Many studies have focused on the metabolic capacity of human gut microbiota to produce short-chain fatty acids and subsequent effects on host physiology. Given scarce data on how SCFAs produced by gut bacteria participate in cross-feeding to influence community structure and function, we evaluated the potential of SCFAs to modulate human gut microbiota in vitro. We employed anaerobic fecal cultivation in chemically defined medium supplemented with one of nine SCFAs to determine effects on both gut microbial community structure via 16S rRNA sequencing and function via genome reconstruction analysis. Each SCFA displayed significant and unique modulatory potential with respect to the relative abundance of bacterial taxa. Analysis of SCFA-supplemented communities revealed that alterations of individual closely related phylotypes displayed coherent changes, although exceptions were also observed which suggest strain-dependent differences in SCFA-induced changes. We used genome reconstruction to evaluate the functional implications of SCFA-mediated restructuring of fecal communities. We note that some SCFA-supplemented cultures displayed a reduction in the predicted abundance of SCFA producers, which suggests a possible undefined negative feedback mechanism. We conclude that SCFAs are not simply end-products of metabolism but also serve to modulate the gut microbiota through cross-feeding that alters the fitness of specified taxa. These results are important in the identification of prebiotics that elevate specific SCFAs for therapeutic benefit and highlight SCFA consumers as a salient part of the overall metabolic flux pertaining to bacterial fermentative processes.The online version contains supplementary material available at 10.1007/s00284-022-02825-5. Bifidobacterium, Bacteroides, Parabacteroides) that encode large suites of glycosyl hydrolases that liberate mono-, di-, and oligo-saccharides from dietary glycans. The cooperative activities of microbes ensure efficient breakdown of varied sugar polymers. Many glycosyl hydrolases are localized on the microbial cell surface which is thought to provide a mechanism of cross-feeding that provides liberated sugars to other bystander saccharolytic microbes and the host. It is estimated that about 10% of the human’s daily calories are derived from this process [One of the central roles of the gut microbiota is to serve as an intermediary between diet and the physiology and health of the host. A well-studied example is the role of gut microbes in the harvest of energy from otherwise indigestible fibers derived from plants, such as vegetables and fruits. The catabolism of dietary fiber is carried out by taxonomically distinct groups of bacteria  and functional redundancy with respect to some of the metabolites these bacteria produce enables some microbial metabolites to reach physiologically relevant levels and produce a significant impact on the host.Sugar fermentation is a property shared by the majority of gut microbes. Under anaerobic conditions present in the colon, these sugars are metabolized by a variety of pathways for energy  leading to the generation of SCFAs, such as formate, acetate, propionate, butyrate, succinate, and lactate. Fermentation of branched chain amino acids leads to the production of additional SCFAs, such as isobutyrate, isovalerate, and others. SCFAs represent end-products of metabolism for many bacteria but may represent potential substrates for others thus defining a form of cross-feeding employed by the gut microbiota. The high density of microbes residing in the distal colon  and guidelines with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Sanford Burnham Prebys Medical Discovery Institute Institutional Review Board. Participants ate their normal diets and donated a morning fecal sample in stool hats (Fisher Scientific). The fecal samples were transferred to conical tubes and stored at −80 °C until subsequent processing.d-lactate, valerate, succinate, isobutyrate, and isovalerate (Sigma).We examined 9 SCFAs which include formate, acetate, propionate, butyrate, 2PO4, 20 mM Na2HPO4, 120 mM NaHCO3, 8 mM of each amino acid except leucine (30 mM), 20 mL ATCC, Trace Mineral Supplement, nucleoside bases (200 mg/L), inosine, xanthine, adenine, guanine, cytosine, thymidine and uracil (800 mg/L), choline (200 mg/L), ascorbic acid (1 g/L), lipoic acid (4 mg/L), hemin (2.4 mg/L), and myo-inositol (800 mg/L). Resazurin (2 mg/L) was added to visually monitor dissolved oxygen. The pH of the media was adjusted to 7.4. CDM contains amino acids but no exogenous carbohydrate. It should be noted that amino acid degradation pathways can generate SCFAs. Despite this complication, all cultures analyzed differed only by the supplementation of a single SCFA, therefore the effect of these additions allow the modulatory effects of SCFAs on fecal communities to be identified, although distinguishing primary vs secondary effects of SCFA supplementation is confounded.A 2× concentrate of chemically defined medium (CDM) containing 100 mM HEPES, 4.4 mM KH2O (4 mL). To ensure equal inoculums across cultures frozen stool sample generated from an equal volume of stool pooled from 12 healthy vegetarian participants were used to inoculate 2× CDM that was then distributed (1 mL) into individual tubes. Individual SCFAs were introduced to reduce H2O to a final concentration of 20 mM. One mL of SCFA solution was added to 2× CDM to generate cultures containing approximately 1 × 106 cells in 1× CDM with or without 10 mM SCFA. Due to the strong buffering capacity of the media, the addition of SCFAs to media had nominal effects on pH, reducing it to 7.2–7.3, therefore no further adjustments were made prior to fecal inoculation. Cultures  were grown statically for 2 days at 37 °C as technical replicates (n = 4) and grown to approximate saturation.CDM (2×) without SCFAs was reduced in an anaerobic chamber (Coy Labs) for 2 days together with several aliquots of HGenomic DNA was isolated from cultures using the procedures of the QiaAmp DNA stool kit (Qiagen) with a modification that included bead beating with the Thermo FastPrep instrument (MP Bio) to ensure uniform lysis of bacterial cells.Multiplexed 16S rDNA libraries were prepared using standard 16S metagenomic sequencing library protocols from Illumina, which uses V3–V4 region of 16S rDNA for target amplification, and subsequent analysis was performed in the CLC Microbial Genomics Module 2.5 (Qiagen) and R  as we haP for each mapped taxa obtained from 16S analysis to possess a certain binary metabolic phenotype as previously described [p values of each taxa multiplied by their relative abundance. CPI provides a fractional representation of cells in the community possessing a specific metabolic pathway .To predict metabolic potential of microbial taxa identified by 16S rRNA analysis we utilized the Phenotype Profiler tool . To predict metabolic capabilities of 2856 reference genomes representing 690 microbial species from human gut, we used a subsystem-based approach implemented in microbial community SEED (mcSEED) platform , as we hescribed . CommuniDifferences in the Shannon alpha diversity measurements, the relative abundance of particular gut microbial taxa, community phenotypes, including SCFA production, vitamin biosynthesis, amino acid biosynthesis, and degradation pathways in experimental compared to control cultures was assessed using a double-tailed Mann–Whitney test. Results of all statistical analyses are provided (Supplementary Table 1).https://www.ncbi.nlm.nih.gov/genbank/. The Bioproject ID is PRJNA725898 and accession numbers are SAMN18914736–SAMN18914775.All 16S rRNA sequences have been deposited at NCBI SRA database and can be found at We profiled the microbiota of a pool consisting of 12 healthy vegetarian stool samples in a chemically defined medium (CDM) supplemented with a single SCFA including formate, acetate, propionate, butyrate, lactate, succinate, valerate, isobutyrate, and isovalerate to assess whether they differentially impacted the fitness of microbes within these communities. The CDM included amino acids but did not provide any carbohydrate sources for SCFA production. We amplified the V3–V4 region of the 16S rDNA gene from replicate cultures and the resulting sequences derived from control cultures (CDM) were compared to cultures supplemented with one of nine tested SCFAs.Sequence data were analyzed at the phylotype level, using CLC, wherein sequences differing by as few as 1 base were maintained separately and no merging of unique sequences was performed. Unique sequences were used to conduct BLAST to identify the most closely related named species (average % identity = 98.6%). In total, we enumerated 457 unique phylotypes that defined 125 bacterial species groups (Supplementary Table S2). We observed 337 phylotypes in control cultures.We calculated β diversity using Bray–Curtis distances of control and SCFA-supplemented communities as determined by Qiime 2 analysis pipeline to assess global changes in microbial community composition Fig. a. The reP = 0.03) and Bacteroidetes (P = 0.03) but increased the relative abundance of Proteobacteria (P = 0.03). The relative abundance of Proteobacteria was increased by several additional SCFAs including acetate, propionate, isobutyrate, and isovalerate but these increases did not achieve statistical significance due to variability in replicate cultures. Propionate-supplemented cultures decreased the relative abundance of Bacteroidetes (P = 0.03). Lactate isovalerate, isobutyrate, and succinate-supplemented cultures did not result in any statistically significant changes at the phylum level.Analysis of community profiles at the phylum level revealed that cultures were dominated by 4 phyla, namely Firmicutes, Bacteroidetes, Proteobacteria, and to a lesser extent Actinobacteria Fig. a. StatisP = 0.03). Isobutyrate and isovalerate also expanded this family but did not reach statistical significance , isovalerate to 27%; (P = 0.03), lactate to 21% (P = 0.03), propionate to 33% (P = 0.03), and valerate to 30% (P = 0.03) increased the relative abundance of Lachnospiraceae. These changes were driven primarily by Blautia spp. and others. Compared to control cultures, succinate (P = 0.03) and isobutyrate (P = 0.03) significantly increased the relative abundance of Acidaminococcaceae, primarily driven by altered relative abundance of Acidaminococcus intestini. Formate-supplemented cultures increased this family more modestly but was also statistically significant (P = 0.03). Most SCFAs reduced the relative abundance of taxa belonging to an unclassified Flavonifractor group, attributed to the reduced relative abundance of Flavonifractor plautii. Neither formate nor lactate impacted this group. All SCFAs reduced the average relative abundance of Bacteroidaceae, resulting from a number of Bacteroides spp.; however only butyrate, isobutyrate, and propionate-supplemented cultures achieved statistical significance (P = 0.03). Butyrate (P = 0.03), isobutyrate (P = 0.03), and valerate supplementation (P = 0.03) led to the virtual elimination of Peptostreptococcaceae, whereas acetate, isovalerate, and propionate reduced the relative abundance of this family but did not achieve statistical significance. These reductions reflected the reduced average relative abundance of Romboutsia sedimentorum. Ruminococcaceae was reduced in most cultures supplemented with SCFA, particularly isobutyrate (P = 0.03), isovalerate (P = 0.03), succinate (P = 0.03), and valerate (P = 0.03) due to reductions in multiple taxa, with the exception of acetate, butyrate, and formate