In most available crystal structures of GATs, the ammonia channel is evident in their native state or upon ligand binding, providing molecular details of the conduit. In addition, conformational changes that enable the coordination of the two catalytic chemistries are also informed by the available structures. In contrast, despite the first structure of a GMPS being published in 1996, the understanding of catalysis in the acceptor domain and inter-domain crosstalk became possible only after the structure of a glutamine-bound mutant of Plasmodium falciparum GMPS was determined. In this review, we present the current status of our understanding of the molecular basis of catalysis in GMPS, becoming the first comprehensive assessment of the biochemical function of this intriguing enzyme.Glutamine amidotransferases (GATs) catalyze the hydrolysis of glutamine and transfer the generated ammonia to diverse metabolites. The two catalytic activities, glutaminolysis and the subsequent amination of the acceptor substrate, happen in two distinct catalytic pockets connected by a channel that facilitates the movement of ammonia. The  GMP serves as the precursor to GTP that supports key cellular processes such as DNA replication, transcription, and translation, along with being an energy source in many cellular processes. Intracellularly, there are three possible routes for the generation of GMP; guanine phosphoribosylation, catalyzed by hypoxanthine guanine phosphoribosyltransferase (HGPRT); guanosine phosphorylation, catalyzed by guanosine kinase; and xanthosine 5′monophosphate (XMP) amination by GMPS. Guanosine kinase is largely restricted to certain bacteria, whereas HGPRT and GMPS have been found to be present in many organisms, including archaea, prokaryotes, and eukaryotes. Despite the presence of alternate cellular pathways for GMP synthesis, GMPS is an essential enzyme in many pathogenic organisms, making the enzyme a key drug target.Guanosine 5′-monophosphate (GMP) synthetase  catalyzes the final step in the Plasmodium berghei and P. falciparum has been established through reverse genetics ppA, the latter being a non-hydrolysable analogue of ATP, also leads to an increase in glutaminase activity, while these ligands have no effect on the activity of the GAT domain of human GMPS [MjGMPS, the combination of XMP + pyrophosphate (PPi) also activates MjGATase, albeit to lower levels [The GATase active site in GMPS has a conserved catalytic triad consisting of a Cys, His, and Glu residue ,41. In aw levels ,36,38,39w levels ,48. The bstrates ,50,51. T and XMP . In the man GMPS ,37,38. Ir levels .l-glutamine to l-glutamic acid, suggesting ammonia channeling. pH-dependent studies of glutamine- and ammonia-dependent activities, in conjunction with 15N-edited proton NMR spectroscopy carried out on PfGMPS, have established that the ammonia released from glutamine is not equilibrated with the external medium, but is channeled to the ATPPase active site [MjGATase and MjATPPase is 1:1, and this supports ammonia channeling in two-subunit GMPS [GMP synthetase converts XMP into GMP with stoichiometric hydrolysis of ATP to AMP and inorganic pyrophosphate, and ive site . It has nit GMPS .EcGMPS, wherein the exchange of 18O from the non-bridge to bridge position of ATP has been observed only in the presence of XMP and Mg2+ [The formation of the AMP-XMP intermediate during the course of the reaction was proposed by positional isotope exchange and kinetic experiments with and Mg2+ .The available crystal structures of free and ligand-bound GMPSs, GATase domains, and subunits, and ligand-complexed subunits of ATPPase from different organisms are summarized in PfGMPS. The synthetase domain is further subdivided into two sub-domains, the N-terminal catalytic core and C-terminal dimerization sub-domains, a feature seen in EcGMPS and in all the other available GMPS structures, except that of human  GMPSs are similar to those observed in the full-length enzymes [PfGMPS and the Pf and PhGATase domains/subunits show weak glutaminase activity [Km values of Gln for the unfused PfGATase and unliganded PfGMPS with that for the substrate-bound enzyme shows more than a 280-fold increase in affinity for glutamine upon ligand binding, indicating K-type (change in Km) activation [PfGMPS GATase active site, are invariant across GMPS sequences and similar to the triad in many hydrolytic enzymes [The GATase domain in GMPS is an independent structural domain with an α/β fold, consisting of nine to twelve β-strands surrounded by five α-helices. In  enzymes . PfGMPS activity ,38, and  enzymes ,58. PfGMPS_C89A/Gln complex [The only crystal structure that informs on the interactions of Gln with the active site residues is that of the  complex   of the catalytic Cys89, with the whole assembly mimicking the pre-catalytic state of the GATase domain [The hydrogen bonding interactions between the carboxyl oxygens of the substrate, the side-chain amide NHe domain  , is not mapped or partially mapped in most available structures of GMPSs. Although fully mapped in human GMPS, the lid loop being present above and distal from the active site of the ATPPase domain does not throw any light on its possible role in catalysis. With the structure of PfGMPS_C89A/Gln, in which the lid loop that is largely mapped except for a short stretch of eight residues, has moved into the active site in the ATPPase domain; the implications of this conformational dynamics have become clear and are discussed in the following sections. The structures of the MjATPPase/XMP and PhATPPase subunits are similar to the structures of the ATPPase domains of two-domain-type native and XMP-bound GMPSs , wherein the GATase domain, when compared to the native PfGMPS structure (PDB-ID 4WIM), is rotated by 85° and translated by 3 Å, relative to the ATPPase domain (PfGMPS_C89A/Gln [PfGMPS_C89A_C113A (PDB-ID 7ZU9), the GATase domain is rotated by 170° and translated by 1.7 Å when compared to the native structure, and by 120° and 4 Å when compared to the structure of PfGMPSC89A/Gln [The available native AMP.PPe domain . This roC89A/Gln . In the C89A/Gln   structures, making this a key residue in discriminating XMP from other purine monophosphates. Arg102 is held in place by H-bonding to the α carboxy group of the last residue Glu310 of the C-terminal loop of the dimerization sub-domain [PfGMPS and MjATPPase structures [2 of Lys302 and backbone NH of Ile307 and Glu308. The side-chain COOH of Glu308 forms a salt bridge with Arg294 from the dimerization sub-domain of the neighboring chain and holds the C-terminal loop  in place for XMP binding [PfGMPS, which correspond to Lys302, Glu308, and Arg294 in MjATPPase, to Leu result in a complete loss of Gln- and NH4Cl-dependent activity on account of impaired adenyl-XMP formation. In addition, GATase activation is reduced by >70%, suggesting that these mutants are defective in binding substrates [P. falciparum (PDB ID: 3UOW), Coxiella burnetii (PDB ID: 3TQI), and Thermus thermophilus (PDB ID: 2YWB and 2YWC). However, in the structures of the native enzymes from E. coli and Neisseria gonorrhoeae, the Arg residue corresponding to the Arg249 of MjATPPase is positioned away from the active site of the neighboring protomer by more than 9 Å  onto that of PfGMPS (PDB ID 4WIO) [PfGMPS_E374L and PfGMPS_D317A have confirmed these structural observations. Activity measurements have indicated that, though PfGMPS_D371A can bind the substrates and activate glutamine hydrolysis in the GATase domain, the adduct adenyl-XMP formation is almost completely compromised. In PfGMPS_E374L, although the rate of adenyl-XMP formation is reduced only by a factor of 3.5, the mutant is completely devoid of glutamine-dependent GMPS activity, and the activation of GATase activity by the binding of substrates to the ATPPase domain is also fully abolished. The mutant, however, exhibits a 72-fold lower NH4Cl-mediated GMP formation. This indicates that Glu374 is involved in both adenyl-XMP formation and inter-domain cross-talk. In the structure of PfGMPS, Glu374 contacts His20 in the GATase domain and the mutant His20Ala shows a 57% lower level of activation, suggesting inter-domain communication via the Glu374–His20 interaction [In the structure of ID 4WIO) . Indeed,eraction . PfGMPS) [EcGMPS structure. While the mutants K386L, H388A, and H389A of PfGMPS show greatly impaired NH4Cl- and Gln-dependent GMP formation, K386L and H389A are fully impaired in adenyl-XMP formation, and H388A shows a significantly lowered rate of adduct formation. The His mutants show a 50% lower GATase activation, but exhibit strong binding of both ATP and XMP, with Kd values in the low micromolar range. Concerning the Lys mutant, GATase activation is impaired to a much greater extent. These results establish that Lys386 is essential for substrate binding and that His388 and His389 play catalytic roles in the synthesis of the adenyl-XMP intermediate. These results clearly indicate that Lys386 participates in ATP binding, whereas His388 and His389 are involved in catalyzing the formation of the adenyl-XMP intermediate, where an increase in the electrophilicity at C2 due to adduct formation facilitates an attack by ammonia and the conversion of XMP into GMP [Apart from the conserved Asp371 and Glu374, the lid loop has an invariant motif IK(T/S)HHN (residues 385–390 in PfGMPS)  that coninto GMP . PfGMPS_C89A/Gln throws light on all key steps in the catalytic process, viz., inter-domain communication, substrate affinity enhancement, the formation of the ammonia channel, and the expulsion of the reaction product [While mutation of Thr387 to Ala impacts activity only marginally, mutation of Asn390 to Ala renders the enzyme completely inactive for GMP synthesis. The rate of adenyl-XMP formation is reduced, while the activation of the GATase domain is greatly enhanced. Taken together, these results indicate that Asn390 plays a key role in a step subsequent to adenyl-XMP formation involving the reaction of ammonia with the adduct to generate GMP. The distinct conformation of the lid loop in the structure of  product ,63. AparPfGMPS_C89A/Gln, Cys113, a residue in the GATase domain, comes proximal to and forms a disulfide bond with Cys377, a residue at the terminus of the helix holding Asp371 and Glu374. Mutation of Cys113 to Ala in PfGMPS_C89A_C113A alters the values of the kinetic parameter Km for NH4Cl, ATP, and XMP, supporting domain rotation and the placement of this region in the GATase domain in close proximity to a catalytically important segment in the ATPPase domain [As a consequence of domain rotation, as seen in the structure of e domain . i-bound structures of GMPSs from both prokaryotes and eukaryotes, the GATase active site is open to the solvent; hence, it does not provide a picture of the channel vital for the transport of ammonia. The 80° GATase domain-rotated structure of PfGMPS_C89A/Gln is the first and only structure that provides a lead on the possible nature of the domain reorganization required to form the channel for the translocation of ammonia. Although a large magnitude domain rotation is observed, the GATase active site is not correctly positioned over the adenyl-XMP bond for a nucleophilic attack by ammonia. Information on the magnitude of the GATase rotation required to position the GATase active site over the adenyl-XMP bond comes from molecular dynamics (MD) simulation studies, wherein a 50° rotation of the GATase domain, starting from the native structure, leads to optimal orientation [In all the available native and XMP- or AMP+PPentation .PfGMPS and PfGMPS/XMP, helix α1 from the GATase and helices α11 and α12 from the ATPPase largely comprise the interface between the two domains  structure, the reduction in the rate of adduct formation by 41% supports domain rotation and relocation of Glu213 that, in turn, impacts catalysis in the ATPPase domain [EcGMPS, where a mutation of His 186 (corresponding to Glu213 in PfGMPS) to Ala has led to a 50-fold increase in the Km value for Gln and an uncoupling of the two reactions [As established in e domain . The inte domain . The inv subunit . Despitee domain ,63. Thiseactions .viz., substrate binding and catalysis, and interdomain cross-talk, thus linking the catalysis in the ATPPase active site with GATase activation. The residues on α11 and α12 that mediate substrate binding are Lys386, Lys411, and Lys 415, while Asp371, His 388, and His 389 catalyze the formation of adenyl-XMP [The large body of kinetic studies on structure-guided mutants has established that the interface helices α11 and α12 (and the lid loop connected to α11) have dual roles, enyl-XMP ,63. MjGMPS and PhGMPS being exceptions. In the case of PhGMPS, the complex of GATase and ATPPase subunits could be isolated in the presence of substrates, whereas the subunits of MjGMPS associate transiently, even in the presence of ATP.Mg and XMP [kcat value setting an upper limit of 0.5 sec for the time scale of subunit association, indicating that glutamine hydrolysis is very tightly regulated in MjGMPS [PfGMPS and other dimeric GMPS structures [MjGMPS, with Lys 20 in helix G α1 of MjGATase interacting with Asp167 of helix α6 of MjATPPase. In addition, the fusion of MjGATase with MjATPPase as a single-chain protein yields an enzyme with characteristics similar to other single-chain dimeric GMPSs [In two-subunit GATs, the GATase and acceptor/synthetase subunits form a tight complex, even in the absence of substrates, with  and XMP ,45. NMR n MjGMPS ,45. Despn MjGMPS . Cross-lructures . The α1 ic GMPSs . These sMjGMPS has a highly stable succinimide arising from the deamidation of Asn109, the presence of which increases the structural stability of the protein at high temperatures [MjGATase remains folded at 100 °C and in 8 M guanidinium chloride, suggesting that sequences that stabilize succinimides from hydrolysis may be evolutionarily selected to confer extreme thermal stability [MjGATase, the molecular basis of the stability of the SNN is revealed to be imparted by electrostatic shielding by the side-chain carboxylate group of its succeeding residue Asp110, as well as through the n/π* interactions between SNN109 and its preceding residue Glu108, both of which prevent water access to the SNN. The loop containing the SNN is locked in an α-turn that contributes to a reduction in protein flexibility. Several archaeal GATases have the SNN-forming tripeptide sequence (E(N/D)(E/D)) conserved, suggesting the role of this post-translational modification in the thermostability of the protein [The GATase subunit of eratures . The pretability . SNNs, i protein .PfGMPS numbering) on the helix preceding the lid loop, along with the residues on the invariant motif IK(T/S)HHN (residues 385–390 in PfGMPS) on the lid loop, participate in ligand binding and catalysis of AMP-XMP formation [viz., the bending of the preceding helix that harbors the residues Asp371 and Glu374, an alteration in GATase–ATPPase domain interaction, and the rotation of the GATase domain, with the latter two events leading to an enhanced affinity of Gln for the GATase active site and its subsequent hydrolysis, generating ammonia. In an intermediate conformation where the GATase domain has rotated by 50°, a channel that connects the GATase active site to the adenyl-XMP bond is formed, resulting in the movement of ammonia and an attack of the adenylated intermediate to form GMP [PfGMPS numbering) play key roles in the step post adenyl-XMP formation [i, and the enzyme returning to its precatalytic state. Taken together, the movement of the lid loop is the trigger that sets the enzyme in action. Studies on two-subunit GMPSs from M. jannaschii and P. horikoshi have indicated that the catalytic mechanism operating in this class is similar to that in the single-chain counterparts [The availability of the various crystal structures of single-chain GMPSs in native and ligand-bound states, along with activity data on structure-guided, site-directed mutants, has enabled the formulation of the catalytic mechanism . Prior tormation ,63. Thisform GMP . The resormation ,63. Substerparts ,37,63.PfGMPS_C89A/Gln exhibited a novel conformational state that enabled an in-depth structure–function analysis, leading to insights into this complex catalytic process [MjGMPS has shown that the subunits associate in a manner similar to domain interactions in single-chain GMPs [MjGATase, contributing to hyperthermostability and the conservation of the SNN-bearing sequences in thermostable archaeal GATases, suggests a widespread occurrence and merits investigation.More than three decades of biochemical and structural studies have provided an in-depth understanding of the catalytic mechanism operating in GMPS. GMPS belongs to the G-type or class I triad family of amidotransferases , with th process . Unlike ain GMPs . Despite


Panicum decompositum) is a native grass species that was used as a staple food by many Australian Aboriginal communities. In this study, the potential for using Native Millet (NM) as a novel flour in the modern food market was investigated. Intact grain and white and wholemeal flours from two populations of NM were compared to bread wheat cv. Spitfire (SW) using a range of physical and chemical tests. The baking properties of NM flour were assessed using basic flatbreads made with 25:75 and 50:50 (NM:SW) mixes of wholemeal flour with 100% SW wholemeal flour used as the control. The grain size of NM was found to be smaller than SW. Milling yield, defined as the proportion of flour obtained from a whole seed, for NM was 4–10% lower than SW under the same moisture conditions used for tempering (drying) wheat. The properties of wholemeal flour indicated that NM flour has lower viscosity and low flour pasting ability compared to SW. This is likely due to the low starch content and high fibre content of NM seed. Wholemeal flour derived from NM had a protein content of 13.6% compared to 12.1% for SW. Based on a sensory analysis using an untrained panel, the distinct colour and texture may negatively affect the acceptance of NM flour by the consumer, but taste and aroma was not found to differ among samples. There were strong indications that the novelty of NM flour may help outweigh any limitations to consumer acceptance, making it a valuable product in future food markets.Native Millet ( For thousands of years, native grasses have been a high carbohydrate staple food for many Australian Aboriginal communities . Seeds fPanicum miliaceum) [Interest in the use of native grasses in an agriculture setting has increased over the past few decades but the possible use for food production in modern markets and consumer acceptance of these products is unknown. As the food industry is driven by movements towards products with descriptors including ‘ancient’, ‘novel’, ‘sustainable’, and ‘back-to-nature’, the potential for introducing products containing native plant species is high. The full or partial substitution of flour derived from current major cereals with native grass species into human food production is of interest to multiple industries and research disciplines. This is not only because native grasses are well adapted to grow in local environments and are thus likely to be environmentally beneficial but also because other closely related grass species