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ResidualBind: Uncovering Sequence-Structure Preferences of RNA-Binding Proteins with Deep Neural Networks
No full textDeep neural networks have demonstrated improved performance at predicting sequence specificities of DNA- and RNA-binding proteins. However, it remains unclear why they perform better than previous methods that rely on k-mers and position weight matrices. Here, we highlight a recent deep learning-based software package, called ResidualBind, that analyzes RNA-protein interactions using only RNA sequence as an input feature and performs global importance analysis for model interpretability. We discuss practical considerations for model interpretability to uncover learned sequence motifs and their secondary structure preferences
Antisense Oligonucleotide Therapeutics for Cystic Fibrosis: Recent Developments and Perspectives
Full text linkAntisense oligonucleotide (ASO) technology has become an attractive therapeutic modality for various diseases, including Mendelian disorders. ASOs can modulate the expression of a target gene by promoting mRNA degradation or changing pre-mRNA splicing, nonsense-mediated mRNA decay, or translation. Advances in medicinal chemistry and a deeper understanding of post-transcriptional mechanisms have led to the approval of several ASO drugs for diseases that had long lacked therapeutic options. For instance, an ASO drug called nusinersen became the first approved drug for spinal muscular atrophy, improving survival and the overall disease course. Mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene cause cystic fibrosis (CF). Although Trikafta and other CFTR-modulation therapies benefit most CF patients, there is a significant unmet therapeutic need for a subset of CF patients. In this review, we introduce ASO therapies and their mechanisms of action, describe the opportunities and challenges for ASO therapeutics for CF, and discuss the current state and prospects of ASO therapies for CF
An expandable FLP-ON::TIR1 system for precise spatiotemporal protein degradation in Caenorhabditis elegans
No full textThe auxin-inducible degradation system has been widely adopted in the Caenorhabditis elegans research community for its ability to empirically control the spatiotemporal expression of target proteins. This system can efficiently degrade auxin-inducible degron (AID)-tagged proteins via the expression of a ligand-activatable AtTIR1 protein derived from A. thaliana that adapts target proteins to the endogenous C. elegans proteasome. While broad expression of AtTIR1 using strong, ubiquitous promoters can lead to rapid degradation of AID-tagged proteins, cell type-specific expression of AtTIR1 using spatially restricted promoters often results in less efficient target protein degradation. To circumvent this limitation, we have developed a FLP/FRT3-based system that functions to reanimate a dormant, high-powered promoter that can drive sufficient AtTIR1 expression in a cell type-specific manner. We benchmark the utility of this system by generating a number of tissue-specific FLP-ON::TIR1 drivers to reveal genetically separable cell type-specific phenotypes for several target proteins. We also demonstrate that the FLP-ON::TIR1 system is compatible with enhanced degron epitopes. Finally, we provide an expandable toolkit utilizing the basic FLP-ON::TIR1 system that can be adapted to drive optimized AtTIR1 expression in any tissue or cell type of interest
Choice selective inhibition drives stability and competition in decision circuits
No full textDuring perceptual decision-making, the firing rates of cortical neurons reflect upcoming choices. Recent work showed that excitatory and inhibitory neurons are equally selective for choice. However, the functional consequences of inhibitory choice selectivity in decision-making circuits are unknown. We developed a circuit model of decision-making which accounts for the specificity of inputs to and outputs from inhibitory neurons. We found that selective inhibition expands the space of circuits supporting decision-making, allowing for weaker or stronger recurrent excitation when connected in a competitive or feedback motif. The specificity of inhibitory outputs sets the trade-off between speed and accuracy of decisions by either stabilizing or destabilizing the saddle-point dynamics underlying decisions in the circuit. Recurrent neural networks trained to make decisions display the same dependence on inhibitory specificity and the strength of recurrent excitation. Our results reveal two concurrent roles for selective inhibition in decision-making circuits: stabilizing strongly connected excitatory populations and maximizing competition between oppositely selective populations
Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making
Full text linkUnderstanding how cortical circuits generate complex behavior requires investigating the cell types that comprise them. Functional differences across pyramidal neuron (PyN) types have been observed within cortical areas, but it is not known whether these local differences extend throughout the cortex, nor whether additional differences emerge when larger-scale dynamics are considered. We used genetic and retrograde labeling to target pyramidal tract, intratelencephalic and corticostriatal projection neurons and measured their cortex-wide activity. Each PyN type drove unique neural dynamics, both at the local and cortex-wide scales. Cortical activity and optogenetic inactivation during an auditory decision task revealed distinct functional roles. All PyNs in parietal cortex were recruited during perception of the auditory stimulus, but, surprisingly, pyramidal tract neurons had the largest causal role. In frontal cortex, all PyNs were required for accurate choices but showed distinct choice tuning. Our results reveal that rich, cell-type-specific cortical dynamics shape perceptual decisions
