CCN proteins: A centralized communication network

The CCN family of proteins includes six members presently known as CCN1, CCN2, CCN3, CCN4, CCN5 and CCN6. These proteins were originally designated CYR61, CTGF, NOV, and WISP-1, WISP-2, WISP-3. Although these proteins share a significant amount of structural features and a partial identity with other large families of regulatory proteins, they exhibit different biological functions. A critical examination of the progress made over the past two decades, since the first CCN proteins were discovered brings me to the conclusion that most of our present knowledge regarding the functions of these proteins was predicted very early after their discovery. In an effort to point out some of the gaps that prevent us to reach a comprehensive view of the functional interactions between CCN proteins, it is necessary to reconsider carefully data that was already published and put aside, either because the scientific community was not ready to accept them, or because they were not fitting with the « consensus » when they were published. This review article points to avenues that were not attracting the attention that they deserved. However, it is quite obvious that the six members of this unique family of tetra-modular proteins must act in concert, either simultaneously or sequentially, on the same sites or at different times in the life of living organisms. A better understanding of the spatio-temporal regulation of CCN proteins expression requires considering the family as such, not as a set of single proteins related only by their name. As proposed in this review, there is enough convincing pieces of evidence, at the present time, in favor of these proteins playing a role in the coordination of multiple signaling pathways, and constituting a Centralized Communication Network. Deciphering the hierarchy of regulatory circuits involved in this complex system is an important challenge for the near future. In this article, I would like to briefly review the concept of a CCN family of proteins and critically examine the progress made over the past 10 years in the understanding of their biological functions and involvement in both normal and pathological processes.     

Immune-associated biomarkers for early diagnosis of Parkinson’s disease based on hematological lncRNA–mRNA co-expression

Early stage diagnosis of Parkinson’s disease (PD) is challenging without significant motor symptoms. The identification of effective molecular biomarkers as a hematological indication of PD may help improve the diagnostic timelines and accuracy. In the present paper, we analyzed and compared the blood samples of PD and control (CTR) patients to identify the disease-related changes and determine the putative biomarkers for PD diagnosis. Based on the RNA sequencing analysis, differentially expressed genes (DEGs) were identified, and the co-expression network of DEGs was constructed using the weighted gene correlation network analysis (WGCNA). The analysis leads to the identification of 87 genes that were exclusively regulated in the PD group, whereas 66 genes were significantly increased and 21 genes were significantly decreased in contrast with the control group. The results indicate that the core lncRNA–mRNA co-expression network greatly changes the immune response in PD patients. Specifically, the results showed that Prader Willi Angelman Region RNA6 (PWAR6), LINC00861, AC83843.1, IRF family, IFIT family and calcium/calmodulin-dependent protein kinase IV (CaMK4) may play important roles in the immune system of PD. Based on the findings from the present study, future research aims at identifying novel therapeutic strategies for PD.     

Changhsingian brachiopod communities along a marine depth gradient in South China and their ecological significance in the end-Permian mass extinction

Diversity indices (dominance and evenness) and ecological spatial structure (lifestyles and relative abundances) are important features of Changhsingian brachiopod communities prior to the end-Permian mass extinction (EPME) and could predict temporal and spatial extinction patterns during the EPME. In South China, Changhsingian brachiopod communities show higher diversity than other contemporaneous brachiopod communities in the world and have been reported from a variety of sedimentary environments. In this paper, brachiopods from 18 sections in South China were selected to divide communities and compare their ecological structure. Based on the results of network analysis, cluster analysis and quantitative data from the selected sections, we show that Changhsingian brachiopod communities in South China can be categorized into three assemblages along a marine depth gradient: the Neochonetes–Fusichonetes–Paryphella Assemblage from the shallow-water clastic-rock facies, Spinomarginifera–Peltichia–Oldhamina Assemblage from the shallow-water carbonate platform facies and Fusichonetes–Crurithyris Assemblage from the deep-water siliciclastic intracontinental basin facies. Compared with communities from carbonate platform facies, the communities from siliciclastic facies were characterized by high dominance, low evenness and low lifestyle diversity, which might be important biotic factors leading to earlier extinctions. After the extinction began in all environments, the whole earliest Triassic brachiopod community was first dominated by Fusichonetes and then by Crurithyris. These patterns of domination and replacement could be explained by morphological and ecological advantages. The domination of these two genera, which were already adapted to the oxygen and food-limited deep-water habitat, indicates that the cooler deep-water environment might have been a relatively less stressed habitat after the beginning of the EPME. This suggests that global warming might be the main trigger among the previously proposed synergistic environmental stresses, while anoxia might not, at least for the beginning of EPME.     

