Additionally, 16S rRNA gene results indicated a distinctive air microbial community, associated primarily with Bacillota phylum particularly of the Bacilli class (>58%), under all conditions. Tracheal results indicated a predominance of Escherichia coli/Shigella at the beginning of the productive cycle, shifting toward the middle and end of the cycle to Gallibacterium. However, at week 3, the dominance of Escherichia coli/Shigella (>99.5%) associated with litter aeration by tumbling stood out. Tracheal and air samples displayed a statistically different community structure, but shared differentially abundant features through time: Enterococcus, Gallibacterium, and Romboutsia ilealis. These results indicate the impact of production management protocols on the birds’ respiratory system that should be considered a breakpoint in poultry farm health.Aerial and respiratory tract-associated bacterial diversity has been scarcely studied in broiler production systems. This study examined the relationship between the environmental air and birds’ respiratory microbiome, considering a longitudinal sampling. Total viable bacteria and coliforms in the air were quantified, and the 16S rRNA gene was sequenced from tracheal and air samples obtained through a novelty protocol. Air results showed a decrease in coliforms over time. However, at week 3, we reported an increase in coliforms (from 143 to 474 CFUc/m Poultry meat production is an essential source of nutrients worldwide . RespiraGallibacterium  the last two weeks of fattening, showing the existence of bacteriophages that can influence bacterial diversity. This study provided a comprehensive view of the ecology of the avian respiratory microbiome and how an infectious agent can disrupt this environment, evolving into a disease. More recently, Zhou et al. [Unlike what happens with the gastrointestinal microbiota, the microbiota of the respiratory system of broiler birds has been characterized by a few studies. Shabbir et al.  and Sohau et al.  analyzed3). In addition, the sequencing of the complete 16S rRNA gene was performed on tracheal and air (nitrocellulose membrane) samples, allowing maximum taxonomic resolution [The environment in intensive broiler production systems becomes relevant because it is characterized by a high animal density that facilitates the accumulation of feces and moisture in the litter. A floor rearing birds’ system creates favorable conditions for bacterial growth and inadequate ammonia concentrations that can lead to deciliation of the respiratory tract and increase susceptibility to infections . Therefosolution . The stuTwo tunnel-ventilated broiler houses (BHs) and one experimental animal facility (AF) were sampled three times during the fattening period, at weeks 1, 3, and 6. The BHs belong to the same farm, housing around 24,000 Cobb male broiler chickens within a vertically integrated broiler system. The floor was freshly covered with wood shavings at the beginning of bird fattening. Alternatively, control birds were reared from day 1 of age in the experimental animal facility unit for birds of the Avian Pathology Laboratory at the Universidad de Chile. These birds were obtained from the same hatchery as the birds destined for the BH system, subjected to the same immunization schedule , and fed®. Viable bacteria (CFU/m3) in the air were sampled using plate count agar (PCA) and McConkey plates in triplicate at each sampling time. For the NGS analysis, one nitrocellulose membrane per time was placed on a PCA plate to collect the sample. On the same day and in parallel with air sampling, birds were collected (n = 3) to perform a tracheal wash to study microbiota during growth  throughout the six weeks of the production cycle. The two BHs were different at week three, where one was sampled 30 min after (BH-I) and the other before (BH-II) the management known as “litter aeration by tumbling”. This consists of breaking and turning the litter—using machinery—allowing it to aerate and dry the substrate [The AF facility was used as a control, where four fans were located in each corner of the room . Here, nine experimental birds plus three backup birds were housed in a wire cage at a density of below 10 kg/mubstrate ,15. Thisg for 10 min. The sedimented bacteria were stored at −80 °C until the subsequent DNA extraction.Healthy birds were euthanized by cervical dislocation after an overnight fast to avoid contamination of respiratory tissues by feed from the birds’ crops. The health status was corroborated by necropsy, and tracheal lavage was performed aseptically as previously described . In brie® impact aero-biocollector. In the AF, the measurement was taken in the center of the room at the height of the birds’ heads. For detecting viable microorganisms in the environment, 5 L of air was impacted on PCA, and 500 L of air was impacted in MacConkey agar plates. Moreover, we designed a novelty protocol: a 0.22 µm nitrocellulose membrane, 47 mm in diameter (Merck GSWG047S6), was aseptically placed on agar plate surfaces to impact a volume of 1000 L of air to recover the environmental DNA (on the air column). Then, they were then stored at −80 °C in 50 mL conical tubes for further processing.In BHs—at the end of the shed and 40 cm from the ground—air samples were taken using the MAS-100 EcoTotal DNA was extracted from tracheal samples using the DNeasy Blood & Tissue Kit . The DNeasy Power Water Kit (Qiagen) was used for the nitrocellulose membrane DNA purification. Once the extracted DNA was purified following the manufacturer’s recommendation, quantity and quality (A260/A280) were measured by spectrophotometry . Additionally, the presence of the bacterial 16S rRNA gene amplicon was evaluated by DNA amplification using the primers 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-CGGTTACCTTGTTACGACTT-3′) followed by electrophoresis in agarose gel. Once the bacterial 16S rRNA gene had been proven, an extracted DNA aliquot was shipped to MR DNA  for 16S rRNA gene library construction and sequencing. ® equipment procedures protocol to obtain the colony-forming units corrected (CFUc). Finally, the CFUc per cubic meter (1000 L) was calculated. In the case of agar plates, incubation was carried out for 16 h at 37 °C, after which total colony-forming units (CFU) were counted. Feller’s statistical correction table was used according to MAS-100 Eco® Express Template Prep Kit 2.0 following the manufacturer’s user guide. Finally, the sequencing procedure was completed at MR DNA  on the PacBio Sequel system following the manufacturer’s guidelines.Primers GM3 (5′-AGAGTTTGATCMTGGCTCAG-3′) and 23S1 (5′-GGGTTTCCCCATTCGGAAATC-3′) with a unique barcode linked to the forward primer were used in the generation of the sequenced amplicon. These primers encompass the entire 16S rRNA gene plus the adjacent internal transcribed spacer (ITS) located between 16S and 23S rRNA genes, which correspond to a region with a high degree of variability, allowing for additional studies on relatedness and discrimination of samples obtained from different sources . A 35-cyUpon completion of the initial DNA sequencing, each library underwent a secondary analysis, Circular Consensus Sequencing (CCS), using PacBio’s CCS algorithm (SMRT Link 9.0.0.92188). The CCS algorithm aligns the subreads individually from each template to produce consensus sequences, allowing the correction of stochastic errors caused in the initial analysis. Sequence data were then processed by applying the MR DNA analysis pipeline . In summary, barcodes were removed of the CCS sequencing data, then oriented 5′ to 3′, and sequences < 150 bp were discarded. Chimera removal and de novo OTU clustering were carried out using the usearch v.7 software package. Operational taxonomic units were defined by clustering at a similarity threshold of 97% (3% divergence). A total of 2172 representative OTU sequences were added to the nonredundant Silva 138 database using the ARB program package ,19. SequAn OPU is the smallest monophyletic clade containing OTU representatives and their closest reference sequence. Where possible, OPUs included a type strain as a reference sequence. When the above was impossible, other available databases were used. For identity values > 98.7% that include a type strain sequence, amplicons were considered as belonging to the same species. When identity values were <98.7 and >94.5% with the closest relative type strain, amplicons were a different unclassified species of the same genus . This apRarefaction curves and alpha diversity indices were calculated using the PAST software v4.05  over proStatistical differences between groups were determined using the stats v4.1.2 R package . Normalip < 0.05) at week 3 and week 6 than week 1. By contrast, counts obtained from broiler houses (BHs) were out of the air sampler working range (over 400 colonies) due to the high bacterial load.Plate count agar (PCA) and MacConkey agar plates were analyzed to assess “total” viable bacteria and Gram-negative enteric bacteria on-air, respectively. In 3). The above coincides with the “litter aeration by tumbling” that was performed in BH-I 30 min before sampling. By contrast, BH-II at week 3, which did not have this litter management done, presented a lower count value. Finally, at the end of the cycle (week 6), all housing conditions remained similar and lower than 25 CFUc/m3 on average at the level of the AF.For MacConkey plate results B, the coThese results showed a low presence of total bacteria in AF compared to the productive environment (BHs), which was impossible to measure on PCA due to the high bacterial load despite using the minimum sampling volume (5 L). A decrease in coliform counts on McConkey plates was observed in BHs throughout the study. The high count of cultivable coliforms in BH-I at week 3 is an important indicator of the high bacterial load we associated with the management of “litter aeration by tumbling” performed before sampling.Thirty-six samples were analyzed, corresponding to 9 environmental air nitrocellulose membrane samples and 27 tracheal wash samples. In addition, a process control for nitrocellulose membrane samples and a control for tracheal wash PBS were included, reporting zero 16S rRNA gene amplification for both. Of all the initial sequences generated by PacBio, those that did not correspond to the bacterial 16S rRNA gene were filtered. It was observed that the tracheal samples needed to eliminate a significantly larger number of sequences from the initial dataset than the air samples, which may be related to the presence of interfering eukaryotic DNA. Thus, the percentage of final filtered sequences regarding the initial sequences in the tracheal samples averaged 88.5% (±20.74%), corresponding to an average of 15,756 (±7861) sequences per sample . On the The reads obtained were grouped into OTUs at the 97% level of sequence identity, generating a total of 1771 different OTUs  with an average of 70 (±68) OTUs for the tracheal samples and 568 (±165) OTUs for environmental samples A. After The rarefaction curves for the number of OTUs and OPUs and the number of reads analyzed are shown in Dominance and lowest Shannon–Weiner index of diversity among all samples analyzed   . The den= 0.002) B. A signIt should be noted that environmental samples at week 3 for both BH-I and BH-II were clustered together A, even tBacillota (>59%), particularly of the class Bacilli (>58%), under all conditions  were consistently observed in AF. In BHs, a new species of the class Bacilli, a new species of the genus Erysipelatoclostridium, Erysipelatoclostridium spiroforme, and a new species of the genus Enterococcus predominated at week 1. Then, a new species of the genus Staphylococcus and a new species of the genus Corynebacterium dominated at week 3. At week 6, a new species of the genus Staphylococcus, a new species of the genus Corynebacterium, and a new species of the family Lactobacillaceae were mainly present in BHs. These results indicated a distinctive microbial community, primarily associated with the Bacilli class.In general, relative abundances showed a predominance of nditions . The prep = 0.009). A significant clustering pattern was observed on axis 1 (45.6%), where the post hoc test indicated two distinct microbiome profiles between BH-I—where litter management was carried out—and BH-II plus AF. A significant clustering pattern was also observed on axis 2 (32.7%), where the post hoc test indicated three distinct microbiome profiles between BH-I, BH-II, and AF. These differences are mainly explained by a high relative abundance of Escherichia coli/Shigella (OPU 437) in all samples in BH-I (>99.5%) and high relative abundance of an unclassified species of the genus Gallibacterium (OPU 447) in samples in BH-II (>98.3%) at week 3 (To compare the microbiota between housing at each sampling time A–C and tt week 3 . No difft week 3 . E. coli (OPU 437) at week 3 in BH-I, it can be presumed that we were seeing the presence of the same clones in the trachea and in the air. This is because OPU 437, which was composed of 22 OTUs obtained through the sequencing of the complete 16S RNA gene plus ITS, shared a similar relative abundance profile of OTUs both in air and the trachea  (p = 0.026)  . For BH-= 0.043) D. A sign= 0.026) E. A signve cycle . No diffve cycle F, where ve cycle . In any Escherichia coli/Shigella  at the beginning of the analyzed productive cycle, with a change toward the middle and at the end of the productive cycle to Gallibacterium . In addition, it was observed that the dominance of Escherichia coli/Shigella at week 3 in BH-I was associated with “litter aeration by tumbling”, which mechanically aerates the litter substrate on which the birds live and increases aerosols in the environment.Altogether, these results indicated a predominance of p = 0.001) showed clusters significantly different for the two groups, either when all samples were analyzed all together . On the other hand, five differentially abundant OPUs were found in the tracheal samples when the same comparison was made (p-adjusted < 0.05). Of these, three significantly different OPUs were shared between the two sample types: Enterococcus sp. (OPU 036), Romboutsia ilealis (OPU 313), and Gallibacterium sp. (OPU 447)  and trachea (20.5), where the latter decreased to a greater extent. Then, the log2FoldChange observed between Gallibacterium sp. of environmental air (−6.2) and trachea (−18.0) showed a greater increase in this species in the trachea. Finally, Romboutsia ilealis showed a higher log2FoldChange in environmental air (−9.4) than in the trachea (−8.3). Thus, the conservative DESeq2 algorithm showed significantly different OPUs shared between tracheal and ambient air samples, despite the different composition of these two communities.To identify significant features, we used the univariate DESeq2 method to analyze week 1 and week 6 tracheal and environmental samples, to explore the existence of differentially abundant OPUs for each dataset over time. A total of 81 differentially abundant OPUs were found in the air samples when comparing week 1 versus week 6 (OPU 447) B. Both Gng times C. These Few studies have investigated the respiratory microbiota composition in broilers, and only one sought to analyze the effect of extrinsic environmental factors on respiratory bacterial communities . Thus, wThe PCA results show a high number of total viable bacteria present in environmental air of commercial broiler houses, finding plate counts out of the operating range of the air sampler machine. Additionally, the AF increased the total bacterial load from week 3, probably associated with the increase in bird size, feces production, movement of the birds, and the consequent increase in aerosols. Further, this work is novel in describing the association of the management of “litter aeration by tumbling” with an increased count of Gram-negative enteric bacteria in the air. Even more, we were able to describe a decreased enteric count over time, finding similar counts at the end of the productive cycle (week 6) in the broiler houses and the experimental animal facility. Through observation, we associate this lower count of coliforms in the environment with a greater compaction of litter and less formation of aerosols from this at week 6. This compaction is generally caused by the accumulation of moisture over time from bird excreta and drinker spillage, among other factors . So far,E. coli OPU 437, in both air and trachea, reinforces this idea  the increase of more than 3 times in the MacConkey counts at week 3 in BH-I B; (2) thhis idea .Escherichia coli/Shigella in the trachea in BH-I at week 3 is related to a high quantity of air bacteria due to litter management. However, the bacterial composition in the air did not differ at week 3 between BH-I and BH-II despite litter management. This highlights the importance of understanding that microbiome data are compositional and cannot report absolute abundance. Thus, selection in the trachea of Escherichia coli/Shigella from ambient air, where there is a high bacterial load and diversity, is presumably due to the preferential ability to colonize this niche. The high presence and capacity for colonization of E. coli could be enhanced by the higher bacterial load and also a temporal increase in ammonia levels related to the “litter aeration by tumbling,” which can take 24 to 48 h to return to normal levels [E. coli such as APEC (avian pathogenic Escherichia coli) is present in the litter environment and environmental air. The ammonia effect was highlighted by a recent study, which reported that an increase in ammonia levels decreases alpha diversity and shifts the structure of the microbial community through inflammatory injury of the respiratory tract [These results support the observation that the dominance of l levels . These try tract . E. coli load at week 3, the tracheal microbiota in BH-I evolves at the end of the fattening cycle to a profile clearly defined by Gallibacterium sp., as in BH-II at week 6. Therefore, we can interpret that this high dominance of E. coli at week 3 is transient, and mucociliary and immune clearance of the tracheal epithelium [Next, we saw that despite the high Enterococcus sp., Gallibacterium sp., and Romboutsia ilealis.Previously, Johnson et al.  reportedEnterococcus sp. (OPU 036), a facultative anaerobe genus, decreased in the trachea and environmental air between week 1 and week 6. It can survive under strict environmental conditions in the gastrointestinal tract, making persistence easier [e easier . Its decGallibacterium sp. (OPU 447) increased in the trachea and environmental air between week 1 and week 6. This genus is known to be a typical inhabitant of the respiratory tract [Gallibacterium sp. high presence at the end of the fattening cycle. Its high relative abundance in tracheas at week 6 means it is most likely spread from the trachea into the ambient air. This observation is supported by a more significant increase at week 6 measured as a log2fold change in tracheal samples, compared to environmental air.ry tract  and facury tract . Mulhollry tract  previousRomboutsia ilealis (OPU 313) is an obligately anaerobic bacteria commonly detected in the gastrointestinal tract [R. ilealis is anaerobic, it might be expected that this bacterium would not colonize the trachea and would be present transiently.al tract . This spBroiler birds are constantly exposed to litter aerosols and bacteria carried in them. We should expect exposure to the litter environment and preferential selection for microbes from this environment with the capacity to colonize. Additionally, some bacterial species may likely be present transiently. Whether there is an establishment or transitory passage of microorganisms through the respiratory tract of birds, they can have a significant effect on their health that requires further investigation. Thus, this work provides new knowledge regarding the influence of ambient air and the bacteria present in it on the respiratory microbiota of broiler chickens. Additionally, we found in litter management a critical point that can significantly influence the respiratory microbiota in broilers through the generation of a higher bacterial load in the air, not previously analyzed, and it must be considered a dissemination point of putative microbial pathogens.E. coli (APEC), described as a pathogen that generates the greatest economic losses in the poultry industry [E. coli could be differentiated from commensal E. coli according to specific virulence determinants, as described previously [E. coli in abundance from the trachea as colonization and invasion, as we demonstrate that there may be a transient establishment in the trachea related to litter aeration by tumbling and aerosol formation. Hence, the knowledge of the influence of ambient air on the respiratory microbiome can help improve the diagnosis and prevention of diseases in broiler chickens.Most studies of respiratory microbiomes in broilers have focused mainly on describing the host bacteria, but the influence of the ambient air microbiome on the respiratory system has not been investigated so far. This is particularly important under conditions where large numbers of animals are housed at high densities. Here, we describe the importance of aerosol formation and the management that can increase it, and its capacity to generate a change in the diversity of the tracheal microbiome. Considering this, we recommend that this point should be taken into consideration in diagnostic and prevention by isolation and characterization of bacteria in circumstances where they may cause disease. This is particularly valid for the case of infections with avian pathogenic industry . These peviously . Thus, o


CC, RB, RMSE, POD and FAR, to compare with daily observations. Overall, all SPPs showed decreasing trends of precipitation from east to west compared to 10 km rainfall data. PERSIANN had the highest values of POD (0.65), RB (91.6%) and FAR (0.59) but worst performed in streamflow. CMORPH, GPM and TRMM fit well with the observations annually but overestimate the precipitation in the southeast during wet seasons. Simulation from GPM and CMORPH yield satisfactory results , while TRMM outperformed GPM in modeling runoff with smaller relative error. Results indicated the potential of GPM and CMORPH in providing alternative rainfall information in YZRB. Accurate evaluation of multi-source SPPs and their hydrological utility in YZRB would benefit further hydrometeorological studies and water resources management in this area.Hydrological modeling in the Third Pole remains challenging due to the complex topography and scarcity of in-situ precipitation observations. In this study, we assessed five satellite precipitation products (SPPs) including TRMM3B42, PERSIANN-CDR, GPM-IMERG, CMORPH, and GSMaP, and simulated daily streamflow in the Yarlung Zangbo River Basin (YZRB) with VIC model. The performance of SPPs was evaluated by  However, the occurrence of satellite deployed PR-related infrared and microwave satellite sensors provides a unique opportunity for precipitation estimation from the gridded scale. Despite various errors and uncertainties, satellite precipitation products (SPPs) have become essential sources of precipitation information, especially in regions where the gauged distribution is sparse and uneven2. Currently, SPPs have been widely used in water resources management4, drought monitoring7, and flood forecasting9.As a critical factor in the atmosphere cycle, precipitation drives the hydrological cycle and influences the energy cycle. There are three main ways to measure precipitation events: observed gauges, radar, and satellite. Gauged observation is the traditional approach to obtaining accurate precipitation estimations at a given point. Due to the complex topography, precipitation and its spatial variability are irregular and unavailable in the watershed with sparse gauges10, such as Tropical Rainfall Measurement Mission (TRMM)11, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)12, National Oceanic and Atmosphere Administration/Climate Prediction Center morphing technology (CMORPH)14, Global Precipitation Measurement (GPM) and Global Satellite Mapping of Precipitation (GSMaP)15. Most SPPs had a good correspondence with gauged estimation since the gauge information is integrated with the correction algorithm17. Purely satellite-based estimation without any