More stories

  • in

    Model-based projections for COVID-19 outbreak size and student-days lost to closure in Ontario childcare centres and primary schools

    Population structureThere are N households in the population, and a single educational institution (either a school or a school, dependent on scenarios to be introduced later) with M rooms and a maximum capacity dependent on the scenario being tested. Effective contacts between individuals occur within each household, as well as rooms and common areas (entrances, bathrooms, hallways, etc.) of the institution. All groups of individuals (households and rooms) in the model are assumed to be well-mixed.Each individual (agent) in the model is assigned an age, household, room in the childcare facility and an epidemiological status. Age is categorical, so that every individual is either considered a child (C) or an adult (A). Epidemiological status is divided into stages in the progression of the disease; agents can either be susceptible (S), exposed to the disease (E), presymptomatic (an initial asymptomatic infections period P), symptomatically infected (I), asymptomatically infected (A) or removed/recovered (R), as shown in Fig. 1b.In the model, some children in the population are enrolled as students in the institution and assigned a classroom based on assumed scenarios of classroom occupancy while some adults are assigned educator/caretaker roles in these classroom (again dependent on the occupancy scenario being tested). Assignments are made such that there is only one educator per household and that children do not attend the same institution as a educator in the household (if there is one), and vice versa.Interaction and disease progressionThe basic unit of time of the model is a single day, over which each attendee (of the institution) spends time at both home and at the institution. The first interactions of each day are established within each household, where all members of the household interact with each other. An asymptomatically infectious individual of age i will transmit the disease to a susceptible housemate with the age j with probability (beta ^H_{i,j}), while symptomatically infectious members will self-isolate (not interact with housemates) for a period of 14 days.The second set of interpersonal interactions occur within the institution. Individuals (both students and educators) in each room interact with each other, where an infectious individual of age i transmits the disease to some susceptible individual of age j with probability (beta ^C_{i,j}). To signify common areas within the building (such as hallways, bathrooms and entrances), each individual will then interact with every other individual in the institution. There, an infectious individual of age j will infect a susceptible individual of age i with probability (beta ^O_{i,j}).To simulate community transmission (for example, public transport, coffee shops and other sources of infection not explicitly modelled here), each susceptible attendee is infected with probability (lambda _S). Susceptible individuals not attending the institution in some capacity are infected at rate (lambda _N), where (lambda _N >lambda _S) to compensate for those consistent effective interactions outside of the institution that are neglected by the model (such as workplace interactions among essential workers and members of the public).Figure 1b shows the progression of the illness experienced by each individual in the model. In each day, susceptible (S) individuals exposed to the disease via community spread or interaction with infectious individuals (those with disease statuses P, A and I) become exposed (E), while previously exposed agents become presymptomatic (P) with probability (delta). Presymptomatic agents develop an infection in each day with probability (delta), where they can either become symptomatically infected (I) with probability (eta) or asymptomatically infected (A) with probability (1-eta).The capacity of the sole educational institution in the model is divided evenly between 5 rooms, with class size and student-educator ratio governed by one of three basic scenarios: seven students and three educators per room (7 : 3), eight students and two educators per room (8 : 2), and fifteen students and two educators per room (15 : 2). Classroom assignments for children can be either randomized or grouped by household (siblings are put in the same class).Symptomatically infected agents (I) are removed from the simulation after 1 day (status R) with probability (gamma _I), upon which they self-isolate for 14 days, and therefore no longer pose a risk to susceptible individuals. Asymptomatically infected agents (A) remain infectious but are presumed able to maintain regular effective contact with other individuals in the population due to their lack of noticeable symptoms; they recover during this period (status R) with probability (gamma _A). Disease statuses are updated at the end of each day, after which the cycles of interaction and infection reoccur the next day.The actions of symptomatic (status I) agents depend on age and role. Individuals that become symptomatic maintain a regular schedule for 1 day following initial infection (including effective interaction within the institution, if attending), after which they serve a mandatory 14-day isolation period at home during which they interaction with no one (including other members of their household). On the second day after the individual’s development of symptoms, their infection is considered a disease outbreak centred in their assigned room, triggering the closure of that room for 14 days. All individuals assigned to that room are sent home, where they self-isolate for 14 days due to presumed exposure to the disease. Symptomatically infected children are not replaced, and simply return to their assigned classroom upon recovery. At the time of classroom reopening, any symptomatic educator is replaced by a substitute for the duration of their recovery, upon which they reprise their previous role in the institution; the selection of a substitute is made under previous constraints on educator selection (one educator per household. with no one chosen from households hosting any children currently enrolled in the institution).ParameterisationThe parameter values are given in Supplementary Table S4. The sizes of households in the simulation was determined from 2016 Statistics Canada census data on the distribution of family sizes42. We note that Statistics Canada data only report family sizes of 1, 2 or 3 children: the relative proportions for 3+ children were obtained by assuming that (65 %) of families of 3+ children had 3 children, (25%) had 4 children, (10%) had 5 children, and none had more than 5 children. Each educator was assumed to be a member of a household that did not have children attending the school. Again using census data, we assumed that (36%) of educators live in homes with no children, where an individual lives alone with probability 0.282, while households hosting 3, 4, 5, 6, and seven adults occur with probability 0.345, 0.152, 0.138, 0.055, 0.021 and 0.009 respectively. Others live with (ge 1) children in households following the size and composition distribution depending on the number of adults in the household. For single-parent households, a household with a single child occurs with probability 0.169, and households with 2, 3, 4 and 5 children occur with probabilities 0.079, 0.019, 0.007 and 0.003 respectively. With two-parent households, those probabilities become 0.284, 0.307, 0.086, 0.033 and 0.012.The age-specific transmission rates in households are given by the matrix:$$begin{aligned} begin{bmatrix} beta ^H_{1,1} &{} beta ^H_{1,2} \ beta ^H_{2,1} &{} beta ^H_{2,2} \ end{bmatrix} equiv beta ^H begin{bmatrix} c^H_{1,1} &{} c^H_{1,2} \ c^H_{2,1} &{} c^H_{2,2} \ end{bmatrix}, end{aligned}$$
    (1)
    where (c^H_{i,j}) gives the number of contacts per day reported between individuals of ages i and j estimated from data28 and the baseline transmission rate (beta ^H) is calibrated. To estimate (c^H_{i,j}) from the data in Ref.28, we used the non-physical contacts of age class 0–9 years and 25–44 years of age with themselves and one another in Canadian households. Based on a meta-analysis, the secondary attack rate of SARS-CoV-2 appears to be approximately (15 %) on average in both Asian and Western households43. Hence, we calibrated (beta ^H) such that a given susceptible person had a (15 %) chance of being infected by a single infected person in their own household over the duration of their infection averaged across all scenarios tested. As such, age specific transmission is given by the matrix$$begin{aligned} beta ^Hcdot begin{bmatrix} 0.5378 &{} 0.3916 \ 0.3632 &{} 0.3335 end{bmatrix}. end{aligned}$$
    (2)
    To determine (lambda _S) we used case notification data from Ontario during lockdown, when schools, workplaces, and schools were closed44. During this period, Ontario reported approximately 200 cases per day. The Ontario population size is 14.6 million, so this corresponds to a daily infection probability of (1.37 times 10^{-5}) per person. However, cases are under-ascertained by a significant factor in many countries. We assumed an under-ascertainment factor of 8.45 based on an empirical estimate of under-reporting45, meaning there are actually 8.45 times more cases than reported in Ontario, giving rise to (lambda _S = 1.16 times 10^{-4}) per day; (lambda _N) was set to (2cdot lambda _S). We emphasize that this number may fall later in the pandemic as testing capacity increases, although some individuals may still never get tested–especially schoolchildren, who are often asymptomatic.The age-specific transmission rates in the school rooms is given by the matrix$$begin{aligned} begin{bmatrix} beta ^C_{1,1} &{} beta ^C_{1,2} \ beta ^C_{2,1} &{} beta ^C_{2,2} \ end{bmatrix} equiv beta ^C begin{bmatrix} c^C_{1,1} &{} c^C_{1,2} \ c^C_{2,1} &{} c^C_{2,2} \ end{bmatrix} equiv beta ^C begin{bmatrix} 1.2356 &{} 0.0588 \ 0.1176 &{} 0.0451 end{bmatrix}, end{aligned}$$
    (3)
    where (c^C_{i,j}) is the number of contacts per day reported between age i and j estimated from data28. To estimate (c^C_{i,j}) from the data in Ref.28, we used the non-physical contacts of age class 0–9 years and 20–54 years of age, with themselves and one another, in Canadian schools. Epidemiological data on secondary attack rates in educational institutions are rare, since childcare centres and schools were closed early in the outbreak in most areas. We note that contacts in families are qualitatively similar in nature and duration to contacts in schools with small group sizes, although these contacts are generally more dispersed among the larger groups in rooms than among the smaller groups in households. On the other hand, rooms may represent equally favourable conditions for aerosol transmission, as opposed to close contact. Hence, we assumed that (beta ^C = alpha _C beta ^H), with a baseline value of (alpha _C = 0.75) based on more dispersed contacts expected in the larger room group, although we varied this assumption in sensitivity analysis.To determine (beta ^O) we assumed that (beta ^O = alpha _O beta ^C) where (alpha _O ll 1) to account for the fact that students spend less time in common areas than in their rooms. To estimate (alpha _O), we note that (beta ^O) is the probability that a given infected person transmits the infection to a given susceptible person. If students and staff have a probability p per hour of visiting a common area, then their chance of meeting a given other student/staff in the same area in that area is (p^2). We assumed that (p=0.05) and thus (alpha _O = 0.0025). The age-specific contact matrix for (beta ^O) was the same as that used for (beta ^C) (Eq. 3).Model initializationUpon population generation, each agent is initially susceptible (S). Individuals are assigned to households as described in the “Parameterisation” section, and children are assigned to rooms either randomly or by household. We assume that parents in households with more than one child will decide to enroll their children in the same institution for convenience with probability (xi =80%), so that each additional child in multi-child households will have probability (1-xi) of not being assigned to the institution being modelled.Households hosting educators are generated separately. As in the “Parameterisation” section, we assume that (36%) of educators live in adult-only houses, while the other educators live in houses with children, both household sizes following the distributions outlined in the “Parameterisation” section. The number of educator households is twice that required to fully supply the school due to the replacement process for symptomatic educators outlined in the “Disease Progression” section.Initially, a proportion of all susceptible agents (R_{init}) is marked as removed/recovered (R) to account for immunity caused by previous infection moving through the population. A single randomly chosen school attendee is chosen as a primary case and is made presymptomatic (P) to introduce a source of infection to the model. All simulations are run until there are no more potentially infectious (E, P, I, A) individuals left in the population and the institution is at full capacity. All results were averaged over 2000 trials.Estimating β
    H
    Agents in the simulation were divided into two classes: “children” (ages 0–9) and “adults” (ages 25–44). Available data on contact rates28 was stratified into age categories of width 5 years starting at age 0 (0–5, 5–9, 10–14, etc.). The mean number of contacts per day (c_{i,j}^H) for each class we considered (shown in Eq. 2) was estimated by taking the mean of the contact rates of all age classes fitting within our presumed age ranges for children and adults.For (beta ^H) calibration, we created populations by generating a sufficient number of households to fill the institution in each of the three tested scenarios; 15 : 2, 8 : 2 and 7 : 3. In each household, a single randomly chosen individual was infected (each member with equal probability) by assigning them a presymptomatic disease status P; all other members were marked as susceptible (disease status S). In each day of the simulation, each member of each household was allowed to interact with the infected member, becoming exposed to the disease with probability given in Eq. 2. Upon exposure, they were assigned disease status E. At the beginning of each subsequent day, presymptomatic individuals proceeded to infected statuses I and A, and infected agents were allowed to recover as dictated by Fig. 1b and Supplementary Table S4. This cycle of interaction and recovery within each household was allowed to continue until all infected individuals were recovered from illness.We did not allow exposed agents (status E) to progress to an infectious stage (I or A) since we were interested in finding out how many infections within the household would result from a single infected household member, as opposed to added secondary infections in later days. At the end of each trial, the specific probability of infection ((pi _n)) in each household (H_n) was calculated by dividing the number of exposed agents in the household ((E_n)) by the size of the household (|H_n|) less 1 (accounting for the member initially infected). Single occupant households ((|H_n|=1)) were excluded from the calculation. The total probability of infection (pi) was then taken as the mean of all (pi _n), so that$$begin{aligned} pi =frac{1}{D}sum _{n}pi _n=frac{1}{D}sum _{|H_n|ge 2}frac{E_n}{|H_n|-1}, end{aligned}$$
    (4)
    where D represents the total number of multiple occupancy households in the simulation. This modified disease simulation was run for 2000 trials each of different prospective values of (beta ^H) ranging from 0 to 0.21. The means of all corresponding final estimates of the infection rate were taken per value of (beta ^H), and the value corresponding to a infection rate of (15%) was interpolated.Simplifying assumptionsOur model makes simplifying assumptions that may influence its predictions. For instance, we assume that classrooms are homogeneously mixing and did not take social structure into account. Social structure might slow the spread of COVID-19 in classrooms. We also assumed that public health authorities will respond to a confirmed case by closing the classroom, although in practice, they may keep the class running if they think the case does not represent an infection risk to children or adults. This would reduce the number of student-days lost to closure. Similarly, we did not account for potential contacts between school children outside of classes, although students of a classroom that has been closed may still interact with their classmates outside of school. Other simplifying assumptions are mentioned in the “Discussion” section. More

  • in

    Quantification of Phytophthora infestans population densities and their changes in potato field soil using real-time PCR

    We modified the reported DNA extraction methods using a commercial DNA extraction kit: the cetyl trimethylammonium bromide (CTAB) method13 with the addition of skim milk to prevent the absorption of DNA and a bead beating method14. In this report, this method is named the modified CTAB-bead method. The proposed real-time PCR assay may be suitable for the quantification of P. infestans population densities, at least in Japanese upland soils, because P. infestans DNA from various kinds of upland soils was well quantified, and there were no false positives in the negative control plots. Thus, we conclude that the P. infestans population density can be represented by the quantity of DNA determined using real-time PCR. One udifluvent and udult soil quantified slightly small amounts of DNA, and there were small differences among soil types at the same population densities. However, this should not be of great consequence because the differences compared with the other upland soils are within tenfold; thus, these small differences are likely due to the soil characteristics. A previous study reported that no single method of cell lysis or purification is appropriate for all soils15. Thus, the proposed real-time PCR assay is available to quantify the pathogen densities in soils such that most soil samples containing 4–400 zoosporangia/g soil plots except decomposed granite soil and sea sand were quantified as approximately 1–100 pg/g soil. Although this method can be used to quantify P. infestans DNA levels in soil, not all soil samples containing the same number of zoosporangia yielded similar results, as the amount of DNA absorbed was dependent on the soil type. Thus, a calibration curve may be required when a new soil type is tested in which a zoosporangia suspension or P. infestans DNA is added to nondiseased soil. Regarding decomposed granite soil and sea sand, which are not upland soils and not suitable for potato cultivation, the reason for the small DNA quantities may be that a large amount of DNA is absorbed onto silica under Na+- or Ca2+-rich conditions16. If the soil type is sandy or clayey, the DNA quantities may be smaller than those in other soil types. For further development of this method, the addition of an internal control, such as GFP-induced plasmid DNA17, to correct the raw data might be effective. Additionally, changing the glass beads used in this method to zirconia or iron beads may also be effective due to the powerful homogenization and lower amount of DNA absorption achieved with the latter two bead types. However, these improvements may be unnecessary because the proposed assay has a small detection limit such that samples containing only 4 zoosporangia/g soil were detected and quantified. Ristaino et al.18 reported that real-time LAMP and droplet digital PCR can be used to quantify P. infestans DNA from plant tissue. Compared with these tools, the proposed real-time PCR assay has some advantages, such as a wide dynamic range. For this reason, this assay may be widely applied to upland soils.This is the first report of the quantification of P. infestans population densities in naturally infested soil samples, and changes in the population densities were analysed using real-time PCR. These results also showed that this quantitative method provides reproducible results, because changes in P. infestans DNA were correlated with symptom development throughout the growth periods. DNA quantities during the epidemics (5 and 18 August 2017 and 2018, respectively) were converted into P. infestans population densities in zoosporangia equivalents based on the results obtained for udant B (experimental field, HARC), as shown in Fig. 1; thus, there were approximately 104–105 and 103–104 zoosporangia in the ridgetop soils. These results indicate that a large amount of P. infestans existed in the field ridgetop soils where the plants were blighted. Quantified DNA may be from zoosporangia, mycelia, or small residue or free DNA but not from oospores. Because the A1 mating type has been dominant in Japan since 200519, sexual reproduction rarely occurs, at least in potato fields. We have not verified the availability of soils containing oospores. In future studies, inoculated soil containing oospores should be tested. However, soils containing P. infestans oospores might be quantified using the proposed assay because previous studies reported that soils containing three potato pathogens and oospores of Pythium spp. have been quantified using CTAB and bead beating methods20,21. If the proposed assay can quantify P. infestans DNA from oospores in soil, we might apply this assay to soil diagnosis before planting.A previous study reported that the inoculum potentials of soil decreased as foliage lesions became less abundant2. Our study corresponds to this previous study because the quantities of P. infestans DNA in soil were consistent with foliage symptom development (in 2017 and 2018) and the number of lesions per plant (in 2018). Hence, the proposed real-time PCR method can be an alternative to bioassays and used as a method to quantify the P. infestans population density. Bioassays require special knowledge and techniques of plant pathology because researchers have to judge whether inoculated tubers were rotted due to P. infestans. On the other hand, real-time PCR assays are easy and require only minimal knowledge and techniques of molecular biology. In 2018, symptom development stopped from late July to early August due to a heat wave. The DNA quantities were reflected in foliage symptoms, with small quantities of DNA estimated during this period. These results imply that this method is highly sensitive for estimating even weekly population changes. The quantities of DNA were decreased to one-tenth of their former numbers in a week after the desiccation of the foliage. As indicated by the decrease, most P. infestans zoospores or zoosporangia cannot survive in/on the soil and quickly die and are degraded by microorganisms and DNase6,22,23. However, if new A2 strains migrated into Japan and oospores were found in field soil, DNA would be detected for a long time even in the noncultivation period. Surprisingly, an infinitesimal quantity of DNA was detected one month after the foliage had disappeared in 2017. This DNA may have been from another plant out of experimental fields or DNA absorbed to some kind of soil material and may persist against DNase24.Figure 5 shows a positive correlation between the quantity of P. infestans DNA and the inoculum potential. Thus, the proposed real-time PCR method is suitable for indirect estimation of P. infestans inoculum potential. In this analysis, two data sets containing zero values were eliminated as outliers because a zero value in this experiment signifies “below the detection limit”; we cannot determine the exact value. Thus, data sets containing zero values cannot be included in the analysis to evaluate the applicability of the proposed real-time PCR assay instead of the bioassay for estimating inoculum potential. Previous estimation methods, such as bioassays, require an incubation period of approximately 2–3 weeks, expert knowledge of P. infestans and incubation space. On the other hand, real-time PCR requires several hours to estimate the population densities, minimal knowledge of molecular biology and no incubation space. For these reasons, we can more easily estimate P. infestans inoculum potential with real-time PCR than with bioassays. In the experiments using commercial potato fields, a larger amount of DNA was quantified from ridge bottom soils than from any other location. This result agrees with a previous report that most rainwater was deposited at the bottoms of ridges, and the rainwater contained fewer than 500 zoosporangia when blight was present on the crop25. According to this result, soils sampled from the bottom of a ridge are suitable for whole field estimation of P. infestans population densities.In this study, the quantities of DNA and inoculum potentials were larger in field A than in fields B and C. This result suggests that the proposed real-time PCR assay may be suitable for comparison among potato fields. In field A, late blight occurred because farmers could not conduct chemical control due to heavy rain. Field B was a non-controlled commercial field, and incomplete chemical spraying gave rise to a non-controlled spot in field C. Fields B and C were not perfectly managed for preventing late blight; however, some control, such as cultural or chemical control, was performed in some part. On the other hand, field A did not receive control measures at all. This might be why field A had larger DNA quantities and inoculum potentials than fields B and C.For the results from non-controlled fields, P. infestans did not percolate through the soil but instead remained at the surface because most soil samples from ridgetops contained larger amounts of DNA than those from the tuber periphery. A previous study reported that more than half of the tubers in the top 5.1 cm of soil were blighted, and the population of blighted tubers decreased with increasing depth26. We can show the same conclusions using real-time PCR. However, in commercial fields, all soils sampled from the tuber periphery contained larger amounts of DNA and had a lower inoculum potential than those from the ridge surfaces. Rainy and cold conditions (approximately at 13 °C) continued from 14 to 18 August 2018, several days before sampling27. The weather might have sustained indirect germination, and many zoospores were released and percolated through the soil. However, zoospores are motile for only a short time28 and cannot survive for a long time. Thus, much of the quantified DNA was from dead P. infestans or free DNA, and less inoculum potential was found near tubers. Soil samples from ridgetops showed larger inoculum potential than those from the tuber periphery. This may be because ridgetop samples contain a large amount of fresh P. infestans from foliage lesions.In this study, we successfully developed a real-time PCR assay to estimate the P. infestans densities in upland soils, and the proposed assay is available not only for the estimation of population density but also inoculum potential. In the future, this research can provide to a new decision support system for predicting and preventing potato storage rot. The P. infestans soil population density is the most important factor influencing potato storage rot. The possibilities or severities of potato storage rot may be predicted by estimating the P. infestans population density in soil before harvesting. Previous storage planning suggested that potato storage rot might occur if many foliage lesions occur during the growing season. In this study, most P. infestans DNA from foliage lesions degraded within one week. Thus, the possibility and severity of storage rot may be low if the quantity of P. infestans DNA immediately before harvesting is small, even if many foliage lesions occur during the growing season. Additionally, the previous quantitative method (bioassay) requires an incubation period of approximately one week or more2,7. On the other hand, the real-time PCR assay does not require an incubation period, and it takes only several hours to quantify the P. infestans population density in the sample soil. Potato storage rot may be reduced because the storage plan can be selected accurately and rapidly by using real-time PCR compared with previous methods. For example, tubers harvested from fields harbouring high levels of P. infestans DNA can be shipped as soon as possible to prevent potato storage rot. However, many other factors may be involved in the spread of this disease, such as surface injury5. A decision support system would allow potato storage companies to evaluate and address factors associated with potato storage rot and establish appropriate countermeasures to prevent economic losses. More