Integrated evaluation of targeted and non-targeted therapies in a network meta-analysis

Individualized therapies for patients with biomarkers are moving more and more into the focus of research interest when developing new treatments. Hereby, the term individualized (or targeted) therapy denotes a treatment specifically developed for biomarker-positive patients. A network meta-analysis model for a binary endpoint combining the evidence for a targeted therapy from individual patient data with the evidence for a non-targeted therapy from aggregate data is presented and investigated. The biomarker status of the patients is either available at patient-level in individual patient data or at study-level in aggregate data. Both types of biomarker information have to be included. The evidence synthesis model follows a Bayesian approach and applies a meta-regression to the studies with aggregate data. In a simulation study, we address three treatment arms, one of them investigating a targeted therapy. The bias and the root-mean-square error of the treatment effect estimate for the subgroup of biomarker-positive patients based on studies with aggregate data are investigated. Thereby, the meta-regression approach is compared to approaches applying alternative solutions. The regression approach has a surprisingly small bias even in the presence of few studies. By contrast, the root-mean-square error is relatively greater. An illustrative example is provided demonstrating implementation of the presented network meta-analysis model in a clinical setting.     

Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network

Abstract

Nipah Virus (NiV) is a newly emergent paramyxovirus that has caused various outbreaks in Asian countries. Despite its acute pathogenicity and lack of approved therapeutics for human use, there is an urgent need to determine inhibitors against NiV. Hence, this work includes prospection of potential entry inhibitors by implementing an integrative structure- and network-based drug discovery approach. FDA-approved drugs were screened against attachment glycoprotein (NiV-G, PDB: 2VSM), one of the prime targets to inhibit viral entry, using a molecular docking approach that was benchmarked both on CCDC/ASTEX and known NIV-G inhibitor set. The predicted small molecules were prioritized on the basis of topological analysis of the chemical-protein interaction network, which was inferred by integrating the drug-target network, NiV-human interaction network, and human protein-protein interaction network. A total of 17 drugs were predicted to be NiV-G inhibitors using molecular docking studies that were further prioritized to 3 novel leads???Nilotinib, Deslanoside and Acetyldigitoxin???on the basis of topological analysis of inferred chemical-protein interaction network. While Deslanoside and Acetyldigitoxin belong to an already known class of anti-NiV inhibitors, Nilotinib belongs to Benzenoids chemical class that has not been reported hitherto for developing anti-NiV inhibitors. These identified drugs are expected to be successful in further experimental evaluation and therefore could be used for anti-Nipah drug discovery. Apart, we also obtained various insights into the underlying chemical-protein interaction network, based on which several important network nodes were predicted. The applicability of our proposed approach was also demonstrated by prospecting for anti-NiV phytochemicals on an independent dataset.

Communicated by Ramaswamy H. Sarma     

ProDCoNN: Protein design using a convolutional neural network

Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this problem as predicting the residue type given the 3D structural environment around the C _α atom of a residue, which is repeated for each residue of a protein. We designed a nine-layer 3D deep convolutional neural network (CNN) that takes as input a gridded box with the atomic coordinates and types around a residue. Several CNN layers were designed to capture structure information at different scales, such as bond lengths, bond angles, torsion angles, and secondary structures. Trained on a very large number of protein structures, the method, called ProDCoNN (protein design with CNN), achieved state-of-the-art performance when tested on large numbers of test proteins and benchmark datasets.     

The genetic program to specify ectodermal cells in ascidian embryos

The ascidian belongs to the sister group of vertebrates and shares many features with them. The gene regulatory network (GRN) controlling gene expression in ascidian embryonic development leading to the tadpole larva has revealed evolutionarily conserved gene circuits between ascidians and vertebrates. These conserved mechanisms are indeed useful to infer the original developmental programs of the ancestral chordates. Simultaneously, these studies have revealed which gene circuits are missing in the ascidian GRN; these gene circuits may have been acquired in the vertebrate lineage. In particular, the GRN responsible for gene expression in ectodermal cells of ascidian embryos has revealed the genetic programs that regulate the regionalization of the brain, formation of palps derived from placode-like cells, and differentiation of sensory neurons derived from neural crest-like cells. We here discuss how these studies have given insights into the evolution of these traits.