Can you shoot 4839 v

In addition to distinct functions, these COL genes show the highest expression at dawn, in contrast to CO which peaks in the afternoon Ledger et al. CO homologues have been isolated from other plants including woody plants, monocotyledons, and even single-celled Chlamydomonas Song et al.

Several CO -like genes have also been identified in Chrysanthemum spp. However, studies in Pharbitis nil and Medicago truncatula indicated that their COL genes are not involved in the control of FT expression and flowering Hayama et al. However, there are several independent examples about the evolution of FT homologues into floral repressors including BvFT1 in sugar beet, three FT homologues in tobacco, and a specific splicing variant of Brachypodium FT Pin et al.

A natural mutant of woodland strawberry F. FvFT1 is normally expressed diurnally with peaks 4 h and 16 h after dawn; its expression is most effectively induced artificially by FR daylength extension in the mutant. Koskela et al. Here, using transgenic overexpression and RNAi lines of woodland strawberry, we demonstrate that FvCO has a major role in the photoperiodic development of this species.

Seedlings or plants clonally propagated from runner cuttings were used for the experiments as indicated in the text and figure legends. Both flowering time and vegetative development were studied in the experiments. To observe flowering time differences between H4 and transgenic lines, either the number of leaves in the primary leaf rosette before the terminal inflorescence or the number of days before the first open flower was recorded.

In addition, the differentiation of axillary buds into either axillary leaf rosettes called branch crowns or runners stolons was observed. Real-time PCR reactions were performed with three technical replicates and two or three biological replicates as mentioned in the figure legends.

Primer efficiencies were almost equal for all primer pairs Rantanen et al. The destination vectors were pK7WG2D. Both vectors contain green fluorescent protein as a positive selection marker. Vectors carrying overexpression and RNAi constructs were electroporated into Agrobacterium tumefaciens strain GV and transformed into H4 as described previously Oosumi et al.

Several transgenic lines were generated for both constructs. Transgenic lines were selected for the experiments based on their phenotypes and FvCO expression levels. MrBayes 3. Two independent runs were performed, the averaged. WAG Whelan and Goldman matrix was used as a substitution model, and gamma distribution was set for among-site rate variation with the rate category of 4. CrCO from Chlamydomonas reinhardtii was used as the outgroup.

ANOVA was conducted on the averages using the general linear model, and differences between means were analysed by Tukey—Kramer test. All statistical analyses were conducted using the R package ver. Accession numbers of the protein sequences used in the phylogenetic analysis are listed in Supplementary Table S2. In total, nine additional putative CO-like protein sequences longer than amino acids were identified.

These protein sequences were subjected to a phylogenetic analysis to identify putative regulators of flowering time. Other predicted proteins clustered in Group II gene and gene or Group III gene, gene, and gene ; gene and gene made up an isolated clade of their own see Supplementary Fig.

A phylogenetic tree of COL proteins from woodland strawberry and other species. A part of the phylogenetic tree containing Group I genes is shown. The full tree structure is available as Supplementary Fig. The list of species and protein accessions is available in Supplementary Table S2. Numbers on each node indicate posterior probabilities. As the phylogenetic tree indicated that FvCO , gene, and gene belong to Group I, conserved domains of the corresponding protein sequences were subjected to further analysis Supplementary Fig.

Predicted protein sequences of these genes were aligned with other CO-like proteins by ClustalW. The alignment showed that two B-box domains Griffiths et al. S2b , c. FvCO showed the highest level of conservation in the M1—M4 conserved regions. Gene had glutamate to aspartate, tryptophan to leucine, and leucine to isoleucine substitutions in the M1 region found in Group Ia—Ic Glu-X 2 -Ser-Trp-Leu-Leu , while the other two have a conserved sequence Supplementary Fig.

As the phylogenetic tree and further analyses on conserved domains indicated that FvCO was the only Group Ia COL protein encoded by the accessible woodland strawberry genome, functional analysis was mainly focused on FvCO. The diurnal expression patterns of FvCO and FvFT1 were investigated in woodland strawberry accessions with contrasting photoperiodic responses. The gene exhibited rhythmic expression, peaking at the same time as FvCO , whereas the expression of gene did not show a clear rhythm Supplementary Fig.

