I think I’ve spent a great amount of time talking about the process of lateral gene transfer (LGT) in general, the main mechanisms, and even how some genes are more prone to being shared than others. I’ve also mentioned the challenges a foreign gene might face upon entering a new bacterial cell. But what I haven’t mentioned so far is: how do we actually study lateral gene transfer? How do we detect it? This question has become one of the most fascinating and challenging parts of my PhD! Although it’s easy to summarize LGT in theory, detecting it is both a technical and conceptual challenge.
It was first observed in 1928, when a British bacteriologist named Frederick Griffith made a groundbreaking discovery revealing how bacteria share genetic information. He studied two strains of Streptococcus pneumoniae, one virulent (causing disease in mice) and one nonvirulent. Griffith found that when he killed the virulent bacteria by heat and mixed their remains with the nonvirulent bacteria, the harmless bacteria somehow became deadly. So, this showed that some material from the dead bacteria was taken up by the living ones, transforming their behavior in real time. We now know that material was DNA! This process is called transformation, one of the main mechanisms of LGT that I’ve mentioned before in previous posts. Later works in the 1940s and 1950s identified other routes: conjugation, where bacteria transfer DNA by direct contact, and transduction, where bacteriophages can move DNA between bacterial cells.
Sometimes, it’s just not possible to run experiments, especially with complex or unculturable microbial communities. So, in these cases, the focus shifts to detecting signals of gene transfer hidden within vast amounts of genetic data. This is where bioinformatics, a field that uses computer science and statistics to analyze DNA sequencings on a large scale, comes in. This field was completely new to me a few years ago when I first began learning and applying it during a research internship on the termite gut microbiome: working with metagenomes to piece together which microbes are there in the termite gut, and what they were doing. When you’re new to this field, it can honestly feel pretty overwhelming at first since the scale of the data is huge, and I won’t even get started on the coding part! Slowly learning to code was very frustrating, but weirdly, somehow, I quickly fell in love with it all. There’s just something so rewarding about watching how these computational tools uncover patterns that we couldn’t see otherwise. Biology increasingly relies on bioinformatics to make sense of complex data. We use various computational methods to sift through genomes and even entire microbial communities, searching for signs that genes have moved from one organism to another.
This task is easier said than done because we often have to distinguish between genes genuinely acquired from unrelated organisms and those that naturally look different due to evolutionary pressures, or even noise in the data. What I mean by this is that one common and insightful approach is to examine patterns in DNA sequences, called genomic signatures. These signatures are patterns unique to each organism’s DNA, made up of the four letters A, C, T, and G that represent the nucleotide bases. Just like how people have unique traits, each microbe tends to also have distinctive characteristics in how their genes are coded or in the frequency of certain nucleotide bases.
When we’re looking at thousands of rows upon rows of this data, all these ACTGs that you see in the picture above, we rely on computational tools to scan through these massive genetic datasets. What we’re searching for are genes or segments that display weird or unique characteristics that deviate from typical DNA patterns, and spotting these deviations raises a red flag that this fragment might actually be foreign, coming from a different organism. These pattern-based methods, called parametric methods, look for these unusual DNA patterns. They are especially useful for flagging potential gene transfers that are recent and still clearly different from the host genome. However, over time, transferred genes can blend in and start to resemble the host’s DNA, making it harder to detect older transfers. Now you can see how it can get pretty confusing. Additionally, sometimes genes might naturally have unique variations, so this can mimic lateral gene transfer signals as well (false hits). This is why pattern-based methods alone don’t provide definitive proof of gene transfer. The real power comes when these genomic signatures are combined thoughtfully with other evidence, like evolutionary relationships, to build a more complete picture.
This brings us to building phylogenetic trees! I’ve actually been building so many of these trees recently, and it’s fascinating how these family trees of genes and species can reveal stories about gene transfer that other methods just can’t show. This is a powerful approach, and in the next blog we’ll take a closer look at why phylogenetic trees are often considered the gold standard for detecting gene transfer.
Image created with Imagen3 with the prompt:
Generate an image of a digital screen with a DNA data background, showing a nucleic acid sequence, for genetic research.
