13273 research outputs found
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MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
Full text linkMultiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps-including biophysically interpretable models-from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise
The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update.
Full text linkGalaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations
Ordered and deterministic cancer genome evolution after p53 loss
Full text linkAlthough p53 inactivation promotes genomic instability1 and presents a route to malignancy for more than half of all human cancers2,3, the patterns through which heterogenous TP53 (encoding human p53) mutant genomes emerge and influence tumorigenesis remain poorly understood. Here, in a mouse model of pancreatic ductal adenocarcinoma that reports sporadic p53 loss of heterozygosity before cancer onset, we find that malignant properties enabled by p53 inactivation are acquired through a predictable pattern of genome evolution. Single-cell sequencing and in situ genotyping of cells from the point of p53 inactivation through progression to frank cancer reveal that this deterministic behaviour involves four sequential phases-Trp53 (encoding mouse p53) loss of heterozygosity, accumulation of deletions, genome doubling, and the emergence of gains and amplifications-each associated with specific histological stages across the premalignant and malignant spectrum. Despite rampant heterogeneity, the deletion events that follow p53 inactivation target functionally relevant pathways that can shape genomic evolution and remain fixed as homogenous events in diverse malignant populations. Thus, loss of p53-the 'guardian of the genome'-is not merely a gateway to genetic chaos but, rather, can enable deterministic patterns of genome evolution that may point to new strategies for the treatment of TP53-mutant tumours
On the parameter combinations that matter and on those that do not: Data-driven studies of parameter (non)identifiability
Full text linkWe present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established analytically
Establishing Physalis as a Solanaceae model system enables genetic reevaluation of the inflated calyx syndrome
Full text linkThe highly diverse Solanaceae family contains several widely studied model and crop species. Fully exploring, appreciating, and exploiting this diversity requires additional model systems. Particularly promising are orphan fruit crops in the genus Physalis, which occupy a key evolutionary position in the Solanaceae and capture understudied variation in traits such as inflorescence complexity, fruit ripening and metabolites, disease and insect resistance, self-compatibility, and most notable, the striking inflated calyx syndrome (ICS), an evolutionary novelty found across angiosperms where sepals grow exceptionally large to encapsulate fruits in a protective husk. We recently developed transformation and genome editing in Physalis grisea (groundcherry). However, to systematically explore and unlock the potential of this and related Physalis as genetic systems, high-quality genome assemblies are needed. Here, we present chromosome-scale references for P. grisea and its close relative P. pruinosa and use these resources to study natural and engineered variation in floral traits. We first rapidly identified a natural structural variant in a bHLH gene that causes petal color variation. Further, and against expectations, we found that CRISPR-Cas9 targeted mutagenesis of 11 MADS-box genes, including purported essential regulators of ICS, had no effect on inflation. In a forward genetics screen, we identified huskless, which lacks ICS due to mutation of an AP2-like gene that causes sepals and petals to merge into a single whorl of mixed identity. These resources and findings elevate Physalis to a new Solanaceae model system, and establish a paradigm in the search for factors driving ICS
Clinical applications of 3D normal and breast cancer organoids: A review of concepts and methods
No full textWhile mouse models and two-dimensional (2D) cell culture systems have dominated as research tools for cancer biology, three-dimensional (3D) cultures have gained traction as a new approach that retains features of in vivo biology within an in vitro system. Over time, 3D culture systems have evolved from spheroids and tumorspheres to organoids, and by doing so, they have become more complex and representative of original tissue. Such technological improvements have mostly benefited the study of heterogeneous solid tumors, like those found in breast cancer (BC), by providing an attractive avenue for scalable drug testing and biobank generation. Experimentally, organoids have been used in the BC field to dissect mechanisms related to cellular invasion and metastasis-and through co-culture methods-epithelial interactions with stromal and immune cells. In addition, organoid studies of wild-type mouse models and healthy donor samples have provided insight into the basic developmental cellular and molecular biology of the mammary gland, which may inform one's understanding of the initial stages of cancer development and progression
Pancreatic Cancer Patient-derived Organoids Can Predict Response to Neoadjuvant Chemotherapy
No full textOBJECTIVE: To evaluate if patient-derived organoids (PDOs) may predict response to neoadjuvant (NAT) chemotherapy in patients with pancreatic adenocarcinoma. BACKGROUND: PDOs have been explored as a biomarker of therapy response and for personalized therapeutics in patients with pancreatic cancer. METHODS: During 2017-2021, patients were enrolled into an IRB-approved protocol and PDO cultures were established. PDOs of interest were analyzed through a translational pipeline incorporating molecular profiling and drug sensitivity testing. RESULTS: One hundred thirty-six samples, including both surgical resections and fine needle aspiration/biopsy from 117 patients with pancreatic cancer were collected. This biobank included diversity in stage, sex, age, and race, with minority populations representing 1/3 of collected cases (16% Black, 9% Asian, 7% Hispanic/Latino). Among surgical specimens, PDO generation was successful in 71% (15 of 21) of patients who had received NAT prior to sample collection and in 76% (39 of 51) of patients who were untreated with chemotherapy or radiation at the time of collection. Pathological response to NAT correlated with PDO chemotherapy response, particularly oxaliplatin. We demonstrated the feasibility of a rapid PDO drug screen and generated data within 7 days of tissue resection. CONCLUSION: Herein we report a large single-institution organoid biobank, including ethnic minority samples. The ability to establish PDOs from chemotherapy-naive and post-NAT tissue enables longitudinal PDO generation to assess dynamic chemotherapy sensitivity profiling. PDOs can be rapidly screened and further development of rapid screening may aid in the initial stratification of patients to the most active NAT regimen
When the levee of sympathetic outflow breaks
No full textIn this issue of Immunity, Bi et al. identify a microglia-neuron signaling axis that is critical for maintaining central control of the sympathetic nervous system. They find that platelet growth factor B released by microglia acts on neurons via PDGFRα to regulate sympathetic outflow. Disrupting this pathway leads to neuronal excitability, highlighting a promising therapeutic target to modulate sympathetic outflow and reduce hypertension