White and black bars above the panels indicate light and dark periods, respectively. The average expression level of three biological replicates is shown for each time point, all normalized to the expression level of FvMSI1.

Error bars indicate the SD. Our data indicated that FvCO expression peaked at dawn under different photoperiods, so we tested whether the dawn signal was critical for the timing of its expression. Under DD conditions, in contrast to the LD control, FvCO expression continued to rise after the subjective dawn the beginning of the light period in the LD control and stayed high during the next 8 h Fig.

These results suggest that the up-regulation of FvCO takes place in darkness and the dawn signal is needed for its down-regulation. In the overexpression lines, strong up-regulation of FvCO was observed especially in the evening ZT16 when its expression level in the wild-type H4 is low.

Flowering phenotypes of FvCO transgenic plants. Samples were taken at ZT4 and ZT We recorded the number of leaves in the primary leaf rosette before the terminal inflorescence in plants that had been subjected to LDs or SDs. Overexpression lines produced slightly fewer leaves before the terminal inflorescence compared with wild-type plants under LDs, whereas a strong promotion of flowering was observed in overexpression lines under SDs Figs 3C , D , 4A ; Supplementary Table S3.

Vegetative and generative development in transgenic lines. NF, no flowering. Axillary buds that did not differentiate to runners or branch crowns remained dormant. Flower-inducing conditions promote the differentiation of axillary buds to axillary leaf rosettes called branch crowns, while in non-inductive conditions vegetative reproduction through runners takes place.

To gain insight into the effect of FvCO and FvFT1 on vegetative development, we studied the differentiation of axillary buds of the primary leaf rosette. In H4, most axillary buds differentiated to runners in SD conditions and only a few branch crowns were observed, whereas the effect of LDs was opposite Fig.

A clear photoperiodic response was also observed in FvCO overexpression lines, although they tended to produce fewer runners and more branch crowns than the wild type. In all RNAi lines, roughly two-thirds of axillary buds differentiated to runners and only very few buds produced branch crowns in both photoperiods.

To explore further the effect of FvCO on the balance between generative and vegetative development, we observed the cumulative number of inflorescences and runners in generative plant materials. FvCO overexpression plants produced slightly more new inflorescences than the wild type Fig. In contrast to the intense flowering, runner production was strongly suppressed in overexpression and wild-type plants, whereas all RNAi lines continuously produced new runners at the rate of approximately one runner per week Fig.

FvCO controls the balance between vegetative and generative development. To obtain generative plant materials in both wild-type H4 and transgenic lines, runner cuttings of flowering plants were rooted. Next, we examined the expression of flowering time genes in FvCO transgenic lines. Error bars indicate the SA. ZT, time h after dawn. Consistent with the observed differences in flowering time, FvAP1 was down-regulated in RNAi lines and highly activated in the stronger SD-grown FvCO overexpression line compared with H4 at 3 weeks after the beginning of the treatment Fig.

However, an equally high FvAP1 expression level was detected in wild-type and overexpression lines in LDs at this time point, but in another experiment, at a 1 week earlier time point, an elevated FvAP1 expression level was detected in overexpression lines compared with H4 in LDs Supplementary Fig.

The average expression level of three biological replicates is shown, all normalized to the expression level of FvMSI1 , and H4 SD is set as 1. Therefore, we studied the diurnal expression patterns of strawberry homologues of GI and FKF1 , genes which encode regulators of FT expression in Arabidopsis Sawa et al.

In the H4 accession under 12 h SDs, the expression of FvGI increased rapidly in the morning and stayed high until ZT12, after which time there was a rapid drop in expression Fig. Plants typically contain a large COL gene family; for example Arabidopsis and rice have 17 and 16 genes, respectively, while 26 genes have been identified in soybean Griffiths et al. A few of these genes encode floral activators, but also repressors as well as regulators, with no effect on flowering Putterill et al.

Here, we have identified 10 COL genes in woodland strawberry and shown that, based on phylogenetic analysis Fig. We have also shown that it plays a major role in the photoperiodic control of reproductive and vegetative development in this species.

Thus, our results do not support the role of FvFT3 in flower induction in H4. In the monocots rice and spring barley, however, the closest CO homologues Hd1 and HvCO2, respectively, activate flowering Izawa et al.

What causes these diverse functions of CO homologues in flowering time regulation is an interesting open question. FvCO overexpression plants, however, produced slightly fewer runners than H4 and, when these plants were moved from SDs to flower-inductive LD conditions, their runner production slowed down earlier than in H4. Consistent with this finding, mutant analysis in A.

Furthermore, approximately genes in A. Accordingly, our modeling approach indicated that the clock is essential to grasp the dynamics of plant growth in a rythmic environment. We have generated two independent experimental data sets, one for parameter estimation experiment 1; experiment 2, treatments A and B , and one for validation of the model experiment 2, treatments C and D; experiment 3.

This procedure is a central requirement for rigorous testing of a mathematical model. The results from model validation were encouraging, indicating that the model is based on plausible principles. Furthermore, the adaptive behavior of the model reflected natural plant growth remarkably well. Hence, the structure of the model as a whole is able to grasp the dynamics of resource allocation and differential growth in plants.

This deviation may be due to emergency programs that allow plants to survive and to grow under minimal P i conditions that would lead to growth arrest or death of the virtual plant.

Obviously, our model does not reflect the full adaptive potential of real plants like, for example, the P i starvation response [ 94 ]. However, our goal is to simulate plant growth within physiological limits relevant for agriculture and not under extreme conditions. In order to test the roles of the central components of the model, individual submodels were either removed or modified to reveal their role in the global behaviour of the model.

Removal of either the starch reserves or of the clock resulted in the death of the plant due to sugar depletion during the night period data not shown. This emphasizes the drastic evolutionary constriction resulting from the rhythmic environment onto plants.

We conclude that starch reserves and a circadian oscillator are indespensable for modeling of plant growth in a rythmic environment. More subtle changes in photoperiod Figs H-J in S1 File revealed another important feature of plant metabolism: The circadian clock has limited flexibility in its ability to adapt to different photoperiod lengths, a fact that may be related to the molecular components of the clock [ 95 ].

It is interesting to note that similar effects on growth and survival have been observed when Arabidopsis plants were grown with inappropriate photoperiods, either due to mutations in components of the clock, or due to manipulation of photoperiod [ 60 ], thus documenting the central importance of the clock for plant fitness and survival. Interestingly, recent evidence documented an intimate association of the clock with carbohydrate metabolism [ 96 ], consistent with the pivotal role of the clock in coordinating the switch between phototrophic metabolism during the day, and heterotrophic metabolism during the night.

As a central element of the model, we addressed the importance of phloem transport, which could potentially represent a limiting factor in plant partitioning. Indeed, increasing or decreasing transport resistance of the phloem had a strong influence, in particular on growth of the heterotrophic root, which represents the major sink for carbohydrate resources in vegetative plants, and therefore depends strongly on efficient carbohydrate supply Fig 8.

In this context it is interesting to note that pathogens that reside in the phloem and interfere with phloem transport lead to comparable negative growth effects that are accompanied with retention of resources in source tissues, and depletion in the sinks [ 97 ].

Light and mineral nutrients are the primordial exogenous determinants of plant growth. Hence, we simulated shoot and root growth under conditions of simultaneous sugar and P i shortage in different combinations. Under these conditions, we obtained predictions that are in good agreement with the expected compromise that plants are forced to reach in their respective allocation of resources to the root and the shoot.

For example, relative root growth, that was strongly affected by growth reductions under low light conditions, recovered partially when, in addition, P i became limiting Fig 9. These results show that our model can simultaneously integrate environmental information from light and P i supply and reach balanced growth strategies.

The model presented here indicates that balanced growth may be an emergent feature of plants. This is an important difference to teleonomic models, in which the balanced growth behavior or any other desired behavior is defined as part of the model.

In order to evaluate the dynamic behavior of our model relative to previously published models of growth and partitioning, we chose to compare it with a standard growth model described by Thornley [ 24 ].

For example, the simulations of P i content Fig F in S2 File , panels d,e and of maximal growth under favorable growth conditions Fig E in S2 File were remarkably close to the experimental data. Hence, a central aspect of adaptive plant growth cannot be reproduced with this model. The good results of the parameter fitting for the submodels of P i uptake Fig B in S2 File , and for shoot and root growth Fig E in S2 File , suggest that the inversed RF does not result from these submodels.

However, the prediction of sugar levels in the shoot and the root were remarkably similar between the two models with largely constant average sugar levels compare Figs D and I in S2 File , except for the diurnal oscillations in our model. This latter behavior is consistent with the virtually stable levels of sucrose in A. Considering the multiple differences between the two models, it appears difficult to pinpoint the reason for their divergent behavior.

Likely, the characteristics of the predictions represent emergent features, hence their differences cannot easily be traced back to a single causal component of one or the other model.

Hence, we decided to carry out a global analysis to compare the behavior of the two models. An important difference between the two models is the number of their parameters. How does this affect the behavior of the two models? However, due to inherent fundamental differences between the two models, the solutions from his model are not part of the solutions of our model.

Importantly, 21 of our parameters were fixed S1 Table before global fitting and the others were varied in narrow biological intervals see S2 Table.

This limits the potentially beneficial effect of the additional degrees of freedom brought about by additional parameters, and it may even compromise the results of global fitting because of the additional complexity and of unexpected interactions between the submodels.

This involved the calculation using a genetic algorithm of a family of parameter sets by minimizing the relative quadratic error RE between simulated and observed values for 1 shoot and root growth, 2 P i concentration, and 3 soluble sugar concentration.

For each optimized parameter set, a point in the 3-dimensional space was obtained the value of these three quadratic errors. The set of all these points is called a Pareto front.

Mathematical analysis revealed that both models produced initial inversions of RF that later turned to a growth behavior consistent with balanced growth. However, the dynamics in the two models were fundamentally different. We describe a combined experimental and modeling approach to examine balanced growth in Petunia hybrida. It is based on a mechanistic model that features the core metabolic pathways involved in the generation and distribution of carbohydrate resources, and in nutrient uptake from the soil.

Our model involves a day-and-night cycle, starch reserves, and realistic regulatory principles for the conversion of sugars to starch during the day, and for starch degradation during the dark phase. Our functional tests of the model show the necessity of all implicated components, including a circadian clock that is required for the coordination of plant metabolism with the environment.

Our model can be used and further developed as a tool for the interpretation of the complex phenotypes of mutants in starch metabolism and in other aspects of primary metabolism and resource partitioning. In addition, such mathematical models will be essential tools for molecular breeders in attempts to manipulate starch production, or other aspects of resource partitioning.

Such strategies have been notoriously difficult and often lead to unexpected results, mostly due to the inherent complexity of the underlying pathways and their interactions in multidimensional networks [ 99 ]. As stated by Shachar-Hill [ ] "The major challenges to success in applying network flux analysis to plant metabolic engineering center on complexity and ignorance". While molecular-genetic studies improve our understanding of the components of metabolic pathways and their individual functions, mathematical modeling is the method of choice to address their interactions in complex networks, and to examine what global properties emerge from such networks.

Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Plants are highly plastic in their potential to adapt to changing environmental conditions. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Funding: This work was supported by SystemsX.

Introduction For optimal development of the plant as a whole, root and shoot biomass have to be balanced. Material and Methods Experimental procedures Plant growth conditions. Analysis of plant architecture. Determination of plant phosphate content. Sugar determination. Determinations of physical parameters. Experimental design and plant treatments.

Download: PPT. Fig 1. Adaptive response of P. Characterization of the model The mathematical model for plant growth is inspired by several previous models in particular of Thornley and Dewar [ 24 , 25 , 36 ], and incorporates additional features that are essential for a realistic understanding of plant growth under natural conditions.

General assumptions and hypotheses. Fig 2. Schematic representation of the plant model and its architecture. Hypotheses and assumptions concerning plant architecture. Hypothesis 1 Leaf architecture. Hypothesis 2 Photosynthetically active leaf surface. Hypothesis 3 Root surface active in nutrient uptake. To account for this behavior, the root surface active in P i uptake in the model is initially proportional to the root volume in the young plant , and later tends towards a constant S max with the root volume: The parameter of M as is denoted m as.