where it was reduced more modestly.Analysis of community profiles at the family level resulted in the identification of a number of coherent changes induced by SCFAs, suggesting that groups of related taxa are altered in their representation by one or more SCFAs. Among the dominant families, butyrate-supplemented cultures resulted in the expansion of Enterobacteriaceae 20 to 41%) compared to control cultures . Additional significant changes were also noted in families of lower abundance , butyrate (3.4%), and very strongly in valerate-supplemented cultures (13%). The relative abundance of Anaerostipes hadrus was elevated in cultures supplemented with lactate and succinate  and significantly decreased by isobutyrate (0.0%), isovalerate (0.001%), and propionate (0.1%). A phylotype with similarity to Blautia hansenii and B. producta was not detected in control cultures but its relative abundance increased to 4.6% and 13.1%, in cultures supplemented with formate and isobutyrate, respectively. Another species group with similarity to Blautia obeum and B. wexlerae, present in control cultures at a relative abundance of 0.3%, was increased by several SCFAs, including acetate (1.3%), isobutyrate, (8.7%), isovalerate (4.7%), propionate (8.2%), and valerate (1.2%). Species groups that were below the limit of detection in control cultures were positively influenced by one or more SCFAs, including a separate species group with similarity to Blautia obeum was increased by isobutyrate (0.45%), propionate (1.0%), and valerate (1.68%), whereas another with similarity to B. luti and B. massiliensis were increased in propionate-supplemented cultures (5.83%).In total, we identified 66 phylotype groups that displayed statistically significant changes in relative abundance in response to one or more SCFAs. Among these cases, we observed more than two times as many instances of reduced (133) relative abundance compared to increased (65) relative abundance (Supplementary Table S3). Among the abundant taxa, Citrobacter freundii, and its relative C. murliniae was present in high and moderate relative abundance in control cultures  and further increased in cultures supplemented with propionate . Both of these species groups were reduced in relative abundance by acetate . Dorea longicatena was positively modulated by a number of SCFAs, including acetate (0.06 to 0.5%), butyrate (7.6%), isobutyrate (3.5%), isovalerate (4.7%), propionate (7.8%), and valerate (6.9%). Phascolarctobacterium faecium present in high abundance in control cultures (8.7%) was further increased by formate (12.4%), isobutyrate (12.8%), and succinate (31.9%) and decreased by valerate (5.5%). The pathobiont Enterococcus faecalis was increased to 1.77% in butyrate-supplemented cultures. The relative abundance of Megamonas funiformis was elevated by butyrate (3.42%) and valerate (0.06%). Finally, Mitsuokella jalaundini was increased by butyrate (3.44%). Among the 10 Bacteroides spp. significantly altered by SCFAs, the majority displayed reduced relative abundance (30 instances) compared to increased relative abundance (3 instances). Six Clostridium spp. displayed similar sensitivity to SCFAs, resulting in reduced relative abundance (21 instances) compared to increased relative abundance . Based on our results it appears that taxa responsive to any SCFA are more likely to be impacted by others as the 66 phylotype groups displaying significant change did so in response to an average of 3 SCFAs of the 9 tested.The pathobiont, Analysis of SCFA-supplemented fecal communities revealed that alterations of closely related phylotypes displayed coherent changes; however, exceptions were also noted suggesting strain-dependent differences in SCFA utilization capacity and/or fitness alterations.Blautia wexlerae, a H2-consuming microbe that plays a role enabling H2-producing fermentation to proceed with continued efficiency. Among the 18 phylotypes, 100% displayed increased relative abundance (>fivefold compared to control cultures) in response to isovalerate supplementation, whereas 89% were increased in response to propionate and 78% and 61% increased in response to isobutyrate and butyrate, respectively (Supplementary Table S1). We identified 10 phylotypes with close relationships to Eubacterium desmolans. All 10 phylotypes displayed reduced relative abundance in cultures supplemented with isovalerate and propionate, whereas 90% and 80% displayed reduced relative abundance in cultures supplemented with succinate and lactate, respectively. The remaining phylotypes defining this species group remained unchanged.Species groups comprising a large number of related strains revealed a number of instances wherein clear trends in relative abundance in response to SCFA supplementation were noted. For example, we identified 18 phylotypes with close relationship to Sutterella wadsworthensis. Cultures supplemented with butyrate, acetate, isobutyrate, isovalerate, and propionate promoted the increased relative abundance of these related phylotypes. The coherence of these changes is conspicuous but not universal as some species groups displayed more heterogeneous responses. For example, 6 phylotypes defining a species group related to Intestinibacillus massiliensis displayed coherent responses (reduced relative abundance) to butyrate, isobutyrate, and isovalerate, whereas in response to succinate, 3 phylotypes were increased and 3 phylotypes were decreased. Taken together, we conclude that SCFA-induced fitness alterations are strain-dependent.Finally, we identified 4 phylotypes with close similarity to P = 0.03), and this was particularly evident in cultures supplemented with butyrate, isobutyrate, isovalerate, and propionate . The relative abundance of acetate producers was largely unaffected by SCFA supplementation with the exception that succinate-supplemented cultures displayed a substantial reduction (P = 0.03). The profile observed for succinate supplementation was also evident for predicted formate and ethanol producers (P = 0.03). d-Lactate-supplemented cultures also decreased the predicted abundance of formate producers (P = 0.03). The impact of SCFA supplementation on the relative abundance of microbes predicted to produce l- and d-lactate were distinct, butyrate-supplemented cultures reduced the relative abundance of l-lactate producers (P = 0.03), whereas it increased the abundance of d-lactate producers (P = 0.03). This pattern was noted for isobutyrate (ns) and isovalerate also (P = 0.03 for l-lactate only). d-lactate supplementation reduced the relative abundance of d-lactate producers but had little effect on the relative abundance of l-lactate producers. This pattern was also evident in succinate-supplemented cultures.We used genome reconstruction of reference genomes pertaining to over 600 gut-associated microbes to predict metabolic phenotypes to calculate the sample-by-sample Community Phenotype Indices (CPIs) for production pathways for four SCFAs and two isoforms of lactate. Compared to control cultures, all SCFA-supplemented cultures (except formate) displayed a reduction in butyrate producers P = 0.0, and thiP = 0.03). Exogenous butyrate, isobutyrate, and isovalerate increased the relative abundance of B vitamin prototrophs, including B1, B2, B3, B6, B7, and B9 (P = 0.03), whereas butyrate and isovalerate increased vitamin K and Q (P = 0.03). Formate and lactate had little effect on any vitamin prototroph representation, only increasing the relative abundance of vitamin B9 prototrophy (P = 0.03). Propionate increased the relative abundance of B1, B2, B3, B9, and Q prototrophs (P = 0.03). Succinate increased the relative abundance of B1, B2, B5, B6, B7, and B9 prototrophs (P = 0.03). Finally, valerate increased the relative abundance of B1, B2, B3, and B9 prototrophs (P = 0.03).We used the same predictive bioinformatics approach of calculate CPIs for microbial vitamin synthesis pathways. Compared to control cultures, multiple SCFA-supplemented cultures altered the abundance of B vitamin producing bacteria or prototrophs  and strongly decreased asparagine representation (P = 0.03).According to genomic reconstructions, the majority of gut microbes are prototrophic for amino acid biosynthesis. Amino acid degradation pathways are less well conserved. These results are too complex to present in detail ; however, certain trends are worth noting. Overall, SCFA supplementation increased the representation of predicted amino acid biosynthetic potential for all amino acids, many achieving statistical significance, whereas others did not. Lactate supplementation represents a contrast to this general trend, resulting in reductions of these pathways, except for cysteine and asparagine biosynthesis pathways. Only isoleucine, valine, leucine, and serine achieved statistical significance. Formate and succinate supplementation had the smallest effect on these pathways, except that succinate increased tryptophan biosynthesis pathway representation (P = 0.03) and decreasing histidine degradation pathway abundance. Valerate-supplemented cultures decreased lysine degradation potential. Isovalerate increased the representation of microbes capable of degrading threonine (P = 0.03).We also analyzed the impact of SCFA supplementation on amino acid degradation pathways. These pathways are relevant as the products of these pathways, for example, tryptophan degradation products act as signaling molecules impacting the host –19. ThesBifidobacterium spp. that increase the amount of lactate in the colon [Recently, the gut microbiota has come into sharp focus as a central intermediary between diet and human health. Many studies have evaluated the modulatory effects of various prebiotic fibers that promote the increased production of SCFAs , 21. Numhe colon , 23, whehe colon , 25. Buthe colon . Butyrathe colon . Butyrathe colon , 29 and he colon , 31. Anihe colon .Despite numerous studies indicating the importance of microbially produced SCFAs on human health, little is known about how SCFAs produced by gut bacteria participate in cross-feeding to influence community structure and function. Herein, we employed in vitro anaerobic cultivation of fecal microbiota to evaluate the impact of SCFAs as potential modulators of gut communities. We speculated that the SCFAs produced by gut microbes may be consumed by others, thereby altering their fitness and relative abundance in microbial communities. We profiled fecal communities by 16S rRNA gene sequencing and identified numerous taxa that were positively or negatively influenced by cultivation in the presence of single-SCFA substrates.Ruminococcus obeum was positively impacted by acetate, propionate, and valerate. The relative abundance of multiple Blautia spp. including B. luti, B. marasmi, B. producta, and B. wexlerae were increased by multiple SCFAs as were Dorea longicatena. Some phylotype groups such as Oscillibacter spp. and Bacteroides spp. were negatively impacted by multiple SCFAs. An explanation for the negative response of some taxa remains unclear. Small but statistically significant reductions in relative abundance of taxa may reflect modulations induced by increases of other taxa. Taxa exhibiting larger negative effects in response to SCFA supplementation may instead highlight phylotypes with an inherent sensitivity to SCFAs that inhibit growth by as yet unknown mechanisms.This study demonstrates the capacity of SCFAs to modulate fecal communities at multiple taxonomic levels. High-resolution analysis of 16S rRNA sequences allowed the identification of 66 species groups that displayed increased/decreased relative abundance as the