Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization

[EN] Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological coin portents. The increasing complexity of the circuits being designed requires performing approximations and model reductions to get handy models. Parameter estimation in these models remains a challenging problem that has usually been addressed by optimizing the weighted combination of different prediction errors to obtain a single solution. The single-objective approach is inadequate to incorporate different kinds of experiments, and to identify parameters for an ensemble of biological circuit models. We present a methodology based on multi-objective optimization to perform parameter estimation that can fully harness to ensembles of local models for biological circuits. The methodology uses a global multi-objective evolutionary algorithm and a multi-criteria decision making strategy to select the most suitable solutions. Our approach finds an approximation to the Pareto optimal set of model parameters that correspond to each experimental scenario. Then, the Pareto set was clustered according to the experimental scenarios. This, in turn, allows to analyze the sensitivity of model parameters for different scenarios. Finally, we show the methodology applicability through the case study of a genetic incoherent feed-forward circuit, under different concentrations of the inducer input signal. (C) 2016 IFAC (International Federation Of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This work is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1). Y.B. thanks grant FP/2013-3242 of Universitat Politecnica de Valencia and Becas Iberoamerica of Santander Group, Spain 2015. G.R.M. thanks the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil. A.V. thanks the Max Planck Society, the CSBD and the MPI-CBG. We are grateful to Dr. C,Bauerl and Dr, D. Provencio at the SB2CLab for their help in plasmid construction and getting experimental data. Also to Dr. V. Monedero at IATACSIC for allowing us to use the POLARstar plate reader at his lab,Boada-Acosta, YF.; Vignoni, A.; Reynoso Meza, G.; Picó, J. (2016). Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization. IFAC-PapersOnLine. 49(26):77-82. https://doi.org/10.1016/j.ifacol.2016.12.106S7782492

R e troalimentación biológica y relajación en pacientes con enfermedad renal crónica terminal en tratamiento de hemodiálisis

El objetivo de este estudio fue evaluar la utilidad de la retroalimentación biológica y la relajación para reducir ansiedad y estrés en pacientes que se encontraban en tratamiento médico de hemodiálisis por enfermedad renal crónico terminal (ERCT) utilizando estrategias derivadas del modelo cognitivo conductual. Participaron ocho pacientes del servicio de Nefrología del Hospital Juárez de México a los que se les aplicaron el inventario de ansiedad IDARE y una escala subjetiva de estrés antes y después de realizar el programa. El diseño fue de caso único con línea base retrospectiva y replicación intrasujeto. Se trabajó con cada paciente durante ocho sesiones en las que se les instruyó sobre el propósito de la retroalimentación y recibieron instrucciones para relajarse ofreciéndoles de manera visual sus cifras de presión arterial y frecuencia cardiaca. Cada paciente disminuyó sus cifras de presión arterial sistólica y diastólica, siendo estas reducciones estadísticamente significativas. Se compararon los puntajes de las escalas de ansiedad y estrés antes y después del tratamiento existiendo reducción en los puntajes de ambas para cada sujeto, siendo estas reducciones estadísticamente significativas, concluyendo que el uso de relajación combinada con retroalimentación biológica resultó eficaz.Palabras clave: Ansiedad, Estrés, Relajación, Retroalimentación biológica (biofeedback), Enfermedad Renal Crónica

La universidad humanista

Esta obra, titulada La universidad humanista, es fruto de la colaboración entre dos instituciones centenarias: la Universidad de Santiago de Compostela, una de las universidades más antiguas de España que ofrece educación superior desde 1495 y la Universidad Autónoma del Estado de México, heredera del Instituto Científico y Literario Autónomo, fundado en 1828, a escasos siete años de que México naciera como entidad política independiente. Ambas instituciones, con un pasado muy diferente, se encuentran hermanadas por la misma vocación de futuro y las preocupaciones propias de las universidades del siglo XXI.Universidad Autónoma del Estado de México y Universidad de Santiago de Compostel

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case

Background: Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized. Results: We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior. Conclusion: The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.Research in this area is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1-R). Yadira Boada thanks grant FPI/2013-3242 of Universitat Politecnica de Valencia; Gilberto Reynoso-Meza gratefully acknowledges the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) for the development of this work. We are grateful to Alejandra Gonzalez-Bosca for her collaboration on this topic while doing her Bachelor thesis, and to Dr. Jose Luis Pitarch from Universidad de Valladolid for his advise in algorithmic implementations and for proof reading the manuscript.Boada Acosta, YF.; Reynoso Meza, G.; Picó Marco, JA.; Vignoni, A. (2016). Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case. BMC Systems Biology. 10:1-19. https://doi.org/10.1186/s12918-016-0269-0S11910ERASynBio. Next steps for european synthetic biology: a strategic vision from erasynbio. Report, ERASynBio. 2014. https://www.erasynbio.eu/lw_resource/datapool/_items/item_58/erasynbiostrategicvision.pdf .Way J, Collins J, Keasling J, Silver P. Integrating biological redesign: Where synthetic biology came from and where it needs to go. Cell. 2014; 157(1):151–61.Canton B, Labno A, Endy D. Refinement and standardization of synthetic biological parts and devices. Nat Biotechnol. 2008; 26(7):787–93.De Lorenzo V, Danchin A. Synthetic biology: discovering new worlds and new words. EMBO Rep. 2008; 9(9):822–7.Church GM, Elowitz MB, Smolke CD, Voigt CA, Weiss R. Realizing the potential of synthetic biology. Nat Rev Mol Cell Biol. 2014; 15(4):289–94.Takahashi CN, Miller AW, Ekness F, Dunham MJ, Klavins E. A low cost, customizable turbidostat for use in synthetic circuit characterization. ACS Synth Biol. 2015; 4(1):32–8. [doi: 10.1021/sb500165g ].Cooling MT, Rouilly V, Misirli G, Lawson J, Yu T, Hallinan J, Wipat A. Standard virtual biological parts: a repository of modular modeling components for synthetic biology. Bioinformatics. 2010; 26(7):925–31.Medema MH, van Raaphorst R, Takano E, Breitling R. Computational tools for the synthetic design of biochemical pathways. Nat Rev Microbiol. 2012; 10(3):191–202.Marchisio MA, Stelling J. Automatic design of digital synthetic gene circuits. PLoS Comput Biol. 2011; 7(2):e1001083. [doi: 10.1371/journal.pcbi.1001083 ].Rodrigo G, Carrera J, Landrain TE, Jaramillo A. Perspectives on the automatic design of regulatory systems for synthetic biology. FEBS Lett. 2012; 586(15):2037–42.Crook N, Alper HS. Model-based design of synthetic, biological systems. Chem Eng Sci. 2013; 103:2–11.Jayanthi S, Nilgiriwala K, Del Vecchio D. Retroactivity controls the temporal dynamics of gene transcription. ACS Synth Biol. 2013; 2(8):431–41.Mélykúti B, Hespanha JP, Khammash M. Equilibrium distributions of simple biochemical reaction systems for time-scale separation in stochastic reaction networks. J R Soc Interface. 2014; 11(97):20140054.Oyarzún DA, Lugagne JB, Stan GB. Noise propagation in synthetic gene circuits for metabolic control. ACS Synth Biol. 2015; 4(2):116–25. [doi: 10.1021/sb400126a ].Picó J, Vignoni A, Picó-Marco E, Boada Y. Modelado de sistemas bioquímicos: De la ley de acción de masas a la aproximación lineal del ruido. Revista Iberoamericana de Automática e Informática Industrial RIAI. 2015; 12(3):241–52.Feng X-j-J, Hooshangi S, Chen D, Li G, Weiss R, Rabitz H. Optimizing genetic circuits by global sensitivity analysis. Biophys J. 2004; 87(4):2195–202.Dasika MS, Maranas CD. Optcircuit: An optimization based method for computational design of genetic circuits. BMC Syst Biol. 2008; 2:24.Rodrigo G, Carrera J, Jaramillo A. Genetdes. Bioinformatics. 2007; 23(14):1857–8.Otero-Muras I, Banga JR. Multicriteria global optimization for biocircuit design. 2014. arXiv preprint arXiv:1402.7323.Banga JR. Optimization in computational systems biology. BMC Syst Biol. 2008; 2:47.Sendin J, Exler O, Banga JR. Multi-objective mixed integer strategy for the optimisation of biological networks. IET Syst Biol. 2010; 4(3):236–48.Miller M, Hafner M, Sontag E, Davidsohn N, Subramanian S, Purnick PE, Lauffenburger D, Weiss R. Modular design of artificial tissue homeostasis: robust control through synthetic cellular heterogeneity. PLoS Comput Biol. 2012; 8(7):1002579.Ellis T, Wang X, Collins JJ. Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat Biotechnol. 2009; 27(5):465–71.Koeppl H, Hafner M, Lu J. Mapping behavioral specifications to model parameters in synthetic biology. BMC Bioinforma. 2013; 14(Suppl 10):9.Chiang AWT, Hwang M-JJ. A computational pipeline for identifying kinetic motifs to aid in the design and improvement of synthetic gene circuits. BMC Bioinforma. 2013; 14 Suppl 16:5.Ma W, Trusina A, El-Samad H, Lim WA, Tang C. Defining network topologies that can achieve biochemical adaptation. Cell. 2009; 138(4):760–73.Chiang AWT, Liu W-CC, Charusanti P, Hwang M-JJ. Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters. BMC Syst Biol. 2014; 8:4.Reynoso-Meza G, Blasco X, Sanchis J, Martínez M. Controller tuning using evolutionary multi-objective optimisation: current trends and applications. Control Eng Pract. 2014; 28:58–73.Alon U. An Introduction To Systems Biology. Design Principles of Biological Circuits. London: Chapman & Hall/ CRC Mathematical and computational Biology Series; 2006.Elowitz MB, Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature. 2000; 403(6767):335–8.Hsiao V, de los Santos ELC, Whitaker WR, Dueber JE, Murray RM. Design and implementation of a biomolecular concentration tracker. ACS Synth Biol. 2015; 4(2):150–61. [doi: 10.1021/sb500024b ].Franco E, Giordano G, Forsberg P-O, Murray RM. Negative autoregulation matches production and demand in synthetic transcriptional networks. ACS Synth Biol. 2014; 3(8):589–99. [doi: 10.1021/sb400157z ].Strelkowa N, Barahona M. Switchable genetic oscillator operating in quasi-stable mode. J R Soc Interface. 2010; 7(48):1071–82.Basu S, Mehreja R, Thiberge S, Chen MT, Weiss R. Spatiotemporal control of gene expression with pulse-generating networks. Proc Natl Acad Sci U S A. 2004; 101(17):6355–60.Bleris L, Xie Z, Glass D, Adadey A, Sontag E, Benenson Y. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Mol Syst Biol. 2011; 7(519):1–12. [doi: 10.1038/msb.2011.49 ].Hart Y, Antebi YE, Mayo AE, Friedman N, Alon U. Design principles of cell circuits with paradoxical components. Proc Natl Acad Sci. 2012; 109(21):8346–51.Zhang Q, Bhattacharya S, Andersen ME. Ultrasensitive response motifs: basic amplifiers in molecular signalling networks. Open Biol. 2013; 3(4):130031.Weber M, Buceta J, Others. Dynamics of the quorum sensing switch: stochastic and non-stationary effects. BMC Syst Biol. 2013; 7(1):6.Womelsdorf T, Valiante TA, Sahin NT, Miller KJ, Tiesinga P. Dynamic circuit motifs underlying rhythmic gain control, gating and integration. Nat Neurosci. 2014; 17(8):1031–9.Arpino JAJ, Hancock EJ, Anderson J, Barahona M, Stan G-BVB, Papachristodoulou A, Polizzi K. Tuning the dials of synthetic biology. Microbiology. 2013; 159(Pt 7):1236–53.Zagaris A, Kaper HGG, Kaper TJJ. Analysis of the computational singular perturbation reduction method for chemical kinetics. J Nonlinear Sci. 2004; 14(1):59–91.Anderson J, Chang Y-C-C, Papachristodoulou A. Model decomposition and reduction tools for large-scale networks in systems biology. Automatica. 2011; 47(6):1165–74.Prescott TP, Papachristodoulou A. Layered decomposition for the model order reduction of timescale separated biochemical reaction networks. J Theor Biol. 2014; 356:113–22.Hancock EJ, Stan GB, Arpino JAJ, Papachristodoulou A. Simplified mechanistic models of gene regulation for analysis and design. J R Soc Interface. 2015; 12(108).Miettinen K, Vol. 12. Nonlinear Multiobjective Optimization. Boston: Kluwer Academic Publishers; 1999.Miettinen K, Ruiz F, Wierzbicki AP. Introduction to multiobjective optimization: interactive approaches. In: Multiobjective Optimization. Berlin: Springer: 2008. p. 27–57.Deb K, Bandaru S, Greiner D, Gaspar-Cunha A, Tutum CC. An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering. Appl Soft Comput. 2014; 15(0):42–56.Ang J, Ingalls B, McMillen D. Probing the input-output behavior of biochemical and genetic systems: System identification methods from control theory In: Johnson ML, Brand L, editors. Methods in Enzymology. Academic Press: 2011. p. 279–317, doi: 10.1016/B978-0-12-381270-4.00010-X .Mattson CA, Messac A. Pareto frontier based concept selection under uncertainty, with visualization. Optim Eng. 2005; 6(1):85–115.Reynoso-Meza G, Sanchis J, Blasco X, Martínez M. Design of continuous controllers using a multiobjective differential evolution algorithm with spherical pruning. Appl Evol Comput. 2010;532–541.Reynoso-Meza G, García-Nieto S, Sanchis J, Blasco X. Controller tuning using multiobjective optimization algorithms: a global tuning framework. IEEE Trans Control Syst Technol. 2013; 21(2):445–58.Reynoso-Meza G, Sanchis J, Blasco X, Herrero JM. Multiobjective evolutionary algortihms for multivariable PI controller tuning. Expert Syst Appl. 2012; 39:7895–907.Anderson C. Anderson promoter collection [online]. 2006. http://parts.igem.org/Promoters/Catalog/Anderson . Accesed 20 Feb 2015.Salis HM, Mirsky EA, Voigt CA. Automated design of synthetic ribosome binding sites to control protein expression. Nat Biotechnol. 2009; 27(10):946–50.Egbert RG, Klavins E. Fine-tuning gene networks using simple sequence repeats. PNAS. 2012; 109(42):16817–22. [doi: 10.1073/pnas.1205693109 ].Hair JF, Suárez MG. Análisis Multivariante vol. 491. Madrid: Prentice Hall; 1999.Blasco X, Herrero JM, Sanchis J, Martínez M. A new graphical visualization of n-dimensional pareto front for decision-making in multiobjective optimization. Inf Sci. 2008; 178(20):3908–24. [doi: 10.1016/j.ins.2008.06.010 ].Reynoso-Meza G, Blasco X, Sanchis J, Herrero JM. Comparison of design concepts in multi-criteria decision-making using level diagrams. Inform Sci. 2013; 221:124–41.Goentoro L, Shoval O, Kirschner MW, Alon U. The incoherent feedforward loop can provide fold-change detection in gene regulation. Mol Cell. 2009; 36(5):894–9.Rodrigo G, Elena SF. Structural discrimination of robustness in transcriptional feedforward loops for pattern formation. PloS ONE. 2011; 6(2):16904.Kim J, Khetarpal I, Sen S, Murray RM. Synthetic circuit for exact adaptation and fold-change detection. Nucleic Acids Res. 2014; 42(2):6078–89. [doi: 10.1093/nar/gku233 ].Chelliah V, Juty N, Ajmera I, Ali R, Dumousseau M, Glont M, Hucka M, Jalowicki G, Keating S, Knight-Schrijver V, et al. Biomodels: ten-year anniversary. Nucleic Acids Res. 2015; 43(D1):542–8.Ang J, Bagh S, Ingalls BP, McMillen DR. Considerations for using integral feedback control to construct a perfectly adapting synthetic gene network. J Theor Biol. 2010; 266(4):723–38.Biobrick Foundation. 2006. Part Registry [online]. http://partsregistry.org/ . Accessed 20 Feb 2015.BIOSS. 2006. BIOSS Toolbox [online]. http://www.bioss.uni-freiburg.de/cms/toolbox-home.html . Accessed 20 Feb 2015.BioFab. 2006. International Open Facility Advancing Biotechnology [online]. http://www.biofab.org/ . Accessed 20 Feb 2015.Vallerio M, Hufkens J, Van Impe J, Logist F. An interactive decision-support system for multi-objective optimization of nonlinear dynamic processes with uncertainty. Expert Syst Appl. 2015; 42(21):7710–31.Frangopol DM, Maute K. Life-cycle reliability-based optimization of civil and aerospace structures. Comput Struct. 2003; 81(7):397–410.Lozano M, Molina D, Herrera F. Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Comput. 2011; 15(11):2085–7.Santana-Quintero LV, Montano AA, Coello CAC. A review of techniques for handling expensive functions in evolutionary multi-objective optimization. In: Computational Intelligence in Expensive Optimization Problems. Berlin: Springer: 2010. p. 29–59

Regulation of Translation by TOR, eIF4E and eIF2 alpha in Plants:Current Knowledge, Challenges and Future Perspectives

An important step in eukaryotic gene expression is the synthesis of proteins from mRNA, a process classically divided into three stages, initiation, elongation, and termination. Translation is a precisely regulated and conserved process in eukaryotes. The presence of plant-specific translation initiation factors and the lack of well-known translational regulatory pathways in this kingdom nonetheless indicate how a globally conserved process can diversify among organisms. The control of protein translation is a central aspect of plant development and adaptation to environmental stress, but the mechanisms are still poorly understood. Here we discuss current knowledge of the principal mechanisms that regulate translation initiation in plants, with special attention to the singularities of this eukaryotic kingdom. In addition, we highlight the major recent breakthroughs in the field and the main challenges to address in the coming years

Behavioral and Cytological Differences between Two Parkinson’s Disease Experimental Models

The knowledge about the biochemical and behavioral changes in humans with PD has allowed proposing animal models for its study; however, the results obtained so far have been heterogeneous. Recently, we established a novel PD model in rodents by manganese chloride (MnCl2) and manganese acetate (Mn (OAc)3) mixture inhalation. After inhaling, the rodents presented bilateral loss of SNc dopaminergic neurons. Later, we conclude that the alterations are of dopamine origin since L-DOPA reverted the alterations. After six months, SNc significantly reduced the number of cells, and striatal dopamine content decreased by 71%. The animals had postural instability, action tremor, and akinesia; these symptoms improved with L-DOPA, providing evidence that Mn mixture inhalation induces comparable alterations that those in PD patients. Thus, this study aimed to compare the alterations in two different PD experimental models: 6-OHDA unilateral lesion and Mn mixture inhalation through open field test, rotarod performance and the number of SNc dopaminergic neurons. The results show that the Mn-exposed animals have motor alterations and bilateral and progressive SNc neurons degeneration; in contrast, in the 6-OHDA model, the neuronal loss is unilateral and acute, demonstrating that the Mn exposure model better recreates the characteristics observed in PD patients

The importance of disease associations and concomitant therapy for the long-term management of psoriasis patients

It is well established that several inflammatory-type conditions, such as arthritis, diabetes, cardiovascular disease, and irritable bowel disease exist comorbidly and at an increased incidence in patients with psoriasis. Psoriasis and other associated diseases are thought to share common inflammatory pathways. Conditions such as these, with similar pathogenic mechanisms involving cytokine dysregulation, are referred to as immune-mediated inflammatory diseases (IMIDs). Considerable evidence for the genetic basis of cormobidities in psoriasis exists. The WHO has reported that the occurrence of chronic diseases, including IMIDs, are a rising global burden. In addition, conditions linked with psoriasis have been associated with increasing rates of considerable morbidity and mortality. The presence of comorbid conditions in psoriasis patients has important implications for clinical management. QoL, direct health care expenditures and pharmacokinetics of concomitant therapies are impacted by the presence of comorbid conditions. For example, methotrexate is contraindicated in hepatic impairment, while patients on ciclosporin should be monitored for kidney function. In addition, some agents, such as beta blockers, lithium, synthetic antimalarial drugs, NSAIDs and tetracycline antibiotics, have been implicated in the initiation or exacerbation of psoriasis. Consequently, collaboration between physicians in different specialties is essential to ensuring that psoriasis treatment benefits the patient without exacerbating associated conditions

The Leishmania donovani Lipophosphoglycan Excludes the Vesicular Proton-ATPase from Phagosomes by Impairing the Recruitment of Synaptotagmin V

We recently showed that the exocytosis regulator Synaptotagmin (Syt) V is recruited to the nascent phagosome and remains associated throughout the maturation process. In this study, we investigated the possibility that Syt V plays a role in regulating interactions between the phagosome and the endocytic organelles. Silencing of Syt V by RNA interference revealed that Syt V contributes to phagolysosome biogenesis by regulating the acquisition of cathepsin D and the vesicular proton-ATPase. In contrast, recruitment of cathepsin B, the early endosomal marker EEA1 and the lysosomal marker LAMP1 to phagosomes was normal in the absence of Syt V. As Leishmania donovani promastigotes inhibit phagosome maturation, we investigated their potential impact on the phagosomal association of Syt V. This inhibition of phagolysosome biogenesis is mediated by the virulence glycolipid lipophosphoglycan, a polymer of the repeating Galβ1,4Manα1-PO4 units attached to the promastigote surface via an unusual glycosylphosphatidylinositol anchor. Our results showed that insertion of lipophosphoglycan into ganglioside GM1-containing microdomains excluded or caused dissociation of Syt V from phagosome membranes. As a consequence, L. donovani promatigotes established infection in a phagosome from which the vesicular proton-ATPase was excluded and which failed to acidify. Collectively, these results reveal a novel function for Syt V in phagolysosome biogenesis and provide novel insight into the mechanism of vesicular proton-ATPase recruitment to maturing phagosomes. We also provide novel findings into the mechanism of Leishmania pathogenesis, whereby targeting of Syt V is part of the strategy used by L. donovani promastigotes to prevent phagosome acidification