Hypothesis 4 Phloem tube length. Hypothesis 5 Number of phloem tubes. Shoot and root growth. Hypotheses and assumptions concerning substrate pools. The following equations reflect the mass balance for the pools of P i , soluble carbohydrate, and starch: Soluble sugar quantity in the shoot: Soluble sugar quantity in the root: P i quantity in the soil: P i quantity in roots: P i quantity in the shoot: where P t is the rate of photosynthesis, the conversion rate of starch into soluble sugar in the shoot compartment, the quantity of sugar anabolised to build 1 cm 3 of shoot roots , the sugar transport rate from shoot to root, R sh R r the shoot root respiration rate, U ph t the P i uptake rate from the soil, the P i transport rate from root to shoot and the P i transport rate from shoot to root.

Hypothesis 6 Phosphate uptake. According to [ 48 ], P i uptake rate per unit of root absorbing surface corresponds to: Where U max is the maximal uptake rate per unit of root active surface and M U a Monod function with parameter m U. Hypothesis 7 Photosynthesis. Hypothesis 8 Rate of photosynthesis. Hypothesis 9 Fraction of carbohydrate stored as starch.

Hypothesis 10 Regulation of the fraction of carbohydrate stored as starch. Hypothesis 11 Starch degradation. Hypothesis 12 Sugar transport from the shoot to the root. Hypothesis 13 Sugar concentrations in the phloem.

Hypothesis 14 Rate of sugar transport between shoot and root. Hypothesis 15 Water flux from the root to the shoot. Hypothesis 16 Phosphate transport from the root to the shoot. Hypothesis 17 Phosphate transport from the shoot to the root. Hypothesis 18 Costs of respiration. The respiration rate in the shoot R sh t Table 2 , Eq 6 and the root R r t Table 2 , Eq 7 corresponds to the sum of maintenance respiration t and , growth respiration and and transport costs and : As suggested by [ 77 , 78 ], maintenance respiration per unit of volume is assumed to be a linear function of soluble carbohydrate concentration: Growth respiration is proportional to growth rate: Relevant transport processes include, among others, P i uptake into root epidermis and phloem loading with photosynthates in the shoot.

Model implementation and parameter estimation The model was implemented in C and compiled in Matlab. Comparison of the two models with Pareto fronts At the structural level, Thornley's and our model are similar, i. Fig 4. Parameter fitting under two different phosphate levels. Fig 5. Model validation and evaluation of adaptive potential of shoot and root growth towards increasing P i supply.

Fig 6. Model validation and evaluation of adaptive potential of shoot and root growth towards decreasing P i supply. Fig 7. Model validation and evaluation of the adaptive potential of plants to a range of different P i concentrations.

Results Adaptive regulation of root:shoot ratio in Petunia hybrida To explore the adaptive potential of P. Modeling of resource partitioning and plant growth In order to address the relative growth of the shoot and the root and their interactions in a systematic and integrated way, we developed a mechanistic mathematical model that includes submodels for photosynthesis, nutrient uptake and transport, and which is embedded in a realistic environment.

Assessing the roles of the submodels and of exogenous cues in the global behaviour of the model With the encouraging results from parameter fitting and model validation, we set out to test the roles of individual components of the model.

Fig 8. Simulation of growth dynamics as a function of phloem resistance. Fig 9. Simulation of the competing effects of limited light irradiance and P i starvation on plant growth and root fraction.

Global quantitative assessment of the two models In order to compare the two models in a more quantitative way, Pareto fronts for plant volume RE V , total P i content RE ph , and soluble sugar content RE su were calculated see Materials and Methods for a detailed description. The dynamics of balanced growth Upon closer inspection, we noticed that the RF exhibited an initial unexpected inversion also in the experimental data set, although only for a short transitional period of 4 days, after which the RF developed in a way consistent with balanced growth, i.

Discussion Adaptive growth responses of plants Plants as sessile organisms have evolved numerous adaptive strategies to cope with environmental stresses such as heat, drought, shading, and nutrient limitation. Concepts of resource partitioning in plants A global understanding of resource partitioning requires an integrated view that includes all plant parts and all involved mechanisms.

A mathematical model to address the dynamics of shoot and root growth In order to understand how the physiological activities of the root and the shoot influence each other, and to address how they impact on growth of the plant as a whole, we took a combined experimental and modeling approach.

Validation and predictions of the model We have generated two independent experimental data sets, one for parameter estimation experiment 1; experiment 2, treatments A and B , and one for validation of the model experiment 2, treatments C and D; experiment 3. Evaluation of critical components of the model In order to test the roles of the central components of the model, individual submodels were either removed or modified to reveal their role in the global behaviour of the model.

Conclusions and Outlook We describe a combined experimental and modeling approach to examine balanced growth in Petunia hybrida.

Supporting Information. S1 File. Model building and parameter estimation. S2 File. Comparison with the model of Thornley S3 File. Mathematical analysis of root fraction in Thornley's model. S1 Table. Fixed parameters of the mathematical model. S2 Table. Fitted parameters of the mathematical model. S3 Table. S4 Table. Original data for Figs 1 — 7. References 1. Effects of photon flux density on carbon partitioning and rhizosphere carbon flow of Lolium perenne. J Exp Bot. Poorter H, Nagel O.

The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Aust J Plant Physiol. Walter A, Nagel KA. Root growth reacts rapidly and more pronounced than shoot growth towards increasing light intensity in tobacco seedlings. Plant Signaling and Behavior. Partitioning of shoot and root dry-matter and carbohydrates in bean plants suffering from phosphorus-, potassium- and magnesium-deficiency.

How do plants respond to nutrient shortage by biomass allocation? Trends Plant Sci. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. Brouwer R. Distribution of dry matter in the plant. Netherland Journal of Agricultural Science. View Article Google Scholar 8. A shoot-root partitioning model. Ann Bot. Shipley B, Meziane D. The balanced-growth hypothesis and the allometry of leaf and root biomass allocation. Funct Ecol.

Concepts of modelling carbon allocation among plant organs. Functional-structural plant modelling in crop production. Wageningen: Springer; Agriculture and the new challenges for photosynthesis research.

Smith AM, Stitt M. Coordination of carbon supply and plant growth. Plant Cell Environ. Patrick JW. Phloem unloading: Sieve element unloading and post-sieve element transport. Kang MZ, de Reffye P. A mathematical approach estimating source and sink functioning of competing organs. Plant and crop modelling: a mathematical approach to plant and crop physiology. Oxford: Clarendon Press; Mathematical models in agriculture: Quantitative methods for the plant, animal and ecological sciences.

Trowbridge: Cromwell Press; Gene targeting approaches using positive-negative selection and large flanking regions. Plant MolBiol. Allocation of new growth between shoot, root and mycorrhiza in relation to carbon, nitrogen and phosphate supply: Teleonomy with maximum growth rate.

Journal of Theoretical Biology. Agren GI, Franklin O. Root: shoot ratios, optimization and nitrogen productivity. A model of shoot-root partitioning with optimal growth. Modelling crop growth and biomass partitioning to shoots and roots in relation to nitrogen and water availability, using a maximization principle.

Model description and validation. Plant Soil. Minchin PEH. Mechanistic modelling of carbon partitioning. Thornley JHM. Grassland dynamics. Modelling shoot-root relations: the only way forward? A balanced quantitative model for root: shoot ratios in vegetative plants. View Article Google Scholar Shoot-root allocation with respect to C, N and P—an investigation and comparison of resistance and teleonomic models.

Influence of light availability on leaf structure and growth of two Eucalyptus globulus ssp globulus provenances. Tree Physiology. Does the photosynthetic light-acclimation need change in leaf anatomy? Responses of leaf structure and photosynthetic properties to intra-canopy light gradients: a common garden test with four broadleaf deciduous angiosperm and seven evergreen conifer tree species.

Modelling biomass production and yield of horticultural crops: a review. Sci Hortic. Phosphorus and nitrogen regulate arbuscular mycorrhizal symbiosis in Petunia hybrida. Identification of temporally and spatially phosphate-starvation responsive genes in Glycine max. Plant Sci. Plant Cell. Monosaccharides and Derivatives. In: Bergmeyer H-U, editor. Methods of Enzymatic Analysis. Weinheim: Verlag Chemie; Beutler H-O. Dewar RC. Niklas KJ.

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