Solanum lycopersicum (tomato) is still unknown. In this study, we find that a tomato methionine synthase (named SlMS1), which could be repressed at the transcriptional level by hydrogen sulfide (H2S), acts as a positive regulator for tomato fruit ripening. By a bioinformatics analysis, it is found that SlMS1 and SlMS2 in tomato are highly homologous to methionine synthases in Arabidopsis thaliana. The expression pattern of SlMS1 and SlMS2 is analyzed in tomato, and SlMS1 expression is up-regulated during fruit ripening, suggesting its potential role in regulating fruit ripening. A potential bipartite nuclear localization signal is found in the amino acid sequence of SlMS1; thus, SlMS1 is tagged with GFP and observed in the leaves of Nicotiana benthamiana. Consistently, SlMS1-GFP shows strong nuclear localization and also cytoplasmic localization. The role of SlMS1 in regulating fruit ripening is investigated in tomato fruit by transient silencing  and transient overexpression. The results show that SlMS1 silencing causes delayed fruit ripening, evidenced by more chlorophyll and less carotenoid accumulation, while SlMS1 overexpression accelerates fruit ripening significantly compared with control. Further investigation shows that SlMS1 overexpression could up-regulate the expression of carotenoid-synthesis-related genes , chlorophyll-degradation-related genes , cell-wall-metabolism-related genes  and ethylene-synthesis-pathway-related genes , while SlMS1 silencing causes the opposite results. The correlation analysis indicates that SlMS1 expression is negatively correlated with chlorophyll content and positively correlated with carotenoid and ripening-related gene expressions. Taken together, our data suggest that SlMS1 is a positive regulator of tomato fruit ripening and a possible target gene for the ripening-delaying effect of H2S.Ethylene is a key phytohormone that regulates the ripening of climacteric fruits, and methionine is an indirect precursor of ethylene. However, whether methionine synthase plays a role in fruit ripening in  Tomato fruit is rich in nutrients and thus a popular healthy vegetable. With a clear genetic background and short growth cycle, tomato is a model organism for the research on plant growth and fruit ripening and senescence. Tomato is a typical climacteric fruit, and ethylene plays a key role in tomato fruit ripening . Tomato ACO1 and ACO3 expression increases strongly and correlates well with the autocatalytic rise in ethylene production, suggesting their important role in regulating fruit ripening [Tomato is a typical climacteric fruit, as it undergoes a rapid rise in respiration and a burst of ethylene production at the onset of the ripening process. The pathway of ethylene biosynthesis involves the conversion of S-adenosylmethionine (SAM) to 1-aminocyclopropane-1-carboxylic acid (ACC) by ACC synthase (ACS) and then by ACC oxidase (ACO) to ethylene ,9. In toripening .SlMS1, corresponding to Solanum lycopersicum methionine synthase in this work, was found to be an ethylene-regulated gene, suggesting that methionine synthase may regulate tomato fruit ripening [Methionine is an indirect precursor for ethylene biosynthesis, and we propose that its influx into the ethylene biosynthesis pathway is critical for ethylene burst during fruit ripening. SAM is generated with methionine as the substrate under the catalyzation of S-adenosylmethionine synthase . Given tripening .2S) can alleviate ripening and senescence of multiple postharvest fruits, such as strawberry, kiwifruit and tomato, by activating the antioxidant system and antagonizing the ethylene pathway [2S in mediating its ripening delay remains unclear. As methionine synthase is the determinant for the influx to the ethylene pathway, we propose that methionine synthase genes may be the targets for H2S signal. In this study, we studied the role of SlMS1 in regulating fruit ripening by transient silencing  and transient overexpression of SlMS1 in tomato fruits. We found that SlMS1 showed both nucleus and cytoplasm localization, and it is a positive regulator for tomato fruit ripening.Hydrogen sulfide  obtained from NCBI was used as a query to obtain homologous proteins in The analysis of the physicochemical properties of AtMS1 showed that AtMS1, AtMS2 and SlMS1 showed the same 765 amino acids and similar pI values, which were 6.09, 6.09 and 6.01, respectively. Consistently, AtMS1, AtMS2 and SlMS1 with similar physicochemical characteristics show more intimate phylogenetic relations.MS genes in tomato plant, the transcription data were explored in tomato roots, stems, leaves, flower buds, flowers and fruits at different ripening stages in a public database. As shown in MS genes in different tissues of tomato. SlMS1 showed an increasing trend of expression until the mature green stage, followed by decline, suggesting that SlMS1 might play an important role in regulating tomato fruit ripening. H2S could act as a repressive signal for fruit ripening by antagonizing the effect of ethylene in tomatoes. Thus, we attempt to establish whether H2S could repress the expression of SlMS1 and SlMS2 in tomato fruit. We explore previous published transcriptome data, where mature green tomato fruits were treated with H2S donor NaHS harvested at 0, 1 and 3 days after storage [2S application repressed the expression of SlMS1 and SlMS2 at 1 and 3 days of treatment and the original transcriptome data were presented in SlMS1 in H2S-treated fruit was less than half of the control at 1 and 3 days of treatment, suggesting that the expression of SlMS1 was repressed by H2S.To explore the expression patterns of  storage . As showA. thaliana and S. lycopersicum were predicted by an online tool, and the results are shown in SlMS1 might play a role in regulating fruit ripening, the potential nuclear localization signal in SlMS1 was further predicted by NLS Mapper with a cut-off score of 5. As shown in SlMS1-GFP plasmids were expressed in tobacco leaves, and fluorescence was observed by a co-focal microscope. As shown in SlMS1-GFP co-localized with the nucleus stain Hoechst 33342, indicating that the localization of SlMS1 was mainly in the nucleus. Moreover, SlMS1-GFP also showed cytoplasm localization.Subcellular localizations of methionine synthases in SlMS1 in regulating tomato fruit ripening, the expression of SlMS1 was transiently silenced by VIGS, and gene expression was achieved by the transient expression of pSAK277-SlMS1 in tomato fruit. The recombinant plasmids TRV-SlMS1 and pSAK277-SlMS1 were transformed into Agrobacterium to infect tomato fruits at 15 days after anthesis and mature green fruits, respectively. As shown in SlMS1-silenced fruit in tomato was observed at 14, 21 and 27 days post infection (DPI). At 21 DPI, the color of the fruits of the control group began to change to yellow, but no color change occurred with TRV-SlMS1. At 27 DPI, the fruits of the control group almost turned red, while TRV-SlMS1 were still in the breaker color stage. To confirm the successful silencing of SlMS1 by VIGS, the expression of SlMS1 was examined by RT-qPCR  by the enzymes cystathionine γ-synthase and cystathionine β-lyase, and both enzymes are only present in the chloroplasts . Plant mlization . L-cysteterminus . The O-aterminus . A recenterminus , suggestSlMS1 in tomato fruit by VIGS and overexpress its expression by pSAK277 plasmid. The results indicate that the silencing of SlMS1 delays tomato fruit ripening, while SlMS1 overexpression promotes fruit ripening, implying that SlMS1 is a positive regulator of fruit ripening. Moreover, the silencing of SlMS1 reduces the accumulation of carotenoid and delays the degradation of chlorophyll. Then, the genes related to carotenoid biosynthesis and chlorophyll degradation are analyzed at the transcriptional level. The data indicate that SlMS1 silencing caused a decreased expression of PSY1, PDS and ZDS compared with control during fruit ripening, whereas SlMS1 overexpression for 10 days induces an increased transcription of PSY1, PDS and ZDS significantly. Further, SlMS1 overexpression enhances the expression of PAO, PPH, NYC1 and SGR1, while SlMS1 silencing reduces their expressions. Thus, the work indicates that SlMS1 overexpression in tomato fruit accelerates chlorophyll degradation and carotenoid biosynthesis by increasing the expression of chlorophyll degradation related genes and carotenoid biosynthesis related genes. Additionally, we find that SlMS1 overexpression enhances the transcription of ethylene-synthesis-related genes, ACS2, ACO1 and ACO3, and genes CEL2, EXP, PG, TBG4 and XTH5, which encode cell wall metabolism related enzymes. To reveal the potential relation between SlMS1 and fruit ripening, the correlation was analyzed among SlMS1, the content of chlorophyll and carotenoids and genes related to fruit ripening. The result suggests that SlMS1 is positively correlated with fruit-ripening-related genes and negatively correlated with chlorophyll, suggesting that SlMS1 acts as a positive regulator of fruit ripening.Methionine is a precursor for ethylene biosynthesis, and therefore, we propose that methionine synthase may participate in regulating fruit ripening. In the present work, we silence the expression of SlMS1 is critical for initiating fruit ripening, as SlMS1 may determine the content of methionine and also ethylene precursor, SAM. Finally, we propose a model of methionine and methionine synthase regulating fruit ripening, as shown in SlMS1) methylates homocysteine to yield Met. Methionine generates methyl donor SAM under the action of SAM synthase, and then, ACC synthase (ACS) and ACC oxidase (ACO) catalyze the biosynthesis of ethylene. Therefore, SlMS1 acts as a positive regulator of tomato fruit ripening, possibly through the synthesis of methionine, which leads to the influx into the ethylene biosynthesis pathway.Methionine synthase is responsible for methionine synthesis, and SAM accounts for about 80% of methionine metabolism, whereas the synthesis of proteins drives about 20% of methionine ,19. Thushttps://phytozome-next.jgi.doe.gov/blast-search, accessed on 11 August 2022 by the program BLASTP-protein query to protein database. The protein molecular weights and theoretical pI values were computed using ProtParam . The predicted NLSs were obtained from NLS mapper  [https://wolfpsort.hgc.jp/, accessed on 15 August 2022. The tertiary structure of proteins was predicted on the website of SWISS-MODEL (expasy.org).With the protein sequence of AtMS1 in Arabidopsis as a query, the homologous genes of methionine synthase in tomato and Arabidopsis were identified in st 2022) . The subArabidopsis thaliana and tomato were investigated using MEGA 7.0 [https://espript.ibcp.fr/ESPript/ESPript/, accessed on 14 August 2022. The domain composition of proteins was predicted on SMART: Main page (embl-heidelberg.de). For the exploration of transcription of methionine synthase genes in different tissues of tomato, the original expression data were obtained from the online tool http://tomexpress.toulouse.inra.fr/query, accessed on 11 August 2022, and the tomato cultivar was Micro Tom.The phylogenetic relationships among methionine synthases from MEGA 7.0  with theSlMS1 without a stop codon was inserted into the p1300-35S-GFP vector for SlMS1-GFP expression. Agrobacterium tumefaciens GV3101 containing the recombinant vectors was infiltrated into 5-week-old N. benthamiana leaves. Then, the tobacco was cultured at 22 °C under a 16 h light/8 h dark photoperiod for 2 days. The location of the nucleus was indicated by treatment with Hoechst 33342 (5 µg/mL) for 5 min. The excitation/emission wavelengths were 488 nm/507 nm for GFP and 346 nm/460 nm for Hoechst 33342. All fluorescence signals were detected using a Zeiss LSM710NLO confocal laser scanning microscope. The primers for the construction of SlMS1-GFP were shown in The CDS of SlMS1 sequence was amplified from the cDNA of tomato by PCR and inserted into pTRV2 to yield pTRV2-SlMS1 at restriction sites of EcoRI and BamHI. Agrobacterium tumefaciens GV3101 containing the vectors was cultured at 28 °C for 16 h in Luria-Bertani medium containing 20 μM AS, 10 mM MES and 50 μg/mL each of the antibiotics kanamycin, gentamycin and rifampicin, according to the method of Fu et al. [SlMS1 vector were then mixed together at a ratio of 1:1 and infected into the pedicels of Micro Tom tomato plants. Tomato petioles infiltrated with pTRV2 without the insert were used as controls. Tomato plants were then stored at 16 °C for 24 h and transferred to normal culture conditions at 22 °C under a 16 h light/8 h dark photoperiod thereafter. The images of fruits were taken at 14, 21 and 27 days post injection. The samples without seeds were sampled at 21 and 27 days post injection and stored in a –80 °C refrigerator for subsequent measurement.A fragment of 400 bp corresponding to nt 1563–1964 of the SlMS1, the coding sequence of SlMS1 was cloned into the plasmid pSAK277 under the 35S promoter at the restriction sites of XbaI and EcoRI. Agrobacterium tumefaciens GV3101 containing pSAK277-SlMS1 or the empty vector cultured overnight was diluted into 20 μM AS (Acetosyringone), 10 mM MES (2-Morpholinoethanesulphonic acid) to OD600 0.8–1.0. After 4 h of culture, the Agrobacterium was injected into one chamber of the tomato fruit at the green mature stage. The infected plant was stored at 16 °C for 24 h in the dark and then placed in normal growth conditions. The images of fruits were taken at 5 and 10 days post injection. The samples without seeds were sampled at 10 days post injection and stored in a –80 °C refrigerator for subsequent measurement. The primers for the construction of pTRV2-SlMS1 and pSAK277-SlMS1 were shown in For transient overexpression of The tomato fruit sample at 0.5 g without the seeds was extracted in ethanol. Afterward, a quantitative determination of chlorophyll and carotenoids was carried out. The chlorophyll and carotenoids levels were measured and calculated based on the equations described by Lichtenthaler and Wellburn .® Green Pro Taq HS Premix, 0.8 μL of cDNA, 0.2 μL of 10 μM F/R of each primer and 3.8 μL RNase free water. The reaction program was 95 °C for 30 s; 95 °C for 5 s; 60 °C for 30 s; 40 cycles, followed by melting curve at 95 °C for 15 s; 60 °C for 60 s; and 95 °C for 15 s. Using the tomato Tubulin gene as an internal reference, the relative expression was calculated according to the 2−△△Ct method. Primers for the qRT-PCR analysis are listed in Reactions were conducted using previously reported methods . Briefly


Comparative and comprehensive omics studies have recently been conducted to provide a comprehensive understanding of the biological mechanisms underlying infertility. However, because these huge omics datasets often contain irrelevant information, editing strategies for summarizing and filtering the data are necessary prerequisite steps for identifying biomarkers of male fertility. Here, we attempted to integrate omics data from spermatozoa with normal and below-normal fertility from boars and bulls, including transcriptomic, proteomic, and metabolomic data. Pathway enrichment analysis was conducted and visualized using g:Profiler, Cytoscape, EnrichmentMap, and AutoAnnotation to determine fertility-related biological functions according to species.In particular, gamete production and protein biogenesis-associated pathways were enriched in bull spermatozoa with below-normal fertility, whereas mitochondrial-associated metabolic pathways were enriched in boar spermatozoa with normal fertility. These results indicate that below-normal fertility may be determined by aberrant regulation of protein synthesis during spermatogenesis, and the modulation of reactive oxygen species generation to maintain capacitation and the acrosome reaction governs boar sperm fertility.Overall, this approach demonstrated that distinct molecular pathways drive sperm fertility in mammals in a species-dependent manner. Moreover, we anticipate that searching for species-specific signaling pathways may aid in the discovery of fertility-related biomarkers within large omics datasets.The online version contains supplementary material available at 10.1186/s40104-023-00836-1. Male infertility is linked to complicated physiological and biochemical mechanisms, and basic semen analysis is insufficient for fully deciphering the underlying causes . ElucidaComprehensive and comparative omics studies have recently been conducted in animals and humans to provide a better understanding of the complex multifactorial processes linked to male fertility at the gene –5, transThe PubMed database was searched for comprehensive and comparative research articles using the terms “transcriptomic”, “boar spermatozoa”, “bovine spermatozoa”, and “fertility”. Further relevant studies were identified by searching the reference lists of the cited articles. Differentially expressed transcripts in spermatozoa with normal or below-normal fertility were integrated according to species, and genes with inconsistent results among the studies were removed Table .Table 1CThe PubMed database was searched for comprehensive and comparative research articles using the terms “proteomic”, “porcine spermatozoa”, “bovine spermatozoa”, and “fertility”. According to species, differentially expressed proteins in spermatozoa with normal or below-normal fertility were integrated, and genes having contradictory results across studies were eliminated.To evaluate the fertility-related proteomic enrichment pathways, four comparative and comprehensive proteomic studies in spermatozoa between normal (high or fertile) and below-normal (low or infertile) fertility bulls were considered for evaluation , 28–30. The PubMed database was searched for comprehensive and comparative research articles using the terms “metabolomic”, “porcine spermatozoa”, “bovine spermatozoa”, and “fertility”. Further relevant studies were identified by searching the reference lists of the cited articles. Differentially expressed metabolites in spermatozoa with normal or below-normal fertility were integrated according to species, and genes with inconsistent results among the studies were removed.Pathway enrichment analysis was performed by following a step-by-step procedure to facilitate the interpretation of transcriptome and proteome data as described by Reimand et al.  with somwww.metaboanalyst.ca).A list of differentially expressed metabolites between normal and below-normal fertility bull spermatozoa was compiled, and enriched pathways were examined using Metabolite Set Enrichment Analysis from MetaboAnalyst 4.0 (n = 5) and a below-normal fertility . NRR was calculated from recent 3-year of historical data had at least 100 breeding with at least ten herds.Frozen semen samples from ten bulls  that represented a wide range of filed fertility levels were obtained from the Hanwoo Improvement Program of the National Agriculture Cooperative Federation (NACF) of Korea. According to our previous study , the 60 n = 3) and below-normal fertility . Average litter size was calculated based on the records of at least 100 sows by artificial insemination.Liquid semen samples from six boars (Yorkshire) with known litter size were obtained from Grand-Grand Parents farm . Based on the average litter size, ten boar semen samples were divided into normal  were lysates with Laemmli sample buffer consisting 63 mmol/L Tris-Cl, 10% glycerol, 10% sodium dodecyl sulphate (SDS), 5% bromophenol blue, and 5% mercaptoethanol. Total sperm cell lysates were loaded into 12% SDS-polyacrylamide gel and transferred to polyvinylidene difluoride membranes . And then membranes were blocked with 5% skim milk, and incubated with primary antibodies against enolase 1 (ENO1), glucose transporter 3 (GLUT3), glutathione peroxidase 4 (GPX4), cytochrome b-c1 complex subunit 1, mitochondrial (UQCRC1), UQCRC2, NADH:Ubiquinone oxidoreductase core subunit S8 (NDUFS8), ATP synthase, H+ transporting, mitochondrial F0 complex (ATP5F), voltage-dependent anion channel 2 (VDAC2), actin-related protein T2 (ACTRT2), cytochrome c oxidase polypeptide 5, mitochondrial (COX5) A, COX5B, COX6B, parkinsonism-associated deglycase (PARK7), cytochrome C (CYC1), and α-tubulin. All antibodies were purchased from Abcam . Protein expression was visualized with Clarity Western ECL substrate  and quantified using ImageJ software .Western blot analysis was conducted with the pooled semen samples from normal and below-normal fertility spermatozoa to minimize individual differences. Moreover, the analysis was repeated at least three times with sperm samples from another set of semen batches from each group. Based on the signaling pathway enrichment analysis, we tried to determine whether the species-specific or fertility-related proteins are differentially expressed between normal and below-normal fertility using Western blot analysis. Both bovine and porcine semen samples were subjected to discontinuous Percoll separation in accordance with our previous studies , 13. TheWe summarized the significant differentially expressed fertility-related transcriptomic markers to construct comprehensive and novel functional enrichment maps. Based on the previous transcriptomic studies, a total 470 transcripts were identified as a subset of transcripts that are possibly relevant for bovine sperm fertility –23, 33. Pathway enrichment analyses of markers that were significantly differentially expressed between spermatozoa with normal and below-normal fertility from bulls . Moreover, sperm flagellum associated with enrichment pathways, including sperm flagellum and myelin sheath, were most highly enriched in fertility-related proteins in bovine spermatozoa . Also, VDAC2, ENO1, and GPX4 proteins were used as the spermatogenesis markers [As shown in Fig.  markers –38 whichP < 0.05).In porcine spermatozoa from normal fertility group, four Biological Process GO terms, including ‘phenol-containing compound metabolic process’, ‘TCA cycle’, ‘generation of precursor metabolites and energy’, and ‘regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway’ were identified by ClueGO analysis Fig. C. HoweveA total of 32 metabolites including 17 metabolomes in highly expressed in normal fertility and 15 metabolomes in highly expressed in below-normal fertility was identified from 3 comparative metabolomic studies , 17, 18 We compiled a list of the metabolomes that were differentially expressed between bull spermatozoa with normal and below-normal fertility . ATP and ROS are both required for boosting the hyperactivation of spermatozoa during capture and fertilization; ROS stimulate adenylyl cyclase to catalyze the synthesis of cyclic AMP from ATP, leading to protein kinase A activation, which stimulates the phosphorylation of tyrosine residues in the fibrous sheath of the sperm flagellum, resulting in hyperactivation of spermatozoa , 49. MorIt is noteworthy that spermatogenesis and structure-associated signaling pathways, such as chaperonin complex associated- and protein folding-related signaling pathways, were specifically identified in bovine spermatozoa, and negatively related to bovine fertility. The endoplasmic reticulum is an essential cellular component that plays a vital role in the folding and assembly of newly synthesized proteins in mammalian cells. However, because the endoplasmic reticulum is eliminated during spermatogenesis, a highly active protein synthesis and folding event occurs before the completion of spermatogenesis . Thus, wThe female reproductive tract contains many chemokines that trigger chemotaxis to control sperm motility and capacitation to optimize contact with oocytes , 56. SevThe dynamic relationships between genes, transcripts, proteins, and metabolites allow biological systems to function as a cohesive unit. Metabolites play an essential role in the biochemical environment because they serve as the primary components of all other biochemical structures, such as proteins, genes, and transcripts . MetabolThis study was to accumulate fertility-related omics data from boar and bull spermatozoa to provide comprehensive fertility-related mechanisms from gene to protein levels. Mitochondrial-associated signaling pathways were commonly identified as the significant fertility-related signaling pathways in both species; we also found species-specific pathways. Our analyses indicate the proper synthesis and folding of proteins during spermatogenesis may determine the bull sperm fertility. In contrast, the modulation of chemotaxis activity may determine boar sperm fertility to maintain the optimal capacitation state and the acrosome reaction. Although the further study is required for confirmation of different gene expression by western blot and RT-PCR between normal and below-normal spermatozoa to identify specific species-dependent mechanisms, this study provide preliminary information about different molecular pathways that govern sperm fertility in mammals depending on the species. Therefore, we propose that screening for species-specific signaling pathways may help identify fertility-related biomarkers within massive omics data. Also, this study may be useful for addressing species-specific fertility control systems to improve animal productivity. Furthermore, employing such evaluation processes may contribute to the elucidation of yet unknown characteristics of spermatozoa, which may contribute to enhancing reproduction.Additional file 1: Table S1. Summary of differentially expressed transcripts in bull spermatozoa with normal and below-normal fertility. Table S2. Summary of differentially expressed transcripts in boar spermatozoa with normal and below-normal fertility. Table S3. Summary of differentially expressed proteins in bull spermatozoa with normal and below-normal fertility. Table S4. Summary of differentially expressed proteins in boar spermatozoa with normal and below-normal fertility. Table S5. Summary of differentially expressed metabolites in bovine spermatozoa with normal and below-normal fertility.


Reformasi in 1998, we found emerging demographic features typically associated with societies in later stages of fertility transition. These include fertility below replacement in some regions; increasing age at first marriage, non-marriage, and divorce rates; and growing diversity in household/family forms. As the vast regions of Indonesia is economically, culturally, and demographically heterogeneous, these key features of SDT are not likely to emerge and unfold in a uniform manner. Further, these demographic shifts are taking place amidst multiple tensions and contradictions in the nature and direction of ideational change pertaining to marriage and the family. We argue that the prevailing ideational change driving the shifts in marriage, fertility, and the family within Indonesia is neither unilinear nor singular in nature. Emerging ideational change embodying individualism, secularism, and post-materialism—originally proposed in SDT theory to be the primary drivers of fertility decline in post-industrial Western Europe—can overlap with popular values promoting de-secularization and the strengthening of familial institutions. As a demographic framework, the SDT theory is an important and useful starting point. But it needs to be reevaluated by considering the complex socio-political and increasingly precarious economic terrains behind fertility transition, as well as marriage and family change in post-Reformasi Indonesia.As a predominantly Muslim and ethnically diverse new democracy in Asia, Indonesia is a timely case to study how the contending forces of development and social change are reflected in changing norms and practices around family formation. This paper examines the extent to which the second demographic transition (SDT) theory can provide a primary framework to understand contemporary patterns of fertility, marriage and family change in Indonesia. Against the backdrop of socio-political change following  Based on the empirical contexts of industrialization in the Western world, the demographic transition theory, later called “first demographic transition” in this paper, was once the dominant framework to explain the association between socioeconomic transformations and demographic change across different parts of the world. In brief, the (first) demographic transition theory outlines the process and drivers of shifts from high to low death and birth rates, and the ensuing changes in the pace of population growth through the course of such shifts. It was hypothesized that by the end of the demographic transition, fertility and mortality would reach replacement level usually measured with Total Fertility Rate (TFR) around 2.1 Kirk, .1 It is By the latter half of the twentieth century, it became apparent that the first demographic transition theory could no longer offer a sufficient framework to understand the continuing decline in fertility to below replacement levels in some parts of Europe. In 1986, the term “Second Demographic Transition” was coined by two European demographers–Dirk van de Kaa and Ron Lesthaeghe–as a novel framework to understand key features of demographic change in the Western world , and parenthood outside of marriage . Along with significant shifts to decentralized governance, Reformasi unleashed a wide array of “alternative” ideas around marriage and family formation, including those emerging with the growing popularity of what Ariel Heryanto  two children are enough; and (2) the ideal ages at marriage for men and women were 25 and 20 respectively. Such social marketing was deemed successful in changing the ideation of family size from “many children bring fortunes” to the norms of small family size.The second is to do with diversity in the nature and direction of ideational change related to norms, attitudes, and practices around marriage and family formation following 0s Hull, . During ugh; and  the ideaHeaton and Cammack  noted hoReformasi also provided a platform for the re-emergence of repressive customs and religious laws and heavily patriarchal stereotypes, and ethno-religious sentiments, in both national and local politics  have actively promoted early(er) marriage among educated young adults to avoid sinful pre-marital relationships  = 2.1 in 2020, but the TFR then slowly declines toward 1.97, just slightly below replacement level, in 2045. In this projection, fertility is allowed to be below replacement level, but will never go deeper. There has not been any official explanation of why fertility is not These two scenarios are in stark contrast to one of the predictions of SDT that all countries will eventually reach sustained below replacement level. Our analysis provides a different picture from the government official “projection” and the “trend” scenario by the Government and the United Nations mentioned earlier. The government uses data from censuses/intercensal surveys which usually provide lower estimates of fertility than those based on Demographic and Health Surveys (DHS) data. Our analysis is based on the more conservative estimates based on DHS. Based on the 2017 DHS ICF, , TFR in Moreover, as also shown in Table As we outlined earlier, Indonesia is a large and diverse country. It has 34 provinces with a relatively large variations in their development levels and TFRs.TFRs in 13 provinces were already as low as 2.2, 2.3, or 2.4, very near to replacement level, the last stage of the FDT, during 2014–2017. They were Jambi, Bengkulu, Lampung, Bangka Belitung, and Riau Island (in Sumatera Island); Jakarta, West Java, Central Java, Yogyakarta, and Banten (in Java Island); South Kalimantan ; and South Sulawesi and North Sulawesi (in Sulawesi Island). Under prevailing trends, these 13 provinces are projected to enter below replacement fertility rate. Altogether, there were already 15 out of 34 provinces that had been or were very close to below replacement during 2014–2017.Furthermore, wanted TFR in eight provinces  were already at replacement level. There were 12 provinces which already had wanted below replacement level: West Sumatera, Jambi, Bengkulu, Lampung, Bangka Belitung, Riau Islands; Jakarta, Yogyakarta, East Java, and Banten; Bali; North Sulawesi. Even Bali’s wanted TFR was already at 1.6, well below replacement level.This may indicate that in more than half of the provinces. The wanted fertility rate may have been below replacement levels in 2014–2017. In other words, 20 out of 34 provinces may have been below replacement level in 2022. It is possible that all provinces in Java Island are at or below replacement level in 2022. The population on Java Island accounts for about 55.5% of the total population in Indonesia. In contrast, there were four provinces with TFR still above 3.0 during 2014–2017. They were East Nusa Tenggara, Maluku, West Papua, and Papua . Their wanted TFR remained high, above 2.5 . Therefore, these four provinces are still far from entering the below replacement level.In short, the majority of provinces may have been at or below replacement level in 2020. Only few are still above or far above (near 3.0) the replacement level. This pattern supports our argument that declining fertility below replacement level may continue.Indonesia’s demographic diversity can also be seen from its ethnic groups. Indonesia had more than 630 ethnic groups in 2010 , the percentage of women aged 20–24 whose age at first marriage was under 18 in Central Kalimantan was 20.94%, while in South Kalimantan it was 23.12%. On the other hand, the figures for Riau Islands, Jakarta, and Yogyakarta were the highest at 23.1 in 2017. Similarly, it was due to the low prevalence of child marriage: the figure for Riau Islands was 4%, Jakarta was 3.18%, and Yogyakarta was 2.21%. The huge variation in median age at first marriage between provinces is largely attributed to significant differences in cultures, religions, and social norms  revealed that 6.9% of male and 3.8% of female students in Indonesia had ever had sex. Determinants of sexual intercourse among students include gender, school performance, substance use , school truancy, suicidal ideation, parental supervision, and peer support  was higher than the median age at first sexual intercourse (24.2), indicating that some men are exposed to pre-marital sexual intercourse. However, we should be cautious in interpreting these data, as people may be reluctant to say when they had their first sexual intercourse if it happened outside marriage, especially in Indonesia where pre-marital sexual intercourse is seen as taboo. O’Donnell et al. , using tIn contrast to Asian countries that face SDT with a declining married population over time, the marriage trend in Indonesia indicates that Indonesians continue to lean towards marriage as a universal norm. In 2017, the majority of women aged 15–49 were currently married (71.5%), with the highest percentage (92.0%) at ages 35–39 ICF, . The perAlthough it is practiced in some regions in Eastern Indonesia , the mean years of schooling for men increased from 7.91 to 8.92, while for women they rose from 6.89 to 8.17 during 2010–2021 , which increases the likelihood of being single .Second, to understand how democratic reforms are shaping ideational change pertaining to marriage and the family, we argue that one must resist the temptation toward binary categorization. This is often the case in many developmental paradigms  (Rakhmani, As another example, the bourgeoning industry of Islamic fashion suggests that many urban young middle-class Muslim women in Indonesia can both be devout Muslims while exhibiting postmaterialist inclination of self-expression and creativity Beta, . Furtheres Beta, . In the Reformasi. As is the case in other parts of the world, social media platforms such as Instagram, WhatsApp, Facebook, TikTok, and Twitter are immensely popular among young adults in Indonesia (DataReportal, Third, we propose that social media plays a major role in facilitating multiple ideational forces relevant to fertility transition after Reformasi is no longer singular nor unidirectional in nature. As articulated by Heryanto (Reformasi Indonesia suggests that “modernity does not imply or require secularization”. This is an important contribution to future research aiming to theorize the variegated nature and direction of ideational change pertaining to fertility transition.In sum, the contending forces directing ideational change on marriage and the family have presented a challenge to the singular narrative of fertility and family ideals of the New Order. Ideational change under Heryanto , p. 76, Reformasi, what might be the future outlook for the fertility transition in Indonesia? We speculate that much of this will be underpinned by two factors: what is happening in the realm of gender, work, and the family; and the impact of the increasing economic precarity for young people brought by the changing nature of work.In light of the contentious terrains of ideational change under First, despite achieving remarkable expansion in education for women, the pervasiveness of patriarchal norms continues to inhibit the progress of gender equality in Indonesia. The female labor force participation rate in Indonesia was stable at around 50% from 2010 to 2020 (Cameron et al., For educated women in urban areas, the privatized/familial support model for childcare has been made possible by an excess pool of women from rural areas with lower education levels being available to work as live-in domestic helpers (Nasution, The current context of gender equality brings the question about the future fertility in Indonesia. Borrowing McDonald ’s terminSecond, while economic and housing precarity have been theorized as key factors behind fertility transition in East Asia Fukuda, , we haveIn sum, the second demographic transition theory was first proposed in the 1980s to explain the transition to below replacement fertility in a specific historical context in Western Europe. Most recently, in light of varying structural and ideational forces that drive the global fertility transition, Lesthaeghe  reiterat


Numerous studies aim to connect negative fertility desires and outcomes with employment conditions deemed to be uncertain. However, there is a lack of consensus about how to define, conceptualise, and measure employment uncertainty. This paper considers issues surrounding the conceptualisation of employment uncertainty. It then reviews existing measures of employment uncertainty in the context of fertility decisions. Finally, it raises considerations about their use. While some aspects of employment uncertainty are well studied, there are still gaps between theory and empirical evidence. Researchers should be aware of existing population heterogeneity, contextual factors, and model selection when considering their conceptualisation of employment uncertainty. Growing employment uncertainty in advanced economies and its relationship to fertility has received significant attention in recent decades . National governments and/or private entities within countries  often collect vital statistics and employment status on a yearly, monthly, or even weekly basis, making large, reliable datasets available. Harmonised definitions allow objective measures to have advantages for cross-country comparisons. Hence, it is the universality, availability, comparability, and perceived straightforwardness that makes them attractive. However, objective measures are not inherently more straightforward, especially when used to measure subjective concepts like employment uncertainty. Almost all other objective measures of employment uncertainty build upon how researchers demarcate employment statuses as either certain or uncertain. Therefore, a study’s conceptualisation of employment status matters. While objective measures may be preferred because they lack the pitfalls of interpersonal subjectivity, they also do not capture differences in the experience or perception of employment uncertainty—something better fit for subjective measures.Subjective measures are generally elicited through individual surveys, which include questions about levels of perceived employment security or proxied via associated stressors. This includes financial and employment security, job-related stressors, and/or job loss resiliency . This includes measures like national and local unemployment rates as well as the share of specific contractual agreements   experiences. They can, however, also be influenced by community or society-wide changes. Therefore,  Adsera, . Recent  Adsera, .income volatility and economic insecurity. Income volatility is defined as income fluctuations from a trend, generally measured as deviations from a predicted mean, and is a backwards-looking objective measure . We screened the articles using screening and eligibility criteria.Numerous studies analyse individuals’ or couples’ employment status in the context of fertility/childbearing. Baizán  explorespermanent employment, self-employment,2atypical employment, and other non-standard jobs.0 indicates insecure work  and 1 indicates secure work (permanent or self-employed).Barbieri et al.  create fAn alternative approach categorises the positional status of a woman in the household by accounting for her partnership status and her partner’s employment status. Alongside women’s employment status, Wood and Neels  create 1The above-mentioned studies focus on current employment. Vignoli et al.  examine Using retrospective employment histories is an approach to quantify time spent in unemployment . Özcan et al.  analyse t-1 to capture the recent experiences of each individual in the labour market. Next, they create a time-varying ratio of years spent in unemployment/short-term employment over permanent employment since union formation. Finally, they include employment status at union formation.Pailhé and Solaz  examine In contrast to measuring unemployment as a count of states or as a ratio, other work attempts to measure more dynamically by combining the number and intensities of unemployment spells into one indicator. Ciganda  uses thePersistent Joblessness Indicator (PJI), which standardises the experience of career joblessness between 0 and 1. Their indicator focuses on being in or out of paid employment as a binary state. Like Ciganda  as well as employment status .The relationship between employment uncertainty and fertility is likely mediated by occupational class. Bernardi and Nazio  apply thperceived work control, is based on the following six features: pace of work, daily organisation of work, power in company policy decisions, requirement to learn new skills, if the job offers variety in tasks and challenges, and if tasks are not closely supervised. The second factor is job strain or the feeling that there is never enough time to finish tasks. Finally, work–family conflict comprises the following four aspects: worrying about work problems when not working, feeling too tired after work to enjoy things one would like to do at home, finding the job prevents from spending time with a partner or family, and perceiving that the partner or the family get fed up with the pressures of the respondent’s job. They further incorporate a weight based on the importance placed on being able to combine family and work when choosing a job.Perceptions of job flexibility, control, and tasks help clarify the relationship between participation in the labour market, hours worked, and the perception of employment uncertainty. Recent work examines job characteristics like working location, perceived work–life balance, and job-related well-being . While this might directly relate to income, it can also concern a position’s prestige and the perception of advancement opportunities. Schmitt  examinesThe most direct way to capture forward-looking subjective perceptions of employment uncertainty is to ask job security questions in surveys. Golsch  includes1 being “employed and secure” and 6 being “unemployed with a low perceived chance of finding a job.” The logic is that the middle scores, 3 or 4,Kreyenfeld  incorpor1 if the person perceives job insecurity and 0 if not.Fahlén and Oláh  combine Various studies have used perceptions of financial security as a measure of general economic uncertainty  child. However, this relationship seems to have reversed across a variety of low-fertility contexts  which influences the type of labour market individuals encounter. EPL could work in two directions (Bastianelli et al., Studies focusing on macroeconomic trends and micro-level employment are still largely walled off from each other. Several studies show that in low-fertility countries TFR is cyclical with the macroeconomic situation (Comolli et al., The first decision in modelling is the outcome variable choice. This is often dictated by data availability, with researchers relying more heavily on measures of fertility preferences when vital statistics data are unavailable. Studying fertility preference allows researchers to observe if there are mismatches between desired and realised fertility. This includes studying the ideal number of lifetime children as well as short-term fertility intentions. However, studies linking women’s employment and fertility preferences show little association (Kuhnt et al., Papers examining the relationship between employment uncertainty and fertility outcomes have largely used event history analysis. These models permit the use of unbalanced panels, overcoming issues of attrition that plague panel surveys. Furthermore, event history analysis allows for the use of time-varying covariates, giving researchers access to richer measures that more fully capture fluctuations in employment uncertainty. Many of the measures discussed in "Where does employment uncertainty fit in the larger landscape of explanations for the continued decline in TFR in low-fertility countries? As previously discussed, several forms of employment uncertainty are linked to negative fertility outcomes. However, since most studies only test one conceptualisation of employment uncertainty it can be hard to draw valid conclusions about the whole concept. There exists a large amount of empirical evidence that unemployment negatively associates with fertility behaviour. However, does this mean that employment uncertainty, rather than something like the loss of income, is the mechanism behind the association? This remains an open question. What makes employment uncertainty particularly difficult to parse from other mechanisms is that it may also be negatively associated with fertility through other life course domains. For example, men appear to have a negative relationship with fertility and employment uncertainty, due to their attractiveness in the marriage market Kalmijn, . IndividAlternative explanations for the decline in TFR include the double burden that many employed women face due to a stalled gender revolution (McDonald, This paper reviews several conceptualisations and measures of employment uncertainty with the goal of helping push the field beyond extrapolating from employment status and contract type. It highlights several open questions within the field. Still, this overview paper is not without its limitations. Employment uncertainty studies tend to be limited to a few high-income countries. Much of the work on employment uncertainty takes a life course perspective, popularised throughout Europe (Huinink & Kohli,


Bull fertility is of paramount importance in bovine industry because semen from a single bull is used to breed several thousands of cows; however, so far, no reliable test is available for bull fertility prediction. In the present study, spermatozoa from high- and low-fertility bulls were subjected to high-throughput transcriptomic, proteomic and metabolomic analysis. Using an integrated multi-omics approach the molecular differences between high- and low-fertility bulls were identified. We identified a total of 18,068 transcripts, 5041 proteins and 3704 metabolites in bull spermatozoa, of which the expression of 4766 transcripts, 785 proteins and 33 metabolites were dysregulated between high- and low-fertility bulls. At transcript level, several genes involved in oxidative phosphorylation pathway were found to be downregulated, while at protein level genes involved in metabolic pathways were significantly downregulated in low-fertility bulls. We found that metabolites involved in Taurine and hypotaurine metabolism were significantly downregulated in low-fertility bulls. Integrated multi-omics analysis revealed the interaction of dysregulated transcripts, proteins and metabolites in major metabolic pathways, including Butanoate metabolism, Glycolysis and gluconeogenesis, Methionine and cysteine metabolism, Phosphatidyl inositol phosphate, pyrimidine metabolism and saturated fatty acid beta oxidation. These findings collectively indicate that molecules governing sperm metabolism potentially influence bull fertility. Use of semen from low-fertility bulls leads to colossal loss to the farmers; therefore selection of bulls for high fertility is very crucial for successful dairying. Currently, bulls are selected based on breeding soundness evaluation (BSE) that mainly focus on examination of phenotypic characteristics and evaluation of few characteristics of spermatozoa like motility and morphology. The bulls that passed all these criteria of breeding soundness evaluation still showed differences in conception rate to the magnitude of 20–25% among themselves4. Over the past decades, in vitro tests to screen the structural and functional integrity of the spermatozoon has increased markedly, but the capacity to estimate the fertility of a semen sample or of the sire from which it has been collected has not5. The methods for assessment of sperm phenotypic and functional characteristics have limited to moderate correlations with fertility and therefore are only of limited value for prediction of bull fertility6. Further, it has been shown that bulls with normal spermiogram, kinematics and morphology varied in their fertility7; the fertility of a considerable proportion of bulls remained suboptimal with a conception rate ranging from 20 to 45%8 leading to considerable economic losses. On the other hand, female fertility has received much attention in the last few decades, while bull fertility has been largely overlooked9.Bull fertility is the most important economic trait in cattle because semen from one bull is used to artificially breed a large number of cows10, sperm DNA integrity12, sperm transcripts16, sperm proteins18, seminal plasma proteins21 and sperm metabolites23. The major advancements in the -omic technologies  have enabled high-throughput screening of a wide range of molecular and cellular dynamics in fertility molecules24. Employing these techniques several studies identified the molecular differences between the spermatozoa of high- and low fertility bulls23. However, it is pertinent to mention here that the most of the earlier studies conducted to identify bull fertility markers have employed assessment of a particular parameter  in relation to bull fertility. Although accumulating studies indicate that the level of specific sperm molecules can be related to bull fertility; so far no single, highly reliable test is available25.Routinely, the bull fertility is estimated based on insemination of large number of cows and assessment of non-return rates/conception rates, which is costly and highly time consuming. Therefore, development of alternate techniques/methods/tools for bull fertility prediction is an area of research that has been active over a period of recent times. Recent developments in high-throughput sequencing technologies that allow the assessment of thousands of molecules in a given time has revolutionized the research on bull fertility. Using such techniques, several studies identified that bull fertility was influenced by several factors including sperm functional attributesomics is challenging due to the complex biochemical regulation involved at multiple levels. Integration of multi-omics data offers several advantages; the biological processes can be investigated in a more comprehensive manner at the transcript and protein levels that regulate the underlying pathway mechanisms. Further, it has the potential to reveal key biological insights into pathways that would otherwise not be made apparent through single-omics studies. Therefore, to develop highly sensitive biomarkers or diagnostic tools for the evaluation of male fertility, an array of high throughput technologies emerging from multi-omics or integrative approaches has to be employed27. Therefore, in the present study, we adopted an integrated multi-omics analysis approach combining transcripts, proteins, and metabolites data from high- and low-fertility bull spermatozoa, for understanding the critical sperm molecular differences associated with bull fertility. To the best of our knowledge this is first study to use integrated multi-omics approach to understand the bull fertility. We report here the differential expression levels of sperm RNA, proteins, and metabolites between high- and low-fertility breeding bulls, and the biological insights into key pathways associated with bull fertility.Interpreting the biological results from data of a single type of -The proportion of membrane intact spermatozoa was lower (P < 0.05) in LF bulls in comparison to the HF bulls. Similarly, the proportion of acrosome intact spermatozoa was also significantly P < 0.01) higher in HF bulls compared to LF bulls. While the proportion of spermatozoa with high mitochondrial membrane potential was higher (P < 0.05) in HF bulls, there was no difference observed in the proportion of spermatozoa with high intracellular calcium between the two groups of bulls , and the significance level associated to this value (y-axis). The demarcation in expression levels of sperm transcripts belonging to high- and low-fertility bulls are shown as volcano plot in Supplementary Fig. The sequencing library resulted in generation of Paired-end 76 bp × 2)  fastq fiWe investigated the potential biological functions of the differentially expressed genes (DEGs) (P ≤ 0.05). Gene ontology (GO) analysis of total 383 DEGs revealed their involvement in 25 BPs, 14 MFs, and 27 CCs . The GO analysis of these translated proteins showed that these proteins were enriched in BPs, mainly including transcription, proteolysis, cell differentiation and negative regulation of protein kinase activity Fig. A. The KEAn integrated network analysis between the total dysregulated transcripts and metabolites was carried out to find out the interaction and pathways in which transcripts and metabolites were involved Fig. A. DysregA protein-metabolite network was generated from the data obtained by the dysregulated proteome and metabolites Fig. A. The neKEGG pathway analysis of DEMs and DEPs having corresponding differential expression at transcript level revealed 14 pathways wherein the metabolites and translated proteins were involved. Among these pathways, significant interaction between the transcript, protein and metabolites was observed in seven pathways viz. Butanoate metabolism, Glycolysis and gluconeogenesis, methionine and cysteine metabolism, phosphatidyl inositol phosphate metabolism and purine and pyrimidine metabolism suggesting these pathways are altered or dysregulated in low-fertility bulls Fig. A–C. FurtCorrelation analysis was carried out between the translated proteins (commonly dysregulated between transcripts and proteins) and sperm functional parameters Fig. . This an28. Molecular content of spermatozoon are dynamic, and the factors affecting spermatogenesis and epididymal maturation may influence the sperm composition and hence it is vital to study the molecular details of sperm in order to predict the fertility of bulls30. Although the advanced semen evaluation methods like Flow cytometry can aid in analysing several sperm quality parameters in relation to fertility, it may not be sufficient to understand the alterations in the molecular health of spermatozoa in infertile/sub-fertile males with normal spermiogram. Greater time (~ 60 days) is needed for spermatogenesis and final sperm maturation in bulls, and there is ample time for molecular, cellular, and physiological errors to occur that can hamper sperm production and render infertility24. Defects in the male germ cells during foetal life may be more probable causes of infertility than defects incurred in later phases of development27. Therefore, more comprehensive and complicated studies spanning developmental stages and robust methods are needed to accurately ascertain semen quality and predict bull fertility. There is a need for additional knowledge on the expression levels and functions of sperm transcripts, proteins, and metabolites in relation to fertility, which can elucidate additional fertility markers that can be used in combination for fertility prediction. In this context, application of integrated omics or mutli-omics helps researchers generate extensive knowledge to better understand the unravelling physiological mechanisms underlying subpar male fertility.Spermatozoa delivers, not just the paternal DNA, but other components such as mRNA, miRNA and transcription factors and other cell signalling molecules, into the oocyte at the time of fertilization23.In the current study, we utilized comparative high-throughput transcriptomic, proteomic and metabolomic analysis to identify the molecules and key pathways associated with bull fertility. We identified a total of 18,068 transcripts, 5041 proteins and 3704 metabolites in bull spermatozoa, of which the expression of 4766 transcripts, 785 proteins and 33 metabolites were dysregulated between high- and low-fertility bulls. The number of transcripts, proteins and metabolites observed in the current study is in agreement with those reported earlier31. The top downregulated genes  were reported for their role in protein phosphorylation, sperm maturation, spermatogenesis, gamete fusion, cryotolerance