Epigenetic profiling reveals key genes and cis-regulatory networks specific to human parathyroids.

In all terrestrial vertebrates, the parathyroid glands are critical regulators of calcium homeostasis and the sole source of parathyroid hormone (PTH). Hyperparathyroidism and hypoparathyroidism are clinically important disorders affecting multiple organs. However, our knowledge regarding regulatory mechanisms governing the parathyroids has remained limited. Here, we present the comprehensive maps of the chromatin landscape of the human parathyroid glands, identifying active regulatory elements and chromatin interactions. These data allow us to define regulatory circuits and previously unidentified genes that play crucial roles in parathyroid biology. We experimentally validate candidate parathyroid-specific enhancers and demonstrate their integration with GWAS SNPs for parathyroid-related diseases and traits. For instance, we observe reduced activity of a parathyroid-specific enhancer of the Calcium Sensing Receptor gene, which contains a risk allele associated with higher PTH levels compared to the wildtype allele. Our datasets provide a valuable resource for unraveling the mechanisms governing parathyroid gland regulation in health and disease.

Exploring the genetics of lithium response in bipolar disorders.

Background: Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N=2,064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. Results: We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. Conclusions: Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.

Corrigendum: Unraveling the interplay between norovirus infection, gut microbiota, and novel antiviral approaches: a comprehensive review.

[This corrects the article DOI: 10.3389/fmicb.2023.1212582.].

Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits.

Genome-wide association studies (GWASs) have identified tens of thousands of genetic loci associated with human complex traits. However, the majority of GWASs were conducted in individuals of European ancestries. Failure to capture global genetic diversity has limited genomic discovery and has impeded equitable delivery of genomic knowledge to diverse populations. Here we report findings from 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia. We identified 968 novel genetic loci, pinpointed novel causal variants through statistical fine-mapping, compared the genetic architecture across TWB, Biobank Japan, and UK Biobank, and evaluated the utility of cross-phenotype, cross-population polygenic risk scores in disease risk prediction. These results demonstrated the potential to advance discovery through diversifying GWAS populations and provided insights into the common genetic basis of human complex traits in East Asia.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment.

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Pleiotropic Genetic Effects between Multiple Sclerosis and Musculoskeletal Traits.

Background: Musculoskeletal disorders were commonly reported in patients with multiple sclerosis. However, the underlying etiology linking Multiple Sclerosis (MS) and musculoskeletal disorders is not well studied. With large-scale Genome-Wide Association Studies (GWAS) publicly available, we conducted genetic correlation analysis to identify shared pleiotropic genetic effects between MS and musculoskeletal traits. We also conducted Mendelian Randomization (MR) to estimate the causal relation between MS and increased risks of musculoskeletal disorders. Methods: Linkage Disequilibrium Score Regression (LDSR) analysis was performed to estimate heritability and genetic correlation. Univariable, multivariable, and bidirectional MR analyses were conducted to estimate the causal relation. These analyses were done by utilizing the recent GWAS summary statistics of MS, fracture, frailty, falls, and several musculoskeletal risk factors, including bone mineral density, lean mass, grip strengths, and vitamin D. Results: LDSR analysis showed a moderate genetic correlation of MS with falls (RG=0.10, p=0.01) but not with fracture and frailty. Genetic variants (rs13191659) in LINC00240 gene which is associated with iron status biomarkers was found to be associated with both MS and falls. In MR analyses after excluding outlier SNPs with potential pleiotropic effects and correcting for multiple testing, MS presented no causal association with fracture and frailty but a minimal association with falls. Falls showed causally increased risks of fracture and frailty. Conclusion: Our study suggests a potential genetic correlation with shared pleiotropic genetic effects between MS and falls. However, we didn't find evidence to support the causal relation between MS and increased risks of falls, fracture, and frailty.

A transcriptomic and proteomic atlas of obesity and type 2 diabetes in cynomolgus monkeys.

Obesity and type 2 diabetes (T2D) remain major global healthcare challenges, and developing therapeutics necessitates using nonhuman primate models. Here, we present a transcriptomic and proteomic atlas of all the major organs of cynomolgus monkeys with spontaneous obesity or T2D in comparison to healthy controls. Molecular changes occur predominantly in the adipose tissues of individuals with obesity, while extensive expression perturbations among T2D individuals are observed in many tissues such as the liver and kidney. Immune-response-related pathways are upregulated in obesity and T2D, whereas metabolism and mitochondrial pathways are downregulated. Moreover, we highlight some potential therapeutic targets, including SLC2A1 and PCSK1 in obesity as well as SLC30A8 and SLC2A2 in T2D. Our study provides a resource for exploring the complex molecular mechanism of obesity and T2D and developing therapies for these diseases, with limitations including lack of hypothalamus, isolated islets of Langerhans, longitudinal data, and body fat percentage.