have nutritional benefits and are gluten-free  ,11,12. FPanicum decompositum or Native Millet (NM). This perennial species is found throughout mainland Australia and produces flowers and branching seed heads (20–40 cm) in summer and autumn. In arid climates, it flowers in response to rainfall [P. miliaceum (Proso), has a comparable fatty acid composition to wheat and barley [P. sumatrense (Little Millet) is a rich source of phenolic antioxidants [One of the most common native Australian species of grass used for food by Aboriginal people was rainfall . Nutritid barley . Similaroxidants .The research described here was carried out in order to evaluate the physical and chemical attributes of seed and flour of NM in comparison to wheat. Such a study is timely, as there is increasing interest in the use of native species worldwide as consumer preference moves towards incorporation of native foods that are putatively more sustainable and nutritious. In Australia, native crops including finger lime, quandong, and lemon myrtle are now widely accepted and used by consumers. According to standardized characterisation of cereal grains, we measured the size, weight, and moisture content of seed and the milling yield (the proportion of flour obtained from a whole grain/seed) at three tempering conditions and the resulting colour of white flour. After recombing white flour with bran to form wholemeal flour, chemical properties, including ash and nitrogen content (as a proxy for protein content) and gelatinisation and pasting quality, were measured or calculated. Furthermore, this research investigated the feasibility of utilising flour from NM as a partial substitute for wheat flour in baking and its acceptance by consumers. To the best of our knowledge, this study represents the first time that seed and flour derived from a native Australian grass has been reported in a peer-reviewed arena.Seed from NM was sourced (machine harvested in 2016) from two wild populations in northern and north-western New South Wales, Australia, located near Quirindi (hereafter referred to as NMQ)  and Werris Creek (NMW) . The seed of NM is not produced commercially and can only be harvested in small quantities from natural populations. Seed was hand cleaned by threshing on a rubber board and via vacuum separation to remove chaff, husks, and dust. This process was repeated three to four times until the raw harvested seed was converted into food-grade grain. Once cleaned, NM grain was stored at room temperature and ambient humidity until required.Grain from bread wheat cv. Spitfire (SW) was used as the bread-making standard in this study. Spitfire is classified as an Australian Prime Hard cultivar and can be sold at a premium to millers both domestically and internationally . Bulk qun = 3) was measured from images of both types of grains arranged alongside the two rulers positioned at a 90° angle. Accuracy was limited using this method , so data were only used for general comparison among grains. Grain weight was measured using the 1000-kernel weight (TKW) method [−1; n = 5), used as an indication of soundness of grain and correlated with flour yield, was measured according to American Association of Cereal Chemists (AACC), Method 55-10.01 [Grain size  method . The man55-10.01 .The moisture content of grain was determined for milling purposes. Duplicate samples of approximately 3 g of seed were weighed, dried at 130 °C for 1 h to constant weight, and re-weighed (AACC Method 44-01.01) . For theWhite flour was used to determine milling yield and flour colour . For theWholemeal flour was used to examine the selected chemical and baking properties of SW compared to NM and for the preparation of flatbreads for sensory analysis. Wholemeal flour was produced using a hammer mill  fitted with a 0.8 mm screen and run at a low feeder speed. Unless otherwise specified, three replicate samples of white or wholemeal flour from NM and SW were analysed.L*, a*, and b* parameters. Maximum L* is 100 and represents white, and minimum L* is zero and represents black. The values for a* and b* have no specific numerical limits; however, positive a* is red, negative a* is green, positive b* is yellow, and negative b* is blue [The colour of white flour was measured using a CR-300 Minolta Chroma Meter . The colour measurements were quantified via the Hunterlab system giving values for  is blue .The nitrogen content (%N) of the duplicate samples of wholemeal flour was measured using an element analyser . Nitrogen content was converted to protein content using the universal conversion factor of 6.25 .The ash content of the duplicate samples of wholemeal flour was determined using the method described by Khoddami et al. . SamplesThe gelatinisation and pasting quality of wholemeal flour was tested using a standard Rapid Visco Analyser (RVA)  according to AACC Method 76-21.02 . This meFlatbreads were prepared using wholemeal flour, salt, and water. Pure wholemeal flour from NM grain was found to be unsuitable for bread-making purposes so the partial substitution of wheat was chosen for product quality testing and sensory analysis. The three formulations used were: 100% SW flour (100 SW), a 25:75 mix of NM and SW flours (25:75 NM:SW), and a 50:50 mix of NM and SW flours (50:50 NM:SW). Given the overall similarity of wholemeal flours produced from grain collected from the two populations of NM  and flattened between two sheets of plastic wrap by hand and a rolling pin with a 2 mm thickness guide attached.The method for the preparation of dough was adapted from Kahlon and Chiu . In a laThe appropriate cooking temperature was determined through the trial baking of flatbreads made from commercial wheat flour at 250 °C (3 min) and at 200 °C (5 min) using a 10 Tray Plus fan-forced Electric Combi Oven . It was decided that 200 °C for 5 min produced a flatbread with the best consistency. The flatbreads were cooled at room temperature for 1 h and stored at room temperature in polyethylene zip-lock bags for further analysis.L*, a*, and b* values were recorded using a portable colorimeter . The loss of moisture (%) from the flatbreads after 7 days of storage was measured by placing three individual wedges from each type of flatbread in separate semi-permeable polyethylene zip-lock bags. Initial weight was taken for each sample and again after 7 days of storage at room temperature.Flatbreads were analysed using several quality measures, including the colour of the baked product and moisture loss after baking and after 7 days of storage. The colour of the cooked flatbread was measured at five random spots on each surface. The n = 50) were briefed prior to testing to confirm that they had no food allergies. A project information sheet, consent form, and sensory analysis questionnaire were provided to each panellist before the sensory test. Panellists were asked to evaluate each type of flatbread according to appearance, colour, flavour, aroma, texture, and overall acceptability using a 1 to 7 hedonic scale for each attribute  using IBM SPSS statistics 27. Grain and flour properties and properties of flatbread were compared using a significance level of −1), but the grain of SW was significantly heavier. The grain of NM had significantly higher moisture content than SW but was not significantly different between the two populations (The grain of NM harvested from both populations (Quirindi and Werris Creek) were smaller  and lighter  than SW A. The teulations .p = 0.002), and tempering moisture had significant effect on flour yield . For a given grain type, tempering moisture content had little influence on white flour yield except for NMQ grain tempered at 15.5% moisture content, which had significantly lower yields compared to 11.5 and 13.5% moisture content. Overall, milling yield for NMW was up to 4% higher than NMQ, but these differences were not significant. There were no observable differences during the milling process for SW compared to NM .White flour yield was higher for SW compared to both NMQ and NMW for all tempering moisture contents tested . The higp < 0.001). For all the physical and chemical properties measured for grain and flour, the ash content of wholemeal flour made from the two populations of NM was the only measure that differed significantly  and greener  and yellower  compared to white flour from SW (p < 0.001), and the effect of adding water for the purpose of tempering the grains was significant for both L* and a* values  but not for b*. In contrast, the colour of flour from grain collected from two populations of NM were generally similar among tempering moisture content  compared to flatbreads made from 100 SW (25.6 ± 3.1%) and 25:75 NM:SW (25.5 ± 3.9%). In comparison, moisture losses from all flatbreads after 7 days of storage were small and were not statistically different (range of 1.1 to 1.2%).As expected, moisture was lost during the baking of flatbreads. However, flatbreads made from 50:50 NM:SW lost significantly more moisture during baking (36.1 ± 3.3%) . Colour changes (browning) occurred on both top and bottom surfaces of all samples during the baking process. Overall, there was a significant difference in browning on top surfaces according to an increase in reddish and yellowish colour  , and a darker colour developed on the bottom surface   .Overall, the sensory analysis of the three types of flatbreads indicated that, for most of the aspects tested, flatbreads made from SW were preferred in comparison to flatbreads made from a mix of flours . FlatbreFree-text comments made by some participants (more than 50% of the cohort) reflected different baking properties of NM flour with the crisper nature of flatbreads made with 50:50 NM:SW, a feature that was both liked and disliked. In addition, some participants noted a grainy or sandy quality to flatbreads containing NM flour, which is very likely to have influenced their scoring pattern. As indicated by the Likert scale, colour was important, and this was confirmed with several participants commenting on the unappealing colour of flatbreads containing NM flour. Few participants commented on aroma but, when they did, they liked the aroma of flatbread with NM flour, particularly after chewing the baked product. Comments regarding flavour or taste of flatbreads with NM flour were mixed with an equal number of responses indicating that the flavour was appealing or unappealing.This study investigated a selection of processing, baking, and sensory properties of NM compared to a cultivar of wheat (SW). We found that, although high in protein compared to wheat, white flour yield was lower. The traditional food made by Aboriginal people from NM was an unleavened bread cooked over hot coals and consumed immediately . The phyL* values (brightness/whiteness) compared to white flour from SW. However, rather than being interpreted solely as differences in bran content, lower L* may be due to the inherent colour of the endosperm and a range of other chemical and physical properties [The small grain size of NM relative to SW resulted in a greater proportion of bran to endosperm. This was reflected in ash content, milling yields, colour, and pasting and gelatinisation profiles. The darker colour of NM flour was partially due to the colour of the bran. The inner husk of NM is usually dark brown and shiny and is difficult to remove . As it woperties ,33,34. Toperties .The RVA profiles assisted to provide more information on product texture. Lower peak, and final viscosities for both NM flours compared to SW flour could be an indication of a lower starch content or a different amylose/amylopectin ratio, which leads to a weak dough that produces a food product with an inferior texture . In addiGluten is an important storage protein in wheat flour and, arguably, its presence or absence has the greatest impact on the baking quality of any type of flour ,38. FlouPost-harvest production costs are a major contributor to the profitability of wheat processing and are usually related to milling yield and quality . Both SWThis research suggests further advantages to using wholemeal NM flour blends as opposed to pure NM flour. This study used the relatively high proportions of NM flour (25 and 50%) to create two composite flours for baking. Studies that have used similar ratios of novel flour in composite mixes found greatest consumer acceptability at a substation rate of 25%   or higheGiven the poor pasting properties, unappealing colour, small seed size, and potentially high cost of NM flour, future research should investigate a smaller proportion of NM in composite flour mixes  to determine whether baked products are more appealing to consumers and, potentially, making them more affordable. Furthermore, baked products that are better suited to NM flour than flatbread should be investigated. According to the sensory analysis, composite NM flour flatbreads had a dry granular texture, unappealing colour, and slight aftertaste. However, panellists noted that these flatbreads were crispy and more like a cracker than a flatbread, indicating a possible alternative use of this grain. Flour derived from NM grain had a dark green-yellow colour compared to SW flour, making it potentially less marketable if substituted into end products that are expected to be a particular colour. End-products are less likely to be accepted or liked by consumers if the colour does not match with their emotional expectations .The use of seed from NM and other Australian native grasses has four broad benefits: health benefits , environFrom an environmental viewpoint, native grasses require minimal additional nutrient and water inputs compared to agricultural cereals  and can Arguably the most compelling issue facing the use of NM grain in a modern food context is the proper recognition of the cultural importance of exploitation of any species of native flora or fauna or other natural resource . One keyThe broad cultural, environmental, economic, and human health benefits associated with use of NM in Australia have driven the need to understand how this ancient grain can be incorporated into modern foods. From the various components of this study, it can be concluded that there is a potential for the inclusion of Australian Native Millet (NM) in certain markets and in certain food products. This market can be reached by using wholemeal flour to reduce costs, blending flour to balance sensory acceptance with nutritional benefits, and ensuring the rich cultural connections of NM are maintained by creating equal and respectful partnerships between Aboriginal people, the food industry, and cross-disciplinary research. Further investigation into uses of NM in a variety of products at various blend ratios and the consumer acceptance of these products are recommended. Native Millet is only one example of a grass species that was traditionally used by Indigenous Aboriginals, so future research should also investigate the potential of other native species, including their ecophysiological responses to environmental conditions and how grain production and composition may be affected.


Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets.In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel  The development of magnetic resonance imaging (MRI), which offers non-invasive examination of the human brain’s structure and composition, has revolutionized medical imaging. High-resolution images of brain anatomy are obtained during MRI scans, allowing medical professionals and researchers to examine different neuroanatomical aspects like cerebral structures, white matter, and grey matter. MRI datasets play a vital role in advancing medical research, not only in aiding in the understanding, diagnosing, and treating of numerous neurological disorders but also in training deep learning models. Radiologists and neurologists use MRI scans to identify abnormalities, such as tumours, lesions, atrophy, or other anatomical changes, that may be signs of disorders like Alzheimer’s, multiple sclerosis, epilepsy, and brain tumours ,2,3. In “domain shift" [Multi-center MRI datasets, incorporating data from multiple imaging centers or institutions, offer a unique opportunity to leverage diverse patient demographics, equipment, imaging platforms, and protocols. These datasets are valuable resources that can improve the generalizability and representativeness of research, producing more robust and reliable findings. Additionally, multi-site databases make it easier to investigate rare disorders, assess novel imaging techniques, and establish clinical benchmarks . Howevern shift" . Domain In a multi-center MRI dataset, domain shift primarily arises due to the heterogeneity among MRI scanners and imaging protocols across different centers. Some examples of domain shift parameters include imaging protocol  and scanner . As a result, the appearance, contrast, intensity distributions, spatial resolutions, and noise levels of MR images differ qualitatively and quantitatively from site to site and study to study.The problem of domain shift creates several challenges in analyzing and interpreting multi-center MRI datasets. Firstly, it impacts the performance and reliability of ML analysis pipelines as models trained in one center may fail to generalize effectively to the data from other centers . This ishttps://github.com/rkushol/DSMRI (accessed on 5 September 2023). The applications and benefits of analyzing and dealing with domain shift in multi-center MRI datasets are numerous. Here are some crucial ones:Addressing the challenges of domain shift in multi-site MRI datasets requires advanced techniques and methodologies. Domain adaptation (DA) ,8 and haImproved generalizability: Domain shift analysis facilitates the development of ML models that can generalize across multiple centers. By identifying and mitigating the variations caused by domain shift, the methods become more robust and applicable to data from different imaging centers.Reliable and reproducible research: It helps overcome biases and confounds triggered by the variations across different sites. By accounting for the domain-specific effects, research studies utilizing multi-center MRI datasets can yield more reliable and reproducible results.Cross-center comparison and validation: It enables meaningful comparisons and validation of imaging biomarkers, algorithms, and protocols across various centers. Thus, researchers and clinicians can assess the performance and consistency of imaging techniques and analysis methods in diverse settings.Enhanced collaborative research: Multi-center collaborations have become prevalent in medical imaging research. Analyzing domain shift encourages data sharing and collaboration among different centers by enabling a harmonized data analysis from various sources. It promotes data integration, pooling, and joint analysis, thereby facilitating large-scale studies and advancing scientific knowledge in the field.Adaptation to new centers and populations: As new imaging centers are established, or new patient cohorts are included in studies, domain shift analysis can guide the adaptation of existing models to these new domain configurations. This reduces the time and effort required to deploy analysis tools in new settings, allowing faster translation of research findings into clinical practice.Quality control (QC) and outlier detection: Analyzing domain shift can serve as a QC measure for MRI datasets. It allows for identifying centers or specific scans that exhibit significant variations compared to others. Such insights can help in data validation as well as detect potential sources of errors or outliers.The proposed DSMRI framework, explicitly designed to analyze the presence of domain shift in multi-center MRI datasets, to our best knowledge, offers several significant contributions for the first time. Firstly, DSMRI integrates insights from diverse domains, including spatial, frequency, wavelet, and texture analysis. This multi-domain approach fortifies the framework’s ability to capture various aspects of domain shift. Secondly, deriving the features from the frequency domain to capture low- and high-frequency image information and incorporating wavelet domain features to measure sparsity and energy within wavelet coefficients enhance the robustness of domain shift analysis. Thirdly, using visualization techniques such as t-SNE  and UMAPPrior studies have widely acknowledged and examined the presence of domain shift in multi-center MRI datasets. Researchers have consistently reported variations and challenges originating from domain shift, highlighting the need for robust analysis techniques.A study by Dadar et al.  examinedIn another study, Lee et al.  exploredThe above studies collectively highlight the pervasive presence of domain shift in multi-center MRI datasets. The observed variations in image characteristics and acquisition parameters across centers pose considerable challenges for analysis and interpretation.MRIQC  is an opOsadebey et al.  presenteEsteban et al.  proposedBottani et al.  introducThese QC studies focus mainly on automatically detecting artifacts or poor-quality samples to reduce manual effort and decide whether a particular scan should be accepted or excluded from the analysis. These studies neither emphasize quantifying the degree of domain shift from these QC features nor analyze which features are correlated to domain shift.The tools introduced by Sadri et al., MRQy , and GuaDomainATM offers visualization of data distribution as well as measures the domain shift distance for the original or synthetic data. Then, they implemented some classical DA methods to show the effectiveness of these methods in reducing the domain shift. However, this tool cannot take raw neuroimaging data, such as NIfTI files, directly as input. To analyze real-world data with DomainATM, the user must process the data with Anatomical Automatic Labeling (AAL) atlas and then extract the grey matter volumes for each region of interest (ROI), making the tool inconvenient for many applications. Most importantly, these grey matter features are not meaningful regarding the domain shift measurement, which is reflected in the experimental section. The proposed framework DSMRI is compared with MRQy and DomainATM to demonstrate the strength of the proposed features in analyzing the domain shift in a multi-center MRI dataset.Seven large-scale multi-center datasets are used in the experimental evaluation of the proposed framework. Publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI)  and the We leverage various image-quality-related metrics to quantify the degree of domain shift in a multi-center MRI dataset. An overview of the proposed DSMRI framework is shown in Spatial domain features are based on prior studies ,32,33,34F and B, respectively.Secondly, relevant noise-related features are extracted by incorporating F and the background (B), such as Peak SNR , CJV, and Entropy Focus Criterion (EFC) features extracted from F. The CV is defined as ollowing .SciPy library [The frequency domain features are calculated after performing a 2D Fast Fourier Transform (FFT) on F. The FFT is performed with the Python  library .1. SNRF: The SNR in the frequency domain assesses signal quality corrupted by noise. It quantifies the power (or energy) ratio in the signal component to the power (or energy) in the noise component in the frequency domain. It can be defined as 2. LFR: The Low Frequency Response (LFR) has the ability to capture low-frequency information in the resulting image. It involves applying a low-pass filter, calculating the amplitude spectrum using the FFT, and measuring the LFR as the square root of the amplitude spectrum. The purpose of the low-pass filter is to attenuate or remove high-frequency components from F, allowing only low-frequency information to pass through. A 3. HFR: Similar to the concept of LFR, the High Frequency Response (HFR) has the ability to capture high-frequency information in an image. Instead of a low-pass filter, a high-pass filter is applied to F, allowing only high-frequency components to pass through while attenuating lower frequencies. This step emphasizes the high-frequency content of an image. A Pywt package [The wavelet domain features are extracted after performing a 2D discrete wavelet transform (DWT) on F. The wavelet decomposition is implemented with the Python  package , and som1. WCS: The Wavelet Coefficient Sparsity (WCS) is a feature used to measure the amount of sparse information present in the wavelet coefficients of a signal or an image. It quantifies the extent to which the wavelet coefficients are concentrated in a few significant coefficients while the majority are close to zero or negligible. First, a 2D DWT is applied to F, which decomposes an image into different frequency subbands, representing different scales or levels of detail. Then, the wavelet coefficients obtained from the wavelet transform are analyzed to determine their sparsity. Various sparsity measurement techniques can be employed, such as counting the number of coefficients above a certain threshold or using sparse representation algorithms like l1-norm minimization. Here, the WCS is measured based on the mean of the coefficients. Coefficients above the mean are considered significant, while those below the mean are considered insignificant. The WCS can be represented as follows: 2. WQS: The Wavelet-based Quality Score (WQS) evaluates the quality of an image by analyzing its spatial frequency content using the wavelet transform. It calculates a quality score based on the magnitude and phase information of the wavelet coefficients. The magnitude represents the strength or energy of each coefficient, while the phase represents the spatial orientation or phase shift. The WQS is calculated by taking the sum of the product of magnitude and cosine form of the phase for each coefficient. The WQS can be expressed as follows: 3. WCE: The Wavelet Coefficient Energy (WCE) measures the amount of energy present in the wavelet coefficients of a signal or an image. It quantifies the overall strength or magnitude of the coefficients, indicating how much information is contained in each coefficient. The energy of each wavelet coefficient is computed by taking the absolute value of its magnitude. The total energy of the wavelet coefficients is then obtained by summing up the energies of all the coefficients. The WCE can be defined as follows: scikit-image package [Texture features are extracted from the widely used GLCM ,38, whic package , which pskimage.feature.greycoprops. Formula:1. Contrast: This feature measures the local variations or differences in intensity between neighbouring pixels in an image. It provides information about the amount of contrast present in the image texture. It is calculated as the sum of squared intensity differences between neighbouring pixel pairs, weighted by the frequencies in the GLCM matrix. A higher contrast value indicates greater variation or sharp transitions between pixel intensities, representing a more textured or detailed image. Command: skimage.feature. greycoprops . Formula:2. Dissimilarity: This feature calculates the average absolute difference between the pixel intensities in the GLCM. It quantifies the amount of local variation in the texture. Higher values indicate greater pixel dissimilarity. It is similar to contrast but focuses on absolute differences rather than squared differences. Command: skimage.feature.greycoprops. Formula:3. Angular Second Moment (ASM): This feature represents the sum of squared elements in the GLCM matrix and reflects the overall uniformity or homogeneity of the image. A higher ASM value indicates a more homogeneous texture, where the pixel pairs are distributed more evenly across the image. Command: skimage.feature.greycoprops. Formula:4. Homogeneity: This feature measures the closeness of the distribution of elements in the GLCM matrix to the diagonal elements, indicating the level of local homogeneity or similarity in an image’s texture. A higher homogeneity value indicates a greater level of similarity between neighbouring pixel pairs in terms of intensity values and spatial relationships. Command: skimage.feature.greycoprops. Formula:5. Correlation: This feature measures the linear dependency between pixel intensities in the image. It indicates how correlated the pixels are in a given direction and provides information about the texture’s pattern and organization. A higher value suggests a higher degree of linear correlation between pixel pairs in the image, representing a more organized and patterned texture. Command: skimage.feature.greycoprops. Formula:6. Energy: This feature reflects the overall uniformity or homogeneity of the image texture. It is calculated by the square root of the sum of squared elements in the GLCM matrix, indicating the concentration or "energy" of pixel pairs with specific intensity values and spatial