gauged corrections tended to overestimate gauged observation, primarily due to the weak relationship between rainfall rate and remote sensing signal, sampling uncertainties together with error caused by human algorithms or atmospheric environmental effects22. Furthermore, the errors in the SPPs can be propagated and expanded in the hydrological utility due to the nonlinearities in the hydrological process17. Therefore, the accurate assessment of SPPs is an indispensable part of their application in both hydrology and meteorology.In the twentieth century, techniques of SPPs with different temporal and spatial resolutions had achieved increasing maturity23. Numerous validation researches on SPPs have been carried out to better understand the uncertainties of different products over different regions31. Because of the unique topography and profound impact on regional climate and even on a global scale32, most of the corresponding researches on SPPs have focused on the Qinghai Tibet Plateau (TP)33. For example, Gao and Liu16 compared TRMM3B42, TRMM3B42-RT, CMORPH, and PERSIANN against gauged observation and found that the four SPPs tended to overestimate light rainfall and underestimate moderate and heavy rainfall while performing better in the humid regions than arid regions. Lu and Yong33 evaluated the capacities of GPM and GSMaP in rainfall detection and found inaccurate records of light rain and snow. Some researchers have devoted their efforts to assessing the suitability of SPPs as input to hydrological models in the TP34. Tong and Su2 investigated the capability of four SPPs  by using a Variable Infiltration Capacity (VIC) model and found that CMORPH and TRMM3B42 performed better in hydrological utilities than TRMM3B42RT and PERSIANN. Similarly, Wu and Guo35 found SPPs performed better in the southeast of the TP and pointed out that China Meteorological Forcing Dataset (CMFD) could yield higher accuracy than TRMM and CHIRPS in streamflow simulation.There were two types of validation methods for SPPs: (1) directly statistical metrics of satellite precipitation against the corresponding gauged observation or the weather radar estimation; and (2) evaluation of the satellited precipitation based on a model frame36 validated the hydrological potential of APHRODITE in the whole Yarlung Zangbo-Brahmaputra River basin and made a trial to improve the accuracy through linear correction. The results revealed that APHRODITE systematically underestimated precipitation in the rainy season. Liu and Xu37 assessed the accuracy of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) in the YZRB and found that the accuracy decreased from east to the west in the basin. Currently, satellite-based precipitation research in YZRB mainly focused on the correction and evaluation of a single remote sensed product. As popular and important as individual SPP might be in YZRB, comprehensive and comparative assessments of multi-satellite products are relatively few, mainly due to limited rainfall data at gauges37. It is therefore useful to evaluate how different the potentials of various SPPs are in estimating precipitation in YZRB..Yarlung Zangbo River Basin (YZRB), characterized by the strong influence of the monsoon, differs from the rest of the TP in hydrologic regimes. Like other areas in TP, the sparsely distributed rainfall gauges in the YZRB result in a lack of in-situ observations and call for the assessment of alternative precipitation data. Luo and Fan39. In this study, a 10 × 10 km reconstructed precipitation dataset that is based on 262 rain gauges39 was introduced to provide gridded rainfall information in the streamflow simulation of YZRB. The primary objective of the study is to evaluate and compare five typical SPPs  in the YZRB. Specifically, the SPPs are (1) compared to gauged observations via diverse statistical metrics , then (2) used as forcing datasets to drive the VIC model for runoff simulation through two scenarios. The results would be quite supportive in assessing SPPs over YZRB and provide feedback on SPPs’ hydrological utility in this area, as well as benefit water resources management in ungauged basins.Reconstructed daily precipitation datasets with high precision may benefit hydrological simulation and SPP evaluation2, with a river length of 1500 km. The YZRB experiences many different climatic patterns, including alpine temperate semiarid, plateau temperate semiarid, and tropical and subtropical monsoon. The annual average precipitation is 429 mm in the basin, and the spatial distribution of precipitation is quite uneven.The Yarlung Zangbo River is located between 82° ~ 97° 7′ E and 28° ~ 31° 16′ N in the southeast of the TP . In this study, the 0.25° × 0.25° 3-hourly CMORPH products from 2003 to 2015 were obtained from ftp://ftp.cpc.ncep.noaa.gov/precip/40.CMORPH, proposed by NOAA  Climate Prediction Center via MORPHing technique, provides the precipitation estimation derived from low-orbiter-satellite passive microwave (PMW) observations11. TMPA products include two versions. In this study, the 3-hourly TMPA 3B42 V7 with a spatial resolution of 0.25° × 0.25° was applied, which was later referred to as TRMM41. The TRMM data in the study are downloaded from http://precip.gsfc.nasa.gov.TMPA is a collaborative product developed by the National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) based on the calibration of TRMM Combined Instrument and TRMM Microwave Imager precipitation products42. The PERSIANN method12 utilizes a neural network function approximation step to convert the IR brightness temperature from a geostationary satellite to precipitation estimation. PERSIANN-CDR differs from the former version in terms of the IR data with the use of GridSat-B1 instead of CPC-IR, and PMWs data is absent in the calibration43. In subsequent articles, we will refer to it simply as PERSIANN44. The PERSIANN precipitation data are available on http://fire.eng.uci.edu/PERSIANN/.PERSIANN is a product with a spatial resolution of 0.25° and a frequency of 3-h invented by the Center for Hydrometeorology and Remote Sensing (CHRS)The fine resolution datasets of IMERG  is the Level 3 precipitation estimation algorithm of GPM, which provides different products, including an Early Run , a Late Run , and a Final Run (Gauged-adjusted with a latency of 4 months) products. The version used in this study was GPM-IMERG Final Run, which was later referred to as GPM.45. The GSMaP algorithm utilizes various PMW radiometers to retrieve quantitative precipitation estimation46. In this study, the version GSMaP-Gauge47 was chosen and applied, which was later referred to as GSMaP.The GSMaP, an hourly SPP with 0.1° grids resolution, is generated by a program aiming to obtain high precision, high-resolution global precipitation map using satellite data sponsored by the Japan Aerospace Exploration Agency Precipitation Measuring MissionAs GSMsP and GPM-IMERG were finer in spatial coverage, the five satellite-gauge SSPs were first aggregated into the uniform 0.25° × 0.25° spatial resolution and accumulated into daily precipitation amount (00 UTC-00 UTC) during the study period from 2003 to 2015 to match the 8:00 to 8:00 local time of the gauge data in China.http://data.cma.cn), including precipitation, maximum and minimum temperature, and the average wind speed. The daily streamflow data at Nuxia from 2003 to 2015 were primarily collected from the Tibet Hydrology and Water Resources Survey Bureau.The daily observation from 2003 to 2015 in the YZRB was obtained from the China Meteorological Administration (CMA)  and Global Land Data Assimilation Systems (GLDAS) data, and the datasets had been extensively assessed and validated in some basins39. The 10 km precipitation data are obtained from the National Tibetan Plateau Scientific Data Center (http://data.tpdc.ac.cn).The daily gridded precipitation data with the spatial resolution of 10 × 10 km, released by Sun and SuCC), Relative Bias (RB), and Root Mean Square Error (RMSE) between the satellite-estimated precipitation data and gauged rainfall observations. CC and RB describe the agreement between the satellite estimation and the reference. RMSE is used to measure the average error magnitude. STD reflects the degree of dispersion for individuals within the group:N is the number of samples; Two evaluation approaches, the general evaluation via statistical metrics and the detection ability evaluation via categorical metrics, are adopted in assessing the hydrologic skills of the SPPs. The conventional statistical analysis was conducted through Correlation Coefficient (POD), and False Alarm Rate (FAR). The POD, with a range from 0 to 1, indicates the ratio of the number of precipitation events correctly detected by satellite among all actual precipitation events. The FAR is the ratio of false alarming precipitation events to the total number of detected precipitation events, ranging from 0 to 1:a denotes observed rainfall correctly detected, b denotes rainfall events detected, and c denotes observed rainfall events. The closer POD value is to 1 and the closer FAR value is to 0, the better skill in detecting precipitation and no-precipitation of the satellite dataset.The skill in detecting precipitation for various satellite products is measured by Probability of Detection  model was set up at 0.25° × 0.25° spatial resolution grids in the YZRB. Information on soil parameters including soil properties and spatial distribution was retrieved from the International Geosphere Biosphere Program Data and Information System (IGBP-DIS)51. The vegetation parameters were obtained from Maryland 1 km global land cover products52 and the topography data was from Advanced Space borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model 53. The main seven parameters were calibrated (Table54. The Nash–Sutcliffe Efficient index (NSE)55 and Relative Error (RE) were used to evaluate model performance. A successive difference of NSE less than 0.001 is used as the stopping condition of the GA program to address the convergence issue56.N is the total number of days in the period.As a distributed hydrological model, the VIC model has been widely used to assess and validate SPPs56. Therefore, in this study, two scenarios were proposed to simulate the runoff processes with diverse SPPs.Despite the variations in accuracy and spatiotemporal resolutions, different satellite-based forcing data might exhibit similar runoff prediction skills after recalibrating the model using the respective precipitation productsScenario I : (a) calibrate and validate the VIC model with the 10 km gridded precipitation dataset in streamflow simulation during 2003 ~ 2015; (b) replace the rainfall reconstruction forcing with precipitation from the five SPPs for independent validation from 2003 ~ 2015 using the reconstruction-calibrated model parameters.Scenario II : Recalibrate VIC using the five SPPs, respectively, over the same calibration period and then simulate runoff using the specific parameter sets calibrated from different products over the same periods as Scenario I.RB between five SPPs and gauged observations during the period from 2003 to 2015. As shown in Fig. RB, especially in the middle of the basin. For GSMaP, the overestimated and underestimated gauges are divided equally, which resulted in a low RB for the whole basin. Noticeably, PERSIANN tended to overestimate all gauges with an average RB of 92%, showing less skill for precipitation estimation compared to other datasets.Figure CC and RMSE of diverse SPPs from 2003 ~ 2015 were also calculated at a gauge scale compared to rainfall observations  at the daily time scale were shown in Fig. POD of 0.75, followed by the GPM (POD = 0.65), CMORPH (POD = 0.59), TRMM (POD = 0.57), GSMaP (POD = 0.50). The regional POD analysis against gauged observations indicated the good capture ability in the middle of the basin for all SPPs. The PERSIANN showed the best performance with the POD range from 0.53 to 0.87. As seen in Fig. POD in the middle regions but showed a poor POD value in the down areas. This is because the POD values were higher in the drier regions and lower in the wetter areas. Conversely, all SPPs demonstrated lower FAR downstream and relatively higher FAR in the middle. Figure FAR of 0.46, followed by the GPM (FAR = 0.48), GSMaP (FAR = 0.51), TRMM (FAR = 0.56), PERSIANN (FAR = 0.59). By contrast, the PERSIANN had the highest POD and highest FAR, probably caused by the overall overestimation of precipitation, indicating the inaccuracy of PERSIANN.The categorical statistical metrics  and wet (June to September) seasons. We can find that all SPPs performed better in the wet season with lower RB than in the dry season under both two Scenarios, and GPM performed best in the wet season, followed by GSMaP, TRMM, CMORPH, and PERSIANN. It was worth noting that TRMM performed better than its successor GPM in dry season simulation. It also indicated slight underestimation in the dry season for all SPPs expect PERSIANN against gauged observations in Fig. 2.Figure 37. In this study, multi-satellite precipitation products  are all incorporated with gauged observations, and were effectively assessed in terms of data reliability and hydrometeorological application potential via the well-calibrated VIC model over the YZRB. Results of the statistical analysis between the SPPs and gauged observation indicated that except for PERSIANN, other SPPs Generally, CMORPH, GPM and GSMaP present significant enhancement in rainfall estimations in comparison with TRMM and PERSIANN with lower RMSE, RB, FAR and higher CC and POD , our study found significant improvements in GPM in both precipitation estimation and hydrological utility, similar to other studies in the TP39 in Scenario I , as the lack of gauged observations may hamper the evaluation of SPPs, especially in capturing extreme events in the historical period62. Moreover, we defined the search space of the VIC model parameters to be strictly within its physical field through the GA optimization procedure and converged the model to the optimal solution to decrease the parameter uncertainty63. However, calibrating the model with an identical parameter set tends to hamper the fairness of the evaluation of different SPPs, although it is widely used by the hydrological community, especially in gauged basins64 and product-specific recalibration might enhance the performances of hydrological modeling56. Results from our study indicated improved performances from CMORPH over calibration and validation periods and more promising estimates from other products over calibration periods when recalibrating the VIC model (Table 67. In the future, the evaluation of SPPs’ applicability in runoff simulation of YZRB could be enhanced by coupling VIC and glacier modules.The uncertainties caused by parameters of hydrological modeling could also be influential on the SPP evaluation results. Ideally, the parameters are obtained by comparing the simulated value with the perfect value, later considered the best possible description of basin characters to run with different SPPs. We have introduced the widely proven high-quality rainfall products reconstructed by Sun and SuIn general, all SPPs represented a similar rainfall pattern in the YZRB, demonstrating a decreasing trend from the east to the west. However, PERSIANN performed worst with an enormous overestimation in the basin. CMORPH performed better among SPPs, with slightly higher correlation and lower bias.The GPM and CMORPH products exhibit comparable ability in streamflow simulations, indicating a great potential in the hydrological application. GPM performed best in daily streamflow simulation, followed by CMORPH, TRMM, GSMaP, and PERSIANN.The GPM performed better for streamflow in the wet season than TRMM, while TRMM performed better in the dry season.By using the VIC model and statistical metrics, the five satellite precipitation products were evaluated in the YZRB on a daily scale. The main conclusions are as follows:Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.Supplementary Information 13.Supplementary Information 14.


The interindividual diversity (beta diversity) was significantly different before and after distal gastrectomy . Alpha and beta diversity were not significantly different between Billroth I and Roux-en-Y reconstructions . Our study demonstrated significant differences in gastric microbiota diversity, composition, and community before and after distal gastrectomy but no difference between Billroth I and Roux-en-Y reconstruction after distal gastrectomy.The changes in gastric microbiota following reconstruction after gastrectomy have not been reported. This study aimed to compare the gastric microbiota following Billroth I and Roux-en-Y reconstructions after distal gastrectomy. We enrolled 71 gastrectomized patients with gastric cancer; 31 and 40 underwent Billroth I and Roux-en-Y reconstructions, respectively. During upper gastrointestinal endoscopy, gastric fluid was collected immediately before and 6 months after distal gastrectomy. Deoxyribonucleic acid isolated from each sample was evaluated using 16S ribosomal ribonucleic acid metagenomic analysis. Analysis revealed that the gastric microbiota’s species richness  was significantly lower after than before distal gastrectomy (operational taxonomic units,  Helicobacter pylori (H. pylori) in 1982, followed by the identification of additional stomach microbiota2. In recent years, the application of bacterial 16S ribosomal ribonucleic acid (16S rRNA) gene sequencing technology for human gut microbiota research has gradually facilitated the comprehensive elucidation of the gastric microbiota3. Gastric mucosa and gastric fluid have been reported to contain 102–104 colony-forming units per g or mL4.For many years, the stomach was believed to be a bacteria-free organ because of its high acidity. However, this concept was refuted by the discovery of 6, and fluctuations in the gastric environment may alter the gastric microbiota. Although changes in the gastric environment after subtotal gastrectomy leads to altered gastric microbiota composition7, their effect on the remnant stomach has not been extensively studied.The gastric microbiota plays an essential role in maintaining gastric homeostasis and has been suggested to be associated with gastric diseases. Studies have demonstrated that the diversity of the gastric microbiota changes with the progression of gastritis, intestinal metaplasia, and gastric cancer8. Distal gastrectomy (DG) is the most common surgical treatment for gastric cancer, and Billroth I (BI) or Roux-en-Y (RY) reconstructions are commonly performed as standard procedures in Japan , 4 who underwent neoadjuvant chemotherapy, 2 who underwent simultaneous resection of other organs for malignant diseases, 10 who had pyloric stenosis, and 16 who were on the following medications: immunosuppressive drugs (n = 1), corticosteroids (n = 2), and acid-suppressing drugs (n = 13). Hence, altogether 71 patients who underwent DG for gastric cancer were included, out of which none used antibiotics during the 3 months preceding surgery.11. At our institute, BI reconstruction is the first-line reconstruction method after DG. BI reconstruction with delta-shaped mechanical anastomosis was performed12. In cases involving a small remnant stomach, hiatal hernia, or reflux esophagitis, RY reconstruction was performed13. Of the 71 patients who underwent DG, 40 underwent reconstruction using the BI method, and 31 using the RY method. The patients’ pathological stages were defined based on the Japanese classification of gastric carcinoma14.Lymph node dissection, surgical procedures, and gastric reconstruction were performed according to the Japanese Gastric Cancer Treatment GuidelinesThis prospective cohort study was conducted in accordance with the Declaration of Helsinki and its latest amendments. The prospective cohort study was approved by the Osaka Medical and Pharmaceutical University Ethics Committee, Takatsuki City, Japan . Written informed consent was obtained from all participants.A preoperative endoscopic examination was performed in each case to evaluate and mark the tumor. Patients were advised to keep an overnight fast until the procedure on the following morning and no pre-procedural prophylactic antibiotics were prescribed. Simultaneously, gastric samples were obtained using a disposable syringe connected to the tube to aspirate as much gastric fluid as possible. After collection, the sample was immediately frozen at − 80 °C before deoxyribonucleic acid (DNA) extraction. Upper gastrointestinal endoscopic examination was performed 6 months after gastrectomy as part of the postoperative workup, and gastric samples were collected in the same manner. None of the patients were on acid-suppressing drugs post DG as removal of gastric fundic glands, the primary source of gastric acid, leads to lower acid concentrations in the remnant stomach.If the amount of gastric fluid was small, the samples were collected with sterile water evenly distributed in the stomach. Effluents from the gastroscope and diluent solutions and media were used as negative controls for bacterial growth to investigate potential contamination.15. The collected gastric fluids were homogenized with 0.1 mm glass beads using a Disruptor Genie homogenizer  for 90 s. Extraction of bacterial genomic DNA was performed from the homogenized samples using GENE PREP STAR PI-480 (Kurabo Industries Ltd.) following the manufacturer’s instructions. The V1–V2 region of the 16S rRNA gene was amplified for 25 cycles using the primer set 27 Fmod: 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACA.DNA extraction, 16S rRNA sequencing, and taxonomic classification were performed using a slightly modified version of methods we have employed previously for saliva samplesGAGRGTTTGATYMTGGCTCAG-3′ and 338R: 5′-GTCTCGTGGGCTCGGAGATG TGTAGTGTATAAGAGACAGTGCTGCCTCCCGTAGGAGT-3′.16. The quality filtering resulted in 4,539,853 sequences, with a mean of 31,971 sequences per sample . We decided to set the minimum depth cut-off for rarefaction at 10,000 and retained all samples for downstream analysis17. The processed sequences were clustered into operational taxonomic units (OTUs), which were defined based on a cut-off of 97% similarity.Each library was prepared using the 16S rRNA sequencing library preparation protocol . DNA sequencing was performed for 500 cycles using the MiSeq Reagent Kit v2 (Illumina). An average of 39,966 sequence reads with 250 bp paired-ends were denoised, and quality reads were filtered using DADA2 in Quantitative Insights Into Microbial Ecology 2 (QIIME2) version 2020.2t-test. A p-value of less than 0.05 was considered reflective of statistical significance.All statistical analyses were performed using JMP pro 15 . For participant characteristics, categorical variables are presented using frequency counts; intergroup comparisons were analyzed using Fisher’s exact test or the Wilcoxon rank-sum test. Continuous variables were compared using Student’s 15. The within-patient alpha diversity of bacterial communities was assessed by the Shannon’s index and the observed OTU index, and the Kruskal–Wallis test was used to compare between groups. The beta diversity between patients was assessed based on the Bray–Curtis dissimilarity, with unweighted and weighted UniFrac distance metrics18. Global differences in the microbiome structure in the UniFrac analysis were visualized using a principal coordinate analysis. The significance of compositional differences between groups was assessed using permutational multivariate analysis of variance (PERMANOVA). These analyses were conducted using the QIIME2 software18.Statistical analysis was performed using a slightly modified version of our previous methodsThe linear discriminant analysis effect size (LEfSe) algorithm was used to detect genera that differed in abundance between the two groups. All analyses were performed with the α parameter of LEfSe set to 0.05 for pairwise tests and the threshold of the logarithmic score for linear discriminant analysis set at 3.0.The eligible patients who underwent DG comprised 40 who underwent BI reconstruction and 31 who underwent RY reconstruction. According to the reconstructive method, patient characteristics are listed in Table Gastric bacteria with a relative abundance of at least 0.1% in the before and after DG groups were classified into 8 phyla, 13 classes, 29 orders, 31 