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Randomized feature selection based semi-supervised latent Dirichlet allocation for microbiome analysis.
Get PDFHealth and disease are fundamentally influenced by microbial communities and their genes (the microbiome). An in-depth analysis of microbiome structure that enables the classification of individuals based on their health can be crucial in enhancing diagnostics and treatment strategies to improve the overall well-being of an individual. In this paper, we present a novel semi-supervised methodology known as Randomized Feature Selection based Latent Dirichlet Allocation (RFSLDA) to study the impact of the gut microbiome on a subject\u27s health status. Since the data in our study consists of fuzzy health labels, which are self-reported, traditional supervised learning approaches may not be suitable. As a first step, based on the similarity between documents in text analysis and gut-microbiome data, we employ Latent Dirichlet Allocation (LDA), a topic modeling approach which uses microbiome counts as features to group subjects into relatively homogeneous clusters, without invoking any knowledge of observed health status (labels) of subjects. We then leverage information from the observed health status of subjects to associate these clusters with the most similar health status making it a semi-supervised approach. Finally, a feature selection technique is incorporated into the model to improve the overall classification performance. The proposed method provides a semi-supervised topic modelling approach that can help handle the high dimensionality of the microbiome data in association studies. Our experiments reveal that our semi-supervised classification algorithm is effective and efficient in terms of high classification accuracy compared to popular supervised learning approaches like SVM and multinomial logistic model. The RFSLDA framework is attractive because it (i) enhances clustering accuracy by identifying key bacteria types as indicators of health status, (ii) identifies key bacteria types within each group based on estimates of the proportion of bacteria types within the groups, and (iii) computes a measure of within-group similarity to identify highly similar subjects in terms of their health status
Genetic and multi-omic risk assessment of Alzheimer\u27s disease implicates core associated biological domains.
Get PDFINTRODUCTION: Alzheimer\u27s disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome.
METHODS: Here we report the development of an informatic pipeline that draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi-omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify system-level changes in expression associated with AD. These two elements combined constitute our target risk score that ranks AD risk genome-wide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive Gene Ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank-and-organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains.
RESULTS: The top AD-risk-associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains.
DISCUSSION: This provides an objective methodology to localize risk within specific biological endophenotypes and drill down into the most significantly associated sets of GO terms and annotated genes for potential therapeutic targets
Quantitative trait loci mapping provides insights into the genetic regulation of dendritic cell numbers in mouse tissues.
Get PDFTo explore the influence of genetics on homeostatic regulation of dendritic cell (DC) numbers, we present a screen of DCs and their progenitors in lymphoid and non-lymphoid tissues in Collaborative Cross (CC) and Diversity Outbred (DO) mice. We report 30 and 71 loci with logarithm of the odds (LOD) scores \u3e8.18 and ranging from 6.67 to 8.19, respectively. The analysis reveals the highly polygenic and pleiotropic architecture of this complex trait, including many of the previously identified genetic regulators of DC development and maturation. Two SNPs in genes potentially underlying variation in DC homeostasis, a splice variant in Gramd4 (rs235532740) and a missense variant in Orai3 (rs216659754), are confirmed by gene editing using CRISPR-Cas9. Gramd4 is a central regulator of DC homeostasis that impacts the entire DC lineage, and Orai3 regulates cDC2 numbers in tissues. Overall, the data reveal a large number of candidate genes regulating DC homeostasis in vivo
Tmod2 Is a Regulator of Cocaine Responses through Control of Striatal and Cortical Excitability and Drug-Induced Plasticity
No full textDrugs of abuse induce neuroadaptations, including synaptic plasticity, that are critical for transition to addiction, and genes and pathways that regulate these neuroadaptations are potential therapeutic targets. Tropomodulin 2 (Tmod2) is an actin-regulating gene that plays an important role in synapse maturation and dendritic arborization and has been implicated in substance abuse and intellectual disability in humans. Here, we mine the KOMP2 data and find that Tmod2 knock-out mice show emotionality phenotypes that are predictive of addiction vulnerability. Detailed addiction phenotyping shows that Tmod2 deletion does not affect the acute locomotor response to cocaine administration. However, sensitized locomotor responses are highly attenuated in these knock-outs, indicating perturbed drug-induced plasticity. In addition, Tmod2 mutant animals do not self-administer cocaine indi- cating lack of hedonic responses to cocaine. Whole-brain MR imaging shows differences in brain volume across multiple regions, although transcriptomic experiments did not reveal perturbations in gene coexpression networks. Detailed electrophysiological characterization of Tmod2 KO neurons showed increased spontaneous firing rate of early postnatal and adult cortical and striatal neurons. Cocaine-induced synaptic plasticity that is critical for sensitization is either missing or reciprocal in Tmod2 KO nucleus accumbens shell medium spiny neurons, providing a mechanistic explanation of the cocaine response phenotypes. Combined, these data, collected from both males and females, provide compelling evidence that Tmod2 is a major regulator of plasticity in the mesolimbic system and regulates the reinforcing and addictive properties of cocaine
Detecting the effect of genetic diversity on brain composition in an Alzheimer\u27s disease mouse model.
Get PDFAlzheimer\u27s disease (AD) is broadly characterized by neurodegeneration, pathology accumulation, and cognitive decline. There is considerable variation in the progression of clinical symptoms and pathology in humans, highlighting the importance of genetic diversity in the study of AD. To address this, we analyze cell composition and amyloid-beta deposition of 6- and 14-month-old AD-BXD mouse brains. We utilize the analytical QUINT workflow- a suite of software designed to support atlas-based quantification, which we expand to deliver a highly effective method for registering and quantifying cell and pathology changes in diverse disease models. In applying the expanded QUINT workflow, we quantify near-global age-related increases in microglia, astrocytes, and amyloid-beta, and we identify strain-specific regional variation in neuron load. To understand how individual differences in cell composition affect the interpretation of bulk gene expression in AD, we combine hippocampal immunohistochemistry analyses with bulk RNA-sequencing data. This approach allows us to categorize genes whose expression changes in response to AD in a cell and/or pathology load-dependent manner. Ultimately, our study demonstrates the use of the QUINT workflow to standardize the quantification of immunohistochemistry data in diverse mice, - providing valuable insights into regional variation in cellular load and amyloid deposition in the AD-BXD model
Lack of effect from genetic deletion of Hdac6 in a humanized mouse model of CMT2D.
No full textBACKGROUND: Inhibition of HDAC6 has been proposed as a broadly applicable therapeutic strategy for Charcot-Marie-Tooth disease (CMT). Inhibition of HDAC6 increases the acetylation of proteins important in axonal trafficking, such as α-tubulin and Miro, and has been shown to be efficacious in several preclinical studies using mouse models of CMT.
AIMS: Here, we sought to expand on previous preclinical studies by testing the effect of genetic deletion of Hdac6 on mice carrying a humanized knockin allele of Gars1, a model of CMT-type 2D.
METHODS: Gars1
RESULTS: The genetic deletion of Hdac6 increased α-tubulin acetylation in the sciatic nerves of both wild-type and Gars1
INTERPRETATION: Our results differ from those of two previous studies that demonstrated the benefit of the HDAC6 inhibitor tubastatin A in mouse models of CMT2D. While we cannot fully explain the different outcomes, our results offer a counterexample to the benefit of inhibiting HDAC6 in CMT2D, suggesting additional research is necessary
Health care utilization and behavior changes after workplace genetic testing at a large US health care system.
Get PDFPURPOSE: This study explored employee health behavior changes and health care utilization after workplace genetic testing (wGT). Wellness-program-associated wGT seeks to improve employee health, but the related health implications are unknown.
METHODS: Employees of a large US health care system offering wGT (cancer, heart disease, and pharmacogenomics [PGx]) were sent electronic surveys. Self-reported data from those who received test results were analyzed. Descriptive statistics characterized responses, whereas logistic regression analyses explored correlates of responses to wGT.
RESULTS: 53.9% (n = 418/776) of respondents (88.3% female, mean age = 44 years) reported receiving wGT results. 12.0% (n = 48/399) received results indicating increased risk (IR) of cancer, 9.5% (n = 38/398) had IR of heart disease, and 31.4% (n = 125/398) received informative PGx results. IR results for cancer and/or heart disease (n = 67) were associated with health behavior changes (adjusted odds ratio: 3.23; 95% CI 1.75, 6.13; P \u3c .001) and health care utilization (adjusted odds ratio: 8.60; 95% CI 4.43, 17.5; P \u3c .001). Informative PGx results (n = 125) were associated with medication changes (PGx-informative: 15.2%; PGx-uninformative: 4.8%; P = .002).
CONCLUSION: This study explored employee responses to wGT, contributing to the understanding of the ethical and social implications of wGT. Receiving IR results from wGT may promote health behavior changes and health care utilization in employees
Human-augmented large language model-driven selection of glutathione peroxidase 4 as a candidate blood transcriptional biomarker for circulating erythroid cells.
Get PDFThe identification of optimal candidate genes from large-scale blood transcriptomic data is crucial for developing targeted assays to monitor immune responses. Here, we introduce a novel, optimized large language model (LLM)-based approach for prioritizing candidate biomarkers from blood transcriptional modules. Focusing on module M14.51 from the BloodGen3 repertoire, we implemented a multi-step LLM-driven workflow. Initial high-throughput screening used GPT-4, Claude 3, and Claude 3.5 Sonnet to score and rank the module\u27s constituent genes across six criteria. Top candidates then underwent high-resolution scoring using Consensus GPT, with concurrent manual fact-checking and, when needed, iterative refinement of the scores based on user feedback. Qualitative assessment of literature-based narratives and analysis of reference transcriptome data further refined the selection process. This novel multi-tiered approach consistently identified Glutathione Peroxidase 4 (GPX4) as the top candidate gene for module M14.51. GPX4\u27s role in oxidative stress regulation, its potential as a future drug target, and its expression pattern across diverse cell types supported its selection. The incorporation of reference transcriptome data further validated GPX4 as the most suitable candidate for this module. This study presents an advanced LLM-driven workflow with a novel optimized scoring strategy for candidate gene prioritization, incorporating human-in-the-loop augmentation. The approach identified GPX4 as a key gene in the erythroid cell-associated module M14.51, suggesting its potential utility for biomarker discovery and targeted assay development. By combining AI-driven literature analysis with iterative human expert validation, this method leverages the strengths of both artificial and human intelligence, potentially contributing to the development of biologically relevant and clinically informative targeted assays. Further validation studies are needed to confirm the broader applicability of this human-augmented AI approach
Presynaptic Nrxn3 is essential for ribbon-synapse maturation in hair cells.
Get PDFHair cells of the inner ear and lateral-line system rely on specialized ribbon synapses to transmit sensory information to the central nervous system. The molecules required to assemble these synapses are not fully understood. We show that Nrxn3, a presynaptic adhesion molecule, is crucial for ribbon-synapse maturation in hair cells. In both mouse and zebrafish models, the loss of Nrxn3 results in significantly fewer intact ribbon synapses. We show in zebrafish that, initially, Nrxn3 loss does not alter pre- and postsynapse numbers but, later, synapses fail to pair, leading to postsynapse loss. We also demonstrate that Nrxn3 subtly influences synapse selectivity in zebrafish lateral-line hair cells that detect anterior flow. Loss of Nrxn3 leads to a 60% loss of synapses in zebrafish, which dramatically reduces pre- and postsynaptic responses. Despite fewer synapses, auditory responses in zebrafish and mice are unaffected. This work demonstrates that Nrxn3 is a crucial and conserved molecule required for the maturation of ribbon synapses. Understanding how ribbon synapses mature is essential to generating new therapies to treat synaptopathies linked to auditory or vestibular dysfunction