Single Cell Multi-Omics of an iPSC Model of Human Sinoatrial Node Development Reveals Genetic Determinants of Heart Rate and Arrhythmia Susceptibility.

Cellular heterogeneity within the sinoatrial node (SAN) is functionally important but has been difficult to model in vitro , presenting a major obstacle to studies of heart rate regulation and arrhythmias. Here we describe a scalable method to derive sinoatrial node pacemaker cardiomyocytes (PCs) from human induced pluripotent stem cells that recapitulates differentiation into distinct PC subtypes, including SAN Head, SAN Tail, transitional zone cells, and sinus venosus myocardium. Single cell (sc) RNA-sequencing, sc-ATAC-sequencing, and trajectory analyses were used to define epigenetic and transcriptomic signatures of each cell type, and to identify novel transcriptional pathways important for PC subtype differentiation. Integration of our multi-omics datasets with genome wide association studies uncovered cell type-specific regulatory elements that associated with heart rate regulation and susceptibility to atrial fibrillation. Taken together, these datasets validate a novel, robust, and realistic in vitro platform that will enable deeper mechanistic exploration of human cardiac automaticity and arrhythmia.

Association of polygenic score and the involvement of cholinergic and glutamatergic pathways with lithium treatment response in patients with bipolar disorder.

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P < 0.05. Li+PGS was positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P = 9.8 × 10-12, R2 = 1.9%) and continuous (P = 6.4 × 10-9, R2 = 2.6%) outcomes. Compared to bipolar patients in the 1st decile of the risk distribution, individuals in the 10th decile had 3.47-fold (95%CI: 2.22-5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P = 3.9 × 10-4, R2 = 0.9%), but not for the continuous outcome (P = 0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGS may be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment.

Unraveling the interplay between norovirus infection, gut microbiota, and novel antiviral approaches: a comprehensive review.

Norovirus infection is a leading cause of acute gastroenteritis worldwide and can also cause harmful chronic infections in individuals with weakened immune systems. The role of the gut microbiota in the interactions between the host and noroviruses has been extensively studied. While most past studies were conducted in vitro or focused on murine noroviruses, recent research has expanded to human noroviruses using in vivo or ex vivo human intestinal enteroids culture studies. The gut microbiota has been observed to have both promoting and inhibiting effects on human noroviruses. Understanding the interaction between noroviruses and the gut microbiota or probiotics is crucial for studying the pathogenesis of norovirus infection and its potential implications, including probiotics and vaccines for infection control. Recently, several clinical trials of probiotics and norovirus vaccines have also been published. Therefore, in this review, we discuss the current understanding and recent updates on the interactions between noroviruses and gut microbiota, including the impact of norovirus on the microbiota profile, pro-viral and antiviral effects of microbiota on norovirus infection, the use of probiotics for treating norovirus infections, and human norovirus vaccine development.

Blood transcriptomic signature in type-2 biomarker-low severe asthma and asthma control.

BACKGROUND: Patients with type-2 (T2) cytokine-low severe asthma often have persistent symptoms despite suppression of T2 inflammation with corticosteroids. OBJECTIVES: We sought to analyze whole blood transcriptome from 738 samples in T2-biomarker-high/-low patients with severe asthma to relate transcriptomic signatures to T2 biomarkers and asthma symptom scores. METHODS: Bulk RNA-seq data were generated for blood samples (baseline, week 24, week 48) from 301 participants recruited to a randomized clinical trial of corticosteroid optimization in severe asthma. Unsupervised clustering, differential gene expression analysis, and pathway analysis were performed. Patients were grouped by T2-biomarker status and symptoms. Associations between clinical characteristics and differentially expressed genes (DEGs) associated with biomarker and symptom levels were investigated. RESULTS: Unsupervised clustering identified 2 clusters; cluster 2 patients were blood eosinophil-low/symptom-high and more likely to be receiving oral corticosteroids (OCSs). Differential gene expression analysis of these clusters, with and without stratification for OCSs, identified 2960 and 4162 DEGs, respectively. Six hundred twenty-seven of 2960 genes remained after adjusting for OCSs by subtracting OCS signature genes. Pathway analysis identified dolichyl-diphosphooligosaccharide biosynthesis and assembly of RNA polymerase I complex as significantly enriched pathways. No stable DEGs were associated with high symptoms in T2-biomarker-low patients, but numerous associated with elevated T2 biomarkers, including 15 that were upregulated at all time points irrespective of symptom level. CONCLUSIONS: OCSs have a considerable effect on whole blood transcriptome. Differential gene expression analysis demonstrates a clear T2-biomarker transcriptomic signature, but no signature was found in association with T2-biomarker-low patients, including those with a high symptom burden.

Viral Pneumonia during the COVID-19 Pandemic, 2019-2021 Evoking Needs for SARS-CoV-2 and Additional Vaccinations.

Coronaviruses can cause pneumonia, with clinical symptoms that may be similar to the symptoms of other viral pneumonias. To our knowledge, there have been no reports regarding cases of pneumonia caused by coronaviruses and other viruses among hospitalized patients in the past 3 years before and during coronavirus disease 2019 (COVID-19). Here, we analysed the causes of viral pneumonia among hospitalized patients during the coronavirus disease 2019 (COVID-19) pandemic (2019-2021). Between September 2019 and April 2021, patients hospitalized at Shuang Ho Hospital in north Taiwan with a diagnosis of pneumonia were enrolled in this study. Age, sex, onset date, and season of occurrence were recorded. Respiratory tract pathogens were identified with molecular detection using the FilmArray® platform from nasopharyngeal swabs. In total, 1147 patients (128 patients aged <18 years and 1019 patients aged ≥18 years) with pneumonia and identified respiratory tract pathogens were assessed. Among the 128 children with pneumonia, the dominant viral respiratory pathogen was rhinovirus (24.2%), followed by respiratory syncytial virus (RSV; 22.7%), parainfluenza virus (1 + 2 + 3 + 4) (17.2%), adenovirus (12.5%), metapneumovirus (9.4%), coronavirus (1.6%), and influenza virus (A + B) (1.6%). Among the 1019 adults with pneumonia, the dominant viral respiratory pathogen was rhinovirus (5.0%), followed by RSV (2.0%), coronavirus (2.0%), metapneumovirus (1.5%), parainfluenza virus (1 + 2 + 3 + 4) (1.1%), adenovirus (0.7%), and influenza virus (A + B) (0%). From 2019-2021, older patients (aged >65 years) with pneumonia tested positive for coronavirus most commonly in autumn. Coronavirus was not detected during summer in children or adults. Among children aged 0-6 years, RSV was the most common viral pathogen, and RSV infection occurred most often in autumn. Metapneumovirus infection occurred most often in spring in both children and adults. In contrast, influenza virus was not detected in patients with pneumonia in any season among children or adults from January 2020 to April 2021. Among all patients with pneumonia, the most common viral pathogens were rhinovirus in spring, adenovirus and rhinovirus in summer, RSV and rhinovirus in autumn, and parainfluenza virus in winter. Among children aged 0-6 years, RSV, rhinovirus, and adenovirus were detected in all seasons during the study period. In conclusion, the proportion of pneumonia cases caused by a viral pathogen was higher in children than the proportion in adults. The COVID-19 pandemic period evoked a need for SARS-CoV-2 (severe acute respiratory disease coronavirus 2) vaccination to prevent the severe complications of COVID-19. However, other viruses were also found. Vaccines for influenza were clinically applied. Active vaccines for other viral pathogens such as RSV, rhinovirus, metapneuomoccus, parainfluenza, and adenovirus may need to be developed for special groups in the future.

Molecular and Genetics-Based Systems for Tracing the Evolution and Exploring the Mechanisms of Human Norovirus Infections.

Human noroviruses (HuNoV) are major causes of acute gastroenteritis around the world. The high mutation rate and recombination potential of noroviruses are significant challenges in studying the genetic diversity and evolution pattern of novel strains. In this review, we describe recent advances in the development of technologies for not only the detection but also the analysis of complete genome sequences of noroviruses and the future prospects of detection methods for tracing the evolution and genetic diversity of human noroviruses. The mechanisms of HuNoV infection and the development of antiviral drugs have been hampered by failure to develop the infectious virus in a cell model. However, recent studies have demonstrated the potential of reverse genetics for the recovery and generation of infectious viral particles, suggesting the utility of this genetics-based system as an alternative for studying the mechanisms of viral infection, such as cell entry and replication.

Association of Polygenic Score and the involvement of Cholinergic and Glutamatergic Pathways with Lithium Treatment Response in Patients with Bipolar Disorder.

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N=2,367) and replicated in the combined PsyCourse (N=89) and BipoLife (N=102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P<����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������.

wnt16 regulates spine and muscle morphogenesis through parallel signals from notochord and dermomyotome.

Bone and muscle are coupled through developmental, mechanical, paracrine, and autocrine signals. Genetic variants at the CPED1-WNT16 locus are dually associated with bone- and muscle-related traits. While Wnt16 is necessary for bone mass and strength, this fails to explain pleiotropy at this locus. Here, we show wnt16 is required for spine and muscle morphogenesis in zebrafish. In embryos, wnt16 is expressed in dermomyotome and developing notochord, and contributes to larval myotome morphology and notochord elongation. Later, wnt16 is expressed at the ventral midline of the notochord sheath, and contributes to spine mineralization and osteoblast recruitment. Morphological changes in wnt16 mutant larvae are mirrored in adults, indicating that wnt16 impacts bone and muscle morphology throughout the lifespan. Finally, we show that wnt16 is a gene of major effect on lean mass at the CPED1-WNT16 locus. Our findings indicate that Wnt16 is secreted in structures adjacent to developing bone (notochord) and muscle (dermomyotome) where it affects the morphogenesis of each tissue, thereby rendering wnt16 expression into dual effects on bone and muscle morphology. This work expands our understanding of wnt16 in musculoskeletal development and supports the potential for variants to act through WNT16 to influence bone and muscle via parallel morphogenetic processes.

In silico discovery of blood cell macromolecular associations.

BACKGROUND: Physical molecular interactions are the basis of intracellular signalling and gene regulatory networks, and comprehensive, accessible databases are needed for their discovery. Highly correlated transcripts may reflect important functional associations, but identification of such associations from primary data are cumbersome. We have constructed and adapted a user-friendly web application to discover and identify putative macromolecular associations in human peripheral blood based on significant correlations at the transcriptional level. METHODS: The blood transcriptome was characterized by quantification of 17,328 RNA species, including 341 mature microRNAs in 105 clinically well-characterized postmenopausal women. Intercorrelation of detected transcripts signal levels generated a matrix with > 150 million correlations recognizing the human blood RNA interactome. The correlations with calculated adjusted p-values were made easily accessible by a novel web application. RESULTS: We found that significant transcript correlations within the giant matrix reflect experimentally documented interactions involving select ubiquitous blood relevant transcription factors (CREB1, GATA1, and the glucocorticoid receptor (GR, NR3C1)). Their responsive genes recapitulated up to 91% of these as significant correlations, and were replicated in an independent cohort of 1204 individual blood samples from the Framingham Heart Study. Furthermore, experimentally documented mRNAs/miRNA associations were also reproduced in the matrix, and their predicted functional co-expression described. The blood transcript web application is available at http://app.uio.no/med/klinmed/correlation-browser/blood/index.php and works on all commonly used internet browsers. CONCLUSIONS: Using in silico analyses and a novel web application, we found that correlated blood transcripts across 105 postmenopausal women reflected experimentally proven molecular associations. Furthermore, the associations were reproduced in a much larger and more heterogeneous cohort and should therefore be generally representative. The web application lends itself to be a useful hypothesis generating tool for identification of regulatory mechanisms in complex biological data sets.

Cognitive Function Associated with Gut Microbial Abundance in Sucrose and S-Adenosyl-L-Methionine (SAMe) Metabolic Pathways.

BACKGROUND: Differential abundance of gut microbiota has found to be associated with Alzheimer's disease (AD). However, the relative abundance of gut microbiota between dementia and mild cognitive impairment (MCI) in AD is not well studied. OBJECTIVE: We attempted to identify differentially enriched gut microbes and their metabolic pathways in AD patients with dementia comparing to AD patients with MCI. METHODS: Fecal samples were collected at Shuang Ho Hospital, Taipei Medical University, Taiwan and analyzed by whole metagenomic sequencing technique. For normal controls without AD (NC), 16S rRNA sequencing was obtained from the Taiwan Microbiome Database. A total of 48 AD (38 dementia and 10 MCI defined by cognitive function scores) and 50 NC were included. Microbiome alpha and beta diversities were estimated. Differentially enriched microbes were identified with HAllA, MaAsLin, DESeq2, and LEfSe statistical modeling approaches. RESULTS: We found significantly increased abundance of Firmicutes but decreased abundance of Bacteroidetes at phylum level in AD compared to NC. In AD patients, cognitive function scores were negatively associated with abundance of Blautia hydrogenotrophica (Firmicutes), Anaerotruncus colihominis (Firmicutes), and Gordonibacter pamelaeae (Actinobacteria). In addition, microbial abundance in the sucrose and S-Adenosyl-L-methionine (SAMe) metabolic pathways was more enriched in AD with MCI than AD with dementia and significantly associated with higher cognitive function scores. CONCLUSION: Gut microbe community diversity was similar in AD patients regardless of MCI or dementia status. However, differential analyses probed in lower-level taxa and metabolic pathways suggested that specific gut microbes in Firmicutes and Actinobacteria might involve in cognitive decline.