families, and 50 genera. The predominant bacteria  at the phylum level included Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria, which comprised 91.4% and 88.3% of the gastric microbiota before and after DG, respectively , of which Neisseria and Prevotella were absent after DG. In contrast, the samples obtained after DG included five genera , of which Lactobacillus was absent before DG , whereas after DG, H. pylori infection was identified in 20 patients (28%). Before DG, the percentages of patients with H. pylori in the BI and RY reconstruction groups were 47.5% and 41.9%, respectively, with no significant between-group differences .As shown in Fig. p = 0.04; weighted, p = 0.001). A PERMANOVA using Bray–Curtis metrics based on the relative abundance of bacterial genera also revealed a significant difference before and after DG (p = 0.001).The principal coordinate analysis of the unweighted and weighted UniFrac distance metric-based results revealed that the gastric samples differed between the “before DG” group and the “after DG” group in three-dimensional space Fig. . This beHelicobacter, Porphyromonas, Alloprevotella, Prevotella, Fusobacterium, Peptostreptococcus, Holdemanella, and Mousegutmetagenoma and a reduction in Rothia and Lactobacillus before DG compared with those after DG . Further, beta diversity was not significantly different between the two groups   with Billroth II reconstruction, the gastric microbiota was significantly more diverse after DG22. It was thought that after stomach resection for gastric cancer, whereby the tumor and peritumor area would have been resected, then the remnant stomach would naturally comprise only the normal region. Therefore, only the gastric microbiota of the original normal region would be retained in the microbiota present in the remnant stomach. However, our technique of collecting the gastric fluid can represent the inclusive microenvironment of the stomach.It has been recently reported that the diversity and composition of the gastric microbiota differ significantly according to the location: whether around the tumor, peritumor, or in the normal region of the stomachWe compared the gastric microbiota after DG with and without postoperative adjuvant chemotherapy. Since there was no significant difference in both alpha and beta diversity , we judged that the effect of postoperative adjuvant chemotherapy on the gastric microbiota was small and included cases with postoperative adjuvant chemotherapy in our study.Helicobacter, Prevotella, Fusobacterium, and Peptostreptococcus were significantly reduced after DG, and these genera were reported to be relatively abundant in the tumor and peritumor regions of the stomach22.In our investigation, the genera 7. The beta diversity of the gastric microbiota significantly increased with the use of acid-suppressing agents, including proton-pump inhibitors23. This situation is considered relatively similar to the environment after DG due to reduced gastric acid secretion. Therefore, it is in agreement with our beta diversity results.The beta diversity exhibited a significant difference before and after DG. As a result of changes in the gastric environment due to the altered gastrointestinal tract, there is an apparent shift in the community composition of the gastric microbiota after DG. Therefore, the gastric microbiota differed before and after surgery. This result is consistent with those of a previous report7. Our LEfSe analyses revealed that the proportions of genera Rothia and Lactobacillus were significantly higher after DG  follow-up study reported an increased richness and diversity of the gut microbiota on stool sample analysis after subtotal gastrectomy with RY reconstruction34. The incidence of remnant gastritis and bile regurgitation is higher after BI than after RY reconstruction33; the procedures are generally equivalent regarding postoperative quality of life34.No significant differences in diversity and community composition between BI and RY reconstructions were observed in this study. Many reports have compared BI and RY reconstruction after DG in terms of quality of life and dysfunction35. Alterations in the gastric microbiota are thought to cause postprandial distress and epigastric pain syndrome. The lack of a difference in the bacterial microbiota between BI and RY reconstruction in this study means that the gastric environment was comparable, possibly suggesting that both reconstructive methods were generally equivalent in terms of postoperative quality of life. However, since there are few published reports in this field and much remains unknown, further research on this topic is needed. Furthermore, examining the relationship between the quality of life and gastric microbiota may provide newer strategies for the betterment of the postoperative quality of life and has important clinical implications.It has been established that there is a discrepancy between endoscopic findings and a patient’s symptoms. The gastric microbiota of patients with functional dyspepsia is altered compared to that in healthy controls22. Gastric fluid is spread evenly throughout the stomach. In this respect, gastric fluid aspiration has the advantage of evenly reflecting the gastric environment. In addition to the difference in the collected samples between this study and others, the measurement times are different. It is unknown when the bacterial microbiota changes; therefore, following the alterations in the bacterial microbiota over time is necessary. Knowledge of these changes and how they are affected by fluctuations in the gastric environment is of importance because it may potentially hold the keys to improved treatment approaches. Finally, in approximately half the patients, H. pylori was detected in the gastric fluid and was not unified. However, the percentage of H. pylori in the gastric fluid after DG is extremely low; hence, we believe that it has little effect on the comparison of gastric microflora with stomach microflora based on the method of reconstruction.The present study has several limitations. First, we aspirated gastric fluid and examined the gastric microbiota, but the amount of gastric fluid that could be aspirated was limited, which could have caused individual differences. Although some reports collected gastric mucosal tissue, the gastric mucosal microbiota differs based on the collection site We investigated the composition and diversity of gastric microbiota before and after DG. Although there were significant differences before and after DG, there were none in terms of diversity, composition, and community between BI and RY reconstructions.Supplementary Information.


Understanding interactions within the gut microbiome and its stability are of critical importance for deciphering ecological issues within the gut ecosystem. Recent studies indicate that long-term instability of gut microbiota is associated with human diseases, and recovery of stability is helpful in the return to health. However, much less is known about such topics in fish, which encompass nearly half of all vertebrate diversity. Here, we examined the assembly and succession of gut microbiota in more than 550 zebrafish, and evaluated the variations of microbial interactions and stability across fish development from larva to adult using molecular ecological network analysis. We found that microbial interactions and stability in the fish gut ecosystem generally increased with host development. This could be attributed to the development of the zebrafish immune system, the increasing amount of space available for microbial colonization within the gut, and the greater stability of nutrients available for the colonized microbiota in adult zebrafish. Moreover, the potential keystone taxa, even those with relatively low abundances, played important roles in affecting the microbial interactions and stability. These findings indicate that regulating rare keystone taxa in adult fish may have great potential in gut microbial management to maintain gut ecosystem stability, which could also provide references for managing gut microbiota in humans and other animals.IMPORTANCE Understanding gut microbial stability and the underlying mechanisms is an important but largely ignored ecological issue in vertebrate fish. Here, using a zebrafish model and network analysis of the gut microbiota we found that microbial interactions and stability in the gut ecosystem increase with fish development. This finding has important implications for microbial management to maintain gut homeostasis and provide better gut ecosystem services for the host. First, future studies should always consider using fish of different age groups to gain a full understanding of gut microbial networks. Second, management of the keystone taxa, even those that are only present at a low abundance, during the adult stage may be a viable pathway to maintain gut ecosystem stability. This study greatly expands our current knowledge regarding gut ecosystem stability in terms of ecological networks affected by fish development, and also highlights potential directions for gut microbial management in humans and other animals. Each fish gut ecosystem is colonized by a diverse community of microorganisms from the surrounding environment during food consumption , 2. Moun3–Bacteroides have even shown species-specific interactions with the host and mediation of gut ecosystem stability  with zebrafish development (P > 0.05) for early sampling points , as well as during 28 to 37 dph, 56 to 63 dph, and 70 to 77 dph (data not shown). However, those among 12, 20, 27, 42, 56, 70, and 98 dph were always significantly different . Also, we observed similar diversity results if only the minimum number of samples  were retained in the dissimilarity tests for all of these sampling points (data not shown).The amplified 16S rRNA gene was sequenced to visualize the diversity patterns of gut microbiota across zebrafish development from 12 to 98 days post-hatching (dph) . Analysielopment . The dis2R > 0.8), and therefore exhibited scale-free characteristics. Moreover, except for 70 dph, the modularity  of each empirical network of gut microbiota was always significantly higher (P < 0.05) than the corresponding randomized networks (Table S2), confirming the networks appear to be modular. However, the complexity of networks visualized by sampling points only significantly increased from 20 dph to 70 dph . We also found that only positive cohesion significantly decreased  with the sampling time. However, the relative fraction of |negative cohesion|: positive cohesion (N:P), which reflects the community stability, showed no significant relationship  different from those of the corresponding randomized stage-dependent networks (Table S2). These metrics indicated that such stage-dependent networks were non-random and unlikely due to chance. Therefore, to gain a better understanding of the assemblage patterns and network stability of zebrafish gut microbiota, the following results were mainly visualized according to the developmental stage rather than by sampling point.Interestingly, the networks constructed for the different developmental stages  indicated that the co-occurrence and interactions among networked OTUs in the last stage (56 to 98 dph) were more complex than those of the early stages . Specifically, the total nodes and total links, as well as the average degree and connectedness tended to be much higher in the last stage . Some other key network topological indices such as geodesic efficiency , harmonic geodesic distance , and centralization of stress centrality  of the empirical networks were also significantly  were further visualized by ternary plots , Venn plots, and detrended correspondence analysis (DCA) ordination. Ternary plots showed a clear selection of gut microbial taxa at different developmental stages. Specifically, the first two stages (12 to 26 and 27 to 42 dph) showed stronger selection for OTUs from the gammaproteobacteria, but there was stronger selection of Bacteroidetes  in the last stage (56 to 98 dph). In addition, some taxa from alphaproteobacteria  and Planctomycetes (OTU150) were also commonly selected at 12 to 26 dph . Venn plSourceTracker analysis was performed to explore the deductive sources for assembling gut microbiota in zebrafish at different developmental stages. We found that microbial sources derived from an adjacent previous stage always accounted for the primary proportion, indicating a considerable part of the colonized gut microbes could be passed on to the next stage. Specifically, 78.36% of the gut microbial sources detected at 15 to 26 dph might be sourced from 12 dph; 67.29% of those at 27 to 42 dph sourced from 12 to 26 dph; and 79.5% of those at 56 to 98 dph sourced from 27 to 42 dph . Meanwhile, zebrafish gut microbes colonized from water environments only accounted for 0.04% to 0.05% in the early stages and increased to 2.32% in the last stage. The remaining 17.58% to 32.66% of microbial sources were classified as unknown . We also compared the affiliation of OTUs by exploring their sharedness or specificity between water microbiota and gut microbiota within developmental stages. We found that the number of OTUs only detected in water was always much smaller (5.46% to 15.58%) than that detected in the gut at the adjacent previous stage . The shared OTUs in water and gut microbiota at the adjacent previous stage accounted for 27.92% to 52.76%, and the remaining 10.76% to 30.22% were classified as unknown .The stage-dependent networks contained 12 to 15 modules with a modularity ranging from 0.374 to 0.489 (Table S2). Overall, taxa tended to co-exclude  rather than co-occur , with negative correlations accounting for 88.60 to 97.98% of observed links in the networks. To identify the possible variation of gut microbial interactions across zebrafish development, we focused on large modules (≥ 5 nodes) in the stage-dependent networks by highlighting the OTU correlations and the proportions of major phyla (Proteobacteria were further divided into classes). The network modules became larger and more connected as the zebrafish developed, as such the 56 to 98 dph network had the largest number of modules  and the two largest modules  that included many more nodes  than those in the early stages /stress centrality (the number of geodesic paths that pass through the ith node)/eigenvector centrality , differed across zebrafish development. They were initially occupied by OTU1662 (Serratia) at 12 to 26 dph, then shared by OTU965 (Vibrio), OTU1021 (Cetobacterium), and OTU7097  at 27 to 42 dph, but dominated by OTU7032  at 56 to 98 dph. Most of these key network nodes were previously defined as “core microbiota” of the zebrafish intestine (iZ) and among module connectivity (iP) of each OTU. Specifically, the potential keystone taxa included network hubs (iZ > 2.5 and iP > 0.62), module hubs (iZ > 2.5 and iP ≤ 0.62), and connectors (iZ ≤ 2.5 and iP > 0.62)  . We founP < 0.05) correlated with the alpha-diversity. However, there were 29 connectors that displayed significant (P < 0.05) correlations with the alpha-diversity at 56 to 98 dph  between the representative potential keystone OTUs  and alpha-diversity indices .To verify which potential keystone taxa may affect the assemblage patterns of gut microbiota, the relationships between each potential keystone OTU and the alpha-diversity were examined. Results indicated that only the connectors showed significant correlations with all three diversity indices , while none of the network hubs and module hubs did . At 12 to 98 dph . Such coP < 0.05) across host developmental stages (P < 0.05) stage by stage, whereas the PD and Shannon of the last stage (56 to 98 dph) were significantly higher than those of the first stage (12 to 26 dph) (P < 0.05). However, the diversity of the middle stage (27 to 42 dph) was similar to that of the first stage  or the last stage  (P = 0.001) (Table S1). To understand whether and how zebrafish development affects the stability of gut microbiota, the stability-related indices  were calculated from the empirical data. We found the positive cohesion significantly decreased  stage by stage, and the values of N:P for the last stage (56 to 98 dph) were also significantly higher than the early stages , indicating that gut microbial community in zebrafish becomes more stable in adults. Similarly, the network vulnerability in the first stage (12 to 26 dph) was much higher than that of the other stages (27 to 42 and 56 to 98 dph), confirming a more stable gut microbiota in adult zebrafish. In addition, the ratio of negative correlations among nodes was much higher in the last stage  than in the early stages , indicating a more stable network in adult zebrafish.The alpha- and beta-diversity indices all indicated that microbial community assembly in zebrafish varied significantly (Shannon) . The disP < 0.001, P < 0.001 or 0.001 < P < 0.01) with stability, indicating the network stability had a negative relationship with gut microbial diversity and potential keystone taxa. In addition, we also found that zebrafish development always showed significant  effects on the gut microbial diversity and potential keystone taxa  was performed as illustrated by Fig. S4. The results indicated that zebrafish development was positively correlated  confirmed our previous study that found fish development was one of the most important forces driving the ecological succession of gut microbiota. The variations of host immunity and gut nutrient niches across fish development could significantly affect the gut microbiota . DevelopVibrio,Pseudomonas, Cetobacterium) in zebrafish intestines  of gut microbiota and result in high stability at the adult stage. Therefore, gut microbial management of the rare keystone taxa at the adult stage may be a good way to regulate gut ecosystem stability. Further clarification of how to precisely regulate the promising targets of rare but key taxa will be useful for maintaining beneficial host-associated microbial communities and controlling microbes-associated diseases .Paramecium (5 to 8 dph), 20 μm mesh filtrated boiled egg yolk (9 to 11 dph), live brine shrimp (12 to 19 dph), and a standard dry fish food from 20 dph onward. To decrease the possible effects of diets, which changed more frequently before 12 dph, we chose the 12 dph as the first sampling point to analyze zebrafish gut microbiota. During the experiment, fully aerated tap water was used without filtering, and no recirculation occurred among the three tanks to make each tank independent. The zebrafish gut samples analyzed herein, included the 189 samples published previously . Therefore, we used days post-hatching (dph) to record the sampling times. The same batch of fertilized zebrafish (AB strain) embryos obtained from China Zebrafish Resource Center were hatched using water from three different environments . Then, tramecium  to 8 dphramecium  to 8 dpheviously . The sameviously ; howevereviously . Many moeviously , while teviously . The colAll protocols involved in the zebrafish experiments were approved by the Institutional Animal Care and Use Committee of the Institute of Hydrobiology, Chinese Academy of Sciences .GTGCCAGCMGCCGCGGTAA-3′) and 907R (5′-CCGTCAATTCMTTTRAGTTT-3′). Each sample was amplified in a 50-μL reaction system containing 1x Premix Taq DNA polymerase , 0.2 mM each primer, and 50 ng genomic DNA using the following program: DNA pre-denaturation for 5 min at 95°C; then 30 cycles of 30 s at 95°C, 30 s at 52°C, and 30 s at 72°C; followed by a post-amplification extension of 10 min at 72°C. PCR products were visualized using 1% agarose gels stained with ethidium bromide, and negative controls were always performed to ensure that no contamination had occurred.Genomic DNA of each collected sample was extracted using the PowerFecal (gut samples) or PowerWater (water samples) DNA isolation kit  following the manufacturer’s instructions. The concentrations and quality of extracted DNA were determined using a NanoDrop One spectrophotometer . Individual samples for which DNA extraction failed were excluded from the following analysis. In total, 652 genomic DNAs  were obtained. All the DNA samples were then diluted to 10 ng/μL for subsequent PCR amplification. The V4 to V5 regions of the 16S rRNA gene was amplified by the primer set of 515F  according to the manufacturer’s instructions. To ensure an identical sequencing depth for each sample, PCR products were equally combined and mixed fully. All mixtures were added to a 2% agarose gel and the excised target DNA band was purified with a QIAquick Gel Extraction Kit . After a re-quantification of the purified DNA, a library was constructed using a NEBNext Ultra DNA Library Prep Kit  following the manufacturer’s instructions. The constructed library was sequenced on the Illumina HiSeq 2500 platform  by Guangdong Magigene Biotechnology Co., Ltd. using a 2 × 250 bp kit .http://mem.rcees.ac.cn:8080/)  . The oven:8080/) , and pooSourceTracker analysis, a Bayesian approach to estimate the proportion of microbes in a given community that come from possible source environments , was usehttp://ieg2.ou.edu/MENA/) as described previously (St) prior to network construction . To ensure the reliability, networks were only constructed for sampling points with more than 27 replicates per time point , whereas those with only 9 replicates were not involved in the time point-dependent network construction. The molecular ecological networks (MENs) were also constructed according to host developmental stage, which were divided into the three stages as referred to in our previous study  . All constructed networks were visualized using Gephi 0.9.2 and Cytoscape 3.8.2.To explore microbial interactions and network stability of gut microbiota across zebrafish development, network analysis was performed using the Molecular Ecological Network Analysis (MENA) pipeline  and among-module connectivity (iP), which were calculated as follows:ib is the number of links of node i to all other nodes in module b; kbσ are the mean and standard deviation of within-module connectivity, respectively; MN is the number of modules in the whole network; ik is the number of links of node i in the whole network; ick is the number of links from node i to all other nodes in module c. This can classify all nodes into four categories: iZ > 2.5 and iP > 0.62 indicated network hubs (highly connected nodes within entire network); iZ > 2.5 and iP ≤ 0.62 indicated module hubs (highly connected nodes within modules); iZ ≤ 2.5 and iP > 0.62 indicated connectors (nodes that connect modules); and Zi ≤ 2.5 and iP ≤ 0.62 indicated peripherals (nodes connected in modules with few outside connections)  in networks was evaluated by its within-module connectivity (ections) . In geneections) , 54, andections) , 57. Briections) , 57.The indices of cohesion and vulnerability were used to evaluate the microbial network complexity and stability across zebrafish development. The cohesion was calculated for each community to quantify the microbial connectivity according to the protocol of Herren and McMahon . Positiva priori model with latent variables, manifest variables, and path diagram . Latent variables were hypothetical variables that could not be measured directly and were taken as underlying variables that help explain the association between two or more manifest variables.The SEM was performed using R package of “plsmp”  to quantThe zOTUs of gut microbiota and networked communities , as well as parameters calculated accordingly involved one individuals or all of the following statistical analyses: (i) alpha-diversity comparisons were conducted to reveal dynamics of gut microbiota throughout zebrafish development as visualized by sampling points and developmental stages; (ii) significance tests were performed through an ANOVA with least-significant-difference (LSD) to examine whether differences among comparisons were significant or not; (iii) correlation analysis was performed to ascertain whether the characteristics interested were significantly correlated or not ; (iv) noOEP002082.The 16S rRNA gene sequencing data are available in the National Omics Data Encyclopedia (NODE) database with an accession number


