Most of the spores investigated showed the presence of chlorophyll in the cytoplasm by red autofluorescence. Bryophyte spores can be stored dry at low temperatures, such as orthodox seeds, supporting the creation of bryophyte spore banks. However, the presence of storage lipids and chlorophyll in the cytoplasm may reduce spore longevity during conventional storage at −20 °C. Alternatively, cryogenic spore storage is possible.Understanding the desiccation and freezing tolerance of bryophyte spores is vital to explain how plants conquered land and current species distribution patterns and help to develop efficient ex situ conservation methods. However, knowledge of these traits is scarce. We investigated tolerance to drying (at 15% relative humidity [RH] for two weeks) and freezing (1 h exposure to liquid nitrogen) on the spores of 12 bryophyte species (23 accessions) from the UK. The presence of storage lipids and their thermal fingerprint, and the levels of unfrozen water content, were determined by differential scanning calorimetry (DSC). The presence of chlorophyll in dry spores was detected by fluorescence microscopy. All species and accessions tested tolerated the drying and freezing levels studied. DSC suggested that 4.1–29.3% of the dry mass is storage lipids, with crystallization and melting temperatures peaking at around −30 °C. Unfrozen water content was determined <0.147 g H The desiccation and freezing tolerance of propagules and vegetative tissues are vital traits that permitted plants to conquer land ,2,3 and 2O g−1 dry weight (DW)) and can be stored at sub-zero temperatures (ca. −20 °C) are considered “orthodox” in relation to their storage behavior and can be stored at the conditions of “conventional” seed banks [Salix L. and Populus L. are known to age and die very quickly in conventional seed banks, typically within two decades of storage, ageing even during a few decades of cryogenic storage [Cuphea P. Browne species and approximately 26% of the native Hawaiian flora [Ulmus glabra Huds.) or beech (Fagus sylvatica L.) also have short lifespans during conventional storage [Salix sp.pl. and Populus sp.pl.), or certain combinations of storage lipids within the dry/glassy cytoplasm that crystallize at the storage temperatures set for orthodox seed storage , are the main issues for the short longevity of many of these seeds , and so storage . Other ean flora ,31,32,33 storage . The preiewed in ). Howeveiewed in . In this context, the spores of lower plants have been highlighted as unique unicellular models to study stress tolerance , which cFor this research, we have empirically measured and quantified the desiccation and freezing tolerance, as well as other traits involved in the potential storability, of bryophyte spores from diverse temperate species. We have used a multidisciplinary cryobiotechnological approach that involves biophysics, ecophysiology, cytology and in vitro technology, applying innovative and non-invasive techniques developed for seed science to bryophyte spores. The hypotheses to test were as follows: (1) bryophyte spores from a variety of species tolerate high desiccation (to 15% RH), (2) dry bryophyte spores from a variety of species tolerate exposure to sub-zero temperatures (liquid nitrogen (LN) exposure), and (3) dry bryophyte spores tolerate freezing because there is no “freezable water” in their cells when dried. We also expected that, analogously to seeds and fern spores , dry bry2O g−1 DW as measured in the four species scanned by Differential Scanning Calorimetry (DSC), for which enough spores were available for WC determinations. From the 23 accessions (acc.) tested, 16  reached 100% germination and seven accessions reached less than 100% germination, four of which  had germination <50%. For most species tested, germination was consistent among accessions. For example, Bryum capillare Hedw. and Polytrichum formosum (Hedw.) G.L. Sm. showed 100% germination in all accessions tested and Funaria hygrometrica Hedw. showed 100% germination in two accessions and near 75% in the third accession tested. Germination was largely different between accessions only for Kindbergia praelonga (Hedw.) Ochyra (100% in acc. 1 and 21% in acc. 2). First germinated spores were detected four days (96 h) after sowing in all the species tested, except for a B. capillare (acc. 3) and Ceratodon purpureus (Hedw.) Brid. for which germination started one day (24 h) after sowing, and for Orthodontium lineare Schwägr. (acc. 2) for which germination started eight days (192 h) after sowing. These observations were supported by the time to reach 50% of the final germination percentage (t50) , whereas most species/accessions ranged between 3 and 6 days.Spores from all bryophyte species (12) and accessions (23) tested were germinated on Knop medium after being harvested and dried to 15% RH for 2 weeks , indicatge (t50) , which wK. praelonga (acc. 2) and Marchantia polymorpha L. for which spores did not germinate. From the 23 accessions tested, 11  reached 100% germination regardless of LN exposure, five accessions showed a significant decrease  in germination from 100% to <90% following exposure to LN, and seven accessions reached less than 100% germination in both treatments. In this last group, only three accessions showed a significant decrease in germination  following LN exposure  P. Beauv., B. capillare (acc. 1), Dicranum scoparium Hedw., F. hygrometrica (acc. 1 and 3), K. praelonga, Leptobryum pyriforme (Hedw.) Wilson and O. lineare (acc. 1)) or O. lineare acc. 2, which showed significantly shorter t50 in LN-exposed spores. These results support a high degree of freezing tolerance in the spores of most bryophyte species and accessions tested.After exposure to LN temperatures (−196 °C), all species and accessions showed some degree of germination, except for one of the accessions of exposure . In summ2O g−1 DW) showed different phase changes (first-order transitions) between −120 and 0 °C in both the melting (P. formosum samples in −1). During the melting scan, the melting profile of these crystallized TAG included different endothermic and exothermic peaks related to the different crystal forms [D. scoparium scan showed no transitions in both the cooling and melting scans . DSC determines changes in the heat capacity of a sample over a temperature scan, so the temperature (°C) in which crystallization (exothermic events) and melting (endothermic events) transitions occur and the enthalpy associated with these events can be measured . Dry bry melting a and coo melting b scans. al forms . These wal forms . Only thng scans a,b. In ang scans , which wn spores ,37,47 anP. formosum from plots of enthalpy  was 329 J g−1 H2O  as a function of the water content of the sample, and moved towards 0 °C. These events were related to ice melting a or wateenthalpy , calculain −1 DW . Once sp g−1 H2O . The amo g−1 H2O . UnfrozeO g−1 DW , indicatO. lineare and P. formosum, respectively . Based ectively .Dicranella heteromalla (Hedw.) Schimp., Hipnum cupressiforme Hedw., K. praelonga, Orthotrichum affine Schrader ex Bridel, Tortula muralis Hedw.); however, there were some species for which some variation in the observations was found  or spore age in B. capillare (aged spores could have shown a lack of red autofluorescence).Spores collected from 36 of the accessions harvested, corresponding to 21 different species , were obas found . For exarescence c,d, whil2O g−1 DW) and, in most cases, a brief (1 h) exposition to LN temperatures. Tolerance of the dry spores to sub-zero temperatures was likely possible due to the removal of the fraction of intracellular water that can freeze (<0.147 g H2O g−1 DW) and the vitrification  of the cytoplasm upon drying. These results, in combination with previous research on the desiccation and freezing tolerance of bryophyte spores  and the presence of chloroplasts. This combination has not been previously observed in seeds and fern spores [The results of this study demonstrate that bryophyte spores from a diversity of species (12) growing in a temperate environment (UK) are highly desiccation- and freezing-tolerant, surviving, in all cases, two weeks of drying at 15% RH  in the rest of the species tested after drying  suggests different possibilities: (1) spore recalcitrance ; (2) short spore longevity in the dry state  and the time for which they were stored dry at 4 °C until the beginning of the germination experiments ); (3) the presence of some form of dormancy that was not broken during the in vitro germination in the culture medium and (4) the low initial quality of the spores harvested. Our data do not support that the spores used in these experiments were recalcitrant (desiccation-sensitive). For example, recalcitrant seeds (or pollen or spores) are those that do not tolerate drying <50% RH or when water content decreases <0.10 g water/g dry weight [−1 DW, plus several weeks of dry storage at such conditions. Hence, we cannot consider, analogously to seeds, that they are “recalcitrant”. This is also supported by observations made by other authors, who found that some of the species with low germination after drying in our experiment are indeed highly tolerant to drying . In addP. formosum by Fourier transform infrared spectroscopy (FT-IR) [These results of the desiccation tolerance of bryophyte spores based on survival under the stress induced by the removal of water are also supported from a physicochemical point of view. For example, bryophyte spores dried at room temperature to low RH (15–50%) not only survive drying but also present a vitrified cytoplasm . This has been measured for  (FT-IR)  and dete (FT-IR) ,58,59,60 (FT-IR) ,47. Finally, from an ecological point of view, it is important to highlight that the spores used in this experiment were from species of temperate origin, where dispersal usually occurs under dry conditions   and, oncM. polymorpha, A. undulatum and acc. 3 of K. praelonga had germination < 25%). However, it would be interesting to check in future experiments if there was some interaction between the intense drying treatment used (15% RH) and the exposure to the ultra-low temperatures of LN. For example, damage due to over-drying has been suggested for seeds dried to very low moisture conditions and stored at cryogenic temperatures [The high tolerance of bryophyte spores to freezing temperatures (around −30 °C), was extensively described by Bernard Otto van Zanten in his experiments on the long-distance dispersal of mosses and liverworts from the southern hemisphere  that the water content level at which ice melting events cannot be detected at the cooling and warming rates used was 0.147 g H2O g−1 DW. Our dry spores had water content far below this limit (0.005 to 0.066 g H2O g−1 DW). This