Gut microbial profile is altered in primary biliary cholangitis and partially restored after UDCA therapy.

OBJECTIVE: A close relationship between gut microbiota and some chronic liver disorders has recently been described. Herein, we systematically performed a comparative analysis of the gut microbiome in primary biliary cholangitis (PBC) and healthy controls. DESIGN: We first conducted a cross-sectional study of 60 ursodeoxycholic acid (UDCA) treatment-naïve patients with PBC and 80 matched healthy controls. Second, an independent cohort composed of 19 treatment-naïve patients and 34 controls was used to validate the results. Finally, a prospective study was performed in a subgroup of 37 patients with PBC who underwent analysis before and after 6 months of UDCA treatment. Faecal samples were collected, and microbiomes were analysed by 16S ribosomal RNA gene sequencing. RESULTS: A significant reduction of within-individual microbial diversity was noted in PBC (p=0.03). A signature defined by decreased abundance of four genera and increased abundance of eight genera strongly correlated with PBC (area under curve=0.86, 0.84 in exploration and validation data, respectively). Notably, the abundance of six PBC-associated genera was reversed after 6 months of UDCA treatment. In particular, Faecalibacterium, enriched in controls, was further decreased in gp210-positive than gp210-negative patients (p=0.002). Of interest was the finding that the increased capacity for the inferred pathway, bacterial invasion of epithelial cells in PBC, highly correlated with the abundance of bacteria belonging to Enterobacteriaceae. CONCLUSIONS: This study presents a comprehensive landscape of gut microbiota in PBC. Dysbiosis was found in the gut microbiome in PBC and partially relieved by UDCA. Our study suggests that gut microbiota is a potential therapeutic target and diagnostic biomarker for PBC.

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study.

BACKGROUND: Proteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard. RESULTS: We systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share at least one common subcellular localization. With the result, for the first time, four graph-based semi-supervised learning algorithms, Majority, χ2-score, GenMultiCut and FunFlow originally proposed for protein function prediction, are introduced to assign "multiplex localization" to proteins. We analyze these approaches by performing a large-scale cross validation on a Saccharomyces cerevisiae proteome compiled from BioGRID and comparing their predictions for 22 protein subcellular localizations. Furthermore, we build an ensemble classifier to associate 529 unlabeled and 137 ambiguously-annotated proteins with subcellular localizations, most of which have been verified in the previous experimental studies. CONCLUSIONS: Physical interaction of proteins has actually provided an essential clue for their co-localization. Compared to the local approaches, the global algorithms consistently achieve a superior performance.

Induced fit or conformational selection for RNA/U1A folding.

The hairpin II of U1 snRNA can bind U1A protein with high affinity and specificity. NMR spectra suggest that the loop region of apo-RNA is largely unstructured and undergoes a transition from unstructured to well-folded upon U1Abinding. However, the mechanism that RNA folding coupled protein binding is poorly understood. To get an insight into the mechanism, we have performed explicit-solvent molecular dynamics (MD) to study the folding kinetics of bound RNA and apo-RNA. Room-temperature MD simulations suggest that the conformation of bound RNA has significant adjustment and becomes more stable upon U1A binding. Kinetic analysis of high-temperature MD simulations shows that bound RNA and apo-RNA unfold via a two-state process, respectively. Both kinetics and free energy landscape analyses indicate that bound RNA folds in the order of RNA contracting, U1A binding, and tertiary folding. The predicted Phi-values suggest that A8, C10, A11, and G16 are key bases for bound RNA folding. Mutant Arg52Gln analysis shows that electrostatic interaction and hydrogen bonds between RNA and U1A (Arg52Gln) decrease. These results are in qualitative agreement with experiments. Furthermore, this method could be used in other studies about biomolecule folding upon receptor binding.

Conformational selection or induced fit for Brinker and DNA recognition.

