Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

Tumor ecosystems are composed of multiple cell types that communicate by ligand-receptor interactions. Targeting ligand-receptor interactions (for instance, with immune checkpoint inhibitors) can provide significant benefits for patients. However, our knowledge of which interactions occur in a tumor and how these interactions affect outcome is still limited. We present an approach to characterize communication by ligand-receptor interactions across all cell types in a microenvironment using single-cell RNA sequencing. We apply this approach to identify and compare the ligand-receptor interactions present in six syngeneic mouse tumor models. To identify interactions potentially associated with outcome, we regress interactions against phenotypic measurements of tumor growth rate. In addition, we quantify ligand-receptor interactions between T cell subsets and their relation to immune infiltration using a publicly available human melanoma dataset. Overall, this approach provides a tool for studying cell-cell interactions, their variability across tumors, and their relationship to outcome. Tumors are composed of cancer cells and many non-malignant cell types, such as immune and stromal cells. To better understand how all cell types in a tumor cooperate to facilitate malignant growth, Kumar et al. studied communication between cells via ligand and receptor interactions using single-cell data and computational modeling. Keywords: computational analysis; single-cell RNA sequencing; cell-cell communication; ligand-receptor interaction; tumor microenvironment; syngeneic mouse models; cancer patient samplesNational Institute of General Medical Sciences (U.S.) (Grant T32-GM008334)National Cancer Institute (U.S.) (Grant U01-CA215798

A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients

Background: Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal tract that is associated with changes in the gut microbiome. Here, we sought to identify strain-specific functional correlates with IBD outcomes. Methods: We performed metagenomic sequencing of monthly stool samples from 20 IBD patients and 12 controls (266 total samples). These were taxonomically profiled with MetaPhlAn2 and functionally profiled using HUMAnN2. Differentially abundant species were identified using MaAsLin and strain-specific pangenome haplotypes were analyzed using PanPhlAn. Results: We found a significantly higher abundance in patients of facultative anaerobes that can tolerate the increased oxidative stress of the IBD gut. We also detected dramatic, yet transient, blooms of Ruminococcus gnavus in IBD patients, often co-occurring with increased disease activity. We identified two distinct clades of R. gnavus strains, one of which is enriched in IBD patients. To study functional differences between these two clades, we augmented the R. gnavus pangenome by sequencing nine isolates from IBD patients. We identified 199 IBD-specific, strain-specific genes involved in oxidative stress responses, adhesion, iron-acquisition, and mucus utilization, potentially conferring an adaptive advantage for this R. gnavus clade in the IBD gut. Conclusions: This study adds further evidence to the hypothesis that increased oxidative stress may be a major factor shaping the dysbiosis of the microbiome observed in IBD and suggests that R. gnavus may be an important member of the altered gut community in IBD. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0490-5) contains supplementary material, which is available to authorized users

A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.National Institute of Allergy and Infectious Diseases (U.S.) (Grant AI121932)United States. Agency for Healthcare Research and Quality (Grant R01HS019388)National Institutes of Health (U.S.). Clinical and Translational Sciences Award Program (Award UL1 TR000153

Trends in Antibiotic Susceptibility in Staphylococcus aureus in Boston, Massachusetts, from 2000 to 2014

ABSTRACT The rate of infection by methicillin-resistant Staphylococcus aureus (MRSA) has declined over the past decade, but it is unclear whether this represents a decline in S. aureus infections overall. To evaluate the trends in the annual rates of infection by S. aureus subtypes and mean antibiotic resistance, we conducted a 15-year retrospective observational study at two tertiary care institutions in Boston, MA, of 31,753 adult inpatients with S. aureus isolated from clinical specimens. We inferred the gain and loss of methicillin resistance through genome sequencing of 180 isolates from 2016. The annual rates of infection by S. aureus declined from 2003 to 2014 by 4.2% (2.7% to 5.6%), attributable to an annual decline in MRSA of 10.9% (9.3% to 12.6%). Penicillin-susceptible S. aureus (PSSA) increased by 6.1% (4.2% to 8.1%) annually, and rates of methicillin-susceptible penicillin-resistant S. aureus (MSSA) did not change. Resistance in S. aureus decreased from 2000 to 2014 by 0.8 antibiotics (0.7 to 0.8). Within common MRSA clonal complexes, 3/14 MSSA and 2/21 PSSA isolates arose from the loss of resistance-conferring genes. Overall, in two tertiary care institutions in Boston, MA, a decline in S. aureus infections has been accompanied by a shift toward increased antibiotic susceptibility. The rise in PSSA makes penicillin an increasingly viable treatment option

A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation

Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.Peer reviewe

Commensal Microbiota Promote Lung Cancer Development via γδ T Cells

Lung cancer is closely associated with chronic inflammation, but the causes of inflammation and the specific immune mediators have not been fully elucidated. The lung is a mucosal tissue colonized by a diverse bacterial community, and pulmonary infections commonly present in lung cancer patients are linked to clinical outcomes. Here, we provide evidence that local microbiota provoke inflammation associated with lung adenocarcinoma by activating lung-resident γδ T cells. Germ-free or antibiotic-treated mice were significantly protected from lung cancer development induced by Kras mutation and p53 loss. Mechanistically, commensal bacteria stimulated Myd88-dependent IL-1β and IL-23 production from myeloid cells, inducing proliferation and activation of Vγ6 + Vδ1 + γδ T cells that produced IL-17 and other effector molecules to promote inflammation and tumor cell proliferation. Our findings clearly link local microbiota-immune crosstalk to lung tumor development and thereby define key cellular and molecular mediators that may serve as effective targets in lung cancer intervention. Lung cancer development is associated with increased bacterial burden and altered bacterial composition in the lung. Depletion of microbiota or blockade of the downstream cellular or molecular immune mediators significantly suppress lung tumor growth.National Cancer Institute (U.S.) ( K99 Award CA226400)United States. Department of Defense. Lung Cancer Research Program ( Concept Award W81XWH-15–1–0623)National Cancer Institute (U.S.) ( R01 Grant CA185020)National Cancer Institute (U.S.) (Core Grant P30-CA14051)National Cancer Institute (U.S.) (Grant P30-ES002109

Epidemiology and genomics of a slow outbreak of methicillin-resistant Staphyloccus aureus (MRSA) in a neonatal intensive care unit: Successful chronic decolonization of MRSA-positive healthcare personnel.

ObjectiveTo describe the genomic analysis and epidemiologic response related to a slow and prolonged methicillin-resistant Staphylococcus aureus (MRSA) outbreak.DesignProspective observational study.SettingNeonatal intensive care unit (NICU).MethodsWe conducted an epidemiologic investigation of a NICU MRSA outbreak involving serial baby and staff screening to identify opportunities for decolonization. Whole-genome sequencing was performed on MRSA isolates.ResultsA NICU with excellent hand hygiene compliance and longstanding minimal healthcare-associated infections experienced an MRSA outbreak involving 15 babies and 6 healthcare personnel (HCP). In total, 12 cases occurred slowly over a 1-year period (mean, 30.7 days apart) followed by 3 additional cases 7 months later. Multiple progressive infection prevention interventions were implemented, including contact precautions and cohorting of MRSA-positive babies, hand hygiene observers, enhanced environmental cleaning, screening of babies and staff, and decolonization of carriers. Only decolonization of HCP found to be persistent carriers of MRSA was successful in stopping transmission and ending the outbreak. Genomic analyses identified bidirectional transmission between babies and HCP during the outbreak.ConclusionsIn comparison to fast outbreaks, outbreaks that are "slow and sustained" may be more common to units with strong existing infection prevention practices such that a series of breaches have to align to result in a case. We identified a slow outbreak that persisted among staff and babies and was only stopped by identifying and decolonizing persistent MRSA carriage among staff. A repeated decolonization regimen was successful in allowing previously persistent carriers to safely continue work duties