indicates that it is likely that the fraction of intracellular water in the dry bryophyte spores tested did not have the freedom to aggregate, nucleate into ice crystals and grow, and may be strongly associated with the macromolecules forming the amorphous solid (glass) at which the cytoplasm of the dry spores was found (see previous subsection). The levels of unfrozen water content measured were relatively low compared to those found in multicellular systems such as seeds and seed embryos  but appear in the range of other unicellular systems such as fern spores [−1 H2O, and in plant systems that tolerate drying and freezing, it is expected that lower enthalpies reflect the presence of solutes or matrices that limit ice formation or promote recrystallization [−1 H2O, very close to that of pure water and higher than detected in fern spores (around 230 J g−1 H2O) or other dry systems (around 150 J g−1 H2O) [2O g−1 DW [The tolerance of the dry spores to sub-zero temperatures  is also n spores ,40. The This study has shown that the spores of four out of five bryophyte species tested presented storage lipids (TAG) in the cytoplasm with crystallization and melting signals between 0 and −120 °C. Based on the temperature and enthalpy of melting of the main peak detected in our DSC scans , TAG conB. capillare and P. formosum). Chloroplast/chlorophyll presence in dry seeds and spores has also been related to a reduction in longevity during storage through oxidative mechanisms induced by the activity of the photosystems in the dry state, but, generally, these propagules are not loaded with many storage oils [Interestingly, these relatively large amounts of storage lipids appeared in combination with the presence of green plastids loaded with chlorophyll in some species  that seBryophyte conservation efforts have recently accelerated, especially after an increase in the number of species included in the IUCN Red List of threatened species. For example, in the European continent, 22.5% of the assessed species are endangered  . Bryophy2O g−1 DW appears as an alternative (or a back-up solution) for the long-term conservation of these propagules. In this respect, we have shown the high tolerance of the dry spores of many temperate species to LN exposure. Additionally, the safe LN storage of dry bryophyte spores for up to 180 days has been demonstrated [F. higrometrica, Dicranum sp.pl., Bryum sp.pl.).The high desiccation and freezing tolerance of bryophyte spores indicates that these propagules could also be potentially stored in ex situ conservation collections ,23,43, anstrated ,53, inclBryophyte samples were collected in different locations and from varying types of habitats, and each collection was considered a different accession . We focuOpen Eppendorf tubes with sporophytes were placed in a drying room (set at 15 °C and 15% RH) for two weeks to allow sporophytes and spores within them to dry. At the end of this drying treatment, Eppendorf tubes were closed, and samples were stored in a fridge (4 °C) until the beginning of the experiments (within 2 months of harvest). To conduct the germination and calorimetry experiments (see below), the sporophytes were slightly crushed with the tip of a sterilized tweezer inside the Eppendorf tubes and then homogenized with a vibrator. This process allowed the release of the dry spores in the Eppendorf tubes, which was monitored using a microscope. Only those samples containing a visible quantity of spores, or more than five mature sporophytes, were chosen for the germination and calorimetry experiments.Other plant tissues (gametophytes) were also collected for identification using sterilized tweezers and collecting paper packets, placed in the drying room for two weeks, at the end of which they were sent to the Natural History Museum (NHM), London, for identification using the taxonomical expertise kindly provided by Dr. Silvia Pressel. The 68 accessions collected corresponded to 25 confirmed species . Atrichum undulatum, Bryum capillare, Ceratodon purpureus, Dicranum scoparium, Funaria hygrometrica, Kindbergia praelonga, Leptobryum pyriforme, Marchantia polymorpha, Mnium hornum Hedw., Orthodontium lineare, Polytrichum formosum and Tortula muralis), as they were the only species/accessions that had clearly released spores from the sporophytes. Two conditions were tested for each species/accession: (1) spore germination after initial desiccation (15% RH) and without liquid nitrogen (LN) exposure and (2) spore germination after desiccation and 1 h of exposure to LN.Germination tests were performed in 12 of the species (23 accessions) collected 2. To produce 1 L of Knop medium, 10 mL of each stock solution was taken; then, 0.3 g FeSO4 was added, and this was finally topped up with distilled water. To solidify the culture medium, it was added to 1.2% (w/v) agar (12 g/L). The medium was sterilized by autoclaving at 120 °C and 1 atmosphere for 25 min. After sterilization, and when the medium was still liquid, approximately 10 mL of the Knop medium was poured into 6-mm-diameter Petri dishes for its solidification. Plates were stored in the fridge until use. Knop medium was used as the culture medium for the bryophyte spore germination tests, following the recipe of Reski and Abel . In detaFor the germination tests, the spores extracted (as explained above) were divided into two tubes, a 1.5 mL Eppendorf tube (for the samples before LN exposure) and a 2 mL cryovial (which was used for the LN exposition treatment). Spores without LN exposition were sown onto Petri dishes containing the Knop medium by adding 1 mL of distilled water inside the Eppendorf tubes using a pipette, suspending the spores, pouring 500 mL of this suspension into two plates and spreading the liquid over the medium. For the LN treatment, spores were exposed to LN for 1 h and then thawed on the lab bench at room temperature (around 21 °C) for 15 min, and finally sown onto Petri dishes using the same procedure indicated above. All the procedures described were conducted under a laminar flow hood to avoid cross-contamination with other species. Petri dishes containing the spores sown on medium were placed in an incubator  and checked for germination.Germination was scored using a light microscope the day after sowing and every 3-4 days until germination was steady or it was impossible to determine if new spores were germinating (due to large protonema growth). Germination was measured selecting a random area of the Petri dish and determining the number of spores germinated and ungerminated, and the proportion of germinated spores per treatment was calculated. This random counting was performed on at least 200 spores per Petri dish. p < 0.05.In order to compare whether the germination of the spores was significantly different before and after exposition to LN, we ran a test of equal proportions for each species and accession. After checking that the proportion of germination between dishes was similar, we pooled the spore germination counts from both Petri dishes used for the germination tests. Proportions were considered to follow a binomial distribution, so a test of equal or given proportion was carried out to quantify whether the proportions (probabilities of success) in a group were the same, or whether they were equal to certain given values . The significance level was taken as 0.05 so that the null hypothesis of no difference was rejected if −1). DSC determines changes in the heat capacity of a sample when cooled and/or warmed and is very useful to detect crystallization and melting transitions , including those occurring in biological cells and tissues [B. capillare, D. scoparium, O. lineare, P. formosum), for which at least 0.2 mg of spores were collected. To investigate the lipid and water crystallization and melting properties of the spores, a total of 20 scans were run: 2 for B. capillare, 1 for D. scoparium, 3 for O. lineare and 14 for P. formosum.The calorimetric analysis of the bryophyte spores was conducted by differential scanning calorimetry (DSC) using a Mettler Toledo DSC-1 , calibrated for temperature with indium (156.6 °C) standards and for energy with indium . HowP. formosum spores were produced in sufficient quantities to allow multiple DSC scans with samples at different moisture content. For these spores, moisture content was manipulated in hermetic chambers using saturated salt solutions of LiCl (which provided 11% relative humidity (RH)), NaCl (75% RH), KCl (81% RH) and KNO3 (94% RH) or distilled water (100% RH) for 2, 6, 8, 20, 26 h. Spore moisture content was manipulated in spores sitting in an open DSC pan, which was sealed with its lid after the moisture manipulation. DSC scans were run immediately after closing the DSC pan. Once the DSC scans were finished, DSC pans were pierced with sterilized tweezers and placed in an oven set at 103 °C for 17 h to allow the complete drying of the spores, as per standard procedures for drying seeds [Spores were obtained from the dry capsules stored at 4 °C , following the same procedure as the one used for germination tests. Between 0.2 and 2.2 mg of spores per species were placed inside a DSC pan, which was subsequently sealed with a lid and weighed to determine the fresh weight of the spore sample. Weights were taken using a Mettler Toledo XPR Microbalance . DSC experiments were mostly conducted at a single moisture content, which was that reached after the spores were initially dried at 15% RH . Only ng seeds . After d−1. The onset temperature of melting and freezing transitions was determined from an intersection between the baseline and a line drawn from the steepest portion of the current transition peak. The enthalpy (∆H) of the transition was determined from the area included by the peak and the baseline. Enthalpies of endothermic and exothermic events are expressed on a per g dry weight basis. All analyses were performed using STARe evaluation software . As the water content (WC) progressively increased in spores of P. formosum (the spores scanned at diverse water content), the transition size increased, which increased the enthalpy of the main melting event (∆Hm) and moved towards 0 °C. This increase in ∆Hm was attributed to the melting of ice crystals. However, the temperature and ∆H of transitions at very low WC did not change with slight increases in WC and so were attributed to triacylglycerols (TAG) [−1 H2O basis. The intersection between this line and the horizontal line attributed to the TAG transitions was the exact point below which melting or freezing transition could not be observed [P. formosum, we assumed that all phase transitions measured in the dry spores were related to TAG. TAG with β’ melting transitions  . The sloobserved . BecauseChlorophyll and chloroplast presence in bryophyte spores were examined by optical and fluorescence microscopy ,75. Spor


Introduction: Phenotype predictions of beef eating quality for individual animals could be used to allocate animals to longer and more expensive feeding regimes as they enter the feedlot if they are predicted to have higher eating quality, and to sort carcasses into consumer or market value categories. Phenotype predictions can include genetic effects , predicted from genetic markers, as well as fixed effects such as days aged and carcass weight, hump height, ossification, and hormone growth promotant (HGP) status.Methods: Here we assessed accuracy of phenotype predictions for five eating quality traits  in striploins from 1701 animals from a wide variety of backgrounds, including Bos indicus and Bos taurus breeds, using genotypes and simple fixed effects including days aged and carcass weight. The genetic components were predicted based on 709k single nucleotide polymorphism (SNP) using BayesR model, which assumes some markers may have a moderate to large effect. Fixed effects in the prediction included principal components of the genomic relationship matrix, to account for breed effects, heterosis, days aged and carcass weight.Results and Discussion: A model which allowed breed effects to be captured in the SNP effects  tended to have slightly higher accuracies (0.43–0.50) compared to when these effects were explicitly fitted as fixed effects (0.42–0.49), perhaps because breed effects when explicitly fitted were estimated with more error than when incorporated into the (random) SNP effects. Adding estimates of effects of days aged and carcass weight did not increase the accuracy of phenotype predictions in this particular analysis. The accuracy of phenotype prediction for beef eating quality traits was sufficiently high that such predictions could be useful in predicting eating quality from DNA samples taken from an animal/carcass as it enters the processing plant, to enable optimal supply chain value extraction by sorting product into markets with different quality. The BayesR predictions identified several novel genes potentially associated with beef eating quality. Historically the value of breeding cattle has been principally based on improved growth, milk and fertility traits and correlated measures of meat quality and quantity. Recent years have seen a shift from producer-driven to consumer-driven beef production, with significant emphasis placed on consumer satisfaction and interest in eating quality indicators, with incentives implemented by beef brands for improved compliance and product quality.Genomic selection  offers tPhenotype predictions, which predict the eating quality of beef from an individual animal, may also be of interest, for example to sort carcasses into consumer or market value categories, or to allocate animals to longer and more expensive feeding regimes as they enter the feedlot if they are predicted to have higher eating quality. Phenotype predictions could include genetic effects, predicted from genetic markers, as well as estimates of fixed effects such as days aged and carcass weight. Our aim here was to determine the most accurate method for predicting phenotypes of beef eating quality traits from genotypes and other independent factors such as carcass weight and days aged, using consumer eating quality data from the striploins of 1701 beef cattle.The phenotype data consisted of eating quality traits from striploin samples from 1701 cattle collected from 65 cohorts between 1997 and 2019 in Australia. The eating quality traits were scores for tenderness (TENDER), juiciness (JUICY), flavour (FLAVOR), overall liking (OVERALL), and MQ4 score formed by weighting the four sensory scores, MQ4 = 0.4 × TENDER + 0.1 × JUICY + 0.2 × FLAVOR + 0.3 × OVERALL, obtained using optimum linear discriminating function . All samBos indicus), 285 Angus, 274 Hereford, 38 Shorthorn, 72 Holstein, 23 Jersey (Bos taurus), 100 Belmont Red, 83 Santa Gertrudis (composite), 121 crossbred and 444 with unknown breed. The animals included 1,319, 345 and 37 steers, heifers, and bulls, respectively, although sex was completely confounded with contemporary group. As these were largely commercial animals, little pedigree was available, however breed information could be reconstructed by genotype as described below.The breed background of the animals was diverse, including 261 Brahman  using findhap4  of the genomic relationship matrix  of <0.01 and those departing from the Hardy-Weinberg equilibrium at findhap4 . The refith SNP is assumed to come from one of four distributions , where , all with a mean of 0 but with different variances.The BayesR approach  with fousR model  to have In strategy 1, the accuracy of predicting phenotype from genomic breeding values only was assessed. A mixed linear model was fitted, including fixed effects of PCs and heterosis, for each of the 5 eating quality traits separately using BayesR . The hety is phenotype; cg is a fixed contemporary group effect (65 groups), days_aged is a covariate of days aged after slaughter , carcass_weight is a covariate on carcass weight , PC1 to PC4 are the first 4 principal components of the genomic relationship matrix, fitted to control for Bos indicus content and breed, heterosis is a regression on marker heterozygosity, Z is matrix allocating genotypes of individuals to SNP effects, g is a vector of SNP effects, and e is a random residual with where Next, phenotype was predicted with a genomic estimated breeding value in a validation set asdays_aged from model 1.In strategy 2, phenotype predictions were made first by fitting the model 1 to the training set like strategy 1, but in a validation set phenotype was predicted including all estimates of fixed effects as below:In strategy 3, exactly the same model as strategy 1 and 2 (Model 1) were used for the training set, however for a validation set, the phenotype was predicted only from genetic effects and fixed effects that were derived from genotypes  as below:y, cg, days_aged, carcass_weight, Z, g, and e are the same as Model 1.Finally, in strategy 4, model 5 was implemented in training set as below:The assumption here is that all genetic effects, including breed effects, are captured by the SNP effects when PCs are not explicitly fitted. Next, phenotype was predicted with a genomic estimated breeding value in a validation set using Model 2.To evaluate accuracies of phenotype prediction from the 4 strategies, five-fold cross-validation was used, with random grouping of animals such that all groups have approximately equal size. In each rotation of the cross validation the phenotypes of 1 group were masked and the remaining 4 groups were used to estimate the GEBV of the group without phenotypes. The accuracy of phenotype prediction for each group was calculated as the Pearson correlation between predictions and raw phenotypes of animals for which their phenotypes were masked. Correlations were averaged across 5 groups and standard error (SE) was calculated as the standard deviation divided by the square root of the number of groups i.e., For each cross-validation fold, the BayesR model simultaneously provides estimates for the SNP effects  to low (0.23), The predictions of eating quality phenotype based on GEBV alone (Strategy 1) was modest, p-value < 8.7 × 10−5).We conducted a follow-up study of potential genes affecting eating quality in cattle. We used the nearest genes within a window of 1 Mb upstream or down stream of top 20 SNPs with highest posterior probability of inclusion for all five eating quality traits. The most strongly enriched pathway identified by Gene Ontology analysis of genes associated with eating quality was “dopamine metabolic process”  and Calpastatin (CAST) genes had the highest probability of inclusion in the model, (MOXD1) had the highest probability of inclusion. However, the same SNP close to the MOXD1 were also included in the prediction model for OVERALL and MQ4 , CAPN1, Kinesin Family 13A (KIF13A) and Apolipoprotein B (APOB) genes were also included in the prediction model  status. Interestingly, adding the estimated effect of carcass weight and days aged did not improve the accuracy of phenotype prediction (Strategy 2), likely because these effects were relatively poorly estimated in our study due to the large number of cg groups and confounding of these effects with the cg group effect . ExpliciCAST and CAPN1 genes had the highest probability of inclusion in the model , the predictive information from PC1 would be irrelevant, and the prediction accuracy would default to that from the GEBV alone .For TENDER, the he model , that isy . These s cattle , and theia grade . It is iKIF13A) is in a pathway associated with skeletal muscle cells increasing insulin signalling, glucose uptake, and maximal oxygen consumption (APOB) is a building block of a type of lipoprotein called a chylomicron. As food is digested, chylomicrons form to carry fat and cholesterol from the intestine into the bloodstream.The results implicate some interesting candidate genes for eating quality. Kinesin Family 13A  models  as well


We also showed that the duodenal subtype of SBA exhibited molecular features more similar to gastric cancer whereas jejunal subtype of SBA more similar to colorectal cancer. Especially, we predicted specific drugs for SBA based on differential gene expression signatures between malignant cells and normal epithelial cells of SBA, and verified more potent inhibitory effects of volasertib and tozasertib for SBA cancer cells than conventional drugs of SBA at the same concentration, which provides new clues for treatments of SBA. In summary, our study provides a blueprint of the molecular signatures of both tumor cells and tumor microenvironment cells in SBA and reveals potential targets and drug candidates for its clinical treatments.Small bowel adenocarcinomas (SBAs) are rare malignant tumors with a high mortality rate, and their molecular characteristics are still largely unexplored. Here we performed single-cell RNA sequencing for tumor samples from 12 SBA patients and predicted drug candidates for SBA. We identified four prevalent subtypes of malignant cells with distinct signatures including cell cycle program, mitochondria program, metabolism program and epithelial–mesenchymal transition (EMT) program. The progression relationships of these four subtypes of malignant cells were also revealed, which started from the cell cycle program, through the mitochondria program and then progressing into either the metabolism program or the EMT program. Importantly, ligand–receptor interaction pairs were found to be specifically enriched in pairs of EMT-program malignant cells and highly exhausted CD8 Although the small intestine accounts for > 