Philippa Kaur

From the phytosociological relevés performed in each sampling area it is evident that hives were positioned in grasslands rich in Alpine herbaceous species (Table S1). In fact, among the 169 identified species, 85% were herbaceous species common in meadows (of Arrhenatherion elatioris and Triseto flavescentis-Polygonion bistortae phytosociological alliance) and acidophilus pastures (Siversio-Nardetum). 15% of the species were trees and shrubs (not abundant in the floristic relevés of the apiary areas considered), including some of beekeeping interest such as: Rhododendron ferrugineum, Castanea sativa and Rubus idaeus. From the MDS biplot (Fig. 2) elevation is the main ecological variable that differentiates sampling areas. In particular, the relevés of stations B and F are characterized by a floristic composition which is different from the areas at higher elevation (characterized by a higher presence of microthermal alpine species). This is due to the separation between the sub-montane belt and the high mountain belt vegetation on the 1.300 m a.s.l. line in the study area25.Figure 2MDS of the phytosociological relevés. Capital letters indicate the six sampling areas, the 1.300 m a.s.l. contour line that separates sub-montane belt and high mountain belt vegetation is highlighted in red.Full size imageAlthough the beehives were positioned in mountain grasslands, melissopalynological analysis presented a different picture. The pollen of numerous species detected through the floristic relevés were found in the honey samples via melissopalynological analysis, although the latter did not totally overlap with the floristic characterization of the area, in particular from a “quantitative” point of view. In fact, the floristic relevés showed a relative richness of herbaceous species (Table S1) peculiar of mountain grasslands that would seem promising for the production of wildflower honeys. Conversely, in the melissopalynological analysis the species considered interesting but not predominant in the botanical description were relevant (Fig. 3 and Table S2).Figure 3MDS of the melissopalynological analysis of the six samples (dots) of mountain wildflower honeys produced in the stations considered. The crosses are the pollens found in the honey samples, the most important are indicated.Full size imageThe premises to produce wildflower honey is that the botanical species contributing must be different and sometimes very numerous, without any of them assuming a dominant character. However, this was not fully evident in our research: although it was possible to identify more than seventy species through melissopalynological analysis and even more through the floristic characterization of the areas, most of them were defined as minor or sporadic pollen (Table S2). Even though apiaries were in mountain grasslands, the most relevant role was played by some woody species/shrubs: Rubus (presumably Rubus idaeous L., identified in the floristic relevés) and rhododendron (Rhododendron ferrugineum L.) for the mountain/subalpine belt and Castanea and Ericaceae (heather) in the submountain belt. Following the rules to define ‘‘unifloral honey’’, three of the wildflower honeys could be defined unifloral or bifloral:

Rhododendron unifloral: honey A (Rhododendron 47.18%), and honey C (Rhododendron 62.93%);

Raspberry unifloral: honey B (Rubus 67.12%)

Raspberry and Rhododendron bifloral: honey D (Rhododendron 34.27% and Rubus 34.74%) as well as honey E (Rubus 44.25%, Rhododendron 34.14%).

Honey F, due to the contribution of pollen from Tilia genus (that was detected only in this sample as an important sporadic pollen, 3.5%) Castanea (96.4% in honey F, but it should be noted that chestnut pollen is an overrepresented pollen) and in the second count Ericaceae (32.45%, that was considered a secondary pollen together with Rubus, with a percentage of 38.59% in honey F) differed from the other honeys (Fig. 3).Rubus pollen was anyway present in good amounts in all the samples considered, and was a dominant pollen in honey B, a secondary pollen in honeys C, D, E and F and a minor pollen in honey A. Sorbus and Tilia pollens were detected only in honey F, while no rhododendron was detected in honey F. Honey D was characterized by a percentage higher than the “rare pollen” category of some important alpine essences, such as Liliaceae, Centaurea, Campanulaceae, Anthyllis f., Polygonum bistorta, Lotus alpinus and Potentilla/fragaria (Table S2).Although wildflower honeys are intrinsically characterized by a high variability compared with unifloral honey, this shows the importance of the formal characterization of honey to obtain a product which satisfies consumer expectations, and it was demonstrated that the botanical origin of honey cannot be based on the claims of local beekeepers by considering the predominant flowers surrounding the hive.Although honeybees are considered supergeneralists in their foraging choices, there are certain key species or plant groups that are particularly important in honeybee foraging2, and many were identified in the botanical characterization of the area, including Rubus idaeus L., Calluna vulgaris L., rhododendron and some present in the broad-leaved woods mentioned such as chestnut (Castanea sativa Mill.) or plants of Tilia genus. In the research work by Hawkins et al.2, Rubus fruticosus L. was among the frequently found species and tree pollen belonging to Castanea sativa L. as well as, for example, species of Malus, Salix and Quercus spp, was frequently seen. These kinds of preferences could relate to the ease of availability and abundance of the plant, the quality and abundance of the nectar and pollen and/or specific nutrients or trace elements provided by these species or neurological aspects (as will be discussed further). As referred by beekeepers, over the last decades the production of mountain wildflower honey, that often does not meet the characteristics expected and presents flavours that are reminiscent of other kinds of honey such as rhododendron or linden or chestnut, is becoming more and more critical and this was absolutely confirmed by this study.This could be linked to the fragmentation of an important habitat of the Alps—mountain grasslands (meaning pastures and meadows) for anthropic and climatic reasons8,9. Honeybees from the same colony forage across areas spanning up to several hundred square kilometres, and at linear distances as far as 9 km from the hive41. Onlooker bees are those in charge of finding nectar sources and of giving instructions to the employed bees, the other foraging bees, that communicate the necessity to look for new resources of food to the onlookers through continuous dance communication42. Among the onlookers, there is a difference between the bees that scout for different nectar sources or recruit to well known floral resources43 and there is an optimal ratio of scouts to recruits, for the most effective collective foraging41. However, this balance may change based on the structure of the landscape in which the bees forage for food44,45,46. Theoretical models47,48 and empirical tests49 suggest that when resources are concentrated into a small number of highly rewarding patches, colonies perform best with few scouts and many recruits, while when resource patches are small, evenly distributed, and easy to locate, successful colonies invest more in scouting than in recruitment. This is strictly linked to climate and social changes in the mountains: mountain grasslands are no longer evenly distributed and easily localizable, as they are scattered among expanding areas of shrublands and forests9 and, for the above-mentioned reasons, it is more efficient for the colony to invest in more recruiters than scouters, as recruiters will identify a small number of highly rewarding patches, such as raspberry or rhododendron shrublands or linden and chestnut woods, that are highly rewarding and very different in quality.This overlaps with individual and collective honeybee behaviour driven by proximate physiological mechanisms that involve the tryptophan metabolism via kynurenine pathway that is one of main neuroprotective mechanisms. In this research, many of the differences/similarities among the samples might be attributed to metabolic alterations within this pathway, represented by relative amounts of kynurenic acid. However, different quinoline structures have also been identified (Fig. 4). Neurotransmitters play a central role in several of the biological processes that honeybees require to perform activities such as foraging behaviour50. A considerable amount of literature highlights the involvement of the neuroprotective kynurenine pathway (KP) final product kynurenic acid (KinA) in the regulation of the stress-related hormone dopamine in the honeybee as well as in other animal species51,52. The major known source of dietary KynA are pollen and nectar produced by sweet chestnuts53,54 and it has been verified that this compound is found in high concentrations in chestnut flowers55. This is coherent with the results of this study: chestnut pollen was found in honey F, produced in the lower station where chestnuts also appear in the floristic relevés, and KynA was found to be a dominant compound in honey F. Interestingly, chestnut pollen was found as sporadic pollen in all the other samples, even those produced in the highest apiary stations (Table S2).Figure 4Kinurenic acid and 3-hydroxyquinaldic acid structure and content in the six honey samples, performed in triplicate. The box diagram representing the median with distribution interval between 25 and 75%.Full size imageFurther, KinA may possess positive properties in a number of pathologies of the gastrointestinal tract, especially colitis, colon obstruction or ulceration56,57. It has been proposed that KinA may also possess antioxidative properties56,57,58,59. This was confirmed by this study, since the wildflower honey with a high component of chestnut pollen was the one with the highest antioxidant properties at the FRSA test (66.61 ± 4.77%), even if lower than manuka honey (84.21 ± 1.04%), a dark honey that is a well-known nutraceutical product and has recently attracted attention for its biological properties, especially for its antioxidant and anti-microbial capacities60. Honey A showed the lowest power (22.40 ± 0.28%) while the other honeys ranked around 40% (Fig. 5). Interestingly, metabolomic analysis revealed the presence of 3-hydroxyquinaldic acid (Fig. 4), which is a kynurenic acid isomer and, although its function has not been elucidated in detail, a few literature data indicate its role as a precursor of naturally occurring peptide antibiotics from the quinomycin family61.Figure 5Results of the FRSA test. Capital letters represent the six honey samples considered. Manuka honey was used as a control.Full size imageIn order to evaluate the ability of honey to induce wound closure, a scratch wound assay was performed (Fig. 6)62. Scratch assay creates a gap in confluent keratinocyte monolayer to mimic a wound. It has already been demonstrated that honeys are able to induce wound closure63 to different extents depending on honey origins and properties.Figure 6The scratch wound test in keratinocytes, HaCaT cells, exposed to honeys. (a) The digitalized pictures of scratched cells after 24 h exposure to 0.5% (w/v) of honeys. (b) The closing percentage wound values after 24 h exposure. Statistics on bars indicate differences compared to the control (CTRL) condition determined by a One-Way ANOVA followed by Dunnett’s test (****p  More

