Postural Hyperventilation as a Cause of Postural Tachycardia Syndrome: Increased Systemic Vascular Resistance and Decreased Cardiac Output When Upright in All Postural Tachycardia Syndrome Variants

BACKGROUND: Postural tachycardia syndrome (POTS) is a heterogeneous condition. We stratified patients previously evaluated for POTS on the basis of supine resting cardiac output (CO) or with the complaint of platypnea or shortness of breath during orthostasis. We hypothesize that postural hyperventilation is one cause of POTS and that hyperventilation-associated POTS occurs when initial reduction in CO is sufficiently large. We also propose that circulatory abnormalities normalize with restoration of CO2. METHODS AND RESULTS: Fifty-eight enrollees with POTS were compared with 16 healthy volunteer controls. Low CO in POTS was defined by a resting supine CO /min. Patients with shortness of breath had hyperventilation with end tidal CO2 photoplethysmography, and CO was measured by ModelFlow. Systemic vascular resistance was defined as mean arterial blood pressure/CO. End tidal CO2 and cerebral blood flow velocity of the middle cerebral artery were only reduced during head-up tilt in the hyperventilation group, whereas blood pressure was increased compared with control. We corrected the reduced end tidal CO2 in hyperventilation by addition of exogenous CO2 into a rebreathing apparatus. With added CO2, heart rate, blood pressure, CO, and systemic vascular resistance in hyperventilation became similar to control. CONCLUSIONS: We conclude that all POTS is related to decreased CO, decreased central blood volume, and increased systemic vascular resistance and that a variant of POTS is consequent to postural hyperventilation

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset

Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms

Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work has been dedicated to the assessment of their stability properties. Here we present a methodical comparison of the performance of a novel method (RegnANN) for gene network inference based on multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER), focussing our analysis on the prediction variability induced by both the network intrinsic structure and the available data. Results: The extensive evaluation on both synthetic data and a selection of gene modules of "Escherichia coli" indicates that all the algorithms suffer of instability and variability issues with regards to the reconstruction of the topology of the network. This instability makes objectively very hard the task of establishing which method performs best. Nevertheless, RegnANN shows MCC scores that compare very favorably with all the other inference methods tested. Availability: The software for the RegnANN inference algorithm is distributed under GPL3 and it is available at the corresponding author home page (http://mpba.fbk.eu/grimaldi/regnann-supmat

DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks

Background Functional genomic and epigenomic research relies fundamentally on sequencing based methods like ChIP-seq for the detection of DNA-protein interactions. These techniques return large, high dimensional data sets with visually complex structures, such as multi-modal peaks extended over large genomic regions. Current tools for visualisation and data exploration represent and leverage these complex features only to a limited extent. Results We present DGW, an open source software package for simultaneous alignment and clustering of multiple epigenomic marks. DGW uses Dynamic Time Warping to adaptively rescale and align genomic distances which allows to group regions of interest with similar shapes, thereby capturing the structure of epigenomic marks. We demonstrate the effectiveness of the approach in a simulation study and on a real epigenomic data set from the ENCODE project. Conclusions Our results show that DGW automatically recognises and aligns important genomic features such as transcription start sites and splicing sites from histone marks. DGW is available as an open source Python package

A Machine learning pipeline for identification of discriminant pathways

Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more generally, in systems biology. This chapter describes a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. Different algorithms can be chosen to implement the workflow steps. Three applications on genome-wide data are presented regarding the susceptibility of children to air pollution, and early and late onset of Parkinsonʼs and Alzheimerʼs disease