{'fraction_non_alphanumeric': 0.01445578231292517, 'fraction_numerical': 0.00935374149659864, 'mean_word_length': 5.843023255813954, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A community overlap strategy reveals central genes and networks in heart failure

Heart failure is one of the leading causes of mortality worldwide, but its underlying molecular mechanisms are poorly understood. To obtain a systems view of the molecular networks that underlie heart failure, we harvested 1352 samples from 313 healthy and failing hearts directly from transplant operating rooms and obtained left-ventricular whole-genome gene expression and genotype measurements. From these data, we built directed regulatory gene networks and gene communities using an approach that combines network and community inference in one framework. Differences in co-expression and global and local centrality parameters pinpointed changes in the molecular interaction network associated with heart failure, as well as its network-wise genetic determinants. Connectivity of one gene, PPP1R3A, previously unassociated with heart failure, changed significantly between healthy and diseased states. Perturbation of in vitro and in vivo systems via time series transcriptome sequencing and murine cardiovascular phenotyping revealed that ablation of PPP1R3A alters disease progression.