Aggregation-prone proteins (APPs) have been implicated in numerous human diseases but the underlying mechanisms are incompletely understood. Here we comparatively analysed cellular responses to different APPs. Our study is based on a systematic proteomic and phosphoproteomic analysis of a set of yeast proteotoxicity models expressing different human disease-related APPs, which accumulate intracellular APP inclusions and exhibit impaired growth. Clustering and functional enrichment analyses of quantitative proteome-level data reveal that the cellular response to APP expression, including the chaperone response, is specific to the APP, and largely differs from the response to a more generalized proteotoxic insult such as heat shock. We further observe an intriguing association between the subcellular location of inclusions and the location of the cellular response, and provide a rich dataset for future mechanistic studies. Our data suggest that care should be taken when designing research models to study intracellular aggregation, since the cellular response depends markedly on the specific APP and the location of inclusions. Further, therapeutic approaches aimed at boosting protein quality control in protein aggregation diseases should be tailored to the subcellular location affected by inclusion formation. SIGNIFICANCE: We have examined the global cellular response, in terms of protein abundance and phosphorylation changes, to the expression of five human neurodegeneration-associated, aggregation-prone proteins (APPs) in a set of isogenic yeast models. Our results show that the cellular response to each APP is unique to that protein, is different from the response to thermal stress, and is associated with processes at the subcellular location of APP inclusion formation. These results further our understanding of how cells, in a model organism, respond to expression of APPs implicated in neurodegenerative diseases like Parkinson's, Alzheimer's, and ALS. They have implications for mechanisms of toxicity as well as of protective responses in the cell. The specificity of the response to each APP means that research models of these diseases should be tailored to the APP in question. The subcellular localization of the response suggest that therapeutic interventions should also be targeted within the cell.

When forecasting time series with a hierarchical structure, the existing state of the art is to forecast each time series independently, and, in a post-treatment step, to reconcile the time series in a way that respects the hierarchy (Hyndman et al., 2011; Wickramasuriya et al., 2018). We propose a new loss function that can be incorporated into any maximum likelihood objective with hierarchical data, resulting in reconciled estimates with confidence intervals that correctly account for additional uncertainty due to imperfect reconciliation. We evaluate our method using a non-linear model and synthetic data on a counterfactual forecasting problem, where we have access to the ground truth and contemporaneous covariates, and show that we largely improve over the existing state-of-the-art method.

The precise coordination of growth and proliferation has a universal prevalence in cell homeostasis. As a prominent property, cell size is modulated by the coordination between these processes in bacterial, yeast, and mammalian cells, but the underlying molecular mechanisms are largely unknown. Here, we show that multifunctional chaperone systems play a concerted and limiting role in cell-cycle entry, specifically driving nuclear accumulation of the G1 Cdk-cyclin complex. Based on these findings, we establish and test a molecular competition model that recapitulates cell-cycle-entry dependence on growth rate. As key predictions at a single-cell level, we show that availability of the Ydj1 chaperone and nuclear accumulation of the G1 cyclin Cln3 are inversely dependent on growth rate and readily respond to changes in protein synthesis and stress conditions that alter protein folding requirements. Thus, chaperone workload would subordinate Start to the biosynthetic machinery and dynamically adjust proliferation to the growth potential of the cell.

Computer science o‚ers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel con€dent their research is reproducible. But this is not exactly true. Jonathan Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. ‘e actual scholarship is the full so‰ware environment, code, and data that produced the result. ‘is implies new workƒows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically di‚erent from other traditional scienti€c journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and so‰ware tests.

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested and are hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.

The Levenberg-Marquardt algorithm is a flexible iterative procedure used to solve non-linear least squares problems. In this work we study how a class of possible adaptations of this procedure can be used to solve maximum likelihood problems when the underlying distributions are in the exponential family. We formally demonstrate a local convergence property and we discuss a possible implementation of the penalization involved in this class of algorithms. Applications to real and simulated compositional data show the stability and efficiency of this approach.

