Microbial Evolution

Quick Guide

• • Lab work: basic microbiology; examining antibiotic resistance

  • Pros: straightforward techniques, connection to human health

  • Challenge: understanding evolution

Overview of the Microbial Evolution project area:

Experimental evolution

When you think of an evolutionary biologist, you might think of a paleontologist who digs for fossils of animals that lived millions of years ago. We tend to think of evolution as a slow process that takes millions of years. However, many evolutionary biologists actually work with microbes, like viruses and bacteria. Microbes evolve rapidly due to their short generation times and large population sizes. An Escherichia coli cell can divide into two cells in just 20 minutes, when growing under its optimal conditions, and a single test tube of E. coli can contain more cells than there are humans on Earth. It’s also possible to stick a culture of E. coli into a freezer set to -80^oC, keep it there for months or even years, and then revive the cells later. This allows researchers to directly compare evolved E. coli populations to their ancestors. Imagine being able to revive a population of Homo erectus (our early hominid cousins) and compare them to modern-day humans! These features make bacteria incredibly useful for studying evolution in action.

Experimental evolution is the use of lab or controlled field experiments to investigate evolutionary processes and dynamics, often using microbes. Simple experiments with bacteria have been extremely powerful, informing us about the processes of evolution like mutation and natural selection. For example, researchers in Richard Lenski’s lab at Michigan State University have been evolving 12 populations of E. coli since 1988. The researchers transfer the 12 populations into fresh growth media every day, and the populations have now undergone 75,000 generations and provided data for hundreds of important research articles. (The same number of generations in humans would take over 2 million years!) Findings about evolutionary processes aren’t just useful for our theoretical understanding of evolution, but can also be extremely relevant for public health. For example, the COVID-19 pandemic and the proliferation of variants demonstrate the impact of novel mutations. In this research area, you will use experimental evolution with E. coli to explore another major public health concern—the evolution of antibiotic resistance.

Antibiotic resistance

Antibiotics are drugs that stop bacterial growth or actively kill bacteria. For thousands of years, people around the world have been using medicines that contain natural antibiotics. For example, traces of the antibiotic tetracycline have been found in skeletal remains of ancient Sudanese Nubians (Bassett et al. 1980), and poultices of mouldy bread were used to treat wounds in Egypt, Greece, and China more than 2000 years ago (Hutchings 2019). Modern antibiotic drugs were discovered in the early 1900s, and mass production of the first widely used antibiotic, penicillin, began in the early 1940s (Hutchings 2019). Penicillin was referred to as a “miracle drug,” and it is estimated that penicillin saved the lives of one in seven British soldiers during the Second World War. The 1950s and 1960s saw a flurry of discovery and development, with dozens of new antibiotics from many different sources.

Since the discovery of modern antibiotics, we’ve taken for granted that scrapes, surgery wounds, and other injuries or medical treatments won’t result in severe and incurable bacterial infections. Many antibiotics kill or inhibit growth of bacteria by blocking key cellular functions, like RNA and protein synthesis, and are extremely effective. However, bacteria can acquire mutations in their genomes that prevent the antibiotics from working. These “antibiotic-resistant” bacteria are able to grow despite the presence of antibiotics, and antibiotic resistance mutations can even be transferred to other strains and species of bacteria through a process called horizontal gene transfer. The overuse of antibiotics in health and agriculture has caused widespread antibiotic resistance (Hutchings 2019), with many common antibiotics rendered useless. Few new antibiotics are being developed, partly because it’s not profitable for pharmaceutical companies to produce an antibiotic that will quickly become less powerful. MRSA (“methicillin-resistant Staphylococcus aureus”) is a strain of bacteria that is resistant to most antibiotics. In 2017, over 100,000 people in the United States became infected with MRSA, and these infections accounted for 20,000 deaths (Kavanagh 2019). Infections of the gastrointestinal tract by antibiotic-resistant strains of Clostridium difficile bacteria are also increasingly common after antibiotic treatment. A report from the United Kingdom predicted that, without action, 10 million people per year may die from antibiotic-resistant bacterial infections by 2050 (O’Neill 2016).

In order to prevent antibiotic resistance, we need to understand it. There are many questions surrounding antibiotic resistance that are important both for basic evolutionary biology and for public health: 1) How does the concentration of antibiotic affect how readily resistance evolves? 2) How does the population size of the bacteria affect the likelihood of resistance evolution? 3) Once bacteria become resistant to an antibiotic, does the level of resistance continue to increase through subsequent mutations? 4) Which mutations give rise to resistance, and how do they prevent the antibiotic from functioning? These questions can all be explored with laboratory experimental evolution.

Microbial communities

Bacteria live in communities of many interacting species. For example, the human gut microbiome contains hundreds of different species. Bacteria are often studied in isolation in the lab, but that is not how they live in the wild. This means that some of the simple lab experiments with a single species may not capture the more complex reality in nature. In the Harcombe Lab at the UMN BioTechnology Institute, researchers are trying to understand the impact of antibiotics in communities of multiple species of bacteria. Interactions between different bacterial species have been shown to alter the impact of antibiotics on pathogens (Bottery et al 2021). For example, in some cases pathogens can be protected by other species that break down antibiotics such as ampicillin (Sorg et al 2016). In other cases, species interactions can cause antibiotics to actually increase the abundance of pathogens, by allowing “competitive release” of the pathogens from antibiotic sensitive competitors that were suppressing the pathogen prior to treatment (Parijs and Steenackers 2016, Varga et al 2021). However, it isn’t all bad news. Researchers have also found that sometimes bacterial interactions make species more susceptible to antibiotics. For example, members of the Harcombe lab have shown that drug resistant Salmonella enterica can be inhibited by low doses of antibiotics that kill other species on which the S. enterica relies for nutrients (Adamowicz et al. 2018). We are only beginning to understand how these ecological effects of species interactions may influence the evolution of antibiotic resistance.

Experiments

In this research area, students will work with the bacteria E. coli to understand the evolution of antibiotic resistance. Students will acquire and strengthen many skills. For example, you will learn how to work with bacteria without contaminating your cultures with other microbes that may be on your skin, clothes, or in the air. You will learn how to wield a pipette to accurately dilute and plate your bacteria on Petri dishes to measure the density of a population of bacteria. You will create environments with different concentrations of antibiotics and subject your E. coli populations to increasing antibiotic concentrations. You will compare your evolved E. coli populations to ancestral populations to measure changes in fitness. Finally, you will sequence your bacteria to identify mutations that may confer resistance. Beyond these practical skills, students will learn how to formulate hypotheses and predictions, collaborate, analyze data, and communicate their findings.

Resources:

Gagneux 2006 The Competitive Cost of Antibiotic Resistance in Mycobacterium tuberculosis

Adamowicz et al. 2018 Cross-feeding modulates antibiotic tolerance in bacterial communities

Adamowicz et al. 2020 Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica