Rhytidocystis. Based on comprehensive molecular phylogenies of Rhabdocoela and Proseriata inferred from both 18S and 28S rDNA sequences, as well as a molecular phylogeny of Marosporida inferred from 18S rDNA sequences, we determine the phylogenetic positions of the microturbellarian hosts and their parasites. Multiple lines of evidence, including morphological and molecular data, show that at least nine new species of Rhytidocystis infect the microturbellarian hosts collected in this study, more than doubling the number of previously recognized species of Rhytidocystis, all of which infect polychaete hosts. A cophylogenetic analysis examining patterns of phylosymbiosis between hosts and parasites suggests a complex picture of overall incongruence between host and parasite phylogenies, and varying degrees of geographic signals and taxon specificity.Microturbellarians are abundant and ubiquitous members of marine meiofaunal communities around the world. Because of their small body size, these microscopic animals are rarely considered as hosts for parasitic organisms. Indeed, many protists, both free-living and parasitic ones, equal or surpass meiofaunal animals in size. Despite several anecdotal records of “gregarines”, “sporozoans”, and “apicomplexans” parasitizing microturbellarians in the literature—some of them dating back to the nineteenth century—these single-celled parasites have never been identified and characterized. More recently, the sequencing of eukaryotic microbiomes in microscopic invertebrates have revealed a hidden diversity of protist parasites infecting microturbellarians and other meiofaunal animals. Here we show that apicomplexans isolated from twelve taxonomically diverse rhabdocoel taxa and one species of proseriate collected in four geographically distinct areas around the Pacific Ocean  and the Caribbean Sea (Curaçao) all belong to the apicomplexan genus  Toxoplasma, Cryptosporidium and Plasmodium), a large and mostly undescribed diversity of these parasites are present in marine animals1. For instance, gregarines are widespread but poorly studied parasites of larger marine invertebrates including annelids, molluscs, nemerteans, phoronids, hemichordates, tunicates and arthropods2. However, research on apicomplexan parasites of microscopic marine invertebrates, such as zooplankton and meiofauna, is very limited.Apicomplexans are one of the most diverse and ubiquitous groups of unicellular eukaryotes infecting animal hosts. While some apicomplexans are well-known pathogens of humans and domesticated animals 7, studies on both meiofauna diversity and their parasites can provide us with additional insight into the ecology and evolution of interstitial communities in particular and marine ecosystems as a whole.Meiofauna constitute an important reservoir of marine biodiversity, comprising taxonomically diverse and extremely abundant communities of microscopic invertebrates associated with sediments and other substrates  in almost every marine habitat around the world8. Meiofaunal flatworms or so-called "microturbellarians" are among the most abundant and diverse groups in these meiofaunal communities10, with the vast majority of microturbellarian species remaining to be discovered12. As predators of microalgae, other meiofauna and smaller macrofauna, microturbellarians likely play an important role in structuring the communities of various marine habitats13. However, our knowledge on ecological interactions between microturbellarians and other organisms, including their symbionts and parasites, remains highly anecdotal.Most animal phyla contain microscopic representatives, and several phyla are exclusively meiofaunal 15. Nevertheless, several taxonomic works on marine or brackish water microturbellarians over the past 140 years have mentioned apicomplexans parasitizing these tiny animals, and either define these infectious cells as “gregarines”23 (German: Gregarinen), “sporozoans”25 (German: Sporozoen) or “apicomplexans”26. Attempts to further identify and characterize these parasites of marine microturbellarians have never been undertaken except for one study by Cannon and Jennings (1988), in which the hyperparasitic gregarine Monocystella epibatis Cannon & Jennings, 1988 of a pterastericolid rhabdocoel that is endobiotic in the crown-of-thorns starfish was described27.A variety of protists, including apicomplexans, are known to infect flatworms. More general overviews on turbellarians and their parasites list larger-sized freshwater triclads and marine polyclads as hosts, with very few references to microturbellariansIn this study, we explore the diversity, identity and host specificity of apicomplexans infecting rhabdocoels and proseriate microturbellarians collected from three geographically distinct locations around the Pacific Ocean and from one location in the Caribbean. In addition to morphological data, we provide comprehensive molecular phylogenies of the hosts inferred from both 18S and 28S rDNA sequences, as well as a molecular phylogeny of the parasites inferred from 18S rDNA sequences. A cophylogenetic analysis examined potential patterns of phylosymbiosis between hosts and parasites.2 decantation method28. Individual worms were isolated and observed under a stereoscope and subsequently whole mounted alive to be visually inspected for the presence of parasites using a compound microscope. Infected individuals were photographed , Curaçao (Kingdom of the Netherlands), and Quadra Island  from 2016 to 2022  using hand-drawn glass pipettes on a CKX-53 inverted microscope (Olympus). Genomic DNA was prepared from these samples using 20 μl FFPE (Lucigen) solution, following the manufacturer’s instructions. The 18S rRNA gene from these single-cell isolates were amplified in a nested reaction with KOD One® PCR Master Mix (Toyobo). The resulting amplicons were visualized on 1.5% agarose gels stained with ethidium bromide, PEG-purified, and sequenced following Wakeman (2020)29. Nested PCR conditions and primers are detailed in Table 30 and subjected to a BLAST search on the NCBI website (http://blast.ncbi.nlm.nih.gov) to verify the taxonomic identity of the hosts and parasites. Sequences were deposited in GenBank under accession numbers detailed in Tables Genomic DNA was extracted from entire host worms and host tissue using the DNeasy Blood & Tissue kit (Qiagen). Manufacturer’s instructions were followed, with the exception that DNA was eluted in 60 μl of preheated AE elution buffer (60 °C). Fragments of the nearly complete 18S (amplicon size range: 1747–2043 bp) and partial 28S rRNA (amplicon size range: 1660–1821 bp) genes of the host and of the 18S rRNA gene of the parasite (amplicon size range: 1154–1708 bp) were PCR amplified using the primers and thermocycling conditions in Table Duplimona sp. were aligned with rDNA sequences of 25 representatives of other proseriates, and of three rhabdocoels as an outgroup  and one dataset for the parasites. Genetic similarities among and between putative species of Rhytidocystis were calculated in Geneious v11.0.15 based on a separate 1638 bp 18S rDNA alignment  containing all 25 Rhytidocystis sequences.The new 18S and 28S rDNA sequences of the rhabdocoel hosts were aligned with rDNA sequences of 110 and 101 representatives of other rhabdocoels, respectively, and with rDNA sequences of three proseriates as an outgroup Table . The 18Sup Table . The 17 is Table  were aliis Table . The 28S37. This partition scheme and models were subsequently used in the phylogenetic analyses of the concatenated datasets. The maximum likelihood (ML) analyses were performed with the RAxML v8.2.11 plugin38 in Geneious v11.0.15 selecting the algorithm for best-scoring ML tree search and non-parametric bootstrapping (1000 replicates). Bayesian analyses were performed on the same datasets in MrBayes v3.2.7a39 through XSEDE in the CIPRES Science Gateway v3.3 (https://www.phylo.org), using default prior and MCMC settings, in two independent simultaneous runs for 40 million generations. Trees were sampled every 400th generation after a 25% burnin. Convergence was assessed through the LogL values, ESS values (estimated sample size) and the average deviation of split frequencies. The remaining 75,000 trees were summarized in a 50% majority-rule consensus tree. Branch support was evaluated with the ML bootstrap values (bs) and Bayesian posterior probabilities (pp) for the ML and Bayesian trees, respectively.The best-fit partitions and models of molecular evolution corresponding to the 18S and 28S rDNA datasets and GTR + GAMMA + I were recovered for the concatenated (18S + 28S) datasets in PartitionFinder v.2.1.1 using a greedy search and all three model selection criteria 40. The number of random permutations of the host-parasite association matrix was set to 100,000. In addition, PACo also assessed the contribution of each individual host-parasite association to the overall fit. To visualize host-parasite relationships, a tanglegram was manually drawn in Adobe Illustrator v26.2.1 (Adobe Inc.) based on the output of a tanglegram generated in Random Tanglegram Partitions (Random TaPas)41. The host and parasite input trees for PACo and Random TaPas were produced in MrBayes v3.2.7a in the same way as described above, but the alignments only included the seventeen 18S and 28S rDNA sequences of the flatworm hosts and seventeen 18S rDNA sequences of their apicomplexan parasites listed in Tables Congruence between host and parasite phylogenies was investigated using the Procrustean Approach to Cophylogeny (PACo) method to compute a goodness-of-fit statistic based on the residual sum of squaresCollection permits for the field work were obtained by the Carmabi Marine Research Institute (Curaçao) and through Fisheries and Oceans Canada (XR 224 2021).42: the Sea of Japan and the Georgia Basin in the Temperate Northern Pacific, the South Kuroshio region in the Central Indo-Pacific, and the southern Caribbean in the Tropical Atlantic  is low (< 5%). A similarly low prevalence was observed in animals from Okinawa and Curaçao. Based on their appearance and location inside the worm host (gut), all the observed parasite cells are thought to be trophozoite stages.Our collections span a large geographical area in various tropical, subtropical and temperate marine ecoregionstic Fig. . ParasitThe trimmed concatenated 18S + 28S rDNA sequence alignment for the rhabdocoel host phylogeny had 130 sequences belonging to 120 taxa and contained 3754 bp , while the trimmed concatenated 18S + 28S rDNA sequence alignment for the proseriate host phylogeny had 29 sequences belonging to 28 taxa and contained 3484 bp . The trimmed concatenated 18S + 28S rDNA sequence alignment for the apicomplexan parasite phylogeny had 70 sequences belonging to 61 taxa and contained 5858 bp . The phylogenetic analyses to determine the positions of the flatworm hosts and their parasites among other rhabdocoel and proseriate flatworms, and among other apicomplexans, respectively, are illustrated in Fig. Parautelga sp., Myobulla sp., and Polycystidinae sp. Among rhabdocoels, the host taxa represent a wide taxonomic diversity, with representatives of nearly all major lineages, except for the predominantly freshwater limnotyphloplanids and the mostly marine neodalyellids . Four other representatives of Rhytidocystis in the tree, R. dobrovolskiji, R. nekhoroshkovae, and Rhytidocystis pertsovi are also intestinal parasites of polychaetes and form the more basal lineages within the genus. Among the flatworm parasites, several clusters of nearly identical haplotypes can be recognized based on genetic similarities: clade b (99.05–99.87%) and clade i (99.55–99.97%) from Curaçao, and clade d (100%) from Quadra  varies between 53.76% and 95.53%.The phylogenetic analyses on the apicomplexan sequences clearly show that they belong to the apicomplexan genus ida Fig. C. The tade b 99.0–99.87% ara Table ”. The geRhytidocystis and their flatworm hosts reveals a complex association pattern suggesting a general incongruence between the two phylogenies  and its p-value (0.076).A tanglegram summarizing the cophylogeny between ies Fig. A. In addDuplominona sp.–Rhytidocystis QU51) and to a lesser extent those of the thalassotyphloplanids  and schizorhynchids  have high squared residuals, thus contributing to the overall incongruence of the host and parasite phylogenies. These high values can be linked to the respective phylogenetic positions of the rhytidocystids from these hosts. For instance, Rhytidocystis QU51 from the proseriate Duplominona sp. is nested among rhytidocystids infecting rhabdocoels, while the rhytidocystids from the thalassotyphloplanids and schizorhynchids are spread over the different clades b, j and k, and clades b and k, respectively  and those of all polycystidid flatworms  have relatively low squared residuals, indicating some phylogenetic congruence for these associations. Both hosts and parasites of these associations cluster together in their respective phylogenies  nor merogony 43. Its two monotypic families, Rhytidocystidae Levine, 1979 and Gemmocystidae Upton & Peters, 1986, contained the genera Rhytidocystis and Gemmocystis Upton & Peters, 1986, respectively. All eight described species of Rhytidocystis infect the intestinal epithelium, coelom and connective tissues of polychaete annelids from the northeast and northwest Atlantic Ocean, the northeast Pacific Ocean and the Arctic Ocean49. The only known species of Gemmocystis is found in the mesenterial filaments of several species of scleractinian corals50. Recent phylogenetic and phylogenomic studies have confirmed the close affinity of rhytidocystids to other coccidiomorphs including coccidians and hematozoans51. In addition, Miroliubova et al. (2020) and Mathur et al. (2021) showed that rhytidocystids form a clade with different representatives of Aggregatidae and Margolisiella, which was subsequently named Eococcidia and Marosporida, respectively49. It is now thought that Gemmocystis cylindricus Upton & Peters, 1986 is likely a “type-N” apicomplexan belonging to the corralicolids54. As a result, the taxonomic validity of the agamococcidians has been questioned34.The genus 5. Moreover, the 18S rDNA sequence of an unidentified apicomplexan from the European oyster Ostrea edulis is represented in our tree as BOU83331  is almost certainly parasitized by apicomplexans, but the cells were at first considered as food items55. Many of the flatworm host genera from this study, including Carcharodorhynchus, Cheliplana, Trigonostomum, Austrorhynchus and Duplominona have global distributions further indicating that we have only started uncovering a small fraction of the diversity of apicomplexan parasites associated with these animals.Our results confirm that rhytidocystids are not confined to annelids, but are widespread among marine flatworms. Their host range is almost certainly wider as rhytidocystids have also been identified in the eukaryotic microbiome of microscopic molluscs and arthropods331 Fig. C and is Rhytidocystis was found to infect representatives of all major marine rhabdocoel lineages, except for the neodalyellids. Despite the collection of many members of this taxon around Vancouver Island56 and Japan (unpublished data), we did not observe any apicomplexans in this group of rhabdocoels. Interestingly, no mentions of “sporozoans” or “gregarines” from free-living neodalyellids could be found in the literature either. The only two records of apicomplexans in this group are both hyperparasites in endosymbiotic neodalyellids from the southern hemisphere, including Monocystella epibatis from an Australian pterastericolid27, and “sporozoans” from an Antarctic umagillid57.Ptychopera westbladi  out of the 300 individuals he observed from the Rhine-Meuse-Scheldt Delta in the Netherlands16. The same author also found the proseriate Monocelis fusca in great numbers around the same region, but states that he “rarely found parasitic gregarines in the body of M. fusca”17. This is in line with our quantitative observations in Japan, Canada and Curaçao.Prevalence of these parasites in flatworm hosts, when recorded, seemed relatively low. For instance, Den Hartog (1964a) found “gregarines” in only one single individual of the rhabdocoel 64, British Columbia67, and Hokkaido68. No marine rhabdocoels are currently known from the Ryukyu Islands. Duplominona sp. is the first record of a proseriate from British Columbia, but several species of proseriates have been documented from the adjacent San Juan Islands, Washington70.The marine rhabdocoel diversity in the sampled regions has been reported in several studies featuring species and records from CuraçaoMariplanella piscadera and Trigonostomum vanmecheleni72, while others, including the two species of Carcharodorhynchus  and Koinocystididae, respectively  the presence of distinct clades and lineages of 18S rDNA sequences in our phylogenetic trees  a ees Fig. , 3) geo geoRhytiees Fig.  and S4. Our results paint a complex picture pertaining to host specificity and patterns of phylosymbiosis between the flatworm hosts and their rhytidocystid parasites. While our cophylogenetic analyses show an overall incongruence between host and parasite phylogenies, several other patterns can be discerned.Rhytidocystis as is the case for the thalassotyphloplanid flatworm Parapharyngiella n. sp. The two infected specimens of this flatworm were collected in roughly the same location tropical sampling locations on Okinawa were also relatively diverse with three putative species of Rhytidocystis infecting three different rhabdocoel taxa. A variety of sampling locations and marine habitats were surveyed for microturbellarians on Curaçao and Okinawa, but only locations with detritus-enriched coral sand seemed to harbour infected hosts.First, the same host species can be infected by more than one putative species of Rhytidocystis can infect various flatworm host species. Species ‘b’ infects hosts belonging to the three major groups of rhabdocoels 63, while species ‘i’ was found in three different species of Polycystididae, one of the most species-rich families of kalyptorhynchs. This suggests varying degrees of host specificity among species of rhytidocystids infecting rhabdocoels—species ‘b’ in any rhabdocoel versus species ‘i’ only in polycystidids—but larger datasets are needed to confirm this trend.Some species of Rhytidocystis with relatively wide host ranges could have been present in several other host species, but probably require the collection of sufficient numbers of individuals for each host species to be found. The rhytidocystids present in an unidentified species of Reinhardorhynchus (Koinocystididae) and an unidentified species of Cheliplana (Cheliplanidae) from Onna Bay (Okinawa) could not be characterized with molecular and/or morphological data in this study.All of our sampling locations are characterized by a relatively high diversity of microturbellarians and other meiofaunal organisms: there were at least eight and four different species of rhabdocoels in the samples from Onna Bay and Igei Beach (Okinawa), respectively; fifteen species of rhabdocoels from Piscadera Bay (Curaçao); seven species of epiphytic rhabdocoels from Otaru (Hokkaido); and no less than fifteen species of rhabdocoels and three species of proseriates from Hyacinthe Bay on Quadra Island. Only some of these species were infected with rhytidocystids, including seven species from Piscadera Bay (Curaçao), three species from Onna Bay (Okinawa), two species from Igei Beach (Okinawa), one species from Otaru (Hokkaido), and one species from Hyacinthe bay (Quadra). As such, species of Rhytidocystis diversity and perceived prevalence are higher in Curaçao and Okinawa  compared to Quadra Island , suggesting that the diversity and prevalence of these parasites might be influenced by geography—i.e., higher diversity and prevalence in (sub)tropical marine areas compared to cooler temperate marine areas. In addition, our results also show the potential existence of a (sub)tropical Rhytidocystis species group (clade k) and a species group (clade j) inhabiting cooler temperate seas (Fig. Finally, eas Fig. A,B. AddiSupplementary Information.


The results showed that injecting a volume equal to 65.62% of the total working reactor volume yielded the best methane production. Specifically, this volume of bioaugmented culture resulted in methane production rates of 196.18 mL g−1 Volatile Solids (VS) and 245.88 mL g−1 VS after 30 and 60 days of AD, respectively. These rates were not significantly different from the control reactors (30d: 205.94 mL CH4 g−1 VS and 60d: 230.26 mL CH4 g−1 VS) operating without ammonia toxicity. Analysis of the microbial community using 16S rRNA gene sequencing revealed the dominance of acetoclastic methanogen members from the genus Methanosaeta in all reactors.This study aimed to investigate the role of the bioaugmented critical biomass that should be injected for successful bioaugmentation for addressing ammonia inhibition in anaerobic reactors used for biogas production. Cattle manure was used as a feedstock for anaerobic digestion (AD). A mixed microbial culture was acclimated to high concentrations of ammonia and used as a bioaugmented culture. Different volumes of bioaugmented culture were injected in batch anaerobic reactors under ammonia toxicity levels i.e., 4 g of NH AD is an environmentally friendly technology as it converts organic waste into a useful source of energy without releasing harmful greenhouse gases into the atmosphere. It also helps to reduce the volume of organic waste in landfills and has a crucial role in waste management in agriculture. AD has a favorable economic impact as the number of biogas plants, which utilize this process, is currently increasing, especially in European countries [Anaerobic digestion (AD) is a promising energy technology in which microorganisms degrade organic waste in the absence of oxygen to produce biogas. Biogas mainly consists of methane (CHountries .4+) and free ammonia (NH3), and their concentration is strongly dependent on the temperature and pH value of the solution. When the concentration of ammonia in an anaerobic reactor exceeds a certain level, it can negatively impact the activity and stability of the microbial community and especially of methanogenic Archaea [AD is a biological process that has limitations and problems related to various biological factors such as inhibition by toxic compounds. Ammonia is a common inhibitor compound in AD, generated by the degradation of nitrogen compounds contained in organic waste, such as proteins, urea, and nucleic acids ,3. In aq Archaea . AmmoniaBioaugmentation is a promising process to alleviate important problematic states of anaerobic reactors, including ammonia inhibition. It is a biological process that involves the addition of selected microbial cultures to enhance the performance of an existing microbial community in a biological system . BioaugmMethanobrevibacter smithii) and syntrophic acetate oxidizing bacteria (Syntrophaceticu schinkii) demonstrated the best results in methane production of ammonia-inhibited anaerobic reactors with a 71% increase compared to control, in a study that tested single and combinations of pure strain cultures [Bioaugmentation can be performed by different types of microbial cultures-inoculum. Pure or mixed cultures are commonly used for tackling ammonia inhibition in anaerobic reactors. Pure cultures are single strains of microorganisms that are often well-characterized and have specific metabolic functions. Studies have shown successful bioaugmentation in anaerobic reactors with ammonia toxicity using pure cultures ,11. Morecultures . Howevercultures . Alternacultures . Successcultures ,16,17. Tcultures . By analThe effectiveness of bioaugmentation can be significantly influenced by the successful delivery of the bioaugmented culture inoculum . SustainThis study aims to investigate the critical biomass required for successful bioaugmentation to address ammonia inhibition in anaerobic reactors that produce biogas. Acclimation of mixed microbial cultures to high ammonia concentrations was performed, to use them as bioaugmented cultures. Different volumes of bioaugmented culture were used to determine the critical biomass injection in batch anaerobic reactors. Methane potential was used to compare the effectiveness and the successful bioaugmentation of the different volumes of the bioaugmented microbial consortia. Microbial analysis based on 16S rRNA gene sequencing was performed to identify the composition of the bioaugmented culture. Moreover, samples were taken at the end of the experimental processes of AD to investigate the adaptation of the bioaugmented culture in the microbial community of the reactors.Inoculum was obtained from a commercial biogas plant, Bioenergy Nigritas S.A. , located in Northern Greece, which operated under mesophilic temperature conditions (37 ± 1 °C). To eliminate any residual methane production, the inoculum was placed in an incubator at a constant temperature of 37 ± 0.3 °C for 5 days.The feedstock for the anaerobic digestion (AD) experiment was fresh cattle manure sourced from a dairy cattle farm, also located in Northern Greece. Coarse materials were removed using a sieve, and the manure was stored at −20 °C after homogenization. Prior to introduction into the anaerobic reactors, the manure was kept at a temperature of 4 °C for 3 days. The characteristics of the inoculum and cattle manure are presented in 4Cl, Merck, Darmstadt, Germany).To achieve high ammonia concentrations in the feedstock, we utilized ammonium chloride with a chemical purity of 99.99%  of purity greater than 99.50% were used as feedstock in a volumetric and volatile solids (2 g VS) ratio of 1:1. The bioaugmentation reactors were flushed with a gas mixture of N2 and CO2 (80/20% v/v) to carry out anaerobic conditions and placed inside an incubator operating at 37 ± 0.3 °C. Ammonium chloride was used to step increase the ammonia concentration during the creation of the bioaugmented culture as described previously [−1 and 254 ± 32 g L−1, respectively. The acclimated enrichment culture was 5× concentrated by centrifugation (10 min of 4500 rpm) in a refrigerated centrifuge . After centrifugation, the supernatant liquid was discarded, and the dense biomass that remained was used as concentrated bioaugmented culture. The characteristics of the bioaugmented culture are presented in A different inoculum obtained from a commercial biogas plant  was used for the creation of the bioaugmented culture . The bioeviously . The fin2 and CO2 . The reactors were maintained under anaerobic conditions by closing them with rubber cups suitable for needle penetration during gas sampling acquisition. To investigate the critical biomass percentage at which bioaugmentation is achieved, seven different sets of reactors (in triplicate) with different contents were used and they are presented in Batch glass reactors were used with a total and working volume of 321 mL and 160 mL, respectively. The anaerobic conditions were achieved by flushing the reactors with a gas mixture of NMeasurements of total Kjeldahl nitrogen (TKN), total ammonia (TAN), total solids (TS), and volatile solids (VS) were performed based on methods of APHA’s Standard Methods . The pH Biogas samples from the headspace of the reactors were collected using a gas-tight syringe that had a pressure lock and a needle attached to it. Throughout the initial 27 days of the experiment, daily biogas samples were collected from the reactors, followed by subsequent sampling intervals of 1 to 3 days until the 60th day from the start of the experiment. Methane content and production was monitored with a gas chromatograph  equipped with a thermal conductivity detector with column characteristics and the method’s parameters described before . Volatil® Soil kit  as per the instructions provided by the manufacturer. Multiplexed amplicon sequencing analysis of the bacterial 16S rRNA gene was performed as described previously [Samples were obtained from the PBIOA, PSA, PS, BIOmin, BIOmed, and BIOmax reactors at the end of the anaerobic digestion (60 days) for high throughput 16S rRNA gene sequencing analysis. Extraction of DNA was performed with the NucleoBondeviously . Brieflyeviously . The 515eviously ,28, whileviously . Before eviously  of the Reviously . A tablep-value was less than 0.05.The mean values and standard deviation were calculated from the derived data of analytical methods. The comparison of means was performed using variance analysis with one factor (ANOVA), and the differences in values per pair were evaluated using the LSD post hoc test performed with the IBM SPSS Statistics, v.25. Statistically significant differences were considered when the The μmax, the maximum specific growth rate, was calculated from the slope of the linear segment of the semi-logarithmic graph of methane production against incubation time .FAN concentrations were conducted based on the following Equation (1):aK is the dissociation constant which at the temperature of 37 °C is equal to 1.29 × 10−9 [Calculations of 9 × 10−9 .−1, and no bioaugmented microbial culture was added to these reactors. The PS reactors served as the control group without ammonia toxicity, with a concentration of 1598 mgL−1, and were used to measure methane production from the cattle manure substrate. In contrast, the BIOmin, BIOmed, and BIOmax reactors, which received bioaugmented microbial culture, exhibited ammonia concentrations of 4256, 4133, and 4011 mg TAN L−1, respectively.The PSA reactors were operated at a TAN of 4379 mgL4 g−1 VS), which had a high ammonia concentration and a bioaugmented population, did not exhibit a significant statistical difference compared to the PS reactors (205.94 ± 24.75 mL CH4 g−1 VS), which were used to measure the methane potential of the substrate (cattle manure). The PSA reactors (123.42 ± 6.92 mL CH4 g−1 VS), where the AD of the substrate with the addition of ammonia was performed, showed noticeably lower methane production compared to the other treatments.Methane production from IN reactors was subtracted from the production of reactors to which inoculum was added. Moreover, the methane production of PBIOA reactors was proportionally subtracted from the production of BIOmin, BIOmed, and BIOmax reactors. The methane potential after 30 and 60 days of anaerobic digestion from all treatments is presented in 4 g−1 VS) and BIOmed (166.17 ± 4.21 mL CH4 g−1 VS) reactors did not exhibit a statistically significant difference between them but showed a significant difference compared to the BIOmax reactors.Methane production in the BIO reactors was significantly higher than in the PSA reactors, indicating that bioaugmentation was achieved regardless of the critical biomass introduced into the batch reactors. However, only the BIOmax reactors managed to reach the performance of the PS reactors, and BIOmin and BIOmax methane production was significantly lower in comparison with the PS reactor’s methane production. The BIOmin (167.84 ± 2.30 mL CH4 g−1 VS), which operated under ammonia toxicity, the microbial population was unable to adapt and approach the methane production achieved by the BIOmin (188.52 ± 13.01 mL CH4 g−1 VS), BIOmed (195.75 ± 10.36 mL CH4 g−1 VS), BIOmax (245.88 ± 13.60 mL CH4 g−1 VS) and PS reactors (230.26 ± 8.78 mL CH4 g−1 VS). In this case, it was confirmed that direct exposure of microbial populations to high levels of ammonia did not lead to adaptation (even after 60 days) as it was also suggested by previous researchers [Notably, even after 60 days of PSA reactors  of the working reactor volume will be preferable for bioaugmentation to alleviate ammonia inhibition in experiments with continuous reactors.Samples were collected from the BIOmin, BIOmed, BIOmax, PS, PSA, and PBIOA reactors for microbial analysis. The analysis was performed with the 515F/806R primer set targeting the V4 region of the 16S rRNA gene