Small herbivores with big impacts: Tundra voles (Microtus oeconomus) alter post-fire ecosystem dynamics.

Fire is an important ecological disturbance that can reset ecosystems and initiate changes in plant community composition, ecosystem biogeochemistry, and primary productivity. As herbivores rely on primary producers for food, changes in vegetation may alter plant-herbivore interactions with important-but often unexplored-feedbacks to ecosystems. Here we examined the impact of post-fire changes in plant community composition and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaskan tundra. In moist acidic tundra where the fire occurred, tundra voles (Microtus oeconomus) are the dominant herbivore and rely on the tussock forming sedge Eriophorum vaginatum for both food and nesting material. Tundra voles were 10 times more abundant at the burned site compared with nearby unburned tundra 7-12 years after the fire. Fire increased the habitat suitability for voles by increasing plant productivity and biomass, food quality, and cover through both taller vegetation and increased microtopography. As a result of elevated vole abundance, Eriophorum mortality caused by vole herbivory was two orders of magnitude higher than natural mortality and approached the magnitude of the mortality rate resulting directly from the fire. These findings suggest that post-fire increases in herbivore pressure on Eriophorum could, in turn, disrupt graminoid recovery and enhance shrub encroachment. Tundra state transitions from graminoid to shrub dominated are also evident following other disturbances and fertilization experiments, suggesting that as Arctic temperatures rise, greater available nutrients and increased frequencies of large-scale disturbances may also alter plant-animal interactions with cascading impacts on plant communities and ecosystem function.

Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System.

Microbial community structure is influenced by the environment and in turn exerts control on many environmental parameters. We applied this concept in a bioreactor study to test whether microbial community structure contains information sufficient to predict the concentration of H2S as the product of sulfate reduction. Microbial sulfate reduction is a major source of H2S in many industrial and environmental systems and is often influenced by the existing physicochemical conditions. Production of H2S in industrial systems leads to occupational hazards and adversely affects the quality of products. A long-term (148 days) experiment was conducted in upflow bioreactors to mimic sulfidogenesis, followed by inhibition with nitrate salts and a resumption of H2S generation when inhibition was released. We determined microbial community structure in 731 samples across 20 bioreactors using 16S rRNA gene sequencing and applied a random forest algorithm to successfully predict different phases of sulfidogenesis and mitigation (accuracy = 93.17%) and sessile and effluent microbial communities (accuracy = 100%). Similarly derived regression models that also included cell abundances were able to predict H2S concentration with remarkably high fidelity (R2 > 0.82). Metabolic profiles based on microbial community structure were also found to be reliable predictors for H2S concentration (R2 = 0.78). These results suggest that microbial community structure contains information sufficient to predict sulfidogenesis in a closed system, with anticipated applications to microbially driven processes in open environments. IMPORTANCE Microbial communities control many biogeochemical processes. Many of these processes are impractical or expensive to measure directly. Because the taxonomic structure of the microbial community is indicative of its function, it encodes information that can be used to predict biogeochemistry. Here, we demonstrate how a machine learning technique can be used to predict sulfidogenesis, a key biogeochemical process in a model system. A distinction of this research was the ability to predict H2S production in a bioreactor from the effluent bacterial community structure without direct observations of the sessile community or other environmental conditions. This study establishes the ability to use machine learning approaches in predicting sulfide concentrations in a closed system, which can be further developed as a valuable tool for predicting biogeochemical processes in open environments. As machine learning algorithms continue to improve, we anticipate increased applications of microbial community structure to predict key environmental and industrial processes.

Detection of Sulfate-Reducing Bacteria as an Indicator for Successful Mitigation of Sulfide Production.

Sulfate-reducing bacteria (SRBs) are one of the main sources of biogenic H2S generation in oil reservoirs. Excess H2S production in these systems leads to oil biosouring, which causes operational risks and health hazards and can increase the cost of refining crude oil. Nitrate salts are often added to the system to suppress sulfidogenesis. Because SRB populations can persist in biofilms even after nitrate treatment, identifying shifts in the sessile community is crucial for successful mitigation. However, sampling the sessile community is hampered by its inaccessibility. Here, we use the results of a long-term (148 days) ex situ experiment to identify particular sessile community members from observations of the sample waste stream. Microbial community structure was determined for 731 samples across 20 bioreactors using 16S rRNA gene sequencing. By associating microbial community structure with specific steps in the mitigation process, we could distinguish between taxa associated with H2S production and mitigation. After initiation of nitrate treatment, certain SRB populations increased in the planktonic community during critical time points, indicating the dissociation of SRBs from the biofilm. Predicted relative abundances of the dissimilatory sulfate reduction pathway also increased during the critical time points. Here, by analyzing the planktonic community structure, we describe a general method that uses high-throughput amplicon sequencing, metabolic inferences, and cell abundance data to identify successful biofilm mitigation. We anticipate that our approach is also applicable to other systems where biofilms must be mitigated but cannot be sampled easily. IMPORTANCE Microbial biofilms are commonly present in many industrial processes and can negatively impact performance and safety. Within the oil industry, subterranean biofilms cause biosouring with implications for oil quality, cost, occupational health, and the environment. Because these biofilms cannot be sampled directly, methods are needed to indirectly assess the success of mitigation measures. This study demonstrates how the planktonic microbial community can be used to assess the dissociation of sulfate-reducing bacterium (SRB)-containing biofilms. We found that an increase in the abundance of a specific SRB population in the effluent after nitrate treatment can be used as a potential indicator for the successful mitigation of biofilm-forming SRBs. Moreover, a method for determining critical time points for detecting potential indicators is suggested. This study expands our knowledge of improving mitigation strategies for biosouring and could have broader implications in other systems where biofilms lead to adverse consequences.

Heavy metals in the Arctic: Distribution and enrichment of five metals in Alaskan soils.

Metal contamination of food and water resources is a known public health issue in Arctic and sub-Arctic communities due to the proximity of many communities to mining and drilling sites. In addition, permafrost thaw may release heavy metals sequestered in previously frozen soils, potentially contaminating food and water resources by increasing the concentration of metals in freshwater, plants, and wildlife. Here we assess the enrichment of selected heavy metals in Alaskan soils by synthesizing publicly available data of soil metal concentrations. We analyzed data of soil concentrations of arsenic, chromium, mercury, nickel, and lead from over 1,000 samples available through the USGS Alaskan Geochemical Database to evaluate 1) the spatial distribution of sampling locations for soil metal analysis, 2) metal concentrations in soils from different land cover types and depths, and 3) the occurrence of soils in Alaska with elevated metal concentrations relative to other soils. We found substantial clustering of sample sites in the southwestern portion of Alaska in discontinuous and sporadic permafrost, while the continuous permafrost zone in Northern Alaska and the more populous Interior are severely understudied. Metal concentration varied by land cover type but lacked consistent patterns. Concentrations of chromium, mercury, and lead were higher in soils below 10 cm depth, however these deeper soils are under-sampled. Arsenic, chromium, mercury, nickel and lead concentrations exceeded average values for US soils by one standard deviation or more in 3.7% to 18.7% of the samples in this dataset. Our analysis highlights critical gaps that impede understanding of how heavy metals in thawing permafrost soils may become mobilized and increase exposure risk for Arctic communities.

The association between educational resource utilization and knowledge/self-management among patients with Type 2 Diabetes in Pune, India.

AIMS: Patients utilize a variety of sources to learn about their condition. This study assessed the association of these resources with patient knowledge, adherence, and physiologic outcomes. METHODS: Adults with Type 2 diabetes recruited at an outpatient clinic in Pune, India provided IRB-approved consent and completed surveys measuring diabetes knowledge, self-management, and educational resource utilization. Lab values were obtained from the patient's medical record. Statistical analysis was conducted in SPSS to determine the association between educational resources and outcomes. RESULTS: We enrolled 82 patients with a mean age of 58.3 years. The most commonly used resource was books/pamphlets. The use of television and books/pamphlets as sources of information was significantly associated with greater knowledge and self-management. Use of books and pamphlets and peers with diabetes was associated with lower fasting and postprandial blood glucose. Regression analyses controlling for age, gender, and years with diabetes revealed that use of newspapers and magazines, books and pamphlets, and television were predictors of overall diabetes knowledge and nutrition knowledge. The use of television as a source of information was a predictor of the hours exercised per week. CONCLUSIONS: Resources such as books and pamphlets, newspapers and magazines, and television are common ways that people with diabetes learn about their medical conditions. The use of television as a source of information predicted the greatest number of positive outcomes among patients, followed by use of books and pamphlets. These methods should be further explored in order to understand how their benefit may be maximized for patients.

A re-emerging infection?

A 38-year-old female with a two-week history of amoxicillin use for fever, cough, and coryza presented with two days of erythematous chest macules. Despite discontinuation of the antibiotic, her rash continued; measles was diagnosed with IgM titers. Three weeks previously she had returned from China. She had received only one vaccination despite travel to this measles-endemic area. Vaccination and herd immunity have reduced the measles prevalence in America by more than 99%; travel, foreign adoption, and refusal of vaccination have, however, increased the risk.

Cyclosporin A is a broad-spectrum multidrug resistance modulator.

PURPOSE: Overexpression of the multidrug resistance proteins P-glycoprotein (Pgp), multidrug resistance protein (MRP-1), breast cancer resistance protein (BCRP), and lung resistance protein (LRP) is associated with treatment failure in acute myeloid leukemia (AML) and other malignancies. The Pgp modulator cyclosporin A has shown clinical efficacy in AML, whereas its analogue PSC-833 has not. Cyclosporin A is known to also modulate MRP-1, and we hypothesized that broad-spectrum multidrug resistance modulation might contribute to its clinical efficacy. EXPERIMENTAL DESIGN: We studied the effects of cyclosporin A and PSC-833 on in vitro drug retention and cytotoxicity in resistant cell lines overexpressing Pgp, MRP-1, and BCRP and on nuclear-cytoplasmic drug distribution and cytotoxicity in cells overexpressing LRP. Cellular drug content was assessed by flow cytometry and nuclear-cytoplasmic drug distribution by confocal microscopy. RESULTS: Cyclosporin A enhanced retention of the substrate drug mitoxantrone in cells overexpressing Pgp (HL60/VCR), MRP-1 (HL60/ADR), and BCRP (8226/MR20, HEK-293 482R) and increased cytotoxicity 6-, 4-, 4-, and 3-fold, respectively. Moreover, cyclosporin A enhanced nuclear distribution of doxorubicin in 8226/MR20 cells, which also express LRP, and increased doxorubicin cytotoxicity 12-fold without an effect on cellular doxorubicin content, consistent with expression of wild-type BCRP, which does not efflux doxorubicin. Cyclosporin A also enhanced nuclear doxorubicin distribution in a second cell line with LRP overexpression, HT1080/DR4. PSC-833 enhanced mitoxantrone retention and cytotoxicity in cells overexpressing Pgp, but had no effect in cells overexpressing MRP-1, BCRP, or LRP. CONCLUSIONS: Cyclosporin A modulates Pgp, MRP-1, BCRP, and LRP, and this broad-spectrum activity may contribute to its clinical efficacy.