Potential health risks to disinfection workers from exposure to active substances in COVID-19 biocidal products.

The importance of disinfection has recently been emphasized due to the increasing risk of the spread of infections such as coronavirus disease-2019 (COVID-19). In addition, disinfection for preventing the spread of COVID-19 is highly recommended. The increased use of biocidal products raises concerns regarding the potential health risks from exposure among disinfection workers. This study aimed to assess these exposure and health risks using questionnaires targeting disinfection workers who were exposed to the active substances in biocidal products used for disinfection during the COVID-19 pandemic. A follow-up survey was conducted among 271 disinfection workers for 10 working days within two weeks, and exposure factors with reference to disinfection were evaluated through interview-administered questionnaires. An exposure algorithm was used to evaluate the exposure of disinfection workers during disinfection. The hazard index (HI) was calculated by dividing the inhalation concentration obtained using the exposure algorithm and the dermal dose according to occupational exposure limits (OEL). A sensitivity analysis was conducted to identify the exposure factors with the greatest impact on the inhalation and dermal exposure algorithms. A logistic regression analysis was performed to verify the relationship with health effects and sociodemographic and exposure characteristics. The average number of disinfections performed during 10 working days was 17.5 ± 12.3 times. The type of disinfection work was divided into 2806 cases of COVID-19 prevention and disinfection and 1956 cases of regular pesticide application to prevent and remove any pests. The HI was ≥1, indicating a potential health risk, with the use of ethanol (6.50E+00), quaternary ammonium compounds (QACs; 1.49E+01), and benzalkonium chloride (BKC; 1.73E+00). Dermal exposure was more hazardous than inhalation exposure for 6 of the 11 active substances in biocidal products. The weight fraction and exposure time were the factors that most significantly influenced the inhalation and dermal exposure algorithms in the sensitivity analysis. Higher exposure concentrations were more likely to affect health (AOR: 3.239, 95% CI: 1.155-9.082). This study provides valuable information regarding the exposure and risk of disinfection workers to 11 biocidal active substances included in common disinfectants. Our results suggest that the use of ethanol, BKC, and QACs has potential health risks to disinfection workers, with a higher possibility of negative health impacts with increasing exposure concentration.

Integrated clinical and genomic models using machine-learning methods to predict the efficacy of paclitaxel-based chemotherapy in patients with advanced gastric cancer.

BACKGROUND: Paclitaxel is commonly used as a second-line therapy for advanced gastric cancer (AGC). The decision to proceed with second-line chemotherapy and select an appropriate regimen is critical for vulnerable patients with AGC progressing after first-line chemotherapy. However, no predictive biomarkers exist to identify patients with AGC who would benefit from paclitaxel-based chemotherapy. METHODS: This study included 288 patients with AGC receiving second-line paclitaxel-based chemotherapy between 2017 and 2022 as part of the K-MASTER project, a nationwide government-funded precision medicine initiative. The data included clinical (age [young-onset vs. others], sex, histology [intestinal vs. diffuse type], prior trastuzumab use, duration of first-line chemotherapy), and genomic factors (pathogenic or likely pathogenic variants). Data were randomly divided into training and validation sets (0.8:0.2). Four machine learning (ML) methods, namely random forest (RF), logistic regression (LR), artificial neural network (ANN), and ANN with genetic embedding (ANN with GE), were used to develop the prediction model and validated in the validation sets. RESULTS: The median patient age was 64 years (range 25-91), and 65.6% of those were male. A total of 288 patients were divided into the training (n = 230) and validation (n = 58) sets. No significant differences existed in baseline characteristics between the training and validation sets. In the training set, the areas under the ROC curves (AUROC) for predicting better progression-free survival (PFS) with paclitaxel-based chemotherapy were 0.499, 0.679, 0.618, and 0.732 in the RF, LR, ANN, and ANN with GE models, respectively. The ANN with the GE model that achieved the highest AUROC recorded accuracy, sensitivity, specificity, and F1-score performance of 0.458, 0.912, 0.724, and 0.579, respectively. In the validation set, the ANN with GE model predicted that paclitaxel-sensitive patients had significantly longer PFS (median PFS 7.59 vs. 2.07 months, P = 0.020) and overall survival (OS) (median OS 14.70 vs. 7.50 months, P = 0.008). The LR model predicted that paclitaxel-sensitive patients showed a trend for longer PFS (median PFS 6.48 vs. 2.33 months, P = 0.078) and OS (median OS 12.20 vs. 8.61 months, P = 0.099). CONCLUSIONS: These ML models, integrated with clinical and genomic factors, offer the possibility to help identify patients with AGC who may benefit from paclitaxel chemotherapy.

A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.

Differential effects of wastewater treatment plant effluents on the antibiotic resistomes of diverse river habitats.

Wastewater treatment plants (WWTPs) are key sources of antimicrobial resistance genes (ARGs) that could influence the resistomes of microbial communities in various habitats of the receiving river ecosystem. However, it is currently unknown which habitats are most impacted and whether ARGs, like certain chemical contaminants, could be accumulated or enriched in the river ecosystem. We conducted a systematic metagenomic survey on the antibiotic resistomes of WWTP effluent, four riverine habitats (water, suspended particles, sediment, epilithic biofilm), and freshwater amphipod gut microbiomes. The impact of WWTP effluent on the downstream habitats was assessed in nine Swiss rivers. While there were significant differences in resistomes across habitats, the wastewater resistome was more similar to the resistome of receiving river water than to the resistomes of other habitats, and river water was the habitat most strongly impacted by the WWTPs effluent. The sulfonamide, beta-lactam, and aminoglycoside resistance genes were among the most abundant ARGs in the WWTP effluents, and especially aadA, sul1, and class A beta-lactamase genes showed significantly increased abundance in the river water of downstream compared to upstream locations (p < 0.05). However, this was not the case for the sediment, biofilm, and amphipod gut habitats. Accordingly, evidence for accumulation or enrichment of ARGs through the riverine food web was not identified. Our study suggests that monitoring riverine antimicrobial resistance determinants could be conducted using "co-occurrence" of aadA, sul1, and class A beta-lactamase genes as an indicator of wastewater-related pollution and should focus on the water as the most affected habitat.

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism.

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.

Surveillance for SARS-CoV-2 and its variants in wastewater of tertiary care hospitals correlates with increasing case burden and outbreaks.

Wastewater-based SARS-CoV-2 surveillance enables unbiased and comprehensive monitoring of defined sewersheds. We performed real-time monitoring of hospital wastewater that differentiated Delta and Omicron variants within total SARS-CoV-2-RNA, enabling correlation to COVID-19 cases from three tertiary-care facilities with >2100 inpatient beds in Calgary, Canada. RNA was extracted from hospital wastewater between August/2021 and January/2022, and SARS-CoV-2 quantified using RT-qPCR. Assays targeting R203M and R203K/G204R established the proportional abundance of Delta and Omicron, respectively. Total and variant-specific SARS-CoV-2 in wastewater was compared to data for variant specific COVID-19 hospitalizations, hospital-acquired infections, and outbreaks. Ninety-six percent (188/196) of wastewater samples were SARS-CoV-2 positive. Total SARS-CoV-2 RNA levels in wastewater increased in tandem with total prevalent cases (Delta plus Omicron). Variant-specific assessments showed this increase to be mainly driven by Omicron. Hospital-acquired cases of COVID-19 were associated with large spikes in wastewater SARS-CoV-2 and levels were significantly increased during outbreaks relative to nonoutbreak periods for total SARS-CoV2, Delta and Omicron. SARS-CoV-2 in hospital wastewater was significantly higher during the Omicron-wave irrespective of outbreaks. Wastewater-based monitoring of SARS-CoV-2 and its variants represents a novel tool for passive COVID-19 infection surveillance, case identification, containment, and potentially to mitigate viral spread in hospitals.

Identification of factors affecting removal of antibiotic resistance genes in full-scale anaerobic digesters treating organic solid wastes.

Efficiencies of removing antibiotic resistance genes (ARGs) and intI1 were explored using eight full-scale anaerobic digesters. The digesters demonstrated different characteristics on the basis of substrate types (food waste, manure or sludge); configuration (single or two-stage); temperature (psychrophilic, mesophilic or thermophilic); hydraulic retention time (HRT) (9.7-44 days); and operation mode (continuous stirred tank reactor or plug flow reactor). Digesters' configuration or operating parameters showed a greater effect on abundance of ARGs than the type of input substrate. Redundancy analysis (RDA) accounted for 85.2% of the total variances and digesters with the same configuration and operational conditions showed similar performance for removal of ARGs. The highest efficiencies of removing ARGs (99.99%) were observed in two-stage thermophilic digesters with relatively long HRTs (32 days). The lowest removal efficiency (97.93%) was observed in single-stage mesophilic with relatively short HRTs (9.7 days), likely due to vertical and horizontal gene transfer.

Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification.

In the fault classification process, filter methods that sequentially remove unnecessary features have long been studied. However, the existing filter methods do not have guidelines on which, and how many, features are needed. This study developed a multi-filter clustering fusion (MFCF) technique, to effectively and efficiently select features. In the MFCF process, a multi-filter method combining existing filter methods is first applied for feature clustering; then, key features are automatically selected. The union of key features is utilized to find all potentially important features, and an exhaustive search is used to obtain the best combination of selected features to maximize the accuracy of the classification model. In the rotating machinery examples, fault classification models using MFCF were generated to classify normal and abnormal conditions of rotational machinery. The obtained results demonstrated that classification models using MFCF provide good accuracy, efficiency, and robustness in the fault classification of rotational machinery.

Wastewater bypass is a major temporary point-source of antibiotic resistance genes and multi-resistance risk factors in a Swiss river.

Untreated combined sewage (bypass) is often discharged by wastewater treatment plants to receiving rivers during stormwater events, where it may contribute to increased levels of antibiotic resistance genes (ARGs) and multi-resistance risk factors (multi-resistant bacteria and multi-resistance genomic determinants (MGDs)) in the receiving water. Other contamination sources, such as soil runoff and resuspended river sediment could also play a role during stormwater events. Here we report on stormwater event-based sampling campaigns to determine temporal dynamics of ARGs and multi-resistance risk factors in bypass, treated effluent, and the receiving river, as well as complimentary data on catchment soils and surface sediments. Both indicator ARGs (qPCR) and resistome (ARG profiles revealed by metagenomics) indicated bypass as the main contributor to the increased levels of ARGs in the river during stormwater events. Furthermore, we showed for the first time that the risk of exposure to bypass-borne multi-resistance risk factors increase under stormwater events and that many of these MGDs were plasmid associated and thus potentially mobile. In addition, elevated resistance risk factors persisted for some time (up to 22 h) in the receiving water after stormwater events, likely due to inputs from distributed overflows in the catchment. This indicates temporal dynamics should be considered when interpreting the risks of exposure to resistance from event-based contamination. We propose that reducing bypass from wastewater treatment plants may be an important intervention option for reducing dissemination of antibiotic resistance.

Deployment of the 1st Area Medical Laboratory to South Korea in Response to the COVID-19 Pandemic.

In December 2019, an outbreak of pneumonia caused by a novel coronavirus, severe acute respiratory syndrome (SARS)-CoV-2, occurred in Wuhan city, Hubei province, China.1 South Korea saw its first confirmed Coronavirus Disease 2019 (COVID-19) case on January 20, 2020, when an infected woman from Wuhan, China arrived in S. Korea via Incheon International Airport.1 By mid-February, SARS-CoV-2 was rapidly spreading in the southern city of Daegu, S. Korea in proximity to three US Forces Korea (USFK) military installations. COVID-19 cases continued to increase during the following weeks, reaching a peak of nearly 1,000 confirmed cases per day by the end of February. As cases surged dramatically, over 28,000 USFK service members, family members, and Department of Defense (DoD) employees were at a risk of exposure to COVID-19. On February 24, clinicians diagnosed the first confirmed case in the USFK population, a 61 year-old widow of a retired service member. This individual, who experienced a mild illness, was the spouse of a retired US military veteran living in S. Korea. The retiree and his spouse both had access to military posts in S. Korea, and the spouse tested positive after she had been on one of the military bases in Area IV (Figure 1). The following day, USFK reported its first confirmed case in a service member, which was the triggering event for the 1st Area Medical Laboratory (AML) to deploy to S. Korea.

Unraveling the riverine antibiotic resistome: The downstream fate of anthropogenic inputs.

River networks are one of the main routes by which the public could be exposed to environmental sources of antibiotic resistance, that may be introduced e.g. via treated wastewater. In this study, we applied a comprehensive integrated analysis encompassing mass-flow concepts, chemistry, bacterial plate counts, resistance gene quantification and shotgun metagenomics to track the fate of the resistome (collective antibiotic resistance genes (ARGs) in a microbial community) of treated wastewater in two Swiss rivers at the kilometer scale. The levels of certain ARGs and the class 1 integron integrase gene (intI1) commonly associated with anthropogenic sources of ARGs decreased quickly over short distances (2-2.5 km) downstream of wastewater discharge points. Mass-flow analysis based on conservative tracers suggested this decrease was attributable mainly to dilution but ARG loadings frequently also decreased (e.g., 55.0-98.5 % for ermB and tetW) over the longest studied distances (6.8 and 13.7 km downstream). Metagenomic analysis confirmed that ARG of wastewater-origin did not persist in rivers after 5 ~ 6.8 km downstream distance. sul1 and intI1 levels and loadings were more variable and even increased sharply at 5 ~ 6.8 km downstream distance on one occasion. While input from agriculture and in-situ positive selection pressure for organisms carrying ARGs cannot be excluded, in-system growth of biomass is a more probable explanation. The potential for direct human exposure to the resistome of wastewater-origin thus appeared to typically abate rapidly in the studied rivers. However, the riverine aquatic resistome was also dynamic, as evidenced by the increase of certain gene markers downstream, without obvious sources of anthropogenic contamination. This study provides new insight into drivers of riverine resistomes and pinpoints key monitoring targets indicative of where human sources and exposures are likely to be most acute.

Long-term enrichment of anaerobic propionate-oxidizing consortia: Syntrophic culture development and growth optimization.

Propionate is a quantitatively important methanogenic intermediate in anaerobic digesters and only limited number of microbes can utilize it under syntrophic association with methanogens. The syntrophic propionate oxidizing bacterias are known to be slow growers due to the low energy yield. Thus, propionate get accumulated frequently in anaerobic digesters and it negatively affect digester performance. In this study, propionate degrading consortia from four different seeding sources were enriched in sequential bath mode in two phases; first adaption phase with 1 g/L of propionate concentration and later, high-strength phase with 3 g/L. From 16s rRNA gene based metagenomics analysis of the former phase, four syntrophic microbial groups, Syntrophaceae, Syntrophomonadaceae, Methanobacterium and Methanosaeta were found to be dominant with complete degradation of propionate. The substrate accelerated microbial shifts were observed at high-strength phase with significant decrease of Syntrophaceae up to 26.9 %. Using Response Surface Methodology, pH 6.8-6.9 and temperature 34.5-34.9 °C were found to be optimum growth conditions for the propionate degradation culture. Observed results could be useful to improve degradation efficiencies and obtained enriched culture can be used to recover propionate-accumulated digesters by bio-augmentation.

Comparing the fate of antibiotic resistance genes in two full-scale thermophilic anaerobic digestion plants treating food wastewater.

This study focus on the fate of ARGs in the full-scale AD of food wastewater (FWW). Residue was collected from two different full-scale thermophilic AD treating FWW. Ten selected ARGs, including tetracycline resistance genes (tetM, tetX, tetQ, tetH and tetG), sulfonamide resistance genes (sul1 and sul2), quinolone resistance genes (qnrD) and macrolide resistance genes (ermB and ermC), were amplified using quantitative polymerase chain reaction (qPCR). Furthermore, the class 1 integron-integrase gene (intI1) was selected as a representative mobile gene element. Remarkable reduction in the ARGs and intI1 was observed in two-stage (acidogenic-methanogenic) AD, particularly, tetG, tetH, tetM, tetQ, tetX and intI1 not detected. Additionally, significant positive correlation (p < 0.01) between ARGs and intI1 suggested a strong likelihood of horizontal gene transfer (HGT). Furthermore, stepwise multiple linear regression analysis revealed significant factors related to the fate of individual ARGs and intI1 during AD.

Associations of exposure to phthalates and environmental phenols with gynecological disorders.

Phthalates and environmental phenols might be associated with some benign diseases that have been found to be hormone-sensitive. Current knowledge on adverse effects of these chemicals among reproductive women is limited and often controversial. Therefore, the purpose of this study was to investigate the association between the urinary concentration of phthalates and environmental phenols and gynecological disorders from 512 women of reproductive age. The association between chemical concentration and disease in the control and case groups was statistically determined with the questionnaire survey data and measurements using the LC-MS/MS. The results have shown that DEHP metabolites, ethyl paraben and 3,4-DHB showed significant direct associations with leiomyoma and benign ovarian tumors (p < 0.05). We found statistically significant positive relationships between exposure to chemicals (some DEHP metabolites, DHB) and prevalence of gynecologic disorders (p < 0.05). Furthermore, the ORs for leiomyoma associated with these compounds in always user for personal care products (PCPs) was higher than those of sometimes user. High levels of urinary concentrations of these compounds such as DEHP metabolites and parabens and their metabolites showed significant associations with leiomyoma and benign ovarian tumors.

Prevalence of glucose-6-phosphate dehydrogenase deficiency, U.S. Armed Forces, May 2004-September 2018.

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is an inherited genetic disorder most commonly associated with hemolytic anemia. Among those with G6PD deficiency, hemolytic anemia may be triggered by bacterial or viral infections and by certain foods and drugs, including the 8-aminoquinoline (8-AQ) class of antimalarials. Because 8-AQ drugs remain the only drugs approved by the U.S. Food and Drug Administration for malaria relapse prevention, the Department of Defense (DoD) requires testing of all service members' G6PD status. To estimate prevalence of G6PD deficiency among DoD service members, Composite Health Care System-generated, Health Level 7-formatted laboratory records for all service members (n=2,311,223) dated between May 2004 and September 2018 were queried for G6PD testing. Corresponding demographic data were obtained from the Defense Enrollment Eligibility Reporting System. Overall prevalence of G6PD deficiency among this cohort was low, at 2.2%. Demographic trends mirrored U.S. statistics; the cohort prevalence among males (2.3%) was higher than among females (1.5%), and the prevalence among non-Hispanic blacks (9.5%) was higher than among those in any other race/ethnicity group.

Association of urinary phthalate metabolites and phenolics with adipokines and insulin resistance related markers among women of reproductive age.

Chemicals such as phthalates and phenolics have been associated with metabolic markers in humans. However, most studies have only looked at a limited number of chemicals, and little is known about their potential effects on adipokines in humans. In the present study, the associations between dozens of urinary chemicals, including phthalate metabolites and phenolics, and markers related to insulin resistance as well as major adipokines, were assessed among the women of reproductive age (n = 459, between 20 and 48 years of age) recruited from major cities in Korea between 2015 and 2016. Adipokines such as adiponectin and leptin, and insulin resistance related markers such as glucose and insulin, were analyzed in serum. Associations between urinary chemicals and the adipokines or insulin resistance related markers were assessed in two steps. First, ordinary least squares (OLS) regression was used to assess the association of each urinary chemical with the adipokines or insulin resistance related markers (single-pollutant model). Second, several chemicals were selected using elastic net regression and were subsequently analyzed with OLS regression model (multi-pollutant model), considering simultaneous exposure to multiple chemicals. In both single- and multi-pollutant models, several urinary chemicals consistently showed significant associations with adipokines or the insulin resistance related markers. The sum of di-(2-ethylhexyl) phthalate metabolites (ΣDEHPm) and ethyl paraben (EtP) were associated with increased serum adiponectin levels. Urinary ΣDEHPm levels also showed positive associations with fasting glucose. Moreover, urinary mono-methyl phthalate (MMP), mono-isobutyl phthalate (MiBP), and bisphenol S (BPS) levels showed positive associations with the homeostasis model assessment for insulin resistance (HOMA-IR). Interestingly, urinary propyl paraben (PrP) levels showed a negative association with HOMA-IR, in both models. Our observations show that among many consumer chemicals, phthalates may affect serum adipokines, and thus glucose, and insulin resistance in adult females. Further confirmation is warranted in other populations.

The Salmonella virulence protein MgtC promotes phosphate uptake inside macrophages.

The MgtC virulence protein from the intracellular pathogen Salmonella enterica is required for its intramacrophage survival and virulence in mice and this requirement of MgtC is conserved in several intracellular pathogens including Mycobacterium tuberculosis. Despite its critical role in survival within macrophages, only a few molecular targets of the MgtC protein have been identified. Here, we report that MgtC targets PhoR histidine kinase and activates phosphate transport independently of the available phosphate concentration. A single amino acid substitution in PhoR prevents its binding to MgtC, thus abrogating MgtC-mediated phosphate transport. Surprisingly, the removal of MgtC's effect on the ability to transport phosphate renders Salmonella hypervirulent and decreases a non-replicating population inside macrophages, indicating that MgtC-mediated phosphate transport is required for normal Salmonella pathogenesis. This provides an example of a virulence protein directly activating a pathogen's phosphate transport inside host.