Reef Cover, a coral reef geomorphic zone classification, developed to support both producers and end-users of global-scale coral reef habitat maps, in a transparent and version-based framework. Scalable classes were created by focusing on attributes that can be observed remotely, but whose membership rules also reflect deep knowledge of reef form and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. Two case studies demonstrate application of the Reef Cover classification scheme and its scientific and conservation benefits: 1) detailed mapping of the Cairns Management Region of the Great Barrier Reef to support management and 2) mapping of the Caroline and Mariana Island chains in the Pacific for conservation purposes.Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications underpinning large-scale reef mapping to date are typically poorly defined, not shared or region-specific, limiting end-users’ ability to interpret outputs. Here we present  Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14397182 Yet for coral reefs, a valuable and rapidly degrading ecosystem3, these accelerating capabilities to collect and analyse data are not often translated into improved environmental outcomes4. One barrier is the transfer of insights gained from remote sensing data from producers (remote sensing scientists) to end-users . Translation of remotely collected observations of coral reefs into user-friendly spatial information for investigation, monitoring, planning and management requires a classification system to discretise continuous data about natural phenomena into spatial units of manageable information.Enhanced earth observation and analytical capabilities have revolutionised the way we view our planet, allowing a global perspective that has the potential to dramatically influence the way humanity manages finite planetary resources5. Maps are an efficient way to share large amounts of spatial data, but to be effective they must be underpinned by a classification framework that a) follows a clear and transparent rationale (transparency), b) defines classes that are meaningful for the application (relevance), c) is clearly and unambiguously described (clarity) and is d) discoverable and intuitive to interpret end-users (accessibility). The latter is particularly important if information is to be meaningfully integrated into practical solutions founded on map data5.Classification is a key process for making data accessible to people that need it6, but diverge in places owing to the disciplinary approach, methodology employed, datasets used, and the scale and scope of investigation. For centuries, traditional reef classification has involved an a priori grouping of natural features into classes based on detailed field observations and expert knowledge, often drawing from multiple disciplines, and making use of diverse ecological and geological datasets  and natural history theory , due to difficulties of integrating diverse information sources and complex knowledge. Divergent geographic, linguistic and disciplinary lenses have occasionally caused divergence in a) nomenclature (terminology of reef features), b) structure (how some of these features are grouped) and c) meaning . For example, the term Back Reef can be interpreted in many ways10. Back Reef may be understood differently by an ecologist vs a geologist, or a Caribbean vs a Pacific-based researcher, or have a different relevance to a conservation practitioner vs ecosystem modeller.Classification of coral reef geomorphology has typically occurred using two broad approaches, which are largely congruent11. Specifically for scaling habitat mapping from remote sensing, this largely computational approach 12. Classification methods developed by remote sensing scientists have driven rapid expansion of reef mapping efforts from reef-scale to ocean-scale extents13. However, these approaches are known to be limited by the spatial, spectral, radiometric and temporal resolutions of source data used in the classifications14, and sometimes do not adequately consider the wealth of natural history, existing “traditional” coral reef classifications and associated terminology, or the requirements of users.In the last 40 years, technological advancements in the field of remote sensing have led to a different approach to reef classificationThe first challenge relates to divergence in how a natural scientist or manager understands a complex system, and how that system can be characterized by biophysical data layers . A mismatch can undermine the relevance of the map product being created to potential users.transparency about classification processes is a prevalent problem in large scale mapping and can be a barrier to effective use of maps. A recent study found just 32% of publications provided sufficient information to replicate a series of mapping studies (99/304 published studies reviewed15). Lack of transparency may confound interpretation of classes by users.Secondly, a lack of clarity) or made available to users (accessibility). A recent review found that only 52% of satellite-derived coral reef and seagrass habitat maps were accompanied by a detailed class descriptor that would allow users to interpret mapped classes .The third challenge relates to communication: map classes are frequently not sufficiently defined or described , and accessibility issues were addressed by making Reef Cover’s detailed descriptors, user-friendly, simple, open access and freely available.To address these challenges we have developed Reef Cover, the classification was trialled in two mapping exercises . Reef Cover’s success was evaluated on how effectively it has been used to support creators of large-scale coral reef maps to generate products that have promoted management and conservation outcomes for reefs.Finally, to demonstrate the application of Reef Cover was specifically developed to support the process used to produce and deliver globally applicable coral reef mapping products from remotely sensed data16. The typology acts as a key to bridge historic and contemporary knowledge, plot-scale and aerial viewpoints, and pixel data with natural history to convert pixel data into information in a form suitable for reef management decisions. Accompanying case-studies19 we use to demonstrate its application are also publicly available. The mapping products described in each case study were developed specifically using Reef Cover to support science and conservation of coral reef ecosystems.We sought to develop a robust system which balances the geomorphic complexity of reefs with the need to develop high accuracy maps of each class in the system. The result is a 17-class system that can be (i) applied to remote sensing datasets for future mapping, (ii) used to interpret coral reef maps (iii) effectively disseminated to users – mainly in coral reef ecology and conservation space – in a way that promotes research and conservation.Review. Existing coral reef geomorphic classification schemes  were carefully reviewed22 to identify synergies in terminology and definitions for reef features, and evaluate how well common features can be described in terms of remote sensing biophysical data. The review allowed us to develop a set of classes that build constructively on previous foundational knowledge on coral reef geomorphology and are relatable to existing mapping and classification efforts, and addresses the challenge of relevance.Step 1. Development. Reef Cover classes were then derived from attributes data, building on established machine-led reef mapping theory13. Physical attributes datasets commonly available to remote sensing scientists were examined to refine a set of 17 meaningful internal reef classes that relate to broader interpretation from a natural history point of view, gathered in Step 1. A workshop was organised to gather feedback on classes. Clarity around how each class relates to attribute data addresses the challenge of transparency.Step 2. Dissemination. Reef Cover classes were then documented23 in a way to promote re-use and cross-walking, with a strong focus on needs of the users, to address the challenges of clarity and accessibility. Development of the Reef Cover document considers and details the 1) relevance e.g. rationale behind why it was important to map this class, but also broader global applicability of the class, 2) simplicity e.g., promoting user-uptake by employing plain language, not over-complicating descriptors and limiting the number of classes to manageable amount, 3) transparency supplying methodological basis behind each class, and exploring caveats and ambiguities in interpretation, 4) accessibility including discoverability, open access and language translations to support users, and 5) flexibility allowing for flexible use of the scheme depending on user needs, allowing for flexible interpretation of classes by providing cross-walk to other schemes and existing maps, and making the classification adaptable, and open to user feedback (versioning).Step 3. Three steps were used in the development of the classification scheme.Reef Cover classification was tested in two large scale coral reef mapping exercises: one in the Great Barrier Reef 24 (Case Study 1) and one across Micronesia19 . During this process, the Reef Cover dataset was reviewed to assess how useful it was for both a) producers using Reef Cover to map large coral reef areas from satellite data, and b) consumers using Reef Cover to interpret map products for application to real world problems.Finally, as a proof-of-concept the 3. Communities and economies are highly dependent on the ecosystem services they provide27. This combination of vulnerability, value and a broad and dispersed global distribution mean global strategies are needed for reef conservation, for which maps (and the classifications that underpin them) play a supporting role. Global coral reef maps have been fundamental to geo-political resource mapping and understanding inequalities29, the valuation of reef ecosystem services26, understanding the past30, present31, and future threats to reefs32, supporting more effective conservation34 and reef restoration strategies36, and facilitating scientific collaborations and research outcomes37. Reef conservation science and practice may particularly benefit from technological advancements that allow delivery of more appropriate map-based information, particularly across broader, more detailed spatial scales and in a consistent manner38.Coral reefs represent pockets of biodiversity that are widely dispersed, often remote/inaccessible and globally threatened39 . Technological developments of the 20th century, such as SCUBA demand regulators and compressed air tanks , acoustic imaging for determining seafloor bathymetry , light aircraft for aerial photography (first applied in the 1950s42) and lightweight submersible drilling rigs for coring (applied in the 1970s43), allowed reef structure to be viewed from fresh perspectives. New aerial, underwater and internal assessments of reef structure expanded the diversity of external and internal classes, with hundreds of new terms for features defined10  - challenging. Divergent regional morphologies  and endemic local features  are created by underlying tectonics, antecedent topography, eustatics, climate and reef accretion rates which can all vary geographically52. The diversity of reef types is reflected in the large number of classes defined in the impressive Millennium Coral Reef Mapping Project (68 classes at the between-reef geomorphic level L3), the most comprehensive globally applicable coral reef classification system to date21.Morphological diversity can make global geomorphic classification – particularly between reefs , developed in parallel to the reef edge and coastlines and generally with a distinct ecology53. These internal patterns of three-dimensional geomorphic structure