Biology subjects

Lloyd, K.

Publications and source records attributed to Lloyd, K..

11 recordsLinked to original sources

bioRxiv2024

Synthetic integrin antibodies discovered by yeast display reveal αV subunit pairing preferences with β subunits

Eight of the 24 integrin heterodimers bind to the tripeptide Arg-Gly-Asp (RGD) motif in their extracellular ligands, and play essential roles in cell adhesion, migration, and homeostasis. Despite similarity in recognizing the RGD motif and some redundancy, these integrins can selectively recognize RGD-containing ligands including fibronectin, vitronectin, fibrinogen, nephronectin and the prodomain of the transforming growth factors to fulfill specific functions in cellular processes. Subtype-specific antibodies against RGD-binding integrins are desirable for investigating their specific functions. In this study, we discovered 11 antibodies that exhibit high specificity and affinity towards integrins V{beta}3, V{beta}5, V{beta}6, V{beta}8, and 5{beta}1 from a synthetic yeast-displayed Fab library. Of these, 6 are function-blocking antibodies containing an R(G/L/T) D motif in their CDR3 sequences. We report antibody binding specificity, kinetics, and binding affinity for purified integrin ectodomains as well as intact integrins on the cell surface. We further employed these antibodies to reveal binding preferences of the V subunit for its 5 {beta}-subunit partners: {beta}6={beta}8>{beta}3>{beta}1={beta}5.

cell biology↗

bioRxiv2024

Do organisms need an impact factor? Citations of key biological resources including model organisms reveal usage patterns and impact.

Research resources like transgenic animals and antibodies are the workhorses of biomedicine, enabling investigators to relatively easily study specific disease conditions. As key biological resources, transgenic animals and antibodies are often validated, maintained, and distributed from university based stock centers. As these centers heavily rely largely on grant funding, it is critical that they are cited by investigators so that usage can be tracked. However, unlike systems for tracking the impact of papers, the conventions and systems for tracking key resource usage and impact lag behind. Previous studies have shown that about 50% of the resources are not findable, making the studies they are supporting irreproducible, but also makes tracking resources difficult. The RRID project is filling this gap by working with journals and resource providers to improve citation practices and to track the usage of these key resources. Here, we reviewed 10 years of citation practices for five university based stock centers, characterizing each reference into two broad categories: findable (authors could use the RRID, stock number, or full name) and not findable (authors could use a nickname or a common name that is not unique to the resource). The data revealed that when stock centers asked their communities to cite resources by RRID, in addition to helping stock centers more easily track resource usage by increasing the number of RRID papers, authors shifted from citing resources predominantly by nickname ([~]50% of the time) to citing them by one of the findable categories ([~]85%) in a matter of several years. In the case of one stock center, the MMRRC, the improvement in findability is also associated with improvements in the adherence to NIH rigor criteria, as determined by a significant increase in the Rigor and Transparency Index for studies using MMRRC mice. From this data, it was not possible to determine whether outreach to authors or changes to stock center websites drove better citation practices, but findability of research resources and rigor adherence was improved.

bioinformatics↗

bioRxiv2023

Reframing dopamine: Controlling the controller at the limbic-motor interface

Pavlovian influences notoriously interfere with operant behaviour; various such may be associated with the release of the neuromodulator dopamine in the nucleus accumbens. One role for cognitive control is suppressing these influences. Here, using the examples of active avoidance and omission behaviour, we examine the possibility that one instrument of control is direct manipulation of the dopamine signal itself.

neuroscience↗

bioRxiv2023

Complex organic matter degradation by secondary consumers in chemolithoautotrophy-based subsurface geothermal ecosystems

