Biology subjects

Hawkins, J.

Publications and source records attributed to Hawkins, J..

11 recordsLinked to original sources

bioRxiv2025

Comparative Analysis of Indole Derivative Production Among Clinical Isolates of Fusobacterium nucleatum

Pathogenic bacteria adapt to distinct disease environments, but whether these adaptations create therapeutic vulnerabilities remains unclear. Fusobacterium nucleatum has emerged as a key microbial player in colorectal cancer (CRC), yet its strain-specific virulence mechanisms remain poorly defined. In this pilot study of 16 clinical F. nucleatum isolates from CRC patients (n=6), Crohns disease patients (n=6), colon of healthy individuals (n=3), and an oral lesion (n=1), a subset of CRC-derived strains produced 3-4-fold higher levels of endogenous indole. Exogenous indole treatment differentially affected growth and biofilm formation, with some strains increasing biofilm despite growth inhibition. Notably, sensitivity to exogenous indole was independent of endogenous production and revealed that isolate 7-1 (EAVG002), a member of the tumorigenic Fna C2 clade, was uniquely hypersensitive to I3CA- and IPA-mediated stress. Invasion assays further showed that indole and its derivatives (I3A, I3CA) reduced invasion of a highly indole-tolerant CRC-derived isolate (SB-CTX3Tcol3) into CRC cells by [~]50%, comparable to antibiotic treatment. Furthermore, in two CRC cell lines, exposure to indole or indole derivatives resulted in substantial variability in adherens junction and tight junction transcript levels, with I3A having the strongest effect on tight junction (CLDN1, CLDN7) transcripts. Collectively, these findings reveal profound strain-level heterogeneity and indole derivative effects, highlighting vulnerabilities that could enable precision therapeutic targeting of pathogenic F. nucleatum populations within the host environment while preserving beneficial commensals.

microbiology↗

bioRxiv2024

Loss of KDM6A-mediated genomic instability and metabolic reprogramming differentially regulates responses to immune checkpoint therapy and chemotherapy in bladder cancer.

Mutations in genes encoding critical epigenetic regulators are frequently noted in bladder cancer, however, the impact of these mutations on therapeutic efficacy is unclear. One of the most common driver mutations in bladder cancer occurs in the KDM6A gene, which encodes a histone demethylase that promotes gene transcription. Retrospective analyses of patients with bladder cancer demonstrated that KDM6A mutations correlate with improved overall survival (OS) with immune checkpoint therapy (ICT), while they are associated with lower OS in patients undergoing cisplatin-based chemotherapy. Mechanistic studies utilizing CRISPR-Cas9 mediated deletion of Kdm6a showed reduced expression of DNA mismatch repair (MMR) and DNA double-stranded base repair (DSBR) genes in tumor cells with improved response to anti-PD-1 therapy and attenuated sensitivity to cisplatin-based chemotherapy in preclinical models of bladder cancer. Additionally, the loss of Kdm6a-mediated reduction in glycolysis and intratumoral lactate accumulation impaired histone 3 lysine 9 lactylation (H3K9la) and histone 3 lysine 18 lactylation (H3K18la) in Tregs with concurrent decrease in the expression of key genes including Foxp3, Tgfb and Pdcd1 and their immune-suppressive function. Further, reduced expansion of PD-1hi Tregs improved the ratio of cytotoxic T cells to Tregs and response to anti-PD-1 therapy in Kdm6a deficient tumor-bearing mice. Collectively, this study provided key insights into the role of KDM6A-mediated epigenetic regulation of DNA repair and metabolic reprogramming which potentially govern response to chemotherapy and ICT thus highlighting the utility of KDM6A mutation status for patient stratification and development of personalized treatment algorithms.

immunology↗

bioRxiv2024

TBX15 regulates a network of immune response genes in adipose tissue and alters fat mass and depot weight in heterozygous knockout mice

Adipose tissue distribution in the body is an indicator of metabolic disease risk, independent of body mass index (BMI), and is indirectly measured by waist-hip-ratio (WHR). T-Box transcription factor-15 (TBX15) has been implicated in regulation of adipose distribution in multiple human and mouse studies, and the TBX15-WARS2 genome-wide association study locus has been associated with BMI-adjusted-WHR signals in multiple investigations. As a potential mediator of this signal, we investigated the role of Tbx15 using heterozygous and homozygous mouse knockout models to determine if loss of this gene alters adipose physiology, and to identify the transcriptional network regulated by Tbx15 in adipose tissue and preadipocyte cells. In a metabolic phenotyping experiment we provided either low fat diet (LFD) or high fat diet (HFD) to male and female heterozygous Tbx15+/- and wildtype Tbx15+/+mice from weaning and maintained for 24 weeks. Only Tbx15+/-mice maintained on LFD weighed less than wildtype LFD controls, and female LFD Tbx15+/- mice had lower fat mass overall. We found that in LFD Tbx15+/- mice, multiple visceral fat depots weighed less than wildtype controls, and this was maintained when corrected for body mass for both gonadal and mesenteric visceral adipose depots. When comparing adipocyte size in multiple adipose depots, some reduction in number of larger adipocytes was detected in the perirenal adipose tissue of female HFD Tbx15+/- vs Tbx15+/+ mice, mesenteric adipose tissue from female LFD Tbx15+/- vs Tbx15+/+ mice and male HFD Tbx15+/- vs Tbx15+/+mice. RNA-sequencing of subcutaneous (inguinal) adipose tissues from 12-week old male and female knockout Tbx15-/-, Tbx15+/- and Tbx15+/+ mice raised on a standard chow diet identified 897 upregulated genes and 2328 downregulated genes in female Tbx15-/- mice compared to Tbx15+/+mice. We then combined this dataset with TBX15 ChIP-sequencing data from mouse preadipocyte 3T3-L1 cells overexpressing TBX15 to identify a credible set of genes directly regulated by TBX15. These 52 genes were enriched for B- and T-cell receptor signalling, JAK-STAT signalling and haematopoietic cell lineage pathways; suggesting a direct regulatory role for TBX15 in these pathways in adipose tissue. Together, these data highlight a role for TBX15 in regulation of differential adipose tissue expansion, particularly under low caloric conditions. Further, we identify a potentially important role for TBX15 in the well described adipocyte-immune cell crosstalk associated with obesity and type 2 diabetes mellitus.

physiology↗

bioRxiv2023

Taste shaped the use of botanical drugs

The perception of taste and flavour (a combination of taste, smell and chemesthesis) here also referred to as chemosensation, enables animals to find high-value foods and avoid toxins. Humans have learned to use unpalatable and toxic substances as medicines, yet the importance of chemosensation in this process is poorly understood. Here we generate tasting-panel data for botanical drugs and apply phylogenetic generalised linear mixed models to test whether intensity and complexity of chemosensory qualities as well as particular tastes and flavours can predict ancient Graeco-Roman drug use. We found chemosensation to be strongly predictive of therapeutic use: botanical drugs with high therapeutic versatility have simple yet intense tastes and flavours, and 21 of 22 chemosensory qualities predicted at least one therapeutic use. In addition to the common notion of bitter tasting medicines, we also found starchy, musky, sweet, cooling, and soapy drugs associated with versatility. In ancient Greece and Rome, illness was thought to arise from imbalance in bodily fluids or humours, yet our study suggests that uses of drugs were based on observed physiological effects that are often consistent with modern understanding of chemesthesis and taste receptor pharmacology.

evolutionary biology↗

bioRxiv2019

Stanford Accelerated Intelligent Neuromodulation Therapy for Treatment-Resistant Depression (SAINT-TRD).

BackgroundCurrent treatments for depression are limited by suboptimal efficacy, delayed response, and frequent side effects. Intermittent theta-burst stimulation (iTBS) is a non-invasive brain stimulation treatment that is FDA-approved for treatment-resistant depression (TRD). Recent methodological advancements suggest iTBS could be improved through 1) treating with multiple sessions per day at optimally-spaced intervals, 2) applying a higher overall pulse-dose of stimulation and 3) precision targeting of the left dorsolateral prefrontal cortex (L-DLPFC) to subgenual anterior cingulate cortex (sgACC) circuit. We examined the feasibility, tolerability, and preliminary efficacy of an accelerated, high-dose, resting-state functional connectivity MRI (fcMRI)-guided iTBS protocol for TRD termed Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT). MethodsTwenty-one participants with TRD received open-label SAINT. FcMRI was used to individually target the region of L-DLPFC most anticorrelated with sgACC. Fifty iTBS sessions (1800 pulses per session, 50-minute inter-session interval) were delivered as 10 daily sessions over 5 consecutive days at 90% resting motor threshold (adjusted for cortical depth). Neuropsychological testing was conducted before and after SAINT. ResultsNineteen of 21 participants (90.48%) met criteria for remission ([≤]10 on the Montgomery-[A]sberg Depression Rating Scale) immediately after SAINT. Neuropsychological testing demonstrated no negative cognitive side-effects. There were no seizures or other severe adverse events. DiscussionOur accelerated, high-dose, iTBS protocol with fcMRI-guided targeting (SAINT) was well tolerated and safe. Efficacy was strikingly high, especially for this treatment-resistant population. Double-blinded sham-controlled trials are required to confirm the high remission rate found in this initial study. Trial registrationClinicalTrials.gov NCT03240692

clinical trials↗

bioRxiv2018

A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex

How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework.

neuroscience↗

bioRxiv2018

Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells

The neocortex is capable of anticipating the sensory results of movement but the neural mechanisms are poorly understood. In the entorhinal cortex, grid cells represent the location of an animal in its environment, and this location is updated through movement and path integration. In this paper, we propose that sensory neocortex incorporates movement using grid cell-like neurons that represent the location of sensors on an object. We describe a two-layer neural network model that uses cortical grid cells and path integration to robustly learn and recognize objects through movement and predict sensory stimuli after movement. A layer of cells consisting of several grid cell-like modules represents a location in the reference frame of a specific object. Another layer of cells which processes sensory input receives this location input as context and uses it to encode the sensory input in the objects reference frame. Sensory input causes the network to invoke previously learned locations that are consistent with the input, and motor input causes the network to update those locations. Simulations show that the model can learn hundreds of objects even when object features alone are insufficient for disambiguation. We discuss the relationship of the model to cortical circuitry and suggest that the reciprocal connections between layers 4 and 6 fit the requirements of the model. We propose that the subgranular layers of cortical columns employ grid cell-like mechanisms to represent object specific locations that are updated through movement.

neuroscience↗

bioRxiv2018

The Effect of Previous Life Cycle Phase on the Growth Kinetics, Morphology and Antibiotic Resistance of Salmonella Typhimurium DT104 in Brain Heart Infusion and Ground Chicken Extract

Growth models are predominately used in the food industry to estimate the potential growth of select microorganisms under environmental conditions. The growth kinetics, cellular morphology and antibiotic resistance were studied throughout the life cycle of Salmonella Typhimurium. The effect of the previous life cycle phase (late log phase [LLP], early stationary phase [ESP], late stationary phase [LSP] and early death phase [EDP]) of Salmonella after reinoculation in brain heart infusion broth (BHI), ground chicken extract (GCE) and BHI at pH 5, 7 and 9 and salt concentrations 2, 3 and 4% was investigated. The growth media and previous life cycle phase had significant effects on the lag time ({lambda}), specific growth rate (max) and maximum population density (Ymax). At 2% and 4% salt concentration the LLP had the significantly (P<0.05) fastest max (1.07 and 0.69 log CFU/mL/h, respectively). As the cells transitioned from the late log phase (LLP) to the early death phase (EDP), the {lambda} significantly (P<0.05) increased. At pH 5 and 9 the EDP had a significantly (P<0.05) lower Ymax than the LLP, ESP and LSP. As the cells transitioned from a rod shape to a coccoid shape in the EDP, the cells were more susceptible to antibiotics. The cells regained their resistance as they transitioned back to a rod shape from the EDP to the log and stationary phase. Our results revealed that growth kinetics, cells length, shape and antibiotic resistance were significantly affected by the previous life cycle phase.

microbiology↗

bioRxiv2017

Untangling Sequences: Behavior vs. External Causes

There are two fundamental reasons why sensory inputs to the brain change over time. Sensory inputs can change due to external factors or they can change due to our own behavior. Interpreting behavior-generated changes requires knowledge of how the body is moving, whereas interpreting externally-generated changes relies solely on the temporal sequence of input patterns. The sensory signals entering the neocortex change due to a mixture of both behavior and external factors. The neocortex must disentangle them but the mechanisms are unknown. In this paper, we show that a single neural mechanism can learn and recognize both types of sequences. In the model, cells are driven by feedforward sensory input and are modulated by contextual input. If the contextual input includes information derived from efference motor copies, the cells learn sensorimotor sequences. If the contextual input consists of nearby cellular activity, the cells learn temporal sequences. Through simulation we show that a network containing both types of contextual input automatically separates and learns both types of input patterns. We review experimental data that suggests the upper layers of cortical regions contain the anatomical structure required to support this mechanism.

neuroscience↗

bioRxiv2017

Why Does the Neocortex Have Layers and Columns, A Theory of Learning the 3D Structure of the World

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

neuroscience↗

bioRxiv2016

The HTM Spatial Pooler: a neocortical algorithm for online sparse distributed coding

1.Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the spatial pooler, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the spatial pooler outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the spatial pooler in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

neuroscience↗