Brinker is the key target protein of the Drosophila Decapentaplegic morphogen signalling pathway. Brinker is widely expressed and can bind with DNA. NMR spectra suggest that apo-Brinker is intrinsically unstructured and undergoes a folding transition upon DNA-binding. However, the coupled mechanism of binding and folding is poorly understood. Here, we performed molecular dynamics (MD) simulations for both bound and apo-Brinker to study the mechanism. Room-temperature MD simulations suggest that Brinker becomes more rigid and stable upon DNA-binding. Kinetic analysis of high-temperature MD simulations shows that both bound and apo-Brinker unfold via a two-state process. The time scale of tertiary unfolding is significantly different between bound and apo-Brinker. The predicted Φ-values suggest that there are more residues with native-like transition state ensembles (TSEs) for bound Brinker than for apo-Brinker. The average RMSD differences between bound and apo-Brinker and Kolmogorov-Smirnov (KS) test analysis illustrate that Brinker folding upon DNA-binding might obey induced-fit mechanism based on MD simulations. These methods can be used for the research of other biomolecular folding upon ligand-binding.

Insight into the stability of cross-beta amyloid fibril from molecular dynamics simulation.

Amyloid fibrils are considered to play causal roles in the pathogenesis of amyloid-related degenerative diseases such as Alzheimer's disease, type II diabetes mellitus, the transmissible spongiform encephalopathies, and prion disease. The mechanism of fibril formation is still hotly debated and remains an important open question. In this study, we utilized molecular dynamics (MD) simulation to analyze the stability of hexamer for eight class peptides. The MD results suggest that VEALYL and MVGGVV-1 are the most stable ones, then SNQNNY, followed by LYQLEN, MVGGVV-2, VQIVYK, SSTSAA, and GGVVIA. The statistics result indicates that hydrophobic residues play a key role in stabilizing the zipper interface. Single point and two linkage mutants of MVGGVV-1 confirmed that both Met1 and Val2 are key hydrophobic residues. This is consistent with the statistics analysis. The stability results of oligomer for MVGGVV-1 suggest that the intermediate state should be trimer (3-0) and tetramer (2-2). These methods can be used in stabilization study of other amyloid fibril.

Inferring Transcriptional Interactions by the Optimal Integration of ChIP-chip and Knock-out Data.

How to combine heterogeneous data sources for reliable prediction of transcriptional regulation is a challenge. Here we present an easy but powerful method to integrate Chromatin immunoprecipitation (ChIP)-chip and knock-out data. Since these two types of data provide complementary (physical and functional) information about transcription, the method combining them is expected to achieve high detection rates and very low false positive rates. We try to seek the optimal integration of these two data using hyper-geometric distribution. We evaluate our method on yeast data and compare our predictions with YEASTRACT, high-quality ChIP-chip data, and literature. The results show that even using low-quality ChIP-chip data, our method uncovers more relations than those inferred before from high-quality data. Furthermore our method achieves a low false positive rate. We find experimental and computational evidence in literature for most transcription factor (TF)-gene relations uncovered by our method.

Insight into the metabolism rate of quinone analogues from molecular dynamics simulation and 3D-QSMR methods.

Molecular dynamics simulation was applied to investigate the metabolism mechanism for quinone analogues. Favourable hydrogen bonds between ligand and NQO1, and parallel orientation between ligand and flavin adenine dinucleotide could explain the difference of metabolism rate (in micromol/min/mg) for quinone analogues. This is consistent with the experimental observation (Structure 2001;9:659-667). Then Support Vector Machines was used to construct quantitative structure-metabolism rate model. The model was evaluated by 14 test set compounds. Some descriptors selected by Support Vector Machine, were introduced into standard fields of three-dimensional quantitative structure-metabolism relationship to improve the statistical parameters of three-dimensional quantitative structure-metabolism relationship models. The results show that the inclusion of highest occupied molecular orbital and lowest unoccupied molecular orbital is meaningful for three-dimensional quantitative structure-metabolism relationship models. These in silico absorption, distribution, metabolism and excretion models are helpful in making quantitative prediction of their metabolic rates for new lead compounds before resorting in vitro and in vivo experimentation.