Complex Biological Systems Research Articles

Advances in Metabolomics based on Mass Spectrometry

Metabonomics, as systems biology's most downstream "omics, is the integrity of the system function change important branch discipline of life. Metabonomics is a research field that focuses on complex biological systems, and there are numerous types of common biological samples, such as biological fluids (urine, saliva, serum, plasma, bile, cerebrospinal fluid, amniotic fluid, tears, pancreatic juice, and intestinal juice, etc.), animal or human tissue (tumor, liver, brain, adipose tissue, etc.), and cells. Metabonomics typically employs two categories of commonly used research methods: mass spectrometry and nuclear magnetic resonance. This paper presents a comprehensive review of the research progress in the field of metabonomics within the medical field. The focus is on disease diagnosis, drug research and development, as well as their relationship to nutrition, toxicology, sports medicine, and other related areas. This review is based on an analysis of mass spectrometry. Despite the significant progress made in science, technology, and research, metabolics research still faces a multitude of challenges and opportunities. The ongoing development and innovation of mass spectrometry technology are crucial for the field's advancement. While mass spectrometry has played a pivotal role in metabolomics research, there is a pressing need to enhance its performance in order to improve sensitivity, resolution, and analysis throughput.

Control Intracellular Protein Condensates with Light.

Protein phase transitions are gaining traction among biologists for their wide-ranging roles in biological regulation. However, achieving precise control over these phenomena in vivo remains a formidable task. Optogenetic techniques present us with a potential means to control protein phase behavior with spatiotemporal precision. This review delves into the design of optogenetic tools, particularly those aimed at manipulating protein phase transitions in complex biological systems. We begin by discussing the pivotal roles of subcellular phase transitions in physiological and pathological processes. Subsequently, we offer a thorough examination of the evolution of optogenetic tools and their applications in regulating these protein phase behaviors. Furthermore, we highlight the tailored design of optogenetic tools for controlling protein phase transitions and the construction of synthetic condensates using these innovative techniques. In the long run, the development of optogenetic tools not only holds the potential to elucidate the roles of protein phase transitions in various physiological processes but also to antagonize pathological ones to reinstate cellular homeostasis, thus bringing about novel therapeutic strategies. The integration of optogenetic techniques into the study of protein phase transitions represents a significant step forward in our understanding and manipulation of biology at the subcellular level.

Spatially Aware Domain Adaptation Enables Cell Type Deconvolution from Multi-Modal Spatially Resolved Transcriptomics.

Spatially Resolved Transcriptomics (SRT) offers unprecedented opportunities to elucidate the cellular arrangements within tissues. Nevertheless, the absence of deconvolution methods that simultaneously model multi-modal features has impeded progress in understanding cellular heterogeneity in spatial contexts. To address this issue, SpaDA is developed, a novel spatially aware domain adaptation method that integrates multi-modal data (i.e., transcriptomics, histological images, and spatial locations) from SRT to accurately estimate the spatial distribution of cell types. SpaDA utilizes a self-expressive variational autoencoder, coupled with deep spatial distribution alignment, to learn and align spatial and graph representations from spatial multi-modal SRT data and single-cell RNA sequencing (scRNA-seq) data. This strategy facilitates the transfer of cell type annotation information across these two similarity graphs, thereby enhancing the prediction accuracy of cell type composition. The results demonstrate that SpaDA surpasses existing methods in cell type deconvolution and the identification of cell types and spatial domains across diverse platforms. Moreover, SpaDA excels in identifying spatially colocalized cell types and key marker genes in regions of low-quality measurements, exemplified by high-resolution mouse cerebellum SRT data. In conclusion, SpaDA offers a powerful and flexible framework for the analysis of multi-modal SRT datasets, advancing the understanding of complex biological systems.

MGDRGCN: A novel framework for predicting metabolite–disease connections using tripartite network and relational graph convolutional network

Metabolites are fundamental to the existence of biomolecules, and numerous studies have demonstrated that uncovering the connections between metabolites and diseases can enhance our understanding of disease pathogenesis. Traditional biological methods can identify potential metabolite–disease relationships, but these approaches often require significant human and material resources. Consequently, computational methods have emerged as a more efficient alternative. However, most computational methods primarily rely on metabolite–disease associations and rarely explore the impact of more biological entities. To address this issue, we propose a novel computational framework based on a metabolite–gene–disease tripartite heterogeneous network and relational graph convolutional network (R-GCN), abbreviated as MGDRGCN. Specifically, we construct three types of similarity networks from multiple data sources, including metabolite and gene functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity for metabolites and diseases. Next, we use principal component analysis to further extract features and construct a tripartite heterogeneous network with genes as the bridge. This network structure comprehensively captures and represents the complex relationships among metabolites, genes and diseases. We employ R-GCN to extract higher-order information from the tripartite heterogeneous network. Finally, we input the embeddings learned from R-GCN into a residual network classifier to predict potential metabolite–disease associations. In five-fold cross-validation experiments, MGDRGCN exhibit outstanding performance, with both AUC (0.9866) and AUPR (0.9865) significantly surpassing other advanced methods. Additionally, case studies further demonstrate MGDRGCN’s superior performance in predicting metabolite–disease associations. Overall, the introduction of MGDRGCN provides new perspectives and methods for future biomedical research, offering promising potential for uncovering the mechanisms of complex biological systems.

Sex-Related Differences in the Immune System Drive Differential Responses to Anti-PD-1 Immunotherapy

Immune checkpoint inhibitors, such as anti-PD-1 antibodies, represent a significant advancement in cancer immunotherapy, but their efficacy varies notably between individuals, influenced by complex biological systems. Recent evidence suggests that sex-related biological differences play a pivotal role in modulating these responses. This study uses a systems biology approach to examine how sex-specific differences in the immune system contribute to variability in the response to treatment. Our model extends previous frameworks by incorporating sex-specific parameters that reflect observed immunological distinctions. The results from the simulation studies align with our clinical observations, showing that on average, males exhibit a more robust response to anti-PD-1 treatment compared to females. Additionally, this study explores the potential of combination therapy with recombinant IL-12, revealing sex-specific differences in treatment efficacy. These findings underscore the need for personalized immunotherapy strategies that consider individual immunological profiles, including sex, to optimize treatment outcomes.

Developing and Applying Computational Models to Uncover Mechanistic Insights into Complex Biological Processes Across Molecular, Cellular, and Systemic Levels

The complexity of biological processes spans molecular, cellular, and systemic levels, requiring advanced computational models to unravel the intricate mechanisms underlying these phenomena. This research explores the development and application of computational models to gain mechanistic insights into diverse biological systems. By integrating multi-scale data from genomics, proteomics, and cellular imaging, this study leverages machine learning algorithms, dynamical systems modeling, and network analysis to simulate and analyze biological interactions. Key areas of focus include understanding signaling pathways, cellular differentiation, and systemic physiological responses. The research also highlights the role of computational tools in bridging experimental data with theoretical predictions, providing a robust framework for hypothesis generation and testing. Challenges such as data heterogeneity, scalability, and model interpretability are addressed, emphasizing the need for interdisciplinary approaches. This study aims to advance the field of computational biology by offering novel insights into complex biological systems and fostering applications in personalized medicine, drug development, and synthetic biology.

Emergent collective behavior evolves more rapidly than individual behavior among acorn ant species

Emergence is a fundamental concept in biology and other disciplines, but whether emergent phenotypes evolve similarly to nonemergent phenotypes is unclear. The hypothesized process of emergent evolution posits that evolutionary change in at least some collective behaviors will differ from evolutionary change in the corresponding intrinsic behaviors of isolated individuals. As a result, collective behavior might evolve more rapidly and diversify more between populations compared to individual behavior. To test whether collective behavior evolves emergently, we conducted a large comparative study using 22 ant species and gathered over 1,500 behavioral rhythm time series from hundreds of colonies and isolated individuals, totaling over 1.5 y of behavioral data. We show that analogous traits measured at individual and collective levels exhibit distinct evolutionary patterns. The estimated rates of phenotypic evolution for the rhythmicity of activity in ant colonies were faster than the evolutionary rates of the same behavior measured in isolated individual ants, and total variation across species in collective behavior was higher than variation in individual behavior. We hypothesize that more rapid evolution and higher variation is a widespread feature of emergent phenotypes relative to lower-level phenotypes across complex biological systems.

ProtPipe: A Multifunctional Data Analysis Pipeline for Proteomics and Peptidomics.

Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analysis. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein-protein interaction analysis, and Major histocompatibility complex (MHC) -peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical postprocessing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available online at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.

AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.

Machine learning (ML) methods offer opportunities for gaining insights into the intricate workings of complex biological systems, and their applications are increasingly prominent in the analysis of omics data to facilitate tasks, such as the identification of novel biomarkers and predictive modeling of phenotypes. For scientists and domain experts, leveraging user-friendly ML pipelines can be incredibly valuable, enabling them to run sophisticated, robust, and interpretable models without requiring in-depth expertise in coding or algorithmic optimization. By streamlining the process of model development and training, researchers can devote their time and energies to the critical tasks of biological interpretation and validation, thereby maximizing the scientific impact of ML-driven insights. Here, we present an entirely automated open-source explainable AI tool, AutoXAI4Omics, that performs classification and regression tasks from omics and tabular numerical data. AutoXAI4Omics accelerates scientific discovery by automating processes and decisions made by AI experts, e.g. selection of the best feature set, hyper-tuning of different ML algorithms and selection of the best ML model for a specific task and dataset. Prior to ML analysis AutoXAI4Omics incorporates feature filtering options that are tailored to specific omic data types. Moreover, the insights into the predictions that are provided by the tool through explainability analysis highlight associations between omic feature values and the targets under investigation, e.g. predicted phenotypes, facilitating the identification of novel actionable insights. AutoXAI4Omics is available at: https://github.com/IBM/AutoXAI4Omics.

Association of body index with fecal microbiome in children cohorts with ethnic-geographic factor interaction: accurately using a Bayesian zero-inflated negative binomial regression model.

The microbiome is closely associated with physical indicators of the body, such as height, weight, age and BMI, which can be used as measures of human health. Accurately identifying which taxa in the microbiome are closely related to indicators of physical development is valuable as microbial markers of regional child growth trajectory. Zero-inflated negative binomial (ZINB) model, a type of Bayesian generalized linear model, can be effectively modeled in complex biological systems. We present an innovative ZINB regression model that is capable of identifying differentially abundant taxa associated with distinctive host phenotypes and quantifying the effects of covariates on these taxa, and demonstrate that its accuracy is superior to traditional Hurdle and INLA models. Our pipeline of integrating bacterial differential abundance in microbiome data and relevant covariates is effective and feasible.

A novel computational approach for the mining of signature pathways using species co-occurrence networks in gut microbiomes

BackgroundAdvances in metagenome sequencing data continue to enable new methods for analyzing biological systems. When handling microbial profile data, metagenome sequencing has proven to be far more comprehensive than traditional methods such as 16s rRNA data, which rely on partial sequences. Microbial community profiling can be used to obtain key biological insights that pave the way for more accurate understanding of complex systems that are critical for advancing biomedical research and healthcare. However, such attempts have mostly used partial or incomplete data to accurately capture those associations.MethodsThis study introduces a novel computational approach for the identification of co-occurring microbial communities using the abundance and functional roles of species-level microbiome data. The proposed approach is then used to identify signature pathways associated with inflammatory bowel disease (IBD). Furthermore, we developed a computational pipeline to identify microbial species co-occurrences from metagenome data at various granularity levels.ResultsWhen comparing the IBD group to a control group, we show that certain co-occurring communities of species are enriched for potential pathways. We also show that the identified co-occurring microbial species operate as a community to facilitate pathway enrichment.ConclusionsThe obtained findings suggest that the proposed network model, along with the computational pipeline, provide a valuable analytical tool to analyze complex biological systems and extract pathway signatures that can be used to diagnose certain health conditions.

The Chemical Probes Portal - 2024: update on this public resource to support best-practice selection and use of small molecules in biomedical research.

The Chemical Probes Portal (www.chemicalprobes.org) is a free, public resource, based on expert-reviews, that supports the assessment, selectionand use of small-molecule compounds that qualify as chemical probes. These high-quality reagents are essential for exploring the function of individual proteins in complex biological systems, such as cells and organisms, and for validating proteins as potential therapeutic targets. The use of reliable chemical probes accelerates protein annotation in basic biological studies and informs drug discovery. However, the use of low-quality compounds has historically led to erroneous conclusions in biomedical research, and experience shows that failure to follow best practice continues, an issue which the Portal aims to address. Here, we describe the latest updates to the Chemical Probes Portal in both content and functionality. The number of chemical probes and human protein targets covered has increased significantly, with improvements in the processes for obtaining expert reviews and user engagement. Moreover, new functionalities and enhanced tools have been introduced to better support biological researchers in selecting and using the best chemical probes for their studies.

Harnessing full-text publications for deep insights into C. elegans and Drosophila biomaps

In the rapidly expanding domain of scientific research, tracking and synthesizing information from the rapidly increasing volume of publications pose significant challenges. To address this, we introduce a novel high-throughput pipeline that employs ChatGPT to systematically extract and analyze connectivity information from the full-texts and abstracts of 24,237 and 150,538 research publications concerning Caenorhabditis elegans and Drosophila melanogaster, respectively. This approach has effectively identified 200,219 and 1,194,587 interactions within the C. elegans and Drosophila biomaps, respectively. Utilizing Cytoscape Web, we have developed a searchable online biomaps that link relevant keywords to their corresponding PubMed IDs, thus providing seamless access to an extensive knowledge network encompassing C. elegans and Drosophila. Our work highlights the transformative potential of integrating artificial intelligence with bioinformatics to deepen our understanding of complex biological systems. By revealing the intricate web of relationships among key entities in C. elegans and Drosophila, we offer invaluable insights that promise to propel advancements in genetics, developmental biology, neuroscience, longevity, and beyond. We also provide details and discuss significant nodes within both biomaps, including the insulin/IGF-1 signaling (IIS) and the notch pathways. Our innovative methodology sets a robust foundation for future research aimed at unravelling complex biological networks across diverse organisms. The two databases are available at worm.bio-map.com and drosophila.bio-map.com.

Crafting Molecular Tools for 15‐Lipoxygenase‐1 in a Single Step

Small molecule modulators are powerful tools for selectively probing and manipulating proteins in native biological systems. However, the development of versatile modulators that exhibit desired properties is hindered by the lack of a rapid and robust synthetic strategy. Here, we develop a facile and reliable one‐step methodology for the generation of multifunctional toolboxes encompassing a wide variety of chemical modulators with different desired features. These modulators bind irreversibly to the protein target via a selective warhead. Key elements are introduced onto the warhead in a single step using multi‐component reactions. To illustrate the power of this new technology, we synthesized a library of diverse modulators designed to explore a highly challenging and poorly understood protein, human 15‐lipoxygenase‐1. Modulators made include; activity‐based/photoaffinity probes, chemosensors, photocrosslinkers, as well as light‐controlled and high‐affinity inhibitors. The efficacy of our compounds was successfully established through the provision of on demand inhibition and labeling of our target protein in vitro, in cellulo and in vivo; thus, proving that this technology has promising potential for applications in many complex biological systems.

Crafting Molecular Tools for 15-Lipoxygenase-1 in a Single Step.

Small molecule modulators are powerful tools for selectively probing and manipulating proteins in native biological systems. However, the development of versatile modulators that exhibit desired properties is hindered by the lack of a rapid and robust synthetic strategy. Here, we develop a facile and reliable one-step methodology for the generation of multifunctional toolboxes encompassing a wide variety of chemical modulators with different desired features. These modulators bind irreversibly to the protein target via a selective warhead. Key elements are introduced onto the warhead in a single step using multi-component reactions. To illustrate the power of this new technology, we synthesized a library of diverse modulators designed to explore a highly challenging and poorly understood protein, human 15-lipoxygenase-1. Modulators made include; activity-based/photoaffinity probes, chemosensors, photocrosslinkers, as well as light-controlled and high-affinity inhibitors. The efficacy of our compounds was successfully established through the provision of on demand inhibition and labeling of our target proteinin vitro,in celluloandin vivo; thus, proving that this technology has promising potential for applications in many complex biological systems.

Automatic construction of Petri net models for computational simulations of molecular interaction network

Petri nets are commonly applied in modeling biological systems. However, construction of a Petri net model for complex biological systems is often time consuming, and requires expertise in the research area, limiting their application. To address this challenge, we developed GINtoSPN, an R package that automates the conversion of multi-omics molecular interaction network extracted from the Global Integrative Network (GIN) into Petri nets in GraphML format. These GraphML files can be directly used for Signaling Petri Net (SPN) simulation. To demonstrate the utility of this tool, we built a Petri net model for neurofibromatosis type I. Simulation of NF1 gene knockout, compared to normal skin fibroblast cells, revealed persistent accumulation of Ras-GTPs as expected. Additionally, we identified several other genes substantially affected by the loss of NF1’s function, exhibiting individual-specific variability. These results highlight the effectiveness of GINtoSPN in streamlining the modeling and simulation of complex biological systems.

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Lensless On-Chip Chemiluminescence Imaging for High-Throughput Single-Cell Heterogeneity Analysis.

High-throughput single-cell heterogeneity imaging and analysis is essential for understanding complex biological systems and for advancing personalized precision disease diagnosis and treatment. Here, we present a miniaturized lensless chemiluminescence chip for high-throughput single-cell functional imaging with subcellular resolution. With the sensitive chemiluminescence sensing and wide field of view of contact lensless imaging, we demonstrated the chemiluminescent imaging of over 1000 single cells, and their membrane glycoprotein and the high-throughput single-cell heterogeneity of membrane protein imaging were examined for precision analysis. Furthermore, the functional adhesion and heterogeneity of single live cells were imaged and explored. This miniaturized lensless on-chip CL-CMOS imaging platform enables high-throughput single-cell imaging and analysis with high sensitivity and subcellular resolution, providing new techniques for the cellular study of biological heterogeneity and has potential application in precision disease diagnosis and treatment at the point-of-care settings.

Discovery of anti-tumor agent targeted MetAP-2 using a special turn-on fluorescent probe

Methionine aminopeptidase 2 (MetAP-2) catalyzes the removal of methionine from the amino-terminus of proteins or peptides and is a known target for anti-tumor therapy. In this study, we developed an enzyme-activated fluorescent probe, DDAO-BMT, specifically for the real-time detection of endogenous MetAP-2. This probe enables the determination of extracellular MetAP-2 activity and visual monitoring of intracellular MetAP-2. Additionally, Sappan Lignum was identified as an active herbal medicine against MetAP-2, with brazilin identified as its key active constituent. Brazilin also exhibited a significant anti-tumor effect, including the inhibition on proliferation, migration, and invasion of tumor cells, and the preliminary mechanism of its activity against cancer has been elucidated. The use of this probe facilitates the monitoring of endogenous MetAP-2 in complex biological system, aiding in the discovery of novel active compounds for cancer treatment.

Selective Methods Promote Protein Solid-State NMR.

Solid-state nuclear magnetic resonance (ssNMR) is indispensable for studying the structures, dynamics, and interactions of insoluble proteins in native or native-like environments. While ssNMR includes numerous nonselective techniques for general analysis, it also provides various selective methods that allow for the extraction of precise details about proteins. This perspective highlights three key aspects of selective methods: selective signals of protein segments, selective recoupling, and site-specific insights into proteins. These methods leverage protein topology, labeling strategies, and the tailored manipulation of spin interactions through radio frequency (RF) pulses, significantly promoting the field of protein ssNMR. With ongoing advancements in higher magnetic fields and faster magic angle spinning (MAS), there remains an ongoing need to enhance the selectivity and efficiency of selective ssNMR methods, facilitating deeper atomic-level insights into complex biological systems.