A key goal of functional genomics is to elucidate how genes and proteins act together in space and time, wired as pathways, to control specific aspects of cell biological function. Here, we develop a method to quantitatively determine proteins’ localization interdependencies at high throughput. We show that this method can be used to systematically obtain weighted, signed and directional pathway relationships, and hence to reconstruct a detailed pathway wiring. As proof-of-principle, we focus on 42 factors that control cell polarity in fission yeast (Schizosaccharomyces pombe) and use high-throughput confocal microscopy and quantitative image analysis to reconstruct their Localization Interdependency Network (LIN). Through this approach we identify 554 pairwise interactions across the factors, including 98% putative new directed links. Validation of an unexpected interaction between two polarity factor subgroups - the polarity landmark proteins and the cell integrity pathway components - by orthogonal phenotyping demonstrates the power of the LIN approach in detecting subtle, systems-level causal connections.

In this paper, we propose a novel application of Generative Adversarial Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy. Compared to natural images, cells tend to have a simpler and more geometric global structure that facilitates image generation. However, the correlation between the spatial pattern of different fluorescent proteins reflects important biological functions, and synthesized images have to capture these relationships to be relevant for biological applications. We adapt GANs to the task at hand and propose new models with casual dependencies between image channels that can generate multi-channel images, which would be impossible to obtain experimentally. We evaluate our approach using two independent techniques and compare it against sensible baselines. Finally, we demonstrate that by interpolating across the latent space we can mimic the known changes in protein localization that occur through time during the cell cycle, allowing us to predict temporal evolution from static images.

BackgroundPublishing in scientific journals is one of the most important ways in which scientists disseminate research to their peers and to the wider public. Pre-publication peer review underpins this process, but peer review is subject to various criticisms and is under pressure from growth in the number of scientific publications.MethodsHere we examine an element of the editorial process ateLife, in which the Reviewing Editor usually serves as one of the referees, to see what effect this has on decision times, decision type, and the number of citations. We analysed a dataset of 8,905 research submissions toeLifesince June 2012, of which 2,747 were sent for peer review. This subset of 2747 papers was then analysed in detail. ResultsThe Reviewing Editor serving as one of the peer reviewers results in faster decision times on average, with the time to final decision ten days faster for accepted submissions (n=1,405) and five days faster for papers that were rejected after peer review (n=1,099). Moreover, editors acting as reviewers had no effect on whether submissions were accepted or rejected, and a very small (but significant) effect on citation rates.ConclusionsAn important aspect ofeLife’s peer-review process is shown to be effective, given that decision times are faster when the Reviewing Editor serves as a reviewer. Other journals hoping to improve decision times could consider adopting a similar approach.

mously reviewed papers for the journal during the preparation of Volume 21. Their generosity, judicious judgment, and prompt response substantially helped us publish a journal that both is timely and has exacting scientific standards. The responsibility for sustaining the quality and relevance of the Journal rests largely in the hands of our reviewers. We at the Journal, and all of our readers, are deeply indebted to them. It is a genuine pleasure to thank them collectively for their dedicated service. (If anyone has been inadvertently omitted from this list, we sincerely apologize and ask them to let us know so that we can include their name in a future list of referees.)

Characterization of normal breast stem cells is important for understanding their role in breast development and in breast cancer. However, the identity of these cells is a subject of controversy and their localization in the breast epithelium is not known. In this study, we utilized a novel approach to analyze the morphogenesis of mammary lobules, by combining one-dimensional theoretical models and computer-generated 3D fractals. Comparing predictions of these models with immunohistochemical analysis of tissue sections for candidate stem cell markers, we defined distinct areas where stem cells reside in the mammary lobule. An increased representation of stem cells was found in smaller, less developed lobules compared to larger, more mature lobules, with marked differences in the gland of nulliparous versus parous women and that of BRCA1/2 mutation carriers versus non-carriers.

