Evaluation of mobility recovery after hip fracture: a scoping review of randomized controlled studies

Few older adults regain their pre-fracture mobility after a hip fracture. Intervention studies evaluating effects on gait typically use short clinical tests or in-lab parameters that are often limited to gait speed only. Measurements of mobility in daily life settings exist and should be considered to a greater extent than today. Less than half of hip fracture patients regain their pre-fracture mobility. Mobility recovery is closely linked to health status and quality of life, but there is no comprehensive overview of how gait has been evaluated in intervention studies on hip fracture patients. The purpose was to identify what gait parameters have been used in randomized controlled trials to assess intervention effects on older people's mobility recovery after hip fracture. This scoping review is a secondary paper that identified relevant peer-reviewed and grey literature from 11 databases. After abstract and full-text screening, 24 papers from the original review and 8 from an updated search and manual screening were included. Records were eligible if they included gait parameters in RCTs on hip fracture patients. We included 32 papers from 29 trials (2754 unique participants). Gait parameters were primary endpoint in six studies only. Gait was predominantly evaluated as short walking, with gait speed being most frequently studied. Only five studies reported gait parameters from wearable sensors. Evidence on mobility improvement after interventions in hip fracture patients is largely limited to gait speed as assessed in a controlled setting. The transition from traditional clinical and in-lab to out-of-lab gait assessment is needed to assess effects of interventions on mobility recovery after hip fracture at higher granularity in all aspects of patients' lives, so that optimal care pathways can be defined.

Minimal Surviving Inoculum in Collective Antibiotic Resistance

A common strategy used by bacteria to resist antibiotics is enzymatic degradation or modification. This reduces the antibiotic threat in the environment and is therefore potentially a collective mechanism that also enhances the survival of nearby cells. Collective resistance is of clinical significance, yet a quantitative understanding at the population level is still incomplete. Here, we develop a general theoretical framework of collective resistance by antibiotic degradation. Our modeling study reveals that population survival crucially depends on the ratio of timescales of two processes: the rates of population death and antibiotic removal. However, it is insensitive to molecular, biological, and kinetic details of the underlying processes that give rise to these timescales. Another important aspect of antibiotic degradation is the degree of cooperativity, related to the permeability of the cell wall to antibiotics and enzymes. These observations motivate a coarse-grained, phenomenological model, with two compound parameters representing the population's race to survival and single-cell effective resistance. We propose a simple experimental assay to measure the dose-dependent minimal surviving inoculum and apply it to Escherichia coli expressing several types of β-lactamase. Experimental data analyzed within the theoretical framework corroborate it with good agreement. Our simple model may serve as a reference for more complex situations, such as heterogeneous bacterial communities. IMPORTANCE Collective resistance occurs when bacteria work together to decrease the concentration of antibiotics in their environment, for example, by actively breaking down or modifying them. This can help bacteria survive by reducing the effective antibiotic concentration below their threshold for growth. In this study, we used mathematical modeling to examine the factors that influence collective resistance and to develop a framework to understand the minimum population size needed to survive a given initial antibiotic concentration. Our work helps to identify generic mechanism-independent parameters that can be derived from population data and identifies combinations of parameters that play a role in collective resistance. Specifically, it highlights the relative timescales involved in the survival of populations that inactivate antibiotics, as well as the levels of cooperation versus privatization. The results of this study contribute to our understanding of population-level effects on antibiotic resistance and may inform the design of antibiotic therapies.

Identifying regulation with adversarial surrogates

Homeostasis, the ability to maintain a relatively constant internal environment in the face of perturbations, is a hallmark of biological systems. It is believed that this constancy is achieved through multiple internal regulation and control processes. Given observations of a system, or even a detailed model of one, it is both valuable and extremely challenging to extract the control objectives of the homeostatic mechanisms. In this work, we develop a robust data-driven method to identify these objectives, namely to understand: "what does the system care about?". We propose an algorithm, Identifying Regulation with Adversarial Surrogates (IRAS), that receives an array of temporal measurements of the system and outputs a candidate for the control objective, expressed as a combination of observed variables. IRAS is an iterative algorithm consisting of two competing players. The first player, realized by an artificial deep neural network, aims to minimize a measure of invariance we refer to as the coefficient of regulation. The second player aims to render the task of the first player more difficult by forcing it to extract information about the temporal structure of the data, which is absent from similar "surrogate" data. We test the algorithm on four synthetic and one natural data set, demonstrating excellent empirical results. Interestingly, our approach can also be used to extract conserved quantities, e.g., energy and momentum, in purely physical systems, as we demonstrate empirically.

A leader cell triggers end of lag phase in populations of Pseudomonas fluorescens

The relationship between the number of cells colonizing a new environment and time for resumption of growth is a subject of long-standing interest. In microbiology this is known as the "inoculum effect." Its mechanistic basis is unclear with possible explanations ranging from the independent actions of individual cells, to collective actions of populations of cells. Here, we use a millifluidic droplet device in which the growth dynamics of hundreds of populations founded by controlled numbers of Pseudomonas fluorescens cells, ranging from a single cell, to one thousand cells, were followed in real time. Our data show that lag phase decreases with inoculum size. The decrease of average lag time and its variance across droplets, as well as lag time distribution shapes, follow predictions of extreme value theory, where the inoculum lag time is determined by the minimum value sampled from the single-cell distribution. Our experimental results show that exit from lag phase depends on strong interactions among cells, consistent with a "leader cell" triggering end of lag phase for the entire population.

Cancer progression as a learning process

Drug resistance and metastasis—the major complications in cancer—both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.

Multiple timescales in bacterial growth homeostasis

In balanced exponential growth, bacteria maintain many properties statistically stable for a long time: cell size, cell cycle time, and more. As these are strongly coupled variables, it is not a-priori obvious which are directly regulated and which are stabilized through interactions. Here, we address this problem by separating timescales in bacterial single-cell dynamics. Disentangling homeostatic set points from fluctuations around them reveals that some variables, such as growth-rate, cell size and cycle time, are "sloppy" with highly volatile set points. Quantifying the relative contribution of environmental and internal sources, we find that sloppiness is primarily driven by the environment. Other variables such as fold-change define "stiff" combinations of coupled variables with robust set points. These results are manifested geometrically as a control manifold in the space of variables: set points span a wide range of values within the manifold, whereas out-of-manifold deviations are constrained. Our work offers a generalizable data-driven approach for identifying control variables in a multidimensional system.

Human genomics of the humoral immune response against polyomaviruses

Human polyomaviruses are widespread in humans and can cause severe disease in immunocompromised individuals. To identify human genetic determinants of the humoral immune response against polyomaviruses, we performed genome-wide association studies and meta-analyses of qualitative and quantitative immunoglobulin G responses against BK polyomavirus (BKPyV), JC polyomavirus (JCPyV), Merkel cellpolyomavirus (MCPyV), WU polyomavirus (WUPyV), and human polyomavirus 6 (HPyV6) in 15,660 individuals of European ancestry from three independent studies. We observed significant associations for all tested viruses: JCPyV, HPyV6, and MCPyV associated with human leukocyte antigen class II variation, BKPyV and JCPyV with variants in FUT2, responsible for secretor status, MCPyV with variants in STING1, involved in interferon induction, and WUPyV with a functional variant in MUC1, previously associated with risk for gastric cancer. These results provide insights into the genetic control of a family of very prevalent human viruses, highlighting genes and pathways that play a modulating role in human humoral immunity.

Epstein-Barr virus infection after adolescence and human herpesvirus 6A as risk factors for multiple sclerosis

BACKGROUND AND PURPOSE

Infections with human herpesvirus 6A (HHV-6A) and Epstein-Barr virus (EBV) have been linked to multiple sclerosis (MS) development. For EBV, late infection has been proposed as a risk factor, but serological support is lacking. The objective of this study was to investigate how age affects the EBV and HHV-6A associated risks of developing MS.

METHODS

In this nested case-control study, Swedish biobanks were accessed to find pre-symptomatically collected blood samples from 670 individuals who later developed relapsing MS and 670 matched controls. A bead-based multiplex assay was used to determine serological response against EBV and HHV-6A. Conditional logistic regression was used to calculate odds ratios and 95% confidence intervals.

RESULTS

Seropositivity against EBV exhibited a pattern where associations switched from a decreased risk of developing MS in the group below 20 years of age to an increased risk amongst individuals aged 20-29 and 30-39 years (p for trend 0.020). The age of transition was estimated to be 18.8 years. In contrast, HHV-6A was associated with increased MS risk in all age groups (total cohort odds ratio 2.1, 95% confidence interval 1.6-2.7).

CONCLUSIONS

This study suggests EBV infection after adolescence and age independent HHV-6A infection as risk factors for MS.

Local and global features of genetic networks supporting a phenotypic switch

Phenotypic switches are associated with alterations in the cell's gene expression profile and are vital to many aspects of biology. Previous studies have identified local motifs of the genetic regulatory network that could underlie such switches. Recent advancements allowed the study of networks at the global, many-gene, level; however, the relationship between the local and global scales in giving rise to phenotypic switches remains elusive. In this work, we studied the epithelial-mesenchymal transition (EMT) using a gene regulatory network model. This model supports two clusters of stable steady-states identified with the epithelial and mesenchymal phenotypes, and a range of intermediate less stable hybrid states, whose importance in cancer has been recently highlighted. Using an array of network perturbations and quantifying the resulting landscape, we investigated how features of the network at different levels give rise to these landscape properties. We found that local connectivity patterns affect the landscape in a mostly incremental manner; in particular, a specific previously identified double-negative feedback motif is not required when embedded in the full network, because the landscape is maintained at a global level. Nevertheless, despite the distributed nature of the switch, it is possible to find combinations of a few local changes that disrupt it. At the level of network architecture, we identified a crucial role for peripheral genes that act as incoming signals to the network in creating clusters of states. Such incoming signals are a signature of modularity and are expected to appear also in other biological networks. Hybrid states between epithelial and mesenchymal arise in the model due to barriers in the interaction between genes, causing hysteresis at all connections. Our results suggest emergent switches can neither be pinpointed to local motifs, nor do they arise as typical properties of random network ensembles. Rather, they arise through an interplay between the nature of local interactions, and the core-periphery structure induced by the modularity of the cell.

Coexistence and cooperation in structured habitats

Background

Natural habitats are typically structured, imposing constraints on inhabiting populations and their interactions. Which conditions are important for coexistence of diverse communities, and how cooperative interaction stabilizes in such populations, have been important ecological and evolutionary questions.

Results

We investigate a minimal ecological framework of microbial population dynamics that exhibits crucial features to show coexistence: Populations repeatedly undergo cycles of separation into compartmentalized habitats and mixing with new resources. The characteristic time-scale is longer than that typical of individual growth. Using analytic approximations, averaging techniques and phase-plane methods of dynamical systems, we provide a framework for analyzing various types of microbial interactions. Population composition and population size are both dynamic variables of the model; they are found to be decoupled both in terms of time-scale and parameter dependence. We present specific results for two examples of cooperative interaction by public goods: collective antibiotics resistance, and enhanced iron-availability by pyoverdine. We find stable coexistence to be a likely outcome.

Conclusions

The two simple features of a long mixing time-scale and spatial compartmentalization are enough to enable coexisting strains. In particular, costly social traits are often stabilized in such an environment—and thus cooperation established.

Visual detection of time-varying signals: Opposing biases and their timescales

Human visual perception is a complex, dynamic and fluctuating process. In addition to the incoming visual stimulus, it is affected by many other factors including temporal context, both external and internal to the observer. In this study we investigate the dynamic properties of psychophysical responses to a continuous stream of visual near-threshold detection tasks. We manipulate the incoming signals to have temporal structures with various characteristic timescales. Responses of human observers to these signals are analyzed using tools that highlight their dynamical features as well. Our experiments show two opposing biases that shape perceptual decision making simultaneously: positive recency, biasing towards repeated response; and adaptation, entailing an increased probability of changed response. While both these effects have been reported in previous work, our results shed new light on the timescales involved in these effects, and on their interplay with varying inputs. We find that positive recency is a short-term bias, inversely correlated with response time, suggesting it can be compensated by afterthought. Adaptation, in contrast, reflects trends over longer times possibly including multiple previous trials. Our entire dataset, which includes different input signal temporal structures, is consistent with a simple model with the two biases characterized by a fixed parameter set. These results suggest that perceptual biases are inherent features which are not flexible to tune to input signals.

