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Presented is an overview of the image-analysis software platform Fiji, a distribution of ImageJ that updates the underlying ImageJ architecture and adds modern software design elements to expand the capabilities of the platform and facilitate collaboration between biologists and computer scientists. Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

Research on event perception has focused on transient elevations in predictive uncertainty or surprise as the primary signal driving event segmentation. Here the authors report behavioral and neuroimaging evidence that suggests that event representations can emerge even in the absence of such cues. They propose that this learning occurs in a manner analogous to the learning of semantic categories. Our experience of the world seems to divide naturally into discrete, temporally extended events, yet the mechanisms underlying the learning and identification of events are poorly understood. Research on event perception has focused on transient elevations in predictive uncertainty or surprise as the primary signal driving event segmentation. We present human behavioral and functional magnetic resonance imaging (fMRI) evidence in favor of a different account, in which event representations coalesce around clusters or 'communities' of mutually predicting stimuli. Through parsing behavior, fMRI adaptation and multivoxel pattern analysis, we demonstrate the emergence of event representations in a domain containing such community structure, but in which transition probabilities (the basis of uncertainty and surprise) are uniform. We present a computational account of how the relevant representations might arise, proposing a direct connection between event learning and the learning of semantic categories.

The genus Legionella comprises 65 species, among which Legionella pneumophila is a human pathogen causing severe pneumonia. To understand the evolution of an environmental to an accidental human pathogen, we have functionally analyzed 80 Legionella genomes spanning 58 species. Uniquely, an immense repository of 18,000 secreted proteins encoding 137 different eukaryotic-like domains and over 200 eukaryotic-like proteins is paired with a highly conserved type IV secretion system (T4SS). Specifically, we show that eukaryotic Rho- and Rab-GTPase domains are found nearly exclusively in eukaryotes and Legionella. Translocation assays for selected Rab-GTPase proteins revealed that they are indeed T4SS secreted substrates. Furthermore, F-box, U-box, and SET domains were present in >70% of all species, suggesting that manipulation of host signal transduction, protein turnover, and chromatin modification pathways are fundamental intracellular replication strategies for legionellae. In contrast, the Sec-7 domain was restricted to L. pneumophila and seven other species, indicating effector repertoire tailoring within different amoebae. Functional screening of 47 species revealed 60% were competent for intracellular replication in THP-1 cells, but interestingly, this phenotype was associated with diverse effector assemblages. These data, combined with evolutionary analysis, indicate that the capacity to infect eukaryotic cells has been acquired independently many times within the genus and that a highly conserved yet versatile T4SS secretes an exceptional number of different proteins shaped by interdomain gene transfer. Furthermore, we revealed the surprising extent to which legionellae have coopted genes and thus cellular functions from their eukaryotic hosts, providing an understanding of how dynamic reshuffling and gene acquisition have led to the emergence of major human pathogens.

Bacterial Toxin-Antitoxin systems (TAS) are involved in key biological functions including plasmid maintenance, defense against phages, persistence and virulence. They are found in nearly all phyla and classified into 6 different types based on the mode of inactivation of the toxin, with the type II TAS being the best characterized so far. We have herein developed a new in silico discovery pipeline named TASmania, which mines the >41K assemblies of the EnsemblBacteria database for known and uncharacterized protein components of type I to IV TAS loci. Our pipeline annotates the proteins based on a list of curated HMMs, which leads to >2.106 loci candidates, including orphan toxins and antitoxins, and organises the candidates in pseudo-operon structures in order to identify new TAS candidates based on a guilt-by-association strategy. In addition, we classify the two-component TAS with an unsupervised method on top of the pseudo-operon (pop) gene structures, leading to 1567 \"popTA\" models offering a more robust classification of the TAs families. These results give valuable clues in understanding the toxin/antitoxin modular structures and the TAS phylum specificities. Preliminary in vivo work confirmed six putative new hits in Mycobacterium tuberculosis as promising candidates. The TASmania database is available on the following server https://shiny.bioinformatics.unibe.ch/apps/tasmania/.

Environmental genomics can describe all forms of organisms—cellular and viral—present in a community. The analysis of such eco-systems biology data relies heavily on reference databases, e.g., taxonomy or gene function databases. Reference databases of symbiosis sensu lato, although essential for the analysis of organism interaction networks, are lacking. By mining existing databases and literature, we here provide a comprehensive and manually curated database of taxonomic links between viruses and their cellular hosts.

The how's and why's of successful drug repositioning Drug repositioning, also known as drug reprofiling or repurposing, has become an increasingly important part of the drug development process. This book examines the business, technical, scientific, and operational challenges and opportunities that drug repositioning offers. Readers will learn how to perform the latest experimental and computational methods that support drug repositioning, and detailed case studies throughout the book demonstrate how these methods fit within the context of a comprehensive drug repositioning strategy. Drug Repositioning is divided into three parts: Part 1, Drug Repositioning: Business Case, Strategies, and Operational Considerations, examines the medical and commercial drivers underpinning the quest to reposition existing drugs, guiding readers through the key strategic, technical, operational, and regulatory decisions needed for successful drug repositioning programs. Part 2, Application of Technology Platforms to Uncover New Indications and Repurpose Existing Drugs, sets forth computational-based strategies, tools, and databases that have been designed for repositioning studies, screening approaches, including combinations of existing drugs, and a look at the development of chemically modified analogs of approved agents. Part 3, Academic and Non-Profit Initiatives & the Role of Alliances in the Drug Repositioning Industry, explores current investigations for repositioning drugs to treat rare and neglected diseases, which are frequently overlooked by for-profit pharmaceutical companies due to their lack of commercial return. The book's appendix provides valuable resources for drug repositioning researchers, including information on drug repositioning and reformulation companies, databases, government resources and organizations, regulatory agencies, and drug repositioning initiatives from academia and non-profits. With this book as their guide, students and pharmaceutical researchers can learn how to use drug repositioning techniques to extend the lifespan and applications of existing drugs as well as maximize the return on investment in drug research and development.

During development, coordinated cell behaviors orchestrate tissue and organ morphogenesis. Detailed descriptions of cell lineages and behaviors provide a powerful framework to elucidate the mechanisms of morphogenesis. To study the cellular basis of limb development, we imaged transgenic fluorescently-labeled embryos from the crustacean Parhyale hawaiensis with multi-view light-sheet microscopy at high spatiotemporal resolution over several days of embryogenesis. The cell lineage of outgrowing thoracic limbs was reconstructed at single-cell resolution with new software called Massive Multi-view Tracker (MaMuT). In silico clonal analyses suggested that the early limb primordium becomes subdivided into anterior-posterior and dorsal-ventral compartments whose boundaries intersect at the distal tip of the growing limb. Limb-bud formation is associated with spatial modulation of cell proliferation, while limb elongation is also driven by preferential orientation of cell divisions along the proximal-distal growth axis. Cellular reconstructions were predictive of the expression patterns of limb development genes including the BMP morphogen Decapentaplegic. During early life, animals develop from a single fertilized egg cell to hundreds, millions or even trillions of cells. These cells specialize to do different tasks; forming different tissues and organs like muscle, skin, lungs and liver. For more than a century, scientists have strived to understand the details of how animal cells become different and specialize, and have created many new techniques and technologies to help them achieve this goal. Limbs – such as arms, legs and wings – form from small lumps of cells called limb buds. Scientists use the shrimp-like crustacean, Parhyale hawaiensis, to study development, including limb growth. This species is useful because it is easy to grow, manipulate and observe its developing young in the laboratory. Understanding how its limbs develop offers important new insights into how limbs develop in other animals too. Wolff, Tinevez, Pietzsch et al. have now combined advanced microscopy with custom computer software, called Massive Multi-view Tracker (MaMuT) to investigate this. As limbs develop in Parhyale, the MaMuT software tracks how cells behave, and how they are organized. This analysis revealed that for cells to produce a limb bud, they need to split at an early stage into separate groups. These groups are organized along two body axes, one that goes from head to tail, and one that runs from back to belly. The limb grows perpendicular to these main body axes, along a new ‘proximal-distal’ axis that goes from nearest to furthest from the body. Wolff et al. found that the cells that contribute to the extremities of the limb divide faster than the ones that stay closer to the body. Finally, the results show that when cells in a limb divide, they mostly divide along the proximal-distal axis, producing one cell that is further from the body than the other. These cell activities may help limbs to get longer as they grow. Notably, the groups of cells seen by Wolff et al. were expressing genes that had previously been identified in developing limbs. This helps to validate the new results and to identify which active genes control the behaviors of the analyzed cells. These findings reveal new ways to study animal development. This approach could have many research uses and may help to link the mechanisms of cell biology to their effects. It could also contribute to new understanding of developmental and genetic conditions that affect human health.

There are currently no disease-modifying treatments for Alzheimer’s disease (AD), and an understanding of preclinical causal biomarkers to help target disease pathogenesis in the earliest phases remains elusive. Here, we investigated whether 19 metabolites previously associated with midlife cognition—a preclinical predictor of AD—translate to later clinical risk, using Mendelian randomization (MR) to tease out AD-specific causal relationships. Summary statistics from the largest genome-wide association studies (GWASs) for AD and metabolites were used to perform bidirectional univariable MR. Bayesian model averaging (BMA) was additionally performed to address high correlation between metabolites and identify metabolite combinations that may be on the AD causal pathway. Univariable MR indicated four extra-large high-density lipoproteins (XL.HDL) on the causal pathway to AD: free cholesterol (XL.HDL.FC: 95% CI = 0.78 to 0.94), total lipids (XL.HDL.L: 95% CI = 0.80 to 0.97), phospholipids (XL.HDL.PL: 95% CI = 0.81 to 0.97), and concentration of XL.HDL particles (95% CI = 0.79 to 0.96), significant at an adjusted P < 0.009. MR–BMA corroborated XL.HDL.FC to be among the top three causal metabolites, in addition to total cholesterol in XL.HDL (XL.HDL.C) and glycoprotein acetyls (GP). Both XL.HDL.C and GP demonstrated suggestive univariable evidence of causality (P < 0.05), and GP successfully replicated within an independent dataset. This study offers insight into the causal relationship between metabolites demonstrating association with midlife cognition and AD. It highlights GP in addition to several XL.HDLs—particularly XL.HDL.FC—as causal candidates warranting further investigation. As AD pathology is thought to develop decades prior to symptom onset, expanding on these findings could inform risk reduction strategies.

Orienting the causal relationship between pairs of traits is a fundamental task in scientific research with significant implications in practice, such as in prioritizing molecular targets and modifiable risk factors for developing therapeutic and interventional strategies for complex diseases. A recent method, called Steiger’s method, using a single SNP as an instrument variable (IV) in the framework of Mendelian randomization (MR), has since been widely applied. We report the following new contributions. First, we propose a single SNP-based alternative, overcoming a severe limitation of Steiger’s method in simply assuming, instead of inferring, the existence of a causal relationship. We also clarify a condition necessary for the validity of the methods in the presence of hidden confounding. Second, to improve statistical power, we propose combining the results from multiple, and possibly correlated, SNPs as multiple instruments. Third, we develop three goodness-of-fit tests to check modeling assumptions, including those required for valid IVs. Fourth, by relaxing one of the three IV assumptions in MR, we propose several methods, including an Egger regression-like approach and its multivariable version (analogous to multivariable MR), to account for horizontal pleiotropy of the SNPs/IVs, which is often unavoidable in practice. All our methods can simultaneously infer both the existence and (if so) the direction of a causal relationship, largely expanding their applicability over that of Steiger’s method. Although we focus on uni-directional causal relationships, we also briefly discuss an extension to bi-directional relationships. Through extensive simulations and an application to infer the causal directions between low density lipoprotein (LDL) cholesterol, or high density lipoprotein (HDL) cholesterol, and coronary artery disease (CAD), we demonstrate the superior performance and advantage of our proposed methods over Steiger’s method and bi-directional MR. In particular, after accounting for horizontal pleiotropy, our method confirmed the well known causal direction from LDL to CAD, while other methods, including bi-directional MR, might fail.