Psychosocial stressors and breast cancer gene expression in the Black Women's Health Study.

PURPOSE: Prior studies indicate that the physiologic response to stress can affect gene expression. We evaluated differential gene expression in breast cancers collected from Black women with high versus low exposure to psychosocial stressors. METHODS: We analyzed tumor RNA sequencing data from 417 Black Women's Health Study breast cancer cases with data on early life trauma and neighborhood disadvantage. We conducted age-adjusted differential gene expression analyses and pathway analyses. We also evaluated Conserved Transcriptional Response to Adversity (CTRA) contrast scores, relative fractions of immune cell types, T cell exhaustion, and adrenergic signaling. Analyses were run separately for estrogen receptor positive (ER+; n = 299) and ER- (n = 118) cases. RESULTS: Among ER+ cases, the top differentially expressed pathways by stress exposure were related to RNA and protein metabolism. Among ER- cases, they were related to developmental biology, signal transduction, metabolism, and the immune system. Targeted analyses indicated greater immune pathway enrichment with stress exposure for ER- cases, and possible relevance of adrenergic signaling for ER+ cases. CTRA contrast scores did not differ by stress exposure, but in analyses of the CTRA components, ER- breast cancer cases with high neighborhood disadvantage had higher pro-inflammatory gene expression (p = 0.039) and higher antibody gene expression (p = 0.006) compared to those with low neighborhood disadvantage. CONCLUSION: There are multiple pathways through which psychosocial stress exposure may influence breast tumor biology. Given the present findings on inflammation and immune response in ER- tumors, further research to identify stress-induced changes in the etiology and progression of ER- breast cancer is warranted.

Analysis of nasopharyngeal microbiome patterns in Zambian infants with fatal acute febrile illness.

Introduction: Associative connections have previously been identified between nasopharyngeal infections and infant mortality. The nasopharyngeal microbiome may potentially influence the severity of these infections. Methods: We conducted an analysis of a longitudinal prospective cohort study of 1,981 infants who underwent nasopharyngeal sampling from 1 week through 14 weeks of age at 2-3-week intervals. In all, 27 microbiome samples from 9 of the infants in the cohort who developed fatal acute febrile illness (fAFI) were analyzed in pooled comparisons with 69 samples from 10 healthy comparator infants. We completed 16S rRNA amplicon gene sequencing all infant NP samples and characterized the maturation of the infant NP microbiome among the fAFI(+) and fAFI(-) infant cohorts. Results: Beta diversity measures of fAFI(-) infants were markedly higher than those of fAFI(+) infants. The fAFI(+) infant NP microbiome was marked by higher abundances of Escherichia, Pseudomonas, Leuconostoc, and Weissella, with low relative presence of Alkalibacterium, Dolosigranulum, Moraxella, and Streptococcus. Conclusions: Our results suggest that nasopharyngeal microbiome dysbiosis precedes fAFI in young infants. Early dysbiosis, involving microbes such as Escherichia, may play a role in the causal pathway leading to fAFI or could be a marker of other pathogenic forces that directly lead to fAFI.

Interactive analysis of single-cell data using flexible workflows with SCTK2.

Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users.

Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data.

Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools.

Postmortem Nasopharyngeal Microbiome Analysis of Zambian Infants With and Without Respiratory Syncytial Virus Disease: A Nested Case Control Study.

BACKGROUND: Respiratory syncytial virus (RSV) is the most common cause of bronchiolitis and lower respiratory tract infections in children in their first year of life, disproportionately affecting infants in developing countries. Previous studies have found that the nasopharyngeal (NP) microbiome of infants with RSV infection has specific characteristics that correlate with disease severity, including lower biodiversity, perturbations of the microbiota and differences in relative abundance. These studies have focused on infants seen in clinical or hospital settings, predominantly in developed countries. METHODS: We conducted a nested case control study within a random sample of 50 deceased RSV+ infants with age at death ranging from 4 days to 6 months and 50 matched deceased RSV- infants who were all previously enrolled in the Zambia Pertussis and RSV Infant Mortality Estimation (ZPRIME) study. All infants died within the community or within 48 hours of facility admittance. As part of the ZPRIME study procedures, all decedents underwent one-time, postmortem NP sampling. The current analysis explored the differences between the NP microbiome profiles of RSV+ and RSV- decedents using the 16S ribosomal DNA sequencing. RESULTS: We found that Moraxella was more abundant in the NP microbiome of RSV+ decedents than in the RSV- decedents. Additionally, Gemella and Staphylococcus were less abundant in RSV+ decedents than in the RSV- decedents. CONCLUSIONS: These results support previously reported findings of the association between the NP microbiome and RSV and suggest that changes in the abundance of these microbes are likely specific to RSV and may correlate with mortality associated with the disease.

Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures.

Rationale: Many blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease, predict risk of progression from infection to disease, and monitor TB treatment outcomes. However, an unresolved issue is whether gene set enrichment analysis (GSEA) of the signature transcripts alone is sufficient for prediction and differentiation, or whether it is necessary to use the original statistical model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data, missing details about the original trained model, and inadequate publicly-available software tools or source code implementing models. To facilitate these signatures' replicability and appropriate utilization in TB research, comprehensive comparisons between gene set scoring methods with cross-data validation of original model implementations are needed. Objectives: We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both re-rebuilt original models and gene set scoring methods to evaluate whether gene set scoring is a reasonable proxy to the performance of the original trained model. We have provided an open-access software implementation of the original models for all 19 signatures for future use. Methods: We considered existing gene set scoring and machine learning methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, as alternative approaches to profile gene signature performance. The sample-size-weighted mean area under the curve (AUC) value was computed to measure each signature's performance across datasets. Correlation analysis and Wilcoxon paired tests were used to analyze the performance of enrichment methods with the original models. Measurement and Main Results: For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original diagnostic models. PLAGE outperformed all other gene scoring methods. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies. Conclusion: Gene set enrichment scoring of existing blood-based biomarker gene sets can distinguish patients with active TB disease from latent TB infection and other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.

Influenza D virus exposure among US cattle workers: A call for surveillance.

Although cattle are a reservoir for influenza D virus (IDV), little is known about human exposure to IDV. We assessed IDV exposure and associated health effects among United States dairy workers, a population at heightened risk of cattle zoonoses. In prospective, cross-shift sampling of 31 workers employed at five large-herd dairy operations in two states, we found evidence of IDV in the nasal washes of 67% of participants at least once during the 5-day study period. IDV exposure was not associated with respiratory symptoms in these workers. These findings suggest that IDV is present in dairy cattle environments and can result in worker exposure.

Development of prognostic scoring system for predicting 1-year mortality among pulmonary tuberculosis patients in South India.

BACKGROUND: Development of a prediction model using baseline characteristics of tuberculosis (TB) patients at the time of diagnosis will aid us in early identification of the high-risk groups and devise pertinent strategies accordingly. Hence, we did this study to develop a prognostic-scoring model for predicting the death among newly diagnosed drug sensitive pulmonary TB patients in South India. METHODS: We undertook a longitudinal analysis of cohort data under the Regional Prospective Observational Research for Tuberculosis India consortium. Multivariable cox regression using the stepwise backward elimination procedure was used to select variables for the model building and the nomogram-scoring system was developed with the final selected model. RESULTS: In total, 54 (4.6%) out of the 1181 patients had died during the 1-year follow-up period. The TB mortality rate was 0.20 per 1000 person-days. Eight variables (age, gender, functional limitation, anemia, leukopenia, thrombocytopenia, diabetes, neutrophil-lymphocyte ratio) were selected and a nomogram was built using these variables. The discriminatory power was 0.81 (95% confidence interval: 0.75-0.86) and this model was well-calibrated. Decision curve analysis showed that the model is beneficial at a threshold probability ~15-65%. CONCLUSIONS: This scoring system could help the clinicians and policy makers to devise targeted interventions and in turn reduce the TB mortality in India.

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference.

Nonignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch effects can lead to difficulty in merging data from multiple sources, as they can severely bias the outcome of the analysis. Many groups have developed approaches for removing batch effects from data, usually by accommodating batch variables into the analysis (one-step correction) or by preprocessing the data prior to the formal or final analysis (two-step correction). One-step correction is often desirable due it its simplicity, but its flexibility is limited and it can be difficult to include batch variables uniformly when an analysis has multiple stages. Two-step correction allows for richer models of batch mean and variance. However, prior investigation has indicated that two-step correction can lead to incorrect statistical inference in downstream analysis. Generally speaking, two-step approaches introduce a correlation structure in the corrected data, which, if ignored, may lead to either exaggerated or diminished significance in downstream applications such as differential expression analysis. Here, we provide more intuitive and more formal evaluations of the impacts of two-step batch correction compared to existing literature. We demonstrate that the undesired impacts of two-step correction (exaggerated or diminished significance) depend on both the nature of the study design and the batch effects. We also provide strategies for overcoming these negative impacts in downstream analyses using the estimated correlation matrix of the corrected data. We compare the results of our proposed workflow with the results from other published one-step and two-step methods and show that our methods lead to more consistent false discovery controls and power of detection across a variety of batch effect scenarios. Software for our method is available through GitHub (https://github.com/jtleek/sva-devel) and will be available in future versions of the $\texttt{sva}$ R package in the Bioconductor project (https://bioconductor.org/packages/release/bioc/html/sva.html).

A comprehensive update to the Mycobacterium tuberculosis H37Rv reference genome.

H37Rv is the most widely used Mycobacterium tuberculosis strain, and its genome is globally used as the M. tuberculosis reference sequence. Here, we present Bact-Builder, a pipeline that uses consensus building to generate complete and accurate bacterial genome sequences and apply it to three independently cultured and sequenced H37Rv aliquots of a single laboratory stock. Two of the 4,417,942 base-pair long H37Rv assemblies are 100% identical, with the third differing by a single nucleotide. Compared to the existing H37Rv reference, the new sequence contains ~6.4 kb additional base pairs, encoding ten new regions that include insertions in PE/PPE genes and new paralogs of esxN and esxJ, which are differentially expressed compared to the reference genes. New sequencing and de novo assemblies with Bact-Builder confirm that all 10 regions, plus small additional polymorphisms, are also present in the commonly used H37Rv strains NR123, TMC102, and H37Rv1998. Thus, Bact-Builder shows promise as an improved method to perform accurate and reproducible de novo assemblies of bacterial genomes, and our work provides important updates to the primary M. tuberculosis reference genome.

Malnutrition leads to increased inflammation and expression of tuberculosis risk signatures in recently exposed household contacts of pulmonary tuberculosis.

Background: Most individuals exposed to Mycobacterium tuberculosis (Mtb) develop latent tuberculosis infection (LTBI) and remain at risk for progressing to active tuberculosis disease (TB). Malnutrition is an important risk factor driving progression from LTBI to TB. However, the performance of blood-based TB risk signatures in malnourished individuals with LTBI remains unexplored. The aim of this study was to determine if malnourished and control individuals had differences in gene expression, immune pathways and TB risk signatures. Methods: We utilized data from 50 tuberculin skin test positive household contacts of persons with TB - 18 malnourished participants (body mass index [BMI] < 18.5 kg/m2) and 32 controls (individuals with BMI ≥ 18.5 kg/m2). Whole blood RNA-sequencing was conducted to identify differentially expressed genes (DEGs). Ingenuity Pathway Analysis was applied to the DEGs to identify top canonical pathways and gene regulators. Gene enrichment methods were then employed to score the performance of published gene signatures associated with progression from LTBI to TB. Results: Malnourished individuals had increased activation of inflammatory pathways, including pathways involved in neutrophil activation, T-cell activation and proinflammatory IL-1 and IL-6 cytokine signaling. Consistent with known association of inflammatory pathway activation with progression to TB disease, we found significantly increased expression of the RISK4 (area under the curve [AUC] = 0.734) and PREDICT29 (AUC = 0.736) progression signatures in malnourished individuals. Conclusion: Malnourished individuals display a peripheral immune response profile reflective of increased inflammation and a concomitant increased expression of risk signatures predicting progression to TB. With validation in prospective clinical cohorts, TB risk biomarkers have the potential to identify malnourished LTBI for targeted therapy.

Celda: a Bayesian model to perform co-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data.

Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and to elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform co-clustering of genes into transcriptional modules and cells into subpopulations. Celda can quantify the probabilistic contribution of each gene to each module, each module to each cell population and each cell population to each sample. In a peripheral blood mononuclear cell dataset, Celda identified a subpopulation of proliferating T cells and a plasma cell which were missed by two other common single-cell workflows. Celda also identified transcriptional modules that could be used to characterize unique and shared biological programs across cell types. Finally, Celda outperformed other approaches for clustering genes into modules on simulated data. Celda presents a novel method for characterizing transcriptional programs and cellular heterogeneity in scRNA-seq data.

Immunopathogenic overlap between COVID-19 and tuberculosis identified from transcriptomic meta-analysis and human macrophage infection.

Current and previous tuberculosis (TB) increase the risk of COVID-19 mortality and severe disease. To identify mechanisms of immunopathogenic interaction between COVID-19 and TB, we performed a systematic review and patient-level meta-analysis of COVID-19 transcriptomic signatures, spanning disease severity, from whole blood, PBMCs, and BALF. 35 eligible signatures were profiled on 1181 RNA-seq samples from 853 individuals across the spectrum of TB infection. Thirteen COVID-19 gene-signatures had significantly higher "COVID-19 risk scores" in active TB and latent TB progressors compared with non-progressors and uninfected controls (p<0·005), in three independent cohorts. Integrative single-cell-RNAseq analysis identified FCN1- and SPP1-expressing macrophages enriched in severe COVID-19 BALF and active TB blood. Gene ontology and protein-protein interaction networks identified 12-gene disease-exacerbation hot spots between COVID-19 and TB. Finally, we in vitro validated that SARS-CoV-2 infection is increased in human macrophages cultured in the inflammatory milieu of Mtb-infected macrophages, correlating with TMPRSS2, IFNA1, IFNB1, IFNG, TNF, and IL1B induction.

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, various technical artifacts can be present in scRNA-seq data and should be assessed before performing downstream analyses. While several tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Here, to streamline the process of generating and visualizing QC metrics for scRNA-seq data, we built the SCTK-QC pipeline within the singleCellTK R package. The SCTK-QC workflow can import data from several single-cell platforms and preprocessing tools and includes steps for empty droplet detection, generation of standard QC metrics, prediction of doublets, and estimation of ambient RNA. It can run on the command line, within the R console, on the cloud platform or with an interactive graphical user interface. Overall, the SCTK-QC pipeline streamlines and standardizes the process of performing QC for scRNA-seq data.

Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform.

BACKGROUND: Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers. METHODS: The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker. RESULTS: Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB. CONCLUSIONS: The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.

The oral microbiome in relation to pancreatic cancer risk in African Americans.

BACKGROUND: African Americans have the highest pancreatic cancer incidence of any racial/ethnic group in the United States. The oral microbiome was associated with pancreatic cancer risk in a recent study, but no such studies have been conducted in African Americans. Poor oral health, which can be a cause or effect of microbial populations, was associated with an increased risk of pancreatic cancer in a single study of African Americans. METHODS: We prospectively investigated the oral microbiome in relation to pancreatic cancer risk among 122 African-American pancreatic cancer cases and 354 controls. DNA was extracted from oral wash samples for metagenomic shotgun sequencing. Alpha and beta diversity of the microbial profiles were calculated. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between microbes and pancreatic cancer risk. RESULTS: No associations were observed with alpha or beta diversity, and no individual microbial taxa were differentially abundant between cases and control, after accounting for multiple comparisons. Among never smokers, there were elevated ORs for known oral pathogens: Porphyromonas gingivalis (OR = 1.69, 95% CI: 0.80-3.56), Prevotella intermedia (OR = 1.40, 95% CI: 0.69-2.85), and Tannerella forsythia (OR = 1.36, 95% CI: 0.66-2.77). CONCLUSIONS: Previously reported associations between oral taxa and pancreatic cancer were not present in this African-American population overall.

Tuberculosis-Learning the Impact of Nutrition (TB LION): protocol for an interventional study to decrease TB risk in household contacts.

BACKGROUND: Comorbidities such as undernutrition and parasitic infections are widespread in India and other tuberculosis (TB)-endemic countries. This study examines how these conditions as well as food supplementation and parasite treatment might alter immune responses to Mycobacterium tuberculosis (Mtb) infection and risk of progression to TB disease. METHODS: This is a 5-year prospective clinical trial at Jawaharlal Institute of Post Graduate Medical Education and Research in Puducherry, Tamil Nadu, India. We aim to enroll 760 household contacts (HHC) of adults with active TB in order to identify 120 who are followed prospectively for 2 years: Thirty QuantiFERON-TB Gold Plus (QFT-Plus) positive HHCs ≥ 18 years of age in four proposed groups: (1) undernourished (body mass index [BMI] < 18.5 kg/m2); (2) participants with a BMI ≥ 18.5 kg/m2 who have a parasitic infection (3) undernourished participants with a parasitic infection and (4) controls-participants with BMI ≥ 18.5 kg/m2 and without parasitic infection. We assess immune response at baseline and after food supplementation (for participants with BMI < 18.5 kg/m2) and parasite treatment (for participants with parasites). Detailed nutritional assessments, anthropometry, and parasite testing through polymerase chain reaction (PCR) and microscopy are performed. In addition, at serial time points, these samples will be further analyzed using flow cytometry and whole blood transcriptomics to elucidate the immune mechanisms involved in disease progression. CONCLUSIONS: This study will help determine whether undernutrition and parasite infection are associated with gene signatures that predict risk of TB and whether providing nutritional supplementation and/or treating parasitic infections improves immune response towards this infection. This study transcends individual level care and presents the opportunity to benefit the population at large by analyzing factors that affect disease progression potentially reducing the overall burden of people who progress to TB disease. Trial registration ClinicalTrials.gov; NCT03598842; Registered on July 26, 2018; https://clinicaltrials.gov/ct2/show/NCT03598842.

Novel temporal and spatial patterns of metastatic colonization from breast cancer rapid-autopsy tumor biopsies.

BACKGROUND: Metastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult and thus poorly understood. METHODS: Via rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole-genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumor's evolution at subclonal resolution. RESULTS: The most unique, previously unreported aspect of the tumor's evolution that we observed in this patient was the presence of "subclone incubators," defined as metastatic sites where substantial tumor evolution occurs before colonization of additional sites and organs by subclones that initially evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g., abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion, subclones at this site were likely the source of all four subsequent metastatic waves, and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known drivers or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patient's metastases for effective clinical intervention. CONCLUSIONS: Our analysis revealed the presence of substantial tumor evolution at metastatic incubator sites in a patient, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.

Wolframin-1-expressing neurons in the entorhinal cortex propagate tau to CA1 neurons and impair hippocampal memory in mice.

Abnormally phosphorylated tau, an early neuropathologic marker of Alzheimer's disease (AD), first occurs in the brain's entorhinal cortex layer II (ECII) and then spreads to the CA1 field of the hippocampus. Animal models of tau propagation aiming to recapitulate this phenomenon mostly show tau transfer from ECII stellate neurons to the dentate gyrus, but tau pathology in the dentate gyrus does not appear until advanced stages of AD. Wolframin-1­expressing (Wfs1+) pyramidal neurons have been shown functionally to modulate hippocampal CA1 neurons in mice. Here, we report that Wfs1+ pyramidal neurons are conserved in the ECII of postmortem human brain tissue and that Wfs1 colocalized with abnormally phosphorylated tau in brains from individuals with early AD. Wfs1+ neuron­specific expression of human P301L mutant tau in mouse ECII resulted in transfer of tau to hippocampal CA1 pyramidal neurons, suggesting spread of tau pathology as observed in the early Braak stages of AD. In mice expressing human mutant tau specifically in the ECII brain region, electrophysiological recordings of CA1 pyramidal neurons showed reduced excitability. Multielectrode array recordings of optogenetically stimulated Wfs1+ ECII axons resulted in reduced CA1 neuronal firing. Chemogenetic activation of CA1 pyramidal neurons showed a reduction in c-fos+ cells in the CA1. Last, a fear conditioning task revealed deficits in trace and contextual memory in mice overexpressing human mutant tau in the ECII. This work demonstrates tau transfer from the ECII to CA1 in mouse brain and provides an early Braak stage preclinical model of AD.