A high-resolution map of human phosphorylation networks was constructed by integrating experimentally determined kinase-substrate relationships with other resources, such as in vivo phosphorylation sites.

No abstract

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3′UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications.

Background: The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.

Superficial liposuction has potential risks for higher complications compared with conventional suction techniques, especially postoperative contour irregularity, which can be minimized with proper selection of candidates for the procedure, avoiding overzealous suctioning of superficial layer, and using a combination of ultrasound energy techniques.

Background: The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM.

Patient-specific human induced pluripotent stem cells (hiPSCs) hold great promise for the modelling of genetic disorders. However, these cells display wide intra-individual and inter-individual variations in gene expression, making it challenging to distinguish true-positive and false-positive phenotypes. Also, data from hiPSC phenotypes and from human embryonic stem cells (hESCs) harbouring the same disease mutation are lacking. Here, we report a comparison of molecular, cellular and functional characteristics of three congruent patient-specific cell types ― hiPSCs, hESCs, and direct lineage-converted cells ― derived from currently available differentiation and direct-reprogramming technologies, for the modelling of Charcot Marie Tooth 1A, a human genetic Schwann-cell disorder featuring a 1.4 megabase chromosomal duplication. In particular, we find that the chemokines CXCL1 and MCP1 are commonly upregulated in all three congruent models and in clinical patient samples. The development of congruent models of a single genetic disease by using somatic cells from a common patient will facilitate the search for convergent phenotypes.

The objective of the study was to identify distinct patterns in inflammatory immune responses of COVID-19 patients and to investigate their association with clinical course and outcome. Data from hospitalized COVID-19 patients were retrieved from electronic medical record. Supervised k-means clustering of serial C-reactive protein levels (CRP), absolute neutrophil counts (ANC), and absolute lymphocyte counts (ALC) was used to assign immune responses to one of three groups. Then, relationships between patterns of inflammatory responses and clinical course and outcome of COVID-19 were assessed in a discovery and validation cohort. Unbiased clustering analysis grouped 105 patients of a discovery cohort into three distinct clusters. Cluster 1 (hyper-inflammatory immune response) was characterized by high CRP levels, high ANC, and low ALC, whereas Cluster 3 (hypo-inflammatory immune response) was associated with low CRP levels and normal ANC and ALC. Cluster 2 showed an intermediate pattern. All patients in Cluster 1 required oxygen support whilst 61% patients in Cluster 2 and no patient in Cluster 3 required supplementary oxygen. Two (13.3%) patients in Cluster 1 died, whereas no patient in Clusters 2 and 3 died. The results were confirmed in an independent validation cohort of 116 patients. We identified three different patterns of inflammatory immune response to COVID-19. Hyper-inflammatory immune responses with elevated CRP, neutrophilia, and lymphopenia are associated with a severe disease and a worse outcome. Therefore, targeting the hyper-inflammatory response might improve the clinical outcome of COVID-19.