of most prokaryotes ,27 for g4Cl solution without bioaugmented culture inoculation.The most dominant bacterial phyla that were detected in the microbial community of the reactors are presented in The archaeal phylum with the highest relative abundance was Halobacterota, accounting for 81.93% of the total archaeal ASVs, followed by Crenarchaeota with 15.21%. The remaining archaeal phyla detected by DNA analysis were Thermoplasmatota and Euryarchaeota, representing 12.02% and 3.45% of the total archaeal ASV population, respectively. In the bioaugmentation culture (PBIOA reactors), the relative abundance of Archaea was higher and reached 21.79% of the total prokaryotic population. Moreover, BIOmin (18.10%), BIOmed (6.93%), and BIOmax (14.84%) reactors had a higher percentage of the mean relative abundance of Archaea compared to the PSA (5.16%) reactors, which can be explained by the introduced bioaugmented culture in the BIO reactors.Methanosaetaceae family were dominant in terms of relative abundance in all treatments. In accordance with a previous study, where Methanosaetaceae family members were found to have a high relative abundance and to be more actively involved in methanogenesis under ammonia-associated inhibition [Methanosaetaceae family was represented only by the genus Methanosaeta, which is generally known as obligate acetoclastic methanogen [Methanosaeta was identified as one of the two most dominant genera, following the use of various bioaugmentation cultures [Methanosarcinaceae family members were detected in all reactors. Even though high relative abundances were observed in the BIOmin and BIOmed reactors, there were no statistically significant differences between the other examined reactors. A significant family in terms of relative abundance was Methanosarcinaceae, which was detected in all AD reactors. A prevalent member of Methanosarcinaceae was the genus Methanosarcina, which are known to grow on several simple carbon compounds using the main three methanogenic pathways: acetoclastic (acetate), hydrogenotrophic (CO2 + H2), and methylotrophic  [Methanosarcinaceae members were often observed in manure-fed reactors and at inhibitory concentration levels of ammonia [Methanosarcina genus which have a high volume-to-surface ratio and are able to create multiple cells structures (clusters) [The relative abundance of archaeal families among the overall archaeal populations detected in samples of the AD reactors are presented in hibition . Specifithanogen ,42. Thiscultures . Methanoulfides) . Methano ammonia , and thelusters) .Methanosaetaceae and Methanomethylophilaceae were the most dominant among families  [Methanospirillaceae family (hydrogenotrophic) were likely present only in the initial inoculum used for the anaerobic digestion (AD) of all the reactors, as they were detected solely in the PS reactors that were not exposed to toxic ammonia concentrations. This result is in accordance with a previous study on the effects of ammonia toxicity on syntrophic propionate oxidation in anaerobic digester sludge where hydrogenotrophic Methanospirillaceae were moderately and severely inhibited at a concentration of 3 g L−1 of ammonium nitrogen [Methanofastidiosaceae, a family known for methane production through the H2-utilizing methylotrophic pathway [Methanofastidiosaceae members were exclusively detected in the PS reactors, suggesting that their presence resulted from the initial inoculum used for anaerobic digestion. This finding strongly indicates the influence of elevated ammonia levels on their populations.In the PS reactors, families . Howevers (CSTR) . Membersnitrogen . Further pathway . These MMethanosaeta, Methanosarcina) showed a statistically significant difference compared to hydrogenotrophic and methylotrophic Archaea in all other treatments. However, Methanosarcina has been reported to potentially act as a hydrogenotroph under high concentrations of ammonia, thereby probably increasing the activity levels of the specific methanogenic pathway [Methanomicrobiaceae, Methanobacteriaceae, Methanocorpusculaceae, and Methanospirillaceae families were detected in the AD reactors. Methanomicrobiaceae exhibited the highest relative abundance in the PS, PSA, BIOmin, and BIOmax treatments, while Methanobacteriaceae was the dominant hydrogenotroph in BIOmed reactors. In the case of methylotrophic methanogens belonging to the order Methanomassiliicoccales, the family Methanomethylophilaceae was identified as the most dominant in terms of relative abundance among methylotrophic Archaea. According to microbial analysis, the pathway of acetoclastic methanogenesis was likely the dominant for the observed methane production. However, the highest methane production was observed in the BIOmax reactor among those with induced ammonia toxicity. DNA analysis revealed a significant presence of hydrogenotrophic and methylotrophic methanogens in this reactor. Therefore, the ratio between the different types of active methanogens in the AD reactors, particularly those with high concentrations of ammonia and supplemented with a bioaugmentation culture, may be the underlying reason for the high methane production.In general, the relative abundance of acetoclastic Archaea  during AD.In this study, bioaugmentation successfully addressed ammonia inhibition on methane production in anaerobic reactors. The BIOmin and BIOmed reactors exhibited a significant increase in methane production compared to the control group of PSA reactors with the same ammonia concentration. However, only the BIOmax reactors reached the mean methane production level of the PS reactors (without ammonia toxicity) within 30 days of anaerobic digestion and surpassed it after 60 days. Therefore, the results of this study demonstrate that the critical biomass of the bioaugmented culture to alleviate inhibition due to high ammonia concentrations should be injected at 65.62% of the working reactor volume in a single application. DNA analysis revealed a dominance of acetoclastic methanogens from the


So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at  Protein–protein interaction (PPI) plays a fundamental role in numerous cellular functional progresses –5. PPI sSo far, various computational methods have been developed for predicting PPI sites, which can be categorized into 2 mainstream strategies . The firHowever, these methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods had a main disadvantage of relatively low prediction accuracy because they only excelled at learning global or local contextual features from primary sequences ,23,40,41http://idrblab.org/ensemppis. EnsemPPIS is applicable for proteome-wide profiling of PPI sites and expected to provide more insights into protein function research and drug discovery.Herein, a novel transformer-based ensemble method for PPI sites prediction, EnsemPPIS, was therefore proposed, which can capture not only global and local patterns but also residue interactions. EnsemPPIS consists of 2 base models, namely, TransformerPPIS and GatCNNPPIS. The transformer framework in TransformerPPIS is equipped with the ability to learn global features and calculate attention weights between residues, making it possible to capture residue dependencies within local structures, while GatCNNPPIS is capable of learning local contextual features using the gated convolutional networks. EnsemPPIS was thoroughly evaluated on multiple PPI sites prediction tasks and exhibited either superior performance or broader applicability compared with various existing methods. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions using only primary sequences, thereby improving the performance of PPI sites prediction. A web server of EnsemPPIS was further established, which is freely available at EnsemPPIS functions through 3 steps, including ProtBERT embedding, feature learning, and prediction, as illustrated in Fig. DeepPPISP task and DELPHI task.Previous studies have generated multiple datasets preserving experimentally validated PPI sites data, which have been widely utilized in developing computational tools, as displayed in Table Performance evaluation on DeepPPISP task(a) DeepPPISP task, as shown in Table EnsemPPIS, along with 12 other competing methods, was first evaluated and compared on the As a result, EnsemPPIS achieved the highest performance among all evaluated methods, achieving the MCC value of 0.277, AUPRC of 0.405, and F1 of 0.405. These 3 evaluation metrics are the most important ones in the imbalanced task of PPI sites prediction . SpecifiDeepPPISP task. Figure EnsemPPIS achieves accurate prediction of PPI sites by integrating 2 separately trained base models. To demonstrate the effectiveness of ensemble learning, 2 variants of EnsemPPIS were constructed by combining the 2 base models into a single model for concurrent training, namely, EnsemPPIS-Va and EnsemPPIS-Vb, as shown in Fig. Furthermore, we additionally assessed the performance of TransformerPPIS using different types of features or feature combinations, namely, ProtBERT, PSSM , DSSP 5, and OnePerformance evaluation on DELPHI task(b) DELPHI task, as shown in Table EnsemPPIS was further assessed on In summary, EnsemPPIS achieved remarkable improvements in residue-level prediction of PPI sites using only protein sequences, outperforming all existing sequence-based methods and comparable to even the most advanced structure-based methods. In this study, protein sequences were input into the pretrained language model ProtBERT to obtain the protein embeddings. Currently, there are some large protein language models that are able to generate informative latent vectors for residues , such asGraphPPIS task, and the results can be found in Table DeepPPISP task were not included in the GraphPPIS task for comparison, such as EGRET, because they were not provided with the training source code, thus preventing their retraining. As a result, EnsemPPIS once again outperformed all sequence-based methods and even achieved better performance than some structure-based approaches. Specifically, considerable improvements in F1, AUPRC, and MCC were achieved by 7.5%, 10.3%, and 17.2%, respectively, compared with the best existing method using protein sequences. In addition, EnsemPPIS also surpassed 2 of the structure-based methods (SPPIDER and DeepPPISP) on F1, AUPRC, and MCC, but slightly lagged behind RGN and GraphPPIS.EnsemPPIS was also evaluated and compared on the GraphPPIS task and only comparable to EGRET in the DeepPPISP task, it promises to be an indispensable tool and is applicable for the whole proteome, because it is free from the inherent limitations of structure-based methods, namely, the acute dependence on precise protein structures and the improper use of protein conformation for model training.Although EnsemPPIS is inferior to RGN and GraphPPIS in the The first limitation of structure-based methods is that the lack of experimentally validated protein structures severely limits their scope of application ,54. ThisProtein conformation undergoes changes when binding with a partner ,65,66, aEnsemPPIS outperforms SOTA ensemble learning method(a) DeepPPISP task. We conducted a comparative analysis between our method and the SOTA ensemble learning method DELPHI to evaluate their performance in predicting individual protein sequences from the Test70 dataset. The results of this comparison were depicted in Fig. EnsemPPIS consistently demonstrated superior performance in predicting PPI sites at the residue level. However, it is worth noting that similar predictive methods are commonly employed for individual protein predictions in downstream research. Therefore, we further assessed the performance of EnsemPPIS in protein-level prediction on the To elucidate the advantage of EnsemPPIS in predicting individual proteins, 2 specific