Many complex systems are composed of coupled networks through different layers, where each layer represents one of many possible types of interactions. A fundamental question is how to extract communities in multi-layer networks. The current algorithms either collapses multi-layer networks into a single-layer network or extends the algorithms for single-layer networks by using consensus clustering. However, these approaches have been criticized for ignoring the connection among various layers, thereby resulting in low accuracy. To attack this problem, a quantitative function (multi-layer modularity density) is proposed for community detection in multi-layer networks. Afterward, we prove that the trace optimization of multi-layer modularity density is equivalent to the objective functions of algorithms, such as kernel K-means, nonnegative matrix factorization (NMF), spectral clustering and multi-view clustering, for multi-layer networks, which serves as the theoretical foundation for designing algorithms for community detection. Furthermore, a Semi-Supervised joint Nonnegative Matrix Factorization algorithm (S2-jNMF) is developed by simultaneously factorizing matrices that are associated with multi-layer networks. Unlike the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of S2-jNMF. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method outperforms state-of-the-art approaches for community detection in multi-layer networks.

Pyroptosis is inflammation-associated caspase-1-dependent programmed cell death, which confers a crucial role in sepsis. The present study intends to investigate the regulatory network and function of the microarray-predicted YTHDF1 in caspase-1-dependent pyroptosis of sepsis. Peripheral blood of patients with sepsis was collected to determine WWP1 and YTHDF1 expression. An in vitro sepsis cell model was induced in RAW264.7 cells using lipopolysaccharide (LPS) and ATP and an in vivo septic mouse model by cecal ligation and perforation (CLP). After gain- and loss-of-function assays in vitro and in vivo, TNF-α and IL-1β levels and the cleavage of gasdermin-D (GSDMD) were detected by ELISA and Western blot assay, followed by determination of lactate dehydrogenase (LDH) activity. Immunoprecipitation and meRIP assay were performed to detect the ubiquitination of NLRP3 and the m6A modification of WWP1 mRNA. The binding of WWP1 to YTHDF1 was explored using RIP-RT-qPCR and dual luciferase gene reporter assay. It was noted that WWP1 and YTHDF1 were downregulated in clinical sepsis samples, LPS + ATP-treated RAW264.7 cells, and CLP-induced mice. The ubiquitination of NLRP3 was promoted after overexpression of WWP1. WWP1 translation could be promoted by YTHDF1. Then, WWP1 or YTHDF1 overexpression diminished LDH activity, NLRP3 inflammasomes and caspase-1-mediated cleavage of GSDMD in LPS + ATP-induced RAW264.7 cells. Overexpressed YTHDF1 restrained inflammatory response in CLP-induced mice. Collectively, the alleviatory effect of m6A reader protein YTHDF1 may be achieved through promotion of NLRP3 ubiquitination and inhibition of caspase-1-dependent pyroptosis by upregulating WWP1.

Anaemia is the most common complication of myeloma and is associated with worse clinical outcomes. Although marrow replacement with myeloma cells is widely considered a mechanistic rationale for anaemia, the exact process has not been fully understood. Our large cohort of 1363 myeloma patients had more than 50% of patients with moderate or severe anaemia at the time of diagnosis. Anaemia positively correlated with myeloma cell infiltration in the bone marrow (BM) and worse patient outcomes. The quantity and erythroid differentiation of HSPCs were affected by myeloma cell infiltration in the BM. The master regulators of erythropoiesis, GATA1 and KLF1, were obviously downregulated in myeloma HSPCs. However, the gene encoding the chemokine CCL3 showed significantly upregulated expression. Elevated CCL3 in the BM plasma of myeloma further inhibited the erythropoiesis of HSPCs via activation of CCL3/CCR1/p38 signalling and suppressed GATA1 expression. Treatment with a CCR1 antagonist effectively recovered GATA1 expression and rescued erythropoiesis in HSPCs. Myeloma cell infiltration causes elevated expression of CCL3 in BM, which suppresses the erythropoiesis of HSPCs and results in anaemia by downregulating the level of GATA1 in HSPCs. Thus, our study indicates that targeting CCL3 would be a potential strategy against anaemia and improve the survival of myeloma patients.

With the increase in the amount of DNA methylation and gene expression data, the epigenetic mechanisms of cancers can be extensively investigate. Available methods integrate the DNA methylation and gene expression data into a network by specifying the anti-correlation between them. However, the correlation between methylation and expression is usually unknown and difficult to determine. To address this issue, we present a novel multiple network framework for epigenetic modules, namely, Epigenetic Module based on Differential Networks (EMDN) algorithm, by simultaneously analyzing DNA methylation and gene expression data. The EMDN algorithm prevents the specification of the correlation between methylation and expression. The accuracy of EMDN algorithm is more efficient than that of modern approaches. On the basis of The Cancer Genome Atlas (TCGA) breast cancer data, we observe that the EMDN algorithm can recognize positively and negatively correlated modules and these modules are significantly more enriched in the known pathways than those obtained by other algorithms. These modules can serve as bio-markers to predict breast cancer subtypes by using methylation profiles, where positively and negatively correlated modules are of equal importance in the classification of cancer subtypes. Epigenetic modules also estimate the survival time of patients, and this factor is critical for cancer therapy. The proposed model and algorithm provide an effective method for the integrative analysis of DNA methylation and gene expression. The algorithm is freely available as an R-package at https://github.com/william0701/EMDN .

Multi-view data effectively model and characterize the underlying complex systems, and multi-view clustering is of great significance for revealing the mechanisms of systems, which groups objects into different clusters with high intra-cluster and low inter-cluster similarity for all views. Current algorithms are criticized for undesirable performance because they solely focus on either the shared features or correlation of objects, failing to address the heterogeneity and structural constraint of various views. To overcome these problems, a novel M ulti-view C lustering with S elf-representation and S tructural C onstraint (MCSSC) is proposed, which is a network-based method by fusing matrix factorization and low-rank representation of various views. Specifically, to remove heterogeneity of multi-view data, a network is constructed for each view, which casts the multi-view clustering into the multi-layer networks clustering problem. To extract the shared features of multiple views, MCSSC factorizes matrices associated with networks by projecting them into a common space and jointly learns an affinity graph for objects in multiple views with self-representation. To facilitate the clustering, the structural constraint is imposed on the affinity graph, where the clusters are identified. Extensive experiments demonstrate that MCSSC significantly outperforms the state-of-the-art in terms of accuracy, implying that the superiority of the proposed method.

Tracking the dynamic modules (modules change over time) during cancer progression is essential for studying cancer pathogenesis, diagnosis, and therapy. However, current algorithms only focus on detecting dynamic modules from temporal cancer networks without integrating the heterogeneous genomic data, thereby resulting in undesirable performance. To attack this issue, we propose a novel algorithm (aka TANMF ) to detect dynamic modules in cancer temporal attributed networks, which integrates the temporal networks and gene attributes. To obtain the dynamic modules, the temporality and gene attributed are incorporated into an overall objective function, which transforms the dynamic module detection into an optimization problem. TANMF jointly decomposes the snapshots at two subsequent time steps to obtain the latent features of dynamic modules, where the attributes are fused via regulations. Furthermore, the $L_{1}$ constraint is imposed to improve the robustness. Experimental results demonstrate that TANMF is more accurate than state-of-the-art methods in terms of accuracy. By applying TANMF to breast cancer data, the obtained dynamic modules are more enriched by the known pathways and associated with patients’ survival time. The proposed model and algorithm provide an effective way for the integrative analysis of heterogeneous omics.