(4fg) Decipher the Complexity of Natural Microbial Communities

Decipher the complexity of natural microbial community

Microbes play an important role in plant/human health, carbon/nitrogen recycling, and biomaterial production. Most of them function in nature as communities, such as human microbiome and soil microbiome. High throughput sequencing has successfully identified who they are in the microbial communities. However, how the microbes interact with each other is still unknown. And the major bottleneck is the lack of tools to decipher the complexity of natural microbial communities. For example, there are normally thousands of species and trillions of cells in a typical human or soil microbiome, which results in hundreds of thousands of pairwise interspecies interaction. And most of the species are unculturable. In my postdoctoral work, I have been developing droplet-based high throughput methods to measure the microbial phenotypes and interactions without prior-isolation, which could phenotype a hundred of microbes in a hundred of conditions in one day, and screen hundreds of thousands of pairwise interactions in one pot. These methods have yielded specific insight to the phenotypes of species and the mechanisms of interspecies interactions.

As I transition to an independent career, I plan to apply those methods to improve our understanding of human and soil microbiomes by directly measuring the phenotypes of each species and the interaction between each other. In addition, to furtherly study the genetic function of the target strain in the community, I have been applying high throughput genetics to identify its genome-wide gene functions during phage infection and biofilm formations.

For my long term goal, I plan to build a high throughput center to expedite the microbiome research by upgrading the whole pipeline using microfluidics and high throughput genetics. The planned high throughput center will be able to do high throughput microbial isolation, single cell sequencing, high throughput RNA-seq, high throughput microbial phenotyping, and microbial interactions screening.

Research experiences:

I have been trained as a chemical engineer with in-depth understanding and skills in biology. My scientific research journey started from studying the polymerization of biodegradable polymers with Dr. Linbo Wu as a master student at Zhejiang University. During my master program, I designed, modeled, and scaled up the melt/solid polymerization of L-lactic acid to produce a biodegradable polymer in a cost-effective way.

Then I switched my gear from materials to the microbe-material interaction, biofilms, and associated microbial antibiotic tolerance in my PhD study with Dr. Dacheng Ren at Syracuse University. Microbial antibiotic resistance/tolerance is a critical problem in public health. Biofilms and persistence strain formation are two major reasons for antibiotic resistance. During my PhD program, I discovered the role of material properties, in particular the stiffness of the materials, on the biofilm formation, human immune cell responses, and discovered a potential drugs therapy of [(Z)-4-bromo-5-(bromomethylene)-3-methylfuran-2(5H)-one] to treat the antibiotic tolerance bacteria.

The natural microbial community, however, is way more complex than the model biofilms. As I seek the solution to study the complex natural microbial community, I realized that the combination of microfluidics and high throughput sequencing could provide a unique opportunity to decipher this complexity. Then I joined Lawrence Berkeley National Laboratory and started working with Dr. Adam Arkin to develop high throughput methods to uncover the complexity of natural microbial communities. Millions of reactions were manipulated parallelly in microdroplets, and sequenced in one batch to identify the growth of millions of cells and interactions with each other.

In my postdoctoral research, I have built multiple droplet-based microfluidic platforms and demonstrated their application in microbiology research. They are (1) a droplet-based cultivation-barcoding-PCR platform to super high throughput screen microbial interactions, (2) a droplet-based parallelly cultivation and sequencing method to measure phenotypes of hundreds of strains in one pot, (3) an agarose droplet-based sorting method using commercial cell sorter to sort active microbes growing in agarose droplets, and (4) a simple, cost-effective and automation-friendly direct PCR approach for bacterial community analysis.

In addition, I have been working on a high-throughput, droplet-based mass spectrometry and sample-storage platform to identify and isolate functional microbes. This method will permit linkage of a metabolic profile to the small communities in hundreds of thousands of droplets, and enable the follow-up study by relocating and cultivating the target communities through the storage.

Teaching Interests:

The reason I would like to pursue a career in academia is because I love both research and teaching. I would like to be an educator to influence young students to achieve their dreams. This is why I have been dedicated to teaching since I was a graduate student. As a teaching assistant in graduate school, I have teached Heat and Mass Transfer, Process Control, and Biological Principles for Engineers, and obtained Syracuse University Outstanding TA award which is the highest honor for the achievement of teaching assistant at Syracuse University. At Berkeley Lab, I have enrolled in the teaching scholar program and actively participate in various K-12 education events. Since the beginning of this year, I have been developing a curriculum of Heat and Mass transfer educational kits for K-8 students.

With the teaching enthusiasm and interdisciplinary training in chemical engineering, materials, microbiology, microfluidics, and genetics, I am prepared and excited to teach a range of classes, such as heat and mass transfer, process control, the principles of polymerization, application of microfluidics in biology, microbial genetics, microbial community analysis, and high throughput sequencing.