Breath Biopsy® to Identify Exhaled Volatile Organic Compounds Biomarkers for Liver Cirrhosis Detection.

Background and Aims: The prevalence of chronic liver disease in adults exceeds 30% in some countries and there is significant interest in developing tests and treatments to help control disease progression and reduce healthcare burden. Breath is a rich sampling matrix that offers non-invasive solutions suitable for early-stage detection and disease monitoring. Having previously investigated targeted analysis of a single biomarker, here we investigated a multiparametric approach to breath testing that would provide more robust and reliable results for clinical use. Methods: To identify candidate biomarkers we compared 46 breath samples from cirrhosis patients and 42 from controls. Collection and analysis used Breath Biopsy OMNI™, maximizing signal and contrast to background to provide high confidence biomarker detection based upon gas chromatography mass spectrometry (GC-MS). Blank samples were also analyzed to provide detailed information on background volatile organic compounds (VOCs) levels. Results: A set of 29 breath VOCs differed significantly between cirrhosis and controls. A classification model based on these VOCs had an area under the curve (AUC) of 0.95±0.04 in cross-validated test sets. The seven best performing VOCs were sufficient to maximize classification performance. A subset of 11 VOCs was correlated with blood metrics of liver function (bilirubin, albumin, prothrombin time) and separated patients by cirrhosis severity using principal component analysis. Conclusions: A set of seven VOCs consisting of previously reported and novel candidates show promise as a panel for liver disease detection and monitoring, showing correlation to disease severity and serum biomarkers at late stage.

A critical-like collective state leads to long-range cell communication in Dictyostelium discoideum aggregation.

The transition from single-cell to multicellular behavior is important in early development but rarely studied. The starvation-induced aggregation of the social amoeba Dictyostelium discoideum into a multicellular slug is known to result from single-cell chemotaxis towards emitted pulses of cyclic adenosine monophosphate (cAMP). However, how exactly do transient, short-range chemical gradients lead to coherent collective movement at a macroscopic scale? Here, we developed a multiscale model verified by quantitative microscopy to describe behaviors ranging widely from chemotaxis and excitability of individual cells to aggregation of thousands of cells. To better understand the mechanism of long-range cell-cell communication and hence aggregation, we analyzed cell-cell correlations, showing evidence of self-organization at the onset of aggregation (as opposed to following a leader cell). Surprisingly, cell collectives, despite their finite size, show features of criticality known from phase transitions in physical systems. By comparing wild-type and mutant cells with impaired aggregation, we found the longest cell-cell communication distance in wild-type cells, suggesting that criticality provides an adaptive advantage and optimally sized aggregates for the dispersal of spores.

Unraveling adaptation in eukaryotic pathways: lessons from protocells.

Eukaryotic adaptation pathways operate within wide-ranging environmental conditions without stimulus saturation. Despite numerous differences in the adaptation mechanisms employed by bacteria and eukaryotes, all require energy consumption. Here, we present two minimal models showing that expenditure of energy by the cell is not essential for adaptation. Both models share important features with large eukaryotic cells: they employ small diffusible molecules and involve receptor subunits resembling highly conserved G-protein cascades. Analyzing the drawbacks of these models helps us understand the benefits of energy consumption, in terms of adjustability of response and adaptation times as well as separation of cell-external sensing and cell-internal signaling. Our work thus sheds new light on the evolution of adaptation mechanisms in complex systems.

A rate-distortion theory for gene regulatory networks and its application to logic gate consistency.

MOTIVATION: A gene regulatory network in which the modes (activation/inhibition) of the transcriptional regulations are known and in which gene expression assumes boolean values can be treated as a system of linear equations over a binary field, i.e. as a constraint satisfaction problem for an information code. RESULTS: For currently available gene networks, we show in this article that the distortion associated with the corresponding information code is much lower than expected from null models, and that it is close to (when not lower than) the Shannon bound determined by the rate-distortion theorem. This corresponds to saying that the distribution of regulatory modes is highly atypical in the networks, and that this atypicality greatly helps in avoiding contradictory transcriptional actions. Choosing a boolean formalism to represent the gene networks, we also show how to formulate criteria for the selection of gates that maximize the compatibility with the empirical information available on the transcriptional regulatory modes. Proceeding in this way, we obtain in particular that non-canalizing gates are upper-bounded by the distortion, and hence that the boolean gene networks are more canalizing than expected from null models.

Common dynamical features of sensory adaptation in photoreceptors and olfactory sensory neurons.

Sensory systems adapt, i.e., they adjust their sensitivity to external stimuli according to the ambient level. In this paper we show that single cell electrophysiological responses of vertebrate olfactory receptors and of photoreceptors to different input protocols exhibit several common features related to adaptation, and that these features can be used to investigate the dynamical structure of the feedback regulation responsible for the adaptation. In particular, we point out that two different forms of adaptation can be observed, in response to steps and to pairs of pulses. These two forms of adaptation appear to be in a dynamical trade-off: the more adaptation to a step is close to perfect, the slower is the recovery in adaptation to pulse pairs and viceversa. Neither of the two forms is explained by the dynamical models currently used to describe adaptation, such as the integral feedback model.

A dynamical feedback model for adaptation in the olfactory transduction pathway.

Olfactory transduction exhibits two distinct types of adaptation, which we denote multipulse and step adaptation. In terms of measured transduction current, multipulse adaptation appears as a decrease in the amplitude of the second of two consecutive responses when the olfactory neuron is stimulated with two brief pulses. Step adaptation occurs in response to a sustained steplike stimulation and is characterized by a return to a steady-state current amplitude close to the prestimulus value, after a transient peak. In this article, we formulate a dynamical model of the olfactory transduction pathway, which includes the kinetics of the CNG channels, the concentration of Ca ions flowing through them, and the Ca-complexes responsible for the regulation. Based on this model, a common dynamical explanation for the two types of adaptation is suggested. We show that both forms of adaptation can be well described using different time constants for the kinetics of Ca ions (faster) and the kinetics of the feedback mechanisms (slower). The model is validated on experimental data collected in voltage-clamp conditions using different techniques and animal species.