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.

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.

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.

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.

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.

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 SARS-CoV-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.

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.

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.

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.

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.

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.

Potential of cannabinoids as treatments for autism spectrum disorders

Current treatments for autism spectrum disorders (ASD) are limited in efficacy and are often associated with substantial side effects. These medications typically ameliorate problem behaviors associated with ASD, but do not target core symptom domains. As a result, there is a significant amount of research underway for development of novel experimental therapeutics. Endocannabinoids are arachidonic acid-derived lipid neuromodulators, which, in combination with their receptors and associated metabolic enzymes, constitute the endocannabinoid (EC) system. Cannabinoid signaling may be involved in the social impairment and repetitive behaviors observed in those with ASD. In this review, we discuss a possible role of the EC system in excitatory-inhibitory (E-I) imbalance and immune dysregulation in ASD. Novel treatments for the core symptom domains of ASD are needed and phytocannabinoids could be useful experimental therapeutics for core symptoms and associated domains.

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.

Author Correction: Compilation of longitudinal microbiota data and hospitalome from hematopoietic cell transplantation patients

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00903-0.

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.

The gut microbiota is associated with immune cell dynamics in humans

The gut microbiota influences development1-3 and homeostasis4-7 of the mammalian immune system, and is associated with human inflammatory8 and immune diseases9,10 as well as responses to immunotherapy11-14. Nevertheless, our understanding of how gut bacteria modulate the immune system remains limited, particularly in humans, where the difficulty of direct experimentation makes inference challenging. Here we study hundreds of hospitalized-and closely monitored-patients with cancer receiving haematopoietic cell transplantation as they recover from chemotherapy and stem-cell engraftment. This aggressive treatment causes large shifts in both circulatory immune cell and microbiota populations, enabling the relationships between the two to be studied simultaneously. Analysis of observed daily changes in circulating neutrophil, lymphocyte and monocyte counts and more than 10,000 longitudinal microbiota samples revealed consistent associations between gut bacteria and immune cell dynamics. High-resolution clinical metadata and Bayesian inference allowed us to compare the effects of bacterial genera in relation to those of immunomodulatory medications, revealing a considerable influence of the gut microbiota-together and over time-on systemic immune cell dynamics. Our analysis establishes and quantifies the link between the gut microbiota and the human immune system, with implications for microbiota-driven modulation of immunity.

The microbe-derived short-chain fatty acids butyrate and propionate are associated with protection from chronic GVHD

Studies of the relationship between the gastrointestinal microbiota and outcomes in allogeneic hematopoietic stem cell transplantation (allo-HCT) have thus far largely focused on early complications, predominantly infection and acute graft-versus-host disease (GVHD). We examined the potential relationship of the microbiome with chronic GVHD (cGVHD) by analyzing stool and plasma samples collected late after allo-HCT using a case-control study design. We found lower circulating concentrations of the microbe-derived short-chain fatty acids (SCFAs) propionate and butyrate in day 100 plasma samples from patients who developed cGVHD, compared with those who remained free of this complication, in the initial case-control cohort of transplant patients and in a further cross-sectional cohort from an independent transplant center. An additional cross-sectional patient cohort from a third transplant center was analyzed; however, serum (rather than plasma) was available, and the differences in SCFAs observed in the plasma samples were not recapitulated. In sum, our findings from the primary case-control cohort and 1 of 2 cross-sectional cohorts explored suggest that the gastrointestinal microbiome may exert immunomodulatory effects in allo-HCT patients at least in part due to control of systemic concentrations of microbe-derived SCFAs.

Diversification and Evolution of Vancomycin-Resistant Enterococcus faecium during Intestinal Domination

Vancomycin-resistant Enterococcus faecium (VRE) is a leading cause of hospital-acquired infections. This is particularly true in immunocompromised patients, where the damage to the microbiota caused by antibiotics can lead to VRE domination of the intestine, increasing a patient's risk for bloodstream infection. In previous studies we observed that the intestinal domination by VRE of patients hospitalized to receive allogeneic bone marrow transplantation can persist for weeks, but little is known about subspecies diversification and evolution during prolonged domination. Here we combined a longitudinal analysis of patient data and in vivo experiments to reveal previously unappreciated subspecies dynamics during VRE domination that appeared to be stable from 16S rRNA microbiota analyses. Whole-genome sequencing of isolates obtained from sequential stool samples provided by VRE-dominated patients revealed an unanticipated level of VRE population complexity that evolved over time. In experiments with ampicillin-treated mice colonized with a single CFU, VRE rapidly diversified and expanded into distinct lineages that competed for dominance. Mathematical modeling shows that in vivo evolution follows mostly a parabolic fitness landscape, where each new mutation provides diminishing returns and, in the setting of continuous ampicillin treatment, reveals a fitness advantage for mutations in penicillin-binding protein 5 (pbp5) that increase resistance to ampicillin. Our results reveal the rapid diversification of host-colonizing VRE populations, with implications for epidemiologic tracking of in-hospital VRE transmission and susceptibility to antibiotic treatment.

Apoptosis in snowflake yeast: novel trait, or side effect of toxic waste?

Recent experiments evolving de novo multicellularity in yeast have found that large cluster-forming genotypes also exhibit higher rates of programmed cell death (apoptosis). This was previously interpreted as the evolution of a simple form of cellular division of labour: apoptosis results in the scission of cell-cell connections, allowing snowflake yeast to produce proportionally smaller, faster-growing propagules. Through spatial simulations, Duran-Nebreda and Solé (J. R. Soc. Interface 12, 20140982 (doi:10.1073/pnas.1115323109)) develop the novel null hypothesis that apoptosis is not an adaptation, per se, but is instead caused by the accumulation of toxic metabolites in large clusters. Here we test this hypothesis by synthetically creating unicellular derivatives of snowflake yeast through functional complementation with the ancestral ACE2 allele. We find that multicellular snowflake yeast with elevated apoptosis exhibit a similar rate of apoptosis when cultured as single cells. We also show that larger snowflake yeast clusters tend to contain a greater fraction of older, senescent cells, which may explain why larger clusters of a given genotype are more apoptotic. Our results show that apoptosis is not caused by side effects of spatial structure, such as starvation or waste product accumulation, and are consistent with the hypothesis that elevated apoptosis is a trait that co-evolves with large cluster size.

Stochasticity and the limits to confidence when estimating R0 of Ebola and other emerging infectious diseases

Dynamic models - often deterministic in nature - were used to estimate the basic reproductive number, R0, of the 2014-5 Ebola virus disease (EVD) epidemic outbreak in West Africa. Estimates of R0 were then used to project the likelihood for large outbreak sizes, e.g., exceeding hundreds of thousands of cases. Yet fitting deterministic models can lead to over-confidence in the confidence intervals of the fitted R0, and, in turn, the type and scope of necessary interventions. In this manuscript we propose a hybrid stochastic-deterministic method to estimate R0 and associated confidence intervals (CIs). The core idea is that stochastic realizations of an underlying deterministic model can be used to evaluate the compatibility of candidate values of R0 with observed epidemic curves. The compatibility is based on comparing the distribution of expected epidemic growth rates with the observed epidemic growth rate given "process noise", i.e., arising due to stochastic transmission, recovery and death events. By applying our method to reported EVD case counts from Guinea, Liberia and Sierra Leone, we show that prior estimates of R0 based on deterministic fits appear to be more confident than analysis of stochastic trajectories suggests should be possible. Moving forward, we recommend including process noise among other sources of noise when estimating R0 CIs of emerging epidemics. Our hybrid procedure represents an adaptable and easy-to-implement approach for such estimation.

A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes

Viral lysis of microbial hosts releases organic matter that can then be assimilated by nontargeted microorganisms. Quantitative estimates of virus-mediated recycling of carbon in marine waters, first established in the late 1990s, were originally extrapolated from marine host and virus densities, host carbon content and inferred viral lysis rates. Yet, these estimates did not explicitly incorporate the cascade of complex feedbacks associated with virus-mediated lysis. To evaluate the role of viruses in shaping community structure and ecosystem functioning, we extend dynamic multitrophic ecosystem models to include a virus component, specifically parameterized for processes taking place in the ocean euphotic zone. Crucially, we are able to solve this model analytically, facilitating evaluation of model behavior under many alternative parameterizations. Analyses reveal that the addition of a virus component promotes the emergence of complex communities. In addition, biomass partitioning of the emergent multitrophic community is consistent with well-established empirical norms in the surface oceans. At steady state, ecosystem fluxes can be probed to characterize the effects that viruses have when compared with putative marine surface ecosystems without viruses. The model suggests that ecosystems with viruses will have (1) increased organic matter recycling, (2) reduced transfer to higher trophic levels and (3) increased net primary productivity. These model findings support hypotheses that viruses can have significant stimulatory effects across whole-ecosystem scales. We suggest that existing efforts to predict carbon and nutrient cycling without considering virus effects are likely to miss essential features of marine food webs that regulate global biogeochemical cycles.

The virus of my virus is my friend: ecological effects of virophage with alternative modes of coinfection

Virophages are viruses that rely on the replication machinery of other viruses to reproduce within eukaryotic hosts. Two different modes of coinfection have been posited based on experimental observation. In one mode, the virophage and the virus enter the host independently. In the other mode, the virophage adheres to the virus so both virophage and virus enter the host together. Here we ask: what are the ecological effects of these different modes of coinfection? In particular, what ecological effects are common to both infection modes, and what are the differences particular to each mode? We develop a pair of biophysically motivated ODE models of viral-host population dynamics, corresponding to dynamics arising from each mode of infection. We find that both modes of coinfection allow for the coexistence of the virophage, virus, and host either at a stable fixed point or through cyclical dynamics. In both models, virophage tends to be the most abundant population and their presence always reduces the viral abundance and increases the host abundance. However, we do find qualitative differences between models. For example, via extensive sampling of biologically relevant parameter space, we only observe bistability when the virophage and the virus enter the host together. We discuss how such differences may be leveraged to help identify modes of infection in natural environments from population level data.

Does mirtazapine have a more rapid onset than SSRIs?

BACKGROUND

A single study utilizing a cross-sectional analysis of scores on the Hamilton Rating Scale for Depression (HAM-D) suggested that mirtazapine has a more rapid onset than selective serotonin reuptake inhibitors (SSRIs). Analysis based on the HAM-D may favor drugs with sleep-producing effects. The purpose of the present study was to determine if a review of all studies comparing an SSRI with mirtazapine, utilizing persistent improvement as the dependent variable, would suggest that mirtazapine had a more rapid onset than SSRIs.

METHOD

All double-blind studies comparing mirtazapine with SSRIs were analyzed. Included in the analysis to determine speed of onset were 298 patients taking mirtazapine and 285 taking an SSRI. Pattern analysis, which has been described and used by other researchers, was employed to study speed of onset.

RESULTS

At the end of each of the 3 studies, the total number of responders for each of the drugs did not differ. However, the proportion of responders with onset of persistent improvement in week 1 was greater for mirtazapine (13%, 38/298) than for the SSRIs (6%, 18/285; chi2 = 6.95, df = 1, p = .008).

CONCLUSION

These data support the possibility that mirtazapine may have a more rapid onset than SSRIs. This observation should be considered preliminary because of the retrospective nature of the analysis and the absence of a placebo group.

Placebo run-in period in studies of depressive disorders. Clinical, heuristic and research implications

BACKGROUND

In spite of the virtually ubiquitous nature of the initial 10-day placebo run-in period (IPR) in drug trials, there is little empirical data establishing its relevance.

METHOD

Data from 593 subjects were examined retrospectively to determine whether or not the prognosis of subjects minimally improved during the IPR was different to those who were unimproved. The IPR period was single-blind and was followed by a six-week double-blind phase in all studies.

RESULTS

Twenty-six per cent of the subjects were minimally improved and 74% were unimproved. Approximately 10% of the subjects who were much improved were not followed systematically. Across a range of diagnosis, severity and chronicity subjects minimally improved (versus unimproved) after IPR had a more favourable prognosis whether assigned to drug or placebo.

CONCLUSIONS

Change during IPR appears to be a meaningful predictor. Stratification should be considered in future antidepressant studies.

Can the effects of antidepressants be observed in the first two weeks of treatment?

Recently the claim that there is a lag in the time of onset of antidepressant effects has been questioned. This issue rests on contrasting the time course of ultimate responders versus nonresponders on imipramine and amitriptyline. It is concluded that in 1 week on antidepressants "clinicians were capable of detecting changes in general states between the groups and the specific effects of depressed mood and anxiety and the physical expression of distress" (Katz et al. 1987). To examine this issue, we first used the design in which ultimate responders and ultimate nonresponders to antidepressants were compared at 1 and 2 weeks. Clearly there were statistically significant differences between ultimate responders and nonresponders on drug. However, the same was true on placebo. When the ultimate responders on placebo were contrasted to the ultimate responders on drug at 1 and 2 weeks using the Clinical global Impression (CGI) scale and the Hamilton Depression Rating Scale (HDRS), there was no difference between drug and placebo. This was also true for a subgroup of patients who met the criteria for melancholia. We conclude that, if the effects of nonspecific improvement are partialed out, there is no evidence of a medication effect at 1 and 2 weeks.

Chronological milestones to guide drug change. When should clinicians switch antidepressants?

BACKGROUND

We attempt to identify the time when patients whose conditions are unimproved while receiving antidepressants are unlikely to respond and should have their treatment changed.

METHODS

A total of 593 patients were studied. The course of treatment for patients was examined to determine the weeks at which patients who received drug therapy had a better chance of being rated as responders at the study end (week 6) vs patients who received placebo.

RESULTS

At the end of week 3, 19 (32%) of the 59 patients who received drug therapy and 6 (10%) of the 57 patients who received placebo and who never minimally improved were rated as responders at week 6. For those who showed no improvement by week 4, the effects of drug therapy and the placebo were equal. Patients who received drug therapy and whose conditions were unimproved but who had been minimally improved at some point had a superior prognosis with drug therapy vs placebo until week 4. Of those unimproved at week 4 but minimally improved at some point previously, 20 (39%) of the 51 patients who received drug therapy vs 3 (8%) of the 36 patients who received placebo were rated as responders at week 6. Of the 75 patients who minimally improved while receiving drug therapy at the end of week 5, 33 (44%) had a chance of being rated a responder at the end of week 6 vs 9 (26%) of the 35 patients receiving placebo.

CONCLUSIONS

Patients tolerant of an adequate dose, whose conditions have never been at least minimally improved by the end of week 4, should have their treatment regimen altered. These patients represented a minority of drug-treated patients in the sample studied (ie, 39/392 [10%]). Patients whose conditions minimally improve at some prior week but not after week 5 should have their treatment changed. Patients whose conditions minimally improve in week 5 should continue treatment until week 6.