Molecular Interaction Networks Research Articles

Multifunctional Antimonene-Silver Nanocomposites for Ultra-Multi-Mode and Multi-Analyte Sensing, Parallel and Batch Logic Computing, Long-Text Information Protection.

To simulate life's emergent functions, mining the multiple sensing capabilities of nanosystems, and digitizing networks of transduction signals and molecular interactions, is an ongoing endeavor. Here, multifunctional antimonene-silver nanocomposites (AM-Ag NCs) are synthesized facilely and fused for molecular sensing and digitization applications (including ultra-multi-mode and multi-analyte sensing, parallel and batch logic computing, long-text information protection). By mixing surfactant, AM, Ag+ and Sodium borohydride (NaBH4) at room temperature for 5min, the resulting NCs are comprised of Ag nanoparticles scattered within AM nanosheets and protected by the surfactant. Interestingly, AM-Ag NCs exhibit ultra-multi-mode sensing ability for multiplex metal ions (Hg2+, Fe3+, or Al3+), which significantly improved selectivity (≈2 times) and sensitivity (≈400 times) when analyzing the combined channels. Moreover, multiple sensing capabilities of AM-Ag NCs enable diverse batch and parallel molecular logic computations (including advanced cascaded logic circuits). Ultra-multi-mode selective patterns of AM-Ag NCs to 18 kinds of metal ions can be converted into a series of binary strings by setting the thresholds, and realized high-density, long-text information protection for the first time. This study provides new ideas and paradigms for the preparation and multi-purpose application of 2D nanocomposites, but also offers new directions for the fusion of molecular sensing and informatization.

Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson’s disease

Parkinson’s disease (PD) is a complex neurodegenerative disorder without a cure. The onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons, limiting early-stage understanding of PD. To shed light on early PD development, we study time series scRNA-seq datasets of mDA neurons obtained from patient-derived induced pluripotent stem cell differentiation. We develop a new data integration method based on Non-negative Matrix Tri-Factorization that integrates these datasets with molecular interaction networks, producing condition-specific “gene embeddings”. By mining these embeddings, we predict 193 PD-related genes that are largely supported (49.7%) in the literature and are specific to the investigated PINK1 mutation. Enrichment analysis in Kyoto Encyclopedia of Genes and Genomes pathways highlights 10 PD-related molecular mechanisms perturbed during early PD development. Finally, investigating the top 20 prioritized genes reveals 12 previously unrecognized genes associated with PD that represent interesting drug targets.

Exploring Lung Cancer Protein Network: Understanding Structure and Function through Metric Space Modeling

Lung cancer remains a significant health threat with high mortality rates. Using graph theory, we modeled the protein-protein interaction (PPI) network in lung cancer to explore its complex structure. This approach allows for the analysis of network properties and the identification of key proteins driving biological processes. Our analysis revealed RPS27A as a central proteinwithin the network, associated with diverse functions related to ribosome biogenesis, translation, cell growth, apoptosis, and cancer progression. This suggests that RPS27A may have multiple functions in cancer development and progression, including in MAPK signaling pathways. Importantly, our study uniquely identifies RPS27A as a central hub in lung cancer PPI networks, shedding light on its pivotal role in disease pathogenesis. Additionally, we identified a central network zone enriched with proteins involved in key signaling pathways, presenting novel insights into potential therapeutic targets for lung cancer treatment. Pathway enrichment analysis further highlighted functional specialization across network zones, providing a comprehensive understanding of the intricate interplay between biological pathways in lung cancer progression. This study underscores the multifaceted roles of central proteins like RPS27A within lung cancer’s PPI network and the network’s potential for pinpointing therapeutic targets, presenting a novel perspective on the intricate network of molecular interactions driving lung cancer pathogenesis.