relationships. A higher energy value suggests a more uniform and homogeneous texture in the image. Command: sklearn.manifold Python library is used to implement t-SNE with the default settings .t-Distributed Stochastic Neighbor Embedding (t-SNE) : It is aumap package with default hyper-parameters . This study’s visual findings are mostly similar for both t-SNE and UMAP. However, the data are more condensed in UMAP and tend to produce more clusters; hence, t-SNE is recommended as the first choice.Uniform Manifold Approximation and Projection (UMAP) : It is aa and b, k. A decrease in the MMD distance between the two domains after DA or harmonization process signifies a reduction in domain shift.Domain shift distance: The MMD is widely recognized as a prominent metric in DA research to assess the dissimilarities in data distribution between two domains . It can sklearn package with a five-fold cross-validation setup.Domain classification accuracy: Consider a scenario where a random number of samples are selected from two distinct domains, each labelled with its corresponding domain. To evaluate the presence of domain shift or dissimilarity, a domain discriminator or classifier is applied to all the samples, aiming to identify the domain from which each sample originates. The classification outcome serves as a measure of domain shift. A high accuracy in classifying the samples based on their domains implies that the two domains exhibit significant differences, indicating a substantial domain shift. It also supports the robustness of the features used to train the classifier. Conversely, the reduction in domain classification accuracy indicates a decrease in domain shift, making it more challenging to distinguish between them. Support Vector Machine (SVM) with a linear kernel and Random Forest (RF) with a 500 estimator size are used in the experiments as domain classifiers. These classifiers follow the implementation of the Python The multi-center datasets used in this study encompass various factors contributing to domain shift, including scanner manufacturer, model, field strength, image acquisition orientation, resolution, and flip angle. However, when applying the DSMRI framework to these datasets, the resulting clusters primarily demonstrated separation based on the scanner manufacturer parameter, corroborating findings from previous studies ,14. FiguThis analysis investigates the impact of different scanner models originating from the same manufacturer. A subset of the ADNI1 dataset comprising five Siemens scanner models, namely Trio, Allegra, Avanto, Sonata, and Symphony, is evaluated to understand the effects of different scanner models. The t-SNE plot in the left panel of 3 are categorized as low-resolution, while images with a resolution of 3 are classified as high-resolution in this study. Within the CALSNIC2 dataset, a total of 86 participants underwent scanning using the Philips Achieva scanner, while 172 participants were scanned using the Siemens Prisma scanner. Notably, the dataset provided two different image resolutions for these participant groups, keeping all other image acquisition parameters constant. Specifically, images with a resolution of This experiment validates the proposed framework’s effectiveness when applied to T2-weighted and FLAIR images. Within the CALSNIC2 dataset, both FLAIR and T2-weighted images were available for the same population. T2-weighted images offer excellent contrast for evaluating pathologies like inflammation, edema, and fluid-filled structures. On the other hand, FLAIR imaging, a variation of T2-weighted imaging, nullifies the signal from fluids like cerebrospinal fluid (CSF) and enhances the visibility of lesions, particularly those adjacent to CSF-filled spaces. In this experiment, our objective is to evaluate the performance of the data after applying commonly used preprocessing neuroimaging pipelines to the CALSNIC1 and CALSNIC2 datasets. As a crucial step in the preprocessing pipeline, we first utilize the FreeSurfer  program This section examines the significance of different proposed features across various datasets and data types. To accomplish this, we employ an RF classifier and extract the feature importance ranking from the model. The ranking of the features is presented in The comparative evaluation of the proposed DSMRI framework involves two related methods, namely DomainATM and MRQy, with a focus on visualizing the data using t-SNE plots. The experimental evaluations conducted in this study encompass diverse large-scale datasets that include different patient cohorts spanning a wide range of ages and multiple MRI modalities. These datasets exhibit a multitude of variations in scanner and protocol parameters, including scanner vendor, model, field strength, flip angle, acquisition orientation, resolution, coil configuration, and so on. Understanding the effects of domain shift caused by these individual factors is both intriguing and essential, provided that the remaining parameters remain constant. However, accessing a dataset that provides such a controlled setup is extremely challenging.Fortunately, the AIBL dataset offers an arrangement that allows for the analysis of three different scanner models from Siemens, thereby examining their effects on domain shift. Additionally, the CALSNIC2 dataset provides an opportunity to analyze the impact of different spatial resolutions while maintaining the homogeneity of other parameters for the same subjects. Furthermore, the CALSNIC2 enables the evaluation and comparison of the performance of T1-weighted, T2-weighted, and FLAIR images for the same population.Neuroscience researchers often apply common preprocessing steps, such as skull stripping and registration, expecting these procedures to mitigate domain shift or scanner bias issues. However, our study reveals that a similar extent of domain shift persists even after applying skull stripping and registration to the MNI-152 template for the same participants in the CALSNIC2 dataset.The domain classification accuracy, evaluated using two commonly employed classic classifiers, consistently achieved high accuracy rates across various experiments, reaching nearly 100% in most cases. This finding indicates that the features proposed in this study are robust and meaningful in effectively distinguishing different domains. In contrast, the DomainATM method reported a domain classification accuracy of only 65% in that paper when classifying data from two scanners in a small subset of the ABIDE dataset. This result demonstrates that the grey matter volume features utilized by DomainATM are ineffective in the context of addressing the issue of domain shift.The proposed frequency domain features, specifically ’HFR’ and ’LFR,’ demonstrated substantial importance in the feature ranking charts, emphasizing their significant contribution to quantifying domain shift. Likewise, the wavelet domain features, namely ’WQS,’ ’WCE,’ and ’WCS,’ played a crucial role in assessing domain shift. We conducted experiments using various wavelet types, including Haar, Daubechies, Discrete Meyer, Symlets, and Coiflets, for wavelet decomposition. The results revealed a high degree of similarity among these wavelet types. However, based on empirical analysis, the Coiflets wavelet type is recommended as the preferred choice in the proposed framework. In the ranking charts, the texture domain features mainly occupied the middle area, signifying their moderate influence on domain shift. Conversely, the noise-related features were predominantly found at lower rankings, indicating that the datasets used in this study were adequately processed and free from noise artifacts.The framework serves as a valuable QC tool, enabling the assessment of MR image datasets. For instance, the presence of noise artifacts can be identified by the lower values of ’PSNR’ or ’CNR,’ indicating the need for denoising prior to analysis. Similarly, higher values of ’EFC’ or ’CJV’ suggest the presence of motion or shading artifacts in the dataset or specific samples. This information can assist radiologists or experts in making informed decisions regarding the inclusion or exclusion of data before performing computational analysis.In the field of neuroscience research, multi-center neuroimaging studies require robust, efficient, and reliable techniques to address the non-biological sources of data variation. ML-based approaches often yield inconsistent results when dealing with data acquired from different MRI scanner models and scanning protocols. This study makes a significant contribution by presenting a simple yet effective unsupervised framework for quantifying the degree of domain shift. After examining a wide range of large multi-center MRI datasets, this study explores the impacts of different scanner manufacturers, models, field strengths, and resolutions in the context of domain shift. Furthermore, the proposed framework demonstrates its adeptness in identifying domain shift, not only in preprocessed MRI data but also across T2-weighted and FLAIR modalities. The findings of this study have important implications for advancing the field of medical imaging and enabling more reliable analysis of multi-center MRI datasets. Moreover, DA and harmonization methods can utilize the proposed framework to validate the effectiveness of their approaches in reducing or eliminating domain shift. Future experiments could explore the application of the DSMRI to more advanced modalities, such as functional MRI (fMRI) and diffusion-weighted images. Such expansion could reveal its versatility and novel advancements in the broader spectrum of neuroimaging research.