result of media supplementation with a single SCFA compared to control cultures. Based on our results, SCFA consumption or fitness alterations are rarely evident for all phylotypes belonging to a species group, which suggests that SCFA-induced alteration in fitness is a strain-dependent phenotype. We noted numerous examples wherein the majority of phylotypes belonging to a species group responded positively or negatively to one or more SCFAs (Supplementary Tables S2 and S3). For example, Acidaminococcus, Sutterella, Phascolarctobacterium, Veillonella, and Alistipes. We speculate that these organisms may have evolved mechanisms to utilize SCFAs for energy given the inability to generate energy from sugar fermentation.We noted that several genera displaying broad positive responses to multiple SCFAs were assacharolytic microbes, including Bifidobacterium adolescentis was co-cultured with Anaerostipes caccae or Eubacterium hallii in the presence of starch, lactate levels remained low due to consumption by A. caccae and E. hallii by converting consumed lactate to butyrate [Anaerostipes spp. consume both lactate and acetate [E. hallii; however, Anaerostipes hadrus was profiled and was positively modulated by lactate. Among the 21 phylotypes mapping to the Anaerostipes hadrus species cluster, nine displayed >fivefold increased relative abundance in acetate-supplemented cultures, whereas 19 displayed >twofold increased relative abundance. Lactate-supplemented cultures increased the relative abundance of 4 phylotypes >fivefold and 14 phylotypes >twofold relative abundance. Nearly identical profiles were observed for cultures supplemented with succinate.There are relatively few studies that examined cross-feeding of SCFAs, although some reports do serve to validate our findings. While a number of gut microbes generate lactate, lactate is not observed in the colon at appreciable concentrations which suggests that it is consumed by microbes . This hybutyrate , 34. Ind acetate . In the Veillonella spp. consume and therefore remediate the acidifying effects of lactate produced by Streptococcus spp., following sugar consumption [Veillonella atypica. Both of these phylotypes displayed increased relative abundance >30-fold in lactate-supplemented cultures. One of these also displayed increased relative abundance in acetate and valerate-supplemented cultures.It has been known for some time that in the oral cavity, where lactate is the principle microbial product promoting dental caries, that sumption , 37. In Paraprevotella xylaniphila, resulted in the increased growth of P Phascolarctobacterium faecium by 2–3 orders of magnitude [P. faecium, nine of which displayed >fivefold increased relative abundance in succinate-supplemented cultures, all 12 displayed >2.5-fold increases relative to control cultures. The related genus Acidaminococcus is also a succinate consumer. Our results indicated that two phylotypes related to A. intestini displayed increased relative abundance of ~2 and eightfold in succinate-supplemented cultures. It is notable that the relative abundance of these phylotypes were more robustly increased in cultures supplemented with butyrate, acetate, and valerate. Utilization of these substrates has not been reported previously.Co-culture experiments in media containing xylan, with the succinate-producing agnitude . Propionl- and d-lactate in SCFA-supplemented cultures were similar in some instances but more often were disparate. For example, butyrate, isobutyrate, and isovalerate supplementation resulted in reduced predicted l-lactate producers but increased d-lactate production. Conversely, succinate-supplemented cultures resulted in increased predicted l-lactate but decreased predicted d-lactate production. It remains unclear whether these observations reflect happenstance changes based on fitness profiles or if instead they highlight regulatory circuits based on positive and/or negative feedback control.To assess the functional implications of SCFA-driven changes in microbiota composition, we applied genome reconstruction of metabolic pathways present in profiled microbes for which one or more complete reference genomes were available. The impact of SCFA supplementation on the relative abundance of the predicted community members possessing complete SCFA production pathways (CPIs) was assessed Fig. . All preE. coli.Analysis of reconstructed pathways for B vitamin biosynthesis, including lipoic acid and vitamins K and Q, further illustrate the functional implications of SCFA-driven restructuring of fecal communities Fig. . SurprisFinally, we analyzed the impact of SCFA supplementation on community’s metabolic potential for amino acid biosynthesis (Supplementary Table S7) and degradation Fig. . AlteratThe study of prebiotics is advancing rapidly based on the large body of data showing that prebiotics alter gut communities and the amount of SCFAs produced. Given, the potential clinical utility of controlling microbial-produced SCFAs levels through prebiotic interventions, little has been elucidated regarding SCFA consumption in gut communities. Ultimately, SCFA levels represents an equilibrium dictated by microbial SCFA production, host absorption, and microbial consumption. Our in vitro study is both a strength and a limitation. By analyzing microbial fitness in response to SCFAs in vitro, we effectively remove host complexities such as absorption that may prevent the identification of SCFA consumers in microbial communities. It remains unclear how the results presented here will translate to in vivo studies. A second limitation of the current study is the inability to attribute the effects of SCFA supplementation as a direct or indirect effect on gut microbiota community structure. It is conceivable that direct effects involving taxa that utilize one or more SCFAs with commensurate fitness gain are relatively innumerous and that the majority of the observed changes in phylotype abundance were driven by indirect effects, mediated by cross-feeding of unknown metabolites or inhibitors produced by taxa directly impacted by treatment.In summary, we identified 66 distinct phylotype groups in cultures supplemented with one or more SCFAs displaying altered fitness as determined by statistically significant changes in relative abundance (Supplementary Table S3). The improved fitness of these species suggest that these fitness changes are driven by the consumption of SCFAs to generate energy. We cannot rule out the possibility that increased fitness may reflect indirect effects. Additional studies are required to verify whether these taxa indeed consume SCFAs and derive fitness advantage as a result. We conclude that SCFA-driven fitness is a strain-dependent phenomenon. This suggests that experimental validation of our predictions may require the isolation of multiple strains pertaining to species of interest. A growing number of laboratories have enhanced gut microbiome isolate banks and therefore are well poised to test candidate SCFA consumers in future work.Supplementary file1 (XLSX 212 kb)Supplementary file2 (DOCX 15 kb)Below is the link to the electronic supplementary material.


Models trained with one system fail to identify other systems accurately because of domain shifts. To perform domain adaptation, numerous studies have been conducted in many fields and have successfully aligned different domains into one domain. The domain shift problem is caused by the difference of distributions between two domains, which is solved by reducing this difference. Source domain data are labeled and used for training the models to extract the features while the target domain data are unlabeled or partially labeled and only used for aligning. Bearings play important roles in rotating machines, so many artificial intelligent models have been developed to diagnose bearings. Bearing diagnosis has also faced a domain shift problem due to various operating conditions such as experimental environment, number of balls, degree of defects, and rotational speed. Cross-domain fault diagnosis has been successfully performed when the systems are the same but operating conditions are different. However, the results are poor when diagnosing different bearing systems because the characteristics of the signals such as specific frequencies depend on the specifications. In this paper, the pre-processing method was used for improving the diagnosis without prior knowledge such as fault frequencies. The signals were first transformed to a common pattern space before entering the models. To develop and to validate the proposed method for different domains, vibration signals measured from two ball-bearing systems (Case Western Reserve University datasets and Paderborn University datasets) were used. One dimensional CNN models were utilized for verification of the proposed method and the results of the models using raw datasets and pre-processed datasets were compared. Even though each of the ball-bearing systems have their own specifications, using the proposed method was very helpful for domain adaptation, and cross-domain fault diagnosis was performed with high accuracy. Rotating machines play a very important role in manufacturing plants. Among the many parts of rotating machines, bearings have a significant impact on the operation of rotating machines. Failures of electro-mechanical drive systems and motors are caused by rolling bearings with high probability . TherefoCNNs are good at extracting features, and therefore many studies on artificial intelligence (AI) models with CNNs for diagnosing bearing faults have been conducted ,17,18,19Despite the remarkable achievement of the studies introduced above, models can only classify the data of the same domain exactly. The domain shift problem occurrs because the distributions of domains are different from each other. Domain adaptation is essential to solve the domain shift problem and it has succeeded in solving this problem in many fields such as classification of text  and imagAll the above studies focus on post-processing methods, but certain domains may not be enough to be accurately adapted. If a suitable signal processing method is implemented, the characteristics of faults can be made easily noticeable, and the training time of the model can be reduced while the accuracy can be improved. Pre-processed signals were used as input for the deep domain generalization network for fault diagnosis (DDGFD) model in . HoweverIn this paper, using a proposed signal processing method is verified in making all signals into a common pattern space without prior knowledge, such as of the characteristics of faults, and using them is helpful for the cross-domain diagnosis problem. For the study, vibration signals acquired from two different ball bearing systems were analyzed in the time domain and the frequency domain to check whether the fault characteristics can be shown in the signals and the need of pre-processing. Using the proposed method we confirmed that not only do the characteristics of faults appear more clearly, but also signals of different systems are placed in the same pattern space. In addition, the results of classification when using pre-processed datasets are greatly improved compared to using raw data.This paper is organized as follows: There are two domains in cross-domain fault diagnosis. One is source domain, and the other is target domain. All labels of the source domain are defined and available. The expression of source domain is written as follows:Since the labels of the target domain are not defined or are only defined for some datasets, the labels cannot be used for