Abstract 6753: Bria-OTS immunotherapy platform: Harnessing gene-modified tumor cells to reinvigorate the cancer immunity cycle for precision anti-tumor responses

Abstract Background: For an immune response to lead to an effective anti-tumor response, a series of stepwise events involving a complex network of iterative cellular and molecular interactions must take place. Cancer, however, often derails this immune cycle, prompting contemporary cancer immunotherapy approaches to seek its reinstatement. Despite significant advances, the inherent disruption of the cancer immunity cycle at multiple junctures, coupled with current therapeutic strategies focusing on isolated stages within this intricate process, has left the attainment of curative therapies as an aspirational goal. We believe that targeting multiple immune processes can restore natural anti-cancer immunity and yield sustained responses. To this end, BriaCell is utilizing gene-modified tumor cells as an immunotherapeutic platform. Our initial version, SV-BR-1-GM, a breast cancer cell line expressing GM-CSF, demonstrates promising clinical outcomes by directly activating CD4+ T-cells in an antigen-specific, HLA-restricted manner. Building on our findings, we hypothesize that tumor cells not only supply antigens but also directly stimulate the immune system. Presenting our second-generation platform, we enhance antigen cross-presentation and direct immune activation through the expression of cytokines, co-stimulatory factors, and HLA alleles. Methods: To achieve this objective, we have developed Bria-OTS cell lines derived from various cancer types with the following genetic modifications: 1) Expression of costimulatory molecules (CD80, CD86, 4-1BBL, CD40) and immunomodulatory cytokines (GM-CSF, IFNα, IL-12 and IL-7). This modification aims to improve the cell's ability to present antigens and activate the host immune system. 2) Expanding HLA alleles to create semi-allogeneic cell lines matching at least one HLA allele across the population aims to enhance the host immune response, enabling recognition of tumor-associated antigens in the context of syngeneic HLA molecules Results: We generated four cell lines for each tumor type, and these cell lines secreted GM-CSF, IFNα, IL12 & IL7, while also expressing CD80, CD86, CD40, and different combinations of both Class I and Class II HLA alleles. In functional assays, our cell lines demonstrated potent antigen presentation, effectively triggering anti-tumor immune responses across diverse immune populations, including T-, NK, and NKT cells. Conclusions: The Bria-OTS cell lines will offer the following key features: 1) multimodal mechanisms of action including thwarting some mechanisms of cancer immune escape. 2) Precision Therapy; Bria-OTS will be precisely matched to individual patients based on their HLA alleles, encompassing over 99% of the U.S. population. 3) rapid, cost-effective treatment as our 'off-the-shelf' cell lines eliminate the need for personalized manufacturing. Citation Format: Miguel A. Lopez-Lago, Vikas Bhardwaj, Pravin Kesarwani, Xiaoyi Zheng, Sagarika Pachhal, Patience Cournoo, Renee Cortez, Charles L. Wiseman, William V. Williams. Bria-OTS immunotherapy platform: Harnessing gene-modified tumor cells to reinvigorate the cancer immunity cycle for precision anti-tumor responses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6753.

Identification of common genes of rhinovirus single/double‑stranded RNA‑induced asthma deterioration by bioinformatics analysis.

Rhinovirus (RV) is the most common respiratory virus affecting humans. The majority of asthma deteriorations are triggered by RV infections. However, whether the effects of RV single- and double-stranded RNA on asthma deterioration have common target genes needs to be further studied. In the present study, two datasets (GSE51392 and GSE30326) were used to screen for common differentially expressed genes (cDEGs). The molecular function, signaling pathways, interaction networks, hub genes, key modules and regulatory molecules of cDEGs were systematically analyzed using online tools such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, STRING and NetworkAnalyst. Finally, the hub genes STAT1 and IFIH1 were verified in clinical samples using reverse transcription-quantitative PCR (RT-qPCR). A total of 85 cDEGs were identified. Function analysis revealed that cDEGs served an important role in the innate immune response to viruses and its regulation. Signal transducer and activator of transcription 1 (STAT1), interferon induced with helicase C domain 1 (IFIH1), interferon regulatory factor 7 (IRF7), DExD/H box helicase 58 (DDX58) and interferon-stimulating gene 15 (ISG15) were detected to be hub genes based on the protein-protein interactions and six topological algorithms. A key module involved in influenza A, the Toll-like receptor signaling pathway, was identified using Cytoscape software. The hub genes were regulated by GATA-binding factor 2 and microRNA-146a-5p. In addition, RT-qPCR indicated that the expression levels of the hub genes STAT1 and IFIH1 were low during asthma deterioration compared with post-treatment recovery samples. The present study enhanced the understanding of the mechanism of RV-induced asthma deterioration.

