Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy

Four classic "de novo" genes all have plausible homologs and likely evolved from retro-duplicated or pseudogenic sequences

Despite being previously regarded as extremely unlikely, the idea that entirely novel protein-coding genes can emerge from non-coding sequences has gradually become accepted over the past two decades. Examples of "de novo origination", resulting in lineage-specific "orphan" genes, lacking coding orthologs, are now produced every year. However, many are likely cases of duplicates that are difficult to recognize. Here, I re-examine the claims and show that four very well-known examples of genes alleged to have emerged completely "from scratch"- FLJ33706 in humans, Goddard in fruit flies, BSC4 in baker's yeast and AFGP2 in codfish-may have plausible evolutionary ancestors in pre-existing genes. The first two are likely highly diverged retrogenes coding for regulatory proteins that have been misidentified as orphans. The antifreeze glycoprotein, moreover, may not have evolved from repetitive non-genic sequences but, as in several other related cases, from an apolipoprotein that could have become pseudogenized before later being reactivated. These findings detract from various claims made about de novo gene birth and show there has been a tendency not to invest the necessary effort in searching for homologs outside of a very limited syntenic or phylostratigraphic methodology. A robust approach is used for improving detection that draws upon similarities, not just in terms of statistical sequence analysis, but also relating to biochemistry and function, to obviate notable failures to identify homologs.

Mapping the Genetic Interaction Network of PARP inhibitor Response

Genetic interactions have long informed our understanding of the coordinated proteins and pathways that respond to DNA damage in mammalian cells, but systematic interrogation of the genetic network underlying that system has yet to be achieved. Towards this goal, we measured 147,153 pairwise interactions among genes implicated in PARP inhibitor (PARPi) response. Evaluating genetic interactions at this scale, with and without exposure to PARPi, revealed hierarchical organization of the pathways and complexes that maintain genome stability during normal growth and defined changes that occur upon accumulation of DNA lesions due to cytotoxic doses of PARPi. We uncovered unexpected relationships among DNA repair genes, including context-specific buffering interactions between the minimally characterized AUNIP and BRCA1-A complex genes. Our work thus establishes a foundation for mapping differential genetic interactions in mammalian cells and provides a comprehensive resource for future studies of DNA repair and PARP inhibitors.

Interpretation of cancer mutations using a multiscale map of protein systems

A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.

Screening for Genes Involved in Autophagy

Autophagy is an important intracellular lysosomal degradation process in cells, which is highly conserved from yeast to mammals. The process of autophagy is roughly divided into the following key steps: the formation of a membrane structure called ISM (isolated membrane) after stimulation, the biogenesis and maturation of autophagosomes, and finally the degradation of autophagosomes. A number of proteins are required to function in the whole process of autophagy. Since the initial genetic screening in yeast cells, multiple genes that play pivotal roles in autophagy have been discovered. These molecules have been named ATG genes (AuTophaGy related genes). The screening for new key molecules involved in autophagy has greatly promoted the characterization of the mechanism of the autophagy machinery and provides multiple targets for the development of autophagy-based regulatory drugs.

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

Environmental robustness of the global yeast genetic interaction network

Phenotypes associated with genetic variants can be altered by interactions with other genetic variants (GxG), with the environment (GxE), or both (GxGxE). Yeast genetic interactions have been mapped on a global scale, but the environmental influence on the plasticity of genetic networks has not been examined systematically. To assess environmental rewiring of genetic networks, we examined 14 diverse conditions and scored 30,000 functionally representative yeast gene pairs for dynamic, differential interactions. Different conditions revealed novel differential interactions, which often uncovered functional connections between distantly related gene pairs. However, the majority of observed genetic interactions remained unchanged in different conditions, suggesting that the global yeast genetic interaction network is robust to environmental perturbation and captures the fundamental functional architecture of a eukaryotic cell.

