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Multidimensional dynamics of the proteome in the degenerative and ageing mammalian brain

The amount of any given protein in the brain is determined by the rates of its synthesis and destruction, which are regulated by different cellular mechanisms. Here, we combine metabolic labelling in live mice with global proteomic profiling to simultaneously quantify both the flux and amount of proteins in mouse models of neurodegeneration. In multiple models, protein turnover increases were associated with increasing pathology. This method distinguishes changes in protein expression mediated by synthesis from those mediated by degradation. In the AppNL-F knockin mouse model of Alzheimers disease increased turnover resulted from imbalances in both synthesis and degradation, converging on proteins associated with synaptic vesicle recycling (Dnm1, Cltc, Rims1) and mitochondria (Fis1, Ndufv1). In contrast to disease models, ageing in wildtype mice caused a widespread decrease in protein recycling associated with a decrease in autophagic flux. This simple multidimensional approach enables the comprehensive mapping of proteome dynamics and identifies affected proteins in mouse models of disease and other live animal test settings.

systems biology

Multiplexed pooled library screening with Cpf1

RNA interference and CRISPR/Cas9-based pooled library screens have revolutionized the field of functional genomics. However, currently available pooled library screens face a trade-off between library effectiveness and library complexity. We developed a multiplexed, high-throughput screening strategy based on an optimized AsCpf1 nuclease that minimizes library size without sacrificing gene targeting efficiency. Our AsCpf1-based multiplexed library performed similarly well compared to currently available CRISPR/Cas9 libraries, but with a single polycistronic crRNA clone targeting each gene. With this strategy, we constructed the smallest whole-genome knock-out library available, \"Mini-human\" for the human genome, which is one-fourth the size of the smallest CRISPR library currently available.

systems biology

MOMO - Multi-Objective Metabolic mixed integer Optimization: application to yeast strain engineering

In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering.\n\nProduction of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. The multi-objective programming framework we developed, called Momo, is open-source and uses PolySCIP{ddagger} as underlying multi-objective solver. Momo is available at http://momo-sysbio.gforge.inria.fr

systems biology

Balancing energy supply during photosynthesis - a theoretical perspective

The photosynthetic electron transport chain (PETC) provides energy and redox equivalents for carbon fixation by the Calvin-Benson-Bassham (CBB) cycle. Both of these processes have been thoroughly investigated and the underlying molecular mechanisms are well known. However, it is far from understood by which mechanisms it is ensured that energy and redox supply by photosynthesis matches the demand of the downstream processes. Here, we deliver a theoretical analysis to quantitatively study the supply-demand regulation in photosynthesis. For this, we connect two previously developed models, one describing the PETC, originally developed to study non-photochemical quenching, and one providing a dynamic description of the photosynthetic carbon fixation in C3 plants, the CBB Cycle. The merged model explains how a tight regulation of supply and demand reactions leads to efficient carbon fixation. The model further illustrates that a stand-by mode is necessary in the dark to ensure that the carbon fixation cycle can be restarted after dark-light transitions, and it supports hypotheses, which reactions are responsible to generate such mode in vivo.

systems biology

The Fate of Glutamine in Human Metabolism. Comparison with Glucose

Genome-scale models of metabolism (GEM) are used to study how metabolism varies in different physiological conditions. However, the great number of reactions involved in GEM makes it difficult to understand these variations. In order to have a more understandable tool, we developed a reduced metabolic model of central carbon and nitrogen metabolism, C2M2N with 77 reactions, 54 internal metabolites and 3 compartments, taking into account the actual stoichiometry of the reactions, including the stoichiometric role of the cofactors and the irreversibility of some reactions. In order to model OXPHOS functioning, the proton gradient through the inner mitochondrial membrane is represented by two pseudo-metabolites DPH ({Delta}pH) and DPSI ({Delta}{psi}). To illustrate the interest of such a reduced and quantitative model of metabolism in mammalian cells, we used Flux Balance Analysis (FBA), to study all the possible fates of glutamine in metabolism. Our analysis shows that glutamine can supply carbon sources for cell energy production and can be used as carbon and nitrogen sources to synthesize essential metabolites. Finally, we studied the interplay between glucose and glutamine for the formation of cell biomass according to ammonia microenvironment. We then propose a quantitative analysis of the Warburg effect.

systems biology

Statistical physics of liquid brains

Liquid neural networks (or \"liquid brains\") are a widespread class of cognitive living networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (\"solid brains\"), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting to external signals.

