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Bacterial community composition
From 36 soil specimens, 3,370,643 high-quality 16S rDNA reads were obtained [(CK1, T1, CK2, and T2 treatments) × 9 replicates] with 74,955–103,931 sequencing reads (mean = 91,908) per sample. The maximum read length was 478 bp and the minimum average length was 341 bp for the 16S rDNA genes. All rarefaction curves for the bacterial samples revealed that the amount of recorded OTUs was generally 7,000 reads per plateau, indicating the assessment adequately covered the microbial variety (see Supplementary Fig. S2a). The bacterium richness (Chao1 and ACE), evenness indexes (Shannon and Simpson), and number of OTUs between CK1 and T1 as well as between CK2 and T2 were not significantly different (see Supplementary Table S1a).
The soil bacterial composition of the two treatment groups at the two growth periods were compared at the level of the phylum. A total of 26 bacterial phyla were identified, with the exception of 1.03% of unclassified sequencing reads. The main phyla of the sequenced bacteria were Proteobacteria, Actinobacteria, and Gemmatimonadetes, which occupied over 64.5% of the total bacterial populations in the sample sequences. Chloroflexi, Acidobacteria, Bacteroidetes, Parcubacteria, Verrucomicrobia, and Firmicutes were also identified at relatively elevated richness (average relative abundance > 1%) (Fig. 1a). Wheat straw addition significantly increased the relative abundances of Actinobacteria, Chloroflexi, and Saccharibacteria, while significantly decreasing the relative abundance of Parcubacteria at both moments of sampling (P  0.05) were detected (see Fig. 1a and Supplementary Table S2a).
Figure 1

Major bacterial (a) and fungal phylum (b) relative abundance in the soil with (T1 and T2) and without (CK1 and CK2) wheat straw addition. Bacterial phyla with > 1% and fungal phyla with > 0.1% average relative abundances. Others included bacterial phyla below 1% relative abundance and unidentified bacterial and fungal phyla. According to the Student’s t-test (n = 9), * and ** represented P  0.1% bacterial reads in T1 soil (only 0.04% in CK1 soil) (see Supplementary Table S3a). In addition, 14 bacterial genera with relative abundance > 0.1% were more prevalent in CK2 samples, including Spororosarcina, Chryseollinea, Nitrososporia, Truepera, Actinomadura, two Planctomycetes (SM1A02 and I-8), and some Ptoteobacteria (Sulfurifustis, Polycyclovorans, Woodsholes, and H16) (Fig. 2b). In contrast, the Actinobacteria (Aeromicrobium, Nonomured, Nocardioides, Dactylosporangium, and Ilumatobacter) group and Proteobacetia (brachysporum_group, Ramlibacter, Dongia, Hyphomicrobium, Rhizobium, Sphingobium, Parablastomonas, Pseudoxanthononas, Dokdonella, and Pseudohoniella) group were more abundant in the T2 soil than in CK2 (see Fig. 2b and Supplementary Table S3b).
Figure 2

LDA histogram scores for bacterial genera with different abundance for the flowering stage (a) and fruiting stage (b) in the watermelon monoculture system.

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With respect to the fungal genera, LEfSe analysis showed that there was higher relative abundance of Schizothecium, Entoloma, Preussia, Lecanicillium, and Bipolairs in T1, whereas there was a higher relative abundance of Thanatephorus, Scopulariopsis, Fusarium, and Conocybe in CK1 (Fig. 3a) for the flowering stage. In the fruiting stage, Psathyrella, Filobasidium, Aphanoascus, Cladosporium, Microascus, and Scopulariopsis were found in CK2, while only Schizothecium was found in T2 (Fig. 3b). Furthermore, the second prevailing genus, Fusarium, accounted for 9.70% of all fungal genera in CK1 (only 0.64% in T1). The relative abundance of Fusarium was higher in CK2 than in T2 (see Supplementary Table S4).
Figure 3

LDA histogram scores for fungal genera with different abundance for the flowering stage (a) and fruiting stage (b) in the watermelon monoculture system.

