Biology subjects

Waters, A.

Publications and source records attributed to Waters, A..

8 recordsLinked to original sources

bioRxiv2024

Opposing lineage specifiers induce a pro-tumor hybrid-identity state in lung adenocarcinoma

The ability of cancer cells to alter their identity, known as lineage plasticity, is crucial for tumor progression and therapy resistance. In lung adenocarcinoma (LUAD), tumor progression is characterized by a gradual loss of lineage fidelity and the emergence of non-pulmonary identity programs. This can lead to hybrid-identity (hybrid-ID) states in which developmentally incompatible identity programs are co-activated within individual cells. However, the molecular mechanisms underlying these identity shifts remain incompletely understood. Here, we identify the gastrointestinal (GI) transcriptional regulator HNF4 as a critical driver of tumor growth and proliferation in KRAS-driven LUAD. In LUAD cells that express the lung lineage specifier NKX2-1, HNF4 can induce a GI/liver-like state by directly binding and activating its canonical targets. HNF4 also forms an aberrant protein complex with NKX2-1, which disrupts NKX2-1 localization and dampens pulmonary identity within hybrid-ID LUAD. Sustained signaling through the RAS/MEK pathway is critical for maintaining the hybrid-ID state. Moreover, RAS/MEK inhibition augments NKX2-1 chromatin binding at pulmonary-specific genes and induces resistance-associated pulmonary signatures. Finally, we demonstrate that HNF4 depletion enhances sensitivity to pharmacologic KRASG12D inhibition. Collectively, our data show that co-expression of opposing lineage specifiers leads to a hybrid identity state that can drive tumor progression and dictate response to targeted therapy in LUAD.

cancer biology↗

bioRxiv2024

Intracranial substrates of meditation-induced neuromodulation in amygdala and hippocampus

Meditation is an accessible mental practice associated with emotional regulation and well-being. Loving-kindness meditation (LKM), a specific sub-type of meditative practice, involves focusing ones attention on thoughts of well-being for oneself and others. Meditation has been proven to be beneficial in a variety of settings, including therapeutical applications, but the neural activity underlying meditative practices and their positive effects are not well understood. In particular, its been difficult to understand the contribution of deep limbic structures given the difficulty of studying neural activity directly in the human brain. Here, we leverage a unique patient population, epilepsy patients chronically implanted with responsive neurostimulation device that allow chronic, invasive electrophysiology recording to investigate the physiological correlates of loving-kindness meditation in the amygdala and hippocampus of novice meditators. We find that LKM-associated changes in physiological activity specific to periodic, but not aperiodic, features of neural activity. LKM was associated with an increase in {gamma} (30-55 Hz) power and an alternation in the duration of {beta} (13-30 Hz) and {gamma} oscillatory bursts in both the amygdala and hippocampus, two regions associated with mood disorders. These findings reveals the nature of LKM-induced modulation of limbic activity in first-time meditators. SIGNIFICANCE STATEMENTWe leverage rare chronic, invasive electrophysiology recordings while participants engage in loving-kindness meditation to demonstrate that meditation induces neural changes in beta and {gamma} activity in the amygdala and hippocampus of novice meditators. These results build on previous findings in experienced meditators and reveal meditations potential for noninvasive neuromodulation of neural mechanisms associated with emotional regulation and mood disorders.

neuroscience↗

bioRxiv2024

Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks

The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the causal significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp EEG) recordings in patients during presurgical intracerebral electrical stimulation (iES). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence of recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show a more functionally integrated processing style than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure.

neuroscience↗

bioRxiv2023

Broad and potent neutralizing mAbs are elicited in vaccinated individuals following Delta/BA.1 breakthrough infection

Despite the success of COVID-19 vaccines in preventing infection and/or severe disease, with the emergence of SARS-CoV-2 variants of concern (VOC) which encode mutations in Spike, and the waning of vaccine induced immunity, there has been an increase in SARS-CoV-2 infections in vaccinated individuals which leads to increased serum neutralization breadth. However, how exposure to a heterologous Spike broadens the neutralizing response at the monoclonal antibody (mAb) level is not fully understood. Through isolation of 119 mAbs from three individuals receiving two-doses of BNT162b2 vaccine before becoming delta or omicron/BA.1-infected, we show that breadth arises from re-activation and maturation of B cells generated through previous COVID-19 vaccination rather than a de novo response specific to the VOC Spike. Isolated mAbs frequently show reduced neutralization of current circulating variants including BA.2.75.2, XBB, XBB.1.5 and BQ.1.1 confirming continuous selective pressure on Spike to evolve and evade neutralization. However, isolation of mAbs that display effective cross-neutralization against all variants indicate the presence of conserved epitopes on RBD and a lesser extent NTD. These findings have implications for selection of Spike antigens for next-generation COVID-19 vaccines.

immunology↗

bioRxiv2021

ChAdOx1 nCoV-19 (AZD1222) vaccine elicits monoclonal antibodies with potent cross-neutralizing activity against SARS-CoV-2 viral variants

Although the antibody response to COVID-19 vaccination has been studied extensively at the polyclonal level using immune sera, little has been reported on the antibody response at the monoclonal level. Here we isolate a panel of 44 anti-SARS-CoV-2 monoclonal antibodies (mAbs) from an individual who received two doses of the ChAdOx1 nCoV-19 (AZD1222) vaccine at a 12-week interval. We show that despite a relatively low serum neutralization titre, mAbs with potent neutralizing activity against the current SARS-CoV-2 variants of concern (B.1.1.7, P.1, B.1.351 and B.1.617.2) were obtained. The vaccine elicited neutralizing mAbs form 8 distinct competition groups and bind epitopes overlapping with neutralizing mAbs elicited following SARS-CoV-2 infection. AZD1222 elicited mAbs are more mutated than mAbs isolated from convalescent donors 1-2 months post infection. Spike reactive IgG+ B cells were still detectable 9-months post boost. These findings give molecular insights into AZD1222 elicited antibody response.

immunology↗

bioRxiv2021

Predicting Moisture Content During Maize Nixtamalization Using Machine Learning with NIR Spectroscopy

Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearmans rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait. Key MessageMoisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.

plant biology↗

bioRxiv2021

Genetic Architecture of Kernel Compositional Variation in a Maize Diversity Panel

Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which overtime has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants. However, much of the underlying genetic architecture controlling these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g. carbohydrates, protein, starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g. dent, sweetcorn, popcorn) were explored and significant differences and correlations were detected. In total, 22.9-71.1% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 70 statistically significant loci for 12 compositional traits. This study provides valuable insights in the phenotypic variation and genetic architecture underlying compositional traits that can be used in breeding programs for improving maize grain quality. Core IdeasO_LIUnderstanding kernel compositional variation is important for food grade corn improvement. C_LIO_LIGenetic and environmental factors account for most of the variation in compositional traits. C_LIO_LIA broad range in trait heritabilities was observed across compositional traits. C_LIO_LICompositional trade-offs will be important to consider when conducting multitrait breeding. C_LIO_LICompositional traits are mostly controlled by a large number of small effect loci. C_LI

plant biology↗

bioRxiv2017

Using Outliers in Freesurfer Segmentation Statistics to Identify Cortical Reconstruction Errors in Structural Scans

IntroductionQuality assurance (QA) is vital for ensuring the integrity of processed neuroimaging data for use in clinical neurosciences research. Manual QA (visual inspection) of processed brains for cortical surface reconstruction errors is resource-intensive, particularly with large datasets. Several semi-automated QA tools use quantitative detection of subjects for editing based on outlier brain regions. There were two project goals: (1) evaluate the adequacy of a statistical QA method relative to visual inspection, and (2) examine whether error identification and correction significantly impacts estimation of cortical parameters and established brain-behavior relationships.\n\nMethodsT1 MPRAGE images (N = 530) of healthy adults were obtained from the NKI-Rockland Sample and reconstructed using Freesurfer 5.3. Visual inspection of T1 images was conducted for: (1) participants (n = 110) with outlier values (z scores {+/-} 3 SD) for subcortical and cortical segmentation volumes (outlier group), and (2) a random sample of remaining participants (n = 110) with segmentation values that did not meet the outlier criterion (non-outlier group).\n\nResultsThe outlier group had 21% more participants with visual inspection-identified errors than participants in the non-outlier group, with a medium effect size ({Phi} = 0.22). Nevertheless, a considerable portion of images with errors of cortical extension were found in the non-outlier group (41%). Sex significantly predicted error rate; men were 2.8 times more likely to have errors than women. Although nine brain regions significantly changed size from pre- to post-editing (with effect sizes ranging from 0.26 to 0.59), editing did not substantially change the correlations of neurocognitive tasks and brain volumes (ps > 0.05).\n\nConclusionsStatistically-based QA, although less resource intensive, is not accurate enough to supplant visual inspection. We discuss practical implications of our findings to guide resource allocation decisions for image processing.

neuroscience↗