Philippa Kaur

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Spillover events, in which a pathogen that originates in animals jumps into people, have probably triggered every viral pandemic that’s occurred since the start of the twentieth century1. What’s more, an August 2021 analysis of disease outbreaks over the past four centuries indicates that the yearly probability of pandemics could increase several-fold in the coming decades, largely because of human-induced environmental changes2.Fortunately, for around US$20 billion per year, the likelihood of spillover could be greatly reduced3. This is the amount needed to halve global deforestation in hotspots for emerging infectious diseases; drastically curtail and regulate trade in wildlife; and greatly improve the ability to detect and control infectious diseases in farmed animals.That is a small investment compared with the millions of lives lost and trillions of dollars spent in the COVID-19 pandemic. The cost is also one-twentieth of the statistical value of the lives lost each year to viral diseases that have spilled over from animals since 1918 (see ‘Spillovers: a growing threat’), and less than one-tenth of the economic productivity erased per year1.

Source: Ref. 1

Yet many of the international efforts to better defend the world from future outbreaks, prompted by the COVID-19 pandemic, still fail to prioritize the prevention of spillover. Take, for example, the Independent Panel for Pandemic Preparedness and Response, established by the World Health Organization (WHO). The panel was convened in September 2020, in part to ensure that any future infectious-disease outbreak does not become another pandemic. In its 86-page report released last May, wildlife is mentioned twice; deforestation once.We urge the decision-makers currently developing three landmark international endeavours to make the prevention of spillover central to each.First, the G20 group of the world’s 20 largest economies provisionally agreed last month to create a global fund for pandemics. If realized, this could provide funding at levels that infectious-disease experts have been recommending for decades — around $5 per person per year globally (see go.nature.com/3yjitwx). Second, an agreement to improve global approaches to pandemics is under discussion by the World Health Assembly (WHA), the decision-making body of the WHO. Third, a draft framework for biodiversity conservation — the post-2020 global biodiversity framework — is being negotiated by parties to the Convention on Biological Diversity.Designed in the right way, these three international endeavours could foster a more proactive global approach to infectious diseases. This opportunity — to finally address the factors that drive major disease outbreaks, many of which also contribute to climate change and biodiversity loss — might not present itself again until the world faces another pandemic.Four actions The risk of spillover is greater when there are more opportunities for animals and humans to make contact, for instance in the trade of wildlife, in animal farming or when forests are cleared for mining, farming or roads. It is also more likely to happen under conditions that increase the likelihood of infected animals shedding viruses – when they are housed in cramped conditions, say, or not fed properly.Decades of research from epidemiology, ecology and genetics suggest that an effective global strategy to reduce the risk of spillover should focus on four actions1,3.First, tropical and subtropical forests must be protected. Various studies show that changes in the way land is used, particularly tropical and subtropical forests, might be the largest driver of emerging infectious diseases of zoonotic origin globally4. Wildlife that survives forest clearance or degradation tends to include species that can live alongside people, and that often host pathogens capable of infecting humans5. For example, in Bangladesh, bats that carry Nipah virus — which can kill 40–75% of people infected — now roost in areas of high human population density because their forest habitat has been almost entirely cleared6.Furthermore, the loss of forests is driving climate change. This could in itself aid spillover by pushing animals, such as bats, out of regions that have become inhospitable and into areas where many people live7.Yet forests can be protected even while agricultural productivity is increased — as long as there is enough political will and resources8. This was demonstrated by the 70% reduction in deforestation in the Amazon during 2004–12, largely through better monitoring, law enforcement and the provision of financial incentives to farmers. (Deforestation rates began increasing in 2013 due to changes in environmental legislation, and have risen sharply since 2019 during Jair Bolsonaro’s presidency.)Second, commercial markets and trade of live wild animals that pose a public-health risk must be banned or strictly regulated, both domestically and internationally.Doing this would be consistent with the call made by the WHO and other organizations in 2021 for countries to temporarily suspend the trade in live caught wild mammals, and to close sections of markets selling such animals. Several countries have already acted along these lines. In China, the trade and consumption of most terrestrial wildlife has been banned in response to COVID-19. Similarly, Gabon has prohibited the sale of certain mammal species as food in markets.

A worker in a crowded chicken farm in Anhui province, China.Credit: Jianan Yu/Reuters

Restrictions on urban and peri-urban commercial markets and trade must not infringe on the rights and needs of Indigenous peoples and local communities, who often rely on wildlife for food security, livelihoods and cultural practices. There are already different rules for hunting depending on the community in many countries, including Brazil, Canada and the United States.Third, biosecurity must be improved when dealing with farmed animals. Among other measures, this could be achieved through better veterinary care, enhanced surveillance for animal disease, improvements to feeding and housing animals, and quarantines to limit pathogen spread.Poor health among farmed animals increases their risk of becoming infected with pathogens — and of spreading them. And nearly 80% of livestock pathogens can infect multiple host species, including wildlife and humans9.Fourth, particularly in hotspots for the emergence of infectious diseases, people’s health and economic security should be improved.People in poor health — such as those who have malnutrition or uncontrolled HIV infection — can be more susceptible to zoonotic pathogens. And, particularly in immunosuppressed individuals such as these, pathogens can mutate before being passed on to others10.What’s more, some communities — especially those in rural areas — use natural resources to produce commodities or generate income in a way that brings them into contact with wildlife or wildlife by-products. In Bangladesh, for example, date palm sap, which is consumed as a drink in various forms, is often collected in pots attached to palm trees. These can become contaminated with bodily substances from bats. A 2016 investigation linked this practice to 14 Nipah virus infections in humans that caused 8 deaths11.Providing communities with both education and tools to reduce the risk of harm is crucial. Tools can be something as simple as pot covers to prevent contamination of date palm sap, in the case of the Bangladesh example.In fact, providing educational opportunities alongside health-care services and training in alternative livelihood skills, such as organic agriculture, can help both people and the environment. For instance, the non-governmental organization Health in Harmony in Portland, Oregon, has invested in community-designed interventions in Indonesian Borneo. During 2007–17, these contributed to a 90% reduction in the number of households that were reliant on illegal logging for their main livelihood. This, in turn, reduced local rainforest loss by 70%. Infant mortality also fell by 67% in the programme’s catchment area12.Systems-oriented interventions of this type need to be better understood, and the most effective ones scaled up.Wise investmentSuch strategies to prevent spillover would reduce our dependence on containment measures, such as human disease surveillance, contact tracing, lockdowns, vaccines and therapeutics. These interventions are crucial, but are often expensive and implemented too late — in short, they are insufficient when used alone to deal with emerging infectious diseases.The COVID-19 pandemic has exposed the real-world limitations of these reactive measures — particularly in an age of disinformation and rising populism. For example, despite the US federal government spending more than $3.7 trillion on its pandemic response as of the end of March, nearly one million people in the United States — or around one in 330 — have died from COVID-19 (see go.nature.com/39jtdfh and go.nature.com/38urqvc). Globally, between 15 million and 21 million lives are estimated to have been lost during the COVID-19 pandemic beyond what would be expected under non-pandemic conditions (known as excess deaths; see Nature https://doi.org/htd6; 2022). And a 2021 model indicates that, by 2025, $157 billion will have been spent on COVID-19 vaccines alone (see go.nature.com/3jqds76).

