Biology subjects

Reynolds, R. C.

Publications and source records attributed to Reynolds, R. C..

11 recordsLinked to original sources

bioRxiv2026

Performance of Rilpivirine-Based Hydrophobic Tags and PROTACs Directed Against HIV-1 Reverse Transcriptase

We aimed to repurpose the non-nucleoside reverse transcriptase (RT) inhibitor (NNRTI) rilpivirine (RPV) as a targeted protein degrader (TPD) of HIV-1 RT. Structure-guided modeling identified a TPD bifunctional linker exit trajectory from RPV that threads the NNRTI entrance channel. Ten RPV analogs (RPV') with varied warhead-linker attachments retained RT inhibition and antiviral activity, guiding selection of an amide-linked connector for degrader construction. We synthesized Proteolysis Targeting Chimeras (PROTACs) designed to recruit CUL4CRBN and CUL2VHL, alongside adamantyl acetic acid-based Hydrophobic Tags (HyTs). TPDs were evaluated for RT inhibition, virus inhibition, biophysical target engagement, and proteasome-dependent degradation. Some PROTACs showed minimal antiviral activity or limited solubility for our assays, whereas the HyT with a tetraethylene glycol (PEG4) linker (PEG4-Ad) exhibited low nanomolar potency to suppress HIV-1 replication and acceptable solubility. PEG4-Ad reduced RT levels in a proteasome-dependent manner without cytotoxicity but did not exhibit superior potency against RPV resistance mutations. MD simulations suggested that PEG4 linkers maximize episodic exposure of the hydrophobic tag, consistent with PEG4-Ad efficacy. These data highlight the promise - and constraints - of antiviral degraders, indicating that linker-controlled hydrophobic tag exposure and subcellular target accessibility may be critical design parameters for prospective therapeutics.

microbiology↗

bioRxiv2025

Domain-general neural effects of associative learning and expectations on pain and hedonic taste perception

Predictive cues significantly influence perception through associative learning. However, it is unknown whether circuits are conserved across domains. We investigated how associative learning influences perceived intensity and valence of pain and hedonic taste and whether the mechanisms that support expectancy-based modulation vary as a function of aversiveness and modality. Sixty participants were randomly assigned to receive either painful heat, unpleasant liquid saline, or pleasant liquid sucrose during fMRI scanning. Following conditioning, cues that were initially associated with low or high intensity outcomes were intermittently followed by stimuli calibrated to elicit medium intensity ratings. Learned cues modulated expectations and subjective outcomes similarly across domains. Consistent with this, the orbitofrontal cortex exhibited domain-general anticipatory activation. The left anterior insula mediated domain-general cue effects on subjective intensity, whereas the thalamus mediated cue effects on subjective valence. Notably, these two regions were nearly identical to those previously implicated in mediating cue effects on pain (nearly identical to those previously implicated in cue effects on mediating pain (Atlas et al., 2010). Pain specificity was evident when we measured variations in stimulus intensity, whether we used univariate or multivariate approaches, but there was minimal evidence of specificity by modality or aversiveness when we examined cue effects on medium trials. These findings suggest that shared neural circuits mediate the effects of learned expectations on perception, linking pain with other areas of affective processing and perception across domains.

neuroscience↗

bioRxiv2024

A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically under-discussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni_proc.py. These items include: a modular HTML document that covers full single subject processing from the raw data through statistical modeling; several review scripts in the results directory of processed data; and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each "QC block", as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.

neuroscience↗

bioRxiv2023

BOLD response is more than just magnitude: improving detection sensitivity through capturing hemodynamic profiles

Typical FMRI analyses assume a canonical hemodynamic response function (HRF) with a focus on the overshoot peak height, while other morphological aspects are largely ignored. Thus, in most reported analyses, the overall effect is reduced from a curve to a single scalar. Here, we adopt a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a response profile at the individual level. Then, we estimate the response in its entirety with a roughness penalty at the population level to improve predictive accuracy, inferential efficiency, and cross-study reproducibility. Using a fast event-related FMRI dataset, we demonstrate the extent of under-fitting and information loss that occurs when adopting the canonical approach. We also address the following questions: O_LIHow much does the HRF shape vary across regions, conditions, and groups? C_LIO_LIDoes an agnostic approach improve sensitivity to detect an effect compared to an assumed HRF? C_LIO_LICan examining HRF shape help validate the presence of an effect complementing statistical evidence? C_LIO_LICould the HRF shape provide evidence for whole-brain BOLD response during a simple task? C_LI

neuroscience↗

bioRxiv2022

Highlight Results, Don't Hide Them: Enhance interpretation, reduce biases and improve reproducibility

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, we propose that studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice---focusing on highlighting results, rather than hiding all but the strongest ones---can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses. HighlightsO_LIMost studies do not present all results of their analysis, hiding subthreshold ones. C_LIO_LIHiding results negatively affects the interpretation and understanding of the study. C_LIO_LINeuroimagers should present all results of their study, highlighting key ones. C_LIO_LIUsing the public NARPS data, we show several benefits of the "highlighting" approach. C_LIO_LIThe highlighting approach improves individual studies and meta-analyses. C_LI

neuroscience↗

bioRxiv2021

Instructions and experiential learning have similar impacts on pain and pain-related brain responses but produce dissociations in value-based reversal learning

Recent data suggest that interactions between systems involved in higher order knowledge and associative learning drive responses during appetitive and aversive learning. However, it is unknown how these systems impact subjective responses, such as pain. We tested how instructions and reversal learning influence pain and pain-evoked brain activation. Healthy volunteers (n = 40) were either instructed about contingencies between cues and aversive outcomes or learned through experience in a paradigm where contingencies reversed three times. We measured predictive cue effects on pain and heat-evoked brain responses using functional magnetic resonance imaging. Predictive cues dynamically modulated pain perception as contingencies changed, regardless of whether participants received contingency instructions. Heat-evoked responses in the insula, anterior cingulate, and putamen updated as contingencies changed, whereas the periaqueductal gray and thalamus responded to initial contingencies throughout the task. Quantitative modeling revealed that expected value was shaped purely by instructions in the Instructed Group, whereas expected value updated dynamically in the Uninstructed Group as a function of error-based learning. These differences were accompanied by dissociations in the neural correlates of value-based learning in the rostral anterior cingulate, medial prefrontal cortex, and orbitofrontal cortex. These results show how predictions impact subjective pain. Moreover, imaging data delineate three types of networks involved in pain generation and value-based learning: those that respond to initial contingencies, those that update dynamically during feedback-driven learning as contingencies change, and those that are sensitive to instruction. Together, these findings provide multiple points of entry for therapies designs to impact pain.

neuroscience↗

bioRxiv2020

EPI Distortion Correction is Easy and Useful, and You Should Use It: A case study with toddler data

Task, resting state, and diffusion MRI data are usually acquired from subjects using echo-planar based imaging techniques. These techniques are highly susceptible to B0 homogeneity effects that result in geometric distortions in the reconstructed images. As researchers work to link the information from these scans back to various developmental stages, or to conditions and diseases in specific regions or structures of the brain, it becomes critical to have accurate correspondence between more geometrically distorted echo-planar images and less geometrically distorted anatomical images. A variety of techniques and tools have been developed to improve this correspondence. The basic premise behind most techniques used to mitigate geometric distortion is to acquire enough information to inform software tools how echo-planar images are warped, then have them undo that warping. Here, we investigate the application of two common methods: B0 correction, and reverse-polarity phase-encoding (or reverse blip) correction. We implement each of these in two separate, widely used software packages in the field: AFNI and FSL. We find that using either technique in either software package results in reduced geometric distortions in the EPI images. We discuss the practical implementations of these methods (e.g., increased scan and processing time). In general, however, both methods possess readily available data acquisition schemes, and are highly efficient to include in processing streams. Due to the overall data improvement, we strongly recommend that researchers include one of these methods in their standard protocols.

biophysics↗

bioRxiv2020

LAYNII: A software suite for layer-fMRI

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed open-source and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain layerification and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data. HighlightsO_LIA new software toolbox is introduced for layer-specific functional MRI: LayNii. C_LIO_LILayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS. C_LIO_LILayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints. C_LIO_LILayNii performs layerification in the native voxel space of functional data. C_LIO_LILayNii performs layer-smoothing, GE-BOLD deveining, QA, and VASO analysis. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/148080v3_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@e12e9org.highwire.dtl.DTLVardef@1fbb564org.highwire.dtl.DTLVardef@41da13org.highwire.dtl.DTLVardef@1545106_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

neuroscience↗

bioRxiv2018

A tail of two sides: Artificially doubled false positive rates in neuroimaging due to the sidedness choice with t-tests

One-sided t-tests are widely used in neuroimaging data analysis. While such a test may be applicable when investigating specific regions and prior information about directionality is present, we argue here that it is often mis-applied, with severe consequences for false positive rate (FPR) control. Conceptually, a pair of one-sided t-tests conducted in tandem (e.g., to test separately for both positive and negative effects), effectively amounts to a two-sided t-test. However, replacing the two-sided test with a pair of one-sided tests without multiple comparisons correction essentially doubles the intended FPR of statements made about the same study; that is, the actual family-wise error (FWE) of results at the whole brain level would be 10% instead of the 5% intended by the researcher. Therefore, we strongly recommend that, unless otherwise explicitly justified, two-sided t-tests be applied instead of two simultaneous one-sided t-tests.

neuroscience↗

bioRxiv2018

FMRI processing with AFNI: Some comments and corrections on \"Exploring the Impact of Analysis Software on Task fMRI Results\"

A recent study posted on bioRxiv by Bowring, Maumet and Nichols aimed to compare results of FMRI data that had been processed with three commonly used software packages (AFNI, FSL and SPM). Their stated purpose was to use \"default\" settings of each softwares pipeline for task-based FMRI, and then to quantify overlaps in final clustering results and to measure similarity/dissimilarity in the final outcomes of packages. While in theory the setup sounds simple (implement each packages defaults and compare results), practical realities make this difficult. For example, different softwares would recommend different spatial resolutions of the final data, but for the sake of comparisons, the same value must be used across all. Moreover, we would say that AFNI does not have an explicit default pipeline available: a wide diversity of datasets and study designs are acquired across the neuroimaging community, often requiring bespoke tailoring of basic processing rather than a \"one-size-fits-all\" pipeline. However, we do have strong recommendations for certain steps, and we are also aware that the choice of a given step might place requirements on other processing steps. Given the very clear reporting of the AFNI pipeline used in Bowring et al. paper, we take this opportunity to comment on some of these aspects of processing with AFNI here, clarifying a few mistakes therein and also offering recommendations. We provide point-by-point considerations of using AFNIs processing pipeline design tool at the individual level, afni_proc.py, along with supplementary programs; while specifically discussed in the context of the present usage, many of these choices may serve as useful starting points for broader processing. It is our intention/hope that the user should examine data quality at every step, and we demonstrate how this is facilitated in AFNI, as well.

neuroscience↗

bioRxiv2017

Regulated release of cryptococcal polysaccharide drives virulence and suppresses immune cell infiltration into the central nervous system

Cryptococcus neoformans is a common environmental yeast and opportunistic pathogen responsible for 15% of AIDS-related deaths worldwide. Mortality primarily results from meningoencephalitis, which occurs when fungal cells disseminate from the initial pulmonary infection site and spread to the brain. A key C. neoformans virulence trait is the polysaccharide capsule. Capsule shields C. neoformans from immune-mediated recognition and destruction. The main capsule component, glucuronoxylomannan (GXM), is found both attached to the cell surface and free in the extracellular space (as exo-GXM). Exo-GXM accumulates in patient serum and cerebrospinal fluid at g/mL concentrations, has well-documented immunosuppressive properties, and correlates with poor patient outcomes. However, it is poorly understood whether exo-GXM release is regulated or the result of shedding during normal capsule turnover. We demonstrate that exo-GXM release is regulated by environmental cues and inversely correlates with surface capsule levels. We identified genes specifically involved in exo-GXM release that do not alter surface capsule thickness. The first mutant, liv7{triangleup}, released less GXM than wild-type cells when capsule is not induced. The second mutant, cnag_00658{triangleup}, released more exo-GXM under capsule-inducing conditions. Exo-GXM release observed in vitro correlated with polystyrene adherence, virulence, and fungal burden during murine infection. Additionally, we find that exo-GXM reduces cell size and capsule thickness in capsule-inducing conditions, potentially influencing dissemination. Finally, we demonstrated that exo-GXM prevents immune cell infiltration into the brain during disseminated infection and highly inflammatory intracranial infection. Our data suggest that exo-GXM performs a different role from capsule GXM during infection, altering cell size and suppressing inflammation.\n\nImportanceCryptococcus neoformans is a leading cause of life-threatening meningoencephalitis in humans. C. neoformans cells produce an immunosuppressive polysaccharide, glucuronoxylomannan (GXM), that is the main component of a protective surface capsule. GXM is also released free into extracellular space as exo-GXM, although the distinction between cell-attached GXM and exo-GXM has been unclear. Exo-GXM influences the outcome of infection, is the basis for current diagnostic tools, and has potential therapeutic applications. This study increases our basic understanding of the fungal biology that regulates polysaccharide release, suggesting that the release of cell-attached GXM and exo-GXM are distinctly regulated. We also introduce a new concept that exo-GXM may alter cell body and capsule size, thereby influencing dissemination in the host. Finally, we provide experimental evidence to confirm clinical observations that exo-GXM influences inflammation during brain infection.

microbiology↗