Bifidobacterium and Lactobacillus to regulate intestinal flora, relieve constipation, and explore the initial mechanism in mice. After four weeks of probiotic intervention, BM1, BM2, and PB2 effectively relieved constipation; however, the pathways involved were different. The Bifidobacteria-dominated formulations BM1 and BM2 mainly changed the composition and structure of the intestinal flora and significantly decreased the relative abundance of Tyzzerella, Enterorhabdus, Faecalibaculum, Gordonibacter, and Mucispirillum in stool; increased the relative abundance of Parabacteroides and the content of short-chain fatty acids (SCFAs) in stool; restored motilin (MTL) and vasoactive intestinal peptide (VIP) levels; and downregulated interleukin 6 (IL-6) and IL-8 levels in serum. This repaired the inflammatory response caused by constipation. Finally, it promoted peristalsis of the gastrointestinal tract, increasing stool water content, and relieving constipation. While Lactobacillus-dominated formula PB2 mainly restored the levels of serum neurotransmitters , VIP and PYY (Peptide YY)) and inflammatory factors , it significantly decreased the relative abundance of Tyzzerella, Enterorhabdus, Faecalibaculum, Gordonibacter and Mucispirillum in stool; it then increased acetic acid content, thereby reducing the level of inflammation and changing stool properties and gastrointestinal motility.Constipation is currently one of the most common gastrointestinal disorders, and its causes are diverse. Multi-strain probiotics are often considered a more effective treatment than single-strain probiotics. In this study, a constipation model was constructed using loperamide hydrochloride to evaluate the ability of a multi-strain probiotic combination of four different ratios of  Chronic constipation can be divided into functional constipation and constipation-type irritable bowel syndrome , which iProbiotics are live microorganisms that, when consumed in sufficient quantities, have a positive influence on host health . There iBifidobacterium infantis BLI-02 (B. infantis BLI-02)  xylanophilum group was significantly and positively correlated with faecal water content (p < 0.05), whereas the relative abundance of Gordonibacter was significantly negatively correlated with faecal water content (p < 0.05). The relative abundances of Bifidobacterium, Gordonibacter, and Tyzzerella were significantly and positively correlated with the time to defecate the first black stool (p < 0.05). The relative abundances of Bifidobacterium, Gordonibacter, Faecalibaculum, and Tyzzerella were significantly and negatively correlated with small intestinal advancement (p < 0.05). In addition to this, the relative abundance of Tyzzerella also showed a significant positive correlation with the levels of 5-HT, IL-6, and IL-8 (p < 0.05); the relative abundance of Faecalibaculum showed a significant positive correlation with the level of IL-6 (p < 0.05); the relative abundance of Gordonibacter was significantly and positively correlated with IL-1, IL-6, and IL-8 levels (p < 0.05) and negatively correlated with IL-10 and AA levels (p < 0.05). The relative abundance of Mucispirillum was significantly and positively correlated with VIP, SS, 5-HT, IL-1, IL-6, and IL-8 contents (p < 0.05) and negatively correlated with MTL, Gas, and IL-10 contents (p < 0.05); lastly, the relative abundance of Parabacteroides was significantly and positively correlated with the content of AA.The correlation between the apparent indicators of constipation in the gastrointestinal tract and each related test index was established based on the results above, as shown in Tyzzerella, Enterorhabdus, Faecalibaculum, Gordonibacter, and Mucispirillum in faeces by decreasing the relative abundance of Parabacteroides, increasing the level of SCFAs in faeces, restoring MTL and VIP levels, and downregulating the levels of the pro-inflammatory cytokines IL-6 and IL-8 in serum, which in turn repairs the inflammatory response triggered by loperamide hydrochloride and increases the peristalsis of the gastrointestinal tract. PB2, on the other hand, mainly increases the relative abundance of Tyzzerella, Enterorhabdus, Faecalibaculum, Gordonibacter, and Mucispirillum; increases the level of acetic acid in the stool; upregulates the levels of MTL and SP; downregulates the levels of VIP and PYY; and regulates the levels of the pro-inflammatory cytokines IL-1, IL-6, and IL-8 in the serum. Specific results are shown in In summary, the above results show that BM1, BM2, and PB2 all relieve constipation, but their pathways and advantages are not the same. BM1 and BM2 mainly restored the relative abundances of Constipation, as one of the common gastrointestinal disorders, is closely related to intestinal flora . AccordiBifidobacterium and Lactobacillus) in the intestinal flora of mice, regulating intestinal flora. Probiotics have been shown in preliminary research to have positive effects on the diseases of the intestinal flora and restore the composition of intestinal flora [B. infantis BLI-02, B. animalis BB-115, and L. rhamnosus MP108) performed well in alleviating constipation but contained the least number of strains (with the highest proportion of Bifidobacteria). This indicates that the degree of improvement in constipation was not proportional to the number of strains but may be related to the proportion of Bifidobacteria. In our previous studies, Bifidobacterium was found to have an advantage over Lactobacillus in terms of constipation relief [Bifidobacterium plays a more reciprocal role in promoting colonization than Lactobacillus [L. paracasei, one of the most common Lactobacillus species, was found to be effective in relieving symptoms of constipation in a combination that contained L. paracasei and other strains, with some dose correlation [Bifidobacterium and one strain of Lactobacillus, whereas BM2 has an additional strain of L. paracasei. Our results showed that both formulations were effective in relieving constipation; however, the effect of BM1 was more pronounced, not only increasing the faecal water content of constipated mice and reducing the time it took to pass the first black stool but also significantly increasing the small intestinal propulsion rate of constipated mice. This result also showed that an increase in Lactobacillus—but not Bifidobacterium—in the probiotic formula for constipation relief did not necessarily increase the effect of constipation relief as was expected.The results of our study confirmed that all four probiotic formulations met the criteria for positive results in the “Test method for regulating intestinal flora” in the “Health Food Function Test and Evaluation Method (2022 edition)”, and considerably boosted the range of beneficial microorganisms . Constipation has been shown to be associated with inflammation in numerous clinical trials . One invTyzzerella, alter the composition of the intestinal flora, and alleviate ulcerative colitis (UC) symptoms [Mucispirillum is associated with gene expression in mucosal tissues, and alterations in its expression produce an inflammatory response [Gordonibacter is an opportunistic pathogenic bacterium, and its increased abundance is linked to the emergence of illnesses, including enteritis and hypertension [Bacteroides fragilis, Bacteroides ovatus, Bifidobacterium longum, Parabacteroides, and Alistipes finegoldii among 76 constipated and healthy children in a comparative analysis of their intestinal flora. Enterorhabdus is considered one of the human intestinal pathogens that participates in the fermentation of undigested proteins in the intestinal tract and may destroy intestinal homeostasis. Its products, ammonia and putrescine, are closely related to the occurrence of colorectal cancer [Porphyromonas and Faecalibaculum in gastritis samples were increased, and the enrichments of Faecalibaculum and Kosakonia in small intestine inflammation samples were elevated compared with those in normal individuals. In this study, we found that the relative abundances of Tyzzerella, Mucispirillum, and Gordonibacter in the faeces of constipated mice were significantly higher, and that of Parabacteroides was lower. After bacterial intervention, the relative abundances of Tyzzerella, Mucispirilum, and Gordonibacter in the BM1, BM2, and PB2 groups were significantly decreased, whereas the relative abundance of Parabacteroides in the BM1 and BM2 groups was significantly increased in comparison to the state of the groups when quantified before intervention. Moreover, the relative abundances of Tyzzerella, Mucispirillum and Gordonibacter were significantly and positively correlated with IL-6 and IL-8 levels, the relative abundance of Gordonibacter was significantly and negatively correlated with acetic acid content, and the relative abundance of Parabacteroides was significantly and positively correlated with acetic acid content. In addition, our results showed that propionic acid content was significantly and positively correlated with faecal water content and the small intestinal propulsion rate, and it significantly and negatively correlated with the time of the first black stool evacuation. Similarly, changes in the composition of the intestinal flora affect the content of SCFAs, which are crucial components for maintaining the homeostasis of the intestinal environment [Bifidobacteria-dominated formulations BM1 and BM2 showed changes in intestinal flora composition and structure; significantly decreased the relative abundance of Tyzzerella, Enterorhabdus, Faecalibaculum, Gordonibacter, and Mucispirillum in faeces; and increased the relative abundance of Parabacteroides in faeces. It also increased the content of SCFAs in faeces, restored the levels of MTL and VIP, and downregulated the levels of the pro-inflammatory cytokines IL-6 and IL-8 in the serum, thus repairing the inflammatory response caused by constipation, promoting peristalsis of the gastrointestinal tract, increasing the water content in faeces, and relieving constipation. Lactobacillus-dominated formula PB2 mainly showed a significant decrease in the relative abundance of Tyzzerella, Enterorhabdus, Faecalibaculum, Gordonibacter, and Mucispirillum; an increase in the level of acetic acid in the stool; upregulation of MTL and SP; downregulation of VIP and PYY; and restoration of inflammatory factor  levels in the serum. Consequently, stool properties changed, and gastrointestinal motility increased.Euphorbia humifusa-Derived Polysaccharides  can reduce the relative abundance of symptoms . Mucispiresponse . Gordonirtension . By contrtension . Tim et rtension  found stl cancer . Chen etl cancer  analysedironment . In summBifidobacteria in the formula; the next step is to verify the above speculation through an all-Bifidobacterial formula. These results will guide the design of probiotic formulas in the future, not only in terms of the number of strains to include but also in considering the synergistic effect between strains, in order to obtain a low-cost and effective probiotic formula.Constipation relief was not improved with more strains but might have been related to the proportion of Bifidobacterium and Lactobacillus involved different pathways. Among them, the Bifidobacteria-dominated formulations BM1 and BM2 mainly showed changes in intestinal flora composition and structure; significantly decreased the relative abundance of Tyzzerella, Enterorhabdus, Faecalibaculum, Gordonibacter, and Mucispirillum; upregulated the relative abundance of Parabacteroides; increased the content of SCFAs in faeces; restored the levels of MTL and VIP; and downregulated the levels of the pro-inflammatory cytokines IL-6 and IL-8 in the serum, thus repairing the inflammatory response caused by constipation. This promoted a peristalsis of the gastrointestinal tract, increasing the water content in faeces and relieving constipation. Lactobacillus-dominated formula PB2 significantly decreased the relative abundance of Tyzzerella, Enterorhabdus, Faecalibaculum, Gordonibacter, and Mucispirillum; increased the level of acetic acid in the stool; upregulated MTL and SP; downregulated VIP and PYY; and restored inflammatory factor  levels in the serum, changing stool properties and increasing gastrointestinal motility.A constipation model was constructed using loperamide hydrochloride to establish the correlation between constipation-related indicators and flora, and the results showed that BM1, BM2, and PB2 could effectively relieve constipation; however, probiotic formulations with different proportions of


Protein kinase C delta (PKC-δ) is an important signaling molecule in human cells that has both proapoptotic as well as antiapoptotic functions. These conflicting activities can be modulated by two classes of ligands, phorbol esters and bryostatins. Phorbol esters are known tumor promoters, while bryostatins have anti-cancer properties. This is despite both ligands binding to the C1b domain of PKC-δ (δC1b) with a similar affinity. The molecular mechanism behind this discrepancy in cellular effects remains unknown. Here, we have used molecular dynamics simulations to investigate the structure and intermolecular interactions of these ligands bound to δC1b with heterogeneous membranes. We observed clear interactions between the δC1b-phorbol complex and membrane cholesterol, primarily through the backbone amide of L250 and through the K256 side-chain amine. In contrast, the δC1b-bryostatin complex did not exhibit interactions with cholesterol. Topological maps of the membrane insertion depth of the δC1b-ligand complexes suggest that insertion depth can modulate δC1b interactions with cholesterol. The lack of cholesterol interactions suggests that bryostatin-bound δC1b may not readily translocate to cholesterol-rich domains within the plasma membrane, which could significantly alter the substrate specificity of PKC-δ compared to δC1b-phorbol complexes. PKC-δ is comprised of an N-terminal regulatory domain that is linked to a C-terminal catalytic domain by a flexible hinge region. The regulatory domain contains the C2-like domain, the pseudosubstrate domain, which auto inhibits the catalytic domain, followed by a membrane-binding C1 domain. The C1 domain is comprised of two zinc finger motifs, C1A and C1B, which bind agonists such as diacylglycerol (DAG) and phorbol esters. Upon agonist and membrane binding, PKC-δ undergoes a conformational change which releases the kinase domain from the pseudosubstrate and allows the binding and phosphorylation of substrates. Overexpression of PKC-δ is associated with increased apoptosis and cell cycle arrest in many cell types through p53 activation [Protein kinase C δ (PKC-δ) is a serine/threonine kinase that plays a central role in regulating cell proliferation , growth tivation ,8. Convetivation , hepatictivation  and breativation ,12,13 cativation ,15. Whiltivation ,16,17, ativation ,19,20,21tivation ,23, manytivation ,25.d = 3–4 nM [The endogenous modulator of PKC-δ is diacylglycerol (DAG) which binds to the C1 domain ,27. The = 3–4 nM ,39 and d= 3–4 nM ,41 throu= 3–4 nM ,39. Coll2H solid-state NMR [The nature of the ligands’ interaction with membrane lipids could be a greater determinant of PKC-δ activity ,43,44,45tate NMR ,53. In atate NMR . DiffereThe requirement for PS in PKC-δ activity has led to most MD simulations ,54,55 anTo assess the potential for δC1b-ligand complexes to interact with different membrane components, we simulated δC1b bound to phorbol and bryostatin  in heterTo determine whether this could be explained by occupancy with other lipids we analyzed the interaction of δC1b-lysine residues with other types of lipids. In δC1b-phorbol simulations we observed transient interactions  with phosphate groups of most phospholipids but found the highest occupancy with phosphatidylethanolamine (PE), phosphatidylinositol (PIP) and, to a lesser extent, phosphatidylcholine (PC) a–c. In fCholesterol is known to enhanced PKC binding to model membranes, primarily through changes in bilayer order parameters ,52. InteOnly one of five bryostatin simulations showed a significant interaction between S240 or K256 b,c, and To determine whether insertion depth can explain the distinct biological function of phorbol and bryostatin, we simulated a bryostatin analogue, Merle27. Merle27 is known to elicit cellular responses that are similar to those initiated by phorbol , despiteA comparison of topological heatmaps of the δC1b-ligand complexes in The C20 fatty acid of Merle27 extends out near L250, sterically hindering access of cholesterol to this residue and explaining the reduced interaction time observed . In δC1bThe mechanism by which different PKC modulators cause disparate cellular phenotypes is unknown. Biochemical and structural studies find little difference in the interaction of modulators, such as phorbol and bryostatin, in terms of binding modes and affinity for PKC to explain the dichotomous phenotypes induced by these modulators. The role of ligand–membrane interactions is known to be important, whereby the ligand influences the insertion depth of PKC and effects membrane order, potentially modulating PKC accessibility to ligands within the membrane. Here, we provide the first indication that insertion depth modulation and ligand hydrogen bonding influences cholesterol interactions with PKC itself and hypothesize how this might impact cell signaling.MD simulations are a powerful predictive tool for studying membrane–protein interactions ,65. TypiThe implication of membrane insertion depth as a distinction between δC1b-phorbol and δC1b-bryostatin were identified in MD simulations by Pande and co-workers in PS membranes . Chelati13C-labeled cholesterol. As the interaction appears to be somewhat dynamic, we would expect low dynamic systems and methods such as low temperature solid-state NMR to perform the best at demonstrating this potential interaction.In model membrane systems, the addition of cholesterol increases the propensity of PS bilayers to form non-lamellar phases . PS promLipid raft partitioning has long been associated with the spatial regulation of cell signaling ,76. It iThe crystal structure of the mouse δC1b regulatory domain of PKC-δ bound to phorbol 13-acetate was obtained from the RCSB Protein Data Bank (PDB ID: 1PTR). The Orientation of Proteins in Membranes (OPM) PPM server  was usedAs above, the crystal structure of the C1b regulatory domain of PKC-δ bound to phorbol 13-acetate was obtained (PDB ID: 1PTR). The ligand was removed using the BIOVIA Discovery Studio Visualizer (BIOVIA (2020), Dassault Systèmes, San Diego, CA, USA). The bryostatin and Merle27 structures were sketched as mol2 files in the BIOVIA Discovery Studio Visualizer. The AutoDock Tools package  was usedmax < 1000 kJ/mol/nm was reached (All 15 systems comprising the three δC1b/ligand complexes were energy minimized, equilibrated, and simulated using the CHARMM36m forcefield  with GRO reached . Equilib reached . All bonData analysis was carried out using home-written PYTHON code, including MDTraj  to read Through molecular dynamics simulations, we show that cholesterol interacts with the δC1b domain. Through topological changes and variations in δC1b-lipid interactions, the δC1b domain displays a significant decrease in cholesterol interactions when bound to bryostatin due to a shallow membrane insertion depth. This decrease in the δC1b domain’s interactions with cholesterol when bound to bryostatin could affect its likelihood of translocating to cholesterol-rich regions of the cell membrane, such as lipid rafts, which could alter substrate accessibility and degradation rates of PKC-δ, thereby influencing cell signaling. This work highlights the importance of including physiologically relevant lipids into simulations of membrane–protein interactions.