can be remarkably predictable, even between oceans. This makes geomorphic zonation a good basis for consistent and comparable mapping at regional to global scales54. Moreover, congruence between geomorphic zones and ecological partitioning means that ecological understanding can be derived from geomorphic habitat classes, making geomorphic mapping valuable to conservation practitioners55.Internally, reef morphology becomes a lot more consistent. Physical boundaries in the depth, slope angle and exposure of the reef surface create partitioning into “geomorphic zones” , monitoring  and management (Marine Ecosystem Benthic Classification60) have included an ecological component. Classifying reef benthos is important as associated metrics, such as abundance of living coral and algae, are widely used indicators of ecosystem change. However, most classifications that consider benthic cover are operational at reef61 to regional scales, due to the need for very high-resolution remote sensing data11  to be able to reliably determine classes such as coral cover and type, soft coral, turf, coralline algae, rubble and sand. A comprehensive benthic coral reef classification23 that met the Reef Cover objective of being globally scalable  but that also fully recognises and includes the rich benthic detail required to address ecological questions at sub-metre scales is beyond the scope of this classification. In the coming years it is likely that further advancements in technology – both downscaling of remote-sensing and up-scaling of field observations63 - will enable us to address this spatial mismatch.Many classifications developed for reef mapping  and geo-ecological processes  below the water that led to the arrangement17. However, these classes frequently also can be related to biophysical information on slope, depth and aspect that can be determined remotely. A thoughtfully prepared classification – that adheres to Stoddart’s (1978) classification principles, which state that classes should be explicit, unique, comprehensible, and should follow the language of prior schemes - can support production of maps and other science  that are still relevant to historic work but that can go forward with consistent definitions21.The challenge of creating the Development of appropriate mapping classes requires a sensitive trade-off between the needs of users , and the input data available and quality of the globally repeatable mapping methods of the map producers.13, with depth and wave exposure being particularly important information to explaining geomorphology64.While vast in terms of scalability, data producers are more constrained in terms of sensor capabilities such as spatial resolution (limited to pixels) and depth detection limits (limited by light penetration), and processing power . Physical conditions and colour derived from remote sensing, along with their textural and spatial relationships, can be linked to reef zonationReef Cover classes that could be defined by attributes available from most commonly available public access or commercial satellite data, but that also corresponded to common classes found in the classification literature, and relevant from a user perspective, we looked for intersectionality between physical attribute data that can be derived from satellites but also help shape and define reef morphology.To select a set of 39. Underlying geomorphic structural features can almost always be characterised in terms of three core characteristics: i) depth, ii) slope angle and iii) exposure to waves 66. Bathymetric data also provides the basis for other critical depth-derived products, slope and aspect, which are used to distinguish geomorphic classes and reef environmental parameters, e.g., exposure to breaking wave energy  from Reef Crest and Reef Flat classes  mean relative depths are more appropriate, which is why absolute numbers were not used in Reef Cover definitions.Reef Flats are defined as being horizontal ‘flattened”69 “flat-topped”70; Fore Reef slope zones often include references to slope angle , and Walls– common on atolls - are “near vertical” features. Variability in slope continuity can also be an important way to demarcate zones71. Montaggioni illustrated a range of representative profiles across atolls and barrier reefs, with convoluted profiles allowing subdivisions of reef slope, particularly across fringing reefs which are less likely to show a uniform reef slope than an atoll53, and Reef Crest is sometimes defined as a demarcation point separating the Fore Reef from the Reef Flat72. Where water depth can be derived from remotely sensed spectral data, bathymetry can be used to directly calculate slope  or by considering the local variance in depth .Slope angle, either absolute angle or discontinuities in angle acting as a break between zones, is an important differentiator of reef zones. Reef Cover classification, slope angle data were used to distinguishing Fore Reef classes such as Reef Slope and Reef Front, from horizontal classes such as Outer and Inner Reef Flat and Lagoons . Exposure influences profile shape and importantly the communities growing in the zone, so that slopes with identical profiles could have very different communities73. Sometimes these zones are related to the communities found there. Meanwhile, exposure across the reef means back-reef zones contain sheltered water bodies. Together with data on water depth and bathymetry, wave energy data was key for distinguishing key Reef Cover classes75.Physical exposure of reefs is a key driver of zonation. 65. Colour and texture information derived from satellites can be used to manually draw polygons around similar geomorphologic units or habitats but provide the basis to drive image-based thematic mapping  and texture, through spectral processing64. Texture measures are also used to improve classification by allowing spectrally similar substrates like corals and macroalgae to be distinguished. Reef Flats, for example, having a single driver of zonation, in contrast to several drivers on most other zones, makes benthic zonation particularly distinct39, and easily detectable as coloured bands in aerial images of reef flats. This allows colour and texture to be used to distinguish Outer Reef Flats, which have a greater component of photosynthetically active corals and algae, from Inner Reef Flats which appear brighter due to a higher proportion of sand build up in this depositional area . Because of the influence of large-scale processes on reef development, these zones occur in order53. Reef Crest is arguably the most defining characteristic of any reef – the break point at which a sharply defined edge divides the shallower platform from a more steeply shelving reef front71, around which other geomorphic zones arranged in parallel77. As a result, spatial arrangement of zones can be informative for mapping (Table Back Reef is often defined as being contiguous to the Reef Crest (Back Reef is often defined as any reef feature found landward of the crest).Natural scientists agree that reefs feature three major geomorphic elements: a ng Table . For exa68) is another feature used classically to define reef zones, but that could also be derived from satellite imagery.Enclosure to semi-enclosure within a bordering reef construction  and which benthic assemblages display heterogeneity (sub-metres)32. Medium spatial resolution multispectral data (5 to 30 m) is the most commonly used satellite information used for coral reef habitat mapping42, and classification of internal geomorphic structures may be best suited to this kind of data.13. More than this can become overwhelming for users and computationally expensive for developers at this point in time. Many coral reef classifications contain four or five hierarchical levels and high numbers of classes: the Millennium Coral Reef Mapping Project (MCRMP) was ambitious in developing a standardised typology that captured much of the reef type diversity, but despite defining over 800 reef classes defined at the finest  scale36, level 3 (68 classes) continue to be more popularly adopted in publications using this dataset. To keep the classification simple, Reef Cover was limited to 17 geomorphic classes, with simple one line definition provided. A limited class was needed 1) to make it manageable for users, 2) to make it computationally manageable for very large  data processing and 3) reduction in classes compared to MCRMP allowed for consistent automated mapping at the global scale – so that whole regions could be directly compared for monitoring and management.Reviews of habitat mapping from remote sensing found the number of map classes averages 18 at continental and global scalesReef Cover class definition also outlines other commonly used terms for concepts (synonymy) and explains different interpretations of the same meanings and understanding of the relations between concepts.Short definitions were provided in plain language for simplicity. To address additional uses issues of semantic interoperability each Simplicity was achieved by (1) choosing an appropriate mapping scale , (2) limiting the number of geomorphic classes (17 classes), and 3) providing clear (1 line) descriptors with additional information to address issues of semantic interoperability.14. Describing how the classification relates to data (Step 2) and producing a detailed descriptor (Step 3) along with a diagram allows classification to be understood and also adopted for different projects. We also attempted to address transparency by relating Reef Cover classes to other major global mapping and monitoring efforts , only 13% were accompanied by a clear classification that defined the meaning of map classes75. Much information remains locked behind paywalls, and additional barriers exist including discoverability. To promote accessibility and encourage use, all data were made publicly available (see Data Records section for access). Terms were translated into different languages, as science published in just one language has been shown to hinder knowledge transfer and new findings getting through to practitioners in the field86.Another barrier to the use of analysing and interpreting big data is access76. Flexibility issues were addressed by 1) not prescribing absolute thresholds to each class, instead providing information on how classes relate to each other  users to interpret with flexibility, 2) providing additional information (Standard Descriptors) including main features, exceptions to rules and broadness as to provide users with a broader understanding of hidden complexities when interpreting class meaning, 3) remaining open to feedback, we hope this Reef Cover version 1 can be improved upon with feedback from the community.One criticism of thematic habitat maps derived from remote sensing is a lack of flexibility: categorical descriptions of habitats are by design a discrete simplification of the ecological continua, thus classifications limit the interpretation and questions that can be askedr Tables  allowingReef Cover classification system (Version 1.0) presented in this paper has been made publicly available as a list of map classes and descriptors “Reef Cover Classification (v1). Internal coral reef class descriptors for global coral reef habitat mapping” (pdf format) through Dryad23.Name - short class name, Standard Label - longer class name, Standard Description – detailed class descriptor, including context and main attributes (highlighted), Translations - Standard Name in different languages, Synonyms - list of commonly used synonyms.The classification system includes 17 Reef Classes Fig.  and theiDiagrams of how each class relates to major reef types, and a Glossary of terms is also provided.Reef Cover Classification document also contains an Attribute Table  to create their own coral reef maps from Reef Cover (Methods)87. The second supplies