The Abca7V1613M variant reduces Aβ generation, plaque load, and neuronal damage

BackgroundVariants in ABCA7, a member of the ABC transporter superfamily, have been associated with increased risk for developing late onset Alzheimer's disease (LOAD).MethodsCRISPR-Cas9 was used to generate an Abca7V1613M variant in mice, modeling the homologous human ABCA7V1599M variant, and extensive characterization was performed.ResultsAbca7V1613M microglia show differential gene expression profiles upon lipopolysaccharide challenge and increased phagocytic capacity. Homozygous Abca7V1613M mice display elevated circulating cholesterol and altered brain lipid composition. When crossed with 5xFAD mice, homozygous Abca7V1613M mice display fewer Thioflavin S-positive plaques, decreased amyloid beta (Aβ) peptides, and altered amyloid precursor protein processing and trafficking. They also exhibit reduced Aβ-associated inflammation, gliosis, and neuronal damage.DiscussionOverall, homozygosity for the Abca7V1613M variant influences phagocytosis, response to inflammation, lipid metabolism, Aβ pathology, and neuronal damage in mice. This variant may confer a gain of function and offer a protective effect against Alzheimer's disease-related pathology.HighlightsABCA7 recognized as a top 10 risk gene for developing Alzheimer's disease. Loss of function mutations result in increased risk for LOAD. V1613M variant reduces amyloid beta plaque burden in 5xFAD mice. V1613M variant modulates APP processing and trafficking in 5xFAD mice. V1613M variant reduces amyloid beta-associated damage in 5xFAD mice

Computational approach to characterize gene dynamics using bulk and single-nucleus RNA sequencing to study Alzheimer’s disease

The brain is a complex organ that controls thought, memory, emotion, touch, motor skills, vision, breathing, temperature, hunger, and many processes that regulate our body. Alzheimer’s disease (AD) is a neurodegenerative disease that is characterized by memory loss and impaired cognitive function. It is associated with the accumulation of plaques and tangles in the brain. The cortex and hippocampus are critical brain regions for learning because of their tasks of neural integration and memory respectively. Therefore, these regions have been characterized exhaustively under different conditions and models to understand the cell subtypes involved. Changes in gene expression and isoforms during development, aging, and disease are controlled by multiple, overlapping programs. The gene expression profiles of distinct cell types arise reflect from complex genomic interactions among multiple simultaneous biological processes within each cell that can be altered by disease progression. Gene functionality is closely connected to its expression specificity across tissue and cell types. These functions can be inferred by the abundance and activity of co-expression networks using bulk RNA-seq. Short-read single-cell RNA-seq is a widely-used method to characterize cellular heterogeneity in complex tissues based on gene expression. A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to identify which genes vary in an informative manner and determine how these genes organize into modules. Because of the limitations of classical clustering methods/detecting modules, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modeling the regulatory network, and/or allowing overlap between modules.Here, I describe my work on characterizing the transcriptome of mouse cortex and hippocampus using bulk RNA-seq in conjunction with single-cell/nucleus RNA-seq to characterize changes during normal development and aging by comparing several mouse models of AD against control mice to study genes associated with neurodegeneration. First, I describe the PyWGCNA package to analyze gene expression and to infer meaningful modules of co-expressed genes that respond to different conditions such as age in different mouse models of AD using bulk RNA-seq. Then, I describe my novel reproducible grade of membership model called Topyfic, which is designed to derive topic models that correspond to cellular programs. I then apply Topyfic to distinct brain RNA-seq datasets from MODEL-AD and ENCODE and detect major changes in microglia, astrocytes, and oligodendrocytes that vary based on genotype and sex. Finally, I investigate possible ways to deconvolve modules into topics and make a connection between them. Together, these new computational methods provide novel insights into cellular programs in health and disease

PyWGCNA: a Python package for weighted gene co-expression network analysis

MotivationWeighted gene co-expression network analysis (WGCNA) is frequently used to identify modules of genes that are co-expressed across many RNA-seq samples. However, the current R implementation is slow, is not designed to compare modules between multiple WGCNA networks, and its results can be hard to interpret as well as to visualize. We introduce the PyWGCNA Python package, which is designed to identify co-expression modules from large RNA-seq datasets. PyWGCNA has a faster implementation than the R version of WGCNA and several additional downstream analysis modules for functional enrichment analysis using GO, KEGG, and REACTOME, inter-module analysis of protein-protein interactions, as well as comparison of multiple co-expression modules to each other and/or external lists of genes such as marker genes from single cell.ResultsWe apply PyWGCNA to two distinct datasets of brain bulk RNA-seq from MODEL-AD to identify modules associated with the genotypes. We compare the resulting modules to each other to find shared co-expression signatures in the form of modules with significant overlap across the datasets.Availability and implementationThe PyWGCNA library for Python 3 is available on PyPi at pypi.org/project/PyWGCNA and on GitHub at github.com/mortazavilab/PyWGCNA. The data underlying this article are available in GitHub at github.com/mortazavilab/PyWGCNA/tutorials/5xFAD_paper

Candida colonization and species identification by two methods in NICU newborn

Background: Over the last two decades invasive candidiasis has become an increasing problem in neonatal intensive care units (NICUs). Colonization of skin and mucous membranes with Candida spp. is important factor in the pathogenesis of neonatal infection and several colonized sites are major risk factors evoking higher frequencies of progression to invasive candidiasis. The aim of this study was to detect Candida colonization in NICU patients. Methods: This cross-sectional study was conducted on 93 neonates in NICUs at Imam Khomeini and Children Medical Center Hospitals in Tehran. Cutaneous and mucous membrane samples obtained at first, third, and seventh days of patients’ stay in NICUs during nine months from August 2013 to May 2014. The samples were primarily cultured on CHROMagar Candida medium. The cultured media were incubated at 35°C for 48h and evaluated based on colony color produced on CHROMagar Candida. In addition, isolated colonies were cultured on Corn Meal Agar medium supplemented with tween 80 for identification of Candida spp. based on their morphology. Finally, polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was performed for definite identification of isolated species. Results: Colonization by Candida spp. was occurred in 20.43% of neonates. Fifteen and four patients colonized with one and two different Candida spp., respectively. Isolated Candida spp. identified as; C. parapsilosis (n: 10), C. albicans (n: 7), C. tropicalis (n: 3), C. guilliermondii (n: 2), and C. krusei (n: 1). In present study non-albicans Candia species were dominant (69.56%) and C. parapsilosis was the most frequent isolate (43.47%). Using Fisher's exact test, the correlation between fungal colonization with low birth weight, low gestational age, and duration of hospital stay was found to be statistically significant (P=0.003). Conclusion: The results of this study imply to the candida species colonization of neonates. Neonates in NICU are at the highest risk for severe infection with Candida parapsilosis. Therefore, isolation of C. parapsilosis as the most common species (43.47%) in present study was noteworthy

Systematic Phenotyping and Characterization of the 3xTg-AD Mouse Model of Alzheimers Disease.

Animal models of disease are valuable resources for investigating pathogenic mechanisms and potential therapeutic interventions. However, for complex disorders such as Alzheimers disease (AD), the generation and availability of innumerous distinct animal models present unique challenges to AD researchers and hinder the success of useful therapies. Here, we conducted an in-depth analysis of the 3xTg-AD mouse model of AD across its lifespan to better inform the field of the various pathologies that appear at specific ages, and comment on drift that has occurred in the development of pathology in this line since its development 20 years ago. This modern characterization of the 3xTg-AD model includes an assessment of impairments in long-term potentiation followed by quantification of amyloid beta (Aβ) plaque burden and neurofibrillary tau tangles, biochemical levels of Aβ and tau protein, and neuropathological markers such as gliosis and accumulation of dystrophic neurites. We also present a novel comparison of the 3xTg-AD model with the 5xFAD model using the same deep-phenotyping characterization pipeline and show plasma NfL is strongly driven by plaque burden. The results from these analyses are freely available via the AD Knowledge Portal (https://modeladexplorer.org/). Our work demonstrates the utility of a characterization pipeline that generates robust and standardized information relevant to investigating and comparing disease etiologies of current and future models of AD

Systematic phenotyping and characterization of the 5xFAD mouse model of Alzheimer's disease.

Mouse models of human diseases are invaluable tools for studying pathogenic mechanisms and testing interventions and therapeutics. For disorders such as Alzheimer's disease in which numerous models are being generated, a challenging first step is to identify the most appropriate model and age to effectively evaluate new therapeutic approaches. Here we conducted a detailed phenotypic characterization of the 5xFAD model on a congenic C57BL/6 J strain background, across its lifespan - including a seldomly analyzed 18-month old time point to provide temporally correlated phenotyping of this model and a template for characterization of new models of LOAD as they are generated. This comprehensive analysis included quantification of plaque burden, Aβ biochemical levels, and neuropathology, neurophysiological measurements and behavioral and cognitive assessments, and evaluation of microglia, astrocytes, and neurons. Analysis of transcriptional changes was conducted using bulk-tissue generated RNA-seq data from microdissected cortices and hippocampi as a function of aging, which can be explored at the MODEL-AD Explorer and AD Knowledge Portal. This deep-phenotyping pipeline identified novel aspects of age-related pathology in the 5xFAD model

BIN1K358R suppresses glial response to plaques in mouse model of Alzheimer's disease

IntroductionThe BIN1 coding variant rs138047593 (K358R) is linked to Late-Onset Alzheimer's Disease (LOAD) via targeted exome sequencing.MethodsTo elucidate the functional consequences of this rare coding variant on brain amyloidosis and neuroinflammation, we generated BIN1K358R knock-in mice using CRISPR/Cas9 technology. These mice were subsequently bred with 5xFAD transgenic mice, which serve as a model for Alzheimer's pathology.ResultsThe presence of the BIN1K358R variant leads to increased cerebral amyloid deposition, with a dampened response of astrocytes and oligodendrocytes, but not microglia, at both the cellular and transcriptional levels. This correlates with decreased neurofilament light chain in both plasma and brain tissue. Synaptic densities are significantly increased in both wild-type and 5xFAD backgrounds homozygous for the BIN1K358R variant.DiscussionThe BIN1 K358R variant modulates amyloid pathology in 5xFAD mice, attenuates the astrocytic and oligodendrocytic responses to amyloid plaques, decreases damage markers, and elevates synaptic densities.HighlightsBIN1 rs138047593 (K358R) coding variant is associated with increased risk of LOAD. BIN1 K358R variant increases amyloid plaque load in 12-month-old 5xFAD mice. BIN1 K358R variant dampens astrocytic and oligodendrocytic response to plaques. BIN1 K358R variant decreases neuronal damage in 5xFAD mice. BIN1 K358R upregulates synaptic densities and modulates synaptic transmission

A Trem2R47H mouse model without cryptic splicing drives age- and disease-dependent tissue damage and synaptic loss in response to plaques

BackgroundThe TREM2 R47H variant is one of the strongest genetic risk factors for late-onset Alzheimer's Disease (AD). Unfortunately, many current Trem2 R47H mouse models are associated with cryptic mRNA splicing of the mutant allele that produces a confounding reduction in protein product. To overcome this issue, we developed the Trem2R47H NSS (Normal Splice Site) mouse model in which the Trem2 allele is expressed at a similar level to the wild-type Trem2 allele without evidence of cryptic splicing products.MethodsTrem2R47H NSS mice were treated with the demyelinating agent cuprizone, or crossed with the 5xFAD mouse model of amyloidosis, to explore the impact of the TREM2 R47H variant on inflammatory responses to demyelination, plaque development, and the brain's response to plaques.ResultsTrem2R47H NSS mice display an appropriate inflammatory response to cuprizone challenge, and do not recapitulate the null allele in terms of impeded inflammatory responses to demyelination. Utilizing the 5xFAD mouse model, we report age- and disease-dependent changes in Trem2R47H NSS mice in response to development of AD-like pathology. At an early (4-month-old) disease stage, hemizygous 5xFAD/homozygous Trem2R47H NSS (5xFAD/Trem2R47H NSS) mice have reduced size and number of microglia that display impaired interaction with plaques compared to microglia in age-matched 5xFAD hemizygous controls. This is associated with a suppressed inflammatory response but increased dystrophic neurites and axonal damage as measured by plasma neurofilament light chain (NfL) level. Homozygosity for Trem2R47H NSS suppressed LTP deficits and loss of presynaptic puncta caused by the 5xFAD transgene array in 4-month-old mice. At a more advanced (12-month-old) disease stage 5xFAD/Trem2R47H NSS mice no longer display impaired plaque-microglia interaction or suppressed inflammatory gene expression, although NfL levels remain elevated, and a unique interferon-related gene expression signature is seen. Twelve-month old Trem2R47H NSS mice also display LTP deficits and postsynaptic loss.ConclusionsThe Trem2R47H NSS mouse is a valuable model that can be used to investigate age-dependent effects of the AD-risk R47H mutation on TREM2 and microglial function including its effects on plaque development, microglial-plaque interaction, production of a unique interferon signature and associated tissue damage