  • in

    Mitogenomic analysis of diversity of key whitefly pests in Kenya and its implication to their sustainable management

    1.Martin, J. H., Mifsud, D. & Rapisarda, C. The whiteflies (Hemiptera: Aleyrodidae) of Europe and the Mediterranean basin. Bull. Entomol. Res. 90, 407–448 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Omongo, C. A. et al. African cassava whitefly, Bemisia tabaci, resistance in African and South American cassava genotypes. J. Integr. Agric. 11, 327–336 (2012).Article 

    Google Scholar 
    3.Omongo, C. A. et al. Host plant resistance to African Bemisia tabaci in local landraces and improved cassava mosaic disease resistant genotypes in Uganda. In 6th International Scientific Meeting of the Cassava Biotechnology Network (Abstracts), Vol. 84, 8–14 (2004).4.Legg, J. P., Sseruwagi, P. & Brown, J. Bemisia whiteflies cause physical damage and yield losses to cassava in Africa. In Sixth International Scientific Meeting of the Cassava Biotechnology Network 78 (2004).5.Lloyd, L. L. The control of the greenhouse white fly (Asterochiton vaporariorum) with notes on its biology 1. Ann Appl Biol. 9, 1–32 (1922).Article 

    Google Scholar 
    6.McAuslane, H. J. & Smith, S. A. Sweet Potato Whitefly B Biotype, Bemisia tabaci (Gennadius) (Insecta: Hemiptera: Aleyrodidae) (University of Florida, 2015).
    Google Scholar 
    7.Viscarret, M. M., Botto, E. N. & Polaszek, A. N. Whiteflies (Hemiptera: Aleyrodidae) of economic importance and their natural enemies (Hymenoptera: Aphelinidae, Signiphoridae) in Argentina. Rev. Chil. Entomol. 26, 5–11 (2000).
    Google Scholar 
    8.Abd-Rabou, S. & Simmons, A. M. Survey of natural enemies of whiteflies (Hemiptera: Aleyrodidae) in Egypt with new local and world records. Entomol. News 124, 38–56 (2014).Article 

    Google Scholar 
    9.Roopa, H. K. et al. Phylogenetic analysis of Trialeurodes spp. (Hemiptera: Aleyrodidae) from India based on differences in mitochondrial and nuclear DNA. Fla Entomol. 1, 1086–94 (2012).Article 

    Google Scholar 
    10.De Barro, P. J., Liu, S. S., Boykin, L. M. & Dinsdale, A. B. Bemisia tabaci: a statement of species status. Annu. Rev. Entomol. 7, 1–9 (2011).Article 
    CAS 

    Google Scholar 
    11.Liu, S. S., Colvin, J. & De Barro, P. J. Species concepts as applied to the whitefly Bemisia tabaci systematics: how many species are there? J. Integr. Agric. 11, 176–186 (2012).Article 

    Google Scholar 
    12.CABI/EPPO. Distribution Maps of Quarantine Pests for Europe (eds Smith I. M. & Charles L. M. F.) 768 (CAB International, 1998).13.Brown, J. K. Current status of Bemisia tabaci as a plant pest and virus vector in agroecosystems worldwide. FAO Plant Prot. Bull. 42, 3–2 (1994).
    Google Scholar 
    14.Brown, J. K. Molecular markers for the identification and global tracking of whitefly vector—begomovirus complexes. Virus Res. 71, 233–260 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Maruthi, M. N., Hillocks, R. J., Rekha, A. R. & Colvin, J. Transmission of Cassava brown streak virus by whiteflies. In Sixth International Scientific Meeting of the Cassava Biotechnology Network—Adding Value to a Small-Farmer Crop 8–14 (2004).16.Mugerwa, H. et al. Genetic diversity and geographic distribution of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) genotypes associated with cassava in East Africa. Ecol. Evol. 2, 2749–2762 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Maruthi, M. N. et al. Reproductive incompatibility and cytochrome oxidase I gene sequence variability amongst host-adapted and geographically separate Bemisia tabaci populations (Hemiptera: Aleyrodidae). Syst. Entomol. 29, 560–568 (2004).Article 

    Google Scholar 
    18.Legg, J. P. et al. Comparing the regional epidemiology of the cassava mosaic and cassava brown streak virus pandemics in Africa. Virus Res. 159, 161–170 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Prijović, M. et al. Genetic variation of the greenhouse whitefly, Trialeurodes vaporariorum (Hemiptera: Aleyrodidae), among populations from Serbia and neighbouring countries, as inferred from COI sequence variability. Bull. Entomol. Res. 104, 357–366 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Palevsky, E. et al. How specific is the phoretic relationship between broad mite, Polyphagotarsonemus latus (Banks) (Acari: Tarsonemidae), and its insect vectors? Exp. Appl. Acarol. 25, 217–224 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Mound, L. A. & Halsey, S. H. Whitefly of the World. A Systematic Catalogue of the Aleyrodidae (Homoptera) with Host Plant and Natural Enemy Data (Wiley, 1978).
    Google Scholar 
    22.Legg, J. P., French, R., Rogan, D., Okao-Okuja, G. & Brown, J. K. A distinct Bemisia tabaci (Gennadius) (Hemiptera: Sternorrhyncha: Aleyrodidae) genotype cluster is associated with the epidemic of severe cassava mosaic virus disease in Uganda. Mol. Ecol. 11, 1219–1229 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Legg, J. P. Epidemiology of a whitefly-transmitted cassava mosaic geminivirus pandemic in Africa. In Bemisia: Bionomics and Management of a Global Pest (eds Stansly, P. A. & Naranjo, S. E.) 233–257 (Springer, 2009).24.Legg, J. P. et al. Spatio-temporal patterns of genetic change amongst populations of cassava Bemisia tabaci whiteflies driving virus pandemics in East and Central Africa. Virus Res. 186, 61–75 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Delatte, H. et al. A new silverleaf-inducing biotype Ms of Bemisia tabaci (Hemiptera: Aleyrodidae) indigenous to the islands of the south-west Indian Ocean. Bull. Entomol. Res. 95, 29–35 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Jones, A. L. & Markham, R. H. Whitefly and whitefly-borne viruses in the tropics: building a knowledge base for global action. CIAT 341, 129–140 (2005).
    Google Scholar 
    27.Hodges, G. S. & Evans, G. A. An identification guide to the whiteflies (Hemiptera: Aleyrodidae) of the Southeastern United States. Fla Entomol. 1, 518–534 (2005).Article 

    Google Scholar 
    28.Calvert, L. A. et al. Morphological and mitochondrial DNA marker analyses of whiteflies (Homoptera: Aleyrodidae) colonizing cassava and beans in Colombia. Ann. Entomol. Soc. Am. 94, 512–519 (2001).CAS 
    Article 

    Google Scholar 
    29.Ovalle, T. M., Parsa, S., Hernández, M. P. & Becerra Lopez-Lavalle, L. A. Reliable molecular identification of nine tropical whitefly species. Ecol. Evol. 4, 3778–3787 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Shatters, R. G. Jr., Powell, C. A., Boykin, L. M., Liansheng, H. E. & McKenzie, C. L. Improved DNA barcoding method for Bemisia tabaci and related Aleyrodidae: development of universal and Bemisia tabaci biotype-specific mitochondrial cytochrome c oxidase I polymerase chain reaction primers. J. Econ. Entomol. 102, 750–758 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    31.Alhudaib, K. A., Rezk, A. A., Abdel-Banat, B. M. & Soliman, A. M. Molecular identification of the biotype of whitefly (Bemisia tabaci) inhabiting the eastern region of Saudi Arabia. J. Biol. Sci. 14, 494–500 (2014).Article 
    CAS 

    Google Scholar 
    32.Cavalieri, V., Manglli, A., Tiberini, A., Tomassoli, L. & Rapisarda, C. Rapid identification of Trialeurodes vaporariorum, Bemisia tabaci (MEAM1 and MED) and tomato-infecting criniviruses in whiteflies and in tomato leaves by real-time reverse transcription-PCR assay. Bull. Insectol. 67, 219–225 (2014).
    Google Scholar 
    33.Dickey, A. M., Stocks, I. C., Smith, T., Osborne, L. & McKenzie, C. L. DNA barcode development for three recent exotic whitefly (Hemiptera: Aleyrodidae) invaders in Florida. Fla Entomol. 98, 473–478 (2015).Article 

    Google Scholar 
    34.Brown, J. K. et al. Molecular diagnostic development for begomovirus-betasatellite complexes undergoing diversification: a case study. Virus Res. 241, 29–41 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Frohlich, D. R., Torres-Jerez, I., Bedford, I. D., Markham, P. G. & Brown, J. K. A phylogeographical analysis of the Bemisia tabaci species complex based on mitochondrial DNA markers. Mol. Ecol. 8, 1683–1691 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
    PubMed 

    Google Scholar 
    37.Xiong, B. & Kocher, T. D. Comparison of mitochondrial DNA sequences of seven morphospecies of black flies (Diptera: Simuliidae). Genome 34, 306–311 (1991).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Narang, S. K., Seawright, J. A. & Suarez, M. F. Genetic structure of natural populations of Anopheles albimanus in Colombia. J. Am. Mosq. Control 7, 437–445 (1991).CAS 

    Google Scholar 
    39.Dinsdale, A., Cook, L., Riginos, C., Buckley, Y. M. & De Barro, P. Refined global analysis of Bemisia tabaci (Hemiptera: Sternorrhyncha: Aleyrodoidea: Aleyrodidae) mitochondrial cytochrome oxidase 1 to identify species level genetic boundaries. Ann. Entomol. Soc. Am. 103, 196–208 (2010).Article 

    Google Scholar 
    40.Berry, S. D. et al. Molecular evidence for five distinct Bemisia tabaci (Homoptera: Aleyrodidae) geographic haplotypes associated with cassava plants in sub-Saharan Africa. Ann. Entomol. Soc. Am. 97, 852–859 (2004).CAS 
    Article 

    Google Scholar 
    41.Brown, J. K. et al. Characterization and distribution of esterase electromorphs in the whitefly, Bemisia tabaci (Genn.) (Homoptera: Aleyrodidae). Biochem. Genet. 33, 205–214 (1995).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.De Barro, P. J. & Carver, M. Cabbage whitefly, Aleyrodes proletella (L.) (Hemiptera: Aleyrodidae), newly discovered in Australia. Aust. J. Entomol. 36, 255–256 (1997).Article 

    Google Scholar 
    43.Springate, S. & Colvin, J. Pyrethroid insecticide resistance in British populations of the cabbage whitefly Aleyrodes proletella. Pest. Manag. Sci. 68, 260–267 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Chen, Z. T., Mu, L. X., Wang, J. R. & Du, Y. Z. Complete mitochondrial genome of the citrus spiny whitefly Aleurocanthus spiniferus (Quaintance) (Hemiptera: Aleyrodidae): implications for the phylogeny of whiteflies. PLoS ONE 11, e0161385 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    45.Boykin, L. M. et al. Global relationships of Bemisia tabaci (Hemiptera: Aleyrodidae) revealed using Bayesian analysis of mitochondrial COI DNA sequences. Mol. Phylogenet. Evol. 44, 1306–1319 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Lee, W., Park, J., Lee, G. S., Lee, S. & Akimoto, S. I. Taxonomic status of the Bemisia tabaci complex (Hemiptera: Aleyrodidae) and reassessment of the number of its constituent species. PLoS ONE 8, e63817 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Manzari, S. & Quicke, D. L. A cladistic analysis of whiteflies, subfamily Aleyrodinae (Hemiptera: Sternorrhyncha: Aleyrodidae). J. Nat. Hist. 40, 2423–2554 (2006).Article 

    Google Scholar 
    48.Gamarra, H., Carhuapoma, P., Mujica, N., Kreuze, J. & Kroschel, J. Greenhouse whitefly, Trialeurodes vaporariorum (Westwood 1956). In Pest Distribution and Risk Atlas for Africa—Potential Global and Regional Distribution and Abundance of Agricultural and Horticultural Pests and Associated Biocontrol Agents Under Current and Future Climates (eds Kroschel, J., Mujica, N., Carhuapoma, P., & Sporleder, M.) 154–168 (International Potato Center (CIP), 2016).49.Khamis, F. M. et al. Insights in the global genetics and gut microbiome of Black Soldier Fly, Hermetia illucens: implications for animal feed safety control. Front. Microbiol. 34, 1538 (2020).Article 

    Google Scholar 
    50.Simon, C. et al. Evolution, weighting, and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers. Ann. Entomol. Soc. Am. 87, 651–701 (1994).CAS 
    Article 

    Google Scholar 
    51.Frohlich, D., Torres-Jerez, I., Bedford, I. D., Markham, P. G. & Brown, J. K. A phylogeographic analysis of the Bemisia tabaci species complex based on mitochondrial DNA markers. Mol. Ecol. 8, 1593–1602 (1999).Article 

    Google Scholar 
    52.Xiong, B. & Kocher, T. D. Comparison of mitochondrial DNA sequences of seven morphospecies of black flies (Diptera: Simuliidae). Genome 34, 306–311 (1991).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Kearse, M. et al. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.Bernt, M. et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.CABI. Invasive Species Compendium (CAB International). Available online at www.cabi.org/isc/datasheet/8925 (2019).60.Fick, S. E. & Hijmans, R. J. Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    61.Taylor, K. E., Stouffer, R. J. & Meeh, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).ADS 
    Article 

    Google Scholar 
    62.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high-resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

    Google Scholar 
    63.Kuhn, M. et al. caret: Classification and Regression Training. R package version 6.0-71. https://CRAN.R-project.org/package=caret (2016). More

  • in

    Mucin O-glycans suppress quorum-sensing pathways and genetic transformation in Streptococcus mutans

    1.Hansson, G. C. Mucins and the microbiome. Annu. Rev. Biochem. 89, 769–793 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Cross, B. W. & Ruhl, S. Glycan recognition at the saliva—oral microbiome interface. Cell. Immunol. 333, 19–33 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Tabak, L. A. In defense of the oral cavity: structure, biosynthesis, and function of salivary mucins. Annu. Rev. Physiol. 57, 547–564 (1995).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Deng, L. et al. Oral streptococci utilize a Siglec-like domain of serine-rich repeat adhesins to preferentially target platelet sialoglycans in human blood. PLoS Pathog. 10, e1004540 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    5.Shanker, E. & Federle, M. J. Quorum sensing regulation of competence and bacteriocins in Streptococcus pneumoniae and mutans. Genes 8, 15 (2017).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    6.Nakano, K., Nomura, R. & Ooshima, T. Streptococcus mutans and cardiovascular diseases. Jpn. Dent. Sci. Rev. 44, 29–37 (2008).Article 

    Google Scholar 
    7.Murchison, H. H., Barrett, J. F., Cardineau, G. A. & Curtiss, R. Transformation of Streptococcus mutans with chromosomal and shuttle plasmid (pYA629) DNAs. Infect. Immun. 54, 273–282 (1986).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Villedieu, A. et al. Prevalence of tetracycline resistance genes in oral bacteria. Antimicrob. Agents Chemother. 47, 878–882 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Chansley, P. E. & Kral, T. A. Transformation of fluoride resistance genes in Streptococcus mutans. Infect. Immun. 57, 1968–1970 (1989).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Hernando-Amado, S., Coque, T. M., Baquero, F. & Martínez, J. L. Defining and combating antibiotic resistance from one health and global health perspectives. Nat. Microbiol. 4, 1432–1442 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Villedieu, A. et al. Genetic basis of erythromycin resistance in oral bacteria. Antimicrob. Agents Chemother. 48, 2298–2301 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Olsen, I., Tribble, G. D., Fiehn, N.-E. & Wang, B.-Y. Bacterial sex in dental plaque. J. Oral Microbiol. 5, 20736 (2013).Article 

    Google Scholar 
    13.Loesche, W. J. Role of Streptococcus mutans in human dental decay. Microbiol. Rev. 50, 353–380 (1986).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Loesche, W. J., Rowan, J., Straffon, L. H. & Loos, P. J. Association of Streptococcus mutans with human dental decay. Infect. Immun. 11, 1252–1260 (1975).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Mathews, S. A., Kurien, B. T. & Scofield, R. H. Oral manifestations of Sjögren’s syndrome. J. Dent. Res. 87, 308–318 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Pramanik, R., Osailan, S. M., Challacombe, S. J., Urquhart, D. & Proctor, G. B. Protein and mucin retention on oral mucosal surfaces in dry mouth patients. Eur. J. Oral. Sci. 118, 245–253 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Frenkel, E. S. & Ribbeck, K. Salivary mucins in host defense and disease prevention. J. Oral Microbiol. 7, 29759 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    18.Ahn, S.-J., Wen, Z. T. & Burne, R. A. Multilevel control of competence development and stress tolerance in Streptococcus mutans UA159. Infect. Immun. 74, 1631–1642 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Ahn, S.-J., Ahn, S.-J., Wen, Z. T., Brady, L. J. & Burne, R. A. Characteristics of biofilm formation by Streptococcus mutans in the presence of saliva. Infect. Immun. 76, 4259–4268 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Duarte, S. et al. Influences of starch and sucrose on Streptococcus mutans biofilms. Oral Microbiol. Immunol. 23, 206–212 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Mitchell, T. J. The pathogenesis of streptococcal infections: from tooth decay to meningitis. Nat. Rev. Microbiol. 1, 219–230 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Frenkel, E. S. & Ribbeck, K. Salivary mucins protect surfaces from colonization by cariogenic bacteria. Appl. Environ. Microbiol. 81, 332–338 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    23.Frenkel, E. S. & Ribbeck, K. Salivary mucins promote the coexistence of competing oral bacterial species. ISME J. 11, 1286–1290 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Levine, M. Salivary proteins may be useful for determining caries susceptibility. J. Evid. Based Dent. Pract. 13, 91–93 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Thomsson, K. A., Schulz, B. L., Packer, N. H. & Karlsson, N. G. MUC5B glycosylation in human saliva reflects blood group and secretor status. Glycobiology 15, 791–804 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Ajdic, D. et al. Genome sequence of Streptococcus mutans UA159, a cariogenic dental pathogen. Proc. Natl Acad. Sci. USA 99, 14434–14439 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Paik, S., Brown, A., Munro, C. L., Cornelissen, C. N. & Kitten, T. The sloABCR operon of Streptococcus mutans encodes an Mn and Fe transport system required for endocarditis virulence and its Mn-dependent repressor. J. Bacteriol. 185, 5967–5975 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Nicolas, G. G. Detection of putative new mutacins by bioinformatic analysis using available web tools. BioData Min. 4, 22 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Aframian, N. & Eldar, A. A bacterial tower of Babel: quorum-sensing signaling diversity and its evolution. Annu. Rev. Microbiol. 74, 587–606 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Merritt, J., Qi, F. & Shi, W. A unique nine-gene comY operon in Streptococcus mutans. Microbiology 151, 157–166 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Underhill, S. A. M. et al. Intracellular signaling by the comRS system in Streptococcus mutans genetic competence. mSphere 3, e00444-18 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Dufour, D., Cordova, M., Cvitkovitch, D. G. & Lévesque, C. M. Regulation of the competence pathway as a novel role associated with a streptococcal bacteriocin. J. Bacteriol. 193, 6552–6559 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Hossain, M. S. & Biswas, I. Mutacins from Streptococcus mutans UA159 are active against multiple streptococcal species. Appl. Environ. Microbiol. 77, 2428–2434 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Merritt, J. & Qi, F. The mutacins of Streptococcus mutans: regulation and ecology. Mol. Oral. Microbiol 27, 57–69 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Son, M., Shields, R. C., Ahn, S. J., Burne, R. A. & Hagen, S. J. Bidirectional signaling in the competence regulatory pathway of Streptococcus mutans. FEMS Microbiol. Lett. 362, fnv159 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    36.Reck, M., Tomasch, J. & Wagner-Döbler, I. The alternative sigma factor SigX controls bacteriocin synthesis and competence, the two quorum sensing regulated traits in Streptococcus mutans. PLoS Genet. 11, e1005353 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    37.Perry, J. A., Cvitkovitch, D. G. & Lévesque, C. M. Cell death in Streptococcus mutans biofilms: a link between CSP and extracellular DNA. FEMS Microbiol. Lett. 299, 261–266 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Wenderska, I. B. et al. A novel function for the competence inducing peptide, XIP, as a cell death effector of Streptococcus mutans. FEMS Microbiol. Lett. 336, 104–112 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Perry, D. & Kuramitsu, H. K. Genetic transformation of Streptococcus mutans. Infect. Immun. 32, 1295–1297 (1981).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Desai, K., Mashburn-Warren, L., Federle, M. J. & Morrison, D. A. Development of competence for genetic transformation of Streptococcus mutans in a chemically defined medium. J. Bacteriol. 194, 3774–3780 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Khan, R. et al. Extracellular identification of a processed type II ComR/ComS pheromone of Streptococcus mutans. J. Bacteriol. 194, 3781–3788 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Khan, R. et al. A positive feedback loop mediated by Sigma X enhances expression of the streptococcal regulator ComR. Sci. Rep. 7, 5984 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    43.Nakano, K. et al. Streptococcus mutans clonal variation revealed by multilocus sequence typing. J. Clin. Microbiol. 45, 2616–2625 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Mukherjee, S. & Bassler, B. L. Bacterial quorum sensing in complex and dynamically changing environments. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-019-0186-5 (2019).45.Visch, L. L., Gravenmade, E. J., Schaub, R. M., Van Putten, W. L. & Vissink, A. A double-blind crossover trial of CMC- and mucin-containing saliva substitutes. Int. J. Oral Max. Surg. 15, 395–400 (1986).CAS 
    Article 

    Google Scholar 
    46.Silverman, H. S. et al. In vivo glycosylation of mucin tandem repeats. Glycobiology 11, 459–471 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Zalewska, A., Zwierz, K., Zółkowski, K. & Gindzieński, A. Structure and biosynthesis of human salivary mucins. Acta Biochim. Pol. 47, 1067–1079 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Wheeler, K. M. et al. Mucin glycans attenuate the virulence of Pseudomonas aeruginosa in infection. Nat. Microbiol. 4, 2146–2154 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    49.Werlang, C., Cárcarmo-Oyarce, G. & Ribbeck, K. Engineering mucus to study and influence the microbiome. Nat. Rev. Mater. https://doi.org/10.1038/s41578-018-0079-7 (2019).50.Wang, B. X. et al. Mucin glycans signal through the sensor kinase RetS to inhibit virulence-associated traits in Pseudomonas aeruginosa. Curr. Biol. 31, 90–102 (2021).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.Huang, Y., Mechref, Y. & Novotny, M. V. Microscale nonreductive release of O-Linked glycans for subsequent analysis through MALDI mass spectrometry and capillary electrophoresis. Anal. Chem. 73, 6063–6069 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    52.Khan, R. et al. Comprehensive transcriptome profiles of Streptococcus mutans UA159 map core streptococcal competence genes. mSystems 1, e00038 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Rayment, S. A., Liu, B., Offner, G. D., Oppenheim, F. G. & Troxler, R. F. Immunoquantification of human salivary mucins MG1 and MG2 in stimulated whole saliva: factors influencing mucin levels. J. Dent. Res. 79, 1765–1772 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    54.Son, M., Ahn, S.-J., Guo, Q., Burne, R. A. & Hagen, S. J. Microfluidic study of competence regulation in Streptococcus mutans: environmental inputs modulate bimodal and unimodal expression of comX. Mol. Microbiol. 86, 258–272 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Ricomini Filho, A. P., Khan, R., Åmdal, H. A. & Petersen, F. C. Conserved pheromone production, response and degradation by Streptococcus mutans. Front. Microbiol. 10, 2140 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Hagen, S. J. & Son, M. Origins of heterogeneity in Streptococcus mutans competence: interpreting an environment-sensitive signaling pathway. Phys. Biol. 14, 015001 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    57.Hillman, J. D., Mo, J., McDonell, E., Cvitkovitch, D. & Hillman, C. H. Modification of an effector strain for replacement therapy of dental caries to enable clinical safety trials. J. Appl. Microbiol. 102, 1209–1219 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    58.Singla, D., Sharma, A., Sachdev, V. & Chopra, R. Distribution of Streptococcus mutans and Streptococcus sobrinus in dental plaque of indian pre-school children using PCR and SB-20M agar medium. J. Clin. Diagn. Res. 10, ZC60–ZC63 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    59.Rodriguez, A. M. et al. Physiological and molecular characterization of genetic competence in Streptococcus sanguinis. Mol. Oral Microbiol. 26, 99–116 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Darch, S. E. et al. Spatial determinants of quorum signaling in a Pseudomonas aeruginosa infection model. Proc. Natl Acad. Sci. USA 115, 4779–4784 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Wu, C. et al. Regulation of ciaXRH operon expression and identification of the CiaR regulon in Streptococcus mutans. J. Bacteriol. 192, 4669–4679 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Qi, F., Merritt, J., Lux, R. & Shi, W. Inactivation of the ciaH gene in Streptococcus mutans diminishes mutacin production and competence development, alters sucrose-dependent biofilm formation, and reduces stress tolerance. Infect. Immun. 72, 4895–4899 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Biswas, S. & Biswas, I. Role of HtrA in surface protein expression and biofilm formation by Streptococcus mutans. Infect. Immun. 73, 6923–6934 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Senadheera, M. D. et al. A VicRK signal transduction system in Streptococcus mutans affects gtfBCD, gbpB, and ftf expression, biofilm formation, and genetic competence development. J. Bacteriol. 187, 4064–4076 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Domenech, A. et al. Proton motive force disruptors block bacterial competence and horizontal gene transfer. Cell Host Microbe 27, 544–555 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Merritt, J., Zheng, L., Shi, W. & Qi, F. Genetic characterization of the hdrRM operon: a novel high-cell-density-responsive regulator in Streptococcus mutans. Microbiology 153, 2765–2773 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Okinaga, T., Niu, G., Xie, Z., Qi, F. & Merritt, J. The hdrRM operon of Streptococcus mutans encodes a novel regulatory system for coordinated competence development and bacteriocin production. J. Bacteriol. 192, 1844–1852 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Alves, L. A. et al. PepO is a target of the two-component systems VicRK and CovR required for systemic virulence of Streptococcus mutans. Virulence 11, 521–536 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Underhill, S. A. M., Shields, R. C., Burne, R. A. & Hagen, S. J. Carbohydrate and PepO control bimodality in competence development by Streptococcus mutans. Mol. Microbiol. 112, 1388–1402 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Kaspar, J. R., Lee, K., Richard, B., Walker, A. R. & Burne, R. A. Direct interactions with commensal streptococci modify intercellular communication behaviors of Streptococcus mutans. ISME J. https://doi.org/10.1038/s41396-020-00789-7 (2020).71.Idone, V. et al. Effect of an orphan response regulator on Streptococcus mutans sucrose-dependent adherence and cariogenesis. Infect. Immun. 71, 4351–4360 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Nagasawa, R., Sato, T. & Senpuku, H. Raffinose induces biofilm formation by Streptococcus mutans in low concentrations of sucrose by increasing production of extracellular DNA and fructan. Appl. Environ. Microbiol. 83, e00869 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Suzuki, Y., Nagasawa, R. & Senpuku, H. Inhibiting effects of fructanase on competence-stimulating peptide-dependent quorum sensing system in Streptococcus mutans. J. Infect. Chemother. 23, 634–641 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Yoshida, A., Ansai, T., Takehara, T. & Kuramitsu, H. K. LuxS-based signaling affects Streptococcus mutans biofilm formation. Appl. Environ. Microbiol. 71, 2372–2380 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    75.Son, M., Ghoreishi, D., Ahn, S.-J., Burne, R. A. & Hagen, S. J. Sharply tuned pH response of genetic competence regulation in Streptococcus mutans: a microfluidic study of the environmental sensitivity of comX. Appl. Environ. Microbiol. 81, 5622–5631 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    76.Nielsen, S. S. in Food Analysis Laboratory Manual 137–141 (Springer, 2017).77.Aoki, K. et al. The diversity of O-linked glycans expressed during Drosophila melanogaster development reflects stage- and tissue-specific requirements for cell signaling. J. Biol. Chem. 283, 30385–30400 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Kumagai, T., Katoh, T., Nix, D. B., Tiemeyer, M. & Aoki, K. In-gel β-elimination and aqueous-organic partition for improved O- and sulfoglycomics. Anal. Chem. 85, 8692–8699 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    79.Anumula, K. R. & Taylor, P. B. A comprehensive procedure for preparation of partially methylated alditol acetates from glycoprotein carbohydrates. Anal. Biochem. 203, 101–108 (1992).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Liu, Y. et al. The minimum information required for a glycomics experiment (MIRAGE) project: improving the standards for reporting glycan microarray-based data. Glycobiology 27, 280–284 (2017).CAS 
    PubMed 

    Google Scholar 
    81.Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 44, D67–D72 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    82.O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).Article 
    CAS 

    Google Scholar 
    83.Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    84.Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    85.Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    86.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).CAS 
    Article 

    Google Scholar 
    87.Thissen, D., Steinberg, L. & Kuang, D. Quick and easy implementation of the Benjamini–Hochberg procedure for controlling the false positive rate in multiple comparisons. J. Educ. Behav. Stat. 27, 77–83 (2002).Article 

    Google Scholar 
    88.Aymanns, S., Mauerer, S., Zandbergen, G., Wolz, C. & Spellerberg, B. High-level fluorescence labeling of Gram-positive pathogens. PLoS ONE 6, e19822 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Takehara, S., Yanagishita, M., Podyma-Inoue, K. A. & Kawaguchi, Y. Degradation of MUC7 and MUC5B in human saliva. PLoS ONE 8, e69059 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Author Correction: Short-range interactions govern the dynamics and functions of microbial communities

    AffiliationsDepartment of Environmental Systems Science, ETH Zurich, Zurich, SwitzerlandAlma Dal Co, Simon van Vliet, Daniel Johannes Kiviet, Susan Schlegel & Martin AckermannDepartment of Environmental Microbiology, Eawag, Duebendorf, SwitzerlandAlma Dal Co, Simon van Vliet, Daniel Johannes Kiviet, Susan Schlegel & Martin AckermannDepartment of Zoology, University of British Columbia, British Columbia, Vancouver, CanadaSimon van VlietAuthorsAlma Dal CoSimon van VlietDaniel Johannes KivietSusan SchlegelMartin AckermannCorresponding authorCorrespondence to
    Alma Dal Co. More

  • in

    Ocean protection needs a spirit of compromise

    Coral reef shoals in the south Pacific, part of which is a marine protected area.Credit: Pete Niesen/Alamy

    After a year of pandemic-induced delays, 2021 is set to be a big year for biodiversity, climate and the ocean. Later this year, world leaders are expected to gather for meetings of the United Nations conventions on biological diversity and climate to set future agendas. Ocean policies will be a priority for both.Momentum is building for what is called the 30 × 30 campaign — a goal to protect 30% of the planet (both land and sea) by 2030. Last December, the 30% ocean goal was backed by the High Level Panel for a Sustainable Ocean Economy, which comprises the heads of state of 14 coastal nations, including some of the largest countries, such as Indonesia, and the smallest, like Palau. This is an important step.But this target is ambitious. At present, 15% of terrestrial surfaces are classed as protected, and only about 7% of the oceans have been designated or proposed as marine protected areas — so named because, within them, fishing and other industrial activities are prohibited or restricted. Just 2.6% of the oceans are either fully or highly protected. Although these numbers have been improving, they are behind schedule — a previous global target was to protect 17% of land and 10% of the oceans by 2020.Achieving the ocean’s full potential for helping humanity will require genuinely sustainable fishing practices, investments in renewable technologies such as offshore wind farms, and zero-emissions shipping. Carbon-hungry seagrasses and mangroves must also be restored. But efforts to achieve these goals inevitably create conflicts, because governments, the conservation community and industry tend to have different priorities. Such disagreements are impeding progress.
    Read the paper: Protecting the global ocean for biodiversity, food and climate
    Research published in Nature this week could help to resolve some of these tensions when establishing protected areas. Conservationist and National Geographic explorer-in-residence Enric Sala and his colleagues present a model showing how the ocean could be protected in a way that optimizes both environmental and fishing-industry benefits1. This model needs to be studied carefully as talks progress, because it could help nations to see where compromises are possible.The researchers assessed data on the distribution of ocean biodiversity (taking in 4,242 species); 1,150 commercially exploited seafood stocks; and carbon in marine sediments. They used these data to model the spaces where marine protected areas could be situated to achieve particular outcomes across three main goals. For example, a plan that protects 71% of the ocean could yield 91% of the maximum biodiversity benefits and 48% of the carbon benefits, but with no change to existing fisheries catches. In another scenario, 28% of the ocean could be protected to obtain a maximum increase in seafood catches while securing 35% of the maximum biodiversity benefits and 27% of the maximum carbon benefits.The model makes it clear that achieving the best outcome on all three goals will require give and take. Nations and stakeholder groups will need to weigh up each goal. That will be hard, but necessary; some countries will have to give a little of their profitable fisheries, for example. And under this model, nations will need to commit to reducing bottom trawling, a fishing practice that stirs up carbon-rich sediments on the sea floor, potentially releasing that carbon. According to one estimate2, the impact of this process on the ocean’s carbon-storage capacity is greater than that of other problems that receive more attention, such as the loss of biological carbon storage when mangroves are cleared.
    Read the paper: Enabling conditions for an equitable and sustainable blue economy
    Countries must also pay attention to equity and access, and ensure that decisions to create protected areas are made in consultation with affected and often vulnerable communities. December’s high-level panel report estimates that the economic opportunities provided by marine genetic resources, ecotourism, fisheries, renewable energy and carbon credits could reel in a net benefit of US$15.5 trillion by 2050. But, as Andrés Cisneros-Montemayor at the University of British Columbia in Vancouver, Canada, and his colleagues point out in this issue3, many coastal nations lack access to the infrastructure or governance needed to promote what is called a ‘sustainable blue economy’. As might be expected, some nations aren’t equipped to ensure that, say, their local fish stocks are protected from being used in farm feed; or that construction of new ports doesn’t unreasonably affect local communities or ecosystems.At present, most of the ocean economy isn’t exactly blue. A study of the 100 largest companies in the ocean economy (which together account for 60% of around US$2 trillion in annual revenue) showed that the majority profit from oil and gas. Even Norway, which co-chaired the high-level panel, recently announced 61 new offshore oil and gas licences, as well as its intention to grant sea-bed mining licences as early as 2023. Such moves are disappointing. Green groups and researchers must continue to put pressure on countries to live up to their promises.World leaders at the upcoming biodiversity and climate meetings have a big task. Expanding the blue economy is difficult given the economic consequences of protecting more of the ocean. But there is now not only momentum in this direction, but also research to show that it can be done. If humanity looks after the ocean, it will look after us. More

  • in

    Social networks strongly predict the gut microbiota of wild mice

    1.Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Monachese M, Burton JP, Reid G. Bioremediation and tolerance of humans to heavy metals through microbial processes: a potential role for probiotics?. Appl Environ Microbiol. 2012;78:6397–404.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Chevalier C, Stojanovi O, Colin DJ, Suarez-Zamorano N, Tarallo V, Veyrat-Durebex C, et al. Gut microbiota orchestrates energy homeostasis during cold. Cell. 2015;163:1360–74.CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Kamada N, Kim Y-G, Sham HP, Vallance BA, Puente JL, Martens EC, et al. Regulated virulence controls the ability of a pathogen to compete with the gut microbiota. Science. 2012;336:1325–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Zhang N, He Q-S. Commensal microbiome promotes resistance to local and systemic infections. Chin Med J. 2015;128:2250–5.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Hooper LV, Littman DR, Macpherson AJ. Interactions between the microbiota and the immune system. Science. 2012;336:1268–73.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Honda K, Littman DR. The microbiota in adaptive immune homeostasis and disease. Nature 2016;535:75–84.CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Thaiss CA, Zmora N, Levy M, Elinav E. The microbiome and innate immunity. Nature. 2016;535:65–74.CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Hanski I, von Hertzen L, Fyhrquist N, Koskinen K, Torppa K, Laatikainen T, et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc Natl Acad Sci USA. 2012;109:8334–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    10.De Luca F, Shoenfeld Y. The microbiome in autoimmune diseases. Clin Exp Immuno l. 2019;195:74–85.Article 
    CAS 

    Google Scholar 
    11.Costello EK, Stagaman K, Dethlefsen L, Bohannan BJM, Relman DA. The application of ecological theory toward an understanding of the human microbiome. Science. 2012;336:1255–62.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci USA. 2010;107:11971–5.PubMed 
    Article 

    Google Scholar 
    13.Ferretti P, Pasolli E, Tett A, Asnicar F, Gorfer V, Fedi S, et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe. 2018;24:133–45.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Lane AA, McGuire MK, McGuire MA, Williams JE, Lackey KA, Hagen EH, et al. Household composition and the infant fecal microbiome: the INSPIRE study. Am J Phys Anthropol. 2019;169:526–39.PubMed 
    Article 

    Google Scholar 
    15.Lehtimäki J, Karkman A, Laatikainen T, Paalanen L, von Hertzen L, Haahtela T, et al. Patterns in the skin microbiota differ in children and teenagers between rural and urban environments. Sci Rep. 2017;7:45651.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    16.Tamburini S, Shen N, Wu H, medicine JC-N. The microbiome in early life: implications for health outcomes. Nat Med. 2016;22:713–22.CAS 
    Article 

    Google Scholar 
    17.Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med. 2009;1:6ra14.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    18.David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–63.CAS 
    PubMed 
    Article 

    Google Scholar 
    19.Ottman N, Ruokolainen L, Suomalainen A, Sinkko H, Karisola P, Lehtimäki J, et al. Soil exposure modifies the gut microbiota and supports immune tolerance in a mouse model. J Allergy Clin Immunol. 2019;143:1198–206.CAS 
    PubMed 
    Article 

    Google Scholar 
    20.Grieneisen LE, Charpentier MJE, Alberts SC, Blekhman R, Bradburd G, Tung J, et al. Genes, geology and germs: gut microbiota across a primate hybrid zone are explained by site soil properties, not host species. Proc R Soc B Biol Sci. 2019;286:20190431.Article 

    Google Scholar 
    21.Sarkar A, Harty S, Johnson KV-A, Moeller AH, Archie EA, Schell LD, et al. Microbial transmission in animal social networks and the social microbiome. Nat Ecol Evol. 2020;4:1020–35.PubMed 
    Article 

    Google Scholar 
    22.Moeller AH, Suzuki TA, Phifer-Rixey M, Nachman MW. Transmission modes of the mammalian gut microbiota. Science. 2018;362:453–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Hufeldt MR, Nielsen DS, Vogensen FK, Midtvedt T, Hansen AK. Variation in the gut microbiota of laboratory mice is related to both genetic and environmental factors. Comp Med. 2010;60:336–47.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Hildebrand F, Nguyen TLA, Brinkman B, Yunta R, Cauwe B, Vandenabeele P, et al. Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice. Genome Biol. 2013;14:R4.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Lees H, Swann J, Poucher SM, Nicholson JK, Holmes E, Wilson ID, et al. Age and microenvironment outweigh genetic influence on the Zucker rat microbiome. PloS One. 2014;9:e100916.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    26.Tung J, Barreiro LB, Burns MB, Grenier JC, Lynch J, Grieneisen LE, et al. Social networks predict gut microbiome composition in wild baboons. Elife. 2015;4:e05224.PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    27.Raulo A, Ruokolainen L, Lane A, Amato K, Knight R, Leigh S, et al. Social behaviour and gut microbiota in red-bellied lemurs (Eulemur rubriventer): In search of the role of immunity in the evolution of sociality. J Anim Ecol. 2018;87:388–99.PubMed 
    Article 

    Google Scholar 
    28.Perofsky AC, Lewis RJ, Abondano LA, Di Fiore A, Meyers LA. Hierarchical social networks shape gut microbial composition in wild Verreaux’s sifaka. Proc Biol Sci. 2017;284:20172274.PubMed 
    PubMed Central 

    Google Scholar 
    29.Moeller AH, Foerster S, Wilson ML, Pusey AE, Hahn BH, Ochman H. Social behavior shapes the chimpanzee pan-microbiome. Sci Adv. 2016;2:e1500997.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    30.Wikberg EC, Christie D, Sicotte P, Ting N. Interactions between social groups of colobus monkeys (Colobus vellerosus) explain similarities in their gut microbiomes. Anim Behav. 2020;163:17–31.Article 

    Google Scholar 
    31.Bennett G, Malone M, Sauther ML, Cuozzo FP, White B, Nelson KE, et al. Host age, social group, and habitat type influence the gut microbiota of wild ring-tailed lemurs (Lemur catta). Am J Primatol. 2016;78:883–92.CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Theis KR, Schmidt TM, Holekamp KE. Evidence for a bacterial mechanism for group-specific social odors among hyenas. Sci Rep. 2012;2:615.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    33.Leclaire S, Nielsen JF, Drea CM. Bacterial communities in meerkat anal scent secretions vary with host sex, age, and group membership. Behav Ecol. 2014;25:996–1004.Article 

    Google Scholar 
    34.Antwis RE, Lea JMD, Unwin B, Shultz S. Gut microbiome composition is associated with spatial structuring and social interactions in semi-feral Welsh Mountain ponies. Microbiome. 2018;6:207.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Song SJ, Lauber C, Costello EK, Lozupone CA, Humphrey G, Berg-Lyons D, et al. Cohabiting family members share microbiota with one another and with their dogs. Elife. 2013;2:e00458.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Grieneisen LE, Livermore J, Alberts S, Tung J, Archie EA. Group living and male dispersal predict the core gut microbiome in wild baboons. Integr Comp Biol. 2017;57:770–85.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Dill-McFarland KA, Tang Z-Z, Kemis JH, Kerby RL, Chen G, Palloni A, et al. Close social relationships correlate with human gut microbiota composition. Sci Rep. 2019;9:703.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    38.Wilson EO. Elementary concepts in sociobiology. In: Wilson EO, editors. Sociobiology: The New Synthesis. 25th ed. Cambridge, Massachutes, USA: Harvard University Press; 2000. p. 8.
    Google Scholar 
    39.Godsall B. Mechanisms of space use in the wood mouse, Apodemus sylvaticus. Doctoral Thesis, London: Imperial College; 2015.40.Wolton RJ. The ranging and nesting behaviour of Wood mice, Apodemus sylvaticus (Rodentia: Muridae), as revealed by radio-tracking. J Zool. 2009;206:203–22.Article 

    Google Scholar 
    41.Stopka P, Macdonald DW. The market effect in the Wood mouse, Apodemus sylvaticus: selling information on reproductive status. Ethology. 2001;105:969–82.42.Walton JB, Andrews JF. Torpor induced by food deprivation in the Wood mouse Apodemus sylvaticus. J Zool. 2009;194:260–3.Article 

    Google Scholar 
    43.Wolton RJ. A possible role for faeces in range-marking by the Wood mouse, Apodemus sylvaticus. J Zool. 2009;206:286–91.Article 

    Google Scholar 
    44.Wolton RJ. Individual recognition by olfaction in the Wood Mouse, Apodemus sylvaticus. Behaviour. 1984;88:191–9.Article 

    Google Scholar 
    45.Godsall B, Coulson T, Malo AF. From physiology to space use: energy reserves and androgenization explain home-range size variation in a woodland rodent. J Anim Ecol. 2014;83:126–35.PubMed 
    Article 

    Google Scholar 
    46.Wang J, Santure AW. Parentage and sibship inference from multilocus genotype data under polygamy. Genetics. 2009;181:1579–94.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Farine DR. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol Evol. 2013;4:1187–94.Article 

    Google Scholar 
    48.Csardi G, Nepusz T. The igraph software package for complex network research. Inter J Complex Syst. 2006;1695:1–9.
    Google Scholar 
    49.Firth JA, Sheldon BC. Social carry-over effects underpin trans-seasonally linked structure in a wild bird population. Ecol Lett. 2016;19:1324–32.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.McKnight DT, Huerlimann R, Bower DS, Schwarzkopf L, Alford RA, Zenger KR. Methods for normalizing microbiome data: an ecological perspective. Methods. Ecol Evol. 2019;10:389–400.
    Google Scholar 
    53.Hsieh TC, Ma KH, Chao A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods. Ecol Evol. 2016;7:1451–6.
    Google Scholar 
    54.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MH, Oksanen MJ, et al. The vegan package. Community Ecol Package. 2007;10:719.
    Google Scholar 
    55.Bürkner PC. brms: an R package for Bayesian multilevel models using stan. J Stat Softw. 2017;80:1–28.Article 

    Google Scholar 
    56.Bürkner PC. Advanced Bayesian multilevel modeling with the R package brms. R J. 2018;10:395–411.Article 

    Google Scholar 
    57.Hadfield JD. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw. 2010;33:1–22.Article 

    Google Scholar 
    58.Dekker D, Krackhardt D, Snijders TA. Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika. 2007;72:563–81.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Ormerod KL, Wood DLA, Lachner N, Gellatly SL, Daly JN, Parsons JD, et al. Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals. Microbiome. 2016;4:36.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Wieczorek AS, Schmidt O, Chatzinotas A, von Bergen M, Gorissen A, Kolb S. Ecological functions of agricultural soil bacteria and microeukaryotes in chitin degradation: a case study. Front Microbiol. 2019;10:1293.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Huang X, Liu L, Zhao J, Zhang J, Cai Z. The families Ruminococcaceae, Lachnospiraceae, and Clostridiaceae are the dominant bacterial groups during reductive soil disinfestation with incorporated plant residues. Appl Soil Ecol. 2019; 135:65–72.62.Moeller AH, Caro-Quintero A, Mjungu D, Georgiev AV, Lonsdorf EV, Muller MN, et al. Cospeciation of gut microbiota with hominids. Science. 2016;353:380–2.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Tew TE, Macdonald DW. Dynamics of space use and male vigour amongst wood mice, Apodemus sylvaticus, in the cereal ecosystem. Behav Ecol Sociobiol. 1994;34:337–45.Article 

    Google Scholar 
    64.Erazo D, Pedersen AB, Gallagher K, Fenton A. Who acquires infection from whom? Estimating herpesvirus transmission rates between wild rodent host groups. 2020, https://www.biorxiv.org/content/10.1101/2020.09.18.302489v1.65.Taylor SE, Klein LC, Lewis BP, Gruenewald TL, Gurung RAR, Updegraff JA. Biobehavioral responses to stress in females: tend-and-befriend, not fight-or-flight. Psychol Rev. 2000;107:411–29.CAS 
    PubMed 
    Article 

    Google Scholar 
    66.Amato KR, Van Belle S, Di Fiore A, Estrada A, Stumpf R, White B, et al. Patterns in gut microbiota similarity associated with degree of sociality among sex classes of a neotropical primate. Micro Ecol. 2017;74:250–8.Article 

    Google Scholar 
    67.Brito IL, Gurry T, Zhao S, Huang K, Young SK, Shea TP, et al. Transmission of human-associated microbiota along family and social networks. Nat Microbiol. 2019;4:964–71.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Johnson KV-A. Gut microbiome composition and diversity are related to human personality traits. Hum Microbiome J. 2020;15:100069.Article 

    Google Scholar 
    69.Levin II, Zonana DM, Fosdick BK, Song SJ, Knight R, Safran RJ. Stress response, gut microbial diversity and sexual signals correlate with social interactions. Biol Lett. 2016;12:20160352.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Mouquet N, Loreau M. Community patterns in source‐sink metacommunities. Am Nat. 2003;162:544–57.PubMed 
    Article 

    Google Scholar 
    71.Altizer S, Nunn CL, Thrall PH, Gittleman JL, Antonovics J, Cunningham AA, et al. Social organization and parasite risk in mammals: Integrating theory and empirical studies. Annu Rev Ecol Evol Syst. 2003;34:517–47.Article 

    Google Scholar 
    72.Loehle C. Social barriers to pathogen transmission in wild animal populations. Ecology. 1995;76:326–35.Article 

    Google Scholar 
    73.Moeller A, Dufva R, Allander K. Parasites and the evolution of host social behavior. Adv Study Behav. 1993;22:65–102.Article 

    Google Scholar 
    74.Reese AT, Dunn RR. Drivers of microbiome biodiversity: a review of general rules, feces, and ignorance. MBio. 2018;9:4.Article 

    Google Scholar 
    75.Shade A. Diversity is the question, not the answer. ISME J. 2017;11:1–6.PubMed 
    Article 

    Google Scholar 
    76.Pallen MJ. The human microbiome and host-pathogen interactions. In: Metagenomics of the human body. New York, NY: Springer; 2011; p 43–61.77.Amato KR, Leigh SR, Kent A, Mackie RI, Yeoman CJ, Stumpf RM, et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Micro Ecol. 2014;69:434–43.Article 
    CAS 

    Google Scholar 
    78.Barribeau SM, Villinger J, Waldman B. Ecological immunogenetics of life-history traits in a model amphibian. Biol Lett. 2012;8:405–7.PubMed 
    Article 

    Google Scholar 
    79.Feng T, Elson CO. Adaptive immunity in the host-microbiota dialog. Mucosal Immunol. 2011;4:15–21.CAS 
    PubMed 
    Article 

    Google Scholar 
    80.Amato KR. Incorporating the gut microbiota into models of human and non-human primate ecology and evolution. Am J Phys Anthropol. 2016;159:196–215.Article 

    Google Scholar 
    81.Knowles SCL, Eccles RM, Baltrūnaitė L. Species identity dominates over environment in shaping the microbiota of small mammals. Ecol Lett. 2019;22:826–37.CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Detecting the effects of predator-induced stress on the global metabolism of an ungulate prey using fecal metabolomic fingerprinting

    1.Schmitz, O. J., Krivan, V. & Ovadia, O. Trophic cascades: the primacy of trait-mediated indirect interactions. Ecol. Lett. 7, 153–163 (2004).Article 

    Google Scholar 
    2.Creel, S. & Christianson, D. Relationships between direct predation and risk effects. Trends Ecol. Evol. 23(4), 194–201 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Ritchie, E. G. et al. Ecosystem restoration with teeth: what role for predators?. Trends Ecol. Evol. 27(5), 265–271 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Terborgh, J. & Estes, J. A. Trophic Cascades: Predators, Prey, and the Changing Dynamics of Nature (Island Press, 2010).
    Google Scholar 
    5.Creel, S. & Winnie, J. A. Responses of elk herd size to fine scale spatial and temporal variation in the risk of predation by wolves. Anim. Behav. 69, 1181–1189 (2005).Article 

    Google Scholar 
    6.Fischhoff, I. R., Sundaresan, S. R., Cordingley, J. & Rubenstein, D. I. Habitat use and movements of plains zebra (Equus burchelli) in response to predation danger from lions. Behav. Ecol. 18, 725–729 (2007).Article 

    Google Scholar 
    7.Latombe, G., Fortin, D. & Parrott, L. Spatio-temporal dynamics in the response of woodland caribou and moose to the passage of grey wolves. J. Anim. Ecol. 83, 185–198 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Prugh, L. R. et al. Designing studies of predation risk for improved inference in carnivore-ungulate systems. Biol. Conserv. 232, 194–207 (2019).Article 

    Google Scholar 
    9.Creel, S., Winnie, J. A. & Christianson, D. Glucocorticoid stress hormones and the effect of predation risk on elk reproduction. PNAS 106(30), 12388–12393 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Dulude-de Broin, F., Hamel, S., Mastromonaco, G. F. & Côté, S. D. Predation risk and mountain goat reproduction: evidence for stress-induced breeding suppression in a wild ungulate. Funct. Ecol. 34(5), 1003–1014 (2020).Article 

    Google Scholar 
    11.Moberg, G. P. & Mench, J. A. The Biology of Animal Stress: Basic Principles and Implications for Animal Welfare (CABI Publishing, 2000).
    Google Scholar 
    12.Boonstra, R. The ecology of stress: a marriage of disciplines. Funct. Ecol. 27, 7–10 (2013).Article 

    Google Scholar 
    13.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 
    14.Kelley, K. W. Immunological consequences of changing environmental stimuli. In Animal Stress (ed. Moberg, G. P.) 193–223 (American Physiological Society, Bethesda, 1985).15.Mӧstl, E. & Palme, R. Hormones as indicators of stress. Domest. Anim. Endocrinol. 23, 67–74 (2002).Article 

    Google Scholar 
    16.Ursin, H. & Eriksen, H. R. The cognitive activation theory of stress. Psychoneuroendocrinology 29(5), 567–592 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Lovallo, W. R. Individual differences in reactivity to stress. In Stress and Health. Biological and Psychological Interactions (ed. Lovallo, W. R.) 203–225 (Sage, 2016).18.Patchev, V. K. & Patchev, A. V. Experimental models of stress. Dialogues Clin. Neurosci. 8(4), 417–432 (2006).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Mills, J. L. Scientific Principles of Stress (University of the West Indie Press, 2012).
    Google Scholar 
    20.Henry, J. P. Biological basis of the stress response. Integr. Physiol. Behav. Sci. 27, 66–83 (1992).CAS 
    PubMed 
    Article 

    Google Scholar 
    21.Wu, Y., Patchev, A. V., Daniel, G., Almeida, O. F. X. & Spengler, D. Early-life stress reduces DNA methylation of the Pomc gene in male mice. Endocrinology 155(5), 1751–1762 (2014).PubMed 
    Article 
    CAS 

    Google Scholar 
    22.Novais, A., Monteiro, S., Roque, S., Correia-Neves, M. & Sousa, N. How age, sex and genotype shape the stress response. Neurob. Stress 6, 44–56 (2017).Article 

    Google Scholar 
    23.Romero, L. M. & Gormally, B. M. G. How truly conserved is the “well-conserved” vertebrate stress response?. Integr. Comp. Biol. 59(2), 273–281 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    24.Millspaugh, J. J. & Washburn, B. E. Use of fecal glucocorticoid metabolite measures in conservation biology research: considerations for application and interpretation. Gen. Comp. Endocrinol. 138, 189–199 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    25.Romero, L. M. Physiological stress in ecology: lessons from biomedical research. Trends Ecol. Evol. 19(5), 249–255 (2004).PubMed 
    Article 

    Google Scholar 
    26.Johnstone, C. P., Reina, R. D. & Lill, A. Interpreting indices of physiological stress in free-living vertebrates. J. Comp. Physiol. B 182, 861–879 (2012).PubMed 
    Article 

    Google Scholar 
    27.Mayer, E. A., Knight, R., Mazmanian, S. K., Cryan, J. F. & Tillisch, K. Gut microbes and the brain: paradigm shift in neuroscience. J. Neurosci. 34, 15490–15496 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Sharon, G., Sampson, T. R., Geschwind, D. H. & Mazmanian, S. K. The central nervous system and the gut microbiome. Cell 167, 915–932 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Mohajeri, M. H., La Fata, G., Steinert, R. E. & Weber, P. Relationship between the gut microbiome and brain function. Nutr. Rev. 76, 481–496 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Bravo, J. A., Forsythe, P., Chew, M. V., Escaravage, E. & Savignac, H. M. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. PNAS 108(38), 16050–16055 (2011).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Beauclercq, S. et al. A multiplatform metabolomic approach to characterize fecal signatures of negative postnatal events in chicks: a pilot study. J Anim. Sci. Biotechnol. 10, 21 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Jianguo, L., Xueyang, J., Cui, W., Changxin, W. & Xuemei, Q. Altered gut metabolome contributes to depression-like behaviors in rats exposed to chronic unpredictable mild stress. Transl. Psychiatry 9, 1–14 (2019).Article 
    CAS 

    Google Scholar 
    33.Valerio, A., Casadei, L., Giuliani, A. & Valerio, M. Fecal metabolomics as a novel non-invasive method for short-term stress monitoring in beef cattle. J. Proteome Res. 19(2), 845–853 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. Science 336, 1262–1267 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    35.Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. Metabolomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153–161 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Lindon, J. C., Nicholson, J. K. & Holmes, E. The Handbook of Metabonomics and Metabolomics (Elsevier, 2007).
    Google Scholar 
    37.Matysik, S., Le Roy, C. I., Liebisch, G. & Claus, S. P. Metabolomics of fecal samples: a practical consideration. Trends Food Sci. Technol. 57, 244–255 (2016).CAS 
    Article 

    Google Scholar 
    38.Nicholson, J. K. & Lindon, J. C. Metabonomics. Nature 455, 1054–1056 (2008).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    39.Viant, M. R. Environmental metabolomics using 1H-NMR spectroscopy. Methods Mol. Biol. 410, 137–150 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    40.Ellis, D. I., Dunn, W. B., Griffin, J. L., Allwood, J. W. & Goodacre, R. Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics 8(9), 1243–1266 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Worley, B. & Powers, R. Multivariate analysis in metabolomics. Curr. Metabolomics 1(1), 92–107 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Rivas-Ubach, A. et al. Ecometabolomics: optimized NMR-based method. Methods Ecol. Evol. 4(5), 464–473 (2013).Article 

    Google Scholar 
    43.Chen, M. X., Wang, S. Y., Kuo, C. H. & Tsai, I. L. Metabolome analysis for investigating host-gut microbiota interactions. JFMA 118(1), S10–S22 (2019).
    Google Scholar 
    44.Emwas, A. H. M. The Strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. In Metabonomics. Methods in Molecular Biology (ed. Bjerrum, J. T.) 1277, 161–193 (Human Press, 2015).45.Emwas, A. H. M. et al. NMR spectroscopy for metabolomics research. Metabolites 9(7), 123 (2019).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    46.Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153–161 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Wiles, G. J., Allen, H. L. & Hayes, G. E. Wolf Conservation and Management Plan for Washington (Washington Department of Fish and Wildlife, 2011).
    Google Scholar 
    48.Schmitz, O. J. & Trussell, G. C. Multiple stressors, state-dependence and predation risk-foraging trade-offs: toward a modern concept of trait-mediated indirect effects in communities and ecosystems. Curr. Opin. Behav. 12, 6–11 (2016).Article 

    Google Scholar 
    49.Brown, J. A. Mortality of Range Livestock in Wolf-Occupied Areas of Washington. Thesis. Washington State University, Pullman, WA, USA (2015).50.Fieberg, J. & Kochanny, C. O. Quantification of home range overlap: the importance of the utilization distribution. J. Wildl. Manag. 69, 1346–1359 (2005).Article 

    Google Scholar 
    51.Robert, K., Garant, D. & Pelletier, F. Keep in touch: does spatial overlap correlate with contact rate frequency?. J. Wildl. Manag. 76(8), 1670–1675 (2012).Article 

    Google Scholar 
    52.Angel, S. P. et al. Climate change and cattle production: impact and adaptation. J. Vet. Med. Res. 5(4), 1134 (2018).
    Google Scholar 
    53.Brosh, A. et al. Energy cost of cows’ grazing activity: use of the heart rate method and the global positioning system for direct field estimation. J. Anim. Sci. 84, 1951–1967 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Provenza, F. D. Postingestive feed-back as an elemental determinant of food preference and intake in ruminants. J. Range Manag. 48, 2–17 (1995).Article 

    Google Scholar 
    55.Provenza, F. D. Acquired aversions as the basis for varied diets of ruminants foraging on rangelands. J. Anim. Sci. 74, 2010–2020 (1996).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Howery, L. D., Provenza, F. D., Ruyle, G. B. & Jordan, N. C. How do animals learn if rangeland plants are toxic or nutritious?. Rangelands 20, 4–9 (1998).
    Google Scholar 
    57.Davitt, B. B. & Nelson, J. R. Methodology for the determination of DAPA in feces of large ruminants. In Proceedings of the Western States and Provinces Elk Workshop (ed. Nelson, R.W.) 133–147 (Edmonton, 1984).58.Church, D. C. Digestive Physiology and Nutrition of Ruminants I (Oxford Press, 1969).
    Google Scholar 
    59.Sato, S. Leadership during actual grazing in a small herd of cattle. Appl. Anim. Ethol. 8, 53–65 (1982).Article 

    Google Scholar 
    60.Frair, J. L. et al. Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data. Philos. Trans. R. Soc. B 365, 2187–2200 (2010).Article 

    Google Scholar 
    61.Deda, O., Gika, H. G., Wilson, I. D. & Theodoridis, G. A. An overview of fecal preparation for global metabolic profiling. J. Pharm. Biomed. 113, 137–150 (2015).CAS 
    Article 

    Google Scholar 
    62.Landakadurai, B. P., Nagato, E. G. & Simpson, M. J. Environmental metabolomics: an emerging approach to study organism responses to environmental stressors. Environ. Rev. 21, 180–205 (2013).Article 
    CAS 

    Google Scholar 
    63.Wiklund, S. et al. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal. Chem. 80, 115–122 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    64.Wishart, D. S. et al. HMDB: a knowledgebase for the human metabolome. Nucl. Acids Res. 37, D603–D610 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    65.Frair, J. L. et al. Scale of movement by elk (Cervus elaphus) in response to heterogeneity in forage resources and predation risk. Landsc. Ecol. 20, 273–287 (2005).Article 

    Google Scholar 
    66.Valerio, A. Stress-Mediated and Habitat-Mediated Risk Effects of Free-Ranging Cattle in Washington. Dissertation. Washington State University, Pullman, WA (2019).67.Winnie, J. & Creel, S. Sex-specific behavioral responses of elk to spatial and temporal variation in the threat of wolf predation. Anim. Behav. 73, 215–225 (2007).Article 

    Google Scholar 
    68.Bundy, J. G., Davey, M. P. & Viant, M. R. Environmental metabolomics: a critical review and future perspectives. Metabolomics 5, 3–21 (2009).CAS 
    Article 

    Google Scholar  More