Like oil blending into larger liquid blobs, the contents of cells can also be separated into droplets. The transformation of a single-phase system into a multiple-phase system is defined as phase separation. Phase separation is one of biology’s hottest questions in recent years. Numerous physical theories and biological experiments have been developed to uncover the underlying principles of phase separation in biology. Whether a solution is able to undergo phase separation in[Detail] ...Download cover
Background: The concept of phase separation has been used to describe and interpret physicochemical phenomena in biological systems for decades. Many intracellular macromolecules undergo phase separation, where it plays important roles in gene regulation, cellular signaling, metabolic reactions and so on, due to its unique dynamic properties and biological effects. As the noticeable importance of phase separation, pioneer researchers have explored the possibility to introduce the synthetically engineered phase separation for applicable cell function.
Results: In this article, we illustrated the application value of phase separation in synthetic biology. We described main states of phase separation in detail, summarized some ways to implement synthetic condensates and several methods to regulate phase separation, and provided a substantial amount of identical examples to illuminate the applications and perspectives of phase separation in synthetic biology.
Conclusions: Multivalent interactions implement phase separation in synthetic biology. Small molecules, light control and spontaneous interactions induce and regulate phase separation. The synthetic condensates are widely used in signal amplifications, designer orthogonally non-membrane-bound organelles, metabolic pathways, gene regulations, signaling transductions and controllable platforms. Studies on quantitative analysis, more standardized modules and precise spatiotemporal control of synthetic phase separation may promote the further development of this field.
Background: Differential allelic expression (DAE) plays a key role in the regulation of many biological processes, and it may also play a role in adaptive evolution. Recently, environment-dependent DAE has been observed in species of marine phytoplankton, and most remarkably, alleles that showed the highest level of DAE also showed the fastest rate of evolution.
Methods: To better understand the role of DAE in adaptive evolution and phenotypic plasticity, we developed a 2-D cellular automata model “DAEsy-World” that builds on the classical Daisyworld model.
Results: Simulations show that DAE delineates the evolution of alternative alleles of a gene, enabling the two alleles to adapt to different environmental conditions and sub-functionalize. With DAE, the build-up of genetic polymorphisms within genes is driven by positive selection rather than strict neutral evolution, and this can enhance phenotypic plasticity. Moreover, in sexually reproducing organisms, DAE also increased the standing genetic variation, augmenting a species’ adaptive evolutionary potential and ability to respond to fluctuating and/or changing conditions (cf. genetic assimilation). We furthermore show that DAE is likely to evolve in fluctuating environmental conditions.
Conclusions: DAE increases the adaptive evolutionary potential of both sexual and asexually reproducing organisms, and it may affect the pattern of nucleotide substitutions of genes.
Background: Functional characterization of the long noncoding RNAs (lncRNAs) in disease attracts great attention, which results in a limited number of experimentally characterized lncRNAs. The major problems underlying the lack of experimental verifications are considered to come from the significant false-positive assignments and extensive genetic-heterogeneity of disease. These problems are even worse when it comes to the functional characterization in comorbidity (simultaneous/sequential presence of multiple diseases in a patient, and showing much wider prevalence, poorer treatment-response and longer illness-course than a single disease).
Methods: Herein, FCCLnc was developed to characterize lncRNA function by (1) integrating diverse SNPs that were associated with 193 diseases standardized by International Classification of Diseases (ICD-11), (2) condition-specific expression of lncRNAs, (3) weighted correlation network of lncRNAs and protein-coding neighboring genes.
Results: FCCLnc can characterize lncRNA function in both disease and comorbidity by not only controlling false discovery but also tolerating their disease heterogeneity. Moreover, FCCLnc can provide interactive visualization and full download of lncRNA-centered co-expression network.
Conclusion: In summary, FCCLnc is unique in characterizing lncRNA function in diverse diseases and comorbidities and is highly expected to emerge to be an indispensable complement to other available tools. FCCLnc is accessible at https://idrblab.org/fcclnc/.
Background: One of the challenges in personalized medicine is to determine specific drugs and their dosages for patient individuals who are undergoing a common disease. The technique of cell lines provides a safe approach to capture the drug responses of patient individuals when given specific drugs with varied dosages. However, it is still costly to determine drug responses in cells w.r.t dosages by biological assays. Computational methods provide a promising screening to infer possible drug responses in the cells of patient individuals on a large scale. Nevertheless, existing computational approaches are insufficient to interpret the underlying reason for drug responses.
Methods: In this work, we propose an interpretable model for analyzing and predicting drug responses across cell lines. The proposed model bridges drug features (e.g., chemical structure fingerprints), cell features (e.g., gene expression profiles), and drug responses across cells (measured by IC50) by a triple matrix factorization (TMF), such that the underlying reason for drug responses in specific cells is possibly interpreted.
Results: The comparison with state-of-the-art computational approaches demonstrates the superiority of our TMF. More importantly, a case study of drug responses in lung-related cell lines shows its interpretable ability to find out highly occurring drug substructures, crucial mutated genes, as well as significant pairs between substructures and mutated genes in terms of drug sensitivity and resistance.
Conclusion: TMF is an effective and interpretable approach for predicting cell lines responses to drugs, and can dig out crucial pairs of chemical substructures and genes, which uncovers the underlying reason for drug responses in specific cells.
Background: The outbreak and continued spread of coronavirus infection (COVID-19) sets the goal of finding new tools and methods to develop analytical procedures and tests to detect, study infection and prevent morbidity.
Methods: The noncovalent binding of cyanine and squarylium dyes of different classes (60 compounds in total) with the proteases NSP3, NSP5, and NSP12 of SARS-CoV-2 was studied by the method of molecular docking.
Results: The interaction energies and spatial configurations of dye molecules in complexes with NSP3, NSP5, and NSP12 have been determined.
Conclusion: A number of anionic dyes showing lower values of the total energy Etot could be recommended for practical research in the development of agents for the detection and inactivation of the coronavirus.
Background: Single-cell RNA sequencing (scRNA-seq) data provides a whole new view to study disease and cell differentiation development. With the explosive increment of scRNA-seq data, effective models are demanded for mining the intrinsic biological information.
Methods: This paper proposes a novel non-negative matrix factorization (NMF) method for clustering and gene co-expression network analysis, termed Adaptive Total Variation Constraint Hypergraph Regularized NMF (ATV-HNMF). ATV-HNMF can adaptively select the different schemes to denoise the cluster or preserve the cluster boundary information between clusters based on the gradient information. Besides, ATV-HNMF incorporates hypergraph regularization, which can consider high-order relationships between cells to reserve the intrinsic structure of the space.
Results: Experiments show that the performances on clustering outperform other compared methods, and the network construction results are consistent with previous studies, which illustrate that our model is effective and useful.
Conclusion: From the clustering results, we can see that ATV-HNMF outperforms other methods, which can help us to understand the heterogeneity. We can discover many disease-related genes from the constructed network, and some are worthy of further clinical exploration.