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.

Assessment of cetuximab-induced infusion reactions and administration rechallenge at an academic medical center.

Cetuximab is approved for treatment of squamous cell carcinoma of the head and neck (SCCHN). Cetuximab is generally well tolerated, but does carry a black box warning for infusion reactions (IRs). Incidence of IR in clinical trials was 15-20% for all grades and 3-5% for grades III-IV. Retrospective studies reported a higher incidence of all grade IRs and grades III-IV IR in areas of the Southeastern United States. Information regarding rechallenge doses after an IR has not been well described. At our institution, we frequently rechallenge on the same day after an initial IR. The primary objective was to determine the incidence, timing, IR grade, and completion of a rechallenge dose in patients who experienced an initial IR. Secondary objectives included: (1) determining the incidence and grade of IR in patients who received a first dose of cetuximab and (2) identifying specific risk factors for cetuximab IR with the first dose. A single-center retrospective chart review was conducted in SCCHN patients treated with cetuximab between June 2008 and September 2015 at the University of Kansas Hospital Cancer Center and inpatient setting. The majority of patients (87.9%) were able to be quickly and successfully rechallenged after an initial IR. Minimal patients (27.6%) experienced a rechallenge IR, resulting in only 1 patient discontinuation. Rechallenge doses were most frequently (37.9%) administered between 30 and 59 min after initial dose discontinuation. This was a single-center retrospective study based on data collected from electronic medical records. Other limitations include interpretation of infusion reactions on a subjective basis by providers. These findings demonstrate the practice of same-day rechallenges in initial IR patients is feasible and safe.

Effects of intraosseous and intravenous administration of Hextend® on time of administration and hemodynamics in a Swine model.

INTRODUCTION: The military recommends that a 500 mL bolus of Hextend® be administered via an intravenous (IV) 18-gauge needle or via an intraosseous (IO) needle for patients in hypovolemic shock. PURPOSES: The purposes of this study were to compare the time of administration of Hextend and the hemodynamics of IV and IO routes in a Class II hemorrhage swine model. METHODS: This was an experimental study using 27 swine. After 30% of their blood volume was exsanguinated, 500 mL of Hextend was administered IV or IO, but not to the control group. Hemodynamic data were collected every 2 minutes until administration was complete. RESULTS: Time for administration was not significant (p=.78). No significant differences existed between the IO and IV groups relative to hemodynamics (p>.05), but both were significantly different than the control group (p<.05). CONCLUSIONS: The IO route is an effective method of administering Hextend.

High-throughput TAIL-PCR as a tool to identify DNA flanking insertions.

Thermal asymmetric interlaced polymerase chain reaction (TAIL-PCR) is a fast and efficient method to amplify unknown sequences adjacent to known insertion sites in Arabidopsis. Nested, insertion-specific primers are used together with arbitrary degenerate primers (AD primers), which are designed to differ in their annealing temperatures. Alternating cycles of high and low annealing temperature yield specific products bordered by an insertion-specific primer on one side and an AD primer on the other. Further specificity is obtained through subsequent rounds of TAIL-PCR, using nested insertion-specific primers. The increasing availability of whole genome sequences renders TAIL-PCR an attractive tool to easily identify insertion sites in large genome tagging populations through the direct sequencing of TAIL-PCR products. For large-scale functional genomics approaches, it is desirable to obtain flanking sequences for each individual in the population in a fast and cost-effective manner. In this chapter, we describe a TAIL-PCR method amenable for high-throughput production (HT-TAIL-PCR) in Arabidopsis. Based on this protocol, HT-TAIL-PCR may be easily adapted for other organisms.

A high-throughput Arabidopsis reverse genetics system.

A collection of Arabidopsis lines with T-DNA insertions in known sites was generated to increase the efficiency of functional genomics. A high-throughput modified thermal asymmetric interlaced (TAIL)-PCR protocol was developed and used to amplify DNA fragments flanking the T-DNA left borders from approximately 100000 transformed lines. A total of 85108 TAIL-PCR products from 52964 T-DNA lines were sequenced and compared with the Arabidopsis genome to determine the positions of T-DNAs in each line. Predicted T-DNA insertion sites, when mapped, showed a bias against predicted coding sequences. Predicted insertion mutations in genes of interest can be identified using Arabidopsis Gene Index name searches or by BLAST (Basic Local Alignment Search Tool) search. Insertions can be confirmed by simple PCR assays on individual lines. Predicted insertions were confirmed in 257 of 340 lines tested (76%). This resource has been named SAIL (Syngenta Arabidopsis Insertion Library) and is available to the scientific community at www.tmri.org.