Members


Lab Alumni:

Marcos Maldonado*, Anika James, Marlea Kudlauskas, Lisa Fetter*, Anna Nguyen, Derek Clark, Ilia Mazin, Nazar Dubchak, Jena Jacobs, Jessica Daniel*, Aviva Bulow, Susan Jett*, Ryan Warren, Tiffany Ashbaugh, Michael McCoy, Ebony Miller, Jonathan Richards*, Laura Roon*, Becky Addison, Jeremy O’Brien, Travis Ingraham, Sarai Graves, Kathryn Norquest*, Stephen Schaffner*, Kyra Brandt, Elina Baravik*, Yerelsy Reyna*, Josh Sowick, Jody Stephens*, Ryan Masterson*, Mason Preusser, Tonya Santaus, Amanda Faux, Matthew Stoddard, Morgan Miller.
(* denotes a researcher with a publication from the lab)


Dr. Andrew J. Bonham

BonhamAndrew_300
Professor of Chemistry & Biochemistry
Dr. Bonham’s Curriculum Vitae

Dr. Bonham’s work focuses on understanding and investigating Transcription Factors, essential human proteins that regulate the bodies growth and response to disease. These transcription factors are essential components of gene regulation, and there is great interest in probing their presence and activity in both academic analysis and clinical diagnostics. Current methods to address these questions are often time-intensive or require specialized reagents, such as antibodies. At Metropolitan State University of Denver, Dr. Bonham is leading an innovative undergraduate research program focused on engineering new tools for sensitive and quick detection of TF:DNA interactions.


Tyler Sodia

maldonado
Lab Member 2018-
Project: Electrochemical DNA Biosensors for Detection of Mannose-capped Lipoarabinomannan

Mycobacterium Tuberculosis (TB) is one of the world’s most prevalent bacterial pathogens. It is estimated that almost 10 million cases of TB emerge every year, and roughly one-fifth of these cases are fatal. The current detection and diagnosis of TB is done primarily via two methods; the TB skin test and the TB blood tests. Neither of these tests can differentiate between latent TB infection and TB disease. In order to differentiate these states, time-consuming sputum tests are required, which rely on culturing the mycobacterium. Designing a sensitive serologic biosensor would dramatically decrease the time line of diagnosis and therefore improve patient outcomes. One possible avenue for improved detection lies in the cell wall of TB, which includes many complex glycolipids—many of which are believed to have immunopathogenic mechanisms in physiologic pathways. Mannose-capped lipoarabinomannan (ManLAM) is one of the most prevalent of these glycolipids, and presents a novel target as a bio-marker for the sensitive detection of TB and related Mycobacterium strains. Here, we have utilized an existing aptamer sequence that binds to ManLAM to generate a sensitive electrochemical, DNA-based biosensor for the detection of TB. This biosensor is able to adopt multiple different folded conformations, only one of which presents the core aptamer sequence in a state capable of binding ManLAM. An appended redox-active tag (methylene blue) generates a measurable difference in electrochemical current upon this conformational change, providing a sensitive and quantitative measurement of ManLAM concentration. Such biosensors may ultimately allow rapid, on site, diagnosis of TB infection within the time constraints of patient-doctor interaction.


Dylan Poch


Lab Member 2019-
Project: Electrochemical DNA Biosensors for Detection of Mannose-capped Lipoarabinomannan

Mycobacterium Tuberculosis (TB) is one of the world’s most prevalent bacterial pathogens. It is estimated that almost 10 million cases of TB emerge every year, and roughly one-fifth of these cases are fatal. The current detection and diagnosis of TB is done primarily via two methods; the TB skin test and the TB blood tests. Neither of these tests can differentiate between latent TB infection and TB disease. In order to differentiate these states, time-consuming sputum tests are required, which rely on culturing the mycobacterium. Designing a sensitive serologic biosensor would dramatically decrease the time line of diagnosis and therefore improve patient outcomes. One possible avenue for improved detection lies in the cell wall of TB, which includes many complex glycolipids—many of which are believed to have immunopathogenic mechanisms in physiologic pathways. Mannose-capped lipoarabinomannan (ManLAM) is one of the most prevalent of these glycolipids, and presents a novel target as a bio-marker for the sensitive detection of TB and related Mycobacterium strains. Here, we have utilized an existing aptamer sequence that binds to ManLAM to generate a sensitive electrochemical, DNA-based biosensor for the detection of TB. This biosensor is able to adopt multiple different folded conformations, only one of which presents the core aptamer sequence in a state capable of binding ManLAM. An appended redox-active tag (methylene blue) generates a measurable difference in electrochemical current upon this conformational change, providing a sensitive and quantitative measurement of ManLAM concentration. Such biosensors may ultimately allow rapid, on site, diagnosis of TB infection within the time constraints of patient-doctor interaction.


Andrew Smith


Lab Member 2019-
Project: Design of electrochemical biosensors for detecting Carrion’s Disease

Carrion’s disease is a neglected tropical disease (NTD) caused by infection by the bacteria B. bacilliformis, endemic to northern Peru, and affects primarily rural, impoverished populations. In rural areas, diagnosis is currently made using Giemsa-stained blood smears, but this technique has a very low sensitivity for the disease (24-36%) and requires trained individuals, which are in short supply. The disease can be diagnosed via qPCR, IFA, and ELISA tests, and show high sensitivity, but are not feasible for diagnosis in rural areas, due to their impoverished nature. Here, we propose start-up activities that should allow the creation of a biosensor-based strategy for field detection of the bacteria responsible for CD. Our CD biosensor will be developing through a process known as SELEX: systematic evolution of ligands by exponential enrichment using the commercial x-aptamer selection system. This process will generate a specific probe against the Pap31 protein shed by the pathogenic bacteri responsible for CD which will be adapted into an electrochemical DNA-based biosensor. E-DNA sensors should provide a rapid means of testing for CD. These sensors have a long shelf-life and are reusable, as well as requiring only small blood samples. Electrochemical sensors provide easily read and interpreted output suitable for point-of-care conditions and can be analyzed using portable equipment on site. Thus, only a finger lancet-derived blood sample obtained in the field would be required. Ultimately, these characteristics offer the broader impact of better, timelier and less invasive diagnostics for those affected by this disease.


Lindsay Armstrong



Lab Member 2017-
Project: Characterizing Binding Interactions and Elucidating Structure of Aptamer-Based Biosensors

Electrochemical biosensors based on the conformational dynamics of DNA aptamers have found success against a wide variety of proteins, toxins, antibodies, and heavy metals. However, the mechanistic underpinnings of the mechanism by which these surface-bound DNA molecules change conformation upon target binding, thus changing the dynamics of an appended redox-active tag and generating a measurable signal, is poorly understood. Our first target for investigation was a previously reported Ricin Chain A binding atamer biosensor. Here, we have investigated this biosensor using a variety of nucleic acid assays, including PCR-termination via basepair modification, fluorescence anisotropy, gel mobility shift assays, and FRET tagging to determine 3D orientation. These have allowed us to better characterize the basepair interactions involved in binding targets, as well as offer clues to the changing three dimensional folded structures of these biosensors. These results will help inform the field of biosensors and aptamers in general on strategies for future optimization.


Austin Haider



Lab Member 2018-
Project: Algorithmic Prediction of DNA Biosensor Structures

Oligonucleotide-based biosensors have been demonstrated as effective tools for detecting heavy metals, small molecule drugs, and protein targets. Recent efforts to move these biosensors from the lab bench to practical use in industry and medicine rely on the ability to rapidly and effectively adapt them to detect new targets of interest. One compelling aspect of DNA and RNA-based biosensors is the great strides that have been made in computational prediction of the three-dimensional structures that they can assume. However, interpreting predicted structures and using that knowledge to design functional biosensors ab initio remains a challenging problem. We hypothesize that by representing a potential DNA biosensor as a node-weighted graph, we can simplify the challenge of automated structure interpretation and sorting. If correct, this would allow a researcher to provide a core sequence of interest (such as a DNA recognition element or artificially-selected aptamer) and have a software tool design the ideal DNA biosensor for the sensitive detection of that target. This process builds on ideas from our oligionucleotide fold scoring algorithm, Fealden. With this new implementation of Fealden, we are achieving significantly more flexibility, allowing us to analyze any structure type. Because of this we are able to traverse a search space of tens of thousands of possible sequences to determine a “best candidate” biosensor. Additionally, with this increased flexibility we hope to implement some very desirable features in our software, generally, different types of design constraints that a researcher can specify for their biosensor.