capacity and sperm motility34. An interesting observation of the current study was that expression of several ribosomal transcripts  involved in the ribosomal pathway were found to be altered in low-fertility bulls. The presence of ribosomal protein transcripts in spermatozoa and their differential expression between high- and low-fertility bulls indicate their crucial role in the production of healthy sperm and fertility35. Defects in ribosome biogenesis could affect the assembly of ribosomes in spermatozoa mitochondria, which further may alter the functions of mitochondria in spermatozoa16. Another important finding was that the transcriptional abundance of several genes  involved in oxidative phosphorylation pathway were found to be downregulated in the low fertility bulls. Any variations in this pathway will alter the sperm metabolism and function38 as this pathway is crucial for the sperm to synthesize ATP and produce energy in all mammals39. Hence, the impaired oxidative phosphorylation could be the major contributing factor for the low infertility in these bulls.Among the total dysregulated genes, the top upregulated genes  were reported to have their roles in sperm motility, apoptosis, early embryonic development and fertilisation24. We observed that expression of 785 sperm proteins were dysregulated between high- and low-fertility bulls.Various proteins exists in the spermatozoal membrane, acrosome, flagellum, cytoplasm and nucleus play important roles in sperm motility, fertilisation and cell signalling which may be vital in determining the fertility of the bulls40. TKTL1 was reported to be associated with normal spermatogenesis as it was not detected in the patients suffering from obstructive or non-obstructive azoospermia41. AMPK pathway is reported to be key mechanism in regulation of energy metabolism homeostasis42, and therefore, it has recently emerged as a new signalling pathway that exerts a necessary control of sperm function43. Arginine and proline metabolism, was found to play an important role in energy metabolism, and some substrates could be used in the sperm medium for storage and cryopreservation44. The network analysis revealed that the downregulated proteins were associated with many important pathways like execution of phase of apoptosis , progesterone mediated oocyte metabolism , sperm motility  and single fertilisation . SLC and TEKT family proteins were reported to play a pivotal role in maintaining the normal flagellar structure, progressive motility and fertility in mouse spermatozoa46. CDC25C and CDC27 were observed to be important for phosphorylation48 and ZP4 is important for sperm egg fusion and acrosome reaction49. All the proteins mentioned in these pathways were observed to be significantly downregulated in low-fertility bull spermatozoa. The top down regulated sperm proteins  in low-fertility bulls are involved in key sperm functions like resistance to oxidative stress50, blastocyst formation51, energy metabolism, sperm motility52 and sperm storage in the female reproductive tract53. It has been reported that TXNDC family genes (Txndc2 and Txndc3) deficiency in spermatozoa lead to loss of motility, damage of DNA at higher rates, increased ROS, enhanced formation of lipid aldehyde-protein adducts, and impaired protamination of the sperm chromatin49. TXNDC9 was reported to diminish the chaperonin TCP1 complex ATPase activity significantly, thus negatively impacts protein folding, including that of actin or tubulin54.GO and KEGG analysis revealed that sperm proteins involved in various important pathways are downregulated in low-fertility bulls. Important proteins like ATP6V1C1, ATP6V1H, AHCY, GAPDHS, POLR3GL, PFKL, ACADS, and TKTL1 are central to the metabolic pathways and are significantly downregulated in low-fertility bulls. GADPHS , a sperm-specific glycolytic enzyme, is reported to be essential for sperm motility and male fertility55 and propanoate metabolism was observed to play an important role in cryopreservation of spermatozoa56. The metabolites involved in Taurine and hypotaurine metabolism were reported to be essential for providing energy and antioxidative protection to the spermatozoa for their progressive motility57. In low-fertility bulls, significantly dysregulated metabolites included Hypotaurine, Selenocysteine, dITP, Maleic acid, d-cysteine, Acetoacetyl-CoA, Phosphatidylinosito and dUTP. It is well documented that hypotaurine plays a vital role in antioxidant activity. In human sperm, the low concentration of hypotaurine were considered as a marker of sperm stress, suggesting that the oxidation of hypotaurine to taurine may occur in poor quality spermatozoa58. Selenocystiene, is reported to be associated with sperm quality while maleic acid is involved in pyruvate oxidation in sperm mitochondria22.We observed that the dysregulated metabolites were found to be involved significantly in pyrimidine metabolism, taurine and hypotaurine metabolism, propanoate metabolism, synthesis and degradation of ketone bodies, nitrogen metabolism, Vitamin B6 metabolism and Valine, leucine and isoleucine metabolism. Pyrimidine metabolites such as uridine may enhance the sperm motility or act as anti-oxidantomic approach and found that 5041 transcripts detected in bull spermatozoa had corresponding proteins also. Integrated transcriptome and proteome network analysis revealed that the translated proteins were found to have relevance in important pathways like positive regulation of cell death , AMPK signalling pathway , sperm motility , single fertilisation , and execution phase of apoptosis . The overlap analysis between the DEG (383 transcripts) and DEP (785 proteins) resulted in a list of 182 translated proteins. These DEPs were found to be involved in important pathways like metabolic pathway , TNF signalling pathway , Rap1 signalling pathways , Prolactin signalling pathway (SOCS2) and pentose pathway . These molecules are reported to have important functions in sperm quality and fertility. For instance, AKT has been shown to mediate various important process like spermatogenesis, sperm maturation, and fertilization59 while low levels of sperm GCDH was reported to be a cause of asthenozoospermia60. ZPR and JUNB were observed to be associated with embryonic cleavage, blastocyst formation, blastocyst development and blastocyst growth, placentation and embryogenesis63. Fibroblast growth factor-5 (FGF5) promotes spermatogonial stem cell proliferation65. These proteins are found to be involved in playing vital role in sperm motility, mobilization of acrosomal calcium during sperm exocytosis, regulation of spermatozoal apoptosis and maintenance of acrosome and chromatin integrity. A correlation analysis was also carried out to correlate the important translated proteins (dysregulated) and sperm functional parameters  cycle in the mitochondrial matrix is cardinal66. TCA cycle is known to be a crucial metabolic pathway that contributes to production of ATP67. These pathways are very important because of their vital role in sperm metabolism, sperm motility and in apoptotic mechanisms.Upon integrating the transcriptome-metabolites and protein-metabolites, we studied their interaction in important pathways associated with sperm quality and fertility. The analysis revealed that Androgen and oestrogen biosynthesis metabolism, Galactose metabolism, TCA cycle, Propanoate metabolism, Purine metabolism and Glycolysis and gluconeogenesis metabolism pathways were to both the integrations. Glycolysis and gluconeogenesis consists of a series of biochemical reactions and metabolites to generate energy in the form of ATP57. Butanoate metabolism, glycolysis and gluconeogenesis are crucial for sperm motility and energy production in spermatozoa69. Glycolysis and gluconeogenesis pathways are very vital for sperm motility as glycolysis could produce ATP adjacent to the site it is required to support active sliding of the flagellar filaments69. Various genes, proteins and metabolites found in these pathways and their function are listed in the Table 70. AHCY is reported to be involved in the energetic metabolic pathway, leading to the production of phosphocreatine, which is essential for carp sperm motility71, while GCDH was reported to be associated with progressive motility in human beings60. From the foregoing information, it is very much evident that the results of integrated multi-omics analysis of bull spermatozoa link up sperm metabolism with bull fertility and it is clearly appreciable that the expression of molecules associated with sperm metabolism was altered in low-fertility bulls.We further integrated the transcriptome, proteome and metabolite data to find the interaction among them. We found the interaction of transcript, protein and metabolite in seven metabolic pathways, which include Butanoate metabolism, Glycolysis and gluconeogenesis, Methionine and cysteine metabolism, Phosphatidyl inositol phosphate, pyrimidine metabolism, saturated fatty acid beta oxidation and Vit B metabolism. Methionine and cysteine metabolism was significant, and the observed metabolites were taurine and hypotaurine, which plays a significant role in maintaining sperm motility, capacitation and the acrosome reactionIt may be concluded that integrated multi-omics analysis of spermatozoa from high- and low-fertility bulls revealed that molecules and pathways associated with Glycolysis and gluconeogenesis, cysteine and methionine metabolism and butanoate metabolism were dysregulated in low-fertility bulls. These molecules and pathways are important for sperm metabolism, energy production and regulation of oxidative phosphorylation. All these findings collectively indicate that molecules governing sperm metabolism potentially influence bull fertility. Therefore, there is a need for an in-depth analysis of the molecules involved in sperm metabolic pathways to expand our understanding on bull fertility and to find out fertility markers for selection of bulls for high fertility.The present study was carried out at the Theriogenology Laboratory, Southern Regional Station of ICAR-National Dairy Research Institute, Bengaluru, India. All the experiments were conducted in accordance with the guidelines and regulations laid down and duly approved by Institute Animal Ethics Committee (CPCSEA/IAEC/LA/SRS-ICAR-NDRI-2019/No.04).10. The adjusted conception rate was used for the calculation of bull fertility. Bulls with conception rate more than Mean + 1 standard deviation (3.95%) were considered as high-fertility and those bulls with conception rate less than Mean − 1 standard deviation were considered as low-fertility. The differences in conception rates between high- and low-fertility bulls were more than 7.9%. The conception rates and number inseminations done per bull are presented in the Supplementary Fig. Holstein Friesian crossbred bulls n = 50) that qualified the breeding soundness examination and regularly used for artificial breeding were utilized for the study. All the experimental bulls have qualified the breeding soundness evaluation and were routinely used for semen collection and artificial breeding. The bulls used in the current study were maintained under tropical climate following standard bull management practices as per the Minimum Standard Protocol for semen production. The bulls were fed with concentrate ration containing 21% crude protein and 70% total digestible nutrients along with ad libitum seasonal green fodders. The bulls had free access to clean drinking water throughout the day. Vaccination, de-worming, regular check-up for communicable diseases and other herd-health programmes were followed as per the farm schedule, to protect the animals from diseases and to produce quality semen. As per the standard method, semen from these bulls were collected using artificial vagina and cryopreserved in Tris-Egg Yolk-Citrate extender using a programmable freezer. The motility of the semen from these bulls were assessed by a single qualified technician immediately after the collection and after freezing using microscopy. The fertility of the bulls were calculated based on conception rates (CR). The mean number of artificial inseminations performed per bull for calculation of conception rates was 1351 (range 841–2202). The CR of the bulls were calculated based on the number of animals conceived out of total number of animals inseminated (up to 3 inseminations). Artificial inseminations (AI) were carried out by certified AI workers in a particular region where the external factors influencing the success of AI are almost similar for all animals including the management practices. Further the effect of non-genetic factors on conception rate was adjusted as previously described0 that quSperm membrane integrity, acrosome integrity, mitochondrial membrane potential and intra cellular calcium levels were assessed in high and low fertility using a Flow Cytometer . Excitation was induced by a blue laser (488 nm). FITC fluorescence was detected with a fluorescence channel (FL) 1 band-pass filter (525/40 nm) and PI fluorescence was measured using a FL3 band-pass filter (585/42 nm). The results of Flow Cytometry analysis are given in Supplementary Fig. 12. SYBR-14 working solution  was added to 2 million spermatozoa in 200 mL of sp TALP and incubated at 37 °C for 10 min in the dark and then 2 mL of PI (2.4 mM) was added and further incubated for 2 min before analysing in flow cytometer.SYBR-14  and Propidium iodide (PI)  was used for assessing the sperm membrane integrity as previously describedAcrosomal integrity was assessed using FITC-PNA (fluorochrome fluorescein isothiocyanate-Peanut agglutinin) and PI combination, 1 µL of 1 mg/mL FITC-PNA was added to approximately 2 million spermatozoa and incubated at 37 °C for 10 min, then 1 µL of 2.4 mM PI was then added and incubated for 2 min and assessed using flowcytometry.Mitochondrial membrane potential was assessed using JC-1 stain. Approximately to 2 million spermatozoa, 5 µL of JC-1 (0.2 mM) was added and incubated at 37 °C for 30 min before analysing in flow cytometer.Intracellular calcium assessment in spermatozoa were carried out using Fluo 3 AM . To 2 million spermatozoa, 3 µL of Fluo 3 AM (1 mM) was added and incubated at 37 °C for 30 min. PI (2 µL) was then added and further incubated for 2 min before analysing in flow cytometer.16. Briefly, the thawed semen (37 °C for 30 s) was carefully layered over the top of prepared Percoll gradient and was centrifuged for 15 min at 950×g to obtain the sperm pellet. The semen pellet was washed with phosphate buffer saline  three times and subjected to RNA isolation. Total RNA was isolated from Percoll-selected spermatozoa using TRIzol  as previously described by Prakash et al.16. The isolated RNA was treated with DNase to eliminate traces of gDNA. The RNA quantification was done using NanoDrop . Samples with optimal purity (OD 260/280 > 1.8 and 50 ng/mL) were selected for further cDNA synthesis using RevertAid First Strand cDNA Synthesis Kit  as per the manufacturer’s instructions. Two representative cDNA samples were made from the six high- fertility (HF) bulls by pooling equal quantities of cDNA from three HF bulls each. Similar method was adopted for low-fertility (LF) bulls. The samples were sequenced using Illumina Nextseq-500 sequencing system .Spermatozoa in semen samples were purified using Percoll gradient (90–45% discontinuous) centrifugation to eliminate the semen extender, cryoprotectant, egg yolk particles and epithelial cell contaminationUseGalaxy72. The sequencing library generated Paired-end (76 bp × 2) fastQ file Raw data for the 4 (2 high- and 2 low-fertility groups) samples. FastQC v0.11.5 was used for checking the quality and GC content of the data generated73. Low quality bases were removed and high quality reads were retained with minimum length of 1574 based on the phred quality score. The removal of low quality bases along with adaptor removal was done using Cutadapt74. For reference based transcriptomics study, the processed high quality data was aligned against the Bos taurus UMD 3.1 genome using Hisat2 v2.1.075. After the alignment, aligned files were sorted with the aid of Samtools v1.1176. Upon alignment, read count was obtained for each transcript in gff reference file through htseq-count v0.9.177. Sample groups with the read count were further analysed for differential expression using DESeq78. Expression changes are seen as log2Fold Change, for upregulated transcripts log2FoldChange > 1 while for downregulated transcripts log2FoldChange < − 1. The raw data for the study is available in the NCBI SRA database under project submission ID-PRJNA516089 (https://www.ncbi.nlm.nih.gov/sra/PRJNA516089).The raw data generated from sequencing was analysed using the cloud server 2. For in solution digestion, 100 µg of the sample was taken and diluted with 50 mM Ammonium Bicarbonate before being used for peptide extraction. This sample was treated with 100 mM DTT at 95 °C for 1 h followed by 250 mM IDA at room temperature in dark for 45 min. Then, digested with Trypsin and incubated overnight at 37 °C. The digested peptides were speed vacuumed and reconstituted in 50 µL of water containing 0.1% formic acid. Following centrifugation at 10,000×g, 10 µL of the sample supernatant was injected onto a C18 UPLC column for peptide separation. Following digestion and column separation, the sample was run on an LC–MS–QTOF instrument (Waters-Synapt G2), for obtaining the high-resolution data79. The samples were administered in triplicate to ensure accurate data collection from each sample and statistical significance. Waters’ MASS LYNX 4.1 program was used to capture and analyze the raw data. The peptides were loaded with buffer A and eluted with buffer B  at a flow rate of 0.3 mL/min. Using the PLGS program 3.0.2, the individual peptides’ MS/MS spectra was matched to the index, taking into account the mass changes caused by various modifications (Carbamidomethylation (C), Oxidation (M), and so on) 80. Following the protein identification of all samples, expression analysis was conducted pair-wise for the samples based on conditions i.e. high-fertility vs low-fertility. The spectral data was auto-normalized and analysed by the ProteinLynx Global server (PLGS) application, for measuring the ratios to assess the up-regulation and down-regulation of common proteins. The parameters used for identification are Peptide Mass Tolerance at MS1 level: 50 ppm and Fragment Mass Tolerance at MS2 level: 100 ppm. Carbamidomethyl on cysteine as fixed modification and oxidation of methionine and N-terminal acetylation were considered as variable modifications for database search. The Score is calculated by the expression analysis extension of PLGS Software based on the relevance of the protein present in both the samples being compared. Differentially expressed proteins were identified by calculating fold change of expression values (log base2) with respect to control samples. High fertility bulls samples were taken in as control and Low Fertility were considered as treated. Differentially expressed proteins include upregulated (> twofold) and downregulated proteins (< 0.5-fold) in the treated sample. The unique Proteins mentioned for each Control and Treated are the proteins that did not have any matching peptides or m/z values between the groups.Purification of the spermatozoa was done using Percoll gradient (90–45% discontinuous) as described above. Protein was extracted from the spermatozoa as per the methods adopted previously by Aslam et al.22. Briefly, 100 μg of protein equivalent samples were taken for the extraction of metabolites. Pooled samples was added with LC/MS grade water (Merck), Methanol, and acetonitrile  in the ratio of 1:2:2 (i.e. v/v/v). The samples were vortexed for 1 min and then subjected to sonication for 10 min and centrifuged at 12,000×g for 15 min at 4 °C. The supernatant was collected and further processed for drying in speedVac (Thermo Fisher Scientific) for 1 h and then processed further for LC–MS/MS analysis.The extraction of metabolites from the spermatozoa was carried out as per the methods previously described by Saraf et al.http://csbg.cnb.csic.es/mbrole2). Antibiotics, pesticides, and heavy metals were also identified as exogenous metabolites in MBrole 2.0, which were excluded from further downstream analysis. Regardless of its polarities, sorted endogenous metabolites of both groups were consolidated to carry out the statistical analysis and enrichment of metabolic pathways in MetaboAnalyst software.The LC–MS/MS analysis was performed on a QTRAP 6500 mass spectrometer  coupled with 1290 infinity II liquid chromatography system (Agilent Technologies). The samples were then injected on to C18 Zorbax rapid resolution high definition analytical columns  with the help of programmable auto sampler. Analyst software version 1.6.3 with the analyst device driver was used to set the parameters for the analysis. The separation of the metabolites was carried out using a 30 min LC method. Solvent A (0.1% formic acid in MilliQ water) and solvent B (0.1% formic acid in 90% acetonitrile) were used for analysis with a standard flow rate of 0.3 mL/min. Elution of metabolites was carried out using the following gradient: 2% B for t = 0 min; 30% B fort = 10 min; 60% B fort = 17 min; 95% B for t = 22–26 min, and 2% B for t = 27.5–30 min. The mass spectrometry data acquisition was carried out with information dependent acquisition (IDA) method, built with enhanced mass spectra (EMS) and enhanced product ion (EPI) i.e. EMS‐IDA‐EPI method in low mass mode. The top five spectra from the EMS mode were used for analysis in the EPI (MS/MS) mode; using high energy collisionally induced dissociation (CID). The metabolite data were acquired in both positive and negative polarities at 4500 V and − 4500 V, respectively, with a probe temperature of 450 °C. The compound parameters were set at a declustering potential of 75 V and collision energy of 45 V. The data was acquired in triplicates. After sorting out the metabolite IDs, the majority of the identified endogenous metabolites are categorized into lipid, phospholipid, glycerophospholipid, amines, common amino acid, fats, and common fatty acid based on functional enrichment of chemical compound in MBrole 2.0  and Cluepedia (Version 1.5.4) plugins in the open source Cytoscape (Version 3.7.1) platform82. A network of interactions between all dysregulated transcripts, proteins and metabolites were obtained in the form of different layouts. All bioinformatic analyses were performed by considering B. taurus as background species.Gene ontology (GO) classification of total dysregulated sperm transcripts, proteins and metabolites were carried out done using Uniprot and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resources (v6.8) and categorized as biological process (BP), cellular component (CC) and molecular function (MF) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. A donut pie chart was plotted for top ten dysregulated BPs, MFs, and CCs using Highcharts. Significantly differentially expressed transcripts, proteins and metabolites with a P-value cut-off of 0.05 were taken in DAVID , an online annotation software, and pathway enrichment was carried using Metscape v3.1.3.83 plugin was used in Cystoscope v3.8.2 tool. DEMs together with DEGs and DEMs with DEPs were analysed together using the Metscape and Metaboanalyst 5.0. Based on the compound and genes, pathway based analysis was carried for integrating the metabolites with genes and proteins.For understanding the biological system, a holistic approach where transcriptomics, proteomics and metabolomics data were considered to find the correct biological interpretation  and various semen parameters, correlation analyses was carried out. Commonly dysregulated genes (n = 182) between transcripts and proteins were functionally annotated. The list of dysregulated genes provides variability in the data and distinguishing factor between the sample group (HF and LF samples). Some of these genes involved in biological processes, molecular function and cellular component  were correlated with sperm-function attributes (Conception rate (CR), Live%, Live acrosome intact (LAI), Live acrosome reacted (LAR), high mitochondrial membrane potential (high MMP), low mitochondrial membrane potential (Low MMP), Live Calcium negative (Live CA−), and Live Calcium positive (LIVE CA+). The abundance of protein  was taken as input to correlate with sperm-function attributes. The R library “corrplot” was used to correlate and generate the visualization.The descriptive analysis of the conception rate calculated was done using SPSS version 22 . The division of bulls into high and low fertility was based on mean ± 1 SD. For the bioinformatic analysis the following statistical procedures were adopted Functional enrichment analyses of gene ontology terms and KEGG pathway terms were carried using DAVID. Based on the input list of genes and genes involved in a particular pathway Fisher Exact P value was used to calculate the EASE score, which provides a significantly enriched term. The EASE score cut-off is given by P-value < 0.01. So the terms whose P-values are less than 0.01 were obtained as the enriched term after enrichment analysis. So lesser the P-value, more is the enriched term. The minimum count of genes considered for the term association is ≥ 2. False discovery rate (FDR) is the corrected P-value obtained by Multiple corrections using Benjamini and Bonferroni testing.Supplementary Figures.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


This study aimed to evaluate the influence of ultrasonic activation (UA) on the physicochemical properties of hydraulic calcium silicate-based sealers. Nine experimental conditions were created based on the hydraulic calcium silicate-based sealers  and the ultrasonic activation . Then the experimental groups were BC-NA, BC-10, BC-20, SPBC-NA, SPBC-10, SPBC-20, BR-NA, BR-10, and BR-20. Activation was performed with an ultrasonic insert 20/.01. The mold for the physicochemical analysis was filled and evaluated according to the ANSI/ADA specification nº. 57: initial and final setting time, flow, radiopacity and solubility. Tests were also performed to evaluate pH and calcium ion release with experimental periods of 1, 24, 72, and 168 hours with a pH meter and colorimetric spectrophotometer. Data were analyzed by one-way analysis of variance and post-hoc Tukey tests. The significance level was set at 5%. The time of UA progressively delayed the initial setting time for all hydraulic calcium silicate-based sealers (p < 0.05). Twenty seconds of UA increased the mean flow values of Sealer Plus BC and Bio-C Sealer compared to NA (p < 0.05). UA did not influence the radiopacity and solubility of the tested sealers (p > 0.05). UA for 20 seconds enhanced the pH levels and the calcium ion release of Sealer Plus BC and Bio-C Sealer at 168h (p < 0.05). UA for twenty seconds interferes with some physicochemical properties of hydraulic calcium silicate-based sealers. The principles of endodontic therapy have been consolidated in the literature for almost a hundred years and are related to the proper sealing of the root canal system (RCS) after chemical-mechanical preparation Hydraulic calcium silicate-based sealers, also known as bioceramic sealers, consist of a recent class of materials used in the medical and dental area and stand out for their biological similarity to hydroxyapatite, which confers excellent biocompatibility ,Bio-C Sealer , BioRoot RCS , and Sealer Plus BC  are ready-to-use sealers. BioRoot RCS is a hydraulic calcium silicate-based sealer with a powder and liquid presentation. It has antimicrobial activity, bioactive capacity by releasing calcium ions ,Ultrasonic activation (UA) of the sealer aims to fill the irregularities, promote the sealer's penetration in difficult access, and improve the adaptation between the sealer and the root canal walls Three hydraulic calcium silicate-based sealers  were tested in the present study. The composition of each tested sealer is displayed in Sealer Plus BC and Bio-C Sealer are presented in a ready-to-use formulation. BioRoot RCS is presented in a powder/liquid formulation, and it was manipulated according to the manufacturer's instructions. All sealers were placed on an analytical balance and the weight was established in 0.3g. Therefore, nine experimental conditions were created based on the hydraulic calcium silicate-based sealers  and the ultrasonic activation  as follow: BC-NA, BC-10, BC-20, SPBC-NA, SPBC-10, SPBC-20, BR-NA, BR-10, and BR-20. A 3 mL syringe was transversally sectioned with a heated instrument at 1 mL mark and filled with 0.3g of the sealer. A 20/.01 ultrasonic tip  coupled in an ultrasonic device  was immersed 5mm into the center of the sealer mass. Circular movements were performed at a power of 20% with the ultrasonic tip during the activation.Tests were performed under controlled temperature and humidity, 37°C ± 1°C and 95% ± 5%, respectively. Setting time was determined according to the specifications of ISO 6876:2012 2 and was obtained from the mean values of three tests.The flow test was performed according to ISO 6876:2012 2 in the center of the samples were calculated concerning their mean values and standard deviations. The radiopacity value was determined according to the radiographic density, which was also converted into millimeters of aluminum (mm Al).Following ISO 6876:2012 Three specimens with 10mm in diameter and 1mm in height Five specimens of each group were produced from polyethylene tubes (10mm in length and 1mm inner diameter) with one of the ends closed, reproducing a closed system The Shapiro-Wilk test was performed to evaluate the distribution of the data. Statistical analysis was performed using a one-way analysis of variance (ANOVA) and the Tukey test at a significance level of 5%.UA progressively delayed the initial setting time for all the hydraulic calcium silicate-based sealers (p < 0.05). Twenty seconds of UA delayed the final setting time for all the sealers compared with groups without activation (p < 0.05). However, ten seconds of UA delayed the Sealer Plus BC (p < 0.05). SPBC-NA and SPBC-20 showed the lowest and the highest initial setting time, respectively (p < 0.05). BioRoot RCS had the lowest final setting time in each experimental condition (p < 0.05).Twenty seconds of UA increased the mean values of flow for Sealer Plus BC and Bio-C Sealer compared to the same sealers without activation (p < 0.05). Ten seconds of UA did not alter the flow rate of the sealers (p < 0.05). The flow rate of BioRoot RCS was higher than Sealer Plus BC without activation (p < 0.05). After ten seconds of UA, Sealer Plus BC showed the lowest flow rate (p < 0.05). Bio-C Sealer showed the highest flow after twenty seconds of activation (p < 0.05).UA did not influence the radiopacity of the sealers (p < 0.05). Bio-C Sealer, BioRoot RCS, and Sealer Plus BC sealers had similar radiopacity (p < 0.05).UA did not affect the solubility of the tested sealers (p < 0.05). The hydraulic calcium silicate-based sealers showed similar solubility (p < 0.05).New techniques and technologies are constantly being used to increase the success rate of endodontic treatment. UA increases the dispersion of the irrigant into the root canal system, improving cleaning, disinfection, and bacterial reduction Setting time is considered the time required for endodontic sealers to achieve their definitive properties. It is related to the sealer compounds, particle size, temperature, and relative humidity Hydraulic calcium silicate-based sealers such as Bio-C Sealer and Sealer Plus BC have water-free thickeners, allowing them to be marketed as a ready-to-use, pre-mixed paste, making the final set of these sealers depend on the presence of moisture In this study, slightly moistened gauzes were placed over the samples to provide Sealer Plus BC and Bio-C Sealer with the necessary moisture to set. This model was based on a previous study Regarding the results presented by the flow test, ten seconds of UA did not impact the flow rate for all the sealers tested. However, twenty seconds of UA increased the mean values of flow for the Sealer Plus BC and Bio-C Sealer compared to the same sealers without activation. UA did not influence the flow of the BioRoot RCS sealer. This result can probably be explained due to the pronounced heat generated when a more prolonged ultrasonic activation was performed, which can increase the flow of sealers in paste-paste or ready-to-use presentations In contrast, BioRoot RCS has an aqueous vehicle in its composition, which confer greater flow even without UA when compared to the ready-to-use sealers. In addition, Bio-C Sealer showed the highest flow after twenty seconds of UA, probably because it presents the smallest particle size among the tested sealers. Nevertheless, all sealers reached the standards determined by ISO 6876:2012, which determines a flow rate of at least 20mm Endodontic sealers must have adequate radiopacity to be distinguishable from adjacent anatomical structures and to allow visualization of the quality of root canal filling. UA did not influence the radiopacity of the sealers. According to the ISO standards Solubility is related to the weight loss of the material when immersed in water Assessing the sealers' pH is essential because it is related to the alkalization promoted by each sealer over time This study showed that the bioceramic sealers had high values of pH and calcium ion release, which increased or remained stable over time. SPBC-20 and BC-20 showed an increase in pH levels and calcium ion release in 168h (p < 0.05). Both sealers present a viscous vehicle. For this reason, it could be hypothesized that the UA for 20 seconds of bioceramic sealers with viscous vehicles could be an exciting strategy during clinical practice, especially in cases of a slow alkalinization of the medium such as root resorptions or dental trauma is required. This approach could promote better alkalinization of the medium with some impact on the disinfection process after root canal filling. Controversially, BioRoot RCS was not influenced by the UA (p < 0.05) but still presented higher pH and calcium ion release values since the first experimental times when compared to other calcium silicate-based sealers (p < 0.05). These results are corroborated by a previous study Based on the findings of this study, it can be concluded that twenty seconds of UA interfere with some physicochemical properties of calcium silicate-based sealers, improving the flow, pH, and calcium ion release of ready-to-use sealers . The initial and final setting time of calcium silicate-based sealers was progressively delayed the longer the UA.