Ecology

Data collectionData were collected in the Sunshine Coast region in Queensland, Australia (− 26.65° S, 153.07° E), from February to April 2019. All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols and methods were approved and carried out in compliance with the ARRIVE guidelines under the approval of the University of the Sunshine Coast (USC) Animal Ethics permit (ANA/16/109T); Human Ethics permit (A181114) and in conjunction with the Sunshine Coast Council (SCC) Local Law permit (OM18/19).
Animals used in the trialsWe recruited 10 domestic cats through an approved media release (males n = 6; females n = 4; weight 2.8–8.4 kg; age 1.5–12 years; body length 38–53 cm; foreleg length 16–19 cm). As per the Sunshine Coast Council local law requirements, all cats had to be neutered, registered and microchipped to participate in the study.
EquipmentWe fitted each cat with a retail harness, to which we attached a tri-axial accelerometer (AX3; Axivity, Newcastle University, UK; 23 × 32.5 × 8.9 mm; 11 g) using cable ties (Fig. 1a). The accelerometer was initialised using the Open Movement Graphical User Interaction application (OMGUI; V1.0.0.37). Because a trade-off exists between data resolution and battery life, we logged data at 50 Hz and with a dynamic range of ± 8 g, with a 13-bit resolution, similar to a previous study23. When combined with the in-built memory storage capacity of 512 MB, and battery limitations, this configuration resulted in a maximum of 8–14 days of data collection. The quartz Real Time Clock and calendar provided a timestamp with a frequency of 32.768 kHz and a precision of ± 50 ppm, with manufacturer specifications indicating a drift of 0.18 s per hour. To overcome this drift over the eight days, we calibrated devices by video recording the signals of five claps/taps on the device, at the start and end of each individual data collection period, and also at random times during the day.Figure 1(a) The anatomical position of the accelerometer (AX3) on the sternum of the cat. (b) The activity of swatting stimulated by the use of a feather. (c) The axis orientation of the accelerometer planes, which are represented in the accelerometer trace data in the MATLAB interface. Fore-aft (surge), lateral (sway) and dorso-ventral (heave) movement is reflected in the X, Y and Z signals.Full size imageWe positioned the accelerometer on the scapular brace-strap of the harness, inverted such that the accelerometer was on the sternum of the cat (Fig. 1a–c). Field trials over four months on four cats in the study determined that this position, in comparison with mounting on the dorsal cranial median plane, did not interfere with the animals’ balance; it also removed all of the abnormal movement behaviours and unnecessary discomfort to the cat2. The positioning of the logging device on the frontal anterior, median plane, resulted in the primary axis for fore-aft (surge), lateral (sway) and dorso-ventral (heave) movement to be reflected in the X, Y and Z signals, respectively (Fig. 1c).The accelerometer harness was used in conjunction with the CatBib for the relevant treatment periods. The total combined mass of the harness, accelerometer and Catbib was to 34.1 g, with a minimum cat mass of 2.8 kg, suggesting the equipment did not weigh above 1.2% of total body weight in any cat studied. The CatBib is a prey protector device, manufactured from a lightweight, washable neoprene material, that is attached to a cat’s safety collar (Fig. 1b). The dimensions of the bib are 17.5 mm × 17.5 mm × 6.5 mm, with a total mass of 23.1 g and it is purple in colour. All cats adjusted to the harness and CatBib within the first hour of deployment and no subsequent adjustments were required. All cats had unrestricted access to roam freely outside during the eight days of field trials.To capture training data, each cat was filmed with a GoPro + 3 Hero device (H.264—1920 × 1080; f/2.8; 60 fps), undertaking natural or stimulated active behaviours through play (Fig. 1b). These activities or behaviours were manually documented to track the activity, date and the timestamps. We conducted two treatments over the eight days: in the first, cats were fitted with CatBib, whereas in the other, bibs were not worn. Each treatment was conducted for four consecutive days, and the sequence of treatments for each cat was randomised. The accelerometer device on the harness was left on the cats for the entire field trial and recorded continuously for the eight days (~ 192 h per cat; total = 2304 h).Data analysisEach accelerometer trace file was exported as a raw binary file through OMIGUI and imported into a custom-built MATLAB GUI. To build our training dataset, the video file timestamp information, determined using Mediainfo (version 18.08, 2018), was used to define the start time for a subset of the accelerometer trace, and the video length to define the end point (Supp. Fig. 1). Offsets between the accelerometer trace and video files were determined using the closest calibrated tap signal trace for each day. We were able to watch each video file in synchrony with the accelerometer trace, and manually annotate each movement/activity from the video files to the accelerometer subset (Clemente et al.)24 (Supp. 1.1. Matlab interface instructions; Supp. Fig. 1).We grouped activities according to behaviour into three classes: Sedentary, Eating and Locomotive and Hunting. We further subdivided each group into behaviours. Sedentary included lying, sitting, grooming and watching; Eating and Locomotive included—eating/drinking, walking, trotting; and for Hunting—galloping, jumping, pouncing, swatting, biting/holding (Supp. Table 1).The accelerometer trace was then further divided into rolling epochs of 50 samples in length, using 1 s duration at 50 Hz to ensure intensive acceleratory bursts of short duration such as jumping and pouncing are captured. The behaviour with the maximum frequency within each epoch was assigned as that epoch’s label. Raw accelerometer data in each epoch was assigned as that epoch’s label. Raw accelerometer data in each epoch was summarized using 26 of the most effective variables for procedure accuracy identified by Tatler et al.25. We included: axial acceleration (X, Y, Z),mean acceleration (X, Y, Z); minimum acceleration.(X, Y, Z); maximum acceleration (X, Y, Z); standard deviation of acceleration (X, Y, Z); Signal Magnitude Area, minimum Overall Dynamic Body Acceleration (ODBA); maximum ODBA, minimum Vectorial Dynamic Body Acceleration VDBA; maximum VDBA, sum ODBA; sum VDBA; correlation (XY, YZ, XZ); skewness (X, Y, Z); and kurtosis (X, Y, Z)25 (See Supp. Table 2 for a detailed description of each variable). Finally, we coded the two treatments: BibON and BibOFF and included this information in the training data set.Classification modellingTo determine whether we could predict cat hunting behaviours, we analysed the training data sets using a Kohonen super Self Organising Map (SOM) in the R package ‘Kohonen’ version 2.0.1926,27.Machine learning procedures such as random forest and support vector machines each provide computationally powerful methods of data classification, however each method is not equal in how it visualises its output. SOMS have been used in behavioural studies10,13,14,15 for their ability to efficiently create easily interpreted maps and identify patterns of behaviour. Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning. In this study, a self-organising map algorithm was chosen for its efficiency in visualising multi-dimensional and complex data onto an easily interpreted two dimensional map output. SOMs also have the ability to visualise which variables are most influential with the use of component planes (Fig. 3b–e) and unlike other procedures mentioned, SOMs use cluster analysis which in this study aids in identifying similar behaviours and visualising them closer together (in clusters) on the map output.To prepare data for the SOM function a random sample of the classifiers for the trained data were extracted, along with their associated behaviour, and combined into a list with 2 elements (measurements and activity). This list was then input into the function supersom.R function, with the grid argument defined using the somgrid.R function [e.g. supersom(TrainingData, grid = somgrid(7, 7, “hexagonal”))]. The 7 × 7 grid function was chosen based on a sensitivity analysis exploring all combinations of grids between 4 to 9 units in length (n = 36, Supp. Fig. 2). The 7 × 7 grid represented the grid which produced the highest accuracy and map symmetry26,28,29. We further tested the effect of the number of times the complete data set is presented to the network by varying the rlen argument in the supersom.R function. We found no obvious increase in overall accuracy with increased iterations, and therefore used the default length of 100 times (Supp. Figure 3). Each supersom procedure created was then tested using the predict.R function, with the newdata argument directed to a testing data set, which was a similar 2 element list containing all samples not included in the training data set. The result of this test was then assembled into a confusion matrix using the table.R function with predictions compared with the known behaviours in the test data set [e.g. table(predictions = ssom.pred$predictions$activity, activity = testData$activity) ]. A confusion matrix is a table where each row represents the instances in a predicted class, while each column represents the instances in the observed class, allowing mislabelled epochs to be easily identified. The confusion matrix was then finally used to compute four specific accuracy metrics—sensitivity (or recall), precision, specificity, as well as overall accuracy.To identify relationships between the size of training dataset, we trained a randomised subset of the BibOFF training data, to predict the remaining BibOFF data from all cats. We tested 35 different subset sample sizes from 100 to 100,000, replicating each sample size ten times (with replacement) to determine variation at each sample size.We then tested the extent to which accelerometer traces are modified by the presence of the CatBib. This modification was indicated by a change in overall prediction accuracy of the SOM between BibOFF and BibON treatments. To do this, we trained the SOM using a subset of the trained data for BibOFF and tested it against annotated classified BibON samples. In order to statistically compare results from bootstrap resampling, we took the median among bootstrap samples as the estimate of performance and quantified uncertainty using the corresponding 2.5th and 97.5th percentiles to represent credible 95% confidence intervals (CIs). We chose the median as a measure of central tendency, because resampling distributions were truncated at 1, so were skewed. If CIs for any pair of estimates (medians) do not overlap, then this is evidence of a significant difference between the estimates. If, however, one estimated median fell within the confidence interval for another estimate, then this was used as evidence of a lack of significant difference. For all other outcomes, differences are equivocal, and we interpreted them tentatively on the basis of the relative overlap in CIs.Finally we compared the output of the SOM with the output from a decision tree classification method using a random forest (RF) approach from the randomForest.R package30. We chose random forest as a comparison as this method has previously been shown to perform better than other similar methods (e.g. k-nearest neighbour, support vector machine, and naïve Bayes) when classifying behavioural data on free moving animals25,31. We trained both the SOM and RF procedures using the same 20,000 randomly selected epochs, and compared the overall accuracy for predicting the behaviour for the remaining ~ 192,000 epochs. The SOM was built using a 7 × 7 grid patterns, with the rlen argument set to 100. The RF was built with the number of trees set to 100 and the number of variables randomly sampled as candidates at each split set to 4. More

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Mya Breitbart has hunted novel viruses in African termite mounds, Antarctic seals and water from the Red Sea. But to hit pay dirt, she has only to step into her back garden in Florida. Hanging around her swimming pool are spiny-backed orbweavers (Gasteracantha cancriformis) — striking spiders with bulbous white bodies, black speckles and six scarlet spikes that make them look like a piece of medieval weaponry. Even more striking for Breitbart, a viral ecologist at the University of South Florida in St Petersburg, was what was inside. When she and her colleagues collected a few spiders and ground them up, they found two viruses previously unknown to science1.Although we humans have been focused on one particularly nasty virus since early 2020, there are legions of other viruses out there waiting to be discovered. Scientists estimate that there are about 1031 individual viral particles inhabiting the oceans alone at any given time — 10 billion times the estimated number of stars in the known Universe.It’s becoming clear that ecosystems and organisms rely on viruses. Tiny but mighty, they have fuelled evolution for millions of years by shuttling genes between hosts. In the oceans, they slice open microorganisms, spilling their contents into the sea and flooding the food web with nutrients. “Without viruses,” says Curtis Suttle, a virologist at the University of British Columbia in Vancouver, Canada, “we would not be alive.”There are just 9,110 named species listed by the International Committee on Taxonomy of Viruses (ICTV), but that’s obviously a pitiful fraction of the total. In part, that’s because officially classifying a virus used to require scientists to culture a virus in its host or host cells — a time-consuming if not impossible process. It’s also because the search has been biased towards viruses that cause diseases in humans or organisms we care about, such as farm animals and crop plants. Yet, as the COVID-19 pandemic has reminded us, it’s important to understand viruses that might jump from one host to another, threatening us, our animals or our crops.
The new scope of virus taxonomy: partitioning the virosphere into 15 hierarchical ranks
Over the past ten years, the number of known and named viruses has exploded, owing to advances in the technology for finding them, plus a recent change to the rules for identifying new species, to allow naming without having to culture virus and host. One of the most influential techniques is metagenomics, which allows researchers to sample the genomes in an environment without having to culture individual viruses. Newer technologies, such as single-virus sequencing, are adding even more viruses to the list, including some that are surprisingly common yet remained hidden until now. It’s an exciting time to be doing this kind of research, says Breitbart. “I think, in many ways, now is the time of the virome.”In 2020 alone, the ICTV added 1,044 species to its official list, and thousands more await description and naming. This proliferation of genomes prompted virologists to rethink the way they classify viruses and helped to clarify their evolution. There is strong evidence that viruses emerged multiple times, rather than sprouting from a single origin.Even so, the true range of the viral world remains mostly uncharted, says Jens Kuhn, a virologist at the US National Institute of Allergy and Infectious Diseases facility at Fort Detrick, Maryland. “We really have absolutely no idea what’s out there.”Here, there and everywhereAll viruses have two things in common: each encases its genome in a protein-based shell, and each relies on its host — be it a person, spider or plant — to reproduce itself. But beyond that general pattern lie endless variations.There are minuscule circoviruses with only two or three genes, and massive mimiviruses that are bigger than some bacteria and carry hundreds of genes. There are lunar-lander-looking phage that infect bacteria and, of course, the killer spiky balls the world is now painfully familiar with. There are viruses that store their genes as DNA, and others that use RNA; there’s even a phage that uses an alternative genetic alphabet, replacing the chemical base A in the standard ACGT system with a different molecule, designated Z.

Studies of the spiny-backed orbweaver found two viruses previously unknown to science.Credit: Scott Leslie/Minden Pictures/Alamy

Viruses are so ubiquitous that they can turn up even when scientists aren’t looking for them. Frederik Schulz did not intend to study viruses as he pored over genome sequences from waste water. As a graduate student at the University of Vienna, in 2015 he was using metagenomics to hunt for bacteria. This involves isolating DNA from a whole mix of organisms, chopping it into bits and sequencing all of them. A computer program then assembles the bits into individual genomes; it’s like solving hundreds of jigsaw puzzles whose pieces have been jumbled up.Among the bacterial genomes, Schulz couldn’t help but notice a whopper of a virus genome — obvious because it carried genes for a viral shell — with a remarkable 1.57 million base pairs2. It turned out to be a giant virus, part of a group whose members are large in terms of both genome size and absolute size (typically, 200 nanometres or more across). These viruses infect amoebae, algae and other protists, putting them in a position to influence ecosystems both aquatic and terrestrial.
Profile of a killer: the complex biology powering the coronavirus pandemic
Schulz, now a microbiologist at the US Department of Energy Joint Genome Institute in Berkeley, California, decided to search for related viruses in metagenome data sets. In 2020, in a single paper3, he and his colleagues described more than 2,000 genomes from the group that contains giant viruses; before that, just 205 such genomes had been deposited in public databases.Virologists have also looked inwards to find new species. Viral bioinformatician Luis Camarillo-Guerrero worked with colleagues at the Wellcome Sanger Institute in Hinxton, UK, to analyse metagenomes from the human gut, and built a database containing more than 140,000 kinds of phage. More than half of these were new to science. Their study4, published in February, matched others’ findings that one of the most common viruses to infect the bacteria in our guts is a group known as crAssphage (named after the cross-assembly software that picked it up in 2014). Despite its abundance, not much is known about how it contributes to our microbiome, says Camarillo-Guerrero, who now works at DNA-sequencing company Illumina in Cambridge, UK.Metagenomics has turned up a wealth of viruses, but it ignores many, too. RNA viruses aren’t sequenced in typical metagenomes, so microbiologist Colin Hill at University College Cork, Ireland, and his colleagues looked for them in databases of RNAs, called metatranscriptomes. Scientists normally use these data to understand the genes in a population that are actively being turned into messenger RNA in to make proteins, but RNA virus genomes can show up, too. Using computational techniques to pull sequences out of the data, the team found 1,015 viral genomes in metatrancriptomes from sludge and water samples5. Again, they’d massively increased the number of known viruses with a single paper.

The giant tupanvirus,found in amoebae, is more than 1,000 nanometres long and has the largest set of protein-coding genes of any known virus.Credit: J. Abrahão et al./Nature Commun.

Although it’s possible for these techniques to accidentally assemble genomes that aren’t real, researchers have quality-control techniques to guard against this. But there are other blind spots. For instance, viral species whose members are very diverse are fiendishly difficult to find because it’s hard for computer programs to piece together the disparate sequences.The alternative is to sequence viral genomes one at a time, as microbiologist Manuel Martinez-Garcia does at the University of Alicante, Spain. He decided to try trickling seawater through a sorting machine to isolate single viruses, amplified their DNA, and got down to sequencing.On his first attempt, he found 44 genomes. One turned out to represent some of the most abundant viruses in the ocean6. This virus is so diverse — its genetic jigsaw pieces so varied from one virus particle to the next — that its genome had never popped up in metagenomics studies. The team calls it 37-F6, for its location on the original laboratory dish, but Martinez-Garcia jokes that, given its ability to hide in plain sight, it should have been named 007, after fictional superspy James Bond.Virus family treesThe James Bond of ocean viruses lacks an official Latin species name, and so do most of the thousands of viral genomes discovered by metagenomics over the past decade. Those sequences presented the ICTV with a dilemma: is a genome enough to name a virus? Until 2016, proposing a new virus or taxonomic group to the ICTV required scientists to have that virus and its host in culture, with rare exceptions. But that year, after a contentious but cordial debate, virologists agreed that a genome was sufficient7.Proposals for new viruses and groups poured in (see ‘Adding to the family’). But the evolutionary relationships between these viruses were often unclear. Virologists usually categorize viruses on the basis of their shapes (long and thin, say, or a head with a tail) or their genomes (DNA or RNA, single- or double-stranded), but this says surprisingly little about shared ancestry. For example, viruses with double-stranded DNA genomes seem to have arisen on at least four separate occasions.

Source: ICTV

The original ICTV viral classification, which is entirely separate from the tree of cellular life, included only the lower rungs of the evolutionary hierarchy, from species and genus up to the order level — a tier equivalent to primates or trees with cones in the classification of multicellular life. There were no higher levels. And many viral families floated alone, with no links to other kinds of virus. So in 2018, the ICTV added higher-order levels: classes, phyla and kingdoms8.At the very top, it invented ‘realms’, intended as counterparts to the ‘domains’ of cellular life — Bacteria, Archaea and Eukaryota — but using a different word to differentiate between the two trees. (Several years ago, some scientists suggested that certain viruses might fit into the cell-based evolutionary tree, but that idea has not gained widespread favour.)The ICTV outlined the branches of the tree, and grouped RNA-based viruses into a realm called Riboviria. SARS-CoV-2 and other coronaviruses, which have single-stranded RNA genomes, are part of this realm. But then it was up to the broader community of virologists to propose further taxonomic groups. As it happened, Eugene Koonin, an evolutionary biologist at the National Center for Biotechnology Information in Bethesda, Maryland, had assembled a team to analyse all the viral genomes, as well as the latest research on viral proteins, to create a first-draft taxonomy9.They reorganized Riboviria and proposed three more realms (see ‘Virus realms’). There was some quibbling over the details, Koonin says, but the taxonomy was ratified without much trouble by ICTV members in 2020. Two further realms got the green light in 2021, but the original four realms will probably remain the largest, he says. Eventually, Koonin speculates, the realms might number up to 25.

Source: ICTV (talk.ictvonline.org/taxonomy); ICTV Coronaviridae Study Group. Nature Microbiol. 5, 536–544 (2020)

That number supports many scientists’ suspicion that there’s no one common ancestor for virus-kind. “There is no single root for all viruses,” says Koonin. “It simply does not exist.” That means that viruses probably arose several times in the history of life on Earth — and there’s no reason to think such emergence can’t happen again. “The de novo origin of new viruses, it’s still ongoing,” says Mart Krupovic, a virologist at the Pasteur Institute in Paris who was involved in both the ICTV decisions and Koonin’s taxonomy team.As to how the realms arose, virologists have several ideas. Perhaps they descended from independent genetic elements at the dawn of life on Earth, before cells even took shape. Maybe they escaped or ‘devolved’ from whole cells, ditching most of the cellular machinery for a minimal lifestyle. Koonin and Krupovic favour a hybrid hypothesis in which those primordial genetic elements stole genes from cellular life to build their virus particles. Because there are multiple origins for viruses, it’s possible there are multiple ways they’ve originated, says Kuhn, who also served on the ICTV committee and worked on the new taxonomy proposal.Thus, although the viral and cellular trees of life are distinct, the branches touch, and genes pass between the two. Whether viruses count as being ‘alive’ depends on your personal definition of life. Many researchers do not consider them to be living things, but others disagree. “I tend to believe that they are living,” says Hiroyuki Ogata, a bioinformatician working on viruses at Kyoto University in Japan. “They are evolving, they have genetic material composed of DNA and RNA, and they are very important in the evolution of all life.”The current classification is widely recognized as just the first attempt, and some virologists say it’s a bit of a mess. A score of families still lack links to any realm. “The good point is, we are trying to put some order in that mess,” says Martinez-Garcia.World changers With the total mass of viruses on Earth equivalent to that of 75 million blue whales, scientists are certain they make a difference to food webs, ecosystems and even the planet’s atmosphere. The accelerating discovery of new viruses “has revealed a watershed of new ways viruses directly impact ecosystems”, says Matthew Sullivan, an environmental virologist at Ohio State University in Columbus. But scientists are still struggling to quantify how much of an impact they have.“We don’t have a very simple story around here at the moment,” says Ogata. In the ocean, viruses can burst out of their microbial hosts, releasing carbon to be recycled by others that eat the host’s innards and then produce carbon dioxide. But, more recently, scientists have also come to appreciate that popped cells often clump together and sink to the bottom of the ocean, sequestering carbon away from the atmosphere.

Viral genomes collected from thawing permafrost at Stordalen Mire in Sweden have genes that could help break down and release carbon.Credit: Bob Gibbons/Alamy

On land, thawing permafrost is a major source of carbon, says Sullivan, and viruses seem to be instrumental in carbon release from microbes in that environment. In 2018, he and his colleagues described 1,907 viral genomes and fragments collected from thawing permafrost in Sweden, including genes for proteins that might influence how carbon compounds break down and, potentially, become greenhouse gases10.Viruses can also influence other organisms by stirring up their genomes. For example, when viruses transfer antibiotic-resistance genes from one bacterium to another, drug-resistant strains can take over. Over time, this kind of transfer can create major evolutionary shifts in a population, says Camarillo-Guerrero. And not just in bacteria — an estimated 8% of human DNA is of viral origin. For example, our mammalian ancestors acquired a gene essential for placental development from a virus.For many questions about viral lifestyles, scientists will need more than just genomes. They will need to find the virus’s hosts. A virus itself might carry clues: it could be toting a recognizable bit of host genetic material in its own genome, for example.Martinez-Garcia and his colleagues used single-cell genomics to identify the microbes that contained the newly discovered 37-F6 virus. The host, too, is one of the most abundant and diverse organisms in the sea, a bacterium known as Pelagibacter11. In some waters, Pelagibacter makes up half the cells present. If just this one type of virus were to suddenly disappear, says Martinez-Garcia, ocean life would be thrown wildly off balance.To understand a virus’s full impact, scientists need to work out how it changes its host, says Alexandra Worden, an evolutionary ecologist at the GEOMAR Helmholtz Centre for Ocean Research in Kiel, Germany. She’s studying giant viruses that carry genes for light-harvesting proteins called rhodopsins. Theoretically, these genes could be useful to the hosts — for purposes such as energy transfer or signalling — but the sequences can’t confirm that. To find out what’s going on with these rhodopsin genes, Worden plans to culture the host and virus together, and study how the pair function in the combined, ‘virocell’ state. “Cell biology is the only way you can say what that true role is, how does this really affect the carbon cycle,” she says.Back in Florida, Breitbart hasn’t cultured her spider viruses, but she’s learnt some more about them. The pair of viruses belong to a category Breitbart calls mind-boggling for their tiny, circular genomes, encoding just one gene for their protein coat and one for their replication protein. One of the viruses is found only in the spider’s body, never its legs, so she thinks it’s actually infecting some creature the spider eats. The other virus is found throughout the spider’s body, and in its eggs and spiderlings, so she thinks it’s transmitted from parent to offspring12. It doesn’t seem to be doing them any harm, as far as Breitbart can tell.With viruses, “finding them’s actually the easy part”, she says. Picking apart how viruses influence host life cycles and ecology is much trickier. But first, virologists must answer one of the toughest questions of all, Breitbart says: “How do you pick which one to study?”

Nature 595, 22-25 (2021)
doi: https://doi.org/10.1038/d41586-021-01749-7

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Filtering threshold for handling spurious sequencesWe first used bacterial communities of known composition (simplified communities) to assess the occurrence of spurious taxa and to determine at which relative abundances they begin to appear. To propose a cutoff that is potentially applicable to different 16S rRNA gene amplicon studies, we included reference data obtained with different variable regions and sequencing pipelines and originating from both in vitro an in vivo communities varying in number and type of species (max. 58) (Tables 1 and 2). To determine a filtering threshold that allowed exclusion of most spurious taxa, we recorded the relative abundance of the first spurious OTU occurring in each of the reference community datasets (Fig. 2a). Median values of approx. 0.12% relative abundance were observed (Fig. 2b). Besides one outlier in the mock communities (0.44% relative abundance), all values were below 0.25% relative abundance.Fig. 2: Determination of filtering thresholds using artificial communities of known composition in vitro (mock; n = 9 different types; 21 replicates in total) and in mice (gnotobiotes; n = 4 different communities; 28 mice in total).a Example of the occurrence of all molecular species detected without filtering in the gut of a gnotobiotic mouse [49]. The arrow indicates the position of the first spurious molecular species, all following taxa being considered as having a high risk of being spurious (light gray bars in the enlarged inset). b Distribution of the relative abundances of first occurring spurious molecular species (as shown in panel a) across all mock communities and samples from gnotobiotes. The orange dashes on the y-axis indicate the consensus threshold of 0.25% relative abundance, above which no spurious taxa occurred with the exception of one outlier in a mock community at a relative abundance of 0.44%. c Comparison of various standard filtering cutoffs (see explanations in the text) in terms of spurious taxa (i.e., those molecular species not matching sequences of the known species contained in the artificial communities). d Corresponding percentages of positive hits retained by the different filtering strategies, with positive hits being defined as the reference sequences found in the respective amplicon datasets. e Percentage of spurious taxa and positive hits in the same reference communities using the DADA2 pipeline for analysis based on amplicon sequence variants (ASVs) [6]. f Effect of filtering thresholds at increments of 0.05% relative abundance on the detection of spurious taxa and positive hits in all mock and gnotobiotic datasets for OTUs (upper panel) and ASVs (lower panel). Lines correspond to mean values; ribbons represent standard deviations.Full size imageWithout any filtering, sequence clustering generated an average of 508 ± 355 OTUs (min. 52; max. 1081) per mock community (10–58 target species in theory) and 105 ± 50 OTUs (min, 55; max. 215) per gnotobiotic community (4–12 target species in theory). Up to 87% of these OTUs were spurious (i.e., they did not match the expected classification of species contained in the corresponding artificial community) (Fig. 2c). On average, the proportion of spurious OTUs in both the mock communities and samples from gnotobiotic mice was slightly lower after removing singletons, although this did not reach statistical significance (50.8 vs. 64.3%, p = 0.227; 57.5% vs. 65.7%; p = 0.70, pairwise comparison by t-test, including Benjamini–Hochberg correction following ANOVA). Interestingly, the proportion of spurious molecular species was higher in gnotobiotic mice independent of filtering (p  0.50) (Fig. 2d). Note that the diversity of reference communities in the gnotobiotic mice was relatively low (4–12 members; Table 2), resulting in a marked drop in the percentage of positive hit (8–25%) when even just one true member is excluded after filtering because of its low relative abundance (which is an expectable event considering a classical, exponentially decreasing distribution of species occurrence in gut environments).We next employed the widely used ASV analysis approach to confirm the aforementioned results. Processing of the same simplified communities generated a total number of 42 ± 25 ASVs (min. 16; max. 98) for mock communities (10–58 target species) and 14 ± 8 ASVs (min. 4; max. 25) for gnotobiotes (4–12 target species). Altogether, a marked decrease in spurious taxa was observed compared with OTU clustering, with an average of 8.6 ± 11.8 and 4.4 ± 6.4% spurious sequences for mock and gnotobiotic communities, respectively (comparison of purple box plots in Fig. 2e, top panels, and Fig. 2c). Of note, the DADA2 pipeline used for the ASV approach does not infer sequence variants that are only supported by a single read (singletons) due to a lack of confidence in their existence relative to sequencing errors. Consequently, data corresponding to “no filtering” with the OTU-based approach were not generated. On average, the first spurious ASV occurred at a relative abundance of 0.10 ± 0.32%. By applying the cutoff of 0.25% relative abundance, spurious sequences were completely removed (except for three outlying samples), albeit with a slight drop in positive hits for both mock and gnotobiotic communities (Fig. 2e).To obtain a more comprehensive view on how filtering thresholds affect the detection of spurious taxa, all datasets (mock and gnotobiotic mice) were processed using a range of relative abundance filtering thresholds (from 0 to 0.5% at increments of 0.05%) after either OTU- or ASV-based processing of raw sequence reads (Fig. 2f). These data indicate that filtering thresholds between 0.1 and 0.3% are appropriate to reduce the occurrence of spurious taxa to 600 of the 678 spurious OTUs occurred in fewer than five of the ten sequencing runs tested, with approximately 450 of them occurring in only one run (Fig. 3c). This observation indicates that the majority of spurious taxa are sporadic cross-contaminations rather than generalist artifacts across sequencing runs, suggesting that fully independent technical replicates would improve data quality. Although most of the spurious taxa were characterized by relative abundances between 0.25 and 2% in the IMNGS-amplicon datasets tested, they represented very dominant populations in a few samples (Fig. 3d).Fig. 3: Origin and occurrence of spurious taxa.a Taxonomic profile and ecological distribution. Inner ring: SILVA-based classification of all non-redundant spurious molecular species at the phylum and family level. Outer colored ring: sample type characterized by the highest prevalence for the given taxon. Outer bars: corresponding highest prevalence values. Only samples with relative abundances >0.25% for any given OTU were counted as positive for prevalence calculation. The total numbers of samples considered were: human, 46,153; soil, 29,864; freshwater, 13,977; mouse, 10,409; marine, 8478. b Distribution of the spurious taxa across sample types. The exclusivity of each OTU for any given sample type was assessed using a Z-test: those assumed to be non-specific for any given sample type appear in red (p 0.25% in at least one replicate were kept). Richness was calculated using ampvis2 [29]. Applying the 0.25% cutoff decreased the number of observed ASVs from 408 ± 71 to 139 ± 5 and, more importantly, the IQR from 101 to 7 (Fig. 6b). Unweighted UniFrac distances within and between runs as calculated using ampvis2 were also compared before and after filtering. Sequences were aligned using MAFFT [30] and phylogeny was inferred using FastTree. Whilst the community makeup in the soil sample varied substantially between sequencing runs without additional filtering, the 0.25% cutoff reduced this variation to the level observed within runs without filtering (Fig. 6c). Replicates within a run were very similar after applying the 0.25% cutoff. Altogether, these data serve as an independent confirmation that stringent filtering delivers more stable values obtained for the exact same sample sequenced in replicates across several sequencing runs. More

2,809 draft MAGs from the rumen ecosystemWe amassed 3.2 terabase pairs (Tbp) of data from 346 publicly available and 66 new rumen metagenome datasets (Supplementary Table 1). The metagenomes were from cattle (312 samples, 2.1 Tbp), sheep (75 samples, 888.4 gigabase pairs (Gbp)), moose (9 samples, 108.8 Gbp), deer (8 samples, 62.9 Gbp), and bison (8 samples, 52.3 Gbp). Metagenomes were assembled independently to reduce the influence of strain variation and improve the recovery of closely related genomes18,19. Following refinement, dereplication, and filtering of resulting population genomes, we identified 2,809 nonredundant MAGs satisfying the following criteria: dRep20 genome quality score ≥60, ≥75% complete, ≤10% contamination, N50 ≥5 kbp, and ≤500 contigs.The median estimated completeness and contamination of the MAGs were 89.7% and 0.9%, respectively (Fig. 1a and Supplementary Data 1). Further, recovered MAGs had a median genome size of 2.2 Mbp, a median of 131 contigs, and a median N50 of 28.3 kbp (Fig. 1b). The proposed minimum information about a MAG (MIMAG) specifies high-quality draft genomes to have an estimated ≥90% completeness, ≤5% contamination, at least 18 tRNAs, and contain 23S, 16S, and 5S rRNA genes21. It remains challenging to reconstruct rRNA genes from short metagenomic reads due to the high sequence similarity of rRNA genes in closely related species. As a result, despite high estimated completeness and low contamination rates, only 20 MAGs meet the MIMAG standards for a high-quality draft genome. We identified a 16S rRNA gene in 197 of the MAGs. The remaining MAGs are characterized as medium-quality MAGs under the MIMAG standards.Fig. 1: Genomic properties of 2,809 rumen MAGs.a CheckM completeness and contamination estimates for the 2,809 population genomes recovered from rumen metagenomes. The size of the point on the scatter plot corresponds to the dRep genome quality score, where Quality = Completeness − (5 ⋅ Contamination) + (Contamination ⋅ (Strain Heterogeneity/100)) + 0.5 ⋅ (({mathrm{log}},)(N50). The reported MAGs meet the following minimum criteria: genome quality score ≥60, ≥75% complete, ≤10% contamination, N50 ≥5 kbp, and ≥500 contigs. b The frequency distribution of the number of contigs and genome sizes of reconstructed MAGs.Full size imageThe majority of bacterial MAGs belonged to phyla Firmicutes or Bacteroidota (2,326; Fig. 2a and Supplementary Data 1). Additionally, we assembled 12 bacterial genomes from the superphylum Patescibacteria. At lower taxonomic ranks, Lachnospiraceae (415) and Prevotella (398) were the dominant family and genus identified among the assembled bacterial genomes. The most prevalent archaeal family and genus were Methanobacteriaceae (45) and Methanobrevibacter (35), respectively (Fig. 2b). The recovered MAGs represent several new taxonomic lineages, as four genomes could not be classified at the rank of order, 16 at the rank of family, and 243 at the genus rank.Fig. 2: Phylogenetic relationships and coverage patterns of near-complete MAGs.a Phylogenomic analysis of 1,163 near-complete (≥90% complete, ≤5% contamination, and N50 ≥15 kbp) bacterial MAGs and (b) 20 near-complete archaeal MAGs inferred from the concatenation of phylogenetically informative proteins. Layers below the genomic trees designate bacterial phylum or archaeal genus based on GTDB taxonomic assignments, genomic size (0–5 Mbp), and the mean number of bases with ≥1× coverage in a rumen metagenomic dataset (layer color indicates the ruminant the data was collected from). The mean number of bases with ≥1× coverage was used as input for hierarchical clustering of rumen metagenomic datasets based on Euclidean distance and Ward linkage. The bacterial and archaeal phylogenetic trees are provided as Supplementary Data 6 and Supplementary Data 7, respectively.Full size imageSpecies-level overlap between reference genomes, the Hungate1000 Collection, and rumen MAGsTo further characterize the assembled genomes, we compared the MAGs to other rumen-specific genome collections, specifically genomes generated from the Hungate1000 project3 and MAGs identified from the Stewart et al. studies4,5. We clustered genomes based on approximate species-level thresholds (≥95% ANI) and calculated the intersection between MAGs in the current study and the Hungate1000 Collection (410 genomes)3, MAGs from Stewart et al. (4,941 genomes)4,5, and a dereplicated genome collection from the GTDB (22,441 genomes, see Methods)22, which includes reference isolate genomes and some environmental MAGs23. It should be noted that we used the raw data from the first of the Stewart et al. studies4 (Supplementary Table 1), but with different assembly and binning approaches. Approximately one-third of the MAGs (1,007) did not exhibit ≥95% ANI with a genome in the GTDB, Stewart et al. MAGs, or the Hungate1000 isolates (Fig. 3a). When considering the pairwise intersections between the datasets, 98 (3.5%), 933 (33.2%), and 1,438 (51.2%) of the MAGs in the current study had ≥95% ANI with a genome in the Hungate1000 Collection3, GTDB22, and Stewart et al.4,5, respectively. One hundred twenty-one (29.5%), 552 (2.5%), and 3,125 (63.2%) of the genomes from the Hungate1000 Collection3, GTDB22, and Stewart et al.4,5 displayed ≥95% ANI with a MAG from the current study. Together, these results indicate that we recovered a majority of previous rumen genomic diversity with additional lineages not previously identified in other major rumen genomic collections.Fig. 3: Genomes sharing ≥95% ANI between databases and the characterization of rumen-specific 95% ANI clusters.a The approximate number of species overlapping amongst rumen-specific and reference genomic datasets. Genomes demonstrating ≥95% ANI were considered to be shared between two datasets. Presented are a subset of intersections in which a MAG from the current study was the query genome. b The number of genomes comprising each of the 3,541 95% ANI clusters generated from 8,160 rumen microbial genomes in the current study, the Hungate1000 Collection3, and Stewart et al. studies4, 5. c Rarefaction analysis based on subsampling 95% ANI clusters at steps of 500 genomes indicates the 8,160 genomes from recently published rumen genomic collections still only represent a fraction of expected microbial species diversity in the rumen ecosystem. Phylogenomic relationships of the 1,781 near-complete bacterial (d) and 35 near-complete archaeal (e) representative genomes with the highest dRep genome quality score from the 3,541 95% ANI clusters generated from 8,160 rumen-specific genomes. Near-complete genomes were defined as being ≥90% complete, having ≤5% contamination, and contig N50 ≥15 kbp. Layers surrounding the genomic trees indicate the bacterial phyla or archaeal genera and the log normalized number of genomes from each rumen genomic collection belonging to the same 95% ANI cluster. The bacterial and archaeal phylogenetic trees are provided as Supplementary Data 8 and Supplementary Data 9, respectively.Full size imageWe applied an additional clustering approach to identify the approximate number of species represented by the rumen-specific genomes assembled in this study, in the Hungate1000 Collection3, and Stewart et al.4,5. A 95% ANI threshold yielded 3,541 clusters from the combination of the datasets (Supplementary Data 2). Of the 3,541 clusters, 2,024 contained a MAG from the current study, and 1,135 were composed exclusively of MAGs from the current study. In comparison, 2,175 and 286 clusters were comprised of genomes from Stewart et al.4,5 and the Hungate1000 Collection3, respectively. The majority of 95% ANI clusters (2,166) are only comprised of a single genome (Fig. 3b). Furthermore, a rarefaction curve suggests the 8,160 genomes from the genomic collections analyzed here only represent a fraction of the estimated microbial species diversity in the rumen (Fig. 3c). The genome with the best dRep score from each cluster was used to generate a phylogenetic tree highlighting the species diversity within each rumen genomic collection and represents the vast diversity of rumen bacterial (Fig. 3d) and archaeal (Fig. 3e) genomes published to date.As stated previously, the median genome size of reconstructed MAGs was 2.2 Mbp, smaller than the median size of genomes from the Hungate1000 project (3.1 Mbp)3. To provide an assessment at a finer resolution, genome sizes of MAGs and Hungate1000 genomes3 belonging to the same 95% ANI cluster were compared (Supplementary Fig. 1). Adjusted sizes of MAGs and Hungate1000 genomes that are ≥95% complete displayed a regression coefficient of 0.96 with a slope of 0.86, indicating the binning process likely did not lead to extensive losses and systematic biases in the reconstructed genomes. Instead, it further highlights that current culturing approaches have not brought large portions of rumen microbial diversity into culture and putatively supports previous findings from the human gut that revealed genome-reduction in uncultured bacteria24.Rumen metagenome classification rates using reference and rumen-specific genomesUtilizing an approach similar to Stewart et al.4,5, we investigated the influence of MAGs on rates of metagenomic read classification. The baseline for read classification was the standard Kraken database containing bacterial, archaeal, fungal, and protozoal RefSeq genomes25. Each rumen-specific dataset was incrementally added to the Kraken RefSeq genomic database in the following order to build new databases: the Hungate1000 Collection3, MAGs from Stewart et al.4,5, and MAGs from the current study. Each individual and collective database was used for classification of sample reads that underpinned metagenomic binning and from a rumen metagenomic dataset not used in the reconstruction of MAGs26. MAGs from the current work classified more reads from deer, moose, and sheep metagenomes, while the more numerous MAGs from Stewart et al.4,5 classified more reads from bison and cattle metagenomes (Supplementary Fig. 2a). The addition of MAGs improves classification relative to databases primarily based on cultured isolates, like the Hungate1000 Collection3 (Supplementary Fig. 2b). Using the combination of all reference and rumen-specific genomes, the median classification rate on an independent set of cattle metagenomes was 62.6%.Phylogenetic characterization of biosynthetic gene clustersMicrobial genome mining is a powerful tool for natural product discovery. We sought to explore the extent of secondary metabolite diversity coded by the MAGs in the current study, the Hungate1000 Collection3, and Stewart et al. MAGs4,5. We identified 14,814 BGCs encoded by the 8,160 rumen-specific genomes using antiSMASH27 (Fig. 4a and Supplementary Data 3). The majority of BGCs were NRPS (5,346), followed by aryl polyenes (2,800), sactipeptides (2,126), and bacteriocins (1,943). Only a few PKS were identified (75). Firmicutes harbored the vast majority of clusters for NRPS, sactipeptide, lantipeptide, lassopeptide, and bacteriocin synthesis (Fig. 4b). At lower taxonomic ranks, DTU089 (979), Bacteroidaceae (934), and Lachnospiraceae (923) coded for the bulk of NRPS gene clusters. Moreover, Acidaminococcaceae genomes contained 21.2% of identified bacteriocins and Ruminococcus spp. possessed the bulk of sactipeptides and lantipeptides. Archaea were predicted to code 737 BGCs, including an average of 3.8 NRPS gene clusters per genome (Fig. 4a).Fig. 4: Characterization of BGCs from 8,160 rumen genomes and MAGs.a Number and types of BGCs identified from select phyla in genomes from the Hungate1000 Collection3, Stewart et al. studies4, 5, and the current study. b Phylogenomic analysis of 1,766 near-complete Firmicutes genomes inferred from the concatenation of phylogenetically informative proteins. The inner layer surrounding the genomic tree designates taxonomic annotations, while the remaining layers depict the log normalized number of BGCs in the genome with the ascribed function. Bacterial class and order labels are displayed for those lineages in which more than 50 genomes were identified. Near-complete genomes were defined as being ≥90% complete, having ≤5% contamination, and contig N50 ≥15 kbp. The phylogenetic tree is provided as Supplementary Data 10. c A relational network of NRPS gene clusters in Firmicutes, Bacteroidota, and Euryarchaeota highlights the similarity of NRPS BGCs from Euryarchaeota and Firmicutes. Edge weight represents the similarity of two BGCs, as determined by BiG-SCAPE (i.e. darker edges demonstrate more similarity between two BGCs). Edges are only shown for BGCs with ≥0.3 BiG-SCAPE similarity. Nodes from each phylum are duplicated to illustrate intra-phylum relationships and nodes along a given axis are ordered alphabetically by taxonomic family. d The association between genome phylogeny and the similarity of NRPS gene clusters coded by near-complete Euryarchaeota genomes. BGCs designated as NRPS were clustered with BiG-SCAPE. The relationship between NRPS clusters was portrayed through the hierarchical clustering of pairwise inter-cluster similarities. The number of NRPS clusters coded by each genome (range of 0–3) is presented alongside the assigned genus. A group of Methanobrevibacter genomes, likely of the same species (≥95% ANI), possessed very similar NRPS clusters (highlighted in blue). Yet, phylogenetically closely related genomes, belonging to two different 95% ANI clusters, did not code for any identified NRPS gene clusters (highlighted in red). The phylogenetic tree is based on the concatenation of 122 phylogenetically informative archaeal proteins and is available as Supplementary Data 11.Full size imageNRPS exhibit high molecular and structural diversity resulting in a wide array of biological activities. The diversity of NRPS, combined with their proteolytic stability and selective bioactivity, has resulted in the development of many NRPS as antimicrobials and other therapeutic agents28. Given the prevalence of NRPS among the recovered MAGs (Fig. 4a), the peptides appear to be important bioactive metabolites in the rumen. To gain fundamental insight into the phylogenetic diversity of rumen NRPS, we built a network based on BGC similarity using BiG-SCAPE29. BiG-SCAPE uses protein domain content, order, copy number, and sequence identity to calculate a distance metric. We assessed the similarity of NRPS gene clusters identified in Firmicutes, Bacteroidota, and Euryarchaeota, as these three phyla coded for 96.4% of assembled NRPS gene clusters from rumen genomes. With a BiG-SCAPE similarity threshold of 0.3, the resulting network consisted of 3,436 nodes (NRPS BGCs on contigs ≥10 kbp) and 79,112 edges (Fig. 4c and Supplementary Data 4). As expected, the network analysis depicted high inter- and intra-phylum genetic diversity among the NRPS gene clusters. The median intra-phylum, -family, and -genus similarity was 0.40, 0.44, and 0.46, respectively, while the median inter-phylum, -family, and -genus similarity was 0.32, 0.34, and 0.34, respectively. Further, only 2.6% of edges were inter-phylum and 69.0% were intra-family. Of the 6,594 Euryarchaeota edges, 8.1% were Euryarchaeota-Firmicutes (median similarity of 0.32) and 2.0% of edges were Euryarchaeota-Bacteroidota (median similarity of 0.31). To further examine the phylogenetic relationships of rumen Euryarchaeota NRPS, we clustered 265 NRPS gene clusters (≥10 kbp) from 85 near-complete Euryarchaeota genomes at a higher similarity threshold of 0.75, yielding 57 NRPS clusters (Fig. 4d). The distribution of NRPS clusters amongst the genomes suggests there exists a strong relationship between methanogen phylogeny and NRPS similarity. Only Methanobrevibacter genomes contain NRPS gene clusters, and genomes of the same species often possessed many of the same NRPS clusters (see genomes highlighted in blue in Fig. 4d). However, there are instances in which closely related methanogens code for a contrasting pattern of NRPS clusters or no NRPS clusters at all (see genomes highlighted in red in Fig. 4d).Bacteriocins likely serve as regulatory elements in complex microbial communities such as the rumen. Consequently, bacteriocins have been studied and characterized for their bactericidal activity and as agents that modulate the microbiota structure and function30. In particular, lanthipeptides, a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) with thioether cross-linked amino acids31, are of pharmaceutical, preservative, and agricultural interest due to their strong antimicrobial properties against gram-positive pathogens31,32,33, low levels of antimicrobial resistance34, and stability35. We identified 195 rumen lanthipeptide BGCs from the Hungate1000 genomes and MAGs from Stewart et al. and the current study. Rumen lanthipeptide BGCs were clustered with 22,870 lanthipeptide BGCs from RefSeq genomes36,37 into gene cluster families (GCFs; groups of BGCs that may generate highly similar products). Clustering with BiG-SCAPE29 yielded 4,565 GCFs, 120 of which contained a rumen lanthipeptide. The 120 GCFs were composed of 519 lanthipeptide BGCs, where 324 were from RefSeq isolates and 195 from rumen genomes (Fig. 5a). The 324 RefSeq BGCs fell into only 18 GCFs. Lanthipeptides from the Hungate1000 isolates clustered into 36 GCFs, while rumen MAG lanthipeptides belonged to 92 GCFs, 82 of which were exclusively composed of MAG lanthipeptides. Together, this evidence suggests rumen MAGs code for diverse and novel lanthipeptides not represented in cultured isolates, including the Hungate Collection.Fig. 5: Phylogenetic diversity of 195 lanthipeptide BGCs coded by rumen genomes.a Network depicting the similarity between lanthipeptide BGCs identified from complete and draft isolate genomes in RefSeq and rumen genomes of the Hungate1000 collection, Stewart et al. MAGs, and MAGs from the current study. The BGCs were clustered into gene cluster families (GCFs) with BiG-SCAPE29. Only the GCFs containing a rumen genome and at least two BGCs were visualized. Nodes in the network represent BGCs and edges connect BGCs with BiG-SCAPE defined similarity ≥0.3. b Phylogenetic relationships of 120 near-complete rumen bacterial genomes coding for lanthipeptide BGCs. Near-complete genomes were defined as being ≥90% complete, having ≤5% contamination, and contig N50 ≥15 kbp. Layers surrounding the genomic trees indicate the bacterial phyla and family, if the genome is a MAG or Hungate Collection isolate, and the class of lanthipeptide, as predicted by antiSMASH27. Genomes without an indicated lanthipeptide class were not classified by antiSMASH. The phylogenetic tree is based on the concatenation of 120 phylogenetically informative bacterial proteins and is available as Supplementary Data 12.Full size imageWe sought to further examine the differences in rumen MAG lanthipeptides relative to isolates and the taxonomic diversity of rumen microbes coding for lanthipeptides. The 195 rumen lanthipeptides were mainly found in Firmicutes genomes, with a subset from Bacteroidota and Actinobacteriota (Fig. 5b). Fifty-two of the 55 lanthipeptides from the Hungate Collection isolates were from Firmicutes (94.5%). At the family-level, these 52 Firmicutes BGCs were distributed evenly between Lachnospiraceae and Streptococcaceae. In contrast, 19.2% and 8.6% of lanthipeptides from rumen MAGs belonged to Bacteroidota and Actinobacteriota, respectively. Lanthipeptides from MAGs were also found in Muribaculaceae and Oscillospiraceae. Moreover, 26.4% of rumen MAG lanthipeptides, compared to 3.6% of Hungate Collection isolates, were found in Eubacterium genomes. The majority of Eubacterium MAG lanthipeptides (62.1%) belonged to a single GCF, suggesting they code for very similar products. Lastly, antiSMASH predicted the bulk of the rumen lanthipeptides were Class II lanthipeptides, with fewer Class I and Class III types (Fig. 5b). Nearly all of the Class I lanthipeptides were from Hungate isolates. The above analysis of lanthipeptide diversity further supports that rumen MAGs code for novel secondary metabolites not represented in cultured isolates.We aligned previously published rumen metatranscriptome data from steers characterized as having high and low feed efficiency to the BGCs to demonstrate if the identified BGCs are active and to explore potential ecological roles of secondary metabolites. Despite data from the metatranscriptome study not being applied to reconstruct genomes in the current study, we identified the expression of 554 gene clusters from rumen-specific genomes in the 20 metatranscriptomes (≥100 aligned reads). Metatranscriptome read count data were normalized independently for each genome to better account for the variation in taxonomic composition across samples38. Genome-specific normalization resulted in the identification of 17 differentially expressed gene clusters between steers with high and low feed efficiency (DESeq239 false discovery rate adjusted P  More

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