Logic motif of combinatorial control in transcriptional networks

Combinatorial control is prevalent in transcriptional regulatory networks. However, whether there are specific logic patterns over- or under-represented in real networks remains uninvestigated. Using a theoretic model and _in-silico_ simulations, we systematically study how the relative abundance of distinct regulatory logic patterns influences the network’s global dynamics. We find that global dynamic characteristics are sensitive to several specific logic patterns regardless of the detailed network topology. We show it is possible to infer logic motifs based on the sensitivity profile and the biological interpretations of these global characteristics

Improvement of adenoviral vector-mediated gene transfer to airway epithelia by folate-modified anionic liposomes

Despite remarkable progress in the development of both viral and nonviral gene delivery vectors for airway disease treatment, poor gene transfer efficiency to the airway epithelium is a major obstacle in clinical application. To take advantage of the unique features of viral and nonviral vectors, we have developed complexes of adenovirus vector and anionic liposomes (AL-Ad5) by the calcium-induced phase change method. In the current study, based on the fact that there are overexpressed folate receptors on the surface of airway epithelia, we further modified the AL-Ad5 complexes with folate (F-AL-Ad5) to improve the transduction ability of Ad5 in airway epithelia. The transduction efficiencies of the obtained F-AL-Ad5 and AL-Ad5 complexes were assessed in primary-cultured airway epithelia in vitro. Our results indicated that compared with naked adenovirus vector, both AL-Ad5 and F-AL-Ad5 could significantly enhance the gene transduction efficiency of adenovirus vector in primary-cultured airway epithelial cells. Moreover, the enhancement mediated by F-AL-Ad5 was more dramatic than that by AL-Ad5. These results suggested that F-AL-Ad5 may be a useful strategy to deliver therapeutic genes to the airway epithelia and is promising in clinical application

Molecular evolution of Cide family proteins: Novel domain formation in early vertebrates and the subsequent divergence

<p>Abstract</p> <p>Background</p> <p>Cide family proteins including Cidea, Cideb and Cidec/Fsp27, contain an N-terminal CIDE-N domain that shares sequence similarity to the N-terminal CAD domain (NCD) of DNA fragmentation factors Dffa/Dff45/ICAD and Dffb/Dff40/CAD, and a unique C-terminal CIDE-C domain. We have previously shown that Cide proteins are newly emerged regulators closely associated with the development of metabolic diseases such as obesity, diabetes and liver steatosis. They modulate many metabolic processes such as lipolysis, thermogenesis and TAG storage in brown adipose tissue (BAT) and white adipose tissue (WAT), as well as fatty acid oxidation and lipogenesis in the liver.</p> <p>Results</p> <p>To understand the evolutionary process of Cide proteins and provide insight into the role of Cide proteins as potential metabolic regulators in various species, we searched various databases and performed comparative genomic analysis to study the sequence conservation, genomic structure, and phylogenetic tree of the CIDE-N and CIDE-C domains of Cide proteins. As a result, we identified signature sequences for the N-terminal region of Dffa, Dffb and Cide proteins and CIDE-C domain of Cide proteins, and observed that sequences homologous to CIDE-N domain displays a wide phylogenetic distribution in species ranging from lower organisms such as hydra (<it>Hydra vulgaris</it>) and sea anemone (<it>Nematostella vectensis</it>) to mammals, whereas the CIDE-C domain exists only in vertebrates. Further analysis of their genomic structures showed that although evolution of the ancestral CIDE-N domain had undergone different intron insertions to various positions in the domain among invertebrates, the genomic structure of <it>Cide </it>family in vertebrates is stable with conserved intron phase.</p> <p>Conclusion</p> <p>Based on our analysis, we speculate that in early vertebrates CIDE-N domain was evolved from the duplication of NCD of Dffa. The CIDE-N domain somehow acquired the CIDE-C domain that was formed around the same time, subsequently generating the Cide protein. Subsequent duplication and evolution have led to the formation of different Cide family proteins that play unique roles in the control of metabolic pathways in different tissues.</p

MeMo: a web tool for prediction of protein methylation modifications

Protein methylation is an important and reversible post-translational modification of proteins (PTMs), which governs cellular dynamics and plasticity. Experimental identification of the methylation site is labor-intensive and often limited by the availability of reagents, such as methyl-specific antibodies and optimization of enzymatic reaction. Computational analysis may facilitate the identification of potential methylation sites with ease and provide insight for further experimentation. Here we present a novel protein methylation prediction web server named MeMo, protein methylation modification prediction, implemented in Support Vector Machines (SVMs). Our present analysis is primarily focused on methylation on lysine and arginine, two major protein methylation sites. However, our computational platform can be easily extended into the analyses of other amino acids. The accuracies for prediction of protein methylation on lysine and arginine have reached 67.1 and 86.7%, respectively. Thus, the MeMo system is a novel tool for predicting protein methylation and may prove useful in the study of protein methylation function and dynamics. The MeMo web server is available at:

