Network biology: understanding the cell's functional organization

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Luteinizing Hormone Receptor Mutation (LHRN316S) Causes Abnormal Follicular Development Revealed by Follicle Single-Cell Analysis and CRISPR/Cas9

Abnormal interaction between granulosa cells and oocytes causes disordered development of ovarian follicles. However, the interactions between oocytes and cumulus granulosa cells (CGs), oocytes and mural granulosa cells (MGs), and CGs and MGs remain to be fully explored. Using single-cell RNA-sequencing (scRNA-seq), we determined the transcriptional profiles of oocytes, CGs and MGs in antral follicles. Analysis of scRNA-seq data revealed that CGs may regulate follicular development through the BMP15-KITL-KIT-PI3K-ARF6 pathway with elevated expression of luteinizing hormone receptor (LHR). Because internalization of the LHR is regulated by Arf6, we constructed LHRN316S mice by CRISPR/Cas9 to further explore mechanisms of follicular development and novel treatment strategies for female infertility. Ovaries of LHRN316S mice exhibited reduced numbers of corpora lutea and ovulation. The LHRN316S mice had a reduced rate of oocyte maturation in vitro and decreased serum progesterone levels. Mating LHRN316S female mice with ICR wild type male mice revealed that the infertility rate of LHRN316S mice was 21.4% (3/14). Litter sizes from LHRN316S mice were smaller than those from control wild type female mice. The oocytes from LHRN316S mice had an increased rate of maturation in vitro after progesterone administration in vitro. Furthermore, progesterone treated LHRN316S mice produced offspring numbers per litter equivalent to WT mice. These findings provide key insights into cellular interactions in ovarian follicles and provide important clues for infertility treatment.

[Critical alterations in the brain and psyche]

Critical alterations in the brain and psyche are often triggered by critical points and feedback effects in closely networked systems. Such crises can occur in the form of neurological disorders, such as epilepsy or mental disorders, such as bipolar disorder and depression. A central mechanism is the excitation-inhibition (EI) balance in the brain, which is responsible for an optimal processing of information. Disruptions in this balance can lead to pathological conditions. The concept of attractors, which represent the stable conditions in neuronal networks, helps to explain the consolidation of memories, behavioral patterns and mental states. These attractor states can be triggered by external stimuli and may become anchored in pathological contexts. Advances in measurement technologies and methods of artificial intelligence enable a deeper analysis of neuronal dynamics and open up new pathways for targeted therapeutic interventions for the treatment of mental disorders.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

mInsc coordinates Par3 and NuMA condensates for assembly of the spindle orientation machinery in asymmetric cell division

As a fundamental process governing the self-renewal and differentiation of stem cells, asymmetric cell division is controlled by several conserved regulators, including the polarity protein Par3 and the microtubule-associated protein NuMA, which orchestrate the assembly and interplay of the Par3/Par6/mInsc/LGN complex at the apical cortex and the LGN/Gαi/NuMA/Dynein complex at the mitotic spindle to ensure asymmetric segregation of cell fate determinants. However, this model, which is well-supported by genetic studies, has been challenged by evidence of competitive interaction between NuMA and mInsc for LGN. Here, the solved crystal structure of the Par3/mInsc complex reveals that mInsc competes with Par6β for Par3, raising questions about how proteins assemble overlapping targets into functional macromolecular complexes. Unanticipatedly, we discover that Par3 can recruit both Par6β and mInsc by forming a dynamic condensate through phase separation. Similarly, the phase-separated NuMA condensate enables the coexistence of competitive NuMA and mInsc with LGN in the same compartment. Bridge by mInsc, Par3/Par6β and LGN/NuMA condensates coacervate, robustly enriching all five proteins both in vitro and within cells. These findings highlight the pivotal role of protein condensates in assembling multi-component signalosomes that incorporate competitive protein-protein interaction pairs, effectively overcoming stoichiometric constraints encountered in conventional protein complexes.

Quantitative omnigenic model discovers interpretable genome-wide associations

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects.

Virtual screening and network pharmacology-based synergistic coagulation mechanism identification of multiple components contained in compound Kushen Injection against hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a primary liver malignancy commonly encountered in the setting of chronic liver disease and cirrhosis. Compound Kushen Injection (CKI) has been widely used in HCC, however, the underlying mechanisms are scarce.

OBJECTIVE

To explore the molecular mechanisms of CKI for HCC.To explore the molecular mechanisms of CKI for HCC.

MATERIALS AND METHODS

The chemical ingredients of CKI were reviewed from published articles and the potential targets were got from Herbal Ingredients' Targets Platform. Coagulation-related targets were from Kyoto Encyclopedia of Genes and Genomes and HCC-related targets were from Therapeutic Target Database, Gene Expression Omnibus, and The Cancer Genome Atlas. Then the CKI-Herb-Target and CKI-Herb-Target-HCC networks were built. The shared targets between CKI and HCC were used for functional enrichment through Metascape and the shared coagulation-related target was used for molecular docking and survival analysis.

RESULTS

A total of 23 chemical ingredients and 41 potential targets shared between CKI and HCC were obtained. The results of functional enrichment indicated that several canonical pathways of CKI mostly participated in the treatment of HCC. Furthermore, a chemical ingredient of CKI formed a stable hydrogen bond link with the ASN-189 on PLG, with a best binding energy of -4.7 kcal/mol. Finally, PLG was confirmed as the shared coagulation-related target and interrelated with the prognosis of HCC.

CONCLUSION

CKI probably improves HCC prognosis through PLG. Our research undoubtedly deepened the understanding of the molecular mechanism of CKI anti-HCC.

Living cognition and the nature of organisms

There is no consensus about what cognition is. Different perspectives conceptualize it in different ways. In the same vein, there is no agreement about which systems are truly cognitive. This begs the question, what makes a process or a system cognitive? One of the most conspicuous features of cognition is that it is a set of processes. Cognition, in the end, is a collection of processes such as perception, memory, learning, decision-making, problem-solving, goal-directedness, attention, anticipation, communication, and maybe emotion. There is a debate about what they mean, and which systems possess these processes. One aspect of this problem concerns the level at which cognition and the single processes are conceptualized. To make this scenario clear, evolutionary and self-maintenance arguments are taken. Given the evolutive landscape, one sees processes shared by all organisms and their derivations in specific taxa. No matter which side of the complexity spectrum one favors, the similarities of the simple processes with the complex ones cannot be ignored, and the differences of some complex processes with their simple versions cannot be blurred. A final cognitive framework must make sense of both sides of the spectrum, their differences and similarities. Here, we discuss from an evolutionary perspective the basic elements shared by all living beings and whether these may be necessary and sufficient for understanding the cognitive process. Following these considerations, cognition can be expanded to every living being. Cognition is the set of informational and dynamic processes an organism must interact with and grasp aspects of its world. Understood at their most basic level, perception, memory, learning, problem-solving, decision-making, action, and other cognitive processes are basic features of biological functioning.

Cell Fate Dynamics Reconstruction Identifies TPT1 and PTPRZ1 Feedback Loops as Master Regulators of Differentiation in Pediatric Glioblastoma-Immune Cell Networks

Pediatric glioblastoma is a complex dynamical disease that is difficult to treat due to its multiple adaptive behaviors driven largely by phenotypic plasticity. Integrated data science and network theory pipelines offer novel approaches to studying glioblastoma cell fate dynamics, particularly phenotypic transitions over time. Here we used various single-cell trajectory inference algorithms to infer signaling dynamics regulating pediatric glioblastoma-immune cell networks. We identified GATA2, PTPRZ1, TPT1, MTRNR2L1/2, OLIG1/2, SOX11, FXYD6, SEZ6L, PDGFRA, EGFR, S100B, WNT, TNF α , and NF-kB as critical transition genes or signals regulating glioblastoma-immune network dynamics, revealing potential clinically relevant targets. Further, we reconstructed glioblastoma cell fate attractors and found complex bifurcation dynamics within glioblastoma phenotypic transitions, suggesting that a causal pattern may be driving glioblastoma evolution and cell fate decision-making. Together, our findings have implications for developing targeted therapies against glioblastoma, and the continued integration of quantitative approaches and artificial intelligence (AI) to understand pediatric glioblastoma tumor-immune interactions.

Identification of a central network hub of key prognostic genes based on correlation between transcriptomics and survival in patients with metastatic solid tumors

Background

Dysregulated pathways in cancer may be hub addicted. Identifying these dysregulated networks for targeting might lead to novel therapeutic options.

Objective

Considering the hypothesis that central hubs are associated with increased lethality, identifying key hub targets within central networks could lead to the development of novel drugs with improved efficacy in advanced metastatic solid tumors.

Design

Exploring transcriptomic data (22,000 gene products) from the WINTHER trial (N = 101 patients with various metastatic cancers), in which both tumor and normal organ-matched tissue were available.

Methods

A retrospective in silico analysis of all genes in the transcriptome was conducted to identify genes different in expression between tumor and normal tissues (paired t-test) and to determine their association with survival outcomes using survival analysis (Cox proportional hazard regression algorithm). Based on the biological relevance of the identified genes, hub targets of interest within central networks were then pinpointed. Patients were grouped based on the expression level of these genes (K-mean clustering), and the association of these groups with survival was examined (Cox proportional hazard regression algorithm, Forest plot, and Kaplan-Meier plot).

Results

We identified four key central hub genes-PLOD3, ARHGAP11A, RNF216, and CDCA8, for which high expression in tumor tissue compared to analogous normal tissue had the most significant correlation with worse outcomes. The correlation was independent of tumor or treatment type. The combination of the four genes showed the highest significance and correlation with the poorer outcome: overall survival (hazard ratio (95% confidence interval (CI)) = 10.5 (3.43-31.9) p = 9.12E-07 log-rank test in a Cox proportional hazard regression model). Findings were validated in independent cohorts.

Conclusion

The expression of PLOD3, ARHGAP11A, RNF216, and CDCA8 constitute, when combined, a prognostic tool, agnostic of tumor type and previous treatments. These genes represent potential targets for intercepting central hub networks in various cancers, offering avenues for novel therapeutic interventions.

Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks

As an alternative to target-driven drug discovery, phenotype-driven approaches identify compounds that counteract the overall disease effects by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for new therapeutic agents. We introduce PDGrapher, a causally-inspired graph neural network (GNN) designed to predict combinatorial perturbagens - sets of therapeutic targets - capable of reversing disease effects. Unlike methods that learn responses to perturbations, PDGrapher solves the inverse problem, which is to infer the perturbagens necessary to achieve a specific response - i.e., directly predicting perturbagens by learning which perturbations elicit a desired response. By encoding gene regulatory networks or protein-protein interactions, PDGrapher can predict unseen chemical or genetic perturbagens, aiding in the discovery of novel drugs or therapeutic targets. Experiments across nine cell lines with chemical perturbations show that PDGrapher successfully predicted effective perturbagens in up to 13.33% additional test samples and ranked therapeutic targets up to 35% higher than the competing methods, and the method shows competitive performance across ten genetic perturbation datasets. A key innovation of PDGrapher is its direct prediction capability, which contrasts with the indirect, computationally intensive models traditionally used in phenotype-driven drug discovery that only predict changes in phenotypes due to perturbations. The direct approach enables PDGrapher to train up to 25 times faster than methods like scGEN and CellOT, representing a considerable leap in efficiency. Our results suggest that PDGrapher can advance phenotype-driven drug discovery, offering a fast and comprehensive approach to identifying therapeutically useful perturbations.

DNA Tetrahedra as Functional Nanostructures: From Basic Principles to Applications

Self-assembled supramolecular DNA tetrahedra composed of programmed sequence-engineered complementary base-paired strands represent elusive nanostructures having key contributions to the development and diverse applications of DNA nanotechnology. By appropriate engineering of the strands, DNA tetrahedra of tuneable sizes and chemical functionalities were designed. Programmed functionalities for diverse applications were integrated into tetrahedra structures including sequence-specific recognition strands (aptamers), catalytic DNAzymes, nanoparticles, proteins, or fluorophore. The article presents a comprehensive review addressing methods to assemble and characterize the DNA tetrahedra nanostructures, and diverse applications of DNA tetrahedra framework are discussed. Topics being addressed include the application of structurally functionalized DNA tetrahedra nanostructure for the assembly of diverse optical or electrochemical sensing platforms and functionalized intracellular sensing and imaging modules. In addition, the triggered reconfiguration of DNA tetrahedra nanostructures and dynamic networks and circuits emulating biological transformations are introduced. Moreover, the functionalization of DNA tetrahedra frameworks with nanoparticles provides building units for the assembly of optical devices and for the programmed crystallization of nanoparticle superlattices. Finally, diverse applications of DNA tetrahedra in the field of nanomedicine are addressed. These include the DNA tetrahedra-assisted permeation of nanocarriers into cells for imaging, controlled drug release, active chemodynamic/photodynamic treatment of target tissues, and regenerative medicine.

Shortest path counting in complex networks based on powers of the adjacency matrix

Complex networks describe a broad range of systems in nature and society. As a fundamental concept of graph theory, the path connecting nodes and edges plays a crucial role in network science, where the computation of shortest path lengths and numbers has garnered substantial focus. It is well known that powers of the adjacency matrix can calculate the number of walks, specifying their corresponding lengths. However, developing methodologies to quantify both the number and length of shortest paths through the adjacency matrix remains a challenge. Here, we extend powers of the adjacency matrix from walks to shortest paths. We address the all-pairs shortest path count problem and propose a fast algorithm based on powers of the adjacency matrix that counts both the number and the length of all shortest paths. Numerous experiments on synthetic and real-world networks demonstrate that our algorithm is significantly faster than the classical algorithms across various network types and sizes. Moreover, we verified that the time complexity of our proposed algorithm significantly surpasses that of the current state-of-the-art algorithms. The superior property of the algorithm allows for rapid calculation of all shortest paths within large-scale networks, offering significant potential applications in traffic flow optimization and social network analysis.

iTraNet: a web-based platform for integrated trans-omics network visualization and analysis

Motivation

Visualization and analysis of biological networks play crucial roles in understanding living systems. Biological networks include diverse types, from gene regulatory networks and protein-protein interactions to metabolic networks. Metabolic networks include substrates, products, and enzymes, which are regulated by allosteric mechanisms and gene expression. However, the analysis of these diverse omics types is challenging due to the diversity of databases and the complexity of network analysis.

Results

We developed iTraNet, a web application that visualizes and analyses trans-omics networks involving four types of networks: gene regulatory networks, protein-protein interactions, metabolic networks, and metabolite exchange networks. Using iTraNet, we found that in wild-type mice, hub molecules within the network tended to respond to glucose administration, whereas in ob/ob mice, this tendency disappeared. With its ability to facilitate network analysis, we anticipate that iTraNet will help researchers gain insights into living systems.

Availability and implementation

iTraNet is available at https://itranet.streamlit.app/.

Networks as tools for defining emergent properties of microbiomes and their stability

The potential promise of the microbiome to ameliorate a wide range of societal and ecological challenges, from disease prevention and treatment to the restoration of entire ecosystems, hinges not only on microbiome engineering but also on the stability of beneficial microbiomes. Yet the properties of microbiome stability remain elusive and challenging to discern due to the complexity of interactions and often intractable diversity within these communities of bacteria, archaea, fungi, and other microeukaryotes. Networks are powerful tools for the study of complex microbiomes, with the potential to elucidate structural patterns of stable communities and generate testable hypotheses for experimental validation. However, the implementation of these analyses introduces a cascade of dichotomies and decision trees due to the lack of consensus on best practices. Here, we provide a road map for network-based microbiome studies with an emphasis on discerning properties of stability. We identify important considerations for data preparation, network construction, and interpretation of network properties. We also highlight remaining limitations and outstanding needs for this field. This review also serves to clarify the varying schools of thought on the application of network theory for microbiome studies and to identify practices that enhance the reproducibility and validity of future work. Video Abstract.

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data

BACKGROUND

RNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level.

RESULTS

Here, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets.

CONCLUSIONS

Scan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Modelling cell type-specific lncRNA regulatory network in autism with Cycle

BACKGROUND

Autism spectrum disorder (ASD) is a class of complex neurodevelopment disorders with high genetic heterogeneity. Long non-coding RNAs (lncRNAs) are vital regulators that perform specific functions within diverse cell types and play pivotal roles in neurological diseases including ASD. Therefore, exploring lncRNA regulation would contribute to deciphering ASD molecular mechanisms. Existing computational methods utilize bulk transcriptomics data to identify lncRNA regulation in all of samples, which could reveal the commonalities of lncRNA regulation in ASD, but ignore the specificity of lncRNA regulation across various cell types.

RESULTS

Here, we present Cycle (Cell type-specific lncRNA regulatory network) to construct the landscape of cell type-specific lncRNA regulation in ASD. We have found that each ASD cell type is unique in lncRNA regulation, and more than one-third and all cell type-specific lncRNA regulatory networks are characterized as scale-free and small-world, respectively. Across 17 ASD cell types, we have discovered 19 rewired and 11 stable modules, along with eight rewired and three stable hubs within the constructed cell type-specific lncRNA regulatory networks. Enrichment analysis reveals that the discovered rewired and stable modules and hubs are closely related to ASD. Furthermore, more similar ASD cell types tend to be connected with higher strength in the constructed cell similarity network. Finally, the comparison results demonstrate that Cycle is a potential method for uncovering cell type-specific lncRNA regulation.

CONCLUSION

Overall, these results illustrate that Cycle is a promising method to model the landscape of cell type-specific lncRNA regulation, and provides insights into understanding the heterogeneity of lncRNA regulation between various ASD cell types.

Discovery and Prediction Study of the Dominant Pharmacological Action Organ of Aconitum carmichaeli Debeaux Using Multiple Bioinformatic Analyses

Herbs, such as Aconitum carmichaeli Debeaux (ACD), have long been used as therapies, but it is difficult to identify which organs of the human body are affected by the various compounds. In this study, we predicted the organ where the drug predominantly acts using bioinformatics and verified it using transcriptomics. We constructed a computer-aided brain system network (BSN) and intestinal system network (ISN). We predicted the action points of ACD using network pharmacology (NP) analysis and predicted the dockable proteins acting in the BSN and ISN using statistical-based docking analysis. The predicted results were verified using ACD-induced transcriptome analysis. The predicted results showed that both the NP and docking analyses predominantly acted on the BSN and showed better hit rates in the hub nodes. In addition, we confirmed through verification experiments that the SW1783 cell line had more than 10 times more differentially expressed genes than the HT29 cell line and that the dominant acting organ is the brain, using network dimension spanning analysis. In conclusion, we found that ACD preferentially acts in the brain rather than in the intestine, and this multi-bioinformatics-based approach is expected to be used in future studies of drug efficacy and side effects.

Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.

A dynamic regulome of shoot-apical-meristem-related homeobox transcription factors modulates plant architecture in maize

BACKGROUND

The shoot apical meristem (SAM), from which all above-ground tissues of plants are derived, is critical to plant morphology and development. In maize (Zea mays), loss-of-function mutant studies have identified several SAM-related genes, most encoding homeobox transcription factors (TFs), located upstream of hierarchical networks of hundreds of genes.

RESULTS

Here, we collect 46 transcriptome and 16 translatome datasets across 62 different tissues or stages from the maize inbred line B73. We construct a dynamic regulome for 27 members of three SAM-related homeobox subfamilies (KNOX, WOX, and ZF-HD) through machine-learning models for the detection of TF targets across different tissues and stages by combining tsCUT&Tag, ATAC-seq, and expression profiling. This dynamic regulome demonstrates the distinct binding specificity and co-factors for these homeobox subfamilies, indicative of functional divergence between and within them. Furthermore, we assemble a SAM dynamic regulome, illustrating potential functional mechanisms associated with plant architecture. Lastly, we generate a wox13a mutant that provides evidence that WOX13A directly regulates Gn1 expression to modulate plant height, validating the regulome of SAM-related homeobox genes.

CONCLUSIONS

The SAM-related homeobox transcription-factor regulome presents an unprecedented opportunity to dissect the molecular mechanisms governing SAM maintenance and development, thereby advancing our understanding of maize growth and shoot architecture.

Implementation of Machine Learning-Based System for Early Diagnosis of Feline Mammary Carcinomas through Blood Metabolite Profiling

Background: Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unspayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration. The diagnosis through these methods is sometimes delayed and unreliable, leading to increased chances of mortality. Objectives: The objective of this study was to identify the biomarkers, including blood metabolites and genes, related to feline mammary carcinoma, study their relationships, and develop a machine learning (ML) model for the early diagnosis of the disease. Methods: We analyzed the blood metabolites of felines with mammary carcinoma using the pathway analysis feature in MetaboAnalyst software, v. 5.0. We utilized machine-learning (ML) methods to recognize FMC using the blood metabolites of sick patients. Results: The metabolic pathways that were elucidated to be associated with this disease include alanine, aspartate and glutamate metabolism, Glutamine and glutamate metabolism, Arginine biosynthesis, and Glycerophospholipid metabolism. Furthermore, we also elucidated several genes that play a significant role in the development of FMC, such as ERBB2, PDGFA, EGFR, FLT4, ERBB3, FIGF, PDGFC, PDGFB through STRINGdb, a database of known and predicted protein-protein interactions, and MetaboAnalyst 5.0. The best-performing ML model was able to predict metabolite class with an accuracy of 85.11%. Conclusion: Our findings demonstrate that the identification of the biomarkers associated with FMC and the affected metabolic pathways can aid in the early diagnosis of feline mammary carcinoma.

Navigating the Intersection of Glycemic Control and Fertility: A Network Perspective

The rising incidence of metabolic diseases is linked to elevated blood glucose levels, contributing to conditions such as diabetes and promoting the accumulation of advanced glycation end products (AGEs). AGEs, formed by non-enzymatic reactions between sugars and proteins, build up in tissues and are implicated in various diseases. This article explores the relationship between glycemic control and AGE accumulation, focusing on fertility implications. A computational model using network theory was developed, featuring a molecular database and a network with 145 nodes and 262 links, categorized as a Barabasi-Albert scale-free network. Three main subsets of nodes emerged, centered on glycemic control, fertility, and immunity, with AGEs playing a critical role. The transient receptor potential vanilloid 1 (TRPV1), a receptor expressed in several tissues including sperm, was identified as a key hub, suggesting that the modulation of TRPV1 in sperm by AGEs may influence fertility. Additionally, a novel link between glycemic control and immunity was found, indicating that immune cells may play a role in endocytosing specific AGEs. This discovery underscores the complex interplay between glycemic control and immune function, with significant implications for metabolic, immune health, and fertility.

Construction and analysis of protein-protein interaction network for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignant tumor of the digestive tract. Clinical findings reveal that the five-year survival rate for mid-to late-stage ESCC patients is merely around 20 %, whereas those diagnosed at an early stage can achieve up to a 95 % survival rate. Consequently, early detection is paramount to improving ESCC patient survival. Protein markers are essential for diagnosing diseases, and the identification of new candidate proteins associated with ESCC through the protein-protein interaction (PPI) network is aimed for in this paper. The PPI network related to ESCC was constructed using protein data, comprising 2094 nodes and 19,660 edges. To assess the nodes' importance in the network, three metrics-degree centrality, betweenness centrality, and closeness centrality-were employed, leading to the identification of 81 key proteins. Subsequently, the biological significance of these proteins in the network was explored, combining biomedical knowledge from three perspectives: network, node, and cluster. The results demonstrated that 52 out of 81 key proteins were confirmed to be linked to ESCC. Among the remaining 29 unreported proteins, 18 displayed significant biological significance, indicating their potential as protein markers related to ESCC.

AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction

Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimer's disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimer's Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.

Inference of single-cell network using mutual information for scRNA-seq data analysis

BACKGROUND

With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.

RESULTS

We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.

CONCLUSIONS

SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM .

Nucleic acid-responsive smart systems for controlled cargo delivery

Stimulus-responsive delivery systems allow controlled, highly regulated, and efficient delivery of various cargos while minimizing side effects. Owing to the unique properties of nucleic acids, including the ability to adopt complex structures by base pairing, their easy synthesis, high specificity, shape memory, and configurability, they have been employed in autonomous molecular motors, logic circuits, reconfigurable nanoplatforms, and catalytic amplifiers. Moreover, the development of nucleic acid (NA)-responsive intelligent delivery vehicles is a rapidly growing field. These vehicles have attracted much attention in recent years due to their programmable, controllable, and reversible properties. In this work, we review several types of NA-responsive controlled delivery vehicles based on locks and keys, including DNA/RNA-responsive, aptamer-responsive, and CRISPR-responsive, and summarize their advantages and limitations.

Biocontrol agents modulate phyllosphere microbiota interactions against pathogen Pseudomonas syringae

The pathogen Pseudomonas syringae, responsible for a variety of diseases, poses a considerable threat to global crop yields. Emerging biocontrol strategies employ antagonistic microorganisms, utilizing phyllosphere microecology and systemic resistance to combat this disease. However, the interactions between phyllosphere microbial dynamics and the activation of the plant defense system remain poorly understood. Here we show significant alterations in phyllosphere microbiota structure and plant gene expression following the application of biocontrol agents. We reveal enhanced collaboration and integration of Sphingomonas and Methylobacterium within the microbial co-occurrence network. Notably, Sphingomonas inhibits P. syringae by disrupting pathogen chemotaxis and virulence. Additionally, both Sphingomonas and Methylobacterium activate plant defenses by upregulating pathogenesis-related gene expression through abscisic acid, ethylene, jasmonate acid, and salicylic acid signaling pathways. Our results highlighted that biocontrol agents promote plant health, from reconstructing beneficial microbial consortia to enhancing plant immunity. The findings enrich our comprehension of the synergistic interplays between phyllosphere microbiota and plant immunity, offering potential enhancements in biocontrol efficacy for crop protection.

Salinity-induced variations in bacterial composition and co-occurrence patterns within Salicornia-based constructed wetlands in mariculture

Constructed wetlands use vegetation and microorganisms to remove contaminants like nitrogen and phosphorus from water. For mariculture, the impact of salinity on the efficiency of wastewater treatment of wetlands is unneglectable. However, little is known about their impact on the microbiome in constructed wetlands. Here, we set four salinity levels (15, 22, 29, and 36) in Salicornia constructed wetlands, and the experiment was conducted for a period of 72 days. The 15 group exhibited the highest removal rates of nitrogen compounds and phosphate, compared to the other salinity groups, the nosZ gene exhibited significantly higher expression in the 22 group (p < 0.05), indicated that microorganisms in 22 salinity have higher denitrification abilities. The three dominant phyla identified within the microbiomes were Proteobacteria, known for their diverse metabolic capabilities; Cyanobacteria, important for photosynthesis and nitrogen fixation; and Firmicutes, which include many fermenters. The ecological network analysis revealed a 'small world' model, characterized by high interconnectivity and short path lengths between microbial species, and had higher co-occurrence (45.13%) observed in this study comparing to the Erdös-Réyni random one (32.35%). The genus Microbulbifer emerged as the sole connector taxon, pivotal for integrating different microbial communities involved in nitrogen removal. A negative correlation was observed between salinity levels and network complexity, as assessed by the number of connections and diversity of interactions within the microbial community. Collectively, these findings underscore the critical role of microbial community interactions in optimizing nitrogen removal in constructed wetlands, with potential applications in the design and management of such systems for improved wastewater treatment in mariculture.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although, there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Transcriptional network analysis of peripheral blood leukocyte subsets in multiple sclerosis identifies a pathogenic role for a cytotoxicity-associated gene network in myeloid cells

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system affecting predominantly adults. It is a complex disease associated with both environmental and genetic risk factors. Although over 230 risk single-nucleotide polymorphisms have been associated with MS, all are common human variants. The mechanisms by which they increase the risk of MS, however, remain elusive. We hypothesized that a complex genetic phenotype such as MS could be driven by coordinated expression of genes controlled by transcriptional regulatory networks. We, therefore, constructed a gene coexpression network from microarray expression analyses of five purified peripheral blood leukocyte subsets of 76 patients with relapsing remitting MS and 104 healthy controls. These analyses identified a major network (or module) of expressed genes associated with MS that play key roles in cell-mediated cytotoxicity which was downregulated in monocytes of patients with MS. Manipulation of the module gene expression was achieved in vitro through small interfering RNA gene knockdown of identified drivers. In a mouse model, network gene knockdown modulated the autoimmune inflammatory MS model disease-experimental autoimmune encephalomyelitis. This research implicates a cytotoxicity-associated gene network in myeloid cells in the pathogenesis of MS.

Proposal of a Knowledge Management Model for Complex Systems: Case of the Supervision and Control Subsystem of the Colombian Health System

BACKGROUND

Considering regulatory, supervision, and control health policy, an innovative knowledge management model is proposed for the Colombian health system, which is recognized as a complex system.

METHODS

A model is constructed through a comparative analysis of various theoretical and conceptual frameworks, and an original methodology is proposed based on an analysis of the macroprocesses of the Supervision and Control System (SSC) of the Colombian General Social Security System in Health (SGSSS). After formulating hypotheses and conceptual references, information errors are determined within the different macroprocesses of the SGSSS, including those of governance and the SSC.

RESULTS

The risks of generating duplicate, wrong, hidden, or non-existent information arise when the associated regulations need more specificity to be applied in all cases, thus leading to the risk of different interpretations by some actors. In this way, it is possible to hinder the generation of unified information, as there is no clarity as to who is responsible for the generation or creation of certain data.

CONCLUSIONS

The proposed model is characterized by its flexibility and adaptability, integrating several processes that can be executed simultaneously or cyclically (depending on the system's needs) and allowing for the generation and feedback of knowledge at different stages, with some processes simultaneously executed to complement each other.

Rhizobium etli CFN42 and Sinorhizobium meliloti 1021 bioinformatic transcriptional regulatory networks from culture and symbiosis

Rhizobium etli CFN42 proteome-transcriptome mixed data of exponential growth and nitrogen-fixing bacteroids, as well as Sinorhizobium meliloti 1021 transcriptome data of growth and nitrogen-fixing bacteroids, were integrated into transcriptional regulatory networks (TRNs). The one-step construction network consisted of a matrix-clustering analysis of matrices of the gene profile and all matrices of the transcription factors (TFs) of their genome. The networks were constructed with the prediction of regulatory network application of the RhizoBindingSites database (http://rhizobindingsites.ccg.unam.mx/). The deduced free-living Rhizobium etli network contained 1,146 genes, including 380 TFs and 12 sigma factors. In addition, the bacteroid R. etli CFN42 network contained 884 genes, where 364 were TFs, and 12 were sigma factors, whereas the deduced free-living Sinorhizobium meliloti 1021 network contained 643 genes, where 259 were TFs and seven were sigma factors, and the bacteroid Sinorhizobium meliloti 1021 network contained 357 genes, where 210 were TFs and six were sigma factors. The similarity of these deduced condition-dependent networks and the biological E. coli and B. subtilis independent condition networks segregates from the random Erdös-Rényi networks. Deduced networks showed a low average clustering coefficient. They were not scale-free, showing a gradually diminishing hierarchy of TFs in contrast to the hierarchy role of the sigma factor rpoD in the E. coli K12 network. For rhizobia networks, partitioning the genome in the chromosome, chromids, and plasmids, where essential genes are distributed, and the symbiotic ability that is mostly coded in plasmids, may alter the structure of these deduced condition-dependent networks. It provides potential TF gen-target relationship data for constructing regulons, which are the basic units of a TRN.

Gene expression and brain imaging association study reveals gene signatures in major depressive disorder

Major depressive disorder is often characterized by changes in the structure and function of the brain, which are influenced by modifications in gene expression profiles. How the depression-related genes work together within the scope of time and space to cause pathological changes remains unclear. By integrating the brain-wide gene expression data and imaging data in major depressive disorder, we identified gene signatures of major depressive disorder and explored their temporal-spatial expression specificity, network properties, function annotations and sex differences systematically. Based on correlation analysis with permutation testing, we found 345 depression-related genes significantly correlated with functional and structural alteration of brain images in major depressive disorder and separated them by directional effects. The genes with negative effect for grey matter density and positive effect for functional indices are enriched in downregulated genes in the post-mortem brain samples of patients with depression and risk genes identified by genome-wide association studies than genes with positive effect for grey matter density and negative effect for functional indices and control genes, confirming their potential association with major depressive disorder. By introducing a parameter of dispersion measure on the gene expression data of developing human brains, we revealed higher spatial specificity and lower temporal specificity of depression-related genes than control genes. Meanwhile, we found depression-related genes tend to be more highly expressed in females than males, which may contribute to the difference in incidence rate between male and female patients. In general, we found the genes with negative effect have lower network degree, more specialized function, higher spatial specificity, lower temporal specificity and more sex differences than genes with positive effect, indicating they may play different roles in the occurrence and development of major depressive disorder. These findings can enhance the understanding of molecular mechanisms underlying major depressive disorder and help develop tailored diagnostic and treatment strategies for patients of depression of different sex.

DNA-controlled protein fluorescence: Design of aptamer-split peptide hetero-modulator for GFP to respond to intracellular ATP levels

Enabling the precise control of protein functions with artificially programmed reaction patterns is beneficial for investigating biological processes. Although several strategies have been established that employ the programmability of nucleic acid, they have been limited to DNA hybridization without external stimuli or target binding. Here, we report an approach for the DNA-mediated control of the tripartite split-GFP assembly via aptamers with responsiveness to intracellular small molecules as stimuli. We designed a novel structure-switching aptamer-peptide conjugate as a hetero modulator for split GFP in response to ATP. By conjugating two peptides (S10/11) derived from the tripartite split-GFP to ATP aptamer, we achieved GFP reassembly using only ATP as a trigger molecule. The response to ATP at ≥4 mM concentrations indicated that it can be applied to respond to intracellular ATP in live cells. Furthermore, our hetero-modulator exhibited high and long-term stability, with a half-life of approximately four days in a serum stability assay, demonstrating resistance to nuclease degradation. We validated that our aptamer-modulator split GFP was successfully reconstituted in the cell in response to intracellular ATP levels. Our aptamer-modulated split GFP platform can be utilized to monitor a wide range of intracellular metabolites by replacing the aptamer sequence.

Path-based reasoning for biomedical knowledge graphs with BioPathNet

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

Voice from both sides: a molecular dialogue between transcriptional activators and repressors in seed-to-seedling transition and crop adaptation

High-quality seeds provide valuable nutrients to human society and ensure successful seedling establishment. During maturation, seeds accumulate storage compounds that are required to sustain seedling growth during germination. This review focuses on the epigenetic repression of the embryonic and seed maturation programs in seedlings. We begin with an extensive overview of mutants affecting these processes, illustrating the roles of core proteins and accessory components in the epigenetic machinery by comparing mutants at both phenotypic and molecular levels. We highlight how omics assays help uncover target-specific functional specialization and coordination among various epigenetic mechanisms. Furthermore, we provide an in-depth discussion on the Seed dormancy 4 (Sdr4) transcriptional corepressor family, comparing and contrasting their regulation of seed germination in the dicotyledonous species Arabidopsis and two monocotyledonous crops, rice and wheat. Finally, we compare the similarities in the activation and repression of the embryonic and seed maturation programs through a shared set of cis-regulatory elements and discuss the challenges in applying knowledge largely gained in model species to crops.

Alzheimer's Disease: Combination Therapies and Clinical Trials for Combination Therapy Development

Alzheimer's disease (AD) is a complex multifaceted disease. Recently approved anti-amyloid monoclonal antibodies slow disease progression by approximately 30%, and combination therapy appears necessary to prevent the onset of AD or produce greater slowing of cognitive and functional decline. Combination therapies may address core features, non-specific co-pathology commonly occurring in patients with AD (e.g., inflammation), or non-AD pathologies that may co-occur with AD (e.g., α-synuclein). Combination therapies may be advanced through co-development of more than one new molecular entity or through add-on strategies including an approved agent plus a new molecular entity. Addressing add-on combination therapy is currently urgent since patients on anti-amyloid monoclonal antibodies may be included in clinical trials for experimental agents. Phase 1 information must be generated for each agent in combination drug development. Phase 2 and Phase 3 of add-on therapies may contrast the new molecular entity, the approved agent as standard of care, and the combination. More complex development programs including standard or modified combinatorial designs are required for co-development of two or more new molecular entities. Biomarkers are markedly affected by anti-amyloid monoclonal antibodies, and these effects must be anticipated in add-on trials. Examining target engagement biomarkers and comparing the magnitude and sequence of biomarker changes in those receiving more than one therapy, compared with those on monotherapy, may be informative. Using network-based medicine approaches, computational strategies may identify rational combinations using disease and drug effect network mapping.

Traditional Chinese Medicine Erhuang Suppository for Treatment of Persistent High-risk Human Papillomavirus Infection and Its Impact on Transcriptome of Uterine Cervix

OBJECTIVE

High-risk human papillomavirus (HR-HPV) infection is the chief cause of cervical intraepithelial neoplasia (CIN) and cervical carcinoma. The Erhuang suppository (EHS) is a traditional Chinese medicine (TCM) prepared from realgar (As2S2), Coptidis rhizoma, alumen, and borneolum syntheticum and has been used for antiviral and antitumor purposes. However, whether EHS can efficiently alleviate HR-HPV infection remains unclear. This study was conducted to evaluate the efficacy of EHS for the treatment of persistent HR-HPV infection in the uterine cervix.

METHODS

In this study, we evaluated the therapeutic efficacy of EHS in a randomized controlled clinical trial with a 3-month follow-up. Totally, 70 patients with persistent HR-HPV infection were randomly assigned to receive intravaginal administration of EHS or placebo. HPV DNA, ThinPrep cytologic test (TCT), colposcopy, and safety evaluation were carried out after treatment. Microarray analysis was performed to compare transcriptome profiles before and after EHS treatment. A K14-HPV16 mouse model was generated to confirm the efficiency of EHS.

RESULTS

After 3 months, 74.3% (26/35) of the patients in the treatment group were HPV negative, compared to 6.9% (2/29) in the placebo group. High-throughput microarrays revealed distinct transcriptome profiles after treatment. The differentially expressed genes were significantly enriched in complement activation, immune response, and apoptotic processes. The K14-HPV16 mouse model also validated the remarkable efficacy of EHS.

CONCLUSION

This study demonstrated that EHS is effective against HR-HPV infection and cervical lesions. Additionally, no obvious systemic toxicity was observed in patients during the trial. The superior efficacy and safety of EHS demonstrated its considerable value as a potential cost-effective drug for the treatment of HPV infection and HPV-related cervical diseases.

Quantitative Online Monitoring of an Immobilized Enzymatic Network by Ion Mobility-Mass Spectrometry

The forward design of in vitro enzymatic reaction networks (ERNs) requires a detailed analysis of network kinetics and potentially hidden interactions between the substrates and enzymes. Although flow chemistry allows for a systematic exploration of how the networks adapt to continuously changing conditions, the analysis of the reaction products is often a bottleneck. Here, we report on the interface between a continuous stirred-tank reactor, in which an immobilized enzymatic network made of 12 enzymes is compartmentalized, and an ion mobility-mass spectrometer. Feeding uniformly 13C-labeled inputs to the enzymatic network generates all isotopically labeled reaction intermediates and products, which are individually detected by ion mobility-mass spectrometry (IMS-MS) based on their mass-to-charge ratios and inverse ion mobilities. The metabolic flux can be continuously and quantitatively monitored by diluting the ERN output with nonlabeled standards of known concentrations. The real-time quantitative data obtained by IMS-MS are then harnessed to train a model of network kinetics, which proves sufficiently predictive to control the ERN output after a single optimally designed experiment. The high resolution of the time-course data provided by this approach is an important stepping stone to design and control sizable and intricate ERNs.

Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks

In this paper, we aim to build a platform that will help bridge the gap between high-dimensional computation and wet-lab experimentation by allowing users to interrogate genomic signatures at multiple molecular levels and identify best next actionable steps for downstream decision making. We introduce Multioviz: a publicly accessible R package and web application platform to easily perform in silico hypothesis testing of generated gene regulatory networks. We demonstrate the utility of Multioviz by conducting an end-to-end analysis in a statistical genetics application focused on measuring the effect of in silico perturbations of complex trait architecture. By using a real dataset from the Wellcome Trust Centre for Human Genetics, we both recapitulate previous findings and propose hypotheses about the genes involved in the percentage of immune CD8+ cells found in heterogeneous stocks of mice. Source code for the Multioviz R package is available at https://github.com/lcrawlab/multio-viz and an interactive version of the platform is available at https://multioviz.ccv.brown.edu/ .

Advances in Parkinson's disease research - A computational network pharmacological approach

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is projected to see a significant rise in incidence over the next three decades. The precise treatment of PD remains a formidable challenge, prompting ongoing research into early diagnostic methodologies. Network pharmacology, a burgeoning field grounded in systems biology, examines the intricate networks of biological systems to identify critical signal nodes, facilitating the development of multi-target therapeutic molecules. This approach systematically maps the components of Parkinson's disease, thereby reducing its complexity. In this review, we explore the application of network pharmacology workflows in PD, discuss the techniques employed in this field, and evaluate the current advancements and status of network pharmacology in the context of Parkinson's disease. The comprehensive insights will pave newer paths to explore early disease biomarkers and to develop diagnosis with a holistic in silico, in vitro, in vivo and clinical studies.

Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network

Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein-protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype diagnosis, the graph propagational network (GPN). By propagating the independent effect of plasma proteins on PPI network, the GPN extracts the globally interactive effects between proteins. Experimental results showed that the interactive effect between proteins yielded to further clarify the differences between dementia subtype groups and contributed to the performance improvement where the GPN outperformed existing methods by 10.4% on average.

MENTOR: Multiplex Embedding of Networks for Team-Based Omics Research

While the proliferation of data-driven omics technologies has continued to accelerate, methods of identifying relationships among large-scale changes from omics experiments have stagnated. It is therefore imperative to develop methods that can identify key mechanisms among one or more omics experiments in order to advance biological discovery. To solve this problem, here we describe the network-based algorithm MENTOR - Multiplex Embedding of Networks for Team-Based Omics Research. We demonstrate MENTOR's utility as a supervised learning approach to successfully partition a gene set containing multiple ontological functions into their respective functions. Subsequently, we used MENTOR as an unsupervised learning approach to identify important biological functions pertaining to the host genetic architectures in Populus trichocarpa associated with microbial abundance of multiple taxa. Moreover, as open source software designed with scientific teams in mind, we demonstrate the ability to use the output of MENTOR to facilitate distributed interpretation of omics experiments.

Enhancing Substrate Channeling with Multi-Enzyme Architectures in Hydrogen-Bonded Organic Frameworks

Hydrogen-bonded organic frameworks (HOF) represent an emerging category of organic structures with high crystallinity and metal-free, which are not commonly observed in alternative porous organic frameworks. These needle-like porous structure can help in stabilizing enzymes and allow transfer of molecules between enzymes participating in cascade reactions for enhanced substrate channelling. Herein, we systematically synthesized and investigated the stability of HOF at extreme conditions followed by one-pot encapsulation of single and bi-enzyme systems. Firstly, we observed HOF to be stable at pH 1 to 14 and at high temperatures (up to 115 °C). Secondly, the encapsulated glucose oxidase enzyme (GOX) showed 80 % and 90 % of its original activity at 70 °C and pH 11, respectively. Thirdly, transient time close to 0 seconds was observed for HOF encapsulated bi-enzyme cascade reaction system demonstrating a 4.25-fold improvement in catalytic activity when compared to free enzymes with enhanced substrate channelling. Our findings showcase a facile system synthesized under ambient conditions to encapsulate and stabilize enzymes at extreme conditions.

Medicinal polypharmacology-a scientific glossary of terminology and concepts

Medicinal polypharmacology is one answer to the complex reality of multifactorial human diseases that are often unresponsive to single-targeted treatment. It is an admittance that intrinsic feedback mechanisms, crosstalk, and disease networks necessitate drugs with broad modes-of-action and multitarget affinities. Medicinal polypharmacology grew to be an independent research field within the last two decades and stretches from basic drug development to clinical research. It has developed its own terminology embedded in general terms of pharmaceutical drug discovery and development at the intersection of medicinal chemistry, chemical biology, and clinical pharmacology. A clear and precise language of critical terms and a thorough understanding of underlying concepts is imperative; however, no comprehensive work exists to this date that could support researchers in this and adjacent research fields. In order to explore novel options, establish interdisciplinary collaborations, and generate high-quality research outputs, the present work provides a first-in-field glossary to clarify the numerous terms that have originated from various individual disciplines.

Metabolomic Approach to Identify the Potential Metabolites from Alpinia malaccensis for Treating SARS-CoV-2 Infection

The advent of the new coronavirus, leading to the SARS-CoV-2 pandemic, has presented a substantial worldwide health hazard since its inception in the latter part of 2019. The severity of the current pandemic is exacerbated by the occurrence of re-infection or co-infection with SARS-CoV-2. Hence, comprehending the molecular process underlying the pathophysiology of sepsis and discerning possible molecular targets for therapeutic intervention holds significant importance. For the first time, 31 metabolites were tentatively identified by GC-MS analysis from Alpinia malaccensis. On the other hand, five phenolic compounds were identified and quantified from the plant in HPLC-DAD analysis, including (-) epicatechin, rutin hydrate, rosmarinic acid, quercetin, and kaempferol. Nine GC-MS and five HPLC-identified metabolites had shown interactions with 45 and 30 COVID-19-associated human proteins, respectively. Among the proteins, PARP1, FN1, PRKCA, EGFR, ALDH2, AKR1C3, AHR, and IKBKB have been found as potential therapeutic targets to mitigate SARS-CoV-2 infection. KEGG pathway analysis also showed a strong association of FN1, EGFR, and IKBKB genes with SARS-CoV-2 viral replication and cytokine overexpression due to viral infection. Protein-protein interaction (PPI) analysis also showed that TP53, MMP9, FN1, EGFR, and NOS2 proteins are highly related to the genes involved in COVID-19 comorbidity. These proteins showed interaction with the plant phytoconstituents as well. As the study offers a robust network-based procedure for identifying biomolecules relevant to COVID-19 disease, A. malaccensis could be a good source of effective therapeutic agents against COVID-19 and related viral diseases.

Gene expression networks regulated by human personality

Genome-wide association studies of human personality have been carried out, but transcription of the whole genome has not been studied in relation to personality in humans. We collected genome-wide expression profiles of adults to characterize the regulation of expression and function in genes related to human personality. We devised an innovative multi-omic approach to network analysis to identify the key control elements and interactions in multi-modular networks. We identified sets of transcribed genes that were co-expressed in specific brain regions with genes known to be associated with personality. Then we identified the minimum networks for the co-localized genes using bioinformatic resources. Subjects were 459 adults from the Young Finns Study who completed the Temperament and Character Inventory and provided peripheral blood for genomic and transcriptomic analysis. We identified an extrinsic network of 45 regulatory genes from seed genes in brain regions involved in self-regulation of emotional reactivity to extracellular stimuli (e.g., self-regulation of anxiety) and an intrinsic network of 43 regulatory genes from seed genes in brain regions involved in self-regulation of interpretations of meaning (e.g., production of concepts and language). We discovered that interactions between the two networks were coordinated by a control hub of 3 miRNAs and 3 protein-coding genes shared by both. Interactions of the control hub with proteins and ncRNAs identified more than 100 genes that overlap directly with known personality-related genes and more than another 4000 genes that interact indirectly. We conclude that the six-gene hub is the crux of an integrative network that orchestrates information-transfer throughout a multi-modular system of over 4000 genes enriched in liquid-liquid-phase-separation (LLPS)-related RNAs, diverse transcription factors, and hominid-specific miRNAs and lncRNAs. Gene expression networks associated with human personality regulate neuronal plasticity, epigenesis, and adaptive functioning by the interactions of salience and meaning in self-awareness.

Network approach reveals preferential T-cell and macrophage association with α-linked β-cells in early stage of insulitis in NOD mice

One of the challenges in studying islet inflammation-insulitis-is that it is a transient phenomenon. Traditional reporting of the insulitis progression is based on cumulative, donor-averaged values of leucocyte density in the vicinity of pancreatic islets, that hinder intra- and inter-islet heterogeneity of disease progression. Here, we aimed to understand why insulitis is non-uniform, often with peri-insulitis lesions formed on one side of an islet. To achieve this, we demonstrated the applicability of network theory in detangling intra-islet multi-cellular interactions during insulitis. Specifically, we asked the question "What is unique about regions of the islet that interact with immune cells first". This study utilized the non-obese diabetic mouse model of type one diabetes and examined the interplay among α-, β-, T-cells, myeloid cells, and macrophages in pancreatic islets during the progression of insulitis. Disease evolution was tracked based on the T/β cell ratio in individual islets. In the early stage, we found that immune cells are preferentially interacting with α-cell-rich regions of an islet. At the islet periphery α-linked β-cells were found to be targeted significantly more compared to those without α-cell neighbors. Additionally, network analysis revealed increased T-myeloid, and T-macrophage interactions with all β-cells.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. Understanding PEx ecology is challenged by high inter-patient variability in airway microbial community profiles. We analyze bacterial communities in 880 CF sputum samples collected during an observational prospective cohort study and develop microbiome descriptors to model community reorganization prior to and during 18 PEx. We identify two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx show hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx display stochasticity and increased diversity. A simulation of antimicrobial treatment predicts better efficacy for hierarchically organized communities. This link between PEx, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.