Identifying Candida albicans Gene Networks Involved in Pathogenicity

Candida albicans is a normal member of the human microbiome. It is also an opportunistic pathogen, which can cause life-threatening systemic infections in severely immunocompromized individuals. Despite the availability of antifungal drugs, mortality rates of systemic infections are high and new drugs are needed to overcome therapeutic challenges including the emergence of drug resistance. Targeting known disease pathways has been suggested as a promising avenue for the development of new antifungals. However, <30% of C. albicans genes are verified with experimental evidence of a gene product, and the full complement of genes involved in important disease processes is currently unknown. Tools to predict the function of partially or uncharacterized genes and generate testable hypotheses will, therefore, help to identify potential targets for new antifungal development. Here, we employ a network-extracted ontology to leverage publicly available transcriptomics data and identify potential candidate genes involved in disease processes. A subset of these genes has been phenotypically screened using available deletion strains and we present preliminary data that one candidate, PEP8, is involved in hyphal development and immune evasion. This work demonstrates the utility of network-extracted ontologies in predicting gene function to generate testable hypotheses that can be applied to pathogenic systems. This could represent a novel first step to identifying targets for new antifungal therapies.

On the relevance of precision autophagy flux control in vivo - Points of departure for clinical translation

Macroautophagy (which we will call autophagy hereafter) is a critical intracellular bulk degradation system that is active at basal rates in eukaryotic cells. This process is embedded in the homeostasis of nutrient availability and cellular metabolic demands, degrading primarily long-lived proteins and specific organelles.. Autophagy is perturbed in many pathologies, and its manipulation to enhance or inhibit this pathway therapeutically has received considerable attention. Although better probes are being developed for a more precise readout of autophagic activity in vitro and increasingly in vivo, many questions remain. These center in particular around the accurate measurement of autophagic flux and its translation from the in vitro to the in vivo environment as well as its clinical application. In this review, we highlight key aspects that appear to contribute to stumbling blocks on the road toward clinical translation and discuss points of departure for reaching some of the desired goals. We discuss techniques that are well aligned with achieving desirable spatiotemporal resolution to gather data on autophagic flux in a multi-scale fashion, to better apply the existing tools that are based on single-cell analysis and to use them in the living organism. We assess how current techniques may be used for the establishment of autophagic flux standards or reference points and consider strategies for a conceptual approach on titrating autophagy inducers based on their effect on autophagic flux . Finally, we discuss potential solutions for inherent controls for autophagy analysis, so as to better discern systemic and tissue-specific autophagic flux in future clinical applications.Abbreviations: GFP: Green fluorescent protein; J: Flux; MAP1LC3/LC3: Microtubule-associated protein 1 light chain 3; nA: Number of autophagosomes; TEM: Transmission electron microscopy; τ: Transition time.

Mmi1, the Yeast Ortholog of Mammalian Translationally Controlled Tumor Protein (TCTP), Negatively Affects Rapamycin-Induced Autophagy in Post-Diauxic Growth Phase

Translationally controlled tumor protein (TCTP) is a multifunctional and highly conserved protein from yeast to humans. Recently, its role in non-selective autophagy has been reported with controversial results in mammalian and human cells. Herein we examine the effect of Mmi1, the yeast ortholog of TCTP, on non-selective autophagy in budding yeast Saccharomyces cerevisiae, a well-established model system to monitor autophagy. We induced autophagy by nitrogen starvation or rapamycin addition and measured autophagy by using the Pho8Δ60 and GFP-Atg8 processing assays in WT, mmi1Δ, and in autophagy-deficient strains atg8Δ or atg1Δ. Our results demonstrate that Mmi1 does not affect basal or nitrogen starvation-induced autophagy. However, an increased rapamycin-induced autophagy is detected in mmi1Δ strain when the cells enter the post-diauxic growth phase, and this phenotype can be rescued by inserted wild-type MMI1 gene. Further, the mmi1Δ cells exhibit significantly lower amounts of reactive oxygen species (ROS) in the post-diauxic growth phase compared to WT cells. In summary, our study suggests that Mmi1 negatively affects rapamycin-induced autophagy in the post-diauxic growth phase and supports the role of Mmi1/TCTP as a negative autophagy regulator in eukaryotic cells.

Biological Functions of Autophagy Genes: A Disease Perspective

The lysosomal degradation pathway of autophagy plays a fundamental role in cellular, tissue, and organismal homeostasis and is mediated by evolutionarily conserved autophagy-related (ATG) genes. Definitive etiological links exist between mutations in genes that control autophagy and human disease, especially neurodegenerative, inflammatory disorders and cancer. Autophagy selectively targets dysfunctional organelles, intracellular microbes, and pathogenic proteins, and deficiencies in these processes may lead to disease. Moreover, ATG genes have diverse physiologically important roles in other membrane-trafficking and signaling pathways. This Review discusses the biological functions of autophagy genes from the perspective of understanding-and potentially reversing-the pathophysiology of human disease and aging.

