Biology subjects

Readhead, B.

Publications and source records attributed to Readhead, B..

10 recordsLinked to original sources

bioRxiv2024

Development of a high-throughput, quantitative platform using human cerebral organoids to study virus-induced neuroinflammation in Alzheimer's disease

Neuroinflammation is a central process in the pathogenesis of several neurodegenerative diseases such as Alzheimers disease (AD), and there are active efforts to target pathways involved in neuroinflammation for molecular biomarker discovery and therapeutic development in neurodegenerative diseases. It was also proposed that there may be an infectious etiology in AD that is associated with viruses such as herpes simplex virus (HSV-1) and influenza A virus (IAV), leading to neuroinflammation-induced AD pathogenesis or disease progression. We sought to develop high-throughput, quantitative molecular biomarker assays using dissociated cells from human cerebral organoids (dcOrgs), that can used for screening compounds to reverse AD-associated neuroinflammation. We found that HSV-1 infection, but not IAV infection, in dcOrgs led to increased intracellular A{beta}42 and phosphorylated Tau-Thr212 (pTau-212) expression, lower ratios of secreted A{beta}42/40, as well as neuronal loss, and increased proportions of astrocytes and microglia, which are hallmarks of AD. Among the glia cell-type markers, Iba1 (microglia) and GFAP (astrocyte) expression were most strongly correlated with HSV-1 expression, which further supported that these biomarkers are perturbed by glia-mediated neuroinflammation. By performing large-scale RNA sequencing, we observed that differentially expressed transcripts in HSV-1 infected dcOrgs were specifically enriched for AD-associated GWAS genes, but not for genes associated with other common neurodegenerative, neuropsychiatric or autoimmune diseases. Immediate treatment of HSV-1 infected dcOrgs with anti-herpetic drug acyclovir (ACV) rescued most of the cellular and transcriptomic biomarkers in a dosage-dependent manner, indicating that it is possible to use our high-throughput platform to identify compounds or target genes that can reverse these neuroinflammation-induced biomarkers associated with AD.

genomics↗

bioRxiv2023

Reduced PIN1 gene expression in neocortical and limbic brain regions in female Alzheimer's patients correlates with cognitive and neuropathological phenotypes

Women have a higher incidence of Alzheimers disease (AD), even after adjusting for increased longevity. Thus, there is an urgent need to identify the molecular networks that underpin the sex-associated risk of AD. Recent efforts have identified PIN1 as a key regulator of tau phosphorylation signaling pathway. Pin1 is the only gene, to date, that when deleted can cause both tau and A{beta}-related pathologies in an age-dependent manner. We analyzed multiple brain transcriptomic datasets focusing on sex differences in PIN1 mRNA levels, in an aging and AD cohort, which revealed reduced PIN1 levels driven by females. Then, we validated this observation in an independent dataset (ROS/MAP) which also revealed that PIN1 is negatively correlated with multiregional neurofibrillary tangle density and global cognitive function, in females only. Additional analysis revealed a decrease in PIN1 in subjects with mild cognitive impairment (MCI) compared with aged individuals, again, driven predominantly by female subjects. Our results show that while both male and female AD patients show decreased PIN1 expression, changes occur before the onset of clinical symptoms of AD in females and correlate to early events associated with AD risk (e.g., synaptic dysfunction). These changes are specific to neurons, and may be a potential prognostic marker to assess AD risk in the aging population and even more so in AD females with increased risk of AD. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/553279v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@95bd4eorg.highwire.dtl.DTLVardef@bf4e3corg.highwire.dtl.DTLVardef@1a9468corg.highwire.dtl.DTLVardef@56c15e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Modeling of mitochondrial genetic polymorphisms reveals induction of heteroplasmy by pleiotropic disease locus MT:10398A>G

Mitochondrial (MT) dysfunction has been associated with several neurodegenerative diseases including Alzheimers disease (AD). While MT-copy number differences have been implicated in AD, the effect of MT heteroplasmy on AD has not been well characterized. Here, we analyzed over 1,800 whole genome sequencing data from four AD cohorts in seven different tissue types to determine the extent of MT heteroplasmy present. While MT-heteroplasmy was present throughout the entire MT genome for blood samples, we detected MT-heteroplasmy only within the MT control region for brain samples. We observed that an MT variant 10398A>G (rs2853826) was significantly associated with overall MT-heteroplasmy in brain tissue while also being linked with the largest number of distinct disease phenotypes of all annotated MT variants in MitoMap. Using gene-expression data from our brain samples, our modeling discovered several gene networks involved in mitochondrial respiratory chain and Complex I function associated with 10398A>G. The variant was also found to be an expression quantitative trait loci (eQTL) for the gene MT-ND3. We further characterized the effect of 10398A>G by phenotyping a population of lymphoblastoid cell-lines (LCLs) with and without the variant allele. Examination of RNA sequence data from these LCLs reveal that 10398A>G was an eQTL for MT-ND4. We also observed in LCLs that 10398A>G was significantly associated with overall MT-heteroplasmy within the MT control region, confirming the initial findings observed in post-mortem brain tissue. These results provide novel evidence linking MT SNPs with MT heteroplasmy and open novel avenues for the investigation of pathomechanisms that are driven by this pleiotropic disease associated loci.

genomics↗

bioRxiv2022

Reliable multiplex generation of pooled induced pluripotent stem cells for genetic testing

Inducing somatic cells into pluripotent stem cells (iPSCs) provides an excellent model for studying systems in-vitro. Understanding the impact of individual donor genetic backgrounds on reprogramming ability would allow researchers to harness these genetic differences and increase the efficiency of the reprogramming process. To better understand the genetic basis of reprogramming cells into iPSCs, we present Induction of Pluripotency from Pooled Cells (iPPC) - an efficient, scalable, and reliable reprogramming procedure. Using our deconvolution algorithm that employs low-coverage pooled sequencing and single nucleotide polymorphisms (SNPs), we estimate individual donor proportions of cell lines within large cohorts. With iPPC, we concurrently reprogrammed over one hundred donor LCLs into iPSCs and found strong correlations of individual donors reprogramming ability across multiple experiments. We note that individual donors reprogramming ability remains consistent across both same-day replicates and multiple experimental runs, and that the expression of certain immunoglobulin precursor genes (IGLV10-54, IGLV3-9, IGLV1-17, IGLV1-6, and IGLV3-1) may impact reprogramming ability. Our process enables a multiplex framework to study the reprogramming ability of different donor cells into iPSCs and also provides a reliable method along with a pooled library of donor iPSCs for downstream research and investigation of other in-vitro phenotypes.

molecular biology↗

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↗

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↗

bioRxiv2018

Rapid Therapeutic Recommendations in the Context of a Global Public Health Crisis using Translational Bioinformatics Approaches: A proof-of-concept study using Nipah Virus Infection

We live in a world of emerging new diseases and old diseases resurging in more aggressive forms. Drug development by pharmaceutical companies is a market-driven and costly endeavor, and thus it is often a challenge when drugs are needed for diseases endemic only to certain regions or which affect only a few patients. However, biomedical open data is accessible and reusable for reanalysis and generation of a new hypotheses and discovery. In this study, we leverage biomedical data and tools to analyze available data on Nipah Virus (NiV) infection. NiV infection is an emerging zoonosis that is transmissible to humans and is associated with high mortality rates. In this study, explored the application of computational drug repositioning and chemogenomic enrichment analyses using host transcriptome data to match drugs that could reverse the virus-induced gene signature. We performed analyses using two gene signatures: i) A previously published gene signature (n=34), and ii) a gene signature generated using the characteristic direction method (n= 5,533). Our predictive framework suggests that several drugs including FDA approved therapies like beclometasone, trihexyphenidyl, S-propranolol etc. could modulate the NiV infection induced gene signatures in endothelial cells. A target specific analysis of CXCL10 also suggests the potential application of Eldelumab, an investigative therapy for Crohns disease and ulcerative colitis, as a putative candidate for drug repositioning. To conclude, we also discuss challenges and opportunities in clinical trials (n-of-1 and adaptive trials) for repositioned drugs. Further follow-up studies including biochemical assays and clinical trials are required to identify effective therapies for clinical use. Our proof-of-concept study highlights that translational bioinformatics methods including gene expression analyses and computational drug repositioning could augment epidemiological investigations in the context of an emerging disease with no effective treatment.

microbiology↗

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↗