Somatic mutation is considered the primary direct cause of tumorigenesis. A cancer patient can carry up to tens of thousands of single base substitution somatic mutations. Like the Butterfly effect, these somatic mutations lead to a series of regulatory function changes, including gene expression dysregulation. Jiang et al. hypothesized that associations between gene expression and somatic mutations can be translated into the associations between gene expression and mutationa[Detail] ...
Background: As one of the representative protein materials, protein nanocages (PNCs) are self-assembled supramolecular structures with multiple advantages, such as good monodispersity, biocompatibility, structural addressability, and facile production. Precise quantitative functionalization is essential to the construction of PNCs with designed purposes.
Results: With three modifiable interfaces, the interior surface, outer surface, and interfaces between building blocks, PNCs can serve as an ideal platform for precise multi-functionalization studies and applications. This review summarizes the currently available methods for precise quantitative functionalization of PNCs and highlights the significance of precise quantitative control in fabricating PNC-based materials or devices. These methods can be categorized into three groups, genetic, chemical, and combined modification.
Conclusion: This review would be constructive for those who work with biosynthetic PNCs in diverse fields.
Background: Synthetic microbial communities, with different strains brought together by balancing their nutrition and promoting their interactions, demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications. The potential of such microbial communities has not been explored, due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.
Results: Genome-scale metabolic models (GEM) have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities, since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbe-habitats and microbe-microbe interactions. In this work, we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities: predicting multi-species interactions, exploring environmental impacts on microbial phenotypes, and optimizing community-level performance.
Conclusions: Although at the infancy stage, GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities. Compared to other methods, especially the use of laboratory cultures, GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality, such as identifying community members, determining media composition, evaluating microbial interaction potential or selecting the best community configuration. Future efforts should be made to overcome the limitations of the approaches, ranging from quality control of GEM reconstructions to community-level modeling algorithms, so that more applications of GEMs in studying phenotypes of microbial communities can be expected.
Background: Mutational signatures computed from somatic mutations, allow an in-depth understanding of tumorigenesis and may illuminate early prevention strategies. Many studies have shown the regulation effects between somatic mutation and gene expression dysregulation.
Methods: We hypothesized that there are potential associations between mutational signature and gene expression. We capitalized upon RNA-seq data to model 49 established mutational signatures in 33 cancer types. Both accuracy and area under the curve were used as performance measures in five-fold cross-validation.
Results: A total of 475 models using unconstrained genes, and 112 models using protein-coding genes were selected for future inference purposes. An independent gene expression dataset on lung cancer smoking status was used for validation which achieved over 80% for both accuracy and area under the curve.
Conclusion: These results demonstrate that the associations between gene expression and somatic mutations can translate into the associations between gene expression and mutational signatures.
Background: A key step in gene expression is the recognition of the stop codon to terminate translation at the correct position. However, it has been observed that ribosomes can misinterpret the stop codon and continue the translation in the 3′UTR region. This phenomenon is called stop codon read-through (SCR). It has been suggested that these events would occur on a programmed basis, but the underlying mechanisms are still not well understood.
Methods: Here, we present a strategy for the comprehensive identification of SCR events in the Drosophila melanogaster transcriptome by evaluating the ribosomal density profiles. The associated ribosomal leak rate was estimated for every event identified. A statistical characterization of the frequency of nucleotide use in the proximal region to the stop codon in the sequences associated to SCR events was performed.
Results: The results show that the nucleotide usage pattern in transcripts with the UGA codon is different from the pattern for those transcripts ending in the UAA codon, suggesting the existence of at least two mechanisms that could alter the translational termination process. Furthermore, a linear regression models for each of the three stop codons was developed, and we show that the models using the nucleotides at informative positions outperforms those models that consider the entire sequence context to the stop codon.
Conclusions: We report that distal nucleotides can affect the SCR rate in a stop-codon dependent manner.
Background: Mass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. However, computational analysis is required to reconstruct such networks with a mechanistic model.
Methods: We propose our Mass cytometry Signaling Network Analysis Code (McSNAC), a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data. McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption often breaks down as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.
Results: We carry out a series of in silico experiments here to show (1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; (2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.
Conclusions: These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.
Background: N6-methyl adenosine (m6A) modifications of mRNA and long non-coding RNA (lncRNAs) are known to play a significant role in regulation of gene expression and organismal development. Besides writer and eraser proteins of this dynamic modification, the YT521-B homology (YTH) domain can recognize the modification involved in numerous cellular processes. The function of proteins containing YTH domain and its binding mode with N6-Methyladenosine RNA has attracted considerable attention. However, the structural and dynamic characteristics of the YTH domain in complex with m6A RNA is still unknown.
Method: This work presents results of accelerated molecular dynamics (aMD) simulations at the timescale of microseconds. Principal component analysis (PCA), molecular mechanics generalized Born surface area (MM/GBSA) calculations, contact analysis and contact-based principal component analysis (conPCA) provide new insights into structure and dynamics of the YTH-RNA complex.
Results: The aMD simulations indicate that the recognition loop has a larger movement away from the binding pocket in the YTH-A3 RNA than that in the YTH-m6A3 RNA. In aMD trajectories of the apo YTH, there is a significant close-open transition of the recognition loop, that is to say, the apo YTH can take both the closed and open structure. We have found that the YTH domain binds more favorably to the methylated RNA than the non-methylated RNA. The per-residue free energy decomposition and conPCA suggest that hydrophobic residues including W380, L383-V385, W431-P434, M437, and M441-L442, may play important roles in favorable binding of the m6A RNA to the YTH domain, which is also supported by aMD simulations of a double mutated system (L383A/M437A).
Conclusion: The results are in good agreement with higher structural stability of the YTH-m6A RNA than that of the YTH-A3 RNA. The addition of a methylation group on A3 can enhance its binding to the hydrophobic pocket in the YTH domain. Our simulations support a ‘conformational selection’ mechanism between the YTH-RNA binding. This work may aid in our understanding of the structural and dynamic characteristics of the YTH protein in complex with the methylated RNA.
Background: Computer simulation studies complement in vitro experiments and provide avenue to understand allosteric regulation in the absence of other molecular viewing techniques. Molecular dynamics captures internal motion within the protein and enables tracing the communication path between a catalytic site and a distal allosteric site. In this article, we have identified the communication pathway between the viral protein genome linked (VPg) binding region and catalytic active site in nuclear inclusion protein-a protease (NIa-Pro).
Methods: Molecular dynamics followed by in silico analyses have been used to map the allosteric pathway.
Results: This study delineates the residue interaction network involved in allosteric regulation of NIa-Pro activity by VPg. Simulation studies indicate that point mutations in the VPg interaction interface of NIa-Pro lead to disruption in these networks and change the orientation of catalytic residues. His142Ala and His167Ala mutations do not show a substantial change in the overall protease structure, but rather in the residue interaction network and catalytic site geometry.
Conclusion: Our mutagenic study delineates the allosteric pathway and facilitates the understanding of the modulation of NIa-Pro activity on a molecular level in the absence of the structure of its complex with the known regulator VPg. Additionally, our in silico analysis explains the molecular concepts and highlights the dynamics behind the previously reported wet lab study findings.
Background: The COVID-19 has a huge negative impact on people’s health. Traditional Chinese Medicine (TCM) has a good effect on viral pneumonia. It is of great practical significance to study its pharmacology.
Methods: The ingredients and targets of each herb in Maxing Shigan Decoction which obtained from Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and the related targets of COVID-19 were screened by GeneCards database based on the network pharmacology. Venn was used to analyze the intersection target between active ingredients and diseases. Cytoscape software was used to construct an active ingredient-disease target network. The Protein-Protein Interaction network was constructed by STRING database and Cytohubba was used to screen out the key targets. Gene Ontology (GO) functional enrichment analysis and KEGG pathway analysis were performed by David database.
Results: In this study, a total of 134 active ingredients and 229 related targets, 198 targets of COVID-19 and 48 common targets of drug-disease were chosen. Enrichment items and pathways were obtained through GO and KEGG pathway analysis. The predicted active ingredients were quercetin, kaempferol, luteolin, naringenin, glycyrol, and the key targets involved IL6, MAPK3, MAPK8, CASP3, IL10, etc. The results showed that the active ingredients of Maxing Shigan Decoction acted on multiple targets which played roles in the treatment of COVID-19 by regulating inflammation, immune system and other pathways.
Conclusions: The main contribution of this paper is to use data to mine the principles of the treatment of COVID-19 from the pharmacology of these prescriptions, and the results can be provided theoretical reference for medical workers.