In Silico Analysis of Glutamate Receptors in Capsicum chinense: Structure, Evolution, and Molecular Interactions

Plant glutamate receptors (GLRs) are integral membrane proteins that function as non-selective cation channels, involved in the regulation of developmental events crucial in plants. Knowledge of these proteins is restricted to a few species and their true agonists are still unknown in plants. Using tomato SlGLRs, a search was performed in the pepper database to identify GLR sequences in habanero pepper (Capsicum chinense Jacq.). Structural, phylogenetic, and orthology analysis of the CcGLRs, as well as molecular docking and protein interaction networks, were conducted. Seventeen CcGLRs were identified, which contained the characteristic domains of GLR. The variation of conserved residues in the M2 transmembrane domain between members suggests a difference in ion selectivity and/or conduction. Also, new conserved motifs in the ligand-binding regions are reported. Duplication events seem to drive the expansion of the species, and these were located in the evolution by using orthologs. Molecular docking analysis allowed us to identify differences in the agonist binding pocket between CcGLRs, which suggest the existence of different affinities for amino acids. The possible interaction of some CcGLRs with proteins leads to suggesting specific functions for them within the plant. These results offer important functional clues for CcGLR, probably extrapolated to other Solanaceae.

ANÁLISE DE REDE DE INTERAÇÕES MOLECULARES PARA ESTUDAR O DESENCADEAMENTO DE DIABETES E HIPERTENSÃO APÓS A INFECÇÃO POR COVID-19 USANDO FERRAMENTAS DE BIOINFORMÁTICA

Background: The association between COVID-19 infection and the development of new-onset diabetes and hypertension is an emerging area of research. However, a comprehensive understanding of the underlying molecular mechanisms is still lacking. Network analysis using bioinformatics tools can provide valuable insights into the complex molecular interactions involved in these conditions after COVID-19 infection. Objective: This study aims to use bioinformatics tools to analyze the network of molecular interactions related to new-onset diabetes and hypertension following COVID-19 infection. Methods: Data Sources: relevant datasets were collected from NCBI databases, including gene expression profiles and protein-protein interaction information. Data Preprocessing: the collected datasets went through pre-processing to ensure data quality and consistency. Integration of Datasets: the gene expression profiles and protein-protein interaction information were integrated to create a comprehensive dataset that captures the molecular interactions related to new-onset diabetes and hypertension following COVID-19 infection. Network Construction: bioinformatics tool STRING was used to construct a network model represented the molecular interactions identified in the integrated dataset. Network Analysis: the network analysis techniques, included network topology analysis and module identification. Functional Annotation and Pathway Enrichment Analysis: The genes and proteins within the network were functionally annotated using databases KEGG. Results: The network analysis revealed several key proteins and pathways related to the pathogenesis of new-onset diabetes and hypertension after COVID-19 infection. Notably, proteins involved in insulin signaling, glucose metabolism, inflammation, and blood pressure regulation were found to be prominently associated. The signaling pathway and the renin-angiotensin system were identified as key pathways in this context. Conclusion: The study demonstrated the interplay between genetics and protein-protein interactions, particularly within signaling pathways, as pivotal factors in the onset of post-COVID-19 development of new-onset diabetes and hypertension

A PDZ scaffolding/CaM-mediated pathway in Cryptochrome signaling.

Cryptochromes are cardinal constituents of the circadian clock, which orchestrates daily physiological rhythms in living organisms. A growing body of evidence points to their participation in pathways that have not traditionally been associated with circadian clock regulation, implying that cryptochromes may be subject to modulation by multiple signaling mechanisms. In this study, we demonstrate that human CRY2 (hCRY2) forms a complex with the large, modular scaffolding protein known as Multi-PDZ Domain Protein 1 (MUPP1). This interaction is facilitated by the calcium-binding protein Calmodulin (CaM) in a calcium-dependent manner. Our findings suggest a novel cooperative mechanism for the regulation of mammalian cryptochromes, mediated by calcium ions (Ca2+ ) and CaM. We propose that this Ca2+ /CaM-mediated signaling pathway may be an evolutionarily conserved mechanism that has been maintained from Drosophila to mammals, most likely in relation to its potential role in the broader context of cryptochrome function and regulation. Further, the understanding of cryptochrome interactions with other proteins and signaling pathways could lead to a better definition of its role within the intricate network of molecular interactions that govern circadian rhythms.

The interplay between Wnt signaling pathways and microtubule dynamics.

Wnt signaling pathways represent an evolutionarily highly conserved, intricate network of molecular interactions that regulates various aspects of cellular behavior, including embryonic development and tissue homeostasis. Wnt signaling pathways share the β-catenin-dependent (canonical) and the multiple β-catenin-independent (non-canonical) pathways. These pathways collectively orchestrate a wide range of cellular processes through distinct mechanisms of action. Both the β-catenin-dependent and β-catenin-independent pathways are closely intertwined with microtubule dynamics, underscoring the complex crosstalk between Wnt signaling and the cellular cytoskeleton. This interplay involves several mechanisms, including how the components of Wnt signaling can influence the stability, organization, and distribution of microtubules. The modulation of microtubule dynamics by Wnt signaling plays a crucial role in coordinating cellular behaviors and responses to external signals. In this comprehensive review, we discussed the current understanding of how Wnt signaling and microtubule dynamics intersect in various aspects of cellular behavior. This study provides insights into our understanding of these crucial cellular processes.

Antioxidant and antiproliferative effects of Teucrium polium extract: computational and in vivo study in rats

The current study aimed to assess the antioxidant and antiproliferative effects of teucrium polium extract: computational and in vivo study in rats. Three groups of animals: Group (i) constitute the control group; Group (ii) HeLa group received an intrafemoral inoculation of HeLa cells and Group (iii) constitue the combination between HeLa + T. polium. The plant was administered by gavage. Our results revealed that HeLa cell injection showed an elevation in aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), total bilirubin (TB), creatinine, urea, calcium and phosphorus. The pretreatment with the plant extract reduced the level of these parameters. Injection of HeLa cells showed a significant decrease in phosphorus and calcium respectively. However, the pretreatment by T. polium modulated the level of these two minerals. Rats treated with HeLa cells line showed an increase in the level of lipid peroxidation as evaluated by the TBARS substances, at the same time, a significant decreases in SOD, CAT and GPx activities were noted in the HeLa group compared to the control. On the other hand, pretreatment with the plant improved the level of these enzymes. Our results revealed that T.polium has a therapeutic effect on some health problems. HeLa cell line induced a small infiltration in liver and kidney. T. polium reduced the damage in both liver and kidney, but did not reveal any proliferation of tumor cells from trabecular bone tissue. The computational study revealed that T. polium compound bound with high free binding energies and established promising network of molecular interactions with COX-2 and TNF-α macromolecules.

Enhanced Sensitivity of A549 Cells to Doxorubicin with WS2 and WSe2 Nanosheets via the Induction of Autophagy.

The excellent physicochemical properties of two-dimensional transition-metal dichalcogenides (2D TMDCs) such as WS2 and WSe2 provide potential benefits for biomedical applications, such as drug delivery, photothermal therapy, and bioimaging. WS2 and WSe2 have recently been used as chemosensitizers; however, the detailed molecular basis underlying WS2- and WSe2-induced sensitization remains elusive. Our recent findings showed that 2D TMDCs with different thicknesses and different element compositions induced autophagy in normal human bronchial epithelial cells and mouse alveolar macrophages at sublethal concentrations. Here, we explored the mechanism by which WS2 and WSe2 act as sensitizers to increase lung cancer cell susceptibility to chemotherapeutic agents. The results showed that WS2 and WSe2 enhanced autophagy flux in A549 lung cancer cells at sublethal concentrations without causing significant cell death. Through the autophagy-specific RT2 Profiler PCR Array, we identified the genes significantly affected by WS2 and WSe2 treatment. Furthermore, the key genes that play central roles in regulating autophagy were identified by constructing a molecular interaction network. A mechanism investigation uncovered that WS2 and WSe2 activated autophagy-related signaling pathways by interacting with different cell surface proteins or cytoplasmic proteins. By utilizing this mechanism, the efficacy of the chemotherapeutic agent doxorubicin was enhanced by WS2 and WSe2 pre-treatment in A549 lung cancer cells. This study revealed a feature of WS2 and WSe2 in cancer therapy, in which they eliminate the resistance of A549 lung cancer cells against doxorubicin, at least partially, by inducing autophagy.

ACAT2 may be a novel predictive biomarker and therapeutic target in lung adenocarcinoma.

Acyl-coenzyme A cholesterol acyltransferase (ACAT) is a membrane-binding enzyme localized in the endoplasmic reticulum. ACAT2 can promote the development of colon cancer, but its efficacy in lung adenocarcinoma (LUAD) remains uncertain. ACAT2 expression was performed by using the TIMER2.0 database. The GEPIA database was utilized to analyze the correlation between ACAT2 expression and pathological stage of the tumor. Clinical prognosis was assessed through the Kaplan-Meier analysis. The CancerSEA database was employed to scrutinize the correlations between the ACAT2 expression and the functional status of various tumors, which were subsequently visualized as a heatmap. Furthermore, molecular interaction network analysis was performed by the STRING tool. High ACAT2 expression was associated with a poor DFS and OS in LUAD patients. Cox regression analysis indicated that the poor outcomes may be related to tumor stage, nodal stage, distant metastatic stage. ACAT2 was found to play a crucial role in various biological processes, including the cell cycle, DNA repair, DNA damage response, and proliferation. Enrichment pathway analysis revealed four ACAT2 related genes, ACOX1, EHHADH, OXCT1, and DLAT. Our study showed that ACAT2 was upregulated in LUAD, and had a worse survival. ACAT2 could be a novel predictive biomarker and therapeutic target in LUAD.

Investigating Lycotoxin-An1a (An1a), a defense antiviral peptide from Alopecosa nagpag venom as prospective anti-dengue agent against DENV-2 NS2B-NS3 protease

Dengue fever is a global health concern with no effective therapy. Screening synthetic chemicals, animal-originated compounds, and phytocompounds against Dengue virus (DENV) targets has failed to find dengue antivirals. The current study examines animal drugs as antagonists against NS2B-NS3Pro, one of DENV's most promising therapeutic targets for dengue fever. Antiviral-Lycotoxin-An1a (An1a), a defence antiviral peptide isolated from the venom of Alopecosa nagpag, a toxic spider. Based on prior in vitro research, it was discovered that the venom peptide suppresses the action of DENV-2 NS2B-NS3Pro. An1a peptide with NS2B-NS3Pro wild type (WT) and two mutants (H51N and S135A) was tested for anti-dengue characteristics using in silico analysis. The WT NS2B-NS3Pro has a catalytic triad of His51, Asp75, and Ser135 in the active site, but the mutants have N51 instead of His51 and Ala135 instead of Ser135. The dynamic sites of the three proteases (WT, H51N, S135A) and the peptide toxin (An1a) were taken into account to achieve molecular docking of An1a with WT NS2B-NS3Pro in conjunction with H51N and S135A. Cluspro-2 performs rigid-flexible docking to predict peptide binding affinity, effectiveness, and inhibitory consistency. Since the ligand had a higher binding affinity, docking score, and molecular interaction network, MD simulations and MM-GBSA free energy calculations were used to investigate the stability of the three protein-peptide complexes. The computer-aided screening and manufacture of spider venom-based anti-dengue medicines yielded intriguing results in the preliminary studies. This study is significant in defining the ideal therapeutic candidate against dengue infections.

Biological Domain Network Analysis Identifies Potential Triadic Interaction between Mitochondria, Nuclear Epigenetics, and Synaptic Viability

AbstractBackgroundThe AD endophenotypes initially described in the Common Alzheimer’s Disease Research Ontology (CADRO) were augmented and transformed into computationally implementable definitions for 19 discrete Biological Domains. These AD biological domains were employed in the development of molecular interaction networks depicting the signaling and protein‐protein interaction as a central bioinformatics resource for the TREAT‐AD consortium.Method19 AD biological domains were identified by iterative literature culling and model testing based on gene set enrichment analysis and gene ontology term identification. All 19 biological domains are defined by an exhaustive set of GO terms, allowing scientists everywhere to use the same criteria to define their large datasets in an unbiased manner. Network models were constructed from the leading‐edge genes from all AD‐risk enriched GO terms within each biological domain. The network models incorporate data from over 1700 transcriptomic brain studies and 1100 proteomic brains studies examining shifts in gene expression. These are employed as a reference tool for the formulation of specific hypotheses.ResultThe analysis of mitochondrial gene expression changes at both the transcriptomic and proteomic level identify deficits in both mitochondrial energy production and metabolism along with a decrease in acetyl‐CoA production, a necessary element for many reactions including histone acetylation. This suggests that mitochondrial metabolic tone may modify epigenetic regulation through lysine acetyltransferase (KATs) independently from ATP production. Examination of decrements in KAT activity from other model systems suggest a particularly robust impact on synaptic gene expression, implying a potential interaction between mitochondrial metabolism and synaptic gene expression independent of energy production. Intriguingly, some elements of a protein module identified by TREAT‐AD large‐scale deep proteomic analysis found to be most tightly associated with AD pathology and cognitive decline, are tightly associated with this model.ConclusionThe biological domains were constructed as a computational resource to facilitate hypothesis generation within the TREAT‐AD consortium, and here are implemented to highlight one emerging interaction between domains that may imply new mechanisms of disease regulation coupling between mitochondria and synaptic viability through shifts in epigenetic regulation.

Biological Domain Network Analysis Identifies Potential Triadic Interaction between Mitochondria, Nuclear Epigenetics, and Synaptic Viability

AbstractBackgroundThe AD endophenotypes initially described in the Common Alzheimer’s Disease Research Ontology (CADRO) were augmented and transformed into computationally implementable definitions for 19 discrete Biological Domains. These AD biological domains were employed in the development of molecular interaction networks depicting the signaling and protein‐protein interaction as a central bioinformatics resource for the TREAT‐AD consortium.Method19 AD biological domains were identified by iterative literature culling and model testing based on gene set enrichment analysis and gene ontology term identification. All 19 biological domains are defined by an exhaustive set of GO terms, allowing scientists everywhere to use the same criteria to define their large datasets in an unbiased manner. Network models were constructed from the leading‐edge genes from all AD‐risk enriched GO terms within each biological domain. The network models incorporate data from over 1700 transcriptomic brain studies and 1100 proteomic brains studies examining shifts in gene expression. These are employed as a reference tool for the formulation of specific hypotheses.ResultThe analysis of mitochondrial gene expression changes at both the transcriptomic and proteomic level identify deficits in both mitochondrial energy production and metabolism along with a decrease in acetyl‐CoA production, a necessary element for many reactions including histone acetylation. This suggests that mitochondrial metabolic tone may modify epigenetic regulation through lysine acetyltransferase (KATs) independently from ATP production. Examination of decrements in KAT activity from other model systems suggest a particularly robust impact on synaptic gene expression, implying a potential interaction between mitochondrial metabolism and synaptic gene expression independent of energy production. Intriguingly, some elements of a protein module identified by TREAT‐AD large‐scale deep proteomic analysis found to be most tightly associated with AD pathology and cognitive decline, are tightly associated with this model.ConclusionThe biological domains were constructed as a computational resource to facilitate hypothesis generation within the TREAT‐AD consortium, and here are implemented to highlight one emerging interaction between domains that may imply new mechanisms of disease regulation coupling between mitochondria and synaptic viability through shifts in epigenetic regulation.

Exploring the molecular mechanism of cold-adaption of an alkaline protease mutant by molecular dynamics simulations and residue interaction network.

Psychrophilic proteases have attracted enormous attention in past decades, due to their high catalytic activity at low temperatures in a wide range of industrial processes, especially in the detergent and leather industries. Among them, H5 is an alkaline protease mutant, which featuring psychrophilic-like behavior, but the reasons that H5 with higher activity at low temperatures are still poorly understood. Herein, the molecular dynamics (MD) simulations combined with residue interaction network (RIN) were utilized to investigate the mechanisms of the cold-adaption of mutant H5. The results demonstrated that two loops involved in the substrate binding G100-S104 and S125-S129 in H5 had higher mobility, and the distance enlargement between the two loops modulated the substrate's accessibility compared with wild type counterpart. Besides, H5 enhanced conformational flexibility by weakening salt bridges and increasing interaction with the solvent. In particular, the absence of Lys251-Asp197-Arg247 salt bridge network may contribute to the structural mobility. Based on the free energy landscape and molecular mechanics Poisson-Boltzmann surface area of the wild type and H5, it was elucidated that H5 possessed a large population of interconvertible conformations, resulting in the weaker substrate binding free energy. The calculated RIN topology parameters such as the average degree, average cluster coefficient, and average path length further verified that the mutant H5 attenuated residue-to-residue interactions. The investigation of the mechanisms by which how the residue mutation affects the stability and activity of enzymes provides a theoretical basis for the development of cold-adapted protease.

Structural insights into intron catalysis and dynamics during splicing

The group II intron ribonucleoprotein is an archetypal splicing system with numerous mechanistic parallels to the spliceosome, including excision of lariat introns1,2. Despite the importance of branching in RNA metabolism, structural understanding of this process has remained elusive. Here we present a comprehensive analysis of three single-particle cryogenic electron microscopy structures captured along the splicing pathway. They reveal the network of molecular interactions that specifies the branchpoint adenosine and positions key functional groups to catalyse lariat formation and coordinate exon ligation. The structures also reveal conformational rearrangements of the branch helix and the mechanism of splice site exchange that facilitate the transition from branching to ligation. These findings shed light on the evolution of splicing and highlight the conservation of structural components, catalytic mechanism and dynamical strategies retained through time in premessenger RNA splicing machines.

Prognostic value of fatty acid metabolism-related genes in colorectal cancer and their potential implications for immunotherapy.

Colorectal cancer is one of the most common gastrointestinal cancers and the second leading cause of cancer-related death. Although colonoscopy screening has greatly improved the early diagnosis of colorectal cancer, its recurrence and metastasis are still significant problems. Tumour cells usually have the hallmark of metabolic reprogramming, while fatty acids play important roles in energy storage, cell membrane synthesis, and signal transduction. Many pathways of fatty acid metabolism (FAM) are involved in the occurrence and development of colon cancer, and the complex molecular interaction network contains a variety of genes encoding key enzymes and related products. Clinical information and RNA sequencing data were collected from TCGA and GEO databases. The prognosis model of colon cancer was constructed by LASSO-Cox regression analysis among the selected fatty acid metabolism genes with differential expression. Nomogram for the prognosis model was also constructed in order to analyze its value in evaluating the survival and clinical stage of the colon cancer patients. The differential expression of the selected genes was verified by qPCR and immunohistochemistry. GSEA and GSVA were used to analyze the enrichment pathways for high- and low-risk groups. CIBERSORT was used to analyze the immune microenvironment of colon cancer and to compare the infiltration of immune cells in the high- and low-risk groups. The "circlize" package was used to explore the correlation between the risk score signature and immunotherapy for colon cancer. We analysed the differential expression of 704 FAM-related genes between colon tumour and normal tissue and screened 10 genes with prognostic value. Subsequently, we constructed a prognostic model for colon cancer based on eight optimal FAM genes through LASSO Cox regression analysis in the TCGA-COAD dataset, and its practicality was validated in the GSE39582 dataset. Moreover, the risk score calculated based on the prognostic model was validated as an independent prognostic factor for colon cancer patients. We further constructed a nomogram composed of the risk score signature, age and American Joint Committee on Cancer (AJCC) stage for clinical application. The colon cancer cohort was divided into high- and low-risk groups according to the optimal cut-off value, and different enrichment pathways and immune microenvironments were depicted in the groups. Since the risk score signature was significantly correlated with the expression of immune checkpoint molecules, the prognostic model might be able to predict the immunotherapy response of colon cancer patients. In summary, our findings expand the prognostic value of FAM-related genes in colon cancer and provide evidence for their application in guiding immunotherapy.

Diffusion-Ordered Spectroscopy (DOSY) as a Powerful NMRMethod to Investigate Molecular Mobitiesand IntermolecularInteractions in Complex Oil Mixtures.

Studying the characteristics and molecular mechanisms of liquid self-diffusion coefficient and viscosity changes is of great significance for, e.g., chemical and petroleum processing. As an example of a highly complex liquid, asphaltene-free high-acid and high-viscosity crude oil and its extracted fractions were studied by comparing their 1H DOSY diffusion maps. The crude oil exhibited a polydisperse diffusion distribution, including multiple diffusion portions with diffusion coefficients much smaller than that of any single fraction in independent diffusion. The main mechanism that leads to the decreases in the diffusion coefficients of crude oil is attributed to diffusion resistance enhanced by Dynamical Molecular-Interaction Networks (DMINs), rather than by enlargement of the diffusion species caused by molecular aggregation. Constructed through the synergistic interactions of various polar molecules in crude oil, DMINs dynamically bind polar molecules, trap polarizable molecules, and spatially hinder the free motion of non-polar molecules. Overall, this reduces the diffusion coefficients of all molecular. This study demonstrates that DOSY is a powerful NMR method to investigate molecular motion abilities in complex mixtures. In addition, the insights in the influence of the interaction matrix on the molecular mobility also help to understand the contribution of "structural viscosity" to the viscosity of heavy oil.

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks.