Combatting SARS-CoV-2 With Digital Contact Tracing and Notification: Navigating Six Points of Failure.

Digital contact tracing and notification were initially hailed as promising strategies to combat SARS-CoV-2; however, in most jurisdictions, they did not live up to their promise. To avert a given transmission event, both parties must have adopted the technology, it must detect the contact, the primary case must be promptly diagnosed, notifications must be triggered, and the secondary case must change their behavior to avoid the focal tertiary transmission event. If we approximate these as independent events, achieving a 26% reduction in the effective reproduction number Rt would require an 80% success rate at each of these 6 points of failure. Here, we review the 6 failure rates experienced by a variety of digital contact tracing and contact notification schemes, including Singapore's TraceTogether, India's Aarogya Setu, and leading implementations of the Google Apple Exposure Notification system. This leads to a number of recommendations, for example, that the narrative be framed in terms of user autonomy rather than user privacy, and that tracing/notification apps be multifunctional and integrated with testing, manual contact tracing, and the gathering of critical scientific data.

Quantifying Shared and Unique Gene Content across 17 Microbial Ecosystems.

Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies, including 6 human associated, 7 nonhuman host associated, and 4 in other nonhuman host environments. In total, we identified 117,629,181 nonredundant genes. The vast majority of genes (66%) occurred in only one sample (i.e., "singletons"). In contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report data sets of other ecology-associated genes (e.g., abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g., at gene sequence identities that are too restrictive). We provide our results and the sets of environmentally differentiating genes described above at http://www.microbial-genes.bio. IMPORTANCE The amount of shared genetic elements has not been quantified between the human microbiome and other host- and non-host-associated microbiomes. Here, we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as "nearly complete" are far from it. Additionally, over two-thirds of all genes only appear in a single sample, and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.

Reconstruction of ancient microbial genomes from the human gut.

Loss of gut microbial diversity1-6 in industrial populations is associated with chronic diseases7, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000-2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces.

The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data.

BACKGROUND: In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression distributions in three different tumor types from the Cancer Genome Atlas (TCGA). RESULTS: Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the shape of the distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of distributions were permitted, i.e. the shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression distribution. CONCLUSIONS: Our results highlight the value of studying a gene's distribution shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of distribution in a patient cohort.

Snai2 Maintains Bone Marrow Niche Cells by Repressing Osteopontin Expression.

Bone marrow (BM) mesenchymal stem and progenitor cells (MSPCs) are a critical constituent of the hematopoietic stem cell (HSC) niche. Previous studies have suggested that the zinc-finger epithelial-mesenchymal transition transcription factor Snai2 (also known as Slug) regulated HSCs autonomously. Here, we show that Snai2 expression in the BM is restricted to the BM stromal compartment where it regulates the HSC niche. Germline or MSPC-selective Snai2 deletion reduces the functional MSPC pool and their mesenchymal lineage output and impairs HSC niche function during homeostasis and after stress. RNA sequencing analysis revealed that Spp1 (osteopontin) expression is markedly upregulated in Snai2-deficient MSPCs. Genetic deletion of Spp1 in Snai2-deficient mice rescues MSPCs' functions. Thus, SNAI2 is a critical regulator of the transcriptional network maintaining MSPCs by the suppression of osteopontin expression.

A direct comparison of interphase FISH versus low-coverage single cell sequencing to detect aneuploidy reveals respective strengths and weaknesses.

Aneuploidy has been reported to occur at remarkably high levels in normal somatic tissues using Fluorescence In Situ Hybridization (FISH). Recently, these reports were contradicted by single-cell low-coverage whole genome sequencing (scL-WGS) analyses, which showed aneuploidy frequencies at least an order of magnitude lower. To explain these seemingly contradictory findings, we used both techniques to analyze artificially generated mock aneuploid cells and cells with natural random aneuploidy. Our data indicate that while FISH tended to over-report aneuploidies, a modified 2-probe approach can accurately detect low levels of aneuploidy. Further, scL-WGS tends to underestimate aneuploidy levels, especially in a polyploid background.

A comparison of survival analysis methods for cancer gene expression RNA-Sequencing data.

Identifying genetic biomarkers of patient survival remains a major goal of large-scale cancer profiling studies. Using gene expression data to predict the outcome of a patient's tumor makes biomarker discovery a compelling tool for improving patient care. As genomic technologies expand, multiple data types may serve as informative biomarkers, and bioinformatic strategies have evolved around these different applications. For categorical variables such as a gene's mutation status, biomarker identification to predict survival time is straightforward. However, for continuous variables like gene expression, the available methods generate highly-variable results, and studies on best practices are lacking. We investigated the performance of eight methods that deal specifically with continuous data. K-means, Cox regression, concordance index, D-index, 25th-75th percentile split, median-split, distribution-based splitting, and KaplanScan were applied to four RNA-sequencing (RNA-seq) datasets from the Cancer Genome Atlas. The reliability of the eight methods was assessed by splitting each dataset into two groups and comparing the overlap of the results. Gene sets that had been identified from the literature for a specific tumor type served as positive controls to assess the accuracy of each biomarker using receiver operating characteristic (ROC) curves. Artificial RNA-Seq data were generated to test the robustness of these methods under fixed levels of gene expression noise. Our results show that methods based on dichotomizing tend to have consistently poor performance while C-index, D-index, and k-means perform well in most settings. Overall, the Cox regression method had the strongest performance based on tests of accuracy, reliability, and robustness.

Aneuvis: web-based exploration of numerical chromosomal variation in single cells.

BACKGROUND: Numerical chromosomal variation is a hallmark of populations of malignant cells. Identifying the factors that promote numerical chromosomal variation is important for understanding mechanisms of carcinogenesis. However, the ability to quantify and visualize differences in chromosome number between experimentally-defined groups (e.g. control vs treated) obtained from single-cell experiments is currently limited by the lack of user-friendly software. RESULTS: Aneuvis is a web application that allows users to determine whether numerical chromosomal variation exists between experimental treatment groups. The web interface allows users to upload molecular cytogenetic or processed single cell whole-genome sequencing data in a cell-by-chromosome matrix format and automatically generates visualizations and summary statistics that reflect the degree of numeric chromosomal variability. CONCLUSIONS: Aneuvis is the first user-friendly web application to help researchers identify the genetic and environmental perturbations that promote numerical chromosomal variation. Aneuvis is freely available as a web application at https://dpique.shinyapps.io/aneuvis/ and the source code for the application is available at https://github.com/dpique/aneuvis .

The nutritional environment determines which and how intestinal stem cells contribute to homeostasis and tumorigenesis.

Sporadic colon cancer accounts for approximately 80% of colorectal cancer (CRC) with high incidence in Western societies strongly linked to long-term dietary patterns. A unique mouse model for sporadic CRC results from feeding a purified rodent Western-style diet (NWD1) recapitulating intake for the mouse of common nutrient risk factors each at its level consumed in higher risk Western populations. This causes sporadic large and small intestinal tumors in wild-type mice at an incidence and frequency similar to that in humans. NWD1 perturbs intestinal cell maturation and Wnt signaling throughout villi and colonic crypts and decreases mouse Lgr5hi intestinal stem cell contribution to homeostasis and tumor development. Here we establish that NWD1 transcriptionally reprograms Lgr5hi cells, and that nutrients are interactive in reprogramming. Furthermore, the DNA mismatch repair pathway is elevated in Lgr5hi cells by lower vitamin D3 and/or calcium in NWD1, paralleled by reduced accumulation of relevant somatic mutations detected by single-cell exome sequencing. In compensation, NWD1 also reprograms Bmi1+ cells to function and persist as stem-like cells in mucosal homeostasis and tumor development. The data establish the key role of the nutrient environment in defining the contribution of two different stem cell populations to both mucosal homeostasis and tumorigenesis. This raises important questions regarding impact of variable human diets on which and how stem cell populations function in the human mucosa and give rise to tumors. Moreover, major differences reported in turnover of human and mouse crypt base stem cells may be linked to their very different nutrient exposures.

Estrogen activates Alzheimer's disease genes.

INTRODUCTION: Women are at increased risk for Alzheimer's disease (AD), but the reason why remains unknown. One hypothesis is that low estrogen levels at menopause increases vulnerability to AD, but this remains unproven. METHODS: We compared neuronal genes upregulated by estrogen in ovariectomized female rhesus macaques with a database of >17,000 diverse gene sets and applied a rare variant burden test to exome sequencing data from 1208 female AD patients with the age of onset < 75 years and 2162 female AD controls. RESULTS: We found a striking overlap between genes upregulated by estrogen in macaques and genes downregulated in the human postmortem AD brain, and we found that estrogen upregulates the APOE gene and that progesterone acts antagonistically to estrogen genome-wide. We also found that female patients with AD have excess rare mutations in the early menopause gene MCM8. DISCUSSION: We show with genomic data that the menopausal loss of estrogen could underlie the increased risk for AD in women.

The Majority of CD45- Ter119- CD31- Bone Marrow Cell Fraction Is of Hematopoietic Origin and Contains Erythroid and Lymphoid Progenitors.

The non-hematopoietic cell fraction of the bone marrow (BM) is classically identified as CD45- Ter119- CD31- (herein referred to as triple-negative cells or TNCs). Although TNCs are believed to contain heterogeneous stromal cell populations, they remain poorly defined. Here we showed that the vast majority of TNCs (∼85%) have a hematopoietic rather than mesenchymal origin. Single cell RNA-sequencing revealed erythroid and lymphoid progenitor signatures among CD51- TNCs. Ly6D+ CD44+ CD51- TNCs phenotypically and functionally resembled CD45+ pro-B lymphoid cells, whereas Ly6D- CD44+ CD51- TNCs were enriched in previously unappreciated stromal-dependent erythroid progenitors hierarchically situated between preCFU-E and proerythroblasts. Upon adoptive transfer, CD44+ CD51- TNCs contributed to repopulate the B-lymphoid and erythroid compartments. CD44+ CD51- TNCs also expanded during phenylhydrazine-induced acute hemolysis or in a model of sickle cell anemia. These findings thus uncover physiologically relevant new classes of stromal-associated functional CD45- hematopoietic progenitors.

Stem cell factor is selectively secreted by arterial endothelial cells in bone marrow.

Endothelial cells (ECs) contribute to haematopoietic stem cell (HSC) maintenance in bone marrow, but the differential contributions of EC subtypes remain unknown, owing to the lack of methods to separate with high purity arterial endothelial cells (AECs) from sinusoidal endothelial cells (SECs). Here we show that the combination of podoplanin (PDPN) and Sca-1 expression distinguishes AECs (CD45- Ter119- Sca-1bright PDPN-) from SECs (CD45- Ter119- Sca-1dim PDPN+). PDPN can be substituted for antibodies against the adhesion molecules ICAM1 or E-selectin. Unexpectedly, prospective isolation reveals that AECs secrete nearly all detectable EC-derived stem cell factors (SCF). Genetic deletion of Scf in AECs, but not SECs, significantly reduced functional HSCs. Lineage-tracing analyses suggest that AECs and SECs self-regenerate independently after severe genotoxic insults, indicating the persistence of, and recovery from, radio-resistant pre-specified EC precursors. AEC-derived SCF also promotes HSC recovery after myeloablation. These results thus uncover heterogeneity in the contribution of ECs in stem cell niches.

pathVar: a new method for pathway-based interpretation of gene expression variability.

Identifying the pathways that control a cellular phenotype is the first step to building a mechanistic model. Recent examples in developmental biology, cancer genomics, and neurological disease have demonstrated how changes in the variability of gene expression can highlight important genes that are under different degrees of regulatory control. Simple statistical tests exist to identify differentially-variable genes; however, methods for investigating how changes in gene expression variability in the context of pathways and gene sets are under-explored. Here we present pathVar, a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets. pathVar is based on a multinomial exact test, or an asymptotic Chi-squared test as a more computationally-efficient alternative. The method can be used for gene expression studies from any technology platform in all biological settings either with a single phenotypic group, or two-group comparisons. To demonstrate its utility, we applied the method to a diverse set of diseases, species and samples. Results from pathVar are benchmarked against analyses based on average expression and two methods of GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation. We also provide recommendations for the choice of variability statistic that have been informed through analyses on simulations and real data. Based on the datasets selected, we show how pathVar can be used to gain insight into expression variability of single cell versus bulk samples, different stem cell populations, and cancer versus normal tissue comparisons.

Self-renewal of a purified Tie2+ hematopoietic stem cell population relies on mitochondrial clearance.

A single hematopoietic stem cell (HSC) is capable of reconstituting hematopoiesis and maintaining homeostasis by balancing self-renewal and cell differentiation. The mechanisms of HSC division balance, however, are not yet defined. Here we demonstrate, by characterizing at the single-cell level a purified and minimally heterogeneous murine Tie2+ HSC population, that these top hierarchical HSCs preferentially undergo symmetric divisions. The induction of mitophagy, a quality control process in mitochondria, plays an essential role in self-renewing expansion of Tie2+ HSCs. Activation of the PPAR (peroxisome proliferator-activated receptor)-fatty acid oxidation pathway promotes expansion of Tie2+ HSCs through enhanced Parkin recruitment in mitochondria. These metabolic pathways are conserved in human TIE2+ HSCs. Our data thus identify mitophagy as a key mechanism of HSC expansion and suggest potential methods of cell-fate manipulation through metabolic pathways.