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An engineered, orthogonal auxin analog/AtTIR1(F79G) pairing improves both specificity and efficacy of the auxin degradation system in Caenorhabditis elegans
No full textThe auxin-inducible degradation system in C. elegans allows for spatial and temporal control of protein degradation via heterologous expression of a single Arabidopsis thaliana F-box protein, transport inhibitor response 1 (AtTIR1). In this system, exogenous auxin (Indole-3-acetic acid; IAA) enhances the ability of AtTIR1 to function as a substrate recognition component that adapts engineered degron-tagged proteins to the endogenous C. elegans E3 ubiquitin ligases complex [SKR-1/2-CUL-1-F-box (SCF)], targeting them for degradation by the proteosome. While this system has been employed to dissect the developmental functions of many C. elegans proteins, we have found that several auxin-inducible degron (AID)-tagged proteins are constitutively degraded by AtTIR1 in the absence of auxin, leading to undesired loss-of-function phenotypes. In this manuscript, we adapt an orthogonal auxin derivative/mutant AtTIR1 pair [C. elegans AID version 2 (C.e.AIDv2)] that transforms the specificity of allosteric regulation of TIR1 from IAA to one that is dependent on an auxin derivative harboring a bulky aryl group (5-Ph-IAA). We find that a mutant AtTIR1(F79G) allele that alters the ligand-binding interface of TIR1 dramatically reduces ligand-independent degradation of multiple AID*-tagged proteins. In addition to solving the ectopic degradation problem for some AID-targets, the addition of 5-Ph-IAA to culture media of animals expressing AtTIR1(F79G) leads to more penetrant loss-of-function phenotypes for AID*-tagged proteins than those elicited by the AtTIR1-IAA pairing at similar auxin analog concentrations. The improved specificity and efficacy afforded by the mutant AtTIR1(F79G) allele expand the utility of the AID system and broaden the number of proteins that can be effectively targeted with it
The 3 '-5 ' Exonuclease Dis3L2 Undergoes Conformational Dynamics During Structured RNA Degradation
No full textSelective time-dependent changes in activity and cell-specific gene expression in human postmortem brain.
Get PDFAs a means to understand human neuropsychiatric disorders from human brain samples, we compared the transcription patterns and histological features of postmortem brain to fresh human neocortex isolated immediately following surgical removal. Compared to a number of neuropsychiatric disease-associated postmortem transcriptomes, the fresh human brain transcriptome had an entirely unique transcriptional pattern. To understand this difference, we measured genome-wide transcription as a function of time after fresh tissue removal to mimic the postmortem interval. Within a few hours, a selective reduction in the number of neuronal activity-dependent transcripts occurred with relative preservation of housekeeping genes commonly used as a reference for RNA normalization. Gene clustering indicated a rapid reduction in neuronal gene expression with a reciprocal time-dependent increase in astroglial and microglial gene expression that continued to increase for at least 24 h after tissue resection. Predicted transcriptional changes were confirmed histologically on the same tissue demonstrating that while neurons were degenerating, glial cells underwent an outgrowth of their processes. The rapid loss of neuronal genes and reciprocal expression of glial genes highlights highly dynamic transcriptional and cellular changes that occur during the postmortem interval. Understanding these time-dependent changes in gene expression in post mortem brain samples is critical for the interpretation of research studies on human brain disorders
Male fertility in Arabidopsis requires active DNA demethylation of genes that control pollen tube function.
Get PDFActive DNA demethylation is required for sexual reproduction in plants but the molecular determinants underlying this epigenetic control are not known. Here, we show in Arabidopsis thaliana that the DNA glycosylases DEMETER (DME) and REPRESSOR OF SILENCING 1 (ROS1) act semi-redundantly in the vegetative cell of pollen to demethylate DNA and ensure proper pollen tube progression. Moreover, we identify six pollen-specific genes with increased DNA methylation as well as reduced expression in dme and dme;ros1. We further show that for four of these genes, reinstalling their expression individually in mutant pollen is sufficient to improve male fertility. Our findings demonstrate an essential role of active DNA demethylation in regulating genes involved in pollen function
Retinal and Callosal Activity-Dependent Chandelier Cell Elimination Shapes Binocularity in Primary Visual Cortex.
