Combined IL6 and CCR2 blockade potentiates antitumor activity of NK cells in HPV-negative head and neck cancer.

BACKGROUND: While T cell-activating immunotherapies against recurrent head and neck squamous cell carcinoma (HNSCC) have shown impressive results in clinical trials, they are often ineffective in the majority of patients. NK cells are potential targets for immunotherapeutic intervention; however, the setback in monalizumab-based therapy in HNSCC highlights the need for an alternative treatment to enhance their antitumor activity. METHODS: Single-cell RNA sequencing (scRNA-seq) and TCGA HNSCC datasets were used to identify key molecular alterations in NK cells. Representative HPV-positive ( +) and HPV-negative ( -) HNSCC cell lines and orthotopic mouse models were used to validate the bioinformatic findings. Changes in immune cells were examined by flow cytometry and immunofluorescence. RESULTS: Through integration of scRNA-seq data with TCGA data, we found that the impact of IL6/IL6R and CCL2/CCR2 signaling pathways on evasion of immune attack by NK cells is more pronounced in the HPV - HNSCC cohort compared to the HPV + HNSCC cohort. In orthotopic mouse models, blocking IL6 with a neutralizing antibody suppressed HPV - but not HPV + tumors, which was accompanied by increased tumor infiltration and proliferation of CD161+ NK cells. Notably, combining the CCR2 chemokine receptor antagonist RS504393 with IL6 blockade resulted in a more pronounced antitumor effect that was associated with more activated intratumoral NK cells in HPV - HNSCC compared to either agent alone. CONCLUSIONS: These findings demonstrate that dual blockade of IL6 and CCR2 pathways effectively enhances the antitumor activity of NK cells in HPV-negative HNSCC, providing a novel strategy for treating this type of cancer.

Integrative Bayesian tensor regression for imaging genetics applications.

Identifying biomarkers for Alzheimer's disease with a goal of early detection is a fundamental problem in clinical research. Both medical imaging and genetics have contributed informative biomarkers in literature. To further improve the performance, recently, there is an increasing interest in developing analytic approaches that combine data across modalities such as imaging and genetics. However, there are limited methods in literature that are able to systematically combine high-dimensional voxel-level imaging and genetic data for accurate prediction of clinical outcomes of interest. Existing prediction models that integrate imaging and genetic features often use region level imaging summaries, and they typically do not consider the spatial configurations of the voxels in the image or incorporate the dependence between genes that may compromise prediction ability. We propose a novel integrative Bayesian scalar-on-image regression model for predicting cognitive outcomes based on high-dimensional spatially distributed voxel-level imaging data, along with correlated transcriptomic features. We account for the spatial dependencies in the imaging voxels via a tensor approach that also enables massive dimension reduction to address the curse of dimensionality, and models the dependencies between the transcriptomic features via a Graph-Laplacian prior. We implement this approach via an efficient Markov chain Monte Carlo (MCMC) computation strategy. We apply the proposed method to the analysis of longitudinal ADNI data for predicting cognitive scores at different visits by integrating voxel-level cortical thickness measurements derived from T1w-MRI scans and transcriptomics data. We illustrate that the proposed imaging transcriptomics approach has significant improvements in prediction compared to prediction using a subset of features from only one modality (imaging or genetics), as well as when using imaging and transcriptomics features but ignoring the inherent dependencies between the features. Our analysis is one of the first to conclusively demonstrate the advantages of prediction based on combining voxel-level cortical thickness measurements along with transcriptomics features, while accounting for inherent structural information.

