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.     

Identification of potential miRNA biomarkers for traumatic osteonecrosis of femoral head

Traumatic osteonecrosis of femoral head (TONFH) is a common orthopedic disease caused by physical injury in hip. However, the unclear pathogenesis mechanism of TONFH and lacking of simple noninvasive early diagnosis method cause the necessity of hip replacement for most patients with TONFH. In this study, we aimed to identify circulating microRNAs (miRNAs) by integrated bioinformatics analyses as potential biomarker of TONFH. mRNA expression profiles were downloaded from the Gene Expression Omnibus database. Then we combined two miRNA screen methods: Weighted gene co-expression network analysis and fold change based differentially expressed miRNAs analysis. As a result, we identified 14 key miRNAs as potential biomarkers for TONFH. Besides, 302 target genes of these miRNAs were obtained and the miRNA–mRNA interaction network was constructed. Furthermore, the results of Kyoto Encyclopedia of Gene and Genome pathway analysis, Gene Ontology function analysis, protein–protein interaction (PPI) network analysis and PPI network module analysis showed close correlation between these 14 key miRNAs and TONFH. Then we established receiver operating characteristic curves and identified 6-miRNA signature with highly diagnosis value including miR-93-5p (area under the curve [AUC] = 0.93), miR-1324 (AUC = 0.92), miR-4666a-3p (AUC = 0.92), miR-5011-3p (AUC = 0.92), and miR-320a (AUC = 0.89), miR-185-5p (AUC = 0.89). Finally, the results of quantitative real-time polymerase chain reaction confirmed the significantly higher expression of miR-93-5p and miR-320a in the serum of patients with ONFH. These circulating miRNAs could serve as candidate early diagnosis markers and potential treatment targets of TONFH.     

The complex fluctuations of probabilistic Boolean networks

Probabilistic Boolean networks (PBNs) are extensions of Boolean networks (BNs), and both have been widely used to model biological systems. In this paper, we study the long-range correlations of PBNs based on their corresponding Markov chains. PBN states are quantified by the deviation of their steady-state distributions. The results demonstrate that, compared with BNs, PBNs can exhibit these dynamics over a wider and higher noise range. In addition, the constituent BNs significantly impact the generation of 1/f dynamics of PBNs, and PBNs with homogeneous steady-state distributions tend to sustain the 1/f dynamics over a wider noise range.