Ananke: temporal clustering reveals ecological dynamics of microbial communities

Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.

Genomic signatures as predictive biomarkers of homologous recombination deficiency in ovarian cancer

DNA repair deficiency is a common hallmark of many cancers and is increasingly recognised as a target for cancer therapeutics. Selecting patients for these treatments requires a functional assessment of multiple redundant DNA repair pathways. With the advent of whole-genome sequencing of cancer genomes, it is increasingly recognised that multiple signatures of mutational and chromosomal alterations can be correlated with specific DNA repair defects. The clinical relevance of this approach is underlined by the use of poly-(ADP-ribose) polymerase inhibitors (PARPi) in homologous recombination (HR) deficient high-grade serous ovarian cancers. Beyond deleterious mutations in HR-related genes such as BRCA1/2, it is recognised that HR deficiency endows ovarian cancers with specific signatures of base substitutions and structural chromosomal variation. Multiple metrics quantifying loss-of-heterozygosity (LOH) events were proposed and implemented in trials with PARPi. However, it was shown that some of the HR-deficient cases, i.e. CDK12-mutated tumours, were not associated with high LOH-based scores, but with distinct patterns of genomic alterations such as tandem duplication. Therefore, more complex signatures of structural genomic variation were identified and quantified. Ultimately, optimal prediction models for treatments targeting DNA repair will need to integrate multiples of these genomic signatures and will also need to assess multiple resistance mechanisms such as genomic reversion events that partially or fully re-activate DNA repair.

Predicting ligand-dependent tumors from multi-dimensional signaling features

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.

Quantitative Systems Biology to decipher design principles of a dynamic cell cycle network: the "Maximum Allowable mammalian Trade-Off-Weight" (MAmTOW)

Network complexity is required to lend cellular processes flexibility to respond timely to a variety of dynamic signals, while simultaneously warranting robustness to protect cellular integrity against perturbations. The cell cycle serves as a paradigm for such processes; it maintains its frequency and temporal structure (although these may differ among cell types) under the former, but accelerates under the latter. Cell cycle molecules act together in time and in different cellular compartments to execute cell type-specific programs. Strikingly, the timing at which molecular switches occur is controlled by abundance and stoichiometry of multiple proteins within complexes. However, traditional methods that investigate one effector at a time are insufficient to understand how modulation of protein complex dynamics at cell cycle transitions shapes responsiveness, yet preserving robustness. To overcome this shortcoming, we propose a multidisciplinary approach to gain a systems-level understanding of quantitative cell cycle dynamics in mammalian cells from a new perspective. By suggesting advanced experimental technologies and dedicated modeling approaches, we present innovative strategies (i) to measure absolute protein concentration in vivo, and (ii) to determine how protein dosage, e.g., altered protein abundance, and spatial (de)regulation may affect timing and robustness of phase transitions. We describe a method that we name “Maximum Allowable mammalian Trade–Off–Weight” (MAmTOW), which may be realized to determine the upper limit of gene copy numbers in mammalian cells. These aspects, not covered by current systems biology approaches, are essential requirements to generate precise computational models and identify (sub)network-centered nodes underlying a plethora of pathological conditions.

NAMPT-Mediated NAD Biosynthesis as the Internal Timing Mechanism: In NAD+ World, Time Is Running in Its Own Way

The biological age of organisms differs from the chronological age and is determined by internal aging clock(s). How cells estimate time on a scale of 24 hours is relatively well studied; however, how biological time is measured by cells, tissues, organs, or organisms in longer time periods (years and decades) is largely unknown. What is clear and widely agreed upon is that the link to age and age-related diseases is not chronological, as it does not depend on a fixed passage of time. Rather, this link depends on the biological age of an individual cell, tissue, organ, or organism and not on time in a strictly chronological sense. Biological evolution does not invent new methods as often as improving upon already existing ones. It should be easier to evolve and remodel the existing (circadian) time clock mechanism to use it for measurement or regulation of longer time periods than to invent a new time mechanism/clock. Specifically, it will be demonstrated that the circadian clock can also be used to regulate circannual or even longer time periods. Nicotinamide phosphoribosyltransferase (NAMPT)-mediated nicotinamide adenine dinucleotide (NAD+) levels, being regulated by the circadian clock, might be the missing link between aging, cell cycle control, DNA damage repair, cellular metabolism and the aging clock, which is responsible for the biological age of an organism. The hypothesis that NAMPT/NAD+/SIRT1 might represent the time regulator that determines the organismal biological age will be presented. The biological age of tissues and organs might be regulated and synchronized through eNAMPT blood secretion. The "NAD World 2.0" concept will be upgraded with detailed insights into mechanisms that regulate NAD⁺-mediated aging clock ticking, the duration and amplitude of which are responsible for the aging rate of humans.