proteins (PDB: 1jtdB and PDB: 1b6cA) were randomly selected as cases to visualize the prediction results of TransformerPPIS, EnsemPPIS, and DELPHI. As shown in Fig. EnsemPPIS is robust on sequences of different lengths(b) Existing sequence-based methods predominantly focused on local sequential features of residues, largely neglecting the sequence interdependency . This ovP > 0.05) according to the Mann–Whitney U test [Therefore, we also evaluated the predictive performance of EnsemPPIS on sequences of varying lengths in the Test70 dataset. All the 70 sequences were grouped into 3 categories, namely, short length (less than 100 residues), medium length (100 to 200 residues), and long length (more than 200 residues). The number of sequences of short length, medium length, and long length was 18, 32, and 20, respectively. We evaluated EnsemPPIS on different lengths in both residue-level and protein-level prediction tasks. As illustrated in Fig. y U test . The resP < 0.05). In the case of the 8-Å range, the correlation coefficient (r) was calculated to be 0.697, with a corresponding P value of 2.71 × 10−5. To gain further insights, we divided the residues within this range into 2 groups based on either the median of the attention scores or the predicted labels. This division allowed us to visualize the distribution of residues and examine their characteristics. As depicted in Fig. P = 1.25 × 10−4) according to the Mann–Whitney U test.The black box nature of deep learning methods calls for careful investigation of interpretability –70. OwinU test was then employed to examine the significant difference of the attention scores between these 2 groups. As illustrated in Fig. P value was 0.0386, indicating that the attention scores of “Close Residues” were significantly higher than those of “Distant Residues.” Similarly, these residues were classified into another 2 groups, namely, “High Attention Score” and “Low Attention Score,” based on the median of their attention scores. As shown in Fig. P value was 0.0351, which implied that residues with higher attention scores were spatially closer in local structure, but might be far apart in sequence. This pattern analysis suggested that residues closer in local space contributed more to the formation of PPI sites, which corroborated the fact that residues closer in space interact more significantly [PPI sites are relatively aggregated in protein structures, and local structural features play a crucial role in the formation of PPI. This implies that the interactions among residues within local structures play a crucial role in predicting PPI sites, and therefore, structure-based methods conduct the prediction by learning the features from spatially proximate amino acids . Howeverficantly .In summary, these findings highlighted that the TransformerPPIS base model within EnsemPPIS is fully capable of learning residue interactions, particularly the long-range interactions within the local structure of PPI sites using only primary sequences. This capability allows the model to extract meaningful connections between protein sequences and structures, ultimately leading to improved performance in predicting PPI sites.A web server that implements EnsemPPIS was constructed in this study, which is convenient for researchers to apply our proposed PPI sites prediction method. The EnsemPPIS server was deployed on a Linux server of an Intel Xeon Gold 6149 3.10GHz CPU with 8 cores and 64 GB of memory based on the Python web framework of Django. As an open online platform, all users could freely access it through popular web browsers, including Google Chrome, Mozilla Firefox, Safari, and Internet Explorer 10 (or later).pickle file containing the embedding vectors; (b) identifying potential PPI sites on all protein sequences and making a downloadable text file containing the prediction results. EnsemPPIS is freely available at http://idrblab.org/ensemppis.EnsemPPIS requires only the FASTA-formatted protein sequences as input, and users should set a project name to associate their PPI sites prediction task. After successful submission, the information necessary to schedule the task would be placed into a MySQL database. Users could find their submitted project displayed on the “Queue” page of the web server. Clicking on the corresponding task information bar will redirect users to the program processing page, which offers 2 key functions: (a) encoding the input protein sequences using the pretrained ProtBERT and providing a downloadable In this study, to improve the accuracy of PPI sites prediction and expand the application scope, a novel transformer-based ensemble learning method for PPI sites prediction, EnsemPPIS, was proposed, which incorporated 2 base models, namely, TransformerPPIS and GatCNNPPIS. EnsemPPIS was designed to extract residue interactions by leveraging the transformer and integrate global and local sequential features through ensemble learning. EnsemPPIS exhibited leading performance across multiple tasks, surpassing all existing sequence-based prediction methods and demonstrating its broader applicability in comparison to structure-based methods. Additionally, EnsemPPIS exhibited superior and robust performance in both residue-level and protein-level prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS revealed its ability to learn residue interactions directly from protein sequences. EnsemPPIS is expected to facilitate in-depth understanding of molecular biology and advance research of drug discovery.DeepPPISP task [GraphPPIS task [DELPHI task [In this study, the performance of our proposed EnsemPPIS was comprehensively assessed on 3 PPI sites prediction tasks, including ISP task , GraphPPPIS task , and DELPHI task . The basDeepPPISP task were obtained from DeepPPISP [2 upon protein binding [The Train352 and Test70 datasets used in the eepPPISP . The DeeeepPPISP , Dset_72eepPPISP , each coeepPPISP  and buil binding . For a f binding . Thus, tGraphPPIS task were originally constructed by GraphPPIS and were also obtained by integrating the 3 datasets mentioned above  [The Train335 and Test60 datasets used in the set_164)  was furtDELPHI task were collected by DELPHI, a recent research of PPI sites prediction using sequences [The Train9982 and Test355 datasets in equences . The Tesequences , which wequences  and consequences , the devequences  and usedequences  to removThe prediction of PPI sites is essentially a binary classification task. In this study, the interaction sites were taken as positive samples and non-interaction sites as negative samples. To fully evaluate the performance of EnsemPPIS and other competing methods, 7 widely used evaluation metrics were adopted in this study, including accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), Matthews correlation coefficient (MCC), area under the receiver operator characteristic curve (AUROC), and area under the precision–recall curve (AUPRC). All metrics were calculated using the Scikit-learn package , and theL × 1,024 matrix for each protein sequence, where L is the sequence length and each amino acid is represented by a 1,024 embedding vector. ProtBERT is a BERT model pretrained on UniRef100 through self-supervised learning, which can capture biophysical features of protein sequences [To convert protein sequences into embeddings, the pretrained protein language model, ProtBERT, was used to generate an equences ,82,83. TL × 64 matrix using the FC and then fed into the gated convolutional network. The hidden layers h0, …, lh in the gated convolutional network are computed as In contrast to the original transformer framework, the encoder of TransformerPPIS uses a gated convolutional network with Conv1D and gated linear unit in place of the self-attention layers . Conv1D X ∈ ℝn×m1 is the input of layer lh; W1 ∈ ℝk×m1×m2, W2 ∈ ℝk×m1×m2, b1 ∈ ℝm2, and b2 ∈ ℝm2 are trainable parameters; l is the number of encoder layers; n is the length of the sequence; m1 and m2 are the dimension of input and hidden features of the gated convolutional network, respectively; k is the kernel size of Conv1D; σ is the sigmoid function; and ⨂ represents the element-wise product between matrices [l is 3, m1 is 64, m2 is 128, and k is 7. The encoder module adopts residual connection and layer normalization to solve the oversmoothing problem [L × 64 matrix, is the final representation of a protein.where matrices . In this problem . The outQ; the keys, K; and the values, V [Q and the global protein feature as K and V, and calculates the attention weight using Q and K. The calculation formula is as follows:The decoder module of TransformerPPIS is specifically designed to learn and capture residue interactions within protein sequences. The input of decoder module contains 2 parts: the global feature of the protein output by the encoder module and the original embedding of a specific residue obtained by ProtBERT. The decoder module mainly consists of multi-head self-attention layers and feedforward layers. The multi-head self-attention layer extracts the interactions between the specific residue and other residues, which takes 3 inputs: the queries, alues, V ,87. Trankd is a scaling factor depending on the dimension of the hidden layer. The mask operation in the original transformer framework is modified in the decoder module to ensure that the complete sequence information is accessible. Accurately identifying PPI sites necessitates careful attention to the features of the local structure surrounding these sites [where se sites . Howeverse sites . After eThe output of decoder module is the interaction feature between the specific residue and the global protein sequence. The interaction feature vector is further fed to the classifier module, which is composed of 3 FCs and the ReLU activation function . FinallyL × 64 matrix), each vector represents the local contextual feature of a particular residue. GatCNNPPIS takes this vector as input and directly feeds it into the classifier module, which outputs the probability of the corresponding residue being an interaction site.The GatCNNPPIS base model presented here can be viewed as a simplified version of TransformerPPIS, consisting solely of the encoder and the classifier modules. In the output of the encoder module , the weighted cross-entropy loss function was adopted to compute the loss values of samples, which were subsequently used to calculate the gradient of parameter update in the backward propagation process . The wei process . In each process ,34,35. Sing cost –94. In oing cost –97.As an ensemble learning framework, the 2 base models of EnsemPPIS (TransformerPPIS and GatCNNPPIS) were separately trained using the same training procedure. To optimize EnsemPPIS, we selected the optimal combinations of base models . After thttp://pytorch.org/) and supported distributed training [Three most influential hyperparameters  were tuned according to the predictive performance on the validation dataset. As a result, the optimal combination of the above 3 hyperparameters was decided . All the hyperparameter settings of EnsemPPIS were summarized in Table training . All scrA comprehensive review on the previously published tools for PPI sites prediction was conducted in this study, which were systematically compared with our proposed EnsemPPIS, as shown in Table


To explore the relationship of MRI morphology of primary rectal cancer with extramural vascular invasion (EMVI), metastasis and local recurrence.This retrospective study included 153 patients with rectal cancer. Imaging factors and histopathological index including nodular projection (NP), cord sign (CS) at primary tumor margin, irregular nodules (IN) of mesorectum, MRI-detected peritoneal reflection invasion (PRI), range of rectal wall invasion (RRWI), patterns and length of tumor growth, maximal extramural depth (EMD), histologically confirmed local node involvement (hLN), MRI T stage, MRI N stage, MRI-detected extramural vascular invasion (mEMVI) and histologically confirmed extramural vascular invasion (hEMVI) were evaluated. Determining the relationship between imaging factors and hEMVI, synchronous metastasis and local recurrence by univariate analysis and multivariable logistic regression, and a nomogram validated internally via Bootstrap self-sampling was constructed based on the latter.P < 0.05). NP and IN remained as significant predictors for hEMVI, and mEMVI was a predictor for synchronous metastasis, while PRI and mEMVI were predictors for local recurrences. The nomogram for predicting hEMVI demonstrated a C-index of 0.868, sensitivity of 86.0%, specificity of 79.6%, and accuracy of 81.7%.Thirty-eight cases of hEMVI, fourteen cases of synchronous metastasis and ten cases of local recurrence were observed among 52 NP cases. There were 50 cases of mEMVI with moderate consistency with hEMVI (Kappa = 0.614). NP, CS, EMD and mEMVI showed statistically significant differences in the negative and positive groups of hEMVI, synchronous metastasis, and local recurrence. Compared to patients with local mass growth, the rectal tumor with circular infiltration had been found to be at higher risk of synchronous metastasis and local recurrence (NP, CS, IN, large EMD, mEMVI, and circular infiltration are significantly associated with several adverse prognostic indicators. The nomogram based on NP has good predictive performance for preoperative EMVI. mEMVI is a risk factor for synchronous metastasis. PRI and mEMVI are risk factors for local recurrence. Rectal cancer is the third most common malignant tumor in global morbidity and mortality , with loIn clinical practices, NP and CS at the tumor margin and IN of mesorectum often appear together with metastasis and EMVI , 11. TheThis study aims to explore the relationship of MRI morphology of primary rectal cancer with EMVI, metastasis and local recurrence.We conducted a retrospective analysis of medical records and MRI data for 198 patients with rectal cancer who were treated at our hospital between October 2014 and April 2019. All patients were pathologically confirmed to have rectal cancer after surgery. The MRI data collected for this study were obtained prior to surgical intervention or neoadjuvant therapy. The synchronous metastasis information accompanying with rectal cancer was also collected. The exclusion criteria included: (i) patient with rectal mucinous adenocarcinoma because of different biologic behavior; (ii) patient treated with palliative surgery; (iii) patient with incomplete pathological data or MRI data, and patient with blurry images ; (iv) patient who had a history of other concurrent malignancies; (v) patient who had an interval of more than 10 weeks between MRI and total mesorectal excision. Finally, 153 patients with rectal cancer who underwent total mesorectal excision were included in the study  which is configure with a phased-array surface coil. Based on the daily dietary intake, patients were advised to consume light meals within 24 h before the scan and ensure timely bowel movements. Prior to MRI scan, bowel cleansing was performed, and antispasmodic drugs were not administered. Pulse sequences was observed by fast spin-echo sagittal HRT2WI with a thickness of 4 mm, an intersection gap of 1 mm, and a TR/TE of 4,000 ms/102 ms, in absence of fat saturation. The matrix size was 320 × 256. The ETL was 16, and NEX was 4. In addition, an oblique axial (perpendicular to the long axis of the rectum) HRT2WI was performed with a contiguous section thickness of 3 mm, TR/TE of 4,000 ms/102 ms, as well as FOV of 16 × 16 cm without fat saturation. The matrix size was 320 × 256. The ETL and NEX was 16 and 4, respectively. The oblique coronal HRT2WI encompassed both the entire tumor and the anterior and posterior walls of the rectum by being performed parallel to the long axis of the diseased bowel, with a contiguous section thickness of 3 mm, TR/TE of 5800 ms/110 ms, the matrix size of 288 × 288, as well as FOV of 26 × 26 cm without fat saturation.For CET1WI, a LAVA/LAVA-XV sequence was conducted in the presence of fat saturation, with a thickness of 3 mm, FOV of 36 × 36 cm, a matrix size of 256 × 192 and a flip angle of 15° for 40 consecutive phases. Gd-DTPA  was intravenously injected at a rate of 2mL/s, followed by a saline flush prior to enhanced sequencing.The morphological data of primary tumor were collected by two experienced radiologists from HRT2WI, including NP, CS, IN, PRI, RRWI, growth pattern, tumor length, and EMD. All these images were obtained from the MR findings before the adjunctive therapy. If there is any dispute, a third radiologist with experience in gastrointestinal imaging examination joined the discussion until reaching a final consensus. All the radiologists were informed of the inclusion criteria in this study, and kept blind to the pathological results of EMVI in patients.mEMVI was determined based on five-scoring system . (i) 0 sNP is defined as a tumor that breaks through the muscle layer and forms more than one nodule in surrounding adipose tissue Fig. A. CS is RRWI is measured around the circumference of the intestinal lumen on oblique axial HRT2WI. Tumor length is the distance between the upper quadrant and lower quadrant of the tumor measured on sagittal T2WI along the longitudinal axis of the tumor . EMD is the distance from the most distal edge of the tumor to the residual intrinsic muscle layer on oblique axial HRT2WI Fig. C. For unThe tumor stage assessed by MRI is largely determined by the signal differences in T2 signal intensity in the tumor, submucosa, muscular layer, and mesorectum on short-axial T2WI. T1 is for tumor invades submucosa; T2 for tumor invades muscularis propria; T3 for tumor invades the muscularis propria and pericolorectal tissues; T4 for tumor penetrates the peritoneal surface of the viscera, or invades other organs or structures. MRI-detected metastatic lymph node was confirmed using three conditions : (i) theLocal lymph node involvement and EMVI diagnosis information from histopathological examination were collected and analyzed. EMVI was confirmed by histopathology when the tumor tissue existed within an extramural space or within a tubular structure lined by endothelial cells, smooth muscles, or elastic fibers . Local rThe statistical package of IBM SPSS Statistics version 26 for Windows was adopted for data analysis. The Shapiro Wilk test was applied to check whether the variables were normally distributed. The continuous variables were compared by T-test, while the variables between groups were classified by the Chi-squared test or Fisher’s exact test. The evaluation of EMVI efficiency was based on the receiver operator characteristic curve (ROC) and area under the curve (AUC). The variables with p < 0.05 in the univariate analysis were included in a binary logistic regression model. In this model, hEMVI, synchronous metastasis and local recurrence were used as dependent variables, while NP, CS and other variables were used as independent variables. The binary logistic stepwise regression (Forward: LR) method was utilized to analyze the impact of NP on hEMVI, synchronous metastasis and local recurrence, after adjusting for confounding factors. Multivariate logistic regression was used to evaluate the variables that are statistically significant in univariate analysis and identify the independent predictors. A nomogram was created using R software (R 4.2.1) based on the results of the multivariable logistic regression analysis, and the C-indices of the nomograms were calculated to determine the differentiation of the models. The comparisons among the ROC curves of the indicators were performed by DeLong test. Interobserver agreement and agreement between pathological findings and imaging assessment results were performed using the Kappa test . A P value of less than 0.05 indicated a statistical significance.A total of 153  rectal cancer patients admitted to our hospital from October 2014 to April 2019 were included in this study. Demographic data, MRI data and histopathology results were presented in Table Among the 153 patients, 50  were confirmed to be hEMVI positive and the other 103 cases were hEMVI negative according to pathological and immune histochemistry results. Forty-eight  local lymph node involvements were confirmed according to histological features, thirty  of which were from hEMVI positive group. There were 26  cases of metastasis, 16  of which were synchronous metastasis . Other pathological results were shown in Table Among the 153 patients, there were 52 52/153, 34%) cases of NP, 38  of which were from hEMVI positive group and 14  of which were from hEMVI negative groups. A total of 50 cases with mEMVI were identified, 37 (74%) of which were from the hEMVI-positive group. mEMVI and hEMVI were moderately consistent, with a Kappa value of 0.614. There is a high degree of agreement between two observers in the evaluation of mEMVI (Kappa = 0.764). Of the 52 cases of NP, 14  and 10  had synchronous metastasis and local recurrences, respectively, with significantly higher incidence than the NP-negative group . According to Table % cases oAll univariately analyzed variables mentioned in Table The rate of distant metastasis from rectal cancer was 25–40% in previous study , and theVascular endothelial growth factor and β-catenin are highly expressed in the marginal cells of tumors with strong biological activity . TherefoRectal cancer can cause a host reaction of desmoplastic reaction (DR) which could evoke to form fibrous tissue around the tumor . In addiCS in this study included DR, EMVI, lymphangitis carcinomatosa  and carcIn our study, IN was defined as irregularly shaped nodes which was mainly metastatic lymph nodes and small proportion of TDs. Therefore, it can be considered that IN was an independent predictor of EMVI. In previous studies, local lymph node metastasis and EMVI were risk factors mutually interacted , 24. SinEMD was significantly associated with EMVI, synchronous metastasis and local recurrence. Koca et al. reported that the degree of tumor invasion was related with local recurrence after surgery , which iPeritoneal carcinomatosis from colorectal cancer occurs in 20% of patients with non-mucinous colorectal cancer, presenting a synchronous or metachronous sign, of which 6–8% occur only in the peritoneum . In the Many pathological and imaging researchers have found that EMVI is closely correlated with adverse prognostic events such as local recurrence, distant metastasis and tumor-related death of rectal cancer –32. ThisThis study has some limitations that must be acknowledged. Firstly, our study’s single-center design and two-year follow-up period resulted in relatively small sample sizes for recurrence  and synchronous metastasis , potentially leading to inflated odds ratios and affecting the predictive performance of the nomogram model. Previous study reported that the local recurrence of rectal cancer after a curative resection was in a range of 5.6 to 11.2%, 35, 36.In conclusion, multiple MRI features of tumor morphology are significantly associated with several adverse prognostic indicators, and the nomogram based on NP has excellent predictive performance for preoperative EMVI. mEMVI is a crucial risk factor for synchronous metastasis and local recurrence. PRI is a crucial risk factor for postoperative local recurrence of rectal cancer. These findings facilitate the preoperative selection of patients with high risk of EMVI, postoperative recurrence and distant metastasis based on MRI. For these patients, appropriate treatment strategy and follow-up examination could improve the prognosis as personalized treatment.


