Conditional Functional Graphical Models.

Graphical modeling of multivariate functional data is becoming increasingly important in a wide variety of applications. The changes of graph structure can often be attributed to external variables, such as the diagnosis status or time, the latter of which gives rise to the problem of dynamic graphical modeling. Most existing methods focus on estimating the graph by aggregating samples, but largely ignore the subject-level heterogeneity due to the external variables. In this article, we introduce a conditional graphical model for multivariate random functions, where we treat the external variables as conditioning set, and allow the graph structure to vary with the external variables. Our method is built on two new linear operators, the conditional precision operator and the conditional partial correlation operator, which extend the precision matrix and the partial correlation matrix to both the conditional and functional settings. We show that their nonzero elements can be used to characterize the conditional graphs, and develop the corresponding estimators. We establish the uniform convergence of the proposed estimators and the consistency of the estimated graph, while allowing the graph size to grow with the sample size, and accommodating both completely and partially observed data. We demonstrate the efficacy of the method through both simulations and a study of brain functional connectivity network.

Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases.

BACKGROUND: Although genome-wide association studies (GWAS) have identified many genomic regions associated with idiopathic pulmonary fibrosis (IPF), the causal genes and functions remain largely unknown. Many single-cell expression data have become available for IPF, and there is increasing evidence suggesting a shared genetic basis between IPF and other diseases. METHODS: We conducted integrative analyses to improve the power of GWAS. First, we calculated global and local genetic correlations to identify IPF genetically associated traits and local regions. Then, we prioritised candidate genes contributing to local genetic correlation. Second, we performed transcriptome-wide association analysis (TWAS) of 44 tissues to identify candidate genes whose genetically predicted expression level is associated with IPF. To replicate our findings and investigate the regulatory role of the transcription factors (TF) in identified candidate genes, we first conducted the heritability enrichment analysis in TF binding sites. Then, we examined the enrichment of the TF target genes in cell-type-specific differentially expressed genes (DEGs) identified from single-cell expression data of IPF and healthy lung samples. FINDINGS: We identified 12 candidate genes across 13 genomic regions using local genetic correlation, including the POT1 locus (p value=0.00041), which contained variants with protective effects on lung cancer but increasing IPF risk. We identified another 13 novel genes using TWAS. Two TFs, MAFK and SMAD2, showed significant enrichment in both partitioned heritability and cell-type-specific DEGs. INTERPRETATION: Our integrative analysis identified new genes for IPF susceptibility and expanded the understanding of the complex genetic architecture and disease mechanism of IPF.

Transcriptomic organization of the human brain in post-traumatic stress disorder.

Despite extensive study of the neurobiological correlates of post-traumatic stress disorder (PTSD), little is known about its molecular determinants. Here, differential gene expression and network analyses of four prefrontal cortex subregions from postmortem tissue of people with PTSD demonstrate extensive remodeling of the transcriptomic landscape. A highly connected downregulated set of interneuron transcripts is present in the most significant gene network associated with PTSD. Integration of this dataset with genotype data from the largest PTSD genome-wide association study identified the interneuron synaptic gene ELFN1 as conferring significant genetic liability for PTSD. We also identified marked transcriptomic sexual dimorphism that could contribute to higher rates of PTSD in women. Comparison with a matched major depressive disorder cohort revealed significant divergence between the molecular profiles of individuals with PTSD and major depressive disorder despite their high comorbidity. Our analysis provides convergent systems-level evidence of genomic networks within the prefrontal cortex that contribute to the pathophysiology of PTSD in humans.

Cortical Transcriptomic Alterations in Association With Appetitive Neuropeptides and Body Mass Index in Posttraumatic Stress Disorder.

BACKGROUND: The molecular pathology underlying posttraumatic stress disorder (PTSD) remains unclear mainly due to a lack of human PTSD postmortem brain tissue. The orexigenic neuropeptides ghrelin, neuropeptide Y, and hypocretin were recently implicated in modulating negative affect. Drawing from the largest functional genomics study of human PTSD postmortem tissue, we investigated whether there were molecular changes of these and other appetitive molecules. Further, we explored the interaction between PTSD and body mass index (BMI) on gene expression. METHODS: We analyzed previously reported transcriptomic data from 4 prefrontal cortex regions from 52 individuals with PTSD and 46 matched neurotypical controls. We employed gene co-expression network analysis across the transcriptomes of these regions to uncover PTSD-specific networks containing orexigenic genes. We utilized Ingenuity Pathway Analysis software for pathway annotation. We identified differentially expressed genes (DEGs) among individuals with and without PTSD, stratified by sex and BMI. RESULTS: Three PTSD-associated networks (P < .01) contained genes in signaling families of appetitive molecules: 2 in females and 1 in all subjects. We uncovered DEGs (P < .05) between PTSD and control subjects stratified by sex and BMI with especially robust changes in males with PTSD with elevated vs normal BMI. Further, we identified putative upstream regulators (P < .05) driving these changes, many of which were enriched for involvement in inflammation. CONCLUSIONS: PTSD-associated cortical transcriptomic modules contain transcripts of appetitive genes, and BMI further interacts with PTSD to impact expression. DEGs and inferred upstream regulators of these modules could represent targets for future pharmacotherapies for obesity in PTSD.

ff14IDPs force field improving the conformation sampling of intrinsically disordered proteins.

Intrinsically disordered proteins are proteins which lack of specific tertiary structure and unable to fold spontaneously without the partner binding. These intrinsically disordered proteins are found to associate with various diseases, such as diabetes, cancer, and neurodegenerative diseases. However, current widely used force fields, such as ff99SB, ff14SB, OPLS/AA, and Charmm27, are insufficient in sampling the conformational characters of intrinsically disordered proteins. In this study, the CMAP method was used to correct the φ/ψ distributions of disorder-promoting amino acids. The simulation results show that the force filed parameters (ff14IDPs) can improve the φ/ψ distributions of the disorder-promoting amino acids, with RMSD less than 0.10% relative to the benchmark data of intrinsically disordered proteins. Further test suggests that the calculated secondary chemical shifts under ff14IDPs are in quantitative agreement with the data of NMR experiment for five tested systems. In addition, the simulation results show that ff14IDPs can still be used to model structural proteins, such as tested lysozyme and ubiquitin, with better performance in coil regions than the original general Amber force field ff14SB. These findings confirm that the newly developed Amber ff14IDPs is a robust model for improving the conformation sampling of intrinsically disordered proteins.

Test and Evaluation of ff99IDPs Force Field for Intrinsically Disordered Proteins.

Over 40% of eukaryotic proteomic sequences have been predicted to be intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs) and confirmed to be associated with many diseases. However, widely used force fields cannot well reproduce the conformers of IDPs. Previously the ff99IDPs force field was released to simulate IDPs with CMAP energy corrections for the eight disorder-promoting residues. In order to further confirm the performance of ff99IDPs, three representative IDP systems (arginine-rich HIV-1 Rev, aspartic proteinase inhibitor IA3, and α-synuclein) were used to test and evaluate the simulation results. The results show that for free disordered proteins, the chemical shifts from the ff99IDPs simulations are in quantitative agreement with those from reported NMR measurements and better than those from ff99SBildn. Thus, ff99IDPs can sample more clusters of disordered conformers than ff99SBildn. For structural proteins, both ff99IDPs and ff99SBildn can well reproduce the conformations. In general, ff99IDPs can successfully be used to simulate the conformations of IDPs and IDRs in both bound and free states. However, relative errors could still be found at the boundaries of ordered residues scattered in long disorder-promoting sequences. Therefore, polarizable force fields might be one of the possible ways to further improve the performance on IDPs.

Revealing the binding mode between respiratory syncytial virus fusion protein and benzimidazole-based inhibitors.

Human respiratory syncytial virus (HRSV) is a major respiratory pathogen in newborn infants and young children and can also be a threat to some elderly and high-risk adults with chronic pulmonary disease and the severely immunocompromised. The RSV fusion (RSVF) protein has been an attractive target for vaccine and drug development. Experimental results indicate a series of benzimidazole-based inhibitors which target RSVF protein to inhibit the viral entry of RSV. To reveal the binding mode between these inhibitors and RSVF protein, molecular docking and molecular dynamics simulations were used to investigate the interactions between the inhibitors and the core domain of RSVF protein. MD results suggest that the active molecules have stronger π-π stacking, cation-π, and other interactions than less active inhibitors. The binding free energy between the active inhibitor and RSVF protein is also found to be significantly lower than that of the less active one using MM/GBSA. Then, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to construct three dimensional quantitative structure-activity (3D-QSAR) models. The cross-validated q(2) values are found to be 0.821 and 0.795 for CoMFA and CoMSIA, respectively. And the non-cross-validated r(2) values are 0.973 and 0.961. Ninety-two test set compounds validated these models. The results suggest that these models are robust with good prediction abilities. Furthermore, these models reveal possible methods to improve the bioactivity of inhibitors.

Synthesis and biological evaluation of 2-oxo-pyrazine-3-carboxamide-yl nucleoside analogues and their epimers as inhibitors of influenza A viruses.

Novel 2-oxo-pyrazine-3-carboxamide-yl nucleoside analogues and their epimers were designed, synthesized and evaluated for their activities against influenza A viruses H1N1 and H3N2 in Madin-Darby canine kidney cells. All the compounds showed low cytotoxicities in these anti-influenza tests. One of the epimers, 4-[(1S, 3R, 4R, 7R)-7-hydroxy-1-(hydroxymethyl)-2,5-dioxabicyclo[2.2.1]heptan-3-yl]-3-oxo-3,4-dihydropyrazine-2-carboxamide 8a, with high antiviral activities (IC50 = 7.41, 5.63 µm for H3N2 and H1N1, respectively) and remarkable low cytotoxicity (TC50 > 200 µm), has great potential for further development as a novel anti-influenza A agent. Molecular docking of compound 8a with RNA-dependent RNA polymerase was performed to understand the binding mode between these inhibitors and the active site of RdRp and to rationalize some SARs.

Insight into the binding mode between HIV-1 integrase and pyrimidone analogue inhibitors with MD simulation and 3D-QSAR.

HIV-1 integrase (HIVIN) plays a key role in the replication of the HIV-1 virus and represents an attractive target for anti-HIV drug design. Experimental observation suggests that pyrimidone analogues have potent anti-HIV activity. Then, we modeled an HIVIN catalytic core domain based on the crystal structure of the prototype foamy virus (PFV) integrase. Molecular docking and molecular dynamics simulations were used to investigate the interaction mechanism between pyrimidone analogues and the HIVIN catalytic core domain. MD results suggest that the most active molecule (6K) has more stable hydrogen bonds and hydrophobic contacts than the FDA approved anti-HIV drug Raltegravir. Furthermore, the analogues and Raltegravir might have similar binding modes with HIVIN. Finally, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to construct three dimensional quantitative structure-activity (3D-QSAR) models. Eleven test set compounds which are not included in the training set were used to evaluate these models. The results suggest that these models are robust and have good prediction abilities.