger interactions when CTAB was in the form of a micelle. Both the complex and the ligand penetrated the lipid interface and were located near the charged head group. These studies demonstrate that a lipid-like interface affects the stability of the complex and raise the possibility that ligand exchange at the interface may be important for the mode of action for these systems. Combined, these studies support recently reported in vivo observations of BMOV penetration into 3T3-L1 adipocyte membranes and increased translocation of a glucose transporter to the plasma membrane.     

EXPRESSION OF cfos IN THE MEDULLA OBLONGATA AFTER CAROTID BARORECEPTOR ACTIVATION BYELEVATED INTRASINUS PRESSURE AND ADENOSINE

在１４只隔离灌流颈动脉窦区的大鼠,观察了窦内压（ＩＳＰ）升高和灌流腺苷（ａｄｅｎｏｓｉｎｅ,Ａｄｏ）激活压力感受器时延髓内ｃｆｏｓ蛋白的表达。结果显示：在孤束核、最后区、延髓腹外侧头端区和中缝苍白核可见Ｆｏｓ蛋白样免疫阳性反应（ＦＬＩ）神经元分布,且其数量随ＩＳＰ升高而增多。在给定ＩＳＰ下,颈动脉窦内灌流Ａｄｏ,可使上述区域中ＦＬＩ表达明显增多。根据以上结果,得出如下结论：ｃｆｏｓ在压力感受器反射延髓通路中的表达,可由ＩＳＰ增高和灌流Ａｄｏ而增强,表明Ａｄｏ对压力感受器反射有易化作用。     

Genomic SELEX for Hfq-binding RNAs identifies genomic aptamers predominantly in antisense transcripts

An unexpectedly high number of regulatory RNAs have been recently discovered that fine-tune the function of genes at all levels of expression. We employed Genomic SELEX, a method to identify protein-binding RNAs encoded in the genome, to search for further regulatory RNAs in Escherichia coli. We used the global regulator protein Hfq as bait, because it can interact with a large number of RNAs, promoting their interaction. The enriched SELEX pool was subjected to deep sequencing, and 8865 sequences were mapped to the E. coli genome. These short sequences represent genomic Hfq-aptamers and are part of potential regulatory elements within RNA molecules. The motif 5′-AAYAAYAA-3′ was enriched in the selected RNAs and confers low-nanomolar affinity to Hfq. The motif was confirmed to bind Hfq by DMS footprinting. The Hfq aptamers are 4-fold more frequent on the antisense strand of protein coding genes than on the sense strand. They were enriched opposite to translation start sites or opposite to intervening sequences between ORFs in operons. These results expand the repertoire of Hfq targets and also suggest that Hfq might regulate the expression of a large number of genes via interaction with cis-antisense RNAs.     

Two different correlations between nanoindentation modulus and mineral content in the bone-cartilage interface

The biomechanical properties of the zone of calcified cartilage (ZCC) in articulating joints are of clinical relevance due to the role ZCC plays in load transfer from cartilage to bone. To determine the micron-level mechanical properties and their correlation to mineral concentration in the ZCC, we combined nanoindentation (for micrometer level stiffness E(r) and hardness H) and quantitative back-scattered electron imaging or qBEI (for micrometer level mean calcium concentration Ca(Mean)) to study the ZCC-subchondral bone junction in 3 embedded human patellae. Nanoindentation line scans were correlated to qBEI analysis in the ZCC. The correlation between local stiffness and local mineral content was different in calcified cartilage compared to bone. The stiffness and hardness of calcified cartilage was typically lower than subchondral bone for the same mineral content. ZCC showed a wider range of variation in calcium content (1-28 wt %) compared to subchondral bone (16-26 wt %). 2D material property maps of the ZCC were generated from the mechanical-mineral correlation, showing that bands of high and low stiffness were found between the bone and tidemark, and between the ZCC and the unmineralized cartilage.     

Keeping the soma free of transposons: programmed DNA elimination in ciliates

Many transposon-related sequences are removed from the somatic macronucleus of ciliates during sexual reproduction. In the ciliate Tetrahymena, an RNAi-related mechanism produces small noncoding RNAs that induce heterochromatin formation, which is followed by DNA elimination. Because RNAi-related mechanisms repress transposon activities in a variety of eukaryotes, the DNA elimination mechanism of ciliates might have evolved from these types of transposon-silencing mechanisms. Nuclear dimorphism allows ciliates to identify any DNA that has invaded the germ-line micronucleus using small RNAs and a whole genome comparison of the micronucleus and the somatic macronucleus.     

Systems biology and combination therapy in the quest for clinical efficacy

Combinatorial control of biological processes, in which redundancy and multifunctionality are the norm, fundamentally limits the therapeutic index that can be achieved by even the most potent and highly selective drugs. Thus, it will almost certainly be necessary to use new 'targeted' pharmaceuticals in combinations. Multicomponent drugs are standard in cytotoxic chemotherapy, but their development has required arduous empirical testing. However, experimentally validated numerical models should greatly aid in the formulation of new combination therapies, particularly those tailored to the needs of specific patients. This perspective focuses on opportunities and challenges inherent in the application of mathematical modeling and systems approaches to pharmacology, specifically with respect to the idea of achieving combinatorial selectivity through use of multicomponent drugs.     

An expanding role for cell biologists in drug discovery and pharmacology

The profound challenges facing clinicians, who must prescribe drugs in the face of dramatic variability in response, and the pharmaceutical industry, which must develop new drugs despite ever-rising costs, represent opportunities for cell biologists interested in rethinking the conceptual basis of pharmacology and drug discovery. Much better understanding is required of the quantitative behaviors of networks targeted by drugs in cells, tissues, and organisms. Cell biologists interested in these topics should learn more about the basic structure of drug development campaigns and hone their quantitative and programming skills. A world of conceptual challenges and engaging industry–academic collaborations awaits, all with the promise of delivering real benefit to patients and strained healthcare systems.Four decades of molecular and cellular biology has fundamentally improved our understanding of human disease, but this undeniable revolution has had less impact than hoped on human health, particularly in the area of discovery and use of therapeutic drugs. The missing link between basic science and useful therapeutics is the quantitative, multifactorial understanding of networks that operate within and between cells and of the changes that drugs induce in these networks (Berger and Iyengar, 2009 ). Contributing to this understanding of drugs and network dynamics represents a significant opportunity for cell biologists interested in careers in industry and for academic scientists seeking industrial collaborations. Success in such “translational” research is not simply a matter of applying known concepts to practical problems; interesting new ideas and science are required (Loscalzo and Barabasi, 2011 ). Fifty years ago, pharmacology and pathophysiology provided cell biologists with many fundamental research problems, and there is every reason to believe this will also be true in the future.Insufficient understanding of pathological and therapeutic mechanisms at a cellular level has contributed to the growing difficulty of bringing new drugs to market. Even when drugs win approval, it is rare that we can predict which patients will benefit from them. As a result, patients have too few treatment options, many serious illnesses remain difficult to treat, and the cost of new medicines is too high (often at the limit of what healthcare systems can support). High-throughput “-omic” approaches have been hailed as a means to understand disease and develop new drugs, but an outstanding opportunity exists for fundamental contributions from cell biologists. A central feature of cell biology is its emphasis on applying diverse conceptual and analytical approaches to biological processes that are inherently multifactorial. This is in contrast to “-omic” approaches, in which the focus is usually on one type of data collected in volume (gene sequences being one example).The role of cell biology in unraveling disease mechanisms is well established, but the value of cell biology in drug development is less well appreciated. Cell and molecular biologists currently play a role during the earliest preclinical stages of drug development in the identification and evaluation of potential drug targets (Figure 1). However, it is increasingly apparent that existing procedures for qualifying targets are inadequate, and this manifests itself as frequent and expensive late-stage failures of efficacy (typically during phase II and III clinical studies (Paul et al., 2010 ). To overcome this problem, we require a much better understanding of the functions of target proteins within the context of cellular networks in normal and diseased cells, both in culture and in the organism (“network biology”). Opportunities exist for cell biologists to help define optimal therapeutic strategies (e.g., aiding in the choice between using a recombinant antibody or small molecule) and to ascertain exposure/response relationships in tissues. Cell biologists also have an important role to play in understanding acquired resistance. A lack of durable responses is the bane of many recently approved targeted drugs. Finally, in diseases such as cancer, we have many plausible targets (the Akt kinase, for example), but it is not clear how to inhibit the target without causing excessive toxicity. It is also unclear why only a subset of patients responds to even the most potent and selective inhibitors. In our opinion, many drugs fail because cell biology is ignored during the later stages of drug development, when selecting indications and drug combinations and determining dosing schedules are the key tasks.Open in a separate windowFIGURE 1:Traditional and emerging roles for cell biologists in drug development and pharmacology. Traditionally, cell biologists have worked on the earliest phases of drug discovery, during the identification and validation of targets. However, by expanding their horizons and adding new skills, cell biologists can become well-suited to other roles later in development, roles in which the stakes are higher and sophisticated understanding of the underlying biology less common. Some of these fields are traditional (e.g., pharmacokinetics and pharmacodynamics [PK/PD]; black) and others are newly emerging (e.g., systems pharmacology; red).Cell biology also has an important role to play in discerning the precise mechanisms of action of existing drugs; it is a remarkable fact that we understand very few drug responses in mechanistic detail. This is as true of the latest generations of targeted therapeutics (many of which aim for selective inhibition of disease-specific mutants) as for older drugs that constitute the mainstay of standard-of-care therapy. The challenge lies less in the interaction between a drug and its intended target than in the consequences of target inhibition for cellular phenotype. This is particularly true when we consider genetic variation from one patient to the next and from one cell to the next within a single patient (particularly with diseases such as cancer). Cellular responses to the microtubule inhibitor and anticancer drug Taxol are an excellent example. Despite being an “old-fashioned” cytotoxic drug, Taxol and its various derivatives are a mainstay of contemporary cancer care, and more patients have probably benefited from taxanes than from all the targeted anticancer drugs combined (Ni Chonghaile et al., 2011 ). Understanding responses to taxanes at a cellular level has also been central to understanding the biology of the spindle assembly checkpoint and mitosis in general. Over the past two decades, checkpoint pathways have been identified and studied in many organisms, and we now understand in detail how processes such as mitotic catastrophe cause cell death (Mitchison, 2012 ). Remarkably, however, the factors that determine whether a cell lives or dies when exposed to Taxol differ dramatically between cultured cells and xenografted tumors (never mind real human tumors); progress through mitosis is always required in culture, but apparently not in the mouse (Orth et al., 2011 ). Understanding this difference represents a fascinating problem in cell biology likely to reveal how cell-autonomous processes, such as mitosis, interact with factors from the local environment in controlling cell fate. Such understanding could also have a real and immediate impact on cancer care.Over the past decade, the success of classical antimitotic chemotherapeutics, such as Taxol, has given rise to efforts to develop other antimitotic agents. For example, drugs that target spindle motors promised to combine the therapeutic antimitotic effects of Taxol, while minimizing neuropathy (motors such as Eg5 are not expressed in neurons [ Huszar et al., 2009 ]). Despite a massive effort by multiple companies, these drugs have proven disappointing in the clinic, as have many drugs that target mitotic kinases. It is now clear that inhibiting mitosis in cancer cells simply does not have the effects we have assumed for the past 50 years, and those antimitotic drugs that do work must do something fundamentally more. Working this out is likely to advance our understanding of the complexities of cell division in humans and animals. However, given the time pressures in industry, there is little opportunity to pursue “failed” drugs, and academic cell biologists have largely ignored problems such as the mechanisms of cell killing by antimitotic agents in real tumors. We must adopt a more holistic and physiological perspective in which we admit that detailed mechanistic understanding is required not only in model organisms and HeLa cells, but also in myriad normal and diseased tissues that have low mitotic index, unusual forms of endo-replication, and complex interactions with neighboring cells. New programs sponsored by the National Center for Advancing Translational Sciences promise to provide some support for this type of research (Allison, 2012 ).More generally, while we all recognize that the “one gene–one disease” paradigm is insufficient for understanding human disease and for selecting patients who will respond to therapy, an effective alternative remains to be developed. Even when the multiplicity of factors involved in a particular disease can be discerned, this understanding does not necessarily reveal how to develop a treatment or cure. For therapy, we must elucidate not only the nature of the initial insult (e.g., a cancer-causing mutation) but also the operation of biological networks that attempt to compensate for the insult (to reestablish homeostasis) and variation in network properties from one individual to the next. It is also important that we identify and understand factors that determine the concentrations and biodistribution of drugs in patients with diverse genotypes. This, in turn, requires a multiscale, network-based approach involving systemic and quantitative study of biological processes at the cellular, tissue, and organismal levels and of the effects of drugs on these processes—precisely the areas in which cell biology has much to contribute.Despite these opportunities, several factors stand in the way of a greater role for cell biologists in drug discovery and development. The first is an unfamiliar vocabulary. We are repeatedly amazed by postdocs who have decided they want to pursue a career in biotechnology or the pharmaceutical industry but who have not spent the time to learn the basics of the drug discovery process from preclinical development to phased clinical trials. Anyone interested in an industrial career should stay abreast of the lively and interesting debates about the best ways to structure and evaluate trials (Kelloff and Sigman, 2012 ). An industrial career usually requires writing more but shorter reports than an academic career, and familiarity with the language of drug discovery makes report writing much easier. A career in industry also benefits from knowledge of the diverse scientific, medical, and business factors that determine success in a drug development campaign. At the same time, it is important to note that some key drug discovery concepts, such as “target identification” or “target qualification,” are widely used but elusive. They imply that the key task is identifying (or cloning) a specific target protein and then screening for agonists and antagonists. As mentioned above, the current challenge increasingly involves understanding targets in the context of biological networks, homeostatic processes, and pathophysiological mechanisms (Wang et al., 2012 ). This implies a more nuanced and holistic approach to understanding the ways the targets and drugs interact (Chene, 2012 ).Many cell biologists in industry find themselves involved in the development or evaluation of assays, particularly for high-throughput screening. Evaluating such screens requires basic understanding of statistics and the trade-offs between false-positive and false-negative results (Atkinson and Lalonde, 2007 ). If high-content screening by imaging is involved, then it is necessary to develop and apply machine vision approaches. Unfortunately, many cell biologists are insufficiently trained in basic statistics, and they have poor programming skills. In our experience, this can be a significant impediment to employment in industry that can be overcome by taking courses in probability and statistics and by gaining practical experience with MatLab or languages such as Python and R. Particularly in biotech, learning the rudiments of intellectual property law can also be a real asset, since it makes it easier to spot patentable inventions.Even the largest drug companies have come to doubt their ability to pursue development projects all the way from target identification to drug approval. It is widely believed that more frequent and effective collaborations between industry and academe are part of the solution (Rubin and Gilliland, 2012 ). This obviously represents a significant opportunity for academic cell biologists. However, the days in which companies were willing to shower academic institutions with generous and unrestricted financial support are long gone. It is now necessary to develop research programs that revolve around concrete goals and deliverables. In our experience, this can be an exciting process for academics accustomed to the conservatism of federal grants, since industry is often willing to pursue ideas that are risky and innovative. Moreover, we have rarely found the perceived difference between applied and basic research to be a significant issue. However, very different expectations over the duration of projects are a major challenge. Industry typically works on 12- to 18-month time lines and academe on a schedule that is at least twice as long. In our experience, even the most effective industry–academic projects tend to underdeliver over the first 18 months, and then only prove their worth in subsequent years. Industry must be more sensitive to the fact that starting a new project in an academic setting means recruiting a new student or postdoc and that there is no way for such an individual to be trained and to succeed with only 18 months of support. However, academics must learn to accommodate the real need for industrial partners to reevaluate projects after approximately 18 months. In our opinion, academics could speed up the initial stages of a project and industry should slow down. We have personally witnessed many industrial projects that were discontinued without reaching a firm conclusion, only to result in an exciting opportunity being missed or to leave open questions that impede progress many years later. A frank discussion of these issues is essential at the outset of any collaborative project.Despite obvious challenges, we envision an expanding role for cell biologists in drug discovery that extends beyond their traditional involvement in early-stage target identification. Significant opportunities exist in better qualifying potential targets and in identifying the role of target proteins in cellular function and pathophysiology. Better understanding of targets in the context of cellular and tissue networks should make it possible to design better therapeutics based on optimizing selectivity, affinity, and type of molecule. Cell biologists can also become more involved in clinical development of new and standard-of-care drugs, particularly with respect to identifying indications, developing diagnostics, and stratifying populations. In this case, learning more about the clinical phases of drug development is valuable. In our personal experience, the most effective approaches are those that involve quantitative analysis and combine experimentation and modeling. This often goes under the name “systems biology” but can easily be viewed as a natural evolution of cell biology in the face of ever-larger data sets and more complex cellular mechanisms. Thus, if we had a single piece of advice for cell biologists interested in pharmacology or drug discovery, it is to acquire or hone skills in statistics, bioinformatics, programming, and applied mathematics in general.Open in a separate windowP. K. SorgerOpen in a separate windowP. K. Sorger     