75% of the length of the entire gastrointestinal tract, small intestinal cancers only account for 2% of all gastrointestinal tract tumors2, which indicates unique mechanisms of carcinogenesis in the small intestine. Relative to gastric cancer (GC) or colorectal cancer (CRC), the incidence of SBA is rare, but the intrinsic reasons for this are still not clear3. Rapid epithelial cell turnover, fast dietary material passage, low bacterial loads, high lymphoid aggregate levels and a relatively alkaline environment may partially explain the low incidence of SBA4. Due to the rarity of pathological studies on SBA, chemotherapy regimens for SBA are usually based on the treatment of CRC as a reference, and specific drugs for SBA are lacking. Recently, immunotherapy-related indicators such as DNA mismatch repair-deficient/microsatellite instability-high (dMMR/MSI-H) status, PD-L1 expression and a high tumor mutational burden (TMB) were reported to appear at higher frequencies in SBA than in other gastrointestinal cancers, and pembrolizumab or nivolumab, with or without ipilimumab, can be used as second-line treatment options for advanced dMMR/MSI-H SBA more widely5.Small bowel adenocarcinomas (SBAs) are rare gastrointestinal cancers with unfavorable prognoses. More than half of SBAs originate in the duodenum, while 25%–29% arise in the jejunum, and 10%–13% arise in the ileum8. However, there are few studies on the SBA transcriptome, and more specific markers of SBA are required for its diagnosis and prognosis. Previous genomic studies of SBA were mainly based on bulk tissue sequencing data, which reveal the average features of tumor cells and microenvironment cells in tumor tissues, and the intratumoral heterogeneity and cell type composition of SBAs have not been comprehensively studied9,10.Genomic studies have revealed different characteristics of SBA compared to GC and CRC+ T cells. Using the transcriptome signatures, we identified that the duodenal subtype of SBA exhibited molecular features more similar to GC whereas jejunal subtype of SBA more similar to CRC, which provides new clues for clinical information of SBA. Finally, drug candidates for the treatment of SBA were also predicted based on differential gene expression signatures between malignant cells and normal epithelial cells. Volasertib and tozasertib were verified more efficient for killing SBA cancer cells than frequently-used clinical drugs of SBA in vitro, which may benefit drug discovery and the clinical treatments of SBA.Here we performed single-cell RNA sequencing (scRNA-seq) for samples of multiple sites from 12 SBA patients. The transcriptomic features of malignant cells were comprehensively analyzed, the intratumoral and microenvironment heterogeneity of SBA were systematically revealed, and inter-regulatory processes were inferred from ligand–receptor interactions. In detail, we identified 4 prevalent subtypes of malignant cells with distinct gene expression signatures including cell cycle program, mitochondria program, metabolism program and epithelial–mesenchymal transition (EMT) program. The progression trajectory of these 4 subtypes of malignant cells started from the cell cycle program, through the mitochondria program and then progressing into the metabolism program or the EMT program. Malignant cells with EMT features were also found to contribute most to the exhaustion of CD812. Overall, in both the STRT and 10× Genomics datasets, 7 main cell types were identified according to well-known markers . However, there were essentially no significant differences in cell type fractions between tumor tissues and adjacent normal tissues, probably due to the limited sample number , and the other epithelial cell clusters as normal epithelial cells Fig. . Despiteent Fig. . The difnts Fig. . We alsonts Fig. . We foun15. We selected all the normal epithelial cells from these datasets and evaluated the corresponding score of each gene set. In terms of absorption and transport functions related to lipids, bile salts, vitamins, water, amino acids, organic solutes, sugar, nucleotides, inorganic solutes and metal ions, the malignant cells from the duodenum, jejunum, and ileum showed declined functions relative to normal intestinal epithelial cells, and this trend was consistent throughout the small intestine  method was applied to elucidate feasible expression programs with co-expressed genes. Based on all of the malignant cells in the STRT dataset, we generated 50-dimensional principal components and extracted 7 co-expressing programs Fig. . The enrTo further illustrate the detailed characteristics of the malignant SBA cells, we focused on the malignant cells of P5 and P6, who were patients with duodenal cancers with metastatic lymph node tumors. For the tumor sampling sites of P5 and P6, the negative and positive poles of the top principal components obtained from principal component analysis (PCA) were taken  in SBA, we enriched fibroblasts from 2 patients in the STRT dataset , LAYN and CTLA4  of T/NK cells from the 10× dataset and performed downstream analysis. Twelve clusters of T/NK cells were identified, which included one cluster of NK cell, four clusters of CD8res Fig. . Among tres Fig. . In geneues Fig. . As expe+ T cells and exhaustion status. Using the cytotoxicity and exhaustion gene sets, we showed that the T cells from tumor tissues presented significantly higher expression of both the cytotoxicity and exhaustion signature genes than T cells from normal tissues  and ex-low (lowly exhausted signature) groups  were enriched in ex-high T cells, as receptor-providing cells. HAVCR2 (encoding TIM3) was one of the main receptors of ex-high T cells, and the IHC results verified that the expression of TIM3 at the protein level was clearly higher in tumor tissues compared with adjacent normal tissues  were mainly enriched in EMT-program and mitochondria-program malignant cells, which corresponded to the more malignant cells in our study. We also used the EMT and highly exhausted CD8+ T cell gene sets to explore the relationship with highly exhausted T cell interactions in the 10× datasets of GC and CRC30. We isolated malignant cells and T cells from these datasets and divided them into cells with high or low expression of the EMT or exhausted T cell signature  method of CRC from the TCGA dataset were used, and we identified the correlation of expressed genes at RNA and protein levels with the MUC1 RNA expression  is not significantly different from the ratio of fibroblasts in unenriched samples (242/2439). Although the attempt of enriching fibroblasts is unsuccessful, the quality of the cells  is not influenced and the cells can still be used for downstream analyses. There were four common fibroblast subclusters, cluster C1, cluster C2, proliferative fibroblasts and myofibroblasts in both the STRT and 10× datasets, which represents the universality and representativeness of the identified fibroblast subclusters. Only the common fibroblast subclusters were analyzed further in our study to make our conclusions more reliable. The identified fibroblast subcluster C1 was also reported in other scRNA-seq studies, such as a single-cell inflammatory bowel disease study and a single-cell colorectal cancer study5; therefore it should be further considered whether these therapies are suitable for duodenal cancers. Although a previous study showed a closer relationship of SBA with CRC than with GC at the CNV level, we think that different dimensions of data may have different features, and it is reasonable about the inconsistency between the CNV-related features and the gene expression-related features. In that study, SBA was similar to CRC at the CNV level, but the differences of individual patients may result from the different genomic mutations. Furthermore, previous SBA studies were based on bulk sequencing, in which signatures of malignant cells from SBA may be covered up by tumor microenvironment cells. Combing with our finding that proportions of fibroblasts increase significantly in tumor samples of SBA, we think that microenvironment cells of tumor samples influence the evaluation of CNV to some extent. As for our single-cell transcriptome dataset, we separated epithelial cells from all the microenvironment cells of GC, SBA and CRC, and selected out malignant cells from normal epithelial cells to represent more specific signatures of different cancer types. In addition, we also divided SBA into duodenal, jejunal and ileal malignant cells, which makes the comparison more accurate. As the spatial order of the gastrointestinal tract from stomach, duodenum, jejunum to colorectum, we found that duodenal malignant cells were more similar to gastric malignant cells based on HVGs, which is corresponding to the spatial distance. As for SBA and CRC, combining with signaling pathway analysis, the EMT signatures of SBA were found to be weaker than those of CRC at the global transcriptome level, which may explain the initial EMT signature identified in SBA and a more mature EMT signature in CRC. Based on the identified complex molecular subtypes of CRC and potential molecular subtypes of SBA, more studies are needed to reveal the EMT signature in gastrointestinal cancers in the future.Based on the current situation of limited treatments for SBA, we utilized our data and published data to evaluate and screen for potential drugs. We compared GC and CRC with SBA and found that duodenal subtypes and GC were more closely related at the transcriptomic level. It should be noted that treatments for CRC are widely referenced for the treatment of SBA50 values were integrated to predict potential drug candidates of SBA. There are relatively few SBA patients, and thus large-scale preclinical or clinical trials are lacking. By using published omics and drug response data, we screened out 14 potential drugs for SBA treatment. The cell viability experiment confirmed that volasertib and tozasertib exhibit more powerful inhibitory effects for the duodenal cancer cell line than conventional drugs for SBA. The high sensitivity and specificity of tozasertib for SBA were also identified in our analysis. Volasertib and tozasertib were predicted through combing our scRNA-seq data and published data and verified to be effective in a duodenal cancer cell line, which may benefit SBA patients. More studies and clinical data for these drugs are worth performing in the future.Moreover, gene expression profiling data and drug response IC+ T cells. The exhaustion marker, TIM3 was highly expressed in T cells of tumor tissues compared to adjacent normal tissues, which is also confirmed by the IHC staining. However, more functional experiments for SBA are needed in the future.We performed a large set of verification experiments to confirm our major