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Background Neisseria gonorrhoeae is one of the most common bacterial sexually transmitted infections. The emergence of antimicrobial-resistant N. gonorrhoeae is an urgent public health threat. Currently, diagnosis of N. gonorrhoeae infection requires expensive laboratory infrastructure, while antimicrobial susceptibility determination requires bacterial culture, both of which are infeasible in low-resource areas where prevalence is highest. Recent advances in molecular diagnostics, such as Specific High-sensitivity Enzymatic Reporter unLOCKing (SHERLOCK) using CRISPR-Cas13a and isothermal amplification, have the potential to provide low-cost detection of pathogen and antimicrobial resistance. Methods and Results We designed and optimized RNA guides and primer-sets for SHERLOCK assays capable of detecting N. gonorrhoeae via the porA gene and of predicting ciprofloxacin susceptibility via a single mutation in the gyrase A (gyrA) gene. We evaluated their performance using both synthetic DNA and purified N. gonorrhoeae isolates. For porA, we created both a fluorescence-based assay and lateral flow assay using a biotinylated FAM reporter. Both methods demonstrated sensitive detection of 14 N. gonorrhoeae isolates and no cross-reactivity with 3 non-gonococcal Neisseria isolates. For gyrA, we created a fluorescence-based assay that correctly distinguished between 20 purified N. gonorrhoeae isolates with phenotypic ciprofloxacin resistance and 3 with phenotypic susceptibility. We confirmed the gyrA genotype predictions from the fluorescence-based assay with DNA sequencing, which showed 100% concordance for the isolates studied. Conclusion We report the development of Cas13a-based SHERLOCK assays that detect N. gonorrhoeae and differentiate ciprofloxacin-resistant isolates from ciprofloxacin-susceptible isolates.

Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infectingBorrelia burgdorferispirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 patient-derivedB. burgdorferisensu stricto (Bbss) isolates from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification ofBbssisolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectiousBbssisolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of ∼800 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, are associated with increased rates of dissemination. OspC type A strains possess a unique constellation of strongly linked genetic changes including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. The patterns of OspC type A strains typify a broader paradigm acrossBbssisolates, in which genetic structure is defined by correlated groups of strain-variable genes located predominantly on plasmids, particularly for expression of surface-exposed lipoproteins. These clusters of genes are inherited in blocks through strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.

While genome-wide association studies (GWAS) and selection scans identify genomic loci driving human phenotypic diversity, functional validation is required to discover the variant(s) responsible. We dissected theIVDlocus, implicated by selection statistics, multiple GWAS, and clinical genetics as important to function and fitness. We combined luciferase assays, CRISPR/Cas9 genome-editing, massively parallel reporter assays (MPRA), and bashing of regulatory loci. We identified three regulatory variants, including an indel, that may underpin GWAS signals for pulmonary fibrosis and testosterone, and that are linked on a positively selected haplotype in the Japanese population. These regulatory variants exhibit synergistic and opposing effects onIVDexpression experimentally. Alleles at these variants lie on a haplotype tagged by the variant most strongly associated withIVDexpression and metabolites, but with no functional evidence itself. This work demonstrates how comprehensive functional investigation and multiple technologies are needed to discover the true genetic drivers of phenotypic diversity.

Identifying the dissemination patterns and impacts of a virus of economic or health importance during a pandemic is crucial, as it informs the public on policies for containment in order to reduce the spread of the virus. In this study, we integrated genomic and travel data to investigate the emergence and spread of the B.1.1.318 and B.1.525 variants of interest in Nigeria and the wider Africa region. By integrating travel data and phylogeographic reconstructions, we find that these two variants that arose during the second wave emerged from within Africa, with the B.1.525 from Nigeria, and then spread to other parts of the world. Our results show how regional connectivity in downsampled regions like Africa can often influence virus transmissions between neighbouring countries. Our findings demonstrate the power of genomic analysis when combined with mobility and epidemiological data to identify the drivers of transmission in the region, generating actionable information for public health decision makers in the region.