Role of TET1-mediated epigenetic modulation in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder influenced by a complex interplay of environmental, epigenetic, and genetic factors. DNA methylation (5mC) and hydroxymethylation (5hmC) are DNA modifications that serve as tissue-specific and temporal regulators of gene expression. TET family enzymes dynamically regulate these epigenetic modifications in response to environmental conditions, connecting environmental factors with gene expression. Previous epigenetic studies have identified 5mC and 5hmC changes associated with AD. In this study, we performed targeted resequencing of TET1 on a cohort of early-onset AD (EOAD) and control samples. Through gene-wise burden analysis, we observed significant enrichment of rare TET1 variants associated with AD (p = 0.04). We also profiled 5hmC in human postmortem brain tissues from AD and control groups. Our analysis identified differentially hydroxymethylated regions (DhMRs) in key genes responsible for regulating the methylome: TET3, DNMT3L, DNMT3A, and MECP2. To further investigate the role of Tet1 in AD pathogenesis, we used the 5xFAD mouse model with a Tet1 KO allele to examine how Tet1 loss influences AD pathogenesis. We observed significant changes in neuropathology, 5hmC, and RNA expression associated with Tet1 loss, while the behavioral alterations were not significant. The loss of Tet1 significantly increased amyloid plaque burden in the 5xFAD mouse (p = 0.044) and lead to a non-significant trend towards exacerbated AD-associated stress response in 5xFAD mice. At the molecular level, we found significant DhMRs enriched in genes involved in pathways responsible for neuronal projection organization, dendritic spine development and organization, and myelin assembly. RNA-Seq analysis revealed a significant increase in the expression of AD-associated genes such as Mpeg1, Ctsd, and Trem2. In conclusion, our results suggest that TET enzymes, particularly TET1, which regulate the methylome, may contribute to AD pathogenesis, as the loss of TET function increases AD-associated pathology.

Unsupervised Hierarchical Clustering of Head and Neck Cancer Patients by Pre-Treatment Plasma Metabolomics Creates Prognostic Metabolic Subtypes.

There is growing evidence that the metabolism is deeply intertwined with head and neck squamous cell carcinoma (HNSCC) progression and survival but little is known about circulating metabolite patterns and their clinical potential. We performed unsupervised hierarchical clustering of 209 HNSCC patients via pre-treatment plasma metabolomics to identify metabolic subtypes. We annotated the subtypes via pathway enrichment analysis and investigated their association with overall and progression-free survival. We stratified the survival analyses by smoking history. High-resolution metabolomics extracted 186 laboratory-confirmed metabolites. The optimal model created two patient clusters, of subtypes A and B, corresponding to 41% and 59% of the study population, respectively. Fatty acid biosynthesis, acetyl-CoA transport, arginine and proline, as well as the galactose metabolism pathways differentiated the subtypes. Relative to subtype B, subtype A patients experienced significantly worse overall and progression-free survival but only among ever-smokers. The estimated three-year overall survival was 61% for subtype A and 86% for subtype B; log-rank p = 0.001. The association with survival was independent of HPV status and other HNSCC risk factors (adjusted hazard ratio = 3.58, 95% CI: 1.46, 8.78). Our findings suggest that a non-invasive metabolomic biomarker would add crucial information to clinical risk stratification and raise translational research questions about testing such a biomarker in clinical trials.

Circadian rhythm sleep disorders and time-of-day-dependent memory deficiency in Presenilin1/2 conditional knockout mice with long noncoding RNA expression profiling changes.

Alzheimer's disease (AD) patients exhibit sleep and circadian disturbances prior to the onset of cognitive decline, and these disruptions worsen with disease severity. However, the molecular mechanisms behind sleep and circadian disruptions in AD patients are poorly understood. In this study, we investigated sleep pattern and circadian rhythms in Presenilin-1/2 conditional knockout (DKO) mice. Assessment of EEG and EMG recordings showed that DKO mice displayed increased NREM sleep time but not REM sleep during the dark phase compared to WT mice at the age of two months; at the age of six months, the DKO mice showed increased wakefulness periods and decreased total time spent in both NREM and REM sleep. WT exhibited time-of-day dependent modulation of contextual and cued memory. Compared with WT mice, 4-month-old DKO mice exhibited the deficiency regardless trained and tested in the same light/night phase or not. Particularly interesting was that DKO showed circadian modulation deficiency when trained in the resting period but not in the active period. Long noncoding RNAs (lncRNAs) are typically defined as transcripts longer than 200 nucleotides, and they have rhythmic expression in mammals. To date no study has investigated rhythmic lncRNA expression in Alzheimer's disease. We applied RNA-seq technology to profile hippocampus expression of lncRNAs in DKO mice during the light (/resting) and dark (/active) phases and performed gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses of the cis lncRNA targets. Expression alteration of lncRNAs associated with immune response and metallodipeptidase activity may contribute to the circadian disruptions of DKO mice. Especially we identified some LncRNAs which expression change oppositely between day and light in DKO mice compared to WT mice and are worthy to be studied further. Our results exhibited the circadian rhythm sleep disorders and a noteworthy time-of-day-dependent memory deficiency in AD model mice and provide a useful resource for studying the expression and function of lncRNAs during circadian disruptions in Alzheimer's disease.

Identification of androgen response-related lncRNAs in prostate cancer.

BACKGROUND: Long noncoding RNAs (lncRNAs) are RNA molecules with over 200 nucleotides that do not code for proteins, but are known to be widely expressed and have key roles in gene regulation and cellular functions. They are also found to be involved in the onset and development of various cancers, including prostate cancer (PCa). Since PCa are commonly driven by androgen regulated signaling, mainly stimulated pathways, identification and determining the influence of lncRNAs in androgen response is useful and necessary. LncRNAs regulated by the androgen receptor (AR) can serve as potential biomarkers for PCa. In the present study, gene expression data analysis were performed to distinguish lncRNAs related to the androgen response pathway. METHODS AND RESULTS: We used publicly available RNA-sequencing and ChIP-seq data to identify lncRNAs that are associated with the androgen response pathway. Using Universal Correlation Coefficient (UCC) and Pearson Correlation Coefficient (PCC) analyses, we found 15 lncRNAs that have (a) highly correlated expression with androgen response genes in PCa and are (b) differentially expressed in the setting of treatment with an androgen agonist as well as antagonist compared to controls. Using publicly available ChIP-seq data, we investigated the role of androgen/AR axis in regulating expression of these lncRNAs. We observed AR binding in the promoter regions of 5 lncRNAs (MIR99AHG, DUBR, DRAIC, PVT1, and COLCA1), showing the direct influence of AR on their expression and highlighting their association with the androgen response pathway. CONCLUSION: By utilizing publicly available multiomics data and by employing in silico methods, we identified five candidate lncRNAs that are involved in the androgen response pathway. These lncRNAs should be investigated as potential biomarkers for PCa.

Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome.

This manuscript presents a comprehensive collection of diverse epigenomic profiling data for the human genome in 100-bp resolution with full genome-wide coverage. The datasets are processed from raw read count data collected from five types of sequencing-based assays collected by the Encyclopedia of DNA Elements consortium (ENCODE, http://www.encodeproject.org). Data from high-throughput sequencing assays were processed and crystallized into a total of 6,305 genome-wide profiles. To ensure the quality of the features, we filtered out assays with low read depth, inconsistent read counts, and poor data quality. The types of sequencing-based experiment assays include DNase-seq, histone and TF ChIP-seq, ATAC-seq, and Poly(A) RNA-seq. Merging of processed data was done by averaging read counts across technical replicates to obtain signals in about 30 million predefined 100-bp bins that tile the entire genome. We provide an example of fetching read counts using disease-related risk variants from the GWAS Catalog. Additionally, we have created a tabix index enabling fast user retrieval of read counts given coordinates in the human genome. The data processing pipeline is replicable for users' own purposes and for other experimental assays. The processed data can be found on Zenodo at https://zenodo.org/record/7015783. These data can be used as features for statistical and machine learning models to predict or infer a wide range of variables of biological interest. They can also be applied to generate novel insights into gene expression, chromatin accessibility, and epigenetic modifications across the human genome. Finally, the processing pipeline can be easily applied to data from any other genome-wide profiling assays, expanding the amount of available data.

NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity.

A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-ß-induced epithelial-mesenchymal transition and macrophage polarization.

A clade C HIV-1 vaccine protects against heterologous SHIV infection by modulating IgG glycosylation and T helper response in macaques.

The rising global HIV-1 burden urgently requires vaccines capable of providing heterologous protection. Here, we developed a clade C HIV-1 vaccine consisting of priming with modified vaccinia Ankara (MVA) and boosting with cyclically permuted trimeric gp120 (CycP-gp120) protein, delivered either orally using a needle-free injector or through parenteral injection. We tested protective efficacy of the vaccine against intrarectal challenges with a pathogenic heterologous clade C SHIV infection in rhesus macaques. Both routes of vaccination induced a strong envelope-specific IgG in serum and rectal secretions directed against V1V2 scaffolds from a global panel of viruses with polyfunctional activities. Envelope-specific IgG showed lower fucosylation compared with total IgG at baseline, and most of the vaccine-induced proliferating blood CD4+ T cells did not express CCR5 and α4ß7, markers associated with HIV target cells. After SHIV challenge, both routes of vaccination conferred significant and equivalent protection, with 40% of animals remaining uninfected at the end of six weekly repeated challenges with an estimated efficacy of 68% per exposure. Induction of envelope-specific IgG correlated positively with G1FB glycosylation, and G2S2F glycosylation correlated negatively with protection. Vaccine-induced TNF-α+ IFN-γ+ CD8+ T cells and TNF-α+ CD4+ T cells expressing low levels of CCR5 in the rectum at prechallenge were associated with decreased risk of SHIV acquisition. These results demonstrate that the clade C MVA/CycP-gp120 vaccine provides heterologous protection against a tier2 SHIV rectal challenge by inducing a polyfunctional antibody response with distinct Fc glycosylation profile, as well as cytotoxic CD8 T cell response and CCR5-negative T helper response in the rectum.

LRcell: detecting the source of differential expression at the sub-cell-type level from bulk RNA-seq data.

Given most tissues are consist of abundant and diverse (sub-)cell types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which (sub-)cell type(s) the differential expression occurs. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell, a computational method aiming to identify specific (sub-)cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative scRNA-seq experiments as options to execute the analyses. We conduct a simulation study to demonstrate the effectiveness and reliability of LRcell. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders. Applying LRcell to bulk RNA-seq results can produce a hypothesis on which (sub-)cell type(s) contributes to the differential expression. LRcell is complementary to cell type deconvolution methods.

UALCAN: An update to the integrated cancer data analysis platform.

Cancer genomic, transcriptomic, and proteomic profiling has generated extensive data that necessitate the development of tools for its analysis and dissemination. We developed UALCAN to provide a portal for easy exploring, analyzing, and visualizing these data, allowing users to integrate the data to better understand the gene, proteins, and pathways perturbed in cancer and make discoveries. UALCAN web portal enables analyzing and delivering cancer transcriptome, proteomics, and patient survival data to the cancer research community. With data obtained from The Cancer Genome Atlas (TCGA) project, UALCAN has enabled users to evaluate protein-coding gene expression and its impact on patient survival across 33 types of cancers. The web portal has been used extensively since its release and received immense popularity, underlined by its usage from cancer researchers in more than 100 countries. The present manuscript highlights the task we have undertaken and updates that we have made to UALCAN since its release in 2017. Extensive user feedback motivated us to expand the resource by including data on a) microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and promoter DNA methylation from TCGA and b) mass spectrometry-based proteomics from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). UALCAN provides easy access to pre-computed, tumor subgroup-based gene/protein expression, promoter DNA methylation status, and Kaplan-Meier survival analyses. It also provides new visualization features to comprehend and integrate observations and aids in generating hypotheses for testing. UALCAN is accessible at http://ualcan.path.uab.edu.

Disease category-specific annotation of variants using an ensemble learning framework.

Understanding the impact of non-coding sequence variants on complex diseases is an essential problem. We present a novel ensemble learning framework-CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example, we demonstrate the potential of CASAVA in revealing variant-disease associations. A website (http://zhanglabtools.org/CASAVA) has been built to facilitate easily access to CASAVA scores.

Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog.

The collection of expression quantitative trait loci (eQTLs) is an important resource to study complex traits through understanding where and how transcriptional regulations are controlled by genetic variations in the non-coding regions of the genome. Previous studies have focused on associating eQTLs with traits to identify the roles of trait-related eQTLs and their corresponding target genes involved in trait determination. Since most genes function as a part of pathways in a systematic manner, it is crucial to explore the pathways' involvements in complex traits to test potentially novel hypotheses and to reveal underlying mechanisms of disease pathogenesis. In this study, we expanded and applied loci2path software to perform large-scale eQTLs enrichment [i.e., eQTLs' target genes (eGenes) enrichment] analysis at pathway level to identify the tissue-specific enriched pathways within trait-related genomic intervals. By utilizing 13,791,909 eQTLs cataloged in the Genotype-Tissue Expression (GTEx) V8 data for 49 tissue types, 2,893 pathway sets reported from MSigDB, and query regions derived from the Phenotype-Genotype Integrator (PheGenI) catalog, we identified intriguing biological pathways that are likely to be involved in ten traits [Alzheimer's disease (AD), body mass index, Parkinson's disease (PD), schizophrenia, amyotrophic lateral sclerosis, non-small cell lung cancer (NSCLC), stroke, blood pressure, autism spectrum disorder, and myocardial infarction]. Furthermore, we extracted the most significant pathways for AD, such as BioCarta D4-GDI pathway and WikiPathways sulfation biotransformation reaction and viral acute myocarditis pathways, to study specific genes within pathways. Our data presented new hypotheses in AD pathogenesis supported by previous studies, like the increased level of caspase-3 in the amygdala that cleaves GDP dissociation inhibitor and binds to beta-amyloid, leading to increased apoptosis and neuronal loss. Our findings also revealed potential pathogenesis mechanisms for PD, schizophrenia, NSCLC, blood pressure, autism spectrum disorder, and myocardial infarction, which were consistent with past studies. Our results indicated that loci2path's eQTLs enrichment test was valuable in unveiling novel biological mechanisms of complex traits. The discovered mechanisms of disease pathogenesis and traits require further in-depth analysis and experimental validation.

Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis.

Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.

Systematic Evaluation of DNA Sequence Variations on in vivo Transcription Factor Binding Affinity.

The majority of the single nucleotide variants (SNVs) identified by genome-wide association studies (GWAS) fall outside of the protein-coding regions. Elucidating the functional implications of these variants has been a major challenge. A possible mechanism for functional non-coding variants is that they disrupted the canonical transcription factor (TF) binding sites that affect the in vivo binding of the TF. However, their impact varies since many positions within a TF binding motif are not well conserved. Therefore, simply annotating all variants located in putative TF binding sites may overestimate the functional impact of these SNVs. We conducted a comprehensive survey to study the effect of SNVs on the TF binding affinity. A sequence-based machine learning method was used to estimate the change in binding affinity for each SNV located inside a putative motif site. From the results obtained on 18 TF binding motifs, we found that there is a substantial variation in terms of a SNV's impact on TF binding affinity. We found that only about 20% of SNVs located inside putative TF binding sites would likely to have significant impact on the TF-DNA binding.

Plasma Metabolic Phenotypes of HPV-Associated versus Smoking-Associated Head and Neck Cancer and Patient Survival.

BACKGROUND: Metabolic differences between human papillomavirus (HPV)-associated head and neck squamous cell carcinoma (HNSCC) and smoking-associated HNSCC may partially explain differences in prognosis. The former relies on mitochondrial oxidative phosphorylation (OXPHOS) while the latter relies on glycolysis. These differences have not been studied in blood. METHODS: We extracted metabolites using untargeted liquid chromatography high-resolution mass spectrometry from pretreatment plasma in a cohort of 55 HPV-associated and 82 smoking-associated HNSCC subjects. Metabolic pathway enrichment analysis of differentially expressed metabolites produced pathway-based signatures. Significant pathways (P < 0.05) were reduced via principal component analysis and assessed with overall survival via Cox models. We classified each subject as glycolytic or OXPHOS phenotype and assessed it with survival. RESULTS: Of 2,410 analyzed metabolites, 191 were differentially expressed. Relative to smoking-associated HNSCC, bile acid biosynthesis (P < 0.0001) and octadecatrienoic acid beta-oxidation (P = 0.01), were upregulated in HPV-associated HNSCC, while galactose metabolism (P = 0.001) and vitamin B6 metabolism (P = 0.01) were downregulated; the first two suggest an OXPHOS phenotype while the latter two suggest glycolytic. First principal components of bile acid biosynthesis [HR = 0.52 per SD; 95% confidence interval (CI), 0.38-0.72; P < 0.001] and octadecatrienoic acid beta-oxidation (HR = 0.54 per SD; 95% CI, 0.38-0.78; P < 0.001) were significantly associated with overall survival independent of HPV and smoking. The glycolytic versus OXPHOS phenotype was also independently associated with survival (HR = 3.17; 95% CI, 1.07-9.35; P = 0.04). CONCLUSIONS: Plasma metabolites related to glycolysis and mitochondrial OXPHOS may be biomarkers of HNSCC patient prognosis independent of HPV or smoking. Future investigations should determine whether they predict treatment efficacy. IMPACT: Blood metabolomics may be a useful marker to aid HNSCC patient prognosis.

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer's disease.

Alzheimer's disease (AD) is influenced by both genetic and environmental factors; thus, brain epigenomic alterations may provide insights into AD pathogenesis. Multiple array-based Epigenome-Wide Association Studies (EWASs) have identified robust brain methylation changes in AD; however, array-based assays only test about 2% of all CpG sites in the genome. Here, we develop EWASplus, a computational method that uses a supervised machine learning strategy to extend EWAS coverage to the entire genome. Application to six AD-related traits predicts hundreds of new significant brain CpGs associated with AD, some of which are further validated experimentally. EWASplus also performs well on data collected from independent cohorts and different brain regions. Genes found near top EWASplus loci are enriched for kinases and for genes with evidence for physical interactions with known AD genes. In this work, we show that EWASplus implicates additional epigenetic loci for AD that are not found using array-based AD EWASs.

Multiomics Analysis of Structural Magnetic Resonance Imaging of the Brain and Cerebrospinal Fluid Metabolomics in Cognitively Normal and Impaired Adults.

INTRODUCTION: Integrating brain imaging with large scale omics data may identify novel mechanisms of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). We integrated and analyzed brain magnetic resonance imaging (MRI) with cerebrospinal fluid (CSF) metabolomics to elucidate metabolic mechanisms and create a "metabolic map" of the brain in prodromal AD. METHODS: In 145 subjects (85 cognitively normal controls and 60 with MCI), we derived voxel-wise gray matter volume via whole-brain structural MRI and conducted high-resolution untargeted metabolomics on CSF. Using a data-driven approach consisting of partial least squares discriminant analysis, a multiomics network clustering algorithm, and metabolic pathway analysis, we described dysregulated metabolic pathways in CSF mapped to brain regions associated with MCI in our cohort. RESULTS: The multiomics network algorithm clustered metabolites with contiguous imaging voxels into seven distinct communities corresponding to the following brain regions: hippocampus/parahippocampal gyrus (three distinct clusters), thalamus, posterior thalamus, parietal cortex, and occipital lobe. Metabolic pathway analysis indicated dysregulated metabolic activity in the urea cycle, and many amino acids (arginine, histidine, lysine, glycine, tryptophan, methionine, valine, glutamate, beta-alanine, and purine) was significantly associated with those regions (P < 0.05). CONCLUSION: By integrating CSF metabolomics data with structural MRI data, we linked specific AD-susceptible brain regions to disrupted metabolic pathways involving nitrogen excretion and amino acid metabolism critical for cognitive function. Our findings and analytical approach may extend drug and biomarker research toward more multiomics approaches.

Author Correction: Truncation of mutant huntingtin in knock-in mice demonstrates exon1 huntingtin is a key pathogenic form.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-19873-9.