Biology subjects

Sabeti, P. C.

Publications and source records attributed to Sabeti, P. C..

30 records · Page 2Linked to original sources

bioRxiv2020

SARS-CoV-2 Spike protein variant D614G increases infectivity and retains sensitivity to antibodies that target the receptor binding domain

The SARS-CoV-2 spike (S) protein variant D614G supplanted the ancestral virus worldwide in a matter of months. Here we show that D614G was more infectious than the ancestral form on human lung cells, colon cells, and cells rendered permissive by ectopic expression of various mammalian ACE2 orthologs. Nonetheless, D614G affinity for ACE2 was reduced due to a faster dissociation rate. Assessment of the S protein trimer by cryo-electron microscopy showed that D614G disrupts a critical interprotomer contact and that this dramatically shifts the S protein trimer conformation toward an ACE2-binding and fusion-competent state. Consistent with the more open conformation, neutralization potency of antibodies targeting the S protein receptor-binding domain was not attenuated. These results indicate that D614G adopts conformations that make virion membrane fusion with the target cell membrane more probable but that D614G retains susceptibility to therapies that disrupt interaction of the SARS-CoV-2 S protein with the ACE2 receptor.

microbiology↗

bioRxiv2020

Single-cell profiling of Ebola virus infection in vivo reveals viral and host transcriptional dynamics

Ebola virus (EBOV) causes epidemics with high case fatality rates, yet remains understudied due to the challenge of experimentation in high-containment and outbreak settings. To better understand EBOV infection in vivo, we used single-cell transcriptomics and CyTOF-based single-cell protein quantification to characterize peripheral immune cell activity during EBOV infection in rhesus monkeys. We obtained 100,000 transcriptomes and 15,000,000 protein profiles, providing insight into pathogenesis. We find that immature, proliferative monocyte-lineage cells with reduced antigen presentation capacity replace conventional circulating monocyte subsets within days of infection, while lymphocytes upregulate apoptosis genes and decline in abundance. By quantifying viral RNA abundance in individual cells, we identify molecular determinants of tropism and examine temporal dynamics in viral and host gene expression. Within infected cells, we observe that EBOV down-regulates STAT1 mRNA and interferon signaling, and up-regulates putative pro-viral genes (e.g., DYNLL1 and HSPA5), nominating cellular pathways the virus manipulates for its replication. Overall, this study sheds light on EBOV tropism, replication dynamics, and elicited immune response, and provides a framework for characterizing interactions between hosts and emerging viruses in a maximum containment setting.

microbiology↗

bioRxiv2020

Integrated sample inactivation, amplification, and Cas13-based detection of SARS-CoV-2

The COVID-19 pandemic has highlighted that new diagnostic technologies are essential for controlling disease transmission. Here, we develop SHINE (SHERLOCK and HUDSON Integration to Navigate Epidemics), a sensitive and specific integrated diagnostic tool that can detect SARS-CoV-2 RNA from unextracted samples. We combine the steps of SHERLOCK into a single-step reaction and optimize HUDSON to accelerate viral inactivation in nasopharyngeal swabs and saliva. SHINEs results can be visualized with an in-tube fluorescent readout -- reducing contamination risk as amplification reaction tubes remain sealed -- and interpreted by a companion smartphone application. We validate SHINE on 50 nasopharyngeal patient samples, demonstrating 90% sensitivity and 100% specificity compared to RT-PCR with a sample-to-answer time of 50 minutes. SHINE has the potential to be used outside of hospitals and clinical laboratories, greatly enhancing diagnostic capabilities.

bioengineering↗

bioRxiv2020

Deployable CRISPR-Cas13a diagnostic tools to detect and report Ebola and Lassa virus cases in real-time

Viral hemorrhagic fevers (VHFs) remain some of the most devastating human diseases, and recent outbreaks of Ebola virus disease (EVD) 1,2 and Lassa fever (LF) 3,4 highlight the urgent need for sensitive, field-deployable tests to diagnose them 5,6. Here we develop CRISPR-Cas13a-based (SHERLOCK) diagnostics targeting Ebola virus (EBOV) and Lassa virus (LASV), with both fluorescent and lateral flow readouts. We demonstrate on laboratory and clinical samples the sensitivity of these assays and the capacity of the SHERLOCK platform to handle virus-specific diagnostic challenges. Our EBOV diagnostic detects both the L and NP genes, thereby eliminating the potential for false positive results caused by the rVSV{Delta}G-ZEBOV-GP live attenuated vaccine. Our two LASV diagnostics together capture 90% of known viral diversity and demonstrate that CRISPR-RNAs (crRNAs) can be effectively multiplexed to provide greater coverage of known viral diversity. We performed safety testing to demonstrate the efficacy of our HUDSON protocol in heat-inactivating and chemically treating VHF viruses before SHERLOCK testing, eliminating the need for an extraction. We developed a user-friendly field protocol and mobile application (HandLens) to report results, facilitating SHERLOCKs use in endemic regions. Finally, we successfully deployed our tests in Sierra Leone and Nigeria in response to recent outbreaks.

molecular biology↗

bioRxiv2020

HCR-FlowFISH: A flexible CRISPR screening method to identify cis-regulatory elements and their target genes

CRISPR screens for cis-regulatory elements (CREs) have shown unprecedented power to endogenously characterize the non-coding genome. To characterize CREs we developed HCR-FlowFISH (Hybridization Chain Reaction Fluorescent In-Situ Hybridization coupled with Flow Cytometry), which directly quantifies native transcripts within their endogenous loci following CRISPR perturbations of regulatory elements, eliminating the need for restrictive phenotypic assays such as growth or transcript-tagging. HCR-FlowFISH accurately quantifies gene expression across a wide range of transcript levels and cell types. We also developed CASA (CRISPR Activity Screen Analysis), a hierarchical Bayesian model to identify and quantify CRE activity. Using >270,000 perturbations, we identified CREs for GATA1, HDAC6, ERP29, LMO2, MEF2C, CD164, NMU, FEN1 and the FADS gene cluster. Our methods detect subtle gene expression changes and identify CREs regulating multiple genes, sometimes at different magnitudes and directions. We demonstrate the power of HCR-FlowFISH to parse genome-wide association signals by nominating causal variants and target genes.

genomics↗

bioRxiv2020

CRISPR-based COVID-19 surveillance using a genomically-comprehensive machine learning approach

The emergence and outbreak of SARS-CoV-2, the causative agent of COVID-19, has rapidly become a global concern and has highlighted the need for fast, sensitive, and specific tools to surveil circulating viruses. Here we provide assay designs and experimental resources, for use with CRISPR-based nucleic acid detection, that could be valuable for ongoing surveillance. We provide assay designs for detection of 67 viral species and subspecies, including: SARS-CoV-2, phylogenetically-related viruses, and viruses with similar clinical presentation. The designs are outputs of algorithms that we are developing for rapidly designing nucleic acid detection assays that are comprehensive across genomic diversity and predicted to be highly sensitive and specific. Of our design set, we experimentally screened 4 SARS-CoV-2 designs with a CRISPR-Cas13 detection system and then extensively tested the highest-performing SARS-CoV-2 assay. We demonstrate the sensitivity and speed of this assay using synthetic targets with fluorescent and lateral flow detection. Moreover, our provided protocol can be extended for testing the other 66 provided designs. Assay designs are available at https://adapt.sabetilab.org/.

genomics↗

bioRxiv2019

Real-time Metagenomic Analysis of Undiagnosed Fever Cases Unveils a Yellow Fever Outbreak in Edo State, Nigeria

We report here the use of metagenomic sequencing to identify and characterize yellow fever virus (YFV) in a cluster of undiagnosed febrile patients from Edo State, Nigeria. Yellow fever, an acute mosquito-borne viral haemorrhagic fever, re-emerged in Nigeria in 2017 after decades of relative control.1 Once a major public health concern in the region,2 the prolonged absence of yellow fever has left a gap in knowledge about circulating virus and lowered clinical suspicion for a disease whose spectrum overlaps with endemic infections (e.g., malaria and Lassa fever). In this context, metagenomic sequencing can be a powerful tool for identifying and tracking pathogens causing infectious disease.3\n\nPlasma samples from thirteen epidemiologically-linked patients with unexplained, often fatal, fever were sent from Irrua Specialist Teaching Hospital to the African Center of Excellence for Genomics of ...

genomics↗

bioRxiv2018

An emergent Ebola virus nucleoprotein variant influences virion budding, oligomerization, transcription and replication

For highly pathogenic viruses, reporter assays that can be rapidly performed are critically needed to identify potentially functional mutations for further study under maximal containment (e.g., biosafety level 4 [BSL-4]). The Ebola virus nucleoprotein (NP) plays multiple essential roles during the viral life cycle, yet few tools exist to study the protein under BSL-2 or equivalent containment. Therefore, we adapted reporter assays to measure NP oligomerization and virion-like particle (VLP) production in live cells and further measure transcription and replication using established minigenome assays. As a proof-of-concept, we examined the NP-R111C substitution, which emerged during the 2013-2016 Western African Ebola virus disease epidemic and rose to high frequency. NP-R111C slightly increased NP oligomerization and VLP budding but slightly decreased transcription and replication. By contrast, a synthetic charge-reversal mutant, NP-R111E, greatly increased oligomerization but abrogated transcription and replication. These results are intriguing in light of recent structures of NP oligomers, which reveal that the neighboring residue, K110, forms a salt bridge with E349 on adjacent NP molecules. By developing and utilizing multiple reporter assays, we find that the NP-111 position mediates a complex interplay between NPs roles in protein structure, virion budding, and transcription and replication.

microbiology↗

bioRxiv2018

Comparative evidence for the independent evolution of hair and sweat gland traits in primates

Humans differ in many respects from other primates, but perhaps no derived human feature is more striking than our naked skin. Long purported to be adaptive, humans unique external appearance is characterized by changes in both the patterning of hair follicles and eccrine sweat glands, producing decreased hair cover and increased sweat gland density. Despite the conspicuousness of these features and their potential evolutionary importance, there is a lack of clarity regarding how they evolved within the primate lineage. We thus collected and quantified the density of hair follicles and eccrine sweat glands from five regions of the skin in three species of primates: macaque, chimpanzee and human. Although human hair cover is greatly attenuated relative to that of our close relatives, we find that humans have a chimpanzee-like hair density that is significantly lower than that of macaques. In contrast, eccrine gland density is on average 10-fold higher in humans compared to chimpanzees and macaques, whose density is strikingly similar. Our findings suggest that a decrease in hair density in the ancestors of humans and apes was followed by an increase in eccrine gland density and a reduction in fur cover in humans. This work answers longstanding questions about the traits that make human skin unique and substantiates a model in which the evolution of expanded eccrine gland density was exclusive to the human lineage.

evolutionary biology↗

bioRxiv2018

Co-circulating mumps lineages at multiple geographic scales

Despite widespread vaccination, eleven thousand mumps cases were reported in the United States (US) in 2016-17, including hundreds in Massachusetts, primarily in college settings. We generated 203 whole genome mumps virus (MuV) sequences from Massachusetts and 15 other states to understand the dynamics of mumps spread locally and nationally, as well as to search for variants potentially related to vaccination. We observed multiple MuV lineages circulating within Massachusetts during 2016-17, evidence for multiple introductions of the virus to the state, and extensive geographic movement of MuV within the US on short time scales. We found no evidence that variants arising during this outbreak contributed to vaccine escape. Combining epidemiological and genomic data, we observed multiple co-circulating clades within individual universities as well as spillover into the local community. Detailed data from one well-sampled university allowed us to estimate an effective reproductive number within that university significantly greater than one. We also used publicly available small hydrophobic (SH) gene sequences to estimate migration between world regions and to place this outbreak in a global context, but demonstrate that these short sequences, historically used for MuV genotyping, are inadequate for tracing detailed transmission. Our findings suggest continuous, often undetected, circulation of mumps both locally and nationally, and highlight the value of combining genomic and epidemiological data to track viral disease transmission at high resolution.

genomics↗

bioRxiv2018

Identifying Gene Expression Programs of Cell-type Identity and Cellular Activity with Single-Cell RNA-Seq

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cells expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here we illustrate and enhance the use of matrix factorization as a solution to this problem. We show with simulations that a method that we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including the relative contribution of programs in each cell. Applied to published brain organoid and visual cortex scRNA-Seq datasets, cNMF refines the hierarchy of cell types and identifies both expected (e.g. cell cycle and hypoxia) and intriguing novel activity programs. We propose that one of the novel programs may reflect a neurosecretory phenotype and a second may underlie the formation of neuronal synapses. We make cNMF available to the community and illustrate how this approach can provide key insights into gene expression variation within and between cell types.

bioinformatics↗

bioRxiv2018

Prognostic models for Ebola virus disease derived from data collected at five treatment units in Sierra Leone and Liberia: performance, external validation, and risk visualization

BackgroundWe created a family of prognostic models for Ebola virus disease from the largest dataset of EVD patients published to date. We incorporated these models into an app, \"Ebola Care Guidelines\", that provides access to recommended, evidence-based supportive care guidelines and highlights the signs/symptoms with the largest contribution to prognosis.\n\nMethodsWe applied multivariate logistic regression on 470 patients admitted to five Ebola treatment units in Liberia and Sierra Leone during the 2014-16 outbreak. We validated the models with two independent datasets from Sierra Leone.\n\nFindingsViral load and age were the most important predictors of death. We generated a parsimonious model including viral load, age, body temperature, bleeding, jaundice, dyspnea, dysphagia, and referral time recorded at triage. We also constructed fallback models for when variables in the parsimonious model are unavailable. The performance of the parsimonious model approached the predictive power of observational wellness assessments by experienced health workers, with Area Under the Curve (AUC) ranging from 0.7 to 0.8 and overall accuracy of 64% to 74%.\n\nInterpretationMachine-learning models and mHealth tools have the potential for improving the standard of care in low-resource settings and emergency scenarios, but data incompleteness and lack of generalizable models are major obstacles. We showed how harmonization of multiple datasets yields prognostic models that can be validated across different cohorts. Similar performance between the parsimonious model and those incorporating expert wellness assessments suggests that clinically-guided machine learning approaches can recapitulate clinical expertise, and thus be useful when such expertise is unavailable. We also demonstrated with our guidelines app how integration of those models with mobile technologies enables deployable clinical management support tools that facilitate access to comprehensive bodies of medical knowledge.\n\nFundingHoward Hughes Medical Institute, US National Institutes of Health

bioinformatics↗