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Microbial community variety and the link between genera abundance and environmental conditions
Non-metric multidimensional scaling (NMDS) clearly indicated that there were considerable variations in the composition of the soil bacterial populations between the samples with (T1 and T2) and without (CK1 and CK2) wheat straw addition in the consecutive watermelon monoculture system in the two growth stages evaluated (Fig. 4a). In the NMDS plot, the nine replicates in the same groups were not closely located for the fungal communities in all the samples, which indicated that there was no distinct difference in fungal community composition between two treatments for the two growth stages (Fig. 4b). RDA revealed that the relative abundances of Planctomyces, Pirellula, and Exiguobacterium were positively correlated with the DI for bacteria (Fig. 5a and Supplementary Table S5a). The relative abundances of Aspergillus, Fusarium, Sopulariopsis, Cladosporium, and Aphanoascus were positively correlated with the DI for fungi (Fig. 5b and Supplementary Table S5b).
Figure 4

Non-metric multidimensional scaling (NMDS) according to the Euclidean distance plot of bacterial (a) and fungal (b) microbiota in the flowering stage (CK1 and T1) and fruiting stage (CK2 and T2).

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Figure 5

Plots of redundancy analysis (RDA) ordination displaying the interactions between the top 10 bacterial (a) and fungal genera (b) and soil environmental variables. AP denotes available phosphorus; pH denotes the solar pH; EC denotes electrolyte conductivity; the disease index (DI) denotes healthy plants as “0”and Fusarium wilt plants as “1”. CK1 represents the soil without wheat straw addition at the watermelon flowering stage while T1 represents the soil with wheat straw addition at the watermelon flowering stage; CK2 represents the soil without wheat straw addition at the watermelon fruiting stage and T2 represents the soil with wheat straw addition at the watermelon fruiting stage.

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Fungal community network analysis
Soil microbial network analysis is widely performed to understand the taxonomic and functional relations within complex microbial communities13. With respect to fungi, the top 300 OTUs of T1 and CK1 soil at the watermelon flowering stage were chosen for pMEN analysis (Fig. 6). The T1 network consisted of 180 nodes (OTUs), 1,036 connections, and 12 modules, with an average connectivity of 11.511, average path length of 2.999, and clustering coefficient of 0.278. The CK1 network consisted of 166 nodes, 741 connections, and 18 modules, with an average connectivity of 8.927, average path length of 2.920, and clustering coefficient of 0.155. The modularity proportion was higher in the T1 network, although fewer total modules were recognized (Table 1). Strikingly, there were more links in T1 soil (1,036 links) than in CK1 soil (741 links). The positive link/negative link ratio (P/N) was higher in T1 soil (P/N = 0.333) than in CK1 soil (P/N = 0.211), demonstrating that the T1 soil had more complex and positive co-occurrence correlations than the CK1 soil.
Figure 6

Network plots of fungal community at the order level from soil without (CK1) (a) and with (T1) (b) wheat straw addition at the watermelon flowering stage. The size of the node corresponds to the relative abundance of the OTUs. The node colors show various phylogenetic associations. Node (edge) connection lines represent co-occurrence with positive (blue) and negative (red) correlations.