conclusions. First, the cell viability assay in the HUTU-80 cell line for the predicted drugs were performed. Inhibitory effects of volasertib and tozasertib are stronger than those of classical clinical drugs for SBA such as oxaliplatin and irinotecan when used at the same concentration, which indicates that the prediction of drug candidates is reliable and effective. Second, we used the IHC staining to verify many markers identified in the study. As for malignant cells, we found several DEGs or markers in malignant cells. MUC1 as a DEG of malignant cells, proved to be expressed more highly in malignant cells than normal epithelial cells by the IHC staining. ERBB2 is found to be highly expressed in malignant cells in SBA, which is also reported to be highly expressed in SBA, and the IHC staining of ERBB2 also confirmed our conclusion. As for fibroblasts, we used the deconvolution analysis to reveal the higher proportion of fibroblasts in tumor tissues compared to adjacent normal tissues, and we used the marker of fibroblast, TAGLN, to verify the higher proportion of fibroblasts in tumor tissues compared to normal tissues. We also found that the ratio of myofibroblasts increases in tumor during SBA tumorigenesis, and the IHC staining of ACTA2, which is a well-known marker of myofibroblasts, confirmed the higher proportion of myofibroblasts in SBA tumor tissues. The DEGs of fibroblasts from tumor tissues compared to adjacent normal tissues were also identified, and one of them, RCN3 is also verified by the IHC staining. As for T cells, the exhaustion signature was found in CD8In summary, our work systematically studied the characteristics of malignant cells and tumor microenvironment cells in SBA, revealing signatures of pathological phenotypes, the connections between malignant cell and microenvironment cell signatures and drug candidates with related targets. SBA is rare but has high mortality rate, and large-scale clinical trials of SBA are difficult to carry out. The enhanced cell–cell interactions between EMT-program malignant cells and highly exhausted T cells may provide clues regarding immunotherapies for SBA. The prediction and verification of approved drugs that may be used to treat SBA could guide clinical treatment decisions.The research was approved by the Ethics Committee of Peking University Third Hospital (License# IRB00006761-M2016170). Informed consents of this study were signed by all the patients. Small bowel cancer tissues were sampled from 12 small bowel cancer patients, who received small bowel resection in Department of General Surgery, Peking University Third Hospital. Normal mucosae at least 5 cm from tumor border were sampled, and lymph node metastases from 3 patients were also sampled. Sampling was performed immediately after surgical resection to retain cell activity.+ fibroblasts.Both normal mucosae and cancer tissues were cut into pieces and digested with the mix of collagenases , followed by incubation on a shaker at 37 °C until no visible pieces were found in the digestion solution. CD90 antibody  was used in FACS to screen CD9012. The library was prepared using a KAPA Hyper Prep Kit (KAPA Biosystems) and sequenced using the 150-bp paired-end sequencing method on Illumina HiSeq 4000 platforms. As for the 10× method, single cell separation, DNA amplification and library construction were performed following the manufacturer’s guidelines of chromium single-cell sequencing technology from 10× Genomics. The scRNA-seq libraries were constructed using the Chromium Single Cell 3ʹ Library and Gel Bead Kit V3. Finally, the prepared libraries were sequenced on Illumina NovaSeq 6000 platforms.The modified STRT method was applied to perform cDNA amplification and library construction, which has been described in detail in previous studiesAbout 200 ng genomic DNA was extracted and fragmented by sonication to 150–200 bp length. Then end repair and ligation by adaptors were performed, fragmented DNA with adapters were subjected to PCR amplification with the mix of NEB universal primer, NEB index primers and 2× KAPA HiFi HotStart ReadyMix . Finally, cDNA libraries were prepared to be captured by WES using SureSelectXT Human All Exon v6 kits .Normal mucosae and cancer tissues were fixed in 10% neutral buffered formalin for 24 h, and then embedded in paraffin. Paraffin-embedded tissues were sectioned into 5 μm-thick slices. The slides were incubated with primary antibody including anti-TAGLN antibody , anti-ACTA2 antibody , anti-RCN3 antibody , anti-ERBB2 antibody , anti-MUC1 antibody  and anti-TIM3 antibody  at 4 °C overnight and then incubated with secondary antibody  at 37 °C for 30 min and visualized using diaminobenzidine (DAB).2.Human duodenum adenocarcinoma cell line HUTU-80 was obtained from Center of Basic Medical Research, Peking University Third Hospital. The cell line was routinely grown in Minimum Essential Medium (MEM) containing non-essential amino acids (NEAA)  supplemented with 10% fetal bovine serum  and penicillin (100 IU/mL)-streptomycin (100 mg/mL) solution  and maintained at 37 °C and 5% CO3 per well) were seeded in 96-well plates. After attachment to the plates and culturing in complete growth medium for 24 h, the cells were treated with Oxaliplatin, Irinotecan, Tozasertib, Volasertib, Mocetinostat and Filanesib  at different final concentrations ranging from 100 nM to100 μM using dimethyl sulfoxide  or H2O as a solvent control. The number of viable cells was determined by CellTiter-Glo Luminescent Cell Viability Assay  following the kit protocol 36 h later and all experiments were performed in triplicate.HUTU-80 cells  were applied to extract barcodes and unique molecular identifiers (UMIs), and seqtk (version 1.3) was used to filter the low-quality reads. Then STAR (version 2.7.1a) was used to align these clean reads to the human GRCh38 genome. We used featureCounts (version 1.6.4) to count read uniquely mapped to the genomes, and UMI-tools were applied to quantify the UMIs. In the STRT dataset, we retained cells with more than 1000 detected genes, 10,000 transcripts and lower than 20% of mitochondrial genes. As for the 10× dataset, we used Cell Ranger (version 3.1.0) with default arguments to process raw data, and the human GRCh38 was used as the reference genome. In the 10× dataset, we retained cells with more than 500 detected genes, 1000 transcripts and lower than 50% of mitochondrial genes. In both the STRT and 10× datasets, UMIs per cell were normalized and transformed to generate gene expression values by log transforming. The log-normalized values were used in the downstream analyses.52. Batch effects caused by experiment batches should be reduced, and we used the R packages RSCORE (version 0.1.0) and Harmony (version 1.0) to correct batch effects54. To obtain different cell types, we performed RSCORE to remove batch effects, with the parameters “max_step = 20” and the human protein–protein interaction network 3.5.173 version from BioGRID. To classify subclusters in separate cell types such as epithelial cells, fibroblasts and T/NK cells, Harmony was used to remove batch effects with the default parameters. Finally, individual cells were clustered through a graph-based clustering approach of Seurat.We mainly used Seurat (version 3.2.2) to perform the downstream analysesWe used the sctransform function to normalize our datasets as well as published datasets including our STRT and 10× datasets, the normal ileum dataset and other gastrointestinal cancer datasets, and utilized the CCA method to integrate these datasets. The default parameters were used, and the expression values in assays of SCT were used to identify cell clusters and calculate DEGs.P values (based on bonferroni correction) < 0.01 were retained as DEGs of malignant cells and normal epithelial cells. DEGs of malignant cells and normal epithelial cells of separate patients were retained with the fold change of 1.5 to involve as many markers as possible. Metascape (http://metascape.org/) was used to perform gene enrichment analysis.DEGs of different cell types were identified through the FindAllMarkers function of Seurat using the wilcox test and the fold change with the value 2. The detailed parameter “test.use = “wilcox”, min.pct = 0.25, logfc.threshold = log(2)”. DEGs of malignant cells and normal epithelial cells in the whole datasets were calculated with the same parameters. Only genes with the adjusted As for the CNV analysis, low-quality and adapter-contaminated reads were reduced and trimmed by Trimmomatic (version 0.39), and then clean reads were aligned to the human GRCh38 genome using BWA (version 0.7.17). CNVkit (version 0.9.6) was used to estimate CNV, which outputted normalized copy ratios of DNA segments.As for somatic mutations, germline mutations were called using HaplotypeCaller built in GATK . Somatic mutations were called with paired peripheral blood samples as control. Somatic mutation calling tools including Mutect2 (built in GATK), MuSE (version 1.0), Varscan2 (version 2.4.3), SomaticSniper (version 1.0.5), Strelka2 (version 2.9.10) and LoFreq (version 2.1.3.1) were also used to call somatic SNVs, and the outputs were filtered and integrated by SomaticSeq (version 2.8.1) with the criterion that SNVs detected by more than four tools were retained. The default parameters of these tools were used. Only the “PASS” somatic mutations were used for downstream analysis.55. In both the STRT and 10× datasets, epithelial cell from adjacent normal tissues were applied as references, and CNVs of epithelial cells from cancer tissues were inferred according to expression pattern. With the inferCNV outputs, we used the mean of squares of deviation as the measurement and the 90th percentile of normal epithelial cells as the threshold value to divide epithelial cells into high CNV levels and low CNV levels. Combining with the cluster information, clusters in which epithelial cells with high CNV levels were dominant were regarded as malignant cell manually, and other clusters were regarded as normal epithelial cells.The R package InferCNV (version 1.1.3) was used to process scRNA-seq data13. The GENIE3 method was used to detect correlations, and the dataset of motifs located 20 kb around TSS from the cisTarget database was used.We used the R package SCENIC (version 1.1.3) to evaluate TF activities56. The input was the mean expression of separate datasets. The “ssgsea” method of GSVA package was used. As for the GSEA analysis, the python package gseapy (version 0.9.17) was used to perform the GSEA analysis. The parameters of “permutation_type = ‘gene_set’, permutation_num = 1000, method = ‘signal_to_noise’” were used.To evaluate gene expression signatures in single cells, we used the AddModuleScore function of Seurat to score the expression