Stable memory with unstable synapses

What is the physiological basis of long-term memory? The prevailing view in Neuroscience attributes changes in synaptic efficacy to memory acquisition, implying that stable memories correspond to stable connectivity patterns. However, an increasing body of experimental evidence points to significant, activity-independent fluctuations in synaptic strengths. How memories can survive these fluctuations and the accompanying stabilizing homeostatic mechanisms is a fundamental open question. Here we explore the possibility of memory storage within a global component of network connectivity, while individual connections fluctuate. We find that homeostatic stabilization of fluctuations differentially affects different aspects of network connectivity. Specifically, memories stored as time-varying attractors of neural dynamics are more resilient to erosion than fixed-points. Such dynamic attractors can be learned by biologically plausible learning-rules and support associative retrieval. Our results suggest a link between the properties of learning-rules and those of network-level memory representations, and point at experimentally measurable signatures.

How are stable memories maintained in the brain despite significant ongoing fluctuations in synaptic strengths? Here, the authors show that a model consistent with fluctuations, homeostasis and biologically plausible learning rules, naturally leads to memories implemented as dynamic attractors.

Timing of HPV16-E6 antibody seroconversion before OPSCC: findings from the HPVC3 consortium

BACKGROUND

Human papillomavirus type 16 (HPV16)-E6 antibodies are detectable in peripheral blood before diagnosis in the majority of HPV16-driven oropharyngeal squamous cell carcinoma (OPSCC), but the timing of seroconversion is unknown.

PATIENTS AND METHODS

We formed the HPV Cancer Cohort Consortium which comprises nine population cohorts from Europe, North America and Australia. In total, 743 incident OPSCC cases and 5814 controls provided at least one pre-diagnostic blood sample, including 111 cases with multiple samples. Median time between first blood collection and OPSCC diagnosis was 11.4 years (IQR = 6-11 years, range = 0-40 years). Antibodies against HPV16-E6 were measured by multiplex serology (GST fusion protein based Luminex assay).

RESULTS

HPV16-E6 seropositivity was present in 0.4% of controls (22/5814; 95% CI 0.2% to 0.6%) and 26.2% (195/743; 95% CI 23.1% to 29.6%) of OPSCC cases. HPV16-E6 seropositivity increased the odds of OPSCC 98.2-fold (95% CI 62.1-155.4) in whites and 17.2-fold (95% CI 1.7-170.5) in blacks. Seropositivity in cases was more frequent in recent calendar periods, ranging from 21.9% pre-1996 to 68.4% in 2005 onwards, in those with blood collection near diagnosis (lead time <5 years). HPV16-E6 seropositivity increased with lead time: 0.0%, 13.5%, 23.7%, and 38.9% with lead times of >30 years (N = 24), 20-30 years (N = 148), 10-20 years (N = 228), and <10 years (N = 301 cases) (p-trend < 0.001). Of the 47 HPV16-E6 seropositive cases with serially-collected blood samples, 17 cases seroconverted during follow-up, with timing ranging from 6 to 28 years before diagnosis. For the remaining 30 cases, robust seropositivity was observed up to 25 years before diagnosis.

CONCLUSIONS

The immune response to HPV16-driven tumorigenesis is most often detectable several decades before OPSCC diagnosis. HPV16-E6 seropositive individuals face increased risk of OPSCC over several decades.

Exploratory adaptation in large random networks

The capacity of cells and organisms to respond to challenging conditions in a repeatable manner is limited by a finite repertoire of pre-evolved adaptive responses. Beyond this capacity, cells can use exploratory dynamics to cope with a much broader array of conditions. However, the process of adaptation by exploratory dynamics within the lifetime of a cell is not well understood. Here we demonstrate the feasibility of exploratory adaptation in a high-dimensional network model of gene regulation. Exploration is initiated by failure to comply with a constraint and is implemented by random sampling of network configurations. It ceases if and when the network reaches a stable state satisfying the constraint. We find that successful convergence (adaptation) in high dimensions requires outgoing network hubs and is enhanced by their auto-regulation. The ability of these empirically validated features of gene regulatory networks to support exploratory adaptation without fine-tuning, makes it plausible for biological implementation.

Recent works suggest that cellular networks may respond to novel challenges on the time-scale of cellular lifetimes through large-scale perturbation of gene expression and convergence to a new state. Here, the authors demonstrate the theoretical feasibility of exploratory adaptation in cellular networks by showing that convergence to new states depends on known features of these networks.

Fluctuations-induced coexistence in public goods dynamics

Cooperative interactions between individuals in a population and their stability properties are central to population dynamics and evolution. We introduce a generic class of nonlinear dynamical systems describing such interactions between producers and non-producers of a rapidly equilibrating common resource extracted from a finite environment. In the deterministic mean field approximation, fast-growing non-producers drive the entire population to extinction. However, the presence of arbitrarily small perturbations destabilizes this fixed point into a stochastic attractor where both phenotypes can survive. Phase space arguments and moment closure are used to characterize the attractor and show that its properties are not determined by the noise amplitude or boundary conditions, but rather it is stabilized by the stochastic nonlinear dynamics. Spatial Monte Carlo simulations with demographic fluctuations and diffusion illustrate a similar effect, supporting the validity of the two-dimensional stochastic differential equation as an approximation. The functional distribution of the noise emerges as the main factor determining the dynamical outcome. Noise resulting from diffusion between different regions, or additive noise, induce coexistence while multiplicative or local demographic noise do not alter the outcome of deterministic dynamics. The results are discussed in a general context of the effect of noise on phase space structure.

Universal protein distributions in a model of cell growth and division

Protein distributions measured under a broad set of conditions in bacteria and yeast were shown to exhibit a common skewed shape, with variances depending quadratically on means. For bacteria these properties were reproduced by temporal measurements of protein content, showing accumulation and division across generations. Here we present a stochastic growth-and-division model with feedback which captures these observed properties. The limiting copy number distribution is calculated exactly, and a single parameter is found to determine the distribution shape and the variance-to-mean relation. Estimating this parameter from bacterial temporal data reproduces the measured distribution shape with high accuracy and leads to predictions for future experiments.

Single-cell protein dynamics reproduce universal fluctuations in cell populations

Protein variability in single cells has been studied extensively in populations, but little is known about temporal protein fluctuations in a single cell over extended times. We present here traces of protein copy number measured in individual bacteria over multiple generations and investigate their statistical properties, comparing them to previously measured population snapshots. We find that temporal fluctuations in individual cells exhibit the same properties as those previously observed in populations. Scaled fluctuations around the mean of each trace exhibit the universal distribution shape measured in populations under a wide range of conditions and in two distinct microorganisms; the mean and variance of the traces over time obey the same quadratic relation. Analyzing the individual protein traces reveals that within a cell cycle protein content increases exponentially, with a rate that varies from cycle to cycle. This leads to a compact description of the trace as a 3-variable stochastic process -exponential rate, cell cycle duration and value at the cycle start- sampled once a cycle. This description is sufficient to reproduce both universal statistical properties of the protein fluctuations. Our results show that the protein distribution shape is insensitive to sub-cycle intracellular microscopic details and reflects global cellular properties that fluctuate between generations.

Synaptic size dynamics as an effectively stochastic process

Long-term, repeated measurements of individual synaptic properties have revealed that synapses can undergo significant directed and spontaneous changes over time scales of minutes to weeks. These changes are presumably driven by a large number of activity-dependent and independent molecular processes, yet how these processes integrate to determine the totality of synaptic size remains unknown. Here we propose, as an alternative to detailed, mechanistic descriptions, a statistical approach to synaptic size dynamics. The basic premise of this approach is that the integrated outcome of the myriad of processes that drive synaptic size dynamics are effectively described as a combination of multiplicative and additive processes, both of which are stochastic and taken from distributions parametrically affected by physiological signals. We show that this seemingly simple model, known in probability theory as the Kesten process, can generate rich dynamics which are qualitatively similar to the dynamics of individual glutamatergic synapses recorded in long-term time-lapse experiments in ex-vivo cortical networks. Moreover, we show that this stochastic model, which is insensitive to many of its underlying details, quantitatively captures the distributions of synaptic sizes measured in these experiments, the long-term stability of such distributions and their scaling in response to pharmacological manipulations. Finally, we show that the average kinetics of new postsynaptic density formation measured in such experiments is also faithfully captured by the same model. The model thus provides a useful framework for characterizing synapse size dynamics at steady state, during initial formation of such steady states, and during their convergence to new steady states following perturbations. These findings show the strength of a simple low dimensional statistical model to quantitatively describe synapse size dynamics as the integrated result of many underlying complex processes.

Coexistence of productive and non-productive populations by fluctuation-driven spatio-temporal patterns

Cooperative interactions, their stability and evolution, provide an interesting context in which to study the interface between cellular and population levels of organization. Here we study a public goods model relevant to microorganism populations actively extracting a growth resource from their environment. Cells can display one of two phenotypes - a productive phenotype that extracts the resources at a cost, and a non-productive phenotype that only consumes the same resource. Both proliferate and are free to move by diffusion; growth rate and diffusion coefficient depend only weakly phenotype. We analyze the continuous differential equation model as well as simulate stochastically the full dynamics. We find that the two sub-populations, which cannot coexist in a well-mixed environment, develop spatio-temporal patterns that enable long-term coexistence in the shared environment. These patterns are purely fluctuation-driven, as the corresponding continuous spatial system does not display Turing instability. The average stability of coexistence patterns derives from a dynamic mechanism in which the producing sub-population equilibrates with the environmental resource and holds it close to an extinction transition of the other sub-population, causing it to constantly hover around this transition. Thus the ecological interactions support a mechanism reminiscent of self-organized criticality; power-law distributions and long-range correlations are found. The results are discussed in the context of general pattern formation and critical behavior in ecology as well as in an experimental context.

Metabolic variability in micro-populations

Biological cells in a population are variable in practically every property. Much is known about how variability of single cells is reflected in the statistical properties of infinitely large populations; however, many biologically relevant situations entail finite times and intermediate-sized populations. The statistical properties of an ensemble of finite populations then come into focus, raising questions concerning inter-population variability and dependence on initial conditions. Recent technologies of microfluidic and microdroplet-based population growth realize these situations and make them immediately relevant for experiments and biotechnological application. We here study the statistical properties, arising from metabolic variability of single cells, in an ensemble of micro-populations grown to saturation in a finite environment such as a micro-droplet. We develop a discrete stochastic model for this growth process, describing the possible histories as a random walk in a phenotypic space with an absorbing boundary. Using a mapping to Polya’s Urn, a classic problem of probability theory, we find that distributions approach a limiting inoculum-dependent form after a large number of divisions. Thus, population size and structure are random variables whose mean, variance and in general their distribution can reflect initial conditions after many generations of growth. Implications of our results to experiments and to biotechnology are discussed.

Universal protein fluctuations in populations of microorganisms

The copy number of any protein fluctuates among cells in a population; characterizing and understanding these fluctuations is a fundamental problem in biophysics. We show here that protein distributions measured under a broad range of biological realizations collapse to a single non-gaussian curve under scaling by the first two moments. Moreover, in all experiments the variance is found to depend quadratically on the mean, showing that a single degree of freedom determines the entire distribution. Our results imply that protein fluctuations do not reflect any specific molecular or cellular mechanism, and suggest that some buffering process masks these details and induces universality.

Cooperation and cheating in microbial exoenzyme production--theoretical analysis for biotechnological applications

The engineering of microorganisms to produce a variety of extracellular enzymes (exoenzymes), for example for producing renewable fuels and in biodegradation of xenobiotics, has recently attracted increasing interest. Productivity is often reduced by "cheater" mutants, which are deficient in exoenzyme production and benefit from the product provided by the "cooperating" cells. We present a game-theoretical model to analyze population structure and exoenzyme productivity in terms of biotechnologically relevant parameters. For any given population density, three distinct regimes are predicted: when the metabolic effort for exoenzyme production and secretion is low, all cells cooperate; at intermediate metabolic costs, cooperators and cheaters coexist; while at high costs, all cells use the cheating strategy. These regimes correspond to the harmony game, snowdrift game, and Prisoner's Dilemma, respectively. Thus, our results indicate that microbial strains engineered for exoenzyme production will not, under appropriate conditions, be outcompeted by cheater mutants. We also analyze the dependence of the population structure on cell density. At low costs, the fraction of cooperating cells increases with decreasing cell density and reaches unity at a critical threshold. Our model provides an estimate of the cell density maximizing exoenzyme production.

Cellular properties and population asymptotics in the population balance equation

Proliferating cell populations at steady-state growth often exhibit broad protein distributions with exponential tails. The sources of this variation and its universality are of much theoretical interest. Here we address the problem by asymptotic analysis of the population balance equation. We show that the steady-state distribution tail is determined by a combination of protein production and cell division and is insensitive to other model details. Under general conditions this tail is exponential with a dependence on parameters consistent with experiment. We discuss the conditions for this effect to be dominant over other sources of variation and the relation to experiments.

Nonequilibrium statistical mechanics of dividing cell populations

We present and study a model for the nonequilibrium statistical mechanics of protein distributions in a proliferating cell population. Our model describes how the total protein variation is shaped by two processes: variation in protein production internal to the cells and variation in division and inheritance at the population level. It enables us to assess the contribution of each of these components separately. We find that, even if production is deterministic, cell division can generate a large variation in protein distribution. In this limit we solve exactly a special case and draw an analogy between protein distribution along cell generations and stress distribution in layers of granular material. At the other limit of extremely noisy protein production, we find that the population structure restrains variation and that the details of division do not affect the tail of the distribution.

Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge

By recruiting the essential HIS3 gene to the GAL regulatory system and switching to a repressing glucose medium, we confronted yeast cells with a novel challenge they had not encountered before along their history in evolution.

Adaptation to this challenge involved a global transcriptional response of a sizeable fraction of the genome, which relaxed on the time scale of the population adaptation, of order of 10 generations.

For a large fraction of the responding genes there is no simple biological interpretation, connecting them to the specific cellular demands imposed by the novel challenge.

Strikingly, repeating the experiment did not reproduce similar transcription patterns neither in the transient phase nor in the adapted state in glucose.

These results suggest that physiological selection operates on the new metabolic configurations generated by the non-specific large scale transcriptional response to eventually stabilize an adaptive state.

Cells adjust their transcriptional state to accommodate environmental and genetic perturbations. Some common perturbations, such as changes in nutrient composition, elicit well-characterized transcriptional responses that can be understood by simple engineering-like design principles as satisfying specific demands imposed by the perturbation. However, cells also have the ability to adapt to novel and unforeseen challenges. This ability is central in realizing the evolvability potential of cells as they respond to dramatic genetic or environmental changes along evolution. Little is known about the mechanisms underlying such adaptations to novel challenges; in particular, the role of the transcriptional regulatory network in such adaptations has not been characterized. Genome-wide measurements have revealed that, in many cases, perturbations lead to a global transcriptional response involving a sizeable fraction of the genome (Gasch et al, 2000; Jelinsky et al, 2000; Causton et al, 2001; Ideker et al, 2001; Lai et al, 2005). Such global behavior suggests that general collective properties of the genetic network, rather than specific pre-designed pathways, determine an important part of the transcriptional response. It is not known however what fraction of genes within such massive transcriptional responses is essential to the specific cellular demands. It is also unknown whether the non-pre-designed part of the response can have a functional role in adaptation to novel challenges.

To study these questions, we confronted yeast cells with a novel challenge they had not encountered before along their history in evolution. A strain of the yeast Saccharomyces cerevisiae was engineered to recruit the gene HIS3, an essential enzyme from the histidine biosynthesis pathway (Hinnebusch, 1992), to the GAL regulatory system, responsible for galactose utilization (Stolovicki et al, 2006). The GAL system is known to be strongly repressed when the cells are exposed to glucose. Therefore, upon switching to a medium containing glucose and lacking histidine, the GAL system and with it HIS3 are highly repressed immediately following the switch and the cells encounter a severe challenge. We have recently shown that a cell population carrying this rewired genome can adapt to grow competitively in a chemostat in a medium containing pure glucose (Stolovicki et al, 2006). This adaptation occurred on a timescale of ∼10 generations; applying a stronger environmental pressure in the form of a competitive inhibitor to HIS3 (3AT) resulted in a similar adaptation albeit with a longer timescale. Figure 1 shows the dynamics of the population's cell density (blue lines, measured by OD) following a medium switch from galactose to glucose in the chemostat without (A) and with (B) 3AT. The experiments revealed that adaptation occurs on physiological timescales (much shorter than required by spontaneous random mutations), but the mechanisms underlying this adaptation have remained unclear (Stolovicki et al, 2006).

Yeast cells had not encountered recruitment of HIS3 to the GAL system along their evolutionary history, and their genome could not possibly have been selected to specifically address glucose repression of HIS3. This experiment, therefore, provides a unique opportunity to characterize the spontaneous transcriptional response during adaptation to a novel challenge and to assess the functional role of the regulatory system in this adaptation. We used DNA microarrays to measure the genome-wide expression levels at time points along the adaptation process, with and without 3AT. These measurements revealed that a sizeable fraction of the genome responded by induction or repression to the switch into glucose. Superimposed on the OD traces, Figure 1 shows the results of a clustering analysis of the expression of genes as measured by the arrays along time in the experiments. This analysis revealed two dominant clusters, each containing hundreds of genes in each experiment, which responded to the medium switch to glucose by a strong transient induction or repression followed by relaxation to steady state on the timescale of the adaptation process, ∼ 10 generations. The two clusters in each experiment show similar but opposite dynamics.

