Accounts of a microbial hunter in France:

Murphy’s Law with a twist - Whatever can go wrong, will go wrong…however, valuable discoveries can be made

“It’s important to grow comfortable with the unknown and unexpected. As these are teachable moments where valuable information and skills can be acquired and refined”

— Erica Dasi

Microbes involved in biologically removing nitrate are ubiquitous to terrestrial and aquatic ecosystems. Perhaps we can have a look for our microbial counterparts at the Calanques in Marseille, France!

If I could summarize these previous weeks, it would go according to Murphy’s law “whatever can go wrong, will go wrong.” However, I realize that this is nature of research - our plans rarely go as anticipated. It’s important to grow comfortable with the unknown and unexpected. As these are teachable moments where valuable information and skills can be acquired and refined. In fact, some of the greatest scientific discoveries were made when the unexpected occurred - penicillin, the pacemaker, and post it notes (I am biased towards this one).

In this blog post, I share my experience with navigating through a particular challenge that halted our analysis. This piece also includes a reflection of my experience with troubleshooting different problems.

Prior to beginning, I extend a warm welcome to you all. You all are my fellow microbial hunters, who are joining this journey to search for microbes involved in treating nitrate contaminated drinking water. For my newest microbial hunters, I would like to direct you to the first blog post that provides information on the motivation of this project and background. This piece is a resource that sets the stage for the subsequent posts of this series.

Let’s proceed with hunting for our microbial counterparts!

Navigating through the unexpected

We are using the Galaxy 16S Microbial Analysis tutorial and Mothur MiSeq SOP as guides to process and analyze our next generation sequencing (NGS) big data. This big data represents the microbes within samples collected from our engineered treatment systems. We have decided to use the workflows of these tutorials as a guide for two reasons: ( 1 ) Both tutorials provide hands-on experience to examine big data associated with the 16S gene, which is frequently used for identifying microbes and other organisms. The data included in the tutorials can be used as a positive control. A positive control produces an expected outcome and can assists to some extent with troubleshooting and for making comparisons with the big data from our studies; ( 2 ) Other researchers have mentioned using the workflow of these tutorials in the methods section of their peer reviewed publications. This substantiates that these tutorials have been accepted by our research community.

We are using the Galaxy 16S Microbial Analysis tutorial as a guide to process and analyze the next generation sequencing big data that represents the microbes collected from our engineered treatment systems.

The beginning of the tutorial presents an optional QC control step, which can be used to remove low quality portions of data. Although the tutorial does not include this step, there is a separate online resource on the Galaxy website to acquire practice with performing it. In our case, we used the Trimmomatic QC tool since it has been applied by other researchers in our field of water research.

It’s important to note that the big data produced by NGS represents individual segment of the organisms that are present within a sample. This is based on how the samples were prepared for NGS. These individual segments must be combined or “glued together” in order to gain useful information about microbial community composition. Therefore, after the QC step, we attempted to assemble our big data into larger segments known as contigs. However, we were unsuccessful with connecting these two steps.

An example of big data from the Galaxy 16S Microbial Analysis tutorial, which has been combined into segments known as contigs.

The Galaxy application has a Gitter page where community members can seek assistance. Galaxy technical advisors respond relatively quickly to post added to the Gitter chatroom.

I used a combination of approaches in attempts to resolve this challenge. I began with creating a post on the Galaxy Help website, where administrators and community members can provide assistance and feedback. Shortly after, I discovered the Galaxy Gitter page. This page is a chatroom where community members can post questions and/or concerns. One advantage of this resource is that users can chat directly with Galaxy technical advisors and receive a response relatively quickly. Both of these resources suggested revisiting my workflow in Galaxy to ensure that input files were included. Furthermore, I received feedback to check the information icon on the error output file to access the stdout page. This page provides the error code, a description of the error, and advice for troubleshooting. If necessary, the error code can be included in a bug report to give additional context to a Galaxy administrator.

Error output files contain the bug report and information icons for seeking assistance and troubleshooting, respectively.

Following the advice from the Galaxy community, I accessed the stdout page. I learned that the underlying issue was related to the naming system, or nomenclature, used to identify the big data for each sample. Interestingly, I realized that certain characters ( . and - ) have specialized functions in the underlying program that Galaxy uses to analyze the microbial community (i.e., Mothur). As a result, using these characters to name the big data representing a sample can interfere with a tool and generate an error output. I proceeded with modifying the sample nomenclature and the problem was resolved. The QC step using Trimmomatic could be connected to the first step of the Galaxy 16S Microbial Analysis tutorial - creating contigs.

Specific characters ( e.g., _ or . ) are sensitive in Mothur and must be omitted to prevent errors from occurring.

Lessons learned

“Instead of avoiding the errors that I encounter, I embrace and see them in a new light - they are opportunities to think critically and creatively. In the end, I am improving my problem solving skills, which can be applied to both my research and life. ”

— Erica Dasi

Error outputs that I received during another halt that I encountered.

Initially, I had anticipated that the analysis of the big data would go smoothy. This was because other researchers have demonstrated their success with following the Galaxy 16S Microbial Analysis tutorial and MiSeq SOP workflows. However, I am grateful for this halt (and the others) occurred because I was able to discover valuable information.

The experience enabled me to learn about different resources that are available to Galaxy members. Now, that I am aware of them, I can visit them in the future to seek assistance. This experience also allowed me to become familiar with the stdout file, which I have used on other occasions for troubleshooting errors.

Furthermore, other halts have allowed me to realize that there is value in learning how the Mothur program performs. The Galaxy application provides operational simplicity for analyzing NGS big data. The user selects their desired parameters and Galaxy performs the computational work needed to operate Mothur. While the workflows of Galaxy 16S Microbial Analysis tutorial and MiSeq SOP can be used as guides, suitable parameters should be selected for a particular dataset. This is important for obtaining results that provide a close representation of a sample’s microbial community composition. Therefore, one must understand the purpose that each parameter serves. Another benefit of possessing this knowledge is that it can assists in resolving errors and refining existing workflows.

Ultimately, the challenges that I have experienced thus far have helped in building my confidence as a researcher. I used to be reluctant to troubleshoot my research out of fear that I might make the problem worst or I simply didn’t know where to begin. However, I have become resourceful in my efforts and no longer afraid of tackling an error output. Instead of avoiding the errors that I encounter, I embrace and see them in a new light - they are opportunities to think critically and creatively. In the end, I am improving my problem solving skills, which can be applied to both my research and life.

Acknowledgements

I would like to thank the Chateaubriand Fellowship, Alfred P. Sloan Foundation, and McKnight Doctoral Fellowship for supporting this project and encouraging opportunities for graduate students to pursue research abroad!

Thank you for your time and I hope that you were able to learn something new from this post. For any additional questions or comments feel free to leave your responses below!

Accounts of a microbial hunter in France:

Searching for “lifelines” to assist with “hunting” for microbes involved in water treatment

Could these be our microbial counterparts in Vieux Porte? Perhaps we are getting closer…

Welcome to the fourth blog post of this series! In the previous posts, I provide an introduction to this project, discuss how formulating research questions can assist in giving direction, and share my experience with using the Galaxy application to search for microbes involved in treating nitrate contaminated water. Feel free to explore the hyperlinks above to gain additional context about this international and multi-disciplinary research experience.

When I was brainstorming this post, the show Who wants to be a millionaire came to mind. Contestants on this game show answer a series of questions in attempt to win $1,000,000. They are provided with three lifelines - a phone call, the audience, and the elimination of two incorrect answers - to assist with answering question they are posed with. You can think about our research in the context of this game show. Our team represents the contestant that is answering a series of research questions to advance towards the grand prize. In this case, our grand prize: ( 1 ) involves increasing knowledge on the biological processes involved in nitrate removal from contaminated water; and ( 2 ) harnessing this information to enhance treatment technologies. In a perfect world, the lifelines from the show would be suitable. However, this is not the case. In this blog post, I discuss our search for appropriate “lifelines” to help in hunting for microbes involved in removing nitrate from contaminated water.

Thank you for joining in on this journey! We truly appreciate your support, questions, and conversation. Buckle up, sit back, and enjoy the ride!

Reference databases can be used as “lifelines” to “hunt” for our microbial counterparts

Silva is an online database that can be used as a reference for processing and classifying the microbes within our engineered treatment systems.

The Galaxy application is a good bioinformatic tool to use for processing and analyzing next generation sequencing (NGS) big data representing the microbes within samples collected from our engineered treatment systems. Feel free to refer to this page, which briefly discusses NGS and how it was applied in this research. However, the application needs additional help in organizing and identifying the organisms involved in this treatment process. Free and publicly available reference databases can be supplied to the Galaxy application and serve as “lifelines” for these critical steps.

A variety of reference databases can be found online (e.g., Silva and the Ribosome Database Project (RDP)), which contain big data representing a variety of organisms (e.g., bacteria and archaea). This information is collected over time from researchers that share big data from their experiments through platforms such as NCBI, EMBL, or DDBJ. These databases can be used as a resource to arrange (i.e., align) the big data from each sample into regions of similarity. More specifically we are interested in aligning the 16S region of the big data, as it is commonly applied for identifying microbes. Organizing the data in this manner can assist with subsequent processing, such as trimming the data to remove portions outside of the 16S region. Reference databases can also be utilized to non-bacterial sequences from the big data, as the NGS procedure used materials for capturing bacteria (i.e., primers specific to regions of the bacterial 16S gene) within our samples.

Reference databases can be used to align the 16S region of the next generation sequencing (NGS) big data. This region is commonly used to identify microbes. The image above is an alignment of the NGS big data from the Galaxy 16S Microbial Analysis with Mothur tutorial. The Silva.V4 reference database was used for this step.

“These databases can be used as a resource to arrange (i.e., align) the big data from each sample into regions of similarity ... we are interested in aligning the 16S region of the big data, as it is commonly applied for identifying microbes. ”

— Erica Dasi

A krona diagram was generated in the Galaxy Application to visualize the big data following the classification step. Data from the 16S Microbial Analysis with Mothur tutorial was used. The Ribosomal Database Project (trainset9_032012) was employed as a reference database for classification.

Another key step of the analysis within the Galaxy application is the classification of the microbes within our samples. This step involves grouping the microbes based on similar characteristics. Microbes within each group can be divided further based on more detailed similarities. The major groups, or ranks, that organisms can be classified as are kingdom, phylum, class, order, family, genus, and species. Big data within classification reference databases can be inputed into the Galaxy analysis workflow to carry out this process. Once big data representing the microbes has undergone this classification process it can be inputted into different visualization tools. Above is a krona diagram, which provides information on the microbial diversity and abundance within a sample. NGS big data from the Galaxy 16S Microbial Analysis with Mothur tutorial was used to create this diagram.

Reflection: The selection of reference databases as “lifelines” needs careful consideration

Reference databases that are commonly employed identifying organisms (microbes in this case) include SILVA, RDP, Greengenes, and NCBI. These databases differ in the sources they are curated from and their sizes (OTT > NCBI > Silva > RDP > Greengenes). Therefore, notable differences in classification can arise when they applied are applied and compared. Prior to proceeding with the analysis of our big data in the Galaxy application, an evaluation of each reference database was performed to determine which would be appropriate to use. This evaluation involved completing the following: ( 1 ) reviewing an article that compares these databases; (2) reviewing the literature to identify which databases are frequently used for characterizing microbial community structure in the fields of environmental science and water treatment; and (3) testing the reference datasets using the 16S microbial analysis tutorial on Galaxy.

Slide from one of my weekly remote meetings with my advisors. Here I am presenting the rationale for the reference databases I am recommending to use for our analysis in the Galaxy application.

Going through this evaluation assisted with gaining a better understanding of the purpose of reference databases in our analysis and the importance of their careful selection. I also learned about the similarities and differences between these databases. As a result, I feel confident in explaining the rationale behind the selection of the reference databases that are currently being employed. I can also provide peer-reviewed evidence that supports this rationale. This is will be important information to include in future manuscripts related to this project and for answering questions (especially from my dissertation committee).

Acknowledgements

I would like to thank the Chateaubriand Fellowship, Alfred P. Sloan Foundation, and McKnight Doctoral Fellowship for supporting this project and encouraging opportunities for graduate students to pursue research abroad!

Thank you for your time and I hope that you were able to learn something new from this post. For any additional questions or comments feel free to leave your responses below!

Accounts of a microbial hunter in France

Sharpening my knowledge on the “tools” used to “hunt” for microbes involved in water treatment

I wonder if we’ll have any luck finding our microbial counterparts in Aix en Provence, France.

In the previous post for this series, I discussed how my first week at the Centre National de la Recherche Scientifique (CNRS) involved creating research questions. These questions would serve as a “map” to search for microbes involved in treating nitrate contaminated water. Formulating these research questions at the beginning assisted with developing a plan and selecting essential research components. This blog post is a recount of the weeks that followed and highlights activities that I was engaged in. It also includes a reflection of those early weeks based on my current mindset (week 11 to be specific) and the experiences that I have collected thus far. I would like to direct anyone who is just joining the series to the first blog post, which serves as an introduction. Information on the motivation, application, and a brief background of the biological treatment method we are harnessing (i.e., denitrification) is provided. The link to this post can be accessed here. Thank you for taking the time to join me on this journey. Buckle up and lets begin the ride!

Prelude:

The research questions formulated for this investigation assisted with the selection of samples and controls to examine the microbial composition in engineered systems aimed to biologically treat nitrate contaminated water in small communities.

The questions formulated for our research activities at CNRS assisted with selecting samples and the appropriate controls. The experimental samples for this investigation originated from engineered systems containing sulfur-bearing minerals. These systems were created in efforts to evaluate their performance in treating nitrate contaminated drinking water.

Once these samples were selected, they were sent to a company to undergo next generation sequencing (NGS). NGS is a method used to determine the DNA sequences of organisms (in this case microbes) within the samples provided. The resulting output of this process is big data representing the organisms within the samples. Biological and computational tools (i.e., bioinformatic tools) are applied to process and analyze the big data to identify the microbes in these engineered systems.

“Biological and computational tools (i.e., bioinformatic tools) are applied to process and analyze the big data to identify the microbes in these engineered systems.”

— Erica Dasi

Galaxy is a free online application that researchers can use to process and analyze next generation sequencing (NGS) big data.

The Galaxy application is the tool of choice to hunt for our microbial counterparts

The Galaxy application offers free online resources for learning how to perform a variety of analyses through their training network.

Galaxy is a bioinformatic tool that can be employed to examine NGS sequencing data. It is a free open-source and web-based application that allows for the processing and analysis of big data using many common tools. The goal of this tool is to develop and maintain a system that enables researchers without informatics expertise to perform computational analysis. There are three Galaxy websites (Galaxy, Galaxy Europe, and Galaxy Australia) and the primary difference between them is where their servers are located. These websites contain similar tools and researchers across the globe can choose which to use based on their needs. While the samples were undergoing NGS, I began learning key tasks of the Galaxy platforms to prepare for subsequent processing and analysis of the NGS big data. This was achieved by taking advantage of the the free resources that are available through the Galaxy Training Network.

Let the training begin!! - Building fundamental skills using the Galaxy application

I began my journey by using the Galaxy application’s free tutorials.

My training journey with Galaxy began with learning how to perform basic tasks using the following link as a guide. This involved creating a “history,” which represents a record of a task or series of tasks. During this exercise, I learned how to upload data onto the Galaxy application and determine whether it was in the appropriate format for subsequent analysis. This exercise also provided instruction on performing an initial quality assessment of the big data using the FastQC tool. This tool is helpful in determining an appropriate quality score to assign in efforts to remove low quality NGS data.

The Galaxy Training Network provides a hands-on tutorial for analyzing NGS sequencing data to identify microorganisms within samples.

After completing this exercise, I began the 16S Microbial Analysis with Mothur. 16S is a gene that is typically used to identify microorganisms in a sample. Furthermore, Mothur is a software that provides tools for processing and analyzing NGS big data. The Galaxy platforms incorporates tools from Mothur. As a result, this prevents the need to input specific programming commands to use the Mothur tools. Thus, Galaxy users need only to provide their data and input desired parameters for the tools selected. Analysis of NGS big data in Galaxy is a much simplified process!

Various visualization tools in Galaxy can be used to represent the big data remaining after the analysis. This bar chart was created using data provided by the Galaxy 16S tutorial. This diagram provides information on the types of gut-bacteria and their abundance in mice over the course of several days.

The 16S Microbial Analysis tutorial provides data for experiments that were carried out in real-life. Questions are also included to remain engaged throughout the activity and to check your progress. At the end of the analysis, I was able to practice visualizing the data that I obtained in a series of diagrams (e.g., rarefaction curves, venn diagrams, heatgraphs, and bar charts). These diagrams serve as visual aids to provide information on the alpha diversity (diversity within a sample) and beta diversity (diversity between samples). The diagrams assisted in taking complex information represented in the big data from the analysis and portraying it in a clear and concise manner that communicates the “take home message.”

Feel free to visit my twitter page, which features additional comments about my experience with using the Galaxy Training Network.

Reflection: I experienced a “learning curve” earlier rather than later & refined knowledge and skills in the process

Above is a photograph of the notes that I took from the 16S Microbial Analysis tutorial. These notes have served as a valuable resource for processing and analyzing the NGS big data.

My early beginnings of using the Galaxy Training Network (especially the 16S Microbial Analysis tutorial) was quite a learning curve. As an undergraduate and master’s student, I studied biological sciences and acquired a basic understanding of NGS and bioinformatic tools. However, I had no previous experiences with analyzing NGS big data before arriving at CNRS and was unsure of what to expect. My knowledge gradually increased with guidance from my mentor (Dr. Emmanuel Talla) and the 16S Microbial Analysis tutorial.

At the beginning of my experience at CNRS, Dr. Talla provided me with papers to use as a resource. These papers provided information to gain a better understanding of how microbial communities can be studied. One paper that I found quite helpful was Hugerth et al., 2017. This document contains a comprehensive description of how microbes can be analyzed - from when the samples are collected to visualize the big data. This paper was valuable for me because it clarified questions that I had and filled knowledge gaps that existed.

During the 16S Microbial Analysis tutorial, I took meticulous notes to ensure that I understood each step of the analysis and why certain tools were used. These notes where helpful in the beginning, especially when I was trying to interpret the data as it was processed and answer questions within the tutorial. For instance, during the analysis there is a step that requires the removal of data known as chimeras. Chimeras are sequences that have combined (i.e., hybridized) during the NGS process and can lead to data artifacts. Thus, it is important to remove these elements during the processing steps to ensure that a proper analysis of the big data is performed. Reviewing the notes that I had taken on the tutorial has helped me in gaining a better understanding of this concept of chimeras and how to remove them. I have also been able to communicate this topic clearly to my major advisor (whose discipline is outside of bioinformatics; environmental engineering to be specific) occasionally during our weekly meetings.

A practice that I found helpful in remaining organized and interpreting the data is creating slides that highlight the various stages of the processing and analysis steps. I used this practice to answer the 16S Microbial Analysis tutorial questions and track my progress. In addition, these slides have been helpful during my current examination of the NGS big data. I have referred to these slides on numerous occasions to interpret the visualization charts generated from the analysis. Furthermore, I have made additional use of these slides during my weekly remote meetings with my major advisor!

Acknowledgements

I would like to thank the Chateaubriand Fellowship, Alfred P. Sloan Foundation, and McKnight Doctoral Fellowship for supporting this project and encouraging opportunities for graduate students to pursue research abroad!

Thank you for your time and I hope that you were able to learn something new from this post. For any additional questions or comments feel free to leave your responses below!

Accounts of a microbial hunter in France:

Using research questions to “hunt” for microbes involved in water treatment

Here is a view of a sample collected from an engineered system that contained sulfur-bearing mineral. This mineral was applied to assess its ability to promote biological nitrate removal. I wonder who this critter is?

Welcome to the second blog post of the “Accounts of a Microbial Hunter in France” series! This series shares my experience at the Centre National de la Recherche Scientifique (CNRS), located in Marseille, France. I am actively searching for microbes (known as denitrifiers) that use sulfur-bearing minerals to biologically removing nitrate (i.e., denitrification) in engineered systems designed for treatment in small communities. Hence the “microbial hunter” part in the title of this blog series. The first blog post of this series serves as an introduction to my research at CNRS. This post is a resource for my audience to use for gaining context about the project (e.g., the motivation, an introduction to denitrification, and the research objectives). Especially, for those who are just joining the series and/or who are unfamiliar with denitrification. Feel free to access this post here.

I will be discussing my first week at CNRS, which involved proposing research questions. This post includes the approach that I used to pose these questions. It also features a reflection of how these questions have assisted me so far.

Prior to proceeding, I would like to extend my gratitude for taking the time to travel through this journey with me. I truly appreciate it. Buckle up and lets begin this ride!

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Research questions were created to serve as a “map” for hunting microbes

During my first week, Dr. Talla (my mentor at CNRS) suggested creating research questions that would serve as a guide to:

I am working with Dr. Talla in the Laboratory of bacterial chemistry at CNRS, located in Maresille, France.

• Search for denitirifiers involved in removing nitrate from contaminated water supplies

• Identify the contribution of my research towards elucidating the biological processes that occur during the treatment process.

The approach that I used to create questions for my research involved thinking about valuable information that could be obtained and potentially applied to the engineered systems. Studying how the microbial community changes in response to the sulfur-bearing minerals within the engineered systems can provide important information. For example, details on how the denitrifiers responds to dynamic environmental conditions (e.g., varying nitrate concentrations) and to each other can be gained. This information can be applied to refine the design of the engineered systems in efforts to improve the treatment process. Therefore, the research questions that I proposed involved examining and comparing microbial community evolution when different sulfur-bearing minerals were used to mediate denitrification. Additional information on why these minerals were employed can be found here.

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Formulating questions assisted with the selection of important research components

Example of a sampling session where I am taking a closer look at the microbes used in an engineered systems.

Once the research questions were posed, I was able to select samples suitable for answering them. The samples could then undergo next generation sequencing to obtain big data representing genetic information (i.e., DNA sequences) of organisms within each sample. Furthermore, these research questions assisted in including positive controls to perform a complete analysis. Positive controls are designed to produce an expected observation and are particularly useful in comparing these results to the experimental samples and validating the procedure.

Analyzing the microbial community throughout the experimental period can be an expensive and a long endeavor. Therefore, samples for specific time points were selected to analyze the microbial community structure. These samples were determined based on the observed nitrate concentrations measured during weekly sampling sessions over the course of three months.

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“... revisiting the research questions have enabled me to return full circle and understand the main take home messages that my research community, peers, and the public will be interested in. ”

— Erica Dasi

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Reflection: Developing research questions beforehand have aided in providing organization and direction

Dr. Talla and I reviewing the big data that has been separated based on the research questions proposed.

These research questions have played a monumental role in gaining organization and direction, and fostering a forward thinking mindset. I have referred to these questions on many occasions to organize the big data representing microbes in the samples into small-groups to obtain clear results. These questions have also served as a reminder of the “big picture” that I am trying to learn about my research. There have been many moments where I became engrossed in the details of processing and analyzing the big data. However, revisiting the research questions have enabled me to return full circle and understand the main take home messages that my research community, peers, and the public will be interested in. Ultimately, reviewing the research questions at the end of the data analysis will aid in determining the denitrifiers that play key roles in the treatment process and pinpoint the contribution of this research to understanding the underlying biological mechanisms of denitrification.

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Acknowledgments:

I would like to thank the Chateaubriand Fellowship, Alfred P. Sloan Foundation, and McKnight Doctoral Fellowship for supporting this project and encouraging opportunities for graduate students to pursue research abroad!

Thank you for your time and I hope that you were able to learn something new from this post. For any additional questions or comments feel free to leave your responses below.

Accounts of a microbial hunter in France: Introduction

Standing in front of the laboratory in Marseille, France.

I am currently conducting research at the Centre National de la Recherche Scientifique (CNRS), located in Marseille, France. I am working there with Dr. Emmanuel Talla to identify the microbial community structure in engineered systems aimed to biologically remove nitrate from small community drinking water systems.

This blog series encompasses my experiences at CNRS. I hope that it serves as a useful resource for those interested in pursuing research opportunities abroad, incorporating an interdisciplinary component to their research, navigating through challenges, and much more. This post serves as an introduction of the research I am pursuing at CNRS. I want to take the time to prime my audience on the basis of this research in efforts to provide context for subsequent posts. Thank you for taking the time to join this journey with me!

Small communities are especially vulnerable to health impacts associated with the consumption of nitrate contaminated drinking water

In 2016, I spent the summer in Ghana working with a team to develop a low-cost and sustainable filter for treating contaminated drinking water.

My passion surrounds providing potable water and treating wastewater in communities around the globe. As an Environmental Engineering PhD student at the University of South Florida, my research seeks to develop low-cost, simple, and sustainable systems for treating nitrate contamination in small community drinking water systems. The consumption of nitrate contaminated water is a concern because it has been linked to causing an illness known as methemoglobinemia, which is fatal in infants and can lead to spontaneous abortion in pregnant women. Small communities are especially vulnerable to these impacts as financial and technological resources are often limited or unavailable. This makes it difficult for these communities to employ traditional physical and chemical methods of nitrate removal.

Denitrification using sulfur-bearing minerals is a promising technology for treating nitrate contaminated drinking water supplies in small communities

Holding a sulfur-bearing mineral specimen in the laboratory.

My research harnesses a natural biological process known as denitrification in engineered systems. Microbes known as denitrifiers are drivers of this process and are responsible for transforming nitrate into nitrogen gas, which is an environmentally benign product. Denitrifiers are ubiquitous throughout the planet. Therefore, the introduction of these organisms into engineered systems can create a potentially affordable and sustainable nitrate removal technology for small communities.

Previous research has demonstrated that sulfur-bearing minerals can mediate wastewater denitrification. However, few studies have evaluated the use of these minerals for drinking water denitrification. My research involves assessing and comparing the performances of various sulfur-bearing minerals to promote drinking water denitrification. The use of sulfur-bearing minerals is specifically attractive because they are abundant and widespread. Therefore, the acquisition and application of these minerals in engineered systems can be achieved with relative ease.

The biological mechanisms governing denitrification facilitated by sulfur-bearing minerals is unclear

Examples of engineered systems containing sulfur-bearing mineral. These systems were used in previous experiments that assessed the performance of sulfur-bearing minerals in promoting drinking water denitrification.

As mentioned previously, denitrifiers are the drivers for denitrification. The underlying biological mechanisms that occur when these minerals are employed for this process are poorly understood. Studying microbial community structure can provide insights into these mechanisms, which can be harnessed to refine the design of engineered systems for enhancing nitrate removal. My research takes an interdisciplinary approach in attempt to treat nitrate contaminated drinking water. Fundamentals of environmental engineering are employed to develop novel engineered systems containing sulfur-bearing minerals for denitrification. Furthermore, molecular and computational tools are used to examine microbial community evolution in these systems.

Next generation sequencing (NGS) technologies are used to study microbial community structure during denitrification mediated by sulfur-bearing minerals

Over of the course of several months, small volumes of sample were removed from the engineered systems to: ( 1 ) monitor how the nitrate concentration changed throughout the experimental period; and ( 2 ) examine the evolution of the microbial community structure. Combining information acquired from both of these tasks can help in gaining a comprehensive view of this specific nitrate removal process. Molecular biology tools and techniques were applied to isolate DNA from the collected samples. Following this, NGS was applied on the samples to obtain data that could be subsequently analyzed to identify microbes within the samples. NGS is a technique that is applied for ancestry kits, such as 23andMe® or AncestryDNA®, which are used to discover a persons family history.

Processing NGS data to obtain information on the microbial community structure in the engineered systems used in this investigation

Dr. Talla and I using a computational tool to analyze our next generation sequencing data.

I am receiving training in bioinformatics at CNRS. Bioinformatics is a multi-disciplinary field that includes molecular biology and computer science. Dr. Talla is advising me through using bioinfomatics to analyze the NGS data representing samples that were previously collected. I am gaining hands-on experience with handling big data obtained from NGS, using computational tools to perform quality control and analysis, and troubleshooting these tools. By the end of this experience, I will have the ability to describe: ( 1 ) the impact of the microbial community on denitrification performance using different sulfur-bearing minerals; and ( 2 ) identify populations that are dominant during peak periods of nitrate removal. This information can be applied in the development of future engineered systems to foster conditions that promote the growth of these dominant populations. This can assist in optimizing nitrate removal efficiencies of these systems during pilot testing within a small community setting.

Acknowledgements

I would like to thank the Chateaubriand Fellowship, Alfred P. Sloan Foundation, and McKnight Doctoral Fellowship for supporting this project and encouraging opportunities for graduate students to pursue research abroad!

Thank you for your time and I hope that you were able to learn something new from this post. For any additional questions or comments feel free to leave your responses below.

Thank you for your time and stay tuned for my next post of this series! If you have any questions or concerns feel free to comment below.

Louis Stoke's Alliance for Minority Participation Research Symposium

Celebrating the Legacy of Congressman Stokes and 25 years of Success

Photo courtesy of Laura Lee.

“Congressman Stokes’ legacy lives on in the students, faculty, and staff who have benefitted from LSAMP.”

— Dr. France Córdova, NSF Director

A few weeks ago, I had the opportunity to attend the Louis Stoke's Alliance for Minority Participation (LSAMP) Research Symposium at the National Harbor in Maryland. The program is named after the late Congressman Stokes who used his elected positions to advocate tirelessly for equal rights and opportunities for African Americans. LSAMP is a program of the National Science Foundation (NSF) that was established in 1991. It is a program dedicated to increasing the quality and quantity of minorities who complete baccalaureate degrees in Science, Technology, Engineering, and Mathematics (STEM). Furthermore, LSAMP aims to increase the number of minority students who continue onto graduate studies in STEM. 

During this conference, students and faculty from various LSAMP institutions as well as a number of individuals from NSF and industry came together to celebrate the legacy of Congressman Stokes and reflect on the 25 years of success of the LSAMP program. In addition, LSAMP fellows had the opportunity to share their research during the conference. The Conference also featured performances by the Lepquinm Gumilgit Gagoadim (Our Own Dance in Our Heart) Tsimshian dancers and Johnny Walker, and speakers that included Dr. Samuel Betances, Dr. Vincent Tinto, Dr. France Córdova, and Dr. Jo Handelsman. Near the end of the conference, a panel of Alumni LSAMP fellows shared their personal narratives of how the LSAMP program enhanced their graduate school experience.

Attending the the LSAMP research conference was such an rewarding experience. Presenting at the conference gave me the opportunity to practice talking about my research to a diverse audience. Being able to communicate your research to a non-technical audience is an essential skill every scientist should have and I am currently working on refining it myself. In addition, I had the opportunity to meet other LSAMP students and hear about research in other disciplines that include chemical engineering, environmental science, civil engineering. Listening to their research allowed me to learn and gain an appreciation about work centered on creating non-invasive technologies for measuring blood glucose levels, understanding biodiversity within the environment, and creating technologies for reducing the concentration of fluoride in water within a developing country. 

Much thanks and appreciation goes out to the people who are involved in organizing the LSAMP program. More specifically, I would like to thank the program directors, Dr. Tasha Inniss and Dr. James Hicks for their hard work in creating the opportunity for students like myself to pursue a graduate degree. I look forward to future LSAMP events and also moving forward in my graduate career as a LSAMP fellow!