The Upsides and Downsides of Organelle Interconnectivity
2017; Cell Press; Volume: 169; Issue: 1 Linguagem: Inglês
10.1016/j.cell.2017.02.030
ISSN1097-4172
AutoresDaniel E. Gottschling, Thomas Nyström,
Tópico(s)Advanced Fluorescence Microscopy Techniques
ResumoInterconnectivity and feedback control are hallmarks of biological systems. This includes communication between organelles, which allows them to function and adapt to changing cellular environments. While the specific mechanisms for all communications remain opaque, unraveling the wiring of organelle networks is critical to understand how biological systems are built and why they might collapse, as occurs in aging. A comprehensive understanding of all the routes involved in inter-organelle communication is still lacking, but important themes are beginning to emerge, primarily in budding yeast. These routes are reviewed here in the context of sub-system proteostasis and complex adaptive systems theory. Interconnectivity and feedback control are hallmarks of biological systems. This includes communication between organelles, which allows them to function and adapt to changing cellular environments. While the specific mechanisms for all communications remain opaque, unraveling the wiring of organelle networks is critical to understand how biological systems are built and why they might collapse, as occurs in aging. A comprehensive understanding of all the routes involved in inter-organelle communication is still lacking, but important themes are beginning to emerge, primarily in budding yeast. These routes are reviewed here in the context of sub-system proteostasis and complex adaptive systems theory. When we try to pick out anything by itself, we find it hitched to everything else in the Universe.—John Muir (1911) Most biologists of the 21st century will nod their head in agreement with Muir's statement; for it is now well-appreciated that all living systems are based upon networks of interactions. This connectivity is observed at every level of biology, occurring between organisms in ecological communities, between tissues, cells, and within cells (Toju et al., 2017Toju H. Yamamichi M. Guimarães P.R. Olesen J.M. Mougi A. Yoshida T. Thompson J.N. Species-rich networks and eco-evolutionary synthesis at the metacommunity level.Nature Ecology Evolution. 2017; 1: 1-11Crossref PubMed Scopus (59) Google Scholar, Vidal et al., 2011Vidal M. Cusick M.E. Barabási A.-L. Interactome networks and human disease.Cell. 2011; 144: 986-998Abstract Full Text Full Text PDF PubMed Scopus (1183) Google Scholar). Progress has been made in identifying connections and mapping numerous networks, and this information has guided our appreciation that there are sub-networks that form discrete functional units within any network. The organelles (mitochondria, nuclei, lysosomes, endoplasmic reticulum, Golgi) are prime examples of such functional units that need to perform specific tasks but also to be fully integrated with, and responding and reacting to, the activity of other sub-systems (Butow and Avadhani, 2004Butow R.A. Avadhani N.G. 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The Macmillan Co., New York1928Google Scholar) organelles, compartments and structures such as peroxisomes, lipid droplets, and the plasma membrane are now appreciated as sub-networks within cells whose activities need to be fully integrated (Costanzo et al., 2016Costanzo M. VanderSluis B. Koch E.N. Baryshnikova A. Pons C. Tan G. Wang W. Usaj M. Hanchard J. Lee S.D. et al.A global genetic interaction network maps a wiring diagram of cellular function.Science. 2016; 353: 6306Crossref Scopus (644) Google Scholar). This is especially true for the protein quality control networks as the functionality of the sub-systems rely on a constant flow and exchange of "pristine" proteins between them. Since the beginning of this century, there has been a great effort to identify and map biological networks and, taking advantage of sequenced genomes, many large experimentally generated datasets are laying a foundation for defining interaction network maps in different organisms (Vidal et al., 2011Vidal M. Cusick M.E. Barabási A.-L. Interactome networks and human disease.Cell. 2011; 144: 986-998Abstract Full Text Full Text PDF PubMed Scopus (1183) Google Scholar, Snider et al., 2015Snider J. Kotlyar M. Saraon P. Yao Z. Jurisica I. Stagljar I. Fundamentals of protein interaction network mapping.Mol. Syst. Biol. 2015; 11: 848Crossref PubMed Scopus (166) Google Scholar, Baryshnikova et al., 2013Baryshnikova A. Costanzo M. Myers C.L. Andrews B.J. Boone C. Genetic interaction networks: toward an understanding of heritability.Annu. Rev. Genomics Hum. Genet. 2013; 14: 111-133Crossref PubMed Scopus (83) Google Scholar, Hughes and de Boer, 2013Hughes T.R. de Boer C.G. Mapping yeast transcriptional networks.Genetics. 2013; 195: 9-36Crossref PubMed Scopus (58) Google Scholar). However, given the relatively limited information on metazoan networks, we focus this review on organelle interaction and interdependency in the budding yeast Saccharomyces cerevisiae. Many of the benefits of this model organism have been presented before, but paramount is that, relative to other organisms, there is an amalgam of deep mechanistic understandings about many different subsystems, including organelles and protein quality control and a sophisticated toolset for developing new approaches and insights (Botstein and Fink, 2011Botstein D. Fink G.R. Yeast: an experimental organism for 21st Century biology.Genetics. 2011; 189: 695-704Crossref PubMed Scopus (359) Google Scholar). In addition, the most detailed, successful mapping efforts performed to date have been made with yeast: the first (nearly) complete genetic interaction network of S. cerevisiae was recently published (Costanzo et al., 2016Costanzo M. VanderSluis B. Koch E.N. Baryshnikova A. Pons C. Tan G. Wang W. Usaj M. Hanchard J. Lee S.D. et al.A global genetic interaction network maps a wiring diagram of cellular function.Science. 2016; 353: 6306Crossref Scopus (644) Google Scholar). Yet, even this massive effort of determining interactions between all pairs of genes is but a first step in developing our understanding of biological complexity at the cellular level. These studies examined a single phenotype (growth) and were constrained to a single genetic background (thus not taking into account that many genes interact to create a phenotype) (Mackay, 2014Mackay T.F.C. Epistasis and quantitative traits: using model organisms to study gene-gene interactions.Nat. Rev. Genet. 2014; 15: 22-33Crossref PubMed Scopus (478) Google Scholar) and to a single environmental condition (i.e., a single temperature and nutrient source). Furthermore, the large-scale efforts define interactions without necessarily explaining function at a similar scale. Nevertheless, from datasets such as the genetic interaction network of S. cerevisiae (Costanzo et al., 2016Costanzo M. VanderSluis B. Koch E.N. Baryshnikova A. Pons C. Tan G. Wang W. Usaj M. Hanchard J. Lee S.D. et al.A global genetic interaction network maps a wiring diagram of cellular function.Science. 2016; 353: 6306Crossref Scopus (644) Google Scholar), the genetic network of protein quality control (Hill et al., 2016Hill S.M. Hao X. Grönvall J. Spikings-Nordby S. Widlund P.O. Amen T. Jörhov A. Josefson R. Kaganovich D. Liu B. Nyström T. Asymmetric inheritance of aggregated proteins and age reset in yeast are regulated by Vac17-dependent vacuolar functions.Cell Rep. 2016; 16: 826-838Abstract Full Text Full Text PDF PubMed Scopus (47) Google Scholar), and functional inter-organelle interdependency (Hughes and Gottschling, 2012Hughes A.L. Gottschling D.E. An early age increase in vacuolar pH limits mitochondrial function and lifespan in yeast.Nature. 2012; 492: 261-265Crossref PubMed Scopus (345) Google Scholar), interesting and unexpected hints are emerging with respect to how groups of genes/proteins known to be part of one organelle, buffer against defects in other organelles and/or compartments. By first identifying the determinants critical for such sub-system interactions and buffering, a deeper mechanistic understanding of how cells/organisms are put together and how they work may indeed be attainable. Uncovering fundamental principles that define networks of interactions and how they assemble into functional sub-networks within a biological system requires network/systems theory. Such principles in biology have often been shaped by engineering, evolution, and computational theories originally conceived decades ago and that continue to evolve and be refined (Capra, 1996Capra F. The Web of Life. Anchor, New York1996Google Scholar, Gleick, 1988Gleick J. Chaos. Penguin Books, 1988Google Scholar, Johnson, 2012Johnson S. Emergence. Simon and Schuster, 2012Google Scholar). There have been numerous thoughtful discussions that apply network/systems theory to various aspects of biology (Barabási and Oltvai, 2004Barabási A.-L. Oltvai Z.N. Network biology: understanding the cell's functional organization.Nat. Rev. Genet. 2004; 5: 101-113Crossref PubMed Scopus (5760) Google Scholar, Sun and Kim, 2011Sun M.G.F. Kim P.M. 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However, we are particularly influenced by considering biological interaction networks as "complex adaptive systems" (Cilliers, 1998Cilliers P. Complexity & Postmodernism. Routledge, London, New York1998Google Scholar, Cohen, 2016Cohen A.A. Complex systems dynamics in aging: new evidence, continuing questions.Biogerontology. 2016; 17: 205-220Crossref PubMed Scopus (76) Google Scholar, Mangel, 2001Mangel M. Complex adaptive systems, aging and longevity.J. Theor. Biol. 2001; 213: 559-571Crossref PubMed Scopus (42) Google Scholar, Miller, 2015Miller J.H. A Crude Look at the Whole: The Science of Complex Systems in Business, Life, and Society. Basic Books, New York2015Google Scholar). In its simplest terms, a complex system consists of a large number of elements (e.g., biological molecules) that interact and self-assemble in such a way that they have "emergent" properties that are not readily predicted by simply knowing about the individual elements. Here, we simply outline a number of important concepts that are most relevant for our discussion (adapted from Cilliers, 1998Cilliers P. Complexity & Postmodernism. Routledge, London, New York1998Google Scholar).•The complex system is dynamic and changes with time.•Interactions do not need to be physical, they can also be considered as transfer of information.•Any element can be influenced by more than one interaction, but most interactions are local. If information travels over longer distances, it has the potential to be modified en route.•Elements often assemble into clusters (subsystems) that can cooperate or compete with one another. Subsystems can themselves have emergent properties that can be considered to understand the larger system.•There can be loops between interactions, consisting of multiple steps. These loops provide both positive and negative feedback.•Complex systems are "open"—they interact with the environment. As a consequence, the border of a complex system is difficult to determine and the complex system is defined by its description.•Complex systems operate under conditions far from equilibrium. Hence a constant flow of energy is required to maintain organization and the system's survival. Equilibrium equals death.•Complex systems have a history. They not only change with time, but past experiences influence present behavior—i.e., initial conditions can matter!•As elements adapt in complex systems, their adaptations are governed by probabilities tied to the system's underlying fitness. There is always a chance that they will be in suboptimal circumstances and fail.•This fail rate will be context-dependent. For instance, if the element/system was optimized for fitness under a certain set of conditions, then when those conditions are no longer present, the response may no longer be as effective. A cell provides some ready examples of these concepts. The biological process of duplicating a cell reminds us that life and its continued propagation is a time-dependent process, and the network of interactions needed to facilitate this are necessarily dynamic. Furthermore, the ability to adapt to situational change is also fundamental to all organisms. Even "simple" changes in metabolism require a "rewiring" of metabolic networks when an organism switches from, for example, using glucose as a carbon source to fatty acids. In fact, an emergent property of complex adaptive systems is the "robustness" of the organism to perturbation (Félix and Barkoulas, 2015Félix M.-A. Barkoulas M. Pervasive robustness in biological systems.Nat. Rev. Genet. 2015; 16: 483-496Crossref PubMed Scopus (162) Google Scholar). The rewiring that occurs during network adaptation may be considered on many timescales, from signaling events that occur in milliseconds within cells, to rewiring of networks that occur over evolutionary time (i.e., when comparing networks between species or even different cells within a species). Here, we will primarily consider timescales that occur within an individual organism's lifetime. Furthermore, the adaptive changes in a network can be described not only with respect to time—i.e., the duration over which the re-wiring is maintained—but also the benefit they provide to the organism. Organelles and large cellular complexes can be viewed as subsystems within a cell, and they provide an experimentally approachable level of phenotypic analysis. There is a rich history of knowledge amassed about processes occurring within organelles and large cellular complexes (Alberts et al., 2014Alberts B. Johnson A. Lewis J. Morgan D. Raff M. Roberts K. Walter P. Molecular Biology of the Cell.Sixth Edition. Garland Science, 2014Crossref Google Scholar), and importantly, quantifiable changes in their structure (size, shape, location, number) are readily followed by microscopy (Cohen and Schuldiner, 2011Cohen Y. Schuldiner M. Advanced methods for high-throughput microscopy screening of genetically modified yeast libraries.Methods Mol. Biol. 2011; 781: 127-159Crossref PubMed Scopus (65) Google Scholar, Styles et al., 2016Styles E.B. Friesen H. Boone C. Andrews B.J. High-throughput microscopy-based screening in Saccharomyces cerevisiae.Cold Spring Harb. Protoc. 2016; 2016 (pdb.top087593)Crossref PubMed Scopus (4) Google Scholar). Furthermore, detailed genetic, chemical, and physiological screens have been carried out (especially in S. cerevisiae) that provide a basis for building a network of interactions that affect organelle structure (reviewed in Giaever and Nislow, 2014Giaever G. Nislow C. The yeast deletion collection: a decade of functional genomics.Genetics. 2014; 197: 451-465Crossref PubMed Scopus (270) Google Scholar). Altogether, this makes organelles a superb experimental platform for exploring complex adaptive systems within cells. Several examples have emerged that lend support to the notion that organelle communication is key to complex adaptive feedback control of subsystem proteostasis. Such interdependency between organelle functions and quality-control systems allows for adaptive, compensatory subsystem responses when one subsystem starts to fail. It should be noted that such adaptive, compensatory responses cannot achieve true cellular proteostasis per se, but rather allow the organism/cell to reach an alternative state of the proteome, which is compatible with function and survival in the face of irreversible damage to one or several subsystems (Figure 1) as might occur during severe stress or aging. Interestingly, these compensatory feedback systems can actually extend lifespan in many organisms when activated by subsystem failures, such as during mitochondrial dysfunction (Scheckhuber et al., 2007Scheckhuber C.Q. Erjavec N. Tinazli A. Hamann A. Nyström T. Osiewacz H.D. Reducing mitochondrial fission results in increased life span and fitness of two fungal ageing models.Nat. Cell Biol. 2007; 9: 99-105Crossref PubMed Scopus (267) Google Scholar, Berendzen et al., 2016Berendzen K.M. Durieux J. Shao L.W. Tian Y. Kim H.E. Wolff S. Liu Y. Dillin A. Neuroendocrine coordination of mitochondrial stress signaling and proteostasis.Cell. 2016; 166 (1553–1563.e10)Abstract Full Text Full Text PDF PubMed Scopus (127) Google Scholar). As exemplified below, several principal ways have emerged by which organelles and cellular compartments can maintain protein quality-control adaptability through inter-organelle communication and exchange of biochemical information. Proteostasis is maintained through a large number of proteins of the protein quality control (PQC) systems. The canonical proteins of the PQCs include molecular chaperones and their co-chaperones and nucleotide exchange factors, organelle-specific proteases, the proteasome machinery together with the ubiquitin tagging proteins (Balchin et al., 2016Balchin D. Hayer-Hartl M. Hartl F.U. In vivo aspects of protein folding and quality control.Science. 2016; 353: aac4354Crossref PubMed Scopus (779) Google Scholar, Buchberger et al., 2010Buchberger A. Bukau B. Sommer T. Protein quality control in the cytosol and the endoplasmic reticulum: brothers in arms.Mol. Cell. 2010; 40: 238-252Abstract Full Text Full Text PDF PubMed Scopus (377) Google Scholar), and in some organisms, proteins such as the yeast Hsp104 disaggregase with a specific role in resolving protein aggregates (Glover and Lindquist, 1998Glover J.R. Lindquist S. 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Dissecting the ER-associated degradation of a misfolded polytopic membrane protein.Cell. 2008; 132: 101-112Abstract Full Text Full Text PDF PubMed Scopus (218) Google Scholar). Thus, a breakdown of PQC in the ER can have repercussions on cytosolic PQC by titrating Hsp70s (titration principle outlined in Figure 1A). Similarly, the titration of the Hsp40 chaperone, Sis1, in the cytosol by misfolded proteins, greatly diminished PQC in the nucleus; i.e., the removal of damaged proteins targeted for Sis1-dependent degradation by the 26S proteasome (Park et al., 2013Park S.H. Kukushkin Y. Gupta R. Chen T. Konagai A. Hipp M.S. Hayer-Hartl M. Hartl F.U. PolyQ proteins interfere with nuclear degradation of cytosolic proteins by sequestering the Sis1p chaperone.Cell. 2013; 154: 134-145Abstract Full Text Full Text PDF PubMed Scopus (255) Google Scholar). Such titration of PQC factors can be used as a cellular surveillance mechanism allowing feedback control adjusting subsystem quality control. 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