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Table 1 Major topological properties of the empirical phylogenetic Molecular Ecological Networks (pMENs) of fungal communities for soil with (T1 and T2) and without (CK1 and CK2) wheat straw addition and their associated random pMENs.
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The top 300 OTUs of T2 and CK2 soil at the watermelon fruiting stage were also chosen for pMEN analysis (see Supplementary Fig. S3). The T2 network consisted of 202 OTU nodes, 1,040 connections, and 14 modules, with an average connectivity of 12.545, average path length of 2.727, and clustering coefficient of 0.214. The CK2 network consisted of 181 nodes, 739 links and 15 modules, with an average connectivity of 8.166, average path length of 3.186, and clustering coefficient of 0.264 (Table 1). Strikingly, there were more links in T2 soil (1,040 links) than in CK2 soil (739 links), which indicated that the T2 soil had more complex and stable microbial networks than the CK2 soil. In T2 soil, the P/N (P/N = 0.218) was lower than that in CK2 soil (P/N = 0.451), indicating that the T2 soil had more negative co-occurrence relationships in the microbial community than those in CK2 soil.
In addition, CK1 and T1 networks shared 49 nodes (Fig. 7). Nodes of the Sordariales, Onygenales, Microascales, Hypocreales, Eurotiales, Agaricales, and Arachnomycetales genera dominated in both networks. The relative abundance of Sordariales and Hypocreales was higher in the two networks. Furthermore, there was a higher proportion of Sordariales-affiliated OTUs and a lower proportion of Hypocreales-affiliated OTUs in T1 compared to CK1 (Fig. 7a). However, 72 nodes were shared between CK2 and T2 networks. Nodes belonging to the Sordariales, Pleosporales, Onygenales, Microascales, Hypocreales, Eurotiales, and Agaricales genera dominated in both networks. Sordariales, Hypocreales, and Agaricales were relatively more abundant in these two networks. However, the relative abundance of Sordariales and Hypocreales had more significant differences in CK2 compared to T2. There was also a higher proportion of Sordariales-affiliated OTUs and a lower proportion of Hypocreales-affiliated OTUs in T2 compared to CK2 (Fig. 7b). These network analysis results suggested that Sordariales dominated in the T1 and T2 soils, which were treated with wheat straw, while Hypocreales dominated in the soil (CK1 and CK2) without wheat straw addition.
Figure 7

Relative abundance of nodes at the order level in modules inside the fungal network created from the flowering stage (a) and fruiting stage (b). Venn diagrams display the amount of shared and unshared network nodes in the soil sample with and without wheat straw addition.

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Bacterial community network analysis
T1 and CK1 soil bacterial community analysis revealed similar sized networks with 224 and 221 nodes, respectively (see Supplementary Fig. S4 and Table S6). The average connectivity for the T1 and CK1 networks was 7.482 and 7.493, with an average path length of 4.273 and 3.557, respectively. The average clustering coefficient value (0.323 or 0.326) was comparable in the T1 and CK1 soil networks, while modularity was somewhat lower in the T1 network (0.403) than in the CK1 network (0.440). However, the number of modules in T1 (27) was higher than that in CK1 (22). In T1 and CK1 soil, the total number of links was 828 and 838 (P/N = 2.33 for T1 soil and P/N = 2.37 for CK1 soil), respectively. However, T2 and CK2 soil had different nodes (228 and 201, respectively) at the watermelon fruiting stage. Furthermore, the average connectivity was higher in T2 (4.263) than in CK2 (3.562) networks. The average path length, average clustering coefficient value, and modularity were higher in CK2 than in T2 networks (see Supplementary Fig. S5 and Table S6). In T2 and CK2 soils, the total number of links was 486 and 358 (P/N = 1.612 for T2 soil and P/N = 1.732 for CK2 soil), respectively. The data suggested that wheat straw addition did not affect the bacterial co-occurrence relationship in both the watermelon flowering and fruiting stages.
Inside the T1 versus the CK1 network, a greater percentage of OTUs associated with Proteobacteria, and a reduced OTU ratio for Chloroflexi, Bacteroidetes, and Acidobacteria were found. (see Supplementary Fig. S6a). However, a higher proportion of Proteobacteria and Actinobacteria- affiliated OTUs and a lower proportion of Gemmatimonadetes, Euryarchaeota, Chloroflexi, and Bacteroidetes-affiliated OTUs inside the modules were identified in the T2 versus CK2 network (see Supplementary Fig. S6b). More

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Co-creating a greenspace is only the beginning; conservation practitioners must also communicate progress and maintain community member’s trust throughout a project’s duration15. Despite residents’ involvement in the co-design process, feelings of “bait and switch” can arise if a developing habitat aesthetically diverges from their expectations. For instance, non-native weedy vegetation may become more abundant within a planting than anticipated. Thus, frequent discussions of all possible or transitory site outcomes, including visual representations of a habitat’s vegetation9,14, can help avoid feelings of contention or disinvestment. Likewise, research tools can be misunderstood and cause concern if not effectively described. For example, neighbors have expressed apprehensions that our native bee traps were releasing stinging insects when in fact they removed the insects for further study. This confusion could have been avoided by better communication at the project’s onset and throughout continued interactions with residents.
To effectively engage a large and diverse urban community, researchers must evaluate multiple options to share their activities and findings14,16. We created a project website, educational video, and social media presence; these have been successful in communicating with other researchers and the media but have largely failed to reach residents. We found that one-on-one discussions of project aims, progress, and outcomes through daily interactions on-site or at community events were far more effective, but still did not reach all stakeholders. For example, our research activity occurred during the day, limiting interactions with those who were away at work, and a high turnover rate in housing occupancy created an influx of new residents unfamiliar with the project. Also, residents may be more comfortable interacting with neighbors or local organizations rather than visiting researchers. To address these issues, scientists should consider pre-existing channels of communication (e.g. neighborhood watch groups, religious organizations, community centers) that are self-identified by community leaders.
Building community relationships requires more than disseminating information; researchers need to actively solicit community opinions in order to gauge and address needs. Regular polling of community opinions through focus groups or surveys can inform habitat management to help resolve community concerns such as aesthetics or perceived safety. While an ecologist may see diverse native plants flourishing, dense and tall plantings can inspire fear of criminal activity17 and community members may consider such habitats as eye-sores indistinguishable from abandoned properties11. Such issues can be mitigated through “cues to care”—the physical signs of intention and upkeep11 advocated by design professionals. These cues can indicate a greenspace has purpose, help combat negative perceptions, and illustrate community consideration11. Common practices such as signage, neatly-mown borders, fences, and/or mulched flower beds around a conservation site can convey a site’s purpose and contribute to community approval9,11. For example, abundant signage and neatly-mown edges were noted as significant factors promoting public support for urban meadows in Bedford and Luton, UK9. We employed similar cues to care and framed our pocket prairie habitats with a mown border, fence, and mulched roadside edge (Fig. 1b). Yet, we received complaints from residents who did not perceive our mown borders as intentional and assumed we had abandoned our mowing efforts. Concern was also expressed that the mulch was a health hazard as stray cats might use it as litter. This illustrates how widely recommended cues to care are not effective in all settings and failure to engage residents in management planning may result in confusion or elicit unanticipated, negative feedback. Conversely, if residents are involved in determining cues to care, creative solutions generating greater satisfaction can be found. For example, hand-painted flags designed by elementary school students were an effective cue to care for the off-season within a sunflower planting in St. Louis, Missouri5 (Fig. 1f).
It is important for community leaders, scientists, and neighbors to recognize the difficulty in reconciling a community’s diverging opinions of greenspace goals. Even with open communication, projects will face challenges in meeting community expectations. For instance, some Cleveland, OH residents prefer the tidy appearance of fabric flowers over the living vegetation of a habitat planting. After 4 years, we are still trying to develop a strategy to meet this concern. Meanwhile, we have also received many positive comments, with residents enjoying the color of our plantings, asking to pick flowers for bouquets, or declaring their general support for helping declining bees. We highlight these variable responses as both precaution and encouragement. It is unlikely that urban conservation sites will garner universal public support2, but iterative assessments and modifications of a site’s management or design can ameliorate some community concerns and shift how greenspace is viewed and valued long-term. More

Wetlands are the largest natural source of methane to the atmosphere. In freshwater mineral-soil wetlands, about 30–90% of methane fluxes are mediated by plants through a reversal of mechanisms in place to transport oxygen into the roots as an adaptation to the predominantly anoxic conditions in wetland soils. The rates of methane transport by plants, regulated by photosynthesis and stomatal conductance, are highly variable and are not well represented in models due to a lack of observational data, leading to high variability in model results.

Credit: Jim West / Alamy Stock Photo

Jorge Villa from Ohio State University, USA, and colleagues investigate methane flux, plant-mediated methane transport and carbon uptake in three plant species (cattail, American lotus and water lily). They find that plant conductance of methane depends on the species as well as leaf area, and varies intra-seasonally. Although methane flux and CO2 uptake were correlated, this relationship cannot be generalized across plant functional types. Nevertheless, using species — distinguished based on whether gas transport is stomatal-controlled — could improve model predictions of wetland methane emissions. More

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Locating seamounts: seamount databases
Two seamount databases were used in this study: the validated Pacific database published by Allain et al. in 2008 (referred to in the text as the “Allain database”) and the most up-to-date global database published by Yesson et al. in 2011 (referred to in the text as the “Yesson database”). The primary analyses were conducted on a representative subsample of the Allain seamount database, the most spatially expansive (45°S–32°N and 130°E–120°W), validated and crosschecked published seamount database16. This area covers a large swath of the Pacific, which contains the vast majority of seamount features on our planet. Only “validated” seamounts, whose location and associated data were confirmed by at least one ship-based dataset rather than purely derived from satellite estimations, were used in the analyses. This subset was further reduced to include only features with validated summit depths deeper than 30 m (the optically shallow cutoff used after Gove et al. 2016) and elevations greater than 1,000 m (to follow the classic definition of a seamount as a feature rising more than 1,000 m above the seafloor). The resulting dataset was then subsampled to meet computational restrictions on database size. All seamounts with summit depths shallower than or equal to 300 m (48) were included, and the remaining features were subsampled such that 5 features were selected from each 100 m height bin and 1,500 m elevation bin for a total of 196 seamounts (of 485).
Second, to examine patterns globally, a subsample of the unvalidated Yesson database was analyzed with identical methodology. This database was based on the same global bathymetry used in this paper to derive underlying water depths for each chlorophyll pixel1. Only seamounts with estimated summit depths deeper than 30 m, elevations greater than 1,000 m, and estimated base areas greater than 500 km2 were selected from because the smallest features have the largest position and depth errors associated with them. Eight features were randomly sampled for each 150 m summit depth bin (ranging from − 30 to − 1,050 m) and from each 1,000 m elevation bin. These cutoffs were selected to create a subset of comparable size to the Allain subset and to maximize the chances of selecting from “real” features (those accurately detected via satellite and the Yesson seamount algorithm)1, 2. Because this database is unvalidated, these added precautions were taken in subset selection. The resulting subset (192 of 2,560) was then examined visually, summit depth estimates were corrected where needed, and features which were mistakenly identified as seamounts were removed from the subset. Despite our subsetting process, the manual revision still revealed problems with the published database, especially in estimated summit depth and location; therefore, approximately 19% of the initially selected seamounts had to be excluded from the final global analysis (final included number of seamounts = 166).
Quantifying chlorophyll-a enhancements around seamounts
Chlorophyll-a (mg/m3) data were derived from the August 2015 version of the level 3 monthly composite, scientific quality, 0.0417° squared (~ 4 km) Moderate Resolution Imaging Spectroradiometer (MODIS) data (https://oceancolor.gsfc.nasa.gov/). Data were accessed through the NOAA ERDDAP, griddap site (https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdMH1chlamday.html). A decade’s worth of chlorophyll data (Jan 2006-Jan 2016) were analyzed around each feature for a seamount-centered square with 100 km sides. Though seamounts whose validated summit depths were shallower than 30 m were excluded from the dataset entirely, an additional 30 m pixel depth (data source described below) cutoff was applied to all chlorophyll data to avoid potential bias from optically shallow waters anywhere in the sampling area, following the methods of Gove et al.19. Additionally, to avoid confusing the island mass effect (IME) with SICE, all seamounts whose sample area included one or more pixels with satellite estimated depths were emergent (≥ 0) were labeled “Emergent”. For all reported analyses these features flagged as ‘emergent’ (N = 19) were removed before statistical anlysis. All analyses included temporal predictors to account for seasonality (month predictor) and annual variability (year predictor) in chlorophyll patterns.
Sea surface temperature
To test for the occurrence of seamount uplifted water, monthly daytime SSTs on the same ~ 4 km resolution from the Aqua MODIS platform were also downloaded for each 100 km sided seamount box (https://coastwatch.pfeg.noaa.gov/infog/MH1_sstMask_las.html). This data is science quality data from the August 2015 reprocessing of the global Level 3, 11 km SST data.
Geophysical drivers
Seamount locations (summit latitude, summit degrees poleward or absolute latitude, summit longitude) and seamount specific information (elevation above the surrounding seafloor and summit depth below sea level) were derived from the published seamount databases described above2,16. Seasonality and annual variability were also included in the model through the incorporation of month and year terms. Each of the predictors was included for their theoretical influence on primary producers around seamounts. Summit location (i.e. latitude, longitude, and degrees poleward—defined as the absolute value of latitude) can influence internal wave dynamics13, mixed layer depth34, and global productivity dynamics including light versus nutrient limitation on production 38. Whether a seamount enhances production may well depend upon the background or long-term average productivity of the area, and this may co-vary with latitude and average SST (oligotrophic gyres are warm) at the summit. Average euphotic layer depth may influence the depth that physical seamount effects would need to reach in order to influence phytoplankton production. Finally, seamount summit depth greatly influences circulation patterns at the feature13 and thus possibly nutrient injection into the euphotic zone. However, seamounts often have complex geomorphologies, and therefore a variety of measures of summit depth were included: the shallowest depth at summit, proportion of pixels with depths shallower than the average euphotic layer depth, and proportion of pixels shallower than 800 m.
Depth data were derived from the Shuttle Radar Topography Mission (SRTM30 PLUS) 30 arc-second global bathymetry grid, which combines high resolution (~ 1 km) ship-based bathymetry data with ~ 9 km satellite-gravity data39 (https://topex.ucsd.edu/WWW_html/srtm30_plus.html). For each selected seamount, bathymetry and chlorophyll data were analyzed from a square region centered on the given summit location measuring 100 km2. Previous research suggested that the island mass effect (IME) extends approximately 30 km from the shore of islands19, and that seamount effects can extend up to 40 km from the summit location18, therefore, a box extending 50 km from the seamount summit was selected in order to ensure that the entire feature and both seamount-influenced waters and the surrounding unmodified open ocean waters were included in the analyses. Depth was extracted for each chlorophyll pixel using the extrapolation methods in the NOAA marmap package (getdepth function)40. In addition, because summit depth uses data from only the single shallowest point on a complex feature, two further depth-based predictors were derived: the proportion of chlorophyll pixels with depths shallower than 800 m (an estimate for the daytime maximum depth of vertical migration) and proportion of pixels shallower than the average euphotic depth at the seamount summit location.
Monthly composite 4 km resolution euphotic depth (in meters) calculated from the Lee algorithm was obtained from the NASA ocean color data product Zeu (e.g.: A200600A20060012006031.L3m_MO_ZLEE_Zeu_lee_4km.nc). The data were downloaded for the same period (2006–2016) as the chlorophyll data for each pixel around each selected seamount feature. The proportion of pixels in the sample region shallower than or equaling the overall average euphotic layer depth was calculated for each seamount.
Decadal average sea surface temperature (SST) at the summit locations were derived from available monthly mean ARGO SST data for each seamount (https://apdrc.soest.hawaii.edu/dods/public_data/Argo_Products/monthly_mean). These are therefore in-situ measured temperatures. Only data from the shallowest depth bin were used to derive these long-term average SSTs.
Statistical models and model selection
All statistical analyses were conducted using the software package R. To identify seamounts characterized by SICE, defined as a statistically significant increase of chlorophyll with shallowing depths, we fit a Gaussian GAM for each seamount in each dataset analyzed. These models use the natural log of chlorophyll as the response and include a spatial predictor (two-dimensional relative latitude and longitude smoother), and a temporal predictor (month) to account for spatial and temporal autocorrelation respectively. Because phytoplankton are naturally patchy throughout the ocean, we included a two-dimensional spatial smoother to detect and account for this natural spatial structure. This approach made it possible to distinguish between depth related chlorophyll enhancements and random patchiness. An alternative approach might be to randomly select a control region away from the seamount for comparison. However, chl-a enhancements are likely to be asymmetrical and background levels are inherently patchy19,41,42,43. Our approach implicitly controls for such patchiness by testing for increases in chl-a with shallowing depth in a seamount-centered region that spreads well beyond the radius of any measured seamount effect, creating a control region that forms a ring around the region of interest instead of a single offset control region whose different position within the larger latitudinal and longitudinal spatial gradients in chlorophyll concentrations could skew the analysis18,19. Gove et al. (2016) took a very similar approach to their analysis of the island mass effect. These GAMs also fit a slope for each seamount between chlorophyll and depth using the decade of chlorophyll data for each corresponding sample area (see Supplementary Information 1 Table 1 for all full model formulas). The seamounts for which the resulting chlorophyll/depth estimate (seamount-specific slopes) were significantly positive (P  More

NEWS AND VIEWS
29 July 2020

Molecules have previously been made that induce protein destruction inside cells. A new class of molecule now induces the degradation of membrane and extracellular proteins — opening up avenues for drug discovery.

Claire Whitworth &

Claire Whitworth is in the Division of Biological Chemistry and Drug Discovery, School of Life Sciences, James Black Centre, University of Dundee, Dundee DD1 5EH, UK.

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Alessio Ciulli

Alessio Ciulli is in the Division of Biological Chemistry and Drug Discovery, School of Life Sciences, James Black Centre, University of Dundee, Dundee DD1 5EH, UK.
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Most drugs act by binding to a specific site in a target protein to block or modulate the protein’s function. The activity of many proteins, however, cannot be altered in this way. An emerging class of drug instead brings proteins into proximity with other molecules, which then alter protein function in unconventional ways1–3. One such approach uses drug molecules called protein degraders, which promote the tagging of proteins with ubiquitin, another small protein. Tagged proteins are then broken down into small peptide molecules by the cell’s proteasome machinery. But because the ubiquitin-mediated degradation pathway occurs inside the cell, protein degraders developed so far attack mainly intracellular targets. Writing in Nature, Banik et al.4 now report a different mechanism that opens up extracellular and membrane-bound proteins for targeted degradation.
The authors report protein degraders that they call lysosome-targeting chimaeras (LYTACs), which are bifunctional (they have two binding regions; Fig. 1). One end carries an oligoglycopeptide group that binds to a transmembrane receptor (the cation-independent mannose-6-phosphate receptor; CI-M6PR) at the cell surface. The other end carries either an antibody or a small molecule that binds to the protein targeted for destruction. These two regions are joined by a chemical linker.

Figure 1 | Mechanism of action of lysosome-targeting chimaeras (LYTACs). Banik et al.4 report LYTAC molecules, which consist of an oligoglycopeptide group (which binds to a cell-surface receptor, CI-M6PR) and an antibody that binds to a specific transmembrane or extracellular protein. The antibody can also be replaced by a small protein-binding molecule (not shown). On simultaneously binding to both CI-M6PR and the target protein, the resulting complex is engulfed by the cell membrane, which forms a transport vesicle. This carries the complex to a lysosome (an organelle that contains protein-degrading enzymes). The protein is degraded and the receptor is recycled; it remains to be seen whether the LYTAC is also degraded. LYTACs are potentially useful for therapeutic applications.

The formation of a trimeric CI-M6PR–LYTAC–target complex at the plasma membrane directs the complex for destruction by protease enzymes in membrane-enclosed organelles called lysosomes. LYTACs are conceptually related, but complementary, to proteolysis-targeting chimaeras5 (PROTACs) — another bifunctional class of protein degrader that mainly targets intracellular proteins by recruiting them to E3 ligases (the enzymes that tag proteins with ubiquitin).
Banik et al. began by making LYTACs of varying size and linker composition, and which used a small molecule called biotin as the protein-binding component — biotin binds with exceptionally high affinity to avidin proteins. The authors observed that these LYTACs rapidly shuttled an extracellular fluorescent avidin protein to intracellular lysosomes in a way that required engagement with CI-M6PR. When the authors replaced biotin with an antibody that recognizes apolipoprotein E4 (a protein implicated in neurodegenerative diseases), this protein was also internalized and degraded by lysosomes. LYTACs can, therefore, repurpose antibodies from their normal immune function to direct extracellular proteins for lysosomal degradation.

