Molecular Systems Biology Research Articles

Challenges in IBD Research: Precision Medicine.

Precision medicine is part of five focus areas of the Challenges in IBD research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. The Challenges in IBD Research document provides a comprehensive overview of current gaps in inflammatory bowel diseases (IBD) research and delivers actionable approaches to address them. It is the result of a multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient centric research prioritization. In particular, the precision medicine section is focused on highlighting the main gap areas that must be addressed to get closer to treatments tailored to the biological and clinical characteristics of each patient, which is the aim of precision medicine. The main gaps were identified in: 1) understanding and predicting the natural history of IBD: disease susceptibility, activity, and behavior; 2) predicting disease course and treatment response; and 3) optimizing current and developing new molecular technologies. Suggested approaches to bridge these gaps include prospective longitudinal cohort studies to identify and validate precision biomarkers for prognostication of disease course, and prediction and monitoring of treatment response. To achieve this, harmonization across studies is key as well as development of standardized methods and infrastructure. The implementation of state-of-the-art molecular technologies, systems biology and machine learning approaches for multi-omics and clinical data integration and analysis will be also fundamental. Finally, randomized biomarker-stratified trials will be critical to evaluate the clinical utility of validated signatures and biomarkers in improving patient outcomes and cost-effective care.

“Systems medicine” in the view of von Bertalanffy's “organismic biology” and systems theory

AbstractCurrently, medicine is transforming towards molecular systems medicine (MSM), based on molecular systems biology (MSB). These approaches should be related to Ludwig von Bertalanffy's vision of “organismic” systems biology/medicine (OSB/OSM) and of general system theory (GST), which he created already in the 1930s. In this paper, on the basis of current diversity of knowledge in medicine, major differences between MSB/MSM and OSB/OSM are highlighted: MSB is based on biochemical high‐throughput technologies, sophisticated mathematical data analytical tools, and supercomputers for computation, whereas OSB is based on developmental biology and is concept and theory oriented. Metatheoretical considerations show that holistic but still reductive MSM cannot bridge the categorical molecule–cell difference, the mind–body difference, and the environment–organism gap by a consistent molecular and mechanistic theory of the organism. In contrast, the options of theoretical interlevel modelling with the help of simply structured but complexly functioning organ models are discussed here. As example, the neurochemical mobile of the brain is discussed. Consequently, a reconsideration of GST in medicine, targeting OSM, seems to be fruitful by linking MSM with a core concept of a systems pathology and with psychosocial and clinical medicine.

Functional characterization of dark sleeper (Odontobutis obscura) IRF3 in IFN regulation

The dark sleeper, Odontobutis obscura (O. obscura), is a commercially important species of freshwater sleeper native to East Asia. However, its molecular biology system is unexplored, including the interferon (IFN) signaling pathway, which is crucial to the antiviral response. In this study, we characterised the IFN regulation pattern of dark sleeper interferon regulatory factor 3 (OdIRF3), supplementing evidence of the conservation of this classical pathway in fish. First, the open reading frame (ORF) of OdIRF3 was cloned from the liver tissue by Rapid amplification of cDNA ends (RACE). Amino acid sequence analysis suggested that OdIRF3 is homologous with other fish IRF3 and that the N-terminal DNA-binding domain (DBD) and the C-terminal IRF-association domain (IAD) are conserved. Then, the cellular distribution demonstrated that OdIRF3 is located in the cytoplasm region and transfers into the nuclear region under stimulation. For the function identification, OdIRF3 activated several types of IFN promoters and induced downstream interferon stimulated genes (ISGs) expression. Finally, the overexpression of OdIRF3 significantly decreased viral proliferation. Taken together, these data systematically characterised the sequence, cellular location, and function in IFN expression of OdIRF3, shedding light on the molecular biology mechanism of the dark sleeper.

On regularities in the spontaneous formation of structural hierarchies in chiral systems of nonliving and living matter

We review a general regularity concerned with the spontaneous formation of alternating-sign L–D hierarchies of chiral structures that are initially nonequilibrium due to homochirality. Mechanical, hydrodynamic, macromolecular, and liquid-crystal systems, as well as proteins and nucleic acids, are considered. Biomacromolecule chirality is related to the presence of an asymmetric carbon atom and, further, to the formation of helical and superhelical intra- and supramolecular structures. Chirality is a physical vehicle that generates stratification and folding in biological macromolecular systems. The hierarchies of alternating-sign chiral structures underlie the formation of a periodic molecular-biological system of cells.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Improved discovery of genetic interactions using CRISPRiSeq across multiple environments.

Large-scale genetic interaction (GI) screens in yeast have been invaluable for our understanding of molecular systems biology and for characterizing novel gene function. Owing in part to the high costs and long experiment times required, a preponderance of GI data has been generated in a single environmental condition. However, an unknown fraction of GIs may be specific to other conditions. Here, we developed a pooled-growth CRISPRi-based sequencing assay for GIs, CRISPRiSeq, which increases throughput such that GIs can be easily assayed across multiple growth conditions. We assayed the fitness of approximately 17,000 strains encompassing approximately 7700 pairwise interactions in five conditions and found that the additional conditions increased the number of GIs detected nearly threefold over the number detected in rich media alone. In addition, we found that condition-specific GIs are prevalent and improved the power to functionally classify genes. Finally, we found new links during respiratory growth between members of the Ras nutrient–sensing pathway and both the COG complex and a gene of unknown function. Our results highlight the potential of conditional GI screens to improve our understanding of cellular genetic networks.

Pluralization through epistemic competition: scientific change in times of data-intensive biology.

We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and the transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call "epistemic competition" that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe-contrary to many other accounts of scientific change-that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape.

Transcriptional Knockdown in Pneumococci Using CRISPR Interference.

Sequence-specific knockdown of gene expression using CRISPR interference (CRISPRi) has recently been developed for Streptococcus pneumoniae. By coexpression of a catalytically inactive Cas9-protein (dCas9) and a single guide RNA (sgRNA), CRISPRi can be used to knock down transcription of any gene of interest. Gene specificity is mediated by a 20bp sequence on the sgRNA, and new genes can be targeted by replacing this 20bp sequence. Here, a protocol is provided for design of sgRNAs and construction of CRIPSRi strains in S. pneumoniae, based on the vectors published by Liu et al. (Mol Syst Biol 13:931, 2017).

Looking for an Acheulean hand-axe in messy knowledge

Scientific knowledge production is a domain of human excellence, but recently this position became progressively challenged by machine intelligence. Some human tasks will be conquered sooner; others, which include design, may still require human intuition. Would the automated knowledge production truly challenge the human, or is one just an extension – a prosthetic – of the other? What could be the place of art in such environment? Mess and messy scientific models have the potential to provide paradigm shifts in molecular modelling, cancer research and systems biology where not only living humans but also machine learning algorithms craft knowledge. Together with art they have the potential for different reading and re-framing, which are the qualities scientists are looking for when engaging works of art within their molecular modelling process. In my opinion, these are the same qualities scientists are also looking for in the outputs of machine learning and computational chemistry modelling.

Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field.

Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.

Developing a circularly permuted variant of Renilla luciferase as a bioluminescent sensor for measuring Caspase-9 activity in the cell-free and cell-based systems

Biosensors and whole cell biosensors consisting of biological molecules and living cells can sense a special stimulus on a living system and convert it to a measurable signal. A major group of them are the bioluminescent sensors derived from luciferases. This type of biosensors has a broad application in molecular biology and imaging systems. In this project, a luciferase-based biosensor for detecting and measuring caspase-9 activity is designed and constructed using the circular permutation strategy. The spectroscopic method results reveal changes in the biosensor structure. Additionally, its activity is examined in a cell-free coupled assay system. Afterward, the biosensor is utilized for measuring the cellular caspase-9 activity upon apoptosis induction in a cancer cell line. In following the gene of biosensor is sub-cloned into a eukaryotic vector and transfected to HEK293T cell line and then its activity is measured upon apoptosis induction in the presence and absence of a caspase-9 inhibitor. The obtained results show that the designed biosensor detects the caspase-9 activity in the cell-free and cell-based systems.

Deep Multi-OMICs and Multi-Tissue Characterization in a Pre- and Postprandial State in Human Volunteers: The GEMM Family Study Research Design.

Cardiovascular disease (CVD) and type 2 diabetes (T2D) are increasing worldwide. This is mainly due to an unhealthy nutrition, implying that variation in CVD risk may be due to variation in the capacity to manage a nutritional load. We examined the genomic basis of postprandial metabolism. Our main purpose was to introduce the GEMM Family Study (Genetics of Metabolic Diseases in Mexico) as a multi-center study carrying out an ongoing recruitment of healthy urban adults. Each participant received a mixed meal challenge and provided a 5-hours’ time course series of blood, buffy coat specimens for DNA isolation, and adipose tissue (ADT)/skeletal muscle (SKM) biopsies at fasting and 3 h after the meal. A comprehensive profiling, including metabolomic signatures in blood and transcriptomic and proteomic profiling in SKM and ADT, was performed to describe tendencies for variation in postprandial response. Our data generation methods showed preliminary trends indicating that by characterizing the dynamic properties of biomarkers with metabolic activity and analyzing multi-OMICS data it could be possible, with this methodology and research design, to identify early trends for molecular biology systems and genes involved in the fasted and fed states.

