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Loop-Mediated Isothermal Amplification Detection of SARS-CoV-2 and Myriad Other Applications
No full textAs the second year of the COVID-19 pandemic begins, it remains clear that a massive increase in the ability to test for SARS-CoV-2 infections in a myriad of settings is critical to controlling the pandemic and to preparing for future outbreaks. The current gold standard for molecular diagnostics is the polymerase chain reaction (PCR), but the extraordinary and unmet demand for testing in a variety of environments means that both complementary and supplementary testing solutions are still needed. This review highlights the role that loop-mediated isothermal amplification (LAMP) has had in filling this global testing need, providing a faster and easier means of testing, and what it can do for future applications, pathogens, and the preparation for future outbreaks. This review describes the current state of the art for research of LAMP-based SARS-CoV-2 testing, as well as its implications for other pathogens and testing. The authors represent the global LAMP (gLAMP) Consortium, an international research collective, which has regularly met to share their experiences on LAMP deployment and best practices; sections are devoted to all aspects of LAMP testing, including preanalytic sample processing, target amplification, and amplicon detection, then the hardware and software required for deployment are discussed, and finally, a summary of the current regulatory landscape is provided. Included as well are a series of first-person accounts of LAMP method development and deployment. The final discussion section provides the reader with a distillation of the most validated testing methods and their paths to implementation. This review also aims to provide practical information and insight for a range of audiences: for a research audience, to help accelerate research through sharing of best practices; for an implementation audience, to help get testing up and running quickly; and for a public health, clinical, and policy audience, to help convey the breadth of the effect that LAMP methods have to offer
Inhibition of Hedgehog Signaling Alters Fibroblast Composition in Pancreatic Cancer.
No full textPURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease characterized by an extensive fibroinflammatory stroma, which includes abundant cancer-associated fibroblast (CAF) populations. PDAC CAFs are heterogeneous, but the nature of this heterogeneity is incompletely understood. The Hedgehog pathway functions in PDAC in a paracrine manner, with ligands secreted by cancer cells signaling to stromal cells in the microenvironment. Previous reports investigating the role of Hedgehog signaling in PDAC have been contradictory, with Hedgehog signaling alternately proposed to promote or restrict tumor growth. In light of the newly discovered CAF heterogeneity, we investigated how Hedgehog pathway inhibition reprograms the PDAC microenvironment. EXPERIMENTAL DESIGN: We used a combination of pharmacologic inhibition, gain- and loss-of-function genetic experiments, cytometry by time-of-flight, and single-cell RNA sequencing to study the roles of Hedgehog signaling in PDAC. RESULTS: We found that Hedgehog signaling is uniquely activated in fibroblasts and differentially elevated in myofibroblastic CAFs (myCAF) compared with inflammatory CAFs (iCAF). Sonic Hedgehog overexpression promotes tumor growth, while Hedgehog pathway inhibition with the smoothened antagonist, LDE225, impairs tumor growth. Furthermore, Hedgehog pathway inhibition reduces myCAF numbers and increases iCAF numbers, which correlates with a decrease in cytotoxic T cells and an expansion in regulatory T cells, consistent with increased immunosuppression. CONCLUSIONS: Hedgehog pathway inhibition alters fibroblast composition and immune infiltration in the pancreatic cancer microenvironment
Asterix/Gtsf1 links tRNAs and piRNA silencing of retrotransposons.