An efficient algorithm for finding all possible input nodes for controlling complex networks

Understanding structural controllability of a complex network requires to identify a Minimum Input nodes Set (MIS) of the network. Finding an MIS is known to be equivalent to computing a maximum matching of the network, where the unmatched nodes constitute an MIS. However, maximum matching is often not unique for a network, and finding all possible input nodes, the union of all MISs, may provide deep insights to the controllability of the network. Here we present an efficient enumerative algorithm for the problem. The main idea is to modify a maximum matching algorithm to make it efficient for finding all possible input nodes by computing only one MIS. The algorithm can also output a set of substituting nodes for each input node in the MIS, so that any node in the set can replace the latter. We rigorously proved the correctness of the new algorithm and evaluated its performance on synthetic and large real networks. The experimental results showed that the new algorithm ran several orders of magnitude faster than an existing method on large real networks.

Structural and Functional View of Polypharmacology

Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.

Emergent properties of bacterial chemotaxis pathway

The chemotaxis pathway of Escherichia coli is the most studied sensory system in prokaryotes. The highly conserved general architecture of this pathway consists of two modules which mediate signal transduction and adaptation. The signal transduction module detects and amplifies changes in environmental conditions and rapidly transmits these signals to control bacterial swimming behavior. The adaptation module gradually resets the activity and sensitivity of the first module after initial stimulation and thereby enables the temporal comparisons necessary for bacterial chemotaxis. Recent experimental and theoretical work has unraveled multiple quantitative features emerging from the interplay between these two modules. This has laid the groundwork for rationalization of these emerging properties in the context of the evolutionary optimization of the chemotactic behavior.

Time-resolved transcriptome analysis and lipid pathway reconstruction of the oleaginous green microalga Monoraphidium neglectum reveal a model for triacylglycerol and lipid hyperaccumulation

BACKGROUND

Oleaginous microalgae are promising production hosts for the sustainable generation of lipid-based bioproducts and as bioenergy carriers such as biodiesel. Transcriptomics of the lipid accumulation phase, triggered efficiently by nitrogen starvation, is a valuable approach for the identification of gene targets for metabolic engineering.

RESULTS

An explorative analysis of the detailed transcriptional response to different stages of nitrogen availability was performed in the oleaginous green alga Monoraphidium neglectum. Transcript data were correlated with metabolic data for cellular contents of starch and of different lipid fractions. A pronounced transcriptional down-regulation of photosynthesis became apparent in response to nitrogen starvation, whereas glucose catabolism was found to be up-regulated. An in-depth reconstruction and analysis of the pathways for glycerolipid, central carbon, and starch metabolism revealed that distinct transcriptional changes were generally found only for specific steps within a metabolic pathway. In addition to pathway analyses, the transcript data were also used to refine the current genome annotation. The transcriptome data were integrated into a database and complemented with data for other microalgae which were also subjected to nitrogen starvation. It is available at https://tdbmn.cebitec.uni-bielefeld.de.

CONCLUSIONS

Based on the transcriptional responses to different stages of nitrogen availability, a model for triacylglycerol and lipid hyperaccumulation is proposed, which involves transcriptional induction of thioesterases, differential regulation of lipases, and a re-routing of the central carbon metabolism. Over-expression of distinct thioesterases was identified to be a potential strategy to increase the oleaginous phenotype of M. neglectum, and furthermore specific lipases were identified as potential targets for future metabolic engineering approaches.

Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica

Complex phenotypes, such as lipid accumulation, result from cooperativity between regulators and the integration of multiscale information. However, the elucidation of such regulatory programs by experimental approaches may be challenging, particularly in context-specific conditions. In particular, we know very little about the regulators of lipid accumulation in the oleaginous yeast of industrial interest Yarrowia lipolytica. This lack of knowledge limits the development of this yeast as an industrial platform, due to the time-consuming and costly laboratory efforts required to design strains with the desired phenotypes. In this study, we aimed to identify context-specific regulators and mechanisms, to guide explorations of the regulation of lipid accumulation in Y. lipolytica. Using gene regulatory network inference, and considering the expression of 6539 genes over 26 time points from GSE35447 for biolipid production and a list of 151 transcription factors, we reconstructed a gene regulatory network comprising 111 transcription factors, 4451 target genes and 17048 regulatory interactions (YL-GRN-1) supported by evidence of protein-protein interactions. This study, based on network interrogation and wet laboratory validation (a) highlights the relevance of our proposed measure, the transcription factors influence, for identifying phases corresponding to changes in physiological state without prior knowledge (b) suggests new potential regulators and drivers of lipid accumulation and

