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.

A multiscale model and imaging data show that cells of the slime mold Dictyostelium discoideum maximize their cell—cell communication range during aggregation by a critical-like state known from phase transitions in physical systems.

Cells are often coupled to each other in cell collectives, such as aggregates during early development, tissues in the developed organism, and tumors in disease. How do cells communicate over macroscopic distances much larger than the typical cell—cell distance to decide how they should behave? Here, we developed a multiscale model of social amoeba, spanning behavior from individuals to thousands of cells. We show that local cell—cell coupling via secreted chemicals may be tuned to a critical value, resulting in emergent long-range communication and heightened sensitivity. Hence, these aggregates are remarkably similar to bacterial biofilms and neuronal networks, all communicating in a pulselike fashion. Similar organizing principles may also aid our understanding of the remarkable robustness in cancer development.

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.