Soil and litter samplingMineral soil (0–15 cm) was collected at the Elizabeth Woods site, a 120-year-old deciduous forest in West Virginia, US (39° 32′ 50.6″ N, − 80° 00′ 00.4″ W). Soils were collected from four 20 × 20 m plots dominated by either AM-associated trees (i.e. Liriodendron tulipifera and Acer saccharum), or ECM-associated trees (i.e. Quercus rubra, Quercus velutina and Carya ovata). These sites have been characterized previously as Culleoka-Westmoreland silt loam soils at the AM sites and Dormont and Guernsey silt loams at the ECM sites40. Soils were also characterized by C:N ratios 11.7 and 14.1 for the AM and ECM soils respectively, with a pH of 6.8 for both soils. Soils with the same mycorrhizal status were pooled and homogenized, air-dried at room temperature for ~ 24 h and sieved through 2.0 mm mesh before the initiation of the experiment. Uniformly 13C labeled litter ( > 97 atom % 13C) from Quercus robur (i.e., ECM substrate) and Liriodendron tulipifera (i.e. AM substrate) leaves (Isolife BV, Wageningen, NL) were incubated in soil mesocosms in a factorial design with five replicates for each treatment combination (2 soil types × 2 substrate types), along with five replicate controls (no 13C substrate addition) for each soil type. The 13C enriched substrates were dried and ground to a powder and added in a suspension of 0.5 ml sterile water to 20 g of soil at a concentration of 400 ug 13C g−1 soil. The control soils received 0.5 ml sterile water additions. These incubations were well mixed and kept at 60% water-holding capacity for the 21-day period at room-temperature18. Chemical characteristics of soils and plant substrates are provided in Table S1.DNA processing and qSIPFor quantitative stable isotope probing, DNA was extracted, quantified, ultracentrifuged, fractionated and sequenced as described in18,26. DNA was extracted using a MoBio PowerSoil HTP Kit following the manufacturer’s instructions. For stable isotope probing, 5 ug of DNA was loaded into a 5-ml ultracentrifuge tube with ~ 3.5 ml of a saturated cesium chloride (CsCl) solution and ~ 900 ml gradient buffer (200 mM Tris, 200 mM KCl, 2 mM EDTA). DNA was separated via ultracentrifugation at 127,000g for 72 h using a TLN-100 rotor in an Optima Max bench top ultracentrifuge (Beckman Coulter, Fullerton, CA, USA). Tubes were fractionated into ~ 25 fractions of 150 µl each, and the density of each fraction was measured with a Raichart AR200 digital refractometer. DNA was purified using an isopropanol precipitation method. The 16S rRNA gene was subsequently quantified and sequenced in samples containing DNA, within the density range 1.660–1.735 gml−1 (~ 10 fractions per sample). To quantify the 16S rRNA gene, quantitative PCR was performed in triplicate using a QuantStudio 5 applied biosystems (Thermo Fisher Scientific) and primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACVSGGGTATCTAAT-3′)41. The PCR program used was as follows: 95 °C for 2 min followed by 45 cycles of 95 °C for 30 s, 64.5 °C for 30 s and 72 °C for 1 min. Libraries were sequenced on an Illumina MiSeq instrument (Illumina, Inc., San Diego, CA, USA) using a 300-cycle v2 reagent kit. Fungal 18S rRNA gene copies in each fraction were also quantified using primers 1380F (5′-CCCTGCCHTTTGTACACAC-3′) and 1510R (5′-CCTTCYGCAGGTTCACCTAC-3′). The PCR program used was as follows: 98 °C for 3 min followed by 40 cycles of 98 °C for 45 s, 60 °C for 45 s and 72 °C for 30 s. DNA fractions were amplified for fungal ITS rRNA genes using primers ITS4F (5′-AGCCTCCGCTTATTGATATGCTTAART-3′) and 5.8SF (5′-AACTTTYRRCAAYGGATCWCT-3′)42 and 300-bp paired-end read chemistry on an IlluminaMiSeq (Illumina, Inc., San Diego, CA, USA). The PCR program used was as follows: 95 °C for 6 min followed by 35 cycles of 95 °C for 15 s, 55 °C for 30 s, and 72 °C for 1 min. DNA fractions were then sequenced using a 500 cycle v2 reagent kit.Files came pre-split and joined multiple paired ends that we combined to pick operational taxonomic units (OTU). Open reference OTUs were picked at 97% identity using SILVA 128 release database for Bacteria and RDP database for