level of the specific gene sets at the single-cell level. We used the Gene Set Variation Analysis (GSVA) enrichment scores to describe enrichment scores of the hallmark gene sets through the R package GSVA (version 1.32.0)57. The count data were input, the “VISION” method and the KEGG pathways built in scMetabolism were used.The R package scMetabolism (version 0.2.1) was used to evaluate metabolism activities at the single-cell resolution16. Signal entropy rate of single cells was calculated by the default parameters.We used the R package SCENT (version 1.0.2) to estimate the ‘differentiation’ potency of single cellshttps://cibersort.stanford.edu/index.php) to perform the deconvolution analysis14. The signature matrix was derived from DEGs of different cell types in our 10× dataset, and the normalized expression matrix was from a published dataset of a microarray experiment of SBA (GSE61465).We employed CIBERSORTx  with the DDR-Tree method and default parameters to perform the single-cell trajectory analysis22. The log-normalized expression values and cell types corresponding to individual cells were used as the input data. The ExtractEdges function calculate expression and specificity of ligand–receptor interactions, and the DiffEdges function identifies changes of ligand–receptor interactions between two conditions. The built-in “lrc2p” database was used to predict interactions, the weight of edges was calculated by the mean method, and the detection threshold value is set to 0.2.We used the python toolkit NATMI to perform cell communication analysis59. As for the analysis for MUC1, the protein expression of MUC1 is not detected in the RPPA assay, and therefore we calculated the correlation of the RNA expression of MUC1 and the other protein expression.The bulk RNA-seq data based on the HiSeq platform and protein expression data using the RPPA method of CRC from the TCGA dataset were used, and the analysis was performed by the LinkedOmics webtool50 AUC values of potential drug responses32. The transcriptomic data of cell lines from CCLE and drug response data from CTRP, PRISM, GDSC1 and GDSC2 were utilized as training sets. Malignant and normal epithelial cells from epithelial cells of the STRT scRNA-seq data were divided into 50 groups randomly, and the mean expression values were used as the pseudo-bulk samples. We used the calcPhenotype function to calculate the IC50 AUC values for each corresponding drug, and selected drugs according to the following criteria: adjusted P values < 0.05 (under t-test), the log2FC value < −0.1, and the correlation with the tumor score < −0.5.We mainly used a ridge regression-based method of the R package pRRophetic (version 0.5) to predict the IC41. The drug perturbation signature collection (PSc) database built in Beyondcell was used. The number of detected genes per cell was corrected following the guidance.The R package Beyondcell (version 1.2.1) was used to identify drug sensibilities of scRNA-seq dataGraphPad Prism software 5 was used to conduct the statistical analysis of cell viability. Data were presented as means ± SD.Supplementary figuresSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Table 7Supplementary Table 8


The Bayesian modelling workflow—and the script—could be used for further model adaptation for the pH of other food products and/or other preservation strategies.Measuring the pH of meat products during storage represents an efficient way to monitor microbial spoilage, since pH is often linked to the growth of several spoilage-associated microorganisms under different conditions. The present work aimed to develop a modelling approach to describe and simulate the pH evolution of fresh meat products, depending on the preservation conditions. The measurement of pH on fresh poultry sausages, made with several lactate formulations and packed under three modified atmospheres (MAP), from several industrial production batches, was used as case-study. A hierarchical Bayesian approach was developed to better adjust kinetic models while handling a low number of measurement points. The pH changes were described as a two-phase evolution, with a first decreasing phase followed by a stabilisation phase. This stabilisation likely took place around the 13th day of storage, under all the considered lactate and MAP conditions. The effects of lactate and MAP on pH previously observed were confirmed herein: (i) lactate addition notably slowed down acidification, regardless of the packaging, whereas (ii) the 50%CO StudyinO2 gases ,11,12,13O2 gases ,15,16,17In this context, Bayesian modelling approaches for describing pH changes can be an interesting way to monitor spoilage. Firstly, Bayesian methods can help to fill data gaps with prior information, possibly provided by experts or other preliminary works ,19. SecoThis study aimed to develop a Bayesian modelling approach to estimate parameters of acidification kinetics, with a limited number of time points in the monitored pH curves. Bayesian modelling has already been applied to meat matrices ,22. Herew/w, corresponding to full normal dose) was used as additive. Furthermore, sausages were battered with a spice mix at a concentration of ∼2.5%; it contained sodium salt ∼40%, dextrose ∼10%, spices ∼15%, aromas ∼22%. Sausages were produced in ten independent batches and under several “process conditions”. These conditions corresponded to three initial concentrations of potassium lactate in sausage formulation combined with three modified atmosphere packaging processes. In practice, for each production batch, sausages were first made from the same batch of turkey meat in sufficient quantity for all conditions. The sausages were provided by two French meat processing companies, and the total quantity of sausages produced within the research project was around 360 kg. Indeed, since within the project microbial, physico-chemical and sensorial analyses on sausages were performed, four different trays with five sausages per tray  were required. Nine conditions were considered in the present study: for simplification purpose, in the present article, we denoted “Full dose”, “Half dose” and “Zero lactate” for the three lactate formulations ; “Air”, “MAP1” and “MAP2” for the three packaging conditions . The level of lactate and type of modified atmosphere were chosen as function of current practice in industry in France. For each condition, analyses were done at four time points . Therefore, the required minimum quantity of sausages was 50 g (sausage) × 5 (sausages/tray) × 4 (trays) × 3 (atmospheres) × 3 (lactate formulation) × 4 (sampling time) = 36 kg per batch. All the above quantities were repeated in ten independent industrial batches which makes finally 360 kg of sausages.The experimental data used in this study were extracted from a data paper monitoring several spoilage responses of fresh turkey sausages with different lactate formulations and conditioned under modified atmospheres during chilled storage (the data paper is available online ). Only dThe pH measurement was done in each condition using the tray destined for microbial analyses. All pH measurements were done in triplicate  for each of four sampling time points. The choice of these points was inspired by the French Norm NF V01-003 used to establish the shelf life of chilled perishable and highly perishable food . It consFirst, it should be noted that not all the pH curves were independent because the different formulations and packaging steps were carried out on the samples from the same batch after production. Second, samples under each process condition were monitored at different times. Consequently, the model was built with successive levels: (i) production batch, (ii) process condition and (iii) time of sampling .We denote by Starting from the same initial value w/w), The acidification rate The stochastic part of the model aimed to describe random variations due to variabilities and uncertainties by assuming probability distributions for model parameters. An initial statistical analysis of the dataset revealed a significant batch effect: pH changes were different from one batch to another . This baFor a given lactate-atmosphere condition With Prior distributions were specified for each model parameter and are given in , et al. . The autpurposes . The effThe main difficulty in the estimation of ated see . When apBayesian inference was performed using the JAGS software linked to the rjags R package ,28. ThreFor all four considered models and all parameters, convergence of the three MCMC chains were successfully checked by visual plotting and according to the chosen convergence criteria. The computed chains could then be considered as a large sample of posterior distribution of the parameters. The DIC criteria for comparing different models were computed and are gathered in In The point estimate of each model parameter, as well as its 95% credibility interval obtained with model 3, are gathered in As the output of model 3, the stabilisation time 2.9 days . Its posw/w (full dose) of lactate and packed under air, an average value of pH at the 15th day can be estimated as follows:The pH reached at the stabilization phase can be deduced from Equation (1) to Equation (4), using the values of parameters  for air packaging and −2.339  for MAP2: 50%CO2-50%N2. These estimations suggested that, for a given sample of formulated sausages, packaging under MAP2: 50%CO2-50%N2 would accelerate the acidification phase in comparison with the air packaging and MAP1:70%O2-30%CO2. In order to better illustrate these effects, the variation in the acidification rate . These files can be used as a first step to perform numerical simulations for the pH kinetics of sausages under different user-defined formulation-atmosphere conditions (pH curves and credible bands). Some simulation examples for two different initial formulations (0.5% and 1.5% w/w) are plotted in The full Bayesian procedure for the four models with all the estimated parameter distributions was prepared (R scripts/RData) and is available online at The provided scripts also allow the possibility to describe pH evolution and estimate acidification rates for other independent data, possibly collected on other food matrices, with other user-defined atmosphere factors and/or other formulations as covariates. Tutorials are available in the provided R script files to help the users get started with our modelling analysis procedure.In the present study, a comparative approach was performed, in order to select the most appropriate model  to describe a two-phase acidification curve, including a likely breaking point between two sampling times. The model selection, based on a penalized-likelihood criterion (DIC), must be a good compromise between improving adjustments and limiting