Intratumoral heterogeneity and clonal evolution induced by HPV integration
Full text linkThe human papillomavirus (HPV) genome is integrated into host DNA in most HPV-positive cancers, but the consequences for chromosomal integrity are unknown. Continuous long-read sequencing of oropharyngeal cancers and cancer cell lines identified a previously undescribed form of structural variation, "heterocateny," characterized by diverse, interrelated, and repetitive patterns of concatemerized virus and host DNA segments within a cancer. Unique breakpoints shared across structural variants facilitated stepwise reconstruction of their evolution from a common molecular ancestor. This analysis revealed that virus and virus-host concatemers are unstable and, upon insertion into and excision from chromosomes, facilitate capture, amplification, and recombination of host DNA and chromosomal rearrangements. Evidence of heterocateny was detected in extrachromosomal and intrachromosomal DNA. These findings indicate that heterocateny is driven by the dynamic, aberrant replication and recombination of an oncogenic DNA virus, thereby extending known consequences of HPV integration to include promotion of intratumoral heterogeneity and clonal evolution
Spatial and temporal correlations in neural networks with structured connectivity
Full text linkThis article is part of the Physical Review Research collection titled Physics of Neuroscience. Correlated fluctuations in the activity of neural populations reflect the network's dynamics and connectivity. The temporal and spatial dimensions of neural correlations are interdependent. However, prior theoretical work mainly analyzed correlations in either spatial or temporal domains, oblivious to their interplay. We show that the network dynamics and connectivity jointly define the spatiotemporal profile of neural correlations. We derive analytical expressions for pairwise correlations in networks of binary units with spatially arranged connectivity in one and two dimensions. We find that spatial interactions among units generate multiple timescales in auto- and cross-correlations. Each timescale is associated with fluctuations at a particular spatial frequency, making a hierarchical contribution to the correlations. External inputs can modulate the correlation timescales when spatial interactions are nonlinear, and the modulation effect depends on the operating regime of network dynamics. These theoretical results open new ways to relate connectivity and dynamics in cortical networks via measurements of spatiotemporal neural correlations
Novel GluN2B-Selective NMDA Receptor Negative Allosteric Modulator Possesses Intrinsic Analgesic Properties and Enhances Analgesia of Morphine in a Rodent Tail Flick Pain Model
Full text linkMany cases of accidental death associated with drug overdose are due to chronic opioid use, tolerance, and addiction. Analgesic tolerance is characterized by a decreased response to the analgesic effects of opioids, requiring increasingly higher doses to maintain the desired level of pain relief. Overactivation of GluN2B-containing N-methyl-d-Aspartate receptors is thought to play a key role in mechanisms underlying cellular adaptation that takes place in the development of analgesic tolerance. Herein, we describe a novel GluN2B-selective negative allosteric modulator, EU93-108, that shows high potency and brain penetrance. We describe the structural basis for binding at atomic resolution. This compound possesses intrinsic analgesic properties in the rodent tail immersion test. EU93-108 has an acute and significant anodyne effect, whereby morphine when combined with EU93-108 produces a higher tail flick latency compared to that of morphine alone. These data suggest that engagement of GluN2B as a target has utility in the treatment of pain, and EU93-108 could serve as an appropriate tool compound to interrogate this hypothesis. Future structure-activity relationship work around this scaffold could give rise to compounds that can be co-administered with opioids to diminish the onset of tolerance due to chronic opioid use, thereby modifying their utility
Shapeshifting bullvalene-linked vancomycin dimers as effective antibiotics against multidrug-resistant gram-positive bacteria.
No full textThe alarming rise in superbugs that are resistant to drugs of last resort, including vancomycin-resistant enterococci and staphylococci, has become a significant global health hazard. Here, we report the click chemistry synthesis of an unprecedented class of shapeshifting vancomycin dimers (SVDs) that display potent activity against bacteria that are resistant to the parent drug, including the ESKAPE pathogens, vancomycin-resistant Enterococcus (VRE), methicillin-resistant Staphylococcus aureus (MRSA), as well as vancomycin-resistant S. aureus (VRSA). The shapeshifting modality of the dimers is powered by a triazole-linked bullvalene core, exploiting the dynamic covalent rearrangements of the fluxional carbon cage and creating ligands with the capacity to inhibit bacterial cell wall biosynthesis. The new shapeshifting antibiotics are not disadvantaged by the common mechanism of vancomycin resistance resulting from the alteration of the C-terminal dipeptide with the corresponding d-Ala-d-Lac depsipeptide. Further, evidence suggests that the shapeshifting ligands destabilize the complex formed between the flippase MurJ and lipid II, implying the potential for a new mode of action for polyvalent glycopeptides. The SVDs show little propensity for acquired resistance by enterococci, suggesting that this new class of shapeshifting antibiotic will display durable antimicrobial activity not prone to rapidly acquired clinical resistance