Fungi. Taxa were analyzed at the ‘OTU’ level from the QIIME L7 table. Calculation of 13C excess atom fraction (EAF) was performed for each taxon as described previously18,19. Briefly, using the CsCl density gradient data, a weighted average density (WAD) was computed for each taxon’s DNA extracted from control soils that did not receive an isotopically enriched substrate. This natural abundance WAD was then compared to the taxon’s WAD following incubation with the 13C enriched material. The change in WAD can be used to quantify the amount of isotope incorporated into the DNA17,18. Preliminary data analysis revealed an effect of ultracentrifuge tube on estimation of phylotype weighted average density, probably a consequence of slight differences in CsCl density gradients between tubes. This technical error was corrected as previously described18,19. In addition to the samples subjected to qSIP analysis we also extracted and analyzed fungal and bacterial OTU’s from control soils where the DNA was extracted prior to incubation.FTICR-MS and lipidomic analysesSoil from substrate-incubated and controls mesocosms were processed and analyzed with Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), using a 12 T Bruker SolariX FTICR mass spectrometer at the Environmental Molecular Sciences Laboratory in Richland, WA, as described in Fudyma et al.43. Briefly, 100 mg of dried soil or litter substrate was extracted using an adjusted Folch extraction44. Extraction was performed on each sample by sequentially adding 2 ml MeOH, followed by a 5 s vortex; 4 ml CHCl3, followed by a 5 s vortex; sonication at 25 °C for 1 h (CPX3800 Ultrasonic Bath, Fisherbrand); addition of 1.25 ml of H2O, followed by a slight mix to achieve bi‐layer separation; and incubated at 4 °C overnight. The top, aqueous layer (metabolite—polar) was pipetted off into 1 ml glass vials and stored at − 80 °C until FTICR‐MS. The bottom, chloroform layer was dried down and stored in 50:50 methanol:chloroform until lipidomics analysis.A standard Bruker electrospray ionization (ESI) source was used to generate negatively charged molecular ions in the metabolite fraction. Samples were then introduced directly to the ESI source. The instrument settings were optimized by tuning on a Suwannee River fulvic acid (SRFA) standard, purchased from International Humic Substances Society (IHCC). Blanks (HPLC grade methanol) were analyzed at the beginning and end of the day to monitor potential carry over from one sample to another. The instrument was flushed between samples using a mixture of water and methanol. One hundred and forty‐four individual scans were averaged for each sample and internally calibrated using an organic matter homologous series separated by 14 Da (CH2 groups). The mass measurement accuracy was less than 1 ppm for singly charged ions across a broad m/z range (m/z 300– 800). Data analysis software (Bruker Daltonik version 4.2) was used to convert raw spectra to a list of m/z values, applying the FTMS peak picker module with a signal-to noise ratio (S/N) threshold set to 7 and absolute intensity threshold set to the default value of 100. Chemical formulae were then assigned using in-house software following the compound identification algorithm that was described in Tolić et al.45. Peaks below 200 and above 800 were dropped to select only for calibrated and assigned peaks. Chemical formulae were assigned based on the following criteria: S/N  > 7 and mass measurement error  800 were not detected in our samples. The m/z values represent the molecular mass (in Dalton) of the detected ions since all detected ions were singly charged ions. While our results do not represent a quantitative characterization of OM, the values presented are relative differences and should be representative of the samples. Finally, we would like to acknowledge that we were not able to see any clear evidence of 13C label in our FTICR-MS analysis of the soil samples. The lack of 13C label in our FTICR-MS analysis of the soil samples even though they received labeled substrate could be either due to the fact that most of the labeled substrates produced by microbial activities were of low molecular weight, which cannot be detected by FTICR-MS and/or the leftover labeled substrate was of low abundance compared to the organic compounds previously present in the soil matrix. As such, we used the FTCIR-MS data to identify shifts in the overall composition of the chemical compounds in each soil.Lipids in the chloroform fraction were analyzed by LC‐MS/MS in both positive and negative ESI modes using a linear trap quadropole (LTQ) Orbitrap Velos mass spectrometer (Thermo Fisher Scientific), as described in detail previously46. Lipid species were identified using the LIQUID tool46 followed by manual data inspection. Confidently identified lipid species were quantified using MZmine47 and the peak intensities were normalized by linear regression and central tendency (i.e., identifying a central or typical value for a probability distribution) using InfernoRDN.Statistical analysisAll data analyses were performed using R 3.2.048. To examine the effects of soil type, substrate type and their interaction in the bacterial, fungal and chemical composition of DOM and the lipid pool; Bray–Curtis distance matrices were compared with permutational multivariate analysis of variance (PerMANOVA) and visualized with Principle Coordinate Analysis (PCoA) using vegan package49. PerMANOVA analysis were run on the relative abundance and on the 13C EAF of individual microbial taxa, separately for both bacterial and fungal communities.The analyses for FTICR-MS were performed separately for control and incubated soils using all assigned molecular formulae remaining after quality filtering31. In all cases, we applied a Z-score standardization before calculating Bray–Curtis distance matrices49. We analyzed the results from FTICR-MS as resulting from the decomposition of the added substrates for two reasons. First, this is a fully factorial design where individual soil samples were split to either receive AM poplar or ECM oak litter substrate. Thus, each soil sample starts with the same characteristics and the changes at the end of the incubation period should reflect the processing of litter. Second, we excluded molecular formulae present in the litters and thus, the differences we report in each soil type are derived from this processing (or the lack of it).We calculated aggregated indices that characterize both the composition and the physicochemical properties of the microbial (both bacteria and fungi) and the SOM and lipid pool34,36. For bacterial and fungal communities, we quantified Shannon–Weaver diversity index for each sample H′ = (-{sum }_{i=1}^{S} pi ln(pi)) (where pi is the proportion of species I) using the relative abundance of individual microbial taxa50. To find the percent of substrate assimilation by individual taxa, we calculated the proportion of C assimilated by each group as previously described18,51 as a percent. For SOM and lipid molecular formulae, we separately calculated weighted means of formula-based characteristics (i.e. m/z, Aromaticity Index—AI; H/C, O/C, and Nominal Oxidation State of Carbon-NOSC) as the sum of the product of the single-formula information (i.e. m/zi, AIi, H/Ci and NOSCi) and the relative intensity (Ii) divided by the sum of all intensities (e.g., m/z sample1 = ({sum }_{i=1}^{S})(m/zi ·Ii)/Σ(Ii)). With these metrics we obtained sample-level information related to the molecular size (i.e. m/z), the molecular bioavailability (i.e. higher H/C ratio), the molecular reactiveness (i.e. lower AI) and the energetic rewards from molecular oxidative degradation (i.e. higher NOSC) of the SOM, which allows to infer the potential of decomposition products to form stable SOM12,31,35. Detailed information of the calculated indices can be found in the literature31,35,36.We further tested the effects of soil type, substrate type and their interaction on each index using the “lm” function from the “stats” package. In these analyses, P values were approximated by an F test using Type II ANOVA tests with Kenward-Roger Degrees of Freedom52. When interactions between soil and substrate type were found at P  More

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OUTLOOK
29 September 2021

Why stem cells might save the northern white rhino

Biologist Jeanne Loring explains how her work could bring endangered animal species back from the brink.

Julianna Photopoulos

Julianna Photopoulos

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Stem-cell researcher Jeanne Loring in her laboratory at Scripps Research.Credit: Nelvin C. Cepeda/SDU-T/Zuma/eyevine

Up to one million plant and animal species face extinction, many within decades, because of human activities. One of these is the northern white rhinoceros (Ceratotherium simum cottoni). Only two individuals remain, both of them female, making the subspecies functionally extinct. Jeanne Loring, a stem-cell biologist and founding director of the Center for Regenerative Medicine at Scripps Research in La Jolla, California, spoke to Nature about how collecting and reprogramming stem cells could save this species and others from extinction.What does stem-cell research have to do with saving endangered animals?Induced pluripotent stem (iPS) cells, which closely resemble embryonic stem cells, can develop into any tissue in the body, including sperm and eggs. The hope is to generate these reproductive cells from the reprogrammed stem cells of endangered animals, and use them in assisted captive-breeding programmes to rescue the species.How did you get involved in this work?My laboratory set out to make iPS cells from endangered animals in 2008, after we visited the San Diego Zoo Safari Park in California. The previous year, a team led by Shinya Yamanaka, who won a Nobel prize for the work, had become the first to make human iPS cells from skin cells called fibroblasts1, and we had immediately started making them too, to treat neurological diseases. The San Diego Zoo’s Institute for Conservation Research had been collecting and freezing fibroblasts from animals since the 1970s. The institute’s director of conservation genetics, Oliver Ryder, was thinking of using stem cells to try to treat musculoskeletal disorders, but nobody had created iPS cells from endangered species before.
Part of Nature Outlook: Stem cells
In 2011, my postdoctoral fellow Inbar Friedrich Ben-Nun was the first to reprogramme stem cells in two animals from endangered species: the northern white rhino and the drill monkey (Mandrillus leucophaeus)2. We’re now focused on saving the northern white rhino — Ryder’s favourite animal — but the techniques we are working on are going to become a standard way of rescuing species from extinction.When did this become a serious venture?Our endangered-species project mostly remained a hobby until 2015, when scientists and conservationists from around the world met in Vienna to explore how cell technologies might aid conservation. We seriously discussed the idea of using stem cells to rescue endangered species, and later published a rescue plan for the northern white rhino3. To begin with, embryos will be created from sperm and egg cells that were collected and stored. They’ll then be implanted into a surrogate mother, a southern white rhino (Ceratotherium simum simum). But we want to be able to create more sperm and eggs from iPS cells and implant them, too — and that’s where our team comes in.After the Vienna meeting, the San Diego Zoo invested in this idea. Staff there built a stem-cell lab and the Rhino Rescue Center, where they brought in six southern white rhinos from Africa, specifically to serve as surrogate mothers for embryos made from northern white rhinos’ cells. The animals should be compatible because southern white and northern white rhinos are closely related, and so have similar reproductive physiologies. A team of reproductive biologists led by Barbara Durrant is now working to perfect the techniques to fertilize eggs in vitro and transfer viable embryos into the southern white rhinos.What progress have you made in creating northern white rhinoceros iPS cells?When we first set out to make the cells from endangered animals, we assumed that human versions of the reprogramming genes would not work in a rhino. So we tried reprogramming the rhino’s fibroblasts with horse genes — the horse is one of the closest relatives of the rhino — but this failed. Surprisingly, the corresponding human genes did work, and we were able to generate pluripotent cells. However, we had used viral vectors to reprogramme the cells, and this has been shown to lead to tumours in mice, so it could not be used for reproduction purposes.After three years of tweaking the technique, we were able to perform the reprogramming without any genetic modification. It’s all trial and error — you just have to keep testing different combinations of variables. Earlier this year, we celebrated a milestone in our efforts to rescue the rhino: Marisa Korody’s lab at the San Diego Zoo was able to reprogramme frozen cells from nine northern white rhinos and two southern white females to become iPS cells4.

Najin (right) and her daughter Fatu are the world’s only remaining northern white rhinos.Credit: Tony Karumba/AFP via Getty

How do you hope to create gametes from iPS cells?The major effort now is to make eggs that can be fertilized with sperm collected from adult males. We’re following in the footsteps of other researchers who have had success, mainly with mice so far. For example, in 2016, Katsuhiko Hayashi and his team at Kyushu University in Fukuoka, Japan, artificially engineered egg cells from reprogrammed mouse skin cells, entirely in a dish, and these were used to birth pups that were healthy and fertile5.That technique required ovarian tissue to be co-cultured with the developing eggs to get them to mature, and it’s impossible to get that kind of tissue from rhinos without putting them at risk. But in July, the same team showed that it could make both egg cells and ovarian tissue from iPS cells, which was a huge improvement6.We are now trying to find an efficient way to make the precursors of gametes, known as primordial germ cells, from the iPS cells of northern white rhinos. We know it’s possible — we’ve seen it happen spontaneously in cultures of these iPS cells — but we need to learn how to generate more of them. And then we have to turn those germ cells into eggs and sperm — or at least, something like sperm. Typically, the process of in vitro fertilization (IVF) involves knocking the tail off a sperm cell and injecting the small head directly into the egg, so we might not need to make sperm with tails. The IVF process itself will need to be adapted, however, to the southern white rhino surrogates — we don’t know for sure that it will work as it does in humans, because it’s never been done before.What advantage is there to using stem-cell technology over other approaches, such as cloning?The San Diego Zoo has frozen fibroblasts from 12 northern white rhinos. We didn’t want to clone those animals, because we would still have only the same 12 individuals. But if we make gametes from them instead — sperm from males, eggs from females and, in theory, sperm from females — then we could make various combinations through IVF to get a new, genetically diverse pool of animals that will help the species to survive. We have found that there is sufficient diversity in combining that group of 12 to exceed the diversity of the current population of southern white rhinos.
More from Nature Outlooks
Another group, at the Leibniz Institute for Zoo and Wildlife Research in Berlin, is instead harvesting eggs from the two living animals in the hope that they can fertilize them and get new animals that way. I’m perfectly happy if that works, but the challenge is getting enough diversity in the population if you have eggs from only one or two animals.Have you encountered opposition to your iPS-cell-mediated approach?If I were doing this with humans there’d be a lot of debate, but with animals there is less. One criticism is that resources for conservation should be invested differently, for example in restoring natural habitats and educating people. One argument we hear is that there’s no purpose in rescuing a species that will be confined to zoos because of poaching. I don’t know how to stop people from hunting rhinos for their horns, but I will do what I can to try to save an animal that humans have forced into extinction.Are you confident that your work will help to save the northern white rhino?It saddens me that as we’ve made progress in the lab, these animals have been dying out. When we started this project there were 8 of them alive, and now there are only 2: Najin, aged 32, and her daughter Fatu, aged 21, who live in a protected park in Kenya. It’s possible that these last two survivors will be gone by the time we succeed. I hope that’s not the case, but we’re working with cells that have been harvested and frozen, so we can try to bring the species back to life if necessary.I can’t predict how long it will take to get there — things have happened much more slowly than I’d like. But I do hope that our efforts will pay off over the next 10 to 20 years. I want to see a new northern white rhino in my lifetime — before I become ‘extinct’!

Nature 597, S18-S19 (2021)
doi: https://doi.org/10.1038/d41586-021-02626-zThis interview has been edited for length and clarity.This article is part of Nature Outlook: Stem cells, an editorially independent supplement produced with the financial support of third parties. About this content.

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The present study was carried out in six fjords within New Zealand’s Fiordland system, specifically Breaksea Sound, Chalky Inlet, Doubtful Sound, Dusky Sound, Long Sound, and Wet Jacket Arm, as described in Tobias-Hünefeldt et al.15. Analyses were divided into three categories: (1) a multi-fjord analysis comprising five of the tested fjords (excluding Long Sound), (2) a high-resolution study along Long Sound’s horizontal axis, and (3) a depth profile of Long Sound’s deepest location (at 421 m). These categories were established to identify trends across multiple fjords, and then test the trends using a fjord analysed at a higher resolution. Total community composition (via 16S and 18S rRNA gene sequencing) and metabolic potential did not significantly covary across the five studied fjords (Mantel, r  More