the number of estimated parameters due to the small amount of data. Surprisingly, arbitrarily setting the stabilization time to one of the four sampling time points did not necessarily improve the selection criterion, compared to other models where the stabilization time was estimated. Indeed, the latter was estimated successfully between two sampling time points (the 8th and 15th day). One possible explanation could be that the chosen sampling time points  were not suitable for studying pH kinetics . Therefore, setting the stabilization time at one sampling time could have strongly biased the fit of the model. In our models, we assumed that the stabilization time was the same for all lactate and atmosphere conditions to avoid an important number of parameters, making their estimation more difficult. In further studies, the experimental designs  may need to be adapted for each condition. In such adapted designs, one could possibly improve the model by assuming different stabilization times, depending on the condition.An advantage of Bayesian simulation techniques is the ability to build a hierarchical structure and easily communicate on it via an acyclic graph, as illustrated in 2: 50%CO2-50%N2 accelerated the acidification phase compared to the Air packaging and MAP1:70%O2-30%CO2, regarding the acidification rates. The quick acidification in MAP2: 50%CO2-50%N2 is probably due to its high initial content of CO2 that favours acid-producing bacteria, such as lactic acid bacteria [2CO3), resulting from the important absorption of CO2 into the meat products [2 and CO2 in the model.The Bayesian model illustrated herein confirmed the effects of lactate and atmosphere on pH kinetics, observed in our previous work , with a bacteria . Anotherproducts ,41,42. BThe initial value of pH (day 0) estimated with our data (at 6.49) was higher than the pH observed by Lerasle et al. , giving 2-30%CO2 packaging slows down acidification of fresh turkey sausages and consequently limits potential sausage spoilage due to lactic acid bacteria. However, before applying such preservation conditions, we recommend also assessing the sensory qualities of the final product. The Bayesian modelling approach could be reused for other studies, especially in the context of empirical modelling framework of meat spoilage. For such a purpose, the Bayesian modelling workflow—and the script—have been provided. The prior distribution could be generated using existing data or previous studies, as done here, but also by seeking experts’ knowledge.The kinetics of turkey sausage pH were monitored and successfully fitted using a Bayesian modelling approach, despite a limited number of experimental data points. The addition of potassium lactate combined with air or 70%O


Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important, yet challenging to study due to disease lesion locations and other complexities.Utilizing the powerful method of spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular hemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications.Our data identified three main 3D global pseudospace-time trajectory bundles that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH stimulation.www.combio-lezhang.online/3dstivh/home).These results are informative for understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injury, strategizing more effective clinical management regimens, and developing novel bioinformatics strategies for the study of other CNS diseases. The algorithm strategies used in this study are searchable via a web service ( IVH is one of the common complications after various acute brain injuries –3 that cFor this reason, this study investigated the pathophysiological mechanism of secondary neurological dysfunction after IVH and the related intervention strategies by developing a bioinformatics analysis workflow based on spatial transcriptome sequencing , 11. Firvia a web service (www.combio-lezhang.online/3dstivh/home).In summary, our study not only identified the brain regions affected by secondary neurological dysfunction after IVH stimulation from a 3D perspective but also allows for the further investigation of driving cell types and molecular pathways, which might benefit the construction of translational intervention strategies for secondary brain injuries after IVH at the molecular and cellular levels in the distant future –22. Fina22.1ad libitum access to food and water. All experiments were reported in compliance with the Animal Research: Reporting In Vivo Experiments (ARRIVE) guidelines. The experimental protocols were approved by the Laboratory Animal Welfare and Ethics Committee of the Third Military Medical University (AMUWEC2020762) and performed according to the Guide for the Care and Use of Laboratory Animals.Five wild-type C57/BL6 male mice  were used in this study. These mice were housed in a temperature-controlled room under specific-pathogen-free conditions and a standard 12-h light/dark cycle, with An IVH model was established according to previously described methods with modified coordinates. Briefly, mice in each group were deeply anesthetized by an intraperitoneal injection of 2 mg of ketamine and 0.4 mg of xylazine in 0.9% saline. A feedback-controlled heating pad was used to maintain body temperature at 37.0°C. A small cranial burr hole was drilled, and a 32-gauge needle was inserted stereotaxically into the right lateral ventricle  to establish the IVH model under stereotactic guidance. Approximately 25 μl of autologous blood was then injected at a rate of 5 μl/min using a microinfusion pump . The burr hole was sealed with bone wax, and the skin incision was closed with sutures after the needle was removed. Sham-operated mice underwent the same surgery without blood injection. The brains of the mice were harvested at different time points after IVH stimulation, after they were deeply anesthetized for further study.2.2We collected wild-type C57/BL6 mouse brains, which were classified into the no-surgery group (control), the needle-puncture group without blood injection (sham), and the needle-puncture group with blood injection on the first, third, and seventh days. These brains were then frozen on dry ice in an optimal cutting temperature (OCT) compound.Frozen brains were then sectioned coronally at 10 μm on a cryostat and mounted on 10X Genomics Visium spatial transcriptome slides. The tissue locations of the library patches started from the emergence of the lateral ventricle, with 20 μm intervals and a 10-μm margin of error  staining. Additionally, all fractions were measured using a bioanalyzer (Agilent 2100) meeting the RNA integrity number RIN>=7.After brightfield imaging, we permeabilized 19 brain sections, the permeabilization time of which are listed in A DNF-915 kit and our library detection instrument were employed for quantification by qPCR. After the library was quantified, we performed PE150 paired-end sequencing using the Illumina NovaSeq 6000 S4 sequencing platform with 50,000–100,000 reads per locus (spot).Finally, genomes and images were aligned to the mouse reference genome mm10-3.0.0 by SpaceRanger (v1.1.0) software. The data were analyzed and normalized by using Seurat (v3.1.2) software and the transform package, respectively.2.32.3.1We used the top 30 up/downregulated 3D transition genes for each 3D subtrajectory  of cell subtypes and N points in the corresponding position    (P_internTrajectory ) (P_AnTrajectory and P_BnTrajectory (n = 1 … N).We used mutual pathway sets for our identified cell subtypes  from trajectory bundle III might reflect the acute and direct effect of hematoma stimulation on the primary cortex.Next, we carried out a union operation for the top 30 planar up/downregulated transition genes for the same subtrajectory in different plane sections at the same time to obtain the top 30 3D up/downregulated transition genes  from trajectory bundle III for further analysis.As discussed in the Initially, we employed our developed algorithm  after IVH stimulation, but these cell subtypes are also different from the cell subtypes of the sham group.It should be noted that we do not have a cell subtype for the first day or the sham group for VL → fxs → CA1 → DG and TH → CA1 → DG, respectively. Additionally, Finally, we found cell types ) that ar3.5n) pairs.Initially, we employed the CellTalkDB database  to locatn) pairs. We then obtained the discrete interaction intensity LRn values for each ligand–receptor pair in the cell subtypes  isFor VL→fxs→CA1→DG from trajectory bundle I, which might be tightly associated with white matter lesions, as we discussed above, the most activated cell type is oligodendroglia, which is consistent with its being widely understood as the main participant in white matter and cognitive functions. Further pathway analysis indicated intense metabolic changes and synaptic activity, as well as gliogenesis and neural cell death in this trajectory. The cell−cell communication strength analysis exhibited strong cell–cell communication after IVH stimulation, which substantially weakened, as shown in Additionally, in TH→CA1→DG from trajectory bundle II, it seems that neuronal cells are the main participants and cell proliferation is the pathway with the most substantial changes, which is supported by a recent study  that sugHowever, pathway analysis .In conclusion, we innovated a bioinformatics algorithm to discover three main 3D global pseudospace-time trajectory groups that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex as well as a direct and integrated effect on the hippocampus after IVH stimulation. To the best of our knowledge, this is the first study to investigate secondary brain injury after IVH stimulation by using spatial transcriptome sequencing and bioinformatics analysis, but verification and further investigation by basic neuroscience and translational interventions are still needed. These data provide helpful information for elucidating the pathophysiological mechanism for the occurrence of IVH in patients after acute brain injury, as well as algorithm strategies for similar studies in the future. The algorithm strategies used in this study are searchable https://www.ncbi.nlm.nih.gov/geo/, GSE214349.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The animal study was reviewed and approved by Laboratory Animal Welfare and Ethics Committee of Third Military Medical University (AMUWEC2020762).LZ and YC conceptualized and designed the experiments and supervised the research. XR, SH, and YC performed animal experiments. LZ, JB, GW, WS, YY, JH, and YC acquired and analyzed the data. LZ, JB, GW, WS, HF, RC, YZ, and YC interpreted the data. LZ, JB, WS, YZ, and YC drafted the manuscript. All authors have read and approved the current version of the manuscript.


Long-term continuous cropping may result in the outbreak and proliferation of soil-borne diseases, as well as reduction in annual crop production. Overcoming the obstacles of continuous cropping is critical for the long-term growth of modern agriculture. Soil microbes are essential for plant health, but the consequences of continuous cropping on soil microbiome are still poorly understood.This study analyzed changes in soil bacterial community composition of Aksu (AKS) and Shihezi (SHZ) in Xinjiang Province during 1–20  years of continuous cropping by 16S amplicon sequencing. The results showed that the incidence of cotton Verticillium wilt rose with the number of cropping years. The bacterial alpha diversity in the AKS soil grew as the number of continuous cropping years increased, however it declined in the SHZ soil.Streptomyces decreased with the increase of continuous cropping years in the AKS and the SHZ soils. In addition, Actinobacteria, Propionibacteriales, and Nocardioidaceae were significantly enriched during the early stages of continuous cropping. Network analysis showed that long-term (≥8  years) continuous cropping interfered with the complexity of soil bacterial co-occurrence networks and reduced collaboration between OTUs.The results of beta diversity analysis showed that there were significant differences in soil bacterial communities between different continuous cropping years and between different soils. The results of community composition changes at the level of main phyla and genus showed that the relative abundance of Actinobacteria, Bacteroidetes and Streptomyces in Actinobacteria might be one of the reasons of continuous cropping obstacles.These findings suggested that continuous cropping and soil origin jointly affected the diversity and structural of bacterial communities, and the loss of Nocardioidaceae and  The region of Xinjiang is China’s most significant cotton-growing site , owing tOvercoming continuous cropping obstacles is an urgent task for the sustainable development of modern agriculture. According to research, changes in soil microbial populations are one of the primary drivers of continuous cropping obstacles . The soiRecently, increasing numbers of studies have indicated that continuous cropping resulted in the disruption of soil microbial community and structure. For example, Verticillium dahliae, is one of the worst plant diseases. V. dahliae can survive as microsclerotia in soil without host plants for more than 14 years. V. dahliae is exceedingly difficult to control due to its long survival time in soil, strong variability, and complex infection process of cotton  region of southern Xinjiang and the Shihezi (SHZ) region of northern Xinjiang over a period of 1–20 years. This study aimed to investigate the response of soil microbial community to continuous cropping, which would provide useful guidance for improving continuous cropping obstacles using microbial techniques in the future.The two study sites are located in Aksu and Shihezi, Xinjiang, China. Aksu belongs to the temperate continental arid climate; the annual average temperature is between 9.2 and 11.5°C, the annual average total sunshine hours are between 2,571 and 2,967 h, and the annual frost-free period is between 183 and 250 days; the annual average rainfall is only 64 mm, while the annual evaporation amounted to 1,890 mm; the distribution of rainfall within the year was extremely uneven, with the main rainfall distributed in summer and almost no rainfall in winter . ShiheziA total of 14 treatments were conducted in this experiment: the farmland with continuous planting of cotton for 1, 3, 5, 8, 10, 15, and 20 years in two sites (Aksu and Shihezi), named AKS1, AKS3, AKS5, AKS8, AKS10, AKS15, AKS20, SHZ1, SHZ3, SHZ5, SHZ8, SHZ10, SHZ15, and SHZ20, respectively. Two cotton fields were selected in each continuous cropping year at each location. There were three blocks in each cotton field and six blocks in each treatment. At the boll formation stage (late August 2020), the incidence of cotton Verticillium wilt was investigated and the incidence rate was calculated . Soil samples were collected with a sampler  using the five-point sampling method, and five soil cores collected in each block were pooled into one sample, with 6 mixed samples per treatment. A total of 84 soil samples were collected. Prior to DNA extraction, the obtained soil was sieved (2 mm) and stored in a refrigerator at −80°C.® High-Fidelity PCR Master Mix (New England Biolabs), 0.2 μM forward primer, 0.2 μM reverse primer, and 10 ng template DNA  was used to generate sequencing libraries, and the quality of each library was assessed using Qubit 2.0 Fluorometer . Finally, the library was sequenced using an Illumina HiSeq 2500 and 250 nucleotide paired-end reads were generated.Soil samples (250 mg) were resuspended in 500 μL of MoBio PowerSoil bead solution, and total genomic DNA was extracted from all 84 soil samples using the MoBio PowerSoil DNA Isolation Kit  following the manufacturer’s instructions. Successful DNA extraction was checked on a 1% agarose gel and DNA concentration was assessed using a spectrophotometer NanoDrop ND-2000 . Barcode primers 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACNNGGGTATCTAAT-3′) were used to amplify the V3-V4 hypervariable region of 16S rRNA. PCR reactions were performed in a 30 μL reaction mix containing 15 μL Phusionlate DNA . PCR conPaired-end reads were assembled using FLASH (v1.2.7) . Qualityp < 0.05 was considered significant) in SPSS 22.0. Beta diversity analysis was performed based on the Bray–Curtis dissimilarity distance matrix and visualized by principal coordinate analysis (PCoA). Analyzes of similarity (ANOSIM) were performed using the vegan package in R to identify significant differences in beta diversity across groups. The effects of continuous cropping years, soil origin and their interactions on bacterial alpha diversity and beta diversity were evaluated through the two-way ANOVA using SPSS 20.0. Linear discriminant analysis (LDA) effect size (LEfSe) was performed on the Galaxy platformr| > 0.8 and p < 0.05 were retained and visualized using ChiPlot.Community alpha diversity was evaluated by Chao1 estimator and Shannon diversity index calculated by QIIME, and visualized using R software (v3.5.0). The alpha diversity index among the groups was statistically analyzed using one-way analysis of variance (ANOVA) and Tukey’s multiple comparison test  (p < 0.05) (p < 0.001) (p < 0.01) (p < 0.001). Two-way ANOVA showed that alpha diversity indexes (Chao1 and Shannon) were significantly affected by the continuous cropping years and soil origin as well as their interactions  were annotated to the genus level. The dilution curves demonstrated that the sequencing data accurately represented the diversity present in the samples from each treatment group . Alpha d < 0.05) . In the  < 0.05) . However< 0.001) ; the Sha < 0.01) ; the Sharactions .R = 0.6621, p = 0.001), and there were also significant differences in community structure between AKS and SHZ soils . In both soils, the distance between samples of continuous cropping for 1 and 3 years was closer, and the distance between the samples of continuous cropping for 5 and 8 years was closer. In particular, the samples of continuous cropping for 15 and 20 years clustered together and separated from the samples of other continuous cropping years. Two-way ANOVA showed that beta diversity indexes (PCo1 and PCo2) were significantly affected by the continuous cropping years and soil origin as well as their interactions  effect size (LEfSe) method was used to analyze the bacterial taxa with significant abundance difference between different continuous cropping years in two soils. A total of 28 taxa were found to be significantly different in relative abundance among continuous cropping years in the AKS soils , and theCo-occurrence networks were constructed to evaluate the co-occurrence patterns of bacterial communities in short-term and long-term continuous cropping in the two soils. In the AKS soil, compared with short-term (≤8 years) continuous cropping, long-term (≥8 years) continuous cropping decreased the network total nodes, total links, average degree, average clustering coefficient and density, while the average path distance increased . In the In this study, the incidence of Verticillium wilt in cotton field soil with different continuous cropping years was assessed in AKS and SHZ, and it was found that the incidence of Verticillium wilt rose with the continuous cropping years, especially after 5 years of continuous cropping. Similar phenomena have been observed in many crops, including cucumber, potato, tomato and vanilla, with an increased incidence of soil-borne diseases and severely stunted growth after years of continuous cropping .Microbial diversity and community structure are important indicators affecting soil health, and many microbes have the potential to improve crop yield and disease suppression . This stStreptomyces belonging to Actinobacteria decreased with the continuous cropping year in both the AKS and the SHZ soils. Streptomyces are the most abundant and important Actinomycetes, which contribute to the maintenance of soil fertility and homeostasis to promote plant growth and to help plants resist biotic and abiotic stresses  of continuous cropping, suggesting that these groups may be important species to maintain soil health in the early stage of continuous cropping. At the same time, we found that some taxa were enriched in the AKS soil with 1–5 years of continuous cropping and also in the SHZ soil with 15–20 years of continuous cropping, which might be due to the differences in the two soils origin and the composition of the native microbial communities.Network analysis has been widely applied to explore the complex inter relationships and co-occurrence patterns of soil microbial communities . PreviouStreptomyces, Propionibacteriales, and Nocardioidaceae, and these taxa that were lost due to the continuous cropping might be one of the causes for continuous cropping obstacles. These findings extend our understanding of the mechanisms by which continuous cropping of cotton affects soil microbes and provide useful guidance for overcoming continuous cropping obstacles through the application of beneficial microbes in the future.The results of this study indicated that continuous cropping and soil origin jointly affected the diversity and structural of the soil bacterial population. The relative abundance of some beneficial bacterial taxa in both soils decreased with increasing years of continuous cropping, such as: Actinobacteria, Bacteroidetes, https://www.ncbi.nlm.nih.gov/), SRP412954.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI Sequence Read Archive .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.