Defining informative priors for ensemble modeling in systems biology.

Ensemble modeling in molecular systems biology requires the reproducible translation of kinetic parameter data into informative probability distributions (priors), as well as approaches that sample parameters from these distributions without violating the thermodynamic consistency of the overall model. Although a number of pioneering frameworks for ensemble modeling have been published, the issue of generating informative priors has not yet been addressed. Here, we present a protocol that aims to fill this gap. This protocol discusses the collection of parameter values from a diverse range of sources (literature, databases and experiments), assessment of their plausibility, and creation of log-normal probability distributions that can be used as informative priors in ensemble modeling. Furthermore, the protocol enables sampling from the generated distributions while maintaining thermodynamic consistency. Once all parameter values have been retrieved from literature and databases, the protocol can be implemented within ~5-10 min per parameter. The aim of this protocol is to facilitate the design and use of informative distributions for ensemble modeling, especially in fields such as synthetic biology and systems medicine.

Model Microbial Consortia as Tools for Understanding Complex Microbial Communities.

A major biological challenge in the postgenomic era has been untangling the composition and functions of microbes that inhabit complex communities or microbiomes. Multi-omics and modern bioinformatics have provided the tools to assay molecules across different cellular and community scales; however, mechanistic knowledge over microbial interactions often remains elusive. This is due to the immense diversity and the essentially undiminished volume of not-yet-cultured microbes. Simplified model communities hold some promise in enabling researchers to manage complexity so that they can mechanistically understand the emergent properties of microbial community interactions. In this review, we surveyed several approaches that have effectively used tractable model consortia to elucidate the complex behavior of microbial communities. We go further to provide some perspectives on the limitations and new opportunities with these approaches and highlight where these efforts are likely to lead as advances are made in molecular ecology and systems biology.

From gene list to gene network: Recognizing functional connections that regulate behavioral traits.

The study of social breeding systems is often gene focused, and the field of insect sociobiology has been successful at assimilating tools and techniques from molecular biology. One common output from sociogenomic studies is a gene list. Gene lists are readily generated from microarray, RNA sequencing, or other molecular screens that typically aim to prioritize genes based on the differences in their expression. Gene lists, however, are often unsatisfying because the information they provide is simply tabular and does not explain how genes interact with each other, or how genetic interactions change in real time under social or environmental circumstances. Here, we promote a view that is relatively common to molecular systems biology, where gene lists are converted into gene networks that better describe the functional connections that regulate behavioral traits. We present a narrative related to honeybee worker sterility to show how network analysis can be used to reprioritize candidate genes based on connectivity rather than their freestanding expression values. Networks can also reveal multigene modules, motifs, clusters or other system-wide properties that might not be apparent from an ab initio list. We argue that because network analyses are not restricted to "genes" as nodes, their implementation can potentially connect multiple levels of biological organization into a single, progressively complex study system.

Identification of Human T-Cell Lymphotropic Virus Type 1 Pro-Invasion in Patients with β-Thalassemia Major Using TaqMan Real-Time PCR in Tonekabon, Iran

Background: Human T-cell lymphotropic virus type 1 (HTLV-1) is a human retrovirus that causes two distinct diseases, adult T-cell leukemia (ATL) and a progressive and degenerative myelin disorder called myelopathy associated with HTLV-1 (tropical spastic paraparesis). Similar to human immunodeficiency virus (HIV), infections caused by HTLV-1 and -2 and retroviruses are persistent and long-lasting. Less than 5% of these infections are HTLV-related illnesses, but their treatment modalities are rare and their prognosis is poor. They are often fatal. Patients with β-thalassemia major are clearly at higher risk for HTLV-1 infection due to the need for frequent blood transfusions. Objectives: The current study aimed at detecting the molecular screening for HTLV-1 infection based on fluorescent probe in patients with β-thalassemia. Methods: The current experimental study was conducted on 80 blood samples collected from patients with β-thalassemia major in Shahid Rajaee Hospital of Tonekabon, Iran. After DNA extraction by the Iranian kit, molecular biological system transfer (MBST) Cinna Gen company (Tehran, Iran), TaqMan real-time polymerase chain reaction (RT-PCR) was employed to detect DNA HTLV-1 genome and a tax gene-specific integrated with the genome of patients’ lymphocyte was obtained. Results: Results of TaqMan RT-PCR indicated that out of 80 patients with β-thalassemia major referred to Shahid Rajai’e Hospital, two (2.5%) had HTLV-1. Conclusions: Compared to other molecular and serologic techniques, HTLV can be applied to detect HTLV-1 in blood banks due to its sensitivity, simplicity, higher speed, and the ability to simultaneously diagnose HTLV and lower costs than that of fluorescence molecular methods.