Microbial communities in terrestrial geothermal systems often contain chemolithoautotrophs with well-characterized distributions and metabolic capabilities. However, the extent to which organic matter produced by these chemolithoautotrophs supports heterotrophs remains largely unknown. Here we compared the abundance and activity of peptidases and carbohydrate active enzymes (CAZymes) that are predicted to be extracellular identified in metagenomic assemblies from 63 springs in the Central American and the Andean convergent margin (Argentinian backarc of the Central Volcanic Zone), as well as the plume-influenced spreading center in Iceland. All assemblies contain two orders of magnitude more peptidases than CAZymes, suggesting that the microorganisms more often use proteins for their carbon and/or nitrogen acquisition instead of complex sugars. The CAZy families in highest abundance are GH23 and CBM50, and the most abundant peptidase families are M23 and C26, all four of which degrade peptidoglycan found in bacterial cells. This implies that the heterotrophic community relies on autochthonous dead cell biomass, rather than allochthonous plant matter, for organic material. Enzymes involved in the degradation of cyanobacterial- and algal-derived compounds are in lower abundance at every site, with volcanic sites having more enzymes degrading cyanobacterial compounds and non-volcanic sites having more enzymes degrading algal compounds. Activity assays showed that many of these enzyme classes are active in these samples. High temperature sites (> 80{degrees}C) had similar extracellular carbon-degrading enzymes regardless of their province, suggesting a less well-developed population of secondary consumers at these sites, possibly connected with the limited extent of the subsurface biosphere in these high temperature sites. We conclude that in < 80{degrees}C springs, chemolithoautotrophic production supports heterotrophs capable of degrading a wide range of organic compounds that do not vary by geological province, even though the taxonomic and respiratory repertoire of chemolithoautotrophs and heterotrophs differ greatly across these regions.

biochemistry↗

bioRxiv2022

Vigilance, arousal, and acetylcholine: Optimal control of attention in a simple detection task

Paying attention to particular aspects of the world or being more vigilant in general can be interpreted as forms of internal action. Such arousal-related choices come with the benefit of increasing the quality and situational appropriateness of information acquisition and processing, but incur potentially expensive energetic and opportunity costs. One implementational route for these choices is widespread ascending neuromodulation, including by acetylcholine (ACh). The key computational question that elective attention poses for sensory processing is when it is worthwhile paying these costs, and this includes consideration of whether sufficient information has yet been collected to justify the higher signal-to-noise ratio afforded by greater attention and, particularly if a change in attentional state is more expensive than its maintenance, when states of heightened attention ought to persist. We offer a partially observable Markov decision-process treatment of optional attention in a detection task, and use it to provide a qualitative model of the results of studies using modern techniques to measure and manipulate ACh in rodents performing a similar task.

neuroscience↗

bioRxiv2022

The role of the orbitofrontal cortex in creating cognitive maps

We use internal models of the external world to guide behavior, but little is known about how these cognitive maps are created. The orbitofrontal cortex (OFC) is typically thought to access these maps to support model-based decision-making, but it has recently been proposed that its critical contribution may be instead to integrate information into existing and new models. We tested between these alternatives using an outcome-specific devaluation task and a high-potency chemogenetic approach. We found that selectively inactivating OFC principal neurons when rats learned distinct cue-outcome associations, but prior to outcome devaluation, disrupted subsequent model-based inference, confirming that the OFC is critical for creating new cognitive maps. However, OFC inactivation surprisingly led to generalized devaluation. Using a novel reinforcement learning framework, we demonstrate that this effect is best explained not by a switch to a model-free system, as would be traditionally assumed, but rather by a circumscribed deficit in defining credit assignment precision during model construction. We conclude that the critical contribution of the OFC to learning is regulating the specificity of associations that comprise cognitive maps. One Sentence SummaryOFC inactivation impairs learning of new specific cue-outcome associations without disrupting model-based learning in general.

neuroscience↗

bioRxiv2021

Prevalent mouse phenotypes in the unexplored druggable genome

Among the estimated ~23,000 protein encoding human genes, the class of druggable genes- defined by their ability to bind drug-like compounds- represents an enticing collection of targets for clinical intervention. Yet many if not most of these genes remain poorly understood and understudied. Here we evaluate three major classes of druggable genes (GPCRs, ion channels, and kinases) and found that a third of these remain largely ignored yet display significant mouse phenotypes upon genetic ablation. We show that both well-studied and understudied druggable genes share a similar number and spectrum of phenotypes. Moreover, many of the mouse phenotypes arising from the ablation of both well-studied and understudied druggable genes show similarities with symptoms in rare human diseases. Collectively these data diminish the notion that most poorly studied genes may not be especially important and highlight therapeutic opportunities and potential disease models among poorly characterized druggable genes.

genetics↗

bioRxiv2021

Local mechanical stimuli shape tissue growth in vertebrate joint morphogenesis

The correct formation of synovial joints is essential for proper motion throughout life. Movement-induced forces are critical to creating correctly shaped joints, but it is unclear how cells sense and respond to these mechanical cues. To determine how mechanical stimuli drive joint morphogenesis, we combined experiments on regenerating axolotl (Ambystoma mexicanum) forelimbs with a poroelastic model of bone rudiment growth. Animals either regrew forelimbs normally (control) or were injected with a TRPV4 agonist to impair chondrocyte mechanosensitivity during joint morphogenesis. We quantified growth and shape in regrown humeri from whole mount light sheet fluorescence images of the regenerated limbs. Results revealed statistically significant differences in morphology and cell proliferation between groups, indicating that mechanical stimuli play a role in the shaping of joints. We simulated local tissue growth in a finite element model with a biological contribution to growth proportional to chondrocyte density, and a mechanical contribution to growth proportional to fluid pressure. Computational predictions agreed with experimental outcomes, suggesting that interstitial pressure driven from cyclic mechanical stimuli promotes local tissue growth. Predictive computational models informed by experimental findings allow us to explore potential physical mechanisms involved in tissue growth to advance our understanding of the mechanobiology of joint morphogenesis.

developmental biology↗

bioRxiv2020

Deep learning classification of canine behavior using a single collar-mounted accelerometer: Real-world validation

Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from over 5,000 videos of over 2,500 dogs and ran the algorithms in production on over 11 million days of device data. We then surveyed project participants representing 10,550 dogs, they provided us 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking, petting, rubbing, scratching, and sniffing. We show that the devices position on the collar has no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1,514 users), and of 94.9% (among 1,491 users) for drinking. The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.

animal behavior and cognition↗

bioRxiv2020

Content and performance of the MiniMUGA genotyping array, a new tool to improve rigor and reproducibility in mouse research

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well annotated genome, wealth of genetic resources and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost effective, informative and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole genome sequencing. Here we describe the content and performance of a new Mouse Universal Genotyping Array (MUGA). MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: 1) chromosomal sex determination, 2) discrimination between substrains from multiple commercial vendors, 3) diagnostic SNPs for popular laboratory strains, 4) detection of constructs used in genetically engineered mice, and 5) an easy to interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA we genotyped 6,899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived and recombinant inbred lines. Here we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, the extension of the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. There is preliminary but striking evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the main stock for a significant action of the genotyped inbred samples. We conclude that MiniMUGA is a valuable platform for genetic QC and important new tool to the increase rigor and reproducibility of mouse research.

genetics↗

bioRxiv2019

Identification of a novel therapeutic agent for Inflammatory Bowel Disease guided by systems medicine

ObjectiveInflammatory bowel diseases cause significant morbidity and mortality. Aberrant NF-{kappa}B signalling is strongly associated with these conditions, and several established drugs influence the NF-{kappa}B signalling network to exert their effect. This study aimed to identify drugs which alter NF-{kappa}B signalling and may be repositioned for use in inflammatory bowel disease. DesignThe SysmedIBD consortium established a novel drug-repurposing pipeline based on a combination of in-silico drug discovery and biological assays targeted at demonstrating an impact on NF-kappaB signalling, and a murine model of IBD. ResultsThe drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic Clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. Clarithromycins effects were validated in several experiments: it influenced NF-{kappa}B mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-{kappa}B protein shuttling in murine reporter enteroids; it suppressed NF-{kappa}B (p65) DNA binding in the small intestine of mice exposed to LPS, and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-{kappa}B (p65) nuclear translocation in human intestinal enteroids. ConclusionsThese findings demonstrate that in-silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of inflammatory bowel disease; and that further clinical assessment of clarithromycin in the management of inflammatory bowel disease is required.

systems biology↗