training extractor. The target domain for which the label is not defined is described as follows:Since the source domain and the target domain are extracted from different distributions, there is a high possibility that they will not be classified using the same classification boundaries. Therefore, an appropriate method must be performed to reduce the discrepancy between the source domain and the target domain. That is the goal of cross-domain fault diagnosis.A Convolutional Neural Network (CNN) is a neural network that employs an operation called convolution instead of general matrix multiplications . CNN is Input data are convoluted with multiple filters (kernels) in convolutional layers. The filters move as much as set stride. The input data are multiplied by trained filters of each convolutional layers, and the convoluted data are extracted as a feature map through an activation function such as ReLU (Rectified Linear Unit).A certain region which is the size of the pooling is replaced with a representative value in pooling layers so that the size of the feature map is reduced. In general, max pooling and average pooling are used in CNN. Max pooling selects the maximum value, while average pooling averages the values in a specific region.The convolutional and pooling layers are stacked in the feature extraction network and the final output is the features, also referred to as feature maps. The extracted features go into the fully connected layers of the classifier network, which is basically the same as a standard neural network. The output layer has as many nodes (neurons) as the number of classes, and it is commonly activated by Softmax functions for classification . The losVarious filters such as minimum-phase filter and lowpass filter are used to find the characteristics of fault more clearly and all signals are transformed to be located in the same domain using a normal dataset of one system.Signals are decomposed into minimum-phase system and all-pass system as in Equation (3) in the minimum-phase filter and the part of  minimum . The sigWhen the signal is transformed with fast Fourier transform (FFT), spectrum signal is obtained, and the spectrum signal has frequency and amplitude information. The magnitude and phase can be separated by taking a logarithm operation after taking absolute values of FFT signal and a cepstrum domain signal is finally produced when this signal transformed with inverse fast Fourier transform (IFFT). Equation (4) is the equation to obtain cesptrum signal.In order to obtain a minimum-phase signal, the cepstrum signal is Fourier transformed by covering a window and finally converted to a minimum-phase signal through exponential operating and IFFT. This process can be mathematically expressed as Equation (5) .(5)yminThe input data for the AI model were prepared as follows. For PU datasets, the window was applied to the resampled signal and then the minimum-phase filter was applied. On the other hand, the minimum-phase parts of CWRU data were extracted after applying the transfer function. Several models which are based in a CNN structure with additional losses or domain adversarial network were compared using raw data and pre-processed data. The classification model was a combination of CNN and domain adaptation methods. The following classification loss was cross-entropy loss which was used for CNN based classification model.Vibration datasets from two different ball-bearing systems were used for training and testing AI models. The first datasets were from Bearing Data Center of Case Western Reserve University, hereinafter referred to as CWRU data and the testbed for acquiring the dataset was configured as shown in BPFO is ball pass frequency outer race, BPFI is ball pass frequency inner race, Before verifying the proposed method, it is important to analyze data in both the time and frequency domains to comprehend the characteristic signals from a defective bearing. Characteristic frequencies of a bearing were computed depending on their specifications . The chaData with 0.007 defect and load 0~3 hp are presented in The next data are from Konstruktions- und Antriebstechnik datacenter in Paderborn University, hereinafter referred to as PU data and the testbed of the datasets is shown in PU data are also analyzed in two domains and some samples of the data are plotted in There are distinct characteristic frequencies in the CWRU data and the PU data, but the shapes of spectrums are different according to the system and the fault type. Since the energy of defect characteristic frequencies is very small compared with the total energy, the shape of spectrum is more dependent on the system energy rather than on the defect energy. Only the energy of the characteristic frequencies was studied and used to identify defects. However, information is needed regarding bearings and a system to calculate the characteristic frequencies in advance. Even though the characteristic defect frequencies can be recovered with the traditional signal processing technique, the system and fault characteristics are not known, or the knowledge to obtain them is often insufficient. Hence, if there is a way to transform signals so that fault characteristics can be seen better without prior knowledge, diagnosis can be performed more efficiently. Since the shapes of the signal in the time domain and the frequency domain vary for many reasons other than the defects, the AI models might be trained with other shapes rather than the defect shapes when diagnosis is performed between datasets of different systems. That is why transformation into the common pattern space is necessary. For this purpose, a pre-processing method is proposed, and the results using raw data and pre-processed data are compared and verified. The proposed method not only makes the signals into the same pattern space, but is also effective in removing noise.The above analysis of the CWRU and the PU data indicates that it is necessary to transform the two datasets into the same pattern space, and to remove the noise by signal processing. The results of the processed datasets (CWRU and PU) are shown in Problems were divided into several cases and addressed. Case 1 was conducted using a CNN with pre-processing method to verify the effectiveness of the pre-processing method. In case 2, the ability of the domain adaptation method was examined using raw data and processed data. Finally, the pre-processing method was combined with domain adaptation methods to confirm that the classification accuracies are improved.Basic CNN model is used for classification. For the validation of proposed method, several methods which are algorithm based on transfer learning are used as follows:CORAL: By aligning the second-order statistics, domain shift between source domain and target domain was minimized ,50. For MK-MMD: MMD was proposed by K.M. Borgwardt et al.  and wideDomain adversarial neural network (DANN): This method was first proposed by Ganin et al.  and usedExtractor, classifier, and discriminator were conducted using Python and signal processing and analysis of results were implemented using Matlab. The model is modified and used as demonstrated in , with loCNN with pre-processing was used in case 1. This procedure was conducted to check the effectiveness of the pre-processing method. Experiments were conducted without pre-processing in case 2. In CNN was combined with pre-processing and domain adaptation methods in case 3 and tested. Features extracted from each model were plotted using PCA and the effectiveness of pre-processing is presented as shown in Cross-domain fault diagnosis with domain adaptation has shown its good performance in some studies. However, domain adaptation is not enough to deal with the big differences between domains in other cases such as bearing fault diagnosis of different kinds of rotating machines. Therefore, in this paper, a signal pre-processing method was developed to overcome the difficulties in domain adaptation in cross-domain fault diagnosis. The developed pre-processing method was good at not only transforming the signals from different machines into a common domain but also reducing noise to enhance the performance in cross-domain analysis. Unlike other frequency analysis methods, the developed method does not require any prior knowledge such as fault frequencies, which is a great advantage for generalization.To develop a pre-processing method, two systems (CWRU and PU), which have different specifications of bearings, were analyzed. Various filters and domain transfer methods were reviewed. First, the effectiveness of the pre-processing method was checked. CNN failed to classify the target system and the features of each system turned out to be located at entirely different regions. With pre-processing, accuracy can be improved by 45 percent. Second, CNN with domain adaptation  was examined. The accuracy was around 30 percent. Both methods were helpful for cross-domain problems but they are not good enough to be used as classifiers. Therefore, pre-processing and domain adaptation method were combined, which improves the accuracy significantly, up to 100 percent. The results were demonstrated and verified with the feature distribution plots using PCA. Therefore, pre-processing with domain adaptation is confirmed to be important for cross-domain fault diagnosis of bearings.


Single-trial electroencephalogram detection has been widely applied in brain-computer interface (BCI) systems. Moreover, an individual generalized model is significant for applying the dynamic visual target detection BCI system in real life because of the time jitter of the detection latency, the dynamics and complexity of visual background. Hence, we developed an unsupervised multi-source domain adaptation network (P3-MSDA) for dynamic visual target detection. In this network, a P3 map-clustering method was proposed for source domain selection. The adversarial domain adaptation was conducted for domain alignment to eliminate individual differences, and prediction probabilities were ranked and returned to guide the input of target samples for imbalanced data classification. The results showed that individuals with a strong P3 map selected by the proposed P3 map-clustering method perform best on the source domain. Compared with existing schemes, the proposed P3-MSDA network achieved the highest classification accuracy and F1 score using five labeled individuals with a strong P3 map as the source domain. These findings can have a significant meaning in building an individual generalized model for dynamic visual target detection. Brain-computer interface has developed a new way for human beings to communicate and control the outside world, and has a great application value and development potential in the fields of medical rehabilitation , MSDA has been widely investigated because of its practice feasibility and performance superiority. The ability of category recognition and domain transformation should be considered in MSDA networks, which bring challenges to the design. Based on the existing domain adaptation network  to develop an individual generalized model for dynamic visual target detection. We designed a P3 map-clustering method to select individuals with a strong P3 map as the source domain. Domain shift was eliminated using the condition domain confrontation networks. The multi-source domain classifiers were weighted to predict target domain categories. Furthermore, the ensemble prediction probabilities were ranked and returned to guide the input of input of target samples to solve the problem of imbalanced data