Universities are particularly vulnerable to infectious disease outbreaks and are also ideal environments to study transmission dynamics and evaluate mitigation and surveillance measures when outbreaks occur. Here, we introduce a SARS-CoV-2 surveillance and response framework based on high-resolution, multimodal data collected during the 2020-2021 academic year at Colorado Mesa University. We analyzed epidemiological and sociobehavioral data (demographics, contact tracing, and wifi-based co-location data) alongside pathogen surveillance data (wastewater, random, and reflexive diagnostic testing; and viral genomic sequencing of wastewater and clinical specimens) to characterize outbreak dynamics and inform policy decisions. We quantified group attributes that increased disease risk, and highlighted parallels between traditional and wifi-based contact tracing. We additionally used clinical and environmental viral sequencing to identify cryptic transmission, cluster overdispersion, and novel lineages or mutations. Ultimately, we used distinct data types to identify information that may help shape institutional policy and to develop a model of pathogen surveillance suitable for the future of outbreak preparedness.

Viral genomic surveillance has been integral in the global response to the SARS-CoV-2 pandemic. Surveillance efforts rely on the availability of representative clinical specimens from ongoing testing activities. However, testing practices have recently shifted due to the widespread availability and use of rapid antigen tests, which could lead to gaps in future monitoring efforts. As such, genomic surveillance strategies must adapt to include laboratory workflows that are robust to sample type. To that end, we compare the results of RT-qPCR and viral genome sequencing using samples from positive BinaxNOW™ COVID-19 Antigen Card swabs (N=555) to those obtained from previously collected nasopharyngeal (NP) swabs used for nucleic acid amplification testing (N=135). We show that swabs obtained from antigen cards are comparable in performance to clinical excess samples from NP swabs, providing a viable alternative. This validation permits the reliable expansion of viral genomic surveillance to cases identified in the clinic or home setting where rapid antigen tests are used.

The Omicron variant of SARS-CoV-2 is highly transmissible in vaccinated and unvaccinated populations. The dynamics governing its establishment and propensity towards fixation (reaching 100% frequency in the SARS-CoV-2 population) in communities remain unknown. In this work, we describe the dynamics of Omicron at three institutions of higher education (IHEs) in the greater Boston area.We use diagnostic and variant-specifying molecular assays and epidemiological analytical approaches to describe the rapid dominance of Omicron following its introduction to three IHEs with asymptomatic surveillance programs.We show that the establishment of Omicron at IHEs precedes that of the state and region, and that the time to fixation is shorter at IHEs (9.5-12.5 days) than in the state (14.8 days) or region. We show that the trajectory of Omicron fixation among university employees resembles that of students, with a 2-3 day delay. Finally, we compare cycle threshold (Ct) values in Omicron vs. Delta variant cases on college campuses, and identify lower viral loads among college affiliates harboring Omicron infections.We document the rapid takeover of the Omicron variant at IHEs, reaching near-fixation within the span of 9.5-12.5 days despite lower viral loads, on average, than the previously dominant Delta variant. These findings highlight the transmissibility of Omicron, its propensity to rapidly dominate small populations, and the ability of robust asymptomatic surveillance programs to offer early insights into the dynamics of pathogen arrival and spread.

Long non-coding RNAs (lncRNAs) are pivotal mediators of systemic immune response to viral infection, yet most studies concerning their expression and functions upon immune stimulation are limited to in vitro bulk cell populations. This strongly constrains our understanding of how lncRNA expression varies at single-cell resolution, and how their cell-type specific immune regulatory roles may differ compared to protein-coding genes. Here, we perform the first in-depth characterization of lncRNA expression variation at single-cell resolution during Ebola virus (EBOV) infection in vivo. Using bulk RNA-sequencing from 119 samples and 12 tissue types, we significantly expand the current macaque lncRNA annotation. We then profile lncRNA expression variation in immune circulating single-cells during EBOV infection and find that lncRNAs’ expression in fewer cells is a major differentiating factor from their protein-coding gene counterparts. Upon EBOV infection, lncRNAs present dynamic and mostly cell-type specific changes in their expression profiles especially in monocytes, the main cell type targeted by EBOV. Such changes are associated with gene regulatory modules related to important innate immune responses such as interferon response and purine metabolism. Within infected cells, several lncRNAs have positively and negatively correlated expression with viral load, suggesting that expression of some of these lncRNAs might be directly hijacked by EBOV to attack host cells. This study provides novel insights into the roles that lncRNAs play in the host response to acute viral infection and paves the way for future lncRNA studies at single-cell resolution.

The COVID-19 pandemic, and the recent rise and widespread transmission of SARS-CoV-2 Variants of Concern (VOCs), have demonstrated the need for ubiquitous nucleic acid testing outside of centralized clinical laboratories. Here, we develop SHINEv2, a Cas13-based nucleic acid diagnostic that combines quick and ambient temperature sample processing and lyophilized reagents to greatly simplify the test procedure and assay distribution. We benchmarked a SHINEv2 assay for SARS-CoV-2 detection against state-of-the-art antigen-capture tests using 96 patient samples, demonstrating 50-fold greater sensitivity and 100% specificity. We designed SHINEv2 assays for discriminating the Alpha, Beta, Gamma and Delta VOCs, which can be read out visually using lateral flow technology. We further demonstrate that our assays can be performed without any equipment in less than 90 minutes. SHINEv2 represents an important advance towards rapid nucleic acid tests that can be performed in any location.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant quickly rose to dominance in mid-2021, displacing other variants, including Alpha. Studies using data from the United Kingdom and India estimated that Delta was 40-80% more transmissible than Alpha, allowing Delta to become the globally dominant variant. However, it was unclear if the ostensible difference in relative transmissibility was due mostly to innate properties of Delta’s infectiousness or differences in the study populations. To investigate, we formed a partnership with SARS-CoV-2 genomic surveillance programs from all six New England US states. By comparing logistic growth rates, we found that Delta emerged 37-163% faster than Alpha in early 2021 (37% Massachusetts, 75% New Hampshire, 95% Maine, 98% Rhode Island, 151% Connecticut, and 163% Vermont). We next computed variant-specific effective reproductive numbers and estimated that Delta was 58-120% more transmissible than Alpha across New England (58% New Hampshire, 68% Massachusetts, 76% Connecticut, 85% Rhode Island, 98% Maine, and 120% Vermont). Finally, using RT-PCR data, we estimated that Delta infections generate on average ∼6 times more viral RNA copies per mL than Alpha infections. Overall, our evidence indicates that Delta’s enhanced transmissibility could be attributed to its innate ability to increase infectiousness, but its epidemiological dynamics may vary depending on the underlying immunity and behavior of distinct populations.

Despite the great success of genome-wide association studies (GWAS) in identifying genetic loci significantly associated with diseases, the vast majority of causal variants underlying disease-associated loci have not been identified1–3. To create an atlas of causal variants, we performed and integrated fine-mapping across 148 complex traits in three large-scale biobanks (BioBank Japan4,5, FinnGen6, and UK Biobank7,8; total n = 811,261), resulting in 4,518 variant-trait pairs with high posterior probability (> 0.9) of causality. Of these, we found 285 high-confidence variant-trait pairs replicated across multiple populations, and we characterized multiple contributors to the surprising lack of overlap among fine-mapping results from different biobanks. By studying the bottlenecked Finnish and Japanese populations, we identified 21 and 26 putative causal coding variants with extreme allele frequency enrichment (> 10-fold) in these two populations, respectively. Aggregating data across populations enabled identification of 1,492 unique fine-mapped coding variants and 176 genes in which multiple independent coding variants influence the same trait (i.e., with an allelic series of coding variants). Our results demonstrate that fine-mapping in diverse populations enables novel insights into the biology of complex traits by pinpointing high-confidence causal variants for further characterization.

Amid COVID-19, many institutions deployed vast resources to test their members regularly for safe reopening. This self-focused approach, however, not only overlooks surrounding communities but also remains blind to community transmission that could breach the institution. To test the relative merits of a more altruistic strategy, we built an epidemiological model that assesses the differential impact on case counts when institutions instead allocate a proportion of their tests to members’ close contacts in the larger community. We found that testing outside the institution benefits the institution in all plausible circumstances, with the optimal proportion of tests to use externally landing at 45% under baseline model parameters. Our results were robust to local prevalence, secondary attack rate, testing capacity, and contact reporting level, yielding a range of optimal community testing proportions from 18% to 58%. The model performed best under the assumption that community contacts are known to the institution; however, it still demonstrated a significant benefit even without complete knowledge of the contact network.

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients’ demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.

3’ untranslated region (3’UTR) variants are strongly associated with human traits and diseases, yet few have been causally identified. We developed the Massively Parallel Reporter Assay for 3’UTRs (MPRAu) to sensitively assay 12,173 3’UTR variants. We applied MPRAu to six human cell lines, focusing on genetic variants associated with genome-wide association studies (GWAS) and human evolutionary adaptation. MPRAu expands our understanding of 3’UTR function, suggesting that low-complexity sequences predominately explain 3’UTR regulatory activity. We adapt MPRAu to uncover diverse molecular mechanisms at base-pair resolution, including an AU-rich element ofLEPRlinked to potential metabolic evolutionary adaptations in East Asians. We nominate hundreds of 3’UTR causal variants with genetically fine-mapped phenotype associations. Using endogenous allelic replacements, we characterize one variant that disrupts a miRNA site regulating the viral defense geneTRIM14, and one that altersPILRBabundance, nominating a causal variant underlying transcriptional changes in age-related macular degeneration.

Diagnostics, particularly for rapidly evolving viruses, stand to benefit from a principled, measurement-driven design that harnesses machine learning and vast genomic data—yet the capability for such design has not been previously built. Here, we develop and extensively validate an approach to designing viral diagnostics that applies a learned model within a combinatorial optimization framework. Concentrating on CRISPR-based diagnostics, we screen a library of 19,209 diagnostic–target pairs and train a deep neural network that predicts, from RNA sequence alone, diagnostic signal better than contemporary techniques. Our model then makes it possible to design assays that are maximally sensitive over the spectrum of a virus’s genomic variation. We introduce ADAPT (https://adapt.guide), a system for fully-automated design, and use ADAPT to design optimal diagnostics for the 1,933 vertebrate-infecting viral species within 2 hours for most species and 24 hours for all but 3. We experimentally show ADAPT’s designs are sensitive and specific down to the lineage level, including against viruses that pose challenges involving genomic variation and specificity. ADAPT’s designs exhibit significantly higher fluorescence and permit lower limits of detection, across a virus’s entire variation, than the outputs of standard design techniques. Our model-based optimization strategy has applications broadly to viral nucleic acid diagnostics and other sequence-based technologies, and, paired with clinical validation, could enable a critically-needed, proactive resource of assays for surveilling and responding to pathogens.

Meningitis and encephalitis are leading causes of central nervous system (CNS) disease and often result in severe neurological compromise or death. Traditional diagnostic workflows largely rely on pathogen-specific diagnostic tests, sometimes over days to weeks. Metagenomic next-generation sequencing (mNGS) is a high-throughput platform that profiles all nucleic acid in a sample. We prospectively enrolled 68 patients from New England with known or suspected CNS infection and performed mNGS from both RNA and DNA to identify potential pathogens. Using a computational metagenomic classification pipeline based on KrakenUniq and BLAST, we detected pathogen nucleic acid in cerebrospinal fluid (CSF) from 22 subjects. This included some pathogens traditionally diagnosed by serology or not typically identified in CSF, including three transmitted by Ixodes scapularis ticks (Powassan virus, Borrelia burgdorferi, Anaplasma phagocytophilum). Among 24 subjects with no clinical diagnosis, we detected enterovirus in two subjects and Epstein Barr virus in one subject. We also evaluated two methods to enhance detection of viral nucleic acid, hybrid capture and methylated DNA depletion. Hybrid capture nearly universally increased viral read recovery. Although results for methylated DNA depletion were mixed, it allowed detection of varicella zoster virus DNA in two samples that were negative by standard mNGS. Overall, mNGS is a promising approach that can test for multiple pathogens simultaneously, with similar efficacy to pathogen-specific tests, and can uncover geographically relevant infectious CNS disease, such as tick-borne infections in New England. With further laboratory and computational enhancements, mNGS may become a mainstay of workup for encephalitis and meningitis.ImportanceMeningitis and encephalitis are leading global causes of central nervous system (CNS) disability and mortality. Current diagnostic workflows remain inefficient, requiring costly pathogen-specific assays and sometimes invasive surgical procedures. Despite intensive diagnostic efforts, 40-60% of people with meningitis or encephalitis have no clear cause of their CNS disease identified. As diagnostic uncertainty often leads to costly inappropriate therapies, the need for novel pathogen detection methods is paramount. Metagenomic next-generation sequencing (mNGS) offers the unique opportunity to circumvent these challenges using unbiased laboratory and computational methods. Here, we performed comprehensive mNGS from 68 patients with suspected CNS infection, and define enhanced methods to improve the detection of CNS pathogens, including those not traditionally identified in the CNS by nucleic acid detection. Overall, our work helps elucidate how mNGS can become a mainstay in the diagnostic toolkit for CNS infections.

Dengue virus is a major and rapidly growing public health concern in tropic and subtropic regions across the globe. In late 2018, Senegal experienced its largest dengue virus outbreak to date, covering several regions. However, little is known about the genetic diversity of dengue virus (DENV) in Senegal. Here we used molecular tools including metagenomic sequencing to identify 19 previously undetected dengue virus cases from the city of Thiès and assemble 17 complete virus genomes. DENV3 was the most frequent serotype; 11 sequences (65%) were DENV3, 4 sequences were DENV2 and 2 were DENV1. Sequences were closest to recent sequences from West Africa, suggesting ongoing local circulation of viral populations; however, detailed inference is limited by the scarcity of available genomic data. We did not find clear associations with reported clinical signs or symptoms, highlighting the importance of testing for diagnosing febrile diseases. Overall, these findings expand the known range of DENV in Senegal, and underscore the need for better genomic characterization of DENV in West Africa.

Developing and deploying new diagnostic tests is difficult, but the need to do so in response to a rapidly emerging pandemic such as COVID-19 is crucially important for an effective response. In the early stages of a pandemic, laboratories play a key role in helping health care providers and public health authorities detect active infection, a task most commonly achieved using nucleic acid-based assays. While the landscape of diagnostics is rapidly evolving, polymerase chain reaction (PCR) remains the gold-standard of nucleic acid-based diagnostic assays, in part due to its reliability, flexibility, and wide deployment. To address a critical local shortage of testing capacity persisting during the COVID-19 outbreak, our hospital set up a molecular based laboratory developed test (LDT) to accurately and safely diagnose SARS-CoV-2. We describe here the process of developing an emergency-use LDT, in the hope that our experience will be useful to other laboratories in future outbreaks and will help to lower barriers to fast and accurate diagnostic testing in crisis conditions.

SARS-CoV-2 has caused a severe, ongoing outbreak of COVID-19 in Massachusetts with 111,070 confirmed cases and 8,433 deaths as of August 1, 2020. To investigate the introduction, spread, and epidemiology of COVID-19 in the Boston area, we sequenced and analyzed 772 complete SARS-CoV-2 genomes from the region, including nearly all confirmed cases within the first week of the epidemic and hundreds of cases from major outbreaks at a conference, a nursing facility, and among homeless shelter guests and staff. The data reveal over 80 introductions into the Boston area, predominantly from elsewhere in the United States and Europe. We studied two superspreading events covered by the data, events that led to very different outcomes because of the timing and populations involved. One produced rapid spread in a vulnerable population but little onward transmission, while the other was a major contributor to sustained community transmission, including outbreaks in homeless populations, and was exported to several other domestic and international sites. The same two events differed significantly in the number of new mutations seen, raising the possibility that SARS-CoV-2 superspreading might encompass disparate transmission dynamics. Our results highlight the failure of measures to prevent importation into MA early in the outbreak, underscore the role of superspreading in amplifying an outbreak in a major urban area, and lay a foundation for contact tracing informed by genetic data.

College campuses are highly vulnerable to infectious disease outbreaks, and there is a pressing need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world reopen to in-person instruction during the COVID-19 pandemic. Towards addressing this need, we applied a stochastic compartmental model to quantify the impact of university-level responses to past mumps outbreaks in college campuses and used it to determine which control interventions are most effective. Mumps is a very relevant disease in such settings, given its airborne mode of transmission, high infectivity, and recurrence of outbreaks despite availability of a vaccine. Our model allows for stochastic variation in small populations, missing or unobserved case data, and changes in disease transmission rates post-intervention. We tested the model and assessed various interventions using data from the 2014 and 2016 mumps outbreaks at Ohio State University and Harvard University, respectively. Our results suggest that in order to decrease infectious disease incidence on their campuses, universities should apply diagnostic protocols that address false negatives from molecular tests, stricter quarantine policies, and effective awareness campaigns among their students and staff. However, one needs to be careful about the assumptions implicit in the model to ensure that the estimated parameters have a reasonable interpretation. This modeling approach could be applied to data from other outbreaks in college campuses and similar small-population settings.

Structured AbstractImportanceCerebral microvascular lesions are common in patients with severe COVID-19. Radiologic-pathologic correlation in one case suggests a combination of microvascular hemorrhagic and ischemic lesions that may reflect an underlying hypoxic mechanism of injury, which requires validation in larger studies.ObjectiveTo determine the incidence, distribution, and clinical and histopathologic correlates of microvascular lesions in patients with severe COVID-19.DesignObservational, retrospective cohort study: March to May 2020.SettingSingle academic medical center.ParticipantsConsecutive patients (n=16) admitted to the intensive care unit with severe COVID-19, undergoing brain MRI for evaluation of coma or focal neurologic deficits.ExposuresNot applicable.Main Outcome and MeasuresHypointense microvascular lesions identified by a prototype ultrafast high-resolution susceptibility-weighted imaging (SWI) MRI sequence, counted by two neuroradiologists and categorized by neuroanatomic location. Clinical and laboratory data (most recent measurements before brain MRI). Brain autopsy and cerebrospinal fluid PCR for SARS-CoV-2 in one patient who died from severe COVID-19.ResultsEleven of 16 patients (69%) had punctate and linear SWI lesions in the subcortical and deep white matter, and eight patients (50%) had >10 SWI lesions. In 4/16 patients (25%), lesions involved the corpus callosum. Brain autopsy in one patient revealed that SWI lesions corresponded to widespread microvascular injury, characterized by perivascular and parenchymal petechial hemorrhages and microscopic ischemic lesions.Conclusions and RelevanceSWI lesions are common in patients with neurological manifestations of severe COVID-19 (coma and focal neurologic deficits). The distribution of lesions is similar to that seen in patients with hypoxic respiratory failure, sepsis, and disseminated intravascular coagulation. Collectively, these radiologic and histopathologic findings suggest that patients with severe COVID-19 are at risk for multifocal microvascular hemorrhagic and ischemic lesions in the subcortical and deep white matter.Key Points SectionQuestionWhat is the prevalence and pathophysiology of cerebral microvascular injury in patients with severe COVID-19?FindingsIn this retrospective cohort study of 16 patients undergoing MRI for neurologic complications of severe COVID-19, microvascular lesions were observed in 11 patients and showed an anatomic distribution similar to that seen in patients with hypoxic respiratory failure and sepsis. In one patient who died, brain autopsy revealed widespread microvascular injury, including perivascular microhemorrhages and microscopic ischemic lesions.MeaningMicrovascular injury is common in patients with severe COVID-19. Radiologic-pathologic correlation, though limited to a single case, provides insights into possible mechanisms of injury.

SUMMARYThe 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.

SummaryEbola 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.

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 rVSVΔG-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 SHERLOCK’s use in endemic regions. Finally, we successfully deployed our tests in Sierra Leone and Nigeria in response to recent outbreaks.

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.

The COVID-19 pandemic has highlighted that new diagnostic technologies are essential for controlling disease transmission. Here, we develop SHINE (Streamlined Highlighting of Infections to Navigate Epidemics), a sensitive and specific diagnostic tool that can detect SARS-CoV-2 RNA from unextracted samples. We identify the optimal conditions to allow RPA-based amplification and Cas13-based detection to occur in a single step, simplifying assay preparation and reducing run-time. We improve HUDSON to rapidly inactivate viruses in nasopharyngeal swabs and saliva in 10 min. SHINE's 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-qPCR with a sample-to-answer time of 50 min. SHINE has the potential to be used outside of hospitals and clinical laboratories, greatly enhancing diagnostic capabilities.

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/.

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

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 NP’s roles in protein structure, virion budding, and transcription and replication.

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.

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.

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 cell’s 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.

Background: The recent Ebola Virus Disease (EVD) outbreaks have revealed the need for field-deployable tools that can be adapted to the heterogeneous spectrum of the disease across widely varying environments and resources. We previously introduced an mHealth approach for EVD prognostication, where machine learning models were integrated into a mobile application (app) and could be updated with new data. Here, we build upon that approach by generating a new family of models derived from the largest published EVD dataset to date and validated on two independent datasets. These models were incorporated into an app, "Ebola Care Guidelines", that calculates prognosis from the patient data available at triage. The app highlights the signs/symptoms with the largest contribution to the risk of death and provides simplified and targeted access to the recommended supportive care guidelines from WHO. Methods and Findings: We generated multivariate logistic regression models from 470 patients admitted to five Ebola treatment units (ETUs) in Liberia and Sierra Leone during the 2014-16 outbreak. We handled missing data by multiple imputation and validated the models with two independent datasets from Sierra Leone. Consistent with previous results, viral load and age were the most important predictors of death. We found that observational wellness assessments by experienced health care providers have high predictive power and are able to explain most of the variation due to individual signs/symptoms. We generated a parsimonious model that included (in addition to viral load and age) body temperature, bleeding, jaundice, weakness, and confusion recorded during triage. We also constructed fallback models for when variables in the parsimonious model are unavailable. Conclusions: This work shows how the integration of prognostic models with mHealth technology can create dynamic and rapidly deployable clinical management support tools that facilitate tailored access to large bodies of medical information. The Ebola Care Guidelines app was created with a novel clinical decision support framework to develop and update guidelines apps quickly as new data and models became available. The performance of the parsimonious model is very similar to that of the models including the wellness scale from experienced health care workers, suggesting that rigorous machine learning approaches can replicate experienced clinical intuition, and could thus be useful when such expertise is unavailable.

Metagenomic sequencing has the potential to transform microbial detection and characterization, but new tools are needed to improve its sensitivity. We developed CATCH (Compact Aggregation of Targets for Comprehensive Hybridization), a computational method to enhance nucleic acid capture for enrichment of diverse microbial taxa. CATCH designs compact probe sets that achieve full coverage of known sequence diversity and that scale well with this diversity. To illustrate applications of CATCH, we focused on capturing viral genomes. We designed, synthesized, and validated multiple probe sets, including one that targets whole genomes of the 356 viral species known to infect humans. Capture with these probe sets enriched unique viral content on average 18× and allowed us to assemble genomes that we could not otherwise recover, while accurately preserving within-sample diversity. We used this approach to recover genomes from the 2018 Lassa fever outbreak in Nigeria and to improve detection of viral infections in samples with unknown content. Together, this work demonstrates a path toward more sensitive, cost-effective metagenomic sequencing.

Methods to identify signatures of selective sweeps in population genomics data have been actively developed, but mostly do not identify the specific mutation favored by the selective sweep. We present a method, iSAFE, that uses a statistic derived solely from population genetics signals to pinpoint the favored mutation even when the signature of selection extends to 5Mbp. iSAFE was tested extensively on simulated data and in human populations from the 1000 Genomes Project, at 22 loci with previously characterized selective sweeps. For 14 of the 22 loci, iSAFE ranked the previously characterized candidate mutation among the 13 highest scoring (out of ∼ 21, 000 variants). Three loci did not show a strong signal. For the remaining loci, iSAFE identified previously unreported mutations as being favored. In these regions, all of which involve pigmentation related genes, iSAFE identified identical selected mutations in multiple non-African populations suggesting an out-of-Africa onset of selection. The iSAFE software can be downloaded from https://github.com/alek0991/iSAFE.

Massively-parallel reporter assays (MPRA) enable unprecedented opportunities to test for regulatory activity of thousands of regulatory sequences. However, MPRA only assay a subset of the genome thus limiting their applicability for genome-wide functional annotations. To overcome this limitation, we have used existing MPRA datasets to train a machine learning model that uses DNA sequence information, regulatory motif annotations, evolutionary conservation, and epigenomic information to predict genomic regions that show enhancer activity when tested in MPRA assays. We used the resulting model to generate global predictions of regulatory activity at single-nucleotide resolution across 14 million common variants. We find that genetic variants with stronger predicted regulatory activity show significantly lower minor allele frequency, indicative of evolutionary selection within the human population. They also show higher over-lap with eQTL annotations across multiple tissues relative to the background SNPs, indicating that their perturbations in vivo more frequently result in changes in gene expression. In addition, they are more frequently associated with trait-associated SNPs from genome-wide association studies (GWAS), enabling us to prioritize genetic variants that are more likely to be causal based on their predicted regulatory activity. Lastly, we use our model to compare MPRA inferences across cell types and platforms and to prioritize the assays most predictive of MPRA assay results, including cell-dependent DNase hypersensitivity sites and transcription factors known to be active in the tested cell types. Our results indicate that high-throughput testing of thousands of putative regions, coupled with regulatory predictions across millions of sites, presents a powerful strategy for systematic annotation of genomic regions and genetic variants.

Zika virus (ZIKV) is currently causing an unprecedented pandemic linked to severe congenital syndromes1,2. In July 2016, mosquito-borne ZIKV transmission was first reported in the continental United States and since then, hundreds of locally acquired infections have been described3. To gain insights into the timing, source, and likely route(s) of introduction into the United States, we tracked the virus from its first detection in Miami, Florida by direct sequencing of ZIKV genomes from infected patients and Aedes aegypti mosquitoes. We detected at least four distinct ZIKV introductions and estimate that 11-52 introductions contributed to the outbreak in Florida. Furthermore, our data suggests that ZIKV transmission likely started in the spring of 2016 - several months before initial detection. By analyzing epidemiological, surveillance, and genetic data, we discovered that several spatially distinct ZIKV transmission zones were likely portions of the same outbreak, rather than isolated events. Our analyses show that most introductions are linked to the Caribbean, which is supported by the high incidence rates and traffic, especially via cruises, from the region into Miami. By comparing mosquito abundance and travel capacity across the United States, we find that southern Florida is especially vulnerable to ZIKV introductions and at risk of repeat occurrences. By tracking the virus from its initial introduction into the United States, we provide a deeper understanding of how ZIKV initiates and sustains transmission in new regions.

Motivation: Efficient simulation of population genetic samples under a given demographic model is a prerequisite for many analyses. Coalescent theory provides an efficient framework for such simulations, but simulating longer regions and higher recombination rates remains challenging. Simulators based on a Markovian approximation to the coalescent scale well, but do not support simulation of selection. Gene conversion is not supported by any published coalescent simulators that support selection. Results: We describe cosi2, an efficient simulator that supports both exact and approximate coalescent simulation with positive selection. cosi2 improves on the speed of existing exact simulators, and permits further speedup in approximate mode while retaining support for selection. cosi2 supports a wide range of demographic scenarios, including recombination hot spots, gene conversion, population size changes, population structure and migration. cosi2 implements coalescent machinery efficiently by tracking only a small subset of the Ancestral Recombination Graph, sampling only relevant recombination events, and using augmented skip lists to represent tracked genetic segments. To preserve support for selection in approximate mode, the Markov approximation is implemented not by moving along the chromosome but by performing a standard backwards-in-time coalescent simulation while restricting coalescence to node pairs with overlapping or near-overlapping genetic material. We describe the algorithms used by cosi2 and present comparisons with existing selection simulators. Availability and implementation: A free C++ implementation of cosi2 is available at http://broadinstitute.org/mpg/cosi2. Contact: ilya@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.