Dissecting the Meanings of "Physiology" to Assess the Vitality of the Discipline.

The vitality of physiology is currently under debate. Some say that physiology is a dying discipline in the era of molecular medicine and systems biology, whereas others claim that physiology remains a key biological and medical discipline, due in part to its integrative nature. In this conceptual review, we argue that any assessment of the vitality of physiology depends heavily on the definition of this discipline adopted. We examine two main conceptions of physiology, one focusing on its object (what physiology is about), and the other on the methods used (how physiologists study the biological reality). We contend that physiology no longer encompasses all biological disciplines and may no longer be the only synoptic biological science. However, far from indicating a sterility of this discipline, this situation should drive physiology to re-invent its relationship with these other biological domains.

Abstract SY31-03: Reuse of genomics data and lessons for cancer research

Abstract Making data and materials used in scientific research available to others is not only a prerequisite for enabling reproducible science and maximizing the return on funding, it is also the very basis of scientific progress, allowing other scientists to build on the previous work of their colleagues (1). In life sciences this principle has been well established since the 1980s, when scientific journals started making it a requirement, that X-ray crystallography and DNA sequence data supporting publications must be deposited in appropriate databases. In 1996, as a part of the Human Genome Project, it was agreed that all the sequence data would be released in publicly accessible databases within twenty-four hours after generation, known as Bermuda principles (2). With advent of new types of data, such as microarray data, it was soon recognized that not only the sequence or microarray data are important, but also the standards how these data are represented and the information about the samples and experiments (3). However, to enable data sharing, a properly built and funded infrastructure is needed (4). Without public data resources, such as Ensembl, UniProt, and Expression Atlas, that add value to molecular data archives, modern life sciences research would be hard to imagine (5-7). The data-sharing mentality is now so firmly embedded in the ethos of life sciences that scientists working in the field struggle to imagine that in other science disciplines the mentality may be different. Data sharing in medical research is a more complex and difficult problem due to multiple reasons, including the need for data security and the patient's confidentiality, complexity of representing health records, and diversity of national legislations. However, there is also an increasing realization that sharing biomedical research data, which is now facilitated by the use of electronic health records, can be an important accelerator of biomedical research (7). Cancer researchers are at the forefront of the data-sharing approach in biomedical research. The International Cancer Genome Consortium is completing the sequencing of genomes, transcriptomes, and epigenomes of over 20,000 patients, making all the data alongside the essential clinical information available to researchers (8). About 10% of these genomes and transcriptomes have been reanalyzed in a standardized way by the Pan-cancer Analysis of the Whole Genomes (PCAWG) group. The PCAWG project provides an important demonstration how data integration can accelerate biomedical research. In this talk I will particularly concentrate on lessons learned from integration of genome, transcriptome, and clinical data of this project. Although at the time of writing this abstract, some of the PCAWG transcriptome analysis is still being finalized, it is already clear that integrating heterogeneous types of data from heterogeneous cancer types provides new insights about this disease, and also that such integrative analysis is challenging (9-13). I will also describe some of the experience in building infrastructure for data integration and sharing at the European Bioinformatics Institute (EMBL-EBI) and some of the data resources provided by EMBL-EBI (14,15), particularly concentrating on cancer genomics data and the benefits that data sharing brings to cancer research.

Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER.

Precise control over microbial cell growth conditions could enable detection of minute phenotypic changes, which would improve our understanding of how genotypes are shaped by adaptive selection. Although automated cell-culture systems such as bioreactors offer strict control over liquid culture conditions, they often do not scale to high-throughput or require cumbersome redesign to alter growth conditions. We report the design and validation of eVOLVER, a scalable DIY framework that can be configured to carry out high-throughput growth experiments in molecular evolution, systems biology, and microbiology. We perform high-throughput evolution of yeast across systematically varied population density niches to show how eVOLVER can precisely characterize adaptive niches. We describe growth selection using time-varying temperature programs on a genome-wide yeast knockout library to identify strains with altered sensitivity to changes in temperature magnitude or frequency. Inspired by large-scale integration of electronics and microfluidics, we also demonstrate millifluidic multiplexing modules that enable multiplexed media routing, cleaning, vial-to-vial transfers and automated yeast mating.

Agent-Based Modeling in Molecular Systems Biology.

Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease.