More stories

  • in

    Vulnerable nations lead by example on Sustainable Development Goals research

    EDITORIAL
    20 July 2021

    Vulnerable nations lead by example on Sustainable Development Goals research

    A United Nations study of world science is a wake-up call that richer countries must also shift science towards the SDGs.

    Share on Twitter
    Share on Twitter

    Share on Facebook
    Share on Facebook

    Share via E-Mail
    Share via E-Mail

    Iraq’s wetlands are threatened by climate change; the country is pivoting its research towards the SDGs.Credit: Murtadha Al-Sudani/Anadolu Agency/Getty

    With the United Nations Sustainable Development Goals, world leaders pledged to end poverty and hunger, protect biodiversity and the climate, and get all children into schools by 2030. How have researchers and funders responded? Has there been a shift in research priorities?The UN’s Paris-based science and education agency has answers to these and other questions in the latest UNESCO Science Report, published last month (see go.nature.com/3zlojva). UNESCO says the 700-page report is a first attempt at understanding the impact of the Sustainable Development Goals (SDGs) on research priorities. The findings are a mixed picture.Using the Scopus database, UNESCO mapped publications from almost 200 countries between 2011 and 2019 on 56 research topics relevant to the SDGs. For the most part, the high-income countries that account for 64% of the world’s research spending — including Japan, South Korea, the United States and many European countries — showed relatively little change in the number of publications produced concerning the SDGs, and a declining share of global research.
    How science can put the Sustainable Development Goals back on track
    But it’s a different story for low- and middle-income countries, which have begun to shift their research priorities towards the goals.For example, the share of publications on photovoltaics — which could address the SDG on boosting renewable energy — from low-income and lower-middle-income countries more than trebled, going from 6.2% to 22% of the world total in the study period. The share of papers on biofuels and biomass nearly trebled, from 8.5% to 23%.Low-income countries more than doubled their share of research publications on crops that are more resilient to climate change, from 5% of the total to 11%. And researchers from sub-Saharan Africa contributed 361 out of 885 publications on smallholder farming in 2019 — more than the European Union’s 294. Ecuador, Ethiopia, Indonesia, Iraq, Russia and Vietnam all increased their output on most topics, albeit from low starting points in some cases.Much of the growth is powered by China. According to UNESCO, China’s researchers now publish around half of the world’s output on battery efficiency, 43% on hydrogen energy and 41% on carbon pricing. Their research on carbon capture and storage increased from 1,300 publications between 2012 and 2015 to 2,049 in 2016–19. By contrast, high-income nations — including France, Germany and the United States — showed declining shares during the same period, and some showed declining numbers. One exception is research into floating marine plastics. The field, which barely existed a decade ago, recorded 853 publications in 2019, mostly from high-income nations. But, overall, wealthier nations reported falls in their share of publishing across 54 out of the 56 fields assessed.
    Does the fight against hunger need its own IPCC?
    It’s disappointing to see so little progress from the richer countries. But it is something of a pattern. UNESCO’s researchers calculated that, between 2000 and 2013, wealthy nations spent less than US$25 billion on international development assistance in environmental areas such as climate change and biodiversity — about one-fifth of the $130 billion given for assistance in industry and innovation.At the same time, it’s heartening to see scientific output being slowly revived in many low-income countries — some of which were engines of scholarship in times past. But UNESCO also finds that funding trends in these countries have become harder to track. Some 98 countries reported funding data in 2015, but this fell to 68 in 2018. Some 28% of high-income and 78% of low- and middle-income countries are not reporting their science-funding data — and that is both problematic and troubling. The ability to correlate funding data with publishing information would provide a richer picture of the gains, and identify areas that would benefit from more resources. Countries need to comply with UNESCO’s requests for information, partly because they are obliged to track these data for the SDGs.Even before the pandemic, the world was not on track to reach most of the Sustainable Development Goals. With less than a decade to go before the 2030 deadline to end poverty and protect the environment, the UNESCO report aptly says that the world is “running out of time”. The report needs to be read closely in every world capital. It’s still not too late for everyone to pivot science to sustainability.

    Nature 595, 472 (2021)
    doi: https://doi.org/10.1038/d41586-021-01992-y

    Related Articles

    Does the fight against hunger need its own IPCC?

    How science can put the Sustainable Development Goals back on track

    The UN Environment Programme needs new powers

    Reset Sustainable Development Goals for a pandemic world

    Subjects

    Developing world

    Sustainability

    Biodiversity

    Government

    Climate change

    Latest on:

    Developing world

    Africa: renewables infrastructure avoids stranded assets
    Correspondence 13 JUL 21

    Cameroon: doubt could mean vaccine doses expire
    Correspondence 29 JUN 21

    Meaningful collaborations can end ‘helicopter research’
    Career Column 29 JUN 21

    Sustainability

    Andes foothills protected by carbon-offset fund
    Correspondence 20 JUL 21

    China wastes almost 30% of its food
    Research Highlight 15 JUL 21

    Italy: Forest harvesting is the opposite of green growth
    Correspondence 13 JUL 21

    Biodiversity

    UK biodiversity: close gap between reality and rhetoric
    Correspondence 06 JUL 21

    Indigenous lands: make Brazil stop mining to secure US deal
    Correspondence 08 JUN 21

    French vote for river barriers defies biodiversity strategy
    Correspondence 01 JUN 21

    Jobs

    Postdoctoral Research Fellow (Columbia University Irving Medical Center, NY)

    Columbia University Medical Center (CUMC), CU
    New York, NY, United States

    Professorships (W2) in Molecular Plant Sciences

    Philipps-Universität Marburg
    Marburg, Germany

    Assistant Editor – Food Science and Nutrition

    John Wiley & Sons, Inc.
    Multiple locations

    Early Detection Research Programme Manager

    Cancer Research UK (CRUK)
    London, United Kingdom

    Nature Briefing
    An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

    Email address

    Yes! Sign me up to receive the daily Nature Briefing email. I agree my information will be processed in accordance with the Nature and Springer Nature Limited Privacy Policy.

    Sign up More

  • in

    Identification of enriched hyperthermophilic microbial communities from a deep-sea hydrothermal vent chimney under electrolithoautotrophic culture conditions

    Archaeoglobales as systematic (electro)lithoautotrophs of the communityWe have evidenced the development of microbial electrotrophic communities and metabolic activity supported by current consumption (Fig. 1), product production (Fig. 2), and qPCRs (Fig. 3). These data suggest that growth did occur from energy supplied by the cathode. Our study is the first to show the possibility of growth of biofilm from environments harboring natural electric current in the total absence of soluble electron donors. To further discuss the putative mechanism, it is necessary to have a look at our conditions unfavorable for water electrolysis (see Supplementary Fig. S2). The equilibrium potential for water reduction into hydrogen at 80 °C, pH 7, and 1 atm was calculated at − 0.490 V vs SHE in pure water. The operational reduction potential is expected to be lower than the theoretical value due to internal resistances (from electrical connections, electrolytes, ionic membranes, etc.)16 and overpotentials (electrode material). This was confirmed with the on-set potential of H2 evolution measured at − 0.830 mV vs SHE in both experimental condition and abiotic control, indicating the absence of catalytic effect of putative hydrogenases secreted by the biofilm or metals from inoculum. Also, during preliminar potentials screening, the increase in current consumption and H2 production was observed only below − 0.7 V vs SHE (Supplementary Figs. S1 and S2). In addition, the presence of catalytic waves observed by CV with midpoint potentials between − 0.217 V to − 0.639 V indicate the implication of enzymes directly connected to the surface of the electrode (see Supplementary Fig. S2). Finally, the fixation of 267–1596 Coulombs day−1 into organics (Fig. 1) exceeds the maximum theoretical abiotic generation of hydrogen from abiotic current (~ 3 C day−1) 90- to 530-fold17.Therefore, under our experimental conditions, the biofilm growth should be largely ensured by a significant part of a direct transfer of electrons from the cathode, thereby demonstrating the presence of electrolithoautotroph microorganisms. This is supported by obtaining a similar biodiversity on sulfate with the cathode poised at − 300 mV [compared to − 590 mV vs SHE (Fig. 3)], whose potential is 190 mV more positive than the Equilibrium potential of H2 evolution (− 490 mV vs SHE), with then no electrochemical possibility of H2 production, even at molecular level.Taxonomic analysis of the enriched microbial communities at the end of the experiments showed the systematic presence of Archaeoglobales on cathodes. Moreover, the qPCR and MiSeq data (Fig. 3) highlighted a strong correlation between current consumption and density of Archaeoglobales in the biofilm (Supplementary Fig. S4, R2 = 0.945).The OTUs were related to some Archaeoglobales strains with 95–98% identities. Thus, we assume that under our experimental conditions new specific electrotrophic metabolisms or new electrolithoautotrophic Archaeoglobaceae species were enriched on the cathode. They were retrieved in all conditions and belonged to the only order in our communities exhibiting autotrophic metabolism. Autotrophic growth in the Archaeoglobales order is ensured mainly through using H2 as energy source and requires both branches of the reductive acetyl-CoA/Wood-Ljungdahl pathway for CO2 fixation18. Terminal electron acceptors used by this order include sulfate, nitrate, poorly crystalline Fe (III) oxide, and sulfur oxyanions19. Moreover, Archaeoglobus fulgidus has been recently shown to grow on iron by directly snatching electrons under carbon starvation during the corrosion process20. Furthermore, Ferroglobus and Geoglobus species were shown to be exoelectrogens in pure culture in a microbial electrosynthesis cell12 and have been enriched within a microbial electrolysis cell11,13. Given these elements, the identified Archaeoglobales species could be, under our electrolithoautotrophic conditions, the first colonizers of the electrode during the first days of growth. This hypothesis was confirmed into a more detailed study focusing on the enrichment on nitrate21.The growth of Archaeoglobales species in presence of oxygen is a surprising finding. Archaeoglobales have a strictly anaerobic metabolism, and the reductive acetyl-CoA pathway is very sensitive to the presence of oxygen22. This can be firstly explained by the low solubility of oxygen at 80 °C. Secondly, carbon cloth mesh reduces oxygen in the environment, allowing for anaerobic development of microorganisms into a protective biofilm23. This observation was supported by the near absence of Archaeoglobales in the liquid medium (Fig. 3). One of the hypotheses concerns direct interspecies electron transfer (DIET)24,25, with Archaeoglobales transferring electrons to another microorganism as an electron acceptor. Research into DIET is in its early stages, and further investigations are required to better understand the diversity of microorganisms and the mechanism of carbon and electron flows in anaerobic environments25 such as hydrothermal ecosystems.Electrosynthesis of organic compoundsAccumulation of pyruvate, glycerol and acetate was measured, while another set of compounds that appeared transiently were essentially detectable in the first few days of biofilm growth (Supplementary Table S1). They included amino acids (threonine, alanine) and volatile fatty acids (formate, succinate, lactate, acetoacetate, 3-hydroxyisovalerate) whose concentrations did not exceed 0.1 mM. Despite their thermostability, this transient production suggests they were used by microbial communities developing on the electrode in interaction with the primary producers during enrichment.On the other hand, in presence of nitrate, sulfate and oxygen as electron acceptors, the liquid media accumulated mainly acetate, glycerol, and pyruvate (Fig. 1). Coulombic efficiency calculations (Fig. 2) showed that electron content of the carbon products represented 60–90% of electrons consumed, the rest being potentially used directly for biomass or transferred to an electron acceptor. This concurs with the energy yield from the Wood-Ljungdahl pathway of Archaeoglobales, with only 5% of carbon flux directed to the production of biomass and the other 95% diverted to the production of small organic end-products excreted from the cell26.Pyruvate is a central intermediate of CO2 uptake by the reducing pathway of the acetyl-CoA/WL pathway27. It can be used to drive the anabolic reactions needed for biosynthesis of cellular constituents. Theoretically, the only explanation for improved production and accumulation of pyruvate (up to 5 mM in the liquid media of sulfate experiment) would be that pyruvate-consuming enzymes were inhibited or that pyruvate influx exceeded its conversion rate. Here we could suggest that in-cell electron over-feeding at the cathode leads to significant production of pyruvate when the electron acceptor runs out.In an ecophysiological context, similar pyruvate and glycerol production could occur on hydrothermal chimney walls into which electric current propagates28. The electrotroph biofilms would continually receive electrons, leading to an excess of intracellular reducing power which would be counterbalanced by overproduction of glycerol and pyruvate29,30. Furthermore, these products can serve as carbon and energy sources for heterotrophic microorganisms or for fermentation. In our experiments, pyruvate and glycerol concentrations varied over time, suggesting they were being consumed by heterotrophic microorganisms. Acetate production would thus result from the fermentation of pyruvate or other compounds produced by electrotrophic Archaeoglobales.Enrichment of rich heterotrophic biodiversity from electrotrophic Archaeoglobales communityDuring our enrichment experiments, the development of effective and specific biodiversity was dependent on the electron acceptors used (Fig. 3). Heatmap analyses (Supplementary Fig. S3) showed four distinct communities for the three electron acceptors and the initial inoculum. Thus, at the lower taxonomic level of the biodiversity analysis, most OTUs are not common to multiple enrichments, except for one OTU of Thermococcales that was found in both the nitrate and sulfate experiments. This suggests a real specificity of the communities and a specific evolution or adaptation of the members of the shared phyla to the different electron acceptors available in the environment. However, the various enrichments also showed the presence of Thermococcales regardless of the electron acceptors used, thus demonstrating a strong interaction between Thermococcales, assumed to be heterotrophs, and Archaeoglobales, the only demonstrated autotrophs. Moreover, members of these two groups have frequently been found together in various hydrothermal sites4,5,31,32, where they are considered potential primary colonizers33,34,35,36,37. After Thermococcales, the rest of the heterotrophic biodiversity was specific to each electron acceptor.On nitrate, two additional phylogenetic groups were retrieved: Desulfurococcales and Thermales. OTUs of Desulfurococcales are mainly affiliated to Thermodiscus or Aeropyrum species, which are hyperthermophilic and heterotrophic Crenarchaeota growing by fermentation of complex organic compounds or sulfur/oxygen reduction (Huber and Stetter, 2015). Concerning Thermales, a new taxon was enriched on cathode and only affiliated to Vulcanithermus mediatlanticus with similarity of 90%. This new taxon of Thermales (OTU 15, Supplementary Fig. S3) was also enriched up to 2% on the cathode of sulfate enrichment. Thermales are thermophilic (30–80 °C) and heterotrophic bacteria whose only four genera (Marinithermus, Oceanithermus, Rhabdothermus, and Vulcanithermus) are all retrieved in marine hydrothermal systems. They can grow under aerobic, microaerophilic and some anaerobic conditions with several inorganic electron acceptors such as nitrate, nitrite, Fe (III) and elemental sulfur38. All of the Thermales species can utilize the pyruvate as carbon and energy source with the sulfate or nitrate as electron acceptors.Pseudomonadales and Bacillales were found in the oxygen experiment. Most Pseudomonas are known to be aerobic and mesophilic bacteria, with a few thermophilic species (up to 65 °C)39,40. There have already been some reports of mesophilic Pseudomonas species growing in thermophilic conditions in composting environments41. Moreover, some Pseudomonas sp. are known to be electroactive in microbial fuel cells through long-distance extracellular electron transport42,43,44, and were dominant on the cathodes of a benthic microbial fuel cell on a deep-ocean cold seep45. In Bacillales, the Geobacillus spp. and some Bacillus sp. are known to be mainly (hyper)thermophilic aerobic and heterotrophic Firmicutes46.Hydrothermal electric current: a new energy source for the development of primary producersThe presence of so many heterotrophs in an initially autotrophic condition points to the hypothesis of a trophic relationship inside the electrotrophic community (Fig. 5). This suggests that the only autotrophs retrieved in all communities, the Archaeoglobales, might be the first colonizer of the electrode, using CO2 as carbon source and the cathode as energy source. Models using the REACT module of the Geochemist’s Workbench (GWB) and based on electron donor acceptor availability predicted low abundances of Archaeoglobales ( More

