A subset of dendritic cells induces CD4+ T cells to produce IFN-γ by an IL-12-independent but CD70-dependent mechanism in vivo

Interferon (IFN)-γ, a cytokine critical for resistance to infection and tumors, is produced by CD4+ helper T lymphocytes after stimulation by cultured dendritic cells (DCs) that secrete a cofactor, interleukin (IL)-12. We have identified a major IL-12-independent pathway whereby DCs induce IFN-γ-secreting T helper (Th)1 CD4+ T cells in vivo. This pathway requires the membrane-associated tumor necrosis family member CD70 and was identified by targeting the LACK antigen from Leishmania major within an antibody to CD205 (DEC-205), an uptake receptor on a subset of DCs. Another major DC subset, targeted with 33D1 anti-DCIR2 antibody, also induced IFN-γ in vivo but required IL-12, not CD70. Isolated CD205 + DCs expressed cell surface CD70 when presenting antigen to T cell receptor transgenic T cells, and this distinction was independent of maturation stimuli. CD70 was also essential for CD205+ DC function in vivo. Detection of the IL-12-independent IFN-γ pathway was obscured with nontargeted LACK, which was presented by both DC subsets. This in situ analysis points to CD70 as a decision maker for Th1 differentiation by CD205+ DCs, even in Th2-prone BALB/c animals and potentially in vaccine design. The results indicate that two DC subsets have innate propensities to differentially affect the Th1/Th2 balance in vivo and by distinct mechanisms

A subset of dendritic cells induces CD4+ T cells to produce IFN-γ by an IL-12–independent but CD70-dependent mechanism in vivo

Interferon (IFN)-γ, a cytokine critical for resistance to infection and tumors, is produced by CD4+ helper T lymphocytes after stimulation by cultured dendritic cells (DCs) that secrete a cofactor, interleukin (IL)-12. We have identified a major IL-12–independent pathway whereby DCs induce IFN-γ–secreting T helper (Th)1 CD4+ T cells in vivo. This pathway requires the membrane-associated tumor necrosis family member CD70 and was identified by targeting the LACK antigen from Leishmania major within an antibody to CD205 (DEC-205), an uptake receptor on a subset of DCs. Another major DC subset, targeted with 33D1 anti-DCIR2 antibody, also induced IFN-γ in vivo but required IL-12, not CD70. Isolated CD205+ DCs expressed cell surface CD70 when presenting antigen to T cell receptor transgenic T cells, and this distinction was independent of maturation stimuli. CD70 was also essential for CD205+ DC function in vivo. Detection of the IL-12–independent IFN-γ pathway was obscured with nontargeted LACK, which was presented by both DC subsets. This in situ analysis points to CD70 as a decision maker for Th1 differentiation by CD205+ DCs, even in Th2-prone BALB/c animals and potentially in vaccine design. The results indicate that two DC subsets have innate propensities to differentially affect the Th1/Th2 balance in vivo and by distinct mechanisms

Prospective Spatio-Temporal and Temporal Cluster Detection by Salmonella Serotype

To improve (Salmonella) cluster detection by serotype in New York City (NYC), we developed an automated daily process to assign serotypes to (Salmonella) cases. We implemented daily analyses using the prospective space-time permutation scan statistic in SaTScan to detect spatio-tempOral and purely tempOral clusters. In 14 weeks of spatio-tempOral analyses, 7 clusters were identified, and in 4 weeks of purely tempOral analyses, 5 clusters were identified. These methods are useful complements to the NYC (Salmonella) surveillance system and could be adopted by other health departments for primary or confirmatory cluster detection

Prospective Spatio-Temporal and Temporal Cluster Detection by Salmonella Serotype

To improve (Salmonella) cluster detection by serotype in New York City (NYC), we developed an automated daily process to assign serotypes to (Salmonella) cases. We implemented daily analyses using the prospective space-time permutation scan statistic in SaTScan to detect spatio-tempOral and purely tempOral clusters. In 14 weeks of spatio-tempOral analyses, 7 clusters were identified, and in 4 weeks of purely tempOral analyses, 5 clusters were identified. These methods are useful complements to the NYC (Salmonella) surveillance system and could be adopted by other health departments for primary or confirmatory cluster detection

Evaluating Twitter for Foodborne Illness Outbreak Detection in New York City

ObjectiveTo incorporate data from Twitter into the New York City Department of Health and Mental Hygiene foodborne illness surveillance system and evaluate its utility and impact on foodborne illness complaint and outbreak detection.IntroductionAn estimated one in six Americans experience illness from the consumption of contaminated food (foodborne illness) annually; most are neither diagnosed nor reported to health departments1. Eating food prepared outside of the home is an established risk factor for foodborne illness2. New York City (NYC) has approximately 24,000 restaurants and >8.5 million residents, of whom 78% report eating food prepared outside of the home at least once per week3. Residents and visitors can report incidents of restaurant-associated foodborne illness to a citywide non-emergency information service, 311. In 2012, the NYC Department of Health and Mental Hygiene (DOHMH) began collaborating with Columbia University to improve the detection of restaurant-associated foodborne illness complaints using a machine learning algorithm and a daily feed of Yelp reviews to identify reports of foodborne illness4. Annually, DOHMH manages over 4,000 restaurant-associated foodborne illness reports received via 311 and identified on Yelp which lead to the detection of about 30 outbreaks associated with a restaurant in NYC. Given the small number of foodborne illness outbreaks identified, it is probable that many restaurant-associated foodborne illness incidents remain unreported. DOHMH sought to incorporate and evaluate an additional data source, Twitter, to enhance foodborne illness complaint and outbreak detection efforts in NYC.MethodsDOHMH epidemiologists continue to collaborate with computer scientists at Columbia University who developed a text mining algorithm that identifies tweets indicating foodborne illness. Twitter data are received via a targeted application program interface query that searches for foodborne illness key words and uses metadata to select for tweets with a possible NYC location. Each tweet is assigned a sick score between 0–1; those meeting a threshold value of 0.5 are manually reviewed by an epidemiologist, and a survey link is tweeted to users who have tweeted about foodborne illness, requesting more information regarding the date and time of the foodborne illness event, restaurant details, and user contact information. Survey data are used to validate complaints and are incorporated in a daily analysis using all sources of complaint data to identify restaurants with multiple foodborne illness complaints within a 30-day period. This system was launched on November 29, 2016.ResultsDuring November 29, 2016–September 27, 2017, 12,015 tweets qualified for review (39/day on average); 2,288 (19.0%) indicated foodborne illness in NYC, and 1,778 (14.8%) were tweeted a survey link (510 foodborne illness tweets were either deleted by the Twitter user or were tweets from a user who was already sent a survey for the same foodborne illness incident). The survey tweets resulted in 92 likes, 12 retweets, 65 replies, 232 profile views and 348 survey link clicks. Of the 1,778 surveys sent, 27 were completed (response rate 1.5%), of which 20 (74.7%) confirmed foodborne illness associated with a NYC restaurant; none had been reported via 311/Yelp. Of those, 11 (55%) provided a phone number, of which 10 (90.9%) completed phone interviews. The completed surveys contributed to the identification of two restaurants with multiple foodborne illness complaints within a 30-day period.ConclusionsThe utility of Twitter for foodborne illness outbreak detection continues to be evaluated. While the survey response rate has been low, the identification of new complaints not otherwise reported to 311 and Yelp suggests this will be a useful tool. Future plans include using feedback data collected by DOHMH epidemiologist review to increase the sensitivity and specificity of the text mining algorithm and improve the location detection for Twitter users. In addition, we plan to implement enhancements to the survey and create a web page to promote survey responses. Furthermore, we intend to share this system with other health departments so that they might incorporate Twitter in their outbreak detection and public health surveillance activities.References1. Scallan E, Griffin PM, Angulo FJ, Tauxe RV, Hoekstra RM. Foodborne illness acquired in the United States--unspecified agents. Emerg Infect Dis. 2011 Jan;17(1):16-22.2. Jones TF, Angulo FJ. Eating in restaurants: a risk factor for foodborne disease? Clin Infect Dis. 2006 Nov 15;43(10):1324-8.3. New York City Health and Nutrition Examination Survey, 2013-2014 [Internet]. New York: New York City Department of Health and Mental Hygiene and The City University of New York; 2017 [cited 2017 Aug 28]. Available from: http://nychanes.org/data/4. Harrison C, Jorder M, Stern H, Stavinsky F, Reddy V, Hanson H, Waechter H, Lowe L, Gravano L, Balter S; Centers for Disease Control and Prevention (CDC).. Using online reviews by restaurant patrons to identify unreported cases of foodborne illness - New York City, 2012-2013. MMWR Morb Mortal Wkly Rep. 2014 May 23;63(20):441-5

Increasing Antibiotic Resistance in Shigella spp. from Infected New York City Residents, New York, USA

Approximately 20% of Shigella isolates tested in New York City, New York, USA, during 2013–2015 displayed decreased azithromycin susceptibility. Case-patients were older and more frequently male and HIV infected than those with azithromycin-susceptible Shigella infection; 90% identified as men who have sex with men. Clinical interpretation guidelines for azithromycin resistance and outcome studies are needed

A Whole-Genome Single Nucleotide Polymorphism-Based Approach To Trace and Identify Outbreaks Linked to a Common Salmonella enterica subsp. enterica Serovar Montevideo Pulsed-Field Gel Electrophoresis Type

In this study, we report a whole-genome single nucleotide polymorphism (SNP)-based evolutionary approach to study the epidemiology of a multistate outbreak of Salmonella enterica subsp. enterica serovar Montevideo. This outbreak included 272 cases that occurred in 44 states between July 2009 and April 2010. A case-control study linked the consumption of salami made with contaminated black and red pepper to the outbreak. We sequenced, on the SOLiD System, 47 isolates with XbaI PFGE pattern JIXX01.0011, a common pulsed-field gel electrophoresis (PFGE) pattern associated with isolates from the outbreak. These isolates represented 20 isolates collected from human sources during the period of the outbreak and 27 control isolates collected from human, food, animal, and environmental sources before the outbreak. Based on 253 high-confidence SNPs, we were able to reconstruct a tip-dated molecular clock phylogeny of the isolates and to assign four human isolates to the actual outbreak. We developed an SNP typing assay to rapidly discriminate between outbreak-related cases and non-outbreak-related cases and tested this assay on an extended panel of 112 isolates. These results suggest that only a very small percentage of the human isolates with the outbreak PFGE pattern and obtained during the outbreak period could be attributed to the actual pepper-related outbreak (20%), while the majority (80%) of the putative cases represented background cases. This study demonstrates that next-generation-based SNP typing provides the resolution and accuracy needed for outbreak investigations of food-borne pathogens that cannot be distinguished by currently used subtyping methods