Learning to read and write in evolution: from static pseudoenzymes and pseudosignalers to dynamic gear shifters

We present a systems biology view on pseudoenzymes that acknowledges that genes are not selfish: the genome is. With network function as the selectable unit, there has been an evolutionary bonus for recombination of functions of and within proteins. Many proteins house a functionality by which they 'read' the cell's state, and one by which they 'write' and thereby change that state. Should the writer domain lose its cognate function, a 'pseudoenzyme' or 'pseudosignaler' arises. GlnK involved in Escherichia coli ammonia assimilation may well be a pseudosignaler, associating 'reading' the nitrogen state of the cell to 'writing' the ammonium uptake activity. We identify functional pseudosignalers in the cyclin-dependent kinase complexes regulating cell-cycle progression. For the mitogen-activated protein kinase pathway, we illustrate how a 'dead' pseudosignaler could produce potentially selectable functionalities. Four billion years ago, bioenergetics may have shuffled 'electron-writers', producing various networks that all served the same function of anaerobic ATP synthesis and carbon assimilation from hydrogen and carbon dioxide, but at different ATP/acetate ratios. This would have enabled organisms to deal with variable challenges of energy need and substrate supply. The same principle might enable 'gear-shifting' in real time, by dynamically generating different pseudo-redox enzymes, reshuffling their coenzymes, and rerouting network fluxes. Non-stationary pH gradients in thermal vents together with similar such shuffling mechanisms may have produced a first selectable proton-motivated pyrophosphate synthase and subsequent ATP synthase. A combination of functionalities into enzymes, signalers, and the pseudo-versions thereof may offer fitness in terms of plasticity, both in real time and in evolution.

Deciphering Epithelial-Mesenchymal Transition Regulatory Networks in Cancer through Computational Approaches

Epithelial–mesenchymal transition (EMT), the process by which epithelial cells can convert into motile mesenchymal cells, plays an important role in development and wound healing but is also involved in cancer progression. It is increasingly recognized that EMT is a dynamic process involving multiple intermediate or “hybrid” phenotypes rather than an “all-or-none” process. However, the role of EMT in various cancer hallmarks, including metastasis, is debated. Given the complexity of EMT regulation, computational modeling has proven to be an invaluable tool for cancer research, i.e., to resolve apparent conflicts in experimental data and to guide experiments by generating testable hypotheses. In this review, we provide an overview of computational modeling efforts that have been applied to regulation of EMT in the context of cancer progression and its associated tumor characteristics. Moreover, we identify possibilities to bridge different modeling approaches and point out outstanding questions in which computational modeling can contribute to advance our understanding of pathological EMT.

Systems metabolic engineering strategies for the production of amino acids

Systems metabolic engineering is a multidisciplinary area that integrates systems biology, synthetic biology and evolutionary engineering. It is an efficient approach for strain improvement and process optimization, and has been successfully applied in the microbial production of various chemicals including amino acids. In this review, systems metabolic engineering strategies including pathway-focused approaches, systems biology-based approaches, evolutionary approaches and their applications in two major amino acid producing microorganisms: Corynebacterium glutamicum and Escherichia coli, are summarized.

Quantitative systems toxicology

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety.

Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategies

Systems orientated research offers the possibility of identifying novel therapeutic targets and relevant diagnostic markers for complex diseases such as osteoarthritis. This review demonstrates that the osteoarthritis research community has been slow to incorporate systems orientated approaches into research studies, although a number of key studies reveal novel insights into the regulatory mechanisms that contribute both to joint tissue homeostasis and its dysfunction. The review introduces both top-down and bottom-up approaches employed in the study of osteoarthritis. A holistic and multiscale approach, where clinical measurements may predict dysregulation and progression of joint degeneration, should be a key objective in future research. The review concludes with suggestions for further research and emerging trends not least of which is the coupled development of diagnostic tests and therapeutics as part of a concerted effort by the osteoarthritis research community to meet clinical needs. © 2017 The Authors. Journal of Orthopaedic Research Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 35:1573-1588, 2017.

The Role of Free/Libre and Open Source Software in Learning Health Systems

Objective: To give an overview of the role of Free/Libre and Open Source Software (FLOSS) in the context of secondary use of patient data to enable Learning Health Systems (LHSs). Methods: We conducted an environmental scan of the academic and grey literature utilising the MedFLOSS database of open source systems in healthcare to inform a discussion of the role of open source in developing LHSs that reuse patient data for research and quality improvement. Results: A wide range of FLOSS is identified that contributes to the information technology (IT) infrastructure of LHSs including operating systems, databases, frameworks, interoperability software, and mobile and web apps. The recent literature around the development and use of key clinical data management tools is also reviewed. Conclusions: FLOSS already plays a critical role in modern health IT infrastructure for the collection, storage, and analysis of patient data. The nature of FLOSS systems to be collaborative, modular, and modifiable may make open source approaches appropriate for building the digital infrastructure for a LHS.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

Quantitative systems pharmacology and the personalized drug-microbiota-diet axis

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the adverse effects of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. While pharmacokinetic models describe in detail a drug's absorption and metabolism, they generally do not account for individual variations in response to environmental influences, in addition to genetic variation. For instance, the human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. Here, we review recent advances in computational modeling approaches that could contribute to a better, mechanism-based understanding of drug-microbiota-diet interactions and their contribution to individual drug responses. By integrating systems biology and quantitative systems pharmacology with microbiology and nutrition, the conceptually and technologically demand for novel approaches could be met to enable the study of individual variability, thereby providing breakthrough support for progress in precision medicine.

Genome-scale modeling of yeast: chronology, applications and critical perspectives

Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.

Why Do Fast-Growing Bacteria Enter Overflow Metabolism? Testing the Membrane Real Estate Hypothesis

Bacteria and other cells show a puzzling behavior. At high growth rates, E. coli switch from respiration (which is ATP-efficient) to using fermentation for additional ATP (which is inefficient). This overflow metabolism results in a several-fold decrease in ATP produced per glucose molecule provided as food. By integrating diverse types of experimental data into a simple biophysical model, we give evidence that this onset is the result of the membrane real estate hypothesis: Fast growth drives cells to be bigger, reducing their surface-to-volume ratios. This decreases the membrane area available for respiratory proteins despite growing demand, causing increased crowding. Only when respiratory proteins reach their crowding limit does the cell activate fermentation, since fermentation allows faster ATP production per unit membrane area. Surface limitation thus creates a Pareto trade-off between membrane efficiency and ATP yield that links metabolic choice to the size and shape of a bacterial cell. By exploring the predictions that emerge from this trade-off, we show how consideration of molecular structures, energetics, rates, and equilibria can provide important insight into cellular behavior.

A Glucose-Sensing Toggle Switch for Autonomous, High Productivity Genetic Control

Many biosynthetic strategies are coupled to growth, which is inherently limiting, as (1) excess feedstock (e.g., sugar) may be converted to biomass, instead of product, (2) essential genes must be maintained, and (3) growth toxicity must be managed. A decoupled growth and production phase strategy could avoid these issues. We have developed a toggle switch that uses glucose sensing to enable this two-phase strategy. Temporary glucose starvation precisely and autonomously activates product pathway expression in rich or minimal media, obviating the requirement for expensive inducers. The switch remains stably in the new state even after reintroduction of glucose. In the context of polyhydroxybutyrate (PHB) biosynthesis, our system enables shorter growth phases and comparable titers to a constitutively expressing PHB strain. This two-phase production strategy, and specifically the glucose toggle switch, should be broadly useful to initiate many types of genetic program for metabolic engineering applications.

Engineering Yarrowia lipolytica for Enhanced Production of Lipid and Citric Acid

Increasing demand for plant oil for food, feed, and fuel production has led to food-fuel competition, higher plant lipid cost, and more need for agricultural land. On the other hand, the growing global production of biodiesel has increased the production of glycerol as a by-product. Efficient utilization of this by-product can reduce biodiesel production costs. We engineered Yarrowia lipolytica (Y. lipolytica) at various metabolic levels of lipid biosynthesis, degradation, and regulation for enhanced lipid and citric acid production. We used a one-step double gene knock-in and site-specific gene knock-out strategy. The resulting final strain combines the overexpression of homologous DGA1 and DGA2 in a POX-deleted background, and deletion of the SNF1 lipid regulator. This increased lipid and citric acid production in the strain under nitrogen-limiting conditions (C/N molar ratio of 60). The engineered strain constitutively accumulated lipid at a titer of more than 4.8 g/L with a lipid content of 53% of dry cell weight (DCW). The secreted citric acid reached a yield of 0.75 g/g (up to ~45 g/L) from pure glycerol in 3 days of batch fermentation using a 1-L bioreactor. This yeast cell factory was capable of simultaneous lipid accumulation and citric acid secretion. It can be used in fed-batch or continuous bioprocessing for citric acid recovery from the supernatant, along with lipid extraction from the harvested biomass.

More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes

The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk.

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

The decreasing cost of genomic technologies has enabled the molecular characterization of large-scale clinical disease samples and of molecular changes upon drug treatment in various disease models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critically important in the discovery of new therapeutics. Here we show that the potency of a drug to reverse cancer-associated gene expression changes positively correlates with that drug's efficacy in preclinical models of breast, liver and colon cancers. Using a systems-based approach, we predict four compounds showing high potency to reverse gene expression in liver cancer and validate that all four compounds are effective in five liver cancer cell lines. The in vivo efficacy of pyrvinium pamoate is further confirmed in a subcutaneous xenograft model. In conclusion, this systems-based approach may be complementary to the traditional target-based approach in connecting diseases to potentially efficacious drugs.

Synthetic biology approaches for the production of plant metabolites in unicellular organisms

Synthetic biology is the repurposing of biological systems for novel objectives and applications. Through the co-ordinated and balanced expression of genes, both native and those introduced from other organisms, resources within an industrial chassis can be siphoned for the commercial production of high-value commodities. This developing interdisciplinary field has the potential to revolutionize natural product discovery from higher plants, by providing a diverse array of tools, technologies, and strategies for exploring the large chemically complex space of plant natural products using unicellular organisms. In this review, we emphasize the key features that influence the generation of biorefineries and highlight technologies and strategic solutions that can be used to overcome engineering pitfalls with rational design. Also presented is a succinct guide to assist the selection of unicellular chassis most suited for the engineering and subsequent production of the desired natural product, in order to meet the global demand for plant natural products in a safe and sustainable manner.

Re-examining the role of Cdc14 phosphatase in reversal of Cdk phosphorylation during mitotic exit

Inactivation of cyclin-dependent kinase (Cdk) and reversal of Cdk phosphorylation are universally required for mitotic exit. In budding yeast (Saccharomyces cerevisiae), Cdc14 is essential for both and thought to be the major Cdk-counteracting phosphatase. However, Cdc14 is not required for mitotic exit in many eukaryotes, despite highly conserved biochemical properties. The question of how similar enzymes could have such disparate influences on mitotic exit prompted us to re-examine the contribution of budding yeast Cdc14. By using an auxin-inducible degron, we show that severe Cdc14 depletion has no effect on the kinetics of mitotic exit and bulk Cdk substrate dephosphorylation, but causes a cell separation defect and is ultimately lethal. Phosphoproteomic analysis revealed that Cdc14 is highly selective for distinct Cdk sites in vivo and does not catalyze widespread Cdk substrate dephosphorylation. We conclude that additional phosphatases likely contribute substantially to Cdk substrate dephosphorylation and coordination of mitotic exit in budding yeast, similar to in other eukaryotes, and the critical mitotic exit functions of Cdc14 require trace amounts of enzyme. We propose that Cdc14 plays very specific, and often different, roles in counteracting Cdk phosphorylation in all species.

Association of breast carcinoma growth with a non-canonical axis of IFNγ/IDO1/TSP1

Reciprocal interactions between cancers and the surrounding microenvironment have an important role in tumour evolution. In this study, our data suggested that through thrombospondin 1 (TSP1), tumour-associated microvessel provides a dormant niche to sustain inactive status of breast invasive ductal carcinoma (IDC) cells. TSP1 levels in the tumour stroma were negatively correlated with vascular indoleamine 2,3-dioxygenase 1 (IDO1) in IDC tissues. IDO1 is an intracellular enzyme initiating the first and rate-limited step of tryptophan breakdown. Lower stromal TSP1 levels and positive tumour vascular IDO1 staining seems to associate with poor survive of patients with IDC. IDC cells induced a significantly increase in IDO1 expression in endothelial cells (ECs). IFNγ exerts a similar effect on ECs. We hypothesized a tryptophan starvation theory that since tryptophan is essential for the synthesis of TSP1, IDO1 induce a decrease in tryptophan availability and a reduction in TSP1 synthesis in ECs, leading to overcoming the dormancy state of IDC cells and exacerbating conditions such as tumour invasion and metastasis. These findings identify a non-canonical role of IFNγ/IDO1/TSP1 axis in microvascular niche-dominated dormancy of breast invasive ductal carcinoma with a solid foundation for further investigation of therapeutic and prognostic relevance.