systems biology

Curcumin induces mild anemia in a DSS-induced colitis mouse model maintained on an iron-sufficient diet

Anemia is frequently encountered in patients with inflammatory bowel disease (IBD), decreasing the quality of life and significantly worsening the prognosis of the disease. The pathogenesis of anemia in IBD is multifactorial and results mainly from intestinal blood loss in inflamed mucosa and impaired dietary iron absorption. Multiple studies have proposed the use of the polyphenolic compound curcumin to counteract IBD pathogenesis since it has significant preventive and therapeutic properties as an anti-inflammatory agent and very low toxicity, even at high dosages. However, curcumin has been shown to possess properties consistent with those of an iron-chelator, such as the ability to modulate proteins of iron metabolism and decrease spleen and liver iron content. Thus, this property may further contribute to the development and severity of anemia of inflammation and iron deficiency in IBD. Herein, we evaluate the effects of curcumin on systemic iron balance in the dextran sodium sulfate (DSS) model of colitis in C57Bl/6 and BALB/c mouse strains that were fed an iron-sufficient diet. In these conditions, curcumin supplementation caused mild anemia, lowered iron stores, worsened colitis and significantly decreased overall survival, independent of the mouse strain. These findings suggest that curcumin usage as an anti-inflammatory supplement should be accompanied by monitoring of erythroid parameters to avoid exacerbation of iron deficiency anemia in IBD.\n\nCore tipOur study shows that curcumin supplementation of an iron-sufficient diet causes mild anemia, depletes iron stores, worsens colitis and decreases survival, independent of the mouse strain. These findings demonstrate that curcumin may have a potential adverse activity in chronic inflammatory diseases, such as IBD, since the iron chelating properties of curcumin may play a role in iron deficiency and the severity of colitis.

systems biology

A population of bang-bang switches of defective interfering particles makes within-host dynamics of dengue virus controllable

The titre of virus in a dengue patient and the duration of this viraemia has a profound effect on whether or not a mosquito will become infected when it feeds on the patient and this, in turn is a key driver of the magnitude of a dengue outbreak. As mosquitoes require 100-1000 times more virus to become infected than a patient, the transmission of dengue virus from a patient to a mosquito is a vulnerability that may be able to be targeted to improve disease control. The intrinsic variability in the within-host dynamics of viraemias is explored for a population of patients using the method of population of models (POMs). A dataset from 207 patients is used to calibrate 20,000 models for the infection kinetics for each of the four dengue virus serotypes. The effect of adding defective dengue virus interfering particles to patients as a therapeutic is evaluated using the calibrated POMs in a bang-bang optimal control setting.\n\nAuthor summaryDengue virions with deletions or defects in their genomes can be recovered from dengue patients. These defective viruses can only replicate with the assistance of fully functional viruses and they reduce the yield of the fully functional viruses. They are known as defective interfering (DI) particles. By administering additional, defined, DI particles to patients it may be possible to reduce the titre and duration of their viraemia. This, in turn may reduce the severity of the disease and the likelihood that dengue virus will be passed from the patient to a mosquito vector. This study estimates the number of DI particles that would need to be administered, and over what period, to have a significant effect on patient viraemia and subsequent dengue fever severity.

systems biology

Sufficiency analysis of estrogen responsive enhancers using synthetic activators

Multiple regulatory regions bound by the same transcription factor have been shown to simultaneously control a single genes expression. However, it remains unclear how these regulatory regions combine to regulate transcription. Here we test the sufficiency of promoter-distal estrogen receptor (ER)-binding sites (ERBS) for activating gene expression by recruiting synthetic activators in the absence of estrogens. Targeting either dCas9-VP16(10x) or dCas9-p300(core) to ERBS induces H3K27ac and activates nearby expression in a manner similar to an estrogen induction, with dCas9-VP16(10x) acting as a stronger activator. The sufficiency of individual ERBS is highly correlated with their necessity, indicating an inherent activation potential that is associated with the binding of RNA polymerase II and several transcription factors. By targeting ERBS combinations, we found that ERBS work independently to control gene expression when bound by synthetic activators. The sufficiency results contrast necessity assays that show synergy between these ERBS, suggesting that synergy occurs between ERBS in terms of activator recruitment, whereas directly recruiting activators leads to independent effects on gene expression.

