CLIPB4 Is a Central Node in the Protease Network that Regulates Humoral Immunity in Anopheles gambiae Mosquitoes.

Insect humoral immune responses are regulated in part by protease cascades, whose components circulate as zymogens in the hemolymph. In mosquitoes, these cascades consist of clip-domain serine proteases (cSPs) and/or their non-catalytic homologs, which form a complex network, whose molecular make-up is not fully understood. Using a systems biology approach, based on a co-expression network of gene family members that function in melanization and co-immunoprecipitation using the serine protease inhibitor (SRPN)2, a key negative regulator of the melanization response in mosquitoes, we identify the cSP CLIPB4 from the African malaria mosquito Anopheles gambiae as a central node in this protease network.

CLIPB4 is a central node in the protease network that regulates humoral immunity in Anopheles gambiae mosquitoes.

Insect humoral immune responses are regulated in part by protease cascades, whose components circulate as zymogens in the hemolymph. In mosquitoes, these cascades consist of clip domain serine proteases (cSPs) and/or their non-catalytic homologs (cSPHs), which form a complex network, whose molecular make-up is not fully understood. Using a systems biology approach, based on a co-expression network of gene family members that function in melanization and co-immunoprecipitation using the serine protease inhibitor (SRPN)2, a key negative regulator of the melanization response in mosquitoes, we identify the cSP CLIPB4 from the African malaria mosquito Anopheles gambiae as a central node in this protease network.

GeCoNet-Tool: a software package for gene co-expression network construction and analysis.

Background: Network analysis is a powerful tool for studying gene regulation and identifying biological processes associated with gene function. However, constructing gene co-expression networks can be a challenging task, particularly when dealing with a large number of missing values.

Results: We introduce GeCoNet-Tool, an integrated gene co-expression network construction and analysis tool.

Feature learning and network structure from noisy node activity data.

In the studies of network structures, much attention has been devoted to developing approaches to reconstruct networks and predict missing links when edge-related information is given. However, such approaches are not applicable when we are only given noisy node activity data with missing values. This work presents an unsupervised learning framework to learn node vectors and construct networks from such node activity data.

A global [Formula: see text] gene co-expression network constructed from hundreds of experimental conditions with missing values.

Background: Gene co-expression networks (GCNs) can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these methodologies provide results that cannot be directly compared. Thus, it is complex to analyze co-expression relations between genes, especially when there are missing values arising for experimental reasons.

Layer reconstruction and missing link prediction of a multilayer network with maximum a posteriori estimation.

From social networks to biological networks, different types of interactions among the same set of nodes characterize distinct layers, which are termed multilayer networks. Within a multilayer network, some layers, confirmed through different experiments, could be structurally similar and interdependent. In this paper, we propose a maximum a posteriori-based method to study and reconstruct the structure of a target layer in a multilayer network.