A Gaussian process-based definition reveals new and bona fide genetic interactions compared to a multiplicative model in the Gram-negative Escherichia coli

MOTIVATION

A digenic genetic interaction (GI) is observed when mutations in two genes within the same organism yield a phenotype that is different from the expected, given each mutation's individual effects. While multiplicative scoring is widely applied to define GIs, revealing underlying gene functions, it remains unclear if it is the most suitable choice for scoring GIs in Escherichia coli. Here, we assess many different definitions, including the multiplicative model, for mapping functional links between genes and pathways in E.coli.

RESULTS

Using our published E.coli GI datasets, we show computationally that a machine learning Gaussian process (GP)-based definition better identifies functional associations among genes than a multiplicative model, which we have experimentally confirmed on a set of gene pairs. Overall, the GP definition improves the detection of GIs, biological reasoning of epistatic connectivity, as well as the quality of GI maps in E.coli, and, potentially, other microbes.

AVAILABILITY AND IMPLEMENTATION

The source code and parameters used to generate the machine learning models in WEKA software were provided in the Supplementary information.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies

Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed approaches that organize omics data into a structured hierarchy of cellular components and pathways, called a "data-driven ontology." Such hierarchies recapitulate known cellular subsystems and discover new ones. To broadly facilitate this type of modeling, we report the development of a software library called the Data-Driven Ontology Toolkit (DDOT), consisting of a Python package (https://github.com/idekerlab/ddot) to assemble and analyze ontologies and a web application (http://hiview.ucsd.edu) to visualize them. Using DDOT, we programmatically assemble a compendium of ontologies for 652 diseases by integrating gene-disease mappings with a gene similarity network derived from omics data. For example, the ontology for Fanconi anemia describes known and novel disease mechanisms in its hierarchy of 194 genes and 74 subsystems. DDOT provides an easy interface to share ontologies online at the Network Data Exchange.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Integrative analysis of Paneth cell proteomic and transcriptomic data from intestinal organoids reveals functional processes dependent on autophagy

Paneth cells are key epithelial cells that provide an antimicrobial barrier and maintain integrity of the small-intestinal stem cell niche. Paneth cell abnormalities are unfortunately detrimental to gut health and are often associated with digestive pathologies such as Crohn's disease or infections. Similar alterations are observed in individuals with impaired autophagy, a process that recycles cellular components. The direct effect of autophagy impairment on Paneth cells has not been analysed. To investigate this, we generated a mouse model lacking Atg16l1 specifically in intestinal epithelial cells, making these cells impaired in autophagy. Using three-dimensional intestinal organoids enriched for Paneth cells, we compared the proteomic profiles of wild-type and autophagy-impaired organoids. We used an integrated computational approach combining protein-protein interaction networks, autophagy-targeted proteins and functional information to identify the mechanistic link between autophagy impairment and disrupted pathways. Of the 284 altered proteins, 198 (70%) were more abundant in autophagy-impaired organoids, suggesting reduced protein degradation. Interestingly, these differentially abundant proteins comprised 116 proteins (41%) that are predicted targets of the selective autophagy proteins p62, LC3 and ATG16L1. Our integrative analysis revealed autophagy-mediated mechanisms that degrade key proteins in Paneth cell functions, such as exocytosis, apoptosis and DNA damage repair. Transcriptomic profiling of additional organoids confirmed that 90% of the observed changes upon autophagy alteration have effects at the protein level, not on gene expression. We performed further validation experiments showing differential lysozyme secretion, confirming our computationally inferred downregulation of exocytosis. Our observations could explain how protein-level alterations affect Paneth cell homeostatic functions upon autophagy impairment.This article has an associated First Person interview with the joint first authors of the paper.

Gyp1 has a dual function as Ypt1 GAP and interaction partner of Atg8 in selective autophagy

Macroautophagy/autophagy is a highly conserved intracellular vesicle transport pathway that prevents accumulation of harmful materials within cells. The dynamic assembly and disassembly of the different autophagic protein complexes at the so-called phagophore assembly site (PAS) is strictly regulated. Rab GTPases are major regulators of cellular vesicle trafficking, and the Rab GTPase Ypt1 and its GEF TRAPPIII have been implicated in autophagy. We show that Gyp1 acts as a Ypt1 GTPase-activating protein (GAP) for selective autophagic variants, such as the Cvt pathway or the selective autophagic degradation of mitochondria (mitophagy). Gyp1 regulates the dynamic disassembly of the conserved Ypt1-Atg1 complex. Thereby, Gyp1 sets the stage for efficient Atg14 recruitment, and facilitates the critical step from nucleation to elongation of the phagophore. In addition, we identified Gyp1 as a new Atg8-interacting motif (AIM)-dependent Atg8 interaction partner. The Gyp1 AIM is required for efficient formation of the cargo receptor-Atg8 complexes. Our findings elucidate the molecular mechanisms of complex disassembly during phagophore formation and suggest potential dual functions of GAPs in cellular vesicle trafficking. Abbreviations AIM, Atg8-interacting motif; Atg, autophagy related; Cvt, cytoplasm-to-vacuole targeting; GAP, GTPase-activating protein; GEF, guanine-nucleotide exchange factor; GFP, green fluorescent protein; log phase, logarithmic growth phase; NHD, N-terminal helical domain; PAS, phagophore assembly site; PE, phosphatidylethanolamine; PtdIns3P, phosphatidylinositol-3-phosphate; WT, wild-type.

Lipid trafficking by yeast Snx4 family SNX-BAR proteins promotes autophagy and vacuole membrane fusion

Cargo-selective and nonselective autophagy pathways employ a common core autophagy machinery that directs biogenesis of an autophagosome that eventually fuses with the lysosome to mediate turnover of macromolecules. In yeast ( Saccharomyces cerevisiae) cells, several selective autophagy pathways fail in cells lacking the dimeric Snx4/Atg24 and Atg20/Snx42 sorting nexins containing a BAR domain (SNX-BARs), which function as coat proteins of endosome-derived retrograde transport carriers. It is unclear whether endosomal sorting by Snx4 proteins contributes to autophagy. Cells lacking Snx4 display a deficiency in starvation induced, nonselective autophagy that is severely exacerbated by ablation of mitochondrial phosphatidylethanolamine synthesis. Under these conditions, phosphatidylserine accumulates in the membranes of the endosome and vacuole, autophagy intermediates accumulate within the cytoplasm, and homotypic vacuole fusion is impaired. The Snx4-Atg20 dimer displays preference for binding and remodeling of phosphatidylserine-containing membrane in vitro, suggesting that Snx4-Atg20-coated carriers export phosphatidylserine-rich membrane from the endosome. Autophagy and vacuole fusion are restored by increasing phosphatidylethanolamine biosynthesis via alternative pathways, indicating that retrograde sorting by the Snx4 family sorting nexins maintains glycerophospholipid homeostasis required for autophagy and fusion competence of the vacuole membrane.

What We Learned From Big Data for Autophagy Research

Autophagy is the process by which cytoplasmic components are engulfed in double-membraned vesicles before being delivered to the lysosome to be degraded. Defective autophagy has been linked to a vast array of human pathologies. The molecular mechanism of the autophagic machinery is well-described and has been extensively investigated. However, understanding the global organization of the autophagy system and its integration with other cellular processes remains a challenge. To this end, various bioinformatics and network biology approaches have been developed by researchers in the last few years. Recently, large-scale multi-omics approaches (like genomics, transcriptomics, proteomics, lipidomics, and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating multi-scale data on the related components. In this review, we outline recent applications of in silico investigations and big data analyses of the autophagy process in various biological systems.

Mapping the Genetic Landscape of Human Cells

Seminal yeast studies have established the value of comprehensively mapping genetic interactions (GIs) for inferring gene function. Efforts in human cells using focused gene sets underscore the utility of this approach, but the feasibility of generating large-scale, diverse human GI maps remains unresolved. We developed a CRISPR interference platform for large-scale quantitative mapping of human GIs. We systematically perturbed 222,784 gene pairs in two cancer cell lines. The resultant maps cluster functionally related genes, assigning function to poorly characterized genes, including TMEM261, a new electron transport chain component. Individual GIs pinpoint unexpected relationships between pathways, exemplified by a specific cholesterol biosynthesis intermediate whose accumulation induces deoxynucleotide depletion, causing replicative DNA damage and a synthetic-lethal interaction with the ATR/9-1-1 DNA repair pathway. Our map provides a broad resource, establishes GI maps as a high-resolution tool for dissecting gene function, and serves as a blueprint for mapping the genetic landscape of human cells.

The LRRK2 signalling system

The LRRK2 gene is a major contributor to genetic risk for Parkinson's disease and understanding the biology of the leucine-rich repeat kinase 2 (LRRK2, the protein product of this gene) is an important goal in Parkinson's research. LRRK2 is a multi-domain, multi-activity enzyme and has been implicated in a wide range of signalling events within the cell. Because of the complexities of the signal transduction pathways in which LRRK2 is involved, it has been challenging to generate a clear idea as to how mutations and disease associated variants in this gene are altered in disease. Understanding the events in which LRRK2 is involved at a systems level is therefore critical to fully understand the biology and pathobiology of this protein and is the subject of this review.

Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping

We have developed a highly parallel strategy, systematic gene-to-phenotype arrays (SGPAs), to comprehensively map the genetic landscape driving molecular phenotypes of interest. By this approach, a complete yeast genetic mutant array is crossed with fluorescent reporters and imaged on membranes at high density and contrast. Importantly, SGPA enables quantification of phenotypes that are not readily detectable in ordinary genetic analysis of cell fitness. We benchmark SGPA by examining two fundamental biological phenotypes: first, we explore glucose repression, in which SGPA identifies a requirement for the Mediator complex and a role for the CDK8/kinase module in regulating transcription. Second, we examine selective protein quality control, in which SGPA identifies most known quality control factors along with U34 tRNA modification, which acts independently of proteasomal degradation to limit misfolded protein production. Integration of SGPA with other fluorescent readouts will enable genetic dissection of a wide range of biological pathways and conditions.

Autophagy in the context of the cellular membrane-trafficking system: the enigma of Atg9 vesicles

Macroautophagy is an intracellular degradation system that involves the de novo formation of membrane structures called autophagosomes, although the detailed process by which membrane lipids are supplied during autophagosome formation is yet to be elucidated. Macroautophagy is thought to be associated with canonical membrane trafficking, but several mechanistic details are still missing. In this review, the current understanding and potential mechanisms by which membrane trafficking participates in macroautophagy are described, with a focus on the enigma of the membrane protein Atg9, for which the proximal mechanisms determining its movement are disputable, despite its key role in autophagosome formation.

Autophagic clearance of proteasomes in yeast requires the conserved sorting nexin Snx4

Turnover of the 26S proteasome by autophagy is an evolutionarily conserved process that governs cellular proteolytic capacity and eliminates inactive particles. In most organisms, proteasomes are located in both the nucleus and cytoplasm. However, the specific autophagy routes for nuclear and cytoplasmic proteasomes are unclear. Here, we investigate the spatial control of autophagic proteasome turnover in budding yeast (Saccharomyces cerevisiae). We found that nitrogen starvation-induced proteasome autophagy is independent of known nucleophagy pathways but is compromised when nuclear protein export is blocked. Furthermore, via pharmacological tethering of proteasomes to chromatin or the plasma membrane, we provide evidence that nuclear proteasomes at least partially disassemble before autophagic turnover, whereas cytoplasmic proteasomes remain largely intact. A targeted screen of autophagy genes identified a requirement for the conserved sorting nexin Snx4 in the autophagic turnover of proteasomes and several other large multisubunit complexes. We demonstrate that Snx4 cooperates with sorting nexins Snx41 and Snx42 to mediate proteasome turnover and is required for the formation of cytoplasmic proteasome puncta that accumulate when autophagosome formation is blocked. Together, our results support distinct mechanistic paths in the turnover of nuclear versus cytoplasmic proteasomes and point to a critical role for Snx4 in cytoplasmic agglomeration of proteasomes en route to autophagic destruction.