Get PDFThe Piwi-interacting RNA (piRNA) pathway safeguards genomic integrity by silencing transposable elements (transposons) in the germline. While Piwi is the central piRNA factor, others including Asterix/Gtsf1 have also been demonstrated to be critical for effective silencing. Here, using enhanced crosslinking and immunoprecipitation (eCLIP) with a custom informatic pipeline, we show that Asterix/Gtsf1 specifically binds tRNAs in cellular contexts. We determined the structure of mouse Gtsf1 by NMR spectroscopy and identified the RNA-binding interface on the protein's first zinc finger, which was corroborated by biochemical analysis as well as cryo-EM structures of Gtsf1 in complex with co-purifying tRNA. Consistent with the known dependence of long terminal repeat (LTR) retrotransposons on tRNA primers, we demonstrate that LTR retrotransposons are, in fact, preferentially de-repressed in Asterix mutants. Together, these findings link Asterix/Gtsf1, tRNAs, and LTR retrotransposon silencing and suggest that Asterix exploits tRNA dependence to identify transposon transcripts and promote piRNA silencing
Patient-derived xenografts and in vitro model show rationale for imatinib mesylate repurposing in HEY1-NCoA2-driven mesenchymal chondrosarcoma
No full textMesenchymal chondrosarcoma (MCS) is a high-grade malignancy that represents 2-9% of chondrosarcomas and mostly affects children and young adults. HEY1-NCoA2 gene fusion is considered to be a driver of tumorigenesis and it has been identified in 80% of MCS tumors. The shortage of MCS samples and biological models creates a challenge for the development of effective therapeutic strategies to improve the low survival rate of MCS patients. Previous molecular studies using immunohistochemical staining of patient samples suggest that activation of PDGFR signaling could be involved in MCS tumorigenesis. This work presents the development of two independent in vitro and in vivo models of HEY1-NCoA2-driven MCS and their application in a drug repurposing strategy. The in vitro model was characterized by RNA sequencing at the single-cell level and successfully recapitulated relevant MCS features. Imatinib, as well as specific inhibitors of ABL and PDGFR, demonstrated a highly selective cytotoxic effect targeting the HEY1-NCoA2 fusion-driven cellular model. In addition, patient-derived xenograft (PDX) models of MCS harboring the HEY1-NCoA2 fusion were developed from a primary tumor and its distant metastasis. In concordance with in vitro observations, imatinib was able to significantly reduce tumor growth in MCS-PDX models. The conclusions of this study serve as preclinical results to revisit the clinical efficacy of imatinib in the treatment of HEY1-NCoA2-driven MCS
Ground tissue circuitry regulates organ complexity in maize and Setaria
No full textMost plant roots have multiple cortex layers that make up the bulk of the organ and play key roles in physiology, such as flood tolerance and symbiosis. However, little is known about the formation of cortical layers outside of the highly reduced anatomy of Arabidopsis. Here, we used single-cell RNA sequencing to rapidly generate a cell-resolution map of the maize root, revealing an alternative configuration of the tissue formative transcription factor SHORT-ROOT (SHR) adjacent to an expanded cortex. We show that maize SHR protein is hypermobile, moving at least eight cell layers into the cortex. Higher-order SHR mutants in both maize and Setaria have reduced numbers of cortical layers, showing that the SHR pathway controls expansion of cortical tissue to elaborate anatomical complexity
Building Back More Equitable STEM Education: Teach Science by Engaging Students in Doing Science
Get PDFThe COVID-19 pandemic is a national tragedy, one that has focused our attention on both the need to improve science education and the need to confront systemic racism in our country. We know that active learning strategies, in particular research experiences, can engage and empower STEM undergraduates, effectively closing the achievement gap for historically excluded persons. The apprenticeship model for STEM training – supervised research under a dedicated mentor – is highly effective, but out of reach for most students. Recent efforts have demonstrated that Course-based Undergraduate Research Experiences (CUREs) can be an effective approach for making STEM research accessible for all. Our meta-analysis of CUREs finds that published examples now cover the breadth of the typical undergraduate biology curriculum. A thoughtfully designed CURE can go beyond foundational knowledge and analytical thinking to include career-related skills, e.g., teamwork and communication. Similarly, it can be designed with equity as a foundational principle, taking into account the unique contributions of all students and their varying needs. We provide here an example framework (The “Do Science Framework”) for making STEM training more effective and inclusive using CUREs. While CUREs do not inherently address equity, there can be no equity in STEM education without equal access to research participation, and progress toward this goal can be achieved using CUREs. However, implementing new CUREs is not a trivial undertaking, particularly at schools with high teaching loads and little or no research infrastructure, including many community colleges. We therefore propose a National Center for Science Engagement to support this transition, building on experiences of current nationally established CUREs as well as the work of many individual faculty. In the aftermath of the COVID-19 pandemic, academia has a renewed responsibility to dismantle structural inequities in education; engaging all STEM students in research can be a key step
A Unified Probabilistic Modeling Framework for Eukaryotic Transcription Based on Nascent RNA Sequencing Data
Get PDFNascent RNA sequencing protocols, such as PRO-seq and NET-seq, are now widely used in the study of eukaryotic transcription, and these experimental techniques have given rise to a variety of statistical and machine-learning methods for data analysis. These computational methods, however, are generally designed to address specialized signal-processing or prediction tasks, rather than directly describing the dynamics of RNA polymerases as they move along the DNA template. Here, I introduce a general probabilistic model that describes the kinetics of transcription initiation, elongation, pause release, and termination, as well as the generation of sequencing read counts. I show that this generative model enables estimation of separate pause-release rates, termination rates, and the initiation/elongation rate ratio up to a proportionality constant. Furthermore, if applied to time-course data in a nonequilibrium setting, the model can be used to estimate elongation rates. This model leads naturally to likelihood ratio tests for differences between genes, conditions, or species in various rates of interest. If read counts are assumed to be Poisson-distributed, convenient, closed-form solutions are available for both parameter estimates and likelihood-ratio-test statistics. Straightforward extensions of the model accommodate uncertainty in the pause site and steric hindrance of initiation by paused polymerases. Additional extensions address Bayesian inference under the Poisson model and a generalized linear model that can be used to discover genomic features associated with rates of elongation. Finally, I address technicalities concerning estimation of library size, normalization and sequencing replicates. Altogether, this modeling framework enables a unified treatment of many common tasks in the analysis of nascent RNA sequencing data
The human Origin Recognition Complex is essential for pre-RC assembly, mitosis and maintenance of nuclear structure.
No full textThe Origin Recognition Complex (ORC) cooperates with CDC6, MCM2-7, and CDT1 to form pre-RC complexes at origins of DNA replication. Here, using tiling-sgRNA CRISPR screens, we report that each subunit of ORC and CDC6 is essential in human cells. Using an auxin-inducible degradation system, we created stable cell lines capable of ablating ORC2 rapidly, revealing multiple cell division cycle phenotypes. The primary defects in the absence of ORC2 were cells encountering difficulty in initiating DNA replication or progressing through the cell division cycle due to reduced MCM2-7 loading onto chromatin in G1 phase. The nuclei of ORC2 deficient cells were also large, with decompacted heterochromatin. Some ORC2 deficient cells that completed DNA replication entered into, but never exited mitosis. ORC1 knockout cells also demonstrated extremely slow cell proliferation and abnormal cell and nuclear morphology. Thus, ORC proteins and CDC6 are indispensable for normal cellular proliferation and contribute to nuclear organization
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman
Get PDFMultiple Sequence Alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for. Here, we implement a smooth and differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF mildly improves contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing the predicted confidence metric, we can learn MSAs that improve structure predictions over the initial MSAs. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment
Predicting flows through microfluidic circuits with fluid walls.
Get PDFThe aqueous phase in traditional microfluidics is usually confined by solid walls; flows through such systems are often predicted accurately. As solid walls limit access, open systems are being developed in which the aqueous phase is partly bounded by fluid walls (interfaces with air or immiscible liquids). Such fluid walls morph during flow due to pressure gradients, so predicting flow fields remains challenging. We recently developed a version of open microfluidics suitable for live-cell biology in which the aqueous phase is confined by an interface with an immiscible and bioinert fluorocarbon (FC40). Here, we find that common medium additives (fetal bovine serum, serum replacement) induce elastic no-slip boundaries at this interface and develop a semi-analytical model to predict flow fields. We experimentally validate the model's accuracy for single conduits and fractal vascular trees and demonstrate how flow fields and shear stresses can be controlled to suit individual applications in cell biology