Delegates at scientific meetings can come from diverse backgrounds and use very different methods in their research. Promoting interactions between these ‘distant’ delegates is challenging but such interactions could lead to novel interdisciplinary collaborations and unexpected breakthroughs. We have developed a network-based ‘speed dating’ approach that allows us to initiate such distant interactions by pairing every delegate with another delegate who might be of interest to them, but whom they might never have encountered otherwise. Here we describe our approach and its algorithmic implementation.

Insulin-like peptides (ILPs) play highly conserved roles in development and physiology. Most animal genomes encode multiple ILPs. Here we identify mechanisms for how the forty Caenorhabditis elegans ILPs coordinate diverse processes, including development, reproduction, longevity and several specific stress responses. Our systematic studies identify an ILP-based combinatorial code for these phenotypes characterized by substantial functional specificity and diversity rather than global redundancy. Notably, we show that ILPs regulate each other transcriptionally, uncovering an ILP-to-ILP regulatory network that underlies the combinatorial phenotypic coding by the ILP family. Extensive analyses of genetic interactions among ILPs reveal how their signals are integrated. A combined analysis of these functional and regulatory ILP interactions identifies local genetic circuits that act in parallel and interact by crosstalk, feedback and compensation. This organization provides emergent mechanisms for phenotypic specificity and graded regulation for the combinatorial phenotypic coding we observe. Our findings also provide insights into how large hormonal networks regulate diverse traits.

Cell polarity is regulated by evolutionarily conserved polarity factors whose precise higher-order organization at the cell cortex is largely unknown. Here we image frontally the cortex of live fission yeast cells using time-lapse and super-resolution microscopy. Interestingly, we find that polarity factors are organized in discrete cortical clusters resolvable to ~50–100 nm in size, which can form and become cortically enriched by oligomerization. We show that forced co-localization of the polarity factors Tea1 and Tea3 results in polarity defects, suggesting that the maintenance of both factors in distinct clusters is required for polarity. However, during mitosis, their co-localization increases, and Tea3 helps to retain the cortical localization of the Tea1 growth landmark in preparation for growth reactivation following mitosis. Thus, regulated spatial segregation of polarity factor clusters provides a means to spatio-temporally control cell polarity at the cell cortex. We observe similar clusters in Saccharomyces cerevisiae and Caenorhabditis elegans cells, indicating this could be a universal regulatory feature. Cell polarity is generated and maintained by the spatial accumulation of polarity factors. By imaging fission yeast cells ‘end-on’, the authors show that the polarity factors Tea1 and Tea3 segregate into distinct clusters, and that surprisingly, their segregation is critical for cell polarization.

We propose stochastic process models as a means for studying and rendering unbounded biological structures, involving mechanisms that extend over geometric space. As an example, we discuss a case study of actin polymerization dynamics, which plays a key role in many cellular activities and enjoys a rich structure. We provide a comparative review of various approaches in the literature for modeling actin. We then illustrate on actin models how otherwise challenging structures can be modeled. In these models the complexity of the structures are incrementally increased with respect to the biological data. We present a geometric representation of these models that we use to generate movies reflecting their dynamics while preserving formal cleanliness as well as loyalty to the biological data.

The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms. Here we carry out a large-scale analysis of the protein-protein interaction (PPI) network of fission yeast (Schizosaccharomyces pombe) and establish a method to identify 'linker' proteins that bridge diverse cellular processes - integrating Gene Ontology and PPI data with network theory measures. We test the method on a highly characterized subset of the genome consisting of proteins controlling the cell cycle, cell polarity and cytokinesis and identify proteins likely to play a key role in controlling the temporal changes in the localization of the polarity machinery. Experimental inspection of one such factor, the polarity-regulating RNB protein Sts5, confirms the prediction that it has a cell cycle dependent regulation. Detailed bibliographic inspection of other predicted 'linkers' also confirms the predictive power of the method. As the method is robust to network perturbations and can successfully predict linker proteins, it provides a powerful tool to study the interplay between different cellular processes.