A detailed analysis of the gene content in the two clusters revealed that only a small portion of the response was induced by a change in carbon source (15% overlap between the corresponding clusters in the two experiments, with and without 3AT). Moreover, it revealed a very low overlap with the universal stress response observed for a wide range of environmental stresses (Gasch et al, 2000; Causton et al, 2001) and with the typical response to amino-acid starvation (Natarajan et al, 2001). Additionally, all known specific responses to stress in the literature are characterized by transient induction or repression with relaxation to steady state within a generation time (Gasch et al, 2000; Koerkamp et al, 2002; Wu et al, 2004), whereas in our experiments relaxation of the transcriptional response occurs over many generations. Taken together, these results show that the transcriptional response observed here is neither a metabolic response to the change in carbon source nor is it a standard response to stress or amino-acid starvation. This raises the possibility that it is a spontaneous collective response that is largely composed of genes that do not have a specific function. This possibility was tested directly by repeating the experiment with different populations and comparing their responses. This procedure revealed reproducible adaptation dynamics and steady states in terms of population density, but showed significantly different transcriptional transient responses and steady states for the two repeated experiments. Thus, a significant portion of the genes that changed their expression during the adaptation process do not have a well-defined and reproducible function in the challenging environment.

The application of a stronger environmental pressure in the form of 3AT had a dramatic effect on the global characteristics of the transcriptional response: it induced a markedly higher correlation among the hundreds of responding genes. Figure 3A compares the array data in color code for the two experiments. It is seen that the emergent pattern of transcription exhibits a higher degree of order by the introduction of high external pressure. Observation of the transcriptional patterns for specific metabolic pathways illustrates the different contributions to the correlated dynamics (Figure 3B–D). A general energetic module such as glycolysis exhibited similar patterns of induction and relaxation in experiments with and without 3AT (Figure 3B). However, in general, we found that more than one-third of the known metabolic modules (30 out of 88 modules described in KEGG) exhibited high expression correlation among their genes when the environmental pressure was high but not when it was low. As an example, Figure 3C shows the histidine biosynthesis pathway and Figure 3D the purine pathway. Note the highly ordered trajectories in the lower panels (with 3AT) compared to the disordered ones in the upper panels (no 3AT). This order extends also between genes belonging to different and even distant metabolic modules. It indicates that a global transcriptional regulatory mechanism is in operation, rather than a local specific one. Surprisingly, genes belonging to the same metabolic pathway exhibited simultaneous positively and negatively correlated dynamics. Thus, an important conclusion of this work is that the global transcriptional response to a novel challenge cannot be explained by a simple cellular or metabolic logic. This is to be expected if the response had not been specifically selected in evolution and was not pre-designed for the challenge.

Our data clearly reveal that the massive transcriptional response underlies the adaptation process to a novel challenge. The novelty of the challenge presented to the cells excludes the possibility that this response has been specifically selected toward this challenge. Thus, transcriptional regulation has dynamic properties resulting in a general massive nonspecific response to a novel perturbation. Such a response in turn allows for metabolic rearrangements, which by feeding back on transcription lead to adaptation of the cells to the unforeseen situation. The drastic change in the expression state of the cell opens multiple new metabolic pathways. Physiological selection works then on these multiple metabolic pathways to stabilize an adaptive state that causes relaxation of the perturbed expression pattern. This scenario, involving the creation of a library of possibilities and physiological selection over this library, is compatible with our understanding of a broad class of biological systems, placing the cellular metabolic/regulatory networks on the same footing as the neural or the immune systems (Gerhart and Kirschner, 1997).

Cells adjust their transcriptional state to accommodate environmental and genetic perturbations. An open question is to what extent transcriptional response to perturbations has been specifically selected along evolution. To test the possibility that transcriptional reprogramming does not need to be ‘pre-designed' to lead to an adaptive metabolic state on physiological timescales, we confronted yeast cells with a novel challenge they had not previously encountered. We rewired the genome by recruiting an essential gene, HIS3, from the histidine biosynthesis pathway to a foreign regulatory system, the GAL network responsible for galactose utilization. Switching medium to glucose in a chemostat caused repression of the essential gene and presented the cells with a severe challenge to which they adapted over approximately 10 generations. Using genome-wide expression arrays, we show here that a global transcriptional reprogramming (>1200 genes) underlies the adaptation. A large fraction of the responding genes is nonreproducible in repeated experiments. These results show that a nonspecific transcriptional response reflecting the natural plasticity of the regulatory network supports adaptation of cells to novel challenges.

Dynamics of protein distributions in cell populations

A population of cells exhibits wide phenotypic variation even if it is genetically homogeneous. In particular, individual cells differ from one another in the amount of protein they express under a given regulatory system under fixed conditions. Here we study how protein distributions in a population of the yeast S. cerevisiae are shaped by a balance of processes: protein production--an intracellular process--and protein dilution due to cell division--a population process. We measure protein distributions by employing reporter green fluorescence protein (gfp) under the regulation of the yeast GAL system under conditions where it is metabolically essential. Cell populations are grown in chemostats, thus allowing control of the environment and stable measurements of distribution dynamics over many generations. Despite the essential functional role of the GAL system in a pure galactose medium, steady-state distributions are found to be universally broad, with exponential tails and a large standard-deviation-to-mean ratio. Under several different perturbations the dynamics of the distribution is observed to be asymmetric, with a much longer time to build a wide expression distribution from below compared with a fast relaxation of the distribution toward steady state from above. These results show that the main features of the protein distributions are largely determined by population effects and are less sensitive to the intracellular biochemical noise.

Synthetic gene recruitment reveals adaptive reprogramming of gene regulation in yeast

The recruitment of a gene to a foreign regulatory system is a major evolutionary event that can lead to novel phenotypes. However, the evolvability potential of cells depends on their ability to cope with challenges presented by gene recruitment. To study this ability, we combined synthetic gene recruitment with continuous culture and online measurements of the metabolic and regulatory dynamics over long timescales. The gene HIS3 from the histidine synthesis pathway was recruited to the GAL system, responsible for galactose utilization in the yeast S. cerevisiae. Following a switch from galactose to glucose--from induced to repressed conditions of the GAL system--in histidine-lacking chemostats (where the recruited HIS3 is essential), the regulatory system reprogrammed to adaptively tune HIS3 expression, allowing the cells to grow competitively in pure glucose. The adapted state was maintained for hundreds of generations in various environments. The timescales involved and the reproducibility of separate experiments render spontaneous mutations an unlikely underlying mechanism. Essentially all cells could adapt, excluding selection over a genetically variable population. The results reveal heritable adaptation induced by the exposure to glucose. They demonstrate that genetic regulatory networks have the potential to support highly demanding events of gene recruitment.

Primary neuroendocrine carcinoma of inguinal lymph node

Ninety-seven percent of neuroendocrine carcinomas are located in the gastrointestinal tract or in the bronchopulmonary tree. Inguinal lymph nodes as the primary tumor site for neuroendocrine carcinoma represent a very unusual location, and have only been described in 2 patient series in the literature. A 64-year-old, previously healthy, Caucasian female presented with a 2-month history of an enlarged inguinal lymph node on the right side. The removed lymph node showed histological and immunohistochemical characteristics of neuroendocrine differentiation (positive for synaptophysin, cytokeratin 20, neuron-specific enolase and chromogranin A). Although extensive investigations including repeated CT and NMR scans, classical endoscopy, wireless capsule endoscopy of the small intestine, octreotide- and MIBG scintigraphy were performed, no other primary tumor was found. Furthermore, there was no evidence of Merkel cell carcinoma on dermatological examinations. A possible explanation for the presence of neuroendocrine carcinomas within the lymph nodes is malignant transformation of preexisting intranodal epithelial nests, which have previously been described in lymph nodes located close to the salivary glands, thyroid gland, breast tissue and pancreas. Since the surgical removal of the affected lymph node, the patient has now been disease-free for 42 months. We therefore consider our case to represent a primary undifferentiated neuroendocrine carcinoma in an inguinal lymph node.

History-dependent multiple-time-scale dynamics in a single-neuron model

History-dependent characteristic time scales in dynamics have been observed at several levels of organization in neural systems. Such dynamics can provide powerful means for computation and memory. At the level of the single neuron, several microscopic mechanisms, including ion channel kinetics, can support multiple-time-scale dynamics. How the temporally complex channel kinetics gives rise to dynamical properties of the neuron is not well understood. Here, we construct a model that captures some features of the connection between these two levels of organization. The model neuron exhibits history-dependent multiple-time-scale dynamics in several effects: first, after stimulation, the recovery time scale is related to the stimulation duration by a power-law scaling; second, temporal patterns of neural activity in response to ongoing stimulation are modulated over time; finally, the characteristic time scale for adaptation after a step change in stimulus depends on the duration of the preceding stimulus. All these effects have been observed experimentally and are not explained by current single-neuron models. The model neuron here presented is composed of an ensemble of ion channels that can wander in a large pool of degenerate inactive states and thus exhibits multiple-time-scale dynamics at the molecular level. Channel inactivation rate depends on recent neural activity, which in turn depends through modulations of the neural response function on the fraction of active channels. This construction produces a model that robustly exhibits nonexponential history-dependent dynamics, in qualitative agreement with experimental results.

Transient responses and adaptation to steady state in a eukaryotic gene regulation system

Understanding the structure and functionality of eukaryotic gene regulation systems is of fundamental importance in many areas of biology. While most recent studies focus on static or short-term properties, measuring the long-term dynamics of these networks under controlled conditions is necessary for their complete characterization. We demonstrate adaptive dynamics in a well-known system of metabolic regulation, the GAL system in the yeast S. cerevisiae. This is a classic model for a eukaryotic genetic switch, induced by galactose and repressed by glucose. We followed the expression of a reporter gfp under a GAL promoter at single-cell resolution in large population of yeast cells. Experiments were conducted for long time scales, several generations, while controlling the environment in continuous culture. This combination enabled us, for the first time, to distinguish between transient responses and steady state. We find that both galactose induction and glucose repression are only transient responses. Over several generations, the system converges to a single robust steady state, independent of external conditions. Thus, at steady state the GAL network loses its hallmark functionality as a sensitive carbon source rheostat. This result suggests that, while short-term dynamics are determined by specific modular responses, over long time scales inter-modular interactions take over and shape a robust steady state response of the regulatory system.

Besonderheiten der implantatassoziierten Infektion in der orthop�dischen Chirurgie

One of the most important risk factors in orthopedic surgery is implant-associated infection. Adhesion and colonization mediated implant infections are extremely resistant to antibiotics and host defences and frequently persist until the biomaterial or foreign body is removed, which is standard therapy. Tissue damage caused by surgery and foreign body implantation increases the susceptibility to infections, activates host defences and stimulates the generation of inflammatory mediators including radicals that are further aggravated by bacterial activity and toxins. Nearly one third of implant-related infections can be prevented by strictly following established infection control guidelines. However, a significant number of implant-associated infections remains. The escape of bacteria from host defence and antibiotic therapy makes the development of infection-resistant materials as anti-microbial drug delivery systems feasible. This concept consists of the sustained delivery of antimicrobial drugs into the local microenvironment of implants avoiding systemic side effects exceeding usual systemic concentrations by magnitudes of order.