HER2+ Cancer Cell Dependence on PI3K vs. MAPK Signaling Axes Is Determined by Expression of EGFR,ERBB3 and CDKN1B

Understanding the molecular pathways by which oncogenes drive cancerous cell growth, and how dependence on such pathways varies between tumors could be highly valuable for the design of anti-cancer treatment strategies. In this work we study how dependence upon the canonical PI3K and MAPK cascades varies across HER2+ cancers, and define biomarkers predictive of pathway dependencies. A panel of 18 HER2+ (ERBB2-amplified) cell lines representing a variety of indications was used to characterize the functional and molecular diversity within this oncogene-defined cancer. PI3K and MAPK-pathway dependencies were quantified by measuring in vitro cell growth responses to combinations of AKT (MK2206) and MEK (GSK1120212; trametinib) inhibitors, in the presence and absence of the ERBB3 ligand heregulin (NRG1). A combination of three protein measurements comprising the receptors EGFR, ERBB3 (HER3), and the cyclin-dependent kinase inhibitor p27 (CDKN1B) was found to accurately predict dependence on PI3K/AKT vs. MAPK/ERK signaling axes. Notably, this multivariate classifier outperformed the more intuitive and clinically employed metrics, such as expression of phospho-AKT and phospho-ERK, and PI3K pathway mutations (PIK3CA, PTEN, and PIK3R1). In both cell lines and primary patient samples, we observed consistent expression patterns of these biomarkers varies by cancer indication, such that ERBB3 and CDKN1B expression are relatively high in breast tumors while EGFR expression is relatively high in other indications. The predictability of the three protein biomarkers for differentiating PI3K/AKT vs. MAPK dependence in HER2+ cancers was confirmed using external datasets (Project Achilles and GDSC), again out-performing clinically used genetic markers. Measurement of this minimal set of three protein biomarkers could thus inform treatment, and predict mechanisms of drug resistance in HER2+ cancers. More generally, our results show a single oncogenic transformation can have differing effects on cell signaling and growth, contingent upon the molecular and cellular context.     

Competing docking interactions can bring about bistability in the MAPK cascade

Mitogen-activated protein kinases are crucial regulators of various cell fate decisions including proliferation, differentiation, and apoptosis. Depending on the cellular context, the Raf-Mek-Erk mitogen-activated protein kinase cascade responds to extracellular stimuli in an all-or-none manner, most likely due to bistable behavior. Here, we describe a previously unrecognized positive-feedback mechanism that emerges from experimentally observed sequestration effects in the core Raf-Mek-Erk cascade. Unphosphorylated/monophosphorylated Erk sequesters Mek into Raf-inaccessible complexes upon weak stimulation, and thereby inhibits cascade activation. Mek, once phosphorylated by Raf, triggers Erk phosphorylation, which in turn induces dissociation of Raf-inaccessible Mek-Erk heterodimers, and thus further amplifies Mek phosphorylation. We show that this positive circuit can bring about bistability for parameter values measured experimentally in living cells. Previous studies revealed that bistability can also arise from enzyme depletion effects in the Erk double (de)phosphorylation cycle. We demonstrate that the feedback mechanism proposed in this article synergizes with such enzyme depletion effects to bring about a much larger bistable range than either mechanism alone. Our results show that stable docking interactions and competition effects, which are common in protein kinase cascades, can result in sequestration-based feedback, and thus can have profound effects on the qualitative behavior of signaling pathways.     

Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors

We present a computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways. The model provides insight into signal-response relationships between the binding of EGF to its receptor at the cell surface and the activation of downstream proteins in the signaling cascade. It shows that EGF-induced responses are remarkably stable over a 100-fold range of ligand concentration and that the critical parameter in determining signal efficacy is the initial velocity of receptor activation. The predictions of the model agree well with experimental analysis of the effect of EGF on two downstream responses, phosphorylation of ERK-1/2 and expression of the target gene, c-fos.