Biology subjects

Dudley, J. T.

Publications and source records attributed to Dudley, J. T..

13 recordsLinked to original sources

bioRxiv2023

A public resource of single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease

The emergence of technologies that can support high-throughput profiling of single cell transcriptomes offers to revolutionize the study of brain tissue from persons with and without Alzheimers disease (AD). Integration of these data with additional complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link observed cell subpopulations and molecular network features within a broader disease-relevant context. We report here single nucleus RNA sequencing (snRNA-seq) profiles generated from superior frontal gyrus cortical tissue samples from 101 exceptionally well characterized, aged subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in peripheral hematological lab parameters, with these observations replicated in an independent, prospective cohort study of ageing and dementia. We also observed an AD-associated CD83(+) microglial subtype with unique molecular networks that encompass many known regulators of AD-relevant microglial biology, and which are associated with immunoglobulin IgG4 production in the transverse colon. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal novel disease biology. The transcriptomic, genetic, phenotypic, and network data resources described within this study are available for access and utilization by the scientific community.

genomics↗

bioRxiv2022

Orally active mGluR2/3 metabotropic antagonist pro-drug mimics the beneficial effects of physical exercise on neurogenesis, behavior, and exercise-related molecular pathways in an Alzheimer's disease mouse model

Modulation of physical activity represents an important intervention that may delay, slow, or prevent mild cognitive impairment (MCI) or dementia due to Alzheimers disease (AD). One mechanism proposed to underlie the beneficial effect of physical exercise involves the apparent stimulation of adult hippocampal neurogenesis (AHN). BCI-838 is a pro-drug whose active metabolite BCI-632 is an antagonist at the group II metabotropic glutamate receptor (mGluR2/3). We previously demonstrated that administration of BCI-838 to a mouse model of cerebrovascular accumulation of oligomeric A{beta}E22Q (APPE693Q = "Dutch APP") reduced learning behavior impairment and anxiety, both of which are associated with the phenotype of the Dutch APP mice. Here we show that (i) administration of BCI-838, physical exercise, or a combination of BCI-838 and physical exercise enhanced AHN in a four-month old mouse model of AD amyloid pathology (APPKM670/671NL/ PSEN1{Delta}exon9 = APP/PS1), (ii) administration of BCI-838 alone or associated with physical exercise led to stimulation of AHN and improvement in both spatial and recognition memory, (iii) significantly, the hippocampal dentate gyrus transcriptome of APP/PS1 mice following BCI-838 treatment up-regulated brain-derived neurotrophic factor (BDNF), PIK3C2A of the PI3K-MTOR pathway, and metabotropic glutamate receptors, and down-regulated EIF5A of ketamine-modulating mTOR activity, and finally (iv) qPCR findings validate a significantly strong association between increased BDNF levels and BCI-838 treatment. Our study points to BCI-838 as a safe and orally active compound capable of mimicking the beneficial effect of exercise on AHN, learning behavior, and anxiety in a mouse model of AD neuropathology.

neuroscience↗

bioRxiv2022

Saracatinib, a Selective Src Kinase Inhibitor, Blocks Fibrotic Responses in In Vitro, In Vivo and Ex Vivo Models of Pulmonary Fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, and often fatal disorder. Two FDA approved anti-fibrotic drugs, nintedanib and pirfenidone, slow the rate of decline in lung function, but responses are variable and side effects are common. Using an in-silico data-driven approach, we identified a robust connection between the transcriptomic perturbations in IPF disease and those induced by saracatinib, a selective Src kinase inhibitor, originally developed for oncological indications. Based on these observations, we hypothesized that saracatinib would be effective at attenuating pulmonary fibrosis. We investigated the anti-fibrotic efficacy of saracatinib relative to nintedanib and pirfenidone in three preclinical models: (i) in vitro in normal human lung fibroblasts (NHLFs); (ii) in vivo in bleomycin and recombinant adenovirus transforming growth factor-beta (Ad-TGF-{beta}) murine models of pulmonary fibrosis; and (iii) ex vivo in precision cut lung slices from these mouse models. In each model, the effectiveness of saracatinib in blocking fibrogenic responses was equal or superior to nintedanib and pirfenidone.

systems biology↗

bioRxiv2021

Deep Learning-Based Brain Transcriptomic Signatures Associated with the Neuropathological and Clinical Severity of Alzheimer's Disease

Brain tissue gene expression from donors with and without Alzheimers disease (AD) have been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyze RNA-seq data from 1,114 brain donors from the AMP-AD consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-{beta} plaque, tau neurofibrillary tangles, and clinical severity in multiple AD dementia populations. Starting from the cross-sectional data in the ROSMAP cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors AD progression. A severity index (SI) was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this dataset, SIs were found to be very closely correlated with all AD neuropathology biomarkers (R [~] 0.5, p < 1e-11) and global cognitive function (R = -0.68, p < 2.2e-16). We then applied the model to additional transcriptomic datasets from different brain regions (MAYO, n = 266; MSBB, n = 214), and observed that the model remained significantly predictive (p < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with AD co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with AD, other age-related neurological disorders and controls; and revealed the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyze heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like AD.

genomics↗

bioRxiv2020

Patient Similarity Network of Newly Diagnosed Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic Features and Clinical Implications

The remarkable genetic heterogeneity of Multiple Myeloma (MM) poses a significant challenge for proper prognostication and clinical management of patients. Accurate dissection of the genetic and molecular landscape of the disease and the robust identification of homogeneous classes of patients are essential steps to reliable risk stratification and the development of novel precision medicine strategies. Here we introduce MM-PSN, the first multi-omics Patient Similarity Network of newly diagnosed MM. MM-PSN has enabled the identification of three broad patient groups and twelve distinct sub-groups defined by five data types generated from genomic and transcriptomic patient profiling of 655 patients. The MM-PSN classification uncovered novel associations between distinct MM hallmarks with significant prognostic implications and allowed further refinement of risk stratification. Our analysis revealed that gain of 1q is the most important single lesion conferring high risk of relapse, and its association with an MMSET translocation is the most significant determinant of poor outcome. We developed a classifier and validated these results in an independent dataset of 559 pts. Our findings suggest that gain of 1q should be incorporated in routine staging systems and risk assessment tools. The MM-PSN classifier is available as a free resource to allow for an easy implementation in most clinical settings.

cancer biology↗

bioRxiv2019

Further evidence of increased human Herpesvirus in Alzheimer's disease

The recent coordination of scientific and political efforts within the field of Alzheimers disease (AD) research coupled with the rapid evolution of molecular profiling technologies has enabled the generation of unprecedented repositories of biological sequence data available for investigation by the scientific community. Next-generation sequences (NGS) represent complex readouts of cellular transcriptomic and genomic states, and hold the potential to illuminate disease biology via unexpected applications of collected data. We recently reported a multiomic study of the Alzheimers associated brain virome, leveraging NGS data to understand the potential relevance of viral activity to AD. We observed increased abundance of human herpesvirus 6A (HHV-6A), HHV-7, and herpes simplex virus 1 (HSV-1) genomes in banked postmortem brains from subjects with AD compared with controls. These results were replicated in two additional, independent and geographically dispersed cohorts. Although these findings might be of general interest to the field, such analyses present novel methodological challenges that have not been well characterized. The viral abundances that we detected were generally quite low, and, in many samples, no virome was detected. We sought to understand whether such sparsity might be associated with the spurious detection of differential abundance. Herein, we employ a simulation approach, which indicates that sparsity in abundance actually biases against the detection of significant differences between AD and control subjects. We also report new results based on previously unpublished whole genome sequences that show increases in both abundance and prevalence of HHV-6A in AD, consistent with our previously reported results. These findings, as well as reports from many other groups employing a variety of approaches, support the dedication of a coordinated effort toward a comprehensive characterization of the microbiome of the brain affected by AD. Such studies should advance our understanding of the potential clinical relevance of these observations.

neuroscience↗

bioRxiv2019

Early life stress alters transcriptomic patterning across reward circuitry in male and female mice

Abuse, neglect, and other forms of early life stress (ELS) significantly increase risk for psychiatric disorders including depression. In this study, we show that ELS in a postnatal sensitive period increases sensitivity to adult stress in female mice, consistent with our earlier findings in male mice. We used RNA-sequencing in the ventral tegmental area, nucleus accumbens, and prefrontal cortex of male and female mice to show that adult stress is distinctly represented in the brains transcriptome depending on ELS history. We identify: 1) biological pathways disrupted after ELS and associated with increased behavioral stress sensitivity, 2) putative transcriptional regulators of the effect of ELS on adult stress response, and 3) subsets of primed genes specifically associated with latent behavioral changes. We also provide transcriptomic evidence that ELS increases sensitivity to future stress through enhancement of known programs of cortical plasticity.

neuroscience↗

bioRxiv2019

Multiscale analysis of acne connects molecular subnetworks with disease status

Acne vulgaris affects millions of individuals and can lead to psychosocial impairment as well as permanent scarring. Previous studies investigating acne pathogenesis have either examined a targeted set of biological parameters in a modest-sized cohort or carried out high-throughput assays on a small number of samples. To develop a more comprehensive understanding of acne pathophysiology, we conducted an in-depth multi-omic study of 56 acne patients and 20 individuals without acne. We collected whole blood, skin punch biopsies, microbiota from skin follicles, and relevant clinical measurements to understand how multiple factors contribute to acne. We provide an integrative analysis of multi-omics data that results in a molecular network of acne. Comparisons of lesional and non-lesional skin highlighted multiple biological processes, including immune cell and inflammatory responses, response to stress, T cell activation, lipid biosynthesis, fatty acid metabolism, keratinocytes, antimicrobial activity, epithelial cell differentiation, and response to wounding, that are differentially altered in acne lesions compared to non-lesions. Our results suggest baseline differences in the skin that may predispose individuals to develop acne. These datasets and findings offer a framework for new target identification and reference for future studies.

systems biology↗

bioRxiv2019

fragilityindex: An R Package for Statistical Fragility Estimates in Biomedicine

Poor reproducibility is a growing crisis in biomedical research. The fragility index was introduced as a convenient measure to estimate how fragile statistical results in clinical trials are to small perturbations in event outcome counts. There is currently no freely available R package to produce this calculation. Furthermore, the original definition of the method is applicable only to 2x2 contingency tables. As such, we developed an R package to calculate fragility index. We have also extended the concept of a statistical fragility index to two of the most commonly used methods in clinical research, survival analysis via weighted log-rank tests and logistic regression, and implemented these technique sin this R package. We describe example applications of these methods to existing publically available datasets. This R package is freely available under the AGPL license on CRAN (https://cran.r-project.org/web/packages/fragilityindex/index.html). The most recent versions may be downloaded and installed via Github (https://github.com/kippjohnson/fragilityindex).

bioinformatics↗

bioRxiv2019

Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits

Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we improve the accuracy of transcriptome prediction and the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge to biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify known and novel compounds that mimic or reverse trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.

genetics↗

bioRxiv2018

Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

Drug repositioning, i.e. identifying new uses for existing drugs and research compounds, is a cost-effective drug discovery strategy that is continuing to grow in popularity. Prioritizing and identifying drugs capable of being repositioned may improve the productivity and success rate of the drug discovery cycle, especially if the drug has already proven to be safe in humans. In previous work, we have shown that drugs that have been successfully repositioned have different chemical properties than those that have not. Hence, there is an opportunity to use machine learning to prioritize drug-like molecules as candidates for future repositioning studies. We have developed a feature engineering and machine learning that leverages data from publicly available drug discovery resources: RepurposeDB and DrugBank. ChemVec is the chemoinformatics-based feature engineering strategy designed to compile molecular features representing the chemical space of all drug molecules in the study. ChemVec was trained through a variety of supervised classification algorithms (Naive Bayes, Random Forest, Support Vector Machines and an ensemble model combining the three algorithms). Models were created using various combinations of datasets as Connectivity Map based model, DrugBank Approved compounds based model, and DrugBank full set of compounds; of which RandomForest trained using Connectivity Map based data performed the best (AUC=0.674). Briefly, our study represents a novel approach to evaluate a small molecule for drug repositioning opportunity and may further improve discovery of pleiotropic drugs, or those to treat multiple indications.

bioinformatics↗

bioRxiv2018

Adaptive landscape of protein variation in human exomes

The human genome contains hundreds of thousands of missense mutations. However, only a handful of these variants are known to be adaptive, which implies that adaptation through protein sequence change is an extremely rare phenomenon in human evolution. Alternatively, existing methods may lack the power to pinpoint adaptive variation. We have developed and applied an Evolutionary Probability Approach (EPA) to discover candidate adaptive polymorphisms (CAPs) through the discordance between allelic evolutionary probabilities and their observed frequencies in human populations. EPA reveals thousands of missense CAPs, which suggest that a large number of previously optimal alleles had experienced a reversal of fortune in the human lineage. We explored non-adaptive mechanisms to explain CAPs, including the effects of demography, mutation rate variability, and negative and positive selective pressures in modern humans. Our analyses suggest that a large proportion of CAP alleles have increased in frequency due to beneficial selection. This conclusion is supported by the facts that a vast majority of adaptive missense variants discovered previously in humans are CAPs, and that hundreds of CAP alleles are protective in genotype-phenotype association data. Our integrated phylogenomic and population genetic EPA approach predicts the existence of thousands of signatures of non-neutral evolution in the human proteome. We expect this collection to be enriched in beneficial variation. EPA approach can be applied to discover candidate adaptive variation in any protein, population, or species for which allele frequency data and reliable multispecies alignments are available.

evolutionary biology↗

bioRxiv2017

Learning and Mapping Lyme Disease Patient Trajectories from Electronic Medical Data for Stratification of Disease Risk and Therapeutic Response

BackgroundLyme disease (LD) is an epidemic, tick-borne illness with approximately 329,000 incidences diagnosed each year in United States. Long-term use of antibiotics is associated with serious complications, including post-treatment Lyme disease syndrome (PTLDS). The landscape of comorbidities and health trajectories associated with LD and associated treatments is not fully understood. Consequently, there is an urgent need to improve clinical management of LD based on a more precise understanding of disease and patient stratification.\n\nMethodsWe used a precision medicine machine-learning approach based on high-dimensional electronic medical records (EMRs) to characterize the heterogeneous comorbidities in a LD population and develop systematic predictive models for identifying medications that influence the risk of subsequent comorbidities.\n\nFindingsWe identified 3, 16, and 17 comorbidities at broad disease categories associated with LD within 2, 5, and 10 years of diagnosis, respectively. At higher resolution of ICD-9 levels, we pinpointed specific co-morbid diseases on a timescale that matched the symptoms associated with PTLDS. We identified 7, 30, and 35 medications that influenced the risks of the reported comorbidities within 2, 5, and 10 years, respectively. These medications included six previously associated with the identified comorbidities and 29 new findings. For instance, the first-line antibiotic doxycycline exhibited a consistently protective effect for typical symptoms of LD, including backache Not Otherwise Specified (NOS) and chronic rhinitis, but consistently increased the risk of cataract NOS, tear film insufficiency NOS, and nocturia.\n\nInterpretationOur approach and findings suggest new hypotheses for precision medicine treatments regimens and drug repurposing opportunities tailored to the phenotypic profiles of LD patients.\n\nFundingThe Steven & Alexandra Cohen Foundation

bioinformatics↗