A farmer in Myanmar gathers sap from a palm tree to make wine. Contamination of the collection pots with excretions from bats can spread diseases to humans.Credit: Wolfgang Kaehler/LightRocket via Getty

Preventing spillover also protects people, domesticated animals and wildlife in the places that can least afford harm — making it more equitable than containment. For example, almost 18 months since COVID-19 vaccines first became publicly available, only 21% of the total population of Africa has received at least one dose. In the United States and Canada, the figure is nearly 80% (see go.nature.com/3vrdpfo). Meanwhile, Pfizer’s total drug sales rose from $43 billion in 2020 to $72 billion in 2021, largely because of the company’s COVID-19 vaccine, the best-selling drug of 202113.Lastly, unlike containment measures, actions to prevent spillover also help to stop spillback, in which zoonotic pathogens move back from humans to animals and then jump again into people. Selection pressures can differ across species, making such jumps a potential source of new variants that can evade existing immunity. Some researchers have suggested that spillback was possibly responsible for the emergence of the Omicron variant of SARS-CoV-2 (see Nature 602, 26–28; 2022).Seize the dayOver the past year, the administration of US President Joe Biden and two international panels (one established in 2020 by the WHO and the other in 2021 by the G20) have released guidance on how to improve approaches to pandemics. All recommendations released so far acknowledge spillover as the predominant cause of emerging infectious diseases. None adequately discusses how that risk might be mitigated. Likewise, a PubMed search for the spike protein of SARS-CoV-2 yields thousands of papers, yet only a handful of studies investigate coronavirus dynamics in bats, from which SARS-CoV-2 is likely to have originated14.Spillover prevention is probably being overlooked for several reasons. Upstream animal and environmental sources of pathogens might be being neglected by biomedical researchers and their funders because they are part of complex systems — research into which does not tend to lead to tangible, profitable outputs. Also, most people working in public health and biomedical sciences have limited training in ecology, wildlife biology, conservation and anthropology.There is growing recognition of the importance of cross-sectoral collaboration, including soaring advocacy for the ‘One Health’ approach — an integrated view of health that recognizes links between the environment, animals and humans. But, in general, this has yet to translate into action to prevent pandemics.Another challenge is that it can take decades to realize the benefits of preventing spillover, instead of weeks or months for containment measures. Benefits can be harder to quantify for spillover prevention, no matter how much time passes, because, if measures are successful, no outbreak occurs. Prevention also runs counter to individual, societal and political tendencies to wait for a catastrophe before taking action.The global pandemic fund, the WHA pandemic agreement and the post-2020 global biodiversity framework all present fresh chances to shift this mindset and put in place a coordinated global effort to reduce the risk of spillover alongside crucial pandemic preparedness efforts.Global fund for pandemicsFirst and foremost, a global fund for pandemics will be key to ensuring that the wealth of evidence on spillover prevention is translated into action. Funding for spillover prevention should not be folded into existing conservation funds, nor draw on any other existing funding streams.Investments must be targeted to those regions and practices where the risk of spillover is greatest, from southeast Asia and Central Africa to the Amazon Basin and beyond. Actions to prevent spillover in these areas, particularly by reducing deforestation, would also help to mitigate climate change and reduce loss of biodiversity. But conservation is itself drastically underfunded. As an example, natural solutions (such as conservation, restoration and improved management of forests, wetlands and grasslands) represent more than one-third of the climate mitigation needed by 2030 to stabilize warming to well below 2 °C15. Yet these approaches receive less than 2% of global funds for climate mitigation16. (Energy systems receive more than half.)In short, the decision-makers backing the global fund for pandemics must not assume that existing funds are dealing with the threat of spillover — they are not. The loss of primary tropical forest was 12% higher in 2020 than in 2019, despite the economic downturn triggered by COVID-19. This underscores the continuing threat to forests.Funding must be sustained for decades to ensure that efforts to reduce the risk of spillover are in place long enough to yield results.WHA pandemic agreementIn 2020, the president of the European Council, Charles Michel, called for a treaty to enable a more coordinated global response to major epidemics and pandemics. Last year, more than 20 world leaders began echoing this call, and the WHA launched the negotiation of an agreement (potentially, a treaty or other international instrument) to “strengthen pandemic prevention, preparedness, and response” at the end of 2021.Such a multilateral agreement could help to ensure more-equitable international action around the transfer of scientific knowledge, medical supplies, vaccines and therapeutics. It could also address some of the constraints currently imposed on the WHO, and define more clearly the conditions under which governments must notify others of a potential disease threat. The COVID-19 pandemic exposed the shortcomings of the International Health Regulations on many of these fronts17. (This legal framework defines countries’ rights and obligations in the handling of public-health events and emergencies that could cross borders.)We urge negotiators to ensure that the four actions to prevent spillover outlined here are prioritized in the WHA pandemic agreement. For instance, it could require countries to create national action plans for pandemics that include reducing deforestation and closing or strictly regulating live wildlife markets. A reporting mechanism should also be developed to evaluate progress in implementing the agreement. This could build on experience from existing schemes, such as the WHO Joint External Evaluation process (used to assess countries’ capacities to handle public-health risks) and the verification regime of the Chemical Weapons Convention.Commitments to expand pathogen surveillance at interfaces between humans, domesticated animals and wildlife — from US mink farms and Asian wet markets to areas of high deforestation in South America — should also be wrapped into the WHA agreement. Surveillance will not prevent spillover, but it could enable earlier detection and better control of zoonotic outbreaks, and provide a better understanding of the conditions that cause them. Disease surveillance would improve simply through investing in clinical care for both people and animals in emerging infectious-disease hotspots.Convention on Biological DiversityWe are in the midst of the sixth mass extinction, and activities that drive the loss of biodiversity, such as deforestation, also contribute to the emergence of infectious disease. Meanwhile, epidemics and pandemics resulting from the exploitation of nature can lead to further conservation setbacks — because of economic damage from lost tourism and staff shortages affecting management of protected areas, among other factors18. Also, pathogens that infect people can be transmitted to other animals and decimate those populations. For instance, an Ebola outbreak in the Republic of Congo in 2002–03 is thought to have killed 5,000 gorillas19.Yet the global biodiversity framework currently being negotiated by the Convention on Biological Diversity fails to explicitly address the negative feedback cycle between environmental degradation, wildlife exploitation and the emergence of pathogens. The first draft made no mention of pandemics. Text about spillover prevention was proposed in March, but it has yet to be agreed on.Again, this omission stems largely from the siloing of disciplines and expertise. Just as the specialists relied on for the WHA pandemic agreement tend to be those in the health sector, those informing the Convention on Biological Diversity tend to be specialists in environmental science and conservation.The global biodiversity framework, scheduled to be agreed at the Conference of the Parties later this year, must strongly reflect the environment–health connection. This means explicitly including spillover prevention in any text relating to the exploitation of wildlife and nature’s contributions to people. Failing to connect these dots weakens the ability of the convention to achieve its own objectives around conservation and the sustainable use of resources.Preventive health careA reactive response to catastrophe need not be the norm. In many countries, preventive health care for chronic diseases is widely embraced because of its obvious health and economic benefits. For instance, dozens of colorectal cancer deaths are averted for every 1,000 people screened using colonoscopies or other methods20. A preventive approach does not detract from the importance of treating diseases when they occur.With all the stressors now being placed on the biosphere — and the negative implications this has for human health — leaders urgently need to apply this way of thinking to pandemics. More

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Cockroach strainsAll cockroaches were maintained on rodent diet (Purina 5001, PMI Nutrition International, St. Louis, MO) and distilled water at 27 °C, ~40% RH, and a 12:12 h L:D cycle. The WT colony (Orlando Normal) was collected in Florida in 1947 and has served as a standard insecticide-susceptible strain. The GA colony (T-164) was collected in 1989, also in Florida, and shown to be aversive to glucose; continued artificial selection with glucose-containing toxic bait fixed the homozygous GA trait in this population (approximately 150 generations as of 2020).Generating recombinant lines and life history dataTo homogenize the genetic backgrounds of the WT and GA strains, two recombinant colonies were initiated in 2013 by crossing 10 pairs of WT♂ × GA♀ and 10 pairs of GA♂ × WT♀ (Fig. 3a). At the F8 generation (free bulk mating without selection), 400 cockroaches were tested in two-choice feeding assays (see below) that assessed their initial response to tastants, as described in previous studies11,26. The cockroaches were separated into glucose-accepting and glucose-rejecting groups by the rapid Acceptance-Rejection assay (described in Feeding Bioassays). These colonies were bred for three more generations, and 200 cockroaches from each group were assayed in the F11 generation and backcrossed to obtain homozygous glucose-accepting (aa) and glucose-averse (AA) lines. Similar results were obtained in both directions of the cross, confirming previous findings of no sex linkage of the GA trait27. These two lines were defined as WT_aa (homozygotes, glucose-accepting) and GA_AA (homozygotes, glucose-averse). To obtain heterozygous GA cockroaches, GA_Aa, a single intercross group was generated from crosses of 10 pairs of WT_aa♂ × GA_AA♀ and 10 pairs of GA_AA♂ × WT_aa♀.The GA trait follows Mendelian inheritance. Therefore, we used backcrosses, guided by two-choice feeding assays and feeding responses in Acceptance-rejection assays, to determine the homozygosity of WT and GA cockroaches. The cross of WT♂ × WT♀ produced homozygous F1 cockroaches showing maximal glucose-acceptance. The cross of GA♂ × GA♀ produced homozygous F1 cockroaches showing maximal glucose-aversion. The cross of WT × GA produced F1 heterozygotes with intermediate glucose-aversion. When the F1 heterozygotes were backcrossed with WT cockroaches, they produced F2 cockroaches with a 1:1 ratio of WT and GA phenotypes.The two-choice feeding assay assessed whether cockroaches accepted or rejected glucose (binary: yes-no). Insects were held for 24 h without water, or starved without food and water. Either 10 adults or 2 day-old first instar siblings (30–40) were placed in a Petri dish (either 90 mm or 60 mm diameter × 15 mm height). Each Petri dish contained two agar discs: one disc contained 1% agar and 1 mmol l−1 red food dye (Allura Red AC), and the second disc contained 1% agar, 0.5 mmol l−1 blue food dye (Erioglaucine disodium salt) and either 1000 mmol l−1 or 3000 mmol l−1 glucose. The assay duration was 2 h during the dark phase of the insects’ L:D cycle. After each assay, the color of the abdomen of each cockroach was visually inspected under a microscope to infer the genotype.We assessed whether the recombinant colonies had different traits from the parental WT and GA lines. We paired single newly eclosed females (day 0) with single 10–12 days-old males of the same line in a Petri dish (90 mm diameter, 15 mm height) with fresh distilled water in a 1.5 ml microcentrifuge tube and a pellet of rodent food, and monitored when they mated. When females formed egg cases, each gravid female was placed individually in a container (95 × 95 × 80 mm) with food and water until the eggs hatched. After removing the female, her offspring were monitored until adult emergence. We recorded the time to egg hatch, first appearance of each nymphal stage, first appearance of adults and the end of adult emergence. The first instar nymphs and adults in each cohort were counted to obtain measures of survivorship. Although there were significant differences in some of these parameters across all four strains, we found no significant differences between the two recombinant lines, except mating success, which was significantly lower in GA_AA♀ than WT_aa♀ (Supplementary Table 11).Mating bioassaysAll mating sequences were recorded using an infra-red-sensitive camera (Polestar II EQ610, Everfocus Electronics, New Taipei City, Taiwan) coupled to a data acquisition board and analyzed by searchable and frame-by-frame capable software (NV3000, AverMedia Information) at 27 °C, ~40% RH and a 12:12 h L:D cycle. For behavioral analysis, tested pairs were classified into two groups: mated (successful courtship) and not-mated (failed courtship). Four distinct behavioral events (Fig. 1c, Contact, Wing raising, Nuptial feeding, and Copulation) were analyzed using seven behavioral parameters as shown in Supplementary Table 2.We extracted behavioral data from successful courtship sequences, defined as courtship that led to Copulation. For failed courtship sequences, we extracted the behavioral data from the first courtship of both mated and not-mated groups, because most pairs in both groups failed to copulate in their first encounter, and there were no significant differences in behavioral parameters between the two groups.To assay female choice, we conducted two-choice mating assays (Fig. 1a). A single focal WT♀ or GA♀ and two males, one WT and one GA, were placed in a Petri dish (90 mm diameter, 15 mm height) with fresh distilled water in a 1.5 ml microcentrifuge tube and a pellet of rodent food (n = 25 WT♀ and 27 GA♀). To assay male choice, a single focal WT♂ or GA♂ was given a choice of two females, one WT♀ and one GA♀ (n = 27 WT♂ and 18 GA♂). Experiments were started using 0 day-old sexually unreceptive females and 10–12 days-old sexually mature males. Newly emerged (0 day-old) females were used to avoid the disruption of introducing a sexually mature female into the bioassay. B. germanica females become sexually receptive at 5–7 days of age, so the mating behavior of the focal insect was video-recorded for several days until they mated. Fertility of mated females was evaluated by the number of offspring produced. We assessed the gustatory phenotype of nymphs (either WT-type or GA-type) to determine which of the two adult cockroaches mated with the focal insect. Each gravid female was maintained individually in a container (95 × 95 × 80 mm) with food and water until the eggs hatched. Two day-old first instar nymphs were starved for one day without water and food, and then they were tested in Two-choice feeding assays using 1000 mmol l−1 glucose-containing agar with 0.5 mmol l−1 blue food dye vs. plain sugar-free agar with 1 mmol l−1 red food dye. If all the nymphs chose the glucose-containing agar, their parents were considered WT♂ and WT♀. When all the nymphs showed glucose-aversion, they were raised to the adult stage. Newly emerged adults were backcrossed with WT cockroaches, and their offspring were tested in the Two-choice assay. When the parents were both GA, 100% of the offspring exhibited glucose-aversion. When the parents were WT and GA, the offspring showed a 1:1 ratio of glucose-accepting and glucose-aversive behavior. Mate choice, mating success ratio and the number of offspring were analyzed statistically.We conducted no-choice mating assay using the WT and GA strains (Fig. 1b, d). A female and a male were placed in a Petri dish with fresh water and a piece of rodent food and video-recorded for 24 h. The females were 5–7 days-old and males were 10–12 days-old. Four treatment pairs were tested: WT♂ × WT♀ (n = 20, 18 and 14 pairs for 5, 6 and 7 day-old females, respectively); GA♂ × GA♀ (n = 23, 22 and 35 pairs); GA♂ × WT♀ (n = 21, 14 and 17 pairs); and WT♂ × GA♀ (n = 33, 19 and 15 pairs).To confirm that gustatory stimuli guide nuptial feeding, we artificially augmented the male nuptial secretion and assessed whether the duration of nuptial feeding and mating success of GA♀ were affected (Fig. 2c). Before starting the mating assay with 5 day-old GA♀, 10–12 days-old WT♂ were separated into three groups: A control group did not receive any augmentation; A water control group received distilled water with 1 mmol l−1 blue dye (+Blue); A fructose group received 3000 mmol l−1 fructose solution with blue dye (+Blue+Fru). Approximately 50 nl of the test solution was placed into the tergal gland reservoirs using a glass microcapillary. No-choice mating assays were carried out for 24 h. n = 20–25 pairs for each treatment.We evaluated the association of short nuptial feeding (Fig. 1c) and the GA trait we conducted no-choice mating assays using females from the recombinant lines (Fig. 3c). Before starting each mating assay with 4 day-old females from the WT, GA and recombinant lines (WT_aa, GA_AA and GA_Aa), the EC50 for glucose was obtained by the instantaneous Acceptance-Rejection assay using 0, 10, 30, 100, 300, 1000 and 3000 mmol l−1 glucose (WT♀ and WT_aa♀, non-starved; GA♀, GA_AA♀ and GA_Aa♀, 1-day starved). After the Acceptance-Rejection assay, GA_Aa♀ were separated into two groups according to their sensitivity for rejecting glucose; the GA_Aa_high sensitivity group rejected glucose at 100 and 300 mmol l−1, whereas the GA_Aa_low sensitivity group rejected glucose at 1000 and 3000 mmol l−1. We paired these females with 10–12 days-old WT♂ (n = 15 WT_aa♀, n = 20 GA_AA♀, n = 20 GA_Aa_high♀ and n = 17 GA_Aa_low♀).Feeding bioassayWe conducted two feeding assays: Acceptance-Rejection assay and Consumption assay. The Acceptance-Rejection assay assessed the instantaneous initial responses (binary: yes-no) of cockroaches to tastants, as previously described7,22,27. Briefly, acceptance means that the cockroach started drinking. Rejection means that the cockroach never initiated drinking. The percentage of positive responders was defined as the Number of insects accepting tastants/Total number of insects tested. The effective concentration (EC50) for each tastant was obtained from dose-response curves using this assay. The Consumption assay was previously described27. Briefly, we quantified the amount of test solution females ingested after they started drinking. Females were observed until they stopped drinking, and we considered this a single feeding bout.We used the Acceptance-Rejection assay and Consumption assay, respectively, to assess the sensitivity of 5 day-old WT♀ and GA♀ for accepting and consuming the WT♂ nuptial secretion (Fig. 2a, b). The secretion was diluted with HPLC-grade water to 0.001, 0.01, 0.03, 0.1, 0.3 and 1 male-equivalents/µl (n = 20 non-starved females each). The amount of nuptial secretion consumed was tested at 0.1 male-equivalents/µl in the Consumption assay (n = 10 each).The Acceptance-Rejection assay was used to calculate the effective concentration (EC50) of glucose for females in the WT, GA and recombinant lines (Fig. 3a, b). A glucose concentration series of 0.1, 1, 10, 100 and 1000 mmol l−1 was tested with one-day starved 4-day old females (n = 65 GA_Aa♀, n = 50 GA_AA♀ and n = 50 GA♀) and non-starved females (n = 50 WT_aa♀ and n = 16 WT♀).The effects of female saliva on feeding responses of 5 day-old WT♀ and GA♀ were tested using the Acceptance-Rejection assay (Fig. 4a). Freshly collected saliva of WT♀ and GA♀ was immediately used in experiments. Assays were prepared as follows: 3 µl of 200 mmol l−1 maltose or maltotriose were mixed with 3 µl of either HPLC-grade water or saliva of WT♀ or GA♀. The final concentration of each sugar was 100 mmol l−1 in a total volume of 6 µl. This concentration represented approximately the acceptance EC70 for WT♀ and GA♀27. Nuptial secretion (1 µl representing 10 male-equivalents) was mixed with 1 µl of either HPLC-grade water or saliva from WT♀ or GA♀, and 8 µl of HPLC-grade water was added to the mix. The final concentration of the nuptial secretion was 1 male-equivalent/µl in a total volume of 10 µl. This concentration also represented approximately the acceptance EC70 for WT♀ and GA♀ (Fig. 2a). The mix of saliva and either sugar or nuptial secretion was incubated for 300 s at 25 °C. Additionally, we tested the effect of only saliva in the Acceptance-Rejection assay. Either 1-day starved or non-starved females were tested with water only and then a 1:1 mixture of saliva and water. Saliva alone did not affect acceptance or rejection of stimuli. n = 20–33 females from each strain.To evaluate whether salivary enzymes are involved in the hydrolysis of oligosaccharides, the contribution of salivary glucosidases was tested using the glucosidase inhibitor acarbose in the Acceptance-Rejection assay (Fig. 4b), as previously described27. We first confirmed that the range of 0–125 mmol l−1 acarbose in HPLC-grade water did not disrupt the acceptance and rejection of tastants. Test solutions were prepared as follows: 2 µl of either HPLC-grade water or saliva of GA♀ was mixed with 1 µl of either 250 µmol l−1 of acarbose or HPLC-grade water, then the mixture was added to 1 µl of 400 mmol l−1 of either maltose or maltotriose solution. The total volume was 4 µl, with the final concentration of sugar being 100 mmol l−1. For assays with nuptial secretion, 1 µl of either HPLC-grade water or saliva from 5 day-old GA♀ was mixed with 0.5 µl of either 250 µmol l−1 of acarbose or HPLC-grade water. This mixture was added to 0.5 µl of 10 male-equivalents of nuptial secretion (i.e., 20 male-equivalents/µl). HPLC-grade water was added for a total volume of 10 µl and a final concentration of 1 male-equivalent/µl. The mix of saliva and either sugars or nuptial secretion was incubated for 5 min at 25 °C. All test solutions contained blue food dye. Test subjects were 5 day-old GA♀ and 20–25 females were tested in each assay.Nuptial secretion and saliva collectionsThe nuptial secretion of WT♂ was collected by the following method: Five 10–12 days-old males were placed in a container (95 × 95 × 80 mm) with 5 day-old GA♀. After the males displayed wing-raising courtship behavior toward the females, individual males were immediately decapitated and the nuptial secretion in their tergal gland reservoirs was drawn into a calibrated borosilicate glass capillary (76 × 1.5 mm) under the microscope. The nuptial secretions from 30 males were pooled in a capillary and stored at −20 °C until use. Saliva from 5 day-old WT♀ and GA♀ was collected by the following method: individual females were briefly anesthetized with carbon dioxide under the microscope and the side of the thorax was gently squeezed. A droplet of saliva that accumulated on the mouthparts was then collected into a microcapillary (10 µl, Kimble Glass). Fresh saliva was immediately used in experiments.GC-MS procedures for analysis of sugarsStandards of D-( + )-glucose (Sigma-Aldrich), D-( + )-maltose (Fisher Scientific) and maltotriose (Sigma-Aldrich) were diluted in HPLC-grade water (Fisher Scientific) at 10, 50, 100, 500 and 1000 ng/µl to generate calibration curves. Samples were vortexed for 20 s and a 10 μl aliquot of each sample was transferred to a Pyrex reaction vial containing a 10 μl solution of 5 ng/μl sorbitol (≥98%) in HPLC-grade water as internal standard and dried under a gentle flow of N2 for 20 min.Samples containing degradation products from nuptial secretions were prepared by adding 15 μl of HPLC-water to each sample in a 1.5 ml Eppendorf tube, vortexed for 30 s and centrifuged at 8000 rpm (5223 RCF) for 5 min to separate lipids from the water layer. The water phase was transferred to a reaction vial using a glass capillary. This procedure was repeated with the remaining lipid layer and the water layers were combined in the same reaction vial containing 10 μl of a solution of 5 ng/μl sorbitol and dried under N2 for 20 min.For derivatization of sugars and samples, each reaction vial received 12 μl of anhydrous pyridine under a constant N2 flow, then vortexed and incubated at 90 °C for 5 min. Three μl of N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA; Sigma-Aldrich) was added to each reaction vial and centrifuged at 1000 rpm (118 RCF) for 2 min. Vials were incubated in a heat block at 90 °C for 1.5 hr and vortexed every 10 min for the first 30 min of incubation.The total volume of sample was ~10 μl, and 1 μl was injected into the GC-MS (6890 GC coupled to a 5975 MS, Agilent Technologies, Palo Alto, CA). The inlet was operated in splitless mode (17.5 psi) at 290 °C. The GC was equipped with a DB-5 column (30 m, 0.25 mm, 0.25 μm, Agilent), and helium was used as the carrier gas at an average velocity of 50 cm/s. The oven temperature program started at 80 °C for 1 min, increased at 10 °C/min to 180 °C, then increased at 5 °C/min to 300 °C, and held for 10 min. The transfer line was set at 250 °C for 24 min, ramped at 5 °C/min to 300 °C and held until the end of program. The ion source operated at 70 eV and 230 °C, while the MS quadrupole was maintained at 200 °C. The MSD was operated in scan mode, starting after 9 min (solvent delay time) with a mass range of 33–650 AMU.For GC-MS data analysis, the sorbitol peak area was obtained from the extracted ion chromatograms with m/z = 205, the sorbitol base peak. The area of peaks of glucose, maltose and maltotriose were obtained from the extracted ion chromatograms using m/z = 204, the base peak of the three sugars. The most abundant peaks of each sugar were selected for quantification36, and these peaks did not coelute with other peaks. Then, the peak areas of the three sugars were divided by the area of the respective sorbitol peak in each sample to normalize the data and to correct technical variability during sample processing. This procedure was performed to obtain the calibration curves and quantification of sugars in our experiments.The results of sugar analysis using GC-MS are reported in Supplementary Figs. 1–4.Analysis of nuptial secretionsWe focused the GC-MS analysis on glucose, maltose and maltotriose in WT♂ nuptial secretion (Fig. 4c). To quantify the time-course of saliva-catalyzed hydrolysis of WT♂ nuptial secretion to glucose, 1 µl of GA♀ saliva was mixed with 1 µl of 10 male-equivalents/µl. We incubated the mixtures for 0, 5, 10 and 300 s at 25 °C, and added 4 µl of methanol to stop the enzyme activity (n = 5 each treatment). Each sample contained the nuptial secretions of 5 males to obtain enough detectable amount of sugars. For the statistical analysis, the amounts of sugars were divided by 5 to obtain the amount of sugars in 1 male (1 male-equivalent). These amounts were also used for generating Fig. 4c and Supplementary Table 9. In calculations of the concentration of the three sugars (mmol l−1), the mass and volume of the nuptial secretion were measured using 70–130 male-equivalents of undiluted secretion of each strain (n = 3). The mass and volume of the nuptial secretion/male, including both lipid and aqueous layers, were approximately 30–50 µg and 40–50 nl. Because it was difficult to separate the lipid layer from the water layer at this small scale, we roughly estimated that the tergal reservoirs of the four cockroach lines had 30 nl of aqueous layer that contained sugars.To quantify the time-course of saliva-catalyzed hydrolysis of maltose and maltotriose to glucose, 1 µl of GA♀ saliva was mixed with 1 µl of 200 mmol l−1 of either maltose or maltotriose (Fig. 4d, e). Incubation time points were 0, 5, 10 and 300 s at 25 °C and methanol was used to stop the enzyme activity. Controls without saliva were also prepared using HPLC-grade water instead of saliva and 300 s incubations. n = 5 for each treatment.PhotomicroscopyThe photographs of the tergal glands and mouthparts (Fig. 5) were obtained using an Olympus Digital camera attached to an Olympus CX41 microscope (Olympus America, Center Valley, PA).Statistics and reproducibilityThe sample size and number of replicates for each experiment are noted in the respective section describing the experimental details. In summary, the samples sizes were: Mating bioassays, n = 18–80; Feeding assays, n = 16–65; Sugar analysis, n = 5; Life history parameters, n  > 14. All statistical analyses were conducted in R Statistical Software (v4.1.0; R Core Team 2021) and JMP Pro 15.2 software (SAS Institute Inc., Carey, NC). For bioassay data and sugar analysis data, we calculated the means and standard errors, and we used the Chi-square test with Holm’s method for post hoc comparisons, t-test, and ANOVA followed by Tukey’s HSD test (all α = 0.05), as noted in each section describing the experimental details, results, and in Supplementary Tables 1–11.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

AffiliationsSanger Institute, Wellcome Trust Genome Campus, Hinxton, UKPhysilia Ying Shi ChuaLaboratory of Genomics and Molecular Medicine, Department of Biology, University of Copenhagen, Copenhagen, DenmarkJacob Agerbo RasmussenCenter for Evolutionary Hologenomics, Globe Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DenmarkJacob Agerbo RasmussenAuthorsPhysilia Ying Shi ChuaJacob Agerbo RasmussenCorresponding authorCorrespondence to
Physilia Ying Shi Chua. More

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In this episode:00:57 Reviving retinas to understand eyesResearch efforts to learn more about diseases of the human eye have been hampered as these organs degrade rapidly after death, and animal eyes are quite different to those from humans. To address this, a team have developed a new method to revive retinas taken from donors shortly after their death. They hope this will provide tissue for new studies looking into the workings of the human eye and nervous system.Research article: Abbas et al.08:05 Research HighlightsA technique that simplifies chocolate making yields fragrant flavours, and 3D imaging reveals some of the largest-known Native American cave art.Research Highlight: How to make a fruitier, more floral chocolateResearch Highlight: Cramped chamber hides some of North America’s biggest cave art10:54 Did life emerge in an ‘RNA world’?How did the earliest biochemical process evolve from Earth’s primordial soup? One popular theory is that life began in an ‘RNA world’ from which proteins and DNA evolved. However, this week a new paper suggests that a world composed of RNA alone is unlikely, and that life is more likely to have begun with molecules that were part RNA and part protein.Research article: Müller et al.News and Views: A possible path towards encoded protein synthesis on ancient Earth17:52 Briefing ChatWe discuss some highlights from the Nature Briefing. This time, the ‘polarised sunglasses’ that helped astronomers identify an ultra-bright pulsar, and how a chemical in sunscreen becomes toxic to coral.Nature: A ‘galaxy’ is unmasked as a pulsar — the brightest outside the Milky WayNature: A common sunscreen ingredient turns toxic in the sea — anemones suggest whySubscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed. More

LookingGlass design and optimizationDataset generationThe taxonomic organization of representative Bacterial and Archaeal genomes was determined from the Genome Taxonomy Database, GTDB51 (release 89.0). The complete genome sequences were downloaded via the NCBI Genbank ftp52. This resulted in 24,706 genomes, comprising 23,458 Bacterial and 1248 Archaeal genomes.Each genome was split into read-length chunks. To determine the distribution of realistic read lengths produced by next-generation short-read sequencing machines, we obtained the BioSample IDs52 for each genome, where they existed, and downloaded their sequencing metadata from the MetaSeek53 database using the MetaSeek API. We excluded samples with average read lengths less than 60 or greater than 300 base pairs. This procedure resulted in 7909 BioSample IDs. The average read lengths for these sequencing samples produced the read-length distribution (Supplementary Fig. 1) with a mean read length of 136 bp. Each genome was split into read-length chunks (with zero overlap in order to maximize information density and reduce data redundancy in the dataset): a sequence length was randomly selected with replacement from the read-length distribution and a sequence fragment of that length was subset from the genome, with a 50% chance that the reverse complement was used. The next sequence fragment was chosen from the genome starting at the end point of the previous read-length chunk, using a new randomly selected read length, and so on. These data were partitioned into a training set used for optimization of the model; a validation set used to evaluate model performance during parameter tuning and as a benchmark to avoid overfitting during training; and a test set used for final evaluation of model performance. To ensure that genomes in the training, validation, and test sets had low sequence similarity, the sets were split along taxonomic branches such that genomes from the Actinomycetales, Rhodobacterales, Thermoplasmata, and Bathyarchaeia were partitioned into the validation set; genomes from the Bacteroidales, Rhizobiales, Methanosarcinales, and Nitrososphaerales were partitioned into the test set; and the remaining genomes remained in the training set. This resulted in 529,578,444 sequences in the training set, 57,977,217 sequences in the validation set, and 66,185,518 sequences in the test set. We term this set of reads the GTDB representative set (Table 1).Table 1 Summary table of datasets used.Full size tableThe amount of data needed for training was also evaluated (Supplementary Fig. 2). Progressively larger amounts of data were tested by selecting at random 1, 10, 100, or 500 read-length chunks from each of the GTDB representative genomes in the GTDB representative training set. Additionally, the performance of smaller but more carefully selected datasets, representing the diversity of the microbial tree of life, were tested by selecting for training one genome at random from each taxonomic class or order in the GTDB taxonomy tree. In general, better accuracy was achieved in fewer epochs with a greater amount of sequencing data (Supplementary Fig. 2); however, a much smaller amount of data performed better if a representative genome was selected from each GTDB taxonomy class.The final LookingGlass model was trained on this class-level partition of the microbial tree of life. We term this dataset the GTDB class set (Table 1). The training, validation, and test sets were split such that no classes overlapped across sets: the validation set included 8 genomes from each of the classes Actinobacteria, Alphaproteobacteria, Thermoplasmata, and Bathyarchaeia (32 total genomes); the test set included 8 genomes from each of the classes Bacteroidia, Clostridia, Methanosarcinia, and Nitrososphaeria (32 total genomes); and the training set included 1 genome from each of the remaining classes (32 archaeal genomes and 298 bacterial genomes for a total of 330 genomes). This resulted in a total of 6,641,723 read-length sequences in the training set, 949,511 in the validation set, and 632,388 in the test set (Supplementary Data 1).Architecture design and trainingRecurrent neural networks (RNNs) are a type of neural network designed to take advantage of the context dependence of sequential data (such as text, video, audio, or biological sequences), by passing information from previous items in a sequence to the current item in a sequence54. Long short-term memory networks (LSTMs)55 are an extension of RNNs, which better learn long-term dependencies by handling the RNN tendency to “forget” information farther away in a sequence56. LSTMs maintain a cell state which contains the “memory” of the information in the previous items in the sequence. LSTMs learn additional parameters which decide at each step in the sequence which information in the cell state to “forget” or “update”.LookingGlass uses a three-layer LSTM encoder model with 1152 units in each hidden layer and an embedding size of 104 based on the results of hyperparameter tuning (see below). It divides the sequence into characters using a kmer size of 1 and a stride of 1, i.e., is a character-level language model. LookingGlass is trained in a self-supervised manner to predict a masked nucleotide, given the context of the preceding nucleotides in the sequence. For each read in the training sequence, multiple training inputs are considered, shifting the nucleotide that is masked along the length of the sequence from the second position to the final position in the sequence. Because it is a character-level model, a linear decoder predicts the next nucleotide in the sequence from the possible vocabulary items “A”, “C”, “G”, and “T”, with special tokens for “beginning of read”, “unknown nucleotide” (for the case of ambiguous sequences), “end of read” (only “beginning of read” was tokenized during LookingGlass training), and a “padding” token (used for classification only).Regularization and optimization of LSTMs require special approaches to dropout and gradient descent for best performance57. The fastai library58 offers default implementations of these approaches for natural language text, and so we adopt the fastai library for all training presented in this paper. We provide the open source fastBio python package59 which extends the fastai library for use with biological sequences.LookingGlass was trained on a Pascal P100 GPU with 16GB memory on Microsoft Azure, using a batch size of 512, a back propagation through time (bptt) window of 100 base pairs, the Adam optimizer60, and utilizing a Cross Entropy loss function (Supplementary Table 1). Dropout was applied at variable rates across the model (Supplementary Table 1). LookingGlass was trained for a total of 12 days for 75 epochs, with progressively decreasing learning rates based on the results of hyperparameter optimization (see below): for 15 epochs at a learning rate of 1e−2, for 15 epochs at a learning rate of 2e−3, and for 45 epochs at a learning rate of 1e−3.Hyperparameter optimizationHyperparameters used for the final training of LookingGlass were tuned using a randomized search of hyperparameter settings. The tuned hyperparameters included kmer size, stride, number of LSTM layers, number of hidden nodes per layer, dropout rate, weight decay, momentum, embedding size, bptt size, learning rate, and batch size. An abbreviated dataset consisting of ten randomly selected read-length chunks from the GTDB representative set was created for testing many parameter settings rapidly. A language model was trained for two epochs for each randomly selected hyperparameter combination, and those conditions with the maximum performance were accepted. The hyperparameter combinations tested and the selected settings are described in the associated Github repository61.LookingGlass validation and analysis of embeddingsFunctional relevanceDataset generation
In order to assess the ability of the LookingGlass embeddings to inform the molecular function of sequences, metagenomic sequences from a diverse set of environments were downloaded from the Sequence Read Archive (SRA)62. We used MetaSeek53 to choose ten metagenomes at random from each of the environmental packages defined by the MIxS metadata standards63: built environment, host-associated, human gut, microbial mat/biofilm, miscellaneous, plant-associated, sediment, soil, wastewater/sludge, and water, for a total of 100 metagenomes. The SRA IDs used are available in (Supplementary Table 2). The raw DNA reads for these 100 metagenomes were downloaded from the SRA with the NCBI e-utilities. These 100 metagenomes were annotated with the mi-faser tool27 with the read-map option to generate predicted functional annotation labels (to the fourth digit of the Enzyme Commission (EC) number), out of 1247 possible EC labels, for each annotatable read in each metagenome. These reads were then split 80%/20% into training/validation candidate sets of reads. To ensure that there was minimal overlap in sequence similarity between the training and validation set, we compared the validation candidate sets of each EC annotation to the training set for that EC number with CD-HIT64, and filtered out any reads with >80% DNA sequence similarity to the reads of that EC number in the training set (the minimum CD-HIT DNA sequence similarity cutoff). In order to balance EC classes in the training set, overrepresented ECs in the training set were downsampled to the mean count of read annotations (52,353 reads) before filtering with CD-HIT. After CD-HIT processing, any underrepresented EC numbers in the training set were oversampled to the mean count of read annotations (52,353 reads). The validation set was left unbalanced to retain a distribution more realistic to environmental settings. The final training set contained 61,378,672 reads, while the validation set contained 2,706,869 reads. We term this set of reads and their annotations the mi-faser functional set (Table 1).
As an external test set, we used a smaller number of DNA sequences from genes with experimentally validated molecular functions. We linked the manually curated entries of Bacterial or Archaeal proteins from the Swiss-Prot database65 corresponding to the 1247 EC labels in the mi-faser functional set with their corresponding genes in the EMBL database66. We downloaded the DNA sequences, and selected ten read-length chunks at random per CDS. This resulted in 1,414,342 read-length sequences in the test set. We term this set of reads and their annotations the Swiss-Prot functional set (Table 1).

Fine-tuning procedure
We fine-tuned the LookingGlass language model to predict the functional annotation of DNA reads, to demonstrate the speed with which an accurate model can be trained using our pretrained LookingGlass language model. The architecture of the model retained the 3-layer LSTM encoder and the weights of the LookingGlass language model encoder, but replaced the language model decoder with a new multiclass classification layer with pooling (with randomly initialized weights). This pooling classification layer is a sequential model consisting of the following layers: a layer concatenating the output of the LookingGlass encoder with min, max, and average pooling of the outputs (for a total dimension of 104*3 = 312), a batch normalization67 layer with dropout, a linear layer taking the 312-dimensional output of the batch norm layer and producing a 50-dimensional output, another batch normalization layer with dropout, and finally a linear classification layer that is passed through the log(Softmax(x)) function to output the predicted functional annotation of a read as a probability distribution of the 1247 possible mi-faser EC annotation labels. We then trained the functional classifier on the mi-faser functional set described above. Because the >61 million reads in the training set were too many to fit into memory, training was done in 13 chunks of ~5-million reads each until one total epoch was completed. Hyperparameter settings for the functional classifier training are seen in Supplementary Table 1.

Encoder embeddings and MANOVA test
To test whether the LookingGlass language model embeddings (before fine-tuning, above) are distinct across functional annotations, a random subset of ten reads per functional annotation was selected from each of the 100 SRA metagenomes (or the maximum number of reads present in that metagenome for that annotation, whichever was greater). This also ensured that reads were evenly distributed across environments. The corresponding fixed-length embedding vectors for each read was produced by saving the output from the LookingGlass encoder (before the embedding vector is passed to the language model decoder) for the final nucleotide in the sequence. This vector represents a contextually relevant embedding for the overall sequence. The statistical significance of the difference between embedding vectors across all functional annotation groups was tested with a MANOVA test using the R stats package68.
Evolutionary relevance
Dataset generation
The OrthoDB database69 provides orthologous groups (OGs) of proteins at various levels of taxonomic distance. For instance, the OrthoDB group “77at2284” corresponds to proteins belonging to “Glucan 1,3-alpha-glucosidase at the Sulfolobus level”, where “2284” is the NCBI taxonomy ID for the genus Sulfolobus.
We tested whether embedding similarity of homologous sequences (sequences within the same OG) is higher than that of nonhomologous sequences (sequences from different OGs). We tested this in OGs at multiple levels of taxonomic distance—genus, family, order, class, and phylum. At each taxonomic level, ten individual taxa at that level were chosen from across the prokaryotic tree of life (e.g., for the genus level, Acinetobacter, Enterococcus, Methanosarcina, Pseudomonas, Sulfolobus, Bacillus, Lactobacillus, Mycobacterium, Streptomyces, and Thermococcus were chosen). For each taxon, 1000 randomly selected OGs corresponding to that taxon were chosen; for each of these OGs, five randomly chosen genes within this OG were chosen.
OrthoDB cross-references OGs to UniProt65 IDs of the corresponding proteins. We mapped these to the corresponding EMBL CDS IDs66 via the UniProt database API65; DNA sequences of these EMBL CDSs were downloaded via the EMBL database API. For each of these sequences, we generated LookingGlass embedding vectors.

Homologous and nonhomologous sequence pairs
To create a balanced dataset of homologous and nonhomologous sequence pairs, we compared all homologous pairs of the five sequences in an OG (total of ten homologous pairs) to an equal number of randomly selected out-of-OG comparisons for the same sequences; i.e., each of the five OG sequences was compared to 2 other randomly selected sequences from any other randomly selected OG (total of ten nonhomologous pairs). We term this set of sequences, and their corresponding LookingGlass embeddings, the OG homolog set (Table 1).

Embedding and sequence similarity
For each sequence pair, the sequence and embedding similarity were determined. The embedding similarity was calculated as the cosine similarity between embedding vectors. The sequence similarity was calculated as the Smith-Waterman alignment score using the BioPython70 pairwise2 package, with a gap open penalty of −10 and a gap extension penalty of −1. The IDs of chosen OGs, the cosine similarities of the embedding vectors, and sequence similarities of the DNA sequences are available in the associated Github repository61.

Comparison to HMM-based domain searches for distant homology detection
Distantly related homologous sequences that share, e.g., Pfam71, domains can be identified using HMM-based search methods. We used hmmscan25 (e-val threshold = 1e−10) to compare homologous (at the phylum level) sequences in the OG homolog set, for which the alignment score was less than 50 bits and the embedding similarity was greater than 0.62 (total: 21,376 gene pairs). Specifically, we identified Pfam domains in each sequence and compared whether the most significant (lowest e-value) domain for each sequence was identified in common for each homologous pair.
Environmental relevance
Encoder embeddings and MANOVA test
The LookingGlass embeddings and the environment of origin for each read in the mi-faser functional set were used to test the significance of the difference between the embedding vectors across environmental contexts. The statistical significance of this difference was evaluated with a MANOVA test using the R stats package68.
Oxidoreductase classifier
Dataset generation
The manually curated, reviewed entries of the Swiss-Prot database65 were downloaded (June 2, 2020). Of these, 23,653 entries were oxidoreductases (EC number 1.-.-.-) of Archaeal or Bacterial origin (988 unique ECs). We mapped their UniProt IDs to both their EMBL CDS IDs and their UniRef50 IDs via the UniProt database mapper API. Uniref50 IDs identify clusters of sequences with >50% amino acid identity. This cross-reference identified 28,149 EMBL CDS IDs corresponding to prokaryotic oxidoreductases, belonging to 5451 unique UniRef50 clusters. We split this data into training, validation, and test sets such that each UniRef50 cluster was contained in only one of the sets, i.e., there was no overlap in EMBL CDS IDs corresponding to the same UniRef50 cluster across sets. This ensures that the oxidoreductase sequences in the validation and test sets are dissimilar to those seen during training. The DNA sequences for each EMBL CDS ID were downloaded via the EMBL database API. These data generation process were repeated for a random selection of non-oxidoreductase UniRef50 clusters, which resulted in 28,149 non-oxidoreductase EMBL CDS IDs from 13,248 unique UniRef50 clusters.
Approximately 50 nucleotide read-length chunks (selected from the representative read-length distribution, as above) were selected from each EMBL CDS DNA sequence, with randomly selected start positions on the gene and a 50% chance of selecting the reverse complement, such that an even number of read-length sequences with “oxidoreductase” and “not oxidoreductase” labels were generated for the final dataset. This procedure produced a balanced dataset with 2,372,200 read-length sequences in the training set, 279,200 sequences in the validation set, and 141,801 sequences in the test set. We term this set of reads and their annotations the oxidoreductase model set (Table 1). In order to compare the oxidoreductase classifier performance to an HMM-based method, reads with “oxidoreductase” labels in the oxidoreductase model test set (71,451 reads) were 6-frame translated and searched against the Swiss-Prot protein database using phmmer25 (reporting e-val threshold = 0.05, using all other defaults).

Fine-tuning procedure
Since our functional annotation classifier addresses a closer classification task to the oxidoreductase classifier than LookingGlass itself, the architecture of the oxidoreductase classifier was fine-tuned starting from the functional annotation classifier, replacing the decoder with a new pooling classification layer (as described above for the functional annotation classifier) and with a final output size of 2 to predict “oxidoreductase” or “not oxidoreductase”. Fine tuning of the oxidoreductase classifier layers was done successively, training later layers in isolation and then progressively including earlier layers into training, using discriminative learning rates ranging from 1e−2 to 5e−4, as previously described72. The fine-tuned model was trained for 30 epochs, over 18 h, on a single P100 GPU node with 16GB memory.

Model performance in metagenomes
Sixteen marine metagenomes from the surface (SRF, ~5 meters) and mesopelagic (MES, 175–800 meters) from eight stations sampled as part of the TARA expedition37 were downloaded from the SRA62 (Supplementary Table 3, SRA accession numbers ERR598981, ERR599063, ERR599115, ERR599052, ERR599020, ERR599039, ERR599076, ERR598989, ERR599048, ERR599105, ERR598964, ERR598963, ERR599125, ERR599176, ERR3589593, and ERR3589586). Metagenomes were chosen from a latitudinal gradient spanning polar, temperate, and tropical regions and ranging from −62 to 76 degrees latitude. Mesopelagic depths from four out of the eight stations were sampled from oxygen minimum zones (OMZs, where oxygen More