users  with downloadable regional case study maps developed using the Reef Cover classification (Maps)24 and presented as case studies in the Technical Validation section.Methods. Methods and code for use in global coral reef habitat mapping are freely available in Zenodo [10.5281/zenodo.3833246]87.Maps. Static coral reef habitat maps of the Cairns Management Region of the Great Barrier Reef Marine Park (GBRMP) produced using the Reef Cover classification are available on Pangaea [10.1594/PANGAEA.925657]24. Caroline and Mariana Islands produced using the Reef Cover classification outlined are available to download from Zenodo [10.5281/zenodo.3953052]19. Updates to the case study, along with maps for the entire globe using Reef Cover will be available in future [https://allencoralatlas.org/atlas/]18. Free online lessons to support users and creators of maps are also available [https://reefresilience.org/online/].Two additional resources accompany this data record in order to improve the uptake and use of Reef Cover classification is to facilitate conversion of large amounts of remote-sensing data into large-scale coral reef mapping products that support the work of coral reef practitioners and scientists. To validate Reef Cover, the classification was applied to two independent large-scale mapping efforts to test whether Reef Cover helped creators and users of the maps. The first, led by researchers in Australia, aimed to map coastal shelf reefs in a section of the Great Barrier Reef from 15 m pansharpened Landsat 8 data using an Object-based approach. Maps were specifically developed to support Great Barrier Reef Marine Park monitoring and management (case study #1). The second was an international collaboration to map two remote oceanic atoll chains in Micronesia as part of a global mapping effort to improve conservation outcomes for reefs, and used 5 m Planet Dove data and an automated cloud-based mapping approach . Each map represents a significant leap forward in scientists’ ability to capture very large areas (thousands of kilometres) of shallow reef habitat in detail in a consistent and repeatable manner (see “Application” section). They also represent improvements in usability of maps: for the two case studies, we detail the challenges for producers in adopting the classification (see “Assessment” section), and successes were assessed on whether products could potentially be translated into real world scientific and conservation outcomes (see “Outcomes” section).The goal of the Reef Cover was applied by a university-based research group to map 237 reefs in the central Great Barrier Reef Marine Park across a 37,000 km2 area to support local management efforts by government  but includes the tourist hubs of Cairns and Port Douglas, making it one of the most highly visited regions of the GBR. The Management Region supports more tourism than any other area as well as cultural activities, shipping, research, commercial and recreational fishing. The platform and ribbon reefs that occur in this region are typical of those on the Queensland Shelf, but very different to the oceanic atolls of case study #2. The high level of use of these reefs makes managing impacts in this area a priority for the Great Barrier Reef Marine Park Authority (GBRMPA), yet only outline extent maps of the large platform and ribbon reefs that make up this section existed previously88.The Reef Cover classification was used to guide the production of geomorphic coral reef habitat maps using object-based analysis (via Trimble eCognition 9 software) following a methodology developed by Roelfsema et al.89. Satellite data, including sub-surface and seafloor reflectance as well as tide-corrected water depth to mean surface level were derived from a mosaic of 8 Landsat 8 Operational Land Imager scenes, pan sharpened from 30 m x 30 m to a 15 m by 15 m resolution, and combined with wave models (simulating waves nearshore (SWAN) model74), field data90 and satellite derived bathymetry to generate a geomorphic map layer, following a ruleset based on Reef Cover. A full methodology is available in Roelfsema et al.36.In this case study, the Reef Cover class. For example, a significant wave height of >2 m was set as the threshold for a Reef Slope (“Fore reef exposed”) and <2 m as a Back Reef class, while a 0.75 m depth threshold was used to distinguish Reef Crest, Outer Reef Flat and Inner Reef Flat from other classes30. Slope angle thresholds (e.g. 5–90 degrees for a Reef Slope or Sheltered Reef Slope) and sub-surface reflectance thresholds were also set, and these rules were then used to assign data to geomorphic classes.A restricted geographic range and local knowledge about geomorphology enabled the researchers to apply hard thresholds to each class which allowed data to be assigned to a particular 2 area, into a sharable online habitat map of some of the planets remotest coral reefs19  and through detailed bathymetry surveys of US jurisdictions of the Mariana Islands by NOAA’s Pacific Islands Benthic Habitat Mapping Center (PIBHMC). The Millennium Coral Reef Mapping Project (MCRMP) used Landsat data to map 6000 km2 of reef across Palau, Federated States of Micronesia, the Marshall Islands and Gilbert Islands91 and the Japanese Ministry of the Environment mapped the region using AVNIR2 and Landsat data in 201078. Many of these maps were used to produce the United Nations Environment Program (UNEP) World Conservation Monitoring Centre (UNEP-WCMC) Global Distribution of Coral Reefs data product, that is used as a global coral extent layer today86.Two remote island chains in Micronesia were targeted. The Mariana Islands are a crescent-shaped archipelago of 15 volcanic islands making up the Commonwealth of the Northern Marianas (CNMI) and the Territory of Guam. The Caroline Islands, an archipelago of over 500 small islands, span 3500 km from Palau’s Hatohobei Reef in the westernmost point to Kosrae (Federated States of Micronesia) in the east. Spread across a 3 million kmReef Cover classification was used to generate coral reef habitat maps of the Marianas and Caroline Islands from satellite data, using an automated cloud-based machine learning approach and combined with object-based clean-up developed by Lyons, et al.38. Sentinel-2, Landsat 8 and Planet Dove satellite-derived relative bathymetry and surrogate for slope angle were used to identify major structural classes such as lagoons or reef slopes. In contrast to Case Study 1, hard thresholds were not applied to Reef Cover but training data for the classifier and neighbourhood rules  were more important in determining class. Expert derived, manually interpreted training data were used to train an algorithm to identify Reef Cover classes from the data. Combined with information on the substrate, calculated from colour, brightness levels and texture produced by spectral reflectance data from the satellites, geomorphic classes were assigned .In both case studies, 2 of shallow water coral reefs were mapped from Innisfail to Lizard Island, with 15 of the 17 Reef Cover classes used . This allowed for a refinement of the total reef area estimate for this region from 3,164 km2 to 1,984 km2 , Outer Reef Flat (15%), Inner Reef Flat (15%) and Reef Crest (12%) which together made up >60% of the shallow mapped reef area36, reflecting the broad flat-topped nature of the platform reefs typical of the Queensland shelf. Following the release of these maps to GBRMPA (the governing body), several applications that could directly support management have been identified, from improved understanding of coral ecological and biophysical processes through much refined extent layers to helping select appropriate areas for reef restoration and adaptation 36.A total of 1,984 km2 of reef were mapped, with eight classes used  comprising 23% of the shallow mapped reef area, the Inner and Outer Reef Flat classes (together 35%) and Shallow Lagoon (18%), reflecting that the region is typified by atolls or high islands with or without expansive lagoons. The spread of classes was largely in agreement with MCRMP outputs, who mapped more than twice as many classes , but found five of these  made up 85% of the map extent suggesting at very large scales some of the additional detail is redundant. A crosswalk found the distribution of classes was comparable with the Allen Coral Atlas classes, with Reef Front (MCRMP Forereef) accounting for 16%, Inner and Outer Reef Flat (seven classes combined) 43% and Shallow Lagoon (three classes combined) 30%. The total reef area mapped by MCRMP across the same area was 15,808 km2 and 38 classes at the L4 level  and Land, this became 3,809 km2 and 24 classes, the higher extent value reflecting MCRMP’s ability to map deeper  and the inclusion of “Variable Depth” classes in this number.A total of 2,188 kmsed Fig. , Table 3Allen Coral Atlas estimated 1,569 km2 of Reef Top, MCRMP 1,293 km2 of Shallow Reef and UNEP-WCMC, who provide an outline of shallow reef, estimated an areal coverage of 1,590 km2 of shallow reef area.Shallow reef extent estimates for the whole region were closer in value: Reef Cover classes (Table very low confidence to 10 very confident) allowed assessment of how applicable map producers working with satellite data found the Reef Cover classes. While all classes were scored > 5, there was some variability in confidence between classes, with mappers having the greatest confidence and consistency in their ability to determine Reef Crest (mean score 8.0 ± 1.3 SD) and Lagoon (8.0 ± 0.9 SD) from remotely sensed data, and the least confidence around Back Reef Slope (5.9 ± 1.7 SD).A team of eight experts involved in the creation of the maps were asked to evaluate their confidence in assigning es Table . ScoringReef Cover classification allowed us to test the usability of the typology to map producers, and it was generally found to be flexible and adaptable, and mappers more confident than not in interpreting and applying classes. It could be aligned with existing schemes, but with just 17 options remained simple enough to allow for automated processing at 1000 km + scales with minimal human supervision. While limitations of the source data meant not all Reef Cover classes could be captured in the exercises, retaining classes like Wall and Slope in the classification is important for producers who may be work with source data with finer spatial resolution or depth. Moreover, the classification proved a good foundation for development of both sets of maps, despite the large differences in reefs being mapped , the approaches used  and the rationale , and maps created are pushing the boundaries of in terms of detail, consistency and spatial extent.Application of the Reef Cover classification was to improve the conversion of large amounts of remote sensing data into a mapped format that not only could visually communicate reef information, but would practically support coral reef science and conservation work. The final test of Reef Cover is whether the classification aided translation of remote sensing data into a real-world practical outcome for users, with success measured in scientific and conservation outcomes.The overarching purpose of developing the 92, but has also rapidly degraded over the last 30 years, and managers are under pressure to halt the declines that have been attributed to global warming, cyclones, pollution and Crown-of-Thorns Starfish outbreaks93. Improved spatial information on reef geomorphic zonation is critical to support scientific work to understand how the GBR is changing, and to support resource management decisions that enable conservation of the reef and its essential ecosystem services. Reef Cover here supported production of geomorphic maps specifically to support governance for a significant portion of GBR shallow reefs at a level of detail not previously available. Compared to existing maps that only outline each reef, the increase in detail provided by these new habitat maps enabled discrete characterisation of each reef’s geomorphology and benthic composition. With the new habitat maps, areas within each reef can be identified as either coral habitat or not coral habitat.The Great Barrier Reef (GBR) is of immense biological, cultural and economic importance36. Living coral cover remains a key metric for managers assessing reef health, and in this Case Study the Reef Crest, Reef Slope and Reef Front classes were further used as a basis to derive an estimate of habitat available for coral colonisation  – a value of 761 km36.Creation of these Cairns Management Region maps has led to refinement of coral and Crown-of-Thorns recruitment modelling and water quality modelling exercises, both of which were previously using reef extent maps, many of which included large areas of deep water overestimating reef size and distribution80. The six nations remain committed to the Micronesia Challenge, an international conservation strategy across the six nations of Micronesia to conserve 30% of marine resources by 2020 in line with UN Sustainable Development goals. Mapping can help support marine spatial planning exercises: habitat maps such as the MCRMP of the area have been previously used to calculate estimates of fish biomass81.The area mapped in Case Study #2 is home to 353,000 islanders whose culture, and many cases livelihoods, are related to coral reefs though fishing, recreation and tourism. These reefs continue to be impacted by pressures from climate change (warming and high intensity storms) and local pollution (associated with crown-of-thorns sea star outbreaks) and overfishingReef Cover classes and displayed in an interactive web platform at AllenCoralAtlas.org. These products are now being used to a) communicate coral reef information and b) support conservation planning by coral reef practitioners in the region, from NGOs and research organisations to the communities that live on the islands. For example, maps of reefs in the Republic of Palau have been integrated into a spatial analysis model by The Nature Conservancy, to help the organisation identify where to site aquaculture as part of a national-scale project in partnership with the Palau Environmental Quality Protection Board, Bureau of Marine Resources and Palau Community College. Meanwhile, maps of the reefs across the Federated States of Micronesia are being used by the Waitt Institute to help plan research expeditions to remote submerged reefs, as well as support marine spatial planning exercises in the area. Researchers commented that, “for a place like the FSM that has a pretty crude current estimate for distribution of coral reef structures, the Atlas is a huge value for them and a very necessary tool in the marine spatial planning process” and “These are some of the most critical pieces of information to create policy.” In Micronesia’s Outer Islands, One People One Reef, a collaboration of communities and scientists, believed the open-access and easy to use format of the products could prove useful in communicating ideas and connecting with remote communities involved in traditional management. Finally, researchers leading the Micronesia Challenge at the University of Guam found the maps to be a dynamic and interactive communication tool during stakeholder meetings, allowing meeting participants to design and review current marine protected areas, and negating the need for an on-site GIS expert during meetings.In the production of the Micronesia maps, 125 GB of remote sensing spatial data (extracted from over 20TB of raw Planet Imagery) was converted into eight 82. On balance, use of Reef Cover helped creators generate large scale consistent maps from remote sensing data, and map classes proved relevant and useful for researchers and managers looking to solve management and conservation challenges.There is still room to improve both the mapping process and user experience: the number of classes used in the typology was refined due to data limitations. A survey of 26 users found 42% of those frequently worked in places with little or no internet connectivity, and 10% needed to work on a mobile deviceReef Cover classification system (Version 1.0) presented should download and read though the full “Reef Cover Classification (v1). Internal coral reef class descriptors for global coral reef habitat mapping” available as a .pdf through Dryad23.Users planning to create their own coral reef geomorphic maps using the The document provides detailed information on how to define and interpret each of the 17 suggested Geomorphic Reef Classes Fig.  and relaDiagrams of how each class relates to major reef types  and in full detail at 10.5281/zenodo.383324687.Finally, for additional support in creating maps using Reef Cover classification document as a basis for creation of new maps from remote sensing, the classification is also intended as a guide or key for map users in the conservation space wanting to interpret the Great Barrier Reef24 or Micronesia maps88 or any future mapping products  that are created around the classification.As well as using the Reef Cover Classification (v1). Internal coral reef class descriptors for global coral reef habitat mapping” available as a .pdf through Dryad69, to get a full understanding of how classes can be interpreted.Users planning to interpret geomorphic mapping products for their own scientific or conservation goals should also download and read though the full “Remote Sensing and Mapping for Coral Reef Conservation Online Course, designed to help marine managers, conservation practitioners, scientists, decision makers, and GIS professionals decide whether these remote sensing products and mapping technologies can help inform their conservation and restoration work. Lesson two is developed around Reef Cover and provides more information in how to interpret the Reef Cover map classes.To further support users, a free online course, Reef Cover class definitions link to Cairns Management Region map class names.Static geomorphic digital maps of the Cairns to Cooktown management area of the Great Barrier Reef are downloadable as a shapefile from 10.1594/PANGAEA.925657. This dataset consists of shapefiles and auxiliary files for each of the geomorphic zonation and dominant benthic cover types on the shallow offshore reefs of the Cairns to Cooktown Management area of the Great Barrier Reef, Australia. See Table 19 [10.5281/zenodo.3953052]. MacOS can open tar and tar.gz files by default with the Archive UtilityUsers, Windows users will need an external program (7-Zip or WinRar) to extract files. The compressed folder Western-Micronesia contains three sub-folders: geomorphic, benthic and boundary. The geomorphic folder contains digital geomorphic coral reef habitat maps described in this study, mapped to 12 Allen Coral Atlas Reef Cover classes  and stored in a variety of standard geospatial formats. Shapefiles are available for use with most GIS software , a KML file for viewing in a browser such as Google Earth (geomorphic.kml), and GeoJSON (geomorphic.geojson). The benthic folder contains the same file formats, but same reefs have been mapped to six benthic classes: Seagrass, Coral Habitat, Rubble, Sand, Microalgal Mats and Rock (see Reef Cover for class descriptors69), and the boundary folder contains the outline of the area mapped, again in the same three formats. Users will be able to access updates and download further regional maps through via this Zenodo link [https://zenodo.org/record/3833246#.Xxo1togzZPY] which links through dynamic maps currently hosted to AllenCoralAtlas.org [http://www.allencoralatlas.org/]18, where up-to-date usage notes are available through the “FAQ” section. Further support is available by emailing support@allencoralatlas.org. The Allen Coral Atlas currently has two mapped themes: one that displays global geomorphic zones (12 classes) and another for global benthic zones (6 classes) commonly associated with shallow water tropical coral reefs. See Table Reef Cover class definitions link to Allen Coral Atlas class names.Static geomorphic digital maps of shallow coral reef habitat across the Caroline and Mariana Islands in western Micronesia (Version 1.0) are downloadable in several formats as a tar file (western-micronesia-aca.tar.gz) from ZenodoAllen Coral Atlas classification process and links to the code used to produce the maps, along with the data themselves can be accessed through Zenodo link [10.5281/zenodo.3833246]87. This link provides resource and citation for code, standards and publications arising from the Allen Coral Atlas18, including Google Earth Engine source code for mapping algorithms: https://github.com/CoralMapping/gee-mapping-source. This repository contains all the Google Earth Engine source code that generates the mapping outputs on the Allen Coral Atlas, including maps and validation statistics.Full methodology for creating your own maps following the Great Barrier Reef mapping methodology are available here. The


Epigenetic alterations have contributed greatly to human carcinogenesis. Conventional epigenetic studies have been predominantly focused on DNA methylation, histone modifications, and chromatin remodelling. Epitranscriptomics is an emerging field that encompasses the study of RNA modifications that do not affect the RNA sequence but affect functionality via a series of RNA binding proteins called writer, reader and eraser. Several kinds of epi-RNA modifications are known, such as 6-methyladenosine (m6A), 5-methylcytidine (m5C), and 1-methyladenosine. M6A modification is the most studied and has large therapeutic implications. In this review, we have summarised the therapeutic potential of m6A-modifiers in controlling haematological disorders, especially acute myeloid leukaemia (AML). AML is a type of blood cancer affecting specific subsets of blood-forming hematopoietic stem/progenitor cells (HSPCs), which proliferate rapidly and acquire self-renewal capacities with impaired terminal cell-differentiation and apoptosis leading to abnormal accumulation of white blood cells, and thus, an alternative therapeutic approach is required urgently. Here, we have described how RNA m6A-modification machineries EEE  could be reformed into potential druggable candidates or as RNA-modifying drugs (RMD) to treat leukaemia. Moreover, we have shed light on the role of microRNAs and suppressors of cytokine signalling (SOCS/CISH) in increasing anti-tumour immunity towards leukaemia. We anticipate, our investigation will provide fundamental knowledge in nurturing the potential of RNA modifiers in discovering novel therapeutics or immunotherapeutic procedures. There are several conventional drugs, including epigenetic-based drugs targeting DNA and histone modifications, that have been already approved by the US food and drug administration (FDA) for the treatment of acute myeloid leukaemia (AML). Targeting RNA-modification machineries as RNA-modifying drugs (RMDs) refuelled the scientific interest to discover novel therapeutic drugs. Although RNA modification was discovered as early in the 1970s , it refuAML is a group of neoplastic diseases involving bone marrow with or without the involvement of peripheral blood. It is the most common type of haematological disorder prevalent in children and adults, globally affecting nearly three million people every year . For somThis section describes the strategies to treat haematological disorders, especially AML by targeting intracellular RNA-modification machineries, like EEE  , microRNVu et al., 2017 have demonstrated the role of m6A writer enzyme ‘Mettl3’ in AML progression  supporteportal.gdc.cancer.gov/) and several leukaemia cell lines and AML cells carrying t(11q23), t, or t translocation)-mutations [cKO or Mettl14fl/fl-CREERT mice decreases cell proliferation and self-renewal capacity of CD45.2+ (of Mettl14cKO) in peripheral blood as compared to the CD45.1+ CD45.2+ (competitor) cells [+ promotes cell-differentiation demonstrated by increase in differentiation markers (CD11b in monocytes and CSF1R in macrophages). These findings suggest that Mettl14 induces leukaemia growth by increasing cell-proliferation and decreasing terminal cell-differentiation. Conversely, the differentiation-inducing agent (OP9 culture medium) [Myb and Myc transcripts [Weng et al., 2018  demonstrutations . Of noteutations . These fr) cells . However medium) , PMA [56 medium)  significnscripts  and SP1 nscripts . Previounscripts ,59,60,61nscripts . Taken tnscripts ,  carrying t(11q23)/MLL-rearrangement and t/PML-RARA mutations, called acute promyelocytic leukaemia (APL) [+ CD14+) and increased (CD11b− CD14−) markers. Conversely, the induction of ATRA significantly decreased FTO and thereby rescued/increased the expression of its target ASB2 and RARA genes [Li, et al., 2017 ; have deia (APL) . FTO affia (APL) ,51, and ia (APL) . These tia (APL) ,66,67,68ia (APL) . Howeveria (APL) ,70 justiia (APL)  characteRA genes ,73. MoreRA genes  , Inv(16), t and t(11q23) mutations, independent of specific TP53-mutation  and TP53-wild type (in MA9.3-ITD and MOLM13) human cell lines [Shen et al., 2020  have demll lines . HoweverKO) model [+Lin−) co-cultured with MLL-AF9 cells into recipient (CD45.1) mice significantly delayed leukaemia progression and prolonged survival with decreased splenomegaly, white blood cells count and immature blast cell (CD11b (Mac-1)+ c-Kit+) populations.To validate the requirement of ALKBH5 in the promotion/transformation of AML cells, Krivtsov et al., 2006 used MLL-AF9 (MA9)-induced leukemogenesis model  model . ALKBH5-O) model . On the Moreover, ALKBH5-deletion inhibited the engraftment of MA9-transformed donor cells in the peripheral blood as compared to the ALHBH5-wild type donor under lethal irradiation conditions. These investigations suggest that ‘ALKBH5’ plays a key role in promoting leukemogenesis of a specific subset of AML cells. The relevant experiments on leukaemia xenograft mouse model further potentiate the involvement of ALKBH5 in self-renewal and maintenance of AML-cells.RNA sequencing of ALKBH5-depleted MOLM13 and NOMO1 cells revealed TACC3 (transforming acidic coiled-coil-containing protein3) could be a positive target of ALKBH5 . FurtherParis et al., 2019 , demonst+CD38−) population is believed to be a major limiting issue during stem cell transplantation therapy, due to a lesser number of HSPCs populations from a single human umbilical cord blood donor. Therefore, ex-vivo expansion of HSPC population is a major and encouraging challenge for its widespread use. Under steady-state, the YTHDF2 sequesters its target gene T-cell acute lymphocytic leukaemia 1 , which is required for the normal proliferation and self-renewal of HSPCs [KO) in HSPCs or hUCB-HSCs significantly boosted (10-fold increase) the number of HSPCs by rescuing the expression of Tal1 gene by enhancing mRNA stability. The requirement of YTHDF2 in the maintenance of HSCs, by targeting pro-inflammatory cytokines, was also demonstrated by Mapperley et al., 2020 [Li et al., 2019 ; have deof HSPCs ,96 and iof HSPCs . The sell., 2020   and non-leukaemia (HPC7) hematopoietic cells [ltd/+ model showed convincing anti-leukaemia effect of STM2120 inhibitors [Recently, Eliza et al., 2021  have disic cells . Moreovehibitors . Similarhibitors ,45,47.Huang et al., 2019 , have exSu et al., 2020  have demIn addition to m6A-modifiers, microRNAs also showed promising outcome in the treatment of acute myeloid leukaemia (AML). Fang et al., 2016  have demmiR-34a was shown to be another microRNA playing a crucial role in controlling elderly AML, who were ineligible for conventional chemotherapy. The authors  have fou+CD38−) compared to the non-LSC  populations isolated from MOLM13 and KG-1a cell lines determined by LTL lectin assays [Liu et al., 2019 ; demonstn assays . HoweverMechanistically, authors showed that FUT4 is regulated by specificity protein 1 (Sp1) transcription factor. Interestingly, in silico analysis showed that miR-29b can target Sp1 transcript and inhibits its expression along with Wnt/β-catenin activation. miR29b-mimic inhibits and anti-miR29b promotes Sp1 expression  and thus+T-cell effector function [−/−) derived from human iPSC (induced pluripotent stem cells) significantly enhance the expansion as well as survival in the tumour microenvironment, confirmed by improved metabolic fitness of NK-cells, characterised by increased glycolytic capacity and mitochondrial respiration (OxPhos activity) via mTOR signalling response. Moreover, increased NK-cell cytotoxicity was reported against multiple tumour cell lines as well as in in-vivo leukaemia xenograft models [In addition to epigenetic modifiers (RMDs) and microRNAs; the role of cytokine-inducible SH2-domain-containing protein (CISH or CIS) as anti-tumour and anti-leukaemia activity, also, cannot be denied because of its versatile role in regulating cytokine signalling via sensitizing immune cells. For example, (i) CISH knockdown has been shown in increasing anti-tumour immunity by enhancing CD8function . (ii) CIfunction  and thusfunction . (iii) Mfunction ,111 and function . Furtherfunction , where tt models . This ret models ,115,116.t models .In this review, we have described the potential of m6A-modifiers in developing cancer precision medicines and explained how ‘epitranscriptomics’ plays a central role in regulating the crucial genes associated with AML. Among these, the m6A-writer proteins Mettl3 and Mettl14 have been found to be aberrantly expressed in specific subtypes of AML and promotes leukemogenesis by regulating MYB, MYC, Bcl2, pTEN, and PI3K-AKT pathways ,49,118. Targeting m6A-modification machineries has revolutionised the class of epigenetic research in discovering novel therapeutic drug targets. Co-targeting of intracellular genes along with m6A-modification machineries will improve the level of immunotherapy against those cancers that are resistant to the immune checkpoint-based therapeutics in patients with blood cancer. Not far in the future targeted therapy in combination with other drugs may become a universal panacea to control many diseases and other forms of cancer. Moreover, targeting suppressors of the cytokine signalling family of proteins would further potentiate the immunotherapeutic potential of RNA modification inhibitors by targeting epigenetic regulator of dendritic cells. Pharmaceutical approaches pertaining to RNA epigenetic modification machinery are expected to shed light on the field of cancer immunotherapy for AML and other forms of blood cancer. Nevertheless, several human clinical trials are required to access the therapeutic value of these inhibitors.Box 1Epitranscriptomics: refers to the post transcription RNA modifications that regulate the activity of the RNA molecule without affecting RNA sequence. The major epitranscriptomic machineries are Writer (editor), Eraser (remover) and Reader (effector) that govern this emerging [emerging .Writers: are the main epitranscriptomic enzymes that incorporate methyl group to various residues of the transcript. Some examples of writers are Mettl-3, Mettl-14, Mettl-16, WTAP, KIAA1429, RBM15/RBM15B and ZC3H13.Erasers: are the main epitranscriptomic enzymes that remove methyl group added by the writers. Some examples of erasers are FTO, ALKBH-3 and ALKBH-5.Readers: are the main epitranscriptomic enzymes/proteins that read n6A-marked transcripts and affect its regulation. Some examples of Reader are YTHDF-1, YTHDF-2, YTHDF-3, YTHDC-1, YTHDC-2 and ELAV1.


Introduction: Carbon ion radiotherapy (CIRT) is a novel treatment for prostate cancer (PCa). However, the underlying mechanism for the individualized response to CIRT is still not clear. Metabolic reprogramming is essential for tumor growth and proliferation. Although changes in metabolite profiles have been detected in patients with cancer treated with photon radiotherapy, there is limited data regarding CIRT-induced metabolic changes in PCa. Therefore, the study aimed to investigate the impact of metabolic reprogramming on individualized response to CIRT in patients with PCa.Materials and Methods: Urine samples were collected from pathologically confirmed patients with PCa before and after CIRT. A UPLC-MS/MS system was used for metabolite detection. XCMS online, MetDNA, and MS-DIAL were used for peak detection and identification of metabolites. Statistical analysis and metabolic pathway analysis were performed on MetaboAnalyst.Results: A total of 1,701 metabolites were monitored in this research. Principal component analysis (PCA) revealed a change in the patient's urine metabolite profiles following CIRT. Thirty-five metabolites were significantly altered, with the majority of them being amino acids. The arginine biosynthesis and histidine metabolism pathways were the most significantly altered pathways. Hierarchical cluster analysis (HCA) showed that after CIRT, the patients could be clustered into two groups according to their metabolite profiles. The arginine biosynthesis and phenylalanine, tyrosine, and tryptophan biosynthesis pathways are the most significantly discriminated pathways.Conclusion: Our preliminary findings indicate that metabolic reprogramming and inhibition are important mechanisms involved in response to CIRT in patients with PCa. Therefore, changes in urine metabolites could be used to timely assess the individualized response to CIRT. Carbon ion radiotherapy (CIRT) is a novel and powerful tool to treat prostate cancer (PCa). Studies have shown an excellent five-year biochemical disease-free survival (bDFS) and low levels of late gastrointestinal and genitourinary toxicities , 2. ThisMetabolic reprogramming is one of the main hallmarks of malignancy, in which tumor cells alter their metabolism, microenvironment, and immune cell function to promote their growth, proliferation, and immune evasion . SignifiA pilot study conducted in Poland evaluated the free serum and urine amino acid profiles in patients with PCa . The resAs urine contains over 2,500 metabolites, it can be easily used to evaluate global metabolic changes in patients with cancer . TherefoFrom July 2020 to December 2020, 15 patients with pathologically confirmed prostate adenocarcinoma were enrolled in this study. CIRT was delivered using the Siemens IONTRIS particle therapy device. The clinical target volume (CTV) included prostate with or without proximal seminal vesicles based on different risk group types. The median CIRT dose to the prostate was 60.4 GyE (range 55.2–65.6 GyE) in 12–16 fractions and was prescribed to the 99% isodose line. Risk stratification was based on NCCN guidelines version 2.2020. The demographic and clinical characteristics of enrolled patients are summarized in The patients' urine samples were collected in the 4 h before receiving the first fraction and 4 h after finishing the last fraction and then stored at 4°C immediately after collection. A 0.22-μm membrane filter was used to remove contaminated bacteria, and 800 μl of chilled methanol/acetonitrile  solution was added to 200 μl of the thawed samples. The supernatant was extracted from the centrifuged mixture, transferred into a new Eppendorf tube, and evaporated into a dry solid. The dry supernatant was redissolved into 200 μL chilled acetonitrile/water  and transferred into smaller sample vials. Quality control (QC) samples were prepared by mixing equal amounts (50 μL) of each sample.High-throughput UPLC-MS/MS  analysis of urine samples was performed on an AB SCIEX ExionLCY system combined with AB SCIEX 500R QTOF. The urinary metabolites were separated in an ACQUITY UPLC BEH Amide 1.7 μm (2.1 × 100 mm) column. Two mobile phases were prepared. Mobile phase A contained water with 10 mM NH4FA and 0.1%FA, and mobile phase B was acetonitrile: water = 95:5 (V/V) with 10 mM NH4FA and 0.1%FA. A 17-min gradient was applied as follows: 2 min, 100% B; 11 min, 45% B; 12 min, 45% B; 12.1 min, 100% B; and 17 min, 100% B. Electrospray ionization mode was performed in the mass spectrometry analysis. Three blanks and six replicates of the QC samples were injected at the beginning of the batch for column conditioning, and the QC sample was analyzed every ten injections. Auto-calibrations were performed every five analyses.A UPLC-MS/MS was used to acquire the raw data, and XCMS online was used for peak detection and dataset alignment. MetDNA and MS-DIAL software were used for the identification of metabolites. The acquired peak tables were then uploaded onto the MetaboAnalyst for statistical analysis and metabolic pathway analysis. Concentrations of metabolites were represented by peak area and normalized according to the creatinine levels.MetaboAnalyst 5.0 was used to analyze data. A volcano plot consisting of a combination of fold change (FC) analysis and non-parametric tests was used to identify any statistically significant differences in the metabolites between the pre-CIRT samples and post-CIRT samples. The unsupervised principal component analysis (PCA) was performed to detect the significant separation shift between compared groups. Supervised multivariate analysis and partial least-squares discriminant analysis (PLS-DA) were performed to achieve maximum separation among the groups. The sparse PLS-DA (sPLS-DA) algorithm was used to reduce the number of variables (metabolites) to produce robust and easy-to-interpret models. Hierarchical cluster analysis (HCA) was used to separate the metabolite profiles between compared groups. Boxplots showed the minimum, lower quartile, median, upper quartile, and maximum values of metabolite concentrations. Error bars stood for the minimum values to the maximum.A total of 1,701 metabolites were monitored by UPLC-MS/MS. Multivariate analysis was performed using PCA, PLS-DA, and sPLS-DA . All samWe further performed pathway enrichment analysis of the identified metabolites, and we found that these metabolites could be enriched in eight pathways , including histidine metabolism, arginine biosynthesis, glutathione metabolism, cysteine and methionine metabolism, pantothenate, and CoA biosynthesis, biotin metabolism, alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism. These metabolic pathways are part of amino acid metabolism, carbohydrate metabolism, and also vitamins and cofactors metabolism. The bubble plot shown in The relation of metabolic clustering with different risk classifications was further explored. Patient risk stratification was performed under the NCCN guidelines. The low-risk and medium-risk patients were considered as a relatively low-risk group, and the high-risk and very high-risk patients were considered as a relatively high-risk group .The patient's urine metabolites in pre-CIRT were further analyzed by PLS-DA and were clustered into two groups. The results were matched with the risk subtype . HoweverThe HCA also revealed that after CIRT, the patients could be clustered into two groups, PM1 and PM2, according to their metabolic profiles . This clPathway enrichment shows the response diversity of PCa to CIRT. The bubble chart of the discriminatory metabolic pathway is shown in Carbon ion radiotherapy is a relatively new radiotherapy technique, and few studies have evaluated the impact of CIRT on cancer metabolism, especially PCa. Therefore, in this study, we evaluated the impact of this new technology on PCa metabolism.Carbon ion radiotherapy showed a strong ability to inhibit metabolism in PCa. Compared to the concentrations of the metabolites in untreated patients' urine samples, almost all discriminated metabolites (33/35) were downregulated after CIRT. This result demonstrates the ability of CIRT to inhibit tumor metabolism. Moreover, CIRT could generally inhibit most of the metabolic processes involved in the proliferation, metastasis, and finally the progression of PCa. The results in this study primarily suggest that CIRT has the ability to significantly downregulate metabolism in patients with PCa.Carbon ion radiotherapy inhibited the production of metabolites that are mainly enriched in the arginine biosynthesis and histidine metabolism pathways. The main alteration of amino acid in the process of prostate tumorigenicity involves histidine and arginine metabolism, and also the metabolism of alanine, aspartate, and glutamate, and some aromatic amino acid metabolism . The argInterestingly, we found the urine metabolites of these patients with PCa have different responses to CIRT. All patients could be clustered into two groups, PM1 and PM2. PM2 showed relatively higher concentrations of metabolites after CIRT. The clustered result was different from the clinical risk stratification. Therefore, the difference in the concentrations of metabolites between PM1 and PM2 can be attributed to tumor sensitivity to CIRT. PCa in the PM2 group patients seemed to be less sensitive to CIRT when compared with the PCa in the PM1 group. However, long-term follow-up is necessary to confirm the role of urine metabolites as treatment response markers for CIRT in PCa.The metabolic results also confirm the response to CIRT of PCa in the PM2 group, shown by the higher levels of metabolites related to arginine biosynthesis and also phenylalanine, tyrosine, and tryptophan biosynthesis. These results further confirm that arginine biosynthesis is important for PCa and may play a central role in response to CIRT.In this study, CIRT showed its strong ability to inhibit metabolism pathways in PCa. CIRT-induced changes in the metabolite profiles mainly enriched in arginine biosynthesis and histidine metabolism. Urine metabolites of patients with PCa had different responses to CIRT. More sensitive PCa showed lower levels of metabolites in urine samples, especially the arginine biosynthesis and also phenylalanine, tyrosine, and tryptophan biosynthesis pathway. CIRT-evoked metabolic reprogramming seems to be one of the most important underlying mechanisms of CIRT to inhibit PCa. Our preliminary results indicate that some urine metabolites could potentially be used to identify the individualized response to CIRT in patients with PCa. However, further longitudinal studies with a larger sample size are recommended to confirm these results.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Shanghai Proton and Heavy Ion Center Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.YP, QZ, and RN finished study design. QZ and YP finished experimental studies. YP and YS finished data analysis. QZ, PL, ZH, YP, WH, and YD collected and proceeded patients' samples and clinical information. YS, QZ, and YP finished manuscript editing. QZ, XG, and YS supervised the study. All authors read and approved the final manuscript.This article was supported by the Science and Technology Development Fund of Shanghai Pudong New Area , the Natural Science Foundation of Shanghai (21ZR1481800).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Saccharomyces cerevisiae rich in β-glucans and mannan-oligosaccharides, can be used as a microbial growth promoter, with potential food safety and health applications. The aim of this study was to assess in vitro the effect of cava lees on the growth of 21 strains of lactic acid bacteria (LAB) species commonly used as starter cultures and/or probiotics. Firstly, 5% of cava lees was selected as the most effective amount for enhancing microbial counts. After screening different LAB, statistically significantly (p < 0.05) higher microbial counts were found in 12 strains as a consequence of cava lees supplementation. Moreover, a greater and faster reduction in pH was observed in most of these strains. The growth-promoting effects of cava lees on LAB strains supports the potential revalorization of this winery by-product, either to improve the safety of fermented products or as a health-promoting prebiotic that may be selectively fermented by probiotic species.The growing trend of circular economy has prompted the design of novel strategies for the revalorization of food industry by-products. Cava lees, a winery by-product consisting of non-viable cells of  Saccharomyces cerevisiae naturally plasmolyzed due to the sustained contact with wine [8 dead cells of S. cerevisiae, is obtained for each bottle of cava [The production of cava, a highly appreciated Spanish sparkling wine, traditionally involves a second fermentation in the bottle and a subsequent biological aging of the base wine on lees for at least 9 months . Cava leith wine ,3,4. In  of cava . Thus, c of cava . These lS. cerevisiae cell wall are β-1,3-glucan (50–55%) and β-1,6-glucan (5–10%), together with mannoproteins (35–40%), and chitin (1–2%) [Cava lees are rich in antioxidant compounds, as well as in soluble and insoluble fibers mainly derived from the yeast cell wall . The prin (1–2%) ,9. In reglucan 5–%, togethn (1–2%)  and, in n (1–2%) ,13. Seven (1–2%) ,15,16,17n (1–2%) ,19,20.The technological functionality of dietary fiber is increasingly being explored for food applications, especially in fermented products ,29,30,31From a health point of view, soluble fiber is well known to have a prebiotic effect that improves the survival and implantation of species within the intestinal microbiota with beneficial effects for the host . PromisiConsidering cava lees are rich in different types of fibers (mainly β-glucans and MOS), they have potential application as a microbial growth promoter, with benefits for food safety and health. If confirmed, this could be a novel strategy for the revalorization of this winery by-product in alignment with current circular economy initiatives. To date, cava lees have not been studied from this perspective. Therefore, the aim of this study was to assess in vitro the potential promoting effect of cava lees on the growth of different strains of LAB from species commonly used as starter cultures and/or probiotics. Cava lees were provided by the winery Freixenet S.A. . To eliminate remaining cava, wet lees were centrifuged at 10,000 rpm for 10 min at 4 °C. The lees were subsequently frozen in an ultra-low temperature freezer (−80 °C), freeze-dried , ground, and preserved in sealed tubes protected from the light and humidity until use.Latilactobacillus sakei (CTC494 and BAP111), Lactiplantibacillus plantarum subsp. plantarum (CECT4180 and CECT220), Latilactobacillus curvatus (BAP202), and Lacticaseibacillus casei (BAP341). An overnight culture of each strain was performed in MRS broth  incubated in optimal growth conditions (w/v)) at the optimal temperature, and with constant shaking (200 rpm) under aerobic conditions. Amounts above 5% of lees were not considered, as they technically hindered the cultivation of the bacterial strains. Plate microbial counts, expressed as log10 CFU/mL, were determined in duplicate at incubation times 0 to 72 h. Three independent assays were performed for each bacterial strain.A scheme of the overall experimental procedure is provided in nditions . Then, aA total of 21 strains of LAB species were selected for their special interest from a technological (starter cultures) and health (probiotics) perspective, and all of them are included in the list of microorganisms with QPS  status . The eff® ©XS instruments, Carpi, Italy). Due to the acidity conferred by lees, the pH value of the culture media supplemented with lees was adjusted with KOH (1M) to equilibrate the pH to that of the control sample.Changes in the pH of the culture media during the assay were measured by a digital instrument . Data of the growth promoter effect of different amounts of cava lees were assessed by an ANOVA and post hoc Tukey tests. Student’s L. sakei strains (p < 0.05). This effect was much more evident after 24 h, when the counts were significantly higher (p < 0.05) for four strains  at all tested amounts of cava lees. The effect of cava lees differed according to the amount used, 2% and 5% producing the strongest statistically significant effect (p < 0.05) on bacterial growth (24 h) and/or survival after the stationary phase (at 48 h). Regarding L. plantarum strains, significantly different results associated with the concentration were not found until 48 h of incubation (p < 0.05). The highest microbial counts of L. plantarum were also obtained with 2% and 5% of lees, indicating that, at lower amounts or control conditions, microbial survival after the stationary growth phase was lower. Therefore, among the different assessed amounts of cava lees, the addition of 5% proved to be the most effective for all the tested strains, resulting in LAB microbial concentrations 0.6 to 1.3 log10 units higher than in the control cultures, either at 24 h or 48 h of incubation. Other studies have also reported a stimulatory effect on LAB growth of different plant-derived by-products at concentrations ranging from 1 to 10% [Lactobacillus delbrueckii subsp. Bulgaricus (previously Lactobacillus bulgaricus) and Streptococcus thermophilus after 48 h of supplemented culture [Supplementation of the growth medium with cava lees resulted in a higher concentration of viable cells of the different LAB strains in comparison with the control medium (without lees). Overall, a strain-dependent and a dose-dependent effect was observed. As can be seen in 1 to 10% ,37,39,40w/v) of cava lees to the 21 tested LAB strains is shown in L. gasseri and P. pentosaceus. The effect, in terms of growth, of adding 5%  when 5% of cava lees was added. Thus, significantly higher microbial counts were obtained at the end of the exponential phase for L. sakei BAP101, BAP102, BAP110, BAP111, BAP115, and CTC494, with differences in the maximum population density ranging from 0.7 to 1.1 log10 units with respect to the control (p < 0.05). Higher counts were generally maintained until the end of the 72 h incubation period (death phase). Overall, the higher microbial counts due to the addition of lees varied according to the strain . RegardiL. curvatus, a significant promotion of growth was observed for both tested strains compared with the control (p < 0.05). L. curvatus BAP202, whose growth rate was not affected in the first hours of incubation, reached a higher maximum population density, with concentrations up to 1.5 log10 greater than the control, and a pH 0.7 points lower. In the case of L. curvatus BAP201, despite an enhanced growth in the presence of cava lees, the level of acidification was very similar with or without the addition of lees. Regarding L. fermentum CECT275 and L. casei BAP341 were also influenced by the addition of lees, although to a lesser extent than the L. sakei and L. curvatus strains (10 cfu/mL compared to the control (p < 0.05). However, a significant effect on acidity was only observed for L. casei BAP341, the final pH value being 0.5 units lower compared to the control (p < 0.05).  strains . SignifiL. plantarum were the bacterial species least affected by the presence of cava lees. Of the six strains tested, only two (L. plantarum CECT220 and L. plantarum CECT4180) showed any differences with respect to the control, though only regarding the survival on the last day of the incubation period. In fact, the addition of cava lees to the culture media significantly enhanced the viability after 36 h (p < 0.05), L. plantarum CECT220 (1.4 log10) and CECT4180 (2 log10), showing a higher concentration compared to the control culture. No differences were observed in the pH values between the supplemented and unsupplemented culture media, which could be explained by the lack of effect of cava lees in the growth phase in both strains.L. casei CTC475. Supplementation with this citrus by-product promoted the growth and survival of LAB, resulting in a higher acidification of sausages without affecting their sensory properties. More recently, the effect of sake lees  on dry-fermented sausages was assessed, and higher LAB counts and lower pH values during the aging period were observed [Various plant-based by-products have been successfully applied to improve the nutritional, textural and sensory properties of dairy and meat fermented products ,25,28,29observed .L. plantarum, L. casei and L. fermentum, among others. As in the current study, the capacity to ferment this substrate varied among strains. Andrade et al. [Several recent studies have focused on the revalorization of by-products with a potential role as prebiotics ,39,40,42e et al.  found anThe bacterial growth-promoting effects of the above-mentioned by-products have been mainly attributed to their content of oligo- or polysaccharides  and pectic substances. Likewise, the observed growth enhancement induced by cava lees could be due to the bacterial use of β-glucans and MOS  as a carbon source. In this regard, the capacity of β-glucans  and MOS to stimulate the growth of several lactobacilli has been extensively reported ,45,46,47Lactobacillus genus, which may endow these bacteria with the ability to use oligo- or polysaccharides as a carbon source [L. plantarum strain with an overexpressed B-glycosidase enzyme. However, the enzymes responsible for this metabolic pathway may be disabled in certain bacterial strains, thus compromising their ability to ferment these substrates [The metabolism of these large carbohydrates requires, as a first step, their hydrolysis into fermentable smaller sugars by glycosidases . The pren source ,48. Thisn source , in whicbstrates ,50,51. TOverall, the enhancement of LAB strain growth in vitro and reduction in pH values induced by cava lees supports the potential revalorization of this winery by-product with applications to improve the safety of fermented foods. Furthermore, the selective fermentation of cava lees by certain probiotic species suggests they could be exploited as prebiotics in health applications. Higher amounts of cava lees could potentially have a growth-promoting effect on those species in which no in vitro effect has been observed. Thus, the results obtained here need to be confirmed in real models of fermented foods, which would also allow us to assess both the effects of higher amounts of cava lees and the impact on organoleptic properties. It should be noted that the observed strain-dependency of the effects of cava lees warrants case-by-case study. The proposed recycling of cava lees represents a novel strategy for the revalorization of a by-product generated in large amounts. This approach is in alignment with the Sustainable Development Goals of the United Nations, focused on reducing food industry waste products and promoting the circular economy.


In prebiotic evolution, self-replicating molecules are believed to have evolved into complex living systems by expanding their information and functions open-endedly. Theoretically, such evolutionary complexification could occur through successive appearance of novel replicators that interact with one another to form replication networks. Here we perform long-term evolution experiments of RNA that replicates using a self-encoded RNA replicase. The RNA diversifies into multiple coexisting host and parasite lineages, whose frequencies in the population initially fluctuate and gradually stabilize. The final population, comprising five RNA lineages, forms a replicator network with diverse interactions, including cooperation to help the replication of all other members. These results support the capability of molecular replicators to spontaneously develop complexity through Darwinian evolution, a critical step for the emergence of life. Long-term experimental evolution shows that a single polymerase-encoding RNA replicator can evolve into a complex replicator network, shedding light on how a molecular replicator could have developed complexity before the emergence of life. How molecular replicators could develop complexity by continuously expanding information and functions is a central issue in prebiotic evolution5. An expected route for complexification is that novel RNA replicators successively emerged and co-replicated so that increased genetic information can be stored at a population level, before their assembly into a long genome8. Although several theoretical studies investigated the possibility of complexification11 and stable coexistence16 of molecular replicators, empirical demonstration has been challenging.An origins-of-life scenario depicts Darwinian evolution from self-replicating molecules, such as RNA, toward complex living systems21. Although considerable efforts have been made to design interactions among these replicators26, spontaneous complexification was generally precluded due to their inability to undergo Darwinian evolution through continuous mutation accumulation and natural selection. The study by Ellinger et al.23 was an exception, but they used a predefined replicator network to initiate evolution, which was also limited to the short-term. Thus, it has remained an open question whether a single molecular replicator can evolve into a complex replicator network.To date, diverse molecular replicators have been constructed based on biomaterials such as DNA, RNA, and peptides27. During replication, mutations are introduced, and occasional recombination deletes replicase-encoded regions to generate a parasitic RNA that replicates by exploiting replicases derived from other RNAs (replicase-encoding “host” RNAs). The RNAs can undergo Darwinian evolution in a serial transfer experiment, and a previous attempt  demonstrated the successive appearance of new host and parasitic RNA lineages showing defense and counter-defense properties29. However, these lineages dominated the population in turn and only transiently, possibly due to a short evolutionary timescale.Previously, we constructed an RNA that replicates using an RNA replicase translated from itselfHere, we continued the serial transfer experiment up to 240 rounds (1200 h). Sequence analysis uncovered that two previously detected host RNA lineages became sustained and further diverged into multiple sublineages of host and parasitic RNAs. The population dynamics of each lineage gradually changed during the evolution, from dynamically fluctuating stages to quasi-stable coexistence, suggesting the appearance of co-replicative relationships among the lineages. Biochemical analyses supported the co-replication of dominant RNAs in the different lineages containing a cooperative RNA that replicates all other members, thus establishing a multiple replicator network.Escherichia coli translation system30. RNA replication occurs through the translation of the replicase subunit, which becomes active in association with elongation factors Tu and Ts (EF-Tu and EF-Ts) in the translation system. The introduction of mutations into the host RNA during replication generates host RNA variants with different features. Spontaneous RNA recombination deletes a part of the replicase gene to generate parasitic RNAs while retaining 5ʹ and 3ʹ terminal sequences for recognition by the replicase. When encapsulated in micro-sized water-in-oil droplets, host RNAs can sustainably replicate in the presence of parasitic RNAs28.The RNA replication system Fig.  consists27). In the experiment, we repetitively (1) performed RNA replication by incubating the replication system in water-in-oil droplets at 37 °C for 5 h, (2) diluted the droplets 5-fold with new droplets containing the fresh translation system, and (3) induced the fusion and division of the droplets by vigorous stirring to mix the contents well  in this study.We performed a long-term replication experiment using the RNA replication system, started with a clonal host RNA population . These results suggested that the mode of evolution changed in the late stage.The population dynamics of the host and parasitic RNAs showed multiple differences between the early (rounds 1–120) and late (rounds 121–240) stages Fig. . First, 29. For rounds 116–240, we recovered RNA mixtures at 18 points and subjected host and ~500 nt parasitic RNAs to PacBio sequencing. For parasitic RNAs, we analyzed only ~500 nt ones, first detected at round 115, because the other sizes of parasitic RNAs were rarely detected in the late stage. From 680 to 10000 reads of host and parasitic RNAs at rounds 116–240  remained dominant in the last 100 rounds. Other RNAs in HL2 accumulated independent mutations around round 129 and formed another set of branches, named HL3, which contains a genotype (HL3–228) that remained in the final round with at least 10 distinct mutations and also exhibited markedly different characteristics from those in HL2 as shown later.To examine the evolutionary trajectory, we performed phylogenetic analysis using the three most frequent host and parasitic RNA genotypes in each sequenced round. The phylogenetic tree is displayed together with the round-by-round frequency and dominant mutations of each genotype Fig. . The hos29, diverged from HL2 around round 115 but was not detected in the last 80 rounds. PL2 emerged around the same round and accumulated unique mutations, and several genotypes  remained in the final round. From PL2, a sublineage of parasitic RNAs appeared, named PL3, which contains a genotype (PL3–228) that accumulated no less than four novel mutations and displayed unique replication abilities as shown later.For parasitic RNAs, two lineages (PL1 and PL2) originated from HL2. PL1, corresponding to parasite-γ in the previous studyNext, we analyzed the population dynamics of each host and parasitic RNA lineage using the 100 most frequent genotypes of all host and parasitic RNAs at each sequenced round. These genotypes covered 46–100% and 80–100% of the total host and parasitic RNA reads of each round, respectively. The genotypes in different lineages show distinct patterns and rates of mutation accumulation  and the most dominant RNA clones in the five sustained lineages  at rounds 120, 155–158, and 228: 3 clones at round 120 , 4 clones at round 155–158 , and 5 clones at round 228 , all of which accumulated different sets of mutations  was replicated by the self-encoded replicase (“HL0 replicase”). At round 120, two host (HL1 and HL2) and one parasitic (PL2) RNAs appeared, but their interaction was limited. Both HL1 and HL2 replicated with their respective replicases without detectable interdependency. HL2 replicase also replicated PL2 as efficiently as HL2, whereas HL1 replicase did not. At rounds 155–158, another host RNA lineage (HL3) appeared, and the replication relationship became complicated. HL1 and HL2 were mainly replicated by their respective replicases, whereas HL3 was primarily replicated by HL2 replicase. PL2 was also preferentially replicated by HL2 replicase. At round 228, another parasitic RNA lineage (PL3) appeared, and the replication relationship became even more complicated. HL1 acquired the ability to utilize HL2 replicase, as well as the self-encoded replicase. HL2 replicase could replicate not only HL2 but also all other RNAs. HL3 replication still largely relied on HL2, although the dependence was weakened compared to rounds 155–158. In parasitic RNA clones, PL2 was only replicated by HL2 replicase, and its replication by the other replicases became negligible. In contrast, PL3 was replicated by all three replicases similarly, but none of the replications was as efficient as that of PL2 by HL2 replicase, indicating that PL2 is an HL2-specific parasite, whereas PL3 is a general parasite. These results demonstrated that the RNAs in each lineage have different biochemical properties, and the replication relationship among the RNAs gradually changed as evolution proceeded to form a larger replicator network.To characterize the biochemical properties underlying the observed replication relationship, we further examined the five RNA clones at round 228  by fully decoupling translation and replication reactions. We first analyzed the synthesis of each encoded replicase during translation and found that HL1- and HL3–228 showed approximately twice as much replicase synthesis than HL2–228, whereas PL2- and PL3–228 did not show detectable translation activity . Similarly, some of the host RNAs in the population may produce dysfunctional replicases and replicate only by utilizing active replicases translated from other host RNAs. Our previous transfer experiment showed that approximately 60% of a host RNA population did not produce functional replicases due to random mutations44. Laboratory evolution of bacterial, eukaryotic, and viral systems has also demonstrated host-parasite coevolution and diversification51, similar to those observed in this study. The simplicity of our RNA replication system, compared with biological organisms, allows us to examine evolutionary events with unprecedented resolution. For example, a small set of defined components enables the detailed characterization of replication strategies for each species  of the entire genome sequences of all replicating species  and HL2–120, were obtained previously as the plasmids encoding round 128 clone29, and additional 120 rounds  of replication were performed by the same method in this study, started with the RNA population in round 120. In the two additional long-term replication experiments  and buffer-saturated oil was as described previously27. Next, the droplets were incubated at 37 °C for 5 h to induce RNA replication through protein translation. From round 2 to 240, 200 μl of water-in-oil droplets in the previous round, 10 μl of the translation system, and 800 μl of buffer-saturated oil were homogenized by the same method to prepare a new droplet population, followed by incubation under the same condition (at 37 °C for 5 h) to induce RNA replication. The concentrations of host and parasitic RNAs were determined after replication. Host RNA concentrations were measured by RT-qPCR with Mx3005P Real-Time PCR System (Agilent technologies) or QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific) after diluting the droplets 100-fold with 1 mM EDTA (pH 8.0), by using One Step TB Green PrimeScript PLUS RT-PCR Kit (Takara) and host RNA-specific primers . The recovered solution was mixed with four volumes of diethyl ether, centrifuged  to remove the diethyl ether phase, and purified using RNeasy Mini kit (Qiagen). Obtained RNA samples were then subjected to 8% polyacrylamide gel electrophoresis in 1× TBE buffer. Fluorescence intensities of parasitic RNA bands were quantified after staining with SYBR Green II (Takara) and visualized using FUSION-SL4 (Vilber-Lourmat), and concentrations were determined based on a dilution series of standard RNA bands. For long-term replication of isolated RNA clones , and PCR-amplified with KOD FX DNA polymerase (Toyobo). Approximately 2000 bp (host) and 500 bp (parasite) PCR products were size-selected through 0.8% agarose gel-electrophoresis with E-Gel CloneWell Gels (Thermo Fisher Scientific), followed by purification with MinElute PCR Purification Kit (Qiagen). The cDNA samples were barcoded through PCR amplification with KOD FX DNA polymerase and sequenced using PacBio RS II  as described previously29 or using PacBio Sequel  at Macrogen Japan. The obtained circular consensus sequences (CCS) reads were quality-filtered as described previously (PacBio RS II)29, or CCS reads with more than Q20 were retained (PacBio Sequel). Host or parasite CCS reads at each round ; rounds 92, 114, 129, 144, 160, 173, 183, 189, 195, 200, 220, and 239 ; and rounds 86, 94, 105, 135, 155, 175, 198, 219, and 237  in the long-term replication experiments were obtained as described above. It should be noted that RNA replication in round 120 of the main experiment was performed in the previous study53 with the default parameters, and host and parasitic RNA lineages were defined based on the trees as described in the main text. The trees were visualized using Interactive Tree Of Life (iTOL)54. Next, to classify the 100 most dominant genotypes of host and parasitic RNAs at each round into the lineages, Hamming distances between all pairs of host RNA genotypes and those of parasitic RNA genotypes were calculated for each experiment. Based on the distance matrices, the genotypes were mapped in two-dimensional spaces  by removing uncommon mutations in each sequence  was incubated at 37 °C for 2 h in the translation system, without UTP to preclude RNA replication. Second, an aliquot of the first reaction was 3-fold diluted in the fresh translation system containing 1.25 mM UTP and further incubated at 37 °C for 2 h with or without another RNA clone , in the presence of 30 μg/ml streptomycin to inhibit further translation. After 0 and 2 h incubation of the second reaction, the concentrations of RNA clones were measured by RT-qPCR using sequence-specific primers (Supplementary Table An RNA clone (300 nM) was incubated at 37 °C for 2 h in the translation system and FluoroTect GreenLys tRNA (Promega), without UTP to preclude RNA replication. After translation, an aliquot was treated with 0.1 mg/ml RNase A (Qiagen) at 37 °C for 15 min, incubated at 95 °C for 4 min in SDS sample buffer , and subjected to 10% SDS-PAGE. The synthesized fluorescently labeled proteins were visualized using FUSION-SL4 (Vilber-Lourmat).55. Briefly, an encoded replicase subunit was co-expressed with EF-Tu and EF-Ts in Escherichia coli, and then, the cell lysate was subjected to ammonium sulfate precipitation, followed by anion and cation exchange chromatography. Purified replicases were analyzed by 10% SDS-PAGE and Coomassie Brilliant Blue staining. For the replication reaction, 10 nM of an RNA clone was replicated at 37 °C for 2 h using 10 nM of each purified replicase. The reaction was performed under the same conditions for the replication reaction in translation-uncoupled replication experiments, and the fold replication of each RNA clone was determined accordingly.Qβ replicase of each RNA clone  was purified as described in the previous studyOne, two, or three RNA clones of choice (10 nM each) were incubated at 37 °C for 5 h in the translation system. After 0, 2, and 5 h incubation, the concentrations of RNA clones were measured by RT-qPCR using sequence-specific primers  examined in the translation-coupled replication experiments  was then calculated for each first order Bahadur coefficient (wx) as follows:The mathematical detail of the analysis was described in the previous studyR2 for wj and wk is 1, by definition, there is no interaction between RNAj and RNAk to affect RNAi replication.If the sum of 31 was modified to simulate the replication dynamics of five RNAs  in a serial transfer format. The developed model includes the following parameters.The previous theoretical modelRNAi]: Concentration of RNAi in each compartment[kij: Rate constant of RNAi replication catalyzed by RNAj in each compartment (1 ≤ j ≤ 5) D: Dilution rate (5)M: Number of compartments (300000)F: Fusion-division number (1.3)kij values for all combinations of i and j were determined from the average fold replications of RNA clones at round 228 obtained in the translation-uncoupled RNA replication experiments  10 (2) RNAs were replicated according to the above differential equation, solved by the Python package scipy.integrate.odeint for a fixed reaction time.D-fold with vacant compartments.(3) The compartments were diluted M  × F times.(4) Total RNAs in two randomly chosen compartments were mixed and redistributed into the two compartments according to the binomial distribution. This process was repeated (5) The processes (2) to (4) were repeated for indicated rounds.We note that the extension of the model to explicitly incorporate replicases (Supplementary Text Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary