Biology subjects

Tanner, J.

Publications and source records attributed to Tanner, J..

13 recordsLinked to original sources

bioRxiv2024

Differential Patterns of Gut and Oral Microbiomes in Hispanic Individuals with Cognitive Impairment

Alzheimers disease and related dementias (ADRD) have been associated with alterations in both oral and gut microbiomes. While extensive research has focused on the role of gut dysbiosis in ADRD, the contribution of the oral microbiome remains relatively understudied. Furthermore, the potential synergistic interactions between oral and gut microbiomes in ADRD pathology are largely unexplored. This study aims to evaluate distinct patterns and potential synergistic effects of oral and gut microbiomes in a cohort of predominantly Hispanic individuals with cognitive impairment (CI) and without cognitive impairment (NC). We conducted 16S rRNA gene sequencing on stool and saliva samples from 32 participants (17 CI, 15 NC; 62.5% female, mean age = 70.4 {+/-} 6.2 years) recruited in San Antonio, Texas, USA. Correlation analysis through MaAslin2 assessed the relationship between participants clinical measurements (e.g., fasting glucose and blood cholesterol) and their gut and saliva microbial contents. Differential abundance analysis evaluated taxa with significant differences between CI and NC groups, and alpha and beta diversity metrics assessed within-sample and group compositional differences. Our analyses revealed no significant differences between NC and CI groups in fasting glucose or blood cholesterol levels. However, a clear association was observed between gut microbiome composition and levels of fasting glucose and blood cholesterol. While alpha and beta diversity metrics showed no significant differences between CI and NC groups, differential abundance analysis revealed an increased presence of oral genera such as Dialister, Fretibacterium, and Mycoplasma in CI participants. Conversely, CI individuals exhibited a decreased abundance of gut genera, including Shuttleworthia, Holdemania, and Subdoligranulum, which are known for their anti-inflammatory properties. No evidence was found for synergistic contributions between oral and gut microbiomes in the context of ADRD. Our findings suggest that similar to the gut microbiome, the oral microbiome undergoes significant modifications as individuals transition from NC to CI. Notably, the identified oral microbes have been previously associated with periodontal diseases and gingivitis. These results underscore the necessity for further investigations with larger sample sizes to validate our findings and elucidate the complex interplay between oral and gut microbiomes in ADRD pathogenesis.

neuroscience↗

bioRxiv2024

Variation in high-amplitude events across the human lifespan

Edge time series decompose functional connections into their fine-scale, framewise contributions. Previous studies have demonstrated that global high-amplitude "events" in edge time series can be clustered into distinct patterns. To date, however, it is unknown whether events and their patterns change or persist throughout the human lifespan. Here, we directly address this question by clustering event frames using the Nathan Kline Institute-Rockland sample that includes subjects with ages spanning the human lifespan. We find evidence of two main clusters that appear across subjects and age groups. We also find that these patterns of clusters systematically change in magnitude and frequency with age. Our results also demonstrate that such event clusters have distinct, heterogeneous relationships with structural connectivity-derived communication measures, which change with age. Finally, event clusters were found to outperform non-events in predicting phenotypes regarding human intelligence and achievement. Collectively, our findings fill several gaps in current knowledge about co-fluctuation patterns in edge time series and human aging, setting the stage for future investigation into the causal origins of changes in functional connectivity throughout the human lifespan.

neuroscience↗

bioRxiv2023

Edge Community Entropy is a Novel Neural Correlate of Aging and Moderator of Fluid Cognition

Decreased neuronal specificity of the brain in response to cognitive demands (i.e., neural dedifferentiation) has been implicated in age-related cognitive decline. Investigations into functional connectivity analogues of these processes have focused primarily on measuring segregation of nonoverlapping networks at rest. Here, we used an edge-centric network approach to derive entropy, a measure of nodal specialization, from spatially overlapping communities during cognitive task fMRI. Using Human Connectome Project Lifespan data (713 participants, 36-100 years old), we characterized a pattern of nodal despecialization differentially affecting the medial temporal lobe and limbic, visual, and subcortical systems. Global entropy uniquely covaried with age when controlling for network segregation. Importantly, relationships between both metrics and fluid cognition were age-dependent, although entropys relationship with cognition was specific to older adults. These results suggest entropy is a potentially important metric for examining how neurological processes in aging affect functional specialization at the nodal, network, and whole-brain level.

neuroscience↗

bioRxiv2023

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation "events" in mouse and human brains

Previous studies have adopted an edge-centric framework to study fine-scale dynamics in human fMRI. To date, however, no studies have applied this same framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

neuroscience↗

bioRxiv2023

A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival

PurposeMonitoring levels of circulating tumor-derived DNA (ctDNA) represents a non-invasive snapshot of tumor burden and potentially clonal evolution. Here we describe how a novel statistical model that uses serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive assessment that is predictive of treatment response and progression-free survival. Patients and MethodsA cohort of 188 metastatic breast cancer patients had DNA extracted from serial plasma samples (total 1098, median=4, mean=5.87). Plasma DNA was assessed using sWGS and the tumor fraction in total cell free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA 15-3 measurements. The longitudinal ichorCNA values were used to develop a Bayesian learning model to predict subsequent treatment response. ResultsWe identified a transition point of 7% estimated tumor fraction to stratify patients into different categories of progression risk using ichorCNA estimates and a time-dependent Cox model, validated across different breast cancer subtypes and treatments, outperforming the alternative methods. We then developed a Bayesian learning model to predict subsequent treatment response with a sensitivity of 0.75 and a specificity of 0.66. ConclusionIn patients with metastatic breast cancer, sWGS of ctDNA and ichorCNA provide prognostic and predictive real-time valuable information on treatment response across subtypes and therapies. A prospective large-scale clinical trial to evaluate clinical benefit of early treatment changes based on ctDNA levels is now warranted.

genomics↗

bioRxiv2022

Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal (combining diffusion and functional MRI data) regression-based, explanatory model that endows reconstructed fiber tracts with directed and signed weights. Benchmarking this method on Human Connectome Project data, we find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples. Next, we analyze the resulting network using graph-theoretic tools from network neuroscience, revealing bilaterally symmetric communities that span cerebral hemispheres. These communities exhibit a clear mapping onto known functional systems. We also study the shortest paths structure of this network, discovering that almost every edge participates in at least one shortest path. We also find evidence of robust asymmetries in edge weights, that the network reconfigures in response to naturalistic stimuli, and that estimated edge weights differ with age. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

neuroscience↗

bioRxiv2022

System-level high-amplitude co-fluctuations

Edge time series decompose interregional correlations (functional connectivity; FC) into their time-varying contributions. Previous studies have revealed that brief, high-amplitude, and globally-defined "events" contribute disproportionately to the time-averaged FC pattern. This whole-brain view prioritizes systems that occupy vast neocortical territory, possibly obscuring extremely high-amplitude co-fluctuations that are localized to smaller brain systems. Here, we investigate local events detected at the system level, assessing their independent contributions to global events and characterizing their repertoire during resting-state and movie-watching scans. We find that, as expected, global events are more likely to occur when large brain systems exhibit events. Next, we study the co-fluctuation patterns that coincide with system events-i.e. events detected locally based on the behavior of individual brain systems. We find that although each system exhibits a distinct co-fluctuation pattern that is dissimilar from those associated with global events, the patterns can nonetheless be grouped into two broad categories, corresponding to events that coincide with sensorimotor and attention systems and, separately, association systems. We then investigate system-level events during movie-watching, discovering that the timing of events in sensorimotor and attention systems decouple, yielding reductions in co-fluctuation amplitude. Next, we show that by associating each edge with its most similar system-averaged edge time series, we recover overlapping community structure, obviating the need for applying clustering algorithms to high-dimensional edge time series. Finally, we focus on cortical responses to system-level events in subcortical areas and the cerebellum. We show that these structures coincide with spatially distributed cortical co-fluctuations, centered on prefrontal and somatosensory systems. Collectively, the findings presented here help clarify the relative contributions of large and small systems to global events, as well as their independent behavior.

neuroscience↗

bioRxiv2022

Hierarchical organization of spontaneous co-fluctuations in densely-sampled individuals using fMRI

Edge time series decompose FC into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames, including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations. Here, we address those questions directly, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multi-scale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge concerning the heterogeneity and richness of co-fluctuation patterns as estimated with edge time series while providing some practical guidance for future studies.

neuroscience↗

bioRxiv2021

Edge-centric analysis of time-varying functional brain networks with applications in autism spectrum disorder

The interaction between brain regions changes over time, which can be characterized using time-varying functional connectivity (tvFC). The common approach to estimate tvFC uses sliding windows and offers limited temporal resolution. An alternative method is to use the recently proposed edge-centric approach, which enables the tracking of moment-to-moment changes in co-fluctuation patterns between pairs of brain regions. Here, we first examined the dynamic features of edge time series and compared them to those in the sliding window tvFC (sw-tvFC). Then, we used edge time series to compare subjects with autism spectrum disorder (ASD) and healthy controls (CN). Our results indicate that relative to sw-tvFC, edge time series captured rapid and bursty network-level fluctuations that synchronize across subjects during movie-watching. The results from the second part of the study suggested that the magnitude of peak amplitude in the collective co-fluctuations of brain regions (estimated as root sum square (RSS) of edge time series) is similar in CN and ASD. However, the trough-to-trough duration in RSS signal is greater in ASD, compared to CN. Furthermore, an edge-wise comparison of high-amplitude co-fluctuations showed that the within-network edges exhibited greater magnitude fluctuations in CN. Our findings suggest that high-amplitude co-fluctuations captured by edge time series provide details about the disruption of functional brain dynamics that could potentially be used in developing new biomarkers of mental disorders.

neuroscience↗

bioRxiv2021

An edge-centric model for harmonizing multi-relational network datasets

Functional and structural connections vary across conditions, measurements, and time. However, how to resolve multi-relational measures of connectivity remains an open challenge. Here, we propose an extension of structural covariance and morphometric similarity methods to integrate multiple estimates of connectivity into a single edge-centric network representation. We highlight the utility of this method through two applications: an analysis of multi-task functional connectivity data and multi-measure structural networks. In these analyses, we use data-driven clustering techniques to identify collections of edges that covary across tasks and measures, revealing overlapping mesoscale architecture. We also link these features to node-level properties such as modularity and canonical descriptors of brain systems. We further demonstrate that, in the case of multi-task functional networks, edge-level features are consistent across individuals yet exhibit subject-specificity. We conclude by highlighting other instances where the edge-centric model may be useful.

neuroscience↗

bioRxiv2020

Ecological niche adaptation of a bacterial pathogen associated with reduced zoonotic potential

The emergence of new bacterial pathogens is a continuing challenge for agriculture and food safety. Salmonella enterica serovar Typhimurium (S. Typhimurium) is a major cause of foodborne illness worldwide, with pigs a major zoonotic reservoir. Two variants, S. Typhimurium phage type U288 and monophasic S. Typhimurium (S. 4,[5],12:i:-) ST34 emerged and have accounted for the majority of isolates from pigs in the UK in the past two decades, but have distinct host range and risk to food safety. ST34 accounts for over 50% of all S. Typhimurium infections in people while U288 less than 2%. U288 and ST34 form distinct phylogenetic clusters within S. Typhimurium, defined by approximately 600 SNPs within their 5 Mbp genomes. Evolution of the U288 clade from an LT2-like ancestor was characterised by the acquisition of AMR genes, insertions and deletions in the virulence plasmid pU288-1, and the accumulation of polymorphisms, some of which resulted in truncation of coding sequences. U288 isolates exhibited lower growth rate and viability following desiccation compared to ST34 isolates, characteristics that could affect transmission through the food chain. U288 and ST34 isolates exhibited distinct outcomes of infection in the murine model of colitis, and colonised pigs in a manner that affected the disease symptoms and distribution in organs. U288 infection was more disseminated in the lymph nodes while ST34 were recovered in greater numbers in the intestinal contents. These data are consistent with the evolution of S. Typhimurium U288 adaptation to pigs that may determine their reduced zoonotic potential. ImportanceBacterial pathogens continually evolve to exploit new ecological niches as they emerge due to human activity including agricultural, medical or societal practice. The consequences of the emergence of new pathogens may affect outcome of infection and risk to human or animal health. Genome sequence can resolve the population structure, identify variants that are evolving as they enter a new niche, and pinpoint potential functional divergence. We report a variant S. Typhimurium that adapted to a unique niche distinct to that occupied by a second S. Typhimurium variant circulating in the same pig populations. Adaptation was accompanied by phenotypic and genotypic changes consistent with a more invasive lifestyle and a decreased zoonotic potential observed in the epidemiological record. Our findings suggest that pathogen genotypic variation may be useful in estimating zoonotic potential and threat to livestock welfare.

microbiology↗

bioRxiv2019

DeepCryoPicker: Fully Automated Deep Neural Network for Single Protein Particle Picking in cryo-EM

Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio (SNR) of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker and RELION, with the significant advantage of not requiring human intervention.

bioinformatics↗

bioRxiv2019

AutoCryoPicker: An Unsupervised Learning Approach for Fully Automated Single Particle Picking in Cryo-EM Images

BackgroundAn important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Currently, particle picking is laborious, time consuming, and potentially biased due to the need of human intervention to initialize the particle picking. The results typically include many false positives and negatives. Adjusting the parameters to eliminate false positives often excludes true particles in certain orientations. The supervised machine learning (e.g. deep learning) methods for particle picking often need a large training dataset, which requires extensive manual annotation. Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and therefore, are not fully automated. These issues motivate us to develop a fully automated, unbiased framework for particle picking. ResultsWe design a fully automated, unsupervised approach for single particle picking in cryo-EM micrographs. Our approach consists of three stages: image preprocessing, particle clustering, and particle picking. The image preprocessing is based on image averaging, normalization, cryo-EM image contrast enhancement correction (CEC), histogram equalization, restoration, adaptive histogram equalization, guided image filtering, and morphological operations significantly improves the quality of original cryo-EM images. Our particle clustering method is based on an intensity distribution model which is much faster and more accurate than traditional K-means and Fuzzy C-Means (FCM) algorithms for single particle clustering. Our particle picking method, based on image cleaning and shape detection with a modified Circular Hough Transform algorithm, effectively detects the shape and the center of each particle and creates a bounding box encapsulating the particles. ConclusionsAutoCryoPicker can automatically and effectively recognizes particle-like objects from in noisy cryo-EM micrographs without the need of labeled training data and human intervention and therefore is a useful tool for cryo-EM protein structure determination.

bioinformatics↗