systems biology

Network-based genetic profiling, and therapeutic target identification of Thyroid Cancer

Pathogenic mechanisms that underlie malignant follicular thyroid carcinoma (FTC) development are poorly understood. To identify key genes and pathways driving malignant behaviour we employed a system biology-based integrative analyses comparing FTC transcriptomes with a similar but benign lesion, follicular thyroid adenoma (FTA). We identified differentially expressed genes (DEGs) in microarray gene expression datasets (n=52) of FTCs and FTA tissues. Pathway analyses of DEGs using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources revealed significant pathways, and pathway hub genes using protein-protein interactions (PPI). We identified 598 DEGs (relative to FTAs) in FTCs and 12 significant pathways with altered expression in FTC. 10 GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression. PPI analysis identified 12 potential hub genes based on degree and betweenness centrality. Moreover, 10 transcription factors (TFs) were identified that may underlie DEG expression as well as a number of microRNA (miRNAs). Thus, we identified DEGs, pathways, TFs and miRNAs that reflect molecular mechanisms differing between FTC and benign FTA. These may constitute biomarkers that distinguish these lesions and, given the similarities and common origin of the lesions, they may also be indicators of malignant progression potential.

bioinformatics

Biomass segregation between biofilm and flocs improves the control of nitrite-oxidizing bacteria in mainstream partial nitritation and anammox processes

The control of nitrite-oxidizing bacteria (NOB) challenges the implementation of partial nitritation and anammox (PN/A) processes under mainstream conditions. The aim of the present study was to understand how operating conditions impact microbial competition and the control of NOB in hybrid PN/A systems, where biofilm and flocs coexist. A hybrid PN/A moving-bed biofilm reactor (MBBR; also referred to as integrated fixed film activated sludge or IFAS) was operated at 15 {degrees}C on aerobically pre-treated municipal wastewater (23 mgNH4-N{middle dot}L-1). Ammonium-oxidizing bacteria (AOB) and NOB were enriched primarily in the flocs, and anammox bacteria (AMX) in the biofilm. After decreasing the dissolved oxygen concentration (DO) from 1.2 to 0.17 mgO2{middle dot}L-1 - with all other operating conditions unchanged - washout of NOB from the flocs was observed. The activity of the minor NOB fraction remaining in the biofilm was suppressed at low DO. As a result, low effluent NO3- concentrations (0.5 mgN{middle dot}L-1) were consistently achieved at aerobic nitrogen removal rates (80 mgN{middle dot}L-1{middle dot}d-1) comparable to those of conventional treatment plants. A simple dynamic mathematical model, assuming perfect biomass segregation with AOB and NOB in the flocs and AMX in the biofilm, was able to qualitatively reproduce the selective washout of NOB from the flocs in response to the decrease in DO-setpoint. Similarly, numerical simulations indicated that flocs removal is an effective operational strategy to achieve the selective washout of NOB. The direct competition for NO2- between NOB and AMX - the latter retained in the biofilm and acting as a \"NO2-sink\" - was identified by the model as key mechanism leading to a difference in the actual growth rates of AOB and NOB (i.e., NOB < AOB in flocs) and allowing for the selective NOB washout. Experimental results and model predictions demonstrate the increased operational flexibility, in terms of variables that can be easily controlled by operators, offered by hybrid systems as compared to solely biofilm systems for the control of NOB in mainstream PN/A applications.\n\nHighlightsO_LIHybrid PN/A systems provide increased operational flexibility for NOB control\nC_LIO_LIAOB and NOB enrich primarily in the flocs, and AMX in the biofilm (\"NO2-sink\")\nC_LIO_LIAMX use NO2- allowing to differentiate AOB and NOB growth rates\nC_LIO_LIA decrease in DO or an increase in floc removal leads to selective NOB washout from flocs\nC_LIO_LIThe activity of the minor NOB fraction in the biofilm is suppressed at limiting DO\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC=\"FIGDIR/small/480780v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (21K):\norg.highwire.dtl.DTLVardef@cad5deorg.highwire.dtl.DTLVardef@9efef3org.highwire.dtl.DTLVardef@7409feorg.highwire.dtl.DTLVardef@3c1043_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology

Blood-based molecular biomarker signatures in Alzheimer’s disease: Insights from systems biomedicine analyses

Background and objectivesAlzheimers disease (AD) is the progressive neurodegenerative disease characterized by dementia, but no peripheral biomarkers available yet that can detect the AD. This study aimed to identify systems biomarker signatures in the AD through integrative analyses.\n\nMaterials and MethodsWe used two microarray transcriptomics datasets of blood from AD patients to identify differentially expressed genes (DEGs). Geneset and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factor interactions, DEGs-MicroRNAs interactions, protein-drug interactions, and protein subcellular localizations analyses were done on common DEGs.\n\nResultsTotal 25 DEGs were detected between the two datasets. Integration of DEGs with biomolecular networks revealed hub proteins (TUBB, ATF3, NOL6, UQCRC1, SND1, CASP2, BTF3, INPP5K, VCAM1, and CSTF1), TFs (FOXC1, ZNF3, GEMIN7, and SMG9), miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-4270, and mir-4441). The analyses revealed candidate blood based biomarkers in the AD. We evaluated the histone modifications of the identified biomolecules. The hub genes and transcription factors (TFs) revealed that they possess several histone modification sites associated with Alzheimers disease. The protein-drug interactions revealed 10 candidate drugs consisting of antineoplastic (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), dermatologicals (Podofilox), and immunosuppressive agents (Colchicine) that may target the candidate systems biomarkers. The subcellular localization analysis revealed the interactions of the DEGs range from nucleus to plasma membrane through cytosol.\n\nConclusionsThis study presents blood based systems molecular biomarker signatures at RNA and protein levels which might be useful as peripheral biomarkers in the AD. The candidate drugs, histone modification sites, and subcellular localizations will be useful in future drug design in the AD.

systems biology

Hfq CLASH uncovers sRNA-target interaction networks enhancing adaptation to nutrient availability

By shaping gene expression profiles, small RNAs (sRNAs) enable bacteria to very efficiently adapt to constant changes in their environment. To better understand how Escherichia coli acclimatizes to changes in nutrient availability, we performed UV cross-linking, ligation and sequencing of hybrids (CLASH) to uncover sRNA-target interactions. Strikingly, we uncovered hundreds of novel Hfq-mediated sRNA-target interactions at specific growth stages, involving many novel 3UTR-derived sRNAs and a plethora of sRNA-sRNA interactions. We discovered sRNA-target interaction networks that play a role in adaptation to changes in nutrient availability. We characterized a novel 3UTR-derived sRNA (MdoR), which is part of a regulatory cascade that enhances maltose uptake by (a) inactivating repressive pathways that block the accumulation of maltose transporters and (b) by reducing the flux of general porins to the outer membrane. Our work provides striking examples of how bacteria utilize sRNAs to integrate multiple regulatory pathways to enhance nutrient stress adaptation.

systems biology

Optimal metabolic states in cells

Cells, in order to thrive, make efficient use of metabolites, proteins, energy, membrane space, and time. How, for example, should they allocate the available amount of protein to different metabolic pathways or cell functions? To model metabolic behaviour as an economic problem, some flux analysis model, kinetic models, and cell models apply optimality principles. However, due to their different assumptions these models are hard to compare and combine. Benefits and costs of metabolic pathways - e.g. favouring high production fluxes and low metabolite and enzyme cost - can be derived from general fitness objectives such as fast cell growth. To define pathway objectives, we may assume "optimistically" that, given a pathway state, any cell variables outside the pathway will be chosen for maximal fitness. The resulting fitness defines an effective pathway objective as a function of the pathway variables. Here I propose a unified theory that considers kinetic models, describes the set of feasible states as a state manifold and score each state by cost and benefit functions for fluxes, metabolite concentrations, and enzyme levels. To screen the state manifold and to find optimal states, the problem can be projected into flux, metabolite, or enzyme space, where effective cost and benefit functions are used. We reobtain existing modelling approaches such as enzyme cost minimisation or nonlinear versions of Flux Balance Analysis. Due to their common origin, the different approaches share mathematical optimality conditions of the same form. A general theory of optimal metabolic states, as proposed here, provides a logical link between existing modelling approaches and can help justify, interconvert, and combine metabolic optimality problems.

systems biology

The value structure of metabolic states

To improve their metabolic performance, cells need to find compromises between high metabolic fluxes, low enzyme investments, and well-adapted metabolite concentrations. In mathematical models, such compromises can be described by optimality problems that trade metabolic benefit against enzyme cost. While many such modelling frameworks exist, they are often hard to compare and combine. To unify these modelling approaches, I propose a theory that characterises metabolic systems by a value structure, that is, a pattern of local costs and benefits assigned to all elements in the network. The economic values of metabolites, fluxes, and enzymes are interlinked by local balance equations. Formally defined as shadow values, the economic variables serve as local proxies for benefits that arise anywhere in the network, but are represented as local costs or benefits in the reaction of interest. Here I derive economic variables and their balance equations for kinetic, stoichiometric, and cell models. Metabolic value theory provides a new perspective on biochemical networks, defines concepts for comparing and combining metabolic optimality problems, and is useful for semi-automatic, layered, and modular modelling.

systems biology

QTG-Finder: a machine-learning algorithm to prioritize causal genes of quantitative trait loci in Arabidopsis and rice

Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene controlling the trait. With the availability of a rich assortment of genomic and functional genomic data, it is possible to develop a computational method to facilitate faster identification of causal genes. We developed QTG-Finder, a machine-learning algorithm to prioritize causal genes by ranking genes within a quantitative trait locus (QTL). Two predictive models were trained separately based on known causal genes in Arabidopsis and rice. With an independent validation analysis, we demonstrate the models can correctly prioritize about 80% and 55% of Arabidopsis and rice causal genes when the top 20% ranked genes were considered. The models can prioritize different types of traits though at different efficiency. We also identified several important features of causal genes including non-synonymous SNPs at conserved protein sequences, paralog copy number, and being a transporter. This work lays the foundation for systematically understanding characteristics of causal genes and establishes a pipeline to predict causal genes based on public data.

systems biology

Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

Mathematical modeling provides the predictive ability to understand the metabolic reprogramming and complex pathways that mediate cancer cells proliferation. We present a mathematical model using a multiscale, multicellular approach to simulate avascular tumor growth, applied to pancreatic cancer. The model spans three distinct spatial and temporal scales. At the extracellular level, reaction diffusion equations describe nutrient concentrations over a span of seconds. At the cellular level, a lattice-based energy driven stochastic approach describes cellular phenomena including adhesion, proliferation, viability and cell state transitions, occurring on the timescale of hours. At the sub-cellular level, we incorporate a detailed kinetic model of intracellular metabolite dynamics on the timescale of minutes, which enables the cells to uptake and excrete metabolites and use the metabolites to generate energy and building blocks for cell growth. This is a particularly novel aspect of the model. Certain defined criteria for the concentrations of intracellular metabolites lead to cancer cell growth, proliferation and necrosis. Overall, we model the evolution of the tumor in both time and space. Starting with a cluster of tumor cells, the model produces an avascular tumor that quantitatively and qualitatively mimics experimental measurements of multicellular tumor spheroids. Through our model simulations, we can investigate the response of individual intracellular species under a metabolic perturbation and investigate how that response contributes to the response of the tumor as a whole. The predicted response of intracellular metabolites under various targeted strategies are difficult to resolve with experimental techniques. Thus, the model can give novel predictions as to the response of the tumor as a whole, identifies potential therapies to impede tumor growth, and predicts the effects of those therapeutic strategies. In particular, the model provides quantitative insight into the dynamic reprogramming of tumor cells at the intracellular level in response to specific metabolic perturbations. Overall, the model is a useful framework to study targeted metabolic strategies for inhibiting tumor growth.

systems biology

Plant-Necrotroph Co-transcriptome Networks Illuminate a Metabolic Battlefield

A central goal of studying host-pathogen interaction research is to understand how the host and pathogen manipulate each other to promote their own fitness in a pathosystem. Co-transcriptomic approaches can simultaneously analyze dual transcriptomes during infection and provide a systematic map of the cross-kingdom communication between two species. Here we used the Arabidopsis-B. cinerea pathosystem to test how plant host and fungal pathogen interaction at the transcriptomic level during infection. We assessed the impact of natural genetic diversity in the pathogen and plant host by utilization of a collection of 96 isolates of B. cinerea infection on Arabidopsis wild-type and two mutants with jasmonate or salicylic acid compromised immunities. We identified ten B. cinerea gene co-expression networks (GCNs) that encode known or novel virulence mechanisms. We constructed a dual interaction network by combining four host-and ten pathogen-GCNs into a single network, which revealed potential connections between the fungal and plant GCNs involving both novel and conserved mechanisms. These co-transcriptome data shed lights on the potential mechanisms underlying host-pathogen interaction and illustrate the continued need for advancements of in planta analysis of dual-species dynamics.

systems biology