AUX/IAA, SAUR, A-RR, and ABF gene expression levels between the red‒blue light treatment and the control buds, especially in regard to the ABF genes, the expression levels of which were completely different between the two groups. The expression of GBF4 and AI5L2 in the control was always low, while the expression under red‒blue light increased. AI5L7 and AI5L5 expression levels showed an upwards trend in the control plant buds and gradually decreased in red‒blue light treatment plant buds. Through weighted gene coexpression network analysis, we determined that the transcription factors WRK48 (WRKY family), EF110 (ERF family), ABR1, CAMTA3 (CAMTA family), and HSFA3 (HSF family) may be involved in the regulation of the GBF4 gene. This study lays a foundation for further analysis of grape flower bud differentiation regulation under red‒blue light.Grape is a globally significant fruit-bearing crop, and the grape flower bud differentiation essential to fruit production is closely related to light quality. To investigate the regulatory mechanism of grape flower bud differentiation under red‒blue light, the transcriptome and hormone content were determined at four stages of flower bud differentiation. The levels of indole-3-acetic acid (IAA) and abscisic acid (ABA) in grape flower buds at all stages of differentiation under red‒blue light were higher than those in the control. However, the levels of cytokinins (CKs) and gibberellic acid  fluctuated continuously over the course of flower bud differentiation. Moreover, many differentially expressed genes were involved in auxin, CK, GA, and the ABA signal transduction pathways. There were significant differences in the  Vitis vinifera L.) is a globally significant fruit-bearing crop that is cultivated on all continents. Whether it is used for making fresh food or wine, it has major economic significance and is one of the most economically profitable fruit crops1. The quality and quantity of grape fruit are the most critical standards for grape cultivation and are directly affected by flower bud differentiation2. Flower bud differentiation of grape is an unusual process, compared with 1-year-old plants3. The shoot meristem generates both nutritional and reproductive structures, and the uncommitted primordia  generated by the stem meristem differentiate into tendrils or inflorescences, which complete flowering in the second year4.Grape , CONSTANS (CO), SUPPRESSOR OF OVEREXPRESSION OF CO1 (SOC1), LEAFY (LFY), APETALA1 (AP1), APETALA2 (AP2), and APETALA3 (AP3), and irreversibly induce the transformation from vegetative meristem to floral meristem7. Flower development pathway genes or genes involved in photoperiod or vernalization have also been identified in the grape genome; most of these genes are flowering signal integrators, floral meristem recognition genes, and floral organ recognition genes, such as MADS-box family genes and VvFT/TFL1 family genes promoting early flowering8. The orthologue of VvFT, the Arabidopsis thaliana gene FLOWERING LOCUST, is related to seasonal flowering induction in latent buds, inflorescence and flower development9. The expression of VvLFY is related to inflorescence and flower developmen10. Homologues of VvFUL and VvAP1 appear in the early stages of lateral meristem development and are maintained in both inflorescence and tendril primordia12.Grape flower bud differentiation is a very complex biological process that is influenced by the integration of the external environment and internal factors13. Light quality is an important signal affecting plant morphogenesis and flower bud formation14. Plants perceive the quality of light through photoreceptors. Photoreceptors are classified as any of the following: photosensitive pigments (Phys) (for absorption of red/far-red light), cryptochromes (Crys) (for absorption of blue/ultraviolet A (UV-A) light), phototaxis proteins, Zeitlupe (ZTL/FKF1/LKP2) family members, and the UV-B absorbing UVR8 family members17. Photosensitizers and cryptochromes are responsible for plant morphological and developmental changes19. Therefore, research on the effect of light quality on plant growth and development mainly focuses on red and blue light. In addition, the combination of red and blue light forms a spectral absorption peak suitable for plant photosynthesis and morphogenesis, forming fully developed flower buds and increasing the number of buds21. Therefore, two wavelengths (blue and red) are necessary for plants.As an essential environmental factor, light drives photosynthesis, participates in most processes regulating the plant life cycle, and plays a key role in plant flower bud differentiation23, the regulatory mechanism of grape flower bud differentiation under red‒blue light is still unclear. In the early stages of this study, we screened red‒blue light for suitablity to grape flower bud differentiation, since it is necessary to study the flower bud differentiation process under red‒blue light if we wish to understand the regulatory mechanism of flower bud differentiation under this particular type of ligh. Therefore, in this study we used the screened red‒blue light for supplementary light treatment, with conditions of no supplementary light as the control, and the hormone content at different stages of flower bud differentiation was determined by high-performance liquid chromatography (HPLC). The differentially expressed genes (DEGs) significantly involved in flower bud differentiation were analysed by comparative transcriptomics. The transcription factors involved in the regulation of critical genes in flower bud differentiation were analysed by using weighted gene coexpression network analysis (WGCNA). To explore the regulation mechanism of grape flower bud differentiation under red‒blue light. This study provides preliminarily clarification of the regulatory mechanism for grape flower bud differentiation under red‒blue light and lays the foundation for further research.Although some progress has been made in studying the grape flower bud differentiation processBefore the transcriptome study, we conducted a morphological analysis of the floral bud development process Fig. . In the Flower bud differentiation is closely related to hormones. Therefore, the levels of indole-3-acetic acid (IAA), cytokinins (CKs), gibberellic acid , and abscisic acid (ABA) in flower buds of the red‒blue light and control plants at the four stages were determined with HPLC. The HPLC analysis showed that the levels of these hormones changed significantly in different flower bud differentiation stages. The IAA content in the control was the highest in the second stage Fig. A and theWe constructed a transcriptome sequencing library for the four stages of flower buds of the treatment and control groups, and 183.27 Gb clean reads were obtained. The clean reads of each sample were above 6.19 Gb, and the Q30 base percentage was above 93.28% , cellular component , catalytic activity , cellular process , metabolic process , membrane component , membrane , organelle , biological regulation  and response to stimulus . Among all the upregulated DEGs, 1995, 1902, 1853, 1673, 1668, 1394, 1173, 1102, 773, and 441 were annotated as these 10 GO terms. In the downregulated DEGs, the numbers were 1783, 1684, 1521, 1445, 1412, 1374, 1204, 953, 747 and 418, respectively  played an essential role in the grape flower bud differentiation. The plant hormone signal transduction pathway was enriched in the downregulated DEGs for the four stages under the red‒blue light treatment and the control. In addition, other enrichment pathways included those for flavonoid biosynthesis (map00941), flavonoid and flavanol biosynthesis (map00944), circadian rhythm-plant (map04712), and glycine, serine, and threonine metabolism (map00260). The plant hormone signal transduction pathway was also significantly enriched in the upregulated genes for the four stages under both the red‒blue light treatment and the control. In addition, the most enriched pathways were those for plant‒pathogen interactions (map04626), and other enrichment pathways included those for MAPK signalling pathway-plant (map04016), glycerophospholipid metabolism (map00564), phenylpropanoid biosynthesis (map00940), and monoterpenoid biosynthesis (map00902) , AUX/IAA , ARF (VIT_11s0016g00640), GH3  and SAUR ; cytokinin pathway CRE1 (VIT_12s0057g00690), B-ARR (VIT_17s0000g10100), A-ARR ; those involved in the gibberellin pathway, which are GID2 (VIT_07s0129g01000), TF ; and those involved in the abscisic acid pathway, which are PYR/PYL (VIT_15s0046g01050), PP2C , SnRK2  and ABF . Further analysis showed that the expression of the TIR1, AUX/IAA, and ARF genes was relatively high in the early stage of flower bud differentiation, while the expression was downregulated in the later stage of differentiation. The expression of GH3 gradually increased during flower bud differentiation. In addition, the expression trend of the SAUR gene was completely different between the red‒blue light and the control plants. In the control plants, the expression of the A-ARR gene was high at the early stage of flower bud differentiation and then decreased, while the expression trend was opposite in the red‒blue light treatment plants. The expression trend of the TF gene was completely different in the control and red‒blue light treatment plants. In the control plants, TF gene expression levels were high at the early stage of flower bud differentiation and were low at the late stage, while in the red‒blue light treatment plants expression levels remained low throughout the flower bud differentiation stages. In the control plants, the expression of the PP2C gene increased over the course of flower bud differentiation, while in the red‒blue light treatment plants, the expression decreased. The expression trends of the ABF gene in the two groups were also different. The control plants’ expression of GBF4 (VIT_18s0072g00470) and AI5L2 (VIT_04s0069g01150) remained at a low level, while the expression in the red‒blue light treatment plants increased over the course of flower bud differentiation, reaching the highest expression levels in the third stage. Expression levels for AI5L7 (VIT_03s0063g00310) and AI5L5 (VIT_18s0001g10450) showed an upwards trend in the control plants but gradually decreased in the red‒blue light treatment plants. The results showed that these signalling genes played an important role in grape flower bud differentiation.Previous studies have shown that grape flower bud differentiation is closely related to plant hormone signalling. In this study, we found that DEGs in plant hormone signal transduction are mainly concentrated in auxin, cytokinin, gibberellin, and abscisic acid pathways Fig. . The 36 ARR4 (VIT_01 s0026g00940), PIF3 (VIT_14s0060g00260), GBF4 (VIT_18s0072g00470), HST (VIT_09s0018g01190), C75A1 (VIT_06s0009g02840), CAMT (VIT_03s0063g00140), CML46 (VIT_14s0108g01000), YODA (VIT_02s0025g03850), and M2K5 (VIT_09s0018g01820). The qRT‒PCR results showed that the expression patterns of these genes were consistent with those in the RNA-seq data, with a correlation coefficient of R = 0.82. These results showed that the gene expression pattern revealed by RNA-seq data was reliable and could be used for further analysis , ABR1 (VIT_07s0031g01980), the bHLH family BH025 (VIT_00s0824g00020), BH025 (VIT_00s0927g00010), the WRKY family WRK48 (VIT_05s0077g00730), and CAMTA3 (VIT_07s0141g00250), the NF-X1 family NFXL2 (VIT_13s0067g00920) and the bZIP family GBF4 (VIT_18s0072g00470)  were selected for analysis , including the following hub transcription factors: the WRKY family WRK48 (VIT_05s0077g00730), the ERF family EF110 (VIT_18s0072g00260), ABR1 (VIT_07s0031g01980), the CAMTA family CAMTA3 (VIT_07s0141g00250), and the HSF family HSFA3 (VIT_08s0007g0390), together with other non-hub transcription factor genes that have a certain regulatory relationship with the GBF4 gene.bZIP family genes are related to ABA signalling. To further analyse the transcription factors regulated by the GBF4 gene in the GA pathway, the genes related to sis Fig. . It was 26. The types and levels of hormones in grape bud varied considerably at different developmental stages. In this study, the ‘Red Globe’ cultivar was used as the experimental material, and the supplemental treatment of red‒blue light was applied, with control conditions of no supplemental light. The four main plant hormones were identified in flower buds of both groups during four stages of flower bud differentiation. IAA was the first plant hormone discovered by researchers, and it is necessary for regular plant growth and development27. The IAA content in flower buds under red‒blue light treatment was higher than that in the control, and it showed a trend of increasing first and then decreasing over the course of flower bud differentiation, reaching its highest level at the third stage. Higher IAA content guarantees good flower bud formation, and this is consistent with the findings of previous studies28. CKs can promote the inflorescence development of lateral meristem in grape plants29. In our study, the content of CKs in flower buds increased during flower bud differentiation under the control, while the CK content decreased over the course of flower bud differentiation in the red‒blue light treatment plants. GAs are related to the initiation of flowering stage transitions and floral primordi31, which is a hot topic in current research32. Unlike CKs, GAs promote the differentiation of lateral meristem and inhibit the development of the inflorescence, which is beneficial to the development of the grape tendril. Grape plants with a mutation in GA-related genes are short in stature, with grape tendrils differentiated into inflorescences33. Interestingly, similar results were also observed in this study. The third stage of flower bud differentiation progressed the fastest, although this was less marked in the red‒blue light treatment plants than in the control. In addition, there was a significant difference in ABA content between normal and malformed flowers34. In this study, the ABA content in the red‒blue light treatment plants was higher than that in the control plants at the early stage of flower bud differentiation and was even 24.6% higher at the third stage, showing that ABA plays a vital role in flower bud differentiation. However, the regulation mechanism of hormone balance is complex. Like other perennial woody plants, grape flower bud differentiation requires a balance between hormones35.Grape yield depends on the amount of flower bud differentiation. Plant hormones are the key factors controlling the induction of flowering and interact with each other during plant developmenTIR1, AUX/IAA, and ARF genes were relatively high at the early stage of flower bud differentiation, while they were downregulated at the late stage of differentiation, which was consistent with the findings of previous studies36. Studies have shown that ARF, combined with auxin response element AUX/IAA, plays a central role in transforming local auxin concentration into specific gene expression output, initiating flowering and regulating the development of floral organs38. In this study, four AUX/IAA genes and one ARF gene regulated the auxin pathway. The expression levels of the IAA17 and ARFS genes in the red‒blue light plants was substantially inconsistent with expression levels in the control plants, indicating that these genes played an essential role in responding to flower bud differentiation under red‒blue light. In the cytokinin signal transduction pathway, the ARR gene participates in the circadian rhythm mechanism under light conditions, activates the function of flowering-induced gene SOC1 to mediate the flowering of Arabidopsis39, and is highly expressed in branches with a high flowering rate in apple trees40. The expression levels of the six ARR genes identified in this study first increased and then decreased during flower bud differentiation and showed significant indigenous differences between the treatment and control groups, confirming the positive effect of ARR family genes on flower bud differentiation. GID and TF are genes that positively regulate the expression of LFY, SOC1, FT, SPL, and other genes that promote flower bud differentiation, which DELLA inhibits. The essential genes involved in flower bud differentiation in these gibberellin signal transduction pathways are also identified in this study. Unlike the results in previous studies, in this study the red–blue light treatment associated with a high flowering rate exhibited a lower expression of gibberellin than the control group, which may be due to the downregulation of gibberellin response genes without the effect of the DELLA gene41. The three signal components of PYR/PYL, PP2C, and SnRK2 constitute the core ABA signalling pathway42, and positively regulate the flowering of long-day plants43. In addition, five ABF genes were also found to be involved in grape flower bud differentiation, indicating that ABF can be phosphorylated by SnRK2, affecting flower bud differentiation44.In this study, 36 DEGs were annotated and identified as being involved in the auxin, cytokinin, gibberellin, and abscisic acid signal transduction pathways. In the auxin signal transduction pathway, the expression levels of 46. In this study, WGCNA was used to evaluate transcript expression and trait changes at different stages of germination and differentiation under red–blue light, and three modules, ‘red’, ‘green’, and ‘magenta’ that were highly related to flower bud differentiation were screened. In these modules, eight transcription factors were identified as hub genes, including genes from the ERF family, bHLH family, WRKY family, CAMTA family, NF-X1 family, and bZIP family, suggesting that these transcription factors may be involved in grape flower bud differentiation under red‒blue light. Previous transcriptome sequencing studies found that transcription factors such as MYB, MADS-box, WRKY, ERF, and bHLH played an essential role in flower bud differentiation47. The MYB gene can regulate floral organ development and even mediate gibberellin signal transduction in flowering responses48, while the MADS-box gene is involved in the regulation of floral meristem initiation and development49. In addition, the bZIP and ERF genes are related to floral bud differentiation hormone signal response50. The bZIP family gene GBF4 is a unique gene in the ABA signal transduction pathway. Thus far, there are few reports on this gene’s regulation of flower bud differentiation. We found that WRKY, ERF, CAMTA, and HSF family genes were involved in the transcriptional regulation of GBF4 through WGCNA. WRKY family members were the key factors in the ABA response pathway, which could accelerate flowering by regulating AP151, and HSF was involved in environmental stress response to regulate the flowering and development of plants52. The above transcription factors involved in plant hormone signal transduction regulation lacked clear evidence, but our study provided a theoretical basis for further confirming the regulatory mechanism of grape flower bud differentiation under red‒blue light.WGCNA has been used to analyse data from numerous species to elucidate the interaction between physiological characteristics and gene expressionOverall, the comparative transcriptome analysis of the differential genes involved in the plant hormone signal transduction pathway showed significant differences in AUX/IAA, SAUR, A-RR, and ABF between red‒blue light treatment and control buds; this was especially true for the ABF genes, which were complete different in the two groups. The GBF4 (VIT_18s0072g00470) and AI5L2 (VIT_04s0069g01150) expression in the control remained low, while the expression under the red‒blue light treatment increased with the process of flower bud differentiation. AI5L7 (VIT_03s0063g00310) and AI5L5 (VIT_18s0001g10450) showed an upwards trend in the control and gradually decreased under red‒blue light. It is speculated that the difference in ABF family gene regulation plays a vital role in the grape flower bud differentiation response to red‒blue light. The WGCNA showed that the hub transcription factors WRK48 (VIT_05s0077g00730), ERF family EF110 (VIT_18s0072g00260), ABR1 (VIT_07s0031g01980), CAMTA family CAMTA3 (VIT_07s0141g00250), and HSF family HSFA3 (VIT_08s0007g0390) may be involved in the regulation of the GBF4 gene.Vitis vinifera cv. ‘Red Globe’ grape plants. The experiment was carried out in the growing season of 2021 in the sunlight greenhouse of Helan County Gardening Industrial Park, Yinchuan City. Our previous studies have shown that a ratio of red light:blue light = 4:1 (S) can significantly increase the flowering rate of ‘Red Globe’ grape plants (unpublished). Therefore, in this study, red light:blue light = 4:1 (S) LED plant lights were selected for the treatment group plants (the wavelengths of red and blue light were 620 and 435 nm) , and conditions of no supplementary light served as a control (T). Light tubes were placed at 30 cm above the test line plants, and the leaf light intensity was maintained at 300 μmol m−2 s−1 for 14 h per day from 8:00 to 22:00. In the first stage , the second stage , the third stage , and the fourth stage  of flower bud differentiation, 26 healthy branches were selected and removed with a sharp scalpel. Twenty buds were fixed in FAA  for paraffin sectioning, and the other buds were immediately frozen in liquid nitrogen and stored at -80℃ until further analysis.The test material was 10-year-old 53, and the flower bud differentiation stage was classified54. Auxins (IAAs), cytokinins (CKs), gibberellins (GAs), and abscisic acid (ABA) were quantified by using ultra-performance liquid chromatography spectrometry , the mobile phase was methanol–water  with a volume ratio of 45:55. The flow rate was 0.25 mL min−1, the column temperature was 30 °C, and the injection volume was 10 μL55, with three biological replicates.Flower buds of four developmental stages were used for paraffin section making and hormone determination. Flower buds fixed in FAA were dehydrated in a gradient ethanol series and then were made transparent, wax-immersed, embedded, and patched in a dehydrator. Paraffin blocks containing flower buds were sliced with a semiautomatic slicer (Leica RM2245), stained with saffron-solid green, and sealed with neutral gum. Observation under an optical microscope (Olympus BX53) was performed using an SC180 image analysis system to obtain imageshttp://plants.ensembl.org/Vitis_vinifera/Info/Index) using HISAT2 software (Version 2.1.0)56 to obtain the mapped reads for subsequent analysis.The RNA of the samples was extracted using the OminiPlant RNA Kit (DNase I) . Nanodrop2000 was used to detect the concentration and purity of RNA, agarose gel electrophoresis was used to detect RNA integrity, and Agilent2100 was used to determine the RNA integrity number (RIN) value for further quantitative real-time reverse transcription-PCR (qRT‒PCR) and RNA-seq determination. The Illumina HiSeq 2500 sequencing platform was used to perform RNA-seq. Raw reads were sequenced and then were processed to obtain clean reads of high quality. The clean reads after quality control were aligned with the reference genome  values and |log2fold changes|≥ 1, and the corrected Padjust < 0.05 as the screening standard to study the function of differential genes. We used the fastcluster (Version 1.2.4) package in R was used for cluster analysis of differentially expressed genes, hierarchical cluster analysis, and complete Gene clustering method. The DEGs in gene concentration were compared with data in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to obtain gene functional annotation and related metabolic pathway information in different samples58.Differential expression analysis of samples was carried out using the expression difference analysis software DESeq2 (Version 1.10.1)Actin1 (GenBank: XP_008654957.1). The primers , and each gene was clustered by hierarchical clustering60. The dynamic tree cutting method was used to divide genes into different coexpression modules. The minimum number of genes in each coexpression module was set at 30, and the coexpression modules with similar clustering were merged with 0.3 as the boundary. The correlation between different modules and the degree of genes in the module  were calculated. Key modules related to flower bud differentiation were identified for subsequent analysis. Cytoscape (Version 3.8.0) was used to visualize the regulatory network mapping.To further study the hormone regulation mechanism and possible transcription factors in grape flower buds, we used the WGCNA (Version 1.63) package in R to construct the coexpression networkThe conducted experiments comply with the laws of China.Supplementary Figures.Supplementary Tables.