  • in

    Appropriate sampling methods and statistics can tell apart fraud from pesticide drift in organic farming

    Pesticide residues in organic productsNon-use of synthetic pesticides is a major characteristic of organic farming, with the objectives of protecting (a) the environment, (b) consumer health, and (c) farm worker health. In consumer studies, “no chemical pesticides” is usually mentioned as one of the most important criteria for buying organic food1,2. These consumer expectations are mostly met in what is referred to in objective (b). Both European and U.S. sources consistently found the percentage of samples with residues of pesticides above the limit of quantification ( > LOQ) to be much lower in organic than in conventional food (Fig. 1a, see also Supplementary Fig. 1). This is especially true when it comes to fresh fruits and vegetables (Fig. 1c), which are known to be the most critical food groups in terms of pesticide residues3. It is elucidating, however, to not only look at the number of samples with an (unknown) level of residues  > LOQ, but to quantify the residues found per sample. In many cases, more than one substance is found in a sample, therefore one meaningful indicator is the mean cumulative pesticide load per sample (MCPL, see Supplementary Table 1). This is represented in Fig. 1b for three out of the four datasets. The food authority CVUA (Chemisches- und Veterinäruntersuchungsamt) in Baden-Württemberg, Germany, has been comparing pesticide residues between organic and conventional food since 2002. In 2019, on average the residues in organic produce were more than 150 times lower than in the corresponding conventional products4 (Ratio Org./Conv., bottom of Fig. 1d). The USDA (U.S. Department of Agriculture) numbers tend to be higher than the European ones, both in percentages (Fig. 1a,c) and in MCPL (Fig. 1b). One reason for this is probably USDA’s risk-oriented sampling approach, in which some highly contaminated commodities are over-represented, as compared to their importance in most people’s diet (Supplementary Table 2, column C). If we correct this possible bias by assuming that every commodity would have been sampled with the same frequency, the MCPL across all commodities is cut by 40% (Supplementary Table 2, last row). Different LOQs and numbers of analytes covered by USDA on one hand, and different European laboratories on the other hand, also make comparison difficult.Figure 1Pesticide residues in conventional and organic food in tests conducted by four organisations: EFSA (European Food Safety Authority) collects official data from all EU member states3, CVUA from one federal state in Germany4, USDA from government laboratories across the U.S.5, while Eurofins is a commercial laboratory in Germany. Figures in brackets represent the number of samples. The legend is valid for (a), (b) and (c). In order to increase the number of samples (represented in brackets) and thus their representativeness, figures from several years were grouped together, as available from each organisation. Black bars symbolise standard errors across years. (a) Shows the percentage of samples with residues above the limit of quantification (LOQ), for all types of food ( available from two organisations only). (b) Represents the mean cumulative pesticide load (MCPL) for fruits and vegetables (available from three organisations). (c) Similar to (a), but for fresh fruits and vegetables only (CVUA uses “above 0.01 mg/kg” instead of LOQ, but this is identical for most substances). The same datasets were used for (b) and (c). (d) Multi-layer sieving model for residue testing at different points of the organic supply chain. The data above the white arrows are from the commercial laboratory Eurofins, and mostly represent the situation before products are released to the market, while the figures below the white arrows are from CVUA, representing the situation on the market (both wholesale and retail). Ratios from “before market” to “on market” are shown in the white arrows. In this process, the MCPL remains in the same range for conventional products (blue rectangle to the right), while it is reduced massively for organic products (green trapezium in the centre). As a result of this sieving mechanism, residues in samples from the market are 150 and more times lower in organic than in conventional produce (trapezium at the bottom). This shows that the process represented by the blue arrows works fairly well—which is not always the case for the investigation of the origin of such residues, symbolised by the yellow arrows.Full size imageOrganic businesses’ testing strategiesUnfortunately, the generally good news for consumers with respect to objective (b) does not always mean that objectives (a) and (c) are also met. With the steady growth of the organic market and globalisation of supply chains, integrity of the system is often at stake. Organic products mostly fetch higher prices, and therefore also attract fraud6,7. Since pesticide residues are easily detectable parameters, often indicating non-compliance with organic production rules, many organic businesses test each batch for such residues, before placing it on the market. Positive results should then lead to an investigation of the origin of the found residues: Did an organic farmer spray? Do the residues come from drift, from ubiquitous contamination, or from (avoidable or unavoidable) contamination during processing, transport, storage? Were organic and conventional products mixed at some point of the supply chain—or is somebody simply labelling conventional products as “organic”? The idea behind this is depicted in Fig. 1d. The filter process as such, and the exclusion of contaminated batches from the organic market, as represented by the blue and red arrows, often work well. Thus, there are remarkably lower average amounts of residues after undergoing this filtering process. Residues in organic produce reported from the market were reduced by 22 and 89 times in fruits and vegetables, respectively, compared to the levels reported by the commercial laboratory, which represent mostly pre-market samples, while the values for conventional samples remained in the same range. This shows that market actors often remove problematic batches by declaring them conventional. In Supplementary Tables 3 and 4 we provide further explanations why the datasets “before release to the market” and “on the market” in Fig. 1d are comparable.We do not have test results from a commercial laboratory in the U.S. that could be compared to Eurofins data. But, as opposed to the other sources of information, the USDA database identifies the country of origin of each sample. Anybody working in international organic certification would expect residues in imported food to be higher than in domestic products, because fraud is more widespread when the distance is bigger between producers on the one hand, and consumers and the competent authorities on the other. The U.S. data, however, suggest the opposite trend: Not only at the aggregate level, but also for most individual commodities, the MCPL is lower in imported than in domestic products (Supplementary Table 2, columns J and K). The reason is probably that samples are tested before signing purchase contracts, and products rejected or bought as conventional, if they do not comply with the expectations.This is good quality control practice—the problem is that the information about the “downgrading” of organic products to conventional is not always reaching the certification bodies (CBs), thus impeding the investigation of the origin of residues and the exclusion of fraudulent actors from the market (yellow arrows in Fig. 1d). It is in the nature of things that these processes are not publicly known and therefore cannot be quantified, but in Supplementary Fig. 2 we present anecdotic evidence, which also suggests that for some market actors the definition of “organic” is limited to “free of pesticide residues”.Certifiers’ testing strategiesThe two most important markets for organic food are the EU, where the “organic” label is legally governed by an EU Regulation, and the USA, where the corresponding rule is the National Organic Program (NOP). Although they have different approaches on how to deal with spray-drift and with residues (Supplementary Table 5), both regulations require CBs to take samples from at least 5% of their clients every year. A large amount of data is being generated through this mechanism, but the sampling procedures and interpretation of results often do not allow deriving clear results. A recent unpublished BSc thesis at the University of Kassel revealed that 80% of the samples by CBs in ten EU member countries are taken of final products, but only 20% from the field or during the production process. This suggests that not only for market actors, but also for many CBs, the purpose of sampling and testing is limited to ensuring that food sold on the market with an organic claim, is free of pesticide residues, without digging deeper to find the origin of contamination.The differentiation between active use and non-intentional contamination is difficult, if only final products are tested. Plant (mainly leaf) samples from the field have several advantages in this regard: (a) Often, there is a long time span between pesticide application and harvest. Because of dissipation of the residues, nothing or only traces may be found in the final product (Supplementary Table 6). Field samples can be taken during or shortly after a suspected pesticide application, so that the dissipation effect is reduced and residues are found even for substances with a short half-life. (b) Leaves have a surface/weight ratio between 10 and 118 cm2/g8, whereas for fruits this ratio is between 0.6 and 2.29, and for seeds between 2 and 10 cm2/g only10,11,12. Residues in leaves are therefore normally higher than in seeds, fruits or roots, which makes interpretation of test results easier. (c) Field sampling allows taking separate samples from centre and margin of the field, as explained below in more detail.Unfortunately, if CBs take field samples at all, they often take them only from field margins13,14 (“let’s see if there is a drift problem”). Positive results are then attributed to spray-drift, and farmers are required to establish buffers—without even considering the possibility of residues originating from an application by the organic farmer. Such procedures open the door for fraudulent use of pesticides by organic farmers.Other CBs have established so-called “action levels”, below which they consider the presence of residues in organic products to be the result of ubiquitous environmental contamination, with no need to investigate their origin13. While such thresholds may be necessary for specific cases (see below concerning the banana industry), using this approach as a general procedure disregards not only the spatial distribution, but also temporal dynamics of pesticides in plant tissue. As opposed to soil, half-lives in plant tissue exposed to UV radiation and weather, are relatively short for most modern pesticides15. A residue level of 0.02 mg/kg, used by some CBs as “action level”, is typically reached one to two months after the application of a pesticide, in some cases even after only five days (Supplementary Table 6).The time that has elapsed since an application, however, is unknown in most cases. Spraying records kept by conventional neighbours are normally not part of the inspection. In case of suspicious test results in samples from the organic farm, such records may sometimes be accessed as part of a follow-up investigation, but at that point the organic farmer may have asked the neighbour to manipulate the records. And if the organic farmer has sprayed, he or she obviously tries to hide this fact. This situation makes interpretation of low levels of residues found in samples from organic fields even more challenging, and increases the importance of being able to differentiate application from drift through other methods.Two forms of spray-driftOver the past decades, a distinction has been made between short distance primary spray-drift during the application, and long distance secondary spray-drift occurring after the application16. The latter was attributed to evaporation and considered to play a role only for pesticides with high vapour pressure17. On the one hand, recent studies have shown that evaporation and long-distance transport can already play a role during, not only after application18. On the other hand, long-distance transport has been found to be linked not only to evaporation. Pesticides adherent to dust from wind erosion can contaminate large areas19. In the present context, we use the terms short-range and long-range drift, instead of primary and secondary drift (Fig. 2).Figure 2Simplified model of short-range vs. long-range drift originating from air-blast spraying in a fruit orchard. The specific values for pesticide concentrations (mg/kg) expected for different downwind distances from the orchard can vary by a factor 10 or more, depending on the applied substance, dose, weather conditions, vegetation, etc., but the graph provides an approximate estimate of the ratios that can be expected. In the case presented here, pesticide concentration in fruit leaves immediately after the application is 15 mg/kg. In the area of short-range direct drift, deposit decreases exponentially, so that at 100 m distance, we can expect to find only 0.01 mg/kg. At further distances, deposits are often below this level.Full size imageLong-range driftLong-range drift is so far poorly understood, can lead to (normally very low) residues at distances as far as thousands of km19, and happens in the form of vapour or molecules adhering to dust. The main factors influencing long-range drift are vapour pressure of the pesticide, capacity of adherence to dust, incidence of wind erosion, and temperature inversion in the atmosphere17. Long-range pesticide drift has recently received more attention21,22,23,24,25. Examples have been used in the context of organic certification for supporting the argument of ubiquity of pesticides, linked to the assumption that low- or even medium-level residues in organic products are often derived from their omnipresence in the environment26,27.Cases from Brazil (endosulfan in soybeans), Montana (USA) and Saskatchewan (Canada) (glyphosate in khorasan wheat) and Germany (pendimethalin and prosulfocarb in different crops) have been quoted to demonstrate the ubiquity of pesticides27. None of these case studies, however, provides solid evidence for the assumption that long-distance transport of pesticides leads to residues in organic food above the level of, say, 0.01 to 0.03 mg/kg. The problem of the herbicides pendimethalin and prosulfocarb being subject to long-distance drift because of their high vapour pressure, has been known for a long time28, but this phenomenon cannot be extrapolated to other substances. Even for these herbicides, there is no evidence that residues at larger distances could be above the indicated levels. Across 15 vegetation samples from nature reserves in Germany, on average, 0.009 mg/kg pendimethalin and 0.004 mg/kg prosulfocarb were found29. Exceptions may exist, e.g., when pesticide applications are followed by heavy wind erosion, as seems to be the case in some of the North American wheat growing areas, where glyphosate is used for cereal desiccation shortly before harvest.In a survey in Switzerland30, neonicotinoid residues were found in 93% of plant samples from organic farms (as compared to 100% of samples from conventional farms), thus supporting the ubiquity suspicion. But there were substantial quantitative differences between organic and conventional farms (Fig. 3). The average sum of neonicotinoid residues in plant and soil samples from organic farms was lower by a factor of 11 than that of plant samples from conventional farms. For soil samples, this factor was as high as 71. Even the highest value for one single substance (imidacloprid) found in organic plants (2.13 µg/kg = 0.00213 mg/kg) would be below the limit of quantification (LOQ) used for this substance in most screenings (0.01 mg/kg).Figure 3Maximum and average residues of neonicotinoid insecticides in soil and plant samples from organic farms, integrated crop production (“IP Suisse”: this program involves reduced pesticide application) and conventional farms in Switzerland. The figures represent the sums of acetamiprid, chlothianidin, imidacloprid, thiacloprid and thiamethoxan. Figures in brackets represent standard errors.(Data from Humann-Guilleminot et al.30).Full size imageIn a study in Germany29, the MCPL in natural vegetation in five reference areas (average distance from arable fields  >3 km) was 0.003 mg/kg, and in 15 nature conservation areas (average distance from arable fields 143 m) it was 0.006 mg/kg, but in three buffer zones (average distance 54 m) it was 5.4 mg/kg. To make figures comparable with other data in this article, we have subtracted the concentration of non-agricultural pesticides from the total amounts, and divided the numbers by a factor five, because the residues in this study refer to dry matter, while all the others use fresh matter. Although 5.4 mg/kg at 54 m distance is a disturbingly high value, the survey confirms that concentrations at larger distances do not exceed the “traces” level. The intention of this article is not to put in doubt the environmental damage caused by such traces. What we try to show is that the “ubiquity” argument may sometimes be hiding cases of fraudulent pesticide use by organic farmers.Short-range driftAs opposed to long-range drift, short-range drift is well understood, has its impact mainly in a range from 1 m up to a maximum of 1,000 m (for aerial spraying), happens in the form of droplets, and is not substance specific. The main factors influencing this form of drift are droplet size, windspeed, and height of the boom (nozzles) above soil17,19,31,32,33. The fact that long-range drift is poorly understood and leads to low concentrations of certain substances over wide areas, should not stop certification bodies (CBs) from using the available knowledge about short-range drift as a tool for assessing farmers’ compliance with organic production rules. The dynamics of short-range spray-drift have been widely studied in the context of preventing liability problems due to herbicide damage, contamination of water bodies and natural habitats, and direct risks for human settlements19,31,32,33,34,35,36. Pesticide deposit decreases exponentially with increasing distance from the field on which the substance is applied. With a tractor boom sprayer, deposit at 25 m distance is expected to be only 1% of that in the target field. While distances are greater for air-blast or aerial spraying, the basic principle of exponential decrease is the same (Fig. 2 and Supplementary Fig. 3).ObjectivesThe objectives of our study are: (I) to demonstrate that appropriate field sampling methods can differentiate the effects of fraudulent pesticide application by the organic farmer, from the results of both short-range and long-range spray-drift, and (II) for the specific case of aerial fungicide spraying in the banana industry, identify appropriate variables, which allow us to interpret the test results correctly for the purpose of this differentiation. More

  • in

    Early-life social experience affects offspring DNA methylation and later life stress phenotype

    1.Harlow, H. F., Dodsworth, R. O. & Harlow, M. K. Total social isolation in monkeys. Proc. Natl Acad. Sci. USA 54, 90–97 (1965).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Levine, S. Infantile experience and resistance to physiological stress. Science 126, 405 (1957).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Liu, D. et al. Maternal care, hippocampal glucocorticoid receptors, and hypothalamic-pituitary-adrenal responses to stress. Science 277, 1659–1662 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Francis, D., Diorio, J., Liu, D. & Meaney, M. J. Nongenomic transmission across generations of maternal behavior and stress responses in the rat. Science 286, 1155–1158 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Caldji, C. et al. Maternal care during infancy regulates the development of neural systems mediating the expression of fearfulness in the rat. Proc. Natl Acad. Sci. USA 95, 5335–5340 (1998).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Vargas, J., Junco, M., Gomez, C. & Lajud, N. Early life stress increases metabolic risk, HPA axis reactivity, and depressive-like behavior when combined with postweaning social isolation in rats. PLoS ONE 11, 1–21 (2016).CAS 

    Google Scholar 
    7.Sánchez, M. M. et al. Alterations in diurnal cortisol rhythm and acoustic startle response in nonhuman primates with adverse rearing. Biol. Psychiatry 57, 373–381 (2005).PubMed 
    Article 
    CAS 

    Google Scholar 
    8.Fries, A. B. W., Shirtcliff, E. A. & Pollak, S. D. Neuroendocrine dysregulation following early social deprivation in children. Dev. Psychobiol. 50, 588–599 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Li, E. & Bird, A. In Epigenetics (eds Allis, C. D., Jenuwein, T., Reinberg, D. & Caparros, M.-L.), 343–356 (Cold Spring Harbor Laboratory Press, 2007).10.Weaver, I. C. G. et al. Epigenetic programming by maternal behavior. Nat. Neurosci. 7, 847–854 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Anier, K. et al. Maternal separation is associated with DNA methylation and behavioural changes in adult rats. Eur. Neuropsychopharmacol. 24, 459–468 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Provencal, N. et al. The signature of maternal rearing in the methylome in rhesus macaque prefrontal cortex and T cells. J. Neurosci. 32, 15626–15642 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Unternaehrer, E. et al. Childhood maternal care is associated with DNA methylation of the genes for brain-derived neurotrophic factor (BDNF) and oxytocin receptor (OXTR) in peripheral blood cells in adult men and women. Stress 18, 451–461 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    14.Sánchez, M. M., Ladd, C. O. & Plotsky, P. M. Early adverse experience as a developmental risk factor for later psychopathology: evidence from rodent and primate models. Dev. Psychopathol. 13, 419–449 (2001).PubMed 
    Article 

    Google Scholar 
    15.Van Bodegom, M., Homberg, J. R. & Henckens, M. J. A. G. Modulation of the hypothalamic-pituitary-adrenal axis by early life stress exposure. Front. Cell. Neurosci. 11, 1–33 (2017).
    Google Scholar 
    16.Moore, S. R. et al. Epigenetic correlates of neonatal contact in humans. Dev. Psychopathol. 29, 1517–1538 (2017).PubMed 
    Article 

    Google Scholar 
    17.Sanchez, M. M. The impact of early adverse care on HPA axis development: nonhuman primate models. Horm. Behav. 50, 623–631 (2006).PubMed 
    Article 

    Google Scholar 
    18.Houtepen, L. C. et al. Genome-wide DNA methylation levels and altered cortisol stress reactivity following childhood trauma in humans. Nat. Commun. 7, 10967 (2016).19.Coley, E. J. L. et al. Cross-generational transmission of early life stress effects on HPA regulators and bdnf are mediated by sex, lineage, and upbringing. Front. Behav. Neurosci. 13, 1–17 (2019).Article 
    CAS 

    Google Scholar 
    20.Kember, R. L. et al. Maternal separation is associated with strain-specific responses to stress and epigenetic alterations to Nr3c1, Avp, and Nr4a1 in mouse. Brain Behav. 2, 455–467 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Dunn, E. C. et al. Sensitive periods for the effect of childhood adversity on DNA methylation: results from a prospective, longitudinal study. Biol. Psychiatry 85, 838–849 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Hennessy, M. B., Hornschuh, G., Kaiser, S. & Sachser, N. Cortisol responses and social buffering: a study throughout the life span. Horm. Behav. 49, 383–390 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Kent, W. J. et al. The Human Genome Browser at UCSC. Genome Res. 12, 996–1006 (2002).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Kent, W. J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Kornienko, O., Clemans, K. H., Out, D. & Granger, D. A. Hormones, behavior, and social network analysis: exploring associations between cortisol, testosterone, and network structure. Horm. Behav. 66, 534–544 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    26.Kornienko, O., Clemans, K. H., Out, D. & Granger, D. A. Friendship network position and salivary cortisol levels. Soc. Neurosci. 8, 385–396 (2013).PubMed 
    Article 

    Google Scholar 
    27.Ponzi, D., Muehlenbein, M. P., Geary, D. C. & Flinn, M. V. Cortisol, salivary alpha-amylase and children’s perceptions of their social networks. Soc. Neurosci. 11, 164–174 (2016).PubMed 
    Article 

    Google Scholar 
    28.Wittig, R. M. et al. Focused grooming networks and stress alleviation in wild female baboons. Horm. Behav. 54, 170–177 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Wey, T. W. & Blumstein, D. T. Social attributes and associated performance measures in marmots: bigger male bullies and weakly affiliating females have higher annual reproductive success. Behav. Ecol. Sociobiol. 66, 1075–1085 (2012).Article 

    Google Scholar 
    30.Priebe, K. et al. Maternal influences on adult stress and anxiety-like behavior in C57BL/6J and BALB/CJ mice: a cross-fostering study. Dev. Psychobiol. 47, 398–407 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.McLaughlin, K. A. et al. Causal effects of the early caregiving environment on development of stress response systems in children. Proc. Natl Acad. Sci. USA 112, 5637–5642 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Sheriff, M. J., Dantzer, B., Delehanty, B., Palme, R. & Boonstra, R. Measuring stress in wildlife: techniques for quantifying glucocorticoids. Oecologia 166, 869–887 (2011).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Goymann, W. On the use of non-invasive hormone research in uncontrolled, natural environments: the problem with sex, diet, metabolic rate and the individual. Methods Ecol. Evol. 3, 757–765 (2012).Article 

    Google Scholar 
    34.Laubach, Z. M. et al. Early life social and ecological determinants of global DNA methylation in wild spotted hyenas. Mol. Ecol. 28, 3799–3812 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Greenberg, J. R. Developmental Flexibility in Spotted Hyneas (Crocuta crocuta): The Role of Maternal and Anthropogenic Effects (Michigan State University, 2017).36.Turner, J. W., Bills, P. S. & Holekamp, K. E. Ontogenetic change in determinants of social network position in the spotted hyena. Behav. Ecol. Sociobiol. 72, 1–5 (2018).37.Smolarek, I. et al. Global DNA methylation changes in blood of patients with essential hypertension. Med. Sci. Monit. 16, 149–155 (2010).
    Google Scholar 
    38.Zinellu, A. et al. Blood global DNA methylation is decreased in non-severe chronic obstructive pulmonary disease (COPD) patients. Pulm. Pharmacol. Ther. 46, 11–15 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Dong, Y. et al. Associations between global DNA methylation and telomere length in healthy adolescents. Sci. Rep. 7, 1–6 (2017).ADS 
    Article 
    CAS 

    Google Scholar 
    40.Wong, J. Y. Y. et al. The association between global DNA methylation and telomere length in a longitudinal study of boilermakers. Genet. Epidemiol. 38, 254–264 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Woo, H. D. & Kim, J. Global DNA hypomethylation in peripheral blood leukocytes as a biomarker for cancer risk: A meta-analysis. PLoS ONE 7, e34615 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Sharma, P. et al. Detection of altered global DNA methylation in coronary artery disease patients. DNA Cell Biol. 27, 357–365 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    43.Ono, H. et al. Association of dietary and genetic factors related to one-carbon metabolism with global methylation level of leukocyte DNA. Cancer Sci. 103, 2159–2164 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Basu, N. et al. Effects of methylmercury on epigenetic markers in three model species: mink, chicken and yellow perch Niladri. Comp. Biochem. Physiol. C 157, 322–327 (2013).CAS 

    Google Scholar 
    45.Laubach, Z. M. et al. Socioeconomic status and DNA methylation from birth through mid-childhood: a prospective study in Project Viva. Epigenomics https://doi.org/10.2217/epi-2019-0040 (2019).46.Meissner, A. et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 33, 5868–5877 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Brown, G. R. et al. Gene: a gene-centered information resource at NCBI. Nucleic Acids Res. 43, D36–D42 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    48.National Library of Medicine (US), National Center for Biotechnology Information. Gene. https://www.ncbi.nlm.nih.gov/gene/ (2004).49.Binns, D. et al. QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics 25, 3045–3046 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Huntley, R. P. et al. The GOA database: Gene Ontology annotation updates for 2015. Nucleic Acids Res. 43, D1057–D1063 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    51.Chang, I. & Parrilla, M. Expression patterns of homeobox genes in the mouse vomeronasal organ at postnatal stages. Gene Expr. Patterns 21, 69–80 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    52.Santos, J. S., Fonseca, N. A., Vieira, C. P., Vieira, J. & Casares, F. Phylogeny of the teashirt-related zinc finger (tshz) gene family and analysis of the developmental expression of tshz2 and tshz3b in the zebrafish. Dev. Dyn. 239, 1010–1018 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Zhou, T. et al. Peripheral blood gene expression as a novel genomic biomarker in complicated sarcoidosis. PLoS ONE 7, 1–13 (2012).CAS 

    Google Scholar 
    54.Scheinfeldt, L. B. et al. Using the Coriell Personalized Medicine Collaborative Data to conduct a genome-wide association study of sleep duration. Am. J. Med. Genet. B 168, 697–705 (2015).Article 

    Google Scholar 
    55.Riku, M. et al. Down-regulation of the zinc-finger homeobox protein TSHZ2 releases GLI1 from the nuclear repressor complex to restore its transcriptional activity during mammary tumorigenesis. Oncotarget 7, 5690–5701 (2016).PubMed 
    Article 

    Google Scholar 
    56.Tapia-Carrillo, D., Tovar, H., Velazquez-Caldelas, T. E. & Hernandez-Lemus, E. Master regulators of signaling pathways: an application to the analysis of gene regulation in breast cancer. Front. Genet. 10, 1–11 (2019).Article 
    CAS 

    Google Scholar 
    57.Yamamoto, M., Cid, E., Bru, S. & Yamamoto, F. Rare and frequent promoter methylation, respectively, of TSHZ2 and 3 genes that are both downregulated in expression in breast and prostate cancers. PLoS ONE 6, 1–10 (2011).Article 

    Google Scholar 
    58.Zhou, S. et al. Proteomic landscape of TGF-β1-induced fibrogenesis in renal fibroblasts. Sci. Rep. 10, 1–17 (2020).Article 
    CAS 

    Google Scholar 
    59.Seto, S., Tsujimura, K. & Koide, Y. Rab GTPases regulating phagosome maturation are differentially recruited to mycobacterial phagosomes. Traffic 12, 407–420 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    60.Kretzer, N. M. et al. RAB43 facilitates cross-presentation of cell-associated antigens by CD8α+ dendritic cells. J. Exp. Med. 213, 2871–2883 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Huang, Z., Liang, H. & Chen, L. Rab43 promotes gastric cancer cell proliferation and metastasis via regulating the pi3k/akt signaling pathway. OncoTargets Ther. 13, 2193–2202 (2020).CAS 
    Article 

    Google Scholar 
    62.Han, M. Z. et al. High expression of RAB43 predicts poor prognosis and is associated with epithelial-mesenchymal transition in gliomas. Oncol. Rep. 37, 903–912 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    63.Blackburn, M. R., Datta, S. K., Wakamiya, M., Vartabedian, B. S. & Kellems, R. E. Metabolic and immunologic consequences of limited adenosine deaminase expression in mice. J. Biol. Chem. 271, 15203–15210 (1996).CAS 
    PubMed 
    Article 

    Google Scholar 
    64.Bradford, K. L., Moretti, F. A., Carbonaro-Sarracino, D. A., Gaspar, H. B. & Kohn, D. B. Adenosine deaminase (ADA)-deficient severe combined immune deficiency (SCID): molecular pathogenesis and clinical manifestations. J. Clin. Immunol. 37, 626–637 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Parish, S. T. et al. Adenosine deaminase modulation of telomerase activity and replicative senescence in human CD8 T lymphocytes. J. Immunol. 184, 2847–2854 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Sánchez-Melgar, A., Albasanz, J. L., Pallàs, M. & Martín, M. Adenosine metabolism in the cerebral cortex from several mice models during aging. Int. J. Mol. Sci. 21, 1–20 (2020).Article 
    CAS 

    Google Scholar 
    67.Geiger, J. D. & Nagy, J. I. Ontogenesis of adenosine deaminase activity in rat brain. J. Neurochem. 48, 147–153 (1987).CAS 
    PubMed 
    Article 

    Google Scholar 
    68.Vasudha, K. C., Nirmal Kumar, A. & Venkatesh, T. Studies on the age dependent changes in serum adenosine deaminase activity and its changes in hepatitis. Indian J. Clin. Biochem. 21, 116–120 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Sims, B., Powers, R. E., Sabina, R. L. & Theibert, A. B. Elevated adenosine monophosphate deaminase activity in Alzheimer’s disease brain. Neurobiol. Aging 19, 385–391 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    70.Singh, L. S. & Sharma, R. Developmental expression and corticosterone inhibition of adenosine deaminase activity in different tissues of mice. Mech. Ageing Dev. 80, 85–92 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    71.McGowan, P. O. et al. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nat. Neurosci. 12, 342–348 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Pan, P., Fleming, A. S., Lawson, D., Jenkins, J. M. & McGowan, P. O. Within- and between-litter maternal care alter behavior and gene regulation in female offspring. Behav. Neurosci. 128, 736–748 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    73.Romero, L. M., Dickens, M. J. & Cyr, N. E. The Reactive Scope Model – a new model integrating homeostasis, allostasis, and stress. Horm. Behav. 55, 375–389 (2009).PubMed 
    Article 

    Google Scholar 
    74.Kamin, H. S. & Kertes, D. A. Cortisol and DHEA in development and psychopathology. Horm. Behav. 89, 69–85 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    75.Engler, H., Bailey, M. T., Engler, A. & Sheridan, J. F. Effects of repeated social stress on leukocyte distribution in bone marrow, peripheral blood and spleen. J. Neuroimmunol. 148, 106–115 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Kruuk, H. The Spotted Hyena: A Study of Predation and Social Behavior (University of Chicago Press, 1972).77.Holekamp, K., Smale, L. & Szykman, M. Rank and reproduction in the female spotted hyaena. J. Reprod. Fertil. 108, 229–237 (1996).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    78.Holekamp, K. E. & Smale, L. Behavioral development in the spotted hyena. Bioscience 48, 997–1005 (1998).Article 

    Google Scholar 
    79.Holekamp, K. E. et al. Patterns of association among female spotted hyenas (Crocuta crocuta). J. Mammal. 78, 55–64 (1997).Article 

    Google Scholar 
    80.Turner, J. W., Robitaille, A. L., Bills, P. S. & Holekamp, K. E. Early-life relationships matter: social position during early life predicts fitness among female spotted hyenas. J. Anim. Ecol. 90, 183–196 (2021).PubMed 
    Article 

    Google Scholar 
    81.Altmann, J. Observational study of behavior: sampling methods. Behaviour 49, 227–267 (1974).CAS 
    PubMed 
    Article 

    Google Scholar 
    82.Karimi, M., Johansson, S. & Ekström, T. J. Using LUMA. A luminometric-based assay for global DNA methylation. Epigenetics 1, 45–48 (2006).PubMed 

    Google Scholar 
    83.Coluccio, A. et al. Individual retrotransposon integrants are differentially controlled by KZFP/KAP1-dependent histone methylation, DNA methylation and TET-mediated hydroxymethylation in naïve embryonic stem cells. Epigenet. Chromatin 11, 1–18 (2018).Article 
    CAS 

    Google Scholar 
    84.Eden, A., Gaudet, F., Waghmare, A. & Jaenisch, R. Chromosomal instability and tumors promoted by DNA hypomethylation. Science 300, 455 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    85.Lev Maor, G., Yearim, A. & Ast, G. The alternative role of DNA methylation in splicing regulation. Trends Genet. 31, 274–280 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    86.Doherty, T. S., Forster, A. & Roth, T. L. Global and gene-specific DNA methylation alterations in the adolescent amygdala and hippocampus in an animal model of caregiver maltreatment. Behav. Brain Res. 298, 55–61 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    87.Noguera, J. C. & Velando, A. Bird embryos perceive vibratory cues of predation risk from clutch mates. Nat. Ecol. Evol. 3, 1225–1232 (2019).PubMed 
    Article 

    Google Scholar 
    88.Crudo, A. et al. Prenatal synthetic glucocorticoid treatment changes DNA methylation states in male organ systems: multigenerational effects. Endocrinology 153, 3269–3283 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Garrett-Bakelman, F. E. et al. Enhanced reduced representation bisulfite sequencing for assessment of DNA nethylation at base pair resolution. J. Vis. Exp. https://doi.org/10.3791/52246, 1–15 (2015).90.Yang, C. et al. A draft genome assembly of spotted hyena, Crocuta crocuta. Sci. Data 7, 1–10 (2020).CAS 
    Article 

    Google Scholar 
    91.Mccormick, J. A. et al. 5’-Heterogeneity of glucocorticoid receptor messenger RNA is tissue specific: differential regulation of variant transcripts by early-life events. Mol. Endocrinol. 14, 506–517 (2000).CAS 
    PubMed 

    Google Scholar 
    92.Szyf, M., Weaver, I. C. G., Champagne, F. A., Diorio, J. & Meaney, M. J. Maternal programming of steroid receptor expression and phenotype through DNA methylation in the rat. Front. Neuroendocrinol. 26, 139–162 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    93.Van Meter, P. E. et al. Fecal glucocorticoids reflect socio-ecological and anthropogenic stressors in the lives of wild spotted hyenas. Horm. Behav. 55, 329–337 (2009).PubMed 
    Article 
    CAS 

    Google Scholar 
    94.Dloniak, S. M. et al. Non-invasive monitoring of fecal androgens in spotted hyenas (Crocuta crocuta). Gen. Comp. Endocrinol. 135, 51–61 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    95.Laubach, Z. M., Murray, E. J., Hoke, K. L., Safran, R. J. & Perng, W. A biologist’s guide to model selection and causal inference. Proc. R. Soc. Ser. B https://doi.org/10.1098/rspb.2020.2815 (2021).96.Engh, A. L., Esch, K., Smale, L. & Holekamp, K. E. Mechanisms of maternal rank ‘inheritance’ in the spotted hyaena, Crocuta crocuta. Anim. Behav. 60, 323–332 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    97.Baron, R. M. & Kenny, D. A. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 1173–1182 (1986).CAS 
    PubMed 
    Article 

    Google Scholar 
    98.Chadeau-Hyam, M. et al. Meeting-in-the-middle using metabolic profiling-a strategy for the identification of intermediate biomarkers in cohort studies. Biomarkers 16, 83–88 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    99.Lea, A. J., Altmann, J., Alberts, S. C. & Tung, J. Resource base influences genome-wide DNA methylation levels in wild baboons (Papio cynocephalus). Mol. Ecol. 25, 1681–1696 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    100.Lea, A. J., Tung, J. & Zhou, X. A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data. PLoS Genet. 11, 1–31 (2015).Article 
    CAS 

    Google Scholar 
    101.van Iterson, M. et al. Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution. Genome Biol. 18, 1–13 (2017).Article 
    CAS 

    Google Scholar 
    102.Hochberg, Y. & Benjamini, Y. More powerful procedures for multiple statistical significance testing. Stat. Med. 9, 811–818 (1990).CAS 
    PubMed 
    Article 

    Google Scholar 
    103.Laubach, Z. M. et al. Early-life social experience affects offspring DNA methylation and later life stress phenotype. https://doi.org/10.5281/zenodo.4967924 (2021). More

  • in

    Response to substrate limitation by a marine sulfate-reducing bacterium

    1.Jørgensen BB. Mineralization of organic matter in the sea bed-the role of sulphate reduction. Nature. 1982;296:643–5.Article 

    Google Scholar 
    2.Kasten S, Jørgensen BB. Sulfate reduction in marine sediments. In: Schulz H, Zabel M, editors. Marine geochemistry. Berlin: Springer; 2000. pp. 263–81.3.Pellerin A, Antler G, Røy H, Findlay A, Beulig F, Scholze C, et al. The sulfur cycle below the sulfate-methane transition of marine sediments. Geochim Cosmochim Acta. 2018;239:74–89.CAS 
    Article 

    Google Scholar 
    4.Reeburgh WS. Oceanic methane biogeochemistry. Chem Rev. 2007;107:486–513.CAS 
    Article 

    Google Scholar 
    5.Holmkvist L, Ferdelman TG, Jørgensen BB. A cryptic sulfur cycle driven by iron in the methane zone of marine sediment (Aarhus Bay, Denmark). Geochim Cosmochim Acta. 2011;75:3581–99.CAS 
    Article 

    Google Scholar 
    6.Starnawski P, Bataillon T, Ettema TJ, Jochum LM, Schreiber L, Chen X, et al. Microbial community assembly and evolution in subseafloor sediment. Proc Natl Acad Sci USA. 2017;114:2940–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Hoehler TM, Jørgensen BB. Microbial life under extreme energy limitation. Nat Rev Microbiol. 2013;11:83–94.CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Jørgensen BB, Marshall IP. Slow microbial life in the seabed. Annu Rev Mar Sci. 2016;8:311–32.Article 

    Google Scholar 
    9.Lever MA, Rogers KL, Lloyd KG, Overmann J, Schink B, Thauer RK, et al. Life under extreme energy limitation: a synthesis of laboratory-and field-based investigations. FEMS Microbiol Rev. 2015;39:688–728.CAS 
    PubMed 
    Article 

    Google Scholar 
    10.Button DK. Kinetics of nutrient-limited transport and microbial growth. Microbiol Rev. 1985;49:270–97.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.De Mattos MT, Neijssel OM. Bioenergetic consequences of microbial adaptation to low-nutrient environments. J Biotechnol. 1997;59:117–26.Article 

    Google Scholar 
    12.Egli T. How to live at very low substrate concentration. Water Res. 2010;44:4826–37.CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Li J, Mara P, Schubotz F, Sylvan JB, Burgaud G, Klein F, et al. Recycling and metabolic flexibility dictate life in the lower oceanic crust. Nature. 2020;579:250–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Zinke LA, Mullis MM, Bird JT, Marshall IP, Jørgensen BB, Lloyd KG, et al. Thriving or surviving? Evaluating active microbial guilds in Baltic Sea sediment. Environ Microbiol Rep. 2017;9:528–36.CAS 
    PubMed 
    Article 

    Google Scholar 
    15.Orsi WD, Jørgensen BB, Biddle JF. Transcriptional analysis of sulfate reducing and chemolithoautotrophic sulfur oxidizing bacteria in the deep subseafloor. Environ Microbiol Rep. 2016;8:452–60.CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Orsi WD, Edgcomb VP, Christman GD, Biddle JF. Gene expression in the deep biosphere. Nature. 2013;499:205–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Cappenberg TE. A study of mixed continuous cultures of sulfate-reducing and methane-producing bacteria. Microb Ecol. 1975;2:60–72.CAS 
    PubMed 
    Article 

    Google Scholar 
    18.Middleton AC, Lawrence AW. Kinetics of microbial sulfate reduction. J Water Pollut Control Fed. 1977;49:1659–70.CAS 

    Google Scholar 
    19.Nethe-Jaenchen R, Thauer RK. Growth yields and saturation constant of Desulfovibrio vulgaris in chemostat culture. Arch Microbiol. 1984;137:236–40.CAS 
    Article 

    Google Scholar 
    20.Ingvorsen K, Zehnder AJ, Jørgensen BB. Kinetics of sulfate and acetate uptake by Desulfobacter postgatei. Appl Environ Microbiol. 1984;47:403–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Cypionka H, Pfennig N. Growth yields of Desulfotomaculum orientis with hydrogen in chemostat culture. Arch Microbiol. 1986;143:396–9.CAS 
    Article 

    Google Scholar 
    22.Okabe S, Characklis WG. Effects of temperature and phosphorous concentration on microbial sulfate reduction by Desulfovibrio desulfuricans. Biotechnol Bioeng. 1992;39:1031–42.CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Okabe S, Nielsen PH, Characklis WG. Factors affecting microbial sulfate reduction by Desulfovibrio desulfuricans in continuous culture: limiting nutrients and sulfide concentration. Biotechnol Bioeng. 1992;40:725–34.CAS 
    PubMed 
    Article 

    Google Scholar 
    24.Habicht KS, Salling L, Thamdrup B, Canfield DE. Effect of low sulfate concentrations on lactate oxidation and isotope fractionation during sulfate reduction by Archaeoglobus fulgidus strain Z. Appl Environ Microbiol. 2005;71:3770–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Davidson MM, Bisher ME, Pratt LM, Fong J, Southam G, Pfiffner SM, et al. Sulfur isotope enrichment during maintenance metabolism in the thermophilic sulfate-reducing bacterium Desulfotomaculum putei. Appl Environ Microbiol. 2009;75:5621–30.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Brysch K, Schneider C, Fuchs G, Widdel F. Lithoautotrophic growth of sulfate-reducing bacteria, and description of Desulfobacterium autotrophicum gen. nov., sp. nov. Arch Microbiol. 1987;148:264–74.CAS 
    Article 

    Google Scholar 
    27.Strittmatter AW, Liesegang H, Rabus R, Decker I, Amann J, Andres S, et al. Genome sequence of Desulfobacterium autotrophicum HRM2, a marine sulfate reducer oxidizing organic carbon completely to carbon dioxide. Environ Microbiol. 2009;11:1038–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Dörries M, Wöhlbrand L, Rabus R. Differential proteomic analysis of the metabolic network of the marine sulfate-reducer Desulfobacterium autotrophicum HRM2. Proteomics. 2016;16:2878–93.PubMed 
    Article 
    CAS 

    Google Scholar 
    29.Petro C, Zäncker B, Starnawski P, Jochum LM, Ferdelman TG, Jørgensen BB, et al. Marine deep biosphere microbial communities assemble in near-surface sediments in Aarhus Bay. Front Microbiol. 2019;10:758.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Jochum LM, Chen X, Lever MA, Loy A, Jørgensen BB, Schramm A, et al. Depth distribution and assembly of sulfate-reducing microbial communities in marine sediments of Aarhus Bay. Appl Environ Microbiol. 2017;83:e01547–17.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Leloup J, Loy A, Knab NJ, Borowski C, Wagner M, Jørgensen BB. Diversity and abundance of sulfate-reducing microorganisms in the sulfate and methane zones of a marine sediment, Black Sea. Environ Microbiol. 2007;9:131–42.CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Tarpgaard IH, Jørgensen BB, Kjeldsen KU, Røy H. The marine sulfate reducer Desulfobacterium autotrophicum HRM2 can switch between low and high apparent half-saturation constants for dissimilatory sulfate reduction. FEMS Microbiol Ecol. 2017;93:fix012.Article 
    CAS 

    Google Scholar 
    33.Marietou A, Røy H, Jørgensen BB, Kjeldsen KU. Sulfate transporters in dissimilatory sulfate reducing microorganisms: a comparative genomics analysis. Front Microbiol. 2018;9:309.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Tarpgaard IH, Røy H, Jørgensen BB. Concurrent low-and high-affinity sulfate reduction kinetics in marine sediment. Geochim Cosmochim Acta. 2011;75:2997–3010.CAS 
    Article 

    Google Scholar 
    35.Volpi M, Lomstein BA, Sichert A, Røy H, Jørgensen BB, Kjeldsen KU. Identity, abundance, and reactivation kinetics of thermophilic fermentative endospores in cold marine sediment and seawater. Front Microbiol. 2017;8:131.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Glombitza C, Pedersen J, Røy H, Jørgensen BB. Direct analysis of volatile fatty acids in marine sediment porewater by two-dimensional ion chromatography-mass spectrometry. Limnol Oceanogr Methods. 2014;12:455–68.CAS 
    Article 

    Google Scholar 
    37.Glombitza C, Jaussi M, Røy H, Seidenkrantz MS, Lomstein BA, Jørgensen BB. Formate, acetate, and propionate as substrates for sulfate reduction in sub-arctic sediments of Southwest Greenland. Front Microbiol. 2015;6:846.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Reese BK, Finneran DW, Mills HJ, Zhu MX, Morse JW. Examination and refinement of the determination of aqueous hydrogen sulfide by the methylene blue method. Aquat Geochem. 2011;17:567.CAS 
    Article 

    Google Scholar 
    39.Beulig F, Røy H, McGlynn SE, Jørgensen BB. Cryptic CH 4 cycling in the sulfate-methane transition of marine sediments apparently mediated by ANME-1 archaea. ISME J. 2019;13:250–62.CAS 
    PubMed 
    Article 

    Google Scholar 
    40.Thorup C, Schramm A, Findlay AJ, Finster KW, Schreiber L. Disguised as a sulfate reducer: growth of the deltaproteobacterium Desulfurivibrio alkaliphilus by sulfide oxidation with nitrate. MBio 2017;8:e00671–17.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, et al. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res. 2012;40:D115–22.CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Rabus R, Venceslau SS, Wöhlbrand L, Voordouw G, Wall JD, Pereira IAC. Chapter two—a post-genomic view of the ecophysiology, catabolism and biotechnological relevance of sulphate-reducing prokaryotes. Adv Micro Physiol. 2015;66:55–321.CAS 
    Article 

    Google Scholar 
    43.Finke N, Vandieken V, Jørgensen BB. Acetate, lactate, propionate, and isobutyrate as electron donors for iron and sulfate reduction in Arctic marine sediments, Svalbard. FEMS Microbiol Ecol. 2007;59:10–22.CAS 
    PubMed 
    Article 

    Google Scholar 
    44.Sonne-Hansen J, Westermann P, Ahring BK. Kinetics of sulfate and hydrogen uptake by the thermophilic sulfate-reducing bacteria Thermodesulfobacterium sp. strain JSP and Thermodesulfovibrio sp. strain R1Ha3. Appl Environ Microbiol. 1999;65:1304–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Keller KL, Wall JD. Genetics and molecular biology of the electron flow for sulfate respiration in Desulfovibrio. Front Microbiol. 2011;2:135.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Molenaar D, Van Berlo R, De Ridder D, Teusink B. Shifts in growth strategies reflect tradeoffs in cellular economics. Mol Syst Biol. 2009;5:323.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Vemuri GN, Altman E, Sangurdekar DP, Khodursky AB, Eiteman MA. Overflow metabolism in Escherichia coli during steady-state growth: transcriptional regulation and effect of the redox ratio. Appl Environ Microbiol. 2006;72:3653–61.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Meyer B, Kuehl JV, Price MN, Ray J, Deutschbauer AM, Arkin AP, et al. The energy-conserving electron transfer system used by Desulfovibrio alaskensis strain G 20 during pyruvate fermentation involves reduction of endogenously formed fumarate and cytoplasmic and membrane-bound complexes, Hdr-Flox and Rnf. Environ Microbiol. 2014;16:3463–86.CAS 
    PubMed 
    Article 

    Google Scholar 
    49.Noguera DR, Brusseau GA, Rittmann BE, Stahl DA. A unified model describing the role of hydrogen in the growth of Desulfovibrio vulgaris under different environmental conditions. Biotechn Bioengin. 1998;59:732–46.CAS 
    Article 

    Google Scholar 
    50.Odom JM, Peck HD Jr. Hydrogen cycling as a general mechanism for energy coupling in the sulfate-reducing bacteria, Desulfovibrio sp. FEMS Microbiol Lett. 1981;12:47–50.CAS 
    Article 

    Google Scholar 
    51.Lupton FS, Conrad R, Zeikus JG. Physiological function of hydrogen metabolism during growth of sulfidogenic bacteria on organic substrates. J Bacteriol. 1984;159:843–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Jin Q, Bethke CM. Cellular energy conservation and the rate of microbial sulfate reduction. Geology. 2009;37:1027–30.CAS 
    Article 

    Google Scholar 
    53.Hoskisson PA, Hobbs G. Continuous culture-making a comeback? Microbiology. 2005;151:3153–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    54.Overbeek R, Fonstein M, D’Souza M, Pusch GD, Maltsev N. The use of gene clusters to infer functional coupling. Proc Natl Acad Sci. 1999;96:2896–901.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Hocking WP, Stokke R, Roalkvam I, Steen IH. Identification of key components in the energy metabolism of the hyperthermophilic sulfate-reducing archaeon Archaeoglobus fulgidus by transcriptome analyses. Front Microbiol. 2014;5:95.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Pereira IA, Ramos AR, Grein F, Marques MC, Da Silva SM, Venceslau SS. A comparative genomic analysis of energy metabolism in sulfate reducing bacteria and archaea. Front Microbiol. 2011;2:69.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Noji H, Yoshida M. The rotary machine in the cell, ATP synthase. J Biol Chem. 2001;276:1665–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    58.Plugge CM, Scholten JC, Culley DE, Nie L, Brockman FJ, Zhang W. Global transcriptomics analysis of the Desulfovibrio vulgaris change from syntrophic growth with Methanosarcina barkeri to sulfidogenic metabolism. Microbiol. 2010;156:2746–56.CAS 
    Article 

    Google Scholar 
    59.Phadtare S. Recent developments in bacterial cold-shock response. Curr Issues Mol Biol. 2004;6:125–36.CAS 
    PubMed 

    Google Scholar 
    60.Rabus R, Brüchert V, Amann J, Könneke M. Physiological response to temperature changes of the marine, sulfate-reducing bacterium Desulfobacterium autotrophicum. FEMS Microbiol Ecol. 2002;42:409–17.CAS 
    PubMed 
    Article 

    Google Scholar 
    61.Barker HA. Amino acid degradation by anaerobic bacteria. Annu Rev Biochem. 1981;50:23–40.CAS 
    PubMed 
    Article 

    Google Scholar 
    62.Zinser ER, Kolter R. Mutations enhancing amino acid catabolism confer a growth advantage in stationary phase. J Bacteriol. 1999;181:5800–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Wick LM, Quadroni M, Egli T. Short- and long-term changes in proteome composition and kinetic properties in a culture of Escherichia coli during transition from glucose-excess to glucose-limited growth conditions in continuous culture and vice versa. Environ Microbiol. 2001;3:588–99.CAS 
    PubMed 
    Article 

    Google Scholar 
    64.Vollmer AC, Bark SJ. Twenty-five years of investigating the universal stress protein: function, structure, and applications. In: Advances in applied microbiology. Academic Press; 2018. pp. 1–36.65.Clark ME, He Q, He Z, Huang KH, Alm EJ, Wan XF, et al. Temporal transcriptomic analysis as Desulfovibrio vulgaris Hildenborough transitions into stationary phase during electron donor depletion. Appl Environ Microbiol. 2006;72:5578–88.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Schauder R, Preuß A, Jetten M, Fuchs G. Oxidative and reductive acetyl CoA/carbon monoxide dehydrogenase pathway in Desulfobacterium autotrophicum. Arch Microbiol. 1988;151:84–9.Article 

    Google Scholar 
    67.Kumari S, Beatty CM, Browning DF, Busby SJ, Simel EJ, Hovel-Miner G, et al. Regulation of acetyl coenzyme A synthetase in. Escherichia coli J Bacteriol. 2000;182:4173–9.CAS 
    PubMed 

    Google Scholar 
    68.Wang Q, Ou MS, Kim Y, Ingram LO, Shanmugam KT. Metabolic flux control at the pyruvate node in an anaerobic Escherichia coli strain with an active pyruvate dehydrogenase. Appl Environ Microbiol. 2010;76:2107–14.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Shimizu K, Matsuoka Y. Regulation of glycolytic flux and overflow metabolism depending on the source of energy generation for energy demand. Biotechnol Adv. 2019;37:284–305.CAS 
    PubMed 
    Article 

    Google Scholar 
    70.Verhagen MF, O’Rourke T, Adams MW. The hyperthermophilic bacterium, Thermotoga maritima, contains an unusually complex iron-hydrogenase: amino acid sequence analyses versus biochemical characterization. Biochim Biophys Acta. 1999;1412:212–29.CAS 
    PubMed 
    Article 

    Google Scholar 
    71.Rabus RA, Hansen TA, Widdel FR. Dissimilatory sulfate-and sulfur-reducing prokaryotes. Prokaryotes. 2006;2:659–768.Article 

    Google Scholar 
    72.Santos AA, Venceslau SS, Grein F, Leavitt WD, Dahl C, Johnston DT, et al. A protein trisulfide couples dissimilatory sulfate reduction to energy conservation. Science. 2015;350:1541–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    73.Buckel W, Thauer RK. Flavin-based electron bifurcation, ferredoxin, flavodoxin, and anaerobic respiration with protons (Ech) or NAD+ (Rnf) as electron acceptors: a historical review. Front Microbiol. 2018;9:401.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Venceslau SS, Stockdreher Y, Dahl C, Pereira IAC. The “bacterial heterodisulfide” DsrC is a key protein in dissimilatory sulfur metabolism. BBA Bioenerg. 2014;1837:1148–64.CAS 
    Article 

    Google Scholar 
    75.Grein F, Ramos AR, Venceslau SS, Pereira IA. Unifying concepts in anaerobic respiration: insights from dissimilatory sulfur metabolism. BBA Bioenerg. 2013;1827:145–60.CAS 
    Article 

    Google Scholar 
    76.Stahlmann J, Warthmann R, Cypionka H. Na+-dependent accumulation of sulfate and thiosulfate in marine sulfate-reducing bacteria. Arch Microbiol. 1991;155:554–8.CAS 
    Article 

    Google Scholar 
    77.Wöhlbrand L, Ruppersberg H, Feenders C, Blasius B, Braun HP, Rabus R. Analysis of membrane-protein complexes of the marine sulfate reducer Desulfobacula toluolica Tol2 by 1D blue native-PAGE complexome profiling and 2D blue native-/SDS-PAGE. Proteomics. 2016;16:973–88.PubMed 
    Article 
    CAS 

    Google Scholar 
    78.Marietou A, Lund MB, Marshall IP, Schreiber L, Jørgensen BB. Complete genome sequence of Desulfobacter hydrogenophilus AcRS1. Mar Genom. 2020;50:100691.Article 

    Google Scholar 
    79.Zhang W, Culley DE, Wu G, Brockman FJ. Two-component signal transduction systems of Desulfovibrio vulgaris: structural and phylogenetic analysis and deduction of putative cognate pairs. J Mol Evol. 2006;62:473–87.CAS 
    PubMed 
    Article 

    Google Scholar 
    80.Rajeev L, Luning EG, Dehal PS, Price MN, Arkin AP, Mukhopadhyay A. Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium. Genome Biol. 2011;12:R99.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    81.Taher R, de Rosny E. A structure-function study of ZraP and ZraS provides new insights into the two-component system Zra. Biochim Biophys Acta. 2020;1865:129810.Article 
    CAS 

    Google Scholar 
    82.Kraft B, Tegetmeyer HE, Sharma R, Klotz MG, Ferdelman TG, Hettich RL, et al. The environmental controls that govern the end product of bacterial nitrate respiration. Science. 2014;345:676–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    83.Yoon S, Cruz-García C, Sanford R, Ritalahti KM, Löffler FE. Denitrification versus respiratory ammonification: environmental controls of two competing dissimilatory NO3−/NO2− reduction pathways in Shewanella loihica strain PV-4. ISME J. 2015;9:1093–104.CAS 
    PubMed 
    Article 

    Google Scholar 
    84.Greene EA, Hubert C, Nemati M, Jenneman GE, Voordouw G. Nitrite reductase activity of sulphate‐reducing bacteria prevents their inhibition by nitrate‐reducing, sulphide‐oxidizing bacteria. Environ Microbiol. 2003;5:607–17.CAS 
    PubMed 
    Article 

    Google Scholar 
    85.Dalsgaard T, Bak F. Nitrate reduction in a sulfate-reducing bacterium, Desulfovibrio desulfuricans, isolated from rice paddy soil: sulfide inhibition, kinetics, and regulation. Appl Environ Microbiol. 1994;60:291–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    86.Ingvorsen K, Jørgensen BB. Kinetics of sulfate uptake by freshwater and marine species of Desulfovibrio. Arch Microbiol. 1984;139:61–6.CAS 
    Article 

    Google Scholar  More

  • in

    Sulfate differentially stimulates but is not respired by diverse anaerobic methanotrophic archaea

    1.Knittel K, Boetius A. Anaerobic oxidation of methane: progress with an unknown process. Annu Rev Microbiol. 2009;63:311–34.CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Reeburgh WS. Oceanic methane biogeochemistry. Chem Rev. 2007;107:486–513.CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Hatzenpichler R, Connon SA, Goudeau D, Malmstrom RR, Woyke T, Orphan VJ. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal−bacterial consortia. Proc Natl Acad Sci USA. 2016;113:E4069–E4078.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Skennerton CT, Chourey K, Iyer R, Hettich RL, Tyson GW, Orphan VJ. Methane-fueled syntrophy through extracellular electron transfer: uncovering the genomic traits conserved within diverse bacterial partners of anaerobic methanotrophic archaea. mBio. 2017;8:e00530–e00517.PubMed 
    PubMed Central 

    Google Scholar 
    5.Orphan VJ, House CH, Hinrichs K-U, McKeegan KD, DeLong EF. Multiple archaeal groups mediate methane oxidation in anoxic cold seep sediments. Proc Natl Acad Sci USA. 2002;99:7663–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Wegener G, Krukenberg V, Ruff SE, Kellermann MY, Knittel K. Metabolic capabilities of microorganisms involved in and associated with the anaerobic oxidation of methane. Front Microbiol. 2016;7:869.Article 

    Google Scholar 
    7.Metcalfe KS, Murali R, Mullin SW, Connon SA, Orphan VJ. Experimentally-validated correlation analysis reveals new anaerobic methane oxidation partnerships with consortium-level heterogeneity in diazotrophy. ISME J. 2020;15:1–20.8.Krukenberg V, Riedel D, Gruber Vodicka HR, Buttigieg PL, Tegetmeyer HE, Boetius A, et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ Microbiol. 2018;20:1651–6.9.Milucka J, Ferdelman TG, Polerecky L, Franzke D, Wegener G, Schmid M, et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature. 2012;491:541–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    10.Schreiber L, Holler T, Knittel K, Meyerdierks A, Amann R. Identification of the dominant sulfate-reducing bacterial partner of anaerobic methanotrophs of the ANME-2 clade. Environ Microbiol. 2010;12:2327–40.CAS 
    PubMed 

    Google Scholar 
    11.Yu H, Susanti D, McGlynn SE, Skennerton CT, Chourey K, Iyer R, et al. Comparative genomics and proteomic analysis of assimilatory sulfate reduction pathways in anaerobic methanotrophic archaea. Front Microbiol. 2018;9:2917.12.Scheller S, Yu H, Chadwick GL, McGlynn SE, Orphan VJ. Artificial electron acceptors decouple archaeal methane oxidation from sulfate reduction. Science. 2016;351:703–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Wegener G, Krukenberg V, Riedel D, Tegetmeyer HE, Boetius A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature. 2015;526:587–90.CAS 
    PubMed 
    Article 

    Google Scholar 
    14.McGlynn SE, Chadwick GL, Kempes CP, Orphan VJ. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature. 2015;526:531–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    15.Liu Y, Beer LL, Whitman WB. Sulfur metabolism in archaea reveals novel processes. Environ Microbiol. 2012;14:2632–44.CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Perona JJ, Rauch BJ, Driggers CM. Sulfur assimilation and trafficking in methanogens. In: Rampelotto PH, editor. Molecular Mechanisms of Microbial Evolution. Cham: Springer International Publishing; 2018. p. 371–408.17.White RH, Allen KD, Wegener G. Identification of a redox active thioquinoxalinol sulfate compound produced by an anaerobic methane-oxidizing microbial consortium. ACS Omega. 2019;4:22613–22.18.Cline JD. Spectrophotometric determination of hydrogen sulfide in natural waters. Limnol Oceanogr. 1969;14:454–8.CAS 
    Article 

    Google Scholar 
    19.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
    Article 
    PubMed 

    Google Scholar 
    21.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
    Article 
    PubMed 

    Google Scholar 
    22.Mason OU, Case DH, Naehr TH, Lee RW, Thomas RB, Bailey JV, et al. Comparison of archaeal and bacterial diversity in methane seep carbonate nodules and host sediments, Eel River Basin and Hydrate Ridge, USA. Microb Ecol. 2015;70:766–84.CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Laczny CC, Sternal T, Plugaru V, Gawron P, Atashpendar A, Margossian HH, et al. VizBin – an application for reference-independent visualization and human-augmented binning of metagenomic data. Microbiome. 2015;3:1.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.Article 
    CAS 

    Google Scholar 
    26.Chen I-MA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–D677.CAS 
    PubMed 
    Article 

    Google Scholar 
    27.Agarwala R, Barrett T, Beck J, Benson DA, Bollin C, Bolton E, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018;46:D8–D13.Article 
    CAS 

    Google Scholar 
    28.Saier MH, Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-Hagelsieb G. The Transporter Classification Database (TCDB): recent advances. Nucleic Acids Res. 2016;44:D372–D379.CAS 
    PubMed 
    Article 

    Google Scholar 
    29.Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Knittel K, Losekann T, Boetius A, Kort R, Amann R. Diversity and distribution of methanotrophic archaea at cold seeps. Appl Environ Microbiol. 2005;71:467–79.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Manz W, Eisenbrecher M, Neu TR, Szewzyk U. Abundance and spatial organization of Gram-negative sulfate-reducing bacteria in activated sludge investigated by in situ probing with specific 16S rRNA targeted oligonucleotides. FEMS Microbiol Ecol. 1998;25:43–61.CAS 
    Article 

    Google Scholar 
    32.Polerecky L, Adam B, Milucka J, Musat N, Vagner T, Kuypers MMM. Look@NanoSIMS – a tool for the analysis of nanoSIMS data in environmental microbiology. Environ Microbiol. 2012;14:1009–23.CAS 
    PubMed 
    Article 

    Google Scholar 
    33.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Kopylova E, Noe L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.CAS 
    Article 

    Google Scholar 
    35.Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:525–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Pimentel H, Bray NL, Puente S, Melsted P, Pachter L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods. 2017;14:687–90.CAS 
    PubMed 
    Article 

    Google Scholar 
    37.McGee WA, Pimentel H, Pachter L, Wu JY. Compositional Data Analysis is necessary for simulating and analyzing RNA-Seq data. bioRxiv 2019;564955.38.Rocha DJP, Santos CS, Pacheco LGC. Bacterial reference genes for gene expression studies by RT-qPCR: survey and analysis. Antonie van Leeuwenhoek. 2015;108:685–93.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
    Article 
    PubMed 

    Google Scholar 
    41.Rinke C, Chuvochina M, Mussig AJ, Chaumeil P-A, Waite DW, Whitman WB, et al. A rank-normalized archaeal taxonomy based on genome phylogeny resolves widespread incomplete and uneven classifications. bioRxiv. 2020. https://doi.org/10.1101/2020.03.01.972265.42.Orphan VJ, Turk KA, Green AM, House CH. Patterns of 15N assimilation and growth of methanotrophic ANME-2 archaea and sulfate-reducing bacteria within structured syntrophic consortia revealed by FISH-SIMS. Environ Microbiol. 2009;11:1777–91.CAS 
    PubMed 
    Article 

    Google Scholar 
    43.Girguis PR, Cozen AE, DeLong EF. Growth and population dynamics of anaerobic methane-oxidizing archaea and sulfate-reducing bacteria in a continuous-flow bioreactor. Appl Environ Microbiol. 2005;71:3725–33.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Nauhaus K, Albrecht M, Elvert M, Boetius A, Widdel F. In vitro cell growth of marine archaeal-bacterial consortia during anaerobic oxidation of methane with sulfate. Environ Microbiol. 2007;9:187–96.CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Meulepas RJW, Jagersma CG, Khadem AF, Buisman CJN, Stams AJM, Lens PNL. Effect of environmental conditions on sulfate reduction with methane as electron donor by an Eckernförde Bay enrichment. Environ Sci Technol. 2009;43:6553–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    46.McGlynn SE. Energy metabolism during anaerobic methane oxidation in ANME archaea. Microbes Environ. 2017;32:5–13.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Wang F-P, Zhang Y, Chen Y, He Y, Qi J, Hinrichs K-U, et al. Methanotrophic archaea possessing diverging methane-oxidizing and electron-transporting pathways. ISME J. 2014;8:1069–78.CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Meyerdierks A, Kube M, Kostadinov I, Teeling H, Glöckner FO, Reinhardt R, et al. Metagenome and mRNA expression analyses of anaerobic methanotrophic archaea of the ANME-1 group. Environ Microbiol. 2010;12:422–39.CAS 
    PubMed 
    Article 

    Google Scholar 
    49.Cai C, Leu AO, Xie G-J, Guo J, Feng Y, Zhao J-X, et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 2018;1:285.
    Google Scholar 
    50.Leu AO, Cai C, McIlroy SJ, Southam G, Orphan VJ, Yuan Z, et al. Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae. ISME J. 2020;14:1030–41.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Yanagawa K, Sunamura M, Lever MA, Morono Y, Hiruta A, Ishizaki O, et al. Niche separation of methanotrophic archaea (ANME-1 and-2) in methane-seep sediments of the eastern Japan Sea offshore Joetsu. Geomicrobiol J. 2011;28:118–29.CAS 
    Article 

    Google Scholar 
    52.Biddle JF, Cardman Z, Mendlovitz H, Albert DB, Lloyd KG, Boetius A, et al. Anaerobic oxidation of methane at different temperature regimes in Guaymas Basin hydrothermal sediments. ISME J. 2012;6:1018–31.CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Holler T, Widdel F, Knittel K, Amann R, Kellermann MY, Hinrichs K-U, et al. Thermophilic anaerobic oxidation of methane by marine microbial consortia. ISME J. 2011;5:1946–56.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Roalkvam I, Jørgensen SL, Chen Y, Stokke R, Dahle H, Hocking WP, et al. New insight into stratification of anaerobic methanotrophs in cold seep sediments. FEMS Microbiol Ecol. 2011;78:233–43.CAS 
    PubMed 
    Article 

    Google Scholar 
    55.Timmers PHA, Widjaja-Greefkes HCA, Ramiro-Garcia J, Plugge CM, Stams AJM. Growth and activity of ANME clades with different sulfate and sulfide concentrations in the presence of methane. Front Microbiol. 2015;6:988.56.Nauhaus K, Treude T, Boetius A, Krüger M. Environmental regulation of the anaerobic oxidation of methane: a comparison of ANME-I and ANME-II communities. Environ Microbiol. 2005;7:98–106.CAS 
    PubMed 
    Article 

    Google Scholar 
    57.Green-Saxena A, Dekas AE, Dalleska NF, Orphan VJ. Nitrate-based niche differentiation by distinct sulfate-reducing bacteria involved in the anaerobic oxidation of methane. ISME J. 2014;8:150–63.CAS 
    PubMed 
    Article 

    Google Scholar 
    58.Wegener G, Niemann H, Elvert M, Hinrichs K-U, Boetius A. Assimilation of methane and inorganic carbon by microbial communities mediating the anaerobic oxidation of methane. Environ Microbiol. 2008;10:2287–98.CAS 
    PubMed 
    Article 

    Google Scholar 
    59.Scherer P, Lippert H, Wolff G. Composition of the major elements and trace elements of 10 methanogenic bacteria determined by inductively coupled plasma emission spectrometry. Biol Trace Elem Res. 1983;5:149–63.CAS 
    PubMed 
    Article 

    Google Scholar 
    60.Marsili E, Baron DB, Shikhare ID, Coursolle D, Gralnick JA, Bond DR. Shewanella secretes flavins that mediate extracellular electron transfer. Proc Natl Acad Sci USA. 2008;105:3968–73.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Kotloski NJ, Gralnick JA. Flavin electron shuttles dominate extracellular electron transfer by Shewanella oneidensis. mBio 2013;4:e00553–12.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Mevers E, Su L, Pishchany G, Baruch M, Cornejo J, Hobert E, et al. An elusive electron shuttle from a facultative anaerobe. eLife. 2019;8:e48054.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Anderson AG, Iii FBC, Odom JM, Weimer PJ. Anthraquinones as inhibitors of sulfide production from sulfate-reducing bacteria. 1991.64.Wang X, Cheng X, Ren Y, Xu G, Tang J. Humic analog AQDS can act as a selective inhibitor to enable anoxygenic photosynthetic bacteria to outcompete sulfate-reducing bacteria under microaerobic conditions. J Chem Technol Biotechnol. 2016;91:2103–10.CAS 
    Article 

    Google Scholar 
    65.Lee YH, Pavlostathis SG. Decolorization and toxicity of reactive anthraquinone textile dyes under methanogenic conditions. Water Res. 2004;38:1838–52.CAS 
    PubMed 
    Article 

    Google Scholar 
    66.Wu Y-W, Ouyang J, Xiao X-H, Gao W-Y, Liu Y. Antimicrobial properties and toxicity of anthraquinones by microcalorimetric bioassay. Chin J Chem. 2006;24:45–50.CAS 
    Article 

    Google Scholar 
    67.Novotný Č, Dias N, Kapanen A, Malachová K, Vándrovcová M, Itävaara M, et al. Comparative use of bacterial, algal and protozoan tests to study toxicity of azo- and anthraquinone dyes. Chemosphere. 2006;63:1436–42.PubMed 
    Article 
    CAS 

    Google Scholar 
    68.Shyu JBH, Lies DP, Newman DK. Protective role of tolC in efflux of the electron shuttle anthraquinone-2,6-disulfonate. J Bacteriol. 2002;184:1806–10.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Lovley DR, Coates JD, Blunt-Harris EL, Phillips EJP, Woodward JC. Humic substances as electron acceptors for microbial respiration. Nature. 1996;382:445–8.CAS 
    Article 

    Google Scholar 
    70.Newman DK, Kolter R. A role for excreted quinones in extracellular electron transfer. Nature. 2000;405:94–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    71.Holmes DE, Ueki T, Tang H-Y, Zhou J, Smith JA, Chaput G, et al. A membrane-bound cytochrome enables Methanosarcina acetivorans to conserve energy from extracellular electron transfer. mBio. 2019;10:e00789–19.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    72.Neuberger A, Du D, Luisi BF. Structure and mechanism of bacterial tripartite efflux pumps. Res Microbiol. 2018;169:401–13.CAS 
    PubMed 
    Article 

    Google Scholar 
    73.Crow A, Greene NP, Kaplan E, Koronakis V. Structure and mechanotransmission mechanism of the MacB ABC transporter superfamily. Proc Natl Acad Sci USA. 2017;114:12572–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Jiménez-Otero F, Chan CH, Bond DR. Identification of different putative outer membrane electron conduits necessary for Fe (III) citrate, Fe (III) oxide, Mn (IV) oxide, or electrode reduction by Geobacter sulfurreducens. J Bacteriol. 2018;200:3061.Article 

    Google Scholar 
    75.Plugge CM, Scholten JCM, Culley DE, Nie L, Brockman FJ, Zhang W. Global transcriptomics analysis of the Desulfovibrio vulgaris change from syntrophic growth with Methanosarcina barkeri to sulfidogenic metabolism. Microbiology. 2010;156:2746–56.CAS 
    PubMed 
    Article 

    Google Scholar 
    76.Walker CB, He Z, Yang ZK, Ringbauer JAJ, He Q, Zhou J, et al. The electron transfer system of syntrophically grown Desulfovibrio vulgaris. J Bacteriol. 2009;191:5793–801.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    77.Wenter R, Hütz K, Dibbern D, Li T, Reisinger V, Plöscher M, et al. Expression-based identification of genetic determinants of the bacterial symbiosis ‘Chlorochromatium aggregatum’. Environ Microbiol. 2010;12:2259–76.CAS 
    PubMed 

    Google Scholar  More

  • in

    The rates of global bacterial and archaeal dispersal

    1.Kruckeberg AR, Rabinowitz D. Biological aspects of endemism in higher plants. Annu Rev Ecol Syst. 1985;16:447–79.Article 

    Google Scholar 
    2.Ceballos G, Brown JH. Global patterns of mammalian diversity, endemism, and endangerment. Conserv Biol. 1995;9:559–68.Article 

    Google Scholar 
    3.Mueller GM, Schmit JP, Leacock PR, Buyck B, Cifuentes J, Desjardin DE, et al. Global diversity and distribution of macrofungi. Biodivers Conserv. 2007;16:37–48.Article 

    Google Scholar 
    4.Prideaux GJ, Warburton NM. An osteology-based appraisal of the phylogeny and evolution of kangaroos and wallabies (macropodidae: Marsupialia). Zool J Linn Soc. 2010;159:954–87.Article 

    Google Scholar 
    5.Finlay BJ, Clarke KJ. Ubiquitous dispersal of microbial species. Nature. 1999;400:828.CAS 
    Article 

    Google Scholar 
    6.Whitaker RJ, Grogan DW, Taylor JW. Geographic barriers isolate endemic populations of hyperthermophilic archaea. Science. 2003;301:976–978.CAS 
    PubMed 
    Article 

    Google Scholar 
    7.Whitfield J. Is everything everywhere? Science. 2005;310:960–61.CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Boenigk J, Pfandl K, Garstecki T, Harms H, Novarino G, Chatzinotas A. Evidence for geographic isolation and signs of endemism within a protistan morphospecies. Appl Environ Microbiol. 2006;72:5159–64.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.DeWit R, Bouvier T. ‘Everything is everywhere, but, the environment selects’; what did Baas Becking and Beijerinck really say? Environ Microbiol. 2006;8:755–8.Article 

    Google Scholar 
    10.van der Gast CJ. Microbial biogeography: the end of the ubiquitous dispersal hypothesis? Environ Microbiol. 2015;17:544–6.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Whittaker KA, Rynearson TA. Evidence for environmental and ecological selection in a microbe with no geographic limits to gene flow. Proc Natl Acad Sci USA. 2017;114:2651–56.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Louca S, Shih PM, Pennell MW, Fischer WW, Parfrey LW, Doebeli M. Bacterial diversification through geological time. Nat Ecol Evol. 2018;2:1458–67.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Martiny JBH, Bohannan BJ, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102–12.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Martiny JBH, Eisen JA, Penn K, Allison SD, Horner-Devine MC. Drivers of bacterial β-diversity depend on spatial scale. Proc Natl Acad Sci USA. 2011;108:7850–54.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Jungblut AD, Lovejoy C, Vincent WF. Global distribution of cyanobacterial ecotypes in the cold biosphere. ISME J. 2010;4:191–202.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Gibbons SM, Caporaso JG, Pirrung M, Field D, Knight R, Gilbert JA. Evidence for a persistent microbial seed bank throughout the global ocean. Proc Natl Acad Sci USA. 2013;110:4651–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Ramirez KS, Leff JW, Barberán A, Bates ST, Betley J, Crowther TW, et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc R Soc Lond B Biol Sci. 2014;281:20141988.18.Gonnella G, Böhnke S, Indenbirken D, Garbe-Schönberg D, Seifert R, Mertens C, et al. Endemic hydrothermal vent species identified in the open ocean seed bank. Nat Microbiol. 2016;1:16086 EP.Article 
    CAS 

    Google Scholar 
    19.Louca S, Mazel F, Doebeli M, Parfrey WL. A census-based estimate of Earth’s bacterial and archaeal diversity. PLoS Biol. 2019;17:e3000106.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Ochman H, Wilson A. Evolution in bacteria: evidence for a universal substitution rate in cellular genomes. J Mol Evol. 1987;26:74–86.CAS 
    PubMed 
    Article 

    Google Scholar 
    21.Kuo CH, Ochman H. Inferring clocks when lacking rocks: the variable rates of molecular evolution in bacteria. Biol Direct. 2009;4:35–35.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    22.Roberts MS, Cohan FM. Recombination and migration rates in natural populations of Bacillus subtilis and Bacillus mojavensis. Evolution. 1995;49:1081–94.PubMed 
    Article 

    Google Scholar 
    23.van Gremberghe I, Leliaert F, Mergeay J, Vanormelingen P, Van der Gucht K, Debeer AE, et al. Lack of phylogeographic structure in the freshwater cyanobacterium Microcystis aeruginosa suggests global dispersal. PLoS ONE. 2011;6:e19561.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    24.Papke RT, Ramsing NB, Bateson MM, Ward DM. Geographical isolation in hot spring cyanobacteria. Environ Microbiol. 2003;5:650–9.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Hongmei J, Aitchison JC, Lacap DC, Peerapornpisal Y, Sompong U, Pointing SB. Community phylogenetic analysis of moderately thermophilic cyanobacterial mats from China, the Philippines and Thailand. Extremophiles. 2005;9:325–32.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    26.Miller SR, Castenholz RW, Pedersen D. Phylogeography of the thermophilic cyanobacterium Mastigocladus laminosus. Appl Environ Microbiol. 2007;73:4751–59.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Takacs-Vesbach C, Mitchell K, Jackson-Weaver O, Reysenbach AL. Volcanic calderas delineate biogeographic provinces among Yellowstone thermophiles. Environ Microbiol. 2008;10:1681–89.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Reno ML, Held NL, Fields CJ, Burke PV, Whitaker RJ. Biogeography of the Sulfolobus islandicus pan-genome. Proc Natl Acad Sci USA. 2009;106:8605–10.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Bahl J, Lau MCY, Smith GJD, Vijaykrishna D, Cary SC, Lacap DC, et al. Ancient origins determine global biogeography of hot and cold desert cyanobacteria. Nat Commun. 2011;2:163.PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    30.Anderson RE, Kouris A, Seward CH, Campbell KM, Whitaker RJ. Structured populations of Sulfolobus acidocaldarius with susceptibility to mobile genetic elements. Genome Biol Evol. 2017;9:1699–710.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Podar PT, Yang Z, Björnsdóttir SH, Podar M. Comparative analysis of microbial diversity across temperature gradients in hot springs from Yellowstone and Iceland. Front Microbiol. 2020;11:1625.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. Genbank. Nucleic Acids Res. 2015;44:D67–D72.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    33.Konstantinidis KT, Tiedje JM. Genomic insights that advance the species definition for prokaryotes. Proc Natl Acad Sci USA. 2005;102:2567–72.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Kim M, Oh HS, Park SC, Chun J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int J Syst Evol Microbiol. 2014;64:346–51.CAS 
    Article 

    Google Scholar 
    35.Olm MR, Crits-Christoph A, Diamond S, Lavy A, Carnevali PBM, Banfield JF. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems. 2020;5:e00731-19.36.Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    37.Shapiro BJ. What microbial population genomics has taught us about speciation. In: Polz MF, Rajora OP, editors. Population Genomics: Microorganisms. Cham, Switzerland: Springer International Publishing; 2019. p. 31–47.38.Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
    PubMed 
    Article 

    Google Scholar 
    39.Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2020;36:1925–27.CAS 

    Google Scholar 
    40.Felsenstein J. Phylogenies and the comparative method. Am Nat. 1985;125:1–15.Article 

    Google Scholar 
    41.Louca S. Phylogeographic estimation and simulation of global diffusive dispersal. Syst Biol. 2021;70:340–59.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Comas I, Coscolla M, Luo T, Borrell S, Holt KE, Kato-Maeda M, et al. Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat Genet. 2013;45:1176–82.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Denef VJ, Banfield JF. In situ evolutionary rate measurements show ecological success of recently emerged bacterial hybrids. Science. 2012;336:462–6.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Bouckaert R, Cartwright R. Phylogeography by diffusion on a sphere: whole world phylogeography. PeerJ. 2016;4:e2406.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Brillinger DR. A particle migrating randomly on a sphere. In: Selected Works of David Brillinger. Cham, Switzerland: Springer; 2012. p. 73–87.46.Ghosh A, Samuel J, Sinha SA. “Gaussian” for diffusion on the sphere. Europhys Lett. 2012;98:30003.Article 
    CAS 

    Google Scholar 
    47.Castenholz RW. The biogeography of hot spring algae through enrichment cultures. SIL Commun. 1978;21:296–315. 1953-1996
    Google Scholar 
    48.Valentine DL. Adaptations to energy stress dictate the ecology and evolution of the archaea. Nat Rev Micro. 2007;5:316–23.CAS 
    Article 

    Google Scholar 
    49.Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–77.CAS 
    PubMed 
    Article 

    Google Scholar 
    50.Smith DJ, Jaffe DA, Birmele MN, Griffin DW, Schuerger AC, Hee J, et al. Free tropospheric transport of microorganisms from Asia to North America. Micro Ecol. 2012;64:973–85.CAS 
    Article 

    Google Scholar 
    51.Pagel M. Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters. Proc R Soc Lond B Biol Sci. 1994;255:37–45.Article 

    Google Scholar 
    52.Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA. 1998;95:6578–83.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Anderson D. The regulation of fishing and related activities in exclusive economic zones. In: Modern Law Sea, Publications on Ocean Development, vol. 59, chap. 11. Leiden, The Netherlands: Brill Nijhoff; 2008. p. 209–27.54.Bullock JM, Clarke RT. Long distance seed dispersal by wind: measuring and modelling the tail of the curve. Oecologia. 2000;124:506–21.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Brynjarsdóttir J, O’Hagan A. Learning about physical parameters: the importance of model discrepancy. Inverse Probl. 2014;30:114007.Article 

    Google Scholar 
    56.Bell T. Experimental tests of the bacterial distance-decay relationship. ISME J. 2010;4:1357–65.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 
    CAS 

    Google Scholar 
    58.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2014;25:1043–55.Article 
    CAS 

    Google Scholar 
    59.Chambat F, Valette B. Mean radius, mass, and inertia for reference Earth models. Phys Earth Planet Inter. 2001;124:237–53.Article 

    Google Scholar 
    60.Data NS, (SEDAC) AC Gridded Population of the World, Version 4 (GPW v4): Population Density, Revision 11. Tech. rep., Palisades, NY: Center for International Earth Science Information Network – CIESIN – Columbia University. 2018. Accessed November 23, 2020.61.Price MN, Dehal PS, Arkin AP. FastTree 2: approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    62.Britton T, Anderson CL, Jacquet D, Lundqvist S, Bremer K. Estimating divergence times in large phylogenetic trees. Syst Biol. 2007;56:741–52.PubMed 
    Article 

    Google Scholar 
    63.Zhu Q, Mai U, Pfeiffer W, Janssen S, Asnicar F, Sanders JG, et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains bacteria and archaea. Nat Commun. 2019;10:5477.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Perrin F. Étude mathématique du movement brownien de rotation. In: Annales scientifiques del’École Normale Supérieure, vol. 45. Paris, France: Elsevier; with 1928. p. 1–51.65.Louca S, Doebeli M. Efficient comparative phylogenetics on large trees. Bioinformatics. 2018;34:1053–55.CAS 
    PubMed 
    Article 

    Google Scholar 
    66.Bloomquist EW, Lemey P, Suchard MA. Three roads diverged? routes to phylogeographic inference. Trends Ecol Evol. 2010;25:626–32.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Lemey P, Rambaut A, Welch JJ, Suchard MA. Phylogeography takes a relaxed random walk in continuous space and time. Mol Biol Evol. 2010;27:1877–85.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Faria NR, Suchard MA, Rambaut A, Lemey P. Toward a quantitative understanding of viral phylogeography. Curr Opin Virol. 2011;1:423–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Faria NR, Suchard MA, Abecasis A, Sousa JD, Ndembi N, Bonfim I, et al. Phylodynamics of the HIV-1 CRF02_AG clade in Cameroon. Infect Genet Evol. 2012;12:453–60.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Lange K. Diffusion processes. In: Applied Probability, chap. 11. New York, NY: Springer New York; 2010. p. 269–95.71.Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using minhash. Genome Biol. 2016;17:132.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    72.Pasolli E, Asnicar F, Manara S, Zolfo M, Karcher N, Armanini F, et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 2019;176:649–62.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Criscuolo A, Gascuel O. Fast NJ-like algorithms to deal with incomplete distance matrices. BMC Bioinforma. 2008;9:166.Article 
    CAS 

    Google Scholar 
    74.Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90.75.Kinene T, Wainaina J, Maina S, Boykin LM, Kliman RM. Methods for rooting trees, vol. 3. Oxford: Academic Press; 2016. p. 489–93.76.van Rossum G. Python tutorial. Tech. Rep. CS-R9526, Amsterdam: Centrum voor Wiskunde en Informatica (CWI); 1995. More

  • in

    Patterns of skeletal integration in birds reveal that adaptation of element shapes enables coordinated evolution between anatomical modules

    1.Cheverud, J. M. Developmental integration and the evolution of pleiotropy. Am. Zool. 36, 44–50 (1996).Article 

    Google Scholar 
    2.Wagner, G. P. & Altenberg, L. Perspective: complex adaptations and the evolution of evolvability. Evolution 50, 967–976 (1996).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Wagner, G. P., Pavlicev, M. & Cheverud, J. M. The road to modularity. Nat. Rev. Genet. 8, 921–931 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Klingenberg, C. P. Morphological integration and developmental modularity. Annu. Rev. Ecol. Evol. Syst. 39, 115–132 (2008).Article 

    Google Scholar 
    5.Klingenberg, C. P. Studying morphological integration and modularity at multiple levels: concepts and analysis. Phil. Trans. R. Soc. B 369, 20130249 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Hallgrímsson, B. et al. Deciphering the palimpsest: studying the relationship between morphological integration and phenotypic covariation. Evol. Biol. 36, 355–376 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Olson, E. & Miller, R. Morphological Integration (Univ. of Chicago Press, 1958).8.Pigliucci, M. Phenotypic integration: studying the ecology and evolution of complex phenotypes. Ecol. Lett. 6, 265–272 (2003).Article 

    Google Scholar 
    9.Eble, G. J. in Phenotypic Integration: Studying the Ecology and Evolution of Complex Phenotypes (eds Pigliucci, M. & Preston, K.) 253–273 (Oxford Univ. Press, 2004).10.Goswami, A., Smaers, J. B., Soligo, C. & Polly, P. D. The macroevolutionary consequences of phenotypic integration: from development to deep time. Phil. Trans. R. Soc. B 369, 20130254 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Goswami, A., Binder, W. J., Meachen, J. & O’Keefe, F. R. The fossil record of phenotypic integration and modularity: a deep-time perspective on developmental and evolutionary dynamics. Proc. Natl Acad. Sci. USA 112, 4891–4896 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Wagner, G. P. & Schwenk, K. Evolutionarily stable configurations: functional integration and the evolution of phenotypic stability. Evol. Biol. 31, 155–217 (2000).
    Google Scholar 
    13.Hallgrímsson, B., Willmore, K. & Hall, B. K. Canalization, developmental stability, and morphological integration in primate limbs. Am. J. Phys. Anthropol. 119, 131–158 (2002).Article 

    Google Scholar 
    14.Gould, S. J. A developmental constraint in cerion, with comments on the definition and interpretation of constraint in evolution. Evolution 43, 516–539 (1989).PubMed 

    Google Scholar 
    15.Arthur, W. Developmental drive: an important determinant of the direction of phenotypic evolution. Evol. Dev. 3, 271–278 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Klingenberg, C. P. in Variation: A Central Concept in Biology (eds Hallgrímsson, B. & Hall, B.) 219–247 (Elsevier, 2005).17.Felice, R. N. & Goswami, A. Developmental origins of mosaic evolution in the avian cranium. Proc. Natl Acad. Sci. USA 115, 555–560 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Bell, E., Andres, B. & Goswami, A. Integration and dissociation of limb elements in flying vertebrates: a comparison of pterosaurs, birds and bats. J. Evol. Biol. 24, 2586–2599 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Gatesy, S. M. & Dial, K. P. Locomotor modules and the evolution of avian flight. Evolution 50, 331–340 (1996).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Gatesy, S. M. & Middleton, K. M. Bipedalism, flight, and the evolution of theropod locomotor diversity. J. Vertebr. Paleontol. 17, 308–329 (1997).Article 

    Google Scholar 
    21.Kulemeyer, C., Asbahr, K., Gunz, P., Frahnert, S. & Bairlein, F. Functional morphology and integration of corvid skulls—a 3D geometric morphometric approach. Front. Zool. 6, 2 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Bright, J. A., Marugán-Lobón, J., Rayfield, E. J. & Cobb, S. N. The multifactorial nature of beak and skull shape evolution in parrots and cockatoos (Psittaciformes). BMC Evol. Biol. 19, 104 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Bright, J. A., Marugán-Lobón, J., Cobb, S. N. & Rayfield, E. J. The shapes of bird beaks are highly controlled by nondietary factors. Proc. Natl Acad. Sci. USA 113, 5352–5357 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Navalón, G., Marugán-Lobón, J., Bright, J. A., Cooney, C. R. & Rayfield, E. J. The consequences of craniofacial integration for the adaptive radiations of Darwin’s finches and Hawaiian honeycreepers. Nat. Ecol. Evol. 4, 270–278 (2020).PubMed 
    Article 

    Google Scholar 
    25.Felice, R. N., Randau, M. & Goswami, A. A fly in a tube: macroevolutionary expectations for integrated phenotypes. Evolution 72, 2580–2594 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Shatkovska, O. V. & Ghazali, M. Integration of skeletal traits in some passerines: impact (or the lack thereof) of body mass, phylogeny, diet and habitat. J. Anat. 236, 274–287 (2020).PubMed 
    Article 

    Google Scholar 
    27.Hieronymus, T. L. Qualitative skeletal correlates of wing shape in extant birds (Aves: Neoaves). BMC Evol. Biol. 15, 30 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Felice, R. N., Tobias, J. A., Pigot, A. L. & Goswami, A. Dietary niche and the evolution of cranial morphology in birds. Proc. R. Soc. B 286, 20182677 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Navalón, G., Bright, J. A., Marugán-Lobón, J. & Rayfield, E. J. The evolutionary relationship among beak shape, mechanical advantage, and feeding ecology in modern birds. Evolution 73, 422–435 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nat. Ecol. Evol. 4, 230–239 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Grant, R. B. & Grant, P. R. What Darwin’s finches can teach us about the evolutionary origin and regulation of biodiversity. BioScience 53, 965–975 (2003).Article 

    Google Scholar 
    32.Van de Ven, T., Martin, R., Vink, T., McKechnie, E. & Cunningham, S. Regulation of heat exchange across the hornbill beak: functional similarities with toucans? PLoS ONE 11, e0154768 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Lamichhaney, S. et al. Rapid hybrid speciation in Darwin’s finches. Science 359, 224–228 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Klingenberg, C. P. & Marugán-Lobón, J. Evolutionary covariation in geometric morphometric data: analyzing integration, modularity, and allometry in a phylogenetic context. Syst. Biol. 62, 591–610 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Dececchi, T. A. & Larsson, H. C. Body and limb size dissociation at the origin of birds: uncoupling allometric constraints across a macroevolutionary transition. Evolution 67, 2741–2752 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Nudds, R., Dyke, G. & Rayner, J. Forelimb proportions and the evolutionary radiation of Neornithes. Proc. R. Soc. Lond. B 271, S324–S327 (2004).
    Google Scholar 
    37.Benson, R. B. & Choiniere, J. N. Rates of dinosaur limb evolution provide evidence for exceptional radiation in Mesozoic birds. Proc. R. Soc. B 280, 20131780 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Videler, J. J. Avian Flight (Oxford Univ. Press, 2006).39.Carrano, M. T. & Sidor, C. A. Theropod hind limb disparity revisited: comments on Gatesy and Middleton (1997). J. Vertebr. Paleontol. 19, 602–605 (1999).Article 

    Google Scholar 
    40.Middleton, K. M. & Gatesy, S. M. Theropod forelimb design and evolution. Zool. J. Linn. Soc. 128, 149–187 (2000).Article 

    Google Scholar 
    41.Young, N. M., Linde-Medina, M., Fondon, J. W., Hallgrímsson, B. & Marcucio, R. S. Craniofacial diversification in the domestic pigeon and the evolution of the avian skull. Nat. Ecol. Evol. 1, 0095 (2017).Article 

    Google Scholar 
    42.Martín-Serra, A. & Benson, R. B. Developmental constraints do not influence long-term phenotypic evolution of marsupial forelimbs as revealed by interspecific disparity and integration patterns. Am. Nat. 195, 547–560 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Dumont, E. R. et al. Selection for mechanical advantage underlies multiple cranial optima in New World leaf-nosed bats. Evolution 68, 1436–1449 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Hedrick, B. P. et al. Morphological diversification under high integration in a hyper diverse mammal clade. J. Mamm. Evol. 27, 563–575 (2020).Article 

    Google Scholar 
    45.Rossoni, D. M., Costa, B. M., Giannini, N. P. & Marroig, G. A multiple peak adaptive landscape based on feeding strategies and roosting ecology shaped the evolution of cranial covariance structure and morphological differentiation in phyllostomid bats. Evolution 73, 961–981 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Prum, R. O. et al. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature 526, 569–573 (2015).CAS 
    Article 

    Google Scholar 
    47.Bjarnason, A. & Benson, R. A 3D geometric morphometric dataset quantifying skeletal variation in birds. MorphoMuseuM 7, e125 (2021).Article 

    Google Scholar 
    48.Adams, D. C., Rohlf, F. J. & Slice, D. E. Geometric morphometrics: ten years of progress following the ‘revolution’. Ital. J. Zool. 71, 5–16 (2004).Article 

    Google Scholar 
    49.R Core Team R: A Language and Environment for Statistical Computing v.3.6.3 (R Foundation for Statistical Computing, 2020).50.Birds of the World (The Cornell Lab of Ornithology, 2021); https://birdsoftheworld.org/bow/home51.Dunning, J. B. Jr CRC Handbook of Avian Body Masses (CRC, 1992).52.The IUCN Red List of Threatened Species (IUCN, 2019); https://www.iucnredlist.org/53.Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

    Google Scholar 
    54.Taylor, G. & Thomas, A. Evolutionary Biomechanics (Oxford Univ. Press, 2014).55.Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. cluster: Cluster analysis basics and extensions. R package version 2.1.0 (2019).56.Grafen, A. The phylogenetic regression. Phil. Trans. R. Soc. Lond. B 326, 119–157 (1989).CAS 
    Article 

    Google Scholar 
    57.Revell, L. J. Size-correction and principal components for interspecific comparative studies. Evolution 63, 3258–3268 (2009).PubMed 
    Article 

    Google Scholar 
    58.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team nlme: Linear and nonlinear mixed effects models. R package version 3.1-145 (2020).59.Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).Article 
    CAS 

    Google Scholar 
    60.Goodall, C. Procrustes methods in the statistical analysis of shape. J. R. Stat. Soc. B 53, 285–321 (1991).
    Google Scholar 
    61.Adams, D., Collyer, M. & Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.2.1 (2020).62.Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).Article 

    Google Scholar 
    63.Adams, D. C. & Felice, R. N. Assessing trait covariation and morphological integration on phylogenies using evolutionary covariance matrices. PLoS ONE 9, e94335 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    64.Rohlf, F. J. & Corti, M. Use of two-block partial least-squares to study covariation in shape. Syst. Biol. 49, 740–753 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Adams, D. C. & Collyer, M. L. On the comparison of the strength of morphological integration across morphometric datasets. Evolution 70, 2623–2631 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Melo, D., Garcia, G., Hubbe, A., Assis, A. P. & Marroig, G. Evolqg—an R package for evolutionary quantitative genetics [version 3; referees: 2 approved, 1 approved with reservations]. F1000Research 4, 925 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Goswami, A. & Polly, P. D. Methods for studying morphological integration and modularity. Paleontol. Soc. Pap. 16, 213–243 (2010).Article 

    Google Scholar 
    68.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6 (2019). More