Successful treatment of SARS-CoV-2 in an immunocompromised patient with persistent infection for 245 days: A case report.

Background: Immunocompromised patients receiving B-cell-depleting therapies are at increased risk of persistent SARS-CoV-2 infection, with many experiencing fatal outcomes. We report a successful outcome in a patient with rheumatoid arthritis (RA) on rituximab diagnosed with COVID-19 in July 2020 with persistent infection for over 245 days. Results: The patient received numerous treatment courses for persistent COVID-19 infection, including remdesivir, baricitinib, immunoglobulin and high doses of corticosteroids followed by a prolonged taper due to persistent respiratory symptoms and cryptogenic organizing pneumonia. Her clinical course was complicated by Pseudomonas aeruginosa sinusitis with secondary bacteremia, and cytomegalovirus (CMV) viremia and pneumonitis. SARS-CoV-2 positive RNA samples were extracted from two nasopharyngeal swabs and sequenced using targeted amplicon Next-Generation Sequencing which were analyzed for virus evolution over time. Viral sequencing indicated lineage B.1.585.3 SARS-CoV-2 accumulated Spike protein mutations associated with immune evasion and resistance to therapeutics. Upon slowly decreasing the patient's steroids, she had resolution of her symptoms and had a negative nasopharyngeal SARS-CoV-2 PCR and serum CMV PCR in March 2021. Conclusion: A patient with RA on B-cell depleting therapy developed persistent SARS-CoV-2 infection allowing for virus evolution and had numerous complications, including viral and bacterial co-infections with opportunistic pathogens. Despite intra-host evolution with a more immune evasive SARS-CoV-2 lineage, it was cleared after 245 days with reconstitution of the patient's immune system.

Sink-traps are a major source for carbapenemase-producing Enterobacteriaceae transmission.

OBJECTIVE: We studied the extent of carbapenemase-producing Enterobacteriaceae (CPE) sink contamination and transmission to patients in a nonoutbreak setting. METHODS: During 2017-2019, 592 patient-room sinks were sampled in 34 departments. Patient weekly rectal swab CPE surveillance was universally performed. Repeated sink sampling was conducted in 9 departments. Isolates from patients and sinks were characterized using pulsed-field gel electrophoresis (PFGE), and pairs of high resemblance were sequenced by Oxford Nanopore and Illumina. Hybrid assembly was used to fully assemble plasmids, which are shared between paired isolates. RESULTS: In total, 144 (24%) of 592 CPE-contaminated sinks were detected in 25 of 34 departments. Repeated sampling (n = 7,123) revealed that 52%-100% were contaminated at least once during the sampling period. Persistent contamination for >1 year by a dominant strain was common. During the study period, 318 patients acquired CPE. The most common species were Klebsiella pneumoniae, Escherichia coli, and Enterobacter spp. In 127 (40%) patients, a contaminated sink was the suspected source of CPE acquisition. For 20 cases with an identical sink-patient strain, temporal relation suggested sink-to-patient transmission. Hybrid assembly of specific sink-patient isolates revealed that shared plasmids were structurally identical, and SNP differences between shared pairs, along with signatures for potential recombination events, suggests recent sharing of the plasmids. CONCLUSIONS: CPE-contaminated sinks are an important source of transmission to patients. Although traditionally person-to-person transmission has been considered the main route of CPE transmission, these data suggest a change in paradigm that may influence strategies of preventing CPE dissemination.

Inferring bacterial transmission dynamics using deep sequencing genomic surveillance data.

Identifying and interrupting transmission chains is important for controlling infectious diseases. One way to identify transmission pairs - two hosts in which infection was transmitted from one to the other - is using the variation of the pathogen within each single host (within-host variation). However, the role of such variation in transmission is understudied due to a lack of experimental and clinical datasets that capture pathogen diversity in both donor and recipient hosts. In this work, we assess the utility of deep-sequenced genomic surveillance (where genomic regions are sequenced hundreds to thousands of times) using a mouse transmission model involving controlled spread of the pathogenic bacterium Citrobacter rodentium from infected to naïve female animals. We observe that within-host single nucleotide variants (iSNVs) are maintained over multiple transmission steps and present a model for inferring the likelihood that a given pair of sequenced samples are linked by transmission. In this work we show that, beyond the presence and absence of within-host variants, differences arising in the relative abundance of iSNVs (allelic frequency) can infer transmission pairs more precisely. Our approach further highlights the critical role bottlenecks play in reserving the within-host diversity during transmission.

High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients.

Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients' longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.

The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia.

Longitudinal microbiome data provide valuable insight into disease states and clinical responses, but they are challenging to mine and view collectively. To address these limitations, we present TaxUMAP, a taxonomically informed visualization for displaying microbiome states in large clinical microbiome datasets. We used TaxUMAP to chart a microbiome atlas of 1,870 patients with cancer during therapy-induced perturbations. Bacterial density and diversity were positively associated, but the trend was reversed in liquid stool. Low-diversity states (dominations) remained stable after antibiotic treatment, and diverse communities had a broader range of antimicrobial resistance genes than dominations. When examining microbiome states associated with risk for bacteremia, TaxUMAP revealed that certain Klebsiella species were associated with lower risk for bacteremia localize in a region of the atlas that is depleted in high-risk enterobacteria. This indicated a competitive interaction that was validated experimentally. Thus, TaxUMAP can chart comprehensive longitudinal microbiome datasets, enabling insights into microbiome effects on human health.

Early Introduction and Rise of the Omicron Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variant in Highly Vaccinated University Populations.

BACKGROUND: The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly transmissible in vaccinated and unvaccinated populations. The dynamics that govern its establishment and propensity toward fixation (reaching 100% frequency in the SARS-CoV-2 population) in communities remain unknown. Here, we describe the dynamics of Omicron at 3 institutions of higher education (IHEs) in the greater Boston area. METHODS: We use diagnostic and variant-specifying molecular assays and epidemiological analytical approaches to describe the rapid dominance of Omicron following its introduction into 3 IHEs with asymptomatic surveillance programs. RESULTS: 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- to 3-day delay. Finally, we compare cycle threshold values in Omicron vs Delta variant cases on college campuses and identify lower viral loads among college affiliates who harbor Omicron infections. CONCLUSIONS: 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.

Understanding Early Pandemic Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in a Medical Center by Incorporating Public Sequencing Databases to Mitigate Bias.

BACKGROUND: Throughout the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, healthcare workers (HCWs) have faced risk of infection from within the workplace via patients and staff as well as from the outside community, complicating our ability to resolve transmission chains in order to inform hospital infection control policy. Here we show how the incorporation of sequences from public genomic databases aided genomic surveillance early in the pandemic when circulating viral diversity was limited. METHODS: We sequenced a subset of discarded, diagnostic SARS-CoV-2 isolates between March and May 2020 from Boston Medical Center HCWs and combined this data set with publicly available sequences from the surrounding community deposited in GISAID with the goal of inferring specific transmission routes. RESULTS: Contextualizing our data with publicly available sequences reveals that 73% (95% confidence interval, 63%-84%) of coronavirus disease 2019 cases in HCWs are likely novel introductions rather than nosocomial spread. CONCLUSIONS: We argue that introductions of SARS-CoV-2 into the hospital environment are frequent and that expanding public genomic surveillance can better aid infection control when determining routes of transmission.

Combining genomic and epidemiological data to compare the transmissibility of SARS-CoV-2 variants Alpha and Iota.

SARS-CoV-2 variants shaped the second year of the COVID-19 pandemic and the discourse around effective control measures. Evaluating the threat posed by a new variant is essential for adapting response efforts when community transmission is detected. In this study, we compare the dynamics of two variants, Alpha and Iota, by integrating genomic surveillance data to estimate the effective reproduction number (Rt) of the variants. We use Connecticut, United States, in which Alpha and Iota co-circulated in 2021. We find that the Rt of these variants were up to 50% larger than that of other variants. We then use phylogeography to show that while both variants were introduced into Connecticut at comparable frequencies, clades that resulted from introductions of Alpha were larger than those resulting from Iota introductions. By monitoring the dynamics of individual variants throughout our study period, we demonstrate the importance of routine surveillance in the response to COVID-19.

A compilation of fecal microbiome shotgun metagenomics from hematopoietic cell transplantation patients.

Hospitalized patients receiving hematopoietic cell transplants provide a unique opportunity to study the human gut microbiome. We previously compiled a large-scale longitudinal dataset of fecal microbiota and associated metadata, but we had limited that analysis to taxonomic composition of bacteria from 16S rRNA gene sequencing. Here we augment those data with shotgun metagenomics. The compilation amounts to a nested subset of 395 samples compiled from different studies at Memorial Sloan Kettering. Shotgun metagenomics describes the microbiome at the functional level, particularly in antimicrobial resistances and virulence factors. We provide accession numbers that link each sample to the paired-end sequencing files deposited in a public repository, which can be directly accessed by the online services of PATRIC to be analyzed without the users having to download or transfer the files. Then, we show how shotgun sequencing enables the assembly of genomes from metagenomic data. The new data, combined with the metadata published previously, enables new functional studies of the microbiomes of patients with cancer receiving bone marrow transplantation.

Optimal Strategy and Benefit of Pulsed Therapy Depend On Tumor Heterogeneity and Aggressiveness at Time of Treatment Initiation.

Therapeutic resistance is a fundamental obstacle in cancer treatment. Tumors that initially respond to treatment may have a preexisting resistant subclone or acquire resistance during treatment, making relapse theoretically inevitable. Here, we investigate treatment strategies that may delay relapse using mathematical modeling. We find that for a single-drug therapy, pulse treatment-short, elevated doses followed by a complete break from treatment-delays relapse compared with continuous treatment with the same total dose over a length of time. For tumors treated with more than one drug, continuous combination treatment is only sometimes better than sequential treatment, while pulsed combination treatment or simply alternating between the two therapies at defined intervals delays relapse the longest. These results are independent of the fitness cost or benefit of resistance, and are robust to noise. Machine-learning analysis of simulations shows that the initial tumor response and heterogeneity at the start of treatment suffice to determine the benefit of pulsed or alternating treatment strategies over continuous treatment. Analysis of eight tumor burden trajectories of breast cancer patients treated at Memorial Sloan Kettering Cancer Center shows the model can predict time to resistance using initial responses to treatment and estimated preexisting resistant populations. The model calculated that pulse treatment would delay relapse in all eight cases. Overall, our results support that pulsed treatments optimized by mathematical models could delay therapeutic resistance.

Spatial-temporal dynamics of a microbial cooperative behavior resistant to cheating.

Much of our understanding of bacterial behavior stems from studies in liquid culture. In nature, however, bacteria frequently live in densely packed spatially-structured communities. How does spatial structure affect bacterial cooperative behaviors? In this work, we examine rhamnolipid production-a cooperative and virulent behavior of Pseudomonas aeruginosa. Here we show that, in striking contrast to well-mixed liquid culture, rhamnolipid gene expression in spatially-structured colonies is strongly associated with colony specific growth rate, and is impacted by perturbation with diffusible quorum signals. To interpret these findings, we construct a data-driven statistical inference model which captures a length-scale of bacterial interaction that develops over time. Finally, we find that perturbation of P. aeruginosa swarms with quorum signals preserves the cooperating genotype in competition, rather than creating opportunities for cheaters. Overall, our data demonstrate that the complex response to spatial localization is key to preserving bacterial cooperative behaviors.

Transmission from vaccinated individuals in a large SARS-CoV-2 Delta variant outbreak.

An outbreak of over 1,000 COVID-19 cases in Provincetown, Massachusetts (MA), in July 2021-the first large outbreak mostly in vaccinated individuals in the US-prompted a comprehensive public health response, motivating changes to national masking recommendations and raising questions about infection and transmission among vaccinated individuals. To address these questions, we combined viral genomic and epidemiological data from 467 individuals, including 40% of outbreak-associated cases. The Delta variant accounted for 99% of cases in this dataset; it was introduced from at least 40 sources, but 83% of cases derived from a single source, likely through transmission across multiple settings over a short time rather than a single event. Genomic and epidemiological data supported multiple transmissions of Delta from and between fully vaccinated individuals. However, despite its magnitude, the outbreak had limited onward impact in MA and the US overall, likely due to high vaccination rates and a robust public health response.

Population impact of SARS-CoV-2 variants with enhanced transmissibility and/or partial immune escape.

SARS-CoV-2 variants of concern exhibit varying degrees of transmissibility and, in some cases, escape from acquired immunity. Much effort has been devoted to measuring these phenotypes, but understanding their impact on the course of the pandemic-especially that of immune escape-has remained a challenge. Here, we use a mathematical model to simulate the dynamics of wild-type and variant strains of SARS-CoV-2 in the context of vaccine rollout and nonpharmaceutical interventions. We show that variants with enhanced transmissibility frequently increase epidemic severity, whereas those with partial immune escape either fail to spread widely or primarily cause reinfections and breakthrough infections. However, when these phenotypes are combined, a variant can continue spreading even as immunity builds up in the population, limiting the impact of vaccination and exacerbating the epidemic. These findings help explain the trajectories of past and present SARS-CoV-2 variants and may inform variant assessment and response in the future.

Coronavirus Disease 2019 Vaccine Impact on Rates of Severe Acute Respiratory Syndrome Coronavirus 2 Cases and Postvaccination Strain Sequences Among Health Care Workers at an Urban Academic Medical Center: A Prospective Cohort Study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) vaccine trials and post-implementation data suggest that vaccination decreases infections. We examine vaccination's impact on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) case rates and viral diversity among health care workers (HCWs) during a high community prevalence period. METHODS: In this prospective cohort study, HCW received 2 doses of BNT162b2 or mRNA-1273. We included confirmed cases among HCWs from 9 December 2020 to 23 February 2021. Weekly SARS-CoV-2 rates per 100,000 person-days and by time from first injection (1-14 and ≥15 days) were compared with surrounding community rates. Viral genomes were sequenced. RESULTS: SARS-CoV-2 cases occurred in 1.4% (96/7109) of HCWs given at least a first dose and 0.3% (17/5913) of HCWs given both vaccine doses. Adjusted rate ratios (95% confidence intervals) were 0.73 (.53-1.00) 1-14 days and 0.18 (.10-.32) ≥15 days from first dose. HCW ≥15 days from initial dose compared to 1-14 days were more often older (46 vs 38 years, P = .007), Latinx (10% vs 8%, P = .03), and asymptomatic (48% vs 11%, P = .0002). SARS-CoV-2 rates among HCWs fell below the surrounding community, an 18% vs 11% weekly decrease, respectively (P = .14). Comparison of 50 genomes from post-first dose cases did not indicate selection pressure toward known spike antibody escape mutations. CONCLUSIONS: Our results indicate an early positive impact of vaccines on SARS-CoV-2 case rates. Post-vaccination isolates did not show unusual genetic diversity or selection for mutations of concern.

Evidence of transmission from fully vaccinated individuals in a large outbreak of the SARS-CoV-2 Delta variant in Provincetown, Massachusetts.

Multiple summer events, including large indoor gatherings, in Provincetown, Massachusetts (MA), in July 2021 contributed to an outbreak of over one thousand COVID-19 cases among residents and visitors. Most cases were fully vaccinated, many of whom were also symptomatic, prompting a comprehensive public health response, motivating changes to national masking recommendations, and raising questions about infection and transmission among vaccinated individuals. To characterize the outbreak and the viral population underlying it, we combined genomic and epidemiological data from 467 individuals, including 40% of known outbreak-associated cases. The Delta variant accounted for 99% of sequenced outbreak-associated cases. Phylogenetic analysis suggests over 40 sources of Delta in the dataset, with one responsible for a single cluster containing 83% of outbreak-associated genomes. This cluster was likely not the result of extensive spread at a single site, but rather transmission from a common source across multiple settings over a short time. Genomic and epidemiological data combined provide strong support for 25 transmission events from, including many between, fully vaccinated individuals; genomic data alone provides evidence for an additional 64. Together, genomic epidemiology provides a high-resolution picture of the Provincetown outbreak, revealing multiple cases of transmission of Delta from fully vaccinated individuals. However, despite its magnitude, the outbreak was restricted in its onward impact in MA and the US, likely due to high vaccination rates and a robust public health response.

Combining genomic and epidemiological data to compare the transmissibility of SARS-CoV-2 lineages.

Emerging SARS-CoV-2 variants have shaped the second year of the COVID-19 pandemic and the public health discourse around effective control measures. Evaluating the public health threat posed by a new variant is essential for appropriately adapting response efforts when community transmission is detected. However, this assessment requires that a true comparison can be made between the new variant and its predecessors because factors other than the virus genotype may influence spread and transmission. In this study, we develop a framework that integrates genomic surveillance data to estimate the relative effective reproduction number (R t ) of co-circulating lineages. We use Connecticut, a state in the northeastern United States in which the SARS-CoV-2 variants B.1.1.7 and B.1.526 co-circulated in early 2021, as a case study for implementing this framework. We find that the R t of B.1.1.7 was 6-10% larger than that of B.1.526 in Connecticut in the midst of a COVID-19 vaccination campaign. To assess the generalizability of this framework, we apply it to genomic surveillance data from New York City and observe the same trend. Finally, we use discrete phylogeography to demonstrate that while both variants were introduced into Connecticut at comparable frequencies, clades that resulted from introductions of B.1.1.7 were larger than those resulting from B.1.526 introductions. Our framework, which uses open-source methods requiring minimal computational resources, may be used to monitor near real-time variant dynamics in a myriad of settings.

Coming to America: Genomic surveillance and how B.1.1.7 arrived in the US.

In this issue of Cell, Washington et al. and Alpert et al. demonstrate the value of genomic surveillance when studying the introduction of the B.1.1.7 variant to the US and illustrate the challenge that results from the lack of good sampling strategies.

COVID-19 vaccine impact on rates of SARS-CoV-2 cases and post vaccination strain sequences among healthcare workers at an urban academic medical center: a prospective cohort study.

BACKGROUND: COVID-19 vaccine trials and post-implementation data suggest vaccination decreases SARS-CoV-2 infections. We examine COVID-19 vaccination's impact on SARS-CoV-2 case rates and viral diversity among healthcare workers (HCW) during a high community prevalence period. METHODS: A prospective cohort study from Boston Medical Center (BMC)'s HCW vaccination program, where staff received two doses of BNT162b2 or mRNA-1273. We included PCR-confirmed SARS-CoV-2 cases among HCWs from December 09, 2020 to February 23, 2021. Weekly SARS-CoV-2 rates per 100,000 person-day overall and by time from first injection (1-14 and >14 days) were compared with surrounding community rates. Viral genomes were sequenced from SARS CoV-2 positive samples. RESULTS: SARS-CoV-2 cases occurred in 1.4% (96/7109) of HCWs given at least a first dose and 0.3% (17/5913) of HCWs given both vaccine doses. Adjusted SARS-CoV-2 infection rate ratios were 0.73 (95% CI 0.53-1.00) 1-14 days and 0.18 (0.10-0.32) >14 days from first dose. HCW SARS-CoV-2 cases >14 days from initial dose compared to within 14 days were more often older (46 versus 38 years, p=0.007), Latinx (10% versus 8%, p=0.03), and asymptomatic (48% versus 11%, p=0.0002). SARS-CoV-2 rates among HCWs fell below those of the surrounding community, with a 18% versus 11% weekly decrease respectively (p=0.14). Comparison of 48 SARS-CoV-2 genomes sequenced from post-first dose cases did not indicate selection pressure towards known spike-antibody escape mutations. CONCLUSIONS: Our results indicate a positive impact of COVID-19 vaccines on SARS-CoV-2 case rates. Post-vaccination isolates did not show unusual genetic diversity or selection for mutations of concern. MAIN POINT: Cases of SARS-CoV-2 among health care workers dropped rapidly with COVID-19 vaccination. Sequencing 48 breakthrough infections (overwhelmingly in 14 days after 1st dose) showed no clear sign of any differences in spike protein compared with time-matched, unvaccinated control sequences.

Compilation of longitudinal microbiota data and hospitalome from hematopoietic cell transplantation patients.

The impact of the gut microbiota in human health is affected by several factors including its composition, drug administrations, therapeutic interventions and underlying diseases. Unfortunately, many human microbiota datasets available publicly were collected to study the impact of single variables, and typically consist of outpatients in cross-sectional studies, have small sample numbers and/or lack metadata to account for confounders. These limitations can complicate reusing the data for questions outside their original focus. Here, we provide comprehensive longitudinal patient dataset that overcomes those limitations: a collection of fecal microbiota compositions (>10,000 microbiota samples from >1,000 patients) and a rich description of the "hospitalome" experienced by the hosts, i.e., their drug exposures and other metadata from patients with cancer, hospitalized to receive allogeneic hematopoietic cell transplantation (allo-HCT) at a large cancer center in the United States. We present five examples of how to apply these data to address clinical and scientific questions on host-associated microbial communities.