Next, Banik et al. investigated whether LYTACs could induce the degradation of membrane proteins that are targets for drug discovery. In several cancer cell lines, LYTACs did indeed induce the internalization and lysosomal degradation of the epidermal growth factor receptor (EGFR) — a membrane protein that drives cell proliferation by activating a signalling pathway. Depletion of EGFR levels by LYTACs in the cancer cell lines reduced signal activation downstream of EGFR, compared with the amount observed when EGFRs were blocked by antibodies alone. This result confirms a previously reported5 advantage of using target degradation in therapeutic applications, rather than target blocking.
Similar outcomes were observed with LYTACs for other single-pass transmembrane proteins (proteins that span the cell membrane only once), including programmed death ligand 1 (PD-L1), which helps cancer cells to evade the immune system. The next step will be to establish whether LYTACs can also induce the degradation of multi-pass proteins that span the membrane several times, such as the ubiquitous G-protein-coupled receptors and proteins that transport materials across membranes (ion channels and solute-carrier proteins, for example). If so, it will be interesting to compare the performance of LYTACs, which would bind to the extracellular domains of such proteins, with that of PROTACs, which can bind to the intracellular domains of these proteins (as was recently demonstrated6 for solute-carrier proteins).
As with any new drug modality, there is scope for improvement. For example, Banik and colleagues’ first PD-L1-targeting LYTACs produced only partial degradation of the protein, which the authors attributed to low expression of CI-M6PR in the cell lines used. When the authors made a second type of LYTAC that incorporated a more potent PD-L1 antibody, degradation increased, albeit in cells that expressed greater levels of CI-M6PR than did the original cell lines. This shows that low abundance of the lysosome-shuttling receptor hijacked by the LYTAC (in this case, CI-M6PR) can reduce the effectiveness of these degraders. Similarly, the loss of core components of E3 ligases is a common mechanism by which cells become resistant to PROTACs7. Lysosome-shuttling receptors other than CI-M6PR could be used by LYTACs as alternatives, should resistance emerge. Degraders that target cell-type-specific receptors might also have improved safety profiles compared with conventional small-molecule therapeutics, which are not always cell-type selective.

What sets PROTACs and LYTACs apart from conventional drugs is their mode of action. For example, after a PROTAC has brought about the destruction of a target protein, the PROTAC is released and can induce further cycles of ubiquitin tagging and degradation, thereby acting as a catalyst at low concentrations1,5. Mechanistic studies are now warranted to determine whether LYTACs also work catalytically.
Another aspect of the mode of action of both PROTACs and LYTACs is that they bring two proteins together, to form a trimeric complex. A general feature of such processes is the hook effect, whereby trimer formation, and thereby the associated biological activity, decreases at high drug concentrations. This is because dimeric complexes generally form preferentially at high drug concentrations — an undesirable effect that can be alleviated by ensuring that all three components interact in such a way that trimer formation is more favourable than is dimer formation1.
Kinetics also matters for protein degraders. For example, stable and long-lived trimeric complexes that involve PROTACs accelerate target degradation, improving drug potency and selectivity8. It will be crucial to understand how the complexes formed by LYTACs can be optimized to improve degradation activity.

PROTACs and LYTACs are larger molecules than conventional drugs. As a result of their size, PROTACs often do not permeate well through biological membranes, which can make them less potent drugs than the biologically active groups they contain. Size should be less of a problem for LYTACs because they do not need to cross the cell membrane, although they would still need to pass through biological barriers to combat diseases of the central nervous system. The development of lysosomal degraders that are smaller and less polar than LYTACs — and therefore more able to pass through membranes — will be eagerly anticipated. Small ‘glue’ molecules that bind to E3 ligases can already do the same job as PROTACs9.
Targeted protein degradation is a promising therapeutic strategy, and the first PROTACs are currently in clinical trials10. LYTACs will need to play catch-up, but they have earned their place as a tool poised to expand the range of proteins that can be degraded. Their development as therapies will require an understanding of their behaviour in the human body — their pharmacokinetics, toxicity, and how they are metabolized, distributed and excreted, for example. It can be challenging to optimize the biological behaviour of molecules that incorporate large groups, such as antibodies and oligoglycopeptides, during drug discovery, but this problem can be overcome by further engineering the structures of these groups11. Banik and colleagues’ new approach to degradation therefore warrants an all-hands-on deck approach.
Scientists working in drug discovery will eagerly await the development of LYTACs and the emergence of other methods for the drug-induced degradation of proteins12. Is no protein beyond the reach of degraders?

doi: 10.1038/d41586-020-02211-w

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Takahashi, D. et al. Mol. Cell 76, 797–810 (2019).

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