Geochemistry and Multiomics Data Differentiate Streams in Pennsylvania Based on Unconventional Oil and Gas Activity.

Unconventional oil and gas (UOG) extraction is increasing exponentially around the world, as new technological advances have provided cost-effective methods to extract hard-to-reach hydrocarbons. While UOG has increased the energy output of some countries, past research indicates potential impacts in nearby stream ecosystems as measured by geochemical and microbial markers. Here, we utilized a robust data set that combines 16S rRNA gene amplicon sequencing (DNA), metatranscriptomics (RNA), geochemistry, and trace element analyses to establish the impact of UOG activity in 21 sites in northern Pennsylvania. These data were also used to design predictive machine learning models to determine the UOG impact on streams. We identified multiple biomarkers of UOG activity and contributors of antimicrobial resistance within the order Burkholderiales. Furthermore, we identified expressed antimicrobial resistance genes, land coverage, geochemistry, and specific microbes as strong predictors of UOG status. Of the predictive models constructed (n = 30), 15 had accuracies higher than expected by chance and area under the curve values above 0.70. The supervised random forest models with the highest accuracy were constructed with 16S rRNA gene profiles, metatranscriptomics active microbial composition, metatranscriptomics active antimicrobial resistance genes, land coverage, and geochemistry (n = 23). The models identified the most important features within those data sets for classifying UOG status. These findings identified specific shifts in gene presence and expression, as well as geochemical measures, that can be used to build robust models to identify impacts of UOG development. IMPORTANCE The environmental implications of unconventional oil and gas extraction are only recently starting to be systematically recorded. Our research shows the utility of microbial communities paired with geochemical markers to build strong predictive random forest models of unconventional oil and gas activity and the identification of key biomarkers. Microbial communities, their transcribed genes, and key biomarkers can be used as sentinels of environmental changes. Slight changes in microbial function and composition can be detected before chemical markers of contamination. Potential contamination, specifically from biocides, is especially concerning due to its potential to promote antibiotic resistance in the environment. Additionally, as microbial communities facilitate the bulk of nutrient cycling in the environment, small changes may have long-term repercussions. Supervised random forest models can be used to identify changes in those communities, greatly enhance our understanding of what such impacts entail, and inform environmental management decisions.

A high prevalence of potential HIV elite controllers identified over 30 years in Democratic Republic of Congo.

BACKGROUND: In-depth analysis of the HIV pandemic at its epicenter in the Congo basin has been hampered by 40 years of political unrest and lack of functional public health infrastructure. In recent surveillance studies (2017-18), we found that the prevalence of HIV in Kinshasa, Democratic Republic of Congo (11%) far exceeded previous estimates. METHODS: 10,457 participants were screened in Kinshasa with rapid tests from 2017-2019. Individuals confirmed as reactive by the Abbott ARCHITECT HIV Ag/Ab Combo assay (n=1968) were measured by the Abbott RealTime HIV-1 viral load assay. Follow up characterization of samples was performed with alternate manufacturer viral load assays, qPCR for additional blood borne viruses, unbiased next generation sequencing, and HIV Western blotting. FINDINGS: Our data suggested the existence of a significant cohort (n=429) of HIV antibody positive/viral load negative individuals. We systematically eliminated collection site bias, sample integrity, and viral genetic diversity as alternative explanations for undetectable viral loads. Mass spectroscopy unexpectedly detected the presence of 3TC antiviral medication in approximately 60% of those tested (209/354), and negative Western blot results indicated false positive serology in 12% (49/404). From the remaining Western blot positives (n=53) and indeterminates (n=31) with reactive Combo and rapid test results, we estimate 2.7-4.3% of infections in DRC to be potential elite controllers. We also analyzed samples from the DRC collected in 1987 and 2001-03, when antiretroviral drugs were not available, and found similarly elevated trends. INTERPRETATION: Viral suppression to undetectable viral loads without therapy occurs infrequently in HIV-1 infected patients around the world. Mining of global data suggests a unique ability to control HIV infection arose early in central Africa and occurs in <1% of founder populations. Identification of this group of elite controllers presents a unique opportunity to study potentially novel genetic mechanisms of viral suppression. FUNDING: Abbott Laboratories funded surveillance in DRC and subsequent research efforts. Additional funding was received from a MIZZOU Award from the University of Missouri. Research was supported in part by the Division of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH.

Bacterial Biomarkers of Marcellus Shale Activity in Pennsylvania.

Unconventional oil and gas (UOG) extraction, also known as hydraulic fracturing, is becoming more prevalent with the increasing use and demand for natural gas; however, the full extent of its environmental impacts is still unknown. Here we measured physicochemical properties and bacterial community composition of sediment samples taken from twenty-eight streams within the Marcellus shale formation in northeastern Pennsylvania differentially impacted by hydraulic fracturing activities. Fourteen of the streams were classified as UOG+, and thirteen were classified as UOG- based on the presence of UOG extraction in their respective watersheds. One stream was located in a watershed that previously had UOG extraction activities but was recently abandoned. We utilized high-throughput sequencing of the 16S rRNA gene to infer differences in sediment aquatic bacterial community structure between UOG+ and UOG- streams, as well as correlate bacterial community structure to physicochemical water parameters. Although overall alpha and beta diversity differences were not observed, there were a plethora of significantly enriched operational taxonomic units (OTUs) within UOG+ and UOG- samples. Our biomarker analysis revealed many of the bacterial taxa enriched in UOG+ streams can live in saline conditions, such as Rubrobacteraceae. In addition, several bacterial taxa capable of hydrocarbon degradation were also enriched in UOG+ samples, including Oceanospirillaceae. Methanotrophic taxa, such as Methylococcales, were significantly enriched as well. Several taxa that were identified as enriched in these samples were enriched in samples taken from different streams in 2014; moreover, partial least squares discriminant analysis (PLS-DA) revealed clustering between streams from the different studies based on the presence of hydraulic fracturing along the second axis. This study revealed significant differences between bacterial assemblages within stream sediments of UOG+ and UOG- streams and identified several potential biomarkers for evaluating and monitoring the response of autochthonous bacterial communities to potential hydraulic fracturing impacts.

Brook trout distributional response to unconventional oil and gas development: Landscape context matters.

We conducted a large-scale assessment of unconventional oil and gas (UOG) development effects on brook trout (Salvelinus fontinalis) distribution. We compiled 2231 brook trout collection records from the Upper Susquehanna River Watershed, USA. We used boosted regression tree (BRT) analysis to predict occurrence probability at the 1:24,000 stream-segment scale as a function of natural and anthropogenic landscape and climatic attributes. We then evaluated the importance of landscape context (i.e., pre-existing natural habitat quality and anthropogenic degradation) in modulating the effects of UOG on brook trout distribution under UOG development scenarios. BRT made use of 5 anthropogenic (28% relative influence) and 7 natural (72% relative influence) variables to model occurrence with a high degree of accuracy [Area Under the Receiver Operating Curve (AUC)=0.85 and cross-validated AUC=0.81]. UOG development impacted 11% (n=2784) of streams and resulted in a loss of predicted occurrence in 126 (4%). Most streams impacted by UOG had unsuitable underlying natural habitat quality (n=1220; 44%). Brook trout were predicted to be absent from an additional 26% (n=733) of streams due to pre-existing non-UOG land uses (i.e., agriculture, residential and commercial development, or historic mining). Streams with a predicted and observed (via existing pre- and post-disturbance fish sampling records) loss of occurrence due to UOG tended to have intermediate natural habitat quality and/or intermediate levels of non-UOG stress. Simulated development of permitted but undeveloped UOG wells (n=943) resulted in a loss of predicted occurrence in 27 additional streams. Loss of occurrence was strongly dependent upon landscape context, suggesting effects of current and future UOG development are likely most relevant in streams near the probability threshold due to pre-existing habitat degradation.