The origin of the temperature divergence between Holocene proxy reconstructions and model simulations remains controversial, but it possibly results from potential biases in the seasonality of reconstructions or in the climate sensitivity of models. Here we present an extensive dataset of Holocene seasonal temperatures reconstructed using 1310 pollen records covering the Northern Hemisphere landmass. Our results indicate that both summer and winter temperatures warmed from the early to mid-Holocene (~11–7 ka BP) and then cooled thereafter, but with significant spatial variability. Strong early Holocene warming trend occurred mainly in Europe, eastern North America and northern Asia, which can be generally captured by model simulations and is likely associated with the retreat of continental ice sheets. The subsequent cooling trend is pervasively recorded except for northern Asia and southeastern North America, which may reflect the cross-seasonal impact of the decreasing summer insolation through climatic feedbacks, but the cooling in winter season is not well reproduced by climate models. Our results challenge the proposal that seasonal biases in proxies are the main origin of model–data discrepancies and highlight the critical impact of insolation and associated feedbacks on temperature changes, which warrant closer attention in future climate modelling. The study reconstructed Holocene seasonal temperatures using 1,310 pollen records covering the Northern Hemisphere landmass, and show that both summer and winter temperatures peaked at ~7 ka BP, but with significant spatial variability. However, the long-term evolution of global temperatures over the past 11 kyr (the Holocene epoch) remains poorly constrained. Multiproxy reconstructions4  multiproxy stacks4  in hemispheric and regional scales. Our extensive dataset indicates evident cooling trends in both summer and winter seasons after ~7 ka BP over the NH landmass, which suggest a low risk of proxy origin of Holocene temperature conundrum and provide insights into the mechanisms of Holocene temperature change.Fossil pollen reflects past vegetation communities that have specific temperature requirements in both cold and warm seasons−1), which was most pronounced during the early to mid-Holocene .Our results show that the trends of NH annual, summer and winter temperatures are all characterised by a rapid warming from the early to mid-Holocene 11–7 ka BP) and gradual cooling after ~7 ka BP , and NH multiproxy reconstructions4 (KA20), while another multiproxy stack2 (MC13) exhibits an HTM phase  from North America and Europe9  analysis of selected records spanning the 11–7 ka BP and 7–0 ka BP periods. The first principal components (PC1) show similar early to mid-Holocene rising Fig.  and mid-14 and our independent proxy compilations (see Methods), as indicated by the EOF results for the 11–7 ka BP . Therefore, along with annual temperatures, a conundrum similar to that derived mainly from marine proxies5 also exists for the winter season at both the hemispheric and regional scales over the NH landmass. The widespread model–data contrast in the spatial comparison also suggests that the uneven distribution of records may be not the main origin of the Holocene conundrum8.For winter and annual temperatures, the warming from ~11 to 7 ka BP is present in both the reconstructions and model results with similar magnitudes  can influence plant growth. These factors may generate “noise” within the overall regional temperature trends  of the warming of annual (winter) temperatures in China18. Simulations using pollen-based vegetation in Europe indicate an entirely warmer than pre-industry mid-Holocene during all seasons with the strongest warming in winter43. The green Sahara and reduced atmospheric dust loading due to a stronger summer-insolation-induced Afro-Asian monsoon during the mid-Holocene, could increase the global annual mean surface temperature by ~0.4 °C42 and ~0.3 °C48, respectively, which could counteract the ~0.3 °C of greenhouse gas‐driven decrease relative to the pre-industry42.Alternatively, potential biases in the models may result from imprecise estimation of the climate sensitivity to forcing factors and the incomplete representation of feedback processes47. The models with stronger sea ice responses to summer insolation changes tend to generate a year-round warmer mid-Holocene in the NH45, and the sea ice loss-induced winter warming can almost counteract the cooling induced by the lower winter insolation in the mid-high latitudes46. Models also indicated an asymmetric pattern of winter temperature responses to Arctic sea ice loss associated with the weakening of mid-high latitude westerlies in the mid-Holocene: the northern Asia cooled, while the Arctic region and North America warmed strongly46. Our reconstructions support the importance of sea ice feedbacks by showing a consistent spatial pattern that there is a warming trend in northern Asia and overall cooling trend in North America after ~7 ka BP  temperatures remain stable (cooling) from ~6 ka BP, evidently diverging from the rising winter  insolation Fig. . As a re6 proposed a solution to the Holocene temperature conundrum based on the assumption that temperatures linearly respond to seasonal insolation changes for oceans between 40° S and 40° N. However, over the NH land, the feedbacks in response to the summer insolation forcing may strongly influence the annual and winter temperature variations and result in the decoupled trends between winter temperature and insolation as the reconstructions indicate. We suggest that, together with the direct insolation forcing, feedbacks could also be important for the Holocene, as well as for future temperature changes54, and these feedbacks need to be better addressed in future climate modelling studies.Bova et al.55 (http://www.neotomadb.org/), European Pollen Database56 (http://www.europeanpollendatabase.net), and the pollen dataset for East Asia58. A total of 1,310 sites  The duration of the record exceeded 5 kyr, and the record included samples younger than 1 ka BP, as required to calculate the anomalies. (4) Sampling resolution was finer than 400 years (relaxing to 1000 years for sites in northern Asia to increase the low coverage there). (5) Samples with <200 pollen count were excluded.The fossil pollen data were obtained from the Neotoma Paleoecology Database60 and interpolated to sample levels using the R package clam61. In addition to ages derived from direct dating, other chronological controls, such as annual lamination counts, biostratigraphic controls, and core-top ages from pollen databases62, were also included in the interpolation.To reduce age uncertainty, all radiocarbon dates were recalibrated using the IntCal20 calibration curve63, European Modern Pollen Database64, East Asian Pollen Database66, and the Eurasia pollen database67  can be used to reduce poor analogue cases and non-climatic influences, and thus provide a more accurate and consistent reflection of the climate69. Therefore, we used PFT scores in our reconstructions instead of the direct pollen taxa assemblages used in Marsicek et al.9. PFT scores were calculated using the taxa–PFT matrixes in biomization procedures for six regions, namely: Europe70, Eurasia71, East Asia72, Canada and the eastern United States73, the western United States74 and Beringia75 ; the total, unique, and shared proportions of variance explained by each variable; and the significance  of each variable in variation explaining (Supplementary Table vegan81 (version 2.5–7) and following the procedure presented in Borcard et al.79.RDA, a widely used constrained gradient analysis technique, is an effective way to tackle this issuep < 0.001; Supplementary Table 77. For each variable, MTCO can explain 9.9% to 20.4% of the total variance, with 5.8% to 13.3% being unique, whereas MTWA explains 6.3% to 20.5%, with 2.3% to 10.2% being unique. The substantial unique contributions of MTCO and MTWA suggest that they have a distinct influence on vegetation changes.Our RDA results demonstrate that both MTCO and MTWA are significant variables with respect to the variance of the modern PFT score data in all regions  is still present there.The correlation between MTCO and MTWA , and has lower proportions of covariance than MTWA in almost all regions  are commonly used to reconstruct the dominant environmental variable83. However, they are susceptible to the covariance problem because they use a constant function calibrated from the modern dataset, but the covariance structure of the variables may change through time76. Instead of using constant calibration functions, MAT does not fit pollen–climate response models, but rather, the climate estimates are based on the modern analogues of fossil samples82, and thus can potentially circumvent the covariance problem to some extent. The modern analogues were chosen as the closest surface sample matches measured using the squared chord distance (SCD)82, and the dissimilarity-weighted climate of the 5–7 closest modern analogues was then assigned to the fossil sample  because of its advantages for large scale reconstructions84. As each variable has a distinct influence on vegetation, as indicated by the partial RDA results, changes in any variable can result in specific changes to the vegetation composition, and consequently, any given vegetation composition reflects a certain group of variables84. Surface pollen records provide a pool of joint vegetation and climate suites. If the fossil records can find close modern analogues, the reconstructions of seasonal temperatures should be robust36.The underlying assumption of MAT is that an individual vegetation composition (here indicated by PFT scores) is the comprehensive result of a specific combination of causal climatic variables (a climate suite)69. Lastly, only fossil samples with close analogues under certain dissimilarity thresholds (Supplementary Table 85. A relatively low SCD (<0.2 throughout the Holocene) between fossil samples and modern analogues  based on leave-one-out cross-validation , although with lower R2 and higher root-mean-square error of prediction (RMSEP) values than leave-one-out cross-validation  using random environmental data91. The reconstructions failing the test may be questionable and should be treated with caution91. However, this test seems to be conservative, because the potentially reliable reconstructions with low variability may be falsely denied (type II errors)92. Following Marsicek et al.9, we adopted a compromised threshold value  for the significant test using the R package palaeoSig88. That is, we only retain the reconstructions that explain more variance than 84.2% of the 999 randomly generated reconstructions. After this selection, 1039 of the 1310 sites were retained for at least one variable, including 792 for ANNT, 780 for MTCO, and 740 for MTWA  model93  to obtain the history of topographic changes for each site by spatial and temporal interpretations using inverse distance weighted and linear methods, respectively. We then calculated the topography-induced temperature changes using the present-day locally (within 300 km) topographic lapse rate of temperatures, which were calculated using the CRU climate dataset68. After the isostatic correction, the composite temperatures induced by climate change became a bit lower, but with negligible amplitude of variation 78. WA-PLS and BRT were implemented with the R package Rioja87 and gbm94, respectively. We also generated reconstructions using the MAT method based on pollen taxa data. 60 and 40 taxa were selected for North America and Europe, respectively, following Marsicek et al.9, while 56 and 74 common taxa from the taxa–PFT matrixes72 were used for northern Asia and East Asia regions, respectively , 781 for MTCO (60%), and 821 for MTWA (63%) in the significance-test step. For WA-PLS, the count of significant records is 308 for ANNT (23%), 325 for MTCO (25%) and 286 for MTWA (22%). Fewer than 200 records, however, yielded significant reconstructions for BRT , and thus we skipped the significance selection for BRT to ensure the spatial coverage. This extremely low ratio (<15%) of significant reconstructions warns that the The Holocene temperature trends over the NH landmass reconstructed using MAT (based on both PFTs and taxa data), WA-PLS and BRT methods are generally consistent with each other, with an early to mid-Holocene warming and thereafter cooling trend for both annual and seasonal temperatures . As for the pollen data, only records with a sample resolution better than 400 years, with a minimum record duration of 5 kyr, and extending to the Common Era, were retained. A total of 135 independent records was compiled, including 26 of annual and 109 of summer temperatures, but with almost no quantitative reconstructions of winter temperatures. In this study, we only retained the chironomid records (N = 83) for regional stacking of the summer temperature in consideration of their robust summer signal32 : orbital insolation, atmospheric greenhouse gas concentration, continental ice sheets, and meltwater flux. The TraCE-21ka simulation was generally consistent with other models in terms of the Holocene temperature trend44, and the output is available at https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm3.trace.html. The annual and seasonal temperatures under full and individual forcings were extracted over pollen sites from the output for detailed model–data comparison and analysis of climate drivers.The global climate over the past 21 kyr has been transiently simulated using a fully coupled atmosphere–ocean general circulation model, CCSM3, developed by the National Center for Atmospheric ResearchSupplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


High seed vigor is crucial for agricultural production owing to its potential in high quality and yield of crops and a better understanding of the molecular mechanism associated with maize seed vigor is highly necessary. To better understand the involvement and regulatory mechanism of miRNAs correlated with maize seed vigor, small RNAs and degradome sequencing of two inbred lines Yu537A and Yu82 were performed. A total of 791 mature miRNAs were obtained with different expressions, among of which 505 miRNAs were newly identified and the rest miRNAs have been reported before by comparing the miRNAs with the sequences in miRbase database. Analysis of miRNA families showed maize seeds contain fewer miRNA families and larger miRNA families compared with animals, indicating that functions of miRNAs in maize seeds were more synergistic than animals. Degradome sequencing was used to identify the targets of miRNAs and the results showed a total of 6,196 targets were obtained. Function analysis of differentially expressed miRNAs and targets showed Glycan degradation and galactose metabolism were closely correlated with improved maize seed vigor. These findings provide valuable information to understand the involvement of miRNAs with maize seed vigor and these putative genes will be valuable resources for improving the seed vigor in future maize breeding. Maize, one of the most popular crops all over the world, plays an important role in overall agricultural production. Maize seed vigor is a kind of comprehensive seed characteristic, including germination rate, germination potential, seedling growth potential, plant resistance and productivity potential, etc., which determines the ability of rapid and healthy germination in the complex field environment and effects crop quality and yield . TherefoIt was suggested that gibberellins (GA) may play an important role in maturity germination. In some species, bioactive GAs are thought to be present and apparently important during early embryogenesis and germination of mature seeds . PreviouMany documents reported that microRNAs (miRNAs) infers to those endogenous, small, singe-stranded non-coding RNAs with the length of usually from 18 to 25 nt, which play an important role at the transcriptional and posttranscriptional regulation level in regulation of seed vigor in maize, rice and many other plants . Some miArabidopsis  with the same filed management conditions in 2016. The seeds were harvested at the same developmental stage . Then these seeds with consistent physiological state were selected and surface-sterilized with 75% ethanol. The seeds of Yu537A were pretreated with 400 mg/L gibberellin (GA3) for its lower seed vigor. Two inbred lines were germinated on the two layers of filter paper with the moisture in sterile petri dishes (the diameter was about 12 cm). The seeds were incubated at a constant temperature of 25°C under a 14/10 h (light/dark) photoperiod with photosynthetically active radiation of 25 μmol photons mThe embryos of five individual seeds were dissected with a sterile knife in each sample and then the samples were immediately frozen in liquid nitrogen and stored at −80°C. Total RNA was extracted using RNAprep Pure Plant Kit (Tiangen) following the procedure of the manufacturer. The concentration was measured by Nanodrop 2000 and integrity of total RNA was tested on 1.0% agarose gel.Small RNA library construction was conducted with TruSeq Small RNA Sample Prep Kits . Small RNA libraries were sequenced with Illumina Hiseq2000/2500 and the sequencing read length is single-ended 1 × 50 bp. The program ACGT101-miR  used for raw data analysis of miRNAs sequencing was independently developed. The main steps are below: (1) removal of 3′ adaptor and junk sequences to obtain clean sequences; (2) screening of miRNA length of 18–25 nt; (3) compared the sequencing data to other database, including Rfam and Repbase; Rfam is a family database of non-coding RNAs (NcRNAs), including rRNA, tRNA, snoRNA, snRNA, miRNA and other non-coding Rnas. We selected Rfam database to annotate the small RNA sequences obtained by sequencing. rRNA, snoRNA, snRNA, tRNA and other non-mirNA sequences were found and removed as far as possible; (4) identification of miRNAs by comparing the precursors to the genome; (5) differential expression analysis of miRNAs; and (6) target prediction of miRNAs.The degradome cDNA library was constructed on the basis of the method  describeIndependently developed program ACGT101-DEG was used for degradome sequencing analysis and target genes prediction analysis was performed with CleaveLand  program.−1, the optimum temperature for color reaction was 80°C, and heating time was 15 min. Under these conditions, an excellent linear relationship between the concentration of glucose and absorbance was obtained and the correlation coefficient was R2 = 0.9991. Based on this optimized method, a standard curve was performed (R2 = 0.9984), suggesting the standard curve has a significant linear relationship. All samples were determinated according to this method and the results showed good repeatability and an accuracy. Each sample contained three replicates.Under the catalysis of α-hydrolase, starch could be converted to maltose, which could react with 3,5-dinitrosalicylic acid to produce brown 3-amino-5-nitrosalicylic acid. Then enzyme activity ofα-hydrolase were determinated according to its absorbance at the wave length of 410 nm. With the PNP β-G3 method, 1.0 g malt flour was used to extract β-hydrolase for 1 h. Then 0.2 ml PNP β-G3 and glucanase mixture were used as substrate, and the absorbance was determinated after the reaction for 10 min at the wave length of 410 nm. Anthrone colorimetric method was applied to determinate the soluble sugar content in maize seeds and the optimum conditions were as follows: the concentration of anthrone was 15 mg/mLIn this study, two maize inbred lines Yu82 with high germination rate and Yu537A with low germination rate were selected  to invesTo investigate the possible miRNAs related with maize seed vigor, we profiled miRNA accumulation and differential expressions during seed germination. Deep sequencing generated a total number 1166415, 1063732, 634,029, 1,885,446, 1,560,914, and 870,636 unique reads from different libraries E_6h, S_6h, E_53h, S_53h, SGA_53h and S_78h, respectively . Length Brassica oleracea  were discovered in the sample E_53h compared with that in the sample E_6h. More than half of the miRNAs were upregulated, suggesting that their complicated functions in the regulation of maize seed vigor. In Yu537A, a maize inbred line with low germination rate, a number of 6 miRNAs with significant expression differences  were identified in sample S_53h compared with that in the sample S_6h. After GA3 treatment, the number of significantly differentially expressed miRNAs increased to 22, including 6 extremely significantly differentially expressed miRNAs, suggesting that more miRNAs were induced to be upregulated to involve in the regulation of maize seed vigor by GA3. From 53 h to 78 h after imbibition, hardly any noticeable changes were observed in Yu537A, which was significantly lower than that in the sample SGA_53h after GA3 treatment. Increased number of significantly differentially expressed miRNAs demonstrated that more complicated regulation mechanism in the sample of SGA_53h, which also indicated that GA3 played an important role in improving the vigor of maize seeds.To identify the miRNAs associated with maize seed vigor, we analyzed significant miRNAs among different samples . In Yu82p value ≦ 0.05). Annotation and expression analysis of these targets  mappable reads were obtained, including 6,559,673 unique mappable reads, while 20,126,668 (82.88%) of total reads were transcript mapped reads, including 4,692,740 unique transcript mappable reads, were obtained. By combing the targets result of miRNA prediction with the degradome sequencing, finally 6,196 targets (transcripts) were obtained . Among o targets , 2 showe targets . This suTo gain a better understanding of functional roles of miRNAs, GO and KEGG analysis of putative targets were conducted. All targets were annotated using GO annotations to describe the functions of genes and gene products, while KEGG analysis is used to understand the pathways of annotated targets. p value = 0.0002] and Galactose metabolism  were discovered and shown in To decipher the complexity of regulatory networks closely related with the improved maize seed vigor, we further studied the significantly enriched pathways with the software ggplot2. Interestingly, two pathways named Glycan degradation [PATH: ko00511, Seed germination is a so complex biological process regulated by a larger number of genes. Based on the whole germination process, GA3 treatment was proved to promote the germination rate of maize seeds significantly by breaking seed dormancy and inducing a series of related genes. In this study, two typical maize inbred lines were selected and used for the miRNAs and degradome sequencing analysis, containing Yu537A with low seed vigor and Yu82 with high seed vigor, which suggested that the materials were novel compared with previous published researches. Two maize inbred lines were derived from Yuzong5, which is an very popular variety and widely planted in China. So the background of two lines were similar, and comparative analysis of miRNA and degradome data could truly reflect the molecular mechanism difference between two inbred lines in the process of seed germination induced by gibberellin (GA3).Seed vigor is a complex trait, including seed germination, seedling emergence and growth, as well as seed storability and stress tolerance, which is directly related with healthy plant seedlings . In our Analysis method of Multi-omics is often used to decode the molecular mechanism of various physiological activities in plants . Seed gehttps://www.ncbi.nlm.nih.gov/, GSE196738.The original contributions presented in the study are publicly available. This data can be found at: Permissions or licenses have been obtained to collect plant samples (two inbred lines Yu537A and Yu82). All methods about experimental research and field studies on plants were carried out in accordance with relevant guidelines and regulations.YJ and ZH designed the experiments and wrote the manuscript. BW and LT helped to carry out the experiments. LZ, SG, HZ, and LX managed the materials in the field and helped to perform the data analysis. All authors contributed to the article and approved the submitted version.This research was supported by grants from the National Natural Science Foundation of China (no. U1504315), Science and Technology Project in Henan Province (no. 212102110244), and the Scientific Research Foundation of Henan University of Science and Technology (no. 13480067).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.