No full textIn mammals with binocular vision, integration of the left and right visual scene relies on information in the center visual field, which are relayed from each retina in parallel and merge in the primary visual cortex (V1) through the convergence of ipsi- and contralateral geniculocortical inputs as well as transcallosal projections between two visual cortices. The developmental assembly of this binocular circuit, especially the transcallosal pathway, remains incompletely understood. Using genetic methods in mice, we found that several days before eye-opening, retinal and callosal activities drive massive apoptosis of GABAergic chandelier cells (ChCs) in the binocular region of V1. Blockade of ChC elimination resulted in a contralateral eye-dominated V1 and deficient binocular vision. As pre-vision retinal activities convey the left-right organization of the visual field, their regulation of ChC density through the transcallosal pathway may prime a nascent binocular territory for subsequent experience-driven tuning during the post-vision critical period
Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.
Get PDFBACKGROUND: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear. METHODS: We used machine learning algorithms to predict COVID-19 severity in 348 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using only clinical variables collected on or before a patient's COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the patient required high levels of oxygen after 3 days), and Non-severe (the patient never required oxygen support). RESULTS: Our algorithm classified patients into these classes with an area under the receiver operating characteristic curve (AUROC) ranging from 70 to 85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables - including basic patient information, pre-existing diagnoses, laboratory and radiological work, and underlying cancer type - suggesting that COVID-19 in cancer patients comes with numerous, combinatorial risk factors. CONCLUSIONS: Overall, we provide a computational tool that can identify high-risk patients early in their disease progression, which could aid in clinical decision-making and selecting treatment options
Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED.
No full textConventional targeted sequencing methods eliminate many of the benefits of nanopore sequencing, such as the ability to accurately detect structural variants or epigenetic modifications. The ReadUntil method allows nanopore devices to selectively eject reads from pores in real time, which could enable purely computational targeted sequencing. However, this requires rapid identification of on-target reads while most mapping methods require computationally intensive basecalling. We present UNCALLED ( https://github.com/skovaka/UNCALLED ), an open source mapper that rapidly matches streaming of nanopore current signals to a reference sequence. UNCALLED probabilistically considers k-mers that could be represented by the signal and then prunes the candidates based on the reference encoded within a Ferragina-Manzini index. We used UNCALLED to deplete sequencing of known bacterial genomes within a metagenomics community, enriching the remaining species 4.46-fold. UNCALLED also enriched 148 human genes associated with hereditary cancers to 29.6× coverage using one MinION flowcell, enabling accurate detection of single-nucleotide polymorphisms, insertions and deletions, structural variants and methylation in these genes
Cell wall protein variation, break-induced replication, and subtelomere dynamics in Candida glabrata.
No full textCandida glabrata is an opportunistic pathogen of humans, responsible for up to 30% of disseminated candidiasis. Adherence of C. glabrata to host cells is mediated by adhesin-like proteins (ALPs), about half of which are encoded in the subtelomeres. We performed a de novo assembly of two C. glabrata strains, BG2 and BG3993, using long single-molecule real-time (SMRT) reads, and constructed high-quality telomere-to-telomere assemblies of all 13 chromosomes to assess differences between C. glabrata strains. We documented variation between strains, and in agreement with earlier studies, found high (~0.5%-1%) frequencies of SNVs across the genome, including within subtelomeric regions. We documented changes in ALP gene structure and complement: there are large length differences in ALP genes in different strains, resulting from copy number variation in tandem repeats. We compared strains to characterize chromosome rearrangement events including within the poorly characterized subtelomeric regions. We show that rearrangements within the subtelomere regions all affect ALP-encoding genes, and 14/16 involve just the most terminal ALP gene. We present evidence that these rearrangements are mediated by break-induced replication. This study highlights the constrained nature of subtelomeric changes impacting ALP gene complement and subtelomere structure
Improving representations of genomic sequence motifs in convolutional networks with exponential activations.
No full textDeep convolutional neural networks (CNNs) trained on regulatory genomic sequences tend to build representations in a distributed manner, making it a challenge to extract learned features that are biologically meaningful, such as sequence motifs. Here we perform a comprehensive analysis of synthetic sequences to investigate the role that CNN activations have on model interpretability. We show that employing an exponential activation in the first layer filters consistently leads to interpretable and robust representations of motifs compared with other commonly used activations. Strikingly, we demonstrate that CNNs with better test performance do not necessarily imply more interpretable representations with attribution methods. We find that CNNs with exponential activations significantly improve the efficacy of recovering biologically meaningful representations with attribution methods. We demonstrate that these results generalize to real DNA sequences across several in vivo datasets. Together, this work demonstrates how a small modification to existing CNNs (that is, setting exponential activations in the first layer) can substantially improve the robustness and interpretabilty of learned representations directly in convolutional filters and indirectly with attribution methods