NBA-Palm: prediction of palmitoylation site implemented in Naïve Bayes algorithm

BACKGROUND: Protein palmitoylation, an essential and reversible post-translational modification (PTM), has been implicated in cellular dynamics and plasticity. Although numerous experimental studies have been performed to explore the molecular mechanisms underlying palmitoylation processes, the intrinsic feature of substrate specificity has remained elusive. Thus, computational approaches for palmitoylation prediction are much desirable for further experimental design. RESULTS: In this work, we present NBA-Palm, a novel computational method based on Naïve Bayes algorithm for prediction of palmitoylation site. The training data is curated from scientific literature (PubMed) and includes 245 palmitoylated sites from 105 distinct proteins after redundancy elimination. The proper window length for a potential palmitoylated peptide is optimized as six. To evaluate the prediction performance of NBA-Palm, 3-fold cross-validation, 8-fold cross-validation and Jack-Knife validation have been carried out. Prediction accuracies reach 85.79% for 3-fold cross-validation, 86.72% for 8-fold cross-validation and 86.74% for Jack-Knife validation. Two more algorithms, RBF network and support vector machine (SVM), also have been employed and compared with NBA-Palm. CONCLUSION: Taken together, our analyses demonstrate that NBA-Palm is a useful computational program that provides insights for further experimentation. The accuracy of NBA-Palm is comparable with our previously described tool CSS-Palm. The NBA-Palm is freely accessible from:

Associative Transformer Is A Sparse Representation Learner

Emerging from the monolithic pairwise attention mechanism in conventional Transformer models, there is a growing interest in leveraging sparse interactions that align more closely with biological principles. Approaches including the Set Transformer and the Perceiver employ cross-attention consolidated with a latent space that forms an attention bottleneck with limited capacity. Building upon recent neuroscience studies of Global Workspace Theory and associative memory, we propose the Associative Transformer (AiT). AiT induces low-rank explicit memory that serves as both priors to guide bottleneck attention in the shared workspace and attractors within associative memory of a Hopfield network. Through joint end-to-end training, these priors naturally develop module specialization, each contributing a distinct inductive bias to form attention bottlenecks. A bottleneck can foster competition among inputs for writing information into the memory. We show that AiT is a sparse representation learner, learning distinct priors through the bottlenecks that are complexity-invariant to input quantities and dimensions. AiT demonstrates its superiority over methods such as the Set Transformer, Vision Transformer, and Coordination in various vision tasks

DBMLoc: a Database of proteins with multiple subcellular localizations

<p>Abstract</p> <p>Background</p> <p>Subcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%.</p> <p>Description</p> <p>We have developed a database of proteins with multiple subcellular localizations, designated DBMLoc. The initial release contains 10470 multiple subcellular localization-annotated entries. Annotations are collected from primary protein databases, specific subcellular localization databases and literature texts. All the protein entries are cross-referenced to GO annotations and SwissProt. Protein-protein interactions are also annotated. They are classified into 12 large subcellular localization categories based on GO hierarchical architecture and original annotations. Download, search and sequence BLAST tools are also available on the website.</p> <p>Conclusion</p> <p>DBMLoc is a protein database which collects proteins with more than one subcellular localization annotation. It is freely accessed at <url>http://www.bioinfo.tsinghua.edu.cn/DBMLoc/index.htm</url>.</p

PEGylated graphene oxide for tumor-targeted delivery of paclitaxel.

AIM: The graphene oxide (GO) sheet has been considered one of the most promising carbon derivatives in the field of material science for the past few years and has shown excellent tumor-targeting ability, biocompatibility and low toxicity. We have endeavored to conjugate paclitaxel (PTX) to GO molecule and investigate its anticancer efficacy. MATERIALS & METHODS: We conjugated the anticancer drug PTX to aminated PEG chains on GO sheets through covalent bonds to get GO-PEG-PTX complexes. The tissue distribution and anticancer efficacy of GO-PEG-PTX were then investigated using a B16 melanoma cancer-bearing C57 mice model. RESULTS: The GO-PEG-PTX complexes exhibited excellent water solubility and biocompatibility. Compared with the traditional formulation of PTX (Taxol®), GO-PEG-PTX has shown prolonged blood circulation time as well as high tumor-targeting and -suppressing efficacy. CONCLUSION: PEGylated graphene oxide is an excellent nanocarrier for paclitaxel for cancer targeting