Protein-protein-interaction networks (PPINs) organize fundamental biological processes, but how oncogenic mutations impact these interactions and their functions at a network-level scale is poorly understood. Here, we analyze how a common oncogenic KRAS mutation (KRAS^G13D) affects PPIN structure and function of the Epidermal Growth Factor Receptor (EGFR) network in colorectal cancer (CRC) cells. Mapping >6000 PPIs shows that this network is extensively rewired in cells expressing transforming levels of KRAS^G13D(mtKRAS). The factors driving PPIN rewiring are multifactorial including changes in protein expression and phosphorylation. Mathematical modelling also suggests that the binding dynamics of low and high affinity KRAS interactors contribute to rewiring. PPIN rewiring substantially alters the composition of protein complexes, signal flow, transcriptional regulation, and cellular phenotype. These changes are validated by targeted and global experimental analysis. Importantly, genetic alterations in the most extensively rewired PPIN nodes occur frequently in CRC and are prognostic of poor patient outcomes.

Modern omics technologies allow us to obtain global information on different types of biological networks. However, integrating these different types of analyses into a coherent framework for a comprehensive biological interpretation remains challenging. Here, we present a conceptual framework that integrates protein interaction, phosphoproteomics, and transcriptomics data. Applying this method to analyze HRAS signaling from different subcellular compartments shows that spatially defined networks contribute specific functions to HRAS signaling. Changes in HRAS protein interactions at different sites lead to different kinase activation patterns that differentially regulate gene transcription. HRAS-mediated signaling is the strongest from the cell membrane, but it regulates the largest number of genes from the endoplasmic reticulum. The integrated networks provide a topologically and functionally resolved view of HRAS signaling. They reveal distinct HRAS functions including the control of cell migration from the endoplasmic reticulum and TP53-dependent cell survival when signaling from the Golgi apparatus.

Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.

Reconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

BACKGROUND: Ergometrine is a uterotonic agent that is recommended in the prevention and management of postpartum hemorrhage. Despite its long-standing use, the mechanism by which it acts in humans has never been elucidated fully. The objective of this study was to investigate the role of adrenoreceptors in ergometrine’s mechanism of action in human myometrium. The study examined the hypothesis that α-adrenoreceptor antagonism would result in the reversal of the uterotonic effects of ergometrine. METHODS: Myometrial samples were obtained from women undergoing elective cesarean delivery. The samples were then dissected into strips and mounted in organ bath chambers. After the generation of an ergometrine concentration–response curve (10^–15 to 10^–5 M), strips were treated with increasing concentrations of ergometrine (10^–15 to 10^–7 M) alone and ergometrine (10^–7 to 10^–5 M) in the presence of phentolamine (10^–7 M), prazosin (10^–7 M), propranolol (10^–6 M), or yohimbine (10^–6 M). The effects of adding ergometrine and the effect of drug combinations were analyzed using linear mixed effects models with measures of amplitude (g), frequency (contractions/10 min), and motility index (g×contractions/10 min). RESULTS: A total of 157 experiments were completed on samples obtained from 33 women. There was a significant increase in the motility index (adding 0.342 g × counts/10 min/μM; 95% confidence interval [CI], 0.253–0.431, P < .001), amplitude (0.078 g/μM; 95% CI, 0.0344–0.121, P = 5e-04), and frequency (0.051 counts/10 min/μM; 95% CI, 0.038–0.063, P < .001) in the presence of ergometrine. The α-adrenergic antagonist phentolamine and the more selective α₁-adrenergic antagonist prazosin inhibited the ergometrine mediated increase in motility index, amplitude, and frequency (−1.63 g × counts/10 min/μM and −16.70 g × counts/10 min/μM for motility index, respectively). CONCLUSIONS: These results provide novel evidence for a role for α-adrenergic signaling mechanisms in the action of ergometrine on human myometrial smooth muscle in the in vitro setting. Information that sheds light on the mechanism of action of ergometrine may have implications for the development of further uterotonic agents.

Background: Hidradenitis suppurativa (HS) is a chronic inflammatory skin disorder with significant morbidity. The pathogenesis remains incompletely understood although immune dysregulation plays an important role. It is challenging to treat and approximately 50% of patients respond clinically to adalimumab, the only licensed treatment. Objectives: To examine differences between lesional and nonlesional HS skin at baseline using bulk RNA sequencing, and to compare the transcriptome in the skin before and after 12 weeks of treatment with adalimumab. To examine transcriptomic differences between adalimumab responders and nonresponders using Hidradenitis Suppurativa Clinical Response and the International Hidradenitis Suppurativa Severity Score System (IHS4); and to compare transcriptomic differences based on disease severity (Hurley stage and IHS4). Methods: We completed bulk RNA sequencing on lesional and nonlesional skin samples of patients before and after 12 weeks of treatment with adalimumab. Results: Baseline differentially expressed genes and pathways between lesional and nonlesional skin highlighted chemokines and antimicrobial peptides produced by keratinocytes; B-cell function; T-cell-receptor, interleukin-17 and nuclear factor-κB signalling; and T-helper-cell differentiation. Transcriptomic differences were identified in lesional skin at baseline, between subsequent responders and nonresponders. Patients with severe HS who did not respond to adalimumab had enriched complement and B-cell activation pathways at baseline. In addition, logistic regression identified CCL28 in baseline lesional HS skin as a potential biomarker of treatment response. Conclusions: This highlights the potential for targeting B-cell and complement pathways in HS treatment and the potential of stratifying patients at baseline to the most suitable treatment based on the skin transcriptome. CCL28 has not previously been identified in HS skin and has potential clinical relevance due to its antimicrobial function and homing of B and T cells at epithelial surfaces. Our results provide data to inform future translational and clinical studies on therapeutics in HS.