Quantification, localization, and expression of IGF-I and TGF-beta1 during growth factor-stimulated fracture healing

Because of the increasing interest on stimulating fracture healing, knowledge about the role and chronology of growth factors during the healing process is important. The purpose of this study was to quantify the protein concentration of IGF-I and TGF-beta1 during rat tibial fracture healing 5, 10, and 15 days after fracture using ELISA methods and to analyze the distribution of the proteins and the related mRNA expression in the fracture callus by immunohistochemistry and in situ hybridization. The following three groups were analyzed: Fractured tibiae intramedullary stabilized with K-wires coated with IGF-I and TGF-beta1 compared with fractures stabilized with uncoated K-wires and unfractured tibiae. The weight of the callus increased during the healing process in both experimental groups. The protein concentration of IGF-I and TGF-beta1 in the fracture callus showed significant changes between the investigated time points and treatment groups compared with the unfractured tibia. IGF-I increased with healing time whereas TGF-beta1 revealed a constantly elevated level at the investigated time points. Mesenchymal cells, osteoblasts, osteocytes, proliferating and immature chondrocytes, and osteoclasts expressed both growth factors. No differences in the expression and localization pattern of the growth factors were detectable among the groups. Using the different methods for quantification and visualization of the growth factors, no differences (except the increased IGF-I concentration at day 15 in the growth factor group) were seen between the normal and the growth factor-stimulated fracture healing as an indication for physiological healing after exogenous growth factor treatment.

Selective adaptation in networks of cortical neurons

A key property of neural systems is their ability to adapt selectively to stimuli with different features. Using multisite electrical recordings from networks of cortical neurons developing ex vivo, we show that neurons adapt selectively to different stimuli invading the network. We focus on selective adaptation to frequent and rare stimuli; networks were stimulated at two sites with two different stimulus frequencies. When both stimuli were presented within the same period, neurons in the network attenuated their responsiveness to the more frequent input, whereas their responsiveness to the rarely delivered stimuli showed a marked average increase. The amplification of the response to rare stimuli required the presence of the other, more frequent stimulation source. By contrast, the decreased response to the frequent stimuli occurred regardless of the presence of the rare stimuli. Analysis of the response of single units suggests that both of these effects are caused by changes in synaptic transmission. By using synaptic blockers, we find that the increased responsiveness to the rarely stimulated site depends specifically on fast GABAergic transmission. Thus, excitatory synaptic depression, the inhibitory sub-network, and their balance play an active role in generating selective gain control. The observation that selective adaptation arises naturally in a network of cortical neurons developing ex vivo indicates that this is an inherent feature of spontaneously organizing cortical networks.

Statistical properties of spike trains: universal and stimulus-dependent aspects

Statistical properties of spike trains measured from a sensory neuron in vivo are studied experimentally and theoretically. Experiments are performed on an identified neuron in the visual system of the blowfly. It is shown that the spike trains exhibit universal behavior over a short time, modulated by a stimulus-dependent envelope over a long time. A model of the neuron as a nonlinear oscillator driven by noise and by an external stimulus is suggested to account for these results. In the short-time universal regime, the main biophysical effect is refractoriness, which can be described as a repulsive ( 1/x) interaction law among spikes. A universal distribution function for intervals is found, defining a point process with special symmetry properties. The long-time modulations in the spike train are related in a simple way to the properties of the input stimulus as seen through the neuronal nonlinearity. Thus our model enables a separation of the effects of internal neuronal properties from the effect of external stimulus properties. Explicit formulas are derived for different statistical properties, which are in very good agreement with the data in both the universal and the stimulus-dependent regimes.

Synergy in a neural code

We show that the information carried by compound events in neural spike trains-patterns of spikes across time or across a population of cells-can be measured, independent of assumptions about what these patterns might represent. By comparing the information carried by a compound pattern with the information carried independently by its parts, we directly measure the synergy among these parts. We illustrate the use of these methods by applying them to experiments on the motion-sensitive neuron H1 of the fly's visual system, where we confirm that two spikes close together in time carry far more than twice the information carried by a single spike. We analyze the sources of this synergy and provide evidence that pairs of spikes close together in time may be especially important patterns in the code of H1.

Adaptive rescaling maximizes information transmission

Adaptation is a widespread phenomenon in nervous systems, providing flexibility to function under varying external conditions. Here, we relate an adaptive property of a sensory system directly to its function as a carrier of information about input signals. We show that the input/output relation of a sensory system in a dynamic environment changes with the statistical properties of the environment. Specifically, when the dynamic range of inputs changes, the input/output relation rescales so as to match the dynamic range of responses to that of the inputs. We give direct evidence that the scaling of the input/output relation is set to maximize information transmission for each distribution of signals. This adaptive behavior should be particularly useful in dealing with the intermittent statistics of natural signals.

[Efficacy of Doppler color echography in the diagnosis of caverno-venous leakage in the impotent patient]

Duplex sonography is now the gold standard for the non-invasive study of cavernous arteries. Many authors consider that this technique allows for the exploration of the veno-occlusive system and can detect venous leakage. The most common criteria is an end diastolic velocity > 5 cm/s, measured in the cavernous arteries. In a retrospective study, 34 impotent patients were reviewed. All of them were explored both by doppler sonography and pharmacocavernosometry to assess the efficacy of duplex sonography in detecting venous leakage. The data show that the doppler detects venous leakage in only 41%. The specificity and the sensitivity are both 50% which is clearly disappointing. Therefore, in our experience, duplex sonography is not a reliable technique to detect venous leakage. We consider that the best method is still pharmacocavernosometry.

Receptor binding radiotracers for the angiotensin II receptor: radioiodinated [Sar1, Ile8]Angiotensin II

The potential for imaging the angiotensin II receptor was evaluated using the radioiodinated peptide antagonist [125I][Sar1, Ile8)angiotensin II. The radioligand provides a receptor-mediated signal in several tissues in rat (kidneys, adrenal and liver). The receptor-mediated signal of 3% ID/g kidney cortex should be sufficient to permit imaging, at least via SPECT. The radiotracer is sensitive to reductions in receptor concentration and can be used to define in vivo dose-occupancy curves of angiotensin II receptor ligands. Receptor-mediated images of [123I][Sar1, Ile8]angiotensin II were obtained in the rat kidney and Rhesus monkey liver.