The impact of photoreceptor noise on retinal gain controls

Multiple retinal mechanisms preserve visual sensitivity as the properties of the light inputs change. Rapid gain controls match the effective signaling range of retinal neurons to the local image statistics. Such gain controls trade an increased sensitivity for some aspects of the inputs for a decreased sensitivity to others. Rapid, local gain control comes at another cost: noise in the signal controlling gain (e.g. from the photoreceptors) will cause gain itself to vary even when the statistics of the light input are constant. Recent advances in identifying retinal pathways and the sites and mechanisms of mean and contrast adaptation have begun to clarify the tradeoffs associated with different gain control locations and how these tradeoffs differ for rod and cone vision.     

Tunable signal processing in synthetic MAP kinase cascades

The flexibility of MAPK cascade responses enables regulation of a vast array of cell fate decisions, but elucidating the mechanisms underlying this plasticity is difficult in endogenous signaling networks. We constructed insulated mammalian MAPK cascades in yeast to explore how intrinsic and extrinsic perturbations affect the flexibility of these synthetic signaling modules. Contrary to biphasic dependence on scaffold concentration, we observe monotonic decreases in signal strength as scaffold concentration increases. We find that augmenting the concentration of sequential kinases can enhance ultrasensitivity and lower the activation threshold. Further, integrating negative regulation and concentration variation can decouple ultrasensitivity and threshold from the strength of the response. Computational analyses show that cascading can generate ultrasensitivity and that natural cascades with different kinase concentrations are innately biased toward their distinct activation profiles. This work demonstrates that tunable signal processing is inherent to minimal MAPK modules and elucidates principles for rational design of synthetic signaling systems.     

Accurate Encoding and Decoding by Single Cells: Amplitude Versus Frequency Modulation

Cells sense external concentrations and, via biochemical signaling, respond by regulating the expression of target proteins. Both in signaling networks and gene regulation there are two main mechanisms by which the concentration can be encoded internally: amplitude modulation (AM), where the absolute concentration of an internal signaling molecule encodes the stimulus, and frequency modulation (FM), where the period between successive bursts represents the stimulus. Although both mechanisms have been observed in biological systems, the question of when it is beneficial for cells to use either AM or FM is largely unanswered. Here, we first consider a simple model for a single receptor (or ion channel), which can either signal continuously whenever a ligand is bound, or produce a burst in signaling molecule upon receptor binding. We find that bursty signaling is more accurate than continuous signaling only for sufficiently fast dynamics. This suggests that modulation based on bursts may be more common in signaling networks than in gene regulation. We then extend our model to multiple receptors, where continuous and bursty signaling are equivalent to AM and FM respectively, finding that AM is always more accurate. This implies that the reason some cells use FM is related to factors other than accuracy, such as the ability to coordinate expression of multiple genes or to implement threshold crossing mechanisms.     

4-aminophenylalanine as a biocompatible nucleophilic catalyst for hydrazone ligations at low temperature and neutral pH

Hydrazone formation and similar reactions are highly versatile and specific, but their application to biological systems has been limited by their characteristically slow reaction kinetics at neutral pH. Catalysis of these reactions through imine formation with aromatic amines such as aniline has broadened the applicability of these reactions to biomolecular labeling. High concentrations of the catalyst are necessary, which may be incompatible with the native structure of certain proteins. In this study, we investigated the utility of 4-aminophenylalanine (4a-Phe) as a catalyst for these reactions. We find that 4a-Phe is nearly as effective as aniline in catalyzing hydrazone formation between the reactive amino acid 3-formyltyrosine (3f-Tyr) and hydrazine-containing fluorophores, both free in solution and incorporated into the protein tubulin. The catalyst 4a-Phe maintains ～70% of the catalytic efficacy of aniline and is less detrimental to the native structure of tubulin. Examination of the temperature dependence of imine formation between 3f-Tyr and 4a-Phe shows an increase in imine concentration accompanying a decrease in temperature, confirming the exothermic nature of the equilibrium reaction. Interestingly, decreasing the temperature of the 4a-Phe-catalyzed hydrazone reaction between 3f-Tyr and the fluorophore 7-hydrazinyl-4-methylcoumarin increases the overall rate of the reaction. This result indicates that the temperature dependence of the catalyst-aldehyde equilibrium is greater than the temperature dependence of the rate constant for hydrazone formation from this intermediate, and that the rate of hydrazone formation a direct function of the concentration of the intermediate imine. These results provide a platform for conducting nucleophilic catalysis under conditions that are more compatible with biomolecular targets than previously demonstrated, thereby expanding the utility of hydrazone ligations in biological systems.     

FGF signaling enhances a sonic hedgehog negative feedback loop at the initiation of spinal cord ventral patterning

A prevalent developmental mechanism for the assignment of cell identities is the production of spatiotemporal concentration gradients of extracellular signaling molecules that are interpreted by the responding cells. One of such signaling systems is the Shh gradient that controls neuronal subtype identity in the ventral spinal cord. Using loss and gain of function approaches in chick and mouse embryos, we show here that the fibroblast growth factor (FGF) signaling pathway is required to restrict the domains of ventral gene expression as neuroepithelial cells become exposed to Shh during caudal extension of the embryo. FGF signaling activates the expression of the Shh receptor and negative pathway regulator Patched 2 (Ptch2) and therefore can enhance a negative feedback loop that restrains the activity of the pathway. Thus, we identify one of the mechanisms by which FGF signaling acts as a modulator of the onset of Shh signaling activity in the context of coordination of ventral patterning and caudal axis extension. © 2015 Wiley Periodicals, Inc. Develop Neurobiol 76: 956–971, 2016     

No Genetic Tradeoffs between Hygienic Behaviour and Individual Innate Immunity in the Honey Bee,Apis mellifera

Many animals have individual and social mechanisms for combating pathogens. Animals may exhibit short-term physiological tradeoffs between social and individual immunity because the latter is often energetically costly. Genetic tradeoffs between these two traits can also occur if mutations that enhance social immunity diminish individual immunity, or vice versa. Physiological tradeoffs between individual and social immunity have been previously documented in insects, but there has been no study of genetic tradeoffs involving these traits. There is strong evidence that some genes influence both innate immunity and behaviour in social insects – a prerequisite for genetic tradeoffs. Quantifying genetic tradeoffs is critical for understanding the evolution of immunity in social insects and for devising effective strategies for breeding disease-resistant pollinator populations. We conducted two experiments to test the hypothesis of a genetic tradeoff between social and individual immunity in the honey bee, Apis mellifera. First, we estimated the relative contribution of genetics to individual variation in innate immunity of honey bee workers, as only heritable traits can experience genetic tradeoffs. Second, we examined if worker bees with hygienic sisters have reduced individual innate immune response. We genotyped several hundred workers from two colonies and found that patriline genotype does not significantly influence the antimicrobial activity of a worker’s hemolymph. Further, we did not find a negative correlation between hygienic behaviour and the average antimicrobial activity of a worker’s hemolymph across 30 honey bee colonies. Taken together, our work indicates no genetic tradeoffs between hygienic behaviour and innate immunity in honey bees. Our work suggests that using artificial selection to increase hygienic behaviour of honey bee colonies is not expected to concurrently compromise individual innate immunity of worker bees.     

Protein Kinase C: The “Masters” of Calcium and Lipid

The coordinated and physiological behavior of living cells in an organism critically depends on their ability to interact with surrounding cells and with the extracellular space. For this, cells have to interpret incoming stimuli, correctly process the signals, and produce meaningful responses. A major part of such signaling mechanisms is the translation of incoming stimuli into intracellularly understandable signals, usually represented by second messengers or second-messenger systems. Two key second messengers, namely the calcium ion and signaling lipids, albeit extremely different in nature, play an important and often synergistic role in such signaling cascades. In this report, we will shed some light on an entire family of protein kinases, the protein kinases C, that are perfectly designed to exactly decode these two second messengers in all of their properties and convey the signaling content to downstream processes within the cell.Once generated, second messengers relay their information content in a plethora of properties, including time, quantity (i.e., concentration), space (i.e., subcellular distribution), and interestingly into any combination of these three characteristics. Nevertheless, such information is meaningless for the cell unless it has a toolkit of read-out systems that can actually interpret such second-messenger properties and relate them further downstream into complex signaling networks, or directly to effector systems. An important system is the family of protein kinase Cs (PKCs) that can read-out lipid signals alone, or combine the ability to read-out simultaneous lipid and Ca²⁺ signals. A common denominator of all PKCs is the property to convey signals downstream by phosphorylation of additional signaling partners or effector proteins. We will briefly introduce the PKC subfamilies with particular emphasis on their signaling ability, discuss the important sensing domains, and their properties, before concentrating on sensing details of the subfamily of conventional PKCs and their role in signal integration in greater depth.     

Learning, memory, and the role of neural network architecture

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.     

Data-driven docking for the study of biomolecular complexes

With the amount of genetic information available, a lot of attention has focused on systems biology, in particular biomolecular interactions. Considering the huge number of such interactions, and their often weak and transient nature, conventional experimental methods such as X-ray crystallography and NMR spectroscopy are not sufficient to gain structural insight into these. A wealth of biochemical and/or biophysical data can, however, readily be obtained for biomolecular complexes. Combining these data with docking (the process of modeling the 3D structure of a complex from its known constituents) should provide valuable structural information and complement the classical structural methods. In this review we discuss and illustrate the various sources of data that can be used to map interactions and their combination with docking methods to generate structural models of the complexes. Finally a perspective on the future of this kind of approach is given.     

Computational Modeling Reveals Optimal Strategy for Kinase Transport by Microtubules to Nerve Terminals

Intracellular transport of proteins by motors along cytoskeletal filaments is crucial to the proper functioning of many eukaryotic cells. Since most proteins are synthesized at the cell body, mechanisms are required to deliver them to the growing periphery. In this article, we use computational modeling to study the strategies of protein transport in the context of JNK (c-JUN NH2-terminal kinase) transport along microtubules to the terminals of neuronal cells. One such strategy for protein transport is for the proteins of the JNK signaling cascade to bind to scaffolds, and to have the whole protein-scaffold cargo transported by kinesin motors along microtubules. We show how this strategy outperforms protein transport by diffusion alone, using metrics such as signaling rate and signal amplification. We find that there exists a range of scaffold concentrations for which JNK transport is optimal. Increase in scaffold concentration increases signaling rate and signal amplification but an excess of scaffolds results in the dilution of reactants. Similarly, there exists a range of kinesin motor speeds for which JNK transport is optimal. Signaling rate and signal amplification increases with kinesin motor speed until the speed of motor translocation becomes faster than kinase/scaffold-motor binding. Finally, we suggest experiments that can be performed to validate whether, in physiological conditions, neuronal cells do indeed adopt such an optimal strategy. Understanding cytoskeletal-assisted protein transport is crucial since axonal and cell body accumulation of organelles and proteins is a histological feature in many human neurodegenerative diseases. In this paper, we have shown that axonal transport performance changes with altered transport component concentrations and transport speeds wherein these aspects can be modulated to improve axonal efficiency and prevent or slowdown axonal deterioration.     

BMP signaling in wing development: A critical perspective on quantitative image analysis

Bone Morphogenetic Proteins (BMPs) are critical for pattern formation in many animals. In numerous tissues, BMPs become distributed in spatially non-uniform profiles. The gradients of signaling activity can be detected by a number of biological assays involving fluorescence microscopy. Quantitative analyses of BMP gradients are powerful tools to investigate the regulation of BMP signaling pathways during development. These approaches rely heavily on images as spatial representations of BMP activity levels, using them to infer signaling distributions that inform on regulatory mechanisms. In this perspective, we discuss current imaging assays and normalization methods used to quantify BMP activity profiles with a focus on the Drosophila wing primordium. We find that normalization tends to lower the number of samples required to establish statistical significance between profiles in controls and experiments, but the increased resolvability comes with a cost. Each normalization strategy makes implicit assumptions about the biology that impacts our interpretation of the data. We examine the tradeoffs for normalizing versus not normalizing, and discuss their impacts on experimental design and the interpretation of resultant data.     

Regulation of biomolecular condensate dynamics by signaling

Biomolecular condensates are mesoscopic biomolecular assemblies devoid of long range order that contribute to important cellular functions. They form reversibly, are stabilized by numerous but relatively weak intermolecular interactions, and their formation can be regulated by various cellular signals including changes in local concentration, post-translational modifications, energy-consuming processes, and biomolecular interactions. Condensates formed by liquid–liquid phase separation are initially liquid but are metastable relative to hydrogels or irreversible solids that have been associated with protein aggregation diseases and are stabilized by stronger, more permanent interactions. As a consequence of this, a series of cellular mechanisms are available to regulate not only biomolecular condensation but also the physical properties of the condensates.     

An introduction to biomolecular simulations and docking

The biomolecules in and around a living cell – proteins, nucleic acids, lipids and carbohydrates – continuously sample myriad conformational states that are thermally accessible at physiological temperatures. Simultaneously, a given biomolecule also samples (and is sampled by) a rapidly fluctuating local environment comprising other biopolymers, small molecules, water, ions, etc. that diffuse to within a few nanometres, leading to inter-molecular contacts that stitch together large supramolecular assemblies. Indeed, all biological systems can be viewed as dynamic networks of molecular interactions. As a complement to experimentation, molecular simulation offers a uniquely powerful approach to analyse biomolecular structure, mechanism and dynamics; this is possible because the molecular contacts that define a complicated biomolecular system are governed by the same physical principles (forces and energetics) that characterise individual small molecules, and these simpler systems are relatively well-understood. With modern algorithms and computing capabilities, simulations are now an indispensable tool for examining biomolecular assemblies in atomic detail, from the conformational motion in an individual protein to the diffusional dynamics and inter-molecular collisions in the early stages of formation of cellular-scale assemblies such as the ribosome. This text introduces the physicochemical foundations of molecular simulations and docking, largely from the perspective of biomolecular interactions.     

Identifying mechanisms that structure ecological communities by snapping model parameters to empirically observed tradeoffs

Theory predicts that interspecific tradeoffs are primary determinants of coexistence and community composition. Using information from empirically observed tradeoffs to augment the parametrisation of mechanism‐based models should therefore improve model predictions, provided that tradeoffs and mechanisms are chosen correctly. We developed and tested such a model for 35 grassland plant species using monoculture measurements of three species characteristics related to nitrogen uptake and retention, which previous experiments indicate as important at our site. Matching classical theoretical expectations, these characteristics defined a distinct tradeoff surface, and models parameterised with these characteristics closely matched observations from experimental multi‐species mixtures. Importantly, predictions improved significantly when we incorporated information from tradeoffs by ‘snapping’ characteristics to the nearest location on the tradeoff surface, suggesting that the tradeoffs and mechanisms we identify are important determinants of local community structure. This ‘snapping’ method could therefore constitute a broadly applicable test for identifying influential tradeoffs and mechanisms.     

Co-Targeting of JNK and HUNK in Resistant HER2-Positive Breast Cancer

Strategies for successful primary treatment of HER2-positive breast cancer include use of the HER2 inhibitors trastuzumab or lapatinib in combination with standard chemotherapy. While successful, many patients develop resistance to these HER2 inhibitors indicating an unmet need. Consequently, current research efforts are geared toward understanding mechanisms of resistance and the signaling modalities that regulate these mechanisms. We have undertaken a study to examine whether signaling molecules downstream of epidermal growth factor receptor, which often act as compensatory signaling outlets to circumvent HER2 inhibition, can be co-targeted to overcome resistance. We identified JNK signaling as a potential area of intervention and now show that inhibiting JNK using the pan-JNK inhibitor, SP600125, is effective in the HER2-positive, resistant JIMT-1 xenograft mammary tumor model. We also investigate potential combination strategies to bolster the effects of JNK inhibition and find that co-targeting of JNK and the protein kinase HUNK can prohibit tumor growth of resistant HER2-positive mammary tumors in vivo.     

Anti-tumor Effects of Gene Therapy with GALV Membrane Fusion Glycoprotein in Lung Adenocarcinoma

The divergence of protein (Hedgehog) in different organisms remains an unresolved issue to comprehend how the pathway in Hh signaling evolves. Insights into this question can help one identify the key molecules in Hh signaling. This work proposes a protein-associated factor in cells. The development of a non-reductionist theory of cellular functions in medicine is not sufficient. It is necessary to find the parameters with regards to molecules/genes that can elicit functions. This work shows that molecular interactions of gene products can be accomplished by biophysics logic. Defining the protein-associated factors in molecular activities and identifying its roles in molecular transduction are important. A misregulation in molecular switch can account for complex biomolecular consequences. This work shows the potential of the factor on homo-sapiens and unlocks its implications to gene multi-tasking functionality. The associated factor notion should facilitate the medical development in cancer therapeutics.     

A general model of local competition for space

Local competition for space across a wide array of taxa typically involves three mechanisms that we denote here as expansion (spreading into unoccupied habitat), lottery (replacing dead competitors), and overgrowth (encroaching on competitors along zones of contact). By formulating and analysing a simple, general model incorporating these features, we identify ecological conditions and life‐history features that lead to stable coexistence or competitive exclusion (with or without initial‐condition dependence) and gain insight by linking these to case studies in the literature. We demonstrate the importance of contact inhibition, a little‐studied feature of overgrowth, and we show how life‐history tradeoffs may influence and be influenced by local competition for space. The general model we present can help indicate whether local interactions are sufficient to explain patterns of coexistence or exclusion and can serve as the foundation for more specific, realistic models of spatial competition.