classification.Thirty-four healthy college participants were recruited for this experiment with an average age of 25 years. All the participants signed the informed consent before the experiment. The experiment was approved by the ethics committee of Henan Provincial People's Hospital.EEG data were induced by unmanned aerial vehicle (UAV) video in the task of video-vehicle detection. The paradigm of UAV video-vehicle detection is shown in An EEG recording system  was used to record EEG data using 64 Ag/AgCl electrodes according to the extended 10–20 system with 61 valid channels. The online sample rate was 600 Hz with a band-pass filter of 0.01–100 Hz and a notching filter of 50 Hz. Signals were filtered to 0.1−30 Hz and resampled to 100 Hz after ICA-based artifact removal. The average reference was used. EEG signals for false reported videos were removed. To ensure the reliability of sample labels, we intercepted deviant samples from deviant video-induced EEG signals and standard samples from standard video-induced EEG signals. Deviant samples were intercepted for 1,500 ms, starting from the target onset time. Since there was only one vehicle in each deviant video, one deviant sample could be obtained from one deviant video. Standard samples were intercepted from standard video-induced signals without overlapping. In this way, several standard samples can be obtained from one standard video. With an ERP alignment method proposed in the previous studies . The labeled samples are Xsj and Ysj denote the sample set and the corresponding label vector, respectively. nsj is the sample number for the j-th source domain. k-th sample. The samples and labels of the target domain are  with data distribution pt . The data distribution between the source and target domain is similar but also different. The similarity ensures that they can share the feature extraction for classification, and the difference is usually expressed as domain shift  for band-pass filters with around 2 Hz frequency resolution. The size of the second convolution kernel is c × 1 for reducing the spatial dimension from multiple channel signals to time-series. After the average pooling layer with double downsampling, the size of the third convolution is 1 × (fs/5 + 1), and the sliding sum of 200 m sec data in time and further down sampling to fs/10 Hz. The obtained feature is transformed into a one-dimensional feature vector expressed as i-th source domain or The feature extractor maps all samples from the target domain and N source domains, there will be N domain discriminators F2 × 1 × T/10. The discriminant probabilities of the target samples and source samples are j-thsource domain is given as follows:vs and vt are a one-hot label for target and source domain. K is the batch size.The domain discriminator distinguishes the distribution distance between the target domain and the source domain by adversary learning. In this study, to eliminate domain shift, a gradient reversal layer . In the testing stage, all the testing data are used to predict detection performance, as the red line shows in The pseudocode of the P3-MSDA algorithm is presented in fs = 100 Hz with 1 s length, sample points T for single-trial signals are 100, and channel number ch is 61. The number of neurons in the fully connected layer for domain discriminators and category classifiers is 80. The feature extraction parameters stated in F1 = 4, F2 = 8, and pdropout = 0.2. For the proposed P3-MSDA algorithm, we set the trade-off hyper-parameters  as , respectively. We fit the model using the Adam optimizer algorithm and run 300 training iterations (epochs). The learning rate is set to 0.0003. The batch size is K = 20, meaning that 20 samples from each source domain are conducted in domain alignment with 20 samples from the target domain. For the labeled source domain, there are equivalent deviant and standard samples in each epoch. For the unlabeled target domain, 20 samples are randomly selected from the first Q = 80% samples with the highest prediction probability as deviant stimuli.Theexperiments were implemented in the PyTorch platform. The sample rate of input signals is To evaluate the proposed P3-MSDA, more schemes are presented and compared. To clearly describe these schemes, a diagram of the composition of the source and target domains is shown in EEGNet. Samples from the single group are directly used as training data for EEGNet network training, and the testing data in the target domain are classified without domain adaption. For strong, medium, weak, and combined groups, the EEGNet network is subdivided into P3-sEEGNet, P3-mEEGNet, P3-wEEGNet, and P3-cEEGNet with the P3 map-clustering method for source domain selection.SDA. This is a DA method with a single source domain. Source samples from the single group are used as one source domain for domain alignment with the target samples. For strong, medium, weak, and combined groups, single domain adaption schemes are subdivided into P3-sSDA, P3-mSDA, P3-wSDA, and P3-cSDA with the P3 map-clustering method for source domain selection.MSDA. This is a DA method with multiple source domains instead of a single source domain. Individuals from the strong group are evenly divided into several source domains for domain alignment with the target samples. Here, sub-classifiers are trained for each source domain. The prediction results from the sub-classifiers are integrated to achieve the final classification. The MSDA scheme can be denoted as P3-MSDA when individuals with a strong P3 map are selected as source domains.g1 = {sub2, sub5, sub8, sub9, sub10, sub11, sub13, sub15, sub22, sub28}, medium P3 map group, g2 = {sub1, sub7, sub12, sub17, sub18, sub19, sub20, sub25, sub26}, and weak P3 map group, g3 = {sub3, sub4, sub6, sub14, sub16, sub21, sub23, sub24, sub27, sub29, sub30, sub31, sub32, sub33, sub34}.Based on theP3 map-clustering method, all individuals can be divided into three groups. The averaged P3 maps for each group are shown in In this study, F1 score is used for performance comparison because of imbalanced samples for the dynamic visual target detection task. The best detection performances of all schemes under different training individual numbers are summarized. They includeF1 score, classification accuracy, hit rate, false alarm rate, and significance level, as shown in Source domain selection is very crucial for the DA network. To verify the validity of the proposed P3 map-clustering method, three other criteria, namely, deviant ERP energy, energy ratio of deviation and standard ERP, and signal-to-noise ratio (SNR), are considered for clustering. The criteria of the P3 map and deviant ERP energy are proposed from a deviant sample perspective. However, the criteria of energy ratio and SNR are proposed based on the difference between deviant and standard samples. For each criterion, all individuals are clustered into three groups: strong, medium, and weak groups, according to the amplitude value.Deviant ERP energy: Since the negative wave at about 700 ms is as strong as the P3 component in the deviant ERP of video target detection, the entire deviant ERP response should be considered to balance more ERP components. Thus, the deviant ERP energy from the entire 1,000 ms with all channels is adopted as the individual feature for individual clustering.Energy ratio: The essence of EEG-based target detection is to distinguish between deviant and standard samples. Thus, the difference between deviant and standard samples should be highlighted for classification. Here, the energy ratio of deviation ERP and standard ERP can be viewed as a criterion for individual clustering. The higher the ratio, the greater the difference. The greatest difference group is the strong group.SNR: In addition to energy ratio, SNR can also reflect the overall signal quality. The higher SNR means better signals and performance. The actual collected signals consist of brain response and noise signals. Here, the brain response signals are denoted by the averaged ERP, and the noise signals are the difference between the collected signals and averaged ERP. The energy ratio of brain response and noise signals denotes the signal SNR. The greatest SNR group is the strong group.0.08 higher F1 score. Compared with the three other criteria, the P3 map criterion can contain the distribution and strength information of brain activity, which might explain why the P3 map criterion achieves the best performance. The response difference among brain regions from the strong P3 map group was maximized for training better classifiers.Here, the EEGNet and SDA networks are used for evaluating the different clustering criteria. Samples from five labeled individuals are combined as source domain, and these individuals are from the same group. For each testing, one of the remaining individuals in each group is selected to form the target domain, and three target individuals from the strong, medium, and weak groups are considered. Finally, detection performance is obtained from the average value of 20-time random validation. The F1 score of detection performance with different clustering criteria is shown in Furthermore, we test the effect of P3 map-clustering method on the target domain, as shown in Since the P3 map-based clustering criterion performs well for source domain selection, we compare the performance among the P3-sEEGNet, P3-mEEGNet, P3-wEEGNet, P3-cEEGNet, P3-sSDA, P3-mSDA, P3-wSDA, and P3-cSDA schemes, and explore the effect of the number of labeled individuals. The averaged F1 scores on the EEGNet network  and the SDA network  are respectively shown in Based on the excellent performance of the P3-sSDA network, we developed a P3-MSDA network, where source domain individuals are selected from the strong P3 map group. Here, P3-MSDA can assume a single individual as one source domain or evenly divide all individuals into several source domains. The F1 scores of P3-MSDA are presented in In this study, we developed an unsupervised multi-source domain adaptation (P3-MSDA) network for dynamic visual target detection, which is an individual generalized model with imbalanced samples. In the P3-MSDA network, a P3 map-clustering method was proposed for source domain selection. The results showed that individuals with a strong P3 map could perform best as source domains, suggesting the superiority of individuals with high-brain activation levels for dynamic visual target detection. Besides, a target sample selector was designed to guide the input of target samples for imbalanced data classification. Based on these, the proposed P3-MSDA could exhibit higher classification accuracy and F1 score than the EEGNet network without domain adaptation and SDA networks with a single source domain, achieving an individual generalized model for dynamic visual target detection.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by Henan Provincial People's Hospital. The patients/participants provided their written informed consent to participate in this study.XS was mainly responsible for research design, data analysis, and manuscript writing of this study. YZ was mainly responsible for data analysis and manuscript writing. LT was mainly responsible for research design and data analysis. JS was mainly responsible for data collection. GB was mainly responsible for data analysis. BY was mainly responsible for research design and document retrieval. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Treponema pallidum, an obligate human pathogen, has an outer membrane (OM) whose physical properties, ultrastructure, and composition differ markedly from those of phylogenetically distant Gram-negative bacteria. We developed structural models for the outer membrane protein (OMP) repertoire (OMPeome) of T. pallidum Nichols using solved Gram-negative structures, computational tools, and small-angle X-ray scattering (SAXS) of selected recombinant periplasmic domains. The T. pallidum “OMPeome” harbors two “stand-alone” proteins (BamA and LptD) involved in OM biogenesis and four paralogous families involved in the influx/efflux of small molecules: 8-stranded β-barrels, long-chain-fatty-acid transporters (FadLs), OM factors (OMFs) for efflux pumps, and T. pallidum repeat proteins (Tprs). BamA (TP0326), the central component of a β-barrel assembly machine (BAM)/translocation and assembly module (TAM) hybrid, possesses a highly flexible polypeptide-transport-associated (POTRA) 1-5 arm predicted to interact with TamB (TP0325). TP0515, an LptD ortholog, contains a novel, unstructured C-terminal domain that models inside the β-barrel. T. pallidum has four 8-stranded β-barrels, each containing positively charged extracellular loops that could contribute to pathogenesis. Three of five FadL-like orthologs have a novel α-helical, presumptively periplasmic C-terminal extension. SAXS and structural modeling further supported the bipartite membrane topology and tridomain architecture of full-length members of the Tpr family. T. pallidum’s two efflux pumps presumably extrude noxious small molecules via four coexpressed OMFs with variably charged tunnels. For BamA, LptD, and OMFs, we modeled the molecular machines that deliver their substrates into the OM or external milieu. The spirochete’s extended families of OM transporters collectively confer a broad capacity for nutrient uptake. The models also furnish a structural road map for vaccine development.IMPORTANCE The unusual outer membrane (OM) of T. pallidum, the syphilis spirochete, is the ultrastructural basis for its well-recognized capacity for invasiveness, immune evasion, and persistence. In recent years, we have made considerable progress in identifying T. pallidum’s repertoire of OMPs. Here, we developed three-dimensional (3D) models for the T. pallidum Nichols OMPeome using structural modeling, bioinformatics, and solution scattering. The OM contains three families of OMP transporters, an OMP family involved in the extrusion of noxious molecules, and two “stand-alone” proteins involved in OM biogenesis. This work represents a major advance toward elucidating host-pathogen interactions during syphilis; understanding how T. pallidum, an extreme auxotroph, obtains a wide array of biomolecules from its obligate human host; and developing a vaccine with global efficacy. Treponema pallidum subsp. pallidum   whose physical properties, ultrastructure, and composition differ markedly from those of phylogenetically distant Gram-negative bacteria    with a mde (LPS) , 11. The12 (–The Gram-negative OM is an asymmetric lipid bilayer consisting of an inner (periplasmic) leaflet of glycerophospholipids and an outer leaflet of LPS, the highly inflammatory glycolipid that establishes the permeability barrier . In dise14–17–24–27–12 . Gram-ne12 , 35. Bam12 –38. The 12 –, 40. In 12 –, 42.T. pallidum strain Nichols rare OMPs was their lack of sequence relatedness to prototypical OMPs of Gram-negative bacteria , coupled with newly developed structural algorithms  OMP repertoire (OMPeome) described here, we refined our structural models using solved structures as the templates, an expanded battery of computational and bioinformatic tools, recently published experimental data, and small-angle X-ray scattering (SAXS) analysis of selected recombinant aqueous soluble periplasmic domains. Our analyses revealed that, in addition to the BamA and LptD “stand-alone” proteins with novel structural features, the spirochete’s OM contains extended families of transporters and efflux pump OMFs that presumably endow the organism with a broad capacity for nutrient uptake and molecule extrusion. For BamA, LptD, and efflux pump OMFs, we identified and modeled the components of the cognate molecular machines that enable these OMPs to deliver their unique cargos into the OM bilayer or the external milieu. SAXS provided new insight into the function of T. pallidum’s β-barrel assembly machine (BAM) apparatus and additional biophysical evidence for the bipartite membrane topology of full-length members of the T. pallidum repeat (Tpr) family. Collectively, the 3D models reveal that T. pallidum has evolved a unique repertoire of OMPs to address its physiological needs while meeting the demands of stealth pathogenicity. Strategies to elicit protective antibodies by immunization with recombinant treponemal proteins require detailed knowledge of the molecular architecture of the spirochete’s OM as well as the membrane topology and structure of candidate vaccinogens. Thus, the models described here also provide a structural road map for the development of a broadly protective vaccine for a disease that has afflicted humankind for centuries.For the iteration of the Escherichia coli, BamA consists of a C-terminal β-barrel and an extended N-terminal periplasmic region containing five polypeptide-transport-associated (POTRA) domains , its first characterized rare OMP , lacks BamBCDE subunits based on genome mining for structural as well as sequence orthologs, and, notably, dissociates in relatively low concentrations of the detergent n-dodecyl-β-d-maltoside (DDM)  , 58.E. coli have established that folding of nascent OMPs occurs within the lumen of the BamA β-barrel and, for one substrate, LptD, have identified residues vital to this process    . The prepallidum  supports folding , have be folding  demonstroli BamA  have beeE. coli) , these dE. coli) . Other ftive OMs , particutive OMs .E. coli BAM complex    , 66. Notnd 5EKQ) . Althoug(TP0327)  and a re(TP0327) , the difE. coli contains a translocation and assembly module (TAM) required for the export of a subset of OMPs, particularly autotransporters, to the cell surface  and likely cotranscribed with it, encodes a large (108-kDa) TamB ortholog with a C-terminal DUF490 domain  of Borrelia burgdorferi, also upstream of bamA (bb0795), is part of the Lyme disease spirochete’s BAM complex. Homology modeling reveals that, like the TamA interaction partner in E. coli  . Consequently, we assessed whether the SAXS data represent a mixture of conformations by employing an ensemble optimization method (EOM) , yielded a multidomain envelope. The EOM-generated structural model of the compact conformation of POTRA1-5 , recombinant POTRA1 to -5 (POTRA1-5) of TP0326  to gain insights into the mechanism of transfer of nascent OMPs. The structural model of POTRA1-5  poorly mod (EOM) . The EOMPOTRA1-5  shows anenvelope . Based otructure  and H anrmations , we can  complex .2FG form an ATP binding cassette (ABC) transporter that uses the energy from the hydrolysis of ATP to extract LPS from the CM and push it along the periplasmic jelly roll bridge formed by LptCAD  (The LPS transport (Lpt) machinery consists of seven essential proteins (LptABCDEFG) that mediate the transfer of newly synthesized LPS from the periplasmic side of the CM to the OM . The innansport) . A transansport) . LptD isansport) . A hydroansport)  38, 75), 752FG fansport) .T. pallidum lacks an LPS biosynthetic pathway . Finally, TP0515 has four cysteines, but their positions are not conserved with respect to the cysteines in Gram-negative LptD . Th. ThT. paI-TASSER  46), co, coT. paI-TASSER . DiscoTocolipids , and thicolipids . Moreovecolipids ; it is tcolipids  of the icolipids . Althougcolipids  needed fcolipids , implyincolipids . InteresE. coli OmpW, Pseudomonas aeruginosa OprG, and Acinetobacter baumannii CarO are the most extensively studied representatives of the single-domain subgroup. The crystal structures of OmpW and OprG revealed a narrow hydrophobic channel leading to a lateral opening in the barrel wall (–2MHL), OprG (PDB accession number 2N6L), and AlkL of Pseudomonas putida  (PDB accession number 6QAM) solved by nuclear magnetic resonance (NMR), and their electrostatic potentials, appear to provide a more uniform picture of structure-function relationships in the 8-stranded β-barrels . The NMR structures revealed that each has a narrow channel, long flexible ECLs, and short β-strands with lengths closely matching the thickness of the OM bilayer (Yersinia pestis binds to negatively charged extracellular matrix (ECM) components via its positively charged ECLs  with OmpA-like domains containing peptidoglycan recognition motifs; neither, however, contains a β-barrel domain  . Knowing that the NMR and crystal structures for 8-stranded barrels can be at variance, we also used the NMR structures of OmpW (PDB accession number 2MHL) (2N6L)  , 94. TP0proteins , best fier 2MHL)  and OprG) (2N6L)  as the t) (2N6L) . We cons) (2N6L) . Calcula) (2N6L)  predict ) (2N6L)  consiste) (2N6L)  and dens) (2N6L) , 78. The barrels  suggests barrels . As Hayn barrels , it remaE. coli FadL (PDB accession number 1T1L), the proposed pathway for fatty acid uptake involves two low-affinity binding sites within a hydrophobic groove formed by ECL3 and -4 and a conserved high-affinity site at the entrance of the barrel   and TodX of Pseudomonas putida (PDB accession number 3BRZ), which transport aromatic hydrocarbons, have been structurally characterized , Gram-negative OMs are a notable exception because of the permeability barrier created by an outer leaflet composed of LPS . Consequig. S8A) . Once inig. S8A) . Disruptig. S8A) . To datecterized . CompareAlthough a previous study proposed that hydrocarbon substrates traverse the channels of TbuX and TodX to reach the periplasm, a recent investigation provided evidence that traversal of the lateral gate would be the preferred mechanism. Although a previous study  proposedT. pallidum obtains LCFAs, which it cannot synthesize , TP0856 and TP0858 (subgroup II), and TP0859 and TP0865 (subgroup III). To better understand the evolutionary and functional relationships between T. pallidum FadL-like proteins and other bacterial orthologs, we performed a sequence-based phylogenetic analysis of 415 unique FadLs from the Pfam database (http://pfam.xfam.org) . Interestingly, the T. pallidum FadL-like proteins are much more closely related to R. pickettii TbuX and P. putida TodX than to E. coli FadL and cluster on the same branch of the tree, suggesting evolution from a single precursor.How nthesize , has beenthesize . Althoug. Additionally, ECL4 of TP0858 was incomplete. We then used trRosetta (www.genome.jp/tools/motif) identified a tetratricopeptide repeat (TPR) motif distributed over two antiparallel α-helices of each helical bundle  of the five genes are cotranscribed , suggesting that their gene produces function in a coordinated fashion. It is widely assumed that an effective syphilis vaccine has to target multiple OMPs rRosetta , and  described by Norgard and colleagues  family of proteins, whose members have sequence homology to the major outer sheath protein (MOSP) of Treponema denticola , a central region that varies in both length and sequence among family members , and a conserved C-terminal domain related to the corresponding domain in MOSP (MOSPC) , a conserved N-terminal domain related to the corresponding domain in the N-terminal portion of  (MOSPC) , 111. Prpallidum , 115. InN and MOSPC domains . A comparative percentage alignment matrix of all Tpr MOSPN and MOSPC domains revealed less than 23% similarity between the two domains (Table S6). This comparison of amino acid sequences between the two domains suggests that they have distinct evolutionary origins. While the percent similarities within the MOSPN and MOSPC domains are similar (Table S7), their stretches of amino acid similarity differ in distribution . With both domains, diversity is generated by block insertions as well as amino acid substitutions. Particularly noteworthy in comparisons with the MOSPN and MOSPC domains are the marked differences in the lengths and amino acid compositions of the CVRs . Subfamily II CVRs are considerably larger than those in the other subfamilies, also as a result of block insertions, while the CVR of TprK is the smallest . Collectively, these results imply that the variable length of CVRs, resulting from the wholesale insertion of sequence cassettes, was a major factor in the evolution of the Tprs  spectroscopy  chromatography without denaturants or detergents and, by size exclusion chromatography (SEC), eluted as monomers with molecular weights of ∼25 kDa . In SAXS experiments, the scattering curves and intraparticle distance distribution functions [P(r)] of both domains showed correct folding without aggregation . As with TprF  consist of an N-terminal nucleotide binding domain (NBD) and four transmembrane (TM) helices and hydrolyze ATP to drive transport (E. coli AcrB) consist of a TM domain with 12 α-helices within the CM, a porter domain in the periplasm containing the substrate binding pocket, and a docking domain that connects the pump to the adaptor; RND transporters are powered by the proton motive force (E. coli TolC) form a trimer with 3-fold symmetry in which 4 β-strands and 6 α-helices from each subunit associate to form a 12-stranded β-barrel within the OM and an α-helical barrel within the periplasm, which interacts with a hexameric adaptor  , and a periplasmic adaptor that connects the two . The pumransport . RND trave force . In bothnd MacA) , 128. Thth pumps .T. pallidum lipoprotein involved in metal homeostasis, resulted in the serendipitous discovery of a large operon (tp0959 to tp0972) encoding a MacB transporter , a periplasmic adaptor (TP0965), and four OMFs (TP0966 to TP0969)  and two identical NBDs (TP0964)   implies that they function in parallel , a  TP0969) . In cont TP0969) , 131, wh(TP0964) . We idenm genome . Multipl, a  TP09er 1TQQ)    . In E. coli, the positively charged periplasmic interface of MacB interacts with the negatively charged projections of MacA, while the converse is true for AcrB and AcrA . We next assembled the complete Mac and Acr efflux system in T. pallidum  (We used Phyre2 and PyMOL to construct a hexameric assembly of the periplasmic adaptor (TP0965), which exhibits a typical MacA-like funnel but is shorter than ctively)  and B. Bpallidum  using thctively) , 128  interacts with multiple adaptors  Gram-negative bacteria in that it contains multiple paralogous OMP families. We postulate that each family enables the bacterium to import a different “class” of substrate and that members within a family have different substrate specificities within that class. With the important exception of the Tprs, T. pallidum has achieved this functional diversity by utilizing known structural orthologs as evolutionary templates. The multidomain Tprs, on the other hand, which have no Gram-negative counterpart, appear to have evolved under a combination of pressures related to the spirochete’s architectural plan  in addiT. pallidum could be accomplished by aggressive public health measures. The explosive increase in new syphilis cases in the United States and globally during the past 20 years clearly argues otherwise  was propagated by intratesticular inoculation of adult male New Zealand White rabbits and harvested in CMRL medium (Gibco) supplemented with 20% heat-inactivated normal rabbit serum at peak orchitis as previously described  and the MOSPN domain of T. denticola strain ATCC 35405 without its signal sequence were described previously (N domain (amino acid residues 29 through 259) of TprK (TP0897) (GenBank accession number WP_010882340) without its signal sequence was PCR amplified from the pUK57 plasmid harboring an E. coli codon-optimized version of tprK (synthesized by GenScript). The resulting amplicon was cloned into linearized pET28a in frame with the N-terminal His6 tag using in-fusion cloning E. coli OverExpress C41(DE3) (Lucigen) at 20°C. Expression was induced at an optical density at 600 nm (OD600) of ∼0.4 by the addition of isopropyl-β-d-1-thiogalactopyranoside (IPTG) to a final concentration of 0.5 mM, followed by 12 to 44 h of growth. Following induction, cells were harvested at 7,000 × g for 20 min at 4°C. The pellet was lysed in Tris-NaCl buffer  supplemented with a protease inhibitor cocktail . The lysate was centrifuged at 14,000 rpm for 40 min at 4°C to remove insoluble proteins and cell debris. The supernatant was mixed with Ni-NTA resin  for 1 h and washed with Tris-NaCl buffer, followed by another wash with the same buffer containing 40 mM imidazole. Elution was carried out in Tris-NaCl buffer with 400 mM imidazole at pH 7.5. Elution fractions were pooled and subjected to size exclusion chromatography (SEC) using a Superdex-200 HR 16/60 column  preequilibrated with a solution containing 50 mM Tris (pH 7.5), 100 mM NaCl, and 1 mM dithiothreitol (DTT). The purity of all proteins was verified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The final proteins (POTRA arm and the MOSPN domains of TprK and MOSP) were concentrated to 1 to 3 mg/ml using an Amicon filtration device, flash-frozen in liquid nitrogen, and stored at −80°C until use.All constructs in this study were overexpressed in T. pallidum OMPs (http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index) (C score (3KGP) from N. gonorrhoeae  . A confid=index)  between d=index) . The higd=index) . Proteind=index) . The acc, LptF (TP0883), LptG (TP0884), LptC (TP0784), and LptA (TP0785) orthologs. Next, the crystal structure of Vibrio cholerae LptB2FGC (PDB accession number 6MJP) . Finally, T. pallidum LptA (TP0785) was manually aligned between the N-terminal domain of LptD (TP0515) and LptC (TP0784).The Phyre2 server  was useder 6MJP)  was applE. coli TolC (PDB accession number 1TQQ)  (5V5S)  and OMFs .The crystal structure of trimeric er 1TQQ)  was used WinCoot . Structu WinCoot . The cryt (5NIK)  and AcrA) (5V5S)  efflux pT. pallidum OMPs on the primary  protein structures. Structures were aligned using the secondary structure matching (SSM) superimposition algorithm in WinCoot (https://pymol.org/) (α atoms in the antigen within a distance of 10 Å (DOG 2.0  was used WinCoot . All strol.org/) . MSA wasol.org/) . PROMALSol.org/)  and ESProl.org/)  were useol.org/) . Discontol.org/) . The sco of 10 Å . All phy of 10 Å  and visu of 10 Å .N domain of TprK. One hundred fifty microliters (∼200 μM) of a protein sample (TprK) was injected into a Superdex Increase 200 10/300 GL column  at a flow rate of 0.45 ml/min. Scattering images were collected continuously at 1-s exposures per frame. The data processing program pyXS  at the 16 LiX beamline  of Natioram pyXS  was usedbeamline  under stbeamline  was usedThe SAXS data of TP0326 POTRA1-5 were collected at the Cornell High-Energy Synchrotron Source (CHESS) beamline using a dual Pilatus 100 K-S SAXS/wide-angle X-ray scattering (WAXS) detector, and background subtraction of SAXS buffer was performed using the open-source software RAW .ab initio envelopes of BamA POTRA1-5, T. denticola MOSPN, and TprK MOSPN from one-dimensional SAXS profiles. Radii of gyration (gR), deduced from the Guinier region of the Guinier plots, were computed using PRIMUS (P(r) curve. Ab initio envelope reconstructions were performed using DAMMIN (The ATSAS software package  was usedg PRIMUS . GNOM (1g PRIMUS  was usedg DAMMIN  and DAMAg DAMMIN . Theoretg DAMMIN . The preg DAMMIN .http://www.softberry.com/berry.phtml?topic=fgenesb&group=programs&subgroup=gfindb) was used for operon prediction for the region encompassing nucleotide positions 937198 to 932966 of the T. pallidum Nichols strain (NCBI accession number NC_021490.2) genome carrying tp0856, tp0858, and tp0859, including intergenic regions between tp0856 and tp0858 and between tp0858 and tp0859.The FGENESB server  as follows: 1 cycle at 98°C for 3 min, followed by 35 cycles of 98°C for 10 s, 55°C for 15 s, and 72°C for 10 s and 1 cycle of 72°C for 3 min.RNA was extracted from testicular tissue of four rabbits inoculated with the escribed . The RNA


We find intermediate structures along the pathway are stabilized by transient interactions between A- and P-domains as well as lipid molecules in the transmembrane helices.Ion pumps (or P-type ATPases) are membrane proteins, which transport ions through biological membranes against a concentration gradient, a function essential for many biological processes, such as muscle contraction, neurotransmission, and metabolism. Molecular mechanisms underlying active ion transport by ion pumps have been investigated by biochemical experiments and high-resolution structure analyses. Here, the transition of sarcoplasmic reticulum Ca 2+-ATPase transports two Ca2+ ions from the cytoplasm to the SR lumen against a large concentration gradient. X-ray crystallography has revealed the atomic structures of the protein before and after the dissociation of Ca2+, while biochemical studies have suggested the existence of intermediate states in the transition between E1P⋅ADP⋅2Ca2+ and E2P. Here, we explore the pathway and free energy profile of the transition using atomistic molecular dynamics simulations with the mean-force string method and umbrella sampling. The simulations suggest that a series of structural changes accompany the ordered dissociation of ADP, the A-domain rotation, and the rearrangement of the transmembrane (TM) helices. The luminal gate then opens to release Ca2+ ions toward the SR lumen. Intermediate structures on the pathway are stabilized by transient sidechain interactions between the A- and P-domains. Lipid molecules between TM helices play a key role in the stabilization. Free energy profiles of the transition assuming different protonation states suggest rapid exchanges between Ca2+ ions and protons when the Ca2+ ions are released toward the SR lumen.Sarcoplasmic reticulum (SR) Ca The transport mechanism was originally described by E1/E2 theory, whereby the protein alternates between Ca2+ high-affinity E1 and low-affinity E2 states. A more-detailed reaction cycle requires multiple steps, including the binding/dissociation of Ca2+, H+-counter transport, ATP-binding and hydrolysis, phosphorylation/dephosphorylation of Asp351, and the dissociation of ADP and Pi . Structurally, the Ca2+-ATPase consists of three cytoplasmic domains  and 10 transmembrane (TM) helices (M1-M10) , have provided essential information for understanding structure–function relationships  the A-domain rotates ∼90°; 2) the threonine–glycine–glutamate–serine (TGES) loop in the A-domain reaches the phosphorylated Asp351 in the P-domain  M2+-ATPase happen on the milliseconds or slower time scales, which are not accessible in brute-force MD simulations  and the Ca2+-ATPase–lipid interactions in the E1P–E2P transition are examined using the simulation trajectories. The predicted interactions between phospholipids and basic sidechains of Ca2+-ATPase are compared with recent X-ray crystallography studies : E2P_dp has the same protonation state as E1P (only Glu908 is protonated), while Glu771, Asp800, and Glu908 are protonated in E2P. In the simulations of E1P, E2P_dp, and E2P, the Cα atoms’ RMSDs from the starting structures increase above 4 Å . Since each cytoplasmic domain and the TM helices are individually stable (RMSD < 3 Å), the increase in RMSD of the whole structure suggests large domain motions. A hierarchal clustering provides a representative structure at the cluster center of each MD simulation trajectory . Large deviations are observed only in the N-domain between the three MD simulations when representative snapshots are superimposed on the starting crystal structures. Interestingly, large motions in M4 and M6 are observed in the simulation of E2P_dp, probably due to mismatches between the TM structure and protonation states of the Ca2+-binding residues. The luminal gate in E2P_dp is more open than in the crystal structure, which may reflect the structural changes occurring just after the dissociation of Ca2+.MD simulations of SR Catuations . Modelin2+ to E2P in the three simulations. First, we use the E1P system and perform MD simulations with the mean-force string method for predicting one of the most probable transition pathways between E1P⋅ADP⋅2Ca2+ and E2P. Due to strong Ca2+–protein interactions, we do not expect that the two Ca2+ ions bound at the TM binding sites are fully released toward the SR lumen, even in the images close to E2P. After the simulation of the first model (E1P), we extract a protein structure from image 50. We then carry out the second simulation with the string method between this image and the representative structure of E2_dp, assuming the same protonation state as E2_dp. In the third simulation of the string method, we connect image 50 and the representative structure of E2P simulation, using the E2P protonation state . In this way, we will answer several questions regarding what series of conformational changes occur in the cytoplasmic and TM domains, how the motions of the cytoplasmic domains are coupled with those of TM helices, and how the exchange between Ca2+ and protons occurs at the TM binding sites. Technical details of the mean-force string method are described in SI Appendix.We next examine how structure changes happen from E1P⋅ADP⋅2CaSI Appendix, Table S2) and are then optimized toward one of the most probable ones by the mean-force string method . The optimized pathway in the first simulation is represented by the 64 images on a two-dimensional map along with the distance RMSDs from representative MD structures of E1P and E2P, respectively. The 64 images are divided into five substates (SSs) using the fixed-radius clustering in Multiscale Modeling Tools for Structural Biology (MMTSB) .The initial paths of the three string simulations are derived from the targeted MD (TMD) method ( (MMTSB) , 36  C. The cl (MMTSB) , which iSI Appendix, Fig. S5). In the first SS transition, both the A- and N-domains show large rotational motions (37° and 25°). However, their rotational axes are almost perpendicular to each other, suggesting their independent motions. In the second and third SS transitions, the rotational axis of the A-domain is almost the same and is parallel to the membrane normal (the z-axis). The TGES loop of the A-domain now approaches the phosphorylated Asp351 in the P-domain. In the final step, from image 44 to 57, DS detects a large motion of the A-domain relative to the P-domain. This large rotation is possible due to the P-domain inclining and pushing M4 toward the SR luminal side.To understand the cytoplasmic domain motion, we compute the rotational axes and angles of the A- and N-domains relative to the P-domain using the domain selection (DS) method  . In the E1P⋅ADP⋅2Ca2+ crystal structure, the A- and N-domains are interconnected via salt bridge interactions between Arg134/Arg139 in the A-domain and Asp426 in the N-domain (Top Left). Biochemical experiments suggested that the salt bridge interactions play an essential role in phosphoenzyme formation . Breaking of the salt-bridge interactions allows independent motions of the A- and N-domains in the first SS transition. When the TGES loop approaches the phosphorylation site in the P-domain during the second SS transition, hydrophobic interactions between Ile179 in the A-domain and Ala725/Val726 in the P-domain seem to be important (Middle). In the next step, different hydrophobic interactions between Leu180 in the A-domain and Val705 in the P-domain are formed. The clockwise rotation of the A-domain seems to be precisely controlled using such a hydrophobic rail on the P-domain. Importantly, the distance between the TGES loop and the phosphorylated Asp351 decreases almost linearly  during the SS transitions. The transient hydrophobic interactions between the A- and P-domains can guide functionally important amino acid residues to meet almost at the end of the transition.These rotational motions start just after the disconnection of ADP from the phosphorylated Asp351 , the distance between the cytoplasmic parts of M2 and M4 , and the distance between the sidechain atoms of Val304 in M4 and Leu793 in M6 . In the first two SSs (images 10 and 23), these values remain close to those in the E1P⋅ADP⋅2Ca2+ crystal structure. The M2 bending angle decreases in the second SS transition because the rotation of the A-domain pulls the A–M2 linker. The M2 bending angle is maintained in the later transitions, which allows the distance between the cytoplasmic halves of M2 and M4 to enlarge slightly. The region is stabilized via hydrophobic interactions observed in the E1P crystal structure (Middle Left). The reduced interaction in the cytoplasmic halves of M2 and M4 seems important for the downward shift of M4. The M4–M6 distance is drastically changed in the last SS transition (from image 44 to 57), and the hydrophobic core formed by Val304 in M4 and Leu793 in M6 is totally broken (Bottom Right). MT indicates that the M1–M2/A-domain (red) and M3–M4/P-domain (green) move together as rigid moving domains (Right). The rotational motion of M6 is likely independent of the cytoplasmic domain motions and happens due to mismatched hydrophobic and electrostatic interactions after the M1–M4 motions.To reduce the Canal gate , 41. The domains A, Right.The sequence of events and the coupling between the cytoplasmic and TM domain motions substantially agree with inferences from structural studies and biochemical experiments. In particular, the rate of phosphoenzyme isomerization from E1P to E2P is affected by the mutations in the cytoplasmic part of M2 and M4 , 43. Bot2+, E1⋅AlF4−⋅ADP⋅2Ca2+, E2⋅AlF4− (TG), and E2(TG)] ] . Two key2+ crystal structure. After disruption of the hydrophobic core formed by the cytoplasmic halves of M2 and M4, a lipid molecule fills the space between the two TM helices . In the second simulation (E2P_dp), not only water molecules but also K+ ions penetrate from the lumen side. Both images 1r and 16r (same as image 50 in the first simulation except for Ca2+ ions) contain three K+ ions at the binding sites   D. These 2+ interactions, we do not observe the second minima toward image 64 (E2P). The second rise in PMF results from a mismatch between the TM structures and the Ca2+-binding states. The barrier height in the third simulation, where the same protonation state of the E2P crystal structure exists, is greatly reduced compared to that in the second simulation with the E1P protonation state. This also supports the existence of a quick exchange of Ca2+ and protons at the ion-binding sites. If we integrate the three PMF curves, the complete energetics of the transition from E1P⋅ADP⋅2Ca2+ to E2P is described.Using umbrella sampling, we compute the potential of mean forces (PMFs) from the three simulations. The PMF obtained in the first simulation shows a flat curve between images 1 and 14 E. Then, 2+-ATPase using atomistic MD simulations. The key results are summarized in 2+ ions are stably bound at the TM binding sites, and ADP/Mg2+ is bound at the cytoplasmic binding sites formed with the N- and P-domains. The crystal structure in the E1P⋅ADP⋅2Ca2+ state shows a compact cytoplasmic structure, which is stabilized by ADP (between the N- and P-domains) and sidechain salt bridge interactions between the N- and A-domains. The disconnection of ADP from the phosphorylated Asp351 and break-up of the salt bridges happen spontaneously without large energetic costs, allowing the A-domain to rotate independently in the beginning. The TGES loop in the A-domain needs to reach the phosphorylated Asp351 in the P-domain at the end of the transition. The large arc-like movement of the A-domain is guided not only by sidechain interactions observed in the crystal structures but also by transient interactions along the way. Hydrophobic interactions with the P-domain act as a rail for fixing the rotation axis of the A-domain. The coupling of the A-domain rotation with the rearrangement of M1–M6 helices occur in almost the final stage of the E1P–E2P transition. One of the key events to initiate the coupling is the change in hydrophobic interactions at the cytoplasmic halves of M2 and M4 helices. With the interactions severed, it is easier for the A-domain rotation and for the inclination of the P-domain to shift M4 downward. The subsequent rotation of M6, which is necessary to open the luminal gate for releasing Ca2+ ions, seems to be driven by mismatched sidechain interactions following the M1–M4 movements.In this study, we examine structures and energetics of the E1P–E2P transitions in SR Ca2+ ions are released toward the lumen, and protonation of the Ca2+ binding residues happens quickly to keep the structural integrity of the mobile TM helices. The opening of the luminal gate means a reduction of protein sidechain interactions, and in compensation, lipid molecules play an important role in stabilizing TM helices. The phosphate head group of a lipid molecule transiently pairs with basic residues in TM region, while the hydrophobic acyl chains fill cavities between TM helices. The current MD simulation studies add information missing from experiments and contribute to a better understanding of the E1P–E2P transition. In addition to the decrease of the interactions between Ca2+ and the binding residues, entropy would be another factor for the energetics in the release of Ca2+. As shown by Rubí et al. . The protein was embedded in a DOPC lipid bilayer. According to a previous simulation study of E1⋅2Ca2+ (2+ (denoted E1P). In the E2P state, two simulation systems were constructed: one of which has the same protonation state as E1P⋅ADP⋅2Ca2+ (denoted E2P_dp), and the other with a different protonation state, in which Glu309, Glu771, and Glu908 are protonated as in the E2P crystal structure (denoted E2P). Detailed simulation methods are described in SI Appendix, and the notations and configurations are summarized in SI Appendix, Table S1.Simulation systems in E1P⋅ADP⋅2CaE1⋅2Ca2+ , Glu908 2+ and in different protonation states of the Ca2+-binding residues were calculated. The first was applied from E2P_dp to the structure with closed luminal gate (image 50) and showed the opening/closing of the luminal gate without Ca2+. The second one simulates the pathway from E2P to image 50, which may mimic the structure after the binding of countertransport protons to the Ca2+-binding sites. Additional details are described in SI Appendix.The pathway from E1P to E2P_dp was calculated as follows. An initial pathway consisting of 64 images was generated by a 200-ns TMD simulation. Then, a string-method simulation was performed, first for 40 ns without updating the images and next for 60 ns while updating the images. To calculate free energy profiles, umbrella sampling was applied to the obtained pathway. The last 46 ns from 50-ns simulation was used for the analysis. In addition, two pathways, with or without CaSupplementary FileSupplementary File