Structure-based control of complex networks with nonlinear dynamics

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

Leucine Biosynthesis Is Involved in Regulating High Lipid Accumulation in Yarrowia lipolytica

The yeast Yarrowia lipolytica is a potent accumulator of lipids, and lipogenesis in this organism can be influenced by a variety of factors, such as genetics and environmental conditions. Using a multifactorial study, we elucidated the effects of both genetic and environmental factors on regulation of lipogenesis in Y. lipolytica and identified how two opposite regulatory states both result in lipid accumulation. This study involved comparison of a strain overexpressing diacylglycerol acyltransferase (DGA1) with a control strain grown under either nitrogen or carbon limitation conditions. A strong correlation was observed between the responses on the transcript and protein levels. Combination of DGA1 overexpression with nitrogen limitation resulted in a high level of lipid accumulation accompanied by downregulation of several amino acid biosynthetic pathways, including that of leucine in particular, and these changes were further correlated with a decrease in metabolic fluxes. This downregulation was supported by the measured decrease in the level of 2-isopropylmalate, an intermediate of leucine biosynthesis. Combining the multi-omics data with putative transcription factor binding motifs uncovered a contradictory role for TORC1 in controlling lipid accumulation, likely mediated through 2-isopropylmalate and a Leu3-like transcription factor.IMPORTANCE The ubiquitous metabolism of lipids involves refined regulation, and an enriched understanding of this regulation would have wide implications. Various factors can influence lipid metabolism, including the environment and genetics. We demonstrated, using a multi-omics and multifactorial experimental setup, that multiple factors affect lipid accumulation in the yeast Yarrowia lipolytica Using integrative analysis, we identified novel interactions between nutrient restriction and genetic factors involving regulators that are highly conserved among eukaryotes. Given that lipid metabolism is involved in many diseases but is also vital to the development of microbial cell factories that can provide us with sustainable fuels and oleochemicals, we envision that our report introduces foundational work to further unravel the regulation of lipid accumulation in eukaryal cells.

Epithelial/mesenchymal plasticity: how have quantitative mathematical models helped improve our understanding?

Phenotypic plasticity, the ability of cells to reversibly alter their phenotypes in response to signals, presents a significant clinical challenge to treating solid tumors. Tumor cells utilize phenotypic plasticity to evade therapies, metastasize, and colonize distant organs. As a result, phenotypic plasticity can accelerate tumor progression. A well‐studied example of phenotypic plasticity is the bidirectional conversions among epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M) phenotype(s). These conversions can alter a repertoire of cellular traits associated with multiple hallmarks of cancer, such as metabolism, immune evasion, invasion, and metastasis. To tackle the complexity and heterogeneity of these transitions, mathematical models have been developed that seek to capture the experimentally verified molecular mechanisms and act as ‘hypothesis‐generating machines’. Here, we discuss how these quantitative mathematical models have helped us explain existing experimental data, guided further experiments, and provided an improved conceptual framework for understanding how multiple intracellular and extracellular signals can drive E/M plasticity at both the single‐cell and population levels. We also discuss the implications of this plasticity in driving multiple aggressive facets of tumor progression.

Computational dynamic approaches for temporal omics data with applications to systems medicine

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

RNA-based ovarian cancer research from 'a gene to systems biomedicine' perspective

Ovarian cancer remains the leading cause of death from a gynecologic malignancy, and treatment of this disease is harder than any other type of female reproductive cancer. Improvements in the diagnosis and development of novel and effective treatment strategies for complex pathophysiologies, such as ovarian cancer, require a better understanding of disease emergence and mechanisms of progression through systems medicine approaches. RNA-level analyses generate new information that can help in understanding the mechanisms behind disease pathogenesis, to identify new biomarkers and therapeutic targets and in new drug discovery. Whole RNA sequencing and coding and non-coding RNA expression array datasets have shed light on the mechanisms underlying disease progression and have identified mRNAs, miRNAs, and lncRNAs involved in ovarian cancer progression. In addition, the results from these analyses indicate that various signalling pathways and biological processes are associated with ovarian cancer. Here, we present a comprehensive literature review on RNA-based ovarian cancer research and highlight the benefits of integrative approaches within the systems biomedicine concept for future ovarian cancer research. We invite the ovarian cancer and systems biomedicine research fields to join forces to achieve the interdisciplinary caliber and rigor required to find real-life solutions to common, devastating, and complex diseases such as ovarian cancer.

ABBREVIATIONS

CAF: cancer-associated fibroblasts; COG: Cluster of Orthologous Groups; DEA: disease enrichment analysis; EOC: epithelial ovarian carcinoma; ESCC: oesophageal squamous cell carcinoma; GSI: gamma secretase inhibitor; GO: Gene Ontology; GSEA: gene set enrichment analyzes; HAS: Hungarian Academy of Sciences; lncRNAs: long non-coding RNAs; MAPK/ERK: mitogen-activated protein kinase/extracellular signal-regulated kinases; NGS: next-generation sequencing; ncRNAs: non-coding RNAs; OvC: ovarian cancer; PI3K/Akt/mTOR: phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin; RT-PCR: real-time polymerase chain reaction; SNP: single nucleotide polymorphism; TF: transcription factor; TGF-β: transforming growth factor-β.

Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle

The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis for Saccharomyces cerevisiae. Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem using network diffusion. Utilizing more than 26,000 regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using time lagged transfer entropy, a method for quantifying information transfer between variables. By picking a set of source nodes, a diffusion process covers a portion of the network. The size of the network cover relates to the influence of the source nodes. The set of nodes that maximizes influence is the solution to the IMP. By solving the IMP over different numbers of source nodes, an influence ranking on genes was produced. The influence ranking was compared to other metrics of network centrality. Although the top genes from each centrality ranking contained well-known cell cycle regulators, there was little agreement and no clear winner. However, it was found that influential genes tend to directly regulate or sit upstream of genes ranked by other centrality measures. The influential nodes act as critical sources of information flow, potentially having a large impact on the state of the network. Biological events that affect influential nodes and thereby affect information flow could have a strong effect on network dynamics, potentially leading to disease. Code and data can be found at: https://github.com/gibbsdavidl/miergolf.

Refactoring the Embden-Meyerhof-Parnas Pathway as a Whole of Portable GlucoBricks for Implantation of Glycolytic Modules in Gram-Negative Bacteria

The Embden–Meyerhof–Parnas (EMP) pathway is generally considered to be the biochemical standard for glucose catabolism. Alas, its native genomic organization and the control of gene expression in Escherichia coli are both very intricate, which limits the portability of the EMP pathway to other biotechnologically important bacterial hosts that lack the route. In this work, the genes encoding all the enzymes of the linear EMP route have been individually recruited from the genome of E. coli K-12, edited in silico to remove their endogenous regulatory signals, and synthesized de novo following a standard (GlucoBrick) that enables their grouping in the form of functional modules at the user’s will. After verifying their activity in several glycolytic mutants of E. coli, the versatility of these GlucoBricks was demonstrated in quantitative physiology tests and biochemical assays carried out in Pseudomonas putida KT2440 and P. aeruginosa PAO1 as the heterologous hosts. Specific configurations of GlucoBricks were also adopted to streamline the downward circulation of carbon from hexoses to pyruvate in E. coli recombinants, thereby resulting in a 3-fold increase of poly(3-hydroxybutyrate) synthesis from glucose. Refactoring whole metabolic blocks in the fashion described in this work thus eases the engineering of biochemical processes where the optimization of carbon traffic is facilitated by the operation of the EMP pathway—which yields more ATP than other glycolytic routes such as the Entner–Doudoroff pathway.

Modeling the Attractor Landscape of Disease Progression: a Network-Based Approach

Genome-wide regulatory networks enable cells to function, develop, and survive. Perturbation of these networks can lead to appearance of a disease phenotype. Inspired by Conrad Waddington's epigenetic landscape of cell development, we use a Hopfield network formalism to construct an attractor landscape model of disease progression based on protein- or gene-correlation networks of Parkinson's disease, glioma, and colorectal cancer. Attractors in this landscape correspond to normal and disease states of the cell. We introduce approaches to estimate the size and robustness of these attractors, and take a network-based approach to study their biological features such as the key genes and their functions associated with the attractors. Our results show that the attractor of cancer cells is wider than the attractor of normal cells, suggesting a heterogeneous nature of cancer. Perturbation analysis shows that robustness depends on characteristics of the input data (number of samples per time-point, and the fraction which converge to an attractor). We identify unique gene interactions at each stage, which reflect the temporal rewiring of the gene regulatory network (GRN) with disease progression. Our model of the attractor landscape, constructed from large-scale gene expression profiles of individual patients, captures snapshots of disease progression and identifies gene interactions specific to different stages, opening the way for development of stage-specific therapeutic strategies.

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

Background

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.

Results

The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and ¹³C Metabolic Flux Analysis. Moreover, it introduces the capability to use ¹³C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale ¹³C Metabolic Flux Analysis (2S-¹³C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user’s specific needs.

Conclusions

jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1615-y) contains supplementary material, which is available to authorized users.

Entrainment of a Bacterial Synthetic Gene Oscillator through Proteolytic Queueing

Internal chemical oscillators (chemical clocks) direct the behavior of numerous biological systems, and maintenance of a given period and phase among many such oscillators may be important for their proper function. However, both environmental variability and fundamental molecular noise can cause biochemical oscillators to lose coherence. One solution to maintaining coherence is entrainment, where an external signal provides a cue that resets the phase of the oscillators. In this work, we study the entrainment of gene networks by a queueing interaction established by competition between proteins for a common proteolytic pathway. Principles of queueing entrainment are investigated for an established synthetic oscillator in Escherichia coli. We first explore this theoretically using a standard chemical reaction network model and a map-based model, both of which suggest that queueing entrainment can be achieved through pulsatile production of an additional protein competing for a common degradation pathway with the oscillator proteins. We then use a combination of microfluidics and fluorescence microscopy to verify that pulse trains modulating the production rate of a fluorescent protein targeted to the same protease (ClpXP) as the synthetic oscillator can entrain the oscillator.

Adipose tissue NAD+ biology in obesity and insulin resistance: From mechanism to therapy

Nicotinamide adenine dinucleotide (NAD⁺ ) biosynthetic pathway, mediated by nicotinamide phosphoribosyltransferase (NAMPT), a key NAD⁺ biosynthetic enzyme, plays a pivotal role in controlling many biological processes, such as metabolism, circadian rhythm, inflammation, and aging. Over the past decade, NAMPT-mediated NAD⁺ biosynthesis, together with its key downstream mediator, namely the NAD⁺ -dependent protein deacetylase SIRT1, has been demonstrated to regulate glucose and lipid metabolism in a tissue-dependent manner. These discoveries have provided novel mechanistic and therapeutic insights into obesity and its metabolic complications, such as insulin resistance, an important risk factor for developing type 2 diabetes and cardiovascular disease. This review will focus on the importance of adipose tissue NAMPT-mediated NAD⁺ biosynthesis and SIRT1 in the pathophysiology of obesity and insulin resistance. We will also critically explore translational and clinical aspects of adipose tissue NAD⁺ biology.

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development.

DOI:http://dx.doi.org/10.7554/eLife.20487.001

Physical, chemical, and metabolic state sensors expand the synthetic biology toolbox for Synechocystis sp. PCC 6803

Many under-developed organisms possess important traits that can boost the effectiveness and sustainability of microbial biotechnology. Photoautotrophic cyanobacteria can utilize the energy captured from light to fix carbon dioxide for their metabolic needs while living in environments not suited for growing crops. Various value-added compounds have been produced by cyanobacteria in the laboratory; yet, the products' titers and yields are often not industrially relevant and lag behind what have been accomplished in heterotrophic microbes. Genetic tools for biological process control are needed to take advantage of cyanobacteria's beneficial qualities, as tool development also lags behind what has been created in common heterotrophic hosts. To address this problem, we developed a suite of sensors that regulate transcription in the model cyanobacterium Synechocystis sp. PCC 6803 in response to metabolically relevant signals, including light and the cell's nitrogen status, and a family of sensors that respond to the inexpensive chemical, l-arabinose. Increasing the number of available tools enables more complex and precise control of gene expression. Expanding the synthetic biology toolbox for this cyanobacterium also improves our ability to utilize this important under-developed organism in biotechnology. Biotechnol. Bioeng. 2017;114: 1561-1569. © 2017 Wiley Periodicals, Inc.

LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks

One of the main challenges of the post-genomic era is the understanding of how gene expression is controlled. Changes in gene expression lay behind diverse biological phenomena such as development, disease and the adaptation to different environmental conditions. Despite the availability of well-established methods to identify these changes, tools to discern how gene regulation is orchestrated are still required. The regulation of gene expression is usually depicted as a Gene Regulatory Network (GRN) where changes in the network structure (i.e., network topology) represent adjustments of gene regulation. Like other networks, GRNs are composed of basic building blocks; small induced subgraphs called graphlets. Here we present LoTo, a novel method that using Graphlet Based Metrics (GBMs) identifies topological variations between different states of a GRN. Under our approach, different states of a GRN are analyzed to determine the types of graphlet formed by all triplets of nodes in the network. Subsequently, graphlets occurring in a state of the network are compared to those formed by the same three nodes in another version of the network. Once the comparisons are performed, LoTo applies metrics from binary classification problems calculated on the existence and absence of graphlets to assess the topological similarity between both network states. Experiments performed on randomized networks demonstrate that GBMs are more sensitive to topological variation than the same metrics calculated on single edges. Additional comparisons with other common metrics demonstrate that our GBMs are capable to identify nodes whose local topology changes between different states of the network. Notably, due to the explicit use of graphlets, LoTo captures topological variations that are disregarded by other approaches. LoTo is freely available as an online web server at http://dlab.cl/loto.