Mass Spectrometry–Based Proteomics and Network Biology

Community Detection in Biological Networks

There is an old saying (at least in English) that what you don't know can't hurt you. In science it is the reverse. So how much are we thinking about the deep unknowns in the life sciences, compared with gathering data? The European Commission set the tone for research with its launch of the “Information Society,” and proclamation (made in 2000 and known as the “Lisbon Agenda”) to make Europe the world-leading knowledge-based economy by 2010. With the rapid technological advances in molecular biology (particularly genome sequencing), some of this knowledge was practically in the bag, so it seemed. It was a “known” entity with predictable growth prospects. Thinking about what we already know, and know how to do, enables us to build on knowledge in an evolutionary, incremental, sense. Thinking about what we don't know, and don't know how to do, on the other hand, raises the truly fascinating questions in biology that are likely to culminate in revolutions in understanding. But at a more practical level, carefully thinking about what we don't know can help us make the right decision as to which data to gather, how to interpret them, and which other advances we need to support this endeavor. It is not necessarily easy to get the concept across these days, because large sectors of the life sciences are concerned with massive data collecting and interpretation projects. It might very well have been more implicitly understood 50 years ago, for instance, when we had but a fraction of the biological data to ponder that we do today. How to sequence (this has been known, in principle, for a very long time) How to decode the sequence (this has also been known for a very long time) But the ambitious aim of understanding the data needed to be matched by ambitious thinking about the unknowns. What we did not know was how to embed the knowledge of the sequence into the model of gene interactions as complex networks with phenotypic outputs that tend to be unpredictable. However, network biology as a discipline had been around for quite some time already. Now we tend to call it systems biology, and systems biology has been a defined discipline for probably more than 40 years: Mihajlo Mesarovic defined it as a research field in 1966 with an international symposium “Systems Theory and Biology” (though Ludwig von Bertalanffy's book “General Systems Theory in Physics and Biology” was published in 1950). Concerted funding for systems biology only really started in the late 1990s. If discovery of information advances faster than the rate at which we are developing the means to understand that information, it is inevitable that we will start misunderstanding it, and – even worse – misapplying it. A hackneyed example is the concept that the human genome sequence would give us the “genes for X, Y, Z”. Unfortunately, this worked against a positive trend in perception that individual genes were not determinants of phenotype.

Minimum network constraint on reverse engineering to develop biological regulatory networks

Attaining a detailed understanding of the various biological networks in an organism lies at the core of the emerging discipline of systems biology. A precise description of the relationships formed between genes, mRNA molecules, and proteins is a necessary step toward a complete description of the dynamic behavior of an organism at the cellular level, and toward intelligent, efficient, and directed modification of an organism. The importance of understanding such regulatory, signaling, and interaction networks has fueled the development of numerous in silico inference algorithms, as well as new experimental techniques and a growing collection of public databases. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, evaluation, and improvement of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to analyze high-throughput gene expression, protein abundance, or protein activation data via a suite of state-of-the-art network inference algorithms. It also allows algorithm developers to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. SEBINI can therefore be used by software developers wishing to evaluate, refine, or combine inference techniques, as well as by bioinformaticians analyzing experimental data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, which is an exploratory data analysis software that enables integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. The collection of edges in a public database, along with the confidence held in each edge (if available), can be fed into CABIN as one "evidence network," using the Cytoscape SIF file format. Using CABIN, one may increase the confidence in individual edges in a network inferred by an algorithm in SEBINI, as well as extend such a network by combining it with species-specific or generic information, e.g., known protein-protein interactions or target genes identified for known transcription factors. Thus, the combined SEBINI-CABIN toolkit aids in the more accurate reconstruction of biological networks, with less effort, in less time.A demonstration web site for SEBINI can be accessed from https://www.emsl.pnl.gov/SEBINI/RootServlet . Source code and PostgreSQL database schema are available under open source license. ronald.taylor@pnl.gov. For commercial use, some algorithms included in SEBINI require licensing from the original developers. CABIN can be downloaded from http://www.sysbio.org/dataresources/cabin.stm . mudita.singhal@pnl.gov.

BackgroundClinical bronchoalveolar lavage fluid (BALF) samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, mass spectrometry (MS)-based proteomic analysis of BALF is challenged by the dynamic range of protein abundance, and potential for interfering contaminants. A robust, MS-based proteomics compatible sample preparation workflow for BALF samples, including those of small and large volume, would be useful for many researchers.ResultsWe have developed a workflow that combines high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, that is compatible with either qualitative or quantitative MS-based proteomic analysis. The workflow includes a value-added collection of endogenous peptides for peptidomic analysis of BALF samples, if desired, as well as amenability to offline semi-preparative or microscale fractionation of complex peptide mixtures prior to LC–MS/MS analysis, for increased depth of analysis. We demonstrate the effectiveness of this workflow on BALF samples collected from COPD patients, including for smaller sample volumes of 1–5 mL that are commonly available from the clinic. We also demonstrate the repeatability of the workflow as an indicator of its utility for quantitative proteomic studies.ConclusionsOverall, our described workflow consistently provided high quality proteins and tryptic peptides for MS analysis. It should enable researchers to apply MS-based proteomics to a wide-variety of studies focused on BALF clinical specimens.

The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This 'systems biology' approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining 'omics' types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform-specific systematic variation. A study of Populus tremula x Populus tremuloides, investigating short-day-induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.

The rapid accumulation of biological network data is creating an urgent need for computational methods on integrative network analysis. Thus far, most such methods focused on the analysis of single biological networks. This chapter discusses a suite of methods we developed to mine patterns across many biological networks. Such patterns include frequent dense subgraphs, frequent dense vertex sets, generic frequent patterns, and differential subgraph patterns. Using the identified network patterns, we systematically perform gene functional annotation, regulatory network reconstruction, and genome to phenome mapping. Finally, tensor computation of multiple weighted biological networks, which filled a gap of integrative network biology, is discussed.

Approaches to investigate biological processes have been of strong interest in the past few years and are the focus of several research areas like systems biology. Biological networks as representations of such processes are crucial for an extensive understanding of living beings. Due to their size and complexity, their growth and continuous change, as well as their compilation from databases on demand, researchers very often request novel network visualization, interaction, and exploration techniques. In this chapter, we first provide background information that is needed for the interactive visual analysis of various biological networks. Fields such as (information) visualization, visual analytics, and automatic layout of networks are highlighted and illustrated by a number of examples. Then, the state of the art in network visualization for the life sciences is presented together with a discussion of standards for the graphical representation of cellular networks and biological processes.

Contributors

Systems biology is an interdisciplinary field that applies engineering principles and computational modeling to understand complex biological systems at multiple scales. This paper examines how systems biology evolved from reductionist approaches to integrative, system-level methodologies that leverage high-throughput technologies and mathematical frameworks. Core topics include the history of systems thinking in biology, computational modeling of metabolic and signaling networks, data integration techniques, and applications in medicine, agriculture, and synthetic biology. With the advent of omics technologies and bioinformatics tools, researchers can now simulate, predict, and design biological functions with increasing precision. However, despite its promise, systems biology faces significant challenges, including data standardization, model validation, and ethical implications. By blending systems engineering with molecular biology, this field aims not only to advance fundamental science but also to offer innovative solutions to pressing societal needs such as disease treatment, food security, and environmental sustainability. Keywords: Systems Biology, Computational Modeling, Biological Networks, Data Integration, Synthetic Biology, Engineering Principles, Omics Technologies.

Mass spectrometry-based proteomics of single cells and organoids: The new generation of cancer research

Simple SummaryThis review provides a roadmap to develop safe and effective OV-based future therapeutics by using several novel synthetic and system biology strategies. Integration of system and synthetic biology can improve the genetic design of OVs backbone, maintaining enough virulence-associated genes. Furthermore, specific computation pipelines can refine target discovery in several types of cancer, identifying the MHC-I and II-restricted peptide repertoire recognized by T-cells and reinforcing anticancer immune responses. Using these versatile approaches could encourage the development of a next-generation of OVs-based therapies, overcoming current challenges such as on-target, off-tumor effects, and variable clinical responses. The efficacy and safety profile of the current OVs-based regimens could be further enhanced with additional long-lasting clinical effects.Oncolytic virus (OV)-based immunotherapy is mainly dependent on establishing an efficient cell-mediated antitumor immunity. OV-mediated antitumor immunity elicits a renewed antitumor reactivity, stimulating a T-cell response against tumor-associated antigens (TAAs) and recruiting natural killer cells within the tumor microenvironment (TME). Despite the fact that OVs are unspecific cancer vaccine platforms, to further enhance antitumor immunity, it is crucial to identify the potentially immunogenic T-cell restricted TAAs, the main key orchestrators in evoking a specific and durable cytotoxic T-cell response. Today, innovative approaches derived from systems biology are exploited to improve target discovery in several types of cancer and to identify the MHC-I and II restricted peptide repertoire recognized by T-cells. Using specific computation pipelines, it is possible to select the best tumor peptide candidates that can be efficiently vectorized and delivered by numerous OV-based platforms, in order to reinforce anticancer immune responses. Beyond the identification of TAAs, system biology can also support the engineering of OVs with improved oncotropism to reduce toxicity and maintain a sufficient portion of the wild-type virus virulence. Finally, these technologies can also pave the way towards a more rational design of armed OVs where a transgene of interest can be delivered to TME to develop an intratumoral gene therapy to enhance specific immune stimuli.

Chapter 32 - Systems biology and the epigenetics of psychiatric disorders

Clinically useful biomarkers are available for some neuropsychiatric disorders like fragile X syndrome, Rett syndrome, and Huntington’s disease. Despite many decades of research on the pathogenesis of neuropsychiatric disorders like schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), the exact pathogenesis of these disorders remains unclear, and there are no clinically useful biomarkers for these disorders. However, there is increasing evidence that abnormal epigenetic mechanisms of gene expression contribute to the pathogenesis of SZ, BD, and MDD. Both systems (or network) biology and epigenetics (a component of systems biology) attempt to make sense of biological systems that are highly dynamic and multi-compartmental. This article suggests that systems biology, emphasizing the epigenetic component of systems biology, could help identify clinically useful biomarkers in neuropsychiatric disorders like SZ, BD, and MDD.

Over the last 10 years, 'Systems Biology' has focused on the integration of biology and medicine with information technology and computation. The current challenge is to use the discoveries of the last 20 years, such as genomics and proteomics, to develop targeted therapeutical strategies. These strategies are the result of understanding the aetiologies of complex diseases. Scientists predict the data will make personalized medicine rapidly available. However, the data need to be considered as a highly complex system comprising multiple inputs and feedback mechanisms. Translational medicine requires the functional and conceptual linkage of genetics to proteins, proteins to cells, cells to organs, organs to systems and systems to the organism. To help understand the complex integration of these systems, a mathematical model of the entire human body, which accurately links the functioning of all organs and systems together, could provide a framework for the development and testing of new hypotheses that will be important in clinical outcomes. There are several efforts to develop a 'Human Physiome', with the strengths and weaknesses of each being presented here. The development of a 'Human Model', with verification, documentation and validation of the underlying and integrative responses, is essential to provide a usable environment. Future development of a 'Human Model' requires integrative physiologists working in collaboration with other scientists, who have expertise in all areas of human biology, to develop the most accurate and usable human model.