The first paper I want to go over provides a nice introduction into this topic broadly [1]. It centers around the human microbiome but all of the concepts can readily be applied to other microbiome systems. The first point I want to highlight in this paper is:

The goal is to understand how microbial traits, and their underlying genetic modifications influence both ecological and evolutionary processes. I think this paper does a wonderful job outlining evolutionary processes and ways to go about measuring them in microbial systems. For instance, interpreting recombination metrics and the challenges in identifying such events within communities. Further, utilizing and understanding various genome estimates (e.g., dN/dS, FST, recombination : selection ratios) are extremely powerful tools to understand population genetics in microbial systems. Inevitably, it comes down to connecting genotype to phenotype, a major goal for my research as well. Definitely worth a read!

So then what qualifies as peak fitness? The idea that natural selection favors traits at any given time is often a misnomer. Natural selection operates at the level of "good enough" with many genes being suboptimal. This next preprint addresses this idea that modules will steadily be improved; however, different modules are improved with fitness effects while others will stall [3]. Especially in regards to microbial clonal interference, modules will essentially compete against one another preventing optimization of other modules, or at least delaying. This observation may explain how, given similar environmental conditions, populations may converge on similar fitnesses, albeit at different trajectories. A recent paper discusses these ideas on how cryptic genetic variation can assist in maneuvering these fitness landscapes (side Figure) by providing access to diverse, otherwise inaccessible fitness trajectories [4]. Together these results suggest that natural selection is a process, slowing improving certain modules to navigate a fitness landscape. However, epistatic interactions render complex fitness landscapes with beneficial mutations in one module affecting fitness trajectories, and possibly leading to suboptimal peaks.

Almost everyone has seen the side figure of the electron transport chain (ETC). It depicts electrons being transferred via membrane-bound proteins to generate a proton (H+) gradient to generate cell's energy, ATP. Oxygen is typically depicted as the terminal electron acceptor and is reduced to water. This is pretty much where my knowledge of the ETC stopped at Biology 101. However, there is so much more to this! I hope to get everyone as excited as I am about how the ETC related to bacterial physiology.

Overall, they recovered 800+ reconstructed genomes in this genus with loads of finer-scale genetic diversity (36 distinct "species" defined at 95% ANI). These clades not only exhibited differential geographic distributions but also were constrained in their extent of gene sharing; in other words, gene content was highly correlated to phylogenetic distance, not geographic proximity (side figure). Probably most interesting was the discovery that a set of integrative conjugative elements (ICEs) were shared across lineages that encoded major functional genes for the Mesorhizobium-chickpea symbiosis.

Next paper will be a shameless plug on my end, as I am extremely excited about the release of my latest paper investigating soil population dynamics. As stated above, we know little of the factors structuring soil populations at varying spatial scales, including global and regional patterns. To get to this level, we first had to establish what would constitute a microbial species. Using geographic sampling across a regional climate gradient, we combined metagenomics, genomics, and physiological assays to define highly similar genomic sequence clusters that share phenotypic traits (paper here). To get at intra-species population dynamics, we examined the phylogenetic structure, extent of recombination, and flexible gene content of this genomic diversity to infer patterns of gene flow [5]. In summary, "our results indicate that bacterial soil populations, similarly to those in other environments, are structured by gene flow discontinuities and exhibit distributional patterns consistent with both isolation by distance and isolation by environment". Just want to add here this project was only possible through the funding by the DOE-SCGSR - if anyone is interested in applying and/or have any questions, please don't hesitate to ask!

On the other hand, metagenomes sound extremely tempting, but these are not without their obstacles. For example. environmental metagenomes suffer from a lack of representative genomes in databases (although it is getting better!) since most genomes are isolated from human-associated systems. MIDAS is an extremely popular metagneomic pipeline to characterize bacterial communities, which is nice since it claims to give strain level profiling of microbial species (as defined by 95% ANI). However, as most metagenomic pipelines go, it is nearly impossible to characterize the microbial communities in environmental samples with coverage often below 10% of the community (see figure). And this is based on single-copy, phylogenetic marker genes. Now imagine trying to resolve functional genes??!?!

Total number of conserved (core) genes and total (pan) genes observed in Curtobacterium isolates (N=75). As you sample more and more genomes, a general trend is a decreasing core and increasing flexible genome.

The existence of this large genetic variability is quite interesting. One major question is whether these genes are neutral, deleterious, or adaptive? Some have argued that the presence of these flexible genes are largely acquired by HGT while others suggest these genes are under frequency-dependent selection (for more information on this part, see [1]). Under a neutral model, most flexible genes acquired by HGT are never actually expressed and thus do not contribute to fitness effects [1]. However, as the above Tweet by Dr. Baltrus above suggests, understanding these processes will require extensive sampling of strains from similar environmental conditions. Far too often, these broad pangenome comparisons are conducted without regard for the source of isolation and/or sampling disparate strains (either taxonomically or environmentally).

All microbial communities exist on environmental gradients of temperature, moisture, pH, etc. How communities will respond to these environmental variables requires an understanding of the variation among taxa [5]. These ideas have been around for decades in studies with macrobes, but only recently have microbial ecologists begun to assess community responses along gradients.

​For instance, I will highlight two papers here:
Glassman et al. used a large-scale reciprocal transplant experiment to look at how decomposition rates would change across a climate gradient co-varying in temperature and precipitation [6]. Another recent study looked at how salinity functioned as an environmental filter to investigate the aggregate trait response and its link to community structure [7].

Much to my surprise, there was a lot of controversy with the release of the paper by A Martiny. Twitter was a whirlwind and it eventually led to a formal response in ISME by some outstanding microbial scientists [3]. This response concentrated on three major issues:
​
1. 16S rRNA gene is biased towards certain taxa
2. Database and analyses bias results (Figure)
3, Inferring 16S to cultures

Based on these computational methods, HUGE advances have been made to characterizing microbial communities, including the discovery of novel lineages. However, a recent preprint very much challenged the validity of these methods, insomuch, to declare that some of these novel lineages derived from MAGs are "unnatural constructs" that were stitched together from environmental DNA [5]. This was absolutely crazy! This paper decidedly claims that binning artifacts and MAGs in general provide a systematic problem for the way we assess microbial diversity.

SPOILER, what he found was the main phylogenetic anomaly described in the preprint can be corrected and this effect disappears (side figure). Basically, the preprint relied on selecting MAGs and isolates and constructing phylogenetic trees for conserved, single-copy ribosomal proteins. They found the MAGs differ from the isolates mainly in the phylogenetic distance between trees was higher than comparisons to isolates (top panel reproduced by ACC). ACC attempted to mitigate these effects by having a 1:1 ratio of representative isolates at the genus and order level; for each MAG there was a similar isolate genome to compare. This reduced or removed the signal altogether (bottom panel).

​From my personal experience in soil microbial communities (leaf litter to be exact), I tried to assembly and bin MAGs for the most abundant bacterium in the system (yes, Curtobacterium). This system harbors >2000 OTUs (defined at 97% similarity) and Curtobacterium comprises anywhere from 8% (from metagenomic data [6]) to 16% (from 16S rRNA data) of the microbial community. We thought, since Curto is so abundant, let's assemble, bin, and analyzed the Curto MAG (top side figure). While we were able to extract two Curto bins, after curation, they did show some inconsistencies with our known isolates from the system using ribosomal genes (bottom side figure). In particular, they formed large outgroups or exhibited long branch lengths. For me, it seemed we are collapsing a lot of fine-scale genetic variation into a conglomerate Curtobacterium "genome", essentially creating a genus-specific mosaic genome. In the end, we decided to not publish the MAGs and concentrate on the genomic and phenotypic variation of our cultured isolates [6].

​For me, this is where the MAG idea falls short or at least needs to assessed the same way researchers are doing for 16S rRNA. Specifically, start to ask the same questions, ​How do we know we captured the diversity in a system and have a representative sample or sequence? The same way we breakdown 16S data, we need to start questioning whether MAGs (or at least low quality MAGs) are actually representative members of the microbial community. Or are some of these MAG constructs providing the same resolution as 16S, collapsing relevant genetic variation into these coarse representations of the true diversity? I don't know the answer to these questions but I think researchers definitely need to start considering the quality of reported MAGs and genomes for that matter. Either way, these past few weeks have provided great conversations and stimulated a lot of relevant talking points for the field. I hope to see more in the future!

Natural products (NPs) are a broad definition including the production of small molecules (usually in the form of secondary metabolites) derived from natural environments. The discovery of these compounds from nature have been instrumental in modern medicine (thanks penicillin!) with some estimates ranging from 50-70% of clinical drugs are derived from natural products or its derivatives. This led to the explosion of new natural products (right Figure) over the first few decades, but the last 10-20 years has seen this number plateaued. Further, we are limited in our ability to describe new natural products with novel structures. One reason for this is the detection of these compounds are mostly derived from bioassay or chemical signature-guided isolation [1]. As such, researchers are turning to new methods to identify novel NPs and elucidate their structures. Below I will outline a few approaches I have seen in my brief time as a postdoc in this field. 

The most studied BGCs allowing for identification of these genetic signatures are the polyketide synthases (PKSs) and the nonribosomal peptide-synthetase (NRPS) enzymes. Tools such as antiSMASH and NaPDoS can identify BGC regions and give a rough estimate to known, well-characterized BGCs. However, linking these BGCs to compounds remains a critical step for NP research. More recent tools, such as BiG-SCAPE, seek to compare BGCs across strains to cluster and dereplicate BGCs. Still subtle differences in the BGC (gene gain/loss) can result in variants of molecules and affect their potency and, therefore, their application to human health.

One way to express and characterize these molecules is through the heterologous expression of whole BGCs. This requires cloning the BGC into a vector and then finding a compatible host to express the BGC. This is another complicated factor as expression of BGCs in hosts can lead to inactivation as this process is highly dependent on host machinery, biochemistry, genetics, etc. However, utilizing this approach can identify the compound of the BGC easily in a host. For example, Salinispora can act as a heterologous host in expressing large BGCs by directly introducing cloned vectors (see left figure)[4].

Historically, ecologists and evolutionary biologists have sought to answer a central question:

​These processes governing community dynamics can be reduced to four main topics:
1. Selection - the environment selects for different species based on fitness differences
2. Ecological Drift - species abundances fluctuate at random due to demographic stochasticity
3. Dispersal - high dispersal will increase local diversity and decrease beta-diversity
4. Speciation - novel lineages can arise to increase species richness and beta-diversity

1. Spatial (environmental) Heterogeneity
2. Niche (resource) Partitioning 
3. Predation  (top-down control)
4. Competition and Species Interactions
5. Pathogens and Virulence
6. Rapid Evolution and Speciation
​7. Temporal Partitioning

1. Priority Effects
2. Community Assembly (if drift > selection)
3. Neutral Theory

I think it makes sense to start here. Bacteria have multiple ways to exchange genetic material. The most common is homologous recombination between closely-related organisms. Despite this, we still have little understanding of the adaptiveness of exchanged genes (especially in free-living environmental microbes). What we do know is that the rate of homologous recombination is expected to decrease exponentially with increasing sequence divergence (see figure)[1]. As a side note, everyone should read the review of bacterial population genetics by EPC Rocha [1].

One of my major questions concerns how prevalent HGT is in natural systems. The first paper I want to highlight does an exceptional job showing that recombination between an invader E. coli strain in a mouse model outweighs adaptive evolution via point mutations [4]. What makes this so interesting is the HGT is mediated by bacteriophages allowing for the colonization of an invader E. coli strain to an intact gut microbial community. What follows is a complex clonal evolution where clonal interference lead to the transfer of new genes while maintaining high genetic diversity (see figure right).