BTI members connect with colleagues in Japan

BTI members connect with colleagues in Japan

BTI members connect with colleagues in Japan

BioTechnology Institute members travel to the University of Tokyo to strengthen research ties.

By Lance Janssen

Despite hours-long flights and an ocean of separation, researchers from the BioTechnology Institute (BTI) and the University of Tokyo have close connections. This past November, eight BTI faculty members traveled to Japan for a shared research symposium as a continued piece of an academic exchange program launched between the two institutions in 2017. With common research focus areas in areas like ​​microbial engineering, synthetic biology, protein design and environmental engineering, as well as opportunities to build connections and train students, the initiative aims to offer opportunities that will advance research and educational efforts for both BTI and the University of Tokyo.

“Our hope is that we can build collaborations with researchers that have shared interest areas and complementary scientific skills and expertise,” says Jeff Gralnick, a professor in the Department of Plant and Microbial Biology and a BTI member. “By establishing a student and postdoc exchange program, we also hope to provide important learning experiences for our trainees.”

Gralnick attended this year’s symposium along with Alptekin Aksan from the College of Science and Engineering, Christine Salomon from the College of Pharmacy and Medical School, as well as Michael Freeman, Kate Adamala, Michael Travisano and Claudia Schmidt-Dannert from the College of Biological Sciences. The symposium not only offered researchers the chance to share some of their research endeavors in Minnesota, but also build closer connections with their Tokyo counterparts. 

“The University of Tokyo is the premier research institution in Japan – with excellent research groups that conduct complementary research to BTI faculty,” says Schmidt-Dannert, head of BTI and a professor in the Department of Biochemistry, Molecular Biology and Biophysics. “Through this symposium we were able to learn first-hand about specific research conducted at the University of Tokyo through lectures, visits to labs and meetings with students, researchers and faculty – both in a formal but also more informal setting – and get insights into the academic environment and culture in Japan.”

Over the course of their visit, the BTI team took part in the symposium, as well as tours to the Mt. Fuji area, a formal banquet with colleagues and a visit to the National Institute of Genetics. These experiences offered researchers the chance to not only learn about research and visit a new city, but also future research opportunities for years to come.

“I think all of us identified areas of overlap for research collaboration,” says Schmidt-Dannert. “Some concrete connections have already been made that will result in material exchange and hosting of students and postdocs. We will use all of the knowledge and experience gathered to identify and apply for joint funding opportunities and develop a program that will allow for an exchange of graduate students and potentially postdocs between BTI and University of Tokyo labs.” 

Symposium presentations

Structural and metabolic insights in RiPP peptide backbone α-N- methylation.
Michael Freeman (University of Minnesota)

Natural product discovery for biocontrol and infectious disease treatment.
Christine Salomon (University of Minnesota)

Molecular Plant-Microbe interactions along the parasitic-mutualistic continuum.
Ke Hirumai (University of Tokyo)

Life but not alive: bioengineering with synthetic cells.
Kate Adamala (University of Minnesota)

Leveraging biological self-organization for the design of functional materials.
Claudia Schmidt-Dannert (University of Minnesota)

Molecular mechanisms of morphological development in the rare actinomycete Actinoplanes missouriensis.
Yasuo Ohnishi (University of Tokyo)

Extracellular electron transfer in Bacteria.
Jeffrey Gralnick (University of Minnesota)

Bioremediation of nitrate pollution in agricultural subsurface drainage.
Satoshi Ishii (University of Minnesota)

Plasmid business: effects to host cell physiology and fate in nature.
Hideaki Nojiri (University of Tokyo)

Active biomaterials for biotechnology applications.
Alptekin Aksan AKSAN (University of Minnesota)

Exploring microbial solutions using Experimental Evolution.
Michael Travisano (University of Minnesota)

Bioinformatics for revealing rules behind microbial genome evolution.
Wataru Iwasaki (University of Tokyo)

BTI publications: June – September 2023

BTI publications: June – September 2023

Publications by BTI faculty

Bohn, B., Chalupova, M., Staley, C., Holtan, S., Maakaron, J., Bachanova, V., & El Jurdi, N. (2023). Temporal variation in oral microbiome composition of patients undergoing autologous hematopoietic cell transplantation with keratinocyte growth factor. BMC Microbiol, 23(1), 258. https://doi.org/10.1186/s12866-023-03000-x

Cai, Z., Donahue, N., Jones, K. C., McNeill, K., Manaia, C., Novak, P. J., & Vikesland, P. J. (2023). Best Papers from 2022 published in the. Environ Sci Process Impacts, 25(7), 1141-1143. https://doi.org/10.1039/d3em90018e

Chang, Y. C., Lin, K., Baxley, R. M., Durrett, W., Wang, L., Stojkova, O., . . . Bielinsky, A. K. (2023). RNF4 and USP7 cooperate in ubiquitin-regulated steps of DNA replication. Open Biol, 13(8), 230068. https://doi.org/10.1098/rsob.230068

Chowdhury, S., Kennedy, J. J., Ivey, R. G., Murillo, O. D., Hosseini, N., Song, X., . . . Paulovich, A. G. (2023). Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer. Cell, 186(16), 3476-3498.e3435. https://doi.org/10.1016/j.cell.2023.07.004

Costa, K. C., & Whitman, W. B. (2023). Model Organisms To Study Methanogenesis, a Uniquely Archaeal Metabolism. J Bacteriol, 205(8), e0011523. https://doi.org/10.1128/jb.00115-23

El Jurdi, N., Holtan, S. G., Hoeschen, A., Velguth, J., Hillmann, B., Betts, B. C., . . . Shields-Cutler, R. (2023). Pre-transplant and longitudinal changes in faecal microbiome characteristics are associated with subsequent development of chronic graft-versus-host disease. Br J Haematol. https://doi.org/10.1111/bjh.19016

Golinski, A. W., Schmitz, Z. D., Nielsen, G. H., Johnson, B., Saha, D., Appiah, S., . . . Martiniani, S. (2023). Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation. ACS Synth Biol, 12(9), 2600-2615. https://doi.org/10.1021/acssynbio.3c00196

Gralnick, J. A., & Bond, D. R. (2023). Electron Transfer Beyond the Outer Membrane: Putting Electrons to Rest. Annu Rev Microbiol, 77, 517-539. https://doi.org/10.1146/annurev-micro-032221-023725

Hassan, A. Z., Ward, H. N., Rahman, M., Billmann, M., Lee, Y., & Myers, C. L. (2023). Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens. Mol Syst Biol, e11657. https://doi.org/10.15252/msb.202311657

Hill, E. R., Chun, C. L., Hamilton, K., & Ishii, S. (2023). High-Throughput Microfluidic Quantitative PCR Platform for the Simultaneous Quantification of Pathogens, Fecal Indicator Bacteria, and Microbial Source Tracking Markers. ACS ES T Water, 3(8), 2647-2658. https://doi.org/10.1021/acsestwater.3c00169

Hu, L. S., D’Angelo, F., Weiskittel, T. M., Caruso, F. P., Fortin Ensign, S. P., Blomquist, M. R., . . . Tran, N. L. (2023). Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures. Nat Commun, 14(1), 6066. https://doi.org/10.1038/s41467-023-41559-1

Huang, S., Bergonzi, C., Smith, S., Hicks, R. E., & Elias, M. H. (2023). Field testing of an enzymatic quorum quencher coating additive to reduce biocorrosion of steel. Microbiol Spectr, e0517822. https://doi.org/10.1128/spectrum.05178-22

Justyna, K., Das, R., Lorimer, E. L., Hu, J., Pedersen, J. S., Sprague-Getsy, A. M., . . . Distefano, M. D. (2023). Synthesis, Enzymatic Peptide Incorporation, and Applications of Diazirine-Containing Isoprenoid Diphosphate Analogues. Org Lett, 25(36), 6767-6772. https://doi.org/10.1021/acs.orglett.3c02736

Kalambokidis, M., & Travisano, M. (2023). Multispecies interactions shape the transition to multicellularity. Proc Biol Sci, 290(2007), 20231055. https://doi.org/10.1098/rspb.2023.1055

Kong, W., Qiu, L., Ishii, S., Jia, X., Su, F., Song, Y., . . . Wei, X. (2023). Contrasting response of soil microbiomes to long-term fertilization in various highland cropping systems. ISME Commun, 3(1), 81. https://doi.org/10.1038/s43705-023-00286-w

Lane, B. R., Anderson, H. M., Dicko, A. H., Fulcher, M. R., & Kinkel, L. L. (2023). Temporal variability in nutrient use among Streptomyces suggests dynamic niche partitioning. Environ Microbiol. https://doi.org/10.1111/1462-2920.16498

Li, J., Arnold, W. A., & Hozalski, R. M. (2023). Spatiotemporal Variability in. Environ Sci Technol, 57(37), 13959-13969. https://doi.org/10.1021/acs.est.3c01767

Lu, M., Lee, Z., Lin, Y. C., Irfanullah, I., Cai, W., & Hu, W. S. (2023). Enhancing the production of recombinant adeno-associated virus in synthetic cell lines through systematic characterization. Biotechnol Bioeng. https://doi.org/10.1002/bit.28562

McConnell, A., & Hackel, B. J. (2023). Protein engineering via sequence-performance mapping. Cell Syst, 14(8), 656-666. https://doi.org/10.1016/j.cels.2023.06.009

Miley, K., Meyer-Kalos, P., Ma, S., Bond, D. J., Kummerfeld, E., & Vinogradov, S. (2023). Causal pathways to social and occupational functioning in the first episode of schizophrenia: uncovering unmet treatment needs. Psychol Med, 53(5), 2041-2049. https://doi.org/10.1017/S0033291721003780

Morra, A., Schreurs, M. A. C., Andrulis, I. L., Anton-Culver, H., Augustinsson, A., Beckmann, M. W., . . . Investigators, k. (2023). Association of the CHEK2 c.1100delC variant, radiotherapy, and systemic treatment with contralateral breast cancer risk and breast cancer-specific survival. Cancer Med, 12(15), 16142-16162. https://doi.org/10.1002/cam4.6272

Ndinga-Muniania, C., Wornson, N., Fulcher, M. R., Borer, E. T., Seabloom, E. W., Kinkel, L., & May, G. (2023). Cryptic functional diversity within a grass mycobiome. PLoS One, 18(7), e0287990. https://doi.org/10.1371/journal.pone.0287990

Qualls, D. A., Lambert, N., Caimi, P. F., Merrill, M. H., Pullarkat, P., Godby, R. C., . . . Salles, G. A. (2023). Tafasitamab and lenalidomide in large B cell lymphoma: real-world outcomes in a multicenter retrospective study. Blood. https://doi.org/10.1182/blood.2023021274

Sakai, A., Jonker, A. J., Nelissen, F. H. T., Kalb, E. M., van Sluijs, B., Heus, H. A., . . . Huck, W. T. S. (2023). Cell-Free Expression System Derived from a Near-Minimal Synthetic Bacterium. ACS Synth Biol, 12(6), 1616-1623. https://doi.org/10.1021/acssynbio.3c00114

Seabloom, E. W., Caldeira, M. C., Davies, K. F., Kinkel, L., Knops, J. M. H., Komatsu, K. J., . . . Borer, E. T. (2023). Globally consistent response of plant microbiome diversity across hosts and continents to soil nutrients and herbivores. Nat Commun, 14(1), 3516. https://doi.org/10.1038/s41467-023-39179-w

van Hees, D., Hanneman, C., Paradis, S., Camara, A. G., Matsumoto, M., Hamilton, T., . . . Kodner, R. B. (2023). Patchy and Pink: Dynamics of a Chlainomonas sp. (Chlamydomonadales, Chlorophyta) algal bloom on Bagley Lake, North Cascades, WA. FEMS Microbiol Ecol. https://doi.org/10.1093/femsec/fiad106

Vitt, J. D., Hansen, E. G., Garg, R., & Bowden, S. D. (2023). Bacteria intrinsic to Medicago sativa (alfalfa) reduce Salmonella enterica growth in planta. J Appl Microbiol, 134(9). https://doi.org/10.1093/jambio/lxad204

Wackett, L. P. (2023a). A microbial evolutionary approach for a sustainable future. Microb Biotechnol, 16(10), 1895-1899. https://doi.org/10.1111/1751-7915.14331

Wackett, L. P. (2023b). Acid stress in microbes: An annotated selection of World Wide Web sites relevant to the topics in environmental microbiology. Environ Microbiol Rep, 15(4), 335-336. https://doi.org/10.1111/1758-2229.13185

Wackett, L. P. (2023c). Cyanobacterial algal blooms: An annotated selection of World Wide Web sites relevant to the topics in environmental microbiology. Environ Microbiol, 25(7), 1375-1376. https://doi.org/10.1111/1462-2920.16061

Wackett, L. P. (2023d). Microbes at low substrate concentrations: An annotated selection of World Wide Web sites relevant to the topics in environmental microbiology. Environ Microbiol, 25(6), 1218-1219. https://doi.org/10.1111/1462-2920.16059

Wackett, L. P. (2023e). Web alert: Fabrication with microbial spores: An annotated selection of World Wide Web sites relevant to the topics in Microbial Biotechnology. Microb Biotechnol, 16(10), 2007-2008. https://doi.org/10.1111/1751-7915.14345

Wackett, L. P. (2023f). Web Alert: Solvent stress on environmental microbes: An annotated selection of World Wide Web sites relevant to the topics in environmental microbiology. Environ Microbiol, 25(8), 1563-1564. https://doi.org/10.1111/1462-2920.16063

Wackett, L. P. (2023g). Web alert: Two-phase biocatalysis. Microb Biotechnol, 16(8), 1702. https://doi.org/10.1111/1751-7915.14314

Wackett, L. P., & McKnight. (2023). Web alert: Microbial biofilm catalysis. Microb Biotechnol, 16(7), 1577-1578. https://doi.org/10.1111/1751-7915.14299

Wang, H., Feyereisen, G. W., Wang, P., Rosen, C., Sadowsky, M. J., & Ishii, S. (2023). Impacts of biostimulation and bioaugmentation on woodchip bioreactor microbiomes. Microbiol Spectr, e0405322. https://doi.org/10.1128/spectrum.04053-22

Wang, Q. Q., Sun, M., Tang, T., Lai, D. H., Liu, J., Maity, S., . . . Long, S. (2023). Functional screening reveals. mBio, 14(4), e0130923. https://doi.org/10.1128/mbio.01309-23

Wang, Z., Ishii, S., & Novak, P. J. (2023). Quantification of depth-dependent microbial growth in encapsulated systems. Microb Biotechnol. https://doi.org/10.1111/1751-7915.14341

Worner, K., Liu, Q., Maschhoff, K. R., & Hu, W. (2023). Identification of RNA-binding proteins’ direct effects on gene expression via the degradation tag system. RNA, 29(10), 1453-1457. https://doi.org/10.1261/rna.079669.123

Zhang, Q., Xuan, Q., Wang, C., Shi, C., Wang, X., Ma, T., . . . Chen, C. (2023). Bioengineered “Molecular Glue”-Mediated Tumor-Specific Cascade Nanoreactors with Self-Destruction Ability for Enhanced Precise Starvation/Chemosynergistic Tumor Therapy. ACS Appl Mater Interfaces, 15(35), 41271-41286. https://doi.org/10.1021/acsami.3c06871

Zhang, W., Dong, H., Wang, X., Zhang, L., Chen, C., & Wang, P. (2023). Engineered. ACS Appl Mater Interfaces. https://doi.org/10.1021/acsami.3c09123

Zhu, B., Du, Z., Dai, Y., Kitaguchi, T., Behrens, S., & Seelig, B. (2023). Nanodroplet-Based Reagent Delivery into Water-in-Fluorinated-Oil Droplets. Biosensors (Basel), 13(8). https://doi.org/10.3390/bios13080768

Zlotorzynska, M., Chea, N., Eure, T., Alkis Ramirez, R., Blazek, G. T., Czaja, C. A., . . . Grigg, C. T. (2023). Residential social vulnerability among healthcare personnel with and without severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in Five US states, May-December 2020. Infect Control Hosp Epidemiol, 1-7. https://doi.org/10.1017/ice.2023.131

Modeling microbial complexity

Modeling microbial complexity

Modeling microbial complexity

Will Harcombe combines experimental and computational models to decode complex microbial interactions.

By Bernard Cook III

The role of E. coli in causing food-borne illness is well known. Less well known, however, is the way microbes interact with each other. Like humans, they exchange resources and live in communities. Understanding these complex interactions could help scientists predict and even control the properties of a microbial community—yielding new tools for treating infections, promoting human and animal health, and improving soil productivity. 

Will Harcombe, an associate professor of Ecology, Evolution and Behavior and a member of the BioTechnology Institute, studies microbial interaction by looking at the exchange of chemicals among microbial species. “One of the fundamental ways [microbes] can influence each other is just by taking up and releasing chemicals into the environment,” he says. “Will they compete by trying to eat the same resource? Or will they facilitate each other by one secreting a product that helps another?”

Model Behavior

To make sense of microbial interactions, Harcombe starts with a model of how he expects microbes to interact. At its core, a model is a tool that describes how the output of a system depends on its inputs. Harcombe’s models consist of equations that relate the abundance of two microbial species, the output, to defined inputs like available nutrients and spatial distribution. Using the model, he can test what happens to the abundance of two microbial species when placed in an environment with defined nutrients. Importantly, this allows him to observe what the microbes do when they interact in the ways he suspects.

After generating computer-based results, Harcombe replicates this experiment in the lab.  Harcombe places two or more species of microbes together and tracks the resources they exchange. At the same time, he observes population-level characteristics such as growth rates, colony shape and species abundance. By simultaneously monitoring resources and community traits, he can answer questions about the spatial organization, the ratio between species and how available nutrients impact the exchange of resources. 

Sometimes experiments align with the results generated by his model. In other cases, they differ from — or even contradict — the model.

Two-Species Surprise

In one study, Harcombe and his colleagues wanted to investigate the dynamics between two microbial species, each of which depends on the other for survival. One (E. coli) was modified to require a nutrient—methionine—produced by the other (S. enterica). S. enterica, for its part, needs acetate excreted by E. coli.

One might expect these microbial colonies would thrive when cultured together — and when cultured alone, neither would make it. Per his computer model, that’s precisely what happened. When cultured in the lab, he saw the same results.

 Then came a surprise. In the lab, Harcombe introduced a virus that infects one species but not the other. Because each species’ survival depends on the other, Harcombe expected that infection would impact both. 

When he introduced a virus that only infected S. enterica, his prediction was spot on. When he introduced a virus that only infected E. coli, he ended with fewer E. coli as predicted, but surprisingly, he also found more S. enterica relative to pre-infection numbers. What was he missing?

Answering that question experimentally would have required painstaking tests, altering one variable at a time until he understood what was happening. While that approach might eventually provide an answer, it would be costly. Without knowing which explanations were plausible, Harcombe could spend years testing possible mechanisms responsible for the odd result. 

Productive Insights

Enter modeling. If the microbes weren’t behaving the way he expected, there must be something else; different metabolites or different types of interactions to explain the observations. So rather than running experiments to identify the mechanisms at play, Harcombe put those possibilities into the model. Once he found a model approximating his observation, he could confirm his conjecture with additional experimentation.

Harcombe suspected some E. coli developed resistance to the virus, protecting them from extinction. He further postulated that others would burst and release a wealth of nutrients. Together, they might provide an abundance of the acetate S. enterica need to thrive. Incorporating these mechanisms into his model yielded results that closely mirrored the experimental model.

“The biggest surprise to me in this study is that bacteria could get the elements they needed from other species by eating the dead,” says Harcombe.

Back in the lab, Harcombe ran additional experiments to test the model. For the most part, they supported his hypothesis. The only thing missing was a second consequence of E. coli developing a resistance to the virus. Virus-resistant E. coli produced more acetate than their nonresistant counterparts.

Harcombe is pleased with the way the experiment-plus-model approach has boosted his ability to solve microbial mysteries, and how the solutions stand to benefit humanity. Harcombe and colleagues use these models to predict how microbes respond to antibiotics and engineer interactions into microbial communities in soils to render crops more resilient to a changing climate. “Going